From 9f5899d141a45674c22d702dd468d57a506d4b43 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Sat, 29 Nov 2025 14:46:07 -0800
Subject: [PATCH 01/49] ---

---
 array_temp/faiman_coefficients.ipynb          | 1055 +++++++++++++++++
 v4/array_temperature/arrayTemperatureModel.py |   80 ++
 2 files changed, 1135 insertions(+)
 create mode 100644 array_temp/faiman_coefficients.ipynb
 create mode 100644 v4/array_temperature/arrayTemperatureModel.py

diff --git a/array_temp/faiman_coefficients.ipynb b/array_temp/faiman_coefficients.ipynb
new file mode 100644
index 0000000..ef739d8
--- /dev/null
+++ b/array_temp/faiman_coefficients.ipynb
@@ -0,0 +1,1055 @@
+{
+ "cells": [
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T19:05:48.463024Z",
+     "start_time": "2025-11-29T19:05:46.602247Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import matplotlib\n",
+    "\n",
+    "\n",
+    "from data_tools import query\n",
+    "from data_tools.collections import TimeSeries\n",
+    "from datetime import datetime, date, time, timezone\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import dill\n",
+    "import os\n",
+    "\n",
+    "#each 5 seconds\n",
+    "utc_offset_h = 2\n",
+    "start_utc = time(5 , 00, 00) #querying i svancouver time, influxdb gives utc\n",
+    "stop_utc = time(4, 45, 40)\n",
+    "date_start = date(2025, 6, 30)\n",
+    "date_stop = date(2025, 7, 2)\n",
+    "start_time = datetime.combine(date_start, start_utc, tzinfo=timezone.utc)\n",
+    "stop_time = datetime.combine(date_stop, stop_utc, tzinfo=timezone.utc)\n",
+    "\n",
+    "client = query.DBClient()\n",
+    "temp_array_expected_aliter: TimeSeries = client.query_time_series(start_time, stop_time, \"MosfetTemperatureA\")\n"
+   ],
+   "id": "7762d7c5f54c9e9f",
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T19:07:28.204093Z",
+     "start_time": "2025-11-29T19:07:18.443470Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "#alternate data:\n",
+    "\n",
+    "from data_tools import query\n",
+    "from data_tools.collections import TimeSeries\n",
+    "from datetime import datetime, date, time, timezone\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import dill\n",
+    "import os\n",
+    "\n",
+    "#each 5 seconds\n",
+    "utc_offset_h = 2\n",
+    "start_utc = time(5 + utc_offset_h , 00, 00) #querying i svancouver time, influxdb gives utc\n",
+    "stop_utc = time(4 +  utc_offset_h, 45, 40)\n",
+    "date_start = date(2025, 7, 1)\n",
+    "date_stop = date(2025, 7, 2)\n",
+    "start_time = datetime.combine(date_start, start_utc, tzinfo=timezone.utc)\n",
+    "stop_time = datetime.combine(date_stop, stop_utc, tzinfo=timezone.utc)\n",
+    "\n",
+    "client = query.DBClient()\n",
+    "temp_array_expected_aliter: TimeSeries = client.query_time_series(start_time, stop_time, \"MosfetTemperatureA\")\n",
+    "speed_kph: TimeSeries = client.query_time_series(start_time, stop_time, \"MotorRotatingSpeed\")\n",
+    "\n",
+    "print(temp_array_expected_aliter)\n",
+    "#this is almost steady state, and expected temperature is roughly 31.8 degrees celsius. note that individual cells do have an upto 5 C difference, which is actually quite a lot.\n",
+    "\n",
+    "\n"
+   ],
+   "id": "a8ea6762121b1311",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[27.46449661 27.46548943 27.46648225 ... 27.29558551 27.29614538\n",
+      " 27.29670525]\n"
+     ]
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T19:07:36.960833Z",
+     "start_time": "2025-11-29T19:07:36.946373Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "#save collected data of 15 minutes\n",
+    "\n",
+    "import os\n",
+    "import dill\n",
+    "\n",
+    "out_dir = os.path.join(\"../../motor_analysis\", \"data\", \"array_temperature_aliter_2025-07-01\")\n",
+    "mosfetA_file_aliter = os.path.join(out_dir, \"mosfetA_aliter.bin\")\n",
+    "\n",
+    "os.makedirs(out_dir, exist_ok=True)\n",
+    "\n",
+    "for filepath, data in zip([mosfetA_file_aliter],\n",
+    "                          [temp_array_expected_aliter]):\n",
+    "    with open(filepath, 'wb') as f:\n",
+    "        dill.dump(data, f)\n",
+    "\n",
+    "\n",
+    "\n",
+    "#time zone matching conventions??"
+   ],
+   "id": "9b566c367639c5e5",
+   "outputs": [],
+   "execution_count": 4
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "alternate data: data over the first 4 days of fsgp 2025",
+   "id": "b59eb1d3085bfd98"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T22:07:18.614759Z",
+     "start_time": "2025-11-29T22:07:18.456316Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from data_tools import query\n",
+    "from data_tools.collections import TimeSeries\n",
+    "from datetime import datetime, date, time, timezone\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import dill\n",
+    "import os\n",
+    "\n",
+    "#each 5 seconds\n",
+    "utc_offset_h = 2\n",
+    "start_utc = time(22 , 00, 00) #querying i svancouver time, influxdb gives utc\n",
+    "stop_utc = time(20, 45, 00)\n",
+    "date_start = date(2025, 7, 1)\n",
+    "date_stop = date(2025, 7, 6)\n",
+    "start_time = datetime.combine(date_start, start_utc, tzinfo=timezone.utc)\n",
+    "stop_time = datetime.combine(date_stop, stop_utc, tzinfo=timezone.utc)\n",
+    "\n",
+    "client = query.DBClient()\n",
+    "temp_array_fsgp: TimeSeries = client.query_time_series(start_time, stop_time, field=  \"MosfetTemperatureA\")\n",
+    "speed_kph: TimeSeries = client.query_time_series(start_time, stop_time, \"MotorRotatingSpeed\")\n",
+    "\n",
+    "#print(temp_array_expected_aliter)\n",
+    "#this is almost steady state, and expected temperature is roughly 31.8 degrees celsius. note that individual cells do have an upto 5 C difference, which is actually quite a lot.\n",
+    "\n",
+    "\n"
+   ],
+   "id": "8b2b4006671bdc31",
+   "outputs": [
+    {
+     "ename": "ApiException",
+     "evalue": "(401)\nReason: Unauthorized\nHTTP response headers: HTTPHeaderDict({'Server': 'nginx/1.27.1', 'Date': 'Sat, 29 Nov 2025 22:07:18 GMT', 'Content-Type': 'application/json; charset=utf-8', 'Content-Length': '55', 'Connection': 'keep-alive', 'X-Influxdb-Build': 'OSS', 'X-Influxdb-Version': 'v2.7.1', 'X-Platform-Error-Code': 'unauthorized'})\nHTTP response body: b'{\"code\":\"unauthorized\",\"message\":\"unauthorized access\"}'\n",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mApiException\u001B[39m                              Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[2]\u001B[39m\u001B[32m, line 20\u001B[39m\n\u001B[32m     17\u001B[39m stop_time = datetime.combine(date_stop, stop_utc, tzinfo=timezone.utc)\n\u001B[32m     19\u001B[39m client = query.DBClient()\n\u001B[32m---> \u001B[39m\u001B[32m20\u001B[39m temp_array_fsgp: TimeSeries = \u001B[43mclient\u001B[49m\u001B[43m.\u001B[49m\u001B[43mquery_time_series\u001B[49m\u001B[43m(\u001B[49m\u001B[43mstart_time\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstop_time\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfield\u001B[49m\u001B[43m=\u001B[49m\u001B[43m  \u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mMosfetTemperatureA\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m     21\u001B[39m speed_kph: TimeSeries = client.query_time_series(start_time, stop_time, \u001B[33m\"\u001B[39m\u001B[33mMotorRotatingSpeed\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m     23\u001B[39m \u001B[38;5;66;03m#print(temp_array_expected_aliter)\u001B[39;00m\n\u001B[32m     24\u001B[39m \u001B[38;5;66;03m#this is almost steady state, and expected temperature is roughly 31.8 degrees celsius. note that individual cells do have an upto 5 C difference, which is actually quite a lot.\u001B[39;00m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\data_tools\\query\\influxdb_query.py:180\u001B[39m, in \u001B[36mDBClient.query_time_series\u001B[39m\u001B[34m(self, start, stop, field, bucket, car, granularity, units, measurement)\u001B[39m\n\u001B[32m    163\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mquery_time_series\u001B[39m(\u001B[38;5;28mself\u001B[39m, start: datetime, stop: datetime, field: \u001B[38;5;28mstr\u001B[39m, bucket: \u001B[38;5;28mstr\u001B[39m = \u001B[33m\"\u001B[39m\u001B[33mCAN_log\u001B[39m\u001B[33m\"\u001B[39m,\n\u001B[32m    164\u001B[39m                       car: \u001B[38;5;28mstr\u001B[39m = \u001B[33m\"\u001B[39m\u001B[33mBrightside\u001B[39m\u001B[33m\"\u001B[39m, granularity: \u001B[38;5;28mfloat\u001B[39m = \u001B[32m0.1\u001B[39m, units: \u001B[38;5;28mstr\u001B[39m = \u001B[33m\"\u001B[39m\u001B[33m\"\u001B[39m,\n\u001B[32m    165\u001B[39m                       measurement: \u001B[38;5;28mstr\u001B[39m = \u001B[38;5;28;01mNone\u001B[39;00m) -> TimeSeries:\n\u001B[32m    166\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    167\u001B[39m \u001B[33;03m    Query the database for a specific field, over a certain time range.\u001B[39;00m\n\u001B[32m    168\u001B[39m \u001B[33;03m    The data will be processed into a TimeSeries, which has homogenous and evenly-spaced (temporally) elements.\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m    178\u001B[39m \u001B[33;03m    :return: a TimeSeries of the resulting time-series data\u001B[39;00m\n\u001B[32m    179\u001B[39m \u001B[33;03m    \"\"\"\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m180\u001B[39m     query_df = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mquery_series\u001B[49m\u001B[43m(\u001B[49m\u001B[43mstart\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstop\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfield\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbucket\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcar\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmeasurement\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    182\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m TimeSeries.from_query_dataframe(query_df, granularity, field, units)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\data_tools\\query\\influxdb_query.py:153\u001B[39m, in \u001B[36mDBClient.query_series\u001B[39m\u001B[34m(self, start, stop, field, bucket, car, measurement)\u001B[39m\n\u001B[32m    151\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m measurement:\n\u001B[32m    152\u001B[39m     query = query.filter(measurement=measurement)\n\u001B[32m--> \u001B[39m\u001B[32m153\u001B[39m query_df = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mquery_dataframe\u001B[49m\u001B[43m(\u001B[49m\u001B[43mquery\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    155\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(query_df, \u001B[38;5;28mlist\u001B[39m):\n\u001B[32m    156\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[33m\"\u001B[39m\u001B[33mQuery returned multiple fields! Please refine your query.\u001B[39m\u001B[33m\"\u001B[39m)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\data_tools\\query\\influxdb_query.py:123\u001B[39m, in \u001B[36mDBClient.query_dataframe\u001B[39m\u001B[34m(self, query)\u001B[39m\n\u001B[32m    120\u001B[39m compiled_query = query.compile_query()\n\u001B[32m    121\u001B[39m compiled_query += \u001B[33m'\u001B[39m\u001B[33m |> pivot(rowKey:[\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m_time\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m], columnKey: [\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m_field\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m], valueColumn: \u001B[39m\u001B[33m\"\u001B[39m\u001B[33m_value\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m) \u001B[39m\u001B[33m'\u001B[39m\n\u001B[32m--> \u001B[39m\u001B[32m123\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_client\u001B[49m\u001B[43m.\u001B[49m\u001B[43mquery_api\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[43mquery_data_frame\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcompiled_query\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\client\\query_api.py:259\u001B[39m, in \u001B[36mQueryApi.query_data_frame\u001B[39m\u001B[34m(self, query, org, data_frame_index, params, use_extension_dtypes)\u001B[39m\n\u001B[32m    225\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mquery_data_frame\u001B[39m(\u001B[38;5;28mself\u001B[39m, query: \u001B[38;5;28mstr\u001B[39m, org=\u001B[38;5;28;01mNone\u001B[39;00m, data_frame_index: List[\u001B[38;5;28mstr\u001B[39m] = \u001B[38;5;28;01mNone\u001B[39;00m, params: \u001B[38;5;28mdict\u001B[39m = \u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[32m    226\u001B[39m                      use_extension_dtypes: \u001B[38;5;28mbool\u001B[39m = \u001B[38;5;28;01mFalse\u001B[39;00m):\n\u001B[32m    227\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    228\u001B[39m \u001B[33;03m    Execute synchronous Flux query and return Pandas DataFrame.\u001B[39;00m\n\u001B[32m    229\u001B[39m \n\u001B[32m   (...)\u001B[39m\u001B[32m    257\u001B[39m \u001B[33;03m            - https://docs.influxdata.com/flux/latest/stdlib/influxdata/influxdb/schema/fieldsascols/\u001B[39;00m\n\u001B[32m    258\u001B[39m \u001B[33;03m    \"\"\"\u001B[39;00m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m259\u001B[39m     _generator = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mquery_data_frame_stream\u001B[49m\u001B[43m(\u001B[49m\u001B[43mquery\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43morg\u001B[49m\u001B[43m=\u001B[49m\u001B[43morg\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata_frame_index\u001B[49m\u001B[43m=\u001B[49m\u001B[43mdata_frame_index\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[43m=\u001B[49m\u001B[43mparams\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    260\u001B[39m \u001B[43m                                              \u001B[49m\u001B[43muse_extension_dtypes\u001B[49m\u001B[43m=\u001B[49m\u001B[43muse_extension_dtypes\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    261\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._to_data_frames(_generator)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\client\\query_api.py:299\u001B[39m, in \u001B[36mQueryApi.query_data_frame_stream\u001B[39m\u001B[34m(self, query, org, data_frame_index, params, use_extension_dtypes)\u001B[39m\n\u001B[32m    265\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    266\u001B[39m \u001B[33;03mExecute synchronous Flux query and return stream of Pandas DataFrame as a :class:`~Generator[DataFrame]`.\u001B[39;00m\n\u001B[32m    267\u001B[39m \n\u001B[32m   (...)\u001B[39m\u001B[32m    295\u001B[39m \u001B[33;03m        - https://docs.influxdata.com/flux/latest/stdlib/influxdata/influxdb/schema/fieldsascols/\u001B[39;00m\n\u001B[32m    296\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m    297\u001B[39m org = \u001B[38;5;28mself\u001B[39m._org_param(org)\n\u001B[32m--> \u001B[39m\u001B[32m299\u001B[39m response = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_query_api\u001B[49m\u001B[43m.\u001B[49m\u001B[43mpost_query\u001B[49m\u001B[43m(\u001B[49m\u001B[43morg\u001B[49m\u001B[43m=\u001B[49m\u001B[43morg\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mquery\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_create_query\u001B[49m\u001B[43m(\u001B[49m\u001B[43mquery\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mdefault_dialect\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    300\u001B[39m \u001B[43m                                                                        \u001B[49m\u001B[43mdataframe_query\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    301\u001B[39m \u001B[43m                                      \u001B[49m\u001B[43masync_req\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_return_http_data_only\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[32m    303\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._to_data_frame_stream(data_frame_index=data_frame_index,\n\u001B[32m    304\u001B[39m                                   response=response,\n\u001B[32m    305\u001B[39m                                   query_options=\u001B[38;5;28mself\u001B[39m._get_query_options(),\n\u001B[32m    306\u001B[39m                                   use_extension_dtypes=use_extension_dtypes)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\service\\query_service.py:285\u001B[39m, in \u001B[36mQueryService.post_query\u001B[39m\u001B[34m(self, **kwargs)\u001B[39m\n\u001B[32m    283\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m.post_query_with_http_info(**kwargs)  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m    284\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m285\u001B[39m     (data) = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mpost_query_with_http_info\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m    286\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m data\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\service\\query_service.py:311\u001B[39m, in \u001B[36mQueryService.post_query_with_http_info\u001B[39m\u001B[34m(self, **kwargs)\u001B[39m\n\u001B[32m    289\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"Query data.\u001B[39;00m\n\u001B[32m    290\u001B[39m \n\u001B[32m    291\u001B[39m \u001B[33;03mRetrieves data from buckets.  Use this endpoint to send a Flux query request and retrieve data from a bucket.  #### Rate limits (with InfluxDB Cloud)  `read` rate limits apply. For more information, see [limits and adjustable quotas](https://docs.influxdata.com/influxdb/cloud/account-management/limits/).  #### Related guides  - [Query with the InfluxDB API](https://docs.influxdata.com/influxdb/latest/query-data/execute-queries/influx-api/) - [Get started with Flux](https://docs.influxdata.com/flux/v0.x/get-started/)\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m    306\u001B[39m \u001B[33;03m         returns the request thread.\u001B[39;00m\n\u001B[32m    307\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m    308\u001B[39m local_var_params, path_params, query_params, header_params, body_params = \\\n\u001B[32m    309\u001B[39m     \u001B[38;5;28mself\u001B[39m._post_query_prepare(**kwargs)  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m311\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mapi_client\u001B[49m\u001B[43m.\u001B[49m\u001B[43mcall_api\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    312\u001B[39m \u001B[43m    \u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m/api/v2/query\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43mPOST\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m    313\u001B[39m \u001B[43m    \u001B[49m\u001B[43mpath_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    314\u001B[39m \u001B[43m    \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    315\u001B[39m \u001B[43m    \u001B[49m\u001B[43mheader_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    316\u001B[39m \u001B[43m    \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m=\u001B[49m\u001B[43mbody_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    317\u001B[39m \u001B[43m    \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43m[\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    318\u001B[39m \u001B[43m    \u001B[49m\u001B[43mfiles\u001B[49m\u001B[43m=\u001B[49m\u001B[43m{\u001B[49m\u001B[43m}\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    319\u001B[39m \u001B[43m    \u001B[49m\u001B[43mresponse_type\u001B[49m\u001B[43m=\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43mstr\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# noqa: E501\u001B[39;49;00m\n\u001B[32m    320\u001B[39m \u001B[43m    \u001B[49m\u001B[43mauth_settings\u001B[49m\u001B[43m=\u001B[49m\u001B[43m[\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    321\u001B[39m \u001B[43m    \u001B[49m\u001B[43masync_req\u001B[49m\u001B[43m=\u001B[49m\u001B[43mlocal_var_params\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43masync_req\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    322\u001B[39m \u001B[43m    \u001B[49m\u001B[43m_return_http_data_only\u001B[49m\u001B[43m=\u001B[49m\u001B[43mlocal_var_params\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m_return_http_data_only\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# noqa: E501\u001B[39;49;00m\n\u001B[32m    323\u001B[39m \u001B[43m    \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m=\u001B[49m\u001B[43mlocal_var_params\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m_preload_content\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    324\u001B[39m \u001B[43m    \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m=\u001B[49m\u001B[43mlocal_var_params\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m_request_timeout\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    325\u001B[39m \u001B[43m    \u001B[49m\u001B[43mcollection_formats\u001B[49m\u001B[43m=\u001B[49m\u001B[43m{\u001B[49m\u001B[43m}\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    326\u001B[39m \u001B[43m    \u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[43m=\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43murlopen_kw\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\api_client.py:343\u001B[39m, in \u001B[36mApiClient.call_api\u001B[39m\u001B[34m(self, resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, async_req, _return_http_data_only, collection_formats, _preload_content, _request_timeout, urlopen_kw)\u001B[39m\n\u001B[32m    304\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"Make the HTTP request (synchronous) and Return deserialized data.\u001B[39;00m\n\u001B[32m    305\u001B[39m \n\u001B[32m    306\u001B[39m \u001B[33;03mTo make an async_req request, set the async_req parameter.\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m    340\u001B[39m \u001B[33;03m    then the method will return the response directly.\u001B[39;00m\n\u001B[32m    341\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    342\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m async_req:\n\u001B[32m--> \u001B[39m\u001B[32m343\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m__call_api\u001B[49m\u001B[43m(\u001B[49m\u001B[43mresource_path\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    344\u001B[39m \u001B[43m                           \u001B[49m\u001B[43mpath_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mheader_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    345\u001B[39m \u001B[43m                           \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfiles\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    346\u001B[39m \u001B[43m                           \u001B[49m\u001B[43mresponse_type\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mauth_settings\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    347\u001B[39m \u001B[43m                           \u001B[49m\u001B[43m_return_http_data_only\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcollection_formats\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    348\u001B[39m \u001B[43m                           \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    349\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m    350\u001B[39m     thread = \u001B[38;5;28mself\u001B[39m.pool.apply_async(\u001B[38;5;28mself\u001B[39m.__call_api, (resource_path,\n\u001B[32m    351\u001B[39m                                    method, path_params, query_params,\n\u001B[32m    352\u001B[39m                                    header_params, body,\n\u001B[32m   (...)\u001B[39m\u001B[32m    356\u001B[39m                                    collection_formats,\n\u001B[32m    357\u001B[39m                                    _preload_content, _request_timeout, urlopen_kw))\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\api_client.py:173\u001B[39m, in \u001B[36mApiClient.__call_api\u001B[39m\u001B[34m(self, resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, _return_http_data_only, collection_formats, _preload_content, _request_timeout, urlopen_kw)\u001B[39m\n\u001B[32m    170\u001B[39m urlopen_kw = urlopen_kw \u001B[38;5;129;01mor\u001B[39;00m {}\n\u001B[32m    172\u001B[39m \u001B[38;5;66;03m# perform request and return response\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m173\u001B[39m response_data = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mrequest\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    174\u001B[39m \u001B[43m    \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m=\u001B[49m\u001B[43mheader_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    175\u001B[39m \u001B[43m    \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m=\u001B[49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    176\u001B[39m \u001B[43m    \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    177\u001B[39m \u001B[43m    \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    179\u001B[39m \u001B[38;5;28mself\u001B[39m.last_response = response_data\n\u001B[32m    181\u001B[39m return_data = response_data\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\api_client.py:388\u001B[39m, in \u001B[36mApiClient.request\u001B[39m\u001B[34m(self, method, url, query_params, headers, post_params, body, _preload_content, _request_timeout, **urlopen_kw)\u001B[39m\n\u001B[32m    379\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m.rest_client.OPTIONS(url,\n\u001B[32m    380\u001B[39m                                     query_params=query_params,\n\u001B[32m    381\u001B[39m                                     headers=headers,\n\u001B[32m   (...)\u001B[39m\u001B[32m    385\u001B[39m                                     body=body,\n\u001B[32m    386\u001B[39m                                     **urlopen_kw)\n\u001B[32m    387\u001B[39m \u001B[38;5;28;01melif\u001B[39;00m method == \u001B[33m\"\u001B[39m\u001B[33mPOST\u001B[39m\u001B[33m\"\u001B[39m:\n\u001B[32m--> \u001B[39m\u001B[32m388\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mrest_client\u001B[49m\u001B[43m.\u001B[49m\u001B[43mPOST\u001B[49m\u001B[43m(\u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    389\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    390\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m=\u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    391\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    392\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    393\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    394\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m=\u001B[49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    395\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    396\u001B[39m \u001B[38;5;28;01melif\u001B[39;00m method == \u001B[33m\"\u001B[39m\u001B[33mPUT\u001B[39m\u001B[33m\"\u001B[39m:\n\u001B[32m    397\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m.rest_client.PUT(url,\n\u001B[32m    398\u001B[39m                                 query_params=query_params,\n\u001B[32m    399\u001B[39m                                 headers=headers,\n\u001B[32m   (...)\u001B[39m\u001B[32m    403\u001B[39m                                 body=body,\n\u001B[32m    404\u001B[39m                                 **urlopen_kw)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\rest.py:311\u001B[39m, in \u001B[36mRESTClientObject.POST\u001B[39m\u001B[34m(self, url, headers, query_params, post_params, body, _preload_content, _request_timeout, **urlopen_kw)\u001B[39m\n\u001B[32m    308\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mPOST\u001B[39m(\u001B[38;5;28mself\u001B[39m, url, headers=\u001B[38;5;28;01mNone\u001B[39;00m, query_params=\u001B[38;5;28;01mNone\u001B[39;00m, post_params=\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[32m    309\u001B[39m          body=\u001B[38;5;28;01mNone\u001B[39;00m, _preload_content=\u001B[38;5;28;01mTrue\u001B[39;00m, _request_timeout=\u001B[38;5;28;01mNone\u001B[39;00m, **urlopen_kw):\n\u001B[32m    310\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"Perform POST HTTP request.\"\"\"\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m311\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mrequest\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mPOST\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    312\u001B[39m \u001B[43m                        \u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m=\u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    313\u001B[39m \u001B[43m                        \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    314\u001B[39m \u001B[43m                        \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    315\u001B[39m \u001B[43m                        \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    316\u001B[39m \u001B[43m                        \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    317\u001B[39m \u001B[43m                        \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m=\u001B[49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    318\u001B[39m \u001B[43m                        \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\rest.py:261\u001B[39m, in \u001B[36mRESTClientObject.request\u001B[39m\u001B[34m(self, method, url, query_params, headers, body, post_params, _preload_content, _request_timeout, **urlopen_kw)\u001B[39m\n\u001B[32m    258\u001B[39m     _BaseRESTClient.log_body(r.data, \u001B[33m'\u001B[39m\u001B[33m<<<\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m    260\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[32m200\u001B[39m <= r.status <= \u001B[32m299\u001B[39m:\n\u001B[32m--> \u001B[39m\u001B[32m261\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m ApiException(http_resp=r)\n\u001B[32m    263\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m r\n",
+      "\u001B[31mApiException\u001B[39m: (401)\nReason: Unauthorized\nHTTP response headers: HTTPHeaderDict({'Server': 'nginx/1.27.1', 'Date': 'Sat, 29 Nov 2025 22:07:18 GMT', 'Content-Type': 'application/json; charset=utf-8', 'Content-Length': '55', 'Connection': 'keep-alive', 'X-Influxdb-Build': 'OSS', 'X-Influxdb-Version': 'v2.7.1', 'X-Platform-Error-Code': 'unauthorized'})\nHTTP response body: b'{\"code\":\"unauthorized\",\"message\":\"unauthorized access\"}'\n"
+     ]
+    }
+   ],
+   "execution_count": 2
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T19:45:37.979942Z",
+     "start_time": "2025-11-29T19:45:37.962502Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "type(temp_array_fsgp)",
+   "id": "52e7b7784852e53d",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "data_tools.collections.time_series.TimeSeries"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 52
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T19:45:39.544344Z",
+     "start_time": "2025-11-29T19:45:39.529624Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df_fsgp = pd.DataFrame(temp_array_fsgp)",
+   "id": "1300423cd026c3ca",
+   "outputs": [],
+   "execution_count": 53
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T19:45:40.816030Z",
+     "start_time": "2025-11-29T19:45:40.810451Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "len(df_fsgp)",
+   "id": "f46c881335aa4926",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2831521"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 54
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T19:45:43.429032Z",
+     "start_time": "2025-11-29T19:45:43.420022Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "len(hourly_data['shortwave_radiation_instant'])",
+   "id": "bbac7524849da8b0",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "120"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 55
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "querying from openmeteo to get solar irradiance data\n",
+    "\n",
+    "- this is hourly irradiance over 4 days\n"
+   ],
+   "id": "bc1ff3d5812338f"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T19:45:50.178966Z",
+     "start_time": "2025-11-29T19:45:50.150527Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import openmeteo_requests\n",
+    "\n",
+    "import pandas as pd\n",
+    "import requests_cache\n",
+    "from retry_requests import retry\n",
+    "\n",
+    "# Setup the Open-Meteo API client with cache and retry on error\n",
+    "cache_session = requests_cache.CachedSession('.cache', expire_after = 3600)\n",
+    "retry_session = retry(cache_session, retries = 5, backoff_factor = 0.2)\n",
+    "openmeteo = openmeteo_requests.Client(session = retry_session)\n",
+    "\n",
+    "# Make sure all required weather variables are listed here\n",
+    "# The order of variables in hourly or daily is important to assign them correctly below\n",
+    "url = \"https://historical-forecast-api.open-meteo.com/v1/forecast\"\n",
+    "params = {\n",
+    "\t\"latitude\": 36.9760,\n",
+    "\t\"longitude\": 86.4491,\n",
+    "\t\"start_date\": \"2025-07-02\",\n",
+    "\t\"end_date\": \"2025-07-06\",\n",
+    "\t\"minutely_15\": [\"temperature_2m\", \"wind_speed_10m\", \"shortwave_radiation_instant\"],\n",
+    "}\n",
+    "responses = openmeteo.weather_api(url, params=params)\n",
+    "\n",
+    "# Process first location. Add a for-loop for multiple locations or weather models\n",
+    "response = responses[0]\n",
+    "print(f\"Coordinates: {response.Latitude()}°N {response.Longitude()}°E\")\n",
+    "print(f\"Elevation: {response.Elevation()} m asl\")\n",
+    "print(f\"Timezone difference to GMT+0: {response.UtcOffsetSeconds()}s\")\n",
+    "\n",
+    "# Process minutely_15 data. The order of variables needs to be the same as requested.\n",
+    "minutely_15 = response.Minutely15()\n",
+    "minutely_15_temperature_2m = minutely_15.Variables(0).ValuesAsNumpy()\n",
+    "minutely_15_shortwave_radiation_instant = minutely_15.Variables(1).ValuesAsNumpy()\n",
+    "minutely_15_wind_speed_10m = minutely_15.Variables(2).ValuesAsNumpy()\n",
+    "\n",
+    "minutely_15_data = {\"date\": pd.date_range(\n",
+    "\tstart = pd.to_datetime(minutely_15.Time(), unit = \"s\", utc = True),\n",
+    "\tend =  pd.to_datetime(minutely_15.TimeEnd(), unit = \"s\", utc = True),\n",
+    "\tfreq = pd.Timedelta(seconds = minutely_15.Interval()),\n",
+    "\tinclusive = \"left\"\n",
+    ")}\n",
+    "\n",
+    "minutely_15_data[\"temperature_2m\"] = minutely_15_temperature_2m\n",
+    "minutely_15_data[\"shortwave_radiation_instant\"] = minutely_15_shortwave_radiation_instant\n",
+    "minutely_15_data[\"wind_speed_10m\"] = minutely_15_wind_speed_10m\n",
+    "\n",
+    "minutely_15_dataframe = pd.DataFrame(data = minutely_15_data)\n",
+    "print(\"\\nMinutely15 data\\n\", minutely_15_dataframe)"
+   ],
+   "id": "f718cf3615289b31",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coordinates: 37.0°N 86.5°E\n",
+      "Elevation: 5139.0 m asl\n",
+      "Timezone difference to GMT+0: 0s\n",
+      "\n",
+      "Minutely15 data\n",
+      "                          date  temperature_2m  shortwave_radiation_instant  \\\n",
+      "0   2025-07-02 00:00:00+00:00           -2.20                     5.588703   \n",
+      "1   2025-07-02 00:15:00+00:00           -2.15                     5.351785   \n",
+      "2   2025-07-02 00:30:00+00:00           -2.15                     5.014219   \n",
+      "3   2025-07-02 00:45:00+00:00           -2.05                     4.680000   \n",
+      "4   2025-07-02 01:00:00+00:00           -1.95                     4.680000   \n",
+      "..                        ...             ...                          ...   \n",
+      "475 2025-07-06 22:45:00+00:00           -1.25                    20.240196   \n",
+      "476 2025-07-06 23:00:00+00:00           -1.05                    19.881649   \n",
+      "477 2025-07-06 23:15:00+00:00           -0.95                    19.917469   \n",
+      "478 2025-07-06 23:30:00+00:00           -0.90                    19.959719   \n",
+      "479 2025-07-06 23:45:00+00:00           -0.80                    20.418695   \n",
+      "\n",
+      "     wind_speed_10m  \n",
+      "0        124.831741  \n",
+      "1        164.539490  \n",
+      "2        205.015503  \n",
+      "3        243.104050  \n",
+      "4        276.808258  \n",
+      "..              ...  \n",
+      "475        0.000000  \n",
+      "476        0.000000  \n",
+      "477       16.716660  \n",
+      "478       48.040825  \n",
+      "479       76.951775  \n",
+      "\n",
+      "[480 rows x 4 columns]\n"
+     ]
+    }
+   ],
+   "execution_count": 56
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "minutely open meteo data starts at 2 july, 12am and goes to 6 july 22 45\n",
+    "therefore on influx i query for 1 july 10  pm to 6 july 20 45\n",
+    "\n",
+    "\n",
+    "utc i s2 hours ahead of vancouver"
+   ],
+   "id": "dd2a5efc6d21b409"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Plot some relevant data. this will further be used to generate the relevant coefficients",
+   "id": "9fe9b268872c73e6"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T21:25:43.719301Z",
+     "start_time": "2025-11-29T21:25:42.242073Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "#this graph shows solar irradiance, array temperature, and ambient temperature over July 2-6 (FSGP 2025)\n",
+    "\n",
+    "fig, ax1 = plt.subplots()\n",
+    "ax_twin = ax1.twinx()\n",
+    "\n",
+    "plt.plot(temp_array_fsgp.datetime_x_axis, temp_array_fsgp, label = \"Array Temperature\")\n",
+    "plt.plot(minutely_15_data['date'],minutely_15_data['temperature_2m'], color = 'green', label = \"Ambient Temperature\")\n",
+    "# plt.plot(minutely_15_data['date'], minutely_15_data['wind_speed_10m'], color = 'blue', label = \"Wind Speed 10m\")\n",
+    "# plt.plot(speed_kph.datetime_x_axis, speed_kph, color = 'yellow', label = \"Speed KPH\")\n",
+    "\n",
+    "ax1.plot(minutely_15_data['date'],minutely_15_data['shortwave_radiation_instant'], color = \"red\", label = \"Solar Irradiance\")\n",
+    "\n",
+    "ax1.set_xlabel(\"Time\")\n",
+    "ax1.set_ylabel(\"Solar Irradiance\")\n",
+    "ax_twin.set_ylabel(\"MosfetTemperatureA\")\n",
+    "\n",
+    "ax1.tick_params(\"x\", rotation  = 90)\n",
+    "\n",
+    "\n",
+    "plt.legend(loc = \"upper left\")\n",
+    "ax1.legend(loc = \"upper left\")\n",
+    "plt.show()"
+   ],
+   "id": "a3fa42c24abb970c",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ],
+      "image/png": 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Q6gF8JqwbKZAZA4CaQ+zPEEE+fJgIJxi4j3T7qVNEXkawGSZYx++9e/faNWBhnykDDWqwT0YkDBkLCUQY/CUR6cLjqBnD8Qv7bgma0rCPxrEj0HBNmMHAwVqKyatxiXT6mjKaR72BAmygakHhCOqycJFArEC0qIFYkUDcoLgRqTuk+9JydJeiRQL/LEcgXKR4UZPecgBp8KoGPzwsE5YTnx331etMXRcgUYtyZ89LEal+HewQXEXNZKH/0Uu36XaCkkJ1JcAYxm+goxkCDFFvbNcQ+CVLEqExBVMrWIQxBiM2NtatDACElePxA1kEWW6CZjCZOVKLMNx3NNUOFPo8/WbcAiIsUKCmCcIHGzFc+hnXZM3ofqcrw2iOxSOQcJCREVZ5YqQaHcYwZqNRo0Z24/EASkRgdSRPtnEMg9WSBNkiNF6hzCcYsAgzMBkzBO7rg3kpivex8fpz7qMvoK4MHS7BBt2qDBN0EaaKdLMIY0KBt99+W3TEY3oMfBZhwv3DDz+IkXYAWQl4VGL6CGqSUbbStWtXkRXC6LhgwOlIA5M5OjKg0TDGPUxdZMnoHxZhTIhSp04dmj17tsjWwOcSkS80VaHbXIImNNQTo3kLdcIwNEcnfbBqXlmEmQS1DxUTXFJYhjF6E2GlSinXPACeMTkdOnQQF1fgOAmBhose4HSkgyGpkUhIsvlUMTpCtTmlmHC7Y3TOuXOpRZhsJoHHHG93DKMbQjoSJu0aYDeAi15tAlyRkHzPzu6AI2H64L7qIJdy/z7dd2FPgW0O3xl/b4zfI2GyIxLGxrBqUY1xYRgmeIS0CIPowoxFHASdzfPTOzERRDcstxMSEomzkfogWSWOExITKSyN7Q8nAizCGL+LsJgYoly5iDBwHl5hLMIYRheEtAgDOADiQOjM+E3PXLuTSOdv24QjPLLCuStPF4Qnwck/2fa9uFDHHAVj/G5RoQYpSYgwpCSdePcxDBN4Ql6EyYOh0VKRZ64r43IkYeHafIbmzZsL0zp0lLgCA6rR5osLk5pb8bZIWHhYOItjJnBgpurly6kjYVKEYXg8ImEMw+gCYykPRpB8TynI37FlI9UolovefOGJgNbaYgQE2nu1BOIvLVGHMUUycuTqAp8wPZAlWrtzG3wuPXifMQZBzr9DvSvSj2pkXRgPgGcY3cCRMAOSfF9RXLOn/UpPd3tVXGMWVtEigZkJh2HVgQajgs7Jri8i+t///ie8XRYvXmx9DPMx9YA6IOlMG2NGZaCjrxi6ntaoKcYkyN8IxrM4psFlhyRHwhhGN3AkzKDdd3fibtN/c2fTE8+/RE1atrFOiZcsX75cHOgxSR5DsjFQu2XLlnTx4kWaP3++mKeFWVzPPPOMmLeoBl17b775phA1mO84cOBAOzsFpCPV6UoY3r388stCnOE18T47duywc9tHinPKlCnif/G6Tz31FN26dUs8/+KLL9KKFSto9OjR1qgWIl9qMHAb4ybkBa79aKqQ9/PmzSuWCeZ8+KzVqlWjP//80+f1gQgd1kVa6wNDuN99910xvzNz5sz0YIsmtGndauvziNBlz55dODRjrhnqDzEcHBHFNm3aiNfEazdr1oy2bt1qt55B586dxbLL+1hfju7OiCJiWR2XG4/j9du2bSse3717N7Vr106sPwxEf/7558UwdcZkIsyxHkwtwk6eDOwyMQzjEhZhKnBgjUuMC8rFE8+oK7cT6b+5c6hEqTJUvFQZeqjLEzTpl4lOXwMCaOzYsWJ6/KlTp8S0eIgVjHP4999/aeHChWIotxoIOgicjRs3CmE0cuRI+umnn1wuz+OPP24VM1u2bKGaNWtSq1at6OrVq9a/OXLkiEir/fPPP+IC0TVs2DDxHN4Dc7teeeUVEe3CxdWQbFcMHTqUJk+eTOPHj6c9e/aI8RXPPfeceB9/rw+InXXr1tG0adNo586d1PGRLvTG84/RiWNHrH8DYTd8+HDxf1g+iEaI0BdeeIFWr14tRm2UKVOG2rdvbxWnEGlg4sSJYp3I++6C5Ub0a82aNWK9QCxDeEKEbt68WUQSMbgW64AJAREmh9QfOxbYZWIYxiWcjlRxJ+kOZRkanLmItwfcpsxRmd1OR86ZPkWIL9CoeWsa8l4vITjU0RCAGVkYagq6d+8uxjlAEJUsWVI89thjj9GyZcvo/ffft/4PBNCoUaNE9KVcuXJivhbuQyQ5AgEBcQIRJjtMkSqE4EIkStaOwRcLEaHY2FhxHxEYzKH8/PPPRRQIYiFTpkzWKfeegEgUZoUhNSmHsOLzYdm+//57EWHy1/pARAsiCdeYPwZ6vPW2iLj9Nf03at+ohngMFijffvutiNBJIIjUYMYZImb4HuH4LNO+eMyb9QJRh8Hr6s8OAYZ1Jfn555/F58OQ27Jly3r8HoyBRJhlGxeRMFjyWHwSGYYJHhwJMyAXTh6l3du30oOdHhX3EaV57PEnaMKECan+tqqqFR3pJwgdKTjkYxBQaurXr2/nXQVhc+jQIVHL5AjSjrdv36ZcuXKJFJe8HDt2TIgbCVJpUoCBAgUKpHpfb8GgVkSakNpTLwMiY+pl8Mf6gCDDNQSMfN9SBXPTlvVr6NQJW0oVIlP93gBRKAg5iCUIUaRDsS4h6LSgVq1aqb4rCEz1Oipfvrx4znE9MSYUYRDymI93/z6nJBlGJ3AkTEWmDJlERCpY7+0u0379RdRttaldwfoYUpGIRCHVpi5Ql1MB1J5oavAYolTeAtEAQYWaK0cQwXG2HFq8r+MyAKQTUZelxtH/Tev1gfdGvRrSsLgGF2/F09XbiZQpc2ZrYT5q0BxNWZGKvHLlikhxFitWTCwrBB6K6NMCBf2OqWdnZsOoT3Nc1o4dO4q0qCP4DhmTizBsf0hJ7tunpCTlPEmGYYIGizAVOEi6mxIMFhBfU3//jd4Z+Bk1aNrC+niJXJnpiccfpalTp9Lrr7/u03ts2LDB7r6sV5IiQw3qv86fPy+icbJw3BsQKXIWaXMHdbG7OvWoFWmtD6T3sNyInjVp0kQ8n/nGXcpyKyHd4ZGo1UKKEnVgADVqjkXyEImO6wVpShTYq9m+fXsqQensu5o5c6b4nvB9MSEmwoAUYUePBnSxGIZxDqcjDQaK2q9fu0adn3qOypSvaL1UrFyZHn30UacpSU+BmOnbty8dOHBAiDoUqvfu3dvp37Zu3VpEb9Cth6J2dDWi6P3DDz8Uxd/uAmEAsYP/hxDxJBqFNCe6E1GMj2J0pNbQZYjlduwa1Xp9IA357LPPUteuXWnWrFkiDbt18yaaMHYkrVzyX5qvCyGHjtF9+/aJz47XQcTMcb2gdg5C99q1a9ZaMqxbpFuRFh08eHAqUeaMnj17imaJp59+WhT5Yz2hdq1bt25eC2DGWCJsWZm69Pgzw+n4UYurPsMwQYVFmMGAyGreohXFZk3tiQURhoMzOvR8AYLi7t27VLduXXHghuBwZc6K6OG8efOoadOm4mAOUQL7iRMnToj6KneBiEJkCVEtRHo8rYv69NNPhXUEuiRhN/Hggw+K9CQsK3wlvfWBwnz8zTvvvCMK97s/9yTt3rGNChQsnO53CWGFCBUaFd566y3RNalmxIgRtGjRIlE8j6gbgN0EPmu/fv2oTp06opsS758eaBxA9A2C64EHHqAqVaoICwukjY02MYJxAoT0hQtpirBuGWvTpiKV6J34YrwKGUYHhKV44o1gQE6fPi0OYEj1FJaO0Rbi4+NF5AIH6owoWDUIN+4m0YkrcXaPlckXSzEZUqcLGf+PcXLkzPW7dOW2ko6sWCArRUYYV+AY9TcSsm75EF+o/UJdoZOUc/H+/4rrknev0tLRzwdhIRlGm+O3WTDu0YGxx9xa2ljwd8EEc2QRoqnp1PyFJ6du5GAYJvCwCDMifJDXNWo5zNKYCcrIonQId9JNyzBM4OEWKQPi7MDOB3v/4Mx6w1ONjIy/oz0FwwS8M9IxEsaGrQwTdDgSFuLBscTk+5SUfF/Mo2S05+ilODp62bOxVAzjdxGWcp9II7NkhmG8hyNhlkhFKHI3MZkOXVSMTqMiwql8gazBXiRToN6cEpLvUUIy0b37KRQZYbxoWKj+NswvwlIUEeZgbswwTGAJ6UiYNLfEyBsjodVh8fzNBFtE7J427vUMvh/zCBf520jPCJYxlgiLgA+ftLNgGCZohHQkDL5U8EiSswIxR9AItTuJ8YmUkpyYykogIsWzr/Pm7bhUr8H4TnJiAqUkJ6dat0ayqkAEDAIMvw38RpxNS2AMno5kEcYwQSekRRjIb+kk0mqYdCCIS0ima3fsu5tSbkZRtIc+YRev3bW7H3XX3q2d8Q54hN1Nso8sRsRlpIhw/Qt8RyDA5G+E0TlcE8YwhiPkRRgiXxheDKdyZ0OQ9ci8XWdpxLKDdo8Nf7QqlS+e06PXeXmWfeffkneaa7J8oc6E2btp3VH7GZB/vt6QcmSOIiOBFCRHwAwCarw8SUeKSNgl/y8XwzBpEvIiTIKDjVEOOAkpkXTmlv2sv+SwSI8dzR1fgx3RteFaQkqqdZshOpoyZoxO939vxifRt8uO0CM1ClL5/NwowbjJ9etECQlui7AUCuPuSIbRAcYpUmGsOLOTYIsJnfu4uVmrP2jObhq/4gi1G71K68VizIyMgmXPjrOpdP8c8yO5Joxhgg+LMANy38kBnZsb9YMzQeyu1cO6o1csf6/5YjFmxoNUJIhOSmARxjA6gEWYEeFImOFEsrPHnHFBZRvCMFqKsO+WH7HezhZ/m9ORDKMDWIQZEGfH8/vuHuUZv+Ms6mUm7zDGmCJs+IL91tstj25WRBj8whiGCRoswgyIs1TVPc5f6YL4pHt07Y69hxtgjczoKR2Z4V4S0b17RFevuvX383ado0e/W0tnrtvb2jAM4xsswkxTmB+URWEcqPv5Ytp95maq9cKRSkZPIiw8ymKX4qY/4hu/baUtJ66JxhGGYbSDRZgB2Xn6RqrH+CCvD27G2zvlM4weRVhYjKWD0kPX/Bt3jeGlyDBGgUWYAZm97UyqxzAgmtEvbCHC6EmERcTEeCXCwg0w1o1hjASLMJPgaU3YLifRNMZ/sEZmdBUJy5RJueHpuDbWYAyjKSzCTIK7PlSS09fu+G1ZGO++n8Tk+2neZxinxMUR3brlkQgjKcI8joTxd8AwWsIizCR4atbKyUvtSasub/3Rq24NZk/rPsOkGQWDsIqNdWsl3c+SRblxJnVpQ1qEcSiMYTSFRZhJSGa/H12nhNccsR/o7Yy4RHvRdZtFGONpKtLNmq0UKdZOnvRoHYfzEYNhNIV/UiZh0F97gr0IIU9azRHupCMv3Iy3u88ijPFHPZjYHrNYRNipUx6tZI6EMYy2RGr8eowfwYF868nrvI4NKMLc6V6NdAgzsAhj/CXCkjNnVm6cPq24P7sZQePmSIbRFo6EGYiZW88I12q9FPcz9iT7KMIc05kswhh/ibCkjJbC/Ph4osvpp8oZxgh8/PHHFBYWZncpX7689fn4+Hjq2bMn5cqVi7JkyUKPPvooXfCwOUVrWIQZiBmbPUsdeAprMP8V5rsjwo5dirO7f5uNXxl/iTAU2OfL51VKkmH0TKVKlejcuXPWy+rVq63Pvf322zR37lyaMWMGrVixgs6ePUtdunQJ6vJyOtJAbDjm3pw3b+E4mP8iYe74hL0zY4fdfY6EMX4TYfdSiIoUUSwqIMJq1nTr/9h0mAkGt27dops3bePgoqOjxcUZkZGRlD9//lSP37hxgyZMmEC///47tWzZUjw2ceJEqlChAq1fv57q169PwYAjYYwVTkf6RloHKG8OXmxRwbjF+fPpijDH33YyPG0gwjyMhHG0nAkGFStWpGzZslkvQ4cOdfm3hw4dooIFC1LJkiXp2WefpZOWDuAtW7ZQUlIStW7d2vq3SFUWLVqU1q1bR8GCI2EhSpITYzGOhGkbCfvt5Xr07E8blOcQefCQW5yOZDSKhDlGYkUkrHBhW3G+m3AkjAkGe/fupUKFClnvu4qC1atXj3755RcqV66cSEV+8skn1KRJE9q9ezedP3+eoqKiKHv27Hb/ky9fPvFcsGARZiByZMpA1+5oM0C3/8xdqR7js1zfuOcgtNT9ZrEZPf+pcTqSSZfERFthfRoi7PLthNQnYQUL2os4N+DxW0wwiI2NpaxZs6b7d+3atbPerlq1qhBlxYoVoz/++INi5LxUncHpSAORoOEYm7tJ91I9lsKxMJ9w7G5EZ46kXZXUNQrpwelIJl1kZ1eGDES5crn8s2X7L6Y2d5aizQMRxiULjJHInj07lS1blg4fPizqxBITE+n6dXubJ3RHOqshCzkRNmzYMHHQ6tOnj67bSYOJv1MBHAnzjXsOUwugwZqVzSNuezPQ4BY75nvO338T/e9/obMxSwGFg0gaJl75smW0u5+UnGITYWfPuv12obJaGXNw+/ZtOnLkCBUoUIBq1apFGTJkoCVLllifP3DggKgZa9CgQWiLsE2bNtH3338vwodq9NhOGkx4B6hvHMvscEjMEBHmsgYvPXsLtqjwkCtXiDp1InrvPaJlyygkcLMzMtxBoCXddz8deVE1yeFmvDblEAzjD959912hFY4fP05r166lzp07U0REBD399NOioL979+7Ut29fWrZsmSjU79atmxBgweqM1IUIg1JFB8OPP/5IOXLkSNVOOnLkSNFOChWLdlKsWLSTMqm5dMu+7sNTWORpO78TkV3pgp+UTjFNt182pXqM05EeMnGi7fbixRQSuCnCCjpEwkSjiPyfq1eJElzvO9p+vdJ6++CF2z4tLsP4k9OnTwvBhcL8J554QmTRoBfy5FEyEqNGjaIOHTqIrFrTpk1FGnLWrFlB/VKCLsKQbnzooYfs2kZ9aSdNSEgQfiLyAn+RUKkJ+3rxQZ9en2vCfMOZIWuGSIsIS+e7W3HwUqrHuDDfQ3791XZ74UIKCdwUYY71iiIymzMnUVSU8kAa3WHqZqCMGYJ+yGAYl0ybNk1kzKADIMhwv1SpUtbnM2bMSOPGjaOrV69SXFycEGDBrAcDQf1FYQVt3brVqeeHt+2keC21nwj8RUIFZ8X2nsCdT9qKMGSAMoSHOY2SuQNbVHgARMahQ7b7W7cqER6z46YI23HqemoRhg1UHoDcrAsrnssyc5JhGGOLsFOnTlHv3r3pt99+E+pUKwYMGCBSmfICf5FQwbHuw1PifRRxoY4zx/wMEZZImBc+YXGJyZosV8jUg925o9zOm1cRZQd9iwwbguPHlWtpvOqCsvli7e5bt0cPbSrYJ4xhTCLCkG68ePEi1axZU4wZwAUFdWPGjBG3EfHypp0UJm7wE5EX+IuECpagi9ecv2ErwGU858hF+3oZfB2RbhbmOwOF+WwJ4CYnTijX2DdUqKDcPnqUTM/+/cq1akixO+JJOOYDDzsk0xrNxTCMgURYq1ataNeuXbR9+3brpXbt2qJIX97WYzupmSNhRy/bD5BmPKNIzkyp05GWSJg3jvk44GnpDWdqLKNJqGhRopIlQ0OE3b5tGzlUrlyaf+oYibU2injomp/WkHqGYQzkmI8IVeXKle0ey5w5s+hmkI/LdtKcOXOKqFavXr2C3k6qZ3zUYKkiOYyH69/JY+5aVLgCxfkZM0TwV+FuJKxYMaISJUJDhB04YEu/osg+DRxPAqzbo4fzIzkSxjDaoutWFz22kwYLZ2mpaoWzUecatnlavsKRMN9w/Ib2nbtFkV7UhH33bE3KHKUIL/YK80KEhUokzM1UpNUXzJkokyJMRhK96ABmGMYksyOXL19ud1+2k+IS6jgexCPDw+ivNxvT0Uu3afa2M5r4fHEkzDcc1z+iDdZ0pAfdkeHhYZQlYyTFJd5jmwpPRZg6HXnsGJm6EaFvX7dFmGMkLNHLSBiLMIYJoUgYk5YRqHItD/JacOxyHNd8aOizBlEmLSo8SUeiti9LtHJ+xF5hbiJFhFqE4bE0TEgNTc+eRBct8yCbNk33z3edvu68MB/rC5w5gwn06b4OpyMZRltYhBk0EhZmqUCS3XdaRMLgM3ZONaKE8QzH9Y+73qQjm5bNbRNh8WxT4Rayu69QIaVGCgOt8YWYcdbs4cNEM2Yot6dOJXr22XT/ZczSw3b3kVUUUS10R2KqQ3KyW+vKKt4YhtEEFmEGwVUkJULlS6GF4z3Sm4x3OK79Y5dve1WYHx0ZIdKRgCNhboAIjjRwhqhAmBhCDMhokZmYNEmZCN+uHdFTT3n9MmKbjIy0eYW5kZLkkjA/8/XXRGg8e/FFoiSe0xkKsAgzCKksDmQ60jKbEGhxksp1YdpRKk8Wry0qOB3pAZcuKaIE4itfPuUxM4swOZz8scfc+nNXXnPW1KIHdWHeTH5g3GTLFqXOb8MGRWhPn86rLgRgEWYQHCMpMv6lTkd6a4Og5sgl9grzFseD3TP1iqoc8z37bjJzTZj7SLd3CC9EdoAUY2ZLR8bFEW3cqNxu3tytfzl97a7Tx63zTJHClXVh6RCfxCLMb3zwgX1Nw6hRvteYMLqHRZhBcCyIdVaYr4UIO3qZ05HekuIkreitY35sqNeEIeLy1ltEMGauV0+5jB/v/vxEs0bC1q1T0lQwWZV+aB4i9x1W2woPRxcxfiAxkWjlSuX2qlXKyQTmn7ppossYF11ZVDCeF8Sqa8I0iYRd5EiYFiosyiKOo7ycHRnyNWFz5hB98439Stm+nahDB5vLu2NRvhQTZhZhP/ygXLdu7bY7s3ofIbbHMKLE5Pu2FLkUryzCgsfOnUTx8UQ5chA1aqR0rcLnDtYr6cwFZYwNR8KM3h2p2sFevp3o9evLGqTzN+O5GNxL1I0RS95pJq6jI5WfWEKyZ8PRQzodiRTMJ58ot19+mWjuXKKGDZVowbBh7kXCzJiO3LvX1hUpPcLcQF2ykDtLVGrbFA/nRzJ+inACFOVDXEvrEOl/x5gWFmFGrQmz7FfDVGfD+8/f9Pr1M0VFUK7MUeL2Ma4L8wpZvoFJBnKOZEaL8z3sPzwhpNORhw4pkYGoKKIvv1SiX4MGKc9hYoZjnYwUYWaPhM2cqVx37EhUpYrb/6ZeXbBMSWWbwunI4LN+vXIt5yJLEebmJAPGuLAIMwiOg5zDNC6aRc0ZuvkA14V5h/VgpxLGMZa5j3cT73mVjoxLDEERJidn4ICE9Axo1owoOloRXAcP2v+9jOCYPRIm1wusKTzgvkqFIXKeqlnEg3Rk3thoj96bcQN8P7IeTIowjN8CLMJMD4swg+BuJMXTg72kfZX8VCpvZnGbbSq8w6rBVI/J4dvnbnhmgps5ShFht0IxEibFhrr7L2NGJVUDfv9dSc1JcQHzUvWBy4yRMDj/r13rUVekRN3Tg9Sk9K5LVRN29WqqCQOOHb/smO8HjhxRCvBhMIy0O+B0ZMjAIswgxDuIMHUaUs0RL8xWRz5RjT5sX5FK5lYiYUcuc3G+N8gDlvqrSbB8b3e8jISFZE3YihXOxUaLFsr1kCFElSopqUp0CsrIGB5zFGHSQ8zooGMOhduI8LkxK1LNfZUKiwgPt0XC5HrJmVNJ/TqJhjmas7Jjvh9933CSkUkpY+BIWOjAIsyoIkx1e9JLda23D1645fFrd6lZmGKiIjgS5odIWMHsMbbnXXj+OHs8NjqDuI4LNREG0YT0IpRsnTr2z3XtSlS1KlHu3Lb6METBIMSyZLFFDwDECl4Dbvp4TaMzfLhy3bmz212REvXmhSiY1TZFljjg9VykJB0HdnMkzI8iTJ5kOEbC2CvM1LAIMwhbT1yzf0C1H25WNg89V1/50R666L3Pl4yE8SBv75D7SnWUMncWWw3NzbvJbo+CsUbCQi0duWePcg0PrMxKetwKHtuxQ/FPkg7jsqC5QgV7cQKfJVkX5oYJqa7ZtYto8WLlM/Xv7/G/q2vCYFchp2zYCSoXImzz8at291mEaQy+G2ciTNpS3L5NdN1++DpjLliEGYSpm9IeKVI2X6y4PuRFJExSOEeM8BFCE8CZ685dtpm0SK2mEGGU3LjrfBacOtpQu5hSiJ45Wvm/24nJLiNophZhFSu6/hscoEqVUqJc33+fOhUp8cAJXtds2mRrTlDXvXlbmB+piNVEdce1CxF28uodu/ucjtSYAweUuadoOpE1jwBpyTx5LF8Cd0iaGRZhBkFd1wEcExJl8ioi7OAF7yNhaF0vlkupSTjKdWHeR8JcPH8rISndg+SYp2vYpSPxlKf1ZKYQYc5ElRpZL4Y5e65Em7ReMLr/lVwnlSt79e/w/lNHwiJlJEztPehiXRXPbR+NxG7IcV/EaNQJjOYTNWxTERKwCDMID1bOn2Zhfpl8Sirx1LU7XndIAmlTwR2SnuPEocKOdUeupCvCsmdSxFfGDOFWp/OQKs53V4R162Z/39kwa7NEwtyJDqbBh7N3W29DgMkpDsluRMLUZtASTkn6oQlFnYqUyKgnG7aaGhZhBgFmqmkVzKL2KGfmKBE58aZDUlIyj8WmwofXCFVskTDnKsyVOFZ/leEWBQeRndnynYeUTcW+fe4JDox2adJEuf3ii87nKEoRZvRIGOw43BGmLjhxxdbtjM1LFua7k450Jrgc9z2MBk752J4d4UhYSMAizCBULZzd7n6yk7b7MnmzeNwh+WaL0nb3rYat7Jrv/dgiF5EwVyle9UFNijAQmzHEOiRRhCw7GUvbb5dOmTpVGWM0Zozz52WKzciRsJs3iU6d8kmEqTXTlhPXrI75dunIdLojc1gitODQRe/rThkVWNeIciE9XNfW4W6FvcJCAhZhBsHRqdqZ9ZG1ON+DDkn1cF/AkTD/1YSdd2HYau/jFJZqnmfIpCOPHVOu4ZKfLVv6f49I1/vvE8XGun7e6JEw2QmKz5Ld/kTMG3q2KE1R0qJCHQlzqAm7Y5nUICNhaquVU1e5aSddtm0jWrhQaR5JLwqGWj9n2zC75ocELMIMgmMCwGkkzFIX5kmHpGPNR0lLJOzirQS6Fe+8kJzxrCasXomcToucndWEqb8O2SEZMulIKcKcpRa9wQyRMGlf0LSpJi/3RvNS1sL8/y08QNfiEu0jYZcu0ZiF+6nioP/ovz3n6Z5lP4P9ROcaiqg9rkpvMk5YsoSoXj2itm2VEVOuzIKXLlWu1V2RajgdGRKwCDMIji4FzsoyvOmQDHcQYdliMlAeS9SNU5Ke8dbUbeL6+GX7tv5aFtsJFNs7457KaV/dcJEl1NKRWouw/JZmlitXiJINug6deUj5ALYvWQt2+XYivffnDuUJGODChwwTNJYeEdevTdliTVkiQls8l3IScZw7p10DT68nn1QMhMGiRbaaPjU3bhBNnqzcfuQR568lvcKQtpSvx5gOFmGGIf1i2LJedEiqa5AkJS0RGx7kTT5bArhjvCpPlCMcvovYUE1HaiXCMI5HrtPLl8lw3LljM6Nt2VKzl1263zZPc8NRixkromNStDqpCUP0rHhuxb7mxBX7kwxGxeDBiuhHY4kstl+9OvUqggC7dUv5uwcfdL4K4RMGYYyTNLPMQGVSwSLMILjTkJTLzQ5JtfmnMzuFUpYC/yMXOe2QHssPXKReU7fR9TuWtI4T0hNTMh3pKIithq0swrwjIsI24siIB7E1a5QICNJSJUv65S1uqbctmZJUIWvCEAkrJiNhnI50zhdf2JpEvvqKqHVr29xPV6lIdPa68rSBMJZTH4xc18ikiXJ0YHSPu6bp6JDccOyq6JCsXChbuq/lGH0BHAlznxcnbrIrovdmGLeMNlhKdWz/ZzFsZRHmAxjkjY5LI86PlAdqpCI9nBfpFU5EmDUSFhFGJSwiDPWiKNzPFMWHD7udqhRgn35K1L694oIvvcAQ7pY/cPzt2rWurSkcvxPUNDp0rTLmgSNhRrM/SAfZIeluXRhHwrTh/A3XHWNxCUpqeNWhy2lGwhwFcUjNj8Q60DodCeToFyNGwmQ9mIapyDRJIxKGKG22TBmsZsKOdY8hz+nTRBcuKNHXd96xueCjyxci6s8/bavo6FFle4yKIqpZ073vhEWYaWERZrZImKUu7HAaXj4paTjvg1JykPeVODZmdPf7SeM5dJm5k2p2TEdmCaV0JOpo4BMGihfXNhIGjBYJi4sj2rxZ06J8t7tJVai7I4FMSaoNYBki2rhRWQ1VqhDFxNjmP779tnJ76FDbapJRsFq1Uo8qcoRFmOlhEWYQ3PWodqdDUl0TZrEMsqMQBnlHhlMiBnlfY08gX5HRSVfY0pFhoZuOlFEwHHTSOzB5I8KMFgk7ckTxmEJzgeyS8zfp1ISB4pbZsse5ON/5kPU6dewff/NNJd2wfbstmvXHH+6La7PMP2VcwiLMIKiFk68dkulFwrDDlfUfPL7I3e/H9XMvNS6R5vcoH3M0zg2pdKQ/UpFGTkceP+6f9eGhCJNGwnLUkbSp4EiYA1u2OBdhuXIR1ahhqw2DmJo3T7n/wgvufyecjjQE99Iy53UBizCT4U6HpFoHOJnPKyiVl0WYZt9J5ijr7ZtOBJX0CXP8LkIqHekvEWbUdKS/1gcR5ctqP30jrXSkLRKmHCqkTcUx9gqzZ/9+WzrSkebNleunn1Y6JpHibdyYqGzZ9L8sFmGG4ODBg9SvXz8qXLiwx//LIsxkNWHuzJBUF/k7psAkJS11YUd4hqR7308az2XMEGHtnrxyO8F1OjJVTVgImbVyJCww68Pi+ZXWAT/frSuptk1ZtmCrCePCfLv6PRTmA2fCSoowOaAethPjxrn3ZUkRxulI3XHnzh2aOHEiNWnShCpWrEgrV66kvn37evw63GNs0hokxabitldmrepI2NE0/MYYG+kZCKDGjhKIrsYlUklLhsxRYKcWYZGpvZzMCkfCnK8PLZsULDimvYv3/5d+f7keNSyRV9goZEqKd1mvKMsUYEiMcoeYKCVaG9IcOqRcw5MONXyOYHQRasMgpKpXJ+re3WnU0SnSJwyRXOwoAmFVwqTJ+vXr6aeffqIZM2ZQ0aJFad++fbRs2TIhxryBI2Ems6hwp0NSHVVz9ZMuZZkhyZEwbYD4Aov2XUj1nDXakKowP4Rqwk6etB9aHOqF+X6sCXusVuqUyTM/bVDsFfLlo6h7SanTkZaDPywqslpqFU9c5Q5JwYEDyrWr9CJc77/5hmjmTKKBA90XYECaDWPsFkYdMW4zbNgwUfPcp08f62Px8fHUs2dPypUrF2XJkoUeffRRugBrETcYMWIEVapUiR577DHKkSOHiHzt2rVLvAdez1tYhJkQT2ZIujqxKmEZXXT5dgLduMtzy5yx+bhl5IsHbDtx3XVNmKNZq+VgdzfpnvmtQuSQ7ULKkGjNC/NxAEtInQoOKc801RBvZ+w7d1NE3g7ksUXf5PB4eYKAA44cRM9eYRYOHlSuy5XT/LsSdheZMxuzrjGIbNq0ib7//nuqWrWq3eNvv/02zZ07V0SxVqxYQWfPnqUuXbq49Zrvv/8+PfLII3TixAn66quvqFq1aposK4swgzBrq+UgpUGHpF0kzIUKi82YwVrAyylJ52w7aRNU6WUJKhbIKq7LF4h12R3pamyR6YvzMUMPF3+IsOzZrYOpDTM/En5pcn34wZ4iMsL5br/d6FWpxiPdik9KVTvKHZIeRsK0OpFgEeYWt2/fpmeffZZ+/PFHEbGS3LhxgyZMmEAjR46kli1bUq1atURN19q1a0WKMT0+/fRTId5KlCghBNnu3btJC1iEGQRXbuvpdUgevng77cL8NNSDLM4/ysX5TvGkPKNVBUtazAn3XAzwjo6MoCjLAdPUIkwWHcfGKhctQXjRaDYV8mALs08ZBQkUJUpQ/RM7rXfldqfeNtkrzAF4gIFKlfzzncjt1ygnEX7g1q1bdPPmTeslIY2oNtKNDz30ELWWszstbNmyhZKSkuweL1++vKjrWrduXbrLMGDAANEFOWXKFDp//jzVq1dPRMNwEn3t2jWvPxuLMJMiOyQPOakLc8eiArBNhfvItI0r5Gr+Y/Mp1wO8nXwZMiVp6g5Jf6UijRpJkGJRLncgKVmSUlSCS27X6k3TOsibbSqUqCU6HkHt2v75Toy2/fqBihUrUrZs2ayXoeoJBCqmTZtGW7dudfo8hFNUVBRlR3RcRb58+cRz7tKsWTOaNGmS+J833nhDRNTwWMOGDUWUzVNYhJkUd2dIZotRbBCcwZGwtFGncrecSPtM6IDFLiQ+yRL2cmKI6UwQWzskzVyc728RZrTifHmwlcsdAJNn69+XKOEgwpykIy1eYWzYipqEbYrvF7ZdJ2a3miCL80NYhO3du1ekE+UFUSlHTp06Rb1796bffvuNMmo5dcMFsbGx9Nprr9GGDRto27ZtVLduXdEM4CkswkyKrAs75MQrTL1bblTa8gN3QilLNI1d833nwcr5XT5nM2tNrcIyyw5JjoSFTiRBw0iYp/0cfY5koPtOImH26UglEnb2RjzFJ3nuEG4q5HxPf0XBjLj9+knwZM2a1XqJjk5tOIx048WLF6lmzZoUGRkpLii+HzNmjLiNiFdiYiJdv27fHIXuyPz5Xe+f0wLdlqBKlSr09ddf0xl5QukBLMJMSmlLh+QhZzVhbp4dl5RdUFfiKFkWLjFWPHHsqVk0h8v1Lw+UjhYVINYiwjgd6QMhHAlLtgzgdpe/Dl2n++ERzrsjV68W6xD1pnK7PHk1xE1bEQmTw7j9BdeEuUWrVq2EZcT27dutl9q1a4sifXk7Q4YMtGTJEuv/HDhwgE6ePEkNGjRw701E5uK+KNIvVKiQsLk4evSoeHzQoEGiXsxTWISZlLQ6JO1nR7p+jULZYyg6MpyS7qXQaR7k7VNhft5YW3gcEQTn6UjXNWGm9grjdKRzEaZBJMyVtcmTtV13Xd5XpXJuynTktq1EMKN85hmRhi9mSUmGfF3YiRPKiipdmvwGR8LcjpZVrlzZ7pI5c2bh4YXbqCXr3r27cLWHuSoiZ926dRMCrH79+u69CRF99tln9Msvv9CXX34paswk8BBDR6ansAgzKeiQzOWiQ9LerNW1kkAdSEmraSs75/uS6lE7i1+9rRi3unIld5aONLVrviyK9VdNTQinI6XZqiMZM7je9adEZ0wdCftvgfIAogg3bqhsKkI8EnbqlN+sRKxwTZhmjBo1ijp06CBMWps2bSrSkLNmzfLoNSZPnkw//PCDiLBFwODYAjol98sZoh7AIszElHbVIWnnE5b2a5TMI8cXsTu2Ize9NLH9Z5fFksGxOzKNwnxTpyOl6JAjWrQmhNOR9+45F2FyIHd6IkwSrj5zW7bMKsJQqhCyINUrZ0b6U4QZbfvVEcuXLxe1WhIU7I8bN46uXr1KcXFxQoB5Wg+Guq/STiKfSFPCAsNTWIQZhLrFncwk06BDMr2Mmm18EUfCXK1fT9lz5qZTEeboEwZiZToyFESYBqIjzUiCUXyWAhAJi5QTuZ1w30nRc3jKfdv3s2gRFctlSUeGsgjD94SDLn63nowi8hQpEhAx9rDblfGPXcaqVatSPf7nn39SjRo1AjfA+/Dhw3TkyBER0ouJiRHFxq7c1xnfaVImN230cEyOqw5JT+ZQluJImEswR88bVh+2FwOy58FpOjLK5BYVd+/a3OH9JcLkXLcrVyjkImEuRNhLjUrQDyuVgmKnIswhyxiRMRrD84iefx5taFTiPR5dZE1FIo2ewbt9gVvINH1iItHVq7btmQkKKMB/4YUXREQM0S9E01DgjzTlP//84/9I2JUrV4TjbNmyZal9+/Z07tw58TgK3t555x2PF4BxjxQNOyTdGVsk4UhYGt+Jiy+laVnnEYwW5Zw/nmY60sGsdcHu8/T4+LVkGmTUBwWuWZXRTpojD1poTb9nAEsFKRY1ONi66o7Mn821j1J8RGpBEY50m3SEP3rUath69sZdSkg2wDo1aj0YgCjOacmEWI63TPDo1KmTmD+5ePFiUfgPUbZv3z7xWJs2bfwvwjAAE54baOvMhLEaFp588klasMBSvMlojjxQa9Ehadcdmc5ryEHeV+IS6fod+4JyxjOauRBn1nRkGhYVSEeevHKHXv91C206fo3enm4ZlWKmVKS/Iumq+XHkw3iRgJCcrEQHgQaiVB0Je6CiezV3CWGpEyRhBQvY5kpeukS5UxIoc1SEOBE5Fao2FYESYepomBzxxQSF5ORkGjJkiJgfuWjRIuFLdufOHVq9ejU98MADXr2mxyJs4cKFNHz4cCpcuLDd42XKlBHTxRn/4E0pgKsOSbVPVXrHPXTnFbCcNR/h4nz778RFfPKhKs4LPSsXypaq9d8+Epa2RUXTr5ZZH5+9zXNTwJCsBwMY4J3Nsu6RztH7GByJBnM0ZU1YZHgYff+8cy+r/Fnto2J3nRTzhxUspKxDS3Qu7Ngx1fiiEBVhgSjKdxRhHAkLKghAwZoCYkwrPBZh6ChQR8Ak6DZw5mLLaEOKhh2S9j5h6UcfbB2SXJxv9524+FIerWl/giKpVcwWkRkyd2/qmjAPHPPrl/S8USNkRRiQ6Ry914XJ+jjUGKk8iHyNhGWNyeDyt54vq/1++2qck4h3YctIqVKllOsjR6xR8pAtzg9kJEwW/rMI04UpLJz4gybCmjRpIgrQJPhhozgN6rBFixaaLRjj2ww4T2dIulcXFqI7WxekuDC4jYxw/rNSHwT/3HI6lVmrs3RkFhci7OZdkxTq+9ueQiLrq4wSCdMgCgaS79kiYY588nAlMTv2885V0n2dMBlJlCnJI0esHZIh6xUWjHQki7Cg065dO+rfvz+9++67NHXqVPr777/tLn7vjoTYghLcvHmzmMPUr18/2rNnj4iErVmzxuMFYNzD285kZx2Snr6WHF/ENhWO30nqFfnV41U9/o7SKsyXFhWO9XhHL98W4s1ZR6Wh4EiY80iYRiJMRsKcibAXGhan5+sXc2sbsp5AyEjY0aNUvDlHwgRcExZSvPHGG+J65MiRTn8n9zxs/vE4Egb7/4MHD1Ljxo1FlwDSk126dBFTxEvJHyiji8J8dYfkQbt0pPJa7tZBy0HenI60x9k30rCU64Ho4O3WZa23ZbOEOwO8b6osKnBAjU+6T+du2o8/MiQXLgQmHWmUSJgUYVmU35xW3ZERLnzB3BXx1k1TFQkrHsrpSBxoZZE8pyNDivv377u8eCrAvPYJwwymDz/80Jt/ZQJcEyYjYZj9eCcxmTLBd8ryYmEepiORdki6d58yuEi3hTpvtkh/ftxbrUrTqMUHxe0TV+OofP6sbqUj1eDgh0aLIxdvi/SnoZEGqtJQNdRrwjROR9oiYRr9ZtWRMEs68sy1u5SYfJ+iIkNov4C0IA64aPrwdyodyPdg13zT4bEImzhxopgc/vjjj9s9PmPGDNGqCRMzRj+RMNkhCYuJIxfjqErhbFZB5665LrqnYjJE0N2ke6IdXc6TDHkcvhIZGUgLrPMKBbLSvnM36dz1eEWEWV7HaXekgwhDkwQMdCHCEJl05UlmGHzwxLpxJ4l6/LaFhnWpSkUtgsDwhq0apyOT0xD4nnBc1n1JEXbiBOWJiaBMURF0J/Eenb4WYvsFWQ+GgnnV/EC/YbT5pyZmyJAhaT4P3zBP8PjUZejQoZTbyVlr3rx56YsvvvD05Rh38WFaheyQPGipC5N6zt3dsjLIW9aFhWDqwU2LCmd1N84oaLH8gNFlegO8cfCEAJbEZsygGqpugu9CpgdlpMoDqg1ZSGuPXLGz7nBJiKYj06oJc1Z7qKZ/u/LW2+dvxNtEB7rgk5Mp7PRpm01FqKUkA1mUD+QxF4bDXswnZLRj9uzZdpc//vhD2HaNGDGC5syZ4/HreSzCYNIKozJHihUrJp5j9BUJU3dIOjrne+KNKQ/8XBdmw/ErcTUixpGClhQiImH2syOd/736+Jk1Y6Q1PYzifMPjYSRs47GrdOV2gnnTkRpHwlA+ABDFTgu10Jc0KpWbhj9ahXJniabercooDyKtKff/qAuTMyRDzSss0CIM26/cYRtlBqpJ2bZtm91l9+7dYnIQGhZhZu93EYaI186dO1M9vmPHDsrFM638hi9zWx07JD2ZHek4Q9JVh2Tx/v+Ki/WMORRFmJtfUoHslkjY9bvpmrWCONW0g4wZImxRyYsGjz7A8PDGDeW2G/uOUYsO0hPfr6Nany0W0wPU3FKZ34ItJ65SvS8W0z87zxorEqZxTdg/O8+5ZSPRsnzqxghsjk/WKUqbP2pN1Ypktz1hZ1OhbIsnOBLmX5DylNswpyR1R9asWemTTz6hgQMH+l+EPf300/TWW2/RsmXLRCcALkuXLqXevXvTU0895fECMO7hgwZL1SFpS0eGeREJS33g33/+pvX2x3/voVBFRh3So2C2GId0pPudakgrlcqtfBfnb8an8g8zFGpBpB4t5ILRSw5Zb8/YYolEWNhz1rYNbjlxjR79bh1duJlAb/6+TYlQGi0SplE6Ur1e0mJgh4pUzhIxd+zMdac4/1ioeYUFOhKmrgvjSJguuXHjhrj4vTD/008/pePHj4vQGyz8AVozu3btyjVhuk1H2ndIWl8pTJtI2IRVx6y3L3mTKjLJd+LuV2RNR95wTEem/4WgRixbpgyUO0sUXb6dSMcuKc0WhhZhMAK17EtcMWOzveg6cN5muQJ2n7lB9UsqkYJHv7MfcL5k3wV6wCiRMI3Tkd0aFqd+M3eKNHZaQHAt6NOEBv21h5buv0hvtixtFVipKFpUuT592tqMwpGwAImwffs4EhZkxowZk8ovEunIKVOmCCNXv4uwqKgomj59uhBjSEHGxMRQlSpVRE0Y4z+k87UWHZI5MmcQj3vSLyVHlFy7kyTGmuTMbBupcsaSVpNRiFBBXQJWPn8sda5hGe2SDnIWJ2rCYE8hLSrccRGQXW6ITF6+fVWIYsOKMA/qwd77074EYutJ++1s1xnXZ6CT1h2nB7qUtYmcxERNRgIZIR2ZIVLZXuzSiWl07n76SGX61IOITHFLOvLk1Tup9gumJpiRME5HBpVRo0bZ3Q8PD6c8efIIZ4gBAwYExicMlC1bVlyYwIA2cF8oky8LXTl6VXRI1i2R0+PCfPiLwZMKggvF+Tkz27rZEGELRWQEq27xnPTH6w3c/r/82TKKdZ94774QxmmZtToSbymwRmQSReqGbpTwwZ4CUUAgt8m/tp+lLzpXoUgn3Q1rDl+hgwkRdKR8I6p65iAVunYtMN5OOkhHWrxa3baj8ahT7/JlyhsbTRkzhAvz4JqfLqLFfZtZu7FNC0S8NBlmERZyHDtmy/xogcc1YagBmzBhAj3zzDPUunVratmypd2F8Q9II/pCmbypOyQ9qQmzH+RtXxeGs+BQxFpQ7+GvCGa3OHiBczfupukTBqJVJpiyBswU8zx9EGEStU9a7c8WU70vltg936aiIrZ6/7GTenQaQI3emEh3L1wKmXSkDNZqOt1KFQlDHaOMhoGpG0OgQ/7MGaX2AFYdcl0EApX4ZYLHSy+9RLfk71QFpgfhOb+LMBTg4wIxhhFG1apVs7sw+oyEOZsh6Sm2A7/r6EuNoumnPcyC1d/LiyhDAUtxfvdJm2mMpeDclaEmDDEltyzji2y+bSaIhHnhESYpo4q6wIbh+h1bl+SSd5pRt0bFxW2Y40q+26R0DKqji39sPkUX9DAGSuN0ZHqdtz6JAUtaTA7y9qQ5xdCcPq1cFy7sWTrBVzgdqQsmTZpEd++mzv7gscmTJ/s/HTlt2jRhTta+fXvyle+++05cUOgPKlWqJNxmZXFbfHw8vfPOO+I9ExISqG3btvTtt99SPr2mEvzIc/WLUZMyuemzf/d59f9l8tk6JK3dkR7uP5wV58v0mKR0CLlmy/XozQGuYPaMtP0U0aVbCXb2E65SwajFU4swKYiPXY4z7iBvWSSfTiTMcRurXiQ7bT913eVYJwnWEYbPo+vvgOrkY/zhBHr0ShzN23WeTl27I17jh5VHRbPD5o/akJnSkXLIfJg/xAAORHfu2EXCML7I9ASjHgxwd2RQuXnzpvg94YJIWMaMSm0vQFBq3rx5wsLL75EwFOaXLp3+jDx3KFy4MA0bNoy2bNlCmzdvFulMDAXfs0exOYDx2dy5c8VIpBUrVtDZs2fFsPBQBGmVl5tY/Hl8iBicunqX7iQle7VjdmZTce2OUpsjSQiFnbCFtJzu3bWpUFMkp/M5kIVy2B6XIqxwjkwUFREu1re6McJs6cjke/epwVBbirFvm7LUtlJ+6/2MURE09pkaQkg1L2dLDb3Xtpy1FuqFhko0TJKYEkZD5u6l4Qv20+8bTgoBpq4z00UkTDMR5oeaMCybbGxAcX7uEBNh0pQ80CIse3abaz4TcLJnz045c+YUvyXUw+fIkcN6wRQhpCJ79uzp/0gYIlOjR4+msWPH+vzD7tixo939zz//XETG1q9fLwQaas9+//13a60Z5lZWqFBBPF+/fn0KZTxd9eoOyUMXlB29p9+fdZD31TvWgb19p++w+5uEZN/SpkbClurx/H8LOBm8nRnD1Z0w4vFq1OTLZXampEhdFs+diQ5euC0ik0VypjM7Ua9DkEF+m6hyBNuajAKCt1qVoc3Hr9o5veMEpX3lAuI3gUjxbxtOWO0qwCM1CtIHs3eJ21XPHaS9BcrQkv0Xnb7fpuNXqU5x79OjPiPTHJm0+T5t9YakHVjRSEmePStSksVy2Trj0WxieiyZGypuL+79DouwoAJvVETBoEdmzpwpBJk6OAWHiIIY6+VvEbZ69WqxMPPnzxfpwwwZFLsDyaxZs8gbEM5DxAvFbQ0aNBDRsaSkJFH8LylfvjwVLVqU1q1b51KEIW2Ji8RZAV2oIjskZV2Yp/vlfFmjKXNUhHBwRzE+IkHrjl4J2UiYjDK44+/lan6kmhhV7ZcatcBSr9+SubMIEYbIZHMl8GO8AmfgYseFHd7IRQdTPa625JCiVEYjYTqKmYdoflCnc0c/VZ3++G0JfTt9IH3bZwR9T4Wdvifq86Z0r0dBF2ExzqOi3p4oaF66hNQYRBgiYaUq2jn0j32GQkOEORnf51fgpwe8MARlfKdZs2bW7sgiRYoIawotiPQmJNe5c2fSil27dgnRhfqvLFmyiIGYFStWpO3btwt1ifdTg3qw8+fPpzlgHOMDGOcdkuuPXrXVx3i4Y0bkDClJeDIh+vLalC2p/iYhKXREmLSW8CYiLA1b1bSu4LrWsUqhbGK9ww5DUipvZqI9Bi7Ox0EcFErtr4axRHvP3aR/LWN31ERHRtATtQvT4n0XqUmZ1N1pagEm6VS9EHWae4IoIY56XdtBP+Qs7NRcd9Why8Lrrlax9B38NefePcX+QEMRZuuODPNbp17+rKlPKEyNtCgIViQMIgwbbyCbAhgr0hP1zp07Yl52ovzNWqhatSr5VYQhJagl5cqVE4ILdv9//vmnMDxD/Ze3wCytb9++1vtnzpwRos5shPnUIen9QRtdeRADzsYXAUTGEMHQtAbFlOlI+wPXmKdruOyOBBNerE3TN56iJ+sUsYuEAVffha5JSrJ5LTlEwjCgu+lXSvrVFV8+Vk1EYtNaZ6mwpA+yXLlIQ16sRAP/cj5i65ulh+iXbnUp4MSrujO1EmH+ioSpOiTVNZHfP1+LTA2M106cCI4Ik5EwiPW4OM3qBhnPuHTpEnXr1k1kA11l9QJi1qoV6kL/WrVq0aZNm0TN2ZNPPikU5vXr1+2iYRcuXKD8adSQREdHi4u6o8GMeCNyZIfk0cvKQTvMTzYVN+OTKVuMfZrajEine4+EgIXcmaPtxPHD1dKuJcgbm5F6tSpj91gpS7OFISNhEGAQCBhX5NBRhAHdzoAZqxqP17sUDleuiG5j/IYwvQCD1CHInqtflKZuPEXLD1wS3Zfowgwo6rZ3zUSYcq35SZGDXcLhz9vRnaR7lDWjyX/3yMKg3AUDtQNdmI86QfxeMPgexfkswoJCnz59hC7ZsGEDNW/eXGTvoEs+++wzGjFihMev55UIQ8QKNhXOQnFbt24lX8AcStR0QZCh3mzJkiX06KOPiucOHDgg3hPpy1AnzEdPJW93zDbDVtcH/gmrjlLfB4xYpOQZ6ZmspoU6etC4tHeGj/K7uHgrQdRGxRrpACjrwQoUcNvt9pl6lpmFGqTQsO1DiEnaVSkgGlfgxzdr6xn6ZskhmvBiHQqKCEPnoUb1Jn7xCVMPXLd06kVGhFNWJ2lg09aDwSMsnXmnmoPvENEwdBUjJYllYALO0qVL6a+//qLatWuLujCkJ9u0aUNZs2YV5VAPPfSQR68X7s3wSoTiUJu1bds2qlu3LuXKlYuOHj3q8fBKpA5XrlwpfMJQG4b7y5cvp2effZayZctG3bt3F6lFNAKgUB/vCwEW6p2R3iI7JCXe7JfdcWofs/QwhQK+WFSAVuWVCJA6xegJiDrksTjvGy4lKevBvOgm0spk1O6pLNFCmL3ZorRIL6N7EkPBjVyUrz5R0Lw4IFSLxINVDybhDsmgg+ZB6QcGewqkJwFmaHsThPJYhMEs9YcffqBvvvlGpBL79etHixYtorfeekvUdXnCxYsXqWvXrqIurFWrViIV+d9//wlVKQdldujQQUTCmjZtKtKQ3nZfmg1vT2zRIWl9DS/+H4O88d437tpsA0IVX2rCwA9da9PWgW2oXH7v3dFhRgqOXjZYSlJGwpwU5TsbNv3lY54Vu6Y79sVZVb7FC0+mhuUkAyOLMFkTprmXb6iKMLndBisKFarrXUdAryArBzAl6Pvvvxe15+PHj6cCiOx7iMfxVKQDGzZsKG7HxMRYLSCef/55EaGCf5i7wAcsLeBIO27cOHFh7PF07qNjhySIS/Dc0wuu7hiaHKpDu7VM9aCmKacqMukNqAvbcOwqHblonkhYkoPNycgnqlGXmoW1E2EonMVBzKHzWvJmy9L0146ztHDvBdp79iZVLJiVjCvC/FQTFqpiQDaTpFGX7Fc4EhZ0MLbxnMXjcPDgwfTggw/Sb7/9JoJSv/zyi/8jYYhGXbWMG4FnF4xTpXeGPOtiAoCX+1TZIemLsaJMSaaFHDRtZkYtUiIlO04Hz8Fa3ShR+oN5VLz/v8LewTAHMydnjrcSklwO6fYJNOzImYxpDEEunTeWHqpSwNopGTD8IcLIT92RLMI0XqG83o3Cc889Ry+++KK4jdr1EydOiCzeqVOnREOh30UY3GL//vtvcRs1WhgthPQh3lxL/zDGP8gOSV+QBeESGLjO6dmIXlSNhznow6Bwo4CB0cGux5LfBdZ3sqUA6KVJm0j3XLxo32Wn4m6icnKA4dsftC8v6rUCURemBs78YP7u83Tg/C3D14RpXpgvRViojdCRHpXBml/MkbCgAgP5UqVK0b59thnOmTJlopo1a4rRRd7gcToS9WDoYASYk4Si/LVr19LDDz9Mr732mlcLwQQOdYfkS41KaBIJm9y9rmjnxwURGRheHjx/i2oWDYLhZYhR2kmjhGwY0DVSBDkZeJtgEbcvNChuN5dQE7CjRHF1GpEwUDZfLLWvkl8M+UY0bOwzNcmYIswPA7xBqEfCgi3CQm296wQ4NsBYXks8joShJTNS1Zr71FNPiY7JXr16iZwoExjCfOiQdBXRchfH/1OfZZezRNr2Byp6EAxQU+SDobCWwHkfMzzVHLscJy6GjYRZRBjqDzVHXZyfDr1aKtGwf3eds476MmpNmN8iYfBhtJyUhwTBrgkL1QikjkDwafjw4ZQMvzYNcCsStnPnTqpcubIQYLidFp5a9jPe4cs+FW3483afs9a9eBt9sS2LSoRZOv1Mm468c4fo8ceJ5s0jev+fYC+NKO5Hh6Sj6P1nx9lU5q5GiISdunrHmlbNFB1cEVahQFZqWykf/bfnAo1ddphGP1WDDNsdGe4nMYDXv32bKGuAmheCCQ66crvhSFjIsmnTJuFfunDhQmFLkTmzfVDCUwcHt0RY9erVxbxGeGPgNg66zorw8binlv1M4Hm3bTlx8RbpTSW5m3gvlQgLWB1NIMHBBkZ8K1fSjWiN02Q+gMikowibu1PHIgxiA+vSSSTs83+VWotGpXP5x33dzZowdTQMImzujrOiTsydphQ91oRpnpDEMkr3dqTGQkGEYZvBcQ+K1sv6H5/hSFjQwQQfaSCvBW6JMHQ+5rHsLHGbMa5FhSbv7RCGU3dCwgIDT1+JS6RLtxJSCTZDM2iQEGA44Hw4eCrRReUI91ap4DrVq4VBy/J5aTVq8i7cFkLYFw8yvyEFEMoXVAdvLPeCPedFdG9Qh0r+eW+HcTvpUblQNmpdIa8YFj5u2WEa+UR1MqZFhWYvaXtB1CchMgQRFugRPsFMRWIbwtiiYMA1YUFH6/nZbgWpYcsvD7y4ndaFCQzBno+dNWOknYGrJCYqgorlzGS+lOR//xF9841ye/p0+sciwED9vWuDt1wONXr5skZTs3KK0Ph7h8VYUq/1YEhFWjbkpHv36ZO5ykDt5+sX8594tAzxJovNjjvITsm/tp+l4/6stfOnRQX5gVArzg92ZyTgSJguQD3Y4sWLhVGr9Eo9e/Ys3ZYRfq0jYdKSwh3QJcn4nyBrMPr3rSb09eJD9EClfFTaYSYlDqDHr9wRKbJGpYMUttcKFB1/9BHR0KHK/UceIXrwQSqycymdumo5aG7apJtIGIrZ4fi+aC9SaOfo3QfKaW/U6SsyCqVKRU5ed4IOXbwtzGvfbl3Wf+8tRdi1a27/S9XC2alFuTy07MAlEQ376vFqTv8OHm1g04etvYsAo97QKJGwUBRhwS7KBxwJCzrwBYNBK4zrMecaFl2xsbGiWB/34ZyvuQh7BAceFY41YeqdPNeEBYZgH1iL5MxEI55wfjBChyTqaGBTYXi+/tomwHr0IBoxgnacum439SbjsSP4ZSJMHJRFVEciEVFqVSEvxWSIoJNX79DO0zecjgHSTSQMNfK3E+jrRQfF7ffalqNsmfyY3pWDpz0QYQD1dRBhs7adEZExbP9quny7xnr7n51nqZs39i9+iIT5lVAVYRwJ0y3fffeduGAeNahUqRINGjTIOtca9hLvvPMOTZs2TQimtm3bilGMmIXtiWM+hnfv2LFDWHRJ4JP6yiuv+CcdCV8weUFHAIrz58+fT9evXxeXefPmCbOyBQsWeLwAjPkol1+p89lv9HTkH38Q9e+v3P76azo/dCT1nXuAOo1bYx3b9NT57VTj7AElXRkkYlUF7Oeux1OmqEhqXVHZqaCg3JELN+OpxIB/6Y/Np0gPkbCvFhygWwnJVLlQVnqitp9ri7xIRwJ43jUpk1t4sCEa5sjWk9ed2sAEPx3pxxpSFmHBi4QhaprE83sdKVy4MA0bNoy2bNlCmzdvFubynTp1oj17lFIHmMvPnTuXZsyYQStWrBApxC5dupAnrFq1ij766KNUllzFixcXMyQ9xePG5T59+tDo0aOFgsyaNau44PbIkSPFEG8mMOgswWRHufxKegzeSveNYBzqSig8/7zY0d19riuNqdSOWvxvOc3aqvzIutQsROsHtKJhJe8p30UQRZgaOVi9Y1XFfuSfnedSfQf1vlgiInn9/kzbbsZvnD6tXBcoIKKKf2xRxOAnD1cSRfl+xctIGOjTWqkN+3PLaTp9zfVoqHhLtzDSk7jEW3zPghIJk2atnI7UriYsmOlIdRdqqEQgPaBjx47Uvn17KlOmDJUtW5Y+//xzypIlixiveOPGDTGvGloF4gwjh1BkD7N5OX7RHRCMcpbxO336tEhL+l2EHTlyRLRoOpItWzZrCJAJbYrnykxREeF0J/GecQd9r1pFKYmJ9Ffrp6lVxa40cvEhYSJas2h2MaIJXXL5s2UkattW+fslS5R2/SABAZMpKoL6tysv7qM4PzZjJJ2/GU+bjnsW9fE7lg7r+8VL0Mdz9wid0KVGIapVzBKlCkQkDILHQ+drLB+sM+Bj9t3yIy4nFGA7mbL+hF3Xp1twOlLf6CEdCVuQLFlCToTdunWLbt68ab0glZgeEEpIO8bFxVGDBg1EdAxjh1q3bm39m/Lly4sZ2OvWrXN7WR544AH6GmUqqtIgFORjmDcEoN9FWJ06dahv3750QW6QYtu8QO+99x7VrVvX4wVgzBcKi4wIp1KWYv0DBk1Jbl+zkx597ivqXetZOnsjngpmy0hjnq5BM3s0FOOZrNSurRzYsUPcsCFoy/tCw+K06+O2VLu4IjKiIyOobaX8Vs8wiWNU7GpcYoCXlIgsJ2uzYorRtpPXxezR9y3i0e/gTFU6l3oRDXvL4qKPVO7Z68oJxsVb8alE2MA5u633k911lPdrOtIPhGo6MpiRsBDtkKxYsaII9MjLUFmn64Rdu3aJ6Fd0dDS9/vrrNHv2bPH/8DpFCtExiIR6MDznLiNGjKA1a9aI10SN2TPPPGNNRaI43+8i7Oeff6Zz584J9Vi6dGlxwW0sAEJ9DAPKW01bbxpqhZy/EU99p2+nRzLUoa2FKlBMWAq906YsLX23ueg6TNUQAb+gFi2U2/AQCyKOqTwsL8D8w+R7ihBAB6IazPoMKAh7HTtGt6JiaNiJcGvRe76sGQPz/hBgPqQk65XMRfVL5qSkeyk0foUSDTt55U6qmjs1r/+61ZqadGZyHQifML8QaiJMDxYVIdohuXfvXpFOlJcBAwa4/Nty5crR9u3bacOGDdSjRw964YUXxP9rWXeGovwPPvhA1JjVqFFD1KFt27ZNGNr7fYA3RBdGFy1atIj2798vHqtQoYII8QW7Yy+U0PuaxgBkIJ3ckbIZveQQ1SmOAufU8wKDDVz/f1h5VBxY5ezCR3ctpn7De1C+yuk4zzduTDRzJtEaW4ecHmhYKpewfEC0a+2RK9S0bB7a7yCKD1+8TXUs0bOAAOFz6xZ907wbXY6/Jzo7uzUqTgEFIuzKFY+L8yXojlx/dANN23iK3mhemjafsBdzE9e4LssoMWCeaEDInSWa8mSJptyxyjUsLXJH56U8OQtTngwxlDUlRZP9qdUnzB/75lASYSg1wDajBxEWgpGw2NhYUX/uDoh2QacA1H1hzBDq2J988klKTEwUzYTqaBgyefk9jG5ifvZzzz3n4adw8Vre/BN+0MiL4sIEB70LXhkJQ2H42GdICJwxSw6Jx44Pe4j0AiITf+84S8Pm76dzN5QIRu28GWngVz2o2q2zRJVGpv8ijRop12vXKr5img/q8z4t3L5Kfvp1/UnRJQkRph4xJUVYQDl2jA7nLEw/1+kk7g7qUFGkTgOKF15hahqUzCVOJjYdv0bfrzxCVQpZDopusvuMi+hwjReUy0aiqC0LFGGWJcpyrQg1u9sWEYd0blD2B6EkwuTIIkS+VbYEQSEEI2G+gEJ61JBBkGXIkEHMfZRjhw4cOCD8vlAz5gn4v2+++Yb27dtnDUS9+eabosYsICIMhW5o78TCQ1mq4Q7JwKBzDUZlVY7nu07fSBWB0QPbTl6jIf/sFXVJoFD2GFHY3uH8Lgo7fwgmM+6t6OrViTJlUg7qiA5XrEh6oWPVgkKEYRzQZ50rW4UmPhaOKYEWYSnHjtOQVq9QcngktSqfl1qU9zx87zMyHellJAyCp3ersvTchA30+4aT9GYL5aw7LdCosrp/C7pwI0H4omGk1yWH68s799GlDJnpVsYslHjvPp25fldc0gOecLljo2wRNdU1/Pr8RiiJMJmKDObIohCOhLkL0pTwBEOJFIr5f//9d1q+fDn9999/opase/fuoqY9Z86cIrLWq1cvIcDq16/v9nvMnDmTnnrqKeEVJsUbuisxzBuNAJ7OlfRYhCHviQ6AO3fuCDGGD3P58mXKlCmTyIeyCGMACtklqw9fpq0nbVEHpCb9bkWQBudu3KUvFxyg2dsUuwl0Fb7RvBS93KSkcJyn9XOVPyzhpuFmhgxKgT5qwrZs0ZUIQ6oRo4wu3EygFQcuic8OGpfOTasOXRYiLDH5PnX+dg11bVCMYqIiKVOGCKvPmJYkJN+jlQcu0sqStSgq5R4N7BCk9eRDTZgEXZLolIU/2DdLFd8wOWPSGd2blKC8sRnFxSWFHyc6c4biN2yiy2UqKsLstjKD1Src5O3bCXT5VgLFJd4T6XNMb7BOcHBCuD/TkaEgBqTBcLBTkYAjYS65ePEide3aVdStQ3RVrVpVCDC42oNRo0ZReHi4EEpqs1ZP6NevnxB7Q4YMsXsc3ZF4zu8iDIVo8OKANT8+JBQgQnzIj8JJlgkMOg+EiWgBxs+MWnyQhi9Qagcl6CYrkC0mzdEvknHP1KSHLJ5XvoJUHNJH45cepPj7yhp8rFZh4dJuVxguh9QX96BWqWpVRYTtDJL3lgvCw8OoQ9WCNGH1MZq78xzdild8xBqWUkQYIi2wW9hz9ia9P3OX9f/2fNKWMkd7FSh3CsRfsy+XU+K9ouJ+9wwXqbjK6d9I6Ui5faM27MWJm0TUCmSLiaJKBbOKdZnWaKn0CvMzZslEhXMol/SIS0gWoswWXVNEm1q4YYpCh2ra/IZCNhIm68GCnYoMNfHrIek1B2bMmJHGjRsnLt4CgQeh5wg00FdffeXx63m8l0XXAYZWQk1GREQINVmyZEn68ssvRReCp+6zjDlrwsDD1QsKEebImWt3nYowR7EGev6+lR6q6lsNGWwZUPeF11fScWFU59QeGrhjFlV9fz6mkdv/g/S7czcSJkUY0JkIk12SEGGL916wzvksmjMT5ciUga7dSXL6HSF9rKVv12/rT1rFSr5bV+jNJp7VUekpHSlpVjYPVSucjXacVkRIxgzhLkUYPNvSxYvuSAhlXIrlyhw8MXDzpq5qIf2CFOxy29FDJIxFWFBo3ry5cM2Xxf+S1atXU5MmTTx+PY9/NYh6QYABpB9RFwYQFTt1KkhjUBhdgs63kk6iHY+NX0cHnfiHqQ0wtQJp0C7fraU+07cLAVYo8SaNmzOU/vj9faq6ZwNCu6n/SUbCvBFhu2zRJL1QtXA2IbqQttp1xiYYHAevq1m09yI9+9N6WrjHff+ctFBnn/uu/pUyV65AQSO3Zaj8ZTdNVNOqDbO46AOs30oFszkVXqjbShMU6BnVrBXLfjvADR6hLMLU4pcJOA8//DC9//77ohD/119/FRfc7t+/v5gf+ffff1svfomEwRMDLZ8YC9CsWTMxHBM1YVOmTKHKlSt785kYE9OyfF46utoialQ8MGqlXZdk15832v3P0v0XrcXy3gAjzS8X7Kc5289a6756NihM3bvUo4zxd4h+/JHo1VeJJk0iatqU6KWXfEtHoogfnDundFJZ5iLqAYiFjtUK0LhlR+xEAUQYOvycIT2w1hy+okk3K2rNJJUuHiMqk47thz+RXj6yzscHWpTLa1d8j0iYZNJLdanLt2vF7cj0aiDVDuBGEWFYTji4w74BKUk3LQQMLcJkKjuYyPUcCmlgHfLGG2+Ia9SSOdaTyefkftfZeCOfI2FffPEFFSig1BdgLlOOHDmEIdqlS5fohx9+8PTlGC/RfzJSoWUF191vsj4JrDxoGepMRD88X4tWv68YoKKmJU2DSwfuJCbTqEUHqeWI5UKAIWv7RO3CtPzd5tTz6g5FgEEwvfwy0TvvKP/UvTvRihW26IgcMF2qlPsfFKNE5N/rMBrW0WLcKskYFeFenVI6YC7igFm7aIXq+3OGWoMUQeos2ssh11ogBbL8nn0AO9oFfZpQp+oFqUfzUlShgE2I5MpsG/CLEV5pIqNgRhJh+HGFSl2YTF1zJCzkuX//vlsXdwSYx5EwHAyRgpQRL9xesGBByH8pwcAAJWGCtIxAl+y7SI/UKOTU3wrt9QB1RBhKnT2T/cR6Z3Vff+04Q8PnHxDzEkHd4jlFB16VwpYDhTRTlfMeMWLi8GGiOXOUAdzNmqH9V3kO+X5Pz+yRkjxyRKkLa9mS9ES5fLFUJm8Wq2N+xsgI62gpb9l47Co98b0yc23qxpNpRszik5R6sCbHtlK20gE2Z/WjCAPl82el0U/VsN7v92A5MXkBKWBJzixR7okwlHqg29YoQIShaN3sIkxP6UiOhJkKj0UYitH27Nkj0pFMMDGGCssQES7sH751Uu/1765zQoTtttQpgbX9FfECq4hsMRmEALt4KyFNEYa6ryFz99L2U0q3UOEcMfRB+wrUrnJ++wYGOaRVGvPhgIfWZYiwHTuUx6QIq2E7qHokwmbP1mVxvpKSLEgjFylF+DFREVTazUjY8gMXqbkq7SaRAky9f3BsGJm/6xyNXXbYWqxe9vJJorpBrAdTpyMhwvxQUA4XfcmU7nWFDUjNoukcvNX1YEY5wwKhEgnTUzoyVNa5jkFJ1rJly4QlBqJeakaOdMPg21sRhoJ8iK8rV66wCGPcpt+D5YUNBDyVHv1OqZEBSGEhJTlprW3MS0FVDVje2GhFhN1MsI5Bcqz7QsfjX5a6LziH92xZml5qVELx+1ITF2cTR2pjvmrVlGstRFiVKsq1DkUYsBNhGSLE+nUH2DAcG9o+3Y5cCK3KDu7xs7adsesWjEmKh700BRVZmI90ATrM/HhgxYgut8Z0Ga0oP9QEQSgV5v/1FyzhlaYlI0VlAwRKsj766CMxoxLDv9X7RW9cCzwuzMegyvfee4++++47LsQPIkY6WZYbJ8wtJRkiwoRJKFKSZfI5j8jkzRot0meXbsenqvsav+Io/bDyiEhzibqvWkXonbZlXZthbt6sRD0KF1Yujl2NZ84o9WBShNWs6fmHlK+1Z49ygA+2s7aTbtXercrQrfhkyq8y03U2lmfdUYsvkgUMAU/Pr23R3gupRJjjmKRyl04E38w2Kkpp84cAQ3G+HqIbRhVhoWIcqqeaMJmOjI8nwsQabM9agd/EU08pr43yDUT2zWw94gWYQ/nzzz/Tiy++SFrg8dqFSdnGjRupWrVqFBMTIxzz1RfGC3DAxtxBDwq6DabBrEIMdUO4vN6slDUlabGPoi4O9WFSUCESJuu+Zm09TS3+t1zMoYQAq1siJ819szENf6xq2m7kct06iqvYWFtB/bJlyhmgs79zh5IllfFF2IGtXk165O02ZWlQR5sImv5q6nEdz9RTDFXVfDx3j4hKymaJbyxzQNUs3nfBaeE++LZTWdo49jnquH8VkRfz1fReFxayIkwKglupLWdMhZ4iYepaVa3F7/Tpyv4LwGJh8mRtX98EhIeHUyM5L1gDPI6Eff3115q9OYPK80QiDEJHdx5COtu32yIqJoqEOdK+SgEx7gUpSZkWy6HqJgPycdSEbTlxVdR9SWPMIjlj6IN2FehBx7ovVxy0GJKWK5f6uYYNlYL6gQNtRfneWEwg8vXcc0ToEv7gA0WI6fyLqlcyFx36vJ0w0N126poYpl1Z5XUlgfBC6veLzlVo8N+7RWTMEaQd4YyvNuKFdxbIdP4M5Y27TlSkiNJJGmzw/R46xCLMV+R3aWafMOyj79xRbush0ID9DNY71jlSklra4UycqFznz6/My+zfn6hzZ1sKlCFMDYLjvlZayCMRlpSUJAZ3Dxw4kEp4YmTJuAYHfmmPACsGdOm5IcKMTvn8scLI9ejlOGtNl6OrOIYQA7i94wKyREdSzxalqVuj4qnrvrwVYU88QTRlii0KJgv3vWHwYGVHhsgm/MYQHTNA8wRGCMkxQmpLEHxHMCTtPW27GFbduUYhWrD7vNPuywMXbonZic/XL5ZKhMUcOWRfNxdsNPQKC+lIWObM5hdhMgqGEyq9eKFhObDOtYyE7dtHtGGDIvI2bSJq1UrZb376KdH//qfd+xicd999lx566CEqVaoUVaxYURjYq5k1a5b/0pF4M0wQZzQCxeLffWeLxgCdprG0BtErRMPA7YRkp67iUoQpf0/0VJ0itPTdZsKPySMBphZhZcumfg6RSFnb4qsIK1jQJvSkqDPgd9OkjFK8/kPX2tSpeiF6snYRcf/x8evovhPbNpjBAoxGkmw4eoUuWVLJMbu2+75utURGD1iE+UYoRMJkPRj2EXqpj/JHQ4SMgj30kFI3KyM9Y8famwmHOG+99ZbojCxbtizlypVLTAtSX/yejnzkkUdozpw5IiTH+Mgffyi1FIiW4EwDQgwRFEQi0kljhRmyKsweiDDYF0iiI+13cOoar+6NStBHHbws6EaNg5wH6UyEobB11CiiHj2UdQ/bCl/Ae+zerQi/du3IiEx8sQ5dv5tk9Wsb0L48Ldl/gS7fTnT6920r5af/LTwo0suPj19LmaIirQauWTNGUpF1y/UpwuRg5mBj1EhYKIgwPdWD+atDEvu9GTOU27Lg/MEHlU5iNCyhe7xuXW3ey+BMmjRJBKMQDdMCj0UYLCqGDBlCa9asoVq1alFmGY5WqUTGTeBPBTAyp1YtjHhXNngcvJ2lzVTovNTILSoUiBUde8cux4n7jtEtdEdK4BnmNaj3wk4GIXyZhnIEOx78qKQo9gWDR8IcDXMBfNpgfIu0pCNP1y0qRiAVy5WJTly5Yx2FhFE9T9UtQm9Vyko5PtmrRBH0siOXtT0+DvHWDBZh+kVPHmH+MmxFfSROVHFCisyAPMjg9zpvHtHGjfr57QYZNCAiFakVHouwCRMmUPbs2WnLli3i4pjGYBHmAXL9wakdGz828pUrldbgdESYeVKS+a0zDaMzOEbCNBptA1d8AIPhtNQroiNaFLnKaJtMgZqEh6sVpP4zd1nrvCRDuyh1Xq0r5LPW7jUqnUsU8RfDiCJEGUH16ko3qh6QB1SOhPkGR8LMkY5ELTJo3NhW5wfq1LGJMEbw8ccf0+DBg2nixImUCd3wgRZhx+RwY8Y3LlxQvKkgCnBwAmh7hQhDXZh6oLQTTBAIs6YkpQiTVhUSFOFLbicqdWNeceKE5wO5QzwS5ko0L3+vOQ2bv18MVp+45hi91NjWoNOqQl6rCPv6yRpKTV9Skk2Evf466QaOhGkrwlDfalb05BHmr3SkFGFypJtERr9QqM8IxowZQ0eOHBFGrcWLF09VmL9161byqwhjNI6C4YAtd2TSe0TOOEwDb5x59UhFFwOPHT8jDEa95uRJ5bpoav8rvyAjYadPKwcnh5S9kcmXNSONelI5aejTuoxIW6rnhCJtjG5La/oYdY+nThHly0f0/POkG1iEaUMoRcLMmo5E0T08Ep2JMETCwP79ynuxVQWhLl5L3BZhffv2devvPJ2bFLJItYxaMIksWkYaC+3CwR7vEgAgtBa+3VQMg25eznUqEMOnfY6EFbNZJ/iVXLmUC1JdqLWQkU6ToRZgAOILsz+T76dQFJosUIf35ZfKk716KTWPegHfD+CaMN8IJRGmx0iYFiIMzWDwQcOJkqOFDMozkEFAvRimjsC2IsQZDBuiYIiwbXKcSwhEZwLC3r3KtdoTDGda6KabP5+oSxei8eOVejGTg7mQzmZDgnlvNaFVhy45dXHXrQiTEU7s3CCoTSrCnJFZlUKmRYuUOZqIBKLzVE/IqAYOsH4Y4u0xXJivX8yejpSpSBTkO/sdICUJEYa6MBZhguvXr9Off/4p0pIY44hifaQhkaIsVMh+8otmIgy+GIyGHD2qXDt2Wfz0kzIyB+Hf5s0V24pnn1VSOaqi5lDRuxULZhUXn5DpyECKMKQkIcJMVhfmEV99pVy//LK+UjnqAyoEGKIJwT7AsgjTL3qMhGmZjsR+CrgSWBBhKCvgujDBzp07qXXr1sIT7Pjx4/TKK68IEQaT1pMnT9JkD0c96cR5LoRFmKMdAsw+kap84w1lgj1+ID17Kp4tKidzxgOPMDRBBLImTF2cb7IOSbdBHdjixcqZdZ8+pDuio221enpISRpdhKGuCE0YZkSPNWFapSNxEoJReY6lMc6K89ev52MQKaVZGN596NAhyqgqsWjfvj2tRGOdh7AICwaon5CDg515UkGIjRunjL0ZMULZMUOMyfFGIRQJ00QMABxwA7kTlcX5oRoJW7VKuUZUN1BdqUa2qTC6CDNzh6QeI2FapSORZoQ3Ik5KXNkiQZwhIHDunM30OoTZtGkTvfbaa6keRxryPOZtegiLsGAgbT5wEEir2wS5ZTRESAdjjI9gPDdqBRACgVSuapuKUIxgShHWpAnpFj0V5xtVhMHfULbom7U4X481YVqlI2UUrFIl2/foCLywZJQsRMbqpUV0dDTddCJ+Dx48SHm88JlkEaanVKQrnnvOFg52HFuEuhtMvHejcSIkQW0dCHSnqfxu8WPVcr6bUZA7az2LMD3ZVBhVhIXCEG89R8K0EmHpNQ/BxDXERdjJkyfp/v379PDDD4upQUmW9DsaEvHc+++/T48++qh/RVhycrJ489PwP2ICJ8JwlgJg7mpBBHWw4+7XT6l5evVV/kbSEmHlywd2/eBgKs+KZHdmqABRg9mZau87PaKndKRM5RnRU87MNhXYx8rh1XqsCUMqEXVd3rJnT+oufWfI37EqEBBqlChRgi5fvkwjRoyg27dvU968eenu3bvUrFkzKl26NMXGxtLnn3/u8et6ZNYaGRlJX331FXXt2tXjN2JUyLy6u7Uy+MFhqr1K/Io42KxZtr+BhwteV6/1N8ECfmvBEGGyEQC1f+jOrFaNQgZpNoyUrKtZnXpARjZkpCOY4GAK9DLWyRPMLMLkthERYV//ppd0JEodsN7lfU+Bj6G6htUVUqShvOLePWV9hBgplrISdEUuWrSIVq9eLTolIchq1qwpOia9wWPH/JYtW9KKFSuEXT8TQAf3ihXtRJjg11/t70+fTvT++/y16CESJi0xMBlBft+hgkxZyBSGXtF6/p4vSAHDIky/9WB66oZCVx5quJASw/brjQiDqJBzdUuXTn9fhug+IoOoaU7v701KmGobaNy4sbj4iscirF27dtS/f3/atWsX1apVizI7hM+RL2Xc7NjzVIQtXGi9G4Yf0NKlyp3evYlGjyaaPZtFmOMO9OJF5XYwBqLL7zfU0pFGKMr3x/w9LSJheoq2uIuZ50dKexu9RXQhBrD9Xr7s/fZ79qwiqhDVSi+ogr/BPhQ1ZDAaD1ERNnDgwHSHdns6NchjEfYG/KtcvBFU4j2EKhn3RFiRIu6vKVkXJtc1dniJiURlyijCCyJswwalbsxDx17TIu0hkMoNxsFNirBQioRhp47UuBFEmJaGl76QnGwrzDdyJEwPYlZr5EkcRvrocfuFCPN2+5VRMAgwV52RjoEAiDCUeIRosGXXrl0UhY5gDacGeSzC0B3A+GgeKn/Ynogwx7CnPOvEj6FAAaLatZWDH7zEnnmGv6JgdkZqKcKwg8U4EYjw775T/Hz0DEabIEWCbbJECdI1eomEqSNIRhRhUszKaJ4ZI2F6FGG+ptNlPRhO5N1B7kflyL0QZPbs2aIgX0vYoiLQyLouhDQ96bZBKFidvsTAVSCLAbl7RV/1YOoxSb6kI//+WxE2EycSvf46GSoVqacaGj1HwqR4iYxUfLeMhhSOZhZhektHqrdfb08iZCTMcXReWpEwdbNTiBHmp/2Zx5EwEBcXJ4rz4Y2RiJSYirfeekurZTMnMiqCKJgnXyr+tm1b2/2kRGUkjBRf9esrKckQbiHWVWckkKIZTtP4nXhzgJXDdWXjxbff6ttLyihF+XqKhKk7I/UuXP0hBvSMntORvkbCpMB0t3xFHQlDTbIRt1UNuiODLsK2bdsmZiTduXNHiDEMroR3BorVEKZjEeaHejAJ0owLlNRFmBwJI89CIcKUL0ipL9HzgTpUImHwCUMXE1LQqNXzND2H1L+qGUN8r4g0IT2pV+SQXz37g+mtO9LInZGA05HGPInw1IQWxfiI1iJ9juNYIGfx6oCJEycKewrMh2zYsKGw7HL0UV27di01bdrUv+nIt99+mzp27EjXrl2jmJgYWr9+PZ04cUJ0Sv7vf//z9OVCD2m4imJxT2ne3L47sn17+9QXztZQ5CtdkB2BjxieDwVgsChNcYMlwnCm6EuHJL5H+Iyh8Pn551NHxvTG9eu2lv70fIf0gF4iOEbujFSLx2Cvx1BLR0oR5q3PnaeDyVG8L+vHQjAl+cILL4iRRS1atKCrTqZs3LhxQzznKR6LsO3bt9M777xD4eHhFBERQQkJCVSkSBH68ssv6YMPPvB4AUIOrcLbiKSp1zcO+IiMAWcibPFiJRKDKIpDCtmUQPSgUxcWKigSDxa+FOcvWKBct2xJ1KGDcnvJEtL9TFQcsIwgKNSu48Hs6jZLJMyMIkzP6cjcuX2b+ODNOCZZFxbCxfkpKSlO68OuXLmSyrLLL+nIDBkyCAEGkH5EXViFChVEmO6UTLUxrkFkA3gx6FNNGHbYjp1ymP81f75zESajlMuWEX32GdGQIaExlQDCM5i1C76IMBn1Qi2grLHatUs52HnrkK2ncVzBRr0OIcSyZw/OcpglEma2wnxkG/TcHSlFGGwqAiXCQrhDskuXLuIaAuzFF18UUTEJrLngno80pd8jYTVq1KBNlroPzEwaNGgQ/fbbb9SnTx+qXLmyxwsQcmglwpzpCjmE1VGE4eCori2SEZZQEGGyQ9FoIgzRkbVrbSKsYEHFzwd1YuiW1CNGE2Go15PNEsGM4hh5ZJGZI2H4PHJupB7TkVKEyWOKL9MA3EWWGcjfegiRLVs2cUEkDHMi5X1c8ufPT6+++ir96jjFxh+RsC+++IJuWXYaGFaJOZI9evSgMmXK0M8//+zxAoQcWokwpTTfuQjbuVOp/ZKFgwMGKGd1aEU+ckSJpqifNyOezufUm00FzjTxHeEMXLaQ4ywLnwvizMs5ZX7FaCJMC8NLLTBLOtJskTCZisT3osdGJ18iYShJkTZHnogw2VDmOEIvRArzAUY2vvvuu16lHjWJhNWuXdtafIZ05IIFC+jmzZu0ZcsWqhZKQ4qDLMJcdq8gpYFuvL/+UgxcsQP54w/FzuLPP5UaKTwvjfrMihQ9wRZh3hbmy8JXtdGsDHUvX066RIowvZu06s2mwizpSLNFwvScilQfQ7wRYepifvkbcAfZUIbSIz9ZNuidwYMHi1Tk4sWL6fvvv7cGpc6ePSuGeXsKm7UGEmy0/hRhEFrSLf+xx5Th0dgo8PjnnyuRsqpVledddVCaBb1EwuSMNUQg4STvqQiThbCgTRubF5ceow5GjITpwaaC05H6RM+dkY6F+Z5OspEiDHWQmAvpqQiDXY63XZl+ZOjQoVSnTh2RLkSQ6JFHHqEDcnydhfj4eOrZsyflypWLsmTJQo8++ihdkN+1G8ANokqVKtSpUyfxOpcsx/Thw4eLCJlfRBjqwGrWrOnWhUlnZys7E/1REwb69LFvKf76a2WGYv/+ymMyWhkqIizYNWEI3yPCgdQihJgvkTC0h0PgQMyhwUJPoLtQRvuMJML0UM9k9HSkXG5sl7KGygzouTMS5Mpl++3BHsbfRfmyjlKKPx024q1YsUIII1hnLVq0iJKSkuiBBx4QnqZqm625c+fSjBkzxN8jgiWL7t2hd+/eIiMobboknTt3piVedK+7VRQENclogIyCYWRROpPYvQYH7Z9+UkZSvPJK6gOijITt2UOmBQeCs2f1IcKgluFThrmeqPNy17PMmQjDa7VrRzRuHNHcufoaogv/OxyEIfyNNEBeT5Ewo6cjpZj1R5Q/GOg9HYnuPJxEYJ0jJenJGDxvRZg8scT7oS5MZyVICxyazn755RcREUO5FExU4eU1YcIE+v3336klrH8stV5weIBwqy9Nz9Ng1apVwpTVcZA3asXOSB9QrUUYcqCMBvgzFamme3fXz8mDupnN9jAmSO6k9HBAQEoRIszddY5oqYyaOQ4fxxkbRBiENnYYSD/roWhYpiKR/vUkvaEXAeFFLYdmGD0die8btaaINuCz6OE3FwoiDCAqBRGGY4snBsnedEaqU5KYzCKL82F5BB/Kt98m6tSJ/MGtW7dE7bkENVlqiwhXQHQBTPYBEGOIjrVWNTaVL1+eihYtSuvWrXNLhN2/f19YUjhy+vRpkQYNWE0YPgzaMXHBKCO95m/NKsK8HiYqD+ow1kRe34zIsxHYOuhhvpmn3jqI4qHGA2dajkazmJqAzwVeflk/Q72NWA+mjj4FU4TJ/YJMLxkRMxbny3SkXmvCfOmQ9CUSpi7OB4jKr1hhE3Z+oGLFinaWENAO7oglWGc1atTIap91/vx5EcHK7uAJmC9fPvGcOyC9+TXKfFTHYhTkI1iFkY5+F2EXL14UYTyIJ8yJxAUji1q1amUtUNNT/lZXyB+KFiLM23/EDgVnBWgSOHiQTIlMReolLSaL692NhMkzTCy/o4hEkwXEl2TyZH3U9xmxM1IvIkxGbqW4NiJ6qK0L1UhYoEWY7PjG/gxNAXJerB9n2u7du1dEteRlAGyX0gHaYvfu3TRt2jRNl2XEiBG0Zs0aIQwRJHrmmWesqUgU5/tdhPXq1UuEBvfs2SPmJ+GCD4pQoafDu5G/hfNspUqVhL0F8rdw4EeUDcj87ciRI4Xwg9hD/hb5WAg3Z2CMEpZFXmT7qC6QPxT5w/EBrwM8+EezpyTVkTA9INc3Boq708WU3nxR7IBmz7bNDv3mGwo6HAnzDmwPZhBhZoyEGUGEyRN6GbXz19xINTKVN28e0dSpygk9Ik1+POmNjY2lrFmzWi/ppSLffPNN+ueff2jZsmVUWLUfhalqYmIiXXdoZEB2Dc+5A15vx44d9OGHH4ogERoXhw0bJjKCyOj5XYRBOH377beikE0CRThu3Diaj5E5PuBp/tYZCFOqw5ZYNt0gZ3wFO+1gdhGmt0gYokNILSL9645fmIyEuRJh6FBCs4w8G4QPnCp6HBQ4Eub9iRk6Z3FypOeDvRFq60IxHSnLFaSQD0QkrFYtpf4MfpO9etmmeuiAlJQUIcBmz55NS5cupRIOkXkEcjB6Ud3FiBIoBH8aNGjg8nXh/IBuSDBkyBAh5J599lkxMxt66OWXX7brlPSrCEOeFR/CETyG57xFq/wtwpTqsCXCmLqLhGkgwnwqdZIO7J4aiBoFvUXCMJmgXDnltjvbo1z+9ERko0bKd4kD3z//UFCRY5mC7cvmKdL1OljiQZ4wIKLhZL9qGMwmwiAwZFRPz+JYijC5HQVChOHg8+qrtqYMBEmkGAsyPXv2FHXq6H5E9Aw6AZe7lvpnBGa6d+9Offv2FVEyBHq6desmBFhaRfn79u2zlkl98sknXpmyusLjuTVIC8InY+rUqVTQcpBDLhRhOdSFeYvM366GEaUPOHZNqDsqzBQJczq2yF1khMWsoyfcFTGBjj5iXBSijw895FskTL0zRB3Gd98p3UpPPklBswSRZ+KyXsQoBLsmTB489XLCYNT1qDVSpEBkqAe96w253QQyEgbQCYmTQJxcevsafuA77AtF/1Jzu8dRxoTSJzBq1CgKDw8XTX4oX2rbtq2IZqVF9erVhVhr3LixiLb973//E42CzsA8bb+KsLFjx9LDDz8sCtGKWOZInTp1SkSvvBleqc7frly50mX+Vh0N8yR/qyv0ko6U4sQLTxNDoMcDmyfF+Z6ISD2kluXyIk1qNHuCYIsHM9SD6WE9ao2sGYKPnB46rLVOR/piUSEbhNywcwg0KW6MUsqYMaMon8LFXVCvju5H6BR0Q6L0KtLJ7GU853cRBuG1detWMTdpPwqNxXGggl3dlicrDIX+yN8uX748zfwtVKu7+VvdizANCvN9wsyRMPwI9RgJq1RJuXbHzsXdSJheRJhMRSIKpucDlqfiAS348NzDOh492r8nDI5WJEZDrkc9NUJpIcIcSmF0LcKw73P39+dLYX4IUq5cOWuXJaJo0CTeFOFrIsKk2mvTpo24+AJSkMjd/vXXX9b8rczboshNnb9FsT66IiDa0svf6hYt05G+HOukOMGBB+laPYfbPQWf584d/UUX5PDtHTsUd3ZXQ3NRV+lJY4GMsMHcFWlBNwwM/SrCjIYrEQb3/2bNFD+9RYuIMBPOEvk3fdTWG8wWCZMTFDwZbh1MEYYaNghHdyNbvqYjQ5j7PtS++1SYj25EhOLUTJ48WUSvoAhfffVVkV/1NH+L4nnkbwsUKGC9TJ8+3fo3yN926NBBRMIwdgBpyFmzZpHhwFmKpjVhPoCRSfLHZ7ZomIyC4QzWX6OhvAEHWQzzxg94zZq0O7LQLYdwvzspd+yEIaLxuocOUVAwgwhz7C7F7FEIMMnff/vn/WVzjDtRTz1jtsJ8o0TC0JEnl9HdlCSO09Kom0WYx0yaNIn+/fdf6/1+/fqJcqmGDRuK4d5+E2Foy4Q3mGTXrl0iSoU0ZP/+/YWhqjsuto7pSGcXWUCnzt/CjwzdCRBghqwHQ5geB1etasJ8TfuYtS5Mb/YUapo2Va5XrnT9N/L7QEeWO91yevB9kyIs2HM6tYzgOA4n/usv/7y/nBDiycgZPWK2SJhRRJg3dWEyCoZ9h5myIAHiiy++sNpRIDgFfQKrity5c4sGRb+JsO3bt9t1PyI/Wq9ePfrxxx9FunDMmDH0B/yKGOfIKBi+PA1m/flceWPWujC92VOoQXoLYMSHFvVgEr2IMH+k6wIlHhAZUM+DkyJMCstly2wHZq1ARAIRNyAtTIyKWWvC9J6O9KZDUoowCExE3BmPQCNiaWQ1iGjOnDkiS4dMIIJQGO7tKW5/AzAqgz+XBCOE2rVrZ72PMUZYOEbnnZESjoQFLxKGYd6uzFXVI4uMIsKkZ58Ri8vVbebq70R+Dy1bKusXUWxXZtRwDS9fnqhJE8/Gx6COD2lkLIMRo/uhUBNmpEiYu15hvnZGhjhZsmShK5bj+cKFC6218cjaST8yv4gwCLBjlhoJ2EagQ1JdHI/xQM5MXBntjVqBz01oHAkLPDAyRd0UDuguJj6kO7JIjyJMOovr2dTSFWhkgBeUo4CQJ5T4HjCdAGBUlDNGjVLSivA4VNWzup2KRBTMaF2lZhdhoZCO5M5Ir4DogkM+LgcPHrQO7Ua5Fqy7/CbC8Eao/UK4Da70mTJloiY487Owc+dOKiWd2Bm/R8J83mVzJEyfdWG+RMJwUFen1AIBIjmXLul/vIsrIH6cCQj5PSDF2rmzchuNSY7mz2i4sVj1WNOW7nLwoDlSkWYuzDdCOtJbEcaRMK9ADRgcGi5dukQzZ86kXJZjOtz3n376af9ZVHz66afUpUsXatasmQjHoUMAI4UkP//8Mz3gxynqhkdv6UiOhAWvLgymxq7qwryJhMFfDxEdtKmjO6dkSQoY2KHLhhOjGbVKIMKQfnIWCYMIq11bSTdCbKHu9eWX7b8vdR0URBiEqTu1NmvXKteqObyGxWyRMCOlI72tCWMR5hXohIRpvSMYZ+QNboswVP7D0R6WEhBhETKEb2HGjBkubfwZHYowjoQFNxK2YYMimuAy72skDL9FdNdhLBJmUwZShMlUJA5WqpMywwsItQhDtKxbN6L331cEtFqEyRQwsgAXLij1NvgeqlVL+z1xwJRt7hYjakPDhfnGqQljEeYzmOIzYcIEMVMSVKpUiV566SXhbeopHrdG4E0cBRiAmao6Msb4OR3paw2JjLQglQQxYAaQGtJ7fVKZMkoRNjrjNm7Uzu0/WHVhEB56Xt/eDPHG70EeqGSU4bHHlGt4vKkjX3J9V6lCVLeucnvLlvTf87fflNQxTHzNFAnDupORUSMTCjVhHAnzis2bN4vSK3iYwjoLl5EjR4rHUCvvKdyfGqo1YfgByiiMu2dQegcH0cREfYyGcgXEs6u6MBzcZYeepxYbwRJhUvQasR7MVRRH/lYxG04ehBFdRLQLAmP5ctv/yvWN9V+9um0qQnqgiN8sUTB1TRhw1flrJIwowrDe3bEI4e5In4AXGOZnHz9+XPiW4oKmRZjK9+nTx+PXYxFm2EiYBi8go2FmMWyVHajwYdOTW74j9eo5P1hLMQwDRU9T+8GOhBlZhEnDSnkAk9sRusfUPzRZ87pgge2x3btt46NkCnL79vTfU0bL6tQhU4AsiBxobIa6MKOMLQLYV0gR7E40jLsjfY6Evf/++3YDvHEbzvl4zlNYhIVqTZg65WUWw1Y9ruO0BJO6q85Xt3+1CENaM1DoPf3rDvJAKw+8rrajhx5SrlGcj3Qy1vPOncpjEGAyEgYRltZ3AF81/OYg8GrUIFOg7jI1umErop1y/qxRHOU9qQvjdKRPYIb1SWlQrQI+qZiB7SkswozqE+Z7QtJ8xflyHes1FSlBp520KFBbSvji9o/CfHTkQUhI89RAYIZImEw5yRSUKxHWtq0SPcZ2NnOm0okKywr4I+I7xQURITwmnfDTioLh783UzGSWDkl1OtUo34/Margzu5BFmE88+eSTYmQjZlxDeOGCCULwDfPGooJFWKCQO3atBIIW3o5mE2FGiYRhFA7q8VC/ph4SLc9ivRFheD3ZFRnIlKQZasLcFWFIP7zyinL7u+9s6WSkIiHEIMAqVUq/Lmz9euW6Vi0yFWYTYTipgfWLEahc2f16RBZhPvG///1P2HV17dpVmLMWK1ZMzLt+7LHHaPjw4R6/HouwQIDUhfxh66UwXy3CzFKYb5RIGHbu0qBTnZL0RYQFqy5MRt2MPHbHVTrS2XbUvbtiCYLC+ilTlMfUdhTu1IUtXqxcN29OpsIshq1y+SEqjTLJQJ0KTw8WYT4BF4jRo0eLUY6Yqb1jxw7RIYluyWgvRLvbPmGMD8idOg6+eir0lAd7s0TCNE75+hWkonDWCsHUoYPvNWFShM2dyyLMX5Ew+d106kQ0a5aSkgQNGrh/MITQk9YklplzpsFskTBpXWIEHOsRXYlHzDaUlkQ8tsgj4APmDjCu9wQWYYFA7tSx0etpar1Z05F6j4TJGi45xFmLmrBgRMKwszdzJMyVmB80SBnmjQMa/MHUO+f0RNjSpYqjPr5/zBE1E2YpzJcizCj1YDIljnQ5olwwGna1bckoGKK5XhSRhzK//PKLSD3WqFGDUjRsfmIRZtBaJU2i5PJgjwhMWmdPRsEo6Ug5aggcPWrcdCREC1LtRu+O9CQSJlOOGMD+009E8AVSm1TLdCQKpPF6jj5TMGkFlqG/psIskTC5/EaKhCENhnpERNc3bUpfhGG7NPr+PsD06NGDpk6dKjzBunXrRs8995wwqfcVHYVlTIwfcvCadEfKtmYUiMsDj5ExSmE+kEX0sjAfItjXdKTsuoRXkIzq+BMZBUMbv5592bQWYVJsffONYuDq+FpovHBWJI3pFH//rdzGGCSzYRYRZsR0JMD0BbBqlXJ9+DDR7Nn2nbpcD+bT4O5z584JP7C5c+dSkSJF6IknnqD//vvPp8gYi7BAgJZ1PXrO4OxJRo3MkJI0YiQMERPYVODAn5TkW2oPaTUZRQtENMwMqUhn6UhfawtdpSTfe0/5jjEQvGpVMh1mLMw3EnISB0QYxBa6b7t0UcyhZR0YizCfQOE9bCgWLVpEe/fuFTMj33jjDdEledvL7Z5FWCCQNRIaijDNIslmqguT4ziMUHCK9Q5bAxyUse7l+s+Tx7dB2IFMSZpFhMlIGE6WUK/la0TVmQhbtIho0iSlJnTECDIlHAkLLk2aKNeYX/jZZ7aTf9jIzJun3GYRphnh4eFihjOiYPfUfo+evo52i8SkK8I0LITUTITJyIm7w1/1jJFGjaAwVqatUBfmaz1YMEWYTGsbFbm9IKUAASYPVBDE3uBshiR8xUCPHraIhdkwW2G+0dKROLFDowgYOdL+QCFrEeVv1si+fkEkISFB1IW1adOGypYtS7t27aKxY8cKB/0sXkZOWYQZNB2pSU2Y+kCDehUjgzMRufM3gghzrAvztR5MwpEw74xupb8PphgAdJp5GwmTxfmYK4noLE5wZC0YRJhZMUskzKjpSDBjBlGjRspt1GnCsgb8958S5ZURd1/3MyHIG2+8QQUKFKBhw4aJYd1wyp8xYwa1b99eRMW8hbsjDRoJ0wwpwmQdjFFRn30bRYSpOyRlCtJIkTA5ssjo6UiZksTnkea56Pb0dsdavLjyPeA7QJQQJ184SUDhtHTUNyNmqQkzaiQMwAQaNWE4AcCJBU70cI3PhJM9FmFeM378eCpatCiVLFmSVqxYIS7OmAUPQQ9gEWbUSJhW6UizRMJkKhI7HKOMGlFHwuRZt1YiDK+JYlxEefyFWWrCnIkwXz4Tfpy//qqYuKLzWJ7gyJFHZsUskTAj+oQ5bn8yLSk9xLZtI9q1i0WYD2BMEWrAtIZFWKhHwmQnodEjYUaqB3MUYYiEye/BVxGGCA6sUFDXhNSaP7vwzCbCwN692tS51aypHPgGDlTc9bFdPv44mRqz1IQZ0ScsLbAPwLa4cyeLMB/NWv0B14QFMhKmRxFmtkiYkUSYOh0JqwotajVwphaolKSZRJgUXbKjUYvPhAgEanTGj1dqwsxyUA+VSJhZvi8ZFUOjiGzA4pow3cCRMINaVGgGR8KCHwlDGkzWV9Wo4fvrQoStXetfEYYaJ7S+m02EyQYJrT4T6spee41CArOIMCMX5jtDRsP//ZcoOVnZJs3wmzUJHAkzrEUFd0caPhKGtKF6eWFZocUZaiAiYUhfo9sKO3RvrRz0hGMamA9SnsOF+foE3ZIQlOoRY+j+ZXQBizDDWlRoHAnD2Z90VTYiep1K4G40DMjWciOIMJmKhACD55nRYRHmOzJyhP0IIi5GxWzpSFhVPPaY7T6nInUFi7BQN2tFQbI8iBp5fqQRI2GgcmX/ibADB/x3MDRTPZizQnyzfK5Aok7fGTklabZ0JHj1VSVqjQhY167BXhpGBcck/Q1SNnquCYOaQzQMNUkozjfqWZJRRdioUYqvFETNCy9o85pIa+IsHmf0hw7ZRJmWmE2EOUbCypYN1pIYF3jd4SAP4Q8hIztOjYbZImEAdil79iglEEhHMrqBI2GB+kFrHQnT7JVMYthqVBEGV/YhQ4h++EG7nT7OeGUxrnp0jpaYORJWqpQ56tyCcUJnhrowo/uEuaJ8eRZgOoRFmL+RUTCk/GJiNHtZTU3jZF2YkW0qjCrC/IUcnaMeIq0lstXdLGfV8jcAOAoWul5hmB9qNp8wRtewCAukR5gf3HY1gSNh5hVh/oqEnT6tXBcuTKZAPaKodu1gLomxkSUXcr9nNNBUACEGWIQxAYBFWCi75Us4EmY+qle3RcLkQUVLTp1SrosUIdMwdChR48ZE77wT7CUxLkYXYeo0KoswJgCwCDOoCOOaMAc4HWkPasJQKI3aLYwv0hozirD+/ZXhx5zS9h657uTv0aj1YJi5agbrFUb3sAjzN3fu+OWsStPMJkfCzAe8gZo0UW4vWKDta2MotSzMN5MIY3zH6JEwM3ZGMrqGRVigftQ4KGqKhirMDDVhRjVr9Sft2inXffoQ/fabdmlJjPbBayHSpi5oZxgZCTOqCDOjR1gIsXLlSurYsSMVLFhQNK/NmTPH7vmUlBQaNGgQFShQgGJiYqh169Z0CDY+QYRFmEEjYZpi9CHeEARyp8+pJBsdOthCps89pwgmfNfwgps2TZuifHVBO8PIkyCjpyP1vL9mXBIXF0fVqlWjcePGOX3+yy+/pDFjxtD48eNpw4YNlDlzZmrbti3FB3FaDJu1BkqEaRwJ80s60qiRMJy9whQXsAizUa4c0fz5RH/9pfiQXb1qe27wYKInn/RuQzJjPRijDUZPR3IkzNC0a9dOXJyBKNjXX39NH330EXXq1Ek8NnnyZMqXL5+ImD311FMUDPg01qDpSL8V5ksxYyTkWTfcujX0YjMFbdsSffutUsMFx+xt25R1hGL9jRu9e00WYYzZC/M5EqYrbt26RTdv3rReEuQwcg84duwYnT9/XqQgJdmyZaN69erRunXrKFiwCPM3RijMh2s7uHfPmDtPdWekXr3Ygg2inRUrKtYVXbooj02e7N1rXbxoLqNWRjuMHgljEaZLKlasKASTvAyFnYyHQIABRL7U4L58LhiwCDNsYb6GREfbdp5GTEmyPYVnyBmVqAvz4ozSWjvIo30Ys4kwTkfqkr1799KNGzeslwEDBpBZYBFm1EiYtglJY9tUsAjzjJYtlYHVqBH791/P17cU6izCGEc4Hcn4gdjYWMqaNav1Eo3AgYfkt8y5vXDhgt3juC+fCwYswvyNESJh6gHGsB8wGizCPAMmlM88o9yeNct7Ecb2FIzZImGcjjQtJUqUEGJryZIl1sdQX4YuyQYNGgRtuViE+Rsj1ISBokWV65MnyXCwCPOcjh1tRq6oBfQEFmGMK9gnjAkit2/fpu3bt4uLLMbH7ZMnTwrfsD59+tBnn31Gf//9N+3atYu6du0qPMUeeeSRoC0zW1T4G6NEwqTdAIuw0ABnfjhgXrlCtGkTUf367v8vizDGnUgY/PuM1ijDkTBDs3nzZmrRooX1ft++fcX1Cy+8QL/88gv169dPeIm9+uqrdP36dWrcuDEtWLCAMmJMVZBgEeZvOBLmf9gt33MyZCBCq/bMmUTLlrkvwjCySK5vTkcyrkQYrG4gaIzmPM+F+YamefPmwg/MFYiGDRkyRFz0AqcjDesTpvEZJqcjQ49q1ZRrTwZ8I3IG4JSfPbt/losxLtjPycHXRqwL40gYE2BYhBl1bJG/asKkEaeR4Jow7yhbVrn2ZHaaTEXCW45HFjGOIP1o5NFFLMKYAMMizN8YpSZMijAYcd69S4aCRZhvIsyTSBjXgzFmLs73Mh2JFNi1u9foyp0rFJ8cvDmEjPHgmjDD+oRpDFJLWEaIRgxoLlOGDAOLMO+Q3zG84a5fdy+9yCKMMbNNhReRsE1nNtEb896gzWc3i/uR4ZHUoHADeqT8I/R67dcpUwadn4AzQYUjYUatCdO66wivZ9S6MFmnJMcvMe6Bs33pD+duSlKa+XJRPuMKI6cjPYyE/bHnD2o8sbFVgIHk+8m06uQqemfhO1TmmzL045YfxWMM4wwWYf4EXRr+qgnzB0YVYRydCVxKUpr5BtFhmtE5Rk5HYooEyJkz3T+dsWcGPTvrWUq8lyiiXufeOUf3Bt2jI28dobHtxlLx7MXp7K2z9Oo/r1LlbyvT+M3j6cjVI5yuZOxgEeZP4uMVIWaEmjCjijCsXxZh3lO8uGcNGfLv5LbCMGaJhGGOqsxcpCPCxmwYQ0/8+YSIcD1T5Rma+cRMyp8lP4WHhVPJHCWpZ92etL/nfvq67deUKyYXHbhygHr824NKf1OaYj6PofJjy9Po9aOFgGNCGxZh/kRGwfxiUeEHjCjCcLadbAn1czrSc2Q68tw5z0SYNPdlGLPUhKntV2Q0z0kB/ldrvqLeC3qL+73r9aZJj0wS4suR6Mho6l2/Nx3tfZSGtRpGjYs2FvViAKKsz399qOWklnT+9nl/fipG57AIC4QIi4oiitS2B8IvRtS+uuZj/A3MPAOJjIIh3RsTE9j3DkURJrcNFmGM2dKRMhWZI4dL+5UBSwZQv8X9xO13GrxDo9qOsgorV2SNzkrvN36fVnVbRXEfxNHFdy/S+IfGU7bobLTm1Bqq/UNt2n1xt/afhzEELMIMak+hu0gY0oLt2ikH9cOHKWBwKjJwg9vxHaNzFrAIY8yWjkynwWfqrqk0fM1wcRvi66s2X3ncIBUVEUV5Mueh12q/Rhtf2UgVclegM7fO0ANTHqAT10/4/hkYw8EizJ8YzfhPbdiaxugHp0ybRrRokXI2+fbbFDBYhAUuEobOSNTN4MBTqJCPb8yYFqNGwtIQYdfjr4v0IfioyUfUp34fnzvUy+YqS2teWkNV8lahc7fP0VMzn6Kke0k+vSZjPFiE+ZNbt5Tr2FjNX1pziwpQuLByDbNWKW7cZcQI2+1//iH67z8KCCzCtBNh6QlvWQ+WL5+SYmcYM9WEpdEZ+fuu3+li3EUhnD5q+pFmb5kjJgf9/fTflD1jdlp/ej19uPRDzV6bMQYswowqwjR/RVSSRtuE2IED7v9fUhLRzp3K7c6dles+fZTHA3X2yr5VvokwCO/0DppclM+EaDpy2u5p4vq1Wq+JgnstgZXFxE4Txe2v1n5F8w7N0/T1GX3DIsygIsw/KoyIqldXrrdtc/9/4DEFwQWDwwkTiPLkIdq/n2jsWPI7HAnzDdQryvRReilJKcxLlPDxTRlTY7J05Ombp4X5Knii0hN+eWv4jL1V9y1xu+vsruI9mdCARZgO55AFlRo1PBdhu3Yp11WqKJ1FX3yh3P/4Y6IzZ8ivsAgLXHG+3CakUGeYEEhHwhUfwGKicFZLpsAPfNnmS6pZoCZduXuFnpn5DLvshwhBFWErV66kjh07UsGCBUWN05w5c1J5sgwaNIgKFChAMTEx1Lp1azrk7ngVs0fC/IU8wG7f7p0IA926EdWtq+yEe/TwvMjfGxHGHmHeI4vs0+uKlduEFOoMEwLpSJmKfKrSU359e6Q5pz82nWKjYkXk7ePlH/v1/Rh9EFQRFhcXR9WqVaNx48Y5ff7LL7+kMWPG0Pjx42nDhg2UOXNmatu2LcXDiZ5CvSbMT/lIeYDdvdt9zy9HERYRQfTzz0QZMhDNnat0Tkru3ydatoxo6VJtPMUuXFCu8+b1/bVClfLlleu9e9OO6srRRizCGHfSkdj/4feuV7BsW7fazJ6diLCj147SprObhBnrYxUf8/silc5Zmn7s+KO4/cWqL2jRkUV+f08mhEVYu3bt6LPPPqPOspjbIQr29ddf00cffUSdOnWiqlWr0uTJk+ns2bOpImah2R1J/htjg50oarzSOiirOXrUfg4hqFSJaOBA5XavXkQXLyq3Bw8matmSqFUroqZNbQOhfRVh6NhjvEOKZymmnbFjhxLRROqS1zXjTiRMvQ/UI4jY16pF1KaNErWXjSeqE7rpu6eL65YlWlK+LIHZxzxZ+UnRAJBCKWI25aErBsr+MOapCTt27BidP39epCAl2bJlo3r16tG6detc/l9CQgLdvHnTerkVzJ2AH2vC/KXBhLrzJCWJA/MJi8lgsWL2z/XvT1S1qnKGWbKkcob5+ee25zdsIPrQx5ZsFmHaiTBEP13xh1IXQw0bavCGjKnJmFG5gOvXSXds2qTsiyZPVu4vX0703ntEx48r+z9VpHfansCkIh2BGSzqwy7duUQP/vYgxSVaPCcZ06FbEQYBBvI5nHXjvnzOGUOHDhViTV4qVqxIQcOINWGeFuej7kOKTUcXdaQjJ05UdsgwrkXhK0Tbiy8qKUkwZYr3xfsYCyXfm6Mz3oOoJYAb/rVrqZ/HdzdpknL75Zd9eCMmZJDF7TLFpyd+/NFWhF+6tHL9ww/KdeXK1nTq3kt7aeeFnZQhPAN1rpA6W+NPYjLE0Pxn51ORrEVESnTIiiEBfX8mcOhWhHnLgAED6MaNG9bLXndTav7AiOlItQhzJxImi7nh0+VsPFPNmkqYf98+5YLGCthYNGumvA/q+3DtiS+ZYxQMIs9oQldP4KAjpyU4i4ahrg9iG9YUDzwQ8MVjDCzCpNjRE0uWKNfffKPs49T7rUaNUqUi25ZuSzljUhu4+pu8mfPSuPZKvfSo9aPo+PXjAV8GJoRFWP78+cX1BXmgtYD78jlnREdHU9asWa2X2GAenKUI80s60o8qTJ2OxFDutJB1FPIg7gwINBR/44IzTwzHhYr8/nslgoW6sEGDPF9OGRHFa/hVlYYAiAC4EmGzZyvXTz7pcrAxw+hehOGE/NdflRpWNA917aqMlOve3fY3TZpYa5Kn75kelFSkmo7lOlLrkq0p6X4SDV4+OGjLwfgP3e5RS5QoIcTWEnnWImxnboouyQYNGpAhkKkyo0XCKlRQ/L5QrCrThulFwrwZ6Fynjm280Z9/KlEyT+B6MP8W50Ngo8t1nsXB20kDDcM4RXYYBluEoQMS0yB++UVJuz//vPI4LHRkA8HIkURTpyqj13CigT6UCzvowJUDlDEyIz1c7uEgfgCiL1p+YR2dxCau5iOoIuz27du0fft2cZHF+Lh98uRJ4RvWp08f0T35999/065du6hr167CU+yRRx4hQ2DUmjDUcj3xhK1myx0RllYkLC2qVSN66CFlZ/nVV579L4sw/0XCcPDEY4gS4GQC32/t2hq+IWNq9FAThu0WNcFIN6ITEmCbbtxYMZKWREYSPfUUUd++SoRM5Q32UJmHKDY6uPvvOoXqUPPizYV567ebvg3qsjAmE2GbN2+mGjVqiAvo27evuA2DVtCvXz/q1asXvfrqq1SnTh0h2hYsWEAZZeeN3jFqTRiQZ4wzZyqF2f4SYbKLEqD4Oz3XdjUswvwTCUPzxIwZSiQUqf9+/ZSh7JyKZIyQjsQ+pHlzZb+rrjWF0MIJ/6pVadY2IhVpNWitHLxUpJo+9fqI6++3fE93ku4Ee3EYs4iw5s2biw3e8fILQsdCaITRkCFDRDckDFoXL15MZdVeVHrHjzVhfgdWBLCVgACTNUHOOHzY93mCODPFBeato0a5/38swrQD9XoQWbAUwPUHHyiPIzowfLhNpDGMntORqGGFwFqxwvYYxqhhX4yUoyXSlRYbzmygEzdOUJaoLNS+THvSAx3KdqCSOUrS1btX6dedvwZ7cZhQqAkzPEivyQiSX9KRfg6FIdQmo2GjRyu1YatXK/UVEkRM5JmmdF33NRo2frz73kIswrQjOppIXWuJgycOWIge+Ik/9/5JFcdVpMghkdRyUkvRis+YhGClI//9l2jPHtt9pCJfecWjE2EZBetUrhNlyuCk4zsIRIRHWAd8D18znBLvaTBthNEFLML8BTys5MxEo9WESV54QbF/2LxZcblH55CsrZAiCNYFiJxIvx1vad+eqFw5pY5j8WLPRFga3bKMByD1DONKmFlCDM+f713DRTpcjLtIXWd3pcdnPE77Lu+jeyn3aNnxZdRmShu6ckeHvlKMcdKROGEESKFjJBGMvdGd7Sb37t+zDuzWSypS8nLNlylf5nziZGXC1gnBXhxGI1iE+TsViYiSM/8sHwmIIwNSjAjrY8QQBJI8UMtRQ/v32/4OkRRfP5Cs04AQcAeOhGkLrD7g34YC/NdeU8a5aAwMMGv9UIum7FQaPvrW70trXlpDJbKXEAcXzMtjTEAwRBjqGTGTFieFPXsq/oOY2OEBy48vp3O3z1GOjDnogVL68sTLHJWZBjVT6qWHrBzCLvomgUWYv0BRs4yC+UExBcwVC63ciExBcMF4FcNupyv+OdZUpBRovtKihXKdni2GhEWYoYDZJKJdaLMvl6scbXplE41oO4IaFmlI4zuMF38zbtM4OnnD0uzBaMbNhJs0Yu0IemTaI9RnQR/afTGNEVVa1oQFKh2JfcGrr9qsVLxsFPpt12/i+vGKj1NURBTpDUTDUBt2/vZ5GrluZLAXh9EAFmH+QtY1wW/LDwTFm1TWiEnbChkJ87UeTIKB3tJUUQ78dgWc9qXQ5ZFFuudS3CUhwM7eOkuV8lSitd3XUu2CNsuLNiXbiDb8hHsJ9MnyT4K6rGYiITmBxmwYQ6XGlKJ3F71Lfx34i0ZvGE1Vv6tKL8x5gRYeWUh/H/ibes/vTZW+rSTq8+Sl0MhC9Oa8N71zaldHwmRZhr/A68O2aP16JQr27rtevUx8cjzN3DdT3H626rOkRyAMP2+pzN/9YvUXXEdpAliE+VuEZc9OpuHpp5Vi7Y0blSiYjFjB60urs2eZPlB3N6UVBYuKss56Y/TJ/ZT71HVOVzp89bBIOy58fmGqMTDohB7aaqi4/cuOX2jdqXVBWlpzcOH2Bfphyw9UYVwF6r2gN12+c5nK5iorDuBdKnShFEqhyTsmU9tf21KnaZ1ozMYxIlWM+jx5gWBGZLLE6BLUZGIT2nRmk/sLIOuwEDn3d0py0SJFgMHfELfr1/fqZf49+K+IGGJeY+OijUmvPFnpSWpZoqUQjT3n9RSOAoxxYRHmL+QgZDciYThbPXH9RKofE+5P3TWVnp/9PP2yXbHtCMjYIlcg4iTrtmB2uGOHcuaJonqtgL+PO3Vh6lQkjyzSNT9u+ZEWHF4g3Mf/fvpvKhhb0Onf1S9cn56t8qwQbU/NfIrO3ToX8GU1OigsRySx+Oji9No/r9Gx68eoQJYC9H2H72nPG3vogyYf0MwnZtKGlzeIdY20MCKTr9V6jf58/E860ecEne17ls70PUPznplHLYq3oPCwcFp9cjU1+rmREG5ugYaePHlsg+H9yedKZEjUgaGByEt+3aVYPzxd+WnxmfUKTli+bf+tiIrhdzVx+8RgLxLjA5G+/DPjeyRs9r7Z1Gt+Lzpz6wwVz16cpnSeIs7Cku4lUbe/ullrFOANowgv9zt9/AIc1NE1N22aLYXoQfeRW3VhY8akXxfG9WCGicj0X6LYjyDSVTmvxZnfBd8+9C1tPLORDl09RE1/aUpft/1aeDXhwMOkjRSvsP4A1fNXp2cqP0M96/ZMZbVQt1Bd+rVL2n5TEMvtyrQTETHso2btm0UvznmRYiJj6PFKj6f/daCzFk08GH+lVbTcEdjmrFypRMG8TEOCI1ePiLQs6FqtK+mdcrnL0UdNPqJBywfRq3OVWrgXq7+oa/HIOIdFWJBEGHaYg5YNos9XfW5XuAy/pAGNB4jZZajfiAyPpDyZ8oiOHYSes4d9Q5EpeSlooOgVhfiyKP/FF7V9fVkXtm+fUtQrC3wdYRFmCN5Z+A5dj79ONQvUpF51e6X791mjs9KC5xaI9BfSlx2mdqAmRZvQ6AdHU40CymQNxjko1IYAi46Ipp8e/klEurQQrxBjiJK9/s/r9MPWH+jZWc9SjpgcYrB0uiIMNhEQYf6OgmE/VKiQ1y8zYt0IsU9uV7odVcpbiYzAh00/pMPXDovoZPe/uwuhjOhY9ozZqWq+qiICWjFPRSqarSiVylFKnOQHewQTkxoWYUESYW8veFvUYcg2/f6N+wuRNWPvDNF+LL6c8Eia/eRsEQloOrEprTm1hlIyTKA8iQOCl4FD+hEeUr16KSaIXTU+a0RBb8GCyuiRo0dZhBmYJUeXiEguIrhIh8Fw0h3Q/bW7x24atnqYKCJfdXKVEGVLX1gqIjhMatafXk8DlgwQtyFYn6v6nPYpsIe+pesJ14WP1lN/PkXbX99OhbMWdv1P0mPOHyIM/oTvvEO0YIGyT3r/fZ9862RK7/1G3r9OoEHUa2KniVQye0n6au1XFJcUJ0Ya4aTHWTNFRFiEEM8QZnkz5xXHl7oF69IL1V9IVaPJBA4WYUGoCftr/19WAfZLp1/EjwBMf2w6Nd3UlCbtmCRGZnzS/BNqWkyJDH330HdUbXw1uhOxhhLCDlIYFaOggbotePL4i+LFFRF2/DhRnTrO/+b8eeWaOyN1CRy935j3hrjds05Pu05Id8DBYnib4fRm3TdFUT/8m9r91o5WvrjSMJGKQIECbZjfYsDzE5WeoFdrWawaNAYievIjk0WEcuu5rdTh9w60/MXlIvIScBHWu7cya1Y2DJUq5fVLoXsU67BeoXrW/a2RhNjg5oNpQJMBSl0xpQgBduzaMTpw5YCwg4EdyYW4C2LkERo0Vp5YaTcdACf9/Rv1p171eulmQkAowSIswJEwdN/Ig9N7Dd+zCjB5tomDDi6OVMlXRdQqQKBdjRpHyfebkWmB+evatYoIc8WZM8o1omYegA4vRGfQpYdoQa5MuUT9HeqQFh9dLHZU2BG3KtnK9cGFSRd05h28cpDyZ8lPn7X8zOs1ViRbEfr7qb+p9ZTW4jt6eNrDtPXVrZQtI3fESoauGipq6JB++qHDD36tn4uOjKYZj8+ghhMaipIJNA3h+3H6nv4QYei07NGD6A/F1Z46diT63/+8frlbCbdEB6iMghm19hBpyDK5yojb6IJ1BgQZRNi2c9vobvJd8dmxL9x1cZeo20RgYEjzIfRSjZcMux6MCIuwAIuw/639nyh0RY5+SAsl7eguaC+fsn02JYYfoXmnP6XPUmaZ88eCSBhwR4QVTiMd4sCc/XOoy3SlPR/0W9xPjAGB8EIoX4KdUeYMmUVEAZ1SdQq5iMYxTkFK5NOVn4rbg5sN9lkwoY4FnXq1f6wtfJFgqfDnE39S7kxBblLRAQcuH6Bha4ZZ05CBEKdIF897dp4QYv8c/IdGrR9FfRv0DYwIGzTIXoD9rRTTe8uPW38U6Tt0iXYq34nMDFLHuKBhQ/Juw3fp912/08BlA8XQ8pfnvkzzDs+jMQ+OoUJZva+xY9yHWykCaNZ6O/E2jd04Vtwe1nqYaNn3BPwocif2JUoJp+1X59CLf71oztEVnogwN4txRQTy3zeEAKtTsI4oXEXK7NTNU0KA5YrJJfx33qzzptgh4zEcXOr+VFcUIuO7Y9zj520/izobFAJ3r9Fdk9WGiCXS9UjTrzixgmr/UJu+3/y9SLuEKrCwQVQd2zEKyh+r+FjA3huNFqPajhK331/8vohSpinC7t3z/U3xOj/+aDOO/vlnn14O6026ziMrEYqdhUgxP1/teTrw5gH6qs1XlCE8g+iCLT+uvDhpZfxP6G11ga4JU0XCvt30LV2LvybCxZ3Ld/bqZWPu16ZcSb0pjMJFV0zpb0rT+M3jhT9QyIgwGECeO+eRCMMBGx2mpXOWppXdVtK217bRrh67aPMrm4V/0sX3LtK0x6bRN+2/oX0999FfT/0lBvhix4wzRTiM4xodVIxrkNpFkbA8sGWIyKDZ6kJRPvyt8B3irP31f1+nkmNKiugmosuhBrbHpceWipO5se3HBjwq/nrt18V4H9SiPfnnk0J424EoNawjkpJsJ02+MGwYUWKiUpM6ebLP1jhYf7AGQven1o0MRgNpZkTFMMcVtXE46Xz0j0dpxfF0TLN1yrhx46h48eKUMWNGqlevHm2EwbhOYREWoHQkQt7o9gIfNvnQ7U4xZ2S514qeL/2DdYZYj397iFqZUzdO2UV+vlzzJfX4p4c4szGsCHPmBg17ivv3Ffd+NwrzIZzGb1FmE6IAFQctiCt4VtUqWEt0C6nPgnEwe7jcwzT10am0qtsqKhRbSJheIiKG4dMQdEiLMalBoS9mP6L7qlv1bpqvInxXmDn5WYvPhHUFvrfZ+2eLyBiMjUPFPRyF5B8s/UDcHth0oNgXBBr8Tn7s+KOor0QxeJ0f64h6Iyv4faK+Exw+7HsU7KefbEbRPoJ9AvaPoE+9PkKEMCRKL7DPwwko1hH2eTATNxLTp0+nvn370uDBg2nr1q1UrVo1atu2LV1MbxRekGAR5g9wIHAQYV+t+UpEweBODf8eXymVtYGI2MDMEqJi3qF5wiW7/k/16aW/XhKz4pAmgPjAGc1b898SUQpDIOu87txRWtEdkWfVBQooO/p0gKs0RFO26Gz0dJWnPVoUDJdGqB71ePCw2n5+u4jAYP2W+aaM1eCRUQ5ssj7p7fpvU0yGGL+sFjRMwCMJEc0dr+8QwgxRzmdmPSPmIRpmO/cBlDVA7OIEAes6WKAGbf6z86lMzjJieRpMaCDmUCJCJw7esmvxyBHf3mjoUFsUrJnvTUmoZdt3eZ/YJ7xW+zWfX89MIHoNcY1tC5FCaf4bTG7dukU3b960XhISXAvDkSNH0iuvvELdunWjihUr0vjx4ylTpkz0s4/pa3/BIswfQDwgBA9y5BDjV1BfBL5o9YVPUTBJtSLZREdM7/q9aV33dWL4MQ6CG85sEJ43clYcWtbBNxu/ESkDQ0QKMPIkk6VV2tncOTkGxc2ifHhNAXT9eNOCnTkqsxj3cvSto9YIDDx20KqPIvEhK4ZwmpKI5h6YK+YPQqz2qN2DAgGimYiMfdzsY+GDNGXnFCHEzJw2RsT74+VKNAjNPf4Su564t298ZSN1KNtBDGBHY0urya0ox/Ac9GCNPTSiAdHOI2u93/ecPGmLgn3i+3B3aZQNsJ1ie2XsQe0l0s1Ado8Gk4oVK1K2bNmsl6EQ5U5ITEykLVu2UOvWNiPh8PBwcX/dOp3Oo00xOadOncIvX1wHjP37sbtJScmcOSXl/v2UHv/0SKGPKaXBTw1S7t+/79NLH7pwM+WPTSdT7t1L/Tonr59M+XnrzymDlg5KmbJjSkrSvSTx+Jx9c1KiP40Wy4DnfF2GgFC4sLION21K/dyYMcpzjz6a7svsvrBbfO7wT8JTjl49qtni3Yy/mfLmv2+K18blkWmPiMdCFWxT9X6sJ9ZF/0X9g7IM/xz4JyVySKRYhq/WfJViRpLvJac0/6W5+IyNJjRKuXf/XoqetoF5B+elPD/r+ZT8/8tv/W3IS9Xvqqb8e/Bfz/c/r7+u/N5bttRkOX/f+btYnmxDs6VcuXNFk9c0I+dunRP7TayrI1ePBPX4vXfv3pQbN25YL/Hx8U7//syZM+Lv165da/f4e++9l1K3bt0UPcKRMH8gQ++lStGBKwdFG7Scnedr8WzpvLH0eO0iFB4e5tRTqVuNbvRJi09EoSmiNQCt17KTCcZ8z81+Tv9dlXJcEUYXuYqEuVGUDyNG8Ej5R6hEDkt9igbANgFF/BMeniAikugkqj+hvoiOhSLoWEQUFiNz+tTvE5RleKjsQzS2ndJ9DPf4XRf8aCgcJGD9AeNaRCp+7vSzrjr6sG/DrMnJnSeLIeBofBlZ+GVqd4goU3I47bywkx76/SFqMamFcPh3C5gyT5igWS0YOiI/WvaRuN2vUT92ik8DePw1K9bMOuM4mMTGxlLWrFmtl+ho89Tw6ecXbCYwbgdyvGQJ0UKO7iGMHmpWPHgGqwgt4wAFYYauoLJjy9KPW34Uy6ZLML7IVTrSTY8wdGtN3jnZWnzrD5DiXPHiCmGUiVQcipPnH5pPocbQ1UOt6yNflvSbJfwFvN0guLFdo3bPTGlJ1Fkh9Q0wBsqVKadeBBlSxW83eofm/UZ0anwMvdfgXSHSIdhRO4auVtRUoonIJUuWKKUdNWsSNWni83KN2zhO1IfCH7B3vd4+v57ZebTCo+J65r6ZLv9mw+kNoizj2l2LI0AQyZ07N0VERNAFOVvYAu7nz5+f9AiLMD9Gwr4pf9PaQv5Nu28o2DvFnnV70uLnFwv/JrT0v/rPq1TluyrOPX6MIMLSiYR9seoL0UUGX7DGRRuTv6hfuD5teXWLuEYXbPvf24tmiP2X91MogBE2C48sFDVZsKUI9naO3xoiRWtPraUJWy1RFIOD3ys61eBzB++1Z6o8Q4YA3ZFhYZTzchx9Wf09OtTrEL1U/SVrVysO3rm+zEXNfmkmTl5S1Y0tX65ct2rl86LAMX7QcqUW7NMWn4paTyZtOldQrJTWnV5HZ27abEZwcoPfVvXx1UUGAGJ6+JrhQV+dUVFRVKtWLVoC8W7h/v374n6DBg1Ij7AI8wdHj9LmgkTvZlRmdH3Z+sugtJA7A9G4/T33i65KGJRCKGA4+KTtljlsRhBhbhTmo2X+u83fWZsh/O2hVCC2AC1/YbmYk4gDDGxBKn9bmV6d+6rdzsuMSOsVtLVrmfL1FriC4yArpyJgpp6RgXh4YMoDwo4GnaBj2ikpdkOAtJE0bT18WJRMTOg0QaQqURQPzzdELTHPECcvSFWuOrHKFsGUIgxdkT7Se0Fv4X+FjufuNbUxETY78FDD+gLSvBXfTbe/ugl3fYyuwv4OdjRv1FHG8QWbvn370o8//kiTJk2iffv2UY8ePSguLk50S+qSFJMTjML88zXLpZTobSvY1msh/PW711MenvqwtXC217xeKbcTbqfogv79lWLc3r3tH8e6jIlRnjt82OW/vzD7BfGZWk1qlRJo0AygXq8xn8WIYnWsb7Nx8PLBlLCPw8Tn3Hl+Z4peQFOKbBSo/1P9lMTkxBQjsvrE6pR8X+UTn6PgiIKaNpcEjBYtlN/r5MlOn8Zneve/d63NQ7gUGVkkZfSkHimHc1BKSnh4SsqNGz4twvhN48XronFDT9upEfjfmv9ZG0FwLBuyfIi4H/FJRMqXq79MuXj7ou6O3998801K0aJFU6KiokRB/vr161P0CkfCvAURmg8+SBWpuX7nKj1Y7yAdy0FUInNh+vnhn3U73xEeP7OfnC3m+0kbC+nAH3SvJVeRMEwiuHs3zeHdSK9imoBshgg0lfJWEo77q7utpkZFGolhufDPqv59ddpxfgeZCRheIkUGewIMmdcLqH3EBAT4QKEI/MOlH5LRbCjg99f0l6Z0Ie4CVclbRZho6iHS6DGlS6dp2IrP9NUDX9HBXgdFqjI2KlaME+t97Dsq3Zuo5AeZ6YNNw+jCbfs6H3eZvnu6MLQGg5oO0tV2agRgc4TmozWn1lDjiY3p4xUfW+sS32v0HuXJnIf0xptvvkknTpwQfmIbNmwQrvl6hUWYt3TurBgIYpSGBaQLHvypOW3Pl0L54sJoYddFlCPGNjtSjyCU/HHzj2nu03PtHPhh/IqQs9M6jWCKMFkPhu7JmNT+SEhtvPbPa0IYoEM0mMO3GxVtJA6cEGRYt0iRNvy5IX236bvgi1wNQJp10o5J1kkEegO1j+ggBBil9O/Bf8kIwHi58neVhd8fUj/Yjtd2X6ubkgaPcdOwtWi2oiJViRFiY643oKbHiTLcD6NjkbdE40f+EflFdyX2SehyTA+Mchu9frToBsf+AOnPj5oqnZGM+yCFPKDxAHEbdZbYJlGXyCldbQhDOIxMzOnTp6lIkSJ06tQpKuymuadbzJtH9NBDoubh7r5dNObMLBHtQGF29rtEy/fXo2pz3GzD1gnYsf2w5QfRBq+eA1chdwVqW6ottS7ZWtSUoejZ78yZowhdFFOuXWt7fMEConbtiKpWJdqROqo0at0o6ruwL+XImIP2v7lfjM/RA1fvXqWn/nyKFh1dJO7DYRzjZmoUqEHlc5e32okYiXcXvksj1o0Q5rVwr9crveb1orGbxooIy/IXl4vh03oFA8krfVtJRE/R6IHazXqF9XsW7xZ//kn0+ONEdeoQuTPDb9cuomrVxOSR28v+owV5lRFsm85usv4JBGnXql3F/gjfq2xeWHJsibCJgejac3GPmDEKXqz+orCT0ZOlh9GODejMnX94Pj1c9mEa2GxgQNblaX8dv3UEizAv2XxmE8374HHamniC1pfLTBfCFN+t6vHZafqP16nsW58QDVI6cYzG3aS7tPrkajHaAx5nOCBIMoRnoGerPksv13hZDFTWckCzHStXKuNJypYlOnDA9jics195RRFiEMIOKZwK4ypQXFIc/dDhB3ql1iukJxClQxQMXm2YaCBByuyBUg+IzsLaBWvrNn3tKCqLjioq1vW8Z+YJfyi9gvE5bX9tK6wRULS//bXtlCuTxYdOR+B8WER6Ds8X/kwLn18o0kCG59gxopIlRZeksO+Rs2Fx/n/5sjKIW27zeAwnt/PnK8Ltjz+sL3PoyiEauW4kzdo/K/WwcBfgZAxNGigaN8LvirGHRZgJ8NeXiBFAf+yx7SCKZStGn7YYQs90GEARp88SLV1K1KIFGR0cbJccXSIiOLggpSbBuI9edXsJ8YD6Mk3ZvZuoShVlB33pku3xDz8k+uILotdeIxqvDOWWAufhqQ+LAxi6eZAG1OtZL3yRRqwdQb/t+k0cTG4l3rI+VypHKRrUbJCYL6rFeCt/gbPiwcsHU7V81Wjba9t0f4DDOoeH28ErB+mxio/RjMdnkN7AjL7HZzwuhNfO13eKcUCm4YEHiBYtInr/faWEIy6OaMwYpa62fn2iJ55Qfuv//KMIr8hIZR9QLvU6gNH01N1ThS0KomPS6xAReojXWgVqicgy/Opwn60ojMvpEIiEcXekD6MvHpvWJWV0y8wpf1SklPi/ZqWk7NundAFFR6ekxMWlmJH1p9anPDnjyZRcw3NZO5lwGyNjNOXcOWVdojMqOdn2eOfOyuNff23356/PfV0sS9SnUYbqfkIXH9bpc7OeE8su12nN72sGbVRIeqCDVn7/U3dNTTEK285tEx1dWG7Nt1cfuZt0V3QEytFipmP2bOV3i87m999Xbqd1+emnYC8xE6pjBwOMPkMFBuDpKk/TjCdn0ltVX6HH9xJFT/pVOdMDjRvbBlCbDNSnoOsMxbPorES92JW7V6jD1A7CTFIz1+Q8eTB5FU57RBdVqYe9e5XrChWsD/2681cav2U8hVEYTX10qqG6n3DGjnU6pfMUutrvKg1vPZyyZ8wuDFDr/VRPl4avGOiL7xxRO0SVjEL1/NWpb4O+4nbPeT3THN115c4VMeT56ZlPU//F/WnbuW1+XTY4uaMjEOnS/o311+TgM506KYar6GwerjL1zJdPuf/II8rzL79M9N9/RN3Zx4sJDbgmzFd27lSKSBE+R7j0+HGla7K/CXekLgo23/nvHVH4DEpkL0FfP/g1PVzuYd9fHI74Z88Sbd5MVKsWUWKiIm7v3SM6dUqsb6RKYfKI5UD7OeZmmqHrsOPUjrTt/DZxUF7z0hrROaYH1HV3EztNFAXPRgLCq+K3FenkjZP0Vt23aHS70an+BmNtmkxsIgq91TQv3pzalGwjRt4ATMLAvEqIZl+Q467uJN0RxeMY/WRKTp5UajlxIoUTrPbtiT75RBlJxDAhmo5kEaYF8CBRd/2gaw/deyHE5rObRT2LrBl7o/Yb9Hmrz307QKGbCgLs77+JOnYk2rOHqHJlTHMlunGDTt86I8ZmICqDiMy0R6fpuo7KE1C4DyGASBisFmBzUTVf8LeprrO70pSdU8QYKMzM1GvdXVrA4gDCHfzx2B/0eKXHrc9BeDX+uTEdu35MdLBCEG05t4Vm7p0pOu6cFX5/0OQDerPum0KUeeOGj5E9EH4QeAueW2DIdeo2yclEmzYpwstEQ5gZ/3CaRZjxCciXiDE6//6rRGhgTIgi1BAExc+frfxMeDJpcYCihx8mmjtXKcBHIb5sda9bl5LXraGWk1rSqpOrqEb+GsJHyav30DE4QDf/pTkduXaEMmXIJGYivlDthaAJzZ0XdgrRCzGy6ZVNopPTqCB6O3L9SFEE/0XLL8RngQcSUq1nbp0RqVY0d2AcFYBIgnjD9oaIFUCR/4ErSudukaxFaEiLIfR81efd+n5kcwYsPhBVhOXC2pfWBnX4OcPojdMswoxPKHyJeuO/w/8Jry6kWeQB6pPmn1DXal09ExCvv070/fdEgwcTffyx0lU1YADRs8/SwJdK0GerPhMeQVtf2ypm0JkRdKeiE3fx0cXiPpzTh7UeRu1KtwtoRyIMGjFjFK7ZcNCe/th0MjLoqINv28x9M1M9h5T6kq5L0nWnhxkoooIDlw0UghlUylNJTKCAZYczPz24vqOzD8PlL91Run5h9YIoriHd8BnGj5wOgeM3pyMZv4ADFEYHDVo+yO4AhWHaHct2dE9ADBmiCDD4gv3wA1GPHiIqNn/gU/RQxHQRkUEhPgZHmxkIBpjQfrH6C2EGDJoWayqK+GHoGQh+2voTvTL3FSEs9r6xV7hom2EbhSs9DIqRRm9QpAG1L92eXqj+gkdRVfjqIYIGYXUt/prVTw+vh8YVDEFGLdrBqwfp7wN/W4dTl81VVozV6ly+s+4tPhgmGJxmEWZ8QuFL1DPODlCYp/hKzVeEA3+hrIVc/7M0ZoV5I/yD2rWjtXsWUJuXo+hOSiK9WvNV+r7j9xQqICo2bPUwGrNhDCXcSxCPwW2/b/2+1K1GN7+57sPLrPzY8uL7G/nASHq7wdt+eR+jA4H81ZqvRKQLNWWuQPr89dqvi3ozI05KYJhAcToEjt8cCWMCAqwrhq8ZTqM3jKb45Hjr44gUoCAZ6Ru4xtsVJcvRUDVqEG3dSuuaFKf2jU7Q9RiiB0s/KIrVTeEo7kWHIoxSEWm8l3JPPFYuVzkRVXmk/COaR1VkMT4sHlALxsIhfY5cPSJG6KDQf8+lPWIqAhossK0bfgwRwwSI0yzCjE8ofIlGAqnJ8ZvHC7drdFSqO87gdI3i5lYlWlF0ZDTR9u1CgF0unJO++LErjV37NSVFEDXMXZMWvbpKFKuHMjfib9DP236mz1d9LjpEAdKTSFMiXakFGF0Fuwx4sK3rvo4FBMMwAeN0CBy/ORLGBDW9tvTYUlp0ZBH9vvt3up14WzwOcYVxIxWylaKdf4ylVcWIEixZm8f2EE2ccJmyxOpv9l8wxRg6UjFXT875hKdVhzIdqE2pNqKY35voGOwxGv3cSHxPfer1oVEPjvLD0jMMwziHRZgJCIUv0Qxcirsk6sZQT3Mh7kKq52uEFaRhk89Smzv5KezsuaAso945d+ucmOmIoesyTSlTvm/Xf5valm7rtukrzEzhlwUXd3Tvwa4hFFO/DMMEj9MhcPzmSBijK1JSUmjXxV3CkgGiougf/1Hrv3ZR+cxFKezESaIGDYjWrg32YuoaeFrN2T9HDFxfcXyFNTomO/LQFNG9RnfKEZPDZQQMKcjDVw+Lwv+VL66kPJnzBPATMAzDEIswMxAKStrUDBxI9NlntvvjxhG98UYwl8hwqUp0p6K2a+OZjdYIGRogUIOHQnGkLBsUbiBc+vF3/Rb3E2aixbIVExEwM9hRMAxjPE6HwPGbI2GMvvnjD6Inn1RuN21KtGyZMneO8UqQwZwUtWPo2EsLFPbPeHwG5c2cl9c0wzBB4XQIiDA2qWH0TbNmRDlzElWqRDRnDgswH8iWMZvwpsIFXapI+coL6vDQAYnxPTAPfbfhu5QhIoN23yPDMAyTChZhjL7Jl4/o/HmiyEgidhXXjMJZC9OL1V8UF9ThYYROTGQMxUbHavcmDMMwTJqwCGP0TwaOyPgT2Fdw2pFhGCbwcHENwzAMwzBMEGARxjAMwzAMEwRYhDEMwzAMwwQBFmEMwzAMwzBBgEUYwzAMwzBMEGARxjAMwzAMEwRYhDEMwzAMwwQBFmEMwzAMwzBBgEUYwzAMwzBMEGARxjAMwzAMEwRYhDEMwzAMwwQBFmEMwzAMwzBBgEUYwzAMwzBMEIgkk3P//n1xfe7cuWAvCsMwDMMwbnLOctyWx3EzYnoRduHCBXFdt27dYC8KwzAMwzBeHMeLFi1qyvUWlpKSkkImJjk5mbZt20b58uWj8HDtsq+3bt2iihUr0t69eyk2Nlaz12V4XQcL3qZ5XZsN3qaNvZ7v378vBFiNGjUoMtKcMSPTizB/cfPmTcqWLRvduHGDsmbNGuzFMTW8rnk9mw3epnk9mwnenr2HC/MZhmEYhmGCAIswhmEYhmGYIMAizEuio6Np8ODB4prxL7yuAwOv58DB65rXs5ng7dl7uCaMYRiGYRgmCHAkjGEYhmEYJgiwCGMYhmEYhgkCLMIYhmEYhmGCAIswhmEYhmGYIMAijGEYhmEYJgiwCGMYxgoP0GAYhgkcLMLc5O7du7R69WoxG8uR+Ph4mjx5stbfTciyb98+mjhxIu3fv1/cx3WPHj3opZdeoqVLlwZ78Uzv94P1z/iPuLg4sX1/+OGHNHbsWLpy5Qqvbg3YunUrHTt2zHp/ypQp1KhRIypSpAg1btyYpk2bxutZA3r16kWrVq3idakR7BPmBgcPHqQHHniATp48SWFhYdYfdIECBcTzGDBasGBBunfvnlbfS8iyYMEC6tSpE2XJkoXu3LlDs2fPpq5du1K1atXEMNcVK1bQwoULqWXLlsFeVEPTt29fp4+PHj2annvuOcqVK5e4P3LkyAAvmfnAYGOcwOXMmZNOnTpFTZs2pWvXrlHZsmXpyJEjYjDx+vXrqUSJEsFeVEODfcSIESOodevW9NNPP9Fbb71Fr7zyClWoUIEOHDggHsP2jZM5xnvCw8PFcbBUqVLUvXt3euGFFyh//vy8Sr2ERZgbdO7cmZKSkuiXX36h69evU58+fUREbPny5VS0aFEWYRrSsGFDIbA+++wzIXTfeOMNEQX7/PPPxfMDBgygLVu2CCHG+LYjxUEre/bsdo9D5NauXZsyZ84sdrQcedRmXZ8/f57y5s0rBC6iNfPmzaNs2bLR7du3xf4lT5489Pvvv2vwbqFLpkyZRBS3WLFiVLNmTbHfgAiTYP1iP7Jnz56gLqcZtudFixbR3Llz6bfffqMbN25Qu3btxLpu3769eJ7xgBQmXfLmzZuyc+dO6/379++nvP766ylFixZNOXLkSMr58+dTwsPDeU1qQNasWVMOHTokbt+7dy8lMjIyZevWrdbnd+3alZIvXz5e1z4ydOjQlBIlSqQsWbLE7nGs7z179vD61ZCwsLCUCxcuiNslS5ZMWbhwod3za9asSSlSpAivcx/JlStXyubNm6377O3bt9s9f/jw4ZSYmBhezxpuz4mJiSnTp09Padu2bUpERERKwYIFUz744APrPpxJH5asbtaDIWUgQYTgu+++o44dO1KzZs1EupLRDqxfgDOqjBkzioiBJDY2Vpx5Mb7Rv39/mj59uogWvPvuuyLSy/h/m0b9qCxjkBQqVIguXbrEq99HEI3Bfhlgv/znn3/aPf/HH39Q6dKleT1rSIYMGeiJJ54QZSRHjx4V0TBEx8qVK8fr2U1YhLlB+fLlafPmzakeR1Et6pcefvhhd9c3kw7FixenQ4cOWe+vW7dOpHwlqMtzPIgx3lGnTh2R2oUAQApy9+7dVrHAaEurVq1EiuzmzZuiPknNiRMnrDV4jPcMHz6clixZIgQYivFRH9akSRN69dVXxWMff/wxDRs2jFexn8B+GusY6XaIMsY9bOEdxiWo2Zg6dSo9//zzToUYCsbHjx/Pa1ADEJlRNzhUrlzZ7vn58+dzUb6GoAFi0qRJov4OBc3cXKI9gwcPTrXO1aC2BmKB8Q00R23btk0ILaxT2K1s3LhRNEOgS3LNmjXiZIPxDdTcRUREuHweJ3Jt2rTh1ewmXJjPMIwAByu0+UOMoTCfYRiG8S8swrwgISHB6qnE+Bde14GB13Pg4HXN69lM8PbsG1wT5iZoyUX7bY4cOUQrNC64jccWL17s49fA8LoOPLxN87o2G7xN83o2GhwJcwPUzLz88sv02GOPUdu2bSlfvnxWk1b4VaELZ8KECU5rxhjP4HUdGHg9Bw5e17yezQRvzxrjho1FyFOmTJmUsWPHulwP48aNSyldunTIryct4HUdGHg9Bw5e17yezQRvz9rCkTA3gFfVjh07XHqfoOW8evXqwk+M8Q1e14GB13Pg4HXN69lM8PasLVwT5gaVKlUS6UZX/Pzzz2I+HOM7vK4DA6/nwMHrmtezmeDtWVs4EuYGmBHZoUMHKlmypGjfV9eEwRwQTsH//vuvGMzL+Aav68DA6zlw8Lrm9WwmeHvWFhZhbnL8+HExEmP9+vViGC/A5PgGDRrQ66+/LpzeGW3gdR0YeD0HDl7XvJ7NBG/P2sEijGEYhmEYJghwTZiXvPHGG3T58mVtvw2G13UQ4W2a17XZ4G2a17Pe4UiYl2TNmpW2b98u6sQY/8LrOjDweg4cvK55PZsJ3p69hyNhXoLhsExg4HXN69ls8DbN69lM8PbsPSzCGIZhGIZhggCnIxmGYRiGYYIAR8LcYMuWLf7/Jhhe1wGEt2le12aDt2lez0aERZgb1KlTh0qXLk1ffPEFnT171v/fSgjD65rXs9ngbZrXs5ng7VlbWIS5ScuWLWn06NFUrFgx4Z4/Z84cunfvnsZfB8PrOnDwNs3r2mzwNs3r2XBoPBDclISFhaVcuHAhJSkpKeXPP/9Mad++fUpERERKvnz5Uvr165dy4MCBYC+iaeB1zevZbPA2zevZTPD2rC0swjzY6NScPn06ZciQISklS5ZMCQ8PT2nSpInGX01owuua17PZ4G2a17OZ4O1ZWzgd6QZhYWGpHitUqBANHDiQjhw5QgsXLqQiRYr4I1AZcvC65vVsNnib5vVsJnh71ha2qHCD8PBwMbQ7b968Gq9+htd1cOBtmte12eBtmtezEeFImBssW7aMcubM6f9vg+F1HSB4mw4cvK55PZsJ3p61hSNhDMMwDMMwQSAyGG9qRBITE4Utxbp160RqEuTPn58aNmxInTp1oqioqGAvomngdc3r2WzwNs3r2Uzw9qwdHAlzg8OHD1Pbtm2FUWu9evUoX7584vELFy7Qhg0bqHDhwjR//nxh6Mr4Bq/rwMDrOXDwuub1bCZ4e9YWFmFu0KZNG8qcOTNNnjyZsmbNavfczZs3qWvXrnT37l3677//NP56Qg9e17yezQZv0/9v7+5CqtjeOI4/2ptFlpL5BplJiZkVFghmlFgIBUZFGV6ECRmZCN5EBr3cROVlEUVEQUFEF9aldVGGpJFZlFqhIFpRqVhElnnjnsOaw9mnfbL/f6zlTHvN9wMb956x2v7WgzytWXsNOZuEetaLJsyBGTNmSEtLi2RlZY15vr293Z4hGx4e1jw8/kPW5GwaapqcTUI968WnIx2IiYmR3t7en55X59T34PeRtTvI2T1kTc4moZ71YmG+A7t377YvOarNWdetWxeyJuzOnTty7Ngxqaqq0jw0/kTW5GwaapqcTUI9a6Z5B35jnTx50kpKSrJv2aBuU6Qe6rk6Vltb6/XbMwpZk7NpqGlyNgn1rA9rwsapp6cnZIuKBQsW6O6LQdauoqbJ2jTUNDmHC9aEjZNqunJzcyUQCEhycvLEjArI2kXUNFmbhpom53DBTNgvUltVPH36VNLS0vSOCMjaI9Q0WZuGmibnPx0zYb/Isiy9IwGy9hg1TdamoabJ+U9HEwYAAOABmrBfdP78+eBWFZhYZO0OcnYPWZOzSajnX8eaMAAAAA8wE+bAwMBAyGu1IL+0tFTy8vJk27Ztcu/evYkaH98ha3I2DTVNziahnvWiCXMgKSkpWHjNzc2Sk5Mjr169spswdQNvdUPTxsZGzUPjT2RNzqahpsnZJNSzXlyOdCAyMtLeoDU+Pl4KCwtl3rx5cvHixeD56upq+ybe6hZG+D1k7Q5ydg9Zk7NJqGe9mAkbp46ODikvLw85pl63tbXpHBeQtWuoabI2DTVNzuGCG3g7NDQ0JFFRUfZj2rRpIefUseHh4YkYH18ia3I2DTVNziahnvVhJsyh9PR0iY2Nld7eXmltbQ059/z5c25hpBFZu4Oc3UPW5GwS6lkfZsIcaGho+GFh4n9vFrtnzx6Nw+JfZE3OpqGmydkk1LNeLMwHAADwAJcjAQAAPEAT5tDZs2dl/fr1Ulxc/MNWFIODg5KWljYR4+NLZE3OpqGmydkk1LM+NGEOnD59Wvbv3y8ZGRn2JyM3btwoJ06cCJ4fHR21N2/F7yNrd5Cze8ianE1CPWtm4f/KzMy0rl69Gnzd1NRkzZ071zp8+LD9uq+vz4qMjCRJDcjaHeTsHrImZ5NQz3rRhDkwffp0q6enJ+RYe3u7lZCQYNXU1NCEaUTW7iBn95A1OZuEetaLLSociIuLkzdv3khqamrwWFZWlty9e1cKCgrk3bt3uicofYusydk01DQ5m4R61os1YQ6sXr1abty48cPxzMxMe5F+fX295mHxL7ImZ9NQ0+RsEupZL2bCHKipqZHHjx+PeW7JkiX2jFhdXZ3mofEnsiZn01DT5GwS6lkvNmsFAADwADNh49DS0iIPHjyQvr4++3ViYqLk5uZKTk7ORI2Pb5E1OZuGmiZnk1DPejAT5sDAwIBs3bpVmpubJSUlRRISEuzj/f398vr1a8nLy7MvR8bHx2saFv8ia3I2DTVNziahnvViYb4D+/btk0AgIC9fvpTe3l55+PCh/VDP1TF1rrKyUvPQ+BNZk7NpqGlyNgn1rBczYQ5ER0dLY2OjZGdnj3leLdrPz8+XoaEhzcPjP2RNzqahpsnZJNSzXsyEOaBuVfT58+efnlfNl/oe/D6ydgc5u4esydkk1LNeNGEO7NixQ0pLS+XmzZshzZh6ro6VlZVJSUmJ5qHxJ7ImZ9NQ0+RsEupZM8078BtpZGTE2rt3rzV16lT7HpFRUVH2Qz1XxyoqKuzvAVmHC2qarE1DTZNzOGJN2DiomS+1/uv7LSpWrlwps2bN0t0b+x5Zu4Oc3UPW5GwS6lkPmjAAAAAPsCbMoW/fvsn9+/flxYsXP5wbGRmRK1eu6B4b3yJrcjYNNU3OJqGeNfL6emg46OzstObPn29FRETY68DWrFljvX37Nni+r6/PPg6yDhfUNFmbhpom53DETJgDBw4ckKysLHun4M7OTnufFHUnebVbPvQia3eQs3vImpxNQj1r5nUXGA7i4+Ottra24OtAIGB/WjIlJcXq7u5mJoysww41TdamoabJORwxE+bw+vfkyf/e6zwiIkLOnTsnRUVFsnbtWunq6tLdG/sWWZOzaahpcjYJ9azXv50FfiojI0NaW1tl8eLFIcfPnDljf920aRPpaULW7iBn95A1OZuEetaLmTAHtmzZIteuXRvznGrE1G75lmVpHhp/ImtyNg01Tc4moZ71Yp8wAAAADzATBgAA4AGaMAAAAA/QhAEAAHiAJgwAAMADNGEA/mi7du2SzZs3e/02AEA79gkD4Bm18fH/cvToUTl16hRbwAAwEk0YAM+8f/8++Pz69ety5MgR+/6s/5g5c6b9AAATcTkSgGcSExODj9mzZ9szY98fUw3Yfy9H5ufnS1VVlVRXV0tsbKwkJCTIhQsX5OvXr1JWVibR0dGycOFCqa+vD/m3Ojo6ZMOGDfbfqf7Mzp07ZXBw0IOfGgD+RhMGIOxcvnxZ4uLipKWlxW7IKioqZPv27bJq1Sp58uSJFBYW2k3W8PCw/f2fPn2SgoICyc7Otm9BduvWLenv75fi4mKvfxQAPkYTBiDsLF++XA4dOiSLFi2SgwcPSlRUlN2UlZeX28fUZc0PHz5IW1tb8PZiqgE7fvy4fe879fzSpUvS0NAgXV1dXv84AHyKNWEAws6yZcuCzydNmiRz5syRpUuXBo+py43KwMCA/fXZs2d2wzXW+rLu7m5JT0935X0DwPdowgCEnSlTpoS8VmvJvj/2z6cuA4GA/fXLly9SVFQktbW1P/xdSUlJE/5+AWAsNGEAjLdixQqpq6uT1NRUmTyZX3sA/gysCQNgvMrKSvn48aOUlJTIo0eP7EuQt2/ftj9NOTo66vXbA+BTNGEAjJecnCxNTU12w6U+OanWj6ktLmJiYiQykl+DALwRYVmW5dG/DQAA4Fv8FxAAAMADNGEAAAAeoAkDAADwAE0YAACAB2jCAAAAPEATBgAA4AGaMAAAAA/QhAEAAHiAJgwAAMADNGEAAAAeoAkDAAAQ9/0FkXxv93Hh1GQAAAAASUVORK5CYII="
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 64
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T21:43:20.307344Z",
+     "start_time": "2025-11-29T21:43:20.296669Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "print(dir(temp_array_fsgp))\n",
+   "id": "d577cfa934535b4f",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['T', '__abs__', '__add__', '__and__', '__array__', '__array_finalize__', '__array_function__', '__array_interface__', '__array_namespace__', '__array_priority__', '__array_struct__', '__array_ufunc__', '__array_wrap__', '__bool__', '__buffer__', '__class__', '__class_getitem__', '__complex__', '__contains__', '__copy__', '__deepcopy__', '__delattr__', '__delitem__', '__dict__', '__dir__', '__divmod__', '__dlpack__', '__dlpack_device__', '__doc__', '__eq__', '__float__', '__floordiv__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__getstate__', '__gt__', '__hash__', '__iadd__', '__iand__', '__ifloordiv__', '__ilshift__', '__imatmul__', '__imod__', '__imul__', '__index__', '__init__', '__init_subclass__', '__int__', '__invert__', '__ior__', '__ipow__', '__irshift__', '__isub__', '__iter__', '__itruediv__', '__ixor__', '__le__', '__len__', '__lshift__', '__lt__', '__matmul__', '__mod__', '__module__', '__mul__', '__ne__', '__neg__', '__new__', '__or__', '__pos__', '__pow__', '__radd__', '__rand__', '__rdivmod__', '__reduce__', '__reduce_ex__', '__repr__', '__rfloordiv__', '__rlshift__', '__rmatmul__', '__rmod__', '__rmul__', '__ror__', '__rpow__', '__rrshift__', '__rshift__', '__rsub__', '__rtruediv__', '__rxor__', '__setattr__', '__setitem__', '__setstate__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__truediv__', '__xor__', '_length', '_meta', '_period', '_start', '_stop', '_units', 'align', 'all', 'any', 'argmax', 'argmin', 'argpartition', 'argsort', 'astype', 'base', 'byteswap', 'choose', 'clip', 'compress', 'conj', 'conjugate', 'copy', 'ctypes', 'cumprod', 'cumsum', 'data', 'datetime_x_axis', 'device', 'diagonal', 'dot', 'dtype', 'dump', 'dumps', 'fill', 'flags', 'flat', 'flatten', 'from_csv', 'from_query_dataframe', 'getfield', 'granularity', 'imag', 'index_of', 'item', 'itemset', 'itemsize', 'length', 'mT', 'max', 'mean', 'meta', 'min', 'nbytes', 'ndim', 'newbyteorder', 'nonzero', 'partition', 'period', 'plot', 'prod', 'promote', 'ptp', 'put', 'ravel', 'real', 'relative_time', 'repeat', 'reshape', 'resize', 'round', 'searchsorted', 'setfield', 'setflags', 'shape', 'size', 'sort', 'squeeze', 'start', 'std', 'stop', 'strides', 'sum', 'swapaxes', 'take', 'to_device', 'tobytes', 'tofile', 'tolist', 'trace', 'transpose', 'units', 'unix_x_axis', 'var', 'view', 'x_axis']\n"
+     ]
+    }
+   ],
+   "execution_count": 71
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T21:44:22.670216Z",
+     "start_time": "2025-11-29T21:44:21.605928Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "#resampling influx data so that i can fit it with irradiance data (queried every 15 minutes)\n",
+    "\n",
+    "ts = temp_array_fsgp\n",
+    "timestamps = pd.to_datetime(ts.datetime_x_axis, utc=True)\n",
+    "values = ts.data\n",
+    "\n",
+    "df = pd.DataFrame({\"value\": values}, index=timestamps)\n",
+    "df_15m = df.resample(\"15T\").mean()\n",
+    "\n",
+    "print(df_15m.head())\n"
+   ],
+   "id": "203be30192c9302a",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                               value\n",
+      "2025-07-02 07:15:00+00:00  37.371079\n",
+      "2025-07-02 07:30:00+00:00  46.801625\n",
+      "2025-07-02 07:45:00+00:00  29.980004\n",
+      "2025-07-02 08:00:00+00:00  45.573409\n",
+      "2025-07-02 08:15:00+00:00  55.745558\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_30480\\3992666424.py:13: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
+      "  df_15m = df.resample(\"15T\").mean()\n"
+     ]
+    }
+   ],
+   "execution_count": 72
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T21:48:01.173879Z",
+     "start_time": "2025-11-29T21:48:01.131446Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df_15m.index",
+   "id": "9d97681681224101",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatetimeIndex(['2025-07-02 07:15:00+00:00', '2025-07-02 07:30:00+00:00',\n",
+       "               '2025-07-02 07:45:00+00:00', '2025-07-02 08:00:00+00:00',\n",
+       "               '2025-07-02 08:15:00+00:00', '2025-07-02 08:30:00+00:00',\n",
+       "               '2025-07-02 08:45:00+00:00', '2025-07-02 09:00:00+00:00',\n",
+       "               '2025-07-02 09:15:00+00:00', '2025-07-02 09:30:00+00:00',\n",
+       "               ...\n",
+       "               '2025-07-05 11:45:00+00:00', '2025-07-05 12:00:00+00:00',\n",
+       "               '2025-07-05 12:15:00+00:00', '2025-07-05 12:30:00+00:00',\n",
+       "               '2025-07-05 12:45:00+00:00', '2025-07-05 13:00:00+00:00',\n",
+       "               '2025-07-05 13:15:00+00:00', '2025-07-05 13:30:00+00:00',\n",
+       "               '2025-07-05 13:45:00+00:00', '2025-07-05 14:00:00+00:00'],\n",
+       "              dtype='datetime64[ns, UTC]', length=316, freq='15min')"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 73
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T21:48:05.682080Z",
+     "start_time": "2025-11-29T21:48:05.548214Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "#this graph shows solar irradiance, array temperature, and ambient temperature over July 2-6 (FSGP 2025)\n",
+    "\n",
+    "fig, ax1 = plt.subplots()\n",
+    "ax_twin = ax1.twinx()\n",
+    "\n",
+    "plt.plot(df_15m.index, df_15m, label = \"Array Temperature\")\n",
+    "plt.plot(hourly_data['date'],hourly_data['temperature_2m'], color = 'green', label = \"Ambient Temperature\")\n",
+    "\n",
+    "ax1.plot(hourly_data['date'],hourly_data['shortwave_radiation_instant'], color = \"red\", label = \"Solar Irradiance\")\n",
+    "\n",
+    "ax1.set_xlabel(\"Time\")\n",
+    "ax1.set_ylabel(\"Solar Irradiance\")\n",
+    "ax_twin.set_ylabel(\"MosfetTemperatureA\")\n",
+    "\n",
+    "ax1.tick_params(\"x\", rotation  = 90)\n",
+    "#ax1.set_xticks(minutely_15.timeformat)\n",
+    "\n",
+    "plt.legend(loc = \"upper left\")\n",
+    "ax1.legend(loc = \"upper left\")\n",
+    "plt.show()"
+   ],
+   "id": "7397cf9f7ec92d1e",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 74
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "8848868de645674f"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "import the Physics Array Temperature Model for validation/sanity checks\n",
+   "id": "11a5c363e4b5b953"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T21:48:56.274247Z",
+     "start_time": "2025-11-29T21:48:56.265960Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from v4.array_temperature.arrayTemperatureModel import arrayTemperatureModel\n",
+    "def model(u0, u1):\n",
+    "    return arrayTemperatureModel(hourly_data['temperature_2m'], hourly_data['shortwave_radiation_instant'], 0, u0, u1)\n",
+    "\n",
+    "model = model(22, 0.8)\n",
+    "print(model.calculateArrayTemperature())\n"
+   ],
+   "id": "2099b64dadb66f1a",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 1.8935347e+00  1.0364137e+01  2.1387180e+01  2.9999233e+01\n",
+      "  2.6391014e+01  2.6875341e+01  2.7645386e+01  2.8025963e+01\n",
+      "  2.5642160e+01  2.4427845e+01  1.8177454e+01  1.7554525e+01\n",
+      "  8.0708389e+00  1.9637234e+00 -8.7300003e-01 -1.2230000e+00\n",
+      " -1.4230000e+00 -1.6230000e+00 -2.3230000e+00 -2.8230000e+00\n",
+      " -3.3230000e+00 -3.7229998e+00 -4.0730000e+00 -4.3730001e+00\n",
+      "  1.2934799e+00  1.0832609e+01  2.1798904e+01  3.0714010e+01\n",
+      "  4.0591778e+01  4.3875725e+01  3.3674473e+01  3.8677563e+01\n",
+      "  3.2507423e+01  3.0509079e+01  2.3399044e+01  1.4952563e+01\n",
+      "  7.7067099e+00  1.6932418e+00 -1.2730000e+00 -1.8230001e+00\n",
+      " -2.6229999e+00 -3.2229998e+00 -3.8229997e+00 -4.0730000e+00\n",
+      " -4.5230002e+00 -4.8730001e+00 -5.2730002e+00 -5.5730000e+00\n",
+      " -1.0213566e-01  9.7276592e+00  2.1370445e+01  3.2780659e+01\n",
+      "  4.2558723e+01  4.9203346e+01  5.2815674e+01  5.2470692e+01\n",
+      "  4.7815193e+01  3.2199566e+01  2.7188547e+01  2.1431530e+01\n",
+      "  1.2343058e+01  3.3490453e+00 -1.1730000e+00 -2.4229999e+00\n",
+      " -3.5730000e+00 -3.5730000e+00 -3.0730000e+00 -3.1729999e+00\n",
+      " -3.1229999e+00 -3.5230000e+00 -3.7730000e+00 -3.8229997e+00\n",
+      " -7.6389313e-03  8.4443426e+00  1.7759680e+01  2.5409929e+01\n",
+      "  3.1721752e+01  3.9829716e+01  2.8951809e+01  4.0317398e+01\n",
+      "  3.3866692e+01  2.9610489e+01  2.6545244e+01  2.2101496e+01\n",
+      "  1.3766469e+01  5.0048113e+00  6.7700005e-01  1.7700000e-01\n",
+      " -4.7300002e-01 -1.0230000e+00 -1.7730001e+00 -2.5230000e+00\n",
+      " -3.0230000e+00 -3.2229998e+00 -3.3729999e+00 -3.3230000e+00\n",
+      "  2.0034187e+00  1.2429336e+01  2.4601358e+01  3.6484211e+01\n",
+      "  4.6292747e+01  5.3362427e+01  5.4850025e+01  5.5464657e+01\n",
+      "  5.2211830e+01  4.7174763e+01  3.9110405e+01  2.9210789e+01\n",
+      "  1.8547527e+01  8.4345522e+00  3.3770001e+00  2.2270000e+00\n",
+      "  1.6270000e+00  1.2770000e+00  1.0270000e+00  1.3770000e+00\n",
+      "  9.2700005e-01  1.2700000e-01 -1.4730000e+00 -1.2730000e+00]\n"
+     ]
+    }
+   ],
+   "execution_count": 76
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T21:48:57.548674Z",
+     "start_time": "2025-11-29T21:48:57.541528Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "faiman_temp = model.calculateArrayTemperature()",
+   "id": "492ea4964124380d",
+   "outputs": [],
+   "execution_count": 77
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T21:48:58.404891Z",
+     "start_time": "2025-11-29T21:48:58.394317Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import numpy as np\n",
+    "np.array(faiman_temp)"
+   ],
+   "id": "bee1dddff65cfb38",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.8935347e+00,  1.0364137e+01,  2.1387180e+01,  2.9999233e+01,\n",
+       "        2.6391014e+01,  2.6875341e+01,  2.7645386e+01,  2.8025963e+01,\n",
+       "        2.5642160e+01,  2.4427845e+01,  1.8177454e+01,  1.7554525e+01,\n",
+       "        8.0708389e+00,  1.9637234e+00, -8.7300003e-01, -1.2230000e+00,\n",
+       "       -1.4230000e+00, -1.6230000e+00, -2.3230000e+00, -2.8230000e+00,\n",
+       "       -3.3230000e+00, -3.7229998e+00, -4.0730000e+00, -4.3730001e+00,\n",
+       "        1.2934799e+00,  1.0832609e+01,  2.1798904e+01,  3.0714010e+01,\n",
+       "        4.0591778e+01,  4.3875725e+01,  3.3674473e+01,  3.8677563e+01,\n",
+       "        3.2507423e+01,  3.0509079e+01,  2.3399044e+01,  1.4952563e+01,\n",
+       "        7.7067099e+00,  1.6932418e+00, -1.2730000e+00, -1.8230001e+00,\n",
+       "       -2.6229999e+00, -3.2229998e+00, -3.8229997e+00, -4.0730000e+00,\n",
+       "       -4.5230002e+00, -4.8730001e+00, -5.2730002e+00, -5.5730000e+00,\n",
+       "       -1.0213566e-01,  9.7276592e+00,  2.1370445e+01,  3.2780659e+01,\n",
+       "        4.2558723e+01,  4.9203346e+01,  5.2815674e+01,  5.2470692e+01,\n",
+       "        4.7815193e+01,  3.2199566e+01,  2.7188547e+01,  2.1431530e+01,\n",
+       "        1.2343058e+01,  3.3490453e+00, -1.1730000e+00, -2.4229999e+00,\n",
+       "       -3.5730000e+00, -3.5730000e+00, -3.0730000e+00, -3.1729999e+00,\n",
+       "       -3.1229999e+00, -3.5230000e+00, -3.7730000e+00, -3.8229997e+00,\n",
+       "       -7.6389313e-03,  8.4443426e+00,  1.7759680e+01,  2.5409929e+01,\n",
+       "        3.1721752e+01,  3.9829716e+01,  2.8951809e+01,  4.0317398e+01,\n",
+       "        3.3866692e+01,  2.9610489e+01,  2.6545244e+01,  2.2101496e+01,\n",
+       "        1.3766469e+01,  5.0048113e+00,  6.7700005e-01,  1.7700000e-01,\n",
+       "       -4.7300002e-01, -1.0230000e+00, -1.7730001e+00, -2.5230000e+00,\n",
+       "       -3.0230000e+00, -3.2229998e+00, -3.3729999e+00, -3.3230000e+00,\n",
+       "        2.0034187e+00,  1.2429336e+01,  2.4601358e+01,  3.6484211e+01,\n",
+       "        4.6292747e+01,  5.3362427e+01,  5.4850025e+01,  5.5464657e+01,\n",
+       "        5.2211830e+01,  4.7174763e+01,  3.9110405e+01,  2.9210789e+01,\n",
+       "        1.8547527e+01,  8.4345522e+00,  3.3770001e+00,  2.2270000e+00,\n",
+       "        1.6270000e+00,  1.2770000e+00,  1.0270000e+00,  1.3770000e+00,\n",
+       "        9.2700005e-01,  1.2700000e-01, -1.4730000e+00, -1.2730000e+00],\n",
+       "      dtype=float32)"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 78
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T21:52:30.256333Z",
+     "start_time": "2025-11-29T21:52:30.246409Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "len(faiman_temp)",
+   "id": "53ffac58d50fb409",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "120"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 87
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T21:54:46.173840Z",
+     "start_time": "2025-11-29T21:54:46.151825Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df_15m.head()",
+   "id": "e2789dc942e61cc",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                               value\n",
+       "2025-07-02 07:15:00+00:00  37.371079\n",
+       "2025-07-02 07:30:00+00:00  46.801625\n",
+       "2025-07-02 07:45:00+00:00  29.980004\n",
+       "2025-07-02 08:00:00+00:00  45.573409\n",
+       "2025-07-02 08:15:00+00:00  55.745558"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2025-07-02 07:15:00+00:00</th>\n",
+       "      <td>37.371079</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-02 07:30:00+00:00</th>\n",
+       "      <td>46.801625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-02 07:45:00+00:00</th>\n",
+       "      <td>29.980004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-02 08:00:00+00:00</th>\n",
+       "      <td>45.573409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-02 08:15:00+00:00</th>\n",
+       "      <td>55.745558</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 90
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T21:52:50.120667Z",
+     "start_time": "2025-11-29T21:52:50.111626Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df_15m.tail()",
+   "id": "87c603ad74af1dd1",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                               value\n",
+       "2025-07-05 13:00:00+00:00  38.086209\n",
+       "2025-07-05 13:15:00+00:00  36.774658\n",
+       "2025-07-05 13:30:00+00:00  35.463106\n",
+       "2025-07-05 13:45:00+00:00  34.271664\n",
+       "2025-07-05 14:00:00+00:00  34.174615"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2025-07-05 13:00:00+00:00</th>\n",
+       "      <td>38.086209</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-05 13:15:00+00:00</th>\n",
+       "      <td>36.774658</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-05 13:30:00+00:00</th>\n",
+       "      <td>35.463106</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-05 13:45:00+00:00</th>\n",
+       "      <td>34.271664</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-05 14:00:00+00:00</th>\n",
+       "      <td>34.174615</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 89
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T21:52:04.209818Z",
+     "start_time": "2025-11-29T21:52:03.938367Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df_15m['faiman'] = faiman_temp",
+   "id": "d57cc2a51597298a",
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Length of values (120) does not match length of index (316)",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mValueError\u001B[39m                                Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[86]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[43mdf_15m\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43mfaiman\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m]\u001B[49m = faiman_temp\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:4322\u001B[39m, in \u001B[36mDataFrame.__setitem__\u001B[39m\u001B[34m(self, key, value)\u001B[39m\n\u001B[32m   4319\u001B[39m     \u001B[38;5;28mself\u001B[39m._setitem_array([key], value)\n\u001B[32m   4320\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m   4321\u001B[39m     \u001B[38;5;66;03m# set column\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m4322\u001B[39m     \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_set_item\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mvalue\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:4535\u001B[39m, in \u001B[36mDataFrame._set_item\u001B[39m\u001B[34m(self, key, value)\u001B[39m\n\u001B[32m   4525\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34m_set_item\u001B[39m(\u001B[38;5;28mself\u001B[39m, key, value) -> \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m   4526\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m   4527\u001B[39m \u001B[33;03m    Add series to DataFrame in specified column.\u001B[39;00m\n\u001B[32m   4528\u001B[39m \n\u001B[32m   (...)\u001B[39m\u001B[32m   4533\u001B[39m \u001B[33;03m    ensure homogeneity.\u001B[39;00m\n\u001B[32m   4534\u001B[39m \u001B[33;03m    \"\"\"\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m4535\u001B[39m     value, refs = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_sanitize_column\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvalue\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   4537\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m (\n\u001B[32m   4538\u001B[39m         key \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m.columns\n\u001B[32m   4539\u001B[39m         \u001B[38;5;129;01mand\u001B[39;00m value.ndim == \u001B[32m1\u001B[39m\n\u001B[32m   4540\u001B[39m         \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(value.dtype, ExtensionDtype)\n\u001B[32m   4541\u001B[39m     ):\n\u001B[32m   4542\u001B[39m         \u001B[38;5;66;03m# broadcast across multiple columns if necessary\u001B[39;00m\n\u001B[32m   4543\u001B[39m         \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m.columns.is_unique \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(\u001B[38;5;28mself\u001B[39m.columns, MultiIndex):\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:5288\u001B[39m, in \u001B[36mDataFrame._sanitize_column\u001B[39m\u001B[34m(self, value)\u001B[39m\n\u001B[32m   5285\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m _reindex_for_setitem(value, \u001B[38;5;28mself\u001B[39m.index)\n\u001B[32m   5287\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m is_list_like(value):\n\u001B[32m-> \u001B[39m\u001B[32m5288\u001B[39m     \u001B[43mcom\u001B[49m\u001B[43m.\u001B[49m\u001B[43mrequire_length_match\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvalue\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mindex\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   5289\u001B[39m arr = sanitize_array(value, \u001B[38;5;28mself\u001B[39m.index, copy=\u001B[38;5;28;01mTrue\u001B[39;00m, allow_2d=\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[32m   5290\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m (\n\u001B[32m   5291\u001B[39m     \u001B[38;5;28misinstance\u001B[39m(value, Index)\n\u001B[32m   5292\u001B[39m     \u001B[38;5;129;01mand\u001B[39;00m value.dtype == \u001B[33m\"\u001B[39m\u001B[33mobject\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m   (...)\u001B[39m\u001B[32m   5295\u001B[39m     \u001B[38;5;66;03m# TODO: Remove kludge in sanitize_array for string mode when enforcing\u001B[39;00m\n\u001B[32m   5296\u001B[39m     \u001B[38;5;66;03m# this deprecation\u001B[39;00m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\common.py:573\u001B[39m, in \u001B[36mrequire_length_match\u001B[39m\u001B[34m(data, index)\u001B[39m\n\u001B[32m    569\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    570\u001B[39m \u001B[33;03mCheck the length of data matches the length of the index.\u001B[39;00m\n\u001B[32m    571\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    572\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(data) != \u001B[38;5;28mlen\u001B[39m(index):\n\u001B[32m--> \u001B[39m\u001B[32m573\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[32m    574\u001B[39m         \u001B[33m\"\u001B[39m\u001B[33mLength of values \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    575\u001B[39m         \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m(\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mlen\u001B[39m(data)\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m) \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    576\u001B[39m         \u001B[33m\"\u001B[39m\u001B[33mdoes not match length of index \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    577\u001B[39m         \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m(\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mlen\u001B[39m(index)\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m)\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    578\u001B[39m     )\n",
+      "\u001B[31mValueError\u001B[39m: Length of values (120) does not match length of index (316)"
+     ]
+    }
+   ],
+   "execution_count": 86
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T21:51:34.085612Z",
+     "start_time": "2025-11-29T21:51:33.854519Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "plt.plot(df_15m.index, df_15m, color = 'red')\n",
+    "plt.plot(df_15m.index, faiman_temp)\n",
+    "plt.xticks(rotation = 90)\n"
+   ],
+   "id": "8737baa73f7c5785",
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "x and y must have same first dimension, but have shapes (316,) and (120,)",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mValueError\u001B[39m                                Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[85]\u001B[39m\u001B[32m, line 2\u001B[39m\n\u001B[32m      1\u001B[39m plt.plot(df_15m.index, df_15m, color = \u001B[33m'\u001B[39m\u001B[33mred\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m----> \u001B[39m\u001B[32m2\u001B[39m \u001B[43mplt\u001B[49m\u001B[43m.\u001B[49m\u001B[43mplot\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdf_15m\u001B[49m\u001B[43m.\u001B[49m\u001B[43mindex\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfaiman_temp\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m      3\u001B[39m plt.xticks(rotation = \u001B[32m90\u001B[39m)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\matplotlib\\pyplot.py:3838\u001B[39m, in \u001B[36mplot\u001B[39m\u001B[34m(scalex, scaley, data, *args, **kwargs)\u001B[39m\n\u001B[32m   3830\u001B[39m \u001B[38;5;129m@_copy_docstring_and_deprecators\u001B[39m(Axes.plot)\n\u001B[32m   3831\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mplot\u001B[39m(\n\u001B[32m   3832\u001B[39m     *args: \u001B[38;5;28mfloat\u001B[39m | ArrayLike | \u001B[38;5;28mstr\u001B[39m,\n\u001B[32m   (...)\u001B[39m\u001B[32m   3836\u001B[39m     **kwargs,\n\u001B[32m   3837\u001B[39m ) -> \u001B[38;5;28mlist\u001B[39m[Line2D]:\n\u001B[32m-> \u001B[39m\u001B[32m3838\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mgca\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[43mplot\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m   3839\u001B[39m \u001B[43m        \u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   3840\u001B[39m \u001B[43m        \u001B[49m\u001B[43mscalex\u001B[49m\u001B[43m=\u001B[49m\u001B[43mscalex\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   3841\u001B[39m \u001B[43m        \u001B[49m\u001B[43mscaley\u001B[49m\u001B[43m=\u001B[49m\u001B[43mscaley\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   3842\u001B[39m \u001B[43m        \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43m(\u001B[49m\u001B[43m{\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mdata\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m}\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mif\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mis\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mnot\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01melse\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43m{\u001B[49m\u001B[43m}\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   3843\u001B[39m \u001B[43m        \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   3844\u001B[39m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:1777\u001B[39m, in \u001B[36mAxes.plot\u001B[39m\u001B[34m(self, scalex, scaley, data, *args, **kwargs)\u001B[39m\n\u001B[32m   1534\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m   1535\u001B[39m \u001B[33;03mPlot y versus x as lines and/or markers.\u001B[39;00m\n\u001B[32m   1536\u001B[39m \n\u001B[32m   (...)\u001B[39m\u001B[32m   1774\u001B[39m \u001B[33;03m(``'green'``) or hex strings (``'#008000'``).\u001B[39;00m\n\u001B[32m   1775\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m   1776\u001B[39m kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D)\n\u001B[32m-> \u001B[39m\u001B[32m1777\u001B[39m lines = [*\u001B[38;5;28mself\u001B[39m._get_lines(\u001B[38;5;28mself\u001B[39m, *args, data=data, **kwargs)]\n\u001B[32m   1778\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m line \u001B[38;5;129;01min\u001B[39;00m lines:\n\u001B[32m   1779\u001B[39m     \u001B[38;5;28mself\u001B[39m.add_line(line)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\matplotlib\\axes\\_base.py:297\u001B[39m, in \u001B[36m_process_plot_var_args.__call__\u001B[39m\u001B[34m(self, axes, data, return_kwargs, *args, **kwargs)\u001B[39m\n\u001B[32m    295\u001B[39m     this += args[\u001B[32m0\u001B[39m],\n\u001B[32m    296\u001B[39m     args = args[\u001B[32m1\u001B[39m:]\n\u001B[32m--> \u001B[39m\u001B[32m297\u001B[39m \u001B[38;5;28;01myield from\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_plot_args\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    298\u001B[39m \u001B[43m    \u001B[49m\u001B[43maxes\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mthis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mambiguous_fmt_datakey\u001B[49m\u001B[43m=\u001B[49m\u001B[43mambiguous_fmt_datakey\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    299\u001B[39m \u001B[43m    \u001B[49m\u001B[43mreturn_kwargs\u001B[49m\u001B[43m=\u001B[49m\u001B[43mreturn_kwargs\u001B[49m\n\u001B[32m    300\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\matplotlib\\axes\\_base.py:494\u001B[39m, in \u001B[36m_process_plot_var_args._plot_args\u001B[39m\u001B[34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001B[39m\n\u001B[32m    491\u001B[39m     axes.yaxis.update_units(y)\n\u001B[32m    493\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m x.shape[\u001B[32m0\u001B[39m] != y.shape[\u001B[32m0\u001B[39m]:\n\u001B[32m--> \u001B[39m\u001B[32m494\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mx and y must have same first dimension, but \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    495\u001B[39m                      \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mhave shapes \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mx.shape\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m and \u001B[39m\u001B[38;5;132;01m{\u001B[39;00my.shape\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m\"\u001B[39m)\n\u001B[32m    496\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m x.ndim > \u001B[32m2\u001B[39m \u001B[38;5;129;01mor\u001B[39;00m y.ndim > \u001B[32m2\u001B[39m:\n\u001B[32m    497\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mx and y can be no greater than 2D, but have \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    498\u001B[39m                      \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mshapes \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mx.shape\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m and \u001B[39m\u001B[38;5;132;01m{\u001B[39;00my.shape\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m\"\u001B[39m)\n",
+      "\u001B[31mValueError\u001B[39m: x and y must have same first dimension, but have shapes (316,) and (120,)"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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rPr74QqR7d5Hrr5e4QfGRy/nABuNn6FRHq0N8tGxpL/4F6H6YRdBDxrKdDzwfk/WERKvV9quv/FsvbPlykVmzJG6YLT7cMh/OlQd1h+6384HngwABFB9mEfTCctlzPsCOHaU9JiEk2t0u27ZlCpwYYbb4cMt84CCg6jQI8YHAKWDo1OwdUdDdLo0a1b2PEJKsssu2r8XHzp0SNyg+3CzwIHIfGzfa1wcckCk+6HyYhboQWhbxq8U7u+yCsh5nfRCS7G6XbRQfyXE+ghIfugHqAUE7Xthuaxa5xIc6H+UGTp3lFo5YJyTZzsfWrfY1nY8EZD6CEh/Zky3pfJiJ7iScZZEgyi6AHS+EmFF22bXLXsIjRrDsUinxwVkfZlLI+fCr7OJ8DgZOCakMEAQqNIJYWG6b4xgVs9ApxUe+zIefgdNczsenn/r3HMRc8ZGv7MLAKSGVwfn/HMTCclu/LrvEsPRC8eHmfKh1HaTz0aGDfb12rX/PQeIjPrLLLuXYsTi7yl7VFjBwSkhlcboRQQZOAcVHjKhk5uPAA+3rNWv8ew4SfXQH4afzgR2ZhqcZOCUkmuIjyMwHoPiIODhL1B18mN0u+lwqPjp2tK8//ti/5yBmll2cmQ6no8KyCyHREB/Y79erR/FhdNll5Eh70BfmboQpPnI5H598ErugEImY+HCeRenjOJ+DmQ9CKt/pAoJqtQUxO46YJz7+/nd7MZ53363rRoTd7aIbJHMf5lAo81GK+NDf0bMrhXM+CInOaHUQZLfLTgZOo40Kjr17c2c+NLQXZLcLJlCq+8HSizkUynyUslPSHZzT9XA+B50PQqLlfHzlU7cLxUeM0A8d4qMSZRfnczF0ah5BlF1yrRdD54OQ6IxWBwycGlx2cToflcx8AIZOzSNI8ZHtfDBwSkhyMx+pFJ2PWKEiAB9cJTMfgM6HWWB7051OrvHq2E4gjIuBZRdCzBMfO3Zk7iuY+UhA5sNv8QGhk098MPNhBs6dQ65VbUvZMRVyPjhenZDkiY9tWccnio8EZD78Dpw61SnLLubiFAG5yi6l7Jj051l2ISTa3S5Bio8attpGm0pkPpzJZpZdzEXFB0SCsyVWv6cUm/soVHah80FINAKnuv//6it/Z3wAOh8xdD6CznzkEh8aOMWcj2Lr/CR+6M4hO++hAhjt1+WIj1zdLmy1JaQysOySE7O7XbxkPpDX8Os5s8UHJq3igIPXgYmrxMxOF4DtoNRBYwycEhJNKD5yYp74KGbOB+73o46Wy/nAwQYCBDB0arb4KKfdloFTQqIJxUdOzBIfEBzqZHgJnPoVOnWKj+xaPztezCHXaPVyp5xyzgch5omPrcx8xAdn+SNf5gNiRA8QfuQ+nOUdresrnPVhDrlGq5frfOTqduF4dUIqC7tdcmKW85FLfGS7EX6HTt1mfCiccmoOQZddOF6dkHh0u+zmnA+zxIez/AHxoR0m2WWXMMUHnQ9zKCQ+ggyc+hGcJoSUV3ZRR31XCcsoZMMhYzF1PnBbxYeb89G8uX29eXP5z0vng1Qy8wHh4cfOjhDij/jY4Rg4WG7mo2VL8+Z8TJ48WaqqqmTs2LG13+vXr5/1Pedl9OjREjnnwylE3JwP7UJZty4c54PdLsknqMxHIfEBOOuDkOiIj507/XM+9t/fv8cMEZejoTcWLlwo999/v/Ts2bPOfaNGjZKbb7659usmzp1gJXEKjnwdKH6LApZdSCVabVHGgbDGdg/xsd9+/BwIqWTgVP/3d/ooPtq0EVmxwgznY9u2bTJixAh54IEHZD+XHRrERrt27Wov1dXVEgmcgiNq4gMb0pYt5T8XMS/zkavbBXBxOUKSWXbZsSOz7GLC2i5jxoyRs88+W8444wzX+6dPny5t2rSRHj16yIQJE+TLPJZvTU2NbNmyJeMSCeejQ4f06PMgxQeCrS1a2LfXrCn/uUj8Mx9+dbsAjlgnJDrdLn6WXXZ/fdKhJ/dJL7vMnDlTFi9ebJVd3Bg+fLh07txZOnToIEuXLpXrrrtOli9fLo8//rjrz0+aNEkmTpwoiXY+cs0TcbbbItiK5zriiPKfj8Q78+FX4NT5XMx8EBId52P3bvu44JY39IruJ7Q5Isni46OPPpKrr75ann/+eWmU4+zt8ssvr7191FFHSfv27aV///6yatUq6dKlS52fhzMyfvz42q/hfHTq1EmMyXzoc739Np2PpBN25gOw7EJIdMSH83+/piYzFF6q+NCxEEkWH4sWLZKNGzfKcccdV/u9PXv2yPz58+Wee+6xSij7ZCm53r17W9crV650FR8NGza0LpF1PtavL1+h5lrATuGgMTOohPig80FIdMSH81i3Y4c/4sME5wMOxrJlyzK+d9lll0n37t2t8kq28ABLliyxruGAVJxinI8DDkh3CmzYkM6ABOV8ALbbmp358HvIGKDzQUh0ul1wDMDlq6/KFwsmiY/mzZtbIVInTZs2ldatW1vfR2llxowZMmjQIOt7yHyMGzdO+vbt69qSGzrFOB8QHpj1AUGASxjig4HTZBP2nA/AwCkh0Qmc6snHtm3+iw8Tul1y0aBBA5kzZ44MGDDAckOuueYaGTp0qMyePVsigdP5cIb6cpVD/HIkCokPll3MwGvZpdjAqf68W7cLyy6ERKfs4uesj127zHE+3Jg7d27tbQRF582bJ5Ell/ORvdKs3+22dD5IGK22LLsQEn3x4desj91ZgVMcZ3DJdZIbMcxaWM4t8+FWcqmU8/HJJ7GzzoiPZZcgMh90PgiJpvjY6XPZxfl8McAs8eHmfERBfLRund44/VhLhkQTttoSYhZhi4+d8Sm9mL2qrVfxUW4QtJD4QNmHodPkU0nxwSFjhFS+28XPzMfu3enH02MLxUdCnI+DDrKvP/rIn+fNV4tju23y8Zr54IRTQpLd7eJn5gPlWhU3LLskJPOhk1Y//FAklSr/eb2ID7bbJpegWm3zdbtwzgchyS677Luvv2vGhIRZZZdinQ8NguID/eyz8p83n/hgu23y0R1Drom+QQ4ZY9mFkHDBfn/vXvs2xYfh4sPN+cg3Nh0bTNu2afejVLw8F8suyUfPVHKJD45XJyQ5OEsgQWY+9qXzkbyyi1+5j2KcD5ZdkgnOgHQ7cHMogsp8sOxCSLTEhx+Zjz170lEAp/goN0cSImY5H8WWXZziww/ng4FTc3EKCrdsBqDzQUhycJZPnft+P/IZu3Zl7k9atrRvb9okccEs8eE2Xr2Q+NDQadDOh7PsonVCkhycO4tczoeeHRW7U6LzQUi0w6bOKdp+iI/dWSczbdrYtz/9VOKCWeIjys4HVv3FBoqfxaRTkiycO4tc4kPHJG/f7n+3CwOnhFS+08WvzMduio/kZz6c7bZBig8cODTcytxH8lB3AgIzV/BYxQdWvCzlsTlenZDoiw8/8hm7d2fuT+h8JNj5CLrsAthum1zyCQSladPixQdCZyy7EBI/8bHTB+cDJ60QIBQfCXY+sLKtU7wEIT7YbptcdGeRT3yU4nw4U+90PgiJ9mj1IMQHoPhIoPOBUgg+YIRAIUDCcD5YdjHT+ShFfBQKsmrmA2dhTvFNCAl/tLoz87HDh7ILxUdCh4ypOFFRUGruw8t4dUDnI7moSMjVZusUHzgj8uqyeRUf+riEkGSVXQCdjwQ6H37kPrwKHa7vklyKcT6K6XjJ7vfPxrmODDteCEmm+Gjd2r5mq22CMh9+tNsycEq8ZD6wk1KB6rX0oo8LV805S0DB9q07vxhNPyQk9lTC+di2LTYOJ+d8eBEf5Q4aY+CUeCm7QDwUm/vw4qhw1gch0ZvzsWOHf/uTFi3SJy7lLIIaImaJj1ImnIbpfGjZZetWkS1bSnsuEk28iAQQhPjQnR3LLoSER67/zSCcj3r1Yld6MUt8lJr5KHfQmFfxgQMPFKyOWSdmlV1AkM4Hyy6EJLPsEsPQqVnio9zMR9BlF8DQaTIJ2vnIV86h80FIMsVHA8f+hOIjwc7H558Xv+5GseKDU06TiReRAOh8EJIMgsx87KbzYYbzgVJIdXXp7kcpzgfLLskiqLKLl8dl4JSQ8GHZJS9mlV3cnI9Cszf8yH2U4nxwyqnZZZdi53wwcEpIvMTH7t2lTx2m82GI81Fu7oPOB2HZhRCzKNTt4hQoxULxYUjmo9x2WxU9XlwWOh/JhK22hJhFIeejnNyHm/g44AD7esMGiQPmOh9626v4KGfQGJ0PEnSrbb4gKzMfhIRPLvGB8nv9+uV1vLj937dvb1+vWydxwCzxUSnnoxTxsXFj5rodJN5U0vngnA9CoiM+/Gi33e3ifFB8xMT52Lu3NOcjaPGBXm09kKxdW/xzkXhnPpo29b/bhXM+CDFHfGzeHIuBguY6H0opgdNUKjjxgfU92G6bPDjhlBCzyOdKNi5z1oeb+MBICBU1MSi9mCU+3NqavIoPFQRQqsWOry1GfACGTpNHFMouXNuFkGg4H43LXG/J7WQGJ67qfsTANTdLfLg5H17nfGADateutNBpseKDzkfyYLcLIWaRT3xUV6cXEfXL+YhZ7sMs8VGO81FO7qNU8cFBY8kh6DkfXrpdYlAHJsQo8bGlxNXLKT4MynyUM2is1LILR6wnh6DHq3NhOUKiBcVHXuh8lCI+wnI+KD6SQ1BlFy/ig5kPQswQHx062NcsuyTM+Sh10Fix68gwcJo8Sim7eOmq0m2LZRdC4nPCUc2yC52PMJwPzZoU63wgsazzSIhZZRcIDy8ZDX3cfNsW53wQkiznY1eOkxkGThPufARddsEGhLYpHFg++aS45yLxLrtoicTryrZ0PghJtvhYvlykZ0+RmTPT32Pg1FDnA/U0/fCDEB9Qs23b2reZ+zBLfKA0pwLESxsenQ9Cki0+5swRWbZMZPp07+IDs6givjyHWWWXcp0PrBqIDxulkGKGuBQrPgBDp8nCSzA0e8eEMcl+Oh8cMkZI/MRHTU3dk5Fc+5PWrdPHmfXrJcqYJT7cnA+vIVAVKqWETksRHwydmul8gP32s6+/+MIf50PFB7b/Yhw7QkjlxceuXXV/NleGDMcoHYYZ8Y4Xs8RHuc5HqbkPOh8kKPHhxfnQwCmg+0FI8CAwni9k3rx5MM5HjEKnZomPcjMfpQ4ao/NBvLbaBuF84MwLAWbAKaeEBI/TYSy31XZXHufDbX8Sk1kfZYmPyZMnS1VVlYwdO7b2ezt37pQxY8ZI69atpVmzZjJ06FDZsGGDJMb5KLbdFvkQbZdl5sNcvLbaBuF8QHgw90FIZU503fb71QFlPkxwPhYuXCj333+/9EQLkINx48bJ7NmzZdasWTJv3jxZu3atnH/++ZIY56PYzEehjTAXDJwmi0pmPgBnfRBSmRNdt1xhdXV6mKDbcclNfMC11Mc1VXxs27ZNRowYIQ888IDspztKK5y/WR566CG544475PTTT5devXrJ1KlT5ZVXXpEFCxaIkc5HoY0wFwycJotKZj4AF5cjJHrOh5elFJwts+p+mCo+UFY5++yz5Ywzzsj4/qJFi2T37t0Z3+/evbscdNBB8uqrr0qinA+v4qNc5wMbW6lT8Eg8W23pfBASbwqddDZsmN4XFNq/q/Ph/Nl8GTIVH8WMg6gARRwNbWbOnCmLFy+2yi7ZrF+/Xho0aCAtW7bM+H7btm2t+9yoqamxLsqWIA+0fjofOCuFYtVx2F6esxjxgcdt0cKe9YBBY06lTOJH0GUXr84Hu10ICR7nSafbMaaqyt6nf/ZZYfFB5wMxh4/k6quvlunTp0ujRo18+YwmTZokLVq0qL10UmchinM+ADYYFQJech+lig/A3IeZ4qNVq+LLLoW2LZZdCAkP52Ki2mmWTXW1t0nGbs6Hl7LLxo2F8yQVpKjTfpRVNm7cKMcdd5zUr1/fuiBU+qtf/cq6DYdj165dsmnTpozfQ7dLOx18ksWECROsrIheIHAi7XwU226rz4kNsNjn0twHR6yb2Wr7+ef+OR8MnBISHnrQz3dyW+3oeIGLPmMG2kWLcz7cTmYwiRvHGnRZQoBElKKOhv3795dly5bJkiVLai/HH3+8FT7V2/vuu6+88MILtb+zfPly+fDDD6VPnz6uj9mwYUOprq7OuEQ681Fs7qOUGR/ZzseaNcX/LokWlWy1BXQ+CAkPLyuZVzvEx6RJIiNGiPz85+U7H3hOCJCIh06LOiI2b95cevTokfG9pk2bWjM99PsjR46U8ePHS6tWrSwhcdVVV1nC48QTT5SKU0nnoxzxQecj/jsi3RkVIz7gIOLsJd82ylZbQqJddvEiPl79uiFj3jyRn/wkt/PhRXxo6QU5y6SIDy/ceeedUq9ePWu4GIKkAwcOlHvvvVcigV/ORzHttuWID7bbmjHtMJf4gPCAzYrgsV/OBwOnhETL+di82V61FqCRI/uEw+l8eGm1VfHx5pvJFh9z587N+BpB1ClTpliXyOGX81HMoDE6H8QpPrxkPpDPQCsedjooveQTH16dD5ZdCImm87Fihcinn6adDXzdrVv5zgeIsPjg2i50PkjQOHceXsRHMbkPr84HA6eERNP5+Mc/Mr//+uuZX5fqfER81odZ4iMI5wOrF3p5zmJbep2ZDySWnQcwEi/0s8M24HU78Co+inU+WHYhJFrOx9Kl+cUHnY+YA5GgC7yVKz4gCtA6i7YotcuCKLu0aZPOCETYPiM+TjcNyvlg2YWQaLXatmmT+bWWWrIHeGY7H3hsPZax7BIDcg1bKcWRQD2+bVtvodNyxAcEDtttzRowFpTzwbILIdEqu1xwgcj++6e/vvhi+1rDp7mcDy8Zsg4dIn/Sak7ZJZf4KMX5KKbdtpSzXidst40/QYoPOh+ExLPs0rq1yK9+lf56wIB0aRQr2OZyPryID818oN22UDQgKa22scp7lCM+kPtAbS5I5wOw3dZM8eF1xDqdD0Li6XyA735X5F//skv4vXrZYgW/izVfdN+fz/nItU/RieL4WTxWdoknApgjPuh8kEpRivul4sNrpohzPgiJDl4bDaqqRG66KfP//pNP7KUVVHzkcz5yPT5ECZwVCA+UXiIoPswpu/jtfHgdNFZu2YXru5jpfDht03xwzgch8XU+soFgABANAMFS57ELzodznahci9bFYNaHOeLDb+fD66Axr2emuWDg1Gzxka9PHzumQql3hYFTQsKj1BELrbPER/aIBTgfmIgK8g0fjMGsD3PER6Wdj1IzHwycxp9S3C9Nq+fbcTi3aZZdCIlXq20p4gPZkA0bMkuzuaDzkXDnAwcHZw0uyLKL25wSkkznQ8UHdjS5tl2n+PDaautM0RNCol12qXHkPZQPPrCvKT4MdT6wZDEOJmhjynd2Wq74gHpFXQ+PUyh8SJIjPrB9YduE4MSE23LXjOGEU0LiW3apXx9LyNu3V670Jj4iPuvDnLKLn0PGAA4M6krkK72Um/nA7+lAM7gfxAzxge1SP/dc4rYY58M54TSiff+EJIZynY/PP890PjDYEickYPly+5rOh6HOh9dBY+VmPgBDp/GmVPer0JmLPi6csUIiWssuWjcmhETP+WjVyt35wImLTkPFXBDnz+aCmY+EZj6cuY98zke5ZRfAdtt4o+l0dR/8Cp0WI2yd4oOLyxESr8xHQ4fzsWJF5s96ER8RdDvNKbtU2vkoR3zQ+Yg3urPo0qW43yvUKldMSQ8/oz/H0Ckh8cp8NGiQFh96TPHqfOD/HfNBIoY54iNX65Mf4iPIzAdgu2280Rqtrlrpd9nF69kVZ30QEn/nQykkPuC0VldHNnRqjvjIJQL8KLsEnflg2SXe/Pvf9nXXrv6WXYoVts7QqYKd0pNPRtKWJcTYOR+ff253urk5H17FR8QHjZkjPnRjyO44CNr5YNnFbBDuXL26POfDj8xHLudj1CiR884Tefrp4l4bISS4ssvevXappBznI+KhU3PER5DOB1Ye3bbN/WcYODWbVatsVwH2Z/bOo9wdR6nOh4oPbJsvvmjffuON4l4bIcT/skvDhul5Hii9lOt8RHjWhzniI5fzUeqcD4ADis7Xz1V68TPzASWM2f4knnmPfItAFZpy6jZFt1jnI7vssnhx+ra27xFCKud8ZOc+6HwkgCCcDy/ttn5kPpo1SweHOGjMjLApwJkOBAMs2PffL1/YZpdd/v73uq+TEFI55yN71kcu5wMnMoUWlgMsu0RoY/BbfBRqt/Wj7OIMna5ZU97jkHiETXXb1N/Txyln28ouu7z8cqb44NpBhETX+WjTJv0z++3n7bEpPiIAPrxDDkmLhbCcDz/KLoDttvEDn/1rr5XufIB84kO3rVLKLhAaTvGBYGyhFZoJIcE7H+3apYPmTucDxxCIDq95D0DxEQFOPdUO/z3zTLydD5Zd4sP//Z/Iu+/aJTNsf+WID7eySLHblrPs8t579plVo0Yihx2W+zkIIeG12jpPaOFyO50PoKUXio8Yki024pD5AJxyGi+WLhW55Rb79q9/nV4kLirOh66QjLOso46ybzN0Skjlyy6dHPOjshelLFV8oFlh+3aJEuZ0uwQlPuh8kFyuB0ob558vMmxY6e+Rlmv8yHw4nQ9dbwahte7d7dsUH4RUvuzSsWNafJTrfMB11f/7iLXbmic+stsd/RIfcD7cpkT6nflg4DT6oNTy2GP27ZtuKu+xtCSC+m/2LJlSnQ+ID13rAeJDBQ7LLoREx/lYsybtfJQqPnC8i+isD4qPcsUHRAE+YCjUTz4JLvPBwGl8uOceW4hicqiWNEoFATNdSjvb/Si12wVlF3U+cGZ08MH2bQZOCam889GpU3q+j8510rLLuefaJ7znnOP98SIaOjVTfDjdj3KGjOlGoelkt9CpX5kPteI2bkyrYRJNdDs46yx/Hi9X6aVY5yNX2cXpqnGNF0Iq32rbqJF9W+f7qPMxYIDIBx+InHmm98ej+IgQTrejXOejUOjUL+cDPd4QOjg4REzBkhyfue4wyqVLF/s6e9BYOXM+nGUXtWXh3mFBK0JI5ZyPqqr0ySY6NN0mcxcDxUeCxUe+0KlfmQ9skCy9xAN1psr9zBXtlMku65XqfGSXXXCWpQOM2MpNSGVbbZ0ntCtXln8iQ/ERIeLofACGTuNBdntcuWjmAyU3v5wPZ9kFcNsixD+KPTHIRp0P7Xah85EQwnQ+/Mp8ADofZooPTbjnEh+lzPlwll0Ah9gREj3nQ/HD+UDHXIQwL3AaZ+eDB4h44Odn7hQfucou5cz50AULKWwJiUbg1LmvV8o5kWGrbYSIY+YD0BqPB2E7H+UGTgEXLiQkGoFTkL0GmR/OB8LkWsaJAHQ+/BQfsLX0gKDQ+TCPIMWHsxW2nPHqdD4Iia7z0bt35olxOfsSDCTT31+/XqICxUe5cz40EKhtsNndAkFkPjjlNNroZ+534BQ7tE2b/B+vDlh2iR733Sdy6KEi77xT6VdCwnY+2rQROflkf5wPdErqLKoIjWmg+PDD+cBjOBcDCtr5gMPCYVDmtNpix6PZDGfppRzng2WXaLNwocj//I895+F3v6v0qyFhB07BkCFSS7knMhFst6X48EN85Aud+pn5wAYEFYuDm65KSpJfdskVOi3V+cDrgwBxC5x+8UX6PlK5A9fFF6e/jtABg4TUapstPsr9n6T4SGjgNF/o1E/nA4+hByGWXswUH344H05UfKD8ovdz0FhlWb06c4VhnXJJzHI+unRJH58OP7y810PxYaDz4WfmA7Dd1rzMR65BY6V0u+gkU9C0aXq7dI50prANn88+Sx+wsluqdcolMSdwquB/8ZVXRI48UsoigrM+WHYJ2vnws+wCGDo1L/Phl/ORfQalrofC0GlleP55+/P9yU8yP+PDDqu7uqkbjz9ud0fgcUgyAqdO0dCnj5RNBGd9UHyE5Xz4dSCi8xH9nY7ueKKW+cgWH9rpotD5qAx33SWyd6/IM89kfsYQH+pU5Sq9oGtp1CiR118X+fa3Rf7615BeNAnF+fALll0MyHwELT7ofEQb55yXqGU+CokPOh/hg7kLzz5r30bOA8JVxQdKbWi1zVd6+cUv7OFROMjBcYMQgZAhyXA+/EKdj7iWXe677z7p2bOnVFdXW5c+ffrIM6rWRaRfv35SVVWVcRk9erQYlfnAHAa1SNEO63fmgweI+IiPoMsu5TofLLtUnunT0weqnTtF3nvPu/jAvJY777Rvox0XGR4cXN58M6xXT4IMnPqJuprYf0RkymlRR96OHTvK5MmTZdGiRfLGG2/I6aefLkOGDJG333679mdGjRol69atq73cdtttEjmcgsOvjQM7cj2T1NyH8wwk7LILDkwDB4r88If+PC8pLu8RRuC0XOcju/uFZZdwwcnJb3+b+T0MFNPPuJD4WLxYZNs221IfNkxkwAD7+08/HfQrJ2G02vpJ69bpQWURyX0UJT4GDx4sgwYNksMOO0y6du0qt956qzRr1kwWLFhQ+zNNmjSRdu3a1V7gkBjhfLiFToM4C/ZadnnrLZG//U3kV7+iDVsJ8YHtys+zHr8yH84Fq9Bh4YSuWrgsWSKybJl9UDj7bPt7OJHTzxifuYqPFSvq/v6iRfb18cfb3UrIfACKj8oTNeejqipyJfuSj7x79uyRmTNnyvbt263yizJ9+nRp06aN9OjRQyZMmCBfwhrMQ01NjWzZsiXjElvxkR06DVJ84H3Kl4BHQh5wIFn8Z3w4xQeGy+mOrZROKuf2nl3/1W0LOQR9bBIc6nqcc47It75VV3zA+dAWSwRKMQAul/gAgwbZ12+8Eak1PIwkas5HBJ3Noo+8y5Yts9yOhg0bWnmOJ554Qo444gjrvuHDh8sjjzwiL730kiU8fv/738v3vve9vI83adIkadGiRe2lkx7Ak+Z8+LURNm+ertXnK7047fmIbGxG4HfA2Gmb4uwFVr06FqXmifTnu3XL/H7btvaZGsSNilcSDPjsZsywb19ySVpkoOziFB9HHy3Ss6edB5k2zV189OplX2P9DhUi7HqpLFFzPiLobBZ95O3WrZssWbJEXnvtNbniiivkkksukXe+Xvjo8ssvl4EDB8pRRx0lI0aMkN/97neWOFmVZ0IfRMrmzZtrLx+5LUsfV+fDefbopwL2kvtwHjwoPuLvfGD7weqUTmFZ6gwZHLQuvdReuMwJdpTakheRHVRiweAoiAwIDGSzvj6Bk3ffTdfkcR8EJ9Z4Afi8NEeGrAd+1ik+gJZe/vKXtMh54gk7nErMbbVNgvPRoEEDOfTQQ6VXr16Wa3H00UfLXehTd6E3Bt9YWancE/rgoGj3jF4S53zgwIGdSJgK1ik+whB0JFjx4Zb7KNX5wJn01KkinTtH/uwoseg+8bjj7M/v4IPttXfQiaAHLg0ZjxhhO57IfUyalM6LwAXD56UrlgLNjiDvhce64w6R888X+cEPQv3zjCdqrbZJEB/Z7N2718ptuAGHBLTXs6mo4BQCYWQ+/LbgvQSHWHZJrvgo1/mI0Q4qsbz/vn0N0aFnyCeemL4fQgSts6BZM5Gbb7Zv33CDyD33iLz6al3XQ8UMxAickfnzRf70p3RLb3bAmAQHyy4FKerIixLJ/PnzZfXq1Vb2A1/PnTvXKrGgtHLLLbdYbbi4/89//rNcfPHF0rdvX2s2SKQIw/kIYsaHwrJLdAlKcLqJjyC2Lzof4S0e5xQfoG/f9G0tuShjx6YFyI9/nJ7vceaZmY+L/Zm6Hw8+KLJwoX3bLTNCgoOB04IUdeTduHGjJSiQ++jfv78sXLhQnnvuOTnzzDOtcsycOXNkwIAB0r17d7nmmmtk6NChMnv2bIkcQYkP7Lixw4ATBGs8iDNTfZ5CZ6fMfCTf+QhC6HB8f7jOxze+kVt8ZAPXA52F27fbuRCc7PzXf9X9OZRZwKOPZuYOnJkRYp7z0bFjustNX18FKeqU6aGHHsp5H7pU5s2bJ7EgiCFjesCB5YkdA0ovegDyW3x4OUCw7JI88ZE9aCyIs6uIzQIwpuwCnGUXCIxscGKDfN0JJ9hfwwlp1Kjuz511FsZNi8yda3+NvAf23Qj+z5mTHkZGzHI+2ra1j30QHtiHVDgOwYXl/HQ+sksvQWc+cokPnN1kiw+UgUgyyi7ZgVM/n4tll+BBCUQ7WpziwzlxFuu8uPHNb9oOxoQJIrnGGECk3Htv+sB34YV2Oy/A94mZzkf9+mnBsXx5pV8NxYfv4sMZOg1K/arzgdKKc5aIgoWmnLYadnYMmyWr7IJ2WR06FUQbNwVrcHzwgX2NQCnmtzhBZwu46qrcv4/1sn760/wHNozRf/JJW2zAUbniCvv7KIOz+83MVltwzDHWlZx3nr19VBA6H3F0PrDMNg5ucDPc5vSr67HffukDFm305IiPf/zDHialgtLP7UtXv9yxw14kkQQbNs1uwb//fpFZs2xxUS4InqrogBhBKQau6G9+k679X3mlyIsvlv9cJPqttgCuGcQo/rfhnrmdvIYExUeQzkdQ4gM7LD1IuIkKDZviYKWvh+IjOZmPbPzcwaHFU4eZcdZHeGFTBW7Id75jt9f6jQ4rQxcMhpxBwE6ZgumQtpBFoFW7Y0jyyi4AxwNkM3/0IzuQHER52CMUH3F0PgqFTtX5QMCINfzkZT4UOFunnZa5Uq0fcNZH+G22YXDuuXYgHuu+oLNGXVMEURFCvfVWkZNOst0XkrzAqYITo5/9TOSoo6SSUHzEMfMB8okKp/OhAaOILKOceIJ0Plq2zPwaAheWediBZlIeutyEm/MRJNhORo1Kn5mfcorIkCH214sX29fYZ8EhYS6kdFAO15bmqDkfEYLiIyjnAwd7XU8hSOcjX9kFzoeKj+wVTEn8xIdzW4UQ0QmYfkPxEeyBSaeTHnushA7CqijfnXyyyNNPZ84J6d/fdj5w4ETuhJSGM+wfRecjIlB8+K1M8Y/dsKG9k1F7NQjxke8A8eabaReGzkdyxIcTlFuCgmWXYF0P/M9i+3DO9QgLZMVwIoK6P9bRQrlFy3n/7/+JDBtm3/7jH8N/bUkUH3Q+ckLx4bfzgcfTnfd77wUvPrKdD8yAwKJSAJaqBlNZdol/5gPcfXc6KBgUdD6CQwcxYlAYwr2VAGfjut+DCML+Ai7I6afbYVfc9/rr6WAsKY6gVjNPGBQffq42m116UfERxAaYK3D62GO28sYCU927s+ySNOcDrZHoSAhyOiFHrAcvPk49VSLD0Uen14NBIBUtuYDuR2nQ+fCE2eLDb9cjO3QahvMB8eGcXorVK8Hw4fa1HqSQA4nAPP/EE1bZJUg4Yt0s8ZHNf/6nfU3xURoUH56g+AgCdT401R6E+ICogGuDg92nn9rfQ8AVA6jABRfY16jn4ucQItOx3CQ4kiQ+MPsB03GJP/z733YXHJxQBDujChamw2tcsiQSY7hjXXZh5iMnFB9BOh/abhWE+MDBTYNiWnrRa6wRoa8BOxH9OeY+4p/5CAPMD9E8Aruk/GPGDPv6jDOC61TyA4x8P/NM+zbdj9KdD5z0BeWuJwAz35mwnI+gQ0fZXQkqPnDm6syyaOiUB5LgSYLzgW2HpRd/QWn0kUcy12+JMt/9rn09cyYXpUzSgLEIQfERBOo6KEGdBWd3JTjFhxO224ZHEsQHYMdL+eD/EeVPiI4FC+wyLFxJTBqNOniN2IbffVfkrbcq/WriRVRHq0cMio8g6Nw5XPHh5nw4ofgIjySUXQBnfZTPn/5kd59ddFG6gwTt70Gs2+I3LVqInHWWfZull+Kg8+EJs8VHUMoUOxctdYRRdvHqfLDsEjx0Pkj2Gku6XaD93Y/VasOCpZfSoPPhCbPFR5BhoK5d07dZdjEHig+iaBfa4MEic+aIvPFG+Ou5lANeN4LHKBfp2i+kMBQfnqD4iLP4yBc4dcLAaXgkRXyw7FI+2to+cKC9bkoQAw2DBA7ut7+dDp4Sb7Ds4gmKDxOcD+2+wYwBEixJyXwwcOqf84H1nuKKDhx79NH06ACSHzofnqD4CEN8BJX50APE5s0iW7ak53hkiw+1ejHldMeOYF4LSabzgW2Kk3HLEx9t2khsQegUDghOXNCxQwpD58MTFB9xFh/Nm9srU4J//tPe6JFjwfoM2UOj8LPggw+CeS0kWeKjbVt7W8I25QxOkuLLLnEWH8h8aGswu168QefDE2aKD629Bhk4PfjgumdAQaAux2uvpQ8a2ZY//l5t/129OrjXQpIjPiCYVcRmL15ICoMSBcbTx118OLteZs2iC+YFOh+eMFN8hNHt4jz4BLk0tdrjWALbreSSXXqh+AiWpGQ+AFe3LZ1Nm9IZibiLjwEDRFq2tEtwf/97pV9N9KHz4QmzxUdYE+iCfB4VG1qPLSQ+WHYJlqQ4H4Aj1ktH3U6UReO+LeD1Y7E5wNKLd/HB8ep5MVt8BL3oz9NPi3zrWyK/+EXwZ6cffWRfd+vm/nN0PsIhieJDyy5Yn+Tee+3VTkny8x5upRdMbHWu2krqou8Px6vnheIjSM4+W+TllzPDp36T7XT06eP+cxQf4YqPJJZd/vpXkTFj7HHhJPlttk5OP90WUvi7Xnyx0q8m2rDs4gmKj7hD8RHNzEeSnA8dYvfKK/Y1FhrTM3uS3DZbJyghfOc79m0OHMsPA6eeoPhIytmpdtig28UN7XZZv15k585wXpuJJLnssnBh+j44esQc8eEcOPbEE+ntnNSFzocnKD6S5HzkKrmA1q1Fmja1b3PSaXAkSXw4R6wj74G1SRR2PeRHnaGklF3AySfbi1Sik+dvf6v0q4kudD48QfERd3BmpQe6fOIDsz6Y+wieJLXaqrDdvl3kzTdFvvgifR/Fh3nOBwKUF15o32bpJTd0PjxB8RF30LHTvbt9+7TT8v8sxUewwB1IkvPRpIk9HVetduc6QRAj27ZV7rVFnSSKD2fXy1NPcamGXND58ATFRxLAgQEJ9COPzP9zFB/Bn/FAgCRFfDjdjyeftK/POcfOD+FvffXVir60SJPEsgs48UT784fwRPcTqQudD0+YLT6S0od9yCGFXQ/AQWPB4gzhJU18oMMFnHCCyCmn2LdZenFn61aRd96pGwhPAijfaumFA8fcofjwhNniI+ghY1GD67uEk/dISubD7eDZrx/FRyGmT7edAQz8O/ZYSRxaesEQRZbe6sKyiycMO/oaLj5YdgnP+UiK+HB2U+Fg2qlTWnxgpD9bLjNB2e2+++zbo0enF7FMEscdJ3LooXbmY/bsSr+a6EHnwxOGHX2/xnTxsXatSE1NpV9N8vjyy/RApqRsW07x0b+/fY2AM4KUmBezaFHFXlokQQ5m6VJ7KfpLLpFEAkGl7gdLL3Wh8+GJhOwhi8RU8YEDBjoYnGvBEP/QNU+CHKcfNs6yyxlnpA8+mPkAmPvIRF0PDOTSTqEkogPHnnnGnvtB0tD58IRhR1/DxQdnfQTLP/5hX2MxwaRwwAGZeQ+FoVP39tpZs9IllyTTo4fIEUfYZTe03ZI0XFjOE4YdfQ0XH4C5j+DQkePqCiSlvj9qlMhPf5p5Jq/iA4Jr796KvbxIMW2aXc7Ee/bNb0riUfeDA8cywVA+oBOliSsGHn0pPixWr670p5AsnPmHJDkfEOi/+Y3IhAmZ30cXB3aumHr69tuVenXRAQLs17+2b19xRTKDptlo7mPOnPRQNSKyebP9LrRsyXcjDxQfpsF222DAuiewoLGwH+auJB2EanWc//z5lX41lQcH4FWrRFq0EBk2TIwA2aZjjrHLDI8/XulXEx00A4NtgeTEbPGRlCFjpYiPDz6o9CtJFgjeqethwlkvYO6jbtD04ovNstu19OK16wUzUFCSSrLzSufDE2aLDxMzHxQf/rN4scjPf27f/s53xBic4kPHypsIVv3VeRf//d9iFDrtdO5ckfXrC/88SnhwCZM8H4TOhycMPPpSfFh8/HE6lU1KB2O0hw61p5uef376TNAEeve2h6lhbsz774uxPPig3V7Zt2/h9ZWSxsEH29sBMi+PPeZNqCW91V/FBzMf/omP++67T3r27CnV1dXWpU+fPvKM2s1W5m6njBkzRlq3bi3NmjWToUOHyoYNGyRymOx8IJOAdUews4AAIaWD8dJYaAsWMnIeOKszpeQCMDPm+OPNnvcB0fnAA+mgqYl4HTgGd0z3OUkWHyy7eKKoo2/Hjh1l8uTJsmjRInnjjTfk9NNPlyFDhsjbX6fdx40bJ7Nnz5ZZs2bJvHnzZO3atXI+zgajhsniA38zRmQD5j5KAzvRn/3MXuEVi4ideqrIa6+JtG4txmF67gPlAzg/mIcSxX1dWKUXiG60mucTFZ99lp6s/OGHklhYdvFEUUffwYMHy6BBg+Swww6Trl27yq233mo5HAsWLJDNmzfLQw89JHfccYclSnr16iVTp06VV155xbo/UujZqYniAzD3UTpYz+J73xP58Y9tEYJhUs8/b0+PNRHTxYcGTUeOTM5KxqWM4NfZNjpkLV/JJcnOB/YJLLt4ouSj7549e2TmzJmyfft2q/wCN2T37t1yho5gtpaA6C4HHXSQvIr1DnJQU1MjW7ZsybgEjsnOB6D4KA1Yxqjrz5hht5ree6998EnKInKloN09//63SBRLrEGyYoXdYou///LLxWi8DBxzig+4RUnMnGHej65uzcxHXoo++i5btsxyOxo2bCijR4+WJ554Qo444ghZv369NGjQQFpmveFt27a17svFpEmTpEWLFrWXTloSCJITThBp3ty2y02E4qN44N4h34CkPsorcDtMrfE7wdRTjNp2TnjNR5KWYL//fvv6rLPSk4NNBaFrnMwtXCjy3nuFxQcCuuvWSeJQ1wPvRbNmlX41yRIf3bp1kyVLlshrr70mV1xxhVxyySXyDhL/JTJhwgSrZKOXj8Kw49BnjsmMP/iBGC0+klx39ZPf/tYWqhDRONBiB+tc58R0vJReIN7wnkH0/+53kojy29Sp9m2KUDvIftpp9vvx6KOFxUdSSy/OvIdJ4fMwxAfcjUMPPdTKdMC1OProo+Wuu+6Sdu3aya5du2RT1gqH6HbBfbmAg6LdM3oJBRMHjCl0PryBs7NrrxW59FJ7eum554q88ordXkiKEx/f/rbIvHn27Zdeiv+7h2zD55/b/0twPkjh0ks+8fFf/2W/jxpIjXunC6ebFqTs0MPevXut3AbEyL777isvvPBC7X3Lly+XDz/80MqEkAhx0EFp58Pk4VD5gIjGAfMXv7C//t//FfnTn+wzd+IuPpYsEXHLbH35pd3pkKQzXg2aIuth8omME3T7IAv1z3+K/OtfucWHZqTUecX/2kMPiTz7rMhzz0msYdg0GPGBEsn8+fNl9erVVvYDX8+dO1dGjBhh5TVGjhwp48ePl5deeskKoF522WWW8DgRsxBIdECuBpYgrGPTQoJeWL7cHpyEnWHjxraNPHGiuQFlL90OcIMwO8YtXO4UHkkQHxBZKCPhQPv971f61USHVq1EBgzIPfNDxQdW/XVuB+++m/6ZP/xBYg1nfHimqL3pxo0b5eKLL7ZyH/3795eFCxfKc889J2eeeaZ1/5133inf/va3reFiffv2tcotj3PBoeiBlkAtHbidoZgMBAeEB7o3INKwZPwFF1T6VcW79ILyhBMcdOLsuOnqtTjTz1NSNnrgGEovzs8Yt1V8qBPuJj7+/Of0kvTohnnzTTufFxc44yMY8YE5HnA9UGaBEJkzZ06t8ACNGjWSKVOmyOeff2614EJ45Mt7kApyxBF1//FNBjtHlFjOPts+e0ELKYKlWDqelCc+1PlQwQvHLVuQxAWUlR55xL7NoGldhgxBkM8+qVm2LP19/E+pqFDxoWUXZ8MCSnQQIDhpbd/edkmw3dxxh+2qwamNsnBl2cUz9b3/KEkUhx9ujwcvo1MpUb35GBaGrhYdGIUZHqYOjSpHfGDSK0KDOABliw+UZ9Bq+8kn9llvHCfCQnjgINq9u7mt+vlA0BLB0SeftEsvPXtmuhwozXTrlvk9PQFCxwzEBdrYUfr89FP7fxDC5ZprMsf6YzkDtwtanlEqrRQsu3iG4sNk8QFMdz4wa+C88+yDJoKDd94pcuWVbJMrlq5d7RHjGzfas1DgHGWLD4gNlLJUfBxzjMQKnHFr0BRila2UubteVHz83//Z75O6HAi76ywnbAdwwfQECJOD4T7C4dClH1B2efFFO+yN+SHYbuCOvPWWfXGjQ4dMQdKlS/o2BE6QnxvLLp6h+DC97GKy84GyCtpnMW0Rw7IQLHVM6CVFgB06RmzDLp8/P1N8aIlFnY7Fi+MZOkWbNQ54OLO+5JJKv5rogi4xuBOrVoksWmQP51PxgdZk/K/hfogIZKtUaFx8sS0+NIeGsQtwmLCvwgkBgKuGx4IQyb7g+bDWEv6fcXEbeofPLpdrgvJOua4Jyy6eofgwFfxT65k//mFMGwWMEekor6Dkgp3bU0+JHHpopV9V/EsvEB/IfUyYUNf5gOWOgw6Io/hQ12PYMPP+X4qhaVNbgEDMw/2A+FCBAecDQhXXEBkYTw9HCWsjoUQD1wLCAfTqVbfDDOW8ww6zL9ngcSB0IULcxAm2OTgtWAj168VQ64CcSS5xgvsKuSac8+EZig+Ta7OowWO9EpReTJnFgsFh119vr0oLsJOcPt0+yyL+5D7QIYT3WedfOMsuejCJm/hAiUAXTWPQ1FvpRcUH/tecZReA0gvEh8710DIwpk/jREBvFwOEAbYxXLCERjYYFJjPNUGYGCdjuGAbzqZRo/yuCYQ1nQ/PUHyYDP7hTRIf2LkMHy7yl7/YX+Ps/JZbOCTKL44+2l7PAu8zOh000+EUH7reRdzEB0ap4+CFs3hcSH4QOsVAPnzOmIniLLsAzX3MnWtfH3VU+eKjEAivwt10czjVNXETJrjg9cMlRZk6V6kanZ3aFkxnrCAUHyaDcgNsz6VLJfGsXClyzjm20MIZzMMP2/Y58Q8M3ULWA2ezKL2o+HBmPhBKjZv4wPA0XUQOQVNSGPyPIU/1+9/bMz+cZRfnta4AixJLtuDwW3x4dU3cnhevM59rgnKLcwFVFVkkJxQfJqNncOhOSDIQWBdeaJ+VoNSEJD7PXoMrvaj4uOqqupkPPePFwCkc1OMwNRatnzjIoFSp65cQbwPHID5QfkHZKrvs4kTFB6ZhI/eBi/5sFMBIeHTN4OIG9i2aNdl//9w/R2qh+DAZrYui+wDKXtdcSAqwUu++W2T8eDuDgB2bDi8iwQ8bw/uPM0pn2QUHFXwP2xvacuMwhHDKFPsaHS4IUxJvYAAlOlt0CQeUPdDqmi0+ECLV7jtkr9ABg7xQnFqZ8XeyJFcUMTjtIIGBxDjO5jQBrvzmN3Y2otgVJlHSwEJbqPFWGrz2UaNErr7aFh44cGA1VQqP4AUtDjKwoHEmCHfDWXaBwNXPIA6lFwy7mj3bvv0//1PpVxMvsB1gBL0CwaFOl1N8oMvFeeIDgYeyDUk0FB8mgx2B1jdff92+xgHjv//bXuCpmBUmcSDBjIwHHrAnP8JtQGsiQlphgzOt/v3tlTLxN2I0MwKD3KEFD95j3abgfiB8CgGiZRegdnocxAe2HYC8kE7mJN5xlqmcZRSn+NCSCzEKig/T0QMFBm6BG29M37d6tbfHgL3+ne/Y/floN0NXACaF4kwRYbMwwURE/E1olYOr89e/iowbFy8LN0mlFy25YLtQ8acHnqiLD9TxdeT+tddW+tXEk3797AxEtviAu6FilOLDSCg+TEdzHxgvjtILhm85O0S8gLZKOCeo3eIxHnww3emAAFZYINiGbgsc1HCWitc0cGB4z09yiw/nOi7Z4gM/A9EYNbBdo3yH9UIwvZWU1gGFsenaiu0E7ylKM3ApiXFQfJgO5nsg3IUdLcYbO1eM9Co+sO4CwIEeO2pMDkWbHdCwWZDA1v/JT+x0PfIr//Efdu4E642Q8DnpJNtpwvajWaJ84mPoUPvsF8FnzF7B4DfniqiVQrd/bEd0zkpn0iR7pdrszMxjj9luqa52TIyC4sN0kD6/7DL7Nnb+AOsrlCI+cBBxPm4Y4gNrOSDUhgWs1B7Har0c8lM58N7raqY6MCqX+EDL7bx5tujF5zZ5sj0EDr+PFtdKots/x+6XBxzRwYMzVzoGCJnGcWVj4gsUH8TOeeiOAZMJL7jAvv3++yJffVW4GwBnt7BXsYPJFh9opwwKvD6cZeMAh9f/u9+J/PznnFgapdILznizD+BO8aH3A4SCnWBGRCVB+BpQfBDiOxQfRKRjR3vMOAZw3XqrfY2DOYQH2lPzWeBY7lqDZeh1V3SSZVDOB14XgqVYZRStmzh7vugifppREx9axkPbc7b4gOWOuSu5As568K8UdD4ICQyKD2Lzwx/aFvixx9rtqTqhb8AAkd69c4sIOB9AA6ZhlF3uvdceYISgIgb7oFMHr5FET3wAhICdHQ3YNmC5I6vzwgt1f/e888IPK2cD0aTig9MqCfEdig/ijnOHixAnOhfyiY/scKeKj+3b7YsfoIUXK4qOGWMPDhsxQmT+fNupIdECblT37vbtH/wg8z6IW+dnduSRaacMIDgMMKjMr22nWCBsMaMEQVOsWkoI8RWKD+IOVqR04rbENMAoZJA9gAmrl+pcBz9yH1gbAi7Mr39tHxCwTDcyAY0bl//YJBiwnDrmZGiGyMm2benb+EzhuCkYVqeB4Uq5H+p6oCTJ4XSE+A7FB3EHZY1C4gMzELROn+18QCD4VXrBqrvIdyDXAVGEcdc/+hHbH6MOOlbQvu3WpqodVpgginkPKj7gMqADQp23SosPllwICQSKD+IOShoYFqZj1zEE6ssvM38GBwbU7SEIVGg48aPjBYFEdLRgSW50HWAY2tln81NLwuwHfKaYPgsw2wMiRfMeetAvN3QKh0VXVC2Gd96xr9npQkggUHwQdxAIxLAwBDpRn0fniwoRt7yH29ltOR0vEDU332zPDkHdH04Mnv/ww/mJJQEMtnOO20YoFRkPiBKn+IAAxjbgddR/NmgdxwKKH3/s/XeeeELk9tvt29j+CSG+Q/FB8gNRgQODW+lF8x65JomWWnaB2EDoUNeZGTvWXqPF2cpLkgfEqq5uqiFPrJQM9wuLHTqn73oNjb78ssjmzSLPPON9PRe4frt324uiff/7Rf4RhBAvUHyQwhQSH7lW+yxFfMCKx/Nh9DIORFiZFovUYYgZMYfsrMXf/paeluqVN95I33Zr6XVj1iy7uwsdOI88krnUOyHENyg+iHfx8eqr6eXRi3E+vGY+0M6LYOk//2mfBWOQGM88zQSlEmf3Cxg2TGTixMxt0Kv4wDA8L84JBAdAUBalIUJIIFB8kMJgNUosgb1pUzqI51wYrHNn998rJvPxwAP26pYIBx53nH3gUNFDzAMtrrfdJnLXXSJPPmlnfnbuFLnpJpHp0709BobPKRDAmIabD+RKIIBRahw+vLzXTwjJC8UHKQxKHjpB1Fl6UUfDrdPF+X0ECXOB2vpVV4lcfrl9+8IL7QOAjuAmZk/dxYAyCN/nnrNXvAUow3lxMVR8tGnjrfQCkaNLBUD8EEICg+KDeENdCAT4NBSqrbfO6ZROnAuIuR0sEAj8j/8Quece+2usTDtzpkiTJvxUSCZwI665xh74hbZv3Q5zgXVjcME0VUzFBZiGmw8skJg9Gp4QEggUH6S00Km6Hpgwimmm+cQHRMqnn9bd0Z9wgl2Lx+/jrPP66zk4jOQGw8eQxQC//GXhdllwxBHpgXkLFuR3TNBZA3QsPCEkMCg+iDf69LHPIrGM/bp1afEB18NtxgfAyrgdOqS7WBRMKD3xRHtI2cEH20HWIUP4SZDCXH21fQ2xim3RDQhblGwAAstY1A7hUWy3WDwxF//6l33NWTKEBA7FB/FGdbXIUUel3Q8NkeYquSgaRkWYD2edP/2pLTQwefK00+y6fI8e/BSIN+BkYI0fdLxouS4blFnQLjtwoC1WUMbDqHeACbluwJlDGRBCOlf3FiHENyg+SGmlF6fzkY9vfCO9VgY6CFBagQjByrQIEcJKJ6QYMHQOYAbM1q2Z9y1ZYgeWdUYM3DqggWmUXvKVXCCWmTkiJHAoPkh54iNXp0u28/GLX9hhUnTO3H+/fdbKAU6kFOBowJ3A5FKsmutkyhT7GiPZsSyAgjJfPudDSy7MexASChQfpHjxgW4DXWvDq/OhgVPU4tFWS0ipwM3Q7AfmgOjQMcyI0RkgcNacqPOB+TFo6c6G4oOQUKH4IN7BQmC6yNxf/lJc5kM5/XS+46R80PXSsqVdzsO6PyjljR5tZz0QMM0eUAenBD+PQWVLl9Z9PIZNCQkVig9S2iJzmKFQjPMBUGZB1wwh5YL27FGj0m23M2bYC9ChrIdpudkdWHBL0NqdXXqBaJk2zR7lD1h2ISQUKD5IcWSfURbKfDiXTce6LZhWSYgfXHml3UKLyaW4DbAS8rHHuv+85j40dIrlArBezGWX2Y4Jumg40p+QUKD4IMWRvXMu5Hygc0B/5tRT+W4T/4CwPe+8tJDA6so/+lHun3eGThGaPuYYkT/+0XZLJk2yyzdcTI6QUKD4IKUtMudVfADU4MGgQXy3STBttxo+bdAg989q2QWrMZ98sj347pBDbCHy4x9TeBASIvXDfDKSoEXmMBYddXVdtCsfDz9sT6Nk3oP4zUknifzsZ3aeCC24+cBMmebN07NB4Jog74EBeoSQUKHzQUovvWBnDjFSiHbtKDxIMEAAo9Qybpy3n7/gAvsaJZo//IHCg5AKQfFBigdLjmeHSQmJA1g5+e677dwH1h4ihFQEll1I8WBNlgcfTGc5CIkL7dunO2MIIfFwPiZNmiTf/OY3pXnz5nLAAQfIueeeK8uXL8/4mX79+klVVVXGZTSG/5BkWd0jR9rdAoQQQkiQ4mPevHkyZswYWbBggTz//POye/duGTBggGzfvj3j50aNGiXr1q2rvdx2223Fvi5CCCGEJJSiyi7PPvtsxtfTpk2zHJBFixZJ3759a7/fpEkTaYeQISGEEEKIn4HTzVhVUkRatWqV8f3p06dLmzZtpEePHjJhwgT58ssvcz5GTU2NbNmyJeNCCCGEkORScuB07969MnbsWPnWt75liQxl+PDh0rlzZ+nQoYMsXbpUrrvuOisX8jjWXciRI5k4cWKpL4MQQgghMaMqlcLKSsVzxRVXyDPPPCMvv/yydOzYMefPvfjii9K/f39ZuXKldOnSxdX5wEWB89GpUyfLVanm8B9CCCEkFuD43aJFC0/H75KcjyuvvFKefvppmT9/fl7hAXpjGqZITvHRsGFD60IIIYQQMyhKfMAkueqqq+SJJ56QuXPnysEHH1zwd5YsWWJdt0d/PSGEEEKMpyjxgTbbGTNmyFNPPWXN+li/fr31fdgsjRs3llWrVln3Dxo0SFq3bm1lPsaNG2d1wvTs2dP4N5sQQgghRWY+MDDMjalTp8qll14qH330kXzve9+Tt956y5r9gezGeeedJzfccIPn/EYxNSNCCCGEJDzzUUinQGxgEBkhhBBCSC64sBwhhBBCQoXigxBCCCEUH4QQQghJLiVPOA0KzZVwzDohhBASH/S47aWPJXLiY+vWrbXhVUIIIYTECxzH0fUSyHj1oMCaMWvXrrXmiORq7Y0DOiYe7cdsGeZ7wm2E/zfcl3D/mvTjTSqVsoQH1narV69evJwPvOBCI9vjBDYEig++J9xG+H/DfQn3ryYcb1oUcDwUdrsQQgghJFQoPgghhBASKhQfAYGVem+88Uau2Mv3hNsI/2+4L+H+NVAaxvB4E7nAKSGEEEKSDZ0PQgghhIQKxQchhBBCQoXigxBCCCGhQvFBCCGEkFAxRnxMmTJFvvGNb0ijRo2kd+/e8vrrr9fet3r1amuaqttl1qxZeR936dKlcsopp1iPiwlzt912W8b9DzzwgHX/fvvtZ13OOOOMjOd2Y926dTJ8+HDp2rWrNXRt7NixdX6mlMeNynvy+OOPy/HHHy8tW7aUpk2byjHHHCO///3vC77euXPnynHHHWclug899FCZNm1aUX9Tkt6PpG8jTmbOnGk95rnnnlvxbSRu70nStxN8vtmPiZ81dV8yrYT3I6xtpA4pA5g5c2aqQYMGqYcffjj19ttvp0aNGpVq2bJlasOGDdb9X331VWrdunUZl4kTJ6aaNWuW2rp1a87H3bx5c6pt27apESNGpN56663UH/7wh1Tjxo1T999/f+3PDB8+PDVlypTUm2++mXr33XdTl156aapFixapNWvW5Hzc999/P/WDH/wg9dvf/jZ1zDHHpK6++uo6P1PK40blPXnppZdSjz/+eOqdd95JrVy5MvXLX/4ytc8++6SeffbZnI/73nvvpZo0aZIaP3689Xt33313nd8p9Dcl6f1I+jbi/DsPPPDA1CmnnJIaMmRI3tcb9DYSx/ck6dvJ1KlTU9XV1RmPvX79+ryvN8n7kqklvB9hbCNuGCE+TjjhhNSYMWNqv96zZ0+qQ4cOqUmTJuX8HXwI3//+9/M+7r333pvab7/9UjU1NbXfu+6661LdunXL+TvY8Jo3b2590F449dRTXTeGch83Su8JOPbYY1M33HBDzvt/9KMfpY488siM7333u99NDRw4sKy/Ka7vhwnbCF7vSSedlHrwwQdTl1xyScEDbdDbSBzfk6RvJzjY4iBYDEnel0wt4f0IYxtxI/Fll127dsmiRYssm0iBtYSvX331Vdffwc8vWbJERo4cmfex8ft9+/aVBg0a1H5v4MCBsnz5cvniiy9cf+fLL7+U3bt3S6tWrUr+m8p93Ci9JxDAL7zwgnU/fi/f4zpfrz6uvt5S/qY4vx8mbCM333yzHHDAAQUfL4xtJK7viQnbybZt26Rz585WGWLIkCHy9ttvF3zcJO9LthX5fpSCH8exxIuPTz/9VPbs2SNt27bN+D6+Xr9+vevvPPTQQ3L44YfLSSedlPex8ftuj6v3uXHddddZK/5lb/zlUszjRuE92bx5szRr1sz6Rzr77LPl7rvvljPPPLPox8Vqjjt27Cjpb4rz+5H0beTll1+2Hg+1Zq8EuY3E9T1J+nbSrVs3efjhh+Wpp56SRx55xFoVHY+7Zs0aI/cl3Up4P0rBj+NY4sVHsWDjmzFjRh0VeuSRR1oHB1zOOuuskh578uTJVlDsiSee8BSKqvTjBvmeNG/e3FL7CxculFtvvVXGjx9vhcDiQBzfjzhtI1iS+6KLLrIOsm3atJG4Esf3JE7bCejTp49cfPHFVkj71FNPtcLb+++/v9x///0SB3bE8P3waxupLwkH/6j77LOPbNiwIeP7+Lpdu3Z1fv6xxx6zLCV8gE7++te/WjYTaNy4sXWN33d7XL3Pye233259aHPmzJGePXv69NeV9rhReE9gRSJlDvCP8u6778qkSZOkX79+rq851+Ni+Wg8N/6eYv6muL8fSd5GVq1aZXUEDB48uPZ+nMGB+vXrWzZzly5dQt1G4vqeJHk7cWPfffeVY489VlauXJnzNSd9X1Ls+1EMfh7HEu98wMbu1auXVUd3/tPia6hENwvsnHPOsdSiE9TQcHDA5cADD7S+h9+fP39+7UYCnn/+ecv6QjuSgnaoW265RZ599lmrpdIvSn3cKLwn2eD5a2pqct6Px3W+Xn1cfb3F/k1xfz+SvI10795dli1bZjlBesFjn3baadZt1LLD3kbi+p4keTtxAyUPvE/t27fP+ZpN2pfs8fB+eMX341jKAND61LBhw9S0adOs1qrLL7/can3KbkFasWJFqqqqKvXMM894etxNmzZZrU8XXXSR1fqE50ELl7P1afLkyVbb1WOPPZbR/pSvpQqgpQmXXr16WW1OuI22rXIfNwrvyU9/+tPU3/72t9SqVaus57799ttT9evXTz3wwAMF2+N++MMfWq1eaPtya4/z8jcl4f1I+jaSjZfOjqC3kTi+J0nfTtCi+txzz1n/O4sWLUr953/+Z6pRo0YZf59J+5KJJbwfYWwjbhghPgB6uQ866CDrDUQr1IIFC+r8zIQJE1KdOnWyWqO88s9//jN18sknWxsbeu/xITnp3LkzVg2uc7nxxhvzPq7b7+Cxyn3cKLwn119/ferQQw+1/inQOtanTx/rH6kQmIeBljS83kMOOcRqKyvlb0rK+5HkbaTUA23Q20gc35Mkbydjx46tfV4cmAcNGpRavHixsfuSsSW+H2FsI9lUff3EhBBCCCGhkPjMByGEEEKiBcUHIYQQQkKF4oMQQgghoULxQQghhJBQofgghBBCSKhQfBBCCCEkVCg+CCGEEBIqFB+EEEIICRWKD0IIIYSECsUHIYQQQkKF4oMQQgghoULxQQghhBAJk/8PPcZ2dgeIsekAAAAASUVORK5CYII="
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 85
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T19:17:19.791409Z",
+     "start_time": "2025-11-29T19:17:19.683835Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "",
+   "id": "ade5efa1f336b655",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x27b9deece00>]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 23
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "#",
+   "id": "1aa707313aeb5993"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/v4/array_temperature/arrayTemperatureModel.py b/v4/array_temperature/arrayTemperatureModel.py
new file mode 100644
index 0000000..a8f131e
--- /dev/null
+++ b/v4/array_temperature/arrayTemperatureModel.py
@@ -0,0 +1,80 @@
+import math
+import numpy as np
+from numpy.typing import NDArray
+
+# include constants for standard testing conditions
+AVG_IRRADIANCE = 1000  # (W/m^2)
+RATED_POWER = 3.76  # (Watt peaks i.e. Wp from maxeon data sheet
+TEMP_CORRECTION = -0.0029  # (%/C) convert from % to /C from data sheet, assumed at 25 C.
+
+
+# initial abstraction considering only the top shell and array. again, we do not consider individual cells but the array as a whole with considering uniform steady state temperature throughout.
+# maxeon cells : individual area is 155cm^2
+# 800w for an average solar irradiance
+
+
+class arrayTemperatureModel():
+
+    def __init__(self, ambient_temperature, irradiance, wind_speed, thermal_loss_coefficient,
+                 convective_loss_coefficient, **kwargs):
+        self.ambient_temperature = ambient_temperature
+        self.irradiance = irradiance
+        self.wind_speed = wind_speed
+        self.thermal_loss_coefficient = thermal_loss_coefficient
+        self.convective_loss_coefficient = convective_loss_coefficient
+
+    # the range of u0  12.5–22 W/m2C and u1 0–6 Ws/m3C
+
+    # temperature rise due to radiation is also caused by heating up of parts of array other than pv module itself
+    # heating due to electrical losses
+
+    # initially considering a stationary vehicle, not considering degradation of cell efficiency etc
+
+    # this method uses the Faiman model. later: incorporate wind speed + car speed later.
+
+    def calculate_speed(self, car_speed):
+        # vectorially add car_speeds and wind_speeds together
+        air_speed = car_speed
+        return air_speed
+
+    def calculateArrayTemperature(self):
+        temperature_array = self.ambient_temperature + self.irradiance / (
+                self.thermal_loss_coefficient + (self.wind_speed * self.convective_loss_coefficient))
+        return temperature_array
+        # ta(array) = t(ambient) + G/(u(0) + u(convec)*wind speeds)
+
+    def power_output(self):
+        temp_array = self.calculateArrayTemperature()
+
+        # using the PVwatts model:
+        # power = irradiance/expected_irradiance * power_rating * (1+ temp_correction (T_array - t_ambient))
+        power_temperature = (self.irradiance / AVG_IRRADIANCE) * RATED_POWER * (
+                1 + TEMP_CORRECTION * (temp_array - self.ambient_temperature))
+
+        return power_temperature
+
+    # another requirement from the DR is the partial derivative of this power output with respect to irradiance and array_temp. this helps us get a sensitivity analysis
+    # also need to extend the faiman model for considering multiple layers
+
+    def partial_irradiance(self):
+        temp_array = self.calculateArrayTemperature()
+
+        temperature_term = TEMP_CORRECTION * ((temp_array - self.ambient_temperature) + self.irradiance / (
+                self.thermal_loss_coefficient + (self.wind_speed * self.convective_loss_coefficient)))
+        partial_irradiance = (RATED_POWER / AVG_IRRADIANCE) * (temperature_term + 1)
+        return partial_irradiance
+
+    def partial_temperature(self):
+        return (self.irradiance / AVG_IRRADIANCE) * RATED_POWER * TEMP_CORRECTION
+
+
+# SHORTWAVE = 729
+
+
+def model():
+    return arrayTemperatureModel(27, 715, 0, 22.5,
+                                 0.8)
+
+
+model = model()
+print(model.calculateArrayTemperature())

From 593db45b6bfa0f47141014e017de07a2b0e2a309 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Sat, 29 Nov 2025 14:46:56 -0800
Subject: [PATCH 02/49] ---

---
 array_temp/temp.ipynb | 720 ++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 720 insertions(+)
 create mode 100644 array_temp/temp.ipynb

diff --git a/array_temp/temp.ipynb b/array_temp/temp.ipynb
new file mode 100644
index 0000000..1b88098
--- /dev/null
+++ b/array_temp/temp.ipynb
@@ -0,0 +1,720 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2025-11-29T22:28:22.534452Z",
+     "start_time": "2025-11-29T22:28:13.880534Z"
+    }
+   },
+   "source": [
+    "from data_tools import query\n",
+    "from data_tools.collections import TimeSeries\n",
+    "from datetime import datetime, date, time, timezone\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import dill\n",
+    "import os\n",
+    "import pytz\n",
+    "from datetime import datetime, time, date\n",
+    "\n",
+    "#each 5 seconds\n",
+    "utc_offset_h = 2\n",
+    "start_utc = time(0, 00, 00)  #querying i svancouver time, influxdb gives utc\n",
+    "stop_utc = time(23, 45, 00)\n",
+    "date_start = date(2025, 7, 2)\n",
+    "date_stop = date(2025, 7, 6)\n",
+    "\n",
+    "vancouver = pytz.timezone(\"America/Vancouver\")\n",
+    "\n",
+    "start_local = vancouver.localize(datetime.combine(date_start, start_utc))\n",
+    "stop_local = vancouver.localize(datetime.combine(date_stop, stop_utc))\n",
+    "\n",
+    "start_time = start_local.astimezone(pytz.utc)\n",
+    "stop_time = stop_local.astimezone(pytz.utc)\n",
+    "\n",
+    "client = query.DBClient()\n",
+    "temp_array_fsgp: TimeSeries = client.query_time_series(start_time, stop_time, field=\"MosfetTemperatureA\")\n",
+    "speed_kph: TimeSeries = client.query_time_series(start_time, stop_time, \"MotorRotatingSpeed\")\n",
+    "\n",
+    "#print(temp_array_expected_aliter)\n",
+    "#this is almost steady state, and expected temperature is roughly 31.8 degrees celsius. note that individual cells do have an upto 5 C difference, which is actually quite a lot.\n",
+    "\n",
+    "\n"
+   ],
+   "outputs": [],
+   "execution_count": 25
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T22:31:03.101565Z",
+     "start_time": "2025-11-29T22:30:54.208893Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from datetime import datetime, date, time\n",
+    "import pytz\n",
+    "\n",
+    "vancouver = pytz.timezone(\"America/Vancouver\")\n",
+    "\n",
+    "date_start = date(2025, 7, 1)\n",
+    "date_stop  = date(2025, 7, 6)\n",
+    "\n",
+    "# Local start/end\n",
+    "start_local = vancouver.localize(datetime.combine(date_start, time(23,45,0)))\n",
+    "stop_local  = vancouver.localize(datetime.combine(date_stop, time(0,0,0)))\n",
+    "\n",
+    "# Convert to UTC\n",
+    "start_utc = start_local.astimezone(pytz.utc)\n",
+    "stop_utc  = stop_local.astimezone(pytz.utc)\n",
+    "\n",
+    "print(\"Start UTC:\", start_utc)  # 2025-07-02 07:00:00+00:00\n",
+    "print(\"Stop UTC:\", stop_utc)    # 2025-07-07 06:45:00+00:00\n",
+    "\n",
+    "client = query.DBClient()\n",
+    "temp_array_fsgp = client.query_time_series(start_utc, stop_utc, field=\"MosfetTemperatureA\")\n",
+    "speed_kph       = client.query_time_series(start_utc, stop_utc, \"MotorRotatingSpeed\")\n"
+   ],
+   "id": "856e8e887bd73795",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Start UTC: 2025-07-02 06:45:00+00:00\n",
+      "Stop UTC: 2025-07-06 07:00:00+00:00\n"
+     ]
+    }
+   ],
+   "execution_count": 29
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T22:31:06.563220Z",
+     "start_time": "2025-11-29T22:31:05.448758Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "#resampling influx data so that i can fit it with irradiance data (queried every 15 minutes)\n",
+    "\n",
+    "ts = temp_array_fsgp\n",
+    "timestamps = pd.to_datetime(ts.datetime_x_axis, utc=True)\n",
+    "values = ts.data\n",
+    "\n",
+    "df = pd.DataFrame({\"value\": values}, index=timestamps)\n",
+    "df_15m = df.resample(\"15T\").mean()\n",
+    "\n",
+    "print(df_15m.head())\n",
+    "print(df_15m.tail())\n"
+   ],
+   "id": "5efaf5364189c268",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                               value\n",
+      "2025-07-01 23:45:00+00:00  26.032398\n",
+      "2025-07-02 00:00:00+00:00  25.083505\n",
+      "2025-07-02 00:15:00+00:00  24.787958\n",
+      "2025-07-02 00:30:00+00:00  24.492412\n",
+      "2025-07-02 00:45:00+00:00  24.675365\n",
+      "                               value\n",
+      "2025-07-05 13:00:00+00:00  38.086209\n",
+      "2025-07-05 13:15:00+00:00  36.774657\n",
+      "2025-07-05 13:30:00+00:00  35.463106\n",
+      "2025-07-05 13:45:00+00:00  34.271664\n",
+      "2025-07-05 14:00:00+00:00  34.174615\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_31364\\2898644639.py:10: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
+      "  df_15m = df.resample(\"15T\").mean()\n"
+     ]
+    }
+   ],
+   "execution_count": 30
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "querying from openmeteo to get solar irradiance data\n",
+    "\n",
+    "- this is hourly irradiance over 4 days\n"
+   ],
+   "id": "8fd8b587bf1e774"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T22:35:59.594077Z",
+     "start_time": "2025-11-29T22:35:59.572711Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import openmeteo_requests\n",
+    "\n",
+    "import pandas as pd\n",
+    "import requests_cache\n",
+    "from retry_requests import retry\n",
+    "\n",
+    "# Setup the Open-Meteo API client with cache and retry on error\n",
+    "cache_session = requests_cache.CachedSession('.cache', expire_after=3600)\n",
+    "retry_session = retry(cache_session, retries=5, backoff_factor=0.2)\n",
+    "openmeteo = openmeteo_requests.Client(session=retry_session)\n",
+    "\n",
+    "# Make sure all required weather variables are listed here\n",
+    "# The order of variables in hourly or daily is important to assign them correctly below\n",
+    "url = \"https://historical-forecast-api.open-meteo.com/v1/forecast\"\n",
+    "params = {\n",
+    "    \"latitude\": 36.9760,\n",
+    "    \"longitude\": 86.4491,\n",
+    "    \"start_date\": \"2025-07-02\",\n",
+    "    \"end_date\": \"2025-07-05\",\n",
+    "    \"minutely_15\": [\"temperature_2m\", \"wind_speed_10m\", \"shortwave_radiation_instant\"],\n",
+    "}\n",
+    "responses = openmeteo.weather_api(url, params=params)\n",
+    "\n",
+    "# Process first location. Add a for-loop for multiple locations or weather models\n",
+    "response = responses[0]\n",
+    "print(f\"Coordinates: {response.Latitude()}°N {response.Longitude()}°E\")\n",
+    "print(f\"Elevation: {response.Elevation()} m asl\")\n",
+    "print(f\"Timezone difference to GMT+0: {response.UtcOffsetSeconds()}s\")\n",
+    "\n",
+    "# Process minutely_15 data. The order of variables needs to be the same as requested.\n",
+    "minutely_15 = response.Minutely15()\n",
+    "minutely_15_temperature_2m = minutely_15.Variables(0).ValuesAsNumpy()\n",
+    "minutely_15_shortwave_radiation_instant = minutely_15.Variables(1).ValuesAsNumpy()\n",
+    "minutely_15_wind_speed_10m = minutely_15.Variables(2).ValuesAsNumpy()\n",
+    "\n",
+    "minutely_15_data = {\"date\": pd.date_range(\n",
+    "    start=pd.to_datetime(minutely_15.Time(), unit=\"s\", utc=True),\n",
+    "    end=pd.to_datetime(minutely_15.TimeEnd(), unit=\"s\", utc=True),\n",
+    "    freq=pd.Timedelta(seconds=minutely_15.Interval()),\n",
+    "    inclusive=\"left\"\n",
+    ")}\n",
+    "\n",
+    "minutely_15_data[\"temperature_2m\"] = minutely_15_temperature_2m\n",
+    "minutely_15_data[\"shortwave_radiation_instant\"] = minutely_15_shortwave_radiation_instant\n",
+    "minutely_15_data[\"wind_speed_10m\"] = minutely_15_wind_speed_10m\n",
+    "\n",
+    "minutely_15_dataframe = pd.DataFrame(data=minutely_15_data)\n",
+    "print(\"\\nMinutely15 data\\n\", minutely_15_dataframe)"
+   ],
+   "id": "75c51cd2fbd7d69",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coordinates: 37.0°N 86.5°E\n",
+      "Elevation: 5139.0 m asl\n",
+      "Timezone difference to GMT+0: 0s\n",
+      "\n",
+      "Minutely15 data\n",
+      "                          date  temperature_2m  shortwave_radiation_instant  \\\n",
+      "0   2025-07-02 00:00:00+00:00           -2.20                     5.588703   \n",
+      "1   2025-07-02 00:15:00+00:00           -2.15                     5.351785   \n",
+      "2   2025-07-02 00:30:00+00:00           -2.15                     5.014219   \n",
+      "3   2025-07-02 00:45:00+00:00           -2.05                     4.680000   \n",
+      "4   2025-07-02 01:00:00+00:00           -1.95                     4.680000   \n",
+      "..                        ...             ...                          ...   \n",
+      "379 2025-07-05 22:45:00+00:00           -2.00                     8.496305   \n",
+      "380 2025-07-05 23:00:00+00:00           -1.95                     8.788720   \n",
+      "381 2025-07-05 23:15:00+00:00           -1.95                     9.085988   \n",
+      "382 2025-07-05 23:30:00+00:00           -1.90                     9.199390   \n",
+      "383 2025-07-05 23:45:00+00:00           -1.85                     9.021574   \n",
+      "\n",
+      "     wind_speed_10m  \n",
+      "0        124.831741  \n",
+      "1        164.539490  \n",
+      "2        205.015503  \n",
+      "3        243.104050  \n",
+      "4        276.808258  \n",
+      "..              ...  \n",
+      "379        0.000000  \n",
+      "380        0.000000  \n",
+      "381       19.759171  \n",
+      "382       51.912174  \n",
+      "383       85.373299  \n",
+      "\n",
+      "[384 rows x 4 columns]\n"
+     ]
+    }
+   ],
+   "execution_count": 49
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T22:36:00.255585Z",
+     "start_time": "2025-11-29T22:36:00.248956Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "minutely_15_dataframe = minutely_15_dataframe[minutely_15_dataframe['date']<'2025-07-05 14:15:00']",
+   "id": "1f24e675de2a0cdd",
+   "outputs": [],
+   "execution_count": 50
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T22:36:01.122614Z",
+     "start_time": "2025-11-29T22:36:01.117851Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "minutely_15_data[\"temperature_2m\"] = minutely_15_temperature_2m\n",
+    "minutely_15_data[\"shortwave_radiation_instant\"] = minutely_15_shortwave_radiation_instant\n",
+    "minutely_15_data[\"wind_speed_10m\"] = minutely_15_wind_speed_10m"
+   ],
+   "id": "cea74153df653c65",
+   "outputs": [],
+   "execution_count": 51
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T22:35:42.524105Z",
+     "start_time": "2025-11-29T22:35:42.511211Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "minutely_15_dataframe.tail()\n",
+   "id": "cc22b957110ee8a8",
+   "outputs": [],
+   "execution_count": 46
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T22:36:04.640616Z",
+     "start_time": "2025-11-29T22:36:04.632028Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "len(minutely_15_dataframe)",
+   "id": "592396d2c42fe633",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "345"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 52
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T22:37:24.889140Z",
+     "start_time": "2025-11-29T22:37:24.879526Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "print(\"\\nMinutely15 data\\n\", minutely_15_dataframe)\n",
+   "id": "b02b2e521e671912",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Minutely15 data\n",
+      "                          date  temperature_2m  shortwave_radiation_instant  \\\n",
+      "0   2025-07-02 00:00:00+00:00           -2.20                     5.588703   \n",
+      "1   2025-07-02 00:15:00+00:00           -2.15                     5.351785   \n",
+      "2   2025-07-02 00:30:00+00:00           -2.15                     5.014219   \n",
+      "3   2025-07-02 00:45:00+00:00           -2.05                     4.680000   \n",
+      "4   2025-07-02 01:00:00+00:00           -1.95                     4.680000   \n",
+      "..                        ...             ...                          ...   \n",
+      "340 2025-07-05 13:00:00+00:00            1.15                    29.548521   \n",
+      "341 2025-07-05 13:15:00+00:00            0.90                    27.475807   \n",
+      "342 2025-07-05 13:30:00+00:00            0.65                    25.202570   \n",
+      "343 2025-07-05 13:45:00+00:00            0.40                    22.461807   \n",
+      "344 2025-07-05 14:00:00+00:00            0.25                    20.056877   \n",
+      "\n",
+      "     wind_speed_10m  \n",
+      "0        124.831741  \n",
+      "1        164.539490  \n",
+      "2        205.015503  \n",
+      "3        243.104050  \n",
+      "4        276.808258  \n",
+      "..              ...  \n",
+      "340       58.205017  \n",
+      "341       22.902834  \n",
+      "342        2.313184  \n",
+      "343        0.000000  \n",
+      "344        0.000000  \n",
+      "\n",
+      "[345 rows x 4 columns]\n"
+     ]
+    }
+   ],
+   "execution_count": 55
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T22:37:47.682054Z",
+     "start_time": "2025-11-29T22:37:47.675658Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "print(df_15m)",
+   "id": "58bd4131e7093a9d",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                               value\n",
+      "2025-07-02 00:00:00+00:00  25.083505\n",
+      "2025-07-02 00:15:00+00:00  24.787958\n",
+      "2025-07-02 00:30:00+00:00  24.492412\n",
+      "2025-07-02 00:45:00+00:00  24.675365\n",
+      "2025-07-02 01:00:00+00:00  25.388537\n",
+      "...                              ...\n",
+      "2025-07-05 13:00:00+00:00  38.086209\n",
+      "2025-07-05 13:15:00+00:00  36.774657\n",
+      "2025-07-05 13:30:00+00:00  35.463106\n",
+      "2025-07-05 13:45:00+00:00  34.271664\n",
+      "2025-07-05 14:00:00+00:00  34.174615\n",
+      "\n",
+      "[345 rows x 1 columns]\n"
+     ]
+    }
+   ],
+   "execution_count": 57
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T22:36:39.558342Z",
+     "start_time": "2025-11-29T22:36:39.549404Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df_15m = df_15m.iloc[1:]",
+   "id": "a7bbd11d36fdb821",
+   "outputs": [],
+   "execution_count": 53
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "minutely open meteo data starts at 2 july, 12am and goes to 6 july 22 45\n",
+    "therefore on influx i query for 1 july 10  pm to 6 july 20 45\n",
+    "\n",
+    "\n",
+    "utc i s2 hours ahead of vancouver\n",
+    "\n",
+    "\n",
+    "\n",
+    "Plot some relevant data. this will further be used to generate the relevant coefficients"
+   ],
+   "id": "600a8c5a1228ac87"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T22:38:36.009117Z",
+     "start_time": "2025-11-29T22:38:35.901346Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "#this graph shows solar irradiance, array temperature, and ambient temperature over July 2-6 (FSGP 2025)\n",
+    "\n",
+    "fig, ax1 = plt.subplots()\n",
+    "ax_twin = ax1.twinx()\n",
+    "\n",
+    "plt.plot(df_15m.index, df_15m, label=\"Array Temperature\")\n",
+    "plt.plot(minutely_15_dataframe['date'], minutely_15_dataframe['temperature_2m'], color='green', label=\"Ambient Temperature\")\n",
+    "# plt.plot(minutely_15_data['date'], minutely_15_data['wind_speed_10m'], color = 'blue', label = \"Wind Speed 10m\")\n",
+    "# plt.plot(speed_kph.datetime_x_axis, speed_kph, color = 'yellow', label = \"Speed KPH\")\n",
+    "\n",
+    "ax1.plot(minutely_15_dataframe['date'], minutely_15_dataframe['shortwave_radiation_instant'], color=\"red\",\n",
+    "         label=\"Solar Irradiance\")\n",
+    "\n",
+    "ax1.set_xlabel(\"Time\")\n",
+    "ax1.set_ylabel(\"Solar Irradiance\")\n",
+    "ax_twin.set_ylabel(\"MosfetTemperatureA\")\n",
+    "\n",
+    "ax1.tick_params(\"x\", rotation=90)\n",
+    "\n",
+    "plt.legend(loc=\"upper left\")\n",
+    "ax1.legend(loc=\"upper left\")\n",
+    "plt.show()"
+   ],
+   "id": "2b7568c9316948b9",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 58
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T22:10:19.460310Z",
+     "start_time": "2025-11-29T22:10:19.455125Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "len(df_15m)",
+   "id": "14e077d6a70439e3",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "381"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 6
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T22:39:26.063136Z",
+     "start_time": "2025-11-29T22:39:25.920959Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plt.plot(minutely_15_dataframe['date'], minutely_15_dataframe['shortwave_radiation_instant'])",
+   "id": "3d07f9ce1c4e6bb4",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1ae53b19430>]"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 60
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T22:10:23.528176Z",
+     "start_time": "2025-11-29T22:10:23.522510Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "len(minutely_15_data['date'])",
+   "id": "b9b13efb8846ffb4",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "480"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 7
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T22:40:45.298102Z",
+     "start_time": "2025-11-29T22:40:45.290457Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from v4.array_temperature.arrayTemperatureModel import arrayTemperatureModel\n",
+    "def model(u0, u1):\n",
+    "    return arrayTemperatureModel(minutely_15_dataframe['temperature_2m'], minutely_15_dataframe['shortwave_radiation_instant'], 0, u0, u1)\n",
+    "\n"
+   ],
+   "id": "f2db910a0a809a42",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0     -1.945968\n",
+      "1     -1.906737\n",
+      "2     -1.922081\n",
+      "3     -1.837273\n",
+      "4     -1.737273\n",
+      "         ...   \n",
+      "340    2.493114\n",
+      "341    2.148900\n",
+      "342    1.795571\n",
+      "343    1.420991\n",
+      "344    1.161676\n",
+      "Length: 345, dtype: float32\n"
+     ]
+    }
+   ],
+   "execution_count": 64
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T22:41:10.031639Z",
+     "start_time": "2025-11-29T22:41:10.022278Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "model2 = model(12, 0.8)\n",
+    "print(model2.calculateArrayTemperature())\n",
+    "faiman_temp = model2.calculateArrayTemperature()"
+   ],
+   "id": "76ddfa495e81e820",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0     -1.734275\n",
+      "1     -1.704018\n",
+      "2     -1.732149\n",
+      "3     -1.660000\n",
+      "4     -1.560000\n",
+      "         ...   \n",
+      "340    3.612377\n",
+      "341    3.189651\n",
+      "342    2.750214\n",
+      "343    2.271817\n",
+      "344    1.921406\n",
+      "Length: 345, dtype: float32\n"
+     ]
+    }
+   ],
+   "execution_count": 68
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T22:41:10.888190Z",
+     "start_time": "2025-11-29T22:41:10.802236Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "plt.plot(df_15m.index, df_15m, color = 'red')\n",
+    "plt.plot(df_15m.index, faiman_temp)\n",
+    "plt.xticks(rotation = 90)\n"
+   ],
+   "id": "eb1c544f0f61380f",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([20271. , 20271.5, 20272. , 20272.5, 20273. , 20273.5, 20274. ,\n",
+       "        20274.5]),\n",
+       " [Text(20271.0, 0, '07-02 00'),\n",
+       "  Text(20271.5, 0, '07-02 12'),\n",
+       "  Text(20272.0, 0, '07-03 00'),\n",
+       "  Text(20272.5, 0, '07-03 12'),\n",
+       "  Text(20273.0, 0, '07-04 00'),\n",
+       "  Text(20273.5, 0, '07-04 12'),\n",
+       "  Text(20274.0, 0, '07-05 00'),\n",
+       "  Text(20274.5, 0, '07-05 12')])"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 69
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "c15bb0d3e9e1a6fd"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From e3bac846a022bd39da5b877e33ca0ff638a9fe60 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Tue, 6 Jan 2026 19:36:41 -0800
Subject: [PATCH 03/49] --

---
 array_temp/faiman_coefficients.ipynb | 32 ++++++++++++++++++++++++----
 array_temp/temp.ipynb                | 19 +++++++++++++----
 pyproject.toml                       |  6 ++++++
 3 files changed, 49 insertions(+), 8 deletions(-)

diff --git a/array_temp/faiman_coefficients.ipynb b/array_temp/faiman_coefficients.ipynb
index ef739d8..9477d88 100644
--- a/array_temp/faiman_coefficients.ipynb
+++ b/array_temp/faiman_coefficients.ipynb
@@ -3,8 +3,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-11-29T19:05:48.463024Z",
-     "start_time": "2025-11-29T19:05:46.602247Z"
+     "end_time": "2026-01-07T03:34:35.885636Z",
+     "start_time": "2026-01-07T03:34:24.042487Z"
     }
    },
    "cell_type": "code",
@@ -34,8 +34,32 @@
     "temp_array_expected_aliter: TimeSeries = client.query_time_series(start_time, stop_time, \"MosfetTemperatureA\")\n"
    ],
    "id": "7762d7c5f54c9e9f",
-   "outputs": [],
-   "execution_count": 2
+   "outputs": [
+    {
+     "ename": "ApiException",
+     "evalue": "(401)\nReason: Unauthorized\nHTTP response headers: HTTPHeaderDict({'Server': 'nginx/1.27.1', 'Date': 'Wed, 07 Jan 2026 03:34:35 GMT', 'Content-Type': 'application/json; charset=utf-8', 'Content-Length': '55', 'Connection': 'keep-alive', 'X-Influxdb-Build': 'OSS', 'X-Influxdb-Version': 'v2.7.1', 'X-Platform-Error-Code': 'unauthorized'})\nHTTP response body: b'{\"code\":\"unauthorized\",\"message\":\"unauthorized access\"}'\n",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[1;31mApiException\u001B[0m                              Traceback (most recent call last)",
+      "Cell \u001B[1;32mIn[1], line 23\u001B[0m\n\u001B[0;32m     20\u001B[0m stop_time \u001B[38;5;241m=\u001B[39m datetime\u001B[38;5;241m.\u001B[39mcombine(date_stop, stop_utc, tzinfo\u001B[38;5;241m=\u001B[39mtimezone\u001B[38;5;241m.\u001B[39mutc)\n\u001B[0;32m     22\u001B[0m client \u001B[38;5;241m=\u001B[39m query\u001B[38;5;241m.\u001B[39mDBClient()\n\u001B[1;32m---> 23\u001B[0m temp_array_expected_aliter: TimeSeries \u001B[38;5;241m=\u001B[39m \u001B[43mclient\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mquery_time_series\u001B[49m\u001B[43m(\u001B[49m\u001B[43mstart_time\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstop_time\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mMosfetTemperatureA\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n",
+      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\data_tools\\query\\influxdb_query.py:180\u001B[0m, in \u001B[0;36mDBClient.query_time_series\u001B[1;34m(self, start, stop, field, bucket, car, granularity, units, measurement)\u001B[0m\n\u001B[0;32m    163\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mquery_time_series\u001B[39m(\u001B[38;5;28mself\u001B[39m, start: datetime, stop: datetime, field: \u001B[38;5;28mstr\u001B[39m, bucket: \u001B[38;5;28mstr\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mCAN_log\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m    164\u001B[0m                       car: \u001B[38;5;28mstr\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mBrightside\u001B[39m\u001B[38;5;124m\"\u001B[39m, granularity: \u001B[38;5;28mfloat\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m0.1\u001B[39m, units: \u001B[38;5;28mstr\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m    165\u001B[0m                       measurement: \u001B[38;5;28mstr\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m TimeSeries:\n\u001B[0;32m    166\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m    167\u001B[0m \u001B[38;5;124;03m    Query the database for a specific field, over a certain time range.\u001B[39;00m\n\u001B[0;32m    168\u001B[0m \u001B[38;5;124;03m    The data will be processed into a TimeSeries, which has homogenous and evenly-spaced (temporally) elements.\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    178\u001B[0m \u001B[38;5;124;03m    :return: a TimeSeries of the resulting time-series data\u001B[39;00m\n\u001B[0;32m    179\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[1;32m--> 180\u001B[0m     query_df \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mquery_series\u001B[49m\u001B[43m(\u001B[49m\u001B[43mstart\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstop\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfield\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbucket\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcar\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmeasurement\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    182\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m TimeSeries\u001B[38;5;241m.\u001B[39mfrom_query_dataframe(query_df, granularity, field, units)\n",
+      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\data_tools\\query\\influxdb_query.py:153\u001B[0m, in \u001B[0;36mDBClient.query_series\u001B[1;34m(self, start, stop, field, bucket, car, measurement)\u001B[0m\n\u001B[0;32m    151\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m measurement:\n\u001B[0;32m    152\u001B[0m     query \u001B[38;5;241m=\u001B[39m query\u001B[38;5;241m.\u001B[39mfilter(measurement\u001B[38;5;241m=\u001B[39mmeasurement)\n\u001B[1;32m--> 153\u001B[0m query_df \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mquery_dataframe\u001B[49m\u001B[43m(\u001B[49m\u001B[43mquery\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    155\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(query_df, \u001B[38;5;28mlist\u001B[39m):\n\u001B[0;32m    156\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mQuery returned multiple fields! Please refine your query.\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n",
+      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\data_tools\\query\\influxdb_query.py:123\u001B[0m, in \u001B[0;36mDBClient.query_dataframe\u001B[1;34m(self, query)\u001B[0m\n\u001B[0;32m    120\u001B[0m compiled_query \u001B[38;5;241m=\u001B[39m query\u001B[38;5;241m.\u001B[39mcompile_query()\n\u001B[0;32m    121\u001B[0m compiled_query \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124m |> pivot(rowKey:[\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_time\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m], columnKey: [\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_field\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m], valueColumn: \u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_value\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m) \u001B[39m\u001B[38;5;124m'\u001B[39m\n\u001B[1;32m--> 123\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_client\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mquery_api\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mquery_data_frame\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcompiled_query\u001B[49m\u001B[43m)\u001B[49m\n",
+      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\influxdb_client\\client\\query_api.py:259\u001B[0m, in \u001B[0;36mQueryApi.query_data_frame\u001B[1;34m(self, query, org, data_frame_index, params, use_extension_dtypes)\u001B[0m\n\u001B[0;32m    225\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mquery_data_frame\u001B[39m(\u001B[38;5;28mself\u001B[39m, query: \u001B[38;5;28mstr\u001B[39m, org\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, data_frame_index: List[\u001B[38;5;28mstr\u001B[39m] \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m, params: \u001B[38;5;28mdict\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[0;32m    226\u001B[0m                      use_extension_dtypes: \u001B[38;5;28mbool\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m):\n\u001B[0;32m    227\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m    228\u001B[0m \u001B[38;5;124;03m    Execute synchronous Flux query and return Pandas DataFrame.\u001B[39;00m\n\u001B[0;32m    229\u001B[0m \n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    257\u001B[0m \u001B[38;5;124;03m            - https://docs.influxdata.com/flux/latest/stdlib/influxdata/influxdb/schema/fieldsascols/\u001B[39;00m\n\u001B[0;32m    258\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[1;32m--> 259\u001B[0m     _generator \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mquery_data_frame_stream\u001B[49m\u001B[43m(\u001B[49m\u001B[43mquery\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43morg\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43morg\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata_frame_index\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdata_frame_index\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mparams\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    260\u001B[0m \u001B[43m                                              \u001B[49m\u001B[43muse_extension_dtypes\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43muse_extension_dtypes\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    261\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_to_data_frames(_generator)\n",
+      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\influxdb_client\\client\\query_api.py:299\u001B[0m, in \u001B[0;36mQueryApi.query_data_frame_stream\u001B[1;34m(self, query, org, data_frame_index, params, use_extension_dtypes)\u001B[0m\n\u001B[0;32m    265\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m    266\u001B[0m \u001B[38;5;124;03mExecute synchronous Flux query and return stream of Pandas DataFrame as a :class:`~Generator[DataFrame]`.\u001B[39;00m\n\u001B[0;32m    267\u001B[0m \n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    295\u001B[0m \u001B[38;5;124;03m        - https://docs.influxdata.com/flux/latest/stdlib/influxdata/influxdb/schema/fieldsascols/\u001B[39;00m\n\u001B[0;32m    296\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[0;32m    297\u001B[0m org \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_org_param(org)\n\u001B[1;32m--> 299\u001B[0m response \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_query_api\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpost_query\u001B[49m\u001B[43m(\u001B[49m\u001B[43morg\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43morg\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mquery\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_create_query\u001B[49m\u001B[43m(\u001B[49m\u001B[43mquery\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdefault_dialect\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    300\u001B[0m \u001B[43m                                                                        \u001B[49m\u001B[43mdataframe_query\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    301\u001B[0m \u001B[43m                                      \u001B[49m\u001B[43masync_req\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_return_http_data_only\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[0;32m    303\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_to_data_frame_stream(data_frame_index\u001B[38;5;241m=\u001B[39mdata_frame_index,\n\u001B[0;32m    304\u001B[0m                                   response\u001B[38;5;241m=\u001B[39mresponse,\n\u001B[0;32m    305\u001B[0m                                   query_options\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_get_query_options(),\n\u001B[0;32m    306\u001B[0m                                   use_extension_dtypes\u001B[38;5;241m=\u001B[39muse_extension_dtypes)\n",
+      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\influxdb_client\\service\\query_service.py:285\u001B[0m, in \u001B[0;36mQueryService.post_query\u001B[1;34m(self, **kwargs)\u001B[0m\n\u001B[0;32m    283\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpost_query_with_http_info(\u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[0;32m    284\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m--> 285\u001B[0m     (data) \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpost_query_with_http_info\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[0;32m    286\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m data\n",
+      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\influxdb_client\\service\\query_service.py:311\u001B[0m, in \u001B[0;36mQueryService.post_query_with_http_info\u001B[1;34m(self, **kwargs)\u001B[0m\n\u001B[0;32m    289\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Query data.\u001B[39;00m\n\u001B[0;32m    290\u001B[0m \n\u001B[0;32m    291\u001B[0m \u001B[38;5;124;03mRetrieves data from buckets.  Use this endpoint to send a Flux query request and retrieve data from a bucket.  #### Rate limits (with InfluxDB Cloud)  `read` rate limits apply. For more information, see [limits and adjustable quotas](https://docs.influxdata.com/influxdb/cloud/account-management/limits/).  #### Related guides  - [Query with the InfluxDB API](https://docs.influxdata.com/influxdb/latest/query-data/execute-queries/influx-api/) - [Get started with Flux](https://docs.influxdata.com/flux/v0.x/get-started/)\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    306\u001B[0m \u001B[38;5;124;03m         returns the request thread.\u001B[39;00m\n\u001B[0;32m    307\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[0;32m    308\u001B[0m local_var_params, path_params, query_params, header_params, body_params \u001B[38;5;241m=\u001B[39m \\\n\u001B[0;32m    309\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_post_query_prepare(\u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[1;32m--> 311\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mapi_client\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcall_api\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m    312\u001B[0m \u001B[43m    \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43m/api/v2/query\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mPOST\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[0;32m    313\u001B[0m \u001B[43m    \u001B[49m\u001B[43mpath_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    314\u001B[0m \u001B[43m    \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    315\u001B[0m \u001B[43m    \u001B[49m\u001B[43mheader_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    316\u001B[0m \u001B[43m    \u001B[49m\u001B[43mbody\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mbody_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    317\u001B[0m \u001B[43m    \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m[\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    318\u001B[0m \u001B[43m    \u001B[49m\u001B[43mfiles\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m{\u001B[49m\u001B[43m}\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    319\u001B[0m \u001B[43m    \u001B[49m\u001B[43mresponse_type\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mstr\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# noqa: E501\u001B[39;49;00m\n\u001B[0;32m    320\u001B[0m \u001B[43m    \u001B[49m\u001B[43mauth_settings\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m[\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    321\u001B[0m \u001B[43m    \u001B[49m\u001B[43masync_req\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mlocal_var_params\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43masync_req\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    322\u001B[0m \u001B[43m    \u001B[49m\u001B[43m_return_http_data_only\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mlocal_var_params\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43m_return_http_data_only\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# noqa: E501\u001B[39;49;00m\n\u001B[0;32m    323\u001B[0m \u001B[43m    \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mlocal_var_params\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43m_preload_content\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    324\u001B[0m \u001B[43m    \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mlocal_var_params\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43m_request_timeout\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    325\u001B[0m \u001B[43m    \u001B[49m\u001B[43mcollection_formats\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m{\u001B[49m\u001B[43m}\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    326\u001B[0m \u001B[43m    \u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43murlopen_kw\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n",
+      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\api_client.py:343\u001B[0m, in \u001B[0;36mApiClient.call_api\u001B[1;34m(self, resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, async_req, _return_http_data_only, collection_formats, _preload_content, _request_timeout, urlopen_kw)\u001B[0m\n\u001B[0;32m    304\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Make the HTTP request (synchronous) and Return deserialized data.\u001B[39;00m\n\u001B[0;32m    305\u001B[0m \n\u001B[0;32m    306\u001B[0m \u001B[38;5;124;03mTo make an async_req request, set the async_req parameter.\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    340\u001B[0m \u001B[38;5;124;03m    then the method will return the response directly.\u001B[39;00m\n\u001B[0;32m    341\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m    342\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m async_req:\n\u001B[1;32m--> 343\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m__call_api\u001B[49m\u001B[43m(\u001B[49m\u001B[43mresource_path\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    344\u001B[0m \u001B[43m                           \u001B[49m\u001B[43mpath_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mheader_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    345\u001B[0m \u001B[43m                           \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfiles\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    346\u001B[0m \u001B[43m                           \u001B[49m\u001B[43mresponse_type\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mauth_settings\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    347\u001B[0m \u001B[43m                           \u001B[49m\u001B[43m_return_http_data_only\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcollection_formats\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    348\u001B[0m \u001B[43m                           \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    349\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m    350\u001B[0m     thread \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpool\u001B[38;5;241m.\u001B[39mapply_async(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m__call_api, (resource_path,\n\u001B[0;32m    351\u001B[0m                                    method, path_params, query_params,\n\u001B[0;32m    352\u001B[0m                                    header_params, body,\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    356\u001B[0m                                    collection_formats,\n\u001B[0;32m    357\u001B[0m                                    _preload_content, _request_timeout, urlopen_kw))\n",
+      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\api_client.py:173\u001B[0m, in \u001B[0;36mApiClient.__call_api\u001B[1;34m(self, resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, _return_http_data_only, collection_formats, _preload_content, _request_timeout, urlopen_kw)\u001B[0m\n\u001B[0;32m    170\u001B[0m urlopen_kw \u001B[38;5;241m=\u001B[39m urlopen_kw \u001B[38;5;129;01mor\u001B[39;00m {}\n\u001B[0;32m    172\u001B[0m \u001B[38;5;66;03m# perform request and return response\u001B[39;00m\n\u001B[1;32m--> 173\u001B[0m response_data \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrequest\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m    174\u001B[0m \u001B[43m    \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mheaders\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mheader_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    175\u001B[0m \u001B[43m    \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbody\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    176\u001B[0m \u001B[43m    \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    177\u001B[0m \u001B[43m    \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    179\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mlast_response \u001B[38;5;241m=\u001B[39m response_data\n\u001B[0;32m    181\u001B[0m return_data \u001B[38;5;241m=\u001B[39m response_data\n",
+      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\api_client.py:388\u001B[0m, in \u001B[0;36mApiClient.request\u001B[1;34m(self, method, url, query_params, headers, post_params, body, _preload_content, _request_timeout, **urlopen_kw)\u001B[0m\n\u001B[0;32m    379\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mrest_client\u001B[38;5;241m.\u001B[39mOPTIONS(url,\n\u001B[0;32m    380\u001B[0m                                     query_params\u001B[38;5;241m=\u001B[39mquery_params,\n\u001B[0;32m    381\u001B[0m                                     headers\u001B[38;5;241m=\u001B[39mheaders,\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    385\u001B[0m                                     body\u001B[38;5;241m=\u001B[39mbody,\n\u001B[0;32m    386\u001B[0m                                     \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39murlopen_kw)\n\u001B[0;32m    387\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m method \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mPOST\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[1;32m--> 388\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrest_client\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mPOST\u001B[49m\u001B[43m(\u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    389\u001B[0m \u001B[43m                                 \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    390\u001B[0m \u001B[43m                                 \u001B[49m\u001B[43mheaders\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    391\u001B[0m \u001B[43m                                 \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    392\u001B[0m \u001B[43m                                 \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    393\u001B[0m \u001B[43m                                 \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    394\u001B[0m \u001B[43m                                 \u001B[49m\u001B[43mbody\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    395\u001B[0m \u001B[43m                                 \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    396\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m method \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mPUT\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[0;32m    397\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mrest_client\u001B[38;5;241m.\u001B[39mPUT(url,\n\u001B[0;32m    398\u001B[0m                                 query_params\u001B[38;5;241m=\u001B[39mquery_params,\n\u001B[0;32m    399\u001B[0m                                 headers\u001B[38;5;241m=\u001B[39mheaders,\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    403\u001B[0m                                 body\u001B[38;5;241m=\u001B[39mbody,\n\u001B[0;32m    404\u001B[0m                                 \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39murlopen_kw)\n",
+      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\rest.py:311\u001B[0m, in \u001B[0;36mRESTClientObject.POST\u001B[1;34m(self, url, headers, query_params, post_params, body, _preload_content, _request_timeout, **urlopen_kw)\u001B[0m\n\u001B[0;32m    308\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mPOST\u001B[39m(\u001B[38;5;28mself\u001B[39m, url, headers\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, query_params\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, post_params\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[0;32m    309\u001B[0m          body\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, _preload_content\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m, _request_timeout\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39murlopen_kw):\n\u001B[0;32m    310\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124;03m\"\"\"Perform POST HTTP request.\"\"\"\u001B[39;00m\n\u001B[1;32m--> 311\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrequest\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mPOST\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    312\u001B[0m \u001B[43m                        \u001B[49m\u001B[43mheaders\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    313\u001B[0m \u001B[43m                        \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    314\u001B[0m \u001B[43m                        \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    315\u001B[0m \u001B[43m                        \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    316\u001B[0m \u001B[43m                        \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    317\u001B[0m \u001B[43m                        \u001B[49m\u001B[43mbody\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    318\u001B[0m \u001B[43m                        \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n",
+      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\rest.py:261\u001B[0m, in \u001B[0;36mRESTClientObject.request\u001B[1;34m(self, method, url, query_params, headers, body, post_params, _preload_content, _request_timeout, **urlopen_kw)\u001B[0m\n\u001B[0;32m    258\u001B[0m     _BaseRESTClient\u001B[38;5;241m.\u001B[39mlog_body(r\u001B[38;5;241m.\u001B[39mdata, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124m<<<\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m    260\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;241m200\u001B[39m \u001B[38;5;241m<\u001B[39m\u001B[38;5;241m=\u001B[39m r\u001B[38;5;241m.\u001B[39mstatus \u001B[38;5;241m<\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;241m299\u001B[39m:\n\u001B[1;32m--> 261\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m ApiException(http_resp\u001B[38;5;241m=\u001B[39mr)\n\u001B[0;32m    263\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m r\n",
+      "\u001B[1;31mApiException\u001B[0m: (401)\nReason: Unauthorized\nHTTP response headers: HTTPHeaderDict({'Server': 'nginx/1.27.1', 'Date': 'Wed, 07 Jan 2026 03:34:35 GMT', 'Content-Type': 'application/json; charset=utf-8', 'Content-Length': '55', 'Connection': 'keep-alive', 'X-Influxdb-Build': 'OSS', 'X-Influxdb-Version': 'v2.7.1', 'X-Platform-Error-Code': 'unauthorized'})\nHTTP response body: b'{\"code\":\"unauthorized\",\"message\":\"unauthorized access\"}'\n"
+     ]
+    }
+   ],
+   "execution_count": 1
   },
   {
    "metadata": {
diff --git a/array_temp/temp.ipynb b/array_temp/temp.ipynb
index 1b88098..54eb311 100644
--- a/array_temp/temp.ipynb
+++ b/array_temp/temp.ipynb
@@ -5,9 +5,8 @@
    "id": "initial_id",
    "metadata": {
     "collapsed": true,
-    "ExecuteTime": {
-     "end_time": "2025-11-29T22:28:22.534452Z",
-     "start_time": "2025-11-29T22:28:13.880534Z"
+    "jupyter": {
+     "is_executing": true
     }
    },
    "source": [
@@ -47,7 +46,19 @@
     "\n"
    ],
    "outputs": [],
-   "execution_count": 25
+   "execution_count": null
+  },
+  {
+   "metadata": {
+    "jupyter": {
+     "is_executing": true
+    }
+   },
+   "cell_type": "code",
+   "source": "print(\"jk\")",
+   "id": "c6cea64b1e62c93e",
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {
diff --git a/pyproject.toml b/pyproject.toml
index 19f4ba0..04fff1a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,3 +23,9 @@ setuptools = "^80.9.0"
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
+
+[dependency-groups]
+dev = [
+    "matplotlib>=3.10.8",
+    "ubc-solar-data-tools>=1.9.1",
+]

From e1d0ff4e834e433094aea484af933bfe04f13df0 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Tue, 6 Jan 2026 19:37:07 -0800
Subject: [PATCH 04/49] --

---
 array_temp/pyproject.toml  |  10 +
 array_temp/temporary.ipynb |  55 +++
 array_temp/uv.lock         | 974 +++++++++++++++++++++++++++++++++++++
 uv.lock                    | 963 ++++++++++++++++++++++++++++++++++++
 4 files changed, 2002 insertions(+)
 create mode 100644 array_temp/pyproject.toml
 create mode 100644 array_temp/temporary.ipynb
 create mode 100644 array_temp/uv.lock
 create mode 100644 uv.lock

diff --git a/array_temp/pyproject.toml b/array_temp/pyproject.toml
new file mode 100644
index 0000000..74ca119
--- /dev/null
+++ b/array_temp/pyproject.toml
@@ -0,0 +1,10 @@
+[project]
+name = "array-temp"
+version = "0.1.0"
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diff --git a/array_temp/temporary.ipynb b/array_temp/temporary.ipynb
new file mode 100644
index 0000000..87860be
--- /dev/null
+++ b/array_temp/temporary.ipynb
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+{
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+  {
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+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2026-01-07T03:33:21.664211Z",
+     "start_time": "2026-01-07T03:33:21.623543Z"
+    }
+   },
+   "source": "print(\"jo\")",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "jo\n"
+     ]
+    }
+   ],
+   "execution_count": 1
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "ff47a17664bb0a1d"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
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From 92a06e14772cd522354a6567f2fddef4f326acb8 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Tue, 6 Jan 2026 20:01:04 -0800
Subject: [PATCH 05/49] --

---
 array_temp/faiman_coefficients.ipynb | 251 ++++-----
 array_temp/temp.ipynb                | 731 ---------------------------
 array_temp/temporary.ipynb           |  55 --
 v3/data_acquisition/get_data.ipynb   |  67 ++-
 4 files changed, 181 insertions(+), 923 deletions(-)
 delete mode 100644 array_temp/temp.ipynb
 delete mode 100644 array_temp/temporary.ipynb

diff --git a/array_temp/faiman_coefficients.ipynb b/array_temp/faiman_coefficients.ipynb
index 9477d88..5a8a122 100644
--- a/array_temp/faiman_coefficients.ipynb
+++ b/array_temp/faiman_coefficients.ipynb
@@ -3,8 +3,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-07T03:34:35.885636Z",
-     "start_time": "2026-01-07T03:34:24.042487Z"
+     "end_time": "2026-01-07T03:45:51.135307Z",
+     "start_time": "2026-01-07T03:45:46.244586Z"
     }
    },
    "cell_type": "code",
@@ -34,38 +34,32 @@
     "temp_array_expected_aliter: TimeSeries = client.query_time_series(start_time, stop_time, \"MosfetTemperatureA\")\n"
    ],
    "id": "7762d7c5f54c9e9f",
-   "outputs": [
-    {
-     "ename": "ApiException",
-     "evalue": "(401)\nReason: Unauthorized\nHTTP response headers: HTTPHeaderDict({'Server': 'nginx/1.27.1', 'Date': 'Wed, 07 Jan 2026 03:34:35 GMT', 'Content-Type': 'application/json; charset=utf-8', 'Content-Length': '55', 'Connection': 'keep-alive', 'X-Influxdb-Build': 'OSS', 'X-Influxdb-Version': 'v2.7.1', 'X-Platform-Error-Code': 'unauthorized'})\nHTTP response body: b'{\"code\":\"unauthorized\",\"message\":\"unauthorized access\"}'\n",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[1;31mApiException\u001B[0m                              Traceback (most recent call last)",
-      "Cell \u001B[1;32mIn[1], line 23\u001B[0m\n\u001B[0;32m     20\u001B[0m stop_time \u001B[38;5;241m=\u001B[39m datetime\u001B[38;5;241m.\u001B[39mcombine(date_stop, stop_utc, tzinfo\u001B[38;5;241m=\u001B[39mtimezone\u001B[38;5;241m.\u001B[39mutc)\n\u001B[0;32m     22\u001B[0m client \u001B[38;5;241m=\u001B[39m query\u001B[38;5;241m.\u001B[39mDBClient()\n\u001B[1;32m---> 23\u001B[0m temp_array_expected_aliter: TimeSeries \u001B[38;5;241m=\u001B[39m \u001B[43mclient\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mquery_time_series\u001B[49m\u001B[43m(\u001B[49m\u001B[43mstart_time\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstop_time\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mMosfetTemperatureA\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n",
-      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\data_tools\\query\\influxdb_query.py:180\u001B[0m, in \u001B[0;36mDBClient.query_time_series\u001B[1;34m(self, start, stop, field, bucket, car, granularity, units, measurement)\u001B[0m\n\u001B[0;32m    163\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mquery_time_series\u001B[39m(\u001B[38;5;28mself\u001B[39m, start: datetime, stop: datetime, field: \u001B[38;5;28mstr\u001B[39m, bucket: \u001B[38;5;28mstr\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mCAN_log\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m    164\u001B[0m                       car: \u001B[38;5;28mstr\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mBrightside\u001B[39m\u001B[38;5;124m\"\u001B[39m, granularity: \u001B[38;5;28mfloat\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m0.1\u001B[39m, units: \u001B[38;5;28mstr\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m    165\u001B[0m                       measurement: \u001B[38;5;28mstr\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m TimeSeries:\n\u001B[0;32m    166\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m    167\u001B[0m \u001B[38;5;124;03m    Query the database for a specific field, over a certain time range.\u001B[39;00m\n\u001B[0;32m    168\u001B[0m \u001B[38;5;124;03m    The data will be processed into a TimeSeries, which has homogenous and evenly-spaced (temporally) elements.\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    178\u001B[0m \u001B[38;5;124;03m    :return: a TimeSeries of the resulting time-series data\u001B[39;00m\n\u001B[0;32m    179\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[1;32m--> 180\u001B[0m     query_df \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mquery_series\u001B[49m\u001B[43m(\u001B[49m\u001B[43mstart\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstop\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfield\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbucket\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcar\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmeasurement\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    182\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m TimeSeries\u001B[38;5;241m.\u001B[39mfrom_query_dataframe(query_df, granularity, field, units)\n",
-      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\data_tools\\query\\influxdb_query.py:153\u001B[0m, in \u001B[0;36mDBClient.query_series\u001B[1;34m(self, start, stop, field, bucket, car, measurement)\u001B[0m\n\u001B[0;32m    151\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m measurement:\n\u001B[0;32m    152\u001B[0m     query \u001B[38;5;241m=\u001B[39m query\u001B[38;5;241m.\u001B[39mfilter(measurement\u001B[38;5;241m=\u001B[39mmeasurement)\n\u001B[1;32m--> 153\u001B[0m query_df \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mquery_dataframe\u001B[49m\u001B[43m(\u001B[49m\u001B[43mquery\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    155\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(query_df, \u001B[38;5;28mlist\u001B[39m):\n\u001B[0;32m    156\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mQuery returned multiple fields! Please refine your query.\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n",
-      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\data_tools\\query\\influxdb_query.py:123\u001B[0m, in \u001B[0;36mDBClient.query_dataframe\u001B[1;34m(self, query)\u001B[0m\n\u001B[0;32m    120\u001B[0m compiled_query \u001B[38;5;241m=\u001B[39m query\u001B[38;5;241m.\u001B[39mcompile_query()\n\u001B[0;32m    121\u001B[0m compiled_query \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124m |> pivot(rowKey:[\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_time\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m], columnKey: [\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_field\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m], valueColumn: \u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_value\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m) \u001B[39m\u001B[38;5;124m'\u001B[39m\n\u001B[1;32m--> 123\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_client\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mquery_api\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mquery_data_frame\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcompiled_query\u001B[49m\u001B[43m)\u001B[49m\n",
-      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\influxdb_client\\client\\query_api.py:259\u001B[0m, in \u001B[0;36mQueryApi.query_data_frame\u001B[1;34m(self, query, org, data_frame_index, params, use_extension_dtypes)\u001B[0m\n\u001B[0;32m    225\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mquery_data_frame\u001B[39m(\u001B[38;5;28mself\u001B[39m, query: \u001B[38;5;28mstr\u001B[39m, org\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, data_frame_index: List[\u001B[38;5;28mstr\u001B[39m] \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m, params: \u001B[38;5;28mdict\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[0;32m    226\u001B[0m                      use_extension_dtypes: \u001B[38;5;28mbool\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m):\n\u001B[0;32m    227\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m    228\u001B[0m \u001B[38;5;124;03m    Execute synchronous Flux query and return Pandas DataFrame.\u001B[39;00m\n\u001B[0;32m    229\u001B[0m \n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    257\u001B[0m \u001B[38;5;124;03m            - https://docs.influxdata.com/flux/latest/stdlib/influxdata/influxdb/schema/fieldsascols/\u001B[39;00m\n\u001B[0;32m    258\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[1;32m--> 259\u001B[0m     _generator \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mquery_data_frame_stream\u001B[49m\u001B[43m(\u001B[49m\u001B[43mquery\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43morg\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43morg\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata_frame_index\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdata_frame_index\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mparams\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    260\u001B[0m \u001B[43m                                              \u001B[49m\u001B[43muse_extension_dtypes\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43muse_extension_dtypes\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    261\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_to_data_frames(_generator)\n",
-      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\influxdb_client\\client\\query_api.py:299\u001B[0m, in \u001B[0;36mQueryApi.query_data_frame_stream\u001B[1;34m(self, query, org, data_frame_index, params, use_extension_dtypes)\u001B[0m\n\u001B[0;32m    265\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m    266\u001B[0m \u001B[38;5;124;03mExecute synchronous Flux query and return stream of Pandas DataFrame as a :class:`~Generator[DataFrame]`.\u001B[39;00m\n\u001B[0;32m    267\u001B[0m \n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    295\u001B[0m \u001B[38;5;124;03m        - https://docs.influxdata.com/flux/latest/stdlib/influxdata/influxdb/schema/fieldsascols/\u001B[39;00m\n\u001B[0;32m    296\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[0;32m    297\u001B[0m org \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_org_param(org)\n\u001B[1;32m--> 299\u001B[0m response \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_query_api\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpost_query\u001B[49m\u001B[43m(\u001B[49m\u001B[43morg\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43morg\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mquery\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_create_query\u001B[49m\u001B[43m(\u001B[49m\u001B[43mquery\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdefault_dialect\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    300\u001B[0m \u001B[43m                                                                        \u001B[49m\u001B[43mdataframe_query\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    301\u001B[0m \u001B[43m                                      \u001B[49m\u001B[43masync_req\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_return_http_data_only\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[0;32m    303\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_to_data_frame_stream(data_frame_index\u001B[38;5;241m=\u001B[39mdata_frame_index,\n\u001B[0;32m    304\u001B[0m                                   response\u001B[38;5;241m=\u001B[39mresponse,\n\u001B[0;32m    305\u001B[0m                                   query_options\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_get_query_options(),\n\u001B[0;32m    306\u001B[0m                                   use_extension_dtypes\u001B[38;5;241m=\u001B[39muse_extension_dtypes)\n",
-      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\influxdb_client\\service\\query_service.py:285\u001B[0m, in \u001B[0;36mQueryService.post_query\u001B[1;34m(self, **kwargs)\u001B[0m\n\u001B[0;32m    283\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpost_query_with_http_info(\u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[0;32m    284\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m--> 285\u001B[0m     (data) \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpost_query_with_http_info\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[0;32m    286\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m data\n",
-      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\influxdb_client\\service\\query_service.py:311\u001B[0m, in \u001B[0;36mQueryService.post_query_with_http_info\u001B[1;34m(self, **kwargs)\u001B[0m\n\u001B[0;32m    289\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Query data.\u001B[39;00m\n\u001B[0;32m    290\u001B[0m \n\u001B[0;32m    291\u001B[0m \u001B[38;5;124;03mRetrieves data from buckets.  Use this endpoint to send a Flux query request and retrieve data from a bucket.  #### Rate limits (with InfluxDB Cloud)  `read` rate limits apply. For more information, see [limits and adjustable quotas](https://docs.influxdata.com/influxdb/cloud/account-management/limits/).  #### Related guides  - [Query with the InfluxDB API](https://docs.influxdata.com/influxdb/latest/query-data/execute-queries/influx-api/) - [Get started with Flux](https://docs.influxdata.com/flux/v0.x/get-started/)\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    306\u001B[0m \u001B[38;5;124;03m         returns the request thread.\u001B[39;00m\n\u001B[0;32m    307\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[0;32m    308\u001B[0m local_var_params, path_params, query_params, header_params, body_params \u001B[38;5;241m=\u001B[39m \\\n\u001B[0;32m    309\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_post_query_prepare(\u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[1;32m--> 311\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mapi_client\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcall_api\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m    312\u001B[0m \u001B[43m    \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43m/api/v2/query\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mPOST\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[0;32m    313\u001B[0m \u001B[43m    \u001B[49m\u001B[43mpath_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    314\u001B[0m \u001B[43m    \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    315\u001B[0m \u001B[43m    \u001B[49m\u001B[43mheader_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    316\u001B[0m \u001B[43m    \u001B[49m\u001B[43mbody\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mbody_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    317\u001B[0m \u001B[43m    \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m[\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    318\u001B[0m \u001B[43m    \u001B[49m\u001B[43mfiles\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m{\u001B[49m\u001B[43m}\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    319\u001B[0m \u001B[43m    \u001B[49m\u001B[43mresponse_type\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mstr\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# noqa: E501\u001B[39;49;00m\n\u001B[0;32m    320\u001B[0m \u001B[43m    \u001B[49m\u001B[43mauth_settings\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m[\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    321\u001B[0m \u001B[43m    \u001B[49m\u001B[43masync_req\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mlocal_var_params\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43masync_req\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    322\u001B[0m \u001B[43m    \u001B[49m\u001B[43m_return_http_data_only\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mlocal_var_params\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43m_return_http_data_only\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# noqa: E501\u001B[39;49;00m\n\u001B[0;32m    323\u001B[0m \u001B[43m    \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mlocal_var_params\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43m_preload_content\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    324\u001B[0m \u001B[43m    \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mlocal_var_params\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43m_request_timeout\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    325\u001B[0m \u001B[43m    \u001B[49m\u001B[43mcollection_formats\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m{\u001B[49m\u001B[43m}\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    326\u001B[0m \u001B[43m    \u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43murlopen_kw\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n",
-      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\api_client.py:343\u001B[0m, in \u001B[0;36mApiClient.call_api\u001B[1;34m(self, resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, async_req, _return_http_data_only, collection_formats, _preload_content, _request_timeout, urlopen_kw)\u001B[0m\n\u001B[0;32m    304\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Make the HTTP request (synchronous) and Return deserialized data.\u001B[39;00m\n\u001B[0;32m    305\u001B[0m \n\u001B[0;32m    306\u001B[0m \u001B[38;5;124;03mTo make an async_req request, set the async_req parameter.\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    340\u001B[0m \u001B[38;5;124;03m    then the method will return the response directly.\u001B[39;00m\n\u001B[0;32m    341\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m    342\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m async_req:\n\u001B[1;32m--> 343\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m__call_api\u001B[49m\u001B[43m(\u001B[49m\u001B[43mresource_path\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    344\u001B[0m \u001B[43m                           \u001B[49m\u001B[43mpath_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mheader_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    345\u001B[0m \u001B[43m                           \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfiles\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    346\u001B[0m \u001B[43m                           \u001B[49m\u001B[43mresponse_type\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mauth_settings\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    347\u001B[0m \u001B[43m                           \u001B[49m\u001B[43m_return_http_data_only\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcollection_formats\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    348\u001B[0m \u001B[43m                           \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    349\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m    350\u001B[0m     thread \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpool\u001B[38;5;241m.\u001B[39mapply_async(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m__call_api, (resource_path,\n\u001B[0;32m    351\u001B[0m                                    method, path_params, query_params,\n\u001B[0;32m    352\u001B[0m                                    header_params, body,\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    356\u001B[0m                                    collection_formats,\n\u001B[0;32m    357\u001B[0m                                    _preload_content, _request_timeout, urlopen_kw))\n",
-      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\api_client.py:173\u001B[0m, in \u001B[0;36mApiClient.__call_api\u001B[1;34m(self, resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, _return_http_data_only, collection_formats, _preload_content, _request_timeout, urlopen_kw)\u001B[0m\n\u001B[0;32m    170\u001B[0m urlopen_kw \u001B[38;5;241m=\u001B[39m urlopen_kw \u001B[38;5;129;01mor\u001B[39;00m {}\n\u001B[0;32m    172\u001B[0m \u001B[38;5;66;03m# perform request and return response\u001B[39;00m\n\u001B[1;32m--> 173\u001B[0m response_data \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrequest\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m    174\u001B[0m \u001B[43m    \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mheaders\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mheader_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    175\u001B[0m \u001B[43m    \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbody\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    176\u001B[0m \u001B[43m    \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    177\u001B[0m \u001B[43m    \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    179\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mlast_response \u001B[38;5;241m=\u001B[39m response_data\n\u001B[0;32m    181\u001B[0m return_data \u001B[38;5;241m=\u001B[39m response_data\n",
-      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\api_client.py:388\u001B[0m, in \u001B[0;36mApiClient.request\u001B[1;34m(self, method, url, query_params, headers, post_params, body, _preload_content, _request_timeout, **urlopen_kw)\u001B[0m\n\u001B[0;32m    379\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mrest_client\u001B[38;5;241m.\u001B[39mOPTIONS(url,\n\u001B[0;32m    380\u001B[0m                                     query_params\u001B[38;5;241m=\u001B[39mquery_params,\n\u001B[0;32m    381\u001B[0m                                     headers\u001B[38;5;241m=\u001B[39mheaders,\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    385\u001B[0m                                     body\u001B[38;5;241m=\u001B[39mbody,\n\u001B[0;32m    386\u001B[0m                                     \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39murlopen_kw)\n\u001B[0;32m    387\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m method \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mPOST\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[1;32m--> 388\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrest_client\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mPOST\u001B[49m\u001B[43m(\u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    389\u001B[0m \u001B[43m                                 \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    390\u001B[0m \u001B[43m                                 \u001B[49m\u001B[43mheaders\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    391\u001B[0m \u001B[43m                                 \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    392\u001B[0m \u001B[43m                                 \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    393\u001B[0m \u001B[43m                                 \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    394\u001B[0m \u001B[43m                                 \u001B[49m\u001B[43mbody\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    395\u001B[0m \u001B[43m                                 \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    396\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m method \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mPUT\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[0;32m    397\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mrest_client\u001B[38;5;241m.\u001B[39mPUT(url,\n\u001B[0;32m    398\u001B[0m                                 query_params\u001B[38;5;241m=\u001B[39mquery_params,\n\u001B[0;32m    399\u001B[0m                                 headers\u001B[38;5;241m=\u001B[39mheaders,\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    403\u001B[0m                                 body\u001B[38;5;241m=\u001B[39mbody,\n\u001B[0;32m    404\u001B[0m                                 \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39murlopen_kw)\n",
-      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\rest.py:311\u001B[0m, in \u001B[0;36mRESTClientObject.POST\u001B[1;34m(self, url, headers, query_params, post_params, body, _preload_content, _request_timeout, **urlopen_kw)\u001B[0m\n\u001B[0;32m    308\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mPOST\u001B[39m(\u001B[38;5;28mself\u001B[39m, url, headers\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, query_params\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, post_params\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[0;32m    309\u001B[0m          body\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, _preload_content\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m, _request_timeout\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39murlopen_kw):\n\u001B[0;32m    310\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124;03m\"\"\"Perform POST HTTP request.\"\"\"\u001B[39;00m\n\u001B[1;32m--> 311\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrequest\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mPOST\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    312\u001B[0m \u001B[43m                        \u001B[49m\u001B[43mheaders\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    313\u001B[0m \u001B[43m                        \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    314\u001B[0m \u001B[43m                        \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    315\u001B[0m \u001B[43m                        \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    316\u001B[0m \u001B[43m                        \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    317\u001B[0m \u001B[43m                        \u001B[49m\u001B[43mbody\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    318\u001B[0m \u001B[43m                        \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n",
-      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\rest.py:261\u001B[0m, in \u001B[0;36mRESTClientObject.request\u001B[1;34m(self, method, url, query_params, headers, body, post_params, _preload_content, _request_timeout, **urlopen_kw)\u001B[0m\n\u001B[0;32m    258\u001B[0m     _BaseRESTClient\u001B[38;5;241m.\u001B[39mlog_body(r\u001B[38;5;241m.\u001B[39mdata, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124m<<<\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m    260\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;241m200\u001B[39m \u001B[38;5;241m<\u001B[39m\u001B[38;5;241m=\u001B[39m r\u001B[38;5;241m.\u001B[39mstatus \u001B[38;5;241m<\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;241m299\u001B[39m:\n\u001B[1;32m--> 261\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m ApiException(http_resp\u001B[38;5;241m=\u001B[39mr)\n\u001B[0;32m    263\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m r\n",
-      "\u001B[1;31mApiException\u001B[0m: (401)\nReason: Unauthorized\nHTTP response headers: HTTPHeaderDict({'Server': 'nginx/1.27.1', 'Date': 'Wed, 07 Jan 2026 03:34:35 GMT', 'Content-Type': 'application/json; charset=utf-8', 'Content-Length': '55', 'Connection': 'keep-alive', 'X-Influxdb-Build': 'OSS', 'X-Influxdb-Version': 'v2.7.1', 'X-Platform-Error-Code': 'unauthorized'})\nHTTP response body: b'{\"code\":\"unauthorized\",\"message\":\"unauthorized access\"}'\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "execution_count": 1
   },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "- overall work flow of this project - we query relevant data from influx - this includes the mppt temperature sensors ad vehicle velocity\n",
+    "- data to be queried from open meteo - every 15 minutes is irradiance, wind speed, ambient temperature\n",
+    "- curve fit to find relevant coefficients needed for the faiman model\n"
+   ],
+   "id": "156948c3c78f7e94"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "d87b11cfda38ce57"
+  },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-11-29T19:07:28.204093Z",
-     "start_time": "2025-11-29T19:07:18.443470Z"
+     "end_time": "2026-01-07T03:45:59.783494Z",
+     "start_time": "2026-01-07T03:45:54.175805Z"
     }
    },
    "cell_type": "code",
@@ -110,13 +104,13 @@
      ]
     }
    ],
-   "execution_count": 3
+   "execution_count": 2
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-11-29T19:07:36.960833Z",
-     "start_time": "2025-11-29T19:07:36.946373Z"
+     "end_time": "2026-01-07T03:49:25.894585Z",
+     "start_time": "2026-01-07T03:49:25.872871Z"
     }
    },
    "cell_type": "code",
@@ -142,7 +136,7 @@
    ],
    "id": "9b566c367639c5e5",
    "outputs": [],
-   "execution_count": 4
+   "execution_count": 3
   },
   {
    "metadata": {},
@@ -153,8 +147,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-11-29T22:07:18.614759Z",
-     "start_time": "2025-11-29T22:07:18.456316Z"
+     "end_time": "2026-01-07T03:49:39.779626Z",
+     "start_time": "2026-01-07T03:49:27.143416Z"
     }
    },
    "cell_type": "code",
@@ -187,38 +181,46 @@
     "\n"
    ],
    "id": "8b2b4006671bdc31",
-   "outputs": [
-    {
-     "ename": "ApiException",
-     "evalue": "(401)\nReason: Unauthorized\nHTTP response headers: HTTPHeaderDict({'Server': 'nginx/1.27.1', 'Date': 'Sat, 29 Nov 2025 22:07:18 GMT', 'Content-Type': 'application/json; charset=utf-8', 'Content-Length': '55', 'Connection': 'keep-alive', 'X-Influxdb-Build': 'OSS', 'X-Influxdb-Version': 'v2.7.1', 'X-Platform-Error-Code': 'unauthorized'})\nHTTP response body: b'{\"code\":\"unauthorized\",\"message\":\"unauthorized access\"}'\n",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mApiException\u001B[39m                              Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[2]\u001B[39m\u001B[32m, line 20\u001B[39m\n\u001B[32m     17\u001B[39m stop_time = datetime.combine(date_stop, stop_utc, tzinfo=timezone.utc)\n\u001B[32m     19\u001B[39m client = query.DBClient()\n\u001B[32m---> \u001B[39m\u001B[32m20\u001B[39m temp_array_fsgp: TimeSeries = \u001B[43mclient\u001B[49m\u001B[43m.\u001B[49m\u001B[43mquery_time_series\u001B[49m\u001B[43m(\u001B[49m\u001B[43mstart_time\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstop_time\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfield\u001B[49m\u001B[43m=\u001B[49m\u001B[43m  \u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mMosfetTemperatureA\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m     21\u001B[39m speed_kph: TimeSeries = client.query_time_series(start_time, stop_time, \u001B[33m\"\u001B[39m\u001B[33mMotorRotatingSpeed\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m     23\u001B[39m \u001B[38;5;66;03m#print(temp_array_expected_aliter)\u001B[39;00m\n\u001B[32m     24\u001B[39m \u001B[38;5;66;03m#this is almost steady state, and expected temperature is roughly 31.8 degrees celsius. note that individual cells do have an upto 5 C difference, which is actually quite a lot.\u001B[39;00m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\data_tools\\query\\influxdb_query.py:180\u001B[39m, in \u001B[36mDBClient.query_time_series\u001B[39m\u001B[34m(self, start, stop, field, bucket, car, granularity, units, measurement)\u001B[39m\n\u001B[32m    163\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mquery_time_series\u001B[39m(\u001B[38;5;28mself\u001B[39m, start: datetime, stop: datetime, field: \u001B[38;5;28mstr\u001B[39m, bucket: \u001B[38;5;28mstr\u001B[39m = \u001B[33m\"\u001B[39m\u001B[33mCAN_log\u001B[39m\u001B[33m\"\u001B[39m,\n\u001B[32m    164\u001B[39m                       car: \u001B[38;5;28mstr\u001B[39m = \u001B[33m\"\u001B[39m\u001B[33mBrightside\u001B[39m\u001B[33m\"\u001B[39m, granularity: \u001B[38;5;28mfloat\u001B[39m = \u001B[32m0.1\u001B[39m, units: \u001B[38;5;28mstr\u001B[39m = \u001B[33m\"\u001B[39m\u001B[33m\"\u001B[39m,\n\u001B[32m    165\u001B[39m                       measurement: \u001B[38;5;28mstr\u001B[39m = \u001B[38;5;28;01mNone\u001B[39;00m) -> TimeSeries:\n\u001B[32m    166\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    167\u001B[39m \u001B[33;03m    Query the database for a specific field, over a certain time range.\u001B[39;00m\n\u001B[32m    168\u001B[39m \u001B[33;03m    The data will be processed into a TimeSeries, which has homogenous and evenly-spaced (temporally) elements.\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m    178\u001B[39m \u001B[33;03m    :return: a TimeSeries of the resulting time-series data\u001B[39;00m\n\u001B[32m    179\u001B[39m \u001B[33;03m    \"\"\"\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m180\u001B[39m     query_df = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mquery_series\u001B[49m\u001B[43m(\u001B[49m\u001B[43mstart\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstop\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfield\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbucket\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcar\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmeasurement\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    182\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m TimeSeries.from_query_dataframe(query_df, granularity, field, units)\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\data_tools\\query\\influxdb_query.py:153\u001B[39m, in \u001B[36mDBClient.query_series\u001B[39m\u001B[34m(self, start, stop, field, bucket, car, measurement)\u001B[39m\n\u001B[32m    151\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m measurement:\n\u001B[32m    152\u001B[39m     query = query.filter(measurement=measurement)\n\u001B[32m--> \u001B[39m\u001B[32m153\u001B[39m query_df = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mquery_dataframe\u001B[49m\u001B[43m(\u001B[49m\u001B[43mquery\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    155\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(query_df, \u001B[38;5;28mlist\u001B[39m):\n\u001B[32m    156\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[33m\"\u001B[39m\u001B[33mQuery returned multiple fields! Please refine your query.\u001B[39m\u001B[33m\"\u001B[39m)\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\data_tools\\query\\influxdb_query.py:123\u001B[39m, in \u001B[36mDBClient.query_dataframe\u001B[39m\u001B[34m(self, query)\u001B[39m\n\u001B[32m    120\u001B[39m compiled_query = query.compile_query()\n\u001B[32m    121\u001B[39m compiled_query += \u001B[33m'\u001B[39m\u001B[33m |> pivot(rowKey:[\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m_time\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m], columnKey: [\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m_field\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m], valueColumn: \u001B[39m\u001B[33m\"\u001B[39m\u001B[33m_value\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m) \u001B[39m\u001B[33m'\u001B[39m\n\u001B[32m--> \u001B[39m\u001B[32m123\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_client\u001B[49m\u001B[43m.\u001B[49m\u001B[43mquery_api\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[43mquery_data_frame\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcompiled_query\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\client\\query_api.py:259\u001B[39m, in \u001B[36mQueryApi.query_data_frame\u001B[39m\u001B[34m(self, query, org, data_frame_index, params, use_extension_dtypes)\u001B[39m\n\u001B[32m    225\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mquery_data_frame\u001B[39m(\u001B[38;5;28mself\u001B[39m, query: \u001B[38;5;28mstr\u001B[39m, org=\u001B[38;5;28;01mNone\u001B[39;00m, data_frame_index: List[\u001B[38;5;28mstr\u001B[39m] = \u001B[38;5;28;01mNone\u001B[39;00m, params: \u001B[38;5;28mdict\u001B[39m = \u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[32m    226\u001B[39m                      use_extension_dtypes: \u001B[38;5;28mbool\u001B[39m = \u001B[38;5;28;01mFalse\u001B[39;00m):\n\u001B[32m    227\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    228\u001B[39m \u001B[33;03m    Execute synchronous Flux query and return Pandas DataFrame.\u001B[39;00m\n\u001B[32m    229\u001B[39m \n\u001B[32m   (...)\u001B[39m\u001B[32m    257\u001B[39m \u001B[33;03m            - https://docs.influxdata.com/flux/latest/stdlib/influxdata/influxdb/schema/fieldsascols/\u001B[39;00m\n\u001B[32m    258\u001B[39m \u001B[33;03m    \"\"\"\u001B[39;00m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m259\u001B[39m     _generator = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mquery_data_frame_stream\u001B[49m\u001B[43m(\u001B[49m\u001B[43mquery\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43morg\u001B[49m\u001B[43m=\u001B[49m\u001B[43morg\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata_frame_index\u001B[49m\u001B[43m=\u001B[49m\u001B[43mdata_frame_index\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[43m=\u001B[49m\u001B[43mparams\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    260\u001B[39m \u001B[43m                                              \u001B[49m\u001B[43muse_extension_dtypes\u001B[49m\u001B[43m=\u001B[49m\u001B[43muse_extension_dtypes\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    261\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._to_data_frames(_generator)\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\client\\query_api.py:299\u001B[39m, in \u001B[36mQueryApi.query_data_frame_stream\u001B[39m\u001B[34m(self, query, org, data_frame_index, params, use_extension_dtypes)\u001B[39m\n\u001B[32m    265\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    266\u001B[39m \u001B[33;03mExecute synchronous Flux query and return stream of Pandas DataFrame as a :class:`~Generator[DataFrame]`.\u001B[39;00m\n\u001B[32m    267\u001B[39m \n\u001B[32m   (...)\u001B[39m\u001B[32m    295\u001B[39m \u001B[33;03m        - https://docs.influxdata.com/flux/latest/stdlib/influxdata/influxdb/schema/fieldsascols/\u001B[39;00m\n\u001B[32m    296\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m    297\u001B[39m org = \u001B[38;5;28mself\u001B[39m._org_param(org)\n\u001B[32m--> \u001B[39m\u001B[32m299\u001B[39m response = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_query_api\u001B[49m\u001B[43m.\u001B[49m\u001B[43mpost_query\u001B[49m\u001B[43m(\u001B[49m\u001B[43morg\u001B[49m\u001B[43m=\u001B[49m\u001B[43morg\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mquery\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_create_query\u001B[49m\u001B[43m(\u001B[49m\u001B[43mquery\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mdefault_dialect\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    300\u001B[39m \u001B[43m                                                                        \u001B[49m\u001B[43mdataframe_query\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    301\u001B[39m \u001B[43m                                      \u001B[49m\u001B[43masync_req\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_return_http_data_only\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[32m    303\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._to_data_frame_stream(data_frame_index=data_frame_index,\n\u001B[32m    304\u001B[39m                                   response=response,\n\u001B[32m    305\u001B[39m                                   query_options=\u001B[38;5;28mself\u001B[39m._get_query_options(),\n\u001B[32m    306\u001B[39m                                   use_extension_dtypes=use_extension_dtypes)\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\service\\query_service.py:285\u001B[39m, in \u001B[36mQueryService.post_query\u001B[39m\u001B[34m(self, **kwargs)\u001B[39m\n\u001B[32m    283\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m.post_query_with_http_info(**kwargs)  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m    284\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m285\u001B[39m     (data) = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mpost_query_with_http_info\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m    286\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m data\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\service\\query_service.py:311\u001B[39m, in \u001B[36mQueryService.post_query_with_http_info\u001B[39m\u001B[34m(self, **kwargs)\u001B[39m\n\u001B[32m    289\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"Query data.\u001B[39;00m\n\u001B[32m    290\u001B[39m \n\u001B[32m    291\u001B[39m \u001B[33;03mRetrieves data from buckets.  Use this endpoint to send a Flux query request and retrieve data from a bucket.  #### Rate limits (with InfluxDB Cloud)  `read` rate limits apply. For more information, see [limits and adjustable quotas](https://docs.influxdata.com/influxdb/cloud/account-management/limits/).  #### Related guides  - [Query with the InfluxDB API](https://docs.influxdata.com/influxdb/latest/query-data/execute-queries/influx-api/) - [Get started with Flux](https://docs.influxdata.com/flux/v0.x/get-started/)\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m    306\u001B[39m \u001B[33;03m         returns the request thread.\u001B[39;00m\n\u001B[32m    307\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m    308\u001B[39m local_var_params, path_params, query_params, header_params, body_params = \\\n\u001B[32m    309\u001B[39m     \u001B[38;5;28mself\u001B[39m._post_query_prepare(**kwargs)  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m311\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mapi_client\u001B[49m\u001B[43m.\u001B[49m\u001B[43mcall_api\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    312\u001B[39m \u001B[43m    \u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m/api/v2/query\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43mPOST\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m    313\u001B[39m \u001B[43m    \u001B[49m\u001B[43mpath_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    314\u001B[39m \u001B[43m    \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    315\u001B[39m \u001B[43m    \u001B[49m\u001B[43mheader_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    316\u001B[39m \u001B[43m    \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m=\u001B[49m\u001B[43mbody_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    317\u001B[39m \u001B[43m    \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43m[\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    318\u001B[39m \u001B[43m    \u001B[49m\u001B[43mfiles\u001B[49m\u001B[43m=\u001B[49m\u001B[43m{\u001B[49m\u001B[43m}\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    319\u001B[39m \u001B[43m    \u001B[49m\u001B[43mresponse_type\u001B[49m\u001B[43m=\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43mstr\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# noqa: E501\u001B[39;49;00m\n\u001B[32m    320\u001B[39m \u001B[43m    \u001B[49m\u001B[43mauth_settings\u001B[49m\u001B[43m=\u001B[49m\u001B[43m[\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    321\u001B[39m \u001B[43m    \u001B[49m\u001B[43masync_req\u001B[49m\u001B[43m=\u001B[49m\u001B[43mlocal_var_params\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43masync_req\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    322\u001B[39m \u001B[43m    \u001B[49m\u001B[43m_return_http_data_only\u001B[49m\u001B[43m=\u001B[49m\u001B[43mlocal_var_params\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m_return_http_data_only\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# noqa: E501\u001B[39;49;00m\n\u001B[32m    323\u001B[39m \u001B[43m    \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m=\u001B[49m\u001B[43mlocal_var_params\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m_preload_content\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    324\u001B[39m \u001B[43m    \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m=\u001B[49m\u001B[43mlocal_var_params\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m_request_timeout\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    325\u001B[39m \u001B[43m    \u001B[49m\u001B[43mcollection_formats\u001B[49m\u001B[43m=\u001B[49m\u001B[43m{\u001B[49m\u001B[43m}\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    326\u001B[39m \u001B[43m    \u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[43m=\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43murlopen_kw\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\api_client.py:343\u001B[39m, in \u001B[36mApiClient.call_api\u001B[39m\u001B[34m(self, resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, async_req, _return_http_data_only, collection_formats, _preload_content, _request_timeout, urlopen_kw)\u001B[39m\n\u001B[32m    304\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"Make the HTTP request (synchronous) and Return deserialized data.\u001B[39;00m\n\u001B[32m    305\u001B[39m \n\u001B[32m    306\u001B[39m \u001B[33;03mTo make an async_req request, set the async_req parameter.\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m    340\u001B[39m \u001B[33;03m    then the method will return the response directly.\u001B[39;00m\n\u001B[32m    341\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    342\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m async_req:\n\u001B[32m--> \u001B[39m\u001B[32m343\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m__call_api\u001B[49m\u001B[43m(\u001B[49m\u001B[43mresource_path\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    344\u001B[39m \u001B[43m                           \u001B[49m\u001B[43mpath_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mheader_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    345\u001B[39m \u001B[43m                           \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfiles\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    346\u001B[39m \u001B[43m                           \u001B[49m\u001B[43mresponse_type\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mauth_settings\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    347\u001B[39m \u001B[43m                           \u001B[49m\u001B[43m_return_http_data_only\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcollection_formats\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    348\u001B[39m \u001B[43m                           \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    349\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m    350\u001B[39m     thread = \u001B[38;5;28mself\u001B[39m.pool.apply_async(\u001B[38;5;28mself\u001B[39m.__call_api, (resource_path,\n\u001B[32m    351\u001B[39m                                    method, path_params, query_params,\n\u001B[32m    352\u001B[39m                                    header_params, body,\n\u001B[32m   (...)\u001B[39m\u001B[32m    356\u001B[39m                                    collection_formats,\n\u001B[32m    357\u001B[39m                                    _preload_content, _request_timeout, urlopen_kw))\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\api_client.py:173\u001B[39m, in \u001B[36mApiClient.__call_api\u001B[39m\u001B[34m(self, resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, _return_http_data_only, collection_formats, _preload_content, _request_timeout, urlopen_kw)\u001B[39m\n\u001B[32m    170\u001B[39m urlopen_kw = urlopen_kw \u001B[38;5;129;01mor\u001B[39;00m {}\n\u001B[32m    172\u001B[39m \u001B[38;5;66;03m# perform request and return response\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m173\u001B[39m response_data = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mrequest\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    174\u001B[39m \u001B[43m    \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m=\u001B[49m\u001B[43mheader_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    175\u001B[39m \u001B[43m    \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m=\u001B[49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    176\u001B[39m \u001B[43m    \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    177\u001B[39m \u001B[43m    \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    179\u001B[39m \u001B[38;5;28mself\u001B[39m.last_response = response_data\n\u001B[32m    181\u001B[39m return_data = response_data\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\api_client.py:388\u001B[39m, in \u001B[36mApiClient.request\u001B[39m\u001B[34m(self, method, url, query_params, headers, post_params, body, _preload_content, _request_timeout, **urlopen_kw)\u001B[39m\n\u001B[32m    379\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m.rest_client.OPTIONS(url,\n\u001B[32m    380\u001B[39m                                     query_params=query_params,\n\u001B[32m    381\u001B[39m                                     headers=headers,\n\u001B[32m   (...)\u001B[39m\u001B[32m    385\u001B[39m                                     body=body,\n\u001B[32m    386\u001B[39m                                     **urlopen_kw)\n\u001B[32m    387\u001B[39m \u001B[38;5;28;01melif\u001B[39;00m method == \u001B[33m\"\u001B[39m\u001B[33mPOST\u001B[39m\u001B[33m\"\u001B[39m:\n\u001B[32m--> \u001B[39m\u001B[32m388\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mrest_client\u001B[49m\u001B[43m.\u001B[49m\u001B[43mPOST\u001B[49m\u001B[43m(\u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    389\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    390\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m=\u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    391\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    392\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    393\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    394\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m=\u001B[49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    395\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    396\u001B[39m \u001B[38;5;28;01melif\u001B[39;00m method == \u001B[33m\"\u001B[39m\u001B[33mPUT\u001B[39m\u001B[33m\"\u001B[39m:\n\u001B[32m    397\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m.rest_client.PUT(url,\n\u001B[32m    398\u001B[39m                                 query_params=query_params,\n\u001B[32m    399\u001B[39m                                 headers=headers,\n\u001B[32m   (...)\u001B[39m\u001B[32m    403\u001B[39m                                 body=body,\n\u001B[32m    404\u001B[39m                                 **urlopen_kw)\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\rest.py:311\u001B[39m, in \u001B[36mRESTClientObject.POST\u001B[39m\u001B[34m(self, url, headers, query_params, post_params, body, _preload_content, _request_timeout, **urlopen_kw)\u001B[39m\n\u001B[32m    308\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mPOST\u001B[39m(\u001B[38;5;28mself\u001B[39m, url, headers=\u001B[38;5;28;01mNone\u001B[39;00m, query_params=\u001B[38;5;28;01mNone\u001B[39;00m, post_params=\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[32m    309\u001B[39m          body=\u001B[38;5;28;01mNone\u001B[39;00m, _preload_content=\u001B[38;5;28;01mTrue\u001B[39;00m, _request_timeout=\u001B[38;5;28;01mNone\u001B[39;00m, **urlopen_kw):\n\u001B[32m    310\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"Perform POST HTTP request.\"\"\"\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m311\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mrequest\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mPOST\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    312\u001B[39m \u001B[43m                        \u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m=\u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    313\u001B[39m \u001B[43m                        \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    314\u001B[39m \u001B[43m                        \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    315\u001B[39m \u001B[43m                        \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    316\u001B[39m \u001B[43m                        \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    317\u001B[39m \u001B[43m                        \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m=\u001B[49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    318\u001B[39m \u001B[43m                        \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\rest.py:261\u001B[39m, in \u001B[36mRESTClientObject.request\u001B[39m\u001B[34m(self, method, url, query_params, headers, body, post_params, _preload_content, _request_timeout, **urlopen_kw)\u001B[39m\n\u001B[32m    258\u001B[39m     _BaseRESTClient.log_body(r.data, \u001B[33m'\u001B[39m\u001B[33m<<<\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m    260\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[32m200\u001B[39m <= r.status <= \u001B[32m299\u001B[39m:\n\u001B[32m--> \u001B[39m\u001B[32m261\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m ApiException(http_resp=r)\n\u001B[32m    263\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m r\n",
-      "\u001B[31mApiException\u001B[39m: (401)\nReason: Unauthorized\nHTTP response headers: HTTPHeaderDict({'Server': 'nginx/1.27.1', 'Date': 'Sat, 29 Nov 2025 22:07:18 GMT', 'Content-Type': 'application/json; charset=utf-8', 'Content-Length': '55', 'Connection': 'keep-alive', 'X-Influxdb-Build': 'OSS', 'X-Influxdb-Version': 'v2.7.1', 'X-Platform-Error-Code': 'unauthorized'})\nHTTP response body: b'{\"code\":\"unauthorized\",\"message\":\"unauthorized access\"}'\n"
-     ]
+   "outputs": [],
+   "execution_count": 4
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-07T03:49:55.294234Z",
+     "start_time": "2026-01-07T03:49:55.277751Z"
     }
+   },
+   "cell_type": "code",
+   "source": [
+    "#save collected data of 15 minutes\n",
+    "\n",
+    "import os\n",
+    "import dill\n",
+    "\n",
+    "out_dir = os.path.join(\"../../motor_analysis\", \"data\", \"array_temperature_aliter_2025-07-06\")\n",
+    "mosfetA_file_aliter = os.path.join(out_dir, \"mosfetA_aliter.bin\")\n",
+    "\n",
+    "os.makedirs(out_dir, exist_ok=True)\n",
+    "\n",
+    "for filepath, data in zip([mosfetA_file_aliter],\n",
+    "                          [temp_array_expected_aliter]):\n",
+    "    with open(filepath, 'wb') as f:\n",
+    "        dill.dump(data, f)\n",
+    "\n",
+    "\n",
+    "\n",
+    "#time zone matching conventions??"
    ],
-   "execution_count": 2
+   "id": "a807d5de05d7c8b0",
+   "outputs": [],
+   "execution_count": 5
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-11-29T19:45:37.979942Z",
-     "start_time": "2025-11-29T19:45:37.962502Z"
+     "end_time": "2026-01-07T03:49:56.326119Z",
+     "start_time": "2026-01-07T03:49:56.315594Z"
     }
    },
    "cell_type": "code",
@@ -231,31 +233,31 @@
        "data_tools.collections.time_series.TimeSeries"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 52
+   "execution_count": 6
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-11-29T19:45:39.544344Z",
-     "start_time": "2025-11-29T19:45:39.529624Z"
+     "end_time": "2026-01-07T03:49:57.060265Z",
+     "start_time": "2026-01-07T03:49:56.982212Z"
     }
    },
    "cell_type": "code",
    "source": "df_fsgp = pd.DataFrame(temp_array_fsgp)",
    "id": "1300423cd026c3ca",
    "outputs": [],
-   "execution_count": 53
+   "execution_count": 7
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-11-29T19:45:40.816030Z",
-     "start_time": "2025-11-29T19:45:40.810451Z"
+     "end_time": "2026-01-07T03:49:57.638445Z",
+     "start_time": "2026-01-07T03:49:57.628343Z"
     }
    },
    "cell_type": "code",
@@ -265,21 +267,21 @@
     {
      "data": {
       "text/plain": [
-       "2831521"
+       "3422130"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 54
+   "execution_count": 8
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-11-29T19:45:43.429032Z",
-     "start_time": "2025-11-29T19:45:43.420022Z"
+     "end_time": "2026-01-07T03:49:58.441874Z",
+     "start_time": "2026-01-07T03:49:58.099405Z"
     }
    },
    "cell_type": "code",
@@ -287,17 +289,18 @@
    "id": "bbac7524849da8b0",
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "120"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "NameError",
+     "evalue": "name 'hourly_data' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
+      "Cell \u001B[1;32mIn[9], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[38;5;28mlen\u001B[39m(\u001B[43mhourly_data\u001B[49m[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mshortwave_radiation_instant\u001B[39m\u001B[38;5;124m'\u001B[39m])\n",
+      "\u001B[1;31mNameError\u001B[0m: name 'hourly_data' is not defined"
+     ]
     }
    ],
-   "execution_count": 55
+   "execution_count": 9
   },
   {
    "metadata": {},
@@ -312,8 +315,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-11-29T19:45:50.178966Z",
-     "start_time": "2025-11-29T19:45:50.150527Z"
+     "end_time": "2026-01-07T03:50:03.609627Z",
+     "start_time": "2026-01-07T03:50:00.071858Z"
     }
    },
    "cell_type": "code",
@@ -408,7 +411,7 @@
      ]
     }
    ],
-   "execution_count": 56
+   "execution_count": 10
   },
   {
    "metadata": {},
@@ -431,8 +434,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-11-29T21:25:43.719301Z",
-     "start_time": "2025-11-29T21:25:42.242073Z"
+     "end_time": "2026-01-07T03:50:09.857859Z",
+     "start_time": "2026-01-07T03:50:07.871779Z"
     }
    },
    "cell_type": "code",
@@ -467,13 +470,13 @@
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ],
-      "image/png": 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Q6gF8JqwbKZAZA4CaQ+zPEEE+fJgIJxi4j3T7qVNEXkawGSZYx++9e/faNWBhnykDDWqwT0YkDBkLCUQY/CUR6cLjqBnD8Qv7bgma0rCPxrEj0HBNmMHAwVqKyatxiXT6mjKaR72BAmygakHhCOqycJFArEC0qIFYkUDcoLgRqTuk+9JydJeiRQL/LEcgXKR4UZPecgBp8KoGPzwsE5YTnx331etMXRcgUYtyZ89LEal+HewQXEXNZKH/0Uu36XaCkkJ1JcAYxm+goxkCDFFvbNcQ+CVLEqExBVMrWIQxBiM2NtatDACElePxA1kEWW6CZjCZOVKLMNx3NNUOFPo8/WbcAiIsUKCmCcIHGzFc+hnXZM3ofqcrw2iOxSOQcJCREVZ5YqQaHcYwZqNRo0Z24/EASkRgdSRPtnEMg9WSBNkiNF6hzCcYsAgzMBkzBO7rg3kpivex8fpz7qMvoK4MHS7BBt2qDBN0EaaKdLMIY0KBt99+W3TEY3oMfBZhwv3DDz+IkXYAWQl4VGL6CGqSUbbStWtXkRXC6LhgwOlIA5M5OjKg0TDGPUxdZMnoHxZhTIhSp04dmj17tsjWwOcSkS80VaHbXIImNNQTo3kLdcIwNEcnfbBqXlmEmQS1DxUTXFJYhjF6E2GlSinXPACeMTkdOnQQF1fgOAmBhose4HSkgyGpkUhIsvlUMTpCtTmlmHC7Y3TOuXOpRZhsJoHHHG93DKMbQjoSJu0aYDeAi15tAlyRkHzPzu6AI2H64L7qIJdy/z7dd2FPgW0O3xl/b4zfI2GyIxLGxrBqUY1xYRgmeIS0CIPowoxFHASdzfPTOzERRDcstxMSEomzkfogWSWOExITKSyN7Q8nAizCGL+LsJgYoly5iDBwHl5hLMIYRheEtAgDOADiQOjM+E3PXLuTSOdv24QjPLLCuStPF4Qnwck/2fa9uFDHHAVj/G5RoQYpSYgwpCSdePcxDBN4Ql6EyYOh0VKRZ64r43IkYeHafIbmzZsL0zp0lLgCA6rR5osLk5pb8bZIWHhYOItjJnBgpurly6kjYVKEYXg8ImEMw+gCYykPRpB8TynI37FlI9UolovefOGJgNbaYgQE2nu1BOIvLVGHMUUycuTqAp8wPZAlWrtzG3wuPXifMQZBzr9DvSvSj2pkXRgPgGcY3cCRMAOSfF9RXLOn/UpPd3tVXGMWVtEigZkJh2HVgQajgs7Jri8i+t///ie8XRYvXmx9DPMx9YA6IOlMG2NGZaCjrxi6ntaoKcYkyN8IxrM4psFlhyRHwhhGN3AkzKDdd3fibtN/c2fTE8+/RE1atrFOiZcsX75cHOgxSR5DsjFQu2XLlnTx4kWaP3++mKeFWVzPPPOMmLeoBl17b775phA1mO84cOBAOzsFpCPV6UoY3r388stCnOE18T47duywc9tHinPKlCnif/G6Tz31FN26dUs8/+KLL9KKFSto9OjR1qgWIl9qMHAb4ybkBa79aKqQ9/PmzSuWCeZ8+KzVqlWjP//80+f1gQgd1kVa6wNDuN99910xvzNz5sz0YIsmtGndauvziNBlz55dODRjrhnqDzEcHBHFNm3aiNfEazdr1oy2bt1qt55B586dxbLL+1hfju7OiCJiWR2XG4/j9du2bSse3717N7Vr106sPwxEf/7558UwdcZkIsyxHkwtwk6eDOwyMQzjEhZhKnBgjUuMC8rFE8+oK7cT6b+5c6hEqTJUvFQZeqjLEzTpl4lOXwMCaOzYsWJ6/KlTp8S0eIgVjHP4999/aeHChWIotxoIOgicjRs3CmE0cuRI+umnn1wuz+OPP24VM1u2bKGaNWtSq1at6OrVq9a/OXLkiEir/fPPP+IC0TVs2DDxHN4Dc7teeeUVEe3CxdWQbFcMHTqUJk+eTOPHj6c9e/aI8RXPPfeceB9/rw+InXXr1tG0adNo586d1PGRLvTG84/RiWNHrH8DYTd8+HDxf1g+iEaI0BdeeIFWr14tRm2UKVOG2rdvbxWnEGlg4sSJYp3I++6C5Ub0a82aNWK9QCxDeEKEbt68WUQSMbgW64AJAREmh9QfOxbYZWIYxiWcjlRxJ+kOZRkanLmItwfcpsxRmd1OR86ZPkWIL9CoeWsa8l4vITjU0RCAGVkYagq6d+8uxjlAEJUsWVI89thjj9GyZcvo/ffft/4PBNCoUaNE9KVcuXJivhbuQyQ5AgEBcQIRJjtMkSqE4EIkStaOwRcLEaHY2FhxHxEYzKH8/PPPRRQIYiFTpkzWKfeegEgUZoUhNSmHsOLzYdm+//57EWHy1/pARAsiCdeYPwZ6vPW2iLj9Nf03at+ohngMFijffvutiNBJIIjUYMYZImb4HuH4LNO+eMyb9QJRh8Hr6s8OAYZ1Jfn555/F58OQ27Jly3r8HoyBRJhlGxeRMFjyWHwSGYYJHhwJMyAXTh6l3du30oOdHhX3EaV57PEnaMKECan+tqqqFR3pJwgdKTjkYxBQaurXr2/nXQVhc+jQIVHL5AjSjrdv36ZcuXKJFJe8HDt2TIgbCVJpUoCBAgUKpHpfb8GgVkSakNpTLwMiY+pl8Mf6gCDDNQSMfN9SBXPTlvVr6NQJW0oVIlP93gBRKAg5iCUIUaRDsS4h6LSgVq1aqb4rCEz1Oipfvrx4znE9MSYUYRDymI93/z6nJBlGJ3AkTEWmDJlERCpY7+0u0379RdRttaldwfoYUpGIRCHVpi5Ql1MB1J5oavAYolTeAtEAQYWaK0cQwXG2HFq8r+MyAKQTUZelxtH/Tev1gfdGvRrSsLgGF2/F09XbiZQpc2ZrYT5q0BxNWZGKvHLlikhxFitWTCwrBB6K6NMCBf2OqWdnZsOoT3Nc1o4dO4q0qCP4DhmTizBsf0hJ7tunpCTlPEmGYYIGizAVOEi6mxIMFhBfU3//jd4Z+Bk1aNrC+niJXJnpiccfpalTp9Lrr7/u03ts2LDB7r6sV5IiQw3qv86fPy+icbJw3BsQKXIWaXMHdbG7OvWoFWmtD6T3sNyInjVp0kQ8n/nGXcpyKyHd4ZGo1UKKEnVgADVqjkXyEImO6wVpShTYq9m+fXsqQensu5o5c6b4nvB9MSEmwoAUYUePBnSxGIZxDqcjDQaK2q9fu0adn3qOypSvaL1UrFyZHn30UacpSU+BmOnbty8dOHBAiDoUqvfu3dvp37Zu3VpEb9Cth6J2dDWi6P3DDz8Uxd/uAmEAsYP/hxDxJBqFNCe6E1GMj2J0pNbQZYjlduwa1Xp9IA357LPPUteuXWnWrFkiDbt18yaaMHYkrVzyX5qvCyGHjtF9+/aJz47XQcTMcb2gdg5C99q1a9ZaMqxbpFuRFh08eHAqUeaMnj17imaJp59+WhT5Yz2hdq1bt25eC2DGWCJsWZm69Pgzw+n4UYurPsMwQYVFmMGAyGreohXFZk3tiQURhoMzOvR8AYLi7t27VLduXXHghuBwZc6K6OG8efOoadOm4mAOUQL7iRMnToj6KneBiEJkCVEtRHo8rYv69NNPhXUEuiRhN/Hggw+K9CQsK3wlvfWBwnz8zTvvvCMK97s/9yTt3rGNChQsnO53CWGFCBUaFd566y3RNalmxIgRtGjRIlE8j6gbgN0EPmu/fv2oTp06opsS758eaBxA9A2C64EHHqAqVaoICwukjY02MYJxAoT0hQtpirBuGWvTpiKV6J34YrwKGUYHhKV44o1gQE6fPi0OYEj1FJaO0Rbi4+NF5AIH6owoWDUIN+4m0YkrcXaPlckXSzEZUqcLGf+PcXLkzPW7dOW2ko6sWCArRUYYV+AY9TcSsm75EF+o/UJdoZOUc/H+/4rrknev0tLRzwdhIRlGm+O3WTDu0YGxx9xa2ljwd8EEc2QRoqnp1PyFJ6du5GAYJvCwCDMifJDXNWo5zNKYCcrIonQId9JNyzBM4OEWKQPi7MDOB3v/4Mx6w1ONjIy/oz0FwwS8M9IxEsaGrQwTdDgSFuLBscTk+5SUfF/Mo2S05+ilODp62bOxVAzjdxGWcp9II7NkhmG8hyNhlkhFKHI3MZkOXVSMTqMiwql8gazBXiRToN6cEpLvUUIy0b37KRQZYbxoWKj+NswvwlIUEeZgbswwTGAJ6UiYNLfEyBsjodVh8fzNBFtE7J427vUMvh/zCBf520jPCJYxlgiLgA+ftLNgGCZohHQkDL5U8EiSswIxR9AItTuJ8YmUkpyYykogIsWzr/Pm7bhUr8H4TnJiAqUkJ6dat0ayqkAEDAIMvw38RpxNS2AMno5kEcYwQSekRRjIb+kk0mqYdCCIS0ima3fsu5tSbkZRtIc+YRev3bW7H3XX3q2d8Q54hN1Nso8sRsRlpIhw/Qt8RyDA5G+E0TlcE8YwhiPkRRgiXxheDKdyZ0OQ9ci8XWdpxLKDdo8Nf7QqlS+e06PXeXmWfeffkneaa7J8oc6E2btp3VH7GZB/vt6QcmSOIiOBFCRHwAwCarw8SUeKSNgl/y8XwzBpEvIiTIKDjVEOOAkpkXTmlv2sv+SwSI8dzR1fgx3RteFaQkqqdZshOpoyZoxO939vxifRt8uO0CM1ClL5/NwowbjJ9etECQlui7AUCuPuSIbRAcYpUmGsOLOTYIsJnfu4uVmrP2jObhq/4gi1G71K68VizIyMgmXPjrOpdP8c8yO5Joxhgg+LMANy38kBnZsb9YMzQeyu1cO6o1csf6/5YjFmxoNUJIhOSmARxjA6gEWYEeFImOFEsrPHnHFBZRvCMFqKsO+WH7HezhZ/m9ORDKMDWIQZEGfH8/vuHuUZv+Ms6mUm7zDGmCJs+IL91tstj25WRBj8whiGCRoswgyIs1TVPc5f6YL4pHt07Y69hxtgjczoKR2Z4V4S0b17RFevuvX383ado0e/W0tnrtvb2jAM4xsswkxTmB+URWEcqPv5Ytp95maq9cKRSkZPIiw8ymKX4qY/4hu/baUtJ66JxhGGYbSDRZgB2Xn6RqrH+CCvD27G2zvlM4weRVhYjKWD0kPX/Bt3jeGlyDBGgUWYAZm97UyqxzAgmtEvbCHC6EmERcTEeCXCwg0w1o1hjASLMJPgaU3YLifRNMZ/sEZmdBUJy5RJueHpuDbWYAyjKSzCTIK7PlSS09fu+G1ZGO++n8Tk+2neZxinxMUR3brlkQgjKcI8joTxd8AwWsIizCR4atbKyUvtSasub/3Rq24NZk/rPsOkGQWDsIqNdWsl3c+SRblxJnVpQ1qEcSiMYTSFRZhJSGa/H12nhNccsR/o7Yy4RHvRdZtFGONpKtLNmq0UKdZOnvRoHYfzEYNhNIV/UiZh0F97gr0IIU9azRHupCMv3Iy3u88ijPFHPZjYHrNYRNipUx6tZI6EMYy2RGr8eowfwYF868nrvI4NKMLc6V6NdAgzsAhj/CXCkjNnVm6cPq24P7sZQePmSIbRFo6EGYiZW88I12q9FPcz9iT7KMIc05kswhh/ibCkjJbC/Ph4osvpp8oZxgh8/PHHFBYWZncpX7689fn4+Hjq2bMn5cqVi7JkyUKPPvooXfCwOUVrWIQZiBmbPUsdeAprMP8V5rsjwo5dirO7f5uNXxl/iTAU2OfL51VKkmH0TKVKlejcuXPWy+rVq63Pvf322zR37lyaMWMGrVixgs6ePUtdunQJ6vJyOtJAbDjm3pw3b+E4mP8iYe74hL0zY4fdfY6EMX4TYfdSiIoUUSwqIMJq1nTr/9h0mAkGt27dops3bePgoqOjxcUZkZGRlD9//lSP37hxgyZMmEC///47tWzZUjw2ceJEqlChAq1fv57q169PwYAjYYwVTkf6RloHKG8OXmxRwbjF+fPpijDH33YyPG0gwjyMhHG0nAkGFStWpGzZslkvQ4cOdfm3hw4dooIFC1LJkiXp2WefpZOWDuAtW7ZQUlIStW7d2vq3SFUWLVqU1q1bR8GCI2EhSpITYzGOhGkbCfvt5Xr07E8blOcQefCQW5yOZDSKhDlGYkUkrHBhW3G+m3AkjAkGe/fupUKFClnvu4qC1atXj3755RcqV66cSEV+8skn1KRJE9q9ezedP3+eoqKiKHv27Hb/ky9fPvFcsGARZiByZMpA1+5oM0C3/8xdqR7js1zfuOcgtNT9ZrEZPf+pcTqSSZfERFthfRoi7PLthNQnYQUL2os4N+DxW0wwiI2NpaxZs6b7d+3atbPerlq1qhBlxYoVoz/++INi5LxUncHpSAORoOEYm7tJ91I9lsKxMJ9w7G5EZ46kXZXUNQrpwelIJl1kZ1eGDES5crn8s2X7L6Y2d5aizQMRxiULjJHInj07lS1blg4fPizqxBITE+n6dXubJ3RHOqshCzkRNmzYMHHQ6tOnj67bSYOJv1MBHAnzjXsOUwugwZqVzSNuezPQ4BY75nvO338T/e9/obMxSwGFg0gaJl75smW0u5+UnGITYWfPuv12obJaGXNw+/ZtOnLkCBUoUIBq1apFGTJkoCVLllifP3DggKgZa9CgQWiLsE2bNtH3338vwodq9NhOGkx4B6hvHMvscEjMEBHmsgYvPXsLtqjwkCtXiDp1InrvPaJlyygkcLMzMtxBoCXddz8deVE1yeFmvDblEAzjD959912hFY4fP05r166lzp07U0REBD399NOioL979+7Ut29fWrZsmSjU79atmxBgweqM1IUIg1JFB8OPP/5IOXLkSNVOOnLkSNFOChWLdlKsWLSTMqm5dMu+7sNTWORpO78TkV3pgp+UTjFNt182pXqM05EeMnGi7fbixRQSuCnCCjpEwkSjiPyfq1eJElzvO9p+vdJ6++CF2z4tLsP4k9OnTwvBhcL8J554QmTRoBfy5FEyEqNGjaIOHTqIrFrTpk1FGnLWrFlB/VKCLsKQbnzooYfs2kZ9aSdNSEgQfiLyAn+RUKkJ+3rxQZ9en2vCfMOZIWuGSIsIS+e7W3HwUqrHuDDfQ3791XZ74UIKCdwUYY71iiIymzMnUVSU8kAa3WHqZqCMGYJ+yGAYl0ybNk1kzKADIMhwv1SpUtbnM2bMSOPGjaOrV69SXFycEGDBrAcDQf1FYQVt3brVqeeHt+2keC21nwj8RUIFZ8X2nsCdT9qKMGSAMoSHOY2SuQNbVHgARMahQ7b7W7cqER6z46YI23HqemoRhg1UHoDcrAsrnssyc5JhGGOLsFOnTlHv3r3pt99+E+pUKwYMGCBSmfICf5FQwbHuw1PifRRxoY4zx/wMEZZImBc+YXGJyZosV8jUg925o9zOm1cRZQd9iwwbguPHlWtpvOqCsvli7e5bt0cPbSrYJ4xhTCLCkG68ePEi1axZU4wZwAUFdWPGjBG3EfHypp0UJm7wE5EX+IuECpagi9ecv2ErwGU858hF+3oZfB2RbhbmOwOF+WwJ4CYnTijX2DdUqKDcPnqUTM/+/cq1akixO+JJOOYDDzsk0xrNxTCMgURYq1ataNeuXbR9+3brpXbt2qJIX97WYzupmSNhRy/bD5BmPKNIzkyp05GWSJg3jvk44GnpDWdqLKNJqGhRopIlQ0OE3b5tGzlUrlyaf+oYibU2injomp/WkHqGYQzkmI8IVeXKle0ey5w5s+hmkI/LdtKcOXOKqFavXr2C3k6qZ3zUYKkiOYyH69/JY+5aVLgCxfkZM0TwV+FuJKxYMaISJUJDhB04YEu/osg+DRxPAqzbo4fzIzkSxjDaoutWFz22kwYLZ2mpaoWzUecatnlavsKRMN9w/Ib2nbtFkV7UhH33bE3KHKUIL/YK80KEhUokzM1UpNUXzJkokyJMRhK96ABmGMYksyOXL19ud1+2k+IS6jgexCPDw+ivNxvT0Uu3afa2M5r4fHEkzDcc1z+iDdZ0pAfdkeHhYZQlYyTFJd5jmwpPRZg6HXnsGJm6EaFvX7dFmGMkLNHLSBiLMIYJoUgYk5YRqHItD/JacOxyHNd8aOizBlEmLSo8SUeiti9LtHJ+xF5hbiJFhFqE4bE0TEgNTc+eRBct8yCbNk33z3edvu68MB/rC5w5gwn06b4OpyMZRltYhBk0EhZmqUCS3XdaRMLgM3ZONaKE8QzH9Y+73qQjm5bNbRNh8WxT4Rayu69QIaVGCgOt8YWYcdbs4cNEM2Yot6dOJXr22XT/ZczSw3b3kVUUUS10R2KqQ3KyW+vKKt4YhtEEFmEGwVUkJULlS6GF4z3Sm4x3OK79Y5dve1WYHx0ZIdKRgCNhboAIjjRwhqhAmBhCDMhokZmYNEmZCN+uHdFTT3n9MmKbjIy0eYW5kZLkkjA/8/XXRGg8e/FFoiSe0xkKsAgzCKksDmQ60jKbEGhxksp1YdpRKk8Wry0qOB3pAZcuKaIE4itfPuUxM4swOZz8scfc+nNXXnPW1KIHdWHeTH5g3GTLFqXOb8MGRWhPn86rLgRgEWYQHCMpMv6lTkd6a4Og5sgl9grzFseD3TP1iqoc8z37bjJzTZj7SLd3CC9EdoAUY2ZLR8bFEW3cqNxu3tytfzl97a7Tx63zTJHClXVh6RCfxCLMb3zwgX1Nw6hRvteYMLqHRZhBcCyIdVaYr4UIO3qZ05HekuIkreitY35sqNeEIeLy1ltEMGauV0+5jB/v/vxEs0bC1q1T0lQwWZV+aB4i9x1W2woPRxcxfiAxkWjlSuX2qlXKyQTmn7ppossYF11ZVDCeF8Sqa8I0iYRd5EiYFiosyiKOo7ycHRnyNWFz5hB98439Stm+nahDB5vLu2NRvhQTZhZhP/ygXLdu7bY7s3ofIbbHMKLE5Pu2FLkUryzCgsfOnUTx8UQ5chA1aqR0rcLnDtYr6cwFZYwNR8KM3h2p2sFevp3o9evLGqTzN+O5GNxL1I0RS95pJq6jI5WfWEKyZ8PRQzodiRTMJ58ot19+mWjuXKKGDZVowbBh7kXCzJiO3LvX1hUpPcLcQF2ykDtLVGrbFA/nRzJ+inACFOVDXEvrEOl/x5gWFmFGrQmz7FfDVGfD+8/f9Pr1M0VFUK7MUeL2Ma4L8wpZvoFJBnKOZEaL8z3sPzwhpNORhw4pkYGoKKIvv1SiX4MGKc9hYoZjnYwUYWaPhM2cqVx37EhUpYrb/6ZeXbBMSWWbwunI4LN+vXIt5yJLEebmJAPGuLAIMwiOg5zDNC6aRc0ZuvkA14V5h/VgpxLGMZa5j3cT73mVjoxLDEERJidn4ICE9Axo1owoOloRXAcP2v+9jOCYPRIm1wusKTzgvkqFIXKeqlnEg3Rk3thoj96bcQN8P7IeTIowjN8CLMJMD4swg+BuJMXTg72kfZX8VCpvZnGbbSq8w6rBVI/J4dvnbnhmgps5ShFht0IxEibFhrr7L2NGJVUDfv9dSc1JcQHzUvWBy4yRMDj/r13rUVekRN3Tg9Sk9K5LVRN29WqqCQOOHb/smO8HjhxRCvBhMIy0O+B0ZMjAIswgxDuIMHUaUs0RL8xWRz5RjT5sX5FK5lYiYUcuc3G+N8gDlvqrSbB8b3e8jISFZE3YihXOxUaLFsr1kCFElSopqUp0CsrIGB5zFGHSQ8zooGMOhduI8LkxK1LNfZUKiwgPt0XC5HrJmVNJ/TqJhjmas7Jjvh9933CSkUkpY+BIWOjAIsyoIkx1e9JLda23D1645fFrd6lZmGKiIjgS5odIWMHsMbbnXXj+OHs8NjqDuI4LNREG0YT0IpRsnTr2z3XtSlS1KlHu3Lb6METBIMSyZLFFDwDECl4Dbvp4TaMzfLhy3bmz212REvXmhSiY1TZFljjg9VykJB0HdnMkzI8iTJ5kOEbC2CvM1LAIMwhbT1yzf0C1H25WNg89V1/50R666L3Pl4yE8SBv75D7SnWUMncWWw3NzbvJbo+CsUbCQi0duWePcg0PrMxKetwKHtuxQ/FPkg7jsqC5QgV7cQKfJVkX5oYJqa7ZtYto8WLlM/Xv7/G/q2vCYFchp2zYCSoXImzz8at291mEaQy+G2ciTNpS3L5NdN1++DpjLliEGYSpm9IeKVI2X6y4PuRFJExSOEeM8BFCE8CZ685dtpm0SK2mEGGU3LjrfBacOtpQu5hSiJ45Wvm/24nJLiNophZhFSu6/hscoEqVUqJc33+fOhUp8cAJXtds2mRrTlDXvXlbmB+piNVEdce1CxF28uodu/ucjtSYAweUuadoOpE1jwBpyTx5LF8Cd0iaGRZhBkFd1wEcExJl8ioi7OAF7yNhaF0vlkupSTjKdWHeR8JcPH8rISndg+SYp2vYpSPxlKf1ZKYQYc5ElRpZL4Y5e65Em7ReMLr/lVwnlSt79e/w/lNHwiJlJEztPehiXRXPbR+NxG7IcV/EaNQJjOYTNWxTERKwCDMID1bOn2Zhfpl8Sirx1LU7XndIAmlTwR2SnuPEocKOdUeupCvCsmdSxFfGDOFWp/OQKs53V4R162Z/39kwa7NEwtyJDqbBh7N3W29DgMkpDsluRMLUZtASTkn6oQlFnYqUyKgnG7aaGhZhBgFmqmkVzKL2KGfmKBE58aZDUlIyj8WmwofXCFVskTDnKsyVOFZ/leEWBQeRndnynYeUTcW+fe4JDox2adJEuf3ii87nKEoRZvRIGOw43BGmLjhxxdbtjM1LFua7k450Jrgc9z2MBk752J4d4UhYSMAizCBULZzd7n6yk7b7MnmzeNwh+WaL0nb3rYat7Jrv/dgiF5EwVyle9UFNijAQmzHEOiRRhCw7GUvbb5dOmTpVGWM0Zozz52WKzciRsJs3iU6d8kmEqTXTlhPXrI75dunIdLojc1gitODQRe/rThkVWNeIciE9XNfW4W6FvcJCAhZhBsHRqdqZ9ZG1ON+DDkn1cF/AkTD/1YSdd2HYau/jFJZqnmfIpCOPHVOu4ZKfLVv6f49I1/vvE8XGun7e6JEw2QmKz5Ld/kTMG3q2KE1R0qJCHQlzqAm7Y5nUICNhaquVU1e5aSddtm0jWrhQaR5JLwqGWj9n2zC75ocELMIMgmMCwGkkzFIX5kmHpGPNR0lLJOzirQS6Fe+8kJzxrCasXomcToucndWEqb8O2SEZMulIKcKcpRa9wQyRMGlf0LSpJi/3RvNS1sL8/y08QNfiEu0jYZcu0ZiF+6nioP/ovz3n6Z5lP4P9ROcaiqg9rkpvMk5YsoSoXj2itm2VEVOuzIKXLlWu1V2RajgdGRKwCDMIji4FzsoyvOmQDHcQYdliMlAeS9SNU5Ke8dbUbeL6+GX7tv5aFtsJFNs7457KaV/dcJEl1NKRWouw/JZmlitXiJINug6deUj5ALYvWQt2+XYivffnDuUJGODChwwTNJYeEdevTdliTVkiQls8l3IScZw7p10DT68nn1QMhMGiRbaaPjU3bhBNnqzcfuQR568lvcKQtpSvx5gOFmGGIf1i2LJedEiqa5AkJS0RGx7kTT5bArhjvCpPlCMcvovYUE1HaiXCMI5HrtPLl8lw3LljM6Nt2VKzl1263zZPc8NRixkromNStDqpCUP0rHhuxb7mxBX7kwxGxeDBiuhHY4kstl+9OvUqggC7dUv5uwcfdL4K4RMGYYyTNLPMQGVSwSLMILjTkJTLzQ5JtfmnMzuFUpYC/yMXOe2QHssPXKReU7fR9TuWtI4T0hNTMh3pKIithq0swrwjIsI24siIB7E1a5QICNJSJUv65S1uqbctmZJUIWvCEAkrJiNhnI50zhdf2JpEvvqKqHVr29xPV6lIdPa68rSBMJZTH4xc18ikiXJ0YHSPu6bp6JDccOyq6JCsXChbuq/lGH0BHAlznxcnbrIrovdmGLeMNlhKdWz/ZzFsZRHmAxjkjY5LI86PlAdqpCI9nBfpFU5EmDUSFhFGJSwiDPWiKNzPFMWHD7udqhRgn35K1L694oIvvcAQ7pY/cPzt2rWurSkcvxPUNDp0rTLmgSNhRrM/SAfZIeluXRhHwrTh/A3XHWNxCUpqeNWhy2lGwhwFcUjNj8Q60DodCeToFyNGwmQ9mIapyDRJIxKGKG22TBmsZsKOdY8hz+nTRBcuKNHXd96xueCjyxci6s8/bavo6FFle4yKIqpZ073vhEWYaWERZrZImKUu7HAaXj4paTjvg1JykPeVODZmdPf7SeM5dJm5k2p2TEdmCaV0JOpo4BMGihfXNhIGjBYJi4sj2rxZ06J8t7tJVai7I4FMSaoNYBki2rhRWQ1VqhDFxNjmP779tnJ76FDbapJRsFq1Uo8qcoRFmOlhEWYQ3PWodqdDUl0TZrEMsqMQBnlHhlMiBnlfY08gX5HRSVfY0pFhoZuOlFEwHHTSOzB5I8KMFgk7ckTxmEJzgeyS8zfp1ISB4pbZsse5ON/5kPU6dewff/NNJd2wfbstmvXHH+6La7PMP2VcwiLMIKiFk68dkulFwrDDlfUfPL7I3e/H9XMvNS6R5vcoH3M0zg2pdKQ/UpFGTkceP+6f9eGhCJNGwnLUkbSp4EiYA1u2OBdhuXIR1ahhqw2DmJo3T7n/wgvufyecjjQE99Iy53UBizCT4U6HpFoHOJnPKyiVl0WYZt9J5ijr7ZtOBJX0CXP8LkIqHekvEWbUdKS/1gcR5ctqP30jrXSkLRKmHCqkTcUx9gqzZ/9+WzrSkebNleunn1Y6JpHibdyYqGzZ9L8sFmGG4ODBg9SvXz8qXLiwx//LIsxkNWHuzJBUF/k7psAkJS11YUd4hqR7308az2XMEGHtnrxyO8F1OjJVTVgImbVyJCww68Pi+ZXWAT/frSuptk1ZtmCrCePCfLv6PRTmA2fCSoowOaAethPjxrn3ZUkRxulI3XHnzh2aOHEiNWnShCpWrEgrV66kvn37evw63GNs0hokxabitldmrepI2NE0/MYYG+kZCKDGjhKIrsYlUklLhsxRYKcWYZGpvZzMCkfCnK8PLZsULDimvYv3/5d+f7keNSyRV9goZEqKd1mvKMsUYEiMcoeYKCVaG9IcOqRcw5MONXyOYHQRasMgpKpXJ+re3WnU0SnSJwyRXOwoAmFVwqTJ+vXr6aeffqIZM2ZQ0aJFad++fbRs2TIhxryBI2Ems6hwp0NSHVVz9ZMuZZkhyZEwbYD4Aov2XUj1nDXakKowP4Rqwk6etB9aHOqF+X6sCXusVuqUyTM/bVDsFfLlo6h7SanTkZaDPywqslpqFU9c5Q5JwYEDyrWr9CJc77/5hmjmTKKBA90XYECaDWPsFkYdMW4zbNgwUfPcp08f62Px8fHUs2dPypUrF2XJkoUeffRRugBrETcYMWIEVapUiR577DHKkSOHiHzt2rVLvAdez1tYhJkQT2ZIujqxKmEZXXT5dgLduMtzy5yx+bhl5IsHbDtx3XVNmKNZq+VgdzfpnvmtQuSQ7ULKkGjNC/NxAEtInQoOKc801RBvZ+w7d1NE3g7ksUXf5PB4eYKAA44cRM9eYRYOHlSuy5XT/LsSdheZMxuzrjGIbNq0ib7//nuqWrWq3eNvv/02zZ07V0SxVqxYQWfPnqUuXbq49Zrvv/8+PfLII3TixAn66quvqFq1aposK4swgzBrq+UgpUGHpF0kzIUKi82YwVrAyylJ52w7aRNU6WUJKhbIKq7LF4h12R3pamyR6YvzMUMPF3+IsOzZrYOpDTM/En5pcn34wZ4iMsL5br/d6FWpxiPdik9KVTvKHZIeRsK0OpFgEeYWt2/fpmeffZZ+/PFHEbGS3LhxgyZMmEAjR46kli1bUq1atURN19q1a0WKMT0+/fRTId5KlCghBNnu3btJC1iEGQRXbuvpdUgevng77cL8NNSDLM4/ysX5TvGkPKNVBUtazAn3XAzwjo6MoCjLAdPUIkwWHcfGKhctQXjRaDYV8mALs08ZBQkUJUpQ/RM7rXfldqfeNtkrzAF4gIFKlfzzncjt1ygnEX7g1q1bdPPmTeslIY2oNtKNDz30ELWWszstbNmyhZKSkuweL1++vKjrWrduXbrLMGDAANEFOWXKFDp//jzVq1dPRMNwEn3t2jWvPxuLMJMiOyQPOakLc8eiArBNhfvItI0r5Gr+Y/Mp1wO8nXwZMiVp6g5Jf6UijRpJkGJRLncgKVmSUlSCS27X6k3TOsibbSqUqCU6HkHt2v75Toy2/fqBihUrUrZs2ayXoeoJBCqmTZtGW7dudfo8hFNUVBRlR3RcRb58+cRz7tKsWTOaNGmS+J833nhDRNTwWMOGDUWUzVNYhJkUd2dIZotRbBCcwZGwtFGncrecSPtM6IDFLiQ+yRL2cmKI6UwQWzskzVyc728RZrTifHmwlcsdAJNn69+XKOEgwpykIy1eYWzYipqEbYrvF7ZdJ2a3miCL80NYhO3du1ekE+UFUSlHTp06Rb1796bffvuNMmo5dcMFsbGx9Nprr9GGDRto27ZtVLduXdEM4CkswkyKrAs75MQrTL1bblTa8gN3QilLNI1d833nwcr5XT5nM2tNrcIyyw5JjoSFTiRBw0iYp/0cfY5koPtOImH26UglEnb2RjzFJ3nuEG4q5HxPf0XBjLj9+knwZM2a1XqJjk5tOIx048WLF6lmzZoUGRkpLii+HzNmjLiNiFdiYiJdv27fHIXuyPz5Xe+f0wLdlqBKlSr09ddf0xl5QukBLMJMSmlLh+QhZzVhbp4dl5RdUFfiKFkWLjFWPHHsqVk0h8v1Lw+UjhYVINYiwjgd6QMhHAlLtgzgdpe/Dl2n++ERzrsjV68W6xD1pnK7PHk1xE1bEQmTw7j9BdeEuUWrVq2EZcT27dutl9q1a4sifXk7Q4YMtGTJEuv/HDhwgE6ePEkNGjRw701E5uK+KNIvVKiQsLk4evSoeHzQoEGiXsxTWISZlLQ6JO1nR7p+jULZYyg6MpyS7qXQaR7k7VNhft5YW3gcEQTn6UjXNWGm9grjdKRzEaZBJMyVtcmTtV13Xd5XpXJuynTktq1EMKN85hmRhi9mSUmGfF3YiRPKiipdmvwGR8LcjpZVrlzZ7pI5c2bh4YXbqCXr3r27cLWHuSoiZ926dRMCrH79+u69CRF99tln9Msvv9CXX34paswk8BBDR6ansAgzKeiQzOWiQ9LerNW1kkAdSEmraSs75/uS6lE7i1+9rRi3unIld5aONLVrviyK9VdNTQinI6XZqiMZM7je9adEZ0wdCftvgfIAogg3bqhsKkI8EnbqlN+sRKxwTZhmjBo1ijp06CBMWps2bSrSkLNmzfLoNSZPnkw//PCDiLBFwODYAjol98sZoh7AIszElHbVIWnnE5b2a5TMI8cXsTu2Ize9NLH9Z5fFksGxOzKNwnxTpyOl6JAjWrQmhNOR9+45F2FyIHd6IkwSrj5zW7bMKsJQqhCyINUrZ0b6U4QZbfvVEcuXLxe1WhIU7I8bN46uXr1KcXFxQoB5Wg+Guq/STiKfSFPCAsNTWIQZhLrFncwk06BDMr2Mmm18EUfCXK1fT9lz5qZTEeboEwZiZToyFESYBqIjzUiCUXyWAhAJi5QTuZ1w30nRc3jKfdv3s2gRFctlSUeGsgjD94SDLn63nowi8hQpEhAx9rDblfGPXcaqVatSPf7nn39SjRo1AjfA+/Dhw3TkyBER0ouJiRHFxq7c1xnfaVImN230cEyOqw5JT+ZQluJImEswR88bVh+2FwOy58FpOjLK5BYVd+/a3OH9JcLkXLcrVyjkImEuRNhLjUrQDyuVgmKnIswhyxiRMRrD84iefx5taFTiPR5dZE1FIo2ewbt9gVvINH1iItHVq7btmQkKKMB/4YUXREQM0S9E01DgjzTlP//84/9I2JUrV4TjbNmyZal9+/Z07tw58TgK3t555x2PF4BxjxQNOyTdGVsk4UhYGt+Jiy+laVnnEYwW5Zw/nmY60sGsdcHu8/T4+LVkGmTUBwWuWZXRTpojD1poTb9nAEsFKRY1ONi66o7Mn821j1J8RGpBEY50m3SEP3rUath69sZdSkg2wDo1aj0YgCjOacmEWI63TPDo1KmTmD+5ePFiUfgPUbZv3z7xWJs2bfwvwjAAE54baOvMhLEaFp588klasMBSvMlojjxQa9Ehadcdmc5ryEHeV+IS6fod+4JyxjOauRBn1nRkGhYVSEeevHKHXv91C206fo3enm4ZlWKmVKS/Iumq+XHkw3iRgJCcrEQHgQaiVB0Je6CiezV3CWGpEyRhBQvY5kpeukS5UxIoc1SEOBE5Fao2FYESYepomBzxxQSF5ORkGjJkiJgfuWjRIuFLdufOHVq9ejU98MADXr2mxyJs4cKFNHz4cCpcuLDd42XKlBHTxRn/4E0pgKsOSbVPVXrHPXTnFbCcNR/h4nz778RFfPKhKs4LPSsXypaq9d8+Epa2RUXTr5ZZH5+9zXNTwJCsBwMY4J3Nsu6RztH7GByJBnM0ZU1YZHgYff+8cy+r/Fnto2J3nRTzhxUspKxDS3Qu7Ngx1fiiEBVhgSjKdxRhHAkLKghAwZoCYkwrPBZh6ChQR8Ak6DZw5mLLaEOKhh2S9j5h6UcfbB2SXJxv9524+FIerWl/giKpVcwWkRkyd2/qmjAPHPPrl/S8USNkRRiQ6Ry914XJ+jjUGKk8iHyNhGWNyeDyt54vq/1++2qck4h3YctIqVKllOsjR6xR8pAtzg9kJEwW/rMI04UpLJz4gybCmjRpIgrQJPhhozgN6rBFixaaLRjj2ww4T2dIulcXFqI7WxekuDC4jYxw/rNSHwT/3HI6lVmrs3RkFhci7OZdkxTq+9ueQiLrq4wSCdMgCgaS79kiYY588nAlMTv2885V0n2dMBlJlCnJI0esHZIh6xUWjHQki7Cg065dO+rfvz+9++67NHXqVPr777/tLn7vjoTYghLcvHmzmMPUr18/2rNnj4iErVmzxuMFYNzD285kZx2Snr6WHF/ENhWO30nqFfnV41U9/o7SKsyXFhWO9XhHL98W4s1ZR6Wh4EiY80iYRiJMRsKcibAXGhan5+sXc2sbsp5AyEjY0aNUvDlHwgRcExZSvPHGG+J65MiRTn8n9zxs/vE4Egb7/4MHD1Ljxo1FlwDSk126dBFTxEvJHyiji8J8dYfkQbt0pPJa7tZBy0HenI60x9k30rCU64Ho4O3WZa23ZbOEOwO8b6osKnBAjU+6T+du2o8/MiQXLgQmHWmUSJgUYVmU35xW3ZERLnzB3BXx1k1TFQkrHsrpSBxoZZE8pyNDivv377u8eCrAvPYJwwymDz/80Jt/ZQJcEyYjYZj9eCcxmTLBd8ryYmEepiORdki6d58yuEi3hTpvtkh/ftxbrUrTqMUHxe0TV+OofP6sbqUj1eDgh0aLIxdvi/SnoZEGqtJQNdRrwjROR9oiYRr9ZtWRMEs68sy1u5SYfJ+iIkNov4C0IA64aPrwdyodyPdg13zT4bEImzhxopgc/vjjj9s9PmPGDNGqCRMzRj+RMNkhCYuJIxfjqErhbFZB5665LrqnYjJE0N2ke6IdXc6TDHkcvhIZGUgLrPMKBbLSvnM36dz1eEWEWV7HaXekgwhDkwQMdCHCEJl05UlmGHzwxLpxJ4l6/LaFhnWpSkUtgsDwhq0apyOT0xD4nnBc1n1JEXbiBOWJiaBMURF0J/Eenb4WYvsFWQ+GgnnV/EC/YbT5pyZmyJAhaT4P3zBP8PjUZejQoZTbyVlr3rx56YsvvvD05Rh38WFaheyQPGipC5N6zt3dsjLIW9aFhWDqwU2LCmd1N84oaLH8gNFlegO8cfCEAJbEZsygGqpugu9CpgdlpMoDqg1ZSGuPXLGz7nBJiKYj06oJc1Z7qKZ/u/LW2+dvxNtEB7rgk5Mp7PRpm01FqKUkA1mUD+QxF4bDXswnZLRj9uzZdpc//vhD2HaNGDGC5syZ4/HreSzCYNIKozJHihUrJp5j9BUJU3dIOjrne+KNKQ/8XBdmw/ErcTUixpGClhQiImH2syOd/736+Jk1Y6Q1PYzifMPjYSRs47GrdOV2gnnTkRpHwlA+ABDFTgu10Jc0KpWbhj9ahXJniabercooDyKtKff/qAuTMyRDzSss0CIM26/cYRtlBqpJ2bZtm91l9+7dYnIQGhZhZu93EYaI186dO1M9vmPHDsrFM638hi9zWx07JD2ZHek4Q9JVh2Tx/v+Ki/WMORRFmJtfUoHslkjY9bvpmrWCONW0g4wZImxRyYsGjz7A8PDGDeW2G/uOUYsO0hPfr6Nany0W0wPU3FKZ34ItJ65SvS8W0z87zxorEqZxTdg/O8+5ZSPRsnzqxghsjk/WKUqbP2pN1Ypktz1hZ1OhbIsnOBLmX5DylNswpyR1R9asWemTTz6hgQMH+l+EPf300/TWW2/RsmXLRCcALkuXLqXevXvTU0895fECMO7hgwZL1SFpS0eGeREJS33g33/+pvX2x3/voVBFRh3So2C2GId0pPudakgrlcqtfBfnb8an8g8zFGpBpB4t5ILRSw5Zb8/YYolEWNhz1rYNbjlxjR79bh1duJlAb/6+TYlQGi0SplE6Ur1e0mJgh4pUzhIxd+zMdac4/1ioeYUFOhKmrgvjSJguuXHjhrj4vTD/008/pePHj4vQGyz8AVozu3btyjVhuk1H2ndIWl8pTJtI2IRVx6y3L3mTKjLJd+LuV2RNR95wTEem/4WgRixbpgyUO0sUXb6dSMcuKc0WhhZhMAK17EtcMWOzveg6cN5muQJ2n7lB9UsqkYJHv7MfcL5k3wV6wCiRMI3Tkd0aFqd+M3eKNHZaQHAt6NOEBv21h5buv0hvtixtFVipKFpUuT592tqMwpGwAImwffs4EhZkxowZk8ovEunIKVOmCCNXv4uwqKgomj59uhBjSEHGxMRQlSpVRE0Y4z+k87UWHZI5MmcQj3vSLyVHlFy7kyTGmuTMbBupcsaSVpNRiFBBXQJWPn8sda5hGe2SDnIWJ2rCYE8hLSrccRGQXW6ITF6+fVWIYsOKMA/qwd77074EYutJ++1s1xnXZ6CT1h2nB7qUtYmcxERNRgIZIR2ZIVLZXuzSiWl07n76SGX61IOITHFLOvLk1Tup9gumJpiRME5HBpVRo0bZ3Q8PD6c8efIIZ4gBAwYExicMlC1bVlyYwIA2cF8oky8LXTl6VXRI1i2R0+PCfPiLwZMKggvF+Tkz27rZEGELRWQEq27xnPTH6w3c/r/82TKKdZ94774QxmmZtToSbymwRmQSReqGbpTwwZ4CUUAgt8m/tp+lLzpXoUgn3Q1rDl+hgwkRdKR8I6p65iAVunYtMN5OOkhHWrxa3baj8ahT7/JlyhsbTRkzhAvz4JqfLqLFfZtZu7FNC0S8NBlmERZyHDtmy/xogcc1YagBmzBhAj3zzDPUunVratmypd2F8Q9II/pCmbypOyQ9qQmzH+RtXxeGs+BQxFpQ7+GvCGa3OHiBczfupukTBqJVJpiyBswU8zx9EGEStU9a7c8WU70vltg936aiIrZ6/7GTenQaQI3emEh3L1wKmXSkDNZqOt1KFQlDHaOMhoGpG0OgQ/7MGaX2AFYdcl0EApX4ZYLHSy+9RLfk71QFpgfhOb+LMBTg4wIxhhFG1apVs7sw+oyEOZsh6Sm2A7/r6EuNoumnPcyC1d/LiyhDAUtxfvdJm2mMpeDclaEmDDEltyzji2y+bSaIhHnhESYpo4q6wIbh+h1bl+SSd5pRt0bFxW2Y40q+26R0DKqji39sPkUX9DAGSuN0ZHqdtz6JAUtaTA7y9qQ5xdCcPq1cFy7sWTrBVzgdqQsmTZpEd++mzv7gscmTJ/s/HTlt2jRhTta+fXvyle+++05cUOgPKlWqJNxmZXFbfHw8vfPOO+I9ExISqG3btvTtt99SPr2mEvzIc/WLUZMyuemzf/d59f9l8tk6JK3dkR7uP5wV58v0mKR0CLlmy/XozQGuYPaMtP0U0aVbCXb2E65SwajFU4swKYiPXY4z7iBvWSSfTiTMcRurXiQ7bT913eVYJwnWEYbPo+vvgOrkY/zhBHr0ShzN23WeTl27I17jh5VHRbPD5o/akJnSkXLIfJg/xAAORHfu2EXCML7I9ASjHgxwd2RQuXnzpvg94YJIWMaMSm0vQFBq3rx5wsLL75EwFOaXLp3+jDx3KFy4MA0bNoy2bNlCmzdvFulMDAXfs0exOYDx2dy5c8VIpBUrVtDZs2fFsPBQBGmVl5tY/Hl8iBicunqX7iQle7VjdmZTce2OUpsjSQiFnbCFtJzu3bWpUFMkp/M5kIVy2B6XIqxwjkwUFREu1re6McJs6cjke/epwVBbirFvm7LUtlJ+6/2MURE09pkaQkg1L2dLDb3Xtpy1FuqFhko0TJKYEkZD5u6l4Qv20+8bTgoBpq4z00UkTDMR5oeaMCybbGxAcX7uEBNh0pQ80CIse3abaz4TcLJnz045c+YUvyXUw+fIkcN6wRQhpCJ79uzp/0gYIlOjR4+msWPH+vzD7tixo939zz//XETG1q9fLwQaas9+//13a60Z5lZWqFBBPF+/fn0KZTxd9eoOyUMXlB29p9+fdZD31TvWgb19p++w+5uEZN/SpkbClurx/H8LOBm8nRnD1Z0w4vFq1OTLZXampEhdFs+diQ5euC0ik0VypjM7Ua9DkEF+m6hyBNuajAKCt1qVoc3Hr9o5veMEpX3lAuI3gUjxbxtOWO0qwCM1CtIHs3eJ21XPHaS9BcrQkv0Xnb7fpuNXqU5x79OjPiPTHJm0+T5t9YakHVjRSEmePStSksVy2Trj0WxieiyZGypuL+79DouwoAJvVETBoEdmzpwpBJk6OAWHiIIY6+VvEbZ69WqxMPPnzxfpwwwZFLsDyaxZs8gbEM5DxAvFbQ0aNBDRsaSkJFH8LylfvjwVLVqU1q1b51KEIW2Ji8RZAV2oIjskZV2Yp/vlfFmjKXNUhHBwRzE+IkHrjl4J2UiYjDK44+/lan6kmhhV7ZcatcBSr9+SubMIEYbIZHMl8GO8AmfgYseFHd7IRQdTPa625JCiVEYjYTqKmYdoflCnc0c/VZ3++G0JfTt9IH3bZwR9T4Wdvifq86Z0r0dBF2ExzqOi3p4oaF66hNQYRBgiYaUq2jn0j32GQkOEORnf51fgpwe8MARlfKdZs2bW7sgiRYoIawotiPQmJNe5c2fSil27dgnRhfqvLFmyiIGYFStWpO3btwt1ifdTg3qw8+fPpzlgHOMDGOcdkuuPXrXVx3i4Y0bkDClJeDIh+vLalC2p/iYhKXREmLSW8CYiLA1b1bSu4LrWsUqhbGK9ww5DUipvZqI9Bi7Ox0EcFErtr4axRHvP3aR/LWN31ERHRtATtQvT4n0XqUmZ1N1pagEm6VS9EHWae4IoIY56XdtBP+Qs7NRcd9Why8Lrrlax9B38NefePcX+QEMRZuuODPNbp17+rKlPKEyNtCgIViQMIgwbbyCbAhgr0hP1zp07Yl52ovzNWqhatSr5VYQhJagl5cqVE4ILdv9//vmnMDxD/Ze3wCytb9++1vtnzpwRos5shPnUIen9QRtdeRADzsYXAUTGEMHQtAbFlOlI+wPXmKdruOyOBBNerE3TN56iJ+sUsYuEAVffha5JSrJ5LTlEwjCgu+lXSvrVFV8+Vk1EYtNaZ6mwpA+yXLlIQ16sRAP/cj5i65ulh+iXbnUp4MSrujO1EmH+ioSpOiTVNZHfP1+LTA2M106cCI4Ik5EwiPW4OM3qBhnPuHTpEnXr1k1kA11l9QJi1qoV6kL/WrVq0aZNm0TN2ZNPPikU5vXr1+2iYRcuXKD8adSQREdHi4u6o8GMeCNyZIfk0cvKQTvMTzYVN+OTKVuMfZrajEine4+EgIXcmaPtxPHD1dKuJcgbm5F6tSpj91gpS7OFISNhEGAQCBhX5NBRhAHdzoAZqxqP17sUDleuiG5j/IYwvQCD1CHInqtflKZuPEXLD1wS3Zfowgwo6rZ3zUSYcq35SZGDXcLhz9vRnaR7lDWjyX/3yMKg3AUDtQNdmI86QfxeMPgexfkswoJCnz59hC7ZsGEDNW/eXGTvoEs+++wzGjFihMev55UIQ8QKNhXOQnFbt24lX8AcStR0QZCh3mzJkiX06KOPiucOHDgg3hPpy1AnzEdPJW93zDbDVtcH/gmrjlLfB4xYpOQZ6ZmspoU6etC4tHeGj/K7uHgrQdRGxRrpACjrwQoUcNvt9pl6lpmFGqTQsO1DiEnaVSkgGlfgxzdr6xn6ZskhmvBiHQqKCEPnoUb1Jn7xCVMPXLd06kVGhFNWJ2lg09aDwSMsnXmnmoPvENEwdBUjJYllYALO0qVL6a+//qLatWuLujCkJ9u0aUNZs2YV5VAPPfSQR68X7s3wSoTiUJu1bds2qlu3LuXKlYuOHj3q8fBKpA5XrlwpfMJQG4b7y5cvp2effZayZctG3bt3F6lFNAKgUB/vCwEW6p2R3iI7JCXe7JfdcWofs/QwhQK+WFSAVuWVCJA6xegJiDrksTjvGy4lKevBvOgm0spk1O6pLNFCmL3ZorRIL6N7EkPBjVyUrz5R0Lw4IFSLxINVDybhDsmgg+ZB6QcGewqkJwFmaHsThPJYhMEs9YcffqBvvvlGpBL79etHixYtorfeekvUdXnCxYsXqWvXrqIurFWrViIV+d9//wlVKQdldujQQUTCmjZtKtKQ3nZfmg1vT2zRIWl9DS/+H4O88d437tpsA0IVX2rCwA9da9PWgW2oXH7v3dFhRgqOXjZYSlJGwpwU5TsbNv3lY54Vu6Y79sVZVb7FC0+mhuUkAyOLMFkTprmXb6iKMLndBisKFarrXUdAryArBzAl6Pvvvxe15+PHj6cCiOx7iMfxVKQDGzZsKG7HxMRYLSCef/55EaGCf5i7wAcsLeBIO27cOHFh7PF07qNjhySIS/Dc0wuu7hiaHKpDu7VM9aCmKacqMukNqAvbcOwqHblonkhYkoPNycgnqlGXmoW1E2EonMVBzKHzWvJmy9L0146ztHDvBdp79iZVLJiVjCvC/FQTFqpiQDaTpFGX7Fc4EhZ0MLbxnMXjcPDgwfTggw/Sb7/9JoJSv/zyi/8jYYhGXbWMG4FnF4xTpXeGPOtiAoCX+1TZIemLsaJMSaaFHDRtZkYtUiIlO04Hz8Fa3ShR+oN5VLz/v8LewTAHMydnjrcSklwO6fYJNOzImYxpDEEunTeWHqpSwNopGTD8IcLIT92RLMI0XqG83o3Cc889Ry+++KK4jdr1EydOiCzeqVOnREOh30UY3GL//vtvcRs1WhgthPQh3lxL/zDGP8gOSV+QBeESGLjO6dmIXlSNhznow6Bwo4CB0cGux5LfBdZ3sqUA6KVJm0j3XLxo32Wn4m6icnKA4dsftC8v6rUCURemBs78YP7u83Tg/C3D14RpXpgvRViojdCRHpXBml/MkbCgAgP5UqVK0b59thnOmTJlopo1a4rRRd7gcToS9WDoYASYk4Si/LVr19LDDz9Mr732mlcLwQQOdYfkS41KaBIJm9y9rmjnxwURGRheHjx/i2oWDYLhZYhR2kmjhGwY0DVSBDkZeJtgEbcvNChuN5dQE7CjRHF1GpEwUDZfLLWvkl8M+UY0bOwzNcmYIswPA7xBqEfCgi3CQm296wQ4NsBYXks8joShJTNS1Zr71FNPiY7JXr16iZwoExjCfOiQdBXRchfH/1OfZZezRNr2Byp6EAxQU+SDobCWwHkfMzzVHLscJy6GjYRZRBjqDzVHXZyfDr1aKtGwf3eds476MmpNmN8iYfBhtJyUhwTBrgkL1QikjkDwafjw4ZQMvzYNcCsStnPnTqpcubIQYLidFp5a9jPe4cs+FW3483afs9a9eBt9sS2LSoRZOv1Mm468c4fo8ceJ5s0jev+fYC+NKO5Hh6Sj6P1nx9lU5q5GiISdunrHmlbNFB1cEVahQFZqWykf/bfnAo1ddphGP1WDDNsdGe4nMYDXv32bKGuAmheCCQ66crvhSFjIsmnTJuFfunDhQmFLkTmzfVDCUwcHt0RY9erVxbxGeGPgNg66zorw8binlv1M4Hm3bTlx8RbpTSW5m3gvlQgLWB1NIMHBBkZ8K1fSjWiN02Q+gMikowibu1PHIgxiA+vSSSTs83+VWotGpXP5x33dzZowdTQMImzujrOiTsydphQ91oRpnpDEMkr3dqTGQkGEYZvBcQ+K1sv6H5/hSFjQwQQfaSCvBW6JMHQ+5rHsLHGbMa5FhSbv7RCGU3dCwgIDT1+JS6RLtxJSCTZDM2iQEGA44Hw4eCrRReUI91ap4DrVq4VBy/J5aTVq8i7cFkLYFw8yvyEFEMoXVAdvLPeCPedFdG9Qh0r+eW+HcTvpUblQNmpdIa8YFj5u2WEa+UR1MqZFhWYvaXtB1CchMgQRFugRPsFMRWIbwtiiYMA1YUFH6/nZbgWpYcsvD7y4ndaFCQzBno+dNWOknYGrJCYqgorlzGS+lOR//xF9841ye/p0+sciwED9vWuDt1wONXr5skZTs3KK0Ph7h8VYUq/1YEhFWjbkpHv36ZO5ykDt5+sX8594tAzxJovNjjvITsm/tp+l4/6stfOnRQX5gVArzg92ZyTgSJguQD3Y4sWLhVGr9Eo9e/Ys3ZYRfq0jYdKSwh3QJcn4nyBrMPr3rSb09eJD9EClfFTaYSYlDqDHr9wRKbJGpYMUttcKFB1/9BHR0KHK/UceIXrwQSqycymdumo5aG7apJtIGIrZ4fi+aC9SaOfo3QfKaW/U6SsyCqVKRU5ed4IOXbwtzGvfbl3Wf+8tRdi1a27/S9XC2alFuTy07MAlEQ376vFqTv8OHm1g04etvYsAo97QKJGwUBRhwS7KBxwJCzrwBYNBK4zrMecaFl2xsbGiWB/34ZyvuQh7BAceFY41YeqdPNeEBYZgH1iL5MxEI55wfjBChyTqaGBTYXi+/tomwHr0IBoxgnacum439SbjsSP4ZSJMHJRFVEciEVFqVSEvxWSIoJNX79DO0zecjgHSTSQMNfK3E+jrRQfF7ffalqNsmfyY3pWDpz0QYQD1dRBhs7adEZExbP9quny7xnr7n51nqZs39i9+iIT5lVAVYRwJ0y3fffeduGAeNahUqRINGjTIOtca9hLvvPMOTZs2TQimtm3bilGMmIXtiWM+hnfv2LFDWHRJ4JP6yiuv+CcdCV8weUFHAIrz58+fT9evXxeXefPmCbOyBQsWeLwAjPkol1+p89lv9HTkH38Q9e+v3P76azo/dCT1nXuAOo1bYx3b9NT57VTj7AElXRkkYlUF7Oeux1OmqEhqXVHZqaCg3JELN+OpxIB/6Y/Np0gPkbCvFhygWwnJVLlQVnqitp9ri7xIRwJ43jUpk1t4sCEa5sjWk9ed2sAEPx3pxxpSFmHBi4QhaprE83sdKVy4MA0bNoy2bNlCmzdvFubynTp1oj17lFIHmMvPnTuXZsyYQStWrBApxC5dupAnrFq1ij766KNUllzFixcXMyQ9xePG5T59+tDo0aOFgsyaNau44PbIkSPFEG8mMOgswWRHufxKegzeSveNYBzqSig8/7zY0d19riuNqdSOWvxvOc3aqvzIutQsROsHtKJhJe8p30UQRZgaOVi9Y1XFfuSfnedSfQf1vlgiInn9/kzbbsZvnD6tXBcoIKKKf2xRxOAnD1cSRfl+xctIGOjTWqkN+3PLaTp9zfVoqHhLtzDSk7jEW3zPghIJk2atnI7UriYsmOlIdRdqqEQgPaBjx47Uvn17KlOmDJUtW5Y+//xzypIlixiveOPGDTGvGloF4gwjh1BkD7N5OX7RHRCMcpbxO336tEhL+l2EHTlyRLRoOpItWzZrCJAJbYrnykxREeF0J/GecQd9r1pFKYmJ9Ffrp6lVxa40cvEhYSJas2h2MaIJXXL5s2UkattW+fslS5R2/SABAZMpKoL6tysv7qM4PzZjJJ2/GU+bjnsW9fE7lg7r+8VL0Mdz9wid0KVGIapVzBKlCkQkDILHQ+drLB+sM+Bj9t3yIy4nFGA7mbL+hF3Xp1twOlLf6CEdCVuQLFlCToTdunWLbt68ab0glZgeEEpIO8bFxVGDBg1EdAxjh1q3bm39m/Lly4sZ2OvWrXN7WR544AH6GmUqqtIgFORjmDcEoN9FWJ06dahv3750QW6QYtu8QO+99x7VrVvX4wVgzBcKi4wIp1KWYv0DBk1Jbl+zkx597ivqXetZOnsjngpmy0hjnq5BM3s0FOOZrNSurRzYsUPcsCFoy/tCw+K06+O2VLu4IjKiIyOobaX8Vs8wiWNU7GpcYoCXlIgsJ2uzYorRtpPXxezR9y3i0e/gTFU6l3oRDXvL4qKPVO7Z68oJxsVb8alE2MA5u633k911lPdrOtIPhGo6MpiRsBDtkKxYsaII9MjLUFmn64Rdu3aJ6Fd0dDS9/vrrNHv2bPH/8DpFCtExiIR6MDznLiNGjKA1a9aI10SN2TPPPGNNRaI43+8i7Oeff6Zz584J9Vi6dGlxwW0sAEJ9DAPKW01bbxpqhZy/EU99p2+nRzLUoa2FKlBMWAq906YsLX23ueg6TNUQAb+gFi2U2/AQCyKOqTwsL8D8w+R7ihBAB6IazPoMKAh7HTtGt6JiaNiJcGvRe76sGQPz/hBgPqQk65XMRfVL5qSkeyk0foUSDTt55U6qmjs1r/+61ZqadGZyHQifML8QaiJMDxYVIdohuXfvXpFOlJcBAwa4/Nty5crR9u3bacOGDdSjRw964YUXxP9rWXeGovwPPvhA1JjVqFFD1KFt27ZNGNr7fYA3RBdGFy1atIj2798vHqtQoYII8QW7Yy+U0PuaxgBkIJ3ckbIZveQQ1SmOAufU8wKDDVz/f1h5VBxY5ezCR3ctpn7De1C+yuk4zzduTDRzJtEaW4ecHmhYKpewfEC0a+2RK9S0bB7a7yCKD1+8TXUs0bOAAOFz6xZ907wbXY6/Jzo7uzUqTgEFIuzKFY+L8yXojlx/dANN23iK3mhemjafsBdzE9e4LssoMWCeaEDInSWa8mSJptyxyjUsLXJH56U8OQtTngwxlDUlRZP9qdUnzB/75lASYSg1wDajBxEWgpGw2NhYUX/uDoh2QacA1H1hzBDq2J988klKTEwUzYTqaBgyefk9jG5ifvZzzz3n4adw8Vre/BN+0MiL4sIEB70LXhkJQ2H42GdICJwxSw6Jx44Pe4j0AiITf+84S8Pm76dzN5QIRu28GWngVz2o2q2zRJVGpv8ijRop12vXKr5img/q8z4t3L5Kfvp1/UnRJQkRph4xJUVYQDl2jA7nLEw/1+kk7g7qUFGkTgOKF15hahqUzCVOJjYdv0bfrzxCVQpZDopusvuMi+hwjReUy0aiqC0LFGGWJcpyrQg1u9sWEYd0blD2B6EkwuTIIkS+VbYEQSEEI2G+gEJ61JBBkGXIkEHMfZRjhw4cOCD8vlAz5gn4v2+++Yb27dtnDUS9+eabosYsICIMhW5o78TCQ1mq4Q7JwKBzDUZlVY7nu07fSBWB0QPbTl6jIf/sFXVJoFD2GFHY3uH8Lgo7fwgmM+6t6OrViTJlUg7qiA5XrEh6oWPVgkKEYRzQZ50rW4UmPhaOKYEWYSnHjtOQVq9QcngktSqfl1qU9zx87zMyHellJAyCp3ersvTchA30+4aT9GYL5aw7LdCosrp/C7pwI0H4omGk1yWH68s799GlDJnpVsYslHjvPp25fldc0gOecLljo2wRNdU1/Pr8RiiJMJmKDObIohCOhLkL0pTwBEOJFIr5f//9d1q+fDn9999/opase/fuoqY9Z86cIrLWq1cvIcDq16/v9nvMnDmTnnrqKeEVJsUbuisxzBuNAJ7OlfRYhCHviQ6AO3fuCDGGD3P58mXKlCmTyIeyCGMACtklqw9fpq0nbVEHpCb9bkWQBudu3KUvFxyg2dsUuwl0Fb7RvBS93KSkcJyn9XOVPyzhpuFmhgxKgT5qwrZs0ZUIQ6oRo4wu3EygFQcuic8OGpfOTasOXRYiLDH5PnX+dg11bVCMYqIiKVOGCKvPmJYkJN+jlQcu0sqStSgq5R4N7BCk9eRDTZgEXZLolIU/2DdLFd8wOWPSGd2blKC8sRnFxSWFHyc6c4biN2yiy2UqKsLstjKD1Src5O3bCXT5VgLFJd4T6XNMb7BOcHBCuD/TkaEgBqTBcLBTkYAjYS65ePEide3aVdStQ3RVrVpVCDC42oNRo0ZReHi4EEpqs1ZP6NevnxB7Q4YMsXsc3ZF4zu8iDIVo8OKANT8+JBQgQnzIj8JJlgkMOg+EiWgBxs+MWnyQhi9Qagcl6CYrkC0mzdEvknHP1KSHLJ5XvoJUHNJH45cepPj7yhp8rFZh4dJuVxguh9QX96BWqWpVRYTtDJL3lgvCw8OoQ9WCNGH1MZq78xzdild8xBqWUkQYIi2wW9hz9ia9P3OX9f/2fNKWMkd7FSh3CsRfsy+XU+K9ouJ+9wwXqbjK6d9I6Ui5faM27MWJm0TUCmSLiaJKBbOKdZnWaKn0CvMzZslEhXMol/SIS0gWoswWXVNEm1q4YYpCh2ra/IZCNhIm68GCnYoMNfHrIek1B2bMmJHGjRsnLt4CgQeh5wg00FdffeXx63m8l0XXAYZWQk1GREQINVmyZEn68ssvRReCp+6zjDlrwsDD1QsKEebImWt3nYowR7EGev6+lR6q6lsNGWwZUPeF11fScWFU59QeGrhjFlV9fz6mkdv/g/S7czcSJkUY0JkIk12SEGGL916wzvksmjMT5ciUga7dSXL6HSF9rKVv12/rT1rFSr5bV+jNJp7VUekpHSlpVjYPVSucjXacVkRIxgzhLkUYPNvSxYvuSAhlXIrlyhw8MXDzpq5qIf2CFOxy29FDJIxFWFBo3ry5cM2Xxf+S1atXU5MmTTx+PY9/NYh6QYABpB9RFwYQFTt1KkhjUBhdgs63kk6iHY+NX0cHnfiHqQ0wtQJp0C7fraU+07cLAVYo8SaNmzOU/vj9faq6ZwNCu6n/SUbCvBFhu2zRJL1QtXA2IbqQttp1xiYYHAevq1m09yI9+9N6WrjHff+ctFBnn/uu/pUyV65AQSO3Zaj8ZTdNVNOqDbO46AOs30oFszkVXqjbShMU6BnVrBXLfjvADR6hLMLU4pcJOA8//DC9//77ohD/119/FRfc7t+/v5gf+ffff1svfomEwRMDLZ8YC9CsWTMxHBM1YVOmTKHKlSt785kYE9OyfF46utoialQ8MGqlXZdk15832v3P0v0XrcXy3gAjzS8X7Kc5289a6756NihM3bvUo4zxd4h+/JHo1VeJJk0iatqU6KWXfEtHoogfnDundFJZ5iLqAYiFjtUK0LhlR+xEAUQYOvycIT2w1hy+okk3K2rNJJUuHiMqk47thz+RXj6yzscHWpTLa1d8j0iYZNJLdanLt2vF7cj0aiDVDuBGEWFYTji4w74BKUk3LQQMLcJkKjuYyPUcCmlgHfLGG2+Ia9SSOdaTyefkftfZeCOfI2FffPEFFSig1BdgLlOOHDmEIdqlS5fohx9+8PTlGC/RfzJSoWUF191vsj4JrDxoGepMRD88X4tWv68YoKKmJU2DSwfuJCbTqEUHqeWI5UKAIWv7RO3CtPzd5tTz6g5FgEEwvfwy0TvvKP/UvTvRihW26IgcMF2qlPsfFKNE5N/rMBrW0WLcKskYFeFenVI6YC7igFm7aIXq+3OGWoMUQeos2ssh11ogBbL8nn0AO9oFfZpQp+oFqUfzUlShgE2I5MpsG/CLEV5pIqNgRhJh+HGFSl2YTF1zJCzkuX//vlsXdwSYx5EwHAyRgpQRL9xesGBByH8pwcAAJWGCtIxAl+y7SI/UKOTU3wrt9QB1RBhKnT2T/cR6Z3Vff+04Q8PnHxDzEkHd4jlFB16VwpYDhTRTlfMeMWLi8GGiOXOUAdzNmqH9V3kO+X5Pz+yRkjxyRKkLa9mS9ES5fLFUJm8Wq2N+xsgI62gpb9l47Co98b0yc23qxpNpRszik5R6sCbHtlK20gE2Z/WjCAPl82el0U/VsN7v92A5MXkBKWBJzixR7okwlHqg29YoQIShaN3sIkxP6UiOhJkKj0UYitH27Nkj0pFMMDGGCssQES7sH751Uu/1765zQoTtttQpgbX9FfECq4hsMRmEALt4KyFNEYa6ryFz99L2U0q3UOEcMfRB+wrUrnJ++wYGOaRVGvPhgIfWZYiwHTuUx6QIq2E7qHokwmbP1mVxvpKSLEgjFylF+DFREVTazUjY8gMXqbkq7SaRAky9f3BsGJm/6xyNXXbYWqxe9vJJorpBrAdTpyMhwvxQUA4XfcmU7nWFDUjNoukcvNX1YEY5wwKhEgnTUzoyVNa5jkFJ1rJly4QlBqJeakaOdMPg21sRhoJ8iK8rV66wCGPcpt+D5YUNBDyVHv1OqZEBSGEhJTlprW3MS0FVDVje2GhFhN1MsI5Bcqz7QsfjX5a6LziH92xZml5qVELx+1ITF2cTR2pjvmrVlGstRFiVKsq1DkUYsBNhGSLE+nUH2DAcG9o+3Y5cCK3KDu7xs7adsesWjEmKh700BRVZmI90ATrM/HhgxYgut8Z0Ga0oP9QEQSgV5v/1FyzhlaYlI0VlAwRKsj766CMxoxLDv9X7RW9cCzwuzMegyvfee4++++47LsQPIkY6WZYbJ8wtJRkiwoRJKFKSZfI5j8jkzRot0meXbsenqvsav+Io/bDyiEhzibqvWkXonbZlXZthbt6sRD0KF1Yujl2NZ84o9WBShNWs6fmHlK+1Z49ygA+2s7aTbtXercrQrfhkyq8y03U2lmfdUYsvkgUMAU/Pr23R3gupRJjjmKRyl04E38w2Kkpp84cAQ3G+HqIbRhVhoWIcqqeaMJmOjI8nwsQabM9agd/EU08pr43yDUT2zWw94gWYQ/nzzz/Tiy++SFrg8dqFSdnGjRupWrVqFBMTIxzz1RfGC3DAxtxBDwq6DabBrEIMdUO4vN6slDUlabGPoi4O9WFSUCESJuu+Zm09TS3+t1zMoYQAq1siJ819szENf6xq2m7kct06iqvYWFtB/bJlyhmgs79zh5IllfFF2IGtXk165O02ZWlQR5sImv5q6nEdz9RTDFXVfDx3j4hKymaJbyxzQNUs3nfBaeE++LZTWdo49jnquH8VkRfz1fReFxayIkwKglupLWdMhZ4iYepaVa3F7/Tpyv4LwGJh8mRtX98EhIeHUyM5L1gDPI6Eff3115q9OYPK80QiDEJHdx5COtu32yIqJoqEOdK+SgEx7gUpSZkWy6HqJgPycdSEbTlxVdR9SWPMIjlj6IN2FehBx7ovVxy0GJKWK5f6uYYNlYL6gQNtRfneWEwg8vXcc0ToEv7gA0WI6fyLqlcyFx36vJ0w0N126poYpl1Z5XUlgfBC6veLzlVo8N+7RWTMEaQd4YyvNuKFdxbIdP4M5Y27TlSkiNJJGmzw/R46xCLMV+R3aWafMOyj79xRbush0ID9DNY71jlSklra4UycqFznz6/My+zfn6hzZ1sKlCFMDYLjvlZayCMRlpSUJAZ3Dxw4kEp4YmTJuAYHfmmPACsGdOm5IcKMTvn8scLI9ejlOGtNl6OrOIYQA7i94wKyREdSzxalqVuj4qnrvrwVYU88QTRlii0KJgv3vWHwYGVHhsgm/MYQHTNA8wRGCMkxQmpLEHxHMCTtPW27GFbduUYhWrD7vNPuywMXbonZic/XL5ZKhMUcOWRfNxdsNPQKC+lIWObM5hdhMgqGEyq9eKFhObDOtYyE7dtHtGGDIvI2bSJq1UrZb376KdH//qfd+xicd999lx566CEqVaoUVaxYURjYq5k1a5b/0pF4M0wQZzQCxeLffWeLxgCdprG0BtErRMPA7YRkp67iUoQpf0/0VJ0itPTdZsKPySMBphZhZcumfg6RSFnb4qsIK1jQJvSkqDPgd9OkjFK8/kPX2tSpeiF6snYRcf/x8evovhPbNpjBAoxGkmw4eoUuWVLJMbu2+75utURGD1iE+UYoRMJkPRj2EXqpj/JHQ4SMgj30kFI3KyM9Y8famwmHOG+99ZbojCxbtizlypVLTAtSX/yejnzkkUdozpw5IiTH+Mgffyi1FIiW4EwDQgwRFEQi0kljhRmyKsweiDDYF0iiI+13cOoar+6NStBHHbws6EaNg5wH6UyEobB11CiiHj2UdQ/bCl/Ae+zerQi/du3IiEx8sQ5dv5tk9Wsb0L48Ldl/gS7fTnT6920r5af/LTwo0suPj19LmaIirQauWTNGUpF1y/UpwuRg5mBj1EhYKIgwPdWD+atDEvu9GTOU27Lg/MEHlU5iNCyhe7xuXW3ey+BMmjRJBKMQDdMCj0UYLCqGDBlCa9asoVq1alFmGY5WqUTGTeBPBTAyp1YtjHhXNngcvJ2lzVTovNTILSoUiBUde8cux4n7jtEtdEdK4BnmNaj3wk4GIXyZhnIEOx78qKQo9gWDR8IcDXMBfNpgfIu0pCNP1y0qRiAVy5WJTly5Yx2FhFE9T9UtQm9Vyko5PtmrRBH0siOXtT0+DvHWDBZh+kVPHmH+MmxFfSROVHFCisyAPMjg9zpvHtHGjfr57QYZNCAiFakVHouwCRMmUPbs2WnLli3i4pjGYBHmAXL9wakdGz828pUrldbgdESYeVKS+a0zDaMzOEbCNBptA1d8AIPhtNQroiNaFLnKaJtMgZqEh6sVpP4zd1nrvCRDuyh1Xq0r5LPW7jUqnUsU8RfDiCJEGUH16ko3qh6QB1SOhPkGR8LMkY5ELTJo3NhW5wfq1LGJMEbw8ccf0+DBg2nixImUCd3wgRZhx+RwY8Y3LlxQvKkgCnBwAmh7hQhDXZh6oLQTTBAIs6YkpQiTVhUSFOFLbicqdWNeceKE5wO5QzwS5ko0L3+vOQ2bv18MVp+45hi91NjWoNOqQl6rCPv6yRpKTV9Skk2Evf466QaOhGkrwlDfalb05BHmr3SkFGFypJtERr9QqM8IxowZQ0eOHBFGrcWLF09VmL9161byqwhjNI6C4YAtd2TSe0TOOEwDb5x59UhFFwOPHT8jDEa95uRJ5bpoav8rvyAjYadPKwcnh5S9kcmXNSONelI5aejTuoxIW6rnhCJtjG5La/oYdY+nThHly0f0/POkG1iEaUMoRcLMmo5E0T08Ep2JMETCwP79ynuxVQWhLl5L3BZhffv2devvPJ2bFLJItYxaMIksWkYaC+3CwR7vEgAgtBa+3VQMg25eznUqEMOnfY6EFbNZJ/iVXLmUC1JdqLWQkU6ToRZgAOILsz+T76dQFJosUIf35ZfKk716KTWPegHfD+CaMN8IJRGmx0iYFiIMzWDwQcOJkqOFDMozkEFAvRimjsC2IsQZDBuiYIiwbXKcSwhEZwLC3r3KtdoTDGda6KabP5+oSxei8eOVejGTg7mQzmZDgnlvNaFVhy45dXHXrQiTEU7s3CCoTSrCnJFZlUKmRYuUOZqIBKLzVE/IqAYOsH4Y4u0xXJivX8yejpSpSBTkO/sdICUJEYa6MBZhguvXr9Off/4p0pIY44hifaQhkaIsVMh+8otmIgy+GIyGHD2qXDt2Wfz0kzIyB+Hf5s0V24pnn1VSOaqi5lDRuxULZhUXn5DpyECKMKQkIcJMVhfmEV99pVy//LK+UjnqAyoEGKIJwT7AsgjTL3qMhGmZjsR+CrgSWBBhKCvgujDBzp07qXXr1sIT7Pjx4/TKK68IEQaT1pMnT9JkD0c96cR5LoRFmKMdAsw+kap84w1lgj1+ID17Kp4tKidzxgOPMDRBBLImTF2cb7IOSbdBHdjixcqZdZ8+pDuio221enpISRpdhKGuCE0YZkSPNWFapSNxEoJReY6lMc6K89ev52MQKaVZGN596NAhyqgqsWjfvj2tRGOdh7AICwaon5CDg515UkGIjRunjL0ZMULZMUOMyfFGIRQJ00QMABxwA7kTlcX5oRoJW7VKuUZUN1BdqUa2qTC6CDNzh6QeI2FapSORZoQ3Ik5KXNkiQZwhIHDunM30OoTZtGkTvfbaa6keRxryPOZtegiLsGAgbT5wEEir2wS5ZTRESAdjjI9gPDdqBRACgVSuapuKUIxgShHWpAnpFj0V5xtVhMHfULbom7U4X481YVqlI2UUrFIl2/foCLywZJQsRMbqpUV0dDTddCJ+Dx48SHm88JlkEaanVKQrnnvOFg52HFuEuhtMvHejcSIkQW0dCHSnqfxu8WPVcr6bUZA7az2LMD3ZVBhVhIXCEG89R8K0EmHpNQ/BxDXERdjJkyfp/v379PDDD4upQUmW9DsaEvHc+++/T48++qh/RVhycrJ489PwP2ICJ8JwlgJg7mpBBHWw4+7XT6l5evVV/kbSEmHlywd2/eBgKs+KZHdmqABRg9mZau87PaKndKRM5RnRU87MNhXYx8rh1XqsCUMqEXVd3rJnT+oufWfI37EqEBBqlChRgi5fvkwjRoyg27dvU968eenu3bvUrFkzKl26NMXGxtLnn3/u8et6ZNYaGRlJX331FXXt2tXjN2JUyLy6u7Uy+MFhqr1K/Io42KxZtr+BhwteV6/1N8ECfmvBEGGyEQC1f+jOrFaNQgZpNoyUrKtZnXpARjZkpCOY4GAK9DLWyRPMLMLkthERYV//ppd0JEodsN7lfU+Bj6G6htUVUqShvOLePWV9hBgplrISdEUuWrSIVq9eLTolIchq1qwpOia9wWPH/JYtW9KKFSuEXT8TQAf3ihXtRJjg11/t70+fTvT++/y16CESJi0xMBlBft+hgkxZyBSGXtF6/p4vSAHDIky/9WB66oZCVx5quJASw/brjQiDqJBzdUuXTn9fhug+IoOoaU7v701KmGobaNy4sbj4iscirF27dtS/f3/atWsX1apVizI7hM+RL2Xc7NjzVIQtXGi9G4Yf0NKlyp3evYlGjyaaPZtFmOMO9OJF5XYwBqLL7zfU0pFGKMr3x/w9LSJheoq2uIuZ50dKexu9RXQhBrD9Xr7s/fZ79qwiqhDVSi+ogr/BPhQ1ZDAaD1ERNnDgwHSHdns6NchjEfYG/KtcvBFU4j2EKhn3RFiRIu6vKVkXJtc1dniJiURlyijCCyJswwalbsxDx17TIu0hkMoNxsFNirBQioRhp47UuBFEmJaGl76QnGwrzDdyJEwPYlZr5EkcRvrocfuFCPN2+5VRMAgwV52RjoEAiDCUeIRosGXXrl0UhY5gDacGeSzC0B3A+GgeKn/Ynogwx7CnPOvEj6FAAaLatZWDH7zEnnmGv6JgdkZqKcKwg8U4EYjw775T/Hz0DEabIEWCbbJECdI1eomEqSNIRhRhUszKaJ4ZI2F6FGG+ptNlPRhO5N1B7kflyL0QZPbs2aIgX0vYoiLQyLouhDQ96bZBKFidvsTAVSCLAbl7RV/1YOoxSb6kI//+WxE2EycSvf46GSoVqacaGj1HwqR4iYxUfLeMhhSOZhZhektHqrdfb08iZCTMcXReWpEwdbNTiBHmp/2Zx5EwEBcXJ4rz4Y2RiJSYirfeekurZTMnMiqCKJgnXyr+tm1b2/2kRGUkjBRf9esrKckQbiHWVWckkKIZTtP4nXhzgJXDdWXjxbff6ttLyihF+XqKhKk7I/UuXP0hBvSMntORvkbCpMB0t3xFHQlDTbIRt1UNuiODLsK2bdsmZiTduXNHiDEMroR3BorVEKZjEeaHejAJ0owLlNRFmBwJI89CIcKUL0ipL9HzgTpUImHwCUMXE1LQqNXzND2H1L+qGUN8r4g0IT2pV+SQXz37g+mtO9LInZGA05HGPInw1IQWxfiI1iJ9juNYIGfx6oCJEycKewrMh2zYsKGw7HL0UV27di01bdrUv+nIt99+mzp27EjXrl2jmJgYWr9+PZ04cUJ0Sv7vf//z9OVCD2m4imJxT2ne3L47sn17+9QXztZQ5CtdkB2BjxieDwVgsChNcYMlwnCm6EuHJL5H+Iyh8Pn551NHxvTG9eu2lv70fIf0gF4iOEbujFSLx2Cvx1BLR0oR5q3PnaeDyVG8L+vHQjAl+cILL4iRRS1atKCrTqZs3LhxQzznKR6LsO3bt9M777xD4eHhFBERQQkJCVSkSBH68ssv6YMPPvB4AUIOrcLbiKSp1zcO+IiMAWcibPFiJRKDKIpDCtmUQPSgUxcWKigSDxa+FOcvWKBct2xJ1KGDcnvJEtL9TFQcsIwgKNSu48Hs6jZLJMyMIkzP6cjcuX2b+ODNOCZZFxbCxfkpKSlO68OuXLmSyrLLL+nIDBkyCAEGkH5EXViFChVEmO6UTLUxrkFkA3gx6FNNGHbYjp1ymP81f75zESajlMuWEX32GdGQIaExlQDCM5i1C76IMBn1Qi2grLHatUs52HnrkK2ncVzBRr0OIcSyZw/OcpglEma2wnxkG/TcHSlFGGwqAiXCQrhDskuXLuIaAuzFF18UUTEJrLngno80pd8jYTVq1KBNlroPzEwaNGgQ/fbbb9SnTx+qXLmyxwsQcmglwpzpCjmE1VGE4eCori2SEZZQEGGyQ9FoIgzRkbVrbSKsYEHFzwd1YuiW1CNGE2Go15PNEsGM4hh5ZJGZI2H4PHJupB7TkVKEyWOKL9MA3EWWGcjfegiRLVs2cUEkDHMi5X1c8ufPT6+++ir96jjFxh+RsC+++IJuWXYaGFaJOZI9evSgMmXK0M8//+zxAoQcWokwpTTfuQjbuVOp/ZKFgwMGKGd1aEU+ckSJpqifNyOezufUm00FzjTxHeEMXLaQ4ywLnwvizMs5ZX7FaCJMC8NLLTBLOtJskTCZisT3osdGJ18iYShJkTZHnogw2VDmOEIvRArzAUY2vvvuu16lHjWJhNWuXdtafIZ05IIFC+jmzZu0ZcsWqhZKQ4qDLMJcdq8gpYFuvL/+UgxcsQP54w/FzuLPP5UaKTwvjfrMihQ9wRZh3hbmy8JXtdGsDHUvX066RIowvZu06s2mwizpSLNFwvScilQfQ7wRYepifvkbcAfZUIbSIz9ZNuidwYMHi1Tk4sWL6fvvv7cGpc6ePSuGeXsKm7UGEmy0/hRhEFrSLf+xx5Th0dgo8PjnnyuRsqpVledddVCaBb1EwuSMNUQg4STvqQiThbCgTRubF5ceow5GjITpwaaC05H6RM+dkY6F+Z5OspEiDHWQmAvpqQiDXY63XZl+ZOjQoVSnTh2RLkSQ6JFHHqEDcnydhfj4eOrZsyflypWLsmTJQo8++ihdkN+1G8ANokqVKtSpUyfxOpcsx/Thw4eLCJlfRBjqwGrWrOnWhUlnZys7E/1REwb69LFvKf76a2WGYv/+ymMyWhkqIizYNWEI3yPCgdQihJgvkTC0h0PgQMyhwUJPoLtQRvuMJML0UM9k9HSkXG5sl7KGygzouTMS5Mpl++3BHsbfRfmyjlKKPx024q1YsUIII1hnLVq0iJKSkuiBBx4QnqZqm625c+fSjBkzxN8jgiWL7t2hd+/eIiMobboknTt3piVedK+7VRQENclogIyCYWRROpPYvQYH7Z9+UkZSvPJK6gOijITt2UOmBQeCs2f1IcKgluFThrmeqPNy17PMmQjDa7VrRzRuHNHcufoaogv/OxyEIfyNNEBeT5Ewo6cjpZj1R5Q/GOg9HYnuPJxEYJ0jJenJGDxvRZg8scT7oS5MZyVICxyazn755RcREUO5FExU4eU1YcIE+v3336klrH8stV5weIBwqy9Nz9Ng1apVwpTVcZA3asXOSB9QrUUYcqCMBvgzFamme3fXz8mDupnN9jAmSO6k9HBAQEoRIszddY5oqYyaOQ4fxxkbRBiENnYYSD/roWhYpiKR/vUkvaEXAeFFLYdmGD0die8btaaINuCz6OE3FwoiDCAqBRGGY4snBsnedEaqU5KYzCKL82F5BB/Kt98m6tSJ/MGtW7dE7bkENVlqiwhXQHQBTPYBEGOIjrVWNTaVL1+eihYtSuvWrXNLhN2/f19YUjhy+vRpkQYNWE0YPgzaMXHBKCO95m/NKsK8HiYqD+ow1kRe34zIsxHYOuhhvpmn3jqI4qHGA2dajkazmJqAzwVeflk/Q72NWA+mjj4FU4TJ/YJMLxkRMxbny3SkXmvCfOmQ9CUSpi7OB4jKr1hhE3Z+oGLFinaWENAO7oglWGc1atTIap91/vx5EcHK7uAJmC9fPvGcOyC9+TXKfFTHYhTkI1iFkY5+F2EXL14UYTyIJ8yJxAUji1q1amUtUNNT/lZXyB+KFiLM23/EDgVnBWgSOHiQTIlMReolLSaL692NhMkzTCy/o4hEkwXEl2TyZH3U9xmxM1IvIkxGbqW4NiJ6qK0L1UhYoEWY7PjG/gxNAXJerB9n2u7du1dEteRlAGyX0gHaYvfu3TRt2jRNl2XEiBG0Zs0aIQwRJHrmmWesqUgU5/tdhPXq1UuEBvfs2SPmJ+GCD4pQoafDu5G/hfNspUqVhL0F8rdw4EeUDcj87ciRI4Xwg9hD/hb5WAg3Z2CMEpZFXmT7qC6QPxT5w/EBrwM8+EezpyTVkTA9INc3Boq708WU3nxR7IBmz7bNDv3mGwo6HAnzDmwPZhBhZoyEGUGEyRN6GbXz19xINTKVN28e0dSpygk9Ik1+POmNjY2lrFmzWi/ppSLffPNN+ueff2jZsmVUWLUfhalqYmIiXXdoZEB2Dc+5A15vx44d9OGHH4ogERoXhw0bJjKCyOj5XYRBOH377beikE0CRThu3Diaj5E5PuBp/tYZCFOqw5ZYNt0gZ3wFO+1gdhGmt0gYokNILSL9645fmIyEuRJh6FBCs4w8G4QPnCp6HBQ4Eub9iRk6Z3FypOeDvRFq60IxHSnLFaSQD0QkrFYtpf4MfpO9etmmeuiAlJQUIcBmz55NS5cupRIOkXkEcjB6Ud3FiBIoBH8aNGjg8nXh/IBuSDBkyBAh5J599lkxMxt66OWXX7brlPSrCEOeFR/CETyG57xFq/wtwpTqsCXCmLqLhGkgwnwqdZIO7J4aiBoFvUXCMJmgXDnltjvbo1z+9ERko0bKd4kD3z//UFCRY5mC7cvmKdL1OljiQZ4wIKLhZL9qGMwmwiAwZFRPz+JYijC5HQVChOHg8+qrtqYMBEmkGAsyPXv2FHXq6H5E9Aw6AZe7lvpnBGa6d+9Offv2FVEyBHq6desmBFhaRfn79u2zlkl98sknXpmyusLjuTVIC8InY+rUqVTQcpBDLhRhOdSFeYvM366GEaUPOHZNqDsqzBQJczq2yF1khMWsoyfcFTGBjj5iXBSijw895FskTL0zRB3Gd98p3UpPPklBswSRZ+KyXsQoBLsmTB489XLCYNT1qDVSpEBkqAe96w253QQyEgbQCYmTQJxcevsafuA77AtF/1Jzu8dRxoTSJzBq1CgKDw8XTX4oX2rbtq2IZqVF9erVhVhr3LixiLb973//E42CzsA8bb+KsLFjx9LDDz8sCtGKWOZInTp1SkSvvBleqc7frly50mX+Vh0N8yR/qyv0ko6U4sQLTxNDoMcDmyfF+Z6ISD2kluXyIk1qNHuCYIsHM9SD6WE9ao2sGYKPnB46rLVOR/piUSEbhNywcwg0KW6MUsqYMaMon8LFXVCvju5H6BR0Q6L0KtLJ7GU853cRBuG1detWMTdpPwqNxXGggl3dlicrDIX+yN8uX748zfwtVKu7+VvdizANCvN9wsyRMPwI9RgJq1RJuXbHzsXdSJheRJhMRSIKpucDlqfiAS348NzDOh492r8nDI5WJEZDrkc9NUJpIcIcSmF0LcKw73P39+dLYX4IUq5cOWuXJaJo0CTeFOFrIsKk2mvTpo24+AJSkMjd/vXXX9b8rczboshNnb9FsT66IiDa0svf6hYt05G+HOukOMGBB+laPYfbPQWf584d/UUX5PDtHTsUd3ZXQ3NRV+lJY4GMsMHcFWlBNwwM/SrCjIYrEQb3/2bNFD+9RYuIMBPOEvk3fdTWG8wWCZMTFDwZbh1MEYYaNghHdyNbvqYjQ5j7PtS++1SYj25EhOLUTJ48WUSvoAhfffVVkV/1NH+L4nnkbwsUKGC9TJ8+3fo3yN926NBBRMIwdgBpyFmzZpHhwFmKpjVhPoCRSfLHZ7ZomIyC4QzWX6OhvAEHWQzzxg94zZq0O7LQLYdwvzspd+yEIaLxuocOUVAwgwhz7C7F7FEIMMnff/vn/WVzjDtRTz1jtsJ8o0TC0JEnl9HdlCSO09Kom0WYx0yaNIn+/fdf6/1+/fqJcqmGDRuK4d5+E2Foy4Q3mGTXrl0iSoU0ZP/+/YWhqjsuto7pSGcXWUCnzt/CjwzdCRBghqwHQ5geB1etasJ8TfuYtS5Mb/YUapo2Va5XrnT9N/L7QEeWO91yevB9kyIs2HM6tYzgOA4n/usv/7y/nBDiycgZPWK2SJhRRJg3dWEyCoZ9h5myIAHiiy++sNpRIDgFfQKrity5c4sGRb+JsO3bt9t1PyI/Wq9ePfrxxx9FunDMmDH0B/yKGOfIKBi+PA1m/flceWPWujC92VOoQXoLYMSHFvVgEr2IMH+k6wIlHhAZUM+DkyJMCstly2wHZq1ARAIRNyAtTIyKWWvC9J6O9KZDUoowCExE3BmPQCNiaWQ1iGjOnDkiS4dMIIJQGO7tKW5/AzAqgz+XBCOE2rVrZ72PMUZYOEbnnZESjoQFLxKGYd6uzFXVI4uMIsKkZ58Ri8vVbebq70R+Dy1bKusXUWxXZtRwDS9fnqhJE8/Gx6COD2lkLIMRo/uhUBNmpEiYu15hvnZGhjhZsmShK5bj+cKFC6218cjaST8yv4gwCLBjlhoJ2EagQ1JdHI/xQM5MXBntjVqBz01oHAkLPDAyRd0UDuguJj6kO7JIjyJMOovr2dTSFWhkgBeUo4CQJ5T4HjCdAGBUlDNGjVLSivA4VNWzup2KRBTMaF2lZhdhoZCO5M5Ir4DogkM+LgcPHrQO7Ua5Fqy7/CbC8Eao/UK4Da70mTJloiY487Owc+dOKiWd2Bm/R8J83mVzJEyfdWG+RMJwUFen1AIBIjmXLul/vIsrIH6cCQj5PSDF2rmzchuNSY7mz2i4sVj1WNOW7nLwoDlSkWYuzDdCOtJbEcaRMK9ADRgcGi5dukQzZ86kXJZjOtz3n376af9ZVHz66afUpUsXatasmQjHoUMAI4UkP//8Mz3gxynqhkdv6UiOhAWvLgymxq7qwryJhMFfDxEdtKmjO6dkSQoY2KHLhhOjGbVKIMKQfnIWCYMIq11bSTdCbKHu9eWX7b8vdR0URBiEqTu1NmvXKteqObyGxWyRMCOlI72tCWMR5hXohIRpvSMYZ+QNboswVP7D0R6WEhBhETKEb2HGjBkubfwZHYowjoQFNxK2YYMimuAy72skDL9FdNdhLBJmUwZShMlUJA5WqpMywwsItQhDtKxbN6L331cEtFqEyRQwsgAXLij1NvgeqlVL+z1xwJRt7hYjakPDhfnGqQljEeYzmOIzYcIEMVMSVKpUiV566SXhbeopHrdG4E0cBRiAmao6Msb4OR3paw2JjLQglQQxYAaQGtJ7fVKZMkoRNjrjNm7Uzu0/WHVhEB56Xt/eDPHG70EeqGSU4bHHlGt4vKkjX3J9V6lCVLeucnvLlvTf87fflNQxTHzNFAnDupORUSMTCjVhHAnzis2bN4vSK3iYwjoLl5EjR4rHUCvvKdyfGqo1YfgByiiMu2dQegcH0cREfYyGcgXEs6u6MBzcZYeepxYbwRJhUvQasR7MVRRH/lYxG04ehBFdRLQLAmP5ctv/yvWN9V+9um0qQnqgiN8sUTB1TRhw1flrJIwowrDe3bEI4e5In4AXGOZnHz9+XPiW4oKmRZjK9+nTx+PXYxFm2EiYBi8go2FmMWyVHajwYdOTW74j9eo5P1hLMQwDRU9T+8GOhBlZhEnDSnkAk9sRusfUPzRZ87pgge2x3btt46NkCnL79vTfU0bL6tQhU4AsiBxobIa6MKOMLQLYV0gR7E40jLsjfY6Evf/++3YDvHEbzvl4zlNYhIVqTZg65WUWw1Y9ruO0BJO6q85Xt3+1CENaM1DoPf3rDvJAKw+8rrajhx5SrlGcj3Qy1vPOncpjEGAyEgYRltZ3AF81/OYg8GrUIFOg7jI1umErop1y/qxRHOU9qQvjdKRPYIb1SWlQrQI+qZiB7SkswozqE+Z7QtJ8xflyHes1FSlBp520KFBbSvji9o/CfHTkQUhI89RAYIZImEw5yRSUKxHWtq0SPcZ2NnOm0okKywr4I+I7xQURITwmnfDTioLh783UzGSWDkl1OtUo34/Margzu5BFmE88+eSTYmQjZlxDeOGCCULwDfPGooJFWKCQO3atBIIW3o5mE2FGiYRhFA7q8VC/ph4SLc9ivRFheD3ZFRnIlKQZasLcFWFIP7zyinL7u+9s6WSkIiHEIMAqVUq/Lmz9euW6Vi0yFWYTYTipgfWLEahc2f16RBZhPvG///1P2HV17dpVmLMWK1ZMzLt+7LHHaPjw4R6/HouwQIDUhfxh66UwXy3CzFKYb5RIGHbu0qBTnZL0RYQFqy5MRt2MPHbHVTrS2XbUvbtiCYLC+ilTlMfUdhTu1IUtXqxcN29OpsIshq1y+SEqjTLJQJ0KTw8WYT4BF4jRo0eLUY6Yqb1jxw7RIYluyWgvRLvbPmGMD8idOg6+eir0lAd7s0TCNE75+hWkonDWCsHUoYPvNWFShM2dyyLMX5Ew+d106kQ0a5aSkgQNGrh/MITQk9YklplzpsFskTBpXWIEHOsRXYlHzDaUlkQ8tsgj4APmDjCu9wQWYYFA7tSx0etpar1Z05F6j4TJGi45xFmLmrBgRMKwszdzJMyVmB80SBnmjQMa/MHUO+f0RNjSpYqjPr5/zBE1E2YpzJcizCj1YDIljnQ5olwwGna1bckoGKK5XhSRhzK//PKLSD3WqFGDUjRsfmIRZtBaJU2i5PJgjwhMWmdPRsEo6Ug5aggcPWrcdCREC1LtRu+O9CQSJlOOGMD+009E8AVSm1TLdCQKpPF6jj5TMGkFlqG/psIskTC5/EaKhCENhnpERNc3bUpfhGG7NPr+PsD06NGDpk6dKjzBunXrRs8995wwqfcVHYVlTIwfcvCadEfKtmYUiMsDj5ExSmE+kEX0sjAfItjXdKTsuoRXkIzq+BMZBUMbv5592bQWYVJsffONYuDq+FpovHBWJI3pFH//rdzGGCSzYRYRZsR0JMD0BbBqlXJ9+DDR7Nn2nbpcD+bT4O5z584JP7C5c+dSkSJF6IknnqD//vvPp8gYi7BAgJZ1PXrO4OxJRo3MkJI0YiQMERPYVODAn5TkW2oPaTUZRQtENMwMqUhn6UhfawtdpSTfe0/5jjEQvGpVMh1mLMw3EnISB0QYxBa6b7t0UcyhZR0YizCfQOE9bCgWLVpEe/fuFTMj33jjDdEledvL7Z5FWCCQNRIaijDNIslmqguT4ziMUHCK9Q5bAxyUse7l+s+Tx7dB2IFMSZpFhMlIGE6WUK/la0TVmQhbtIho0iSlJnTECDIlHAkLLk2aKNeYX/jZZ7aTf9jIzJun3GYRphnh4eFihjOiYPfUfo+evo52i8SkK8I0LITUTITJyIm7w1/1jJFGjaAwVqatUBfmaz1YMEWYTGsbFbm9IKUAASYPVBDE3uBshiR8xUCPHraIhdkwW2G+0dKROLFDowgYOdL+QCFrEeVv1si+fkEkISFB1IW1adOGypYtS7t27aKxY8cKB/0sXkZOWYQZNB2pSU2Y+kCDehUjgzMRufM3gghzrAvztR5MwpEw74xupb8PphgAdJp5GwmTxfmYK4noLE5wZC0YRJhZMUskzKjpSDBjBlGjRspt1GnCsgb8958S5ZURd1/3MyHIG2+8QQUKFKBhw4aJYd1wyp8xYwa1b99eRMW8hbsjDRoJ0wwpwmQdjFFRn30bRYSpOyRlCtJIkTA5ssjo6UiZksTnkea56Pb0dsdavLjyPeA7QJQQJ184SUDhtHTUNyNmqQkzaiQMwAQaNWE4AcCJBU70cI3PhJM9FmFeM378eCpatCiVLFmSVqxYIS7OmAUPQQ9gEWbUSJhW6UizRMJkKhI7HKOMGlFHwuRZt1YiDK+JYlxEefyFWWrCnIkwXz4Tfpy//qqYuKLzWJ7gyJFHZsUskTAj+oQ5bn8yLSk9xLZtI9q1i0WYD2BMEWrAtIZFWKhHwmQnodEjYUaqB3MUYYiEye/BVxGGCA6sUFDXhNSaP7vwzCbCwN692tS51aypHPgGDlTc9bFdPv44mRqz1IQZ0ScsLbAPwLa4cyeLMB/NWv0B14QFMhKmRxFmtkiYkUSYOh0JqwotajVwphaolKSZRJgUXbKjUYvPhAgEanTGj1dqwsxyUA+VSJhZvi8ZFUOjiGzA4pow3cCRMINaVGgGR8KCHwlDGkzWV9Wo4fvrQoStXetfEYYaJ7S+m02EyQYJrT4T6spee41CArOIMCMX5jtDRsP//ZcoOVnZJs3wmzUJHAkzrEUFd0caPhKGtKF6eWFZocUZaiAiYUhfo9sKO3RvrRz0hGMamA9SnsOF+foE3ZIQlOoRY+j+ZXQBizDDWlRoHAnD2Z90VTYiep1K4G40DMjWciOIMJmKhACD55nRYRHmOzJyhP0IIi5GxWzpSFhVPPaY7T6nInUFi7BQN2tFQbI8iBp5fqQRI2GgcmX/ibADB/x3MDRTPZizQnyzfK5Aok7fGTklabZ0JHj1VSVqjQhY167BXhpGBcck/Q1SNnquCYOaQzQMNUkozjfqWZJRRdioUYqvFETNCy9o85pIa+IsHmf0hw7ZRJmWmE2EOUbCypYN1pIYF3jd4SAP4Q8hIztOjYbZImEAdil79iglEEhHMrqBI2GB+kFrHQnT7JVMYthqVBEGV/YhQ4h++EG7nT7OeGUxrnp0jpaYORJWqpQ56tyCcUJnhrowo/uEuaJ8eRZgOoRFmL+RUTCk/GJiNHtZTU3jZF2YkW0qjCrC/IUcnaMeIq0lstXdLGfV8jcAOAoWul5hmB9qNp8wRtewCAukR5gf3HY1gSNh5hVh/oqEnT6tXBcuTKZAPaKodu1gLomxkSUXcr9nNNBUACEGWIQxAYBFWCi75Us4EmY+qle3RcLkQUVLTp1SrosUIdMwdChR48ZE77wT7CUxLkYXYeo0KoswJgCwCDOoCOOaMAc4HWkPasJQKI3aLYwv0hozirD+/ZXhx5zS9h657uTv0aj1YJi5agbrFUb3sAjzN3fu+OWsStPMJkfCzAe8gZo0UW4vWKDta2MotSzMN5MIY3zH6JEwM3ZGMrqGRVigftQ4KGqKhirMDDVhRjVr9Sft2inXffoQ/fabdmlJjPbBayHSpi5oZxgZCTOqCDOjR1gIsXLlSurYsSMVLFhQNK/NmTPH7vmUlBQaNGgQFShQgGJiYqh169Z0CDY+QYRFmEEjYZpi9CHeEARyp8+pJBsdOthCps89pwgmfNfwgps2TZuifHVBO8PIkyCjpyP1vL9mXBIXF0fVqlWjcePGOX3+yy+/pDFjxtD48eNpw4YNlDlzZmrbti3FB3FaDJu1BkqEaRwJ80s60qiRMJy9whQXsAizUa4c0fz5RH/9pfiQXb1qe27wYKInn/RuQzJjPRijDUZPR3IkzNC0a9dOXJyBKNjXX39NH330EXXq1Ek8NnnyZMqXL5+ImD311FMUDPg01qDpSL8V5ksxYyTkWTfcujX0YjMFbdsSffutUsMFx+xt25R1hGL9jRu9e00WYYzZC/M5EqYrbt26RTdv3rReEuQwcg84duwYnT9/XqQgJdmyZaN69erRunXrKFiwCPM3RijMh2s7uHfPmDtPdWekXr3Ygg2inRUrKtYVXbooj02e7N1rXbxoLqNWRjuMHgljEaZLKlasKASTvAyFnYyHQIABRL7U4L58LhiwCDNsYb6GREfbdp5GTEmyPYVnyBmVqAvz4ozSWjvIo30Ys4kwTkfqkr1799KNGzeslwEDBpBZYBFm1EiYtglJY9tUsAjzjJYtlYHVqBH791/P17cU6izCGEc4Hcn4gdjYWMqaNav1Eo3AgYfkt8y5vXDhgt3juC+fCwYswvyNESJh6gHGsB8wGizCPAMmlM88o9yeNct7Ecb2FIzZImGcjjQtJUqUEGJryZIl1sdQX4YuyQYNGgRtuViE+Rsj1ISBokWV65MnyXCwCPOcjh1tRq6oBfQEFmGMK9gnjAkit2/fpu3bt4uLLMbH7ZMnTwrfsD59+tBnn31Gf//9N+3atYu6du0qPMUeeeSRoC0zW1T4G6NEwqTdAIuw0ABnfjhgXrlCtGkTUf367v8vizDGnUgY/PuM1ijDkTBDs3nzZmrRooX1ft++fcX1Cy+8QL/88gv169dPeIm9+uqrdP36dWrcuDEtWLCAMmJMVZBgEeZvOBLmf9gt33MyZCBCq/bMmUTLlrkvwjCySK5vTkcyrkQYrG4gaIzmPM+F+YamefPmwg/MFYiGDRkyRFz0AqcjDesTpvEZJqcjQ49q1ZRrTwZ8I3IG4JSfPbt/losxLtjPycHXRqwL40gYE2BYhBl1bJG/asKkEaeR4Jow7yhbVrn2ZHaaTEXCW45HFjGOIP1o5NFFLMKYAMMizN8YpSZMijAYcd69S4aCRZhvIsyTSBjXgzFmLs73Mh2JFNi1u9foyp0rFJ8cvDmEjPHgmjDD+oRpDFJLWEaIRgxoLlOGDAOLMO+Q3zG84a5fdy+9yCKMMbNNhReRsE1nNtEb896gzWc3i/uR4ZHUoHADeqT8I/R67dcpUwadn4AzQYUjYUatCdO66wivZ9S6MFmnJMcvMe6Bs33pD+duSlKa+XJRPuMKI6cjPYyE/bHnD2o8sbFVgIHk+8m06uQqemfhO1TmmzL045YfxWMM4wwWYf4EXRr+qgnzB0YVYRydCVxKUpr5BtFhmtE5Rk5HYooEyJkz3T+dsWcGPTvrWUq8lyiiXufeOUf3Bt2jI28dobHtxlLx7MXp7K2z9Oo/r1LlbyvT+M3j6cjVI5yuZOxgEeZP4uMVIWaEmjCjijCsXxZh3lO8uGcNGfLv5LbCMGaJhGGOqsxcpCPCxmwYQ0/8+YSIcD1T5Rma+cRMyp8lP4WHhVPJHCWpZ92etL/nfvq67deUKyYXHbhygHr824NKf1OaYj6PofJjy9Po9aOFgGNCGxZh/kRGwfxiUeEHjCjCcLadbAn1czrSc2Q68tw5z0SYNPdlGLPUhKntV2Q0z0kB/ldrvqLeC3qL+73r9aZJj0wS4suR6Mho6l2/Nx3tfZSGtRpGjYs2FvViAKKsz399qOWklnT+9nl/fipG57AIC4QIi4oiitS2B8IvRtS+uuZj/A3MPAOJjIIh3RsTE9j3DkURJrcNFmGM2dKRMhWZI4dL+5UBSwZQv8X9xO13GrxDo9qOsgorV2SNzkrvN36fVnVbRXEfxNHFdy/S+IfGU7bobLTm1Bqq/UNt2n1xt/afhzEELMIMak+hu0gY0oLt2ikH9cOHKWBwKjJwg9vxHaNzFrAIY8yWjkynwWfqrqk0fM1wcRvi66s2X3ncIBUVEUV5Mueh12q/Rhtf2UgVclegM7fO0ANTHqAT10/4/hkYw8EizJ8YzfhPbdiaxugHp0ybRrRokXI2+fbbFDBYhAUuEobOSNTN4MBTqJCPb8yYFqNGwtIQYdfjr4v0IfioyUfUp34fnzvUy+YqS2teWkNV8lahc7fP0VMzn6Kke0k+vSZjPFiE+ZNbt5Tr2FjNX1pziwpQuLByDbNWKW7cZcQI2+1//iH67z8KCCzCtBNh6QlvWQ+WL5+SYmcYM9WEpdEZ+fuu3+li3EUhnD5q+pFmb5kjJgf9/fTflD1jdlp/ej19uPRDzV6bMQYswowqwjR/RVSSRtuE2IED7v9fUhLRzp3K7c6dles+fZTHA3X2yr5VvokwCO/0DppclM+EaDpy2u5p4vq1Wq+JgnstgZXFxE4Txe2v1n5F8w7N0/T1GX3DIsygIsw/KoyIqldXrrdtc/9/4DEFwQWDwwkTiPLkIdq/n2jsWPI7HAnzDdQryvRReilJKcxLlPDxTRlTY7J05Ombp4X5Knii0hN+eWv4jL1V9y1xu+vsruI9mdCARZgO55AFlRo1PBdhu3Yp11WqKJ1FX3yh3P/4Y6IzZ8ivsAgLXHG+3CakUGeYEEhHwhUfwGKicFZLpsAPfNnmS6pZoCZduXuFnpn5DLvshwhBFWErV66kjh07UsGCBUWN05w5c1J5sgwaNIgKFChAMTEx1Lp1azrk7ngVs0fC/IU8wG7f7p0IA926EdWtq+yEe/TwvMjfGxHGHmHeI4vs0+uKlduEFOoMEwLpSJmKfKrSU359e6Q5pz82nWKjYkXk7ePlH/v1/Rh9EFQRFhcXR9WqVaNx48Y5ff7LL7+kMWPG0Pjx42nDhg2UOXNmatu2LcXDiZ5CvSbMT/lIeYDdvdt9zy9HERYRQfTzz0QZMhDNnat0Tkru3ydatoxo6VJtPMUuXFCu8+b1/bVClfLlleu9e9OO6srRRizCGHfSkdj/4feuV7BsW7fazJ6diLCj147SprObhBnrYxUf8/silc5Zmn7s+KO4/cWqL2jRkUV+f08mhEVYu3bt6LPPPqPOspjbIQr29ddf00cffUSdOnWiqlWr0uTJk+ns2bOpImah2R1J/htjg50oarzSOiirOXrUfg4hqFSJaOBA5XavXkQXLyq3Bw8matmSqFUroqZNbQOhfRVh6NhjvEOKZymmnbFjhxLRROqS1zXjTiRMvQ/UI4jY16pF1KaNErWXjSeqE7rpu6eL65YlWlK+LIHZxzxZ+UnRAJBCKWI25aErBsr+MOapCTt27BidP39epCAl2bJlo3r16tG6detc/l9CQgLdvHnTerkVzJ2AH2vC/KXBhLrzJCWJA/MJi8lgsWL2z/XvT1S1qnKGWbKkcob5+ee25zdsIPrQx5ZsFmHaiTBEP13xh1IXQw0bavCGjKnJmFG5gOvXSXds2qTsiyZPVu4vX0703ntEx48r+z9VpHfansCkIh2BGSzqwy7duUQP/vYgxSVaPCcZ06FbEQYBBvI5nHXjvnzOGUOHDhViTV4qVqxIQcOINWGeFuej7kOKTUcXdaQjJ05UdsgwrkXhK0Tbiy8qKUkwZYr3xfsYCyXfm6Mz3oOoJYAb/rVrqZ/HdzdpknL75Zd9eCMmZJDF7TLFpyd+/NFWhF+6tHL9ww/KdeXK1nTq3kt7aeeFnZQhPAN1rpA6W+NPYjLE0Pxn51ORrEVESnTIiiEBfX8mcOhWhHnLgAED6MaNG9bLXndTav7AiOlItQhzJxImi7nh0+VsPFPNmkqYf98+5YLGCthYNGumvA/q+3DtiS+ZYxQMIs9oQldP4KAjpyU4i4ahrg9iG9YUDzwQ8MVjDCzCpNjRE0uWKNfffKPs49T7rUaNUqUi25ZuSzljUhu4+pu8mfPSuPZKvfSo9aPo+PXjAV8GJoRFWP78+cX1BXmgtYD78jlnREdHU9asWa2X2GAenKUI80s60o8qTJ2OxFDutJB1FPIg7gwINBR/44IzTwzHhYr8/nslgoW6sEGDPF9OGRHFa/hVlYYAiAC4EmGzZyvXTz7pcrAxw+hehOGE/NdflRpWNA917aqMlOve3fY3TZpYa5Kn75kelFSkmo7lOlLrkq0p6X4SDV4+OGjLwfgP3e5RS5QoIcTWEnnWImxnboouyQYNGpAhkKkyo0XCKlRQ/L5QrCrThulFwrwZ6Fynjm280Z9/KlEyT+B6MP8W50Ngo8t1nsXB20kDDcM4RXYYBluEoQMS0yB++UVJuz//vPI4LHRkA8HIkURTpyqj13CigT6UCzvowJUDlDEyIz1c7uEgfgCiL1p+YR2dxCau5iOoIuz27du0fft2cZHF+Lh98uRJ4RvWp08f0T35999/065du6hr167CU+yRRx4hQ2DUmjDUcj3xhK1myx0RllYkLC2qVSN66CFlZ/nVV579L4sw/0XCcPDEY4gS4GQC32/t2hq+IWNq9FAThu0WNcFIN6ITEmCbbtxYMZKWREYSPfUUUd++SoRM5Q32UJmHKDY6uPvvOoXqUPPizYV567ebvg3qsjAmE2GbN2+mGjVqiAvo27evuA2DVtCvXz/q1asXvfrqq1SnTh0h2hYsWEAZZeeN3jFqTRiQZ4wzZyqF2f4SYbKLEqD4Oz3XdjUswvwTCUPzxIwZSiQUqf9+/ZSh7JyKZIyQjsQ+pHlzZb+rrjWF0MIJ/6pVadY2IhVpNWitHLxUpJo+9fqI6++3fE93ku4Ee3EYs4iw5s2biw3e8fILQsdCaITRkCFDRDckDFoXL15MZdVeVHrHjzVhfgdWBLCVgACTNUHOOHzY93mCODPFBeato0a5/38swrQD9XoQWbAUwPUHHyiPIzowfLhNpDGMntORqGGFwFqxwvYYxqhhX4yUoyXSlRYbzmygEzdOUJaoLNS+THvSAx3KdqCSOUrS1btX6dedvwZ7cZhQqAkzPEivyQiSX9KRfg6FIdQmo2GjRyu1YatXK/UVEkRM5JmmdF33NRo2frz73kIswrQjOppIXWuJgycOWIge+Ik/9/5JFcdVpMghkdRyUkvRis+YhGClI//9l2jPHtt9pCJfecWjE2EZBetUrhNlyuCk4zsIRIRHWAd8D18znBLvaTBthNEFLML8BTys5MxEo9WESV54QbF/2LxZcblH55CsrZAiCNYFiJxIvx1vad+eqFw5pY5j8WLPRFga3bKMByD1DONKmFlCDM+f713DRTpcjLtIXWd3pcdnPE77Lu+jeyn3aNnxZdRmShu6ckeHvlKMcdKROGEESKFjJBGMvdGd7Sb37t+zDuzWSypS8nLNlylf5nziZGXC1gnBXhxGI1iE+TsViYiSM/8sHwmIIwNSjAjrY8QQBJI8UMtRQ/v32/4OkRRfP5Cs04AQcAeOhGkLrD7g34YC/NdeU8a5aAwMMGv9UIum7FQaPvrW70trXlpDJbKXEAcXzMtjTEAwRBjqGTGTFieFPXsq/oOY2OEBy48vp3O3z1GOjDnogVL68sTLHJWZBjVT6qWHrBzCLvomgUWYv0BRs4yC+UExBcwVC63ciExBcMF4FcNupyv+OdZUpBRovtKihXKdni2GhEWYoYDZJKJdaLMvl6scbXplE41oO4IaFmlI4zuMF38zbtM4OnnD0uzBaMbNhJs0Yu0IemTaI9RnQR/afTGNEVVa1oQFKh2JfcGrr9qsVLxsFPpt12/i+vGKj1NURBTpDUTDUBt2/vZ5GrluZLAXh9EAFmH+QtY1wW/LDwTFm1TWiEnbChkJ87UeTIKB3tJUUQ78dgWc9qXQ5ZFFuudS3CUhwM7eOkuV8lSitd3XUu2CNsuLNiXbiDb8hHsJ9MnyT4K6rGYiITmBxmwYQ6XGlKJ3F71Lfx34i0ZvGE1Vv6tKL8x5gRYeWUh/H/ibes/vTZW+rSTq8+Sl0MhC9Oa8N71zaldHwmRZhr/A68O2aP16JQr27rtevUx8cjzN3DdT3H626rOkRyAMP2+pzN/9YvUXXEdpAliE+VuEZc9OpuHpp5Vi7Y0blSiYjFjB60urs2eZPlB3N6UVBYuKss56Y/TJ/ZT71HVOVzp89bBIOy58fmGqMTDohB7aaqi4/cuOX2jdqXVBWlpzcOH2Bfphyw9UYVwF6r2gN12+c5nK5iorDuBdKnShFEqhyTsmU9tf21KnaZ1ozMYxIlWM+jx5gWBGZLLE6BLUZGIT2nRmk/sLIOuwEDn3d0py0SJFgMHfELfr1/fqZf49+K+IGGJeY+OijUmvPFnpSWpZoqUQjT3n9RSOAoxxYRHmL+QgZDciYThbPXH9RKofE+5P3TWVnp/9PP2yXbHtCMjYIlcg4iTrtmB2uGOHcuaJonqtgL+PO3Vh6lQkjyzSNT9u+ZEWHF4g3Mf/fvpvKhhb0Onf1S9cn56t8qwQbU/NfIrO3ToX8GU1OigsRySx+Oji9No/r9Gx68eoQJYC9H2H72nPG3vogyYf0MwnZtKGlzeIdY20MCKTr9V6jf58/E860ecEne17ls70PUPznplHLYq3oPCwcFp9cjU1+rmREG5ugYaePHlsg+H9yedKZEjUgaGByEt+3aVYPzxd+WnxmfUKTli+bf+tiIrhdzVx+8RgLxLjA5G+/DPjeyRs9r7Z1Gt+Lzpz6wwVz16cpnSeIs7Cku4lUbe/ullrFOANowgv9zt9/AIc1NE1N22aLYXoQfeRW3VhY8akXxfG9WCGicj0X6LYjyDSVTmvxZnfBd8+9C1tPLORDl09RE1/aUpft/1aeDXhwMOkjRSvsP4A1fNXp2cqP0M96/ZMZbVQt1Bd+rVL2n5TEMvtyrQTETHso2btm0UvznmRYiJj6PFKj6f/daCzFk08GH+lVbTcEdjmrFypRMG8TEOCI1ePiLQs6FqtK+mdcrnL0UdNPqJBywfRq3OVWrgXq7+oa/HIOIdFWJBEGHaYg5YNos9XfW5XuAy/pAGNB4jZZajfiAyPpDyZ8oiOHYSes4d9Q5EpeSlooOgVhfiyKP/FF7V9fVkXtm+fUtQrC3wdYRFmCN5Z+A5dj79ONQvUpF51e6X791mjs9KC5xaI9BfSlx2mdqAmRZvQ6AdHU40CymQNxjko1IYAi46Ipp8e/klEurQQrxBjiJK9/s/r9MPWH+jZWc9SjpgcYrB0uiIMNhEQYf6OgmE/VKiQ1y8zYt0IsU9uV7odVcpbiYzAh00/pMPXDovoZPe/uwuhjOhY9ozZqWq+qiICWjFPRSqarSiVylFKnOQHewQTkxoWYUESYW8veFvUYcg2/f6N+wuRNWPvDNF+LL6c8Eia/eRsEQloOrEprTm1hlIyTKA8iQOCl4FD+hEeUr16KSaIXTU+a0RBb8GCyuiRo0dZhBmYJUeXiEguIrhIh8Fw0h3Q/bW7x24atnqYKCJfdXKVEGVLX1gqIjhMatafXk8DlgwQtyFYn6v6nPYpsIe+pesJ14WP1lN/PkXbX99OhbMWdv1P0mPOHyIM/oTvvEO0YIGyT3r/fZ9862RK7/1G3r9OoEHUa2KniVQye0n6au1XFJcUJ0Ya4aTHWTNFRFiEEM8QZnkz5xXHl7oF69IL1V9IVaPJBA4WYUGoCftr/19WAfZLp1/EjwBMf2w6Nd3UlCbtmCRGZnzS/BNqWkyJDH330HdUbXw1uhOxhhLCDlIYFaOggbotePL4i+LFFRF2/DhRnTrO/+b8eeWaOyN1CRy935j3hrjds05Pu05Id8DBYnib4fRm3TdFUT/8m9r91o5WvrjSMJGKQIECbZjfYsDzE5WeoFdrWawaNAYievIjk0WEcuu5rdTh9w60/MXlIvIScBHWu7cya1Y2DJUq5fVLoXsU67BeoXrW/a2RhNjg5oNpQJMBSl0xpQgBduzaMTpw5YCwg4EdyYW4C2LkERo0Vp5YaTcdACf9/Rv1p171eulmQkAowSIswJEwdN/Ig9N7Dd+zCjB5tomDDi6OVMlXRdQqQKBdjRpHyfebkWmB+evatYoIc8WZM8o1omYegA4vRGfQpYdoQa5MuUT9HeqQFh9dLHZU2BG3KtnK9cGFSRd05h28cpDyZ8lPn7X8zOs1ViRbEfr7qb+p9ZTW4jt6eNrDtPXVrZQtI3fESoauGipq6JB++qHDD36tn4uOjKYZj8+ghhMaipIJNA3h+3H6nv4QYei07NGD6A/F1Z46diT63/+8frlbCbdEB6iMghm19hBpyDK5yojb6IJ1BgQZRNi2c9vobvJd8dmxL9x1cZeo20RgYEjzIfRSjZcMux6MCIuwAIuw/639nyh0RY5+SAsl7eguaC+fsn02JYYfoXmnP6XPUmaZ88eCSBhwR4QVTiMd4sCc/XOoy3SlPR/0W9xPjAGB8EIoX4KdUeYMmUVEAZ1SdQq5iMYxTkFK5NOVn4rbg5sN9lkwoY4FnXq1f6wtfJFgqfDnE39S7kxBblLRAQcuH6Bha4ZZ05CBEKdIF897dp4QYv8c/IdGrR9FfRv0DYwIGzTIXoD9rRTTe8uPW38U6Tt0iXYq34nMDFLHuKBhQ/Juw3fp912/08BlA8XQ8pfnvkzzDs+jMQ+OoUJZva+xY9yHWykCaNZ6O/E2jd04Vtwe1nqYaNn3BPwocif2JUoJp+1X59CLf71oztEVnogwN4txRQTy3zeEAKtTsI4oXEXK7NTNU0KA5YrJJfx33qzzptgh4zEcXOr+VFcUIuO7Y9zj520/izobFAJ3r9Fdk9WGiCXS9UjTrzixgmr/UJu+3/y9SLuEKrCwQVQd2zEKyh+r+FjA3huNFqPajhK331/8vohSpinC7t3z/U3xOj/+aDOO/vlnn14O6026ziMrEYqdhUgxP1/teTrw5gH6qs1XlCE8g+iCLT+uvDhpZfxP6G11ga4JU0XCvt30LV2LvybCxZ3Ld/bqZWPu16ZcSb0pjMJFV0zpb0rT+M3jhT9QyIgwGECeO+eRCMMBGx2mpXOWppXdVtK217bRrh67aPMrm4V/0sX3LtK0x6bRN+2/oX0999FfT/0lBvhix4wzRTiM4xodVIxrkNpFkbA8sGWIyKDZ6kJRPvyt8B3irP31f1+nkmNKiugmosuhBrbHpceWipO5se3HBjwq/nrt18V4H9SiPfnnk0J424EoNawjkpJsJ02+MGwYUWKiUpM6ebLP1jhYf7AGQven1o0MRgNpZkTFMMcVtXE46Xz0j0dpxfF0TLN1yrhx46h48eKUMWNGqlevHm2EwbhOYREWoHQkQt7o9gIfNvnQ7U4xZ2S514qeL/2DdYZYj397iFqZUzdO2UV+vlzzJfX4p4c4szGsCHPmBg17ivv3Ffd+NwrzIZzGb1FmE6IAFQctiCt4VtUqWEt0C6nPgnEwe7jcwzT10am0qtsqKhRbSJheIiKG4dMQdEiLMalBoS9mP6L7qlv1bpqvInxXmDn5WYvPhHUFvrfZ+2eLyBiMjUPFPRyF5B8s/UDcHth0oNgXBBr8Tn7s+KOor0QxeJ0f64h6Iyv4faK+Exw+7HsU7KefbEbRPoJ9AvaPoE+9PkKEMCRKL7DPwwko1hH2eTATNxLTp0+nvn370uDBg2nr1q1UrVo1atu2LV1MbxRekGAR5g9wIHAQYV+t+UpEweBODf8eXymVtYGI2MDMEqJi3qF5wiW7/k/16aW/XhKz4pAmgPjAGc1b898SUQpDIOu87txRWtEdkWfVBQooO/p0gKs0RFO26Gz0dJWnPVoUDJdGqB71ePCw2n5+u4jAYP2W+aaM1eCRUQ5ssj7p7fpvU0yGGL+sFjRMwCMJEc0dr+8QwgxRzmdmPSPmIRpmO/cBlDVA7OIEAes6WKAGbf6z86lMzjJieRpMaCDmUCJCJw7esmvxyBHf3mjoUFsUrJnvTUmoZdt3eZ/YJ7xW+zWfX89MIHoNcY1tC5FCaf4bTG7dukU3b960XhISXAvDkSNH0iuvvELdunWjihUr0vjx4ylTpkz0s4/pa3/BIswfQDwgBA9y5BDjV1BfBL5o9YVPUTBJtSLZREdM7/q9aV33dWL4MQ6CG85sEJ43clYcWtbBNxu/ESkDQ0QKMPIkk6VV2tncOTkGxc2ifHhNAXT9eNOCnTkqsxj3cvSto9YIDDx20KqPIvEhK4ZwmpKI5h6YK+YPQqz2qN2DAgGimYiMfdzsY+GDNGXnFCHEzJw2RsT74+VKNAjNPf4Su564t298ZSN1KNtBDGBHY0urya0ox/Ac9GCNPTSiAdHOI2u93/ecPGmLgn3i+3B3aZQNsJ1ie2XsQe0l0s1Ado8Gk4oVK1K2bNmsl6EQ5U5ITEykLVu2UOvWNiPh8PBwcX/dOp3Oo00xOadOncIvX1wHjP37sbtJScmcOSXl/v2UHv/0SKGPKaXBTw1S7t+/79NLH7pwM+WPTSdT7t1L/Tonr59M+XnrzymDlg5KmbJjSkrSvSTx+Jx9c1KiP40Wy4DnfF2GgFC4sLION21K/dyYMcpzjz6a7svsvrBbfO7wT8JTjl49qtni3Yy/mfLmv2+K18blkWmPiMdCFWxT9X6sJ9ZF/0X9g7IM/xz4JyVySKRYhq/WfJViRpLvJac0/6W5+IyNJjRKuXf/XoqetoF5B+elPD/r+ZT8/8tv/W3IS9Xvqqb8e/Bfz/c/r7+u/N5bttRkOX/f+btYnmxDs6VcuXNFk9c0I+dunRP7TayrI1ePBPX4vXfv3pQbN25YL/Hx8U7//syZM+Lv165da/f4e++9l1K3bt0UPcKRMH8gQ++lStGBKwdFG7Scnedr8WzpvLH0eO0iFB4e5tRTqVuNbvRJi09EoSmiNQCt17KTCcZ8z81+Tv9dlXJcEUYXuYqEuVGUDyNG8Ej5R6hEDkt9igbANgFF/BMeniAikugkqj+hvoiOhSLoWEQUFiNz+tTvE5RleKjsQzS2ndJ9DPf4XRf8aCgcJGD9AeNaRCp+7vSzrjr6sG/DrMnJnSeLIeBofBlZ+GVqd4goU3I47bywkx76/SFqMamFcPh3C5gyT5igWS0YOiI/WvaRuN2vUT92ik8DePw1K9bMOuM4mMTGxlLWrFmtl+ho89Tw6ecXbCYwbgdyvGQJ0UKO7iGMHmpWPHgGqwgt4wAFYYauoLJjy9KPW34Uy6ZLML7IVTrSTY8wdGtN3jnZWnzrD5DiXPHiCmGUiVQcipPnH5pPocbQ1UOt6yNflvSbJfwFvN0guLFdo3bPTGlJ1Fkh9Q0wBsqVKadeBBlSxW83eofm/UZ0anwMvdfgXSHSIdhRO4auVtRUoonIJUuWKKUdNWsSNWni83KN2zhO1IfCH7B3vd4+v57ZebTCo+J65r6ZLv9mw+kNoizj2l2LI0AQyZ07N0VERNAFOVvYAu7nz5+f9AiLMD9Gwr4pf9PaQv5Nu28o2DvFnnV70uLnFwv/JrT0v/rPq1TluyrOPX6MIMLSiYR9seoL0UUGX7DGRRuTv6hfuD5teXWLuEYXbPvf24tmiP2X91MogBE2C48sFDVZsKUI9naO3xoiRWtPraUJWy1RFIOD3ys61eBzB++1Z6o8Q4YA3ZFhYZTzchx9Wf09OtTrEL1U/SVrVysO3rm+zEXNfmkmTl5S1Y0tX65ct2rl86LAMX7QcqUW7NMWn4paTyZtOldQrJTWnV5HZ27abEZwcoPfVvXx1UUGAGJ6+JrhQV+dUVFRVKtWLVoC8W7h/v374n6DBg1Ij7AI8wdHj9LmgkTvZlRmdH3Z+sugtJA7A9G4/T33i65KGJRCKGA4+KTtljlsRhBhbhTmo2X+u83fWZsh/O2hVCC2AC1/YbmYk4gDDGxBKn9bmV6d+6rdzsuMSOsVtLVrmfL1FriC4yArpyJgpp6RgXh4YMoDwo4GnaBj2ikpdkOAtJE0bT18WJRMTOg0QaQqURQPzzdELTHPECcvSFWuOrHKFsGUIgxdkT7Se0Fv4X+FjufuNbUxETY78FDD+gLSvBXfTbe/ugl3fYyuwv4OdjRv1FHG8QWbvn370o8//kiTJk2iffv2UY8ePSguLk50S+qSFJMTjML88zXLpZTobSvY1msh/PW711MenvqwtXC217xeKbcTbqfogv79lWLc3r3tH8e6jIlRnjt82OW/vzD7BfGZWk1qlRJo0AygXq8xn8WIYnWsb7Nx8PLBlLCPw8Tn3Hl+Z4peQFOKbBSo/1P9lMTkxBQjsvrE6pR8X+UTn6PgiIKaNpcEjBYtlN/r5MlOn8Zneve/d63NQ7gUGVkkZfSkHimHc1BKSnh4SsqNGz4twvhN48XronFDT9upEfjfmv9ZG0FwLBuyfIi4H/FJRMqXq79MuXj7ou6O3998801K0aJFU6KiokRB/vr161P0CkfCvAURmg8+SBWpuX7nKj1Y7yAdy0FUInNh+vnhn3U73xEeP7OfnC3m+0kbC+nAH3SvJVeRMEwiuHs3zeHdSK9imoBshgg0lfJWEo77q7utpkZFGolhufDPqv59ddpxfgeZCRheIkUGewIMmdcLqH3EBAT4QKEI/MOlH5LRbCjg99f0l6Z0Ie4CVclbRZho6iHS6DGlS6dp2IrP9NUDX9HBXgdFqjI2KlaME+t97Dsq3Zuo5AeZ6YNNw+jCbfs6H3eZvnu6MLQGg5oO0tV2agRgc4TmozWn1lDjiY3p4xUfW+sS32v0HuXJnIf0xptvvkknTpwQfmIbNmwQrvl6hUWYt3TurBgIYpSGBaQLHvypOW3Pl0L54sJoYddFlCPGNjtSjyCU/HHzj2nu03PtHPhh/IqQs9M6jWCKMFkPhu7JmNT+SEhtvPbPa0IYoEM0mMO3GxVtJA6cEGRYt0iRNvy5IX236bvgi1wNQJp10o5J1kkEegO1j+ggBBil9O/Bf8kIwHi58neVhd8fUj/Yjtd2X6ubkgaPcdOwtWi2oiJViRFiY643oKbHiTLcD6NjkbdE40f+EflFdyX2SehyTA+Mchu9frToBsf+AOnPj5oqnZGM+yCFPKDxAHEbdZbYJlGXyCldbQhDOIxMzOnTp6lIkSJ06tQpKuymuadbzJtH9NBDoubh7r5dNObMLBHtQGF29rtEy/fXo2pz3GzD1gnYsf2w5QfRBq+eA1chdwVqW6ottS7ZWtSUoejZ78yZowhdFFOuXWt7fMEConbtiKpWJdqROqo0at0o6ruwL+XImIP2v7lfjM/RA1fvXqWn/nyKFh1dJO7DYRzjZmoUqEHlc5e32okYiXcXvksj1o0Q5rVwr9crveb1orGbxooIy/IXl4vh03oFA8krfVtJRE/R6IHazXqF9XsW7xZ//kn0+ONEdeoQuTPDb9cuomrVxOSR28v+owV5lRFsm85usv4JBGnXql3F/gjfq2xeWHJsibCJgejac3GPmDEKXqz+orCT0ZOlh9GODejMnX94Pj1c9mEa2GxgQNblaX8dv3UEizAv2XxmE8374HHamniC1pfLTBfCFN+t6vHZafqP16nsW58QDVI6cYzG3aS7tPrkajHaAx5nOCBIMoRnoGerPksv13hZDFTWckCzHStXKuNJypYlOnDA9jics195RRFiEMIOKZwK4ypQXFIc/dDhB3ql1iukJxClQxQMXm2YaCBByuyBUg+IzsLaBWvrNn3tKCqLjioq1vW8Z+YJfyi9gvE5bX9tK6wRULS//bXtlCuTxYdOR+B8WER6Ds8X/kwLn18o0kCG59gxopIlRZeksO+Rs2Fx/n/5sjKIW27zeAwnt/PnK8Ltjz+sL3PoyiEauW4kzdo/K/WwcBfgZAxNGigaN8LvirGHRZgJ8NeXiBFAf+yx7SCKZStGn7YYQs90GEARp88SLV1K1KIFGR0cbJccXSIiOLggpSbBuI9edXsJ8YD6Mk3ZvZuoShVlB33pku3xDz8k+uILotdeIxqvDOWWAufhqQ+LAxi6eZAG1OtZL3yRRqwdQb/t+k0cTG4l3rI+VypHKRrUbJCYL6rFeCt/gbPiwcsHU7V81Wjba9t0f4DDOoeH28ErB+mxio/RjMdnkN7AjL7HZzwuhNfO13eKcUCm4YEHiBYtInr/faWEIy6OaMwYpa62fn2iJ55Qfuv//KMIr8hIZR9QLvU6gNH01N1ThS0KomPS6xAReojXWgVqicgy/Opwn60ojMvpEIiEcXekD6MvHpvWJWV0y8wpf1SklPi/ZqWk7NundAFFR6ekxMWlmJH1p9anPDnjyZRcw3NZO5lwGyNjNOXcOWVdojMqOdn2eOfOyuNff23356/PfV0sS9SnUYbqfkIXH9bpc7OeE8su12nN72sGbVRIeqCDVn7/U3dNTTEK285tEx1dWG7Nt1cfuZt0V3QEytFipmP2bOV3i87m999Xbqd1+emnYC8xE6pjBwOMPkMFBuDpKk/TjCdn0ltVX6HH9xJFT/pVOdMDjRvbBlCbDNSnoOsMxbPorES92JW7V6jD1A7CTFIz1+Q8eTB5FU57RBdVqYe9e5XrChWsD/2681cav2U8hVEYTX10qqG6n3DGjnU6pfMUutrvKg1vPZyyZ8wuDFDr/VRPl4avGOiL7xxRO0SVjEL1/NWpb4O+4nbPeT3THN115c4VMeT56ZlPU//F/WnbuW1+XTY4uaMjEOnS/o311+TgM506KYar6GwerjL1zJdPuf/II8rzL79M9N9/RN3Zx4sJDbgmzFd27lSKSBE+R7j0+HGla7K/CXekLgo23/nvHVH4DEpkL0FfP/g1PVzuYd9fHI74Z88Sbd5MVKsWUWKiIm7v3SM6dUqsb6RKYfKI5UD7OeZmmqHrsOPUjrTt/DZxUF7z0hrROaYH1HV3EztNFAXPRgLCq+K3FenkjZP0Vt23aHS70an+BmNtmkxsIgq91TQv3pzalGwjRt4ATMLAvEqIZl+Q467uJN0RxeMY/WRKTp5UajlxIoUTrPbtiT75RBlJxDAhmo5kEaYF8CBRd/2gaw/deyHE5rObRT2LrBl7o/Yb9Hmrz307QKGbCgLs77+JOnYk2rOHqHJlTHMlunGDTt86I8ZmICqDiMy0R6fpuo7KE1C4DyGASBisFmBzUTVf8LeprrO70pSdU8QYKMzM1GvdXVrA4gDCHfzx2B/0eKXHrc9BeDX+uTEdu35MdLBCEG05t4Vm7p0pOu6cFX5/0OQDerPum0KUeeOGj5E9EH4QeAueW2DIdeo2yclEmzYpwstEQ5gZ/3CaRZjxCciXiDE6//6rRGhgTIgi1BAExc+frfxMeDJpcYCihx8mmjtXKcBHIb5sda9bl5LXraGWk1rSqpOrqEb+GsJHyav30DE4QDf/pTkduXaEMmXIJGYivlDthaAJzZ0XdgrRCzGy6ZVNopPTqCB6O3L9SFEE/0XLL8RngQcSUq1nbp0RqVY0d2AcFYBIgnjD9oaIFUCR/4ErSudukaxFaEiLIfR81efd+n5kcwYsPhBVhOXC2pfWBnX4OcPojdMswoxPKHyJeuO/w/8Jry6kWeQB6pPmn1DXal09ExCvv070/fdEgwcTffyx0lU1YADRs8/SwJdK0GerPhMeQVtf2ypm0JkRdKeiE3fx0cXiPpzTh7UeRu1KtwtoRyIMGjFjFK7ZcNCe/th0MjLoqINv28x9M1M9h5T6kq5L0nWnhxkoooIDlw0UghlUylNJTKCAZYczPz24vqOzD8PlL91Run5h9YIoriHd8BnGj5wOgeM3pyMZv4ADFEYHDVo+yO4AhWHaHct2dE9ADBmiCDD4gv3wA1GPHiIqNn/gU/RQxHQRkUEhPgZHmxkIBpjQfrH6C2EGDJoWayqK+GHoGQh+2voTvTL3FSEs9r6xV7hom2EbhSs9DIqRRm9QpAG1L92eXqj+gkdRVfjqIYIGYXUt/prVTw+vh8YVDEFGLdrBqwfp7wN/W4dTl81VVozV6ly+s+4tPhgmGJxmEWZ8QuFL1DPODlCYp/hKzVeEA3+hrIVc/7M0ZoV5I/yD2rWjtXsWUJuXo+hOSiK9WvNV+r7j9xQqICo2bPUwGrNhDCXcSxCPwW2/b/2+1K1GN7+57sPLrPzY8uL7G/nASHq7wdt+eR+jA4H81ZqvRKQLNWWuQPr89dqvi3ozI05KYJhAcToEjt8cCWMCAqwrhq8ZTqM3jKb45Hjr44gUoCAZ6Ru4xtsVJcvRUDVqEG3dSuuaFKf2jU7Q9RiiB0s/KIrVTeEo7kWHIoxSEWm8l3JPPFYuVzkRVXmk/COaR1VkMT4sHlALxsIhfY5cPSJG6KDQf8+lPWIqAhossK0bfgwRwwSI0yzCjE8ofIlGAqnJ8ZvHC7drdFSqO87gdI3i5lYlWlF0ZDTR9u1CgF0unJO++LErjV37NSVFEDXMXZMWvbpKFKuHMjfib9DP236mz1d9LjpEAdKTSFMiXakFGF0Fuwx4sK3rvo4FBMMwAeN0CBy/ORLGBDW9tvTYUlp0ZBH9vvt3up14WzwOcYVxIxWylaKdf4ylVcWIEixZm8f2EE2ccJmyxOpv9l8wxRg6UjFXT875hKdVhzIdqE2pNqKY35voGOwxGv3cSHxPfer1oVEPjvLD0jMMwziHRZgJCIUv0Qxcirsk6sZQT3Mh7kKq52uEFaRhk89Smzv5KezsuaAso945d+ucmOmIoesyTSlTvm/Xf5valm7rtukrzEzhlwUXd3Tvwa4hFFO/DMMEj9MhcPzmSBijK1JSUmjXxV3CkgGiougf/1Hrv3ZR+cxFKezESaIGDYjWrg32YuoaeFrN2T9HDFxfcXyFNTomO/LQFNG9RnfKEZPDZQQMKcjDVw+Lwv+VL66kPJnzBPATMAzDEIswMxAKStrUDBxI9NlntvvjxhG98UYwl8hwqUp0p6K2a+OZjdYIGRogUIOHQnGkLBsUbiBc+vF3/Rb3E2aixbIVExEwM9hRMAxjPE6HwPGbI2GMvvnjD6Inn1RuN21KtGyZMneO8UqQwZwUtWPo2EsLFPbPeHwG5c2cl9c0wzBB4XQIiDA2qWH0TbNmRDlzElWqRDRnDgswH8iWMZvwpsIFXapI+coL6vDQAYnxPTAPfbfhu5QhIoN23yPDMAyTChZhjL7Jl4/o/HmiyEgidhXXjMJZC9OL1V8UF9ThYYROTGQMxUbHavcmDMMwTJqwCGP0TwaOyPgT2Fdw2pFhGCbwcHENwzAMwzBMEGARxjAMwzAMEwRYhDEMwzAMwwQBFmEMwzAMwzBBgEUYwzAMwzBMEGARxjAMwzAMEwRYhDEMwzAMwwQBFmEMwzAMwzBBgEUYwzAMwzBMEGARxjAMwzAMEwRYhDEMwzAMwwQBFmEMwzAMwzBBgEUYwzAMwzBMEIgkk3P//n1xfe7cuWAvCsMwDMMwbnLOctyWx3EzYnoRduHCBXFdt27dYC8KwzAMwzBeHMeLFi1qyvUWlpKSkkImJjk5mbZt20b58uWj8HDtsq+3bt2iihUr0t69eyk2Nlaz12V4XQcL3qZ5XZsN3qaNvZ7v378vBFiNGjUoMtKcMSPTizB/cfPmTcqWLRvduHGDsmbNGuzFMTW8rnk9mw3epnk9mwnenr2HC/MZhmEYhmGCAIswhmEYhmGYIMAizEuio6Np8ODB4prxL7yuAwOv58DB65rXs5ng7dl7uCaMYRiGYRgmCHAkjGEYhmEYJgiwCGMYhmEYhgkCLMIYhmEYhmGCAIswhmEYhmGYIMAijGEYhmEYJgiwCGMYxgoP0GAYhgkcLMLc5O7du7R69WoxG8uR+Ph4mjx5stbfTciyb98+mjhxIu3fv1/cx3WPHj3opZdeoqVLlwZ78Uzv94P1z/iPuLg4sX1/+OGHNHbsWLpy5Qqvbg3YunUrHTt2zHp/ypQp1KhRIypSpAg1btyYpk2bxutZA3r16kWrVq3idakR7BPmBgcPHqQHHniATp48SWFhYdYfdIECBcTzGDBasGBBunfvnlbfS8iyYMEC6tSpE2XJkoXu3LlDs2fPpq5du1K1atXEMNcVK1bQwoULqWXLlsFeVEPTt29fp4+PHj2annvuOcqVK5e4P3LkyAAvmfnAYGOcwOXMmZNOnTpFTZs2pWvXrlHZsmXpyJEjYjDx+vXrqUSJEsFeVEODfcSIESOodevW9NNPP9Fbb71Fr7zyClWoUIEOHDggHsP2jZM5xnvCw8PFcbBUqVLUvXt3euGFFyh//vy8Sr2ERZgbdO7cmZKSkuiXX36h69evU58+fUREbPny5VS0aFEWYRrSsGFDIbA+++wzIXTfeOMNEQX7/PPPxfMDBgygLVu2CCHG+LYjxUEre/bsdo9D5NauXZsyZ84sdrQcedRmXZ8/f57y5s0rBC6iNfPmzaNs2bLR7du3xf4lT5489Pvvv2vwbqFLpkyZRBS3WLFiVLNmTbHfgAiTYP1iP7Jnz56gLqcZtudFixbR3Llz6bfffqMbN25Qu3btxLpu3769eJ7xgBQmXfLmzZuyc+dO6/379++nvP766ylFixZNOXLkSMr58+dTwsPDeU1qQNasWVMOHTokbt+7dy8lMjIyZevWrdbnd+3alZIvXz5e1z4ydOjQlBIlSqQsWbLE7nGs7z179vD61ZCwsLCUCxcuiNslS5ZMWbhwod3za9asSSlSpAivcx/JlStXyubNm6377O3bt9s9f/jw4ZSYmBhezxpuz4mJiSnTp09Padu2bUpERERKwYIFUz744APrPpxJH5asbtaDIWUgQYTgu+++o44dO1KzZs1EupLRDqxfgDOqjBkzioiBJDY2Vpx5Mb7Rv39/mj59uogWvPvuuyLSy/h/m0b9qCxjkBQqVIguXbrEq99HEI3Bfhlgv/znn3/aPf/HH39Q6dKleT1rSIYMGeiJJ54QZSRHjx4V0TBEx8qVK8fr2U1YhLlB+fLlafPmzakeR1Et6pcefvhhd9c3kw7FixenQ4cOWe+vW7dOpHwlqMtzPIgx3lGnTh2R2oUAQApy9+7dVrHAaEurVq1EiuzmzZuiPknNiRMnrDV4jPcMHz6clixZIgQYivFRH9akSRN69dVXxWMff/wxDRs2jFexn8B+GusY6XaIMsY9bOEdxiWo2Zg6dSo9//zzToUYCsbHjx/Pa1ADEJlRNzhUrlzZ7vn58+dzUb6GoAFi0qRJov4OBc3cXKI9gwcPTrXO1aC2BmKB8Q00R23btk0ILaxT2K1s3LhRNEOgS3LNmjXiZIPxDdTcRUREuHweJ3Jt2rTh1ewmXJjPMIwAByu0+UOMoTCfYRiG8S8swrwgISHB6qnE+Bde14GB13Pg4HXN69lM8PbsG1wT5iZoyUX7bY4cOUQrNC64jccWL17s49fA8LoOPLxN87o2G7xN83o2GhwJcwPUzLz88sv02GOPUdu2bSlfvnxWk1b4VaELZ8KECU5rxhjP4HUdGHg9Bw5e17yezQRvzxrjho1FyFOmTJmUsWPHulwP48aNSyldunTIryct4HUdGHg9Bw5e17yezQRvz9rCkTA3gFfVjh07XHqfoOW8evXqwk+M8Q1e14GB13Pg4HXN69lM8PasLVwT5gaVKlUS6UZX/Pzzz2I+HOM7vK4DA6/nwMHrmtezmeDtWVs4EuYGmBHZoUMHKlmypGjfV9eEwRwQTsH//vuvGMzL+Aav68DA6zlw8Lrm9WwmeHvWFhZhbnL8+HExEmP9+vViGC/A5PgGDRrQ66+/LpzeGW3gdR0YeD0HDl7XvJ7NBG/P2sEijGEYhmEYJghwTZiXvPHGG3T58mVtvw2G13UQ4W2a17XZ4G2a17Pe4UiYl2TNmpW2b98u6sQY/8LrOjDweg4cvK55PZsJ3p69hyNhXoLhsExg4HXN69ls8DbN69lM8PbsPSzCGIZhGIZhggCnIxmGYRiGYYIAR8LcYMuWLf7/Jhhe1wGEt2le12aDt2lez0aERZgb1KlTh0qXLk1ffPEFnT171v/fSgjD65rXs9ngbZrXs5ng7VlbWIS5ScuWLWn06NFUrFgx4Z4/Z84cunfvnsZfB8PrOnDwNs3r2mzwNs3r2XBoPBDclISFhaVcuHAhJSkpKeXPP/9Mad++fUpERERKvnz5Uvr165dy4MCBYC+iaeB1zevZbPA2zevZTPD2rC0swjzY6NScPn06ZciQISklS5ZMCQ8PT2nSpInGX01owuua17PZ4G2a17OZ4O1ZWzgd6QZhYWGpHitUqBANHDiQjhw5QgsXLqQiRYr4I1AZcvC65vVsNnib5vVsJnh71ha2qHCD8PBwMbQ7b968Gq9+htd1cOBtmte12eBtmtezEeFImBssW7aMcubM6f9vg+F1HSB4mw4cvK55PZsJ3p61hSNhDMMwDMMwQSAyGG9qRBITE4Utxbp160RqEuTPn58aNmxInTp1oqioqGAvomngdc3r2WzwNs3r2Uzw9qwdHAlzg8OHD1Pbtm2FUWu9evUoX7584vELFy7Qhg0bqHDhwjR//nxh6Mr4Bq/rwMDrOXDwuub1bCZ4e9YWFmFu0KZNG8qcOTNNnjyZsmbNavfczZs3qWvXrnT37l3677//NP56Qg9e17yezQZv0/9v7+5CqtjeOI4/2ptFlpL5BplJiZkVFghmlFgIBUZFGV6ECRmZCN5EBr3cROVlEUVEQUFEF9aldVGGpJFZlFqhIFpRqVhElnnjnsOaw9mnfbL/f6zlTHvN9wMb956x2v7WgzytWXsNOZuEetaLJsyBGTNmSEtLi2RlZY15vr293Z4hGx4e1jw8/kPW5GwaapqcTUI968WnIx2IiYmR3t7en55X59T34PeRtTvI2T1kTc4moZ71YmG+A7t377YvOarNWdetWxeyJuzOnTty7Ngxqaqq0jw0/kTW5GwaapqcTUI9a6Z5B35jnTx50kpKSrJv2aBuU6Qe6rk6Vltb6/XbMwpZk7NpqGlyNgn1rA9rwsapp6cnZIuKBQsW6O6LQdauoqbJ2jTUNDmHC9aEjZNqunJzcyUQCEhycvLEjArI2kXUNFmbhpom53DBTNgvUltVPH36VNLS0vSOCMjaI9Q0WZuGmibnPx0zYb/Isiy9IwGy9hg1TdamoabJ+U9HEwYAAOABmrBfdP78+eBWFZhYZO0OcnYPWZOzSajnX8eaMAAAAA8wE+bAwMBAyGu1IL+0tFTy8vJk27Ztcu/evYkaH98ha3I2DTVNziahnvWiCXMgKSkpWHjNzc2Sk5Mjr169spswdQNvdUPTxsZGzUPjT2RNzqahpsnZJNSzXlyOdCAyMtLeoDU+Pl4KCwtl3rx5cvHixeD56upq+ybe6hZG+D1k7Q5ydg9Zk7NJqGe9mAkbp46ODikvLw85pl63tbXpHBeQtWuoabI2DTVNzuGCG3g7NDQ0JFFRUfZj2rRpIefUseHh4YkYH18ia3I2DTVNziahnvVhJsyh9PR0iY2Nld7eXmltbQ059/z5c25hpBFZu4Oc3UPW5GwS6lkfZsIcaGho+GFh4n9vFrtnzx6Nw+JfZE3OpqGmydkk1LNeLMwHAADwAJcjAQAAPEAT5tDZs2dl/fr1Ulxc/MNWFIODg5KWljYR4+NLZE3OpqGmydkk1LM+NGEOnD59Wvbv3y8ZGRn2JyM3btwoJ06cCJ4fHR21N2/F7yNrd5Cze8ianE1CPWtm4f/KzMy0rl69Gnzd1NRkzZ071zp8+LD9uq+vz4qMjCRJDcjaHeTsHrImZ5NQz3rRhDkwffp0q6enJ+RYe3u7lZCQYNXU1NCEaUTW7iBn95A1OZuEetaLLSociIuLkzdv3khqamrwWFZWlty9e1cKCgrk3bt3uicofYusydk01DQ5m4R61os1YQ6sXr1abty48cPxzMxMe5F+fX295mHxL7ImZ9NQ0+RsEupZL2bCHKipqZHHjx+PeW7JkiX2jFhdXZ3mofEnsiZn01DT5GwS6lkvNmsFAADwADNh49DS0iIPHjyQvr4++3ViYqLk5uZKTk7ORI2Pb5E1OZuGmiZnk1DPejAT5sDAwIBs3bpVmpubJSUlRRISEuzj/f398vr1a8nLy7MvR8bHx2saFv8ia3I2DTVNziahnvViYb4D+/btk0AgIC9fvpTe3l55+PCh/VDP1TF1rrKyUvPQ+BNZk7NpqGlyNgn1rBczYQ5ER0dLY2OjZGdnj3leLdrPz8+XoaEhzcPjP2RNzqahpsnZJNSzXsyEOaBuVfT58+efnlfNl/oe/D6ydgc5u4esydkk1LNeNGEO7NixQ0pLS+XmzZshzZh6ro6VlZVJSUmJ5qHxJ7ImZ9NQ0+RsEupZM8078BtpZGTE2rt3rzV16lT7HpFRUVH2Qz1XxyoqKuzvAVmHC2qarE1DTZNzOGJN2DiomS+1/uv7LSpWrlwps2bN0t0b+x5Zu4Oc3UPW5GwS6lkPmjAAAAAPsCbMoW/fvsn9+/flxYsXP5wbGRmRK1eu6B4b3yJrcjYNNU3OJqGeNfL6emg46OzstObPn29FRETY68DWrFljvX37Nni+r6/PPg6yDhfUNFmbhpom53DETJgDBw4ckKysLHun4M7OTnufFHUnebVbPvQia3eQs3vImpxNQj1r5nUXGA7i4+Ottra24OtAIGB/WjIlJcXq7u5mJoysww41TdamoabJORwxE+bw+vfkyf/e6zwiIkLOnTsnRUVFsnbtWunq6tLdG/sWWZOzaahpcjYJ9azXv50FfiojI0NaW1tl8eLFIcfPnDljf920aRPpaULW7iBn95A1OZuEetaLmTAHtmzZIteuXRvznGrE1G75lmVpHhp/ImtyNg01Tc4moZ71Yp8wAAAADzATBgAA4AGaMAAAAA/QhAEAAHiAJgwAAMADNGEA/mi7du2SzZs3e/02AEA79gkD4Bm18fH/cvToUTl16hRbwAAwEk0YAM+8f/8++Pz69ety5MgR+/6s/5g5c6b9AAATcTkSgGcSExODj9mzZ9szY98fUw3Yfy9H5ufnS1VVlVRXV0tsbKwkJCTIhQsX5OvXr1JWVibR0dGycOFCqa+vD/m3Ojo6ZMOGDfbfqf7Mzp07ZXBw0IOfGgD+RhMGIOxcvnxZ4uLipKWlxW7IKioqZPv27bJq1Sp58uSJFBYW2k3W8PCw/f2fPn2SgoICyc7Otm9BduvWLenv75fi4mKvfxQAPkYTBiDsLF++XA4dOiSLFi2SgwcPSlRUlN2UlZeX28fUZc0PHz5IW1tb8PZiqgE7fvy4fe879fzSpUvS0NAgXV1dXv84AHyKNWEAws6yZcuCzydNmiRz5syRpUuXBo+py43KwMCA/fXZs2d2wzXW+rLu7m5JT0935X0DwPdowgCEnSlTpoS8VmvJvj/2z6cuA4GA/fXLly9SVFQktbW1P/xdSUlJE/5+AWAsNGEAjLdixQqpq6uT1NRUmTyZX3sA/gysCQNgvMrKSvn48aOUlJTIo0eP7EuQt2/ftj9NOTo66vXbA+BTNGEAjJecnCxNTU12w6U+OanWj6ktLmJiYiQykl+DALwRYVmW5dG/DQAA4Fv8FxAAAMADNGEAAAAeoAkDAADwAE0YAACAB2jCAAAAPEATBgAA4AGaMAAAAA/QhAEAAHiAJgwAAMADNGEAAAAeoAkDAAAQ9/0FkXxv93Hh1GQAAAAASUVORK5CYII="
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 64
+   "execution_count": 11
   },
   {
    "metadata": {
@@ -499,8 +502,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-11-29T21:44:22.670216Z",
-     "start_time": "2025-11-29T21:44:21.605928Z"
+     "end_time": "2026-01-07T03:50:21.450203Z",
+     "start_time": "2026-01-07T03:50:20.047261Z"
     }
    },
    "cell_type": "code",
@@ -525,29 +528,29 @@
      "output_type": "stream",
      "text": [
       "                               value\n",
-      "2025-07-02 07:15:00+00:00  37.371079\n",
-      "2025-07-02 07:30:00+00:00  46.801625\n",
-      "2025-07-02 07:45:00+00:00  29.980004\n",
-      "2025-07-02 08:00:00+00:00  45.573409\n",
-      "2025-07-02 08:15:00+00:00  55.745558\n"
+      "2025-07-01 15:00:00+00:00  31.502800\n",
+      "2025-07-01 15:15:00+00:00  32.612457\n",
+      "2025-07-01 15:30:00+00:00  32.363079\n",
+      "2025-07-01 15:45:00+00:00  31.748017\n",
+      "2025-07-01 16:00:00+00:00  31.132956\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_30480\\3992666424.py:13: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_13384\\1903960078.py:10: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
       "  df_15m = df.resample(\"15T\").mean()\n"
      ]
     }
    ],
-   "execution_count": 72
+   "execution_count": 12
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-11-29T21:48:01.173879Z",
-     "start_time": "2025-11-29T21:48:01.131446Z"
+     "end_time": "2026-01-07T03:50:23.625393Z",
+     "start_time": "2026-01-07T03:50:23.615067Z"
     }
    },
    "cell_type": "code",
@@ -557,32 +560,32 @@
     {
      "data": {
       "text/plain": [
-       "DatetimeIndex(['2025-07-02 07:15:00+00:00', '2025-07-02 07:30:00+00:00',\n",
-       "               '2025-07-02 07:45:00+00:00', '2025-07-02 08:00:00+00:00',\n",
-       "               '2025-07-02 08:15:00+00:00', '2025-07-02 08:30:00+00:00',\n",
-       "               '2025-07-02 08:45:00+00:00', '2025-07-02 09:00:00+00:00',\n",
-       "               '2025-07-02 09:15:00+00:00', '2025-07-02 09:30:00+00:00',\n",
+       "DatetimeIndex(['2025-07-01 15:00:00+00:00', '2025-07-01 15:15:00+00:00',\n",
+       "               '2025-07-01 15:30:00+00:00', '2025-07-01 15:45:00+00:00',\n",
+       "               '2025-07-01 16:00:00+00:00', '2025-07-01 16:15:00+00:00',\n",
+       "               '2025-07-01 16:30:00+00:00', '2025-07-01 16:45:00+00:00',\n",
+       "               '2025-07-01 17:00:00+00:00', '2025-07-01 17:15:00+00:00',\n",
        "               ...\n",
        "               '2025-07-05 11:45:00+00:00', '2025-07-05 12:00:00+00:00',\n",
        "               '2025-07-05 12:15:00+00:00', '2025-07-05 12:30:00+00:00',\n",
        "               '2025-07-05 12:45:00+00:00', '2025-07-05 13:00:00+00:00',\n",
        "               '2025-07-05 13:15:00+00:00', '2025-07-05 13:30:00+00:00',\n",
        "               '2025-07-05 13:45:00+00:00', '2025-07-05 14:00:00+00:00'],\n",
-       "              dtype='datetime64[ns, UTC]', length=316, freq='15min')"
+       "              dtype='datetime64[ns, UTC]', length=381, freq='15min')"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 73
+   "execution_count": 13
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-11-29T21:48:05.682080Z",
-     "start_time": "2025-11-29T21:48:05.548214Z"
+     "end_time": "2026-01-07T03:50:24.895375Z",
+     "start_time": "2026-01-07T03:50:24.699623Z"
     }
    },
    "cell_type": "code",
@@ -610,26 +613,42 @@
    ],
    "id": "7397cf9f7ec92d1e",
    "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'hourly_data' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
+      "Cell \u001B[1;32mIn[14], line 7\u001B[0m\n\u001B[0;32m      4\u001B[0m ax_twin \u001B[38;5;241m=\u001B[39m ax1\u001B[38;5;241m.\u001B[39mtwinx()\n\u001B[0;32m      6\u001B[0m plt\u001B[38;5;241m.\u001B[39mplot(df_15m\u001B[38;5;241m.\u001B[39mindex, df_15m, label \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mArray Temperature\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m----> 7\u001B[0m plt\u001B[38;5;241m.\u001B[39mplot(\u001B[43mhourly_data\u001B[49m[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mdate\u001B[39m\u001B[38;5;124m'\u001B[39m],hourly_data[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtemperature_2m\u001B[39m\u001B[38;5;124m'\u001B[39m], color \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mgreen\u001B[39m\u001B[38;5;124m'\u001B[39m, label \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mAmbient Temperature\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m      9\u001B[0m ax1\u001B[38;5;241m.\u001B[39mplot(hourly_data[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mdate\u001B[39m\u001B[38;5;124m'\u001B[39m],hourly_data[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mshortwave_radiation_instant\u001B[39m\u001B[38;5;124m'\u001B[39m], color \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mred\u001B[39m\u001B[38;5;124m\"\u001B[39m, label \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mSolar Irradiance\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m     11\u001B[0m ax1\u001B[38;5;241m.\u001B[39mset_xlabel(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mTime\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n",
+      "\u001B[1;31mNameError\u001B[0m: name 'hourly_data' is not defined"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 74
+   "execution_count": 14
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-07T03:50:25.378205Z",
+     "start_time": "2026-01-07T03:50:25.369392Z"
+    }
+   },
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": "",
-   "id": "8848868de645674f"
+   "id": "8848868de645674f",
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
diff --git a/array_temp/temp.ipynb b/array_temp/temp.ipynb
deleted file mode 100644
index 54eb311..0000000
--- a/array_temp/temp.ipynb
+++ /dev/null
@@ -1,731 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "id": "initial_id",
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "is_executing": true
-    }
-   },
-   "source": [
-    "from data_tools import query\n",
-    "from data_tools.collections import TimeSeries\n",
-    "from datetime import datetime, date, time, timezone\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "import dill\n",
-    "import os\n",
-    "import pytz\n",
-    "from datetime import datetime, time, date\n",
-    "\n",
-    "#each 5 seconds\n",
-    "utc_offset_h = 2\n",
-    "start_utc = time(0, 00, 00)  #querying i svancouver time, influxdb gives utc\n",
-    "stop_utc = time(23, 45, 00)\n",
-    "date_start = date(2025, 7, 2)\n",
-    "date_stop = date(2025, 7, 6)\n",
-    "\n",
-    "vancouver = pytz.timezone(\"America/Vancouver\")\n",
-    "\n",
-    "start_local = vancouver.localize(datetime.combine(date_start, start_utc))\n",
-    "stop_local = vancouver.localize(datetime.combine(date_stop, stop_utc))\n",
-    "\n",
-    "start_time = start_local.astimezone(pytz.utc)\n",
-    "stop_time = stop_local.astimezone(pytz.utc)\n",
-    "\n",
-    "client = query.DBClient()\n",
-    "temp_array_fsgp: TimeSeries = client.query_time_series(start_time, stop_time, field=\"MosfetTemperatureA\")\n",
-    "speed_kph: TimeSeries = client.query_time_series(start_time, stop_time, \"MotorRotatingSpeed\")\n",
-    "\n",
-    "#print(temp_array_expected_aliter)\n",
-    "#this is almost steady state, and expected temperature is roughly 31.8 degrees celsius. note that individual cells do have an upto 5 C difference, which is actually quite a lot.\n",
-    "\n",
-    "\n"
-   ],
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {
-    "jupyter": {
-     "is_executing": true
-    }
-   },
-   "cell_type": "code",
-   "source": "print(\"jk\")",
-   "id": "c6cea64b1e62c93e",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T22:31:03.101565Z",
-     "start_time": "2025-11-29T22:30:54.208893Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "from datetime import datetime, date, time\n",
-    "import pytz\n",
-    "\n",
-    "vancouver = pytz.timezone(\"America/Vancouver\")\n",
-    "\n",
-    "date_start = date(2025, 7, 1)\n",
-    "date_stop  = date(2025, 7, 6)\n",
-    "\n",
-    "# Local start/end\n",
-    "start_local = vancouver.localize(datetime.combine(date_start, time(23,45,0)))\n",
-    "stop_local  = vancouver.localize(datetime.combine(date_stop, time(0,0,0)))\n",
-    "\n",
-    "# Convert to UTC\n",
-    "start_utc = start_local.astimezone(pytz.utc)\n",
-    "stop_utc  = stop_local.astimezone(pytz.utc)\n",
-    "\n",
-    "print(\"Start UTC:\", start_utc)  # 2025-07-02 07:00:00+00:00\n",
-    "print(\"Stop UTC:\", stop_utc)    # 2025-07-07 06:45:00+00:00\n",
-    "\n",
-    "client = query.DBClient()\n",
-    "temp_array_fsgp = client.query_time_series(start_utc, stop_utc, field=\"MosfetTemperatureA\")\n",
-    "speed_kph       = client.query_time_series(start_utc, stop_utc, \"MotorRotatingSpeed\")\n"
-   ],
-   "id": "856e8e887bd73795",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Start UTC: 2025-07-02 06:45:00+00:00\n",
-      "Stop UTC: 2025-07-06 07:00:00+00:00\n"
-     ]
-    }
-   ],
-   "execution_count": 29
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T22:31:06.563220Z",
-     "start_time": "2025-11-29T22:31:05.448758Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "#resampling influx data so that i can fit it with irradiance data (queried every 15 minutes)\n",
-    "\n",
-    "ts = temp_array_fsgp\n",
-    "timestamps = pd.to_datetime(ts.datetime_x_axis, utc=True)\n",
-    "values = ts.data\n",
-    "\n",
-    "df = pd.DataFrame({\"value\": values}, index=timestamps)\n",
-    "df_15m = df.resample(\"15T\").mean()\n",
-    "\n",
-    "print(df_15m.head())\n",
-    "print(df_15m.tail())\n"
-   ],
-   "id": "5efaf5364189c268",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                               value\n",
-      "2025-07-01 23:45:00+00:00  26.032398\n",
-      "2025-07-02 00:00:00+00:00  25.083505\n",
-      "2025-07-02 00:15:00+00:00  24.787958\n",
-      "2025-07-02 00:30:00+00:00  24.492412\n",
-      "2025-07-02 00:45:00+00:00  24.675365\n",
-      "                               value\n",
-      "2025-07-05 13:00:00+00:00  38.086209\n",
-      "2025-07-05 13:15:00+00:00  36.774657\n",
-      "2025-07-05 13:30:00+00:00  35.463106\n",
-      "2025-07-05 13:45:00+00:00  34.271664\n",
-      "2025-07-05 14:00:00+00:00  34.174615\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_31364\\2898644639.py:10: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
-      "  df_15m = df.resample(\"15T\").mean()\n"
-     ]
-    }
-   ],
-   "execution_count": 30
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": [
-    "querying from openmeteo to get solar irradiance data\n",
-    "\n",
-    "- this is hourly irradiance over 4 days\n"
-   ],
-   "id": "8fd8b587bf1e774"
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T22:35:59.594077Z",
-     "start_time": "2025-11-29T22:35:59.572711Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "import openmeteo_requests\n",
-    "\n",
-    "import pandas as pd\n",
-    "import requests_cache\n",
-    "from retry_requests import retry\n",
-    "\n",
-    "# Setup the Open-Meteo API client with cache and retry on error\n",
-    "cache_session = requests_cache.CachedSession('.cache', expire_after=3600)\n",
-    "retry_session = retry(cache_session, retries=5, backoff_factor=0.2)\n",
-    "openmeteo = openmeteo_requests.Client(session=retry_session)\n",
-    "\n",
-    "# Make sure all required weather variables are listed here\n",
-    "# The order of variables in hourly or daily is important to assign them correctly below\n",
-    "url = \"https://historical-forecast-api.open-meteo.com/v1/forecast\"\n",
-    "params = {\n",
-    "    \"latitude\": 36.9760,\n",
-    "    \"longitude\": 86.4491,\n",
-    "    \"start_date\": \"2025-07-02\",\n",
-    "    \"end_date\": \"2025-07-05\",\n",
-    "    \"minutely_15\": [\"temperature_2m\", \"wind_speed_10m\", \"shortwave_radiation_instant\"],\n",
-    "}\n",
-    "responses = openmeteo.weather_api(url, params=params)\n",
-    "\n",
-    "# Process first location. Add a for-loop for multiple locations or weather models\n",
-    "response = responses[0]\n",
-    "print(f\"Coordinates: {response.Latitude()}°N {response.Longitude()}°E\")\n",
-    "print(f\"Elevation: {response.Elevation()} m asl\")\n",
-    "print(f\"Timezone difference to GMT+0: {response.UtcOffsetSeconds()}s\")\n",
-    "\n",
-    "# Process minutely_15 data. The order of variables needs to be the same as requested.\n",
-    "minutely_15 = response.Minutely15()\n",
-    "minutely_15_temperature_2m = minutely_15.Variables(0).ValuesAsNumpy()\n",
-    "minutely_15_shortwave_radiation_instant = minutely_15.Variables(1).ValuesAsNumpy()\n",
-    "minutely_15_wind_speed_10m = minutely_15.Variables(2).ValuesAsNumpy()\n",
-    "\n",
-    "minutely_15_data = {\"date\": pd.date_range(\n",
-    "    start=pd.to_datetime(minutely_15.Time(), unit=\"s\", utc=True),\n",
-    "    end=pd.to_datetime(minutely_15.TimeEnd(), unit=\"s\", utc=True),\n",
-    "    freq=pd.Timedelta(seconds=minutely_15.Interval()),\n",
-    "    inclusive=\"left\"\n",
-    ")}\n",
-    "\n",
-    "minutely_15_data[\"temperature_2m\"] = minutely_15_temperature_2m\n",
-    "minutely_15_data[\"shortwave_radiation_instant\"] = minutely_15_shortwave_radiation_instant\n",
-    "minutely_15_data[\"wind_speed_10m\"] = minutely_15_wind_speed_10m\n",
-    "\n",
-    "minutely_15_dataframe = pd.DataFrame(data=minutely_15_data)\n",
-    "print(\"\\nMinutely15 data\\n\", minutely_15_dataframe)"
-   ],
-   "id": "75c51cd2fbd7d69",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Coordinates: 37.0°N 86.5°E\n",
-      "Elevation: 5139.0 m asl\n",
-      "Timezone difference to GMT+0: 0s\n",
-      "\n",
-      "Minutely15 data\n",
-      "                          date  temperature_2m  shortwave_radiation_instant  \\\n",
-      "0   2025-07-02 00:00:00+00:00           -2.20                     5.588703   \n",
-      "1   2025-07-02 00:15:00+00:00           -2.15                     5.351785   \n",
-      "2   2025-07-02 00:30:00+00:00           -2.15                     5.014219   \n",
-      "3   2025-07-02 00:45:00+00:00           -2.05                     4.680000   \n",
-      "4   2025-07-02 01:00:00+00:00           -1.95                     4.680000   \n",
-      "..                        ...             ...                          ...   \n",
-      "379 2025-07-05 22:45:00+00:00           -2.00                     8.496305   \n",
-      "380 2025-07-05 23:00:00+00:00           -1.95                     8.788720   \n",
-      "381 2025-07-05 23:15:00+00:00           -1.95                     9.085988   \n",
-      "382 2025-07-05 23:30:00+00:00           -1.90                     9.199390   \n",
-      "383 2025-07-05 23:45:00+00:00           -1.85                     9.021574   \n",
-      "\n",
-      "     wind_speed_10m  \n",
-      "0        124.831741  \n",
-      "1        164.539490  \n",
-      "2        205.015503  \n",
-      "3        243.104050  \n",
-      "4        276.808258  \n",
-      "..              ...  \n",
-      "379        0.000000  \n",
-      "380        0.000000  \n",
-      "381       19.759171  \n",
-      "382       51.912174  \n",
-      "383       85.373299  \n",
-      "\n",
-      "[384 rows x 4 columns]\n"
-     ]
-    }
-   ],
-   "execution_count": 49
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T22:36:00.255585Z",
-     "start_time": "2025-11-29T22:36:00.248956Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "minutely_15_dataframe = minutely_15_dataframe[minutely_15_dataframe['date']<'2025-07-05 14:15:00']",
-   "id": "1f24e675de2a0cdd",
-   "outputs": [],
-   "execution_count": 50
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T22:36:01.122614Z",
-     "start_time": "2025-11-29T22:36:01.117851Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "minutely_15_data[\"temperature_2m\"] = minutely_15_temperature_2m\n",
-    "minutely_15_data[\"shortwave_radiation_instant\"] = minutely_15_shortwave_radiation_instant\n",
-    "minutely_15_data[\"wind_speed_10m\"] = minutely_15_wind_speed_10m"
-   ],
-   "id": "cea74153df653c65",
-   "outputs": [],
-   "execution_count": 51
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T22:35:42.524105Z",
-     "start_time": "2025-11-29T22:35:42.511211Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "minutely_15_dataframe.tail()\n",
-   "id": "cc22b957110ee8a8",
-   "outputs": [],
-   "execution_count": 46
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T22:36:04.640616Z",
-     "start_time": "2025-11-29T22:36:04.632028Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "len(minutely_15_dataframe)",
-   "id": "592396d2c42fe633",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "345"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 52
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T22:37:24.889140Z",
-     "start_time": "2025-11-29T22:37:24.879526Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "print(\"\\nMinutely15 data\\n\", minutely_15_dataframe)\n",
-   "id": "b02b2e521e671912",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Minutely15 data\n",
-      "                          date  temperature_2m  shortwave_radiation_instant  \\\n",
-      "0   2025-07-02 00:00:00+00:00           -2.20                     5.588703   \n",
-      "1   2025-07-02 00:15:00+00:00           -2.15                     5.351785   \n",
-      "2   2025-07-02 00:30:00+00:00           -2.15                     5.014219   \n",
-      "3   2025-07-02 00:45:00+00:00           -2.05                     4.680000   \n",
-      "4   2025-07-02 01:00:00+00:00           -1.95                     4.680000   \n",
-      "..                        ...             ...                          ...   \n",
-      "340 2025-07-05 13:00:00+00:00            1.15                    29.548521   \n",
-      "341 2025-07-05 13:15:00+00:00            0.90                    27.475807   \n",
-      "342 2025-07-05 13:30:00+00:00            0.65                    25.202570   \n",
-      "343 2025-07-05 13:45:00+00:00            0.40                    22.461807   \n",
-      "344 2025-07-05 14:00:00+00:00            0.25                    20.056877   \n",
-      "\n",
-      "     wind_speed_10m  \n",
-      "0        124.831741  \n",
-      "1        164.539490  \n",
-      "2        205.015503  \n",
-      "3        243.104050  \n",
-      "4        276.808258  \n",
-      "..              ...  \n",
-      "340       58.205017  \n",
-      "341       22.902834  \n",
-      "342        2.313184  \n",
-      "343        0.000000  \n",
-      "344        0.000000  \n",
-      "\n",
-      "[345 rows x 4 columns]\n"
-     ]
-    }
-   ],
-   "execution_count": 55
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T22:37:47.682054Z",
-     "start_time": "2025-11-29T22:37:47.675658Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "print(df_15m)",
-   "id": "58bd4131e7093a9d",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                               value\n",
-      "2025-07-02 00:00:00+00:00  25.083505\n",
-      "2025-07-02 00:15:00+00:00  24.787958\n",
-      "2025-07-02 00:30:00+00:00  24.492412\n",
-      "2025-07-02 00:45:00+00:00  24.675365\n",
-      "2025-07-02 01:00:00+00:00  25.388537\n",
-      "...                              ...\n",
-      "2025-07-05 13:00:00+00:00  38.086209\n",
-      "2025-07-05 13:15:00+00:00  36.774657\n",
-      "2025-07-05 13:30:00+00:00  35.463106\n",
-      "2025-07-05 13:45:00+00:00  34.271664\n",
-      "2025-07-05 14:00:00+00:00  34.174615\n",
-      "\n",
-      "[345 rows x 1 columns]\n"
-     ]
-    }
-   ],
-   "execution_count": 57
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T22:36:39.558342Z",
-     "start_time": "2025-11-29T22:36:39.549404Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "df_15m = df_15m.iloc[1:]",
-   "id": "a7bbd11d36fdb821",
-   "outputs": [],
-   "execution_count": 53
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": [
-    "minutely open meteo data starts at 2 july, 12am and goes to 6 july 22 45\n",
-    "therefore on influx i query for 1 july 10  pm to 6 july 20 45\n",
-    "\n",
-    "\n",
-    "utc i s2 hours ahead of vancouver\n",
-    "\n",
-    "\n",
-    "\n",
-    "Plot some relevant data. this will further be used to generate the relevant coefficients"
-   ],
-   "id": "600a8c5a1228ac87"
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T22:38:36.009117Z",
-     "start_time": "2025-11-29T22:38:35.901346Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "#this graph shows solar irradiance, array temperature, and ambient temperature over July 2-6 (FSGP 2025)\n",
-    "\n",
-    "fig, ax1 = plt.subplots()\n",
-    "ax_twin = ax1.twinx()\n",
-    "\n",
-    "plt.plot(df_15m.index, df_15m, label=\"Array Temperature\")\n",
-    "plt.plot(minutely_15_dataframe['date'], minutely_15_dataframe['temperature_2m'], color='green', label=\"Ambient Temperature\")\n",
-    "# plt.plot(minutely_15_data['date'], minutely_15_data['wind_speed_10m'], color = 'blue', label = \"Wind Speed 10m\")\n",
-    "# plt.plot(speed_kph.datetime_x_axis, speed_kph, color = 'yellow', label = \"Speed KPH\")\n",
-    "\n",
-    "ax1.plot(minutely_15_dataframe['date'], minutely_15_dataframe['shortwave_radiation_instant'], color=\"red\",\n",
-    "         label=\"Solar Irradiance\")\n",
-    "\n",
-    "ax1.set_xlabel(\"Time\")\n",
-    "ax1.set_ylabel(\"Solar Irradiance\")\n",
-    "ax_twin.set_ylabel(\"MosfetTemperatureA\")\n",
-    "\n",
-    "ax1.tick_params(\"x\", rotation=90)\n",
-    "\n",
-    "plt.legend(loc=\"upper left\")\n",
-    "ax1.legend(loc=\"upper left\")\n",
-    "plt.show()"
-   ],
-   "id": "2b7568c9316948b9",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 58
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T22:10:19.460310Z",
-     "start_time": "2025-11-29T22:10:19.455125Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "len(df_15m)",
-   "id": "14e077d6a70439e3",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "381"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 6
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T22:39:26.063136Z",
-     "start_time": "2025-11-29T22:39:25.920959Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "plt.plot(minutely_15_dataframe['date'], minutely_15_dataframe['shortwave_radiation_instant'])",
-   "id": "3d07f9ce1c4e6bb4",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1ae53b19430>]"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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uXpTns6cpTw0Yzxp4gzkEHwAAGlxueeS9k+L6/ddOoxfvWETPf3MhvfTtxXTV1DTq7bfQf71TNOLP8zJNhdrNMjzzwcs11roPAxc0euI1+NM25TW4b/U0a5eQL+Sq/5csGjYiBB8AABrzztEa0e3AbZdfnpc9JHD46arJ4vpHxXVU39Zt9+crmzrFkkFocCClxYRf8PXsBLTbuvoa+EKemq3i7JWFi30MCMEHAIDGvH6wUlxeOzPrgsJSnoA5PStWBBdvHlG6MIYrVvd0GZcUabdOQRadViDzMeZrsGbWha+Bt2XFR1BQoNJuW9tqP8DUOwQfAAAaUtfWTR+fqhPXr52Vafd7rp2pfP6Rd0/Svz32Cd305G7aX9Zo/fo+9fqsEXZdHWy3xbKLPedbbV4D9bn2pdDgQMqMDzd00SmCDwAADdlRfF5kNaZlxtL45KHFohJ3XvABqr69RxREfnyqnv74rlKfwPaWKh0y88Yl2v15Wb9QprbjwlC/+9cx8RrMzImncSO8Bt6Wlyg7Xoz5GqHVFgBAQ/aUKlkL7nAZSUZcBL1x1zIqOd9GrV199OOXD4kz9drWLrFT7cEKpRNmpLkUvBzDztQZ86zaHVuPVNFrByrFKPX7Vk/12/3I5dfolHEzHwg+AAA0ZKyshTQpLUZ8sBc+LRPbsL95qIouyo6jnr4BsQHaSJkTeTbPSzzc1RETPrj3i9m9pI5Sv+PiCTTHy6PURyOHwxm13RbLLgAAGtHU0UMn1WJRZ6ZpyroE3u59Z3G9uD43L2HEQsnY8BARnDCjnlm7gjdyk8HfFy/yzq61Tne81Btz2QXBBwCARuw7qyy5TEiJoqToMId/jmtAIkOD6GhlCz2izqZYPjl11J+R2Y8zBt4/xFmnzrdRS1efGOw2NTPWr/clz+CDxhB8AABorN7D2T1EUmLC6BdXK/UJPBZi0YRE+vK8nFF/Rm5eVorgw+b5V7Ies3PjKSTIv4fHXHXZpamjl5o7esloEHwAAGjE0UqlUJS7LJz1tQU5dP3sLDEU67++MkvMiRjN+GS16NSgaX1X7HMx+POGqLBgSlazX3KTQCNBwSkAgEacqG4Vl1MynE/5c33HH2+c5fD3y2UXZD4UPElUbuI3VrGvL5de6tq6RV3OjGznA1ItQ+YDAEAD+CDDw61YodrF4k3WZRcUnAr7y5vEXjdcOzNP3cDP3/LUug+5SaCRIPgAANCAIjXrwQccTrn76sDW0N5Dbd19ZHav7T8nLnnjvshQbSwK5Bl40BiCDwAADThe1SIuJ6d7P+vBeLZHbHiwdSM6M+NdgN84VCWuXzc7i7QiTw0QjZidQvABAKCheo/J6b5r8cyUe7yYPPjYWVwnMkDJ0aG0rCCZtCJXLrsg+AAAAG84Ua1kPqZk+CbzYbvBnNkzH6+qSy7XzMikYD+32Nqbclrd0kVdvf1kJNp5lgEATKqvf4CK1cmm/sh8mDn4aO/uo3eP1WhuyYUlRoVSjFr/U26wolMEHwAAfsZr+t19A2Kyphwu5dvgo4vM6p1j1dTZ2y8225uZHUdaEhAQYF16MdoYfAQfAAAaWXIpTI+hwDGGg3lSVgJqPnafVmZ7rLooY8S9cLRRdNpORoLgAwDAz05Utfq83oNlxYeT2ZddZOB3UZa2sh5Srtpua7RZHwg+AAA0cgD0Zb2H7bJLdXMX9Q9YyGz4MRfVyC4j3wZ+Tm8wh2UXAADwpONV/jkApsaEiz1g+gYs1umqZsLDu7p6Byg8JNC6hb3W5Bl0yikyHwAAftTS1Wuds+HrzAcHHumx4aad9SGnyk5KixlzIz5/yVODoorGDtEVZRQIPgAA/OikegDMiAunuMgQn///Zp71cbxa20sujIPD0KBA6u23UFWzcbqSEHwAAPhRSZ3SxZCfEu2X/z8z3ryZjxPWkfa+zTg5gzMy49UdiE+q9SlGgOADAMCP5Jb245J9N9/DlpkHjRXXtllbnLVsitoFJff/MQIEHwAAfiTnN4xP9lfmw5zBh8VisWZ7fDnYzRVTMmKHFCYbAYIPAAA/Kjkvgw//HABlzcc5k005rW/voZ6+AeK5Ymlq0a1WTbYGH8h8AACAmwYGLNb5Dch8+JbM9KTGhFFocKAull3O1LdTR08fGYG2n3EAAAOrae0S+4pwUWG2OurcXwWnzZ291NZtjAObM8GHXHbSstSYcEqODiWLhYtOlToVvUPwAQDgJ2fUYlOuOQjx01buMeEhFBOu7JxaZaK6D7mZXmac9oOPoXUfxlh6QfABAOAnpXXKkgvvqOpPg3UfnSbMfGi73sOR4IOLZ//47kla8cft1u4prUPwAQDgx/HebJw6x8FfBjtezFN0Wtmsn2WXsdptH9x6gv68rZhO1bbR0x+fIT1A8AEA4CcVjcoBMDtBG5kPM7Xbyu4e/QQfsdYdkDnTIe0tbaDHt5dY//3Pg5XU3ddPWofgAwDATyrUg708+PtLhrr0YKbgo1Ijz72jeAIuj1lv7e6zBq0chGx464S4/uW52ZQWG0ZNHb30wYla0joEHwAAfnLOmvnw7wFQbi7H3TdmwJkBuYuvXjIfIUGBVJCqDKI7pi697Ciuo31nG8WuvD9aWUhfmp0tPv+vw9WkdQg+AAD8oKu3n+raujVx9i2HbNW0KPfH6GqalccZFhxICX7YzM9TRafbjteIyy/NzhKv4bKCZPHvI+eaSesQfAAA+DHtHxkaRPF+PgByup7VtJgj89HQ0SMuk6PDKIBHnOrElGFFpztO1YnLSyelDvk6j+zX+swWBB8AAH4g21o57e/vA2Cqmvlo7eqjzh7tFyu6q0kNPuIi9JP1YDOy48Xl7pIGMSOGR/PzgLrF+Uni80nRYWIJjetRi6q1PQ8EwQcAgB/rPfy95MJiwoIpIiRIXK81Qd0HT3Nl/s44OWtuXoIILvj+37/lqPjcrJz4IUHU1ExlaeZYJYIPAAAYIfOR5ediU8aZl1Tr0ku3aYIPvWU+ggID6LrZWeL6h0XnxeXFE5U6D2mqWhcii1K1CpkPAACTZz5YWowsOjV+5oPbUfWY+WDXz1GCDzkZ998X5ZEtvWQ+lIH+AADglxkf/m6zlQYzH12myXzE6izzwSalxdAti/OorKGDHvryTFE0a2uaGnycqG6lvv4BCvbTnkFjQfABAOAHVep47wyNbGwm221r1fkXpsh8RISSHj2wZvqIX8tJiKTosGDR7VJS1y6CFS3SZkgEAGBgPJlS1lZkxGljYzPZbltrosyHHpddxhIYGGBtudXy0guCDwAAPxz8evoGxPWUmKFpc38x06Cx5k59tto6Sg9Fpwg+AAB8TB7g+cw7XG1x9bdUWXBqglbbwWUXgwYfmdovOkXwAQDgY7KoU3aYaGvZxUSttgZcdmFTM+KsmQ/bHXC1BMEHAICfgg/ZYaIFcsopFypqfTS3u5p0OufDURPTosVMkIb2Hs0uoyH4AADwMdlRIusstIA7JKJCgwxfdMob+sl6m/hIfXa7jIWX8gpSlB1wj2t0zDqCDwAAP2U+5Fb2WmGGdltZ78GZARlsGVFOotLCXdWkzUASwQcAgI9VN6s1HxpadjHLoLEmtdOFi039vaGfN6WrLdzV6jwZrUHwAQDgYzVqZkHWWWgu86HROgFPaO4wdrGpJIfXVaqBrtYg+AAA8DFZU6Glmo+hsz60ecDyBKMXm0pyeJ3Msuk6+HjsscdoxowZFBsbKz4WL15Mb731lvXrXV1ddOedd1JSUhJFR0fTDTfcQDU1Nd643wAAujQwYLEpONXYsos68ExmZgw93dTgwUe6GnzIMf66Dj6ys7PpwQcfpH379tHevXtp+fLltGbNGjp69Kj4+j333ENbtmyhzZs30/bt26myspKuv/56b913AADdqW/vof4BC3G5wfBNwfzNDJkP67KLwYOPTHXZpaq5S5OzPpzaWG716tVD/v273/1OZEN2794tApOnnnqKXnzxRRGUsGeeeYamTJkivr5o0SLP3nMAAB06p+5mmxIdRiEa23FUZj7OGzjz0arOMIkJN0fmo6Onn1q6+jQXbLn8m9/f30+bNm2i9vZ2sfzC2ZDe3l5asWKF9XsmT55Mubm5tGvXrhFvp7u7m1paWoZ8AAAYVWldu7gclxRFWmOb+dDi2bIntKvBR2SYcdts5ayPBLWoVot1H04HH4cPHxb1HGFhYfSd73yHXn31VZo6dSpVV1dTaGgoxcfHD/n+tLQ08bWRbNiwgeLi4qwfOTk5rj0SAAAdOCODj+RI0hrZastny0adctrRozyu6FCnEv8673jpJN0HH4WFhXTgwAH69NNP6bvf/S7deuutdOzYMZfvwPr166m5udn6UV5e7vJtAQBo3dl6GXxoL/MRGRpMMeHKQVmrY7nd1dbdLy4jw8wQfIRrNvPh9LPP2Y2CggJxfe7cubRnzx7605/+RDfeeCP19PRQU1PTkOwHd7ukp6ePeHucQeEPAAAzOFPfIS7Ha3DZRS69tHa1iaWXglRlRLeRdKgZnWiDL7sM7XjRXvDhdrXTwMCAqNvgQCQkJIS2bdtm/VpRURGVlZWJmhAAALCp+dBg5kPrZ8ueIJeTokyV+egkrQl2dolk1apVooi0tbVVdLZ8+OGH9Pbbb4t6jbVr19K6desoMTFRzAG56667ROCBThcAAKLG9h7rnAktFpza7jej1fkQ7uJ6FhZlgpqPZLWVm3e31Rqnnv3a2lq65ZZbqKqqSgQbPHCMA48rr7xSfP3hhx+mwMBAMVyMsyErV66kRx991Fv3HQBAV86o9R58gI/Q6KZmGRpO1Xuy28UMmY8kNfioa9N58MFzPEYTHh5OGzduFB8AADDSkov2Ol2kdLVDwqjLLu1qt0ukRoM/T0qMChWX9e3aKx7W1oQbAAADK2vo0PSSC8uIN3rmQ1l2iTZB5iM5Wg0+NJj5QPABAOAjlep006x4Jbug6SJFA45Y58Fp1syHCbpdktRlF65zkfNNtALBBwCAj0erZ2o5+IiNsBYpdvUqWQKj6OztJzm41QyZj6jQIAoLDtRk9gPBBwCAj1Q2dWk++IiNCKaIkCBD1n3INlve1E8+RiMLCAiwdrzwhoZaguADQCcOVzTTjY/vomW/f5++/sRuaulSWjZBPyn/czpYduEDllE7XjrUeo+o0GDxOM0gyVr3oa2iUwQfABrX0zdA//VOEV336Mf06ZkGqmjspE9O19OTH5X4+66BE/jMk19LPubJyZNaJe9fdUunITMfZuh0kZJkxwuWXQDAUb39A3T7s5/RX94/Rf0DFrp6Rgb96pqp4mtP7jxDdRo7m4Gxi0152/pQdR1eq7Q8ltsTA8bMUO9xwawPjbXbavsvAMDE+voH6P4tR+njU/WicGzj1+eIj9uXjqMZ2XHijfSZj8/4+26Ck8GHlus9jD5i3UwDxi5cdkHNBwCMURvw5uEquurhj+j53WXicw/fOEtkPRivVX/n0nxx/eV9FSJIAe07p4NiUyk1Rgk+zrdq62zZXWZcdkmOUgtONZYlNU/4B6AD3X399P0X99O7x2qsEwp/tmoyXTVt6M7QK6akia/xtucfFZ+n5ZPT/HSPwUgzPqSUmDBDBh9y1oW5ll3klFNkPgDAjtrWLvrmX/eKwIN78+9eMZG2//gy+sq8nAu+l2sGvjQ7S1z/x55yPJ96WnbReLHpkOBDY2fL7mpTu10iTRV8hGlyfxfUfABoZM8PXmbZUVxH4SGB9PRt8+nuFZMoJjxkxJ+RQcm247WGO0M18mh1PSy7pKgHrNqWbrEMaBQd6rJLtAmmm17Y7aKt9wgEHwAa8Pzus9TU0UsTU6Pple8upaUFyWP+TGF6DM3Miae+AQu9ur/CJ/cTXMOdSqdq28T1SWkxusl88ETQdrVDxAjarJvKmSfzkagGH40dPZoKJBF8APgZvyFsPVotrt971SSamhnr8M/eqGY/Xtpboak3FhiqtL6duvsGxFTN3ETt7mgrcTcId1gxI2XVrEPGTLTskhCpBB+9/RZrwa0WIPgA8LOjlS1icBgvt1w6KdWpn109M0P8HJ9VHznX4rX7CO45UdUqLielRVNgoD4maxqx6NTaamuibpeI0CDrKPnGdu1MRUbwAeBnW48oWY/LJqWKNwpncE3I5YVKwLL1aJVX7h+4r6i6xbpUphdGDD7kmb+ZMh+2Sy8NHdopOkXwAeBnn51pEJfLpziX9ZC+MD19SBAD2nOiWsl8FKY7vqSmlVkf3IVlFGaccMoSopTC9UYNtdsi+ADwI67TOKGeFU/PjHPpNi6fnEohQQF0+nw7napVDnKgLUU1yusyBZkPvzLjkDHbuo8GBB8AwKpbuqilq4+CAgMoPzXKpSclNjzE2h3z7rFaPLEarDOQbbZYdvEvMw4ZG97xohXIfAB44exq65EqanVgy3tZiJifEkVhwa6fjV08MUVc7i6pd/k2wDsOVTQTNyLxfily4JMeyFkfRho01m7CIWNazXyY6xUA8LL3T9TQT14+JKYJXpQVR3//1qJRz7I8VQuwaEKiuNxb2iD2egkOwnmFVnxe1igu5+QmkJ6kxBqv4LTdmvkw17JLIjIfAMbFKd17XzpoHWN8+Fwzfeu5vdTVO/KQJlnvMdnNWoAp6bEUFxEiBkIdqUTLrZZ8flYNPvL0FXykqt0uNS1dhmu1NdOQMZYQpb3MB06PADzkpT3l1NjRK4ZI/d93l4hZAp+crqcfbtpvXWserkjNfLgbfPDsiAXjlewHll60VVA8mPmIJz2RG+BxMD1aAK2nTRt50JYpW20j1ZoPzPkAMN747Cd2nBHX77hkAs3NS6D/vWUehQYF0ttHa8S+LSXnlfHaUmdPP51WP+eJQsSFCD4050xduwhIeSPAaS52M/kLZ9LkcKrq5i7DTDc125Ax21ZbzPkAMBiutTjX1CnesL88N1t8jjtQnrptnjiD5Ammj354esjPfHqmXpyJ8dc9sc36oglJ6n1pFHUf4H/71CWXGVlxIgDRk4CAAMqMDx+yI68R2mx5x2iz1URZaz6w7AJgLO8eqxGXV0xJpXD1bFF2ofzH9ReJ67tO1w/Zf2VncZ24XFaQLN7o3TUlI5Ziw4PFmyyPbAf/+7ysSVxyJkyP5A68HFgbZcCY2ZZchiy7dPTQwIA29oAyV/gH4AUcULx7XAk+rpySdsHX549LoODAAPEGXt4w+Ca+85QSfCydOPYOto7gWSELxivZD9R9aKvYdLbOOl2kzDgl+Khs6jLQaHVzLbmweDX44LijxYERAL6A4APATbyp29n6DlHfcckkZd6GLa6sn60WG+4qqbO2L8o226X5SsDgCbLlFsGH//Gb/El14uycPH0Vmw7PfBhh2UUWfUeZrNOF8ZJfjJrx0UrHC4IPADd9pC6fLMpPGjGlu1itx+DuF/EzJ8+Ly6kZsR4dPCXrPvaUNmpq+2wzOlDWJIaL5SRGWPdJ0RtrzUdzp3F2tDXhsottu61Wppwi+PBC18Mf3z1JV/95h/iQcxzAuA6WK+v680dZ11+ijj9//3itOCOWNSIrXNxMbrS6j+yECBF4rH/l8JAaE/At2WI7V6dLLizLQJkPOd3UrMFHXITS8dLciWUXw+ntH6DvvbCP/rytWBT88cezH5f6+26Blx2sUIKPmTkjp9YXjEukianR1NrdR0/uOEPb1czHlVOVHWk9hes+/vTVWaLGZMvBSvqwSPl/wPc+LWnQ5XAxWxnW4KNL94GsnG5qtjZbKTZCCbpaOrWREUXmw4Ne2H1WzHTgtf8vTFMOKu8dr9VMdTF4XlNHj6j3YDOy40YdAva9y/PFdQ5OO3v7xV4f07M8v8X63LxE+je13XcX9nrx2+/FZ6VK8HGpnTogveDfUca/r00d2jhjdr/gFJkPLUDw4SGcSv/z+6fE9V+unkp//tpsUeBT19ZNB9QzYzDmpmEsLynSWlE+ktUzMqkwbXCY2JVT0zzSYmuPPNs+oC4JgW+9f6JWLMHy5Nq8JNd2K9YCbhtPUmsF9N5uK4eMmTbzEa4su7Rg2cVY/rbrrKgi5t1JvzY/R1QXX1qonPHI9X0wnkNqYDkje+xuBh5s9NJ3FtMvr5lK18/JojsvL/Da/ZqlLgEdOdcsDoLgW/Jv/qqpF7Ze601abLghNphD5iNEPA+o+TDom80dF0+wTs/jM1v29pFq3a+Xgn0H1czHzFGWXIYXfa1dNp7++JVZ1jd1b8hPiRZneDxYiVuBwXd4IzZZa3OVuvyqZ6nq7ra1rV3GaLU16bJLLIIPY67vyjNgme1gyyenigxISV07Ha9S+v3BWE7WtFpbZrWEC0+nZ8UN6cYB3/jV60dEjQTPdpmWqa3fC/d2t+02RreLWZddItRlFwwZM46PT9WLyXGT0qIpQ50IyGLCQ+hyNRh541ClH+8heANvDFfWoBSbTrSp5dAKufQiu3HA+7YeqRZF59xttOH6i7xW0+NLMkOn98yH7HaJNGnmIw6ZD+ORA6N4H4/hrpmRKS63HKqknj5s9mUkvCMtr6bFR4ZQcvToxab+IFt/EXz4Bq+lc9aDfefSfJqcrv+sh7EyH0rwEW364KOPtADdLh4g2xkvtrNHB280xl0vvKfH9174nGpb9H32AIOK1dHZk1JjNHmGK4OPE1Wt1NU7uJ04eMfvt56g2tZumpAcRd9f7r1iYl9LtWY+9B18tJl8yFhsuJzzoY2WaQQfbuL1M5l6n52TYHdfj/++aY6o/XjveA0t2rCNbn36M9q8t5wqGpWfA30qrlEKOSemRZMWZcaFi4xM34CFjlVh0q43fXamgV78tExc512MbXc2Nkrm47zOT5wG93YxzmvjyrILgg+D4LNKOYY4LlJ5cYfjIUN/+8YCmpMbL2pDeLrlj18+RMt+/wFd/If3rcs2oC8nZfCRqs3gg7MxM9UWYBSdeg93sv3yNWW55avzc6z76xjFYM1Ht64HJpp9b5dYNfjgKctaaL9H5sNNx9UzyikZoxccLpyQRK98bym9f++l9IPlBaISnjsSeDnmnn8cEB0zoNNlFw0Wm15Q94GOF6/hbRSKalopPCSQ1q+aQkaTrG58yBk0rWxK5l63S7CpMx+sVQMdLwg+PBZ8OFZcNiElmtZdVUivfm8pHfjVleKsub69h/7z7SJ37wr4OIWr5U4XSY58l5NYwfPePFwlLi8vTB0x+6lnvGScqE451WvRKZ/pc/sziwoz57JLSFAgRapLTloYNIbgw03HnAw+bHEr7gNrpovrm/dWjFoU+M7Ravr7Z2UYVqYRfDDnThfe+yJFXRPXIjl5lWfNyLQzeHbJRQYfX7wow7BPraz70Gu7raz3MPOyy9AR6/5/L0Dw4Ya+/gEqqm51OfhgiyYkirRmT/8AHT7XPGJL53df+Fxskc5zBMD/9pcpszN4+UzL+IxVps0x6dTzeHhgaX0HhQUHiqGCRmXteNFp5kOOVuf5K/xamVWchmZ9mPdV8AB+0+nuGxCprLzESJeLAufmKQewvaWNdr/nobeLrAVCD7xxDGewGrC/rHHEDietkQWxxRiz7nFvHVGyHpcVphj6jDpN55kPuSMvH3y12BbvKwg+DOKUWnBYkBottkx31by8RHG576yyBbct3hjsrSPVxDfPqc+q5i569pNSN+41eCLVvr9cH5kPxpN3bQtkwXO/B/8ywZILS1KzZ1yfpkfyTN+INTnOiI1QZ32g4FTfZBq7IMW9Vsu545Sz531nGy+o6Xh65xlxee3MTPrpFyaL68/tKqXefkxL9ZfK5i6xw2ewzf4pWlagFsSeUluDwTO4w6XkfLsoyDTykgtLjFIO2o06DT5sMx9mFotlF2M4fb5dXOa7OedhemacWIds7OgV9R0Spzh5LDu7fel4umZmhihu5IpzWeQG/ltymZoZq4thUlh28Y43Dyv1V5dMTBHF40aWEKl0uzSoB3G9ae5UgqZ4swcf4aj5MFbmw83gg8+cFoxXll62HBwMKv7xWTn19lvEBmE8ryEsOIhuXpQnvvaCOk0R/Fhsqs7Q0EvwUd7YITbD84b3jtWIKZ9mIk8Arp6RTkaXpO5d1KjzZZd4NYgyqwT18ctMkD+h4NRFPOlPZincDT7Yv83NFpcv76sQxaW8/PLagXPiczctzLV+35dmZ4nLvaUNun0jMEyxaa72i03lej13vfCKnm1mzVP2lDbQN5/bS195fBf9v1cP63oKpqOKa1rFyUdoUCBdMSWNjM6a+dDpew6WXRQJ6vKZFoZaIvhwUVVLF3X09FNIUIDLnS62Vk5LFxv/nGvqpO0na+lEdatY1uE3t5XTB8+schIjaXJ6jBjT/kFRrdv/Lzinu6+fjlS26KbYVJIBsjeKTnn+jMT7m7xzzPjt4LLQlDeTlKlsI5NDxhr0XnBq8mWXOPXxa2FSLYIPN5dcxiVFUXCQ+08j1w7IrAbvfrvqTzusLXzD39yunKqcaW07juDDH3MdevoGxJtxrgeCTp/XfXi46JSr5uXyw0J16fAfe8rJ6N4+WiMuVxm8y2V48MFTQr21dOdNTQg+BCy7GMBpD9V72Fp3ZaE4k+rqHexkWT0z84LvW6GmeT8sqhUHQvDHfI94Xc0LkPvPeHrWxz8PVIrfV27nffCGGeJzvHFiVXMnGRVPIi6qVrJfl0xMJjOIDgsWWV6tnDU7S+7kGm/yVtsEdflMC68hMh8u4uI9lpvkubNf7kH/6+0L6JEbZ9EXpqXTdbMy6appF64nX5QVR0lRodTe008Hxtgw7FhlC333+X20s7jOY/fTzOQGbVwErCeDmQ/PLru8tFfJcnxlXg6NT44S2Q9eEnzlc6VeyYh4qjE/Rv4b1PJofU/iQFvPdR+y5sPswUe8+vi5s3L4WAdfQ/DhoupmZdJfZlyEJ18PMazsutlZ9D83z6VHvjpbdLjY+57F+cq23R+fqht1/Pvd/9gvhpTd+sxn9LpawAqu41ocNi3LtXH6/lKgDhrjzfBG20PI2U0VeY8bPiO+fo5SMC2XDo28DYDtZpJ6yn6Zue6jSW21NXvNR4L6GnLGXG605y8IPtwYNMXS45Q9D3xtaYGS7v3k9MjBxz/2ltPJmjYxHZU7aH7zxjG/R7t6xoPdZLeIXMbQi5ToMPHGy2fsPBjLE2RtB9cgyQPTiqlp4veN9yni4mljBx/6+h1wl3yNtZCyd1azdciYuVtto0KDbJbP/Ntui+DDRdXqmranMx+OWpqfbJ05YW+3Uj5IPvjmCXF9/aopFB4SSHVtPSIYAdeU1rWLuSv8B5wV75/X3VV8hj44bMwzSy/vHqsZ0ibOeBO7eeMSrTsxG7Xo2J3NJPV+1qy3zAefeLV0Ke+RZs98BAQEWGed+HtUA4IPF8+Aa1u7/Zr54FqT7IQI6huwiAI/uQ/Mox+eEuPXb39mD7V299H8cQl029JxNF89IIyWKYGxx2mzSekxuky3T1SXXjyxu21tS5fIbHCWY8F4ZQnQtm2c8XKf0XDm0HbZxUwSNXLQclarzT4mZg8+WEKkNna2RfDhAg48ePWCZ3Bw0Zm/yE6YB986IeYrXP/oJ/SHrUX0q9ePirX9zLhwevSmuRQSFEhL1EzJJ6fr/XZ/9e6kWu9RqLMlF0neb14ScdfnatcPLz9xJ4StL16ULoISnnh6ps4zSzxaUdHYKYJ6Tl3nu7mnk25rPnS27CKLTTljydOkzS5eIx0veCVcUKWuZafFhbm1m6277ry8gDLiwkWgwZMle/oHaE5uPC2ekET3rJhEW+5aZq3GX6IWqO4uqRdpSHAj86HT4ENOZOWlOnenkH6ujpifk3fhlNeMuAi6dFKKuL5pj7G2ATimZj0mpsaY7kCm14JTDBizn/lAzYcO8bb2LCPWv+v+fMb5uy9NF7urcpaDA47N31lCf//WIvrhionWbbAZ774aEx5MrV191rQxOEfWyxSm6zP4UDbCCxRvxiVuZiQ+P6tkPuaMMGL+awuULQFe3lshpsIahVmXXPRc82EdMGbyfV2keLXotgk1H/ojByhlxPun3sPW8slpdOT+lfTxz5aLgCNohEwMf16e+cqUOTiOD6Bn65UDtizc1BtefpuRHT8keHAFt+kdUpduONNmD28xnx4bTvXtPfTSnnLDdFmZtdPFtuZDC5uSOUPuYxIXMXR50Kzio3SY+diwYQPNnz+fYmJiKDU1la677joqKioa8j1dXV105513UlJSEkVHR9MNN9xANTVKVbzRMh/+Kja1N5rdkQJIeaBw58BjVuUNHaJNldeN9TxYaq66TLLPjd+Bkro2EYBwJo0Hi9nDWw587/J8cf2hd07SzPvfEUuDeg9CZKfLVBNmPmSxpr8LFZ3FXX62y0Zml2ANInVU87F9+3YRWOzevZveffdd6u3tpauuuora2wdTuPfccw9t2bKFNm/eLL6/srKSrr/+ejKSqibvDBjzNpki34fMh9PO1CkTbcclR+my00Wa64HfgTPqnJAJKdGjPhc3zs8RLcl8sOJWRy6Kfm7XWdIr7prg+iqzLrvEqpkDOapcL8rUjGVuov1A2bw1Hz1+vR9O5aG2bt065N/PPvusyIDs27ePLrnkEmpubqannnqKXnzxRVq+fLn4nmeeeYamTJkiApZFixaRUXa01VLmw1Gzcnk/Ej6L76Tzrd26PoP3x4wPGXzomdyJl9tt+cxHVr47Q9aL5I/xXPB03v/8txn02PbTlBYbTi/vq6Df/uuYWJLh3Zn1Ot2Wl5Nk/YMZMx+8rQOPG+BlPD0orVdPHDy4FYYxul16/Xo/3Prt4WCDJSYqMyQ4COFsyIoVK6zfM3nyZMrNzaVdu3bZvY3u7m5qaWkZ8qGXbhe9ZT54d9xJqcpaNeo+nHNGPXsan6Tv4IOLkOVSyf4x9gUaiZyQOtKSi60lBcn0t7ULRRCyrCBZDGl75L1i0iMz13uwGJvdtfWU/ZDZqjyd/+2aetnF1sDAAN199920dOlSmj59uvhcdXU1hYaGUnz80CK0tLQ08bWR6kji4uKsHzk5OaRlHPGfb/PvgDF3yA3RDle4P+vBTIyS+bBdfnO19udMndL1Mz7F8eeCl2d+vLJQXH91f4VHBp352km11bow3XxLLrJoPUad6SInhmod72/F9VpsXDIyH/J5WL9qMt29YhLpMvjg2o8jR47Qpk2b3LoD69evFxkU+VFeruwXoVU1LV2aGDDm7pRLLhoE54OP8QZ4A3O36FQODpuQ7FzXz8yceLq8MEUU7r6tw9HrxWqr9ST1b8iMYnVWdFrZ1CWmQIcFB1JajP5OFr0hNSacvn1pvtjAVHfBx/e//31644036IMPPqDs7MF9HdLT06mnp4eamoamc7nbhb9mT1hYGMXGxg750MNutv4eMOaqfLVN9HStsSZPehPvAis3EhxngNTtnDwl+3WgvEmcGTqDR2vLtWJXziSXTUzRbceVzNbwgDGzBx96WXYpVZdL85Iidfl+bWROBR/cJseBx6uvvkrvv/8+jR8/fsjX586dSyEhIbRt2zbr57gVt6ysjBYvXkxGIA9CPMVRjwrUkdB89opJp445qxascWupEdr1+ODJ6fOOnn7rxE5ni015qF1kqPNzE6zt3mWNumq7rW/rFjNLWH6q/gNQV8lZGWNlPoqqW8XGg/5+jeVsHtR76Dz44KWW559/XnSz8KwPruPgj85OpQCTazbWrl1L69atE1kRLkC9/fbbReBhlE4XuZstjzXXo8z4CJGC5FHsFY3KQRUcW2YYr/M2W9u1+yUFyrj9Nw9Xu/ZcOFHvYWtaZpwYS87ZEz3t+yKzHryZoytBl1Fw0fpYwcef3iumVX/6iO54bq/Tv1+ehk4XgwQfjz32mKjLuOyyyygjI8P68Y9//MP6PQ8//DBdc801YrgYt9/ycssrr7xCRsFriHrOfPCBR3YpnD6Pug9nUrdGWHKR1sxS1nu3HKx0ap+XEvV3xpFOF3s48LgoK27I/jB6UGxdcjFvvYdtu22LzU6xtviE5uH3Toq6HsY7bPuTDHBzDfS3a9plF3sft912m/V7wsPDaePGjdTQ0CCGj3HgMVK9hx7Jmg+9Zj4Y6j7M2+ki8awN3hvoXFOnUwPHXC02tVfwqqd2b2u9h043FfRVwaksyuUghU90Pj3TQMVql5Cv8bHpkNrVZ8aJtFqnjykxWtzXRc/Bh1r3gcyHs8su+u90sR3Jv3KaclLw+oFzPlt20euY/+Ja5QBagMzHqAWn8j2FZ7pcMTlVXPfXVFuuz6tr6xYbb07LRPChNQg+XN3RVqfLLixfPXDocdaCPxhx2YWtmZUpLv91qErMrxkLL88MZj7cCT4SrHMzeGS5HpSq4/XdedxGEBsuR6z3jRp88HvMbUvGieub95U7tBMuT13+2+6z9LddpVSrTpF2x0F1iN7kjBgRbIO2IPhwAm+mJQeMaWFHW1fJ9Xp5UIWRdfT0UU1Lt1t1Dlq1JD+JkqNDRfHnzuK6Mb+/srmTuvt4rHYAZSe4ngVKjQ0Xe75wXcDBcu0Pu+N25Gr1YOjO4zaCuMjRl11kCz8v7S7OTxL1PV29Aw7Vfvx6y1H65WtH6JevH6V7Nx/0WPAxU93JGbQFwYcTOPCQA8bk9tJ6JGsXeLdHvZx5+vuMNz4yxKV9ULSMd569Zkamw0svMuvBbYu8nu+OOTqq++BsJ7elc9CVavL9kMYqOB3MfCibDn7rkgni389+Ujrqew3XZ3xaUm/9985TdWKgozt4jo0cbgfag+DDCfKPgTdk0/PAGm6Xk9NZ5QwLMNeSi7R6phJ88EwGHqbmyJ4unlh6sJ33oXVclCvb1PX8d+/tVlseQCdnocgs4arp6TQhJYqaOnrpyR1nRrxd3uyST4Y4wONsCZ/kcSeWq9q7++jwueYhW0qAtiD4cEKtmn5PjdX/2Y/Mfuhp1oK/Z3wY0eyceLFLK+9U+snpOq8Xmw7veNlf1uRUq68/nGvstM74MDuZ+bAXfMgtG3gAXZS6Bwxn1+69UtnT58kdJSJAsUcGoTwH5ivzlf29Xt3veCH0cBzo8BA9nmwqC+xBWxB8OKG2VR2tboA9AuSZvGwjBfvkplS5OtwC3hF8Jn/VtDRxfeuRaoemm3oi8zElI5bCQwLFQUzerlZVqMEH16mYne149eHTS2W9x4RhB3vOfkxOjxEB7kgBhQw+uBj56osyiBNMRytbrN2Fzk6j/d+PTovrP7qq0O0lQvAOBB8mzXzItlG5VTyM3t2UqeMC47F8QW255aWX0fZ6sc478cASVEhQIM3I0sfSy7kmJQDNijdmAOpK5oOTVW3dfRcUJLOcxIgLAtybFuaK6y/tLbc7ct0afOTFiy0MLlKLRD85NVgH4qg3DlWJQIfbazmQAW1C8OFCzUdabLhhll2Q+XDsNU/XcWv1WBaMTxQHFe56kevk9jq95Dh+Ty1Bzc7Tx7wPWfOBZRcSWzPwlFrGdRyOvj9eOzNL/NyJ6lY6cm7ofkIcxByvah3Shs2dWOzjMZYC7dl+8ry1nsnsNTpahuDDCbWt3daCU8Msu6DgdFSyxZLrIoyK1+Xn2dRg2MOBB5/tRoYGeez3Xx5otJ75sC67oOZDdLDIYvXhsztkS7q9vxVu0ZVD7f53R8mQr+0pbRDdRLy0yUW9bGl+srjcdbreqc3puGiaf4ZdOknZQRm0CcGHC8GHEdrtZOaD30DG2qHSrPiNTJ7dGTn4YLPV7pP9anviyFuTe25zPRl88L4pI7Vu+hsXw1Yi8zFEUrQSfNS3K++Hw7eeGCkz/O1LJhD/6nAXy76zDdbP71aDhcUTlGyHLEjmkQa87OlMUfze0kbq7O0X79FcZwLaheDDCbUGWnbhfT3kGSyWXuyTb6ZcGBmrbiVuVLPVQGD/CFkIOe/EkyPm+fePz3b5xPaARjeZ4xOO3n6LKFo0egDqqKQo5X2jvm145mP098fpWXH0lblKJ8sDbxy3djntUud78FAyKSI0SNR/sE/U4MQRHxTVWrMeRtiB2sgQfDiIx0/LHnYjZD7YeOvSC4pOx1pyMfob2YzsOHFWyksMsqtrpMyHJ2l93ocsNuXfAV6eAtvMR8+QmiD577RRCvLvXTmJokKDxPTRfx6sFBmvI2qdkW3wwZaoSy9jtYBLvHTzxiFlNsgVU5QOLtAu/DU5iPcdYDwEJ8Egky7HyY4Xjbc6+r/Y1PhnvDHhITQpVUlT28tCyNogGbB6ipx0uk+jRaeo97hQcrTMfAwuu8iAVUx/VmtC7EmNCafvXV4grm946zj99o1jopaI27eHZ0yWFijBCNdwODILZkfxeVF3khAZInZtBm1D8OFssWm0vqeb2kLHi2PLLmZJt8tJkHIbcltyaY6HNnmSrPvgUdhaHDYmg49szPiwksGF7bKLDNR5DMFYWcK1y8bTuKRIESi8tLdCfO6HKyZe8H0zsuNFloS7sI5XD+2QseflfcptrZmldNaAtuEVcna0uoEORIPLLhixPtqyS5oJMh9y909WVKO0PXqzzdb6f6bHUERIELV29dEpdV8QLUGb7YVkt0udzbKL7HRxpB6Od5h96duLRXaCa2l+cfUUETDYmwXDbeBMdrCMNgzw7aPKkLwb5mSPeR/A/xB8OLnsYpR6jyGZD9R82GW2zEdhWox1q3tvt9lKXEfB9SZanfeBZRfHll2c/VvhnY2fvm0+Hfn1Svrmxcrmc/bIuo+PT41e9/Ff7xSJwuBlBcl0kfr7BNqG4MNBck8CGfUbgUyhcztpU4f9PRfMTGY+MkyS+ZiktiaWNXRQR0+fV9ts7e3zosWi03Nqxic7AdNNhxec2s75qFFrPpyd/sxdLaNZotZ9fHamQRT928MZj9cOKIWmP1s12an/H/wHwYeDeN2RGWlb9cjQYGtlOopORx6nb4TWakfPaDm45tbXU7VtXm2ztT9sTFvttjzcSi67YF+XQUnWzEePdQBYjZeyhFPSY0UBKY9LP1TRZLe19nsvfC6uf21BrmjnBX1A8OEgmRmIj1T2NjCKwUmn6HixxW+qdWpaWaaZzWCSuvRSVN3q9Tbb4QPOOODRUgaOW0e7egdEC3KGgff2cZbM/vb0D1Crur+L3APJ051hXNwvW3A/HrbPS3dfP/3ytSOixfa6WZn0mzXTPPp/g3ch+HBQkzoFlKNwI5G7tcptw0HB23F39ylp3tFaB42mMP3Cug9vtdnanklz98NoE1b9Qf5NcJ1XWPDoywNmwgWjPKTQtuNF7v7sjeWpxWrdx85hdR9/23VW1ORw9nbD9TMwh0VnEHw4qNGa+TDWgUjWM8gzF6Ah69m8kRYXWpot88EbgHm7zdbe0st+DRWdWotN0WY7Srttt+iGqlLro+TJjCddXJBsrfvg2SAfnKgVc0X+8v4p8fl7rywcs3YEtAfBh4PkHh9GGTAmZahvrAg+hpLTGjnFbPTppvbabeUuo95ss7U121p0qp3Mh3zcKDYdfcop18Vw6Qe3TCern/d0V96Prpokrj++vYRuf3YPXfHQdrEnFbdq3zAXrbV6ZOwNKzzIqDUfco0WwYf97qZEL7yZahkX+PEMPa534b2MeLtzb7XZ2pprM2yM1/B5/oO/nVWXEryZ8dH7/i78e8JBB8tJjPBaoP795RNF7dVrB87R0XMt1loT7m7Rwu8KOA/BhwN48qLc+dVowUdmnMx8oObDXubDaJmusXD6uiA1mk7WtNGRysFJp95qs7WtNeFplhzsFNe20uT0WPK3MrXWxRtLCXqXqRbg8tKU3PHe28/TVxfkio9jlS207qUDYjNE3kAO9AnBhwN48yM5+Tk+ItSQmQ9eVurs6cfaqapB3S7cSHNdHDUtM04JPs61WJcbp2Z4Nxjgs9eZOfFiB1Pe50ULwYe3u3z0TD4nHKANqNGHr5anpmbG0ta7L/HJ/wXeg5oPB8g3YD4zM9qeAbHhweJxMWQ/Lsx8JKrpZTOZlqkc+Hm3UTmyeuU07+8Sap33cdb/dR9c61KpzviQnTgwKE/NcnCAJjtdkCECZxjrSOolRu10YZxKR93HKDUfUcZaZnOEHNT0zrEaUUzIa/qX+CC9PSdPmfexXwOTTvlxc7aTH7s3a130viM2Zz54Ii5D8AHOQPDhTKeLQQ9Emeh4GbHV1oyZD05r27p8coqY7eBts3OUzEdJXbs1+POXs+qSCx9QzdTt5CheYuGnhQs/eXmO5aA2BpyA4MMBTZ09hqz3kORI5Co1zQy2yy7GfM1HExseQt++dIJ1Oe6r83N98v8mRIXShBSllmBPaQP501lZbIolF7s4GM2wGaXONTvc7QLgKBScOqCx3ZidLhfM+lAHBYHNRoIma7WV1q+aQj+6qlBsde/LAGzRhCQqOd9Ou0rq6app6eTv4AP1HiPjwKxSHU64JD9J7BUF4ChkPpyY8WHUtkvrlFNkPsjsrba2QoICfZ75WaqO0t51eug+Hr5W1qAuu6DTZcx9odjVF2X44mUBA0Hw4dSOtgbNfGDQ2AWdDnzGb9ZWW39aNCHROt5dbuznD5x9Ych8jMy2FMafWSrQJwQfTmwqZ8RuF5ZhHTSGZRfb7iZex46LMGbAqVW8yRyPzGa7S+r9FnzK6aY8cA3sWzMrS1wuGJdoytoocA+CD2dGqxv0QCS3C+cprh09yhm/mclOF97BmLf0Bt9aqm4k9t6xGr8tufCIdy64lcXYYL8+5427ltHTt8/H0wNOQ/DhgBY1BR9r0OAjJsx20BiyH4PBB87m/GH1zExx+eaRamvg70unapUll/zUaLTZOjATJjoMhabgPAQfDmjrUpZdYsKN+UfGcwxkx0s1gg9Tt9lqwczsOLH0wssfT+wosU4a9ZXT59vEZX4KllwAvMWYR1MPk8WHRo7wuej0VG2bz9/otahBLXQ0a5utFoLhry3Ipfv+eZQ2fnCaHvvwNF03K0tsrS5FhQXTTQtzvTL8bDD4wJ4uAN5i3KOpF4IPHr5kVOh4GdSgdjch8+E/N8zNFvvK8LwNHnX+yv5zF3zP+dZusaW6p51WO12Q+QDwHgQfY+jrH6DO3n5DL7uwdHS8XLCjbSJqPvyGs4wv3rFIXOddbrccrKTuvgHrLtP/OlRFf/2klNYuG+/RvVcsFgudrlUzH+h0AfAa4x5NPaSte7D7I9rAwUemddYHll0G93XBsosWzM1LEB+2AUJFQwcdrGim/9l+mn55zVSP1vvw3zzPsMjDaHUAr0HBqYNLLuEhgWLio1Gh4HRQfZsafESbb1M5vdSErLuqUFx/fvdZjxZJy9tKjg6jsGDvb6YHYFbGPZp6vNjUuPUetjUfKDgdHDKGZRftumRiMs3LSxBLMRs/OOWx25Wt5vLvAQC8A8HHGFrVNttYAy+5sEy11ZZnmvCauplh2UUv2Y9J4vqmPWWi+NQTqtVlRwwXA/AuBB8O1nwYudhUFvilqoV73HJrVgMDFutePmi11bYl+ck0Myeeevst9PK+Co/cJjIfAL6B4MPRZReDBx9sYpoyVMnMwQdnfXi0NsOEU+27aUGuNfvBgaOnaj5k9xcAeAeCjzG0ysyHwWs+2MRUZUMvMwcfcropj5wPDcafh9ZdMzNDvFY8D2T3Gfc3okPmA8A38O7qYM2HGTIfcq6BmYMPa70HppvqQmRoMF0xJVVc/+xMg9u3V90iMx8oOAXwJgQfDi67GL3mg01Ug4/i2lYis7fZYsaHbnDdBztc0ezW7fD8EDnnBt0uAN6F4GMMbdbgw/jLLgVq8FHR2EmdPcpUV7O22SYh+NCNGdlK8MFDxziAcFVzZy919SpTVNNikfkA8CYEHw4uu/C6stHxATchMoT4/VturmXWZRcUm+rH1IxYCgoMoLq2buuyiTv1Hpz18saGdQAwCMHHGMzSaitnJ8jsh1mDDzkvItmD+4WAd0WEBtGkNKVY+pAbSy/WThdkPQC8DsHHGHjollkKTlmB2vFSXGPO4EOu+cu9bkAfZmTFictDFU0u30ZFY4e4zIzHaw/gbQg+HC44NX7NByswedGpPPvFmr++TM+KFZfHq1z/vT19vl1c5qcofwMA4D0IPsbQ1q3WfJgk8zHR5O22g3MeMGRKT8YnK7+3Z+uVAMIV8ncewQeA9yH4cDTzYYKCU9spp6X1HdTTp1T+m0Vv/wCdb1NqPjDnQV/ykiLFZXljp8uTTmWdk5x3AwDeg+BjFNy2Z6ZWW1lsx/u88Ihxd84ih+Pdcn/7xjE6VtlCWi425U6fkKAAtNrqDM/lCA4MEAGzKx0vXFgus175KVFeuIcAYAvBxyi4579PPYsyS8Epd7zIM79iDy298MyQtX/dS0/uPEM3P/UpnWtSijq1Rh58UmPCKTAwwN93B5wQHBRI2QnKUhmPWndWiZr1SI4OpfjIUDz3AF6G4GOMoUOMz6iiQs3T91+Q4tm6j99vPUHHq1qse6d8+297NTnETBabYrqlPuUmKRmLsgbnM3ao9wDwLQQfo2jqVAZOxUWEiIyAWci6DxkwuLt0teVgpbj+wJppYoDTkXMt9LNXDrk1jdKbbbao99CnvMRIa72Ss1DvAeBbCD5G0dShZD7iIs1R7yHNzUuwbtTlboDAZ5Sc7QgPCaSvzs+ljV+fI6ZRvn6gkp7YUUJagsyHMYpOy1wIPoqqW4dk/QDAuxB8OBB8xEeYK/iYkR0nggUOGtxdetmt7jQ6JzdBbFG/OD+JfnXNVPG5B986QR8W1ZJWDO5oijZbPcpTl13OOrns0tc/QJ+WKL+n88YpgTcAaCz4+Oijj2j16tWUmZkpliJee+21IV/nM+Vf/epXlJGRQREREbRixQoqLi4mPWpRaz7MVoAWFhxkzX7sLql367Y+VX9+4fgk6+duWZxHN87LIa7l/d4Ln7u9G6mnYLy2MTIfZ+s6nMrYHTrXTK3dfWJ5dVqmMikVADQWfLS3t9PMmTNp48aNdr/+hz/8gf785z/T//zP/9Cnn35KUVFRtHLlSurqcn3DJy3UfJjNIjVY2K2eEbqCDwCfqpmPhRMSrZ/noPU3102nZQXJ1NHTT7c/+5lLqXJP43ZgloHx2roNPji7xoGEM3UfHxfXiculBUliSRAANBh8rFq1in7729/Sl770JbsHm0ceeYR+8Ytf0Jo1a2jGjBn03HPPUWVl5QUZEl3VfJgx+MhXgo9dJfUuD22qbO4SszO4W2hWjrLtucQHicf+fY7YkbSurYdufeYz66ZuEs8EueGxT+jiP7xP33h2j0cKYEdiOx9CtmyC/jJ2co+XvaWOB807T8ngI9lr9w0AvFjzcebMGaqurhZLLVJcXBwtXLiQdu3aRXpttY03WcEpm5kdL9qLeYv5Yy4e9ItrlCK+8clRdrco58Ftz94+n7LiI+hMXTv9+5Of0iPvnRQfD797UswE2Xe2kcobOun9E7V0zV920ifqgcIbSy4cY4UFB1JKNHa01Su5XMi/N47O95Dfy5k4ANBh8MGBB0tLSxvyef63/Npw3d3d1NLSMuRDK5pk8GHCzAdnJpaob8bbT5536TZksarcrM6e1NhweuGbCyklJoyKalrpkfeKxcefthWLglfeMOzvdyyiSyeliKmrP3/tCHX3eX5GiNzRlLMeZmqrNnvw8R9vHheDBC8vTLEWrAKA9/l9bOeGDRvo/vvvJy1qNmmrrXTJpBR691gNbS86T3deXuBy8CE3qxvJuOQoevk7i+n53WdFDYiUFBVKty8dTwlRoTQtK5au+K/tIkPCSzD3rJhE88YN1pG4q9wafChFi6Dv4IOn8zZ19IxaLM7F0O8drxXLgj+/WunAAgAdBh/p6enisqamRnS7SPzvWbNm2f2Z9evX07p166z/5sxHTk4OaangND7CXN0u0qUTU8Tl52WN1NLVS7FO7m9jnRrpwEZdfNY52gGA/+8Hrp1Gd774OX18qp72lH5Kb/3wYo/tQFrRqBSbot5D35Kiw8QyHwepnP24YsrQLKytJ3acEZdfnpczanYOADS+7DJ+/HgRgGzbtm1IMMFdL4sXL7b7M2FhYRQbGzvkQ2s1H2bNfOQmRdKElCiRln59/zmnfpaLj+XeMBNTYzxyf1ZdlEHv3HMpLRyfKApEf/LyIbEU49ngA5kPvVukdlbtOl0/4u8mFy9vO1Ej/r122Xif3j8AcCH4aGtrowMHDogPWWTK18vKysRa+d133y26Yf75z3/S4cOH6ZZbbhEzQa677jrdPd9mHTJm67Yl48Tlxg9OU1ev47UW3MHCwRuXT3AA4yl8hvrHG2eJnXf5zPa5XaUer/kAfVucr9QqfTIs+Gjt6qXbn/mMZj3wLq360w6xg/FlhSnIegDoIfjYu3cvzZ49W3wwXjLh6zxYjP3kJz+hu+66i771rW/R/PnzRbCydetWCg8PJz3hM+rWrj7TttpKN87PERutcRvqps/KHP654lql0yU3MdJup4s7uDvmZ6smi+t/2FrkkRkhWHYxDpn5OF7dQo3tytIpe+bjUvqg6PxgUJwcRfdeWejHewpgXk7XfFx22WWjTg/k7McDDzwgPvRMTjc1e/DBsxO+v7yAfv7qEdr44Wn66oJch4KJk+peGWMVm7rq6wty6Y1DlWII2k//7xC9eMdCl7tUhs74wLKL3qXGhIvfO172+/Lju2j1jEyRfXtqp1Lj8dvrptO/zc32eFAMAI7D3i5jtNnGhAVTcJC5n6Yvz80R2QYeAsZnj444UN4kLi/KGjpczFMCAwPo9zfMEHvQ8CC0v39W7tZuthZ1xkdytDmLi41miTokj4ueH37vJN319/0i45GfEkVfczCABgADt9pqFbfpmbnYdPjMjx9cUUA//b/D9PutJ2jz3nIKCwmiAHVZ5la1LsTWfjX4mJXrneBDdsj8eOVk+s0bx+ihd4roy/OyKcSFQNF2yQUzPoxh7bIJdK6pSwSTXDDNQUhdWzfdt3oaRqgDaACCjzEyH2ZecrF1w5xsMe78xc/KqKRucNfQ3/3rOF05NY0y4wcLNXkq6lm1DmNWtveCD3br4jx67MNTosB1Z3EdXT451Y1iUyy5GKlT68lb5/n7bgDACMy9nuDAgDEzjla3h5ee7l8znT752RX0t7ULxMe8vATq6R+gjR+cGvK9B9WsBxf0eTtzxPfr6ouUmTL/PFjp0m2g2BQAwLcQfIyAU7QsKQr7fNjiMegXT0wRHz9eqXQKvLS3nMobOi5cchm2mZy3XDsrU1y+c7SaOm0mpDpK3vecRGQ+AAB8AcHHCDiNz5KxydiIFk5IEtuQ9/ZbhmQ/5F4ws71Y72FrTm6CqNdo7+m3Do5yBjIfAAC+heBjrMwHuh9GxXussJf3VYh5GzyKnZddQoMC6QvTB0fsexMXia6eqWQ//nnA+aUXTDcFAPAtBB8jqFeDD2yvPjre3O3iicmio+Dnrx2mJz4qsS6F8BKNr6xRl14+VIdIOYp3yK1plTM+MN0UAMAXEHyMseyCzMfYfn3tNDEjY0dxHb11pFp87valF7bfetPk9FialBYtCmDfVu+DI6qausSMD54XwrvoAgCA9yH4GGPZBTUfY+OdZeW4cz6I/3r1VJqWGUe+dtVUZVflvWcbXFpywYwPAADfwJwPO3h8fL0sOPXh0oHeN6CbkhFL45KiKD3OP/v48P/PitTR7o7AhnIAAL6H4MOOlq4+kb5nSMU7hrMGiyYoI639pTA9RlyerGmjgQGLGME+lrOyzRYDxgAAfAbLLqMUm/K+LtgDQj/GJUWKUfCdvf1Urk4tHcvZemVa67jkKC/fOwAAkBB82IFiU33iaadyF90TDi69nKnrsAYuAADgGwg+7ECxqX4VpsU4XPfBtT3IfAAA+B6Cj1GWXdDpoj+y7qOoZuzg43xrN3X09BOXhmDGBwCA7yD4sOM8Znzo1iQ1+Ch2IPgoVXfe5R15w4KDvH7fAABAgeDDDiy76Fd2vDKltLpZmVo6mlK12HQ8ik0BAHwKwYcdlU3K4KnUWMz40JvUmHBru3RX7+g73JbWKcFHHopNAQB8CsGHHcerWqwju0FfYiOCxah3WdMxmrPqsgsPRgMAAN9B8GFnyaWmpZsCAjj4UOoHQF/DzmTGqqZl9KWXEjXzgeADAMC3EHyMkPUYnxRFUWEYAKtHaerSS+0omQ+egFpyvk1cL1BngwAAgG8g+BjmaKUSfEzJxJKLXjmS+TjX1EndfQMUGhSINlsAAB9D8DHMMTX4mKpuUgb6LTodLfNxWs16cLEpT0YFAADfMdW7bmtXr3WA2EiOqcsuU5H5MHTm4/R5pd4jPwVLLgAAvhZoplqOa/6yk36waT/1D1jsfk9ta5f1jHgagg/dZz5G63aRr3N+KjpdAAB8zTTBR0hQoDgYfXyqnv77/VN2v2frkWqyWIhm5cRbD2CgP2mOZD5q1eADmQ8AAJ8zTfDBHQ2/+9J0cf2RbSfpUEXTBd/z5uEqcXn1RRk+v3/g65oPLLsAAPiLaYIP9qXZ2bR6ZqbIbvzn20VDvsZZkc/ONIjrqy5K99M9BE9mPpo6eu1OOW1s77GO0J+QgmUXAABfM1XwwX6yspBCggJoR3EdfXKqzvr5LQcriUtBZubEU3ZCpF/vI7gnLiKEQkeZcnqgXMl6TUiOopjwEDzdAAA+ZrrgIycxkr6+IFdc//3bRWThNAgRvbyvQlzeMCfLr/cPPDPlVG4wV96gjFC3te9so7icnZuApxsAwA9MF3yw7y+fSJGhQXSwvIme/aSU/nmwUrTY8sCp1TMy/X33wAPkZnGl6v4t9oKPuXkIPgAA/MGU88NTYsLoG0vH039/cIru33LM+vkrpqRSQlSoX+8beEae2CzuPJ2tVwpLpb7+ATqoFhsj+AAA8A9TBh/s25dOoEPnmqmiUTkzjgoNpjsvL/D33QIPGZ+sFJKWDgs+TlS3UkdPP8WEBdNE7OkCAOAXpg0+uNDwuW8s8PfdAC8vu5wdtuwisx6zcuMpMDAAzz8AgB+YsuYDjG9c0mDmQxYVs5PVreISe/cAAPgPgg8wpKyECAoKDKCu3oEhw8aKapTgY2JajB/vHQCAuSH4AMOO089OUNptS+uUug/OgBSpmY9CBB8AAH6D4AMM3vEyWHRa19ZDjR29FBCgjNsHAAD/QPABhjVJDTAOn2sWl8XqkkteYiRFhAb59b4BAJgZgg8wrPnjE8Wl3LNH1ntMwpILAIBfIfgAw5o/Tgk+Tta0UUN7j3VPl8J0FJsCAPgTgg8wrMSoUJqUpiy9/ODv++n1A5Xi+qIJSX6+ZwAA5obgAwxtgbr0slPdwfiu5QW0tCDZz/cKAMDcEHyAoS3JHww0fnDFRFp35SS/3h8AADDxeHUwh5XT0uk3a6bRjOx4mpkT7++7AwAACD7A6HjK6c2Lx/n7bgAAgA0suwAAAIBPIfgAAAAAn0LwAQAAAD6F4AMAAAB8CsEHAAAA+BSCDwAAAPApBB8AAADgUwg+AAAAwKcQfAAAAIBPIfgAAAAAn0LwAQAAAD6F4AMAAAAQfAAAAIBxBZPGWCwWcdnS0uLvuwIAAAAOksdteRzXVfDR2toqLnNycvx9VwAAAMCF43hcXNyo3xNgcSRE8aGBgQGqrKykmJgYCggI8HhUxkFNeXk5xcbGkpngseN1x++8OeBvHX/rsX46vnE4wYFHZmYmBQYG6ivzwXc4Ozvbq/8HvzBmeyOW8NjxupuNWX/nzfq4GR57rN+e+7EyHhK6XQAAAMCnEHwAAACAT5kq+AgLC6P77rtPXJoNHjted7Mx6++8WR83w2O/Tzevu+YKTgEAAMDYTJX5AAAAAP9D8AEAAAA+heADAAAAfArBBwAAAJgn+Ni4cSONGzeOwsPDaeHChfTZZ59Zv1ZaWiomnNr72Lx586i3e+jQIbr44ovF7fJE0z/84Q9Dvv7EE0+IryckJIiPFStWDPm/R/Lhhx/SnDlzRDVxQUEBPfvss049Jr0+9qqqKvr6179OkyZNEkPg7r777gu+x5nb9ddjf+WVV2jevHkUHx9PUVFRNGvWLPrb3/5GZnjdXXnsRnndbW3atEnc5nXXXUdmeN1deexGed359Rp+m/y9vnrd9fS4qzz8mjvE4iebNm2yhIaGWp5++mnL0aNHLXfccYclPj7eUlNTI77e19dnqaqqGvJx//33W6Kjoy2tra0j3m5zc7MlLS3NctNNN1mOHDli+fvf/26JiIiwPP7449bv+frXv27ZuHGjZf/+/Zbjx49bbrvtNktcXJyloqJixNstKSmxREZGWtatW2c5duyY5S9/+YslKCjIsnXrVocfk14f+5kzZyw/+MEPLH/9618ts2bNsvzwhz+84HscvV1/PvYPPvjA8sorr4jX79SpU5ZHHnnkgtfQqK+7K4/dKK+77ePJysqyXHzxxZY1a9ZYRmOU192Vx26U1/2ZZ56xxMbGDrnt6upqn7zuenvcZzz4mjvKb8HHggULLHfeeaf13/39/ZbMzEzLhg0bRvwZflK+8Y1vjHq7jz76qCUhIcHS3d1t/dxPf/pTS2Fh4Yg/w78IMTEx4okfyU9+8hPLtGnThnzuxhtvtKxcudLpx6S3x27r0ksvtfuL6ejtaumxs9mzZ1t+8YtfmO51d+SxG+l15/u1ZMkSy5NPPmm59dZbxzwAG+l1d/axG+V154MwHxyd4anXXW+P25OvuaP8suzS09ND+/btE2kbiVM9/O9du3bZ/Rn+/gMHDtDatWtHvW3++UsuuYRCQ0Otn1u5ciUVFRVRY2Oj3Z/p6Oig3t5eSkxMHPV2be+vvF15fx19THp87K6wd7taeuwceG/btk18nX/OTK+7o4/dSK/7Aw88QKmpqWPenhFfd2cfu5Fe97a2NsrLyxPLE2vWrKGjR496/XXX4+N2hbvHDr8EH3V1ddTf309paWlDPs//rq6utvszTz31FE2ZMoWWLFky6m3zz9u7Xfk1e37605+KXfiG/9I5cru8g2RnZ6fDj0mPj90V9m5XC4+9ubmZoqOjxR/v1VdfTX/5y1/oyiuvNMXr7uxjN8rrvnPnTnF7vGbtKKO87q48dqO87oWFhfT000/T66+/Ts8//7zYMZ1vt6Kiwquvux4ftyvcPXbootuFX/QXX3zxgqhw2rRp4s2UP1atWuXSbT/44IOiEOvVV191qBjJ1/T42D11u9547DExMeIMY8+ePfS73/2O1q1bJwrMtEaPj12Lrztv733zzTeLg29ycjJpnR4fuxZfd7Z48WK65ZZbRHH1pZdeKoquU1JS6PHHHyct6dTh4/bEax5MfsB/CEFBQVRTUzPk8/zv9PT0C77/5ZdfFikefkJtvfnmmyLtwyIiIsQl/7y925Vfs/XQQw+JJ/G9996jGTNmjHqfR7pd3rqZ/29+PI48Jj0+dmeMdrtaeOyc/uQKdsZ/nMePH6cNGzbQZZddZvjX3dnHboTX/fTp06KzYPXq1dav85kgCw4OFunq/Px8Q77urj52I7zu9oSEhNDs2bPp1KlTIz4eT7zuenzczvDUscMvmQ9O+86dO1esO9v+UfC/OWqzl5K69tprRfRmi9e0+M2UP7KyssTn+Oc/+ugj64vG3n33XZGK4vYgiduTfvOb39DWrVtFC+JY+HZt76+8XXl/HX1MenzsjhrrdrXw2Ifj/7+7u9sUr7uzj90Ir/vkyZPp8OHDIuMjP/i2L7/8cnGd18SN+rq7+tiN8Lrbw0sh/HxkZGSM+Jg88brr8XE7yqPHDoufcCtSWFiY5dlnnxUtTd/61rdEK9LwlqDi4mJLQECA5a233nLodpuamkQr0s033yxakfj/4dYp21akBx98ULRBvfzyy0PakUZrcZItWD/+8Y9FmxG3HNlrwXLkMentsTNur+KPuXPnipYrvs4tZM7erj8f+3/8x39Y3nnnHcvp06fF//3QQw9ZgoODLU888YThX3dXHrtRXvfhHOn4MMrr7spjN8rrzq2rb7/9tvid37dvn+WrX/2qJTw8fMjj8NbrrrfH7cnX3FF+Cz4Y91Dn5uaKB8StSbt3777ge9avX2/JyckRrUqOOnjwoGXZsmXixefedn7SbOXl5fFOvhd83HfffaPeLs9K4HYovr8TJkwQLU2uPCY9PnZ7P8O35crt+uux//znP7cUFBSIP0RuV1u8eLH44x2LEV53Vx+7EV53Vw/ARnjdXX3sRnjd7777buv/ywfsL37xi5bPP//cZ6+73h43efA1d0SA+p8CAAAA+IQuul0AAADAOBB8AAAAgE8h+AAAAACfQvABAAAAPoXgAwAAAHwKwQcAAAD4FIIPAAAA8CkEHwAAAOBTCD4AAADApxB8AAAAgE8h+AAAAACfQvABAAAA5Ev/H9+Jywv+Oh5QAAAAAElFTkSuQmCC"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 60
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T22:10:23.528176Z",
-     "start_time": "2025-11-29T22:10:23.522510Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "len(minutely_15_data['date'])",
-   "id": "b9b13efb8846ffb4",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "480"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 7
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T22:40:45.298102Z",
-     "start_time": "2025-11-29T22:40:45.290457Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "from v4.array_temperature.arrayTemperatureModel import arrayTemperatureModel\n",
-    "def model(u0, u1):\n",
-    "    return arrayTemperatureModel(minutely_15_dataframe['temperature_2m'], minutely_15_dataframe['shortwave_radiation_instant'], 0, u0, u1)\n",
-    "\n"
-   ],
-   "id": "f2db910a0a809a42",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0     -1.945968\n",
-      "1     -1.906737\n",
-      "2     -1.922081\n",
-      "3     -1.837273\n",
-      "4     -1.737273\n",
-      "         ...   \n",
-      "340    2.493114\n",
-      "341    2.148900\n",
-      "342    1.795571\n",
-      "343    1.420991\n",
-      "344    1.161676\n",
-      "Length: 345, dtype: float32\n"
-     ]
-    }
-   ],
-   "execution_count": 64
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T22:41:10.031639Z",
-     "start_time": "2025-11-29T22:41:10.022278Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "model2 = model(12, 0.8)\n",
-    "print(model2.calculateArrayTemperature())\n",
-    "faiman_temp = model2.calculateArrayTemperature()"
-   ],
-   "id": "76ddfa495e81e820",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0     -1.734275\n",
-      "1     -1.704018\n",
-      "2     -1.732149\n",
-      "3     -1.660000\n",
-      "4     -1.560000\n",
-      "         ...   \n",
-      "340    3.612377\n",
-      "341    3.189651\n",
-      "342    2.750214\n",
-      "343    2.271817\n",
-      "344    1.921406\n",
-      "Length: 345, dtype: float32\n"
-     ]
-    }
-   ],
-   "execution_count": 68
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T22:41:10.888190Z",
-     "start_time": "2025-11-29T22:41:10.802236Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "plt.plot(df_15m.index, df_15m, color = 'red')\n",
-    "plt.plot(df_15m.index, faiman_temp)\n",
-    "plt.xticks(rotation = 90)\n"
-   ],
-   "id": "eb1c544f0f61380f",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([20271. , 20271.5, 20272. , 20272.5, 20273. , 20273.5, 20274. ,\n",
-       "        20274.5]),\n",
-       " [Text(20271.0, 0, '07-02 00'),\n",
-       "  Text(20271.5, 0, '07-02 12'),\n",
-       "  Text(20272.0, 0, '07-03 00'),\n",
-       "  Text(20272.5, 0, '07-03 12'),\n",
-       "  Text(20273.0, 0, '07-04 00'),\n",
-       "  Text(20273.5, 0, '07-04 12'),\n",
-       "  Text(20274.0, 0, '07-05 00'),\n",
-       "  Text(20274.5, 0, '07-05 12')])"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 69
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
-   "source": "",
-   "id": "c15bb0d3e9e1a6fd"
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/array_temp/temporary.ipynb b/array_temp/temporary.ipynb
deleted file mode 100644
index 87860be..0000000
--- a/array_temp/temporary.ipynb
+++ /dev/null
@@ -1,55 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "id": "initial_id",
-   "metadata": {
-    "collapsed": true,
-    "ExecuteTime": {
-     "end_time": "2026-01-07T03:33:21.664211Z",
-     "start_time": "2026-01-07T03:33:21.623543Z"
-    }
-   },
-   "source": "print(\"jo\")",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "jo\n"
-     ]
-    }
-   ],
-   "execution_count": 1
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
-   "source": "",
-   "id": "ff47a17664bb0a1d"
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/v3/data_acquisition/get_data.ipynb b/v3/data_acquisition/get_data.ipynb
index d8a9956..e4eead0 100644
--- a/v3/data_acquisition/get_data.ipynb
+++ b/v3/data_acquisition/get_data.ipynb
@@ -2,20 +2,13 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 11,
    "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Creating client with API Token: Hgnv70HqoniXv8D5S9rS-4jLIfhG4QlneuR4igLx31MOAB7aPo6NmzLAejbhDtDnBdSU2GQXmoNgfKpoJcqjnA==\n",
-      "Creating client with Org: 8a0b66d77a331e96\n"
-     ]
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2026-01-07T03:39:13.710644Z",
+     "start_time": "2026-01-07T03:39:11.916754Z"
     }
-   ],
+   },
    "source": [
     "from data_tools import DBClient, FluxQuery\n",
     "import pathlib\n",
@@ -25,12 +18,12 @@
     "from datetime import datetime, timezone\n",
     "\n",
     "client = DBClient()"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 1
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "outputs": [],
    "source": [
     "things_of_interest = [\"VehicleVelocity\", \"AcceleratorPosition\", \"BatteryCurrent\", \"BatteryVoltage\", \"BatteryCurrentDirection\", \"MechBrakePressed\"]\n",
     "\n",
@@ -60,13 +53,17 @@
     "end_time = datetime(2024, 7, 21, 00, 0, 0, tzinfo=timezone.utc)"
    ],
    "metadata": {
-    "collapsed": false
-   }
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2026-01-07T03:39:18.818820Z",
+     "start_time": "2026-01-07T03:39:14.734974Z"
+    }
+   },
+   "outputs": [],
+   "execution_count": 2
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "outputs": [],
    "source": [
     "for thing_of_interest in things_of_interest:\n",
     "    data_df = client.query_dataframe(FluxQuery().range(start_time.isoformat(), end_time.isoformat()).filter(field=thing_of_interest).from_bucket(\"CAN_log\"))\n",
@@ -81,8 +78,36 @@
     "        pickle.dump(data_ts, f, protocol=pickle.HIGHEST_PROTOCOL)"
    ],
    "metadata": {
-    "collapsed": false
-   }
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2026-01-07T03:39:22.570884Z",
+     "start_time": "2026-01-07T03:39:21.950318Z"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ApiException",
+     "evalue": "(401)\nReason: Unauthorized\nHTTP response headers: HTTPHeaderDict({'Server': 'nginx/1.27.1', 'Date': 'Wed, 07 Jan 2026 03:39:22 GMT', 'Content-Type': 'application/json; charset=utf-8', 'Content-Length': '55', 'Connection': 'keep-alive', 'X-Influxdb-Build': 'OSS', 'X-Influxdb-Version': 'v2.7.1', 'X-Platform-Error-Code': 'unauthorized'})\nHTTP response body: b'{\"code\":\"unauthorized\",\"message\":\"unauthorized access\"}'\n",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[1;31mApiException\u001B[0m                              Traceback (most recent call last)",
+      "Cell \u001B[1;32mIn[3], line 2\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m thing_of_interest \u001B[38;5;129;01min\u001B[39;00m things_of_interest:\n\u001B[1;32m----> 2\u001B[0m     data_df \u001B[38;5;241m=\u001B[39m \u001B[43mclient\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mquery_dataframe\u001B[49m\u001B[43m(\u001B[49m\u001B[43mFluxQuery\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrange\u001B[49m\u001B[43m(\u001B[49m\u001B[43mstart_time\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43misoformat\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mend_time\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43misoformat\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfilter\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfield\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mthing_of_interest\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfrom_bucket\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mCAN_log\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m      3\u001B[0m     data_df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m_time\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m=\u001B[39m pd\u001B[38;5;241m.\u001B[39mto_datetime(data_df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m_time\u001B[39m\u001B[38;5;124m'\u001B[39m])\n\u001B[0;32m      4\u001B[0m     data_df\u001B[38;5;241m.\u001B[39mset_index(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m_time\u001B[39m\u001B[38;5;124m'\u001B[39m, inplace\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n",
+      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\data_tools\\query\\influxdb_query.py:123\u001B[0m, in \u001B[0;36mDBClient.query_dataframe\u001B[1;34m(self, query)\u001B[0m\n\u001B[0;32m    120\u001B[0m compiled_query \u001B[38;5;241m=\u001B[39m query\u001B[38;5;241m.\u001B[39mcompile_query()\n\u001B[0;32m    121\u001B[0m compiled_query \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124m |> pivot(rowKey:[\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_time\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m], columnKey: [\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_field\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m], valueColumn: \u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_value\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m) \u001B[39m\u001B[38;5;124m'\u001B[39m\n\u001B[1;32m--> 123\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_client\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mquery_api\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mquery_data_frame\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcompiled_query\u001B[49m\u001B[43m)\u001B[49m\n",
+      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\influxdb_client\\client\\query_api.py:259\u001B[0m, in \u001B[0;36mQueryApi.query_data_frame\u001B[1;34m(self, query, org, data_frame_index, params, use_extension_dtypes)\u001B[0m\n\u001B[0;32m    225\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mquery_data_frame\u001B[39m(\u001B[38;5;28mself\u001B[39m, query: \u001B[38;5;28mstr\u001B[39m, org\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, data_frame_index: List[\u001B[38;5;28mstr\u001B[39m] \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m, params: \u001B[38;5;28mdict\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[0;32m    226\u001B[0m                      use_extension_dtypes: \u001B[38;5;28mbool\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m):\n\u001B[0;32m    227\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m    228\u001B[0m \u001B[38;5;124;03m    Execute synchronous Flux query and return Pandas DataFrame.\u001B[39;00m\n\u001B[0;32m    229\u001B[0m \n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    257\u001B[0m \u001B[38;5;124;03m            - https://docs.influxdata.com/flux/latest/stdlib/influxdata/influxdb/schema/fieldsascols/\u001B[39;00m\n\u001B[0;32m    258\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[1;32m--> 259\u001B[0m     _generator \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mquery_data_frame_stream\u001B[49m\u001B[43m(\u001B[49m\u001B[43mquery\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43morg\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43morg\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata_frame_index\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdata_frame_index\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mparams\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    260\u001B[0m \u001B[43m                                              \u001B[49m\u001B[43muse_extension_dtypes\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43muse_extension_dtypes\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    261\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_to_data_frames(_generator)\n",
+      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\influxdb_client\\client\\query_api.py:299\u001B[0m, in \u001B[0;36mQueryApi.query_data_frame_stream\u001B[1;34m(self, query, org, data_frame_index, params, use_extension_dtypes)\u001B[0m\n\u001B[0;32m    265\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m    266\u001B[0m \u001B[38;5;124;03mExecute synchronous Flux query and return stream of Pandas DataFrame as a :class:`~Generator[DataFrame]`.\u001B[39;00m\n\u001B[0;32m    267\u001B[0m \n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    295\u001B[0m \u001B[38;5;124;03m        - https://docs.influxdata.com/flux/latest/stdlib/influxdata/influxdb/schema/fieldsascols/\u001B[39;00m\n\u001B[0;32m    296\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[0;32m    297\u001B[0m org \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_org_param(org)\n\u001B[1;32m--> 299\u001B[0m response \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_query_api\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpost_query\u001B[49m\u001B[43m(\u001B[49m\u001B[43morg\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43morg\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mquery\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_create_query\u001B[49m\u001B[43m(\u001B[49m\u001B[43mquery\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdefault_dialect\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    300\u001B[0m \u001B[43m                                                                        \u001B[49m\u001B[43mdataframe_query\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    301\u001B[0m \u001B[43m                                      \u001B[49m\u001B[43masync_req\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_return_http_data_only\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[0;32m    303\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_to_data_frame_stream(data_frame_index\u001B[38;5;241m=\u001B[39mdata_frame_index,\n\u001B[0;32m    304\u001B[0m                                   response\u001B[38;5;241m=\u001B[39mresponse,\n\u001B[0;32m    305\u001B[0m                                   query_options\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_get_query_options(),\n\u001B[0;32m    306\u001B[0m                                   use_extension_dtypes\u001B[38;5;241m=\u001B[39muse_extension_dtypes)\n",
+      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\influxdb_client\\service\\query_service.py:285\u001B[0m, in \u001B[0;36mQueryService.post_query\u001B[1;34m(self, **kwargs)\u001B[0m\n\u001B[0;32m    283\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpost_query_with_http_info(\u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[0;32m    284\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m--> 285\u001B[0m     (data) \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpost_query_with_http_info\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[0;32m    286\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m data\n",
+      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\influxdb_client\\service\\query_service.py:311\u001B[0m, in \u001B[0;36mQueryService.post_query_with_http_info\u001B[1;34m(self, **kwargs)\u001B[0m\n\u001B[0;32m    289\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Query data.\u001B[39;00m\n\u001B[0;32m    290\u001B[0m \n\u001B[0;32m    291\u001B[0m \u001B[38;5;124;03mRetrieves data from buckets.  Use this endpoint to send a Flux query request and retrieve data from a bucket.  #### Rate limits (with InfluxDB Cloud)  `read` rate limits apply. For more information, see [limits and adjustable quotas](https://docs.influxdata.com/influxdb/cloud/account-management/limits/).  #### Related guides  - [Query with the InfluxDB API](https://docs.influxdata.com/influxdb/latest/query-data/execute-queries/influx-api/) - [Get started with Flux](https://docs.influxdata.com/flux/v0.x/get-started/)\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    306\u001B[0m \u001B[38;5;124;03m         returns the request thread.\u001B[39;00m\n\u001B[0;32m    307\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[0;32m    308\u001B[0m local_var_params, path_params, query_params, header_params, body_params \u001B[38;5;241m=\u001B[39m \\\n\u001B[0;32m    309\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_post_query_prepare(\u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[1;32m--> 311\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mapi_client\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcall_api\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m    312\u001B[0m \u001B[43m    \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43m/api/v2/query\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mPOST\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[0;32m    313\u001B[0m \u001B[43m    \u001B[49m\u001B[43mpath_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    314\u001B[0m \u001B[43m    \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    315\u001B[0m \u001B[43m    \u001B[49m\u001B[43mheader_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    316\u001B[0m \u001B[43m    \u001B[49m\u001B[43mbody\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mbody_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    317\u001B[0m \u001B[43m    \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m[\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    318\u001B[0m \u001B[43m    \u001B[49m\u001B[43mfiles\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m{\u001B[49m\u001B[43m}\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    319\u001B[0m \u001B[43m    \u001B[49m\u001B[43mresponse_type\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mstr\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# noqa: E501\u001B[39;49;00m\n\u001B[0;32m    320\u001B[0m \u001B[43m    \u001B[49m\u001B[43mauth_settings\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m[\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    321\u001B[0m \u001B[43m    \u001B[49m\u001B[43masync_req\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mlocal_var_params\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43masync_req\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    322\u001B[0m \u001B[43m    \u001B[49m\u001B[43m_return_http_data_only\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mlocal_var_params\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43m_return_http_data_only\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# noqa: E501\u001B[39;49;00m\n\u001B[0;32m    323\u001B[0m \u001B[43m    \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mlocal_var_params\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43m_preload_content\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    324\u001B[0m \u001B[43m    \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mlocal_var_params\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43m_request_timeout\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    325\u001B[0m \u001B[43m    \u001B[49m\u001B[43mcollection_formats\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m{\u001B[49m\u001B[43m}\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    326\u001B[0m \u001B[43m    \u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43murlopen_kw\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n",
+      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\api_client.py:343\u001B[0m, in \u001B[0;36mApiClient.call_api\u001B[1;34m(self, resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, async_req, _return_http_data_only, collection_formats, _preload_content, _request_timeout, urlopen_kw)\u001B[0m\n\u001B[0;32m    304\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Make the HTTP request (synchronous) and Return deserialized data.\u001B[39;00m\n\u001B[0;32m    305\u001B[0m \n\u001B[0;32m    306\u001B[0m \u001B[38;5;124;03mTo make an async_req request, set the async_req parameter.\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    340\u001B[0m \u001B[38;5;124;03m    then the method will return the response directly.\u001B[39;00m\n\u001B[0;32m    341\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m    342\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m async_req:\n\u001B[1;32m--> 343\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m__call_api\u001B[49m\u001B[43m(\u001B[49m\u001B[43mresource_path\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    344\u001B[0m \u001B[43m                           \u001B[49m\u001B[43mpath_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mheader_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    345\u001B[0m \u001B[43m                           \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfiles\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    346\u001B[0m \u001B[43m                           \u001B[49m\u001B[43mresponse_type\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mauth_settings\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    347\u001B[0m \u001B[43m                           \u001B[49m\u001B[43m_return_http_data_only\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcollection_formats\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    348\u001B[0m \u001B[43m                           \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    349\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m    350\u001B[0m     thread \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpool\u001B[38;5;241m.\u001B[39mapply_async(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m__call_api, (resource_path,\n\u001B[0;32m    351\u001B[0m                                    method, path_params, query_params,\n\u001B[0;32m    352\u001B[0m                                    header_params, body,\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    356\u001B[0m                                    collection_formats,\n\u001B[0;32m    357\u001B[0m                                    _preload_content, _request_timeout, urlopen_kw))\n",
+      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\api_client.py:173\u001B[0m, in \u001B[0;36mApiClient.__call_api\u001B[1;34m(self, resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, _return_http_data_only, collection_formats, _preload_content, _request_timeout, urlopen_kw)\u001B[0m\n\u001B[0;32m    170\u001B[0m urlopen_kw \u001B[38;5;241m=\u001B[39m urlopen_kw \u001B[38;5;129;01mor\u001B[39;00m {}\n\u001B[0;32m    172\u001B[0m \u001B[38;5;66;03m# perform request and return response\u001B[39;00m\n\u001B[1;32m--> 173\u001B[0m response_data \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrequest\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m    174\u001B[0m \u001B[43m    \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mheaders\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mheader_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    175\u001B[0m \u001B[43m    \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbody\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    176\u001B[0m \u001B[43m    \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    177\u001B[0m \u001B[43m    \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    179\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mlast_response \u001B[38;5;241m=\u001B[39m response_data\n\u001B[0;32m    181\u001B[0m return_data \u001B[38;5;241m=\u001B[39m response_data\n",
+      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\api_client.py:388\u001B[0m, in \u001B[0;36mApiClient.request\u001B[1;34m(self, method, url, query_params, headers, post_params, body, _preload_content, _request_timeout, **urlopen_kw)\u001B[0m\n\u001B[0;32m    379\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mrest_client\u001B[38;5;241m.\u001B[39mOPTIONS(url,\n\u001B[0;32m    380\u001B[0m                                     query_params\u001B[38;5;241m=\u001B[39mquery_params,\n\u001B[0;32m    381\u001B[0m                                     headers\u001B[38;5;241m=\u001B[39mheaders,\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    385\u001B[0m                                     body\u001B[38;5;241m=\u001B[39mbody,\n\u001B[0;32m    386\u001B[0m                                     \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39murlopen_kw)\n\u001B[0;32m    387\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m method \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mPOST\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[1;32m--> 388\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrest_client\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mPOST\u001B[49m\u001B[43m(\u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    389\u001B[0m \u001B[43m                                 \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    390\u001B[0m \u001B[43m                                 \u001B[49m\u001B[43mheaders\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    391\u001B[0m \u001B[43m                                 \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    392\u001B[0m \u001B[43m                                 \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    393\u001B[0m \u001B[43m                                 \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    394\u001B[0m \u001B[43m                                 \u001B[49m\u001B[43mbody\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    395\u001B[0m \u001B[43m                                 \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    396\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m method \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mPUT\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[0;32m    397\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mrest_client\u001B[38;5;241m.\u001B[39mPUT(url,\n\u001B[0;32m    398\u001B[0m                                 query_params\u001B[38;5;241m=\u001B[39mquery_params,\n\u001B[0;32m    399\u001B[0m                                 headers\u001B[38;5;241m=\u001B[39mheaders,\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    403\u001B[0m                                 body\u001B[38;5;241m=\u001B[39mbody,\n\u001B[0;32m    404\u001B[0m                                 \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39murlopen_kw)\n",
+      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\rest.py:311\u001B[0m, in \u001B[0;36mRESTClientObject.POST\u001B[1;34m(self, url, headers, query_params, post_params, body, _preload_content, _request_timeout, **urlopen_kw)\u001B[0m\n\u001B[0;32m    308\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mPOST\u001B[39m(\u001B[38;5;28mself\u001B[39m, url, headers\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, query_params\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, post_params\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[0;32m    309\u001B[0m          body\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, _preload_content\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m, _request_timeout\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39murlopen_kw):\n\u001B[0;32m    310\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124;03m\"\"\"Perform POST HTTP request.\"\"\"\u001B[39;00m\n\u001B[1;32m--> 311\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrequest\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mPOST\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    312\u001B[0m \u001B[43m                        \u001B[49m\u001B[43mheaders\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    313\u001B[0m \u001B[43m                        \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    314\u001B[0m \u001B[43m                        \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    315\u001B[0m \u001B[43m                        \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    316\u001B[0m \u001B[43m                        \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    317\u001B[0m \u001B[43m                        \u001B[49m\u001B[43mbody\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    318\u001B[0m \u001B[43m                        \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n",
+      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\rest.py:261\u001B[0m, in \u001B[0;36mRESTClientObject.request\u001B[1;34m(self, method, url, query_params, headers, body, post_params, _preload_content, _request_timeout, **urlopen_kw)\u001B[0m\n\u001B[0;32m    258\u001B[0m     _BaseRESTClient\u001B[38;5;241m.\u001B[39mlog_body(r\u001B[38;5;241m.\u001B[39mdata, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124m<<<\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m    260\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;241m200\u001B[39m \u001B[38;5;241m<\u001B[39m\u001B[38;5;241m=\u001B[39m r\u001B[38;5;241m.\u001B[39mstatus \u001B[38;5;241m<\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;241m299\u001B[39m:\n\u001B[1;32m--> 261\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m ApiException(http_resp\u001B[38;5;241m=\u001B[39mr)\n\u001B[0;32m    263\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m r\n",
+      "\u001B[1;31mApiException\u001B[0m: (401)\nReason: Unauthorized\nHTTP response headers: HTTPHeaderDict({'Server': 'nginx/1.27.1', 'Date': 'Wed, 07 Jan 2026 03:39:22 GMT', 'Content-Type': 'application/json; charset=utf-8', 'Content-Length': '55', 'Connection': 'keep-alive', 'X-Influxdb-Build': 'OSS', 'X-Influxdb-Version': 'v2.7.1', 'X-Platform-Error-Code': 'unauthorized'})\nHTTP response body: b'{\"code\":\"unauthorized\",\"message\":\"unauthorized access\"}'\n"
+     ]
+    }
+   ],
+   "execution_count": 3
   },
   {
    "cell_type": "code",

From 73a1078ba71e88f910c2ffd2af2042503ce25402 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Tue, 6 Jan 2026 20:01:14 -0800
Subject: [PATCH 06/49] cleaning

---
 array_temp/.cache.sqlite                 |  Bin 0 -> 40960 bytes
 array_temp/coefficient_fitting.ipynb     |  866 ++++++++++++++++++
 motor_analysis/faiman_coefficients.ipynb | 1055 ++++++++++++++++++++++
 3 files changed, 1921 insertions(+)
 create mode 100644 array_temp/.cache.sqlite
 create mode 100644 array_temp/coefficient_fitting.ipynb
 create mode 100644 motor_analysis/faiman_coefficients.ipynb

diff --git a/array_temp/.cache.sqlite b/array_temp/.cache.sqlite
new file mode 100644
index 0000000000000000000000000000000000000000..be53ebb1f1e66e5045c1af19d58ab7927122f66c
GIT binary patch
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diff --git a/array_temp/coefficient_fitting.ipynb b/array_temp/coefficient_fitting.ipynb
new file mode 100644
index 0000000..8a54043
--- /dev/null
+++ b/array_temp/coefficient_fitting.ipynb
@@ -0,0 +1,866 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2026-01-07T03:57:28.810983Z",
+     "start_time": "2026-01-07T03:57:09.262265Z"
+    }
+   },
+   "source": [
+    "from data_tools import query\n",
+    "from data_tools.collections import TimeSeries\n",
+    "from datetime import datetime, date, time, timezone\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import dill\n",
+    "import os\n",
+    "import pytz\n",
+    "from datetime import datetime, time, date\n",
+    "\n",
+    "#each 5 seconds\n",
+    "utc_offset_h = 2\n",
+    "start_utc = time(0, 00, 00)  #querying i svancouver time, influxdb gives utc\n",
+    "stop_utc = time(23, 45, 00)\n",
+    "date_start = date(2025, 7, 2)\n",
+    "date_stop = date(2025, 7, 6)\n",
+    "\n",
+    "vancouver = pytz.timezone(\"America/Vancouver\")\n",
+    "\n",
+    "start_local = vancouver.localize(datetime.combine(date_start, start_utc))\n",
+    "stop_local = vancouver.localize(datetime.combine(date_stop, stop_utc))\n",
+    "\n",
+    "start_time = start_local.astimezone(pytz.utc)\n",
+    "stop_time = stop_local.astimezone(pytz.utc)\n",
+    "\n",
+    "client = query.DBClient()\n",
+    "temp_array_fsgp: TimeSeries = client.query_time_series(start_time, stop_time, field=\"MosfetTemperatureA\")\n",
+    "speed_kph: TimeSeries = client.query_time_series(start_time, stop_time, \"MotorRotatingSpeed\")\n",
+    "\n",
+    "#print(temp_array_expected_aliter)\n",
+    "#this is almost steady state, and expected temperature is roughly 31.8 degrees celsius. note that individual cells do have an upto 5 C difference, which is actually quite a lot.\n",
+    "\n",
+    "\n"
+   ],
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-07T03:59:14.746195Z",
+     "start_time": "2026-01-07T03:59:14.675268Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "#save collected data of 15 minutes\n",
+    "\n",
+    "import os\n",
+    "import dill\n",
+    "\n",
+    "out_dir = os.path.join(\"../../motor_analysis\", \"data\", \"array_temperature_2025-07-02\")\n",
+    "mosfetA_file_aliter = os.path.join(out_dir, \"mosfetA_aliter.bin\")\n",
+    "\n",
+    "os.makedirs(out_dir, exist_ok=True)\n",
+    "\n",
+    "for filepath, data in zip([mosfetA_file_aliter],\n",
+    "                          [temp_array_fsgp, speed_kph]):\n",
+    "    with open(filepath, 'wb') as f:\n",
+    "        dill.dump(data, f)\n",
+    "\n",
+    "\n",
+    "\n",
+    "#time zone matching conventions??"
+   ],
+   "id": "98caafc58a09e0c8",
+   "outputs": [],
+   "execution_count": 19
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-07T03:59:42.337364Z",
+     "start_time": "2026-01-07T03:59:30.836156Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from datetime import datetime, date, time\n",
+    "import pytz\n",
+    "\n",
+    "vancouver = pytz.timezone(\"America/Vancouver\")\n",
+    "\n",
+    "date_start = date(2025, 7, 1)\n",
+    "date_stop  = date(2025, 7, 6)\n",
+    "\n",
+    "# Local start/end\n",
+    "start_local = vancouver.localize(datetime.combine(date_start, time(23,45,0)))\n",
+    "stop_local  = vancouver.localize(datetime.combine(date_stop, time(0,0,0)))\n",
+    "\n",
+    "# Convert to UTC\n",
+    "start_utc = start_local.astimezone(pytz.utc)\n",
+    "stop_utc  = stop_local.astimezone(pytz.utc)\n",
+    "\n",
+    "print(\"Start UTC:\", start_utc)  # 2025-07-02 07:00:00+00:00\n",
+    "print(\"Stop UTC:\", stop_utc)    # 2025-07-07 06:45:00+00:00\n",
+    "\n",
+    "client = query.DBClient()\n",
+    "temp_array_fsgp_aliter = client.query_time_series(start_utc, stop_utc, field=\"MosfetTemperatureA\")\n",
+    "speed_kph_aliter       = client.query_time_series(start_utc, stop_utc, \"MotorRotatingSpeed\")\n"
+   ],
+   "id": "856e8e887bd73795",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Start UTC: 2025-07-02 06:45:00+00:00\n",
+      "Stop UTC: 2025-07-06 07:00:00+00:00\n"
+     ]
+    }
+   ],
+   "execution_count": 20
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-07T03:59:46.674302Z",
+     "start_time": "2026-01-07T03:59:46.595670Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "#save collected data of 15 minutes\n",
+    "\n",
+    "import os\n",
+    "import dill\n",
+    "\n",
+    "out_dir = os.path.join(\"../../motor_analysis\", \"data\", \"array_temperature_2025-07-06\")\n",
+    "mosfetA_file_aliter = os.path.join(out_dir, \"mosfetA_aliter.bin\")\n",
+    "\n",
+    "os.makedirs(out_dir, exist_ok=True)\n",
+    "\n",
+    "for filepath, data in zip([mosfetA_file_aliter],\n",
+    "                          [temp_array_fsgp_aliter, speed_kph_aliter]):\n",
+    "    with open(filepath, 'wb') as f:\n",
+    "        dill.dump(data, f)\n",
+    "\n",
+    "\n",
+    "\n",
+    "#time zone matching conventions??"
+   ],
+   "id": "5f382e13f6628392",
+   "outputs": [],
+   "execution_count": 21
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-07T03:57:39.894705Z",
+     "start_time": "2026-01-07T03:57:38.856358Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "#resampling influx data so that i can fit it with irradiance data (queried every 15 minutes)\n",
+    "\n",
+    "ts = temp_array_fsgp\n",
+    "timestamps = pd.to_datetime(ts.datetime_x_axis, utc=True)\n",
+    "values = ts.data\n",
+    "\n",
+    "df = pd.DataFrame({\"value\": values}, index=timestamps)\n",
+    "df_15m = df.resample(\"15T\").mean()\n",
+    "\n",
+    "print(df_15m.head())\n",
+    "print(df_15m.tail())\n"
+   ],
+   "id": "5efaf5364189c268",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                               value\n",
+      "2025-07-01 23:45:00+00:00  26.032398\n",
+      "2025-07-02 00:00:00+00:00  25.083505\n",
+      "2025-07-02 00:15:00+00:00  24.787958\n",
+      "2025-07-02 00:30:00+00:00  24.492412\n",
+      "2025-07-02 00:45:00+00:00  24.675365\n",
+      "                               value\n",
+      "2025-07-05 13:00:00+00:00  38.086209\n",
+      "2025-07-05 13:15:00+00:00  36.774657\n",
+      "2025-07-05 13:30:00+00:00  35.463106\n",
+      "2025-07-05 13:45:00+00:00  34.271664\n",
+      "2025-07-05 14:00:00+00:00  34.174615\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_8744\\2898644639.py:10: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
+      "  df_15m = df.resample(\"15T\").mean()\n"
+     ]
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "querying from openmeteo to get solar irradiance data\n",
+    "\n",
+    "- this is hourly irradiance over 4 days\n"
+   ],
+   "id": "8fd8b587bf1e774"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-07T03:57:43.206073Z",
+     "start_time": "2026-01-07T03:57:39.923337Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import openmeteo_requests\n",
+    "\n",
+    "import pandas as pd\n",
+    "import requests_cache\n",
+    "from retry_requests import retry\n",
+    "\n",
+    "# Setup the Open-Meteo API client with cache and retry on error\n",
+    "cache_session = requests_cache.CachedSession('.cache', expire_after=3600)\n",
+    "retry_session = retry(cache_session, retries=5, backoff_factor=0.2)\n",
+    "openmeteo = openmeteo_requests.Client(session=retry_session)\n",
+    "\n",
+    "# Make sure all required weather variables are listed here\n",
+    "# The order of variables in hourly or daily is important to assign them correctly below\n",
+    "url = \"https://historical-forecast-api.open-meteo.com/v1/forecast\"\n",
+    "params = {\n",
+    "    \"latitude\": 36.9760,\n",
+    "    \"longitude\": 86.4491,\n",
+    "    \"start_date\": \"2025-07-02\",\n",
+    "    \"end_date\": \"2025-07-05\",\n",
+    "    \"minutely_15\": [\"temperature_2m\", \"wind_speed_10m\", \"shortwave_radiation_instant\"],\n",
+    "}\n",
+    "responses = openmeteo.weather_api(url, params=params)\n",
+    "\n",
+    "# Process first location. Add a for-loop for multiple locations or weather models\n",
+    "response = responses[0]\n",
+    "print(f\"Coordinates: {response.Latitude()}°N {response.Longitude()}°E\")\n",
+    "print(f\"Elevation: {response.Elevation()} m asl\")\n",
+    "print(f\"Timezone difference to GMT+0: {response.UtcOffsetSeconds()}s\")\n",
+    "\n",
+    "# Process minutely_15 data. The order of variables needs to be the same as requested.\n",
+    "minutely_15 = response.Minutely15()\n",
+    "minutely_15_temperature_2m = minutely_15.Variables(0).ValuesAsNumpy()\n",
+    "minutely_15_shortwave_radiation_instant = minutely_15.Variables(1).ValuesAsNumpy()\n",
+    "minutely_15_wind_speed_10m = minutely_15.Variables(2).ValuesAsNumpy()\n",
+    "\n",
+    "minutely_15_data = {\"date\": pd.date_range(\n",
+    "    start=pd.to_datetime(minutely_15.Time(), unit=\"s\", utc=True),\n",
+    "    end=pd.to_datetime(minutely_15.TimeEnd(), unit=\"s\", utc=True),\n",
+    "    freq=pd.Timedelta(seconds=minutely_15.Interval()),\n",
+    "    inclusive=\"left\"\n",
+    ")}\n",
+    "\n",
+    "minutely_15_data[\"temperature_2m\"] = minutely_15_temperature_2m\n",
+    "minutely_15_data[\"shortwave_radiation_instant\"] = minutely_15_shortwave_radiation_instant\n",
+    "minutely_15_data[\"wind_speed_10m\"] = minutely_15_wind_speed_10m\n",
+    "\n",
+    "minutely_15_dataframe = pd.DataFrame(data=minutely_15_data)\n",
+    "print(\"\\nMinutely15 data\\n\", minutely_15_dataframe)"
+   ],
+   "id": "75c51cd2fbd7d69",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coordinates: 37.0°N 86.5°E\n",
+      "Elevation: 5139.0 m asl\n",
+      "Timezone difference to GMT+0: 0s\n",
+      "\n",
+      "Minutely15 data\n",
+      "                          date  temperature_2m  shortwave_radiation_instant  \\\n",
+      "0   2025-07-02 00:00:00+00:00           -2.20                     5.588703   \n",
+      "1   2025-07-02 00:15:00+00:00           -2.15                     5.351785   \n",
+      "2   2025-07-02 00:30:00+00:00           -2.15                     5.014219   \n",
+      "3   2025-07-02 00:45:00+00:00           -2.05                     4.680000   \n",
+      "4   2025-07-02 01:00:00+00:00           -1.95                     4.680000   \n",
+      "..                        ...             ...                          ...   \n",
+      "379 2025-07-05 22:45:00+00:00           -2.00                     8.496305   \n",
+      "380 2025-07-05 23:00:00+00:00           -1.95                     8.788720   \n",
+      "381 2025-07-05 23:15:00+00:00           -1.95                     9.085988   \n",
+      "382 2025-07-05 23:30:00+00:00           -1.90                     9.199390   \n",
+      "383 2025-07-05 23:45:00+00:00           -1.85                     9.021574   \n",
+      "\n",
+      "     wind_speed_10m  \n",
+      "0        124.831741  \n",
+      "1        164.539490  \n",
+      "2        205.015503  \n",
+      "3        243.104050  \n",
+      "4        276.808258  \n",
+      "..              ...  \n",
+      "379        0.000000  \n",
+      "380        0.000000  \n",
+      "381       19.759171  \n",
+      "382       51.912174  \n",
+      "383       85.373299  \n",
+      "\n",
+      "[384 rows x 4 columns]\n"
+     ]
+    }
+   ],
+   "execution_count": 4
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-07T03:57:43.289544Z",
+     "start_time": "2026-01-07T03:57:43.266224Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "minutely_15_dataframe = minutely_15_dataframe[minutely_15_dataframe['date']<'2025-07-05 14:15:00']",
+   "id": "1f24e675de2a0cdd",
+   "outputs": [],
+   "execution_count": 5
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-07T03:57:43.322372Z",
+     "start_time": "2026-01-07T03:57:43.307373Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "minutely_15_data[\"temperature_2m\"] = minutely_15_temperature_2m\n",
+    "minutely_15_data[\"shortwave_radiation_instant\"] = minutely_15_shortwave_radiation_instant\n",
+    "minutely_15_data[\"wind_speed_10m\"] = minutely_15_wind_speed_10m"
+   ],
+   "id": "cea74153df653c65",
+   "outputs": [],
+   "execution_count": 6
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-07T03:57:43.365624Z",
+     "start_time": "2026-01-07T03:57:43.343293Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "minutely_15_dataframe.tail()\n",
+   "id": "cc22b957110ee8a8",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                         date  temperature_2m  shortwave_radiation_instant  \\\n",
+       "340 2025-07-05 13:00:00+00:00            1.15                    29.548521   \n",
+       "341 2025-07-05 13:15:00+00:00            0.90                    27.475807   \n",
+       "342 2025-07-05 13:30:00+00:00            0.65                    25.202570   \n",
+       "343 2025-07-05 13:45:00+00:00            0.40                    22.461807   \n",
+       "344 2025-07-05 14:00:00+00:00            0.25                    20.056877   \n",
+       "\n",
+       "     wind_speed_10m  \n",
+       "340       58.205017  \n",
+       "341       22.902834  \n",
+       "342        2.313184  \n",
+       "343        0.000000  \n",
+       "344        0.000000  "
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>temperature_2m</th>\n",
+       "      <th>shortwave_radiation_instant</th>\n",
+       "      <th>wind_speed_10m</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>340</th>\n",
+       "      <td>2025-07-05 13:00:00+00:00</td>\n",
+       "      <td>1.15</td>\n",
+       "      <td>29.548521</td>\n",
+       "      <td>58.205017</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>341</th>\n",
+       "      <td>2025-07-05 13:15:00+00:00</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>27.475807</td>\n",
+       "      <td>22.902834</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>342</th>\n",
+       "      <td>2025-07-05 13:30:00+00:00</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>25.202570</td>\n",
+       "      <td>2.313184</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>343</th>\n",
+       "      <td>2025-07-05 13:45:00+00:00</td>\n",
+       "      <td>0.40</td>\n",
+       "      <td>22.461807</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>344</th>\n",
+       "      <td>2025-07-05 14:00:00+00:00</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>20.056877</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 7
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-07T03:57:43.447030Z",
+     "start_time": "2026-01-07T03:57:43.436435Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "len(minutely_15_dataframe)",
+   "id": "592396d2c42fe633",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "345"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 8
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-07T03:57:43.501909Z",
+     "start_time": "2026-01-07T03:57:43.481229Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "print(\"\\nMinutely15 data\\n\", minutely_15_dataframe)\n",
+   "id": "b02b2e521e671912",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Minutely15 data\n",
+      "                          date  temperature_2m  shortwave_radiation_instant  \\\n",
+      "0   2025-07-02 00:00:00+00:00           -2.20                     5.588703   \n",
+      "1   2025-07-02 00:15:00+00:00           -2.15                     5.351785   \n",
+      "2   2025-07-02 00:30:00+00:00           -2.15                     5.014219   \n",
+      "3   2025-07-02 00:45:00+00:00           -2.05                     4.680000   \n",
+      "4   2025-07-02 01:00:00+00:00           -1.95                     4.680000   \n",
+      "..                        ...             ...                          ...   \n",
+      "340 2025-07-05 13:00:00+00:00            1.15                    29.548521   \n",
+      "341 2025-07-05 13:15:00+00:00            0.90                    27.475807   \n",
+      "342 2025-07-05 13:30:00+00:00            0.65                    25.202570   \n",
+      "343 2025-07-05 13:45:00+00:00            0.40                    22.461807   \n",
+      "344 2025-07-05 14:00:00+00:00            0.25                    20.056877   \n",
+      "\n",
+      "     wind_speed_10m  \n",
+      "0        124.831741  \n",
+      "1        164.539490  \n",
+      "2        205.015503  \n",
+      "3        243.104050  \n",
+      "4        276.808258  \n",
+      "..              ...  \n",
+      "340       58.205017  \n",
+      "341       22.902834  \n",
+      "342        2.313184  \n",
+      "343        0.000000  \n",
+      "344        0.000000  \n",
+      "\n",
+      "[345 rows x 4 columns]\n"
+     ]
+    }
+   ],
+   "execution_count": 9
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-07T03:57:43.540177Z",
+     "start_time": "2026-01-07T03:57:43.528157Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "print(df_15m)",
+   "id": "58bd4131e7093a9d",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                               value\n",
+      "2025-07-01 23:45:00+00:00  26.032398\n",
+      "2025-07-02 00:00:00+00:00  25.083505\n",
+      "2025-07-02 00:15:00+00:00  24.787958\n",
+      "2025-07-02 00:30:00+00:00  24.492412\n",
+      "2025-07-02 00:45:00+00:00  24.675365\n",
+      "...                              ...\n",
+      "2025-07-05 13:00:00+00:00  38.086209\n",
+      "2025-07-05 13:15:00+00:00  36.774657\n",
+      "2025-07-05 13:30:00+00:00  35.463106\n",
+      "2025-07-05 13:45:00+00:00  34.271664\n",
+      "2025-07-05 14:00:00+00:00  34.174615\n",
+      "\n",
+      "[346 rows x 1 columns]\n"
+     ]
+    }
+   ],
+   "execution_count": 10
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-07T03:57:43.693592Z",
+     "start_time": "2026-01-07T03:57:43.689899Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df_15m = df_15m.iloc[1:]",
+   "id": "a7bbd11d36fdb821",
+   "outputs": [],
+   "execution_count": 11
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "minutely open meteo data starts at 2 july, 12am and goes to 6 july 22 45\n",
+    "therefore on influx i query for 1 july 10  pm to 6 july 20 45\n",
+    "\n",
+    "\n",
+    "utc i s2 hours ahead of vancouver\n",
+    "\n",
+    "\n",
+    "\n",
+    "Plot some relevant data. this will further be used to generate the relevant coefficients"
+   ],
+   "id": "600a8c5a1228ac87"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-07T03:57:43.948394Z",
+     "start_time": "2026-01-07T03:57:43.792528Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "#this graph shows solar irradiance, array temperature, and ambient temperature over July 2-6 (FSGP 2025)\n",
+    "\n",
+    "fig, ax1 = plt.subplots()\n",
+    "ax_twin = ax1.twinx()\n",
+    "\n",
+    "plt.plot(df_15m.index, df_15m, label=\"Array Temperature\")\n",
+    "plt.plot(minutely_15_dataframe['date'], minutely_15_dataframe['temperature_2m'], color='green', label=\"Ambient Temperature\")\n",
+    "# plt.plot(minutely_15_data['date'], minutely_15_data['wind_speed_10m'], color = 'blue', label = \"Wind Speed 10m\")\n",
+    "# plt.plot(speed_kph.datetime_x_axis, speed_kph, color = 'yellow', label = \"Speed KPH\")\n",
+    "\n",
+    "ax1.plot(minutely_15_dataframe['date'], minutely_15_dataframe['shortwave_radiation_instant'], color=\"red\",\n",
+    "         label=\"Solar Irradiance\")\n",
+    "\n",
+    "ax1.set_xlabel(\"Time\")\n",
+    "ax1.set_ylabel(\"Solar Irradiance\")\n",
+    "ax_twin.set_ylabel(\"MosfetTemperatureA\")\n",
+    "\n",
+    "ax1.tick_params(\"x\", rotation=90)\n",
+    "\n",
+    "plt.legend(loc=\"upper left\")\n",
+    "ax1.legend(loc=\"upper left\")\n",
+    "plt.show()"
+   ],
+   "id": "2b7568c9316948b9",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ],
+      "image/png": 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3da65R48erF69mo0bN7Jnzx6uv/56DAaD+fHg4GDi4+PZtGkTeXl5lJSUEBAQwIQJEzhz5gwvvfQSKSkpLF68mN9++63ev9GMGTOIiopiypQprFu3jtTUVNasWcP9999v44FrDqoMlvdPS55zKZF4LT/9JKJfXbvC0KEwZgy0bQtnz8L69e5enaQRSBHmYXzwwQcMHz4cvV5PWVkZWq2Wc+fOkZOTw5QpU9i+fTupqan4+4uIXmlpKZXayjr3mZyczCOPPMKiRYvo27cvn3zyCffdd5/dbW+44QYqKyu57bbbGvw73H777bz66qv8+OOP9OvXj9GjR/PRRx/RuXNnAgICzGb2srIyTpw4YfaNgRCC/v7+pKWlmX1vp0+fNj8+btw4unXrxp133snNN9/MFVdcwaOPPkpZWRmFhYU89thj3Hfffbz77rtcccUVXHLJJfzyyy8kJCTUueZFixbRtm1bJkyYwGWXXca4ceNITk6moqICECnCu+++m6NHj9K1a1eio6MpLCy0mfqQnJzM1q1beeihh+r9GwUFBfHPP//QsWNHpk6dSu/evZk5cyYVFRXNHhmzjqOWVTpW7SmRSFyIkoq87jpQqUTkS0lDbtjgvnVJGo3K6OWnthkZGSQkJHDq1KkakZvCwkJOnjxJt27dbMzcLRmDwUBmZiZFRUVotVqMRiMajYY2bdoQHx9vFixarZZTp05RVFSEOjgSdXCEzX4CVHp0BTn06dMHEIInLS2N8vJyNBoN7du3JyMjg9jYWLNZffv27Wzfvp3//ve/ZGZm1tom4ciRIwQGBtKxY0cA9u7da7MfEB6tU6dOMXDgQJvnOrKOiooK0tLSKC0txd/fn4SEBI4dO8bChQspKyvjq6++Yt++fSQlJREUFITBYODkyZMUFBQAEBkZiY+PD4WFhebfPzU1laqqKnN6EiA9PZ3y8nJzBanBYOD06dOcO3cOvV6PRqMhPj7eHLmrqKggIyOD4uJi8/8lPDycDh062PXOGQwGtFot/v7+NkKzJVFRUcHho8eZ9VMGp4ur2DxvHHHhAe5elkTSeqishNBQcblvH/TtK+5ftAjmzIHJk+HHH926xKairu9vb0Emkj0MtVrt0JvR39/fLCiyCss5U6y1eTwwMIge7fuYbwcFBdl4mUCIFYWysjLatGnDG2+8wV133VVnnypFtCj079+/xjZRUVFm8WJNfesAzA1erRkyZAgajYaysjL8/f0ZMmSI+TG1Wk3nzp1rvJb139He44qItN5PQkJCrVEza6O/N2F9llai1QFShEkkzUZamhBgQUHQx3LMZtgwcbl1KxiN4GCRlKRl0TJPvyUuxV6s09n450svvUSvXr2Ii4tj3rx5rlmYxCOwfq8UVehr31Aikbie48fFZbdutkJr4ECRlszJgfR096xN0mikCGsF2BVh9Zj1q/Pkk0+i0+lYtWpVk859bAwfffQR33//vbuX4XVYv1eKpQiTSJoXRYRVqyQnMBCULMPWrc27JonLkCKsFWBPcBm82gkocSXWIr64wj1jvSSSVot1JKw6550nLrdsab71SFyKFGGtAHuRMIN312NIXIiNJ0xGwiSS5qUuEaYUNu3f33zrkbgUKcLw/t5HrvCESVyPp7zvbCNhUoRJJM2KadKHXRHWu7e4PHy4+dYjcSmtujrSzzT4VK/Xo9frW2ybgMZiL+plMBptmo1KmhdlFJNKpXJ49JO7sPWEyXSkRNJs6HSiOhLsizClEj09XTRz9ZBWSxILrVqEqdVqfH19UalUduf5eQtVBkuDTRUivaT0qJK4D7VajZ+fX4sXYcjqSInEPaSng14PAQHQrl3Nx6OiIDISzp0TEbPk5OZfo6RRtGoRBmLAsp+fn7nDvDeiKqkCDHSICMTfV03K2VJA5dW/syfgCVEwqN4nTIowiaTZUPxgXbuCvUyNSgW9esHGjSIlKUWYx9HqRRiIL0NvTUWC5UtUrbb8ngaw+zuPGTOGAQMGsGjRolr3l5iYyOzZs5k9e7bL1yppeVh711yZjiwoqyRQ44O/r4/L9imReBVHjojL6u0prOnVi9RDaYQeOk7N9teSlo73Ko9WwKZNm/Dx8eGyyy6rczvlO1SlUqFWKfc13BS+bds27rzzzgY/3x5jxoypU9SlpaWZI0e1/Xz00UcuXVNLQKVSub33mfU7xVXG/HOllYx44W9uXCL7G0kktaKIsGoTQqzJ6t6Xi+58j2HlNSeTSFo+MhLmwSxZsoT77ruPJUuWkJmZSTt7ngEsxmoVmNNfjekTFh0d3fAnN5CEhASysrLMt1955RVWrlzJX3/9Zb4vPDy82dfVEKqqqpo9+lpZWVnnqKm6sNbrrkpHHs4qoqyyir0ZBRiNRo9Iy0okzY5S9VhtFJw1W2O6QQEYVDKm4onI/5qHUlJSwldffcW///1vLrvsshpRoDVr1qBSqfj999+ZMm4kw7rFM3nSRM6eyWX96j+ZMmYYYWFhXH/99ZSVldk8V6/Xc++99xIeHk5UVBSPP/64TeQsMTHRJl1ZUFDA7bffTnR0NGFhYYwdO5Y9e/aYH3/yyScZMGAAn376KYmJiYSHh3PddddRXFwMwC233MLatWt57bXXzFGtNKUiyISPjw9xcXHmn5CQEHx9fc23Y2JiWLRoEZ07dyYwMJDk5GSWL19u9+8xcOBAAgMDGTt2LLm5ufz222/07t3b7t9jzJgx3HvvvXX+PbRaLQ899BDt27cnODiY8847jzVr1pgf/+ijj4iIiODHH38kKSkJf39/0tPT2bZtGxMmTCAqKorw8HBGjx7Nzp07bf7OAFdddRUqlcp8+5ZbbuHKK6+0+fvMnj2bMWPG1Fj37NmziYqK4uKLLwZg//79TJo0iZCQEGJjY7nxxhs5e/YsdWGbjnSNCMsqrACgQmegrLKqnq0lklaKEgmrQ4TlR8aarxt00rPpaUgRZoXRaKS0stQtP86mB7/++mt69epFz549ueGGG/jwww/t7uPJJ5/k8ede4ePvf+d0RgbTp1/HZx+8w4I33uenn37mjz/+4I033rB5zscff4yvry9bt27ltddeY+HChXzwwQe1ruVf//qXWczs2LGDQYMGMW7cOM6dO2feJiUlhe+//56ff/6Zn3/+mbVr1/LCCy8A8NprrzF8+HDuuOMOsrKyyMrKqnVIdm0sWLCATz75hHfeeYcDBw4wZ84cbrjhBtauXVvj7/Hmm2+yceNGTp06xTXXXMOiRYv44osv+OWXXxr097j33nvZtGkTy5YtY+/evfzrX//ikksu4ZjS3wcxAP3FF1/kgw8+4MCBA8TExFBcXMzNN9/M+vXr2bx5M927d+fSSy81i9Nt27YBsHTpUrKyssy3HeXjjz9Go9GwYcMG3nnnHQoKChg7diwDBw5k+/btrFy5kpycHK655po692ObjnSNJyy7qMJ8/VxppUv2KZF4FcXFcPq0uF6HCCsIsmQAtEeP1bqdpGUi05FWlOnKCFngnrmIJfNKCNYEO7z9kiVLuOGGGwC45JJLKCwsZO3atTbREIBnn32W9n2GUqk3cNMttzL/8cf4Zf0uOnRKJKldGFdffTWrV6/m0UcfNT8nISGBV199FZVKRc+ePdm3bx+vvvoqd9xxR411rF+/nq1bt5Kbm2uutnzllVf4/vvvWb58udk7ZjAY+OijjwgNDQXgxhtvZNWqVTz33HOEh4ej0WgICgoiLi7Oqb8biEjU888/z19//cXw4cMB6NKlC+vXr+fdd99l9OjRNn+PCy64AICZM2cyb948UlJS6NKlC4DTf4/09HSWLl1Kenq6OR380EMPsXLlSpYuXcrzzz8PgE6n46233iLZqnpp7NixNr/He++9R0REBGvXruXyyy83p30jIiIa9Hfp3r07L730ks3vPnDgQPOaAD788EMSEhI4evQoPXr0sLufphjgnV1oEWF5pZUkRMr+RhKJDUePisvoaNGGohYKrD6TFXv2Edind1OvTOJCZCTMAzly5Ahbt25l+vTpAPj6+nLttdeyZMmSGtv279/f/CUaGxtDUFAQCZ0SAfHlGhsbS25urs1zzj//fBuPzvDhwzl27BhVVTXTRnv27KGkpIS2bdsSEhJi/klNTSUlJcW8XWJiolmAAcTHx9d43YZy/PhxysrKmDBhgs0aPvnkE5s1gPh7KMTGxhIUFGQWYMp9zvw99u3bR1VVFT169LB57bVr19q8tkajsXltgJycHO644w66d+9OeHg4YWFhlJSUkJ6e7pK/y+DBg21u79mzh9WrV9uss5fJ8Fv972SNdSSsUm9Aq298+jCr0DoSJvvVSSQ1cMCUD3Cm2PL5qdh/qClXJGkCZCTMiiC/IErmlbjttR1lyZIl6PV6GyO+0WjE39+fN99808ag7ufnh9GUQbJuDmo0Gs2G6MZ0zi8pKSE+Pt7GA6UQERFhsw5rGvu61dcA8Msvv9C+fXubx6r3QrNeh9IjrjHrKikpwcfHhx07duDjY9tqISTEElUNDAysYT6/+eabycvL47XXXqNTp074+/szfPhwKivrTs+p1eoaqWd7zYaDg20jqyUlJUyePJkXX3yxxrbx8fG1v2C11yqp0OMf0ri2EtlF5ebrZ0tkOlIiqYEDfjCwTe1XHD7alCuSNAFShFmhUqmcSgm6A71ezyeffML//vc/Jk6caPPYlVdeyZdffsndd99tc78R2y9RtUpUR9ZWIbllyxab24pfqbrIABg0aBDZ2dn4+vqajeMNQaPR2I20OYK12d069egq6vp7DBw4kKqqKnJzcxk1apRT+92wYQNvvfUWl156KQCnTp2qYZL38/Or8XeJjo5mf7WBvbt3764hKKszaNAgvv32WxITE/H1dfyjX/1tcjCriAu6RqFWN7yiMbtQesIkkjpR0pG12AQUrD9LFcdqj2hLWiYyHelh/Pzzz+Tn5zNz5kz69u1r8zNt2jS7KUlznzDTbSUiU1sxQHp6OnPnzuXIkSN8+eWXvPHGGzzwwAN2tx0/fjzDhw/nyiuv5I8//iAtLY2NGzfy2GOPsX37dod/r8TERLZs2UJaWhpnz551KhoVGhrKQw89xJw5c/j4449JSUlh586dvPHGG3z88ccO76c26vp79OjRgxkzZnDTTTexYsUKUlNT2bp1KwsWLOCXX36pc7/du3fn008/5dChQ2zZsoUZM2YQGBhos01iYiKrVq0iOzub/Px8QHjJtm/fzieffMKxY8eYP39+DVFmj1mzZnHu3DmmT5/Otm3bSElJ4ffff+fWW2+tUwBXf5vcuGQrX2xteMpUq6+yiX5JESaR2OHECXFpb2akCYPBSI51JCwvX4wwkngMUoR5GEuWLGH8+PF2e2JNmzaN7du3s3fvXpv7Lc1abS9ri4TddNNNlJeXM2zYMGbNmsUDDzxQa3NWlUrFr7/+yoUXXsitt95Kjx49uO666zh58iSxsbF2n2OPhx56CB8fH5KSkoiOjnbaF/XMM8/w+OOPs2DBAnr37s0ll1zCL7/8QufOnZ3ajz3q+3ssXbqUm266iQcffJCePXty5ZVXsm3bNjp27FjnfpcsWUJ+fj6DBg3ixhtv5P777ycmJsZmm//973/8+eefJCQkMHDgQAAuvvhiHn/8cR555BGGDh1KcXExN910U72/R7t27diwYQNVVVVMnDiRfv36MXv2bCIiIursWaZEUoM0lkjoppS8el+vNnKLbD1gZ0ukJ0wiqYEiwuo4hp0t1aK3OpBX+PqDAydkkpaDytiY1ukeQEZGBgkJCZw6dYoOHTrYPFZRUUFqaiqdO3cmICDATStsWoxGI/tOFwKQFB+Gr4+aoznFVOiq6BIVTEhA3Sms1o4jY5y8mYqKCjbsOsgTf+fy8rVDWHkgm082naRPuzB+ud+59KvCtrRz/OudTebbY3pG89Gtw1y1ZInE8ykuhrAwcb2gAGppRL0vo5DJb6433176zXwumn8/OHBS5gnU9f1tjyeffJKnnnrK5r6ePXty2NT0tqKiggcffJBly5ah1Wq5+OKLeeutt5wKGLgaGQnzcqwVtjkSZrrdmK75ktaD8jZJjArmpuGJAKSddb63nYJ1ZSTIdKREUoPUVHHZtm2tAgwgq7Dc5rbWVwPVGl23Nvr06WPuN5mVlcX69RaROmfOHH766Se++eYb1q5dS2ZmJlOnTnXjaqUx3+ux/p5UmeSXWvGE1bBcSyS2iCpacT3Az4eoEH/UKiitrOJMiZaYUOcjyNmmL45ObYM4mVdGnqyOlEhsUVKRVu1z7GHtBwNTOrKVizBlkkp1CgsLWbJkCV988YW5R+PSpUvp3bs3mzdv5vzzz2/upQIyEub1WEcrqnvCvDsR7RrWrFnTalORYPse8fdVo/FV076NKB5IO1tWy7PqRhFdPWNF37g82SdMIrHFQRFWUGbbmkbr6wcnTzbVqtxGcXExRUVF5h+ttvZjxrFjx2jXrh1dunRhxowZZn/xjh070Ol0jB8/3rxtr1696NixI5s2baptd02OFGFejvIdqkJlropUImEyHSmpD4OVCvP3FYeLxLaijUtaXmmD9plfJkRY1xjRR03Mj5Qz7yQSM0o6sp7CopJqnxtvjYQlJSURHh5u/lmwYIHd7c477zw++ugjVq5cydtvv01qaiqjRo2iuLiY7OxsNBqNTf9KEA26s7Ozm+G3sI9MR1J7qwZvQPndrPuEWiJh3vt7S1yDwWAEjKhVKnx9LCJs3bGzpJ1tmAg7VyrO3ju0CUTjq6ZSbyCvpJKgSHk4kkgAhyNhZVrb1jIVvho4dQqqqsBOX0dP5eDBgzaNuKs34VaYNGmS+Xr//v0577zz6NSpE19//XWN9j8thVYdCVOaW5aVNSyt4glUb08BMhImcZyy8jJ0VUbKrE64E6MaFwkrMEXC2gRpaBusAaQ5XyKxwUERVqqtFgnTBIBOB1lZTbUytxAaGkpYWJj5pzYRVp2IiAh69OjB8ePHiYuLo7KykoKCApttcnJyGjSb11W06lNPHx8fIiIizLMCg4KCaoyW8XS0uiqM+kqMajUVFcLEWaXTYtTrqNSqqPCTSkxSE6PRSFlZGWdyz7DqRAlGleV8rXOUGLGV2kBPmJKOjAjyIyJIQ1ZhBQXlNccuSSStEoPBko6sR4SVmERYgJ+aCp2Bisgo8cDJk+BASwdvp6SkhJSUFG688UYGDx6Mn58fq1atYtq0aYCYw5yens7w4cPdtsZWLcIAswJ21TDplkal3kBusRYftQqfUlHJVlCmo0SrpyLAl6JA1/QJMxiMFFboqDIY8ff1ITSg1b+1vAK9byArDpUSH26pgowOUd5HDYteKWbiNkEaIkzvv4buSyLxOrKyQKsV6cSEhDo3LasU6ci2wf6cLiinok1b8UBaGlxwQRMvtOXx0EMPMXnyZDp16kRmZibz58/Hx8eH6dOnEx4ezsyZM5k7dy6RkZGEhYVx3333MXz4cLdVRoIUYahUKuLj44mJibE7BNnTOZhZyJM/7iIuPIDPb+8NwLtrU/h6eybXDEngrtGN7ygP8NXWdN5bJ6pQfNQqfrl/FH4+rTrb7fH4+fmx81QhRsDfz+IvCTR1zle+AJzBaDSao16RwRrCTSKsSEbCJBKBEgXr2BHqmfGqRMLahmiECAtrIx7wQnO+I2RkZDB9+nTy8vKIjo5m5MiRbN68mejoaABeffVV1Go106ZNs2nW6k5avQhT8PHxsTug2tPRq8o4XVyFfwDmqQBVKl9OF1dxrsLoskkBu7PE6yjkV0DHtt45haA1UaET/1OlMhIs44vKGyDCiir0VJnMiCIdqUTCpAiTSACH/WCAuao40uSt1Iaauux7YZsKR1i2bFmdjwcEBLB48WIWL17cTCuqHxmq8HJ0VWIQtsYqKqVENSp0jg/Jro+jOcU2t9PPeW+xQ2tCa3qP2ETCTNcrqwzoq5x7D+WbDPhBGh/8fX0IV0SYjIRJJAInRFip1pKOBKgIEr33WmskzBORIszLqTR9SVqnBpWohlbvfCTDHlUGI8dzSwDobKqcO5UvRZg3oNWbRJhVJCzQapB3uc6591C+VWUkQESguJSRMInEhBMiTElHRoWIz1FFgCiaaa2RME9EijAvR6dXRJil6lOJajj7BVobp86VodUb8PdVc0E3YQyVkTDvQElHBlhFwvx91ahNbydnU5KK2FLSkMplYbk05kskgEWE1dOoFWqmIyv8TK0bTp4UVZaSFo8UYV6Orkr4b6wjYW1MX3z5Loo+KKnIrtEh5m7qUoR5B/YiYSqVypySdNacXz0SphjzC2U6UiIRONieQquvMh/f24aY0pE+fqBWi+rKBlb855VoeeX3I6TnyWN4cyBFmJdj9oRZfYkq/oG8EtfM7DtmSkX2iA2hY6QIh5+SIswrsGfMBwjUiJoe59OR1SJhgdKYL5GYKS+HzExxvd5GrZbPXmSw+BxV6I2gdJZvoC9s2bZTvLn6OO+vO9Gg50ucQ4owL8eeJ6xtiGu7lCuRsO6xoXRsK0SYjIR5BxX6mulIsFRIOhsJK6geCZPGfInEgiKcwsIgMrLOTUutGrUGmU6KKvRVkJgoNmigLyy7UDT1llMsmgcpwrwcXVVNT5gyKia/TOd0dZs9skwf2o6RQSS0ESKsoEwnU0xeQHGFONCHBdg29VXSkc56wpQDexvTezDCJMYKy3RylqlEYm3Kr2d6S6nJDxbi72s+SdLqDNCpk9iggZGws6YMSVGFPH43B1KEeTkWY77lXx0RpDEbq8+5oFN5pek1Av18CPb3NVfqyJSk56MI6bBA25aCSoWks+lIS7d823RkZZXBZYUiEonH4oQpX0lHBvv7EuAnju8VOqtIWANFWF6J+E4oqTaXUtI0SBHm5SjGTes+YT5qlbmaRvnANQbFvK34zpQ2Fcdyi2t9jsQzUDrZV4+EWdKRzh2oqxvzgzQ++JrOCGTkVNLqcdCUD5Z0ZJDGlwBfpfdj49ORSiRMiYJLmhYpwrwQrb6K/3y3jz8OZNv1hIG1Od8VkTBxRqaIsD7twgHYf7qo0fuWuJciJR0Z6Jp0pCK0FC+YSqWSXfMlEgWnGrUq6UgfczqyQm+VjlQEnZOcMYmwEinCmgUpwryQHWn5fLElnTf+Pm7xhPna+gsUc35eaeMrJCurVWD2bS9E2L7ThY3et8S9WCJhrklHKkb+YI1lf+GyQlIiETgjwkyfpSCNJR1ZZTCi69zVsi+9c0JKq68yR8CKpSesWZAizAtRvhhFHxn7kTBXpiMrq/WS6mcSYQczizAYpNnak1HMudUjYQ2tjlTSl0FWXffN5nzZsFXSmjEaGxgJ87WpXta2aw+BgVBZ6XQ0zPr7oLSyyjznVdJ0SBHmhSjCS28w2vWEAUSZmvu5IhJWvaFn1+hgAvzUlGj1pOaVNnr/EvehRMLCa4gwU58wJ0WYsr31l4Zs2CqRAGfOQGmpqIpUUop1oBjng/19bPr4VVQZoWdPcePwYaeWcLZa70hpzm96pAjzQhThZTAYzVGqmp4w10fCND7ii9XXR03v+DAA9suUpMdiNBopKrfvCQto4OgrZXubSJhMR0oklqhV+/bg71/v5paosi8qlcosxCp0VdCrl9jo0CGnllD9+0CKsKZHijAvxDYSVosIM0XCzrpShFmdjSkpSSnCPBet3mD2+1X3hDUkHamrMphPEKxFmGzYKpHgVCoSLC0qQvzFZ9NsztcZoHdvsZGTkbAz1SJh0hfW9EgR5oXoTV90VdYirImM+QaDEb3JN2AtwqQ53/NRUpFqla2RHiwiqtyJFhXWgi3QJhIm3osyEiZp1TgpwpQoVZC/+Cy5IhJWIx0pKySbHCnCvBAlelFVpyfMNenISquO+9a+hL6mNhUHTktzvqeimPJDA/xQq21FfEPSkcocSrXK9v2otKiQxnxJq8ZJEVZm1TEfLJ9Jrb7KNhLmxCSK6t8HsldY0yNFmBeitxFh4rpPtS/RyGAlHalt1LgYrc4iwqwjYd1jQ9D4qinW6uUcSQ+l0OQHq27Kh4alI8usSupVViNZpDFfIsGpbvkAJVrbdi/KZ7JUWwXdu4NaDQUFkJPj8BKqR8KKpSesyZEizAtRol9VRqO5xNi3WiQsPjwAH7WKssoqcosbnpLUVokDgUqFufM5CA+aYs6XKUnPpKiWkUVgnY50RoSJA7p1KhKsPGEyHSlpzTjRLR+gzKo6EiDU5NssrtBDQIClwvLoUYeXUDMSJj+TTY0UYV6IzmCKhFVZ/Fq+dtJJXaPFeKEDmQ0XSZbKSLVNdAOgbztThWQj9i9xH+YeYQE1I2ENSUeWV9asjARZHSmRUFkJp06J6056woJN6Ujlc2oWTt26icuUFIeXUWCyBCh9JKUnrOmRIswL0emF8NIbLJGw6ulIcM14IXuVkQqyQtKzqW1uJDSsT5iSjgz0qybCzM1apQiTtFLS08FgEE1WY2MdekqpVYsKqBYJA+hq6pzvhAhTPs8xof62+5I0GS1GhL3wwguoVCpmz55tvq+iooJZs2bRtm1bQkJCmDZtGjlO5LdbK3olEmasPRIG0McUqWpMJMzSqNWnxmN921tEXmN8ZxL3YJkbWXs60hlPmBI1q56OVCJhJVq92cMoaeGsWwf/+Q9UVLh7Jd6BtSlfVfNYbY+yai0qQqtHwhQRdvy4w8uoMHl8o00iTPYJa3pahAjbtm0b7777Lv3797e5f86cOfz000988803rF27lszMTKZOneqmVXoO1tWRVQb7xnywRMIOZDY+EuZvJxLWIzYUjY+awnIdGfnlDX4NiXuorVs+uDYdad0ItkhGw1o++flw1VWwYAG8+667V+MdOGnKB9uO+WCJhBU1IhKmVDBHm/pIFklPWJPjdhFWUlLCjBkzeP/992nTpo35/sLCQpYsWcLChQsZO3YsgwcPZunSpWzcuJHNmze7ccUtH9s+YbWnI5NMkbCM/HIKG+jHqT682xqNr5qecaGANOd7IoV1piMbYsxX0pG2kTUftcr8BSIbtnoA8+dDXp64/uGHTrVAkNSCk6Z8fZXBnIVQqiOVk5miRnjClJMqcyRMpiObHLeLsFmzZnHZZZcxfvx4m/t37NiBTqezub9Xr1507NiRTZs21bo/rVZLUVGR+ae4uLjJ1t5SsU7pKNftpSPDA/1IiAwEGm6etzbm20M2bfVcahveDRYRVlllMLdEqQ97w7sVImSFpGdQUABvvSWu+/jA3r2wa5dbl+QVONst3+rkJ9i/Fk+Ysq/8fDh3rt59Go1GSyRMesKaDbeKsGXLlrFz504WLFhQ47Hs7Gw0Gg0RERE298fGxpKdnV3rPhcsWEB4eLj5JykpydXLbvEo0S+w9PHyUdv/V/dvHwHAnoyCBr2WOR3pZ3//0pzvuVjmRtb0hFkP4C5zMCWpHOCrG/PB0jVfNmxt4ezfD1VVkJAAV18t7vv0U/euyRtwemSR+GxqfNTmLEQNT1hwMMTFiesORMMqqwwofbWlJ6z5cJsIO3XqFA888ACff/45AQEBLtvvvHnzKCwsNP8cPHjQZfv2FKwjE5V1RMIABiREALDnVEGDXkurF1+stUfCLIO8pTnfs1AO5iH+NSNh/r5qlLdUhYMpSXM6so5ImKyQbOEox9M+feCKK8T1rVvdtx5vQKezjBfq0cOhp5ijyv6Wz5LZE1ZuJZyc8IVVWDXeVjxhsk9Y0+M2EbZjxw5yc3MZNGgQvr6++Pr6snbtWl5//XV8fX2JjY2lsrKSgoICm+fl5OQQp6h7O/j7+xMWFmb+CQ0NbeLfpOVhnY5UIlX2PGEAySYRtrvBIqx2TxhAz7hQ/HxU5JfpOF0gzfmehOIPCbYjmlQqlbk03tEKybJajPlgSXnKdGQL58ABcdmnDwwYIK7v2SPaK0gaxr59UF4OERGi070DVO+WD1Z9wrRWnyHFF+ZAhaTWaqxYpGmsnfRoNj1uE2Hjxo1j37597N692/wzZMgQZsyYYb7u5+fHqlWrzM85cuQI6enpDB8+3F3L9gh0VrMalUhVbZGwfu3D8VGryCnSklXovEiqq08YiNYVPWKFEJYpSc9CEU0BdkQTWKqyHE1Z1FYdCbJhq8dgLcJ69AB/fygttaTTJM6jFJqdd54YNeQApdUqIwHCqnvCAHr2FJdHjtS7z3Iru0D7COEVLijTyQrJJsZtIiw0NJS+ffva/AQHB9O2bVv69u1LeHg4M2fOZO7cuaxevZodO3Zw6623Mnz4cM4//3x3Ldsj0Okdj4QFanzoaRJJDUlJmqsja0lHgmWYd2OawkqaH8XDZU80gWX+aF6pYz4uS5+wmh4zmY70EBQRlpQEvr7Qt6+4vWeP+9bk6SgizInvtdJq3fLB2hOmt1g/evUSl0q6sw6UdGSAnw+hAX5EmaJhaWdLHV6XxHncXh1ZF6+++iqXX34506ZN48ILLyQuLo4VK1a4e1ktHr1NJMzkCfOpvQGgkpLc1QARphj//e2YrRX6dpAVkp5IbR3uFZSDdF6JY7NH69qfYswvKJPG/BbLuXOgFEUpBU/JyeJSirCG0xARZvKEhdiIMHG9ymC0WAR69xaXhw/X20pEOUlSim4S24qxdqlShDUpNU9J3ciaNWtsbgcEBLB48WIWL17sngV5KPY9YbXr7YEJEXy5NZ3d6QVOv5YjkTDrCkmj0VhjxqSk5WE0GmvtcK/QNlgRYY5GwmpvUaE0hJWRsBaMYsrv2BEUr621L0ziHOfOwdNPw7Fj4vawYQ4/VfGEWX+WgjQ++KhVVBmMFFfoRZSsa1cRsSwthYwMUdVaCxVmESaO5YlRwWw/mU/a2TJnfzOJE7ToSJikYViLsLrGFikM6BgBiEhVlcG5Csb6PGEAveJC8VGryCutJLtIjjnxBLR6g/nEubZIWFtTBdXZUicjYfZEmNInTIqwlouS0rJu+yMjYQ3DaIQbb4TXXhO3hw+HyEiHn15mJx2pUqmseoWZPkd+fhZzfj0pyeonXZ2jRCQsLU9GwpoSKcK8EOs+YQrqOqJPXaNDCNb4UFZZxbFc55rb1jW2SCHAz4fuMSEA7MuQKUlPwLrisXYRJiJhZ4sdjIQ5YMxv6OQGSTOgVNhZV/Ap6a70dKiUqWSH+ewz+PVX0Gjgyy/hzz+derrZE1bNX1ljdBHYpiTrQKmODPCV6cjmRIowL8ReB/O6PGE+ahX9O0QAOJ2SNPcJq0OEgWza6mkoZ8UaHzW+taSao8zGfMciYeV1NWsNkiXxLR5FhCmRFYCoKAgMFJGdU6fcsy5Pw2gUo59AXF53nWis6gRKx3zrSBhAqH+1hq3gsDm/eiQsMSoIkJGwpkaKMC+k0k4krLbqSAUlJels53xHImFgGV+0vxHDwiXNR7nJ+FubHwwskTBHPWGONmuVTX1bKErDT6UBKIBKBZ06ietpac2+JI9kyxYxKzI4GGbPbtAulEhYiL/tZ6nOSFg9IkypjvSvFgkrKNPJgpkmRIowL8RuJKweEZZsioTtcjIS5ogxH+QMSU+jvFL8X2tLRYLFE+ZodaQlHVmzHkgx5lcZjHJUSkvEaLQvwsAiwk6ebN41eSpffikup0yBoKAG7UL5jFT/LClNj20iYUoX/noatiqfT8WYH+zvS2yY+IyfkCnJJkOKMC9EZ0eE1RcJG2iKhB3NKTaPxHCE+jrmKyTFh6FWwZliLTnSnN/iqWvYtoJSHXm2tLLe6JV1taW9fQb4+ZijqbJhaxOh08GddwoT+Pnni2q8YcPg2Wfrf25uLpSUiMhX5862j0kR5jg6HXz9tbg+fXqDd6NElUOqpyPtNWxV0senT4vO/LVQoa9pF1AabR/Jds4rLHEcKcK8EHvGfN96OjHHhgUQFxaAweiced6R6kgQKahuJnO+9IW1fOprTwGWdGSl3lBv9KqyymCuvK1tn7JhaxPzzDPw/vuiL9WWLbBtm/h5/HGor/+iEgVLSBBd8q1JTBSXUoTVz9NPi15r0dEwcWKDd2OOhFVLR4ZVH+INouoyXGQiSE2tdZ8VlbZ9wkBUtgMczpI2kqZCijAvpCGRMLAM83ZmjqRWb+sjqAuZkvQcyutp1AoiFaJEterzhZU7UG1padgqRZjL2bEDnntOXH/+efjhB/jpJ7jrLnHfHXdAXl7tz68tFQkyEuYoO3aIvz3Am2+KysgGYq9jPljS+udKrT5DKpXl/1ZHSrLCdCy3PknqHR8GwKEsGQlrKqQI80L0dnp91ecJg4aZ8x2NhIGskPQkHImEgZU5v54KSWV/fj4q/GrxD4bLSFjT8dZbYsj21VfDvHlwxRVw+eXw+utiDuS5c7BsWe3PlyKs8Sj/g3/9C665plG7qi0dGRsWAMCZ4mqWD+X/pvwf7WBu1mp1LO8VZxJh2UWyYKaJkCLMC7GeHangU0eLCoXkBrSpcEaEmSsk5QzJFk99I4sUopSGrfVEwsxz6eqImCpn8QXlshLLpWi18O234vq999o+ptHAzJniumIYt4cSQalLhJ06BVVVNR+X2P4PZs1q9O4sxnzbz5NipM8pqnZS5IAIMxvzrfbZLSYEX7WK4go9pwtq95NJGo4UYV6IzuB8dSRA/w7hqFWQWVhBroPmeUerI0GY81UqyC6q4EyxYxV1EvdQ3/BuhbZKr7B6RJjST87fr/b3idKwVaYjXcxvv0FhIbRvD6NG1Xz82mtFymrDBtE0dOdOOHPGdpvt28Vlnz41nx8fL0bj6PWQmen69XsD9f0PnKRMW3N2JEBMqIiE1Sh+Usz5JhFmNBq569Pt3PHJdnOES0lHWp8oaXzVZi/vYZmSbBKkCPNCnO2YrxDs72uuhnHUF2b+cnUgEhbs70vXaGnO9wTq6ulljTLEO7d6+qMa1XsQ2UMa85uIr74Sl9ddB/YKdNq1g9GjxfWJE2HwYBE5UYZ1nz0LR46I6yNG1Hy+j4+YJwmyV5g9jEZ4+21xvbb/gRMYDMZam7UqkbCzJVrbEXTVImFFFXp+P5DDnwdzyCwUn91yO8Z8sPjCDmfLDEZTIEWYl2E0Gu3Of3QkEgZWKUkHRZijzVoVpC/MM7B0t6/Z08uadhGBAGTWk6pQRqLUGQlTuubLxpCuw2iE1avF9SlTat/uv/8V0ZL27UUH/OJii3jbuFFc9u4Nbdvaf36XLuKyjnRXq+XDD+GPP0Tq9/bbG727Mp0l5Vt9bFHbEH/UKjAYq/XvU0RYairo9TaFMvml4vOmnFAHamw/o0qFpKeZ81944QVUKhWzrRriVlRUMGvWLNq2bUtISAjTpk0jJyfHfYtEijCvw14UDByrjgSLOd9ZEeaIJwygTztxViUrJFs25upITd3/1/YmEVafX8SRKlql0aSMhLmQlBTIyRECYOjQ2rcbNw6OHYOMDHjxRXGf4hFbv15cXnBB7c9X5kkeO9b4NXsT5eUwd664/uyzlhFCjaDCSoQFVDup8VGriA614wtr3160FtHr4dQpSq16QSrWEHMkrNpntFe8xZzvKWzbto13332X/v3729w/Z84cfvrpJ7755hvWrl1LZmYmU6dOddMqBVKEeRm1tadQOZCOBEskbG9GIQY7EbXqOCvCZCTMM7A0a607EtahjRBhGfl1izBz5ZX0hDUvGzaIy6FDISDAsedcc41ImW3ZIkScso+RI2t/jiLCjh5t+Fq9ka1boahI+OYUMdZIzMdcH7Xd47pSIWnjC1OrLU12U1Io01qEnGIlUJq1BmiqpyNFJCztbKlNBK2lUlJSwowZM3j//fdp06aN+f7CwkKWLFnCwoULGTt2LIMHD2bp0qVs3LiRzZs3u229UoR5GfoGzI20pkdsCIF+PpRo9aScKal3e7Mx39FImEmEZRZWODzuRtL8lCvVjPVUR3aIFGNXMgvK6xTtWgfS1tIT1gQ4EsWqTmysiIwB3HefEBL17UNGwuyjCNhRo4R3zgUoJ9p+tVS8xyiRsOo+TStzvnUkLLeo7khYdIg/bYM1GIxiooo7KC4upqioyPyj1db+3TFr1iwuu+wyxo8fb3P/jh070Ol0Nvf36tWLjh07smnTpiZbe31IEeZlVDZgbqTNtj5q+nUQQmmXAylJrQOGa2tC/H3pEiUGw8ph3i2XcgfGFgHEhvrjo1ahqzKSW0fFqyUSVocxXzZrdT2KCKsrimWP//5XVEz+9ptIYV16qeVL3B6KCDt+XPjQJIKGiOB6qC/7EGOOhGnZf7qQuz7dTtrZUhtzvvVoOuVzqxTPVC/GUalUVk1b3XPMTkpKIjw83PyzYMECu9stW7aMnTt32n08OzsbjUZDRESEzf2xsbFkK0UobkCKMC9Db6c9hTORMLB0zt/jiAhzMhIG1v3CZEqypWIx5tctwnx91MSHi4N+Rn5ZrdvJSJgbyM+Hw4fFdXtVjXVx4YWW9FlUFCxZUvf2nTuLlFdpKWRlOb9Wb8RgsBQ1OCuC66DSHAmz/1mKDbU0bH1x5WF+P5DDy38csemaX2ovHVmHZcA8vshNMyQPHjxIYWGh+WfevHk1tjl16hQPPPAAn3/+OQGOpt5bAFKEeRk6fcMrIxUcHV9kNBpt/AmOovjCnJlRKWleHG1RARZfWF3mfGeM+eW6KhvzsaSB7NsnLjt1qr2qsS6eew4WLhSVfXFxdW+r0VhmSMqUpODAAdEbLDgYqhnEG4NSfFWrCDO1qTiaU8KmFDGK6s8DOeR3tFSw2o+E2U9HAm6PhIWGhhIWFmb+8a8+vxSRbszNzWXQoEH4+vri6+vL2rVref311/H19SU2NpbKykoKCgpsnpeTk0Ncfe/vJkSKMC/DXqNWHyf70igi7HB2cZ1GTOtKTGciYX3aiw/0/kwpwloqyv+9vnQkQIc2whdWlznfEWN+qL8vyvlCkYyGNZ69e8VlQwWAvz/MmQMDBzq2vfSF2bJypbgcMUI0s3UR9bUFUoz5O07mm0fYVVYZ+M4QJTZISTHPngThCTMajXWOKusVr7SpaLnji8aNG8e+ffvYvXu3+WfIkCHMmDHDfN3Pz49Vq1aZn3PkyBHS09MZPny429btuneGpEVgrzrS2UhYfHgA0aH+nCnWciCzkCGJkXa3U/rKgON9wsCSjszILye/tJI2wQ0fZCtpGhxNR4KlTYVj6cja96dWqwgP9CO/TEdBuc7sbZE0kMaKMGfp3h1+/12KMIXPPxeXV1/t0t3q6klH9ooPxc9HZT5J7hgZRPq5Mn7O1HGbKWVcds5yAnymWEtllQGlrsZeJEwZX1RUoSersMLcH7AlERoaSt++fW3uCw4Opm3btub7Z86cydy5c4mMjCQsLIz77ruP4cOHc/7557tjyYCMhHkdja2OBGHEdCQlqbWaUemMCAsL8COxrYieyGhYy6S8AenIuiJhWgciYWDdsFVGwhqNko5sLhGmtEBIT2+e12vJ7N8Pe/aAn5/LRZjZE+Zr/7geHx7IOzcMJljjg1oFj1zSE4CUs2WQkABAae45m/3lWvUUC7DTG9Df18c87cRdKUlX8Oqrr3L55Zczbdo0LrzwQuLi4lixYoVb1yQjYV6GvepIZ0UYiJTknwdz6hRhSorJ39d+v5q66NM+nLS8MvafLmJU92in1ydpWsodHOAN0KmtqHY9caa01m0ciYSBZYi3NOc3EoOh+UWYMrpIijBLFOyyyyDSfiahoTjiwx3XO5Y/547mbInWLJ4Ky3UU9kgi/ORJyvLyAUs0Ky1PfHZVqtr32ys+lCM5xRzOLmZc71gX/TZNy5o1a2xuBwQEsHjxYhYvXuyeBdlBRsK8DHuRMGfTkeCYOd8yD9D5t5Fs2tqyKTcP8K7/PE3xi5wuKK915JCjM0bDzQ1b5eiiRpGaKioVAwLqbi3hShQRdvJk87xeS8VggC++ENdnzHD57utLRyq0iwikf4cIgv19iQoRRvaTXUVarrTQtgfkXlORVEyof60n1Io5/6AHR8JaIlKEeRm1dcx3ln4dwlGpRIrpbC1NVZUv1voaetrdv1IhKUVYi6NSbzAbeh2JhIUF+JEQKc6qa5svV+Fg81fZpsJFKH6wPn1cagqvk06dxGVmJuha8f9v/XoRDQwLg8svd/nudQ1oC9TJZP842U5USJaV2FoHtqSK9GSCqcjGHuY2FVKEuRQpwrwMV4mwsAA/cxi7tn5hjn6x2kOZIZl+roxC6f9pUVhXxDriCQPoHVf3WbI5ElafJ0yOLnINzW3KB4iOFhWVRiOcPt18r9vSsDbkN0G/qoa0BepkmmyRHiFaMZRUiM9XsOnzveWEaGXRMbJ2EZZkioSlni2VLWRciBRhXoa9Ad6+tYy3qI/6UpKOmq3tERGkMUdPDkhzfotCGdTbNlhT62iU6iS1q7uPkFmw1+cJMxnzZSSskbhDhKnVZuN3q/WFGQywfLm43gSpSIDKevqE2aOjKRKW7i8yEGWmY3d/06xgxbOZUIcIiw71J9LN44u8ESnCvAy93UhYw/7N9YmwCrPPp2Ez0WRKsmXyz9EzAIzqHuVwwYXZL1LLKCpHI2FmT5iVCPtpTybT3t5YZwsMSTXcIcJAmvPT0uDcOdG89sILm+QldPWMLbKHOR1ZJT5fpaaavP6mEXUKdYkwMb5ISUlKEeYqpAjzMho7O9Ia6/FF9oYza83pyIa9jfq0M5nz5QzJFsU/x4QIu7CH41WrSqriWG6xOV1ijdbBIo6IasZ8rb6Kp346wI6T+Xy+pZV+sTtLSQmkpIjr/fo172u3dhHWDF68+sYW2aNjpKhgTi/QQnQ0ZRqRJu1XTYTVlY4E6FWP7aC1U1XlfJpWijAvwxV9whR6xoXi76umqEJPal7N9gMVjTDmg6yQbImcLdGy/7Q4wDrTOqRDm0AigvzQVRnZd7qgxuPmqKmTxvyf9mRxtkQIstWHcx1eT6vmwAHhy4qPFz6t5qS1izClLUgTil9LJMzx47oirrKKKtB270mpn7CCdIkKMfvCALNFpDaUiPfhbCnCrDl69CiPPPIIHTp0cPq5UoR5Ga7omK/g56M2C6Xd6QU1Hre0qGicCEs9W0pRhfQAuZsSrZ4nftgPiMhWdGjN+Wy1oVKpuKCbGIuy9ujZGo87HAkLsjXmL92Qan7scHYx2YUVDq+p1eKuVCRIEdYMf3slEuaMMT8qREOovy9GI6T0GGCOhIX4+9I1JsS8P2X4d20oFZKHsopb7Pii5qKsrIylS5cyatQokpKS+Oeff5irDL13AinCvAydnbRhQyNhAMlKSjKjoMZj5matDUxHtgnWmEfeHDgtz6zcSVmlnhuXbOHXfdn4qlXMusj53lKjTZGzdaZ0pjWON2u1GPNLtXoOmFLVXaJFOmXtURkNqxcpwtxHM4owZ9KRKpXKPC5uT/uelGnEcTfI34dupir4Dm0CUdfzXdEtJgQftYrCch3ZRa3zhGjz5s3cfvvtxMfHs3DhQjZt2sTq1avZvHkzDz/8sNP7kyLMy7BvzG+4CKvLnK98sdZX8VYXfU3DvGWFpHt5+Ju97EovICLIj6/uGs5l/eOd3seoHiIStudUQY22I44M8AaLMb+oQkdWoehlFOLvyxXJ7QD4x06UTVKNQ4fEZZ8+zf/a1g1bW1ukpKzMMjezCUWYTm+qjnSySXb/BCHCtgS3M98XrLFEwjrU4wcDYT3pajoham3m/P/973/06dOHq6++mjZt2vDPP/+wb98+VCoVbdu2bfB+pQjzMlyZjgSLCDuUVVSjN4yjX6x1ISsk3Y9WX8Uv+7IAeO/GIQzu1KZB+4kPD6R7TAgGI2xIsRVLzo4tMhrhaI7o6h0b5s8w0xB5exFZSTWOHhWXPXo0/2srLSpKSqCgoPlf350cPCjeuNHRENt0Y30qTeZvZ9KRAMmmdhSbKkUUTGU0EOCr4orkdozo2pZbRnRyaD+t1Zz/6KOPcuWVV3Ly5ElefvllkpOTXbJfKcK8DHt9whoTCevQJpC2wRp0VcYaH7rGNGtV6CtFmNsp01rE9aCOEY3a1xCTWKreqsJRwa7xVZuNwoezxZl2XHgAfUzvk4z82kcjSYDycjh1Slzv3r35Xz8oyFIM0NpSkkoUrHfvJn0ZJRLmTIsKsJzw5pSL43ZwZQWq06dJiAziizvOZ2wvx4SjxZzfuiJhzzzzDN988w2dO3fm0UcfZf/+/S7ZrxRhXob9SFjD/80qlcqSkqxmzrce4N1Q+lqZ80u0+gbvR9JwlDmRGh81vk6eXVenmym1cTzXMpvOaDQ6HAkD0cgX4IipAis2LIDwQD9zhdd+6R+snePHxWWbNtCIFEmjaK2+MOX37eRYRKmhWGZHOndy3aFNIJHBGvPtoMpy2LPH6ddXZsXW1pjZW5k3bx5Hjx7l008/JTs7m/POO4/k5GSMRiP5+fkN3q8UYV6GK1tUKCTX4gsze8IaEQmLCvEnPjwAo7H2Rp+SpqWssvFpZQXFL5JyxiLCrHvXOVLEoaQkj5jOtOPDTT2NlJYm0j9YO0o0pnt3cLDRbl1k5JeZG+06TGsXYcrv30RoG1AdCeKEOtmqL1hIA0WY0hPwxJmSVjm+aPTo0Xz88cdkZ2dzzz33MHjwYEaPHs2IESNYuHCh0/uTIszLUM6SrH1gjfGEgVXT1mp+nMaMLbJGpiTdi3IgDdI0vrmkEglLyys1F4koaWtwrIhDEWFpeaJDflyYEGHyfeIAih+skalIfZWB5345yMgXV/Pg105+UUsR1qQvo/QJc9aYD3DtUMvaCgJDGyTCYkL9aRPkh8EIx3JK6n+ClxIaGspdd93Fli1b2LVrF8OGDeOFF15wej9ShHkZiifMOkXY6EiYydB5Mq+Mc6UWP05jm7Uq9DV1zj8gv1zdghIJC3JwWHddtAsPJMBPja7KyKl8Ud2oRFJUKsdSKEqvMIVYswgTZ+CyuW8dKJGwRpry316TwvvrRI+2n/dm2Z2CUCutVYQpXjylOKGJ0DWgRYXCxX0svq9zQeENEmFifJFpVmwrb9paUSHadPTr149FixZxugGD66UI8zLKdcJXFWgV1WjoAG+F8CA/ukSJNNMeq5RkhYMNOOujXwfxgZYRDvdQrnONmAZQq1V0ibL1hWmthnc7MouyQxvbrt1xpnSkItZP5pXJAd+10cBI2MbjZ7n67Y3sNUW7N6bk2Txe6/xYXRXP/HyQ//1xhHzlBK21irBmioQp6f2GHHdVKhV/zLmQ7pEBPPP7YiHaS2tOQwFgyxYxC9MOSoVka2tTAWAwGHjmmWdo3749ISEhnDhxAoAnnniCTz/91On9SRHmZShffN1igs33NTYSBvb7hWldFQkzpZlSzpRQVinN+c1Nuelv7opIGGDuO6T4whwd3q0wukeMzW0lHWnT3Ff6wuxj7QlzkLMlWu77chfbT+bzzloxc/LEWfG/UwTxxpSa/dkq9Qb+/dkOlqxP5Y2/jzP2f2s4mVfaOkVYcTEo5uymjoQpfcIaWETTIzaUPx8Zx41ZO0VLjQMHam70+utw/vliCLm+5jFZGeR9MKv1fQ6fffZZPvroI1566SU0GkuhQ58+fXj//fed3p8UYV6E0Wg0n5kow7EBfFxg0LVnzm/s2CKFmNAAYkL9MRjrrrjJyC/jwa/3kG7yCklcg5KODHSRCFM6cKeYTgicjZgO6xxpc7ttiGV8kmLOlxMW7FBaCjk54nrXrg49xWg0Mm/FPvJMUay1R86QV6Ilp0gLwA3ni0q/jcfzajz3441prD5yhgA/NV2igskv0/HSyiMWEZaZCbpWErFUUpERERAW1qQvpW1EOtIGpc9V9ZTk8uXwwAPi+qlT8McfNZ6a1E5psl3U6sYXffLJJ7z33nvMmDEDHx/LMTM5OZnDhw87vT8pwryI0wXlFGv1+Pmo6BEbYr7fpxEtKhSszfnKh84VzVoVzE1bM2o/s/pgXSrf7szgzdXHGv16EgtKOjLQBelIgK4xthWSzkZMNb5q2oVbZthZR3L7dZDm/FpRIk9hYaJFhQN8u/M0fx7Mwc9HRXigH6WVVXyxRewnKsSfSX3jANh1Kt/m5MdoNPL1diE8HrssibduGIRKBb/sy2KXVgMaDRgM0ACPjEfSTKlIsB7g3QQirKICHnzQdruPPqrx1B6xoWh81RRX6DnZyk6KT58+TbduNce6GQwGdA046ZAizItQomBdo0NsvvAa6wkD0aBP46OmoExn/tBZ+oQ1/svbUvlWe4TjaI74/fbWIdQkzlPuQmM+iPcfiNS40Wh0eHi3Nf8aIlI61QV+H9MZuGxTYYeTJ8Wlg32qCsoqeepHkYqaM6EHl5tGVb37j/C4dIkOpmNkEMMSI9FVGbnnix0UVYgvmX2nCzmWW4K/r5opA9rRKy6Mqwd1AGDBb0cwdujA2aBwlq47YX6OV9OcIqyBfcJqYE+ELV4sfpf27WHjRnHfDz9YUq0m/HzU9DYN825tJ0RJSUmsW7euxv3Lly9n4MCBTu+vwSLs+PHj/P7775SXiwqo1haSbIkcNlWq9I4Ps4keuMITpvFVm0PQSkrS0ies8VpeEWF1eX2OmdJbR3OKzcJB0njKXZyO7BwVjEoFRRV6zpZUNqif3P3juvPYpb1ZfvcIm/tlc986cFKEbUzJo1irp0t0MHdd2JUJSaJyTvm7do0OQaVSsei6AbQJ8mP/6SJGv7San/dm8tlm8VoT+8QRFiCqWedO7IG/r5qtaef4q++FPHjZHJ46UM49n+2kyuDl3w/NVBkJjTPm26DMt9y71zLn8+23xeWTT8Lw4dCrF1RWwvr1NZ6uRKVbW7XyE088wb333suLL76IwWBgxYoV3HHHHTz33HM88cQTTu/P6f9iXl4e48ePp0ePHlx66aVkZYmZczNnzuTB6mFMSbNyyNTcsldcqI0PrLF9whSqm/NdMbZIQUlHHsu13wCwoKySM8XCp2Iwtk5DaFNRZk5HNr5PGIj3Q0Ib0d0+xaqhozNfGj5qFXdc2MUsuhSsm/vKlibVUESYg9GYbWnnABjVLQoftYpR3aNJiLRUpiqNd9tFBLLklqF0jRa+r3u/2MXX2zMAuG6oRXTEhwcyc2RnAB7uMZm1XYYAsP74WV5f5eUWAiXt2r59k7+UuU9YYz1hvXqJtHFRkaiCTEkRP76+cO21YpuhQ8Xl7t01nt5a5/5OmTKFn376ib/++ovg4GCeeOIJDh06xE8//cSECROc3p/T/8U5c+bg6+tLeno6QUGWqevXXnstK1eudHoBEtegqzKYxwr1jAt1eSQMYKBprqA5EuaCsUUKsWH+RIX4U2WoOaMSbMfggExJuhJXpyPBtnO+MyOLHKGvuXO+NOfb4OTYnO1pIsWkzPv0Uau4dURn8+OdoywV1oM6tuH32Rdy4/mWfT9ySU8u6BZls8+7x3SlW0wIBT6imKKPUZwYvv73Mf45esbJX8iDOCcELVFRdW/nAiqrGlcdacbPD5KSxPXt2+HPP8X1ESMgVKQaGTBAXO7aVePpfa18vK0lGqbX63n66afp3Lkzf/75J7m5uZSVlbF+/XomTpzYoH06/V/8448/ePHFF+nQoYPN/d27d+ekciYmaXbeX3eC0wXltAnyY1CnNjbCy1WRMKVp68HMIrT6Kpc1awXRv6auZpzVRVhdBn6Jc7g6HQkWX9hj3+1n9le7AdeIdbAaX9RKDvwO40Q6skSrN6f+hyRaTPzXWEW2FPuBgq+Pmqen9OGVfyWz+PpB3DOmpjk5LMCP5XcPZ6xfMb1yU/ko+y+mD+uI0QgPLNtFZkE5p86V8fLvh3n/nxMYjUbOlmg9P12peKYiI+vezgVUmo67jTbmA4wdKy6/+cZSBWktJhSPk51IWI/YUEL9fSnW6rn8jfUs/ONI49fTwvH19eWll15Cb6dtR4P36ewTSktLbSJgCufOncPf39/OMyRNTdrZUl77S4T7/3tZEmEBftUiYa758uvUNog2QX7kl+k4kFlk7s7vChEG4st1zZEzdr9cFT9Yl+hgTpwpZa/8AnYZZS6ujgTL+CJrQgJck+6UnfNrwQkRtis9H4NR9AGLD7ekIEP8fflr7mgKy3U29yuoVCquHtyhxv3WRARp+LCnHp69D8aNY/7kJPZmFHAgs4ir395ITrFFdO06lc9v+7NpFx7I7PHdzQUZHocSCXOwKrUxKMddZ2dH2uXGG2HhQiHCFKxFmGLeT02FggLRgsOEn4+aL+88n/f+OcFv+7MY3TO68evxAMaNG8fatWtJTEx0yf6cPiqOGjWKTz75hGeeeQYQH0qDwcBLL73ERRdd5JJFSRzHaDTyn+/2odUbGNU9iqmDhCfB10p4uSoSplKpSE6IYM2RM2xNPWe+3xXGfKi7QlIRYVOS2/PqX0fNw2NdJQBbM00SCbMSYckdwunXIZzrhrqmcqx6c19XzLz0eHQ6iy/JARG26lAuAMMSa0Zu7Alop2nXTlxmZhLg58PbMwZz2RvryCwUY146RwWTeraUX/dlA6K9zsPL99I5KticHvUoFBHWDJGwxowtqkFyMvTtC/v3i9sJCTBokOXxyEjhMUxPFwb+Cy+0eXrf9uG8Pn0g50r70KbauDFvZdKkSfzf//0f+/btY/DgwQQHB9s8fsUVVzi1P6ePXi+99BLjxo1j+/btVFZW8sgjj3DgwAHOnTvHhg0bnN2dpJF8syODjSl5BPipee7KfuaxMNbBL1d5wkCkJNccOcPmE5bmja7y+pjN+TnFNgLrcHYRG4+Ljt0X9Yrm401pnCut5GhOMf1NKVJJw1FGXbnSE6Y0bAV4/PIkl36xKs19c4u1HMws8swvbVdz+rToy6XRQGxsnZsWlunMPb6uHNhERnIrEQbQsW0QH9w0hC+2pnPt0ASGdIpk8hvrOZJTzPRhCZRXVvH97kzmrdjHL/ePck2qrTlR0pFNHAkzGIzoTVFEl/yNVCq47TaYOxfi4+G338Cn2nFgwAAhwnbvriHCFCKDNXbv90buueceABYuXFjjMZVKRVWVc5X7Touwvn37cvToUd58801CQ0MpKSlh6tSpzJo1i/j4eGd3J2kEZ4q1PPfLIQDmjO9Bx7aWNLF1JMyVImyAyZyviDA/H5XL9h8fHkBksIZzpZUcyS4mOSECg8HII8v3ojcYmZgUS7/24fSOD2XD8TwOZRVJEeYCzJEwF0YV2wRreGlaf/QGY5OIpH7tw1l1OJf9pwulCAPbysh67AefbTlJWWUVvePDGNW9iYzkiggrLBSd/IODOa9LW87r0ta8ydd3DWfnqXwu7B5NcYWOdcfOciy3hPf+SeHesc7NvnQrFRVgatXU1JEwpT0FuKBPmMK994qCgnHjLP83awYMgB9/tOsLa40YDE4Ms3eABsXxw8PDeeyxx1y6EInzPPXTAQrLdfRpF2YuDVfwacJIGFi1p3BRFAwUc344/xw9w77ThSQnRHAgs4i9GYUEa3x49sq+qFQqesWFmURY6xse2xS4emyRgrXJ29X0NYmwupr7tiqUVGSHuv1a2YUVvLNGzIe8Y1RnhwaqN4jQUAgOFgIsKwvsdBgPD/Ljop5iTmhEkIYnJifxwLLdvP73cS7tF0+XaBekRZsDJQrm49PkI4t0NiLMRdFCPz/hDasNpUJSirAmwWkRtnTpUkJCQvjXv/5lc/8333xDWVkZN998s8sWJ6mdvw/n8PPeLHzUKl6c1h/fah9InybwhIEIO3dqG2Tumu/vYk9Wv/Zh/HPUYs5XOqMP6tSGGNMg597x4kBX15xJieMoY4tcmY5sahxp7tuqMKX97PWpWnMkl6+2nWJEtyh+2ZtJsVZPckIEUwY0YU8rlUpEVY4dE2uzI8Kqc0VyO77deZp/jp7hse/288Ud5zWdSHQlih8sIkL83k1Ipd4iwlxizHcEpUJy/37RuFXTelKP9nj66afrfNzZhq1Oi7AFCxbw7rvv1rg/JiaGO++8U4qwZqBEq+e/3wkj5cyRnWs0tARb4eXKSBiInkFmEeZi70bfdkoPKPHlqjQCtB5I3jte9LA5lCWGx3rEgboFo6QjPanIoXpzX09ae5OgRMLspJMW/XWM3acK+G2/MMFrfNS8cnV/lx8XahAfL0SYqaF3fahUKp67si8TXl3LphN5LN+R4RnVks1qyhd+MF+1CnVT//8UOnWC8HCRWj50yFIx2Ur57rvvbG7rdDpSU1Px9fWla9euToswp79B09PT6dy5c437O3XqRLrSLFDSpLzy+xEyCytIiAxkzvgedrdRW3fMd5V3wMRIqwaNrqqMVFAE5ZHsYrT6KnNXdKUtAYjqLV+1iqIKvbnaStJwLM1aPafKUDT31VBlMMqIKFgiYXZE2AnTIPUQf1+uGtiez+84j+6xoU2/pmrmfEdIiAxi7gRxTHvu10OcLdE2xcpcSzOZ8sESCXNZKtIRVCqZkrRi165dNj/79+8nKyuLcePGMWfOHKf35/R/MiYmhr1799a4f8+ePbRt29bOMySuZFd6Ph9vSgPg+av61erjsRZeruoTpmBt5nX1DMcObQKJCPJDV2XkYGaReRRTX6tImL+vDz1MXyK70vPt7kfiOJ6YjlT8gyD7hQG1irD80kqKKkT167bHxvPqtQMY2lyFDA0QYQC3XdCZpPgwCsp0PPvzwSZYmItpxkiYYsxv9upRKcLqJCwsjKeeeorHH3/c6ec6/Z+cPn06999/P6tXr6aqqoqqqir+/vtvHnjgAa677jqnFyBxHF2VgXkr9mE0wtSB7RnVvfbmeE3RMV9B8WYBLo9EqVQqs+D6ftdpKvUGQv196Rhp2yBY6fK946QUYY2hUm8wl7x7WkpPeZ+0ttl1dqnFE3bibCkA7cIDXF54US8NFGG+PmoWTO2HWgXf785s+eOOmrFbvkt7hDlDHeOLJILCwkIKC50/Fjmdf3jmmWdIS0tj3Lhx+PqKpxsMBm666Saef/55pxcgcZz3/jnB4exiIoM1/PfypDq3tR7g3RTej4TIQE6dK3f5fkGkJNcfP8uXW0Uvo6R2YTX8D4M7teGTTSelCGsk5VbD0j0pEgZWMyStKiRziioI9vclxN9zUquNxmisNRKWahJhiVHB1Z/V9DRQhAEkJ0Rwy4jOfLghlce+38cfs0c3v4h0lGbslq+kIzUutpjUS58+4vKYlw9id4DXX3/d5rbRaCQrK4tPP/2USZMmOb0/p49UGo2Gr776imeeeYY9e/YQGBhIv3796OTg0FhJwzhxpoTXVokPwOOX9663OV5TRsIA3r9pCNe8s4l7Lqq/6slZFNO1Enof1rnmGabSG+pAZpHsmt4IlHSyr1rV/GfXjaRfB/E+OWpq7nu6oJzLXl9Hj9hQvr/nguYzLrubggLRqwqEGd6KNJMI6+xhIgzgwYk9WLk/i1Pnylm06ijzJvV24eJciBu65Td7OjLBVCCRlSWmM/i1ju749nj11VdtbqvVaqKjo7n55puZN2+e0/tr8DdXjx496NHDvilc4loMBiPzVuyjUm/gwh7RXOlAablPE1ZHAvSKC2PP/IlNUplobcIHuNZOv6n2EYHEhweQVVjB7lMFjOjaRE0nvZyySuEXarFRhjpoFx5gnmV6NKeYn/ZkUqEzsDejkH+OnWGMqQeV16NURkZGQkCAzUOpLUGEOVgdWZ1gf1+entKX2z/ZzgfrUpmS3L7GUPEWQXMa892VjoyJEcJLp4PsbIsoa4Wkpqa6dH9O/yerqqpYsmQJ119/PePHj2fs2LE2PxLX8/X2U2xJPUegnw/PmRqW1kdTtqhQaKrWENb+r3bhAXRoU3NgPIiUJMCONJmSbCieaMpXsDbn70ovYMXO0+bHPtyQ5qZVuYE6KiPdKsKUqFxxsfhpAOOTYrm0XxxVBiPzVuw1D/5uUTSnMd8d1ZEgpjAofsNTp5r3tVsYt912G8V23s+lpaXcdtttTu/P6f/kAw88wAMPPEBVVRV9+/YlOTnZ5kfiWnKLK3j+VzGa6MGJPUiItC9IqtPUkbCmRKVScXn/eNQqeOVftb+nzCJMVkg2mKYYWdScKCLstVXHyCutJDzQD7UK/jl6hmM5rWSiQi0iTFdlIC3PjZ6wkBDROR8aHA0DeHJyH0IDfNmTUcgnpsrwFkWzGvNdODfSWZRpDBkZzf/aLYiPP/6Y8vKafujy8nI++eQTp/fndDpy2bJlfP3111x66aVOv1h13n77bd5++23S0tIA6NOnD0888YTZ3FZRUcGDDz7IsmXL0Gq1XHzxxbz11lvE1jOg1pt46seDFFXo6dc+nFtGJDr8PFtPmGd5fQBeuro/D1/ck05ta//yGNJJHPR2nszHYDC2Hg+QC1EiYYEe6qlT/IPnSisBmD6sI6lnS/j9QA4fbkhlwdT+7lxe81CLCHvljyOUVVYRHuhHQi3R5CanXTs4ckSssYH2lZiwAP5vUi8e+24/r/x+hIv7xNEuItDFC20EzWjMN3vC3OHfbOUirKhINAc3Go0UFxcTYJX6r6qq4tdffyUmxnkLhNP/SY1GQzcHRlA4QocOHXjhhRfYsWMH27dvZ+zYsUyZMoUDBw4AMGfOHH766Se++eYb1q5dS2ZmJlOnTnXJa3sCfx3M4Zd9YjTRC9P61RhNVBeeHAkD0Ti0LgEGonN+kMaHogo9x3JLmmll3kVRuckT5uKmu81FP6tpEWoV3HZBIjNHdgFgxc7TZnHm1djplr/qUA7vrj0BwIKp/dwTObFeUwPN+QrTh3ZkSKc2lFZW8cQP+zEaW1Ba0g3NWt3y/1R8YK00HRkREUFkZCQqlYoePXrQpk0b809UVBS33XYbs2bNcnq/Tp/+Pvjgg7z22mu8+eabjfYETZ482eb2c889x9tvv83mzZvp0KEDS5Ys4YsvvjB7zZYuXUrv3r3ZvHkz559/fqNeu6VTXKHj8R/EaKLbR3W2GdvjCE1dHdkS8PVRMyAhgo0peWw/eY6ecc3QBdzL+Hmv+HJ09v3VUujQxhIRGdc7lpiwAKJD/enXPpx9pwv5fPNJ7hvX3Y0rbAZSxEBuEhMByMgvY+7XewC4ZUQil/aLr+WJzYCLRJhareL5qf247PV1/HUol5X7s5nkzt/LmhLTCWBo0x9/LMZ8NxzTW3kkbPXq1RiNRsaOHcu3335LpFX6WaPR0KlTJ9rZ8WXWh9MibP369axevZrffvuNPn364FetVHXFihVOLwJEOO+bb76htLSU4cOHs2PHDnQ6HePHjzdv06tXLzp27MimTZtqFWFarRat1jLqwp6BzhN4+fcjZBVW0DEyiNnjnA/je3okzFGGdGrDxpQ8dqTlM+M82SbFGU4XlPP7ATFP8Mbhnvm3U6lUzB7fnb8P5/LclX3N980c2ZnZX+3mk80nuXN0F/x9PdPz5hBK76bu3anUG7j3i10UlutI7hDOvEt7uXdtjayQtKZHbCj/Ht2V1/8+zvwfD3BB9yjCAtzcKkGnA+X7phlEWL4pshsW6Ibfu5WLsNGjRwOiOjIhIQG1i2w+Tu8lIiKCq666itGjRxMVFUV4eLjNj7Ps27ePkJAQ/P39ufvuu/nuu+9ISkoiOzsbjUZDRESEzfaxsbFkZ2fXur8FCxbYrCcpqe6mpi2RHSfz+XTzSaDu0UR1Ye0Dc/XsyJbEYFO/MGnOd44SrZ75P+zHYIQRXduax0B5IrPH9+DHe0faTHK4tF88sWH+nCnW8vOexguAFktlJZg8tXTvzgu/HWb3qQLCAnx58/pB7hefSoVkIyNhCvdc1I0uUcHkFmt5aeXherd/7a9jXPrauqZLS5dY2SBCQprmNazIMk0oiQ93gyfOA9KRb7/9Nv379ycsLIywsDCGDx/Ob7/9Zn68oqKCWbNm0bZtW0JCQpg2bRo5OTlOvUanTp1Qq9WUlZVx+PBh9u7da/PjLE5HwpYuXer0i9RFz5492b17N4WFhSxfvpybb76ZtWvXNnh/8+bNY+7cuebbp0+f9ighVqk3MG/FXoxGmDaoAyO7N6z/lXXwy1vTkQADEiIAOJlXxtkSLVEh/u5dUAunoKySz7ek8/HGNHKLtfiqVdw71vUNd92NxlfNTcMTefn3IyxZn8rUQe2brKWKW0lLA4MBgoJYma/mww2ih9H/rhngcCV1k6JEwk6frns7Bwnw8+G5q/ox/f3NfLY5nasGtmdwJ/tViUajkSXrT1BUoeevQzlcM6QJelspmRZ//2ZpYJpZIKry2kUE1LNlE6BEwrKyQK8H35ZXzKP4zLt3747RaOTjjz9mypQp7Nq1iz59+jBnzhx++eUXvvnmG8LDw7n33nuZOnUqGzZscPg1zpw5w6233moj7qypqnJunrLb3biK0X/w4MEsWLCA5ORkXnvtNeLi4qisrKSgoMBm+5ycHOLi4mrdn7+/v1kFh4WFEdoMIWJX8s7aFI7mlNA2WMN/L2t4h2iVSmVOQ7p6gHdLIjzQj+4x4gx0V3qBexfTgjlxpoT/fr+P8xes4uXfj5BbrKV9RCBf3XW+1za6vX5YRwL81BzMKmJL6jl3L6dpMKUi0/sN5eHl4iz8zgu7MCGphVSQK72lXBQJAxjetS3XDBGCQGlibY+0vDLz8PI9pwpc9vo2KCKsGaJg4OZIWEyMEF4Gg0vSy03B5MmTufTSS+nevTs9evTgueeeIyQkhM2bN1NYWMiSJUtYuHAhY8eOZfDgwSxdupSNGzeyefNmh19j9uzZFBQUsGXLFgIDA1m5ciUff/wx3bt358cff3R6zQ2SssuXL+frr78mPT2dykrbMO/OnTsbskszBoMBrVbL4MGD8fPzY9WqVUybNg2AI0eOkJ6ezvDhwxv1Gi2V47klvPn3cQCemJxEm3pGE9WHj1pFlcFoM0fSGxnUsQ3HckvYmZ7fcr58WgBGo5FNJ/JYsi6VVYdzzfcnxYdx+6jOXN6/nfuq5pqBNsEapg7qwBdb0lmyPpXzu7StdVtdlQGD0ej+9J2zHDtGhY8f95x/K8UVegZ3asPDF/d096osWPuIjEZw0bHoP5f2ZtWhXI7mlPDePyncO7Zm8YW18NqTUVDjcZfQjKZ8gKxCEQmLD3dDJMzHB+LixP+ymbvmFxcXU1RkmRHr7++Pv3/dWQ9X+cyr8/fff/PDDz8wZMgQ1Go1nTp1YsKECYSFhbFgwQIuu+wyp343p4/Ar7/+OrfeeiuxsbHs2rWLYcOG0bZtW06cOOH08Mp58+bxzz//kJaWxr59+5g3bx5r1qxhxowZhIeHM3PmTObOncvq1avZsWMHt956K8OHD/fKykiDwch/VuyjssrAmJ7RXJHsfJVFdcb3jqFXXCjx7ghdNyMDO0YAol+YBLT6KpbvyODS19dz/ftbWHU4F5UKxveO5cs7zueX+0cydVAHrxZgCrdd0BmAvw7lmOcoWmM0ipFgA576g+Sn/jB3mPcYjh3jubEz2R8QRZsgP96YPrBlzQBV0pFaLZw967LdRgRpeGKysJm8/vdxTpyp2aJmt5UIO5wl5osCnCnW8v4/Jygs0zV+IUokrBlEmFZfxdkSEfRwW580pQ9Wbm7d27mYpKQkG6/3ggULat3W1T7z6pSWlpr7gbVp04YzZ84A0K9fvwYFoZyOhL311lu89957TJ8+nY8++ohHHnmELl268MQTT3DunHMh/9zcXG666SaysrIIDw+nf//+/P7770yYMAEQgzLVajXTpk2zadbqjSzbdoqtaecI0vjwrIOjierjrRmDMRqN3umFsWKQqXP+3oxCdFWGlvUl1IycK63k880n+WTzSc4Ui4qtQD8f/jWkA7de0Nk9o2vcTLeYEMb0jGbNkTN8tDGNJ6/oY/P4ybwyvtyabr799+FcZo7s3NzLbDA/nYVPB10OwKvXDmhZTUwBNBqIjYWcHBFBiY522a6vSG7H8h0ZrDt2lse+288Xd5xnc6zbaxX90huMHMgsZHCnSF7+/TBfb8/g1/1ZrPj3iMYdH5VIWDOkI3MKxWfa31dNmyA3VYW6SYQdPHiQ9u0tM5PrioK52mdub/9HjhwhMTGR5ORk3n33XRITE3nnnXeIj3e+bYrTIiw9PZ0RI0YAEBgYaG4BceONN3L++efz5ptvOryvJUuW1Pl4QEAAixcvZvHixc4u06PIKapgwW/KaKKetc5KbAjeLsAAukWHEBHkR0GZjt2nChia2PTjQ1oSx3NL+HBDKt/uyEBr8sfEhvlz84hErh/WkYigxqW1PZ2ZIzuz5sgZvt5+ijkTehBuVd6vjPVRaDLvUBNw4kwJ/9dB9FCc1VXTcgeWd+hgEWEDB7pstyqViueu7MfERWvZdCKP5Tsy+JfJfK+rMrA/U6SvukYHk3KmlN2nChmQ0Iavt4sWC7vSC/hpbxZDOrXhk00nOXGmhG4xIVzQLYrIYA0RQX60CdIQUNdIr2aMhGUWKqb8QPcd1xURZor+NBehoaGEhTk2vN26ofzgwYPZtm0br732Gtdee63ZZ24dDavPZ16dBx54gCyTJ27+/PlccsklfP7552g0Gj766COH96PgtAiLi4vj3LlzdOrUiY4dO7J582aSk5NJTU1tWV2MPYj5PxyguEJPcgfnRhNJBGq1itE9ovlhdyarDuW2ChFmNBrZcDyPJetPsPqI5YDYt30Yt4/swqX94ltFutERRnaLokdsCEdzSvhqWzp3XtjV/Nipc2UABPipqdAZbKInLZkKXRX3fL6TUr8Azkvfx5x7Zrh7SbWTkAA7djRJf6mObYOYM74HC347zHO/HmJsrxjahvhzJLuYSr2BsABfrhzQnv/9eZQ9pwpsUpQAL/9+mO4xofxt8kz+cTCHt9ak2Gzj76s2C7LwQHEZEeRHRJCGiNMqIvpPJCK2DxEn8qwe83O5v1Dxg8WFudFe4qZIWGNwtc/8hhtuMF8fPHgwJ0+e5PDhw3Ts2JGoKOeLnJwWYWPHjuXHH39k4MCB3HrrrcyZM4fly5ezffv2VjVSyFX8fiCblQey8VGrWDC1v1c3Vm1KxvaKMYmwHP5vkpsbVDYhWn0VP+zO5MP1qRzOFmfhKhVM6B3LzJGdGdY5slVEP51BpVJx2wWd+b8V+/h440luu6CzeQTYyTwhwi7r145vd2aQlldGQVlli48ePvnjAQ5nFxNVms8bvy7E99OH3b2k2lHM+U3UX+q2kZ35fncmh7KKePaXQ7x67QCz2EpOiGBgR2FX2H2qgITDoifU+N6xbEnN49S5ck6dE+Lm4Yt7si+jkGO5xRSW6ygo06E3GNHqDeQUackp0tp59TYw6X5x9T3bCrtAPx/aBPkRHqQhItCPNsF+hAdqaGMSaRGm+yOCNKbt/IgI1NR68pRZYKqMdKfHt4WLsHnz5jFp0iQ6duxIcXExX3zxBWvWrOH333+38ZlHRkYSFhbGfffd55TPXKfT0atXL37++Wd69xbdC4KCghg0aFCD1+y0CHvvvfcwGETKQ2l6tnHjRq644gruuuuuBi+kNVJUoeMJ02iiOy/sQlI7x8KtkpqM6RGDj1rFsdwS0vPK6Ni2BfRIciF5JVo+25zOp5tPcrZEfBkEaXy4ZkgCt4xIJLEV+r2c4cqB7Xnp9yOmKQE5XNZfeDdOmiJh/TuEszM9n9SzpezJKGR0D9d5l1zN8h0ZLNt2ChXw2k+vENMmWFSutVSauNO6n4+aF6b248q3NvDdrtNcNbC9OaLZv0M4/TqIJuLp58r4fpdolXF5f9HM9/Mtwg84NLENsy6y7ZdnNBop0eopKBOCrKC8kvwyHYVl4rKgTEfBpq0UHE2loHN3CmI7UFCuo6CsEoMRynVVlBdWkWlqK+EowRofIdCqibV9pwsBaOeO9hQKLVyENbXP3M/Pj4oK5/6f9eG0CFOr1Tbt+q+77jquu+46ly6qtfDSysPkFGlJbBvEA94+366JCQ/yY0inNmxJPcfKA1k2KSdP5lhOMUvWp7Ji12lzP6T48ABuGZHIdUM7Et4Yg25ZGaxbJxovjhwJDZh44SkE+Plww3kdef3v4yxZf8IswtJNkbCObYPo3yGc1LOl7D1V0GJF2L6MQh77bh8As+O0XHByD1x4oZtXVQ/NMO4mOSGCW0YksnRDGo99v888MSS5QwThgX50iQ7mxJlSTheUo/FRc1HPGDpHBZtF2L8G12y3oFKpCA3wIzTAj4TaHA5/vw/fLoL/+z94SKSpDAYjxVo9hWU68ssqzcJMEXP5ZZUUlpseK9OZrxeW6zAaobSyitLKck6bGrNWJyFSirDaaA6f+axZs3jxxRf54IMP8HVBw1qH9rB371769u2LWq2uty1///79G72o1sD2tHN8tlkcAJ6f2q9u86fEIa4Y0I4tqef4ensGd4zqgkqlorhCx9fbM5iYFOtwB/Hyyiq+23WabjEhDE1s0+zpPaPRyLpjZ/lgfSr/HLX4vfp3COf2UV2Y1Deu8RWga9fCrbdCquiwzoUXwpo1Luvj1BK5YXgn3ll7gp3pBexKz2dAQgTppkhYp8ggkjtE8MPuzKbrKdVI8kq03P3ZDrR6A+N6xXBf9t/igY4d3buw+mimmYMPTuzJyv3Z5vQiWCZqDOgQwYkzoghjXO8YwoP86B8YziV94jhdUG4W5U5jpzpSrVYRHuhHeKCfUxF5g8FIUYUSdTMJszKLWCsoq8Tfz4fL+ze+fVGDaeEirDnYtm0bq1at4o8//qBfv34EB9tmIZydn+2QCBswYADZ2dnExMQwYMAAVCqVXRO+SqVyumV/a0Srr+L/Voiz2WuGdPDajuXNzRXJ7Xj250McNzVuTYoP57aPtrEtLZ8tJ/J476Yh9e5jX0Yh//58Bxn54kAeFaIhJjSAd24Y3OQpzgpdFT/sPs2S9akczREHd7UKJibFMXNUZ4Z0coEg1OvhoYfgtdfE7dhYyM+Hf/6B336DSy9t5G/RcokJDWBysvB+LVmfyhOTkyjXVaFSQYc2QSQniEjg7lOFLa61i8Fg5N4vdnG6oJwuUcG8et0A1A9+JB70JBHmwoat1Qnx9+WZKX25/ZPtgIgYK/NEkxMiWLFLjE6aNkisR6VS8c6Ngxv3oi6sjlSrVaY0ZAv2I1qLsCb8X7ZkIiIizMZ+V+CQCEtNTSXa1N8lVTlzljSYt9ekcDy3hKgQDf+5tOGjiSS2hAb4cVn/eJbvyODNv4+jUqnYliYauG5KyUNfZTAbsu2h1Vdx35c7ycgvJzrUn6JyHWdLKjlbUsmv+7O4e3TTpDjPFGv5bPNJPtt8kjzToOFgjQ/XDE3g1hGdXSv+HnnEIsDuvBNefhmefVZc/uc/MGmSVx9YZ47szLc7M/htfzYT+4iy9HbhgWh81fRpF46PWsXZEi1ZhRXkFFXwyh9HmDepNwVlOvadLmR87xi6u2HY+a5TBWw6kUegnw/v3jiYsAA/SDf1N+vUqdnX4xRKf6fycjh3DtrWPrmgsYxPiuXSfnH8ui/b3MQZMFdMR4X4M7qnC1PNzTy2yO0ofd50OigshGqNT1sDrp6f7ZAI62T1Ie/U0j/wLZxjOcUsXi1GE82f3Kdln/V4ILeMSOSH3afNbRsC/NSoVSqKtXr2nS40V0rZ44N1qaTllREd6s9fc0cD8H/f7uW3/dkcy6nZkbuxHMkuZsn6E3y/O9Ps92ofEcgtIxK5dliC+KJ1Ffn58Pzz8Oqr4vYXX8D06eL6o4/C22/Dnj3CI9bSPUaNIKldGMO7tGXTiTxe/O0wAB1NaeoAPx96xYVyILOI7SfzWfTnUU6cLeWZnw+yN6OQcl0VL648zNszBjGpXwPTVw1kU4roNn9Rr2iLCFREWEuPhAUECOGVlydmDjahCANYcFV/esaGMWWAJW2X1C6MD24aQofIQNc2c27msUVuJyBA/K7FxSIa1gpFGIBer2fNmjWkpKRw/fXXExoaSmZmJmFhYYQ4KcgdEmHODKW84oornFpAa8JgMPJ/K/ahqzIytlcMlzfUhyCplb7tw3lj+iBmfbETFfD2jMEs25bO7wdyuOGDLSQnRPDG9IG0DbHtuKyrMvDuWtEf6D+X9jI39JwyoB2/7c/meG6xS9ZnNBpZe/QMS9ansu6YZYzLgIQIbh/VmUv6xNUZrXMag0EIr2efhYICcd/jj1sEGIgvxeuugw8+gCVLvFqEgWhpsOlEntn4fFEvS2Skf4cIDmQWMeer3VQZhOWi+vDv3/ZnN7sI25iSB8Bwa+uCp4gwEDMH8/LEzMG+fZv0pcKD/HhgfM1Cp/FNMVe2GZu1thhiYiwirEcPd6+m2Tl58iSXXHIJ6enpaLVaJkyYQGhoKC+++CJarZZ33nnHqf05JMKuvPJKm9vVPWHW3gnpCaudz7ems+NkPsEaH55x0WgiSU0u6RvHygdGYQR6xIaSfq6M3w/kUFpZxcaUPD7amMaDE22HHO8+VUBRhZ42QX5ckWwZj9EtRhxcj+WWNMonVKETZv8l61M5nmvxe13SN46ZI7swuFPtEboGYzDAPffAu++K2337wnPPweTJNbe97TYhwr75Bl5/3asrJcf1iiGxbRBpeWWM7hHNzJFdzI8NSAjny62YBZi/r9o8heCintGsPnKGzSfymtUzVqGrYrtpLupwZQh5UZFFVDfjIOUGExcHBw6ISJg30Yxji1oMMTGQktJqzfkPPPAAQ4YMYc+ePbS1iupeddVV3HHHHU7vz6FTboPBYP75448/GDBgAL/99hsFBQUUFBTw66+/MmjQIFauXOn0AloL2YUV5vTHQxf3pH1Lm/HmZXSPDaWHKW1zQTfbwocvt6aj1dueLChViCO7R9s0zO3UNgg/HxVllc73+wHILa5g4R9HGPHC38xbsY/juSWE+Psyc2Rn1j58EW/NGNw0Aiw9HSZOFAJMrYY334Tdu+GKK+x7vs4/H3r3Fr6dr75y/XpaEGq1ikXXDeTfY7ry+vSBNv/vwZ0svQhuu6AzcyeIM30ftYqnp/RF46smt1hLyplSs1BzBn2VgZ3p+RiceO66Y2ep1BuICfWna7SpEktpfNqmjWdEYZSZek4MSvYIWmskDFqtCFu3bh3//e9/0WhsrUSJiYmcPn3a6f053eRi9uzZvPPOO4wcOdJ838UXX0xQUBB33nknhw4dcnoRrYEnfthPiVbPgIQIbhqe6O7ltCq6xYSwYGo/AvzUvPjbEbKLKvhlbxZTTVVSYBFhF3a3FWx+Pmo6RwVzNKeE47klDovnQ1lFLFmfyo+7M6mssvi9br0gkWuHJhDqSr+XPa65BrZsgcBAkWK0Tj/aQ6WCmTNF5eSSJcK078UMSIgwty+wpltMCO/eOJioEA2DO0VyrrSSX/ZlMbxrWxIigxjUMYLNJ84xfuFa2kcE8sHNQ+gd73iT5Vf/Osri1SnMndCD+x3oDbh49XFe+eMIABf2iLZE3w6LEzq6dKnlmS0MZTaft4mw1hoJg1YrwgwGg92MX0ZGBqENEONOm09SUlJshl8qhIeHk5aW5vQCWgMr92fxx8EcfNUqXpjWT44mcgPTh3XkqoEduOF84Z/5eGOa+bH80kr2mrpRj+pes3Kqu5KSzKnbF2YwGFl9OJcZH2xm0mvrWL4jg8oqA4M6RvDWjEGsfXgMt4/q0vQC7MQJIcB8fGDXrvoFmMKNN4KvL2zdCvv3N+0aWzAX94kzR8QigzX8eO9I5k0SVcznd7GkH04XlHPNu5t46qcDtTbWtKZSb+DLrSKC9f66ExRV6OrcflvaOV754whGI1zSJ852HNc+0eKGfv2c+dXchzeKMIOh9RnzAZT5iHl57l2Hm5g4cSKLFi0y31apVJSUlDB//nwubUCLH6dF2NChQ5k7dy45OTnm+3Jycnj44YcZNmyY0wvwdgrLdTzxwwEA7h7dlV5xcjSRO5k+rCMaXzV7MgrZlS58NrtPFWA0QtfoYOLCa85l6xYjznJrq5Asr6zi8y0nGf/qWm79aBsbjufho1ZxWf94VtwzghX3XMCl/eJda7ivC6VZ4Jgx0LNnnZvaEBNj8Yt9+KHLl+UNTEyKQ62CPu3CGNKpDcUVepZuSOPadzdxztRepDb+Ppxj3qa4Qs+nm07Wuq2+ysCjy/diNIpegu/cOJgo62ISKcLcT1mZ5XprioQpIuzs2bq381L+97//sWHDBpKSkqioqOD66683pyJffPFFp/fndDryww8/5KqrrqJjx44kmAyhp06donv37nz//fdOL8DbeeG3w+QWa+kSFcy9Y7vV/wRJk9I2xJ/J/UXDzo83pjGwYxuOmSofe9WSVuoVJ85y92cW2tyfW1TBJ5tO8vmWk+SXiahGqL8v08/ryM0jEt3n+/v2W3E5darzz73lFvjuO/j6a3jlFeEnk5hJahfGlv+MJzJYg8Fo5O/DuTz3yyHSz5Vx7xc7+eS2YXbFdqlWzwfrRI/FHrEhHM0pYemGNO68sIvdlgk7TuZz4mwp4YF+PHZZUs2FKCLMUyaUKJ4wbzLmK34wtVqk/VsLrVyEdejQgT179rBs2TL27t1LSUkJM2fOZMaMGQQ24H3gtAjr1q0be/fu5c8//+SwyZfQu3dvxo8fL6v9qrHlRB5fbpWjiVoat4xI5NudGfyyL4v/XNbbXK3YLdr+2ewgk3H+UFYRpVo9aXmlLFmfyk97MtFVCYN1QmQgt47ozDVDEwjxb/w8sQaTmwubNwuP11VXOf/8iRPFWf3p07B9O8jodg2iQ0VEygcVF/eJI7FtMFe9tYGNKXk8/+thnphsK5rOlmi55t1NnDhTisZHzeLrBzH9/S2cLdHy18Ecu+0u/j4i/DZje8WY26WYKSuD46LXoIyEuRFrP1hr+u5r5SIMwNfXlxtuuME1+2rIk1QqFRMnTmTixIkuWYQ3UqGrYp5p0O51QxNsvCQS99KvQziDOkawM72AL7akW0RYjH0RFhsWQPuIQE4XlHPL0q3mLvwAQxPbMHNkZyYkxbUMr9+uXeKyZ09L9MEZAgLgsstEheS330oR5gA940JZeE0yd3+2kw83pNKnXRjTBouijyqDkfu/3MWJM6XEhQXw+vSBdI8N5ZohHXhrTQpfbE23K8LWHBaFIhf1iqn5ggcPipEx0dFi7JQnoIiw/HzQasHfv+7tPYHWWBkJUoQBR44c4Y033jAXIvbu3Zt7772XXr161fPMmjRIhJWWlrJ27VrS09OprLT1Qdx///0N2aXX8dbq45w4U0pUiL/Z1CtpOdw8IpGd6bv5fEs6FTpR6VKbCAMY3KkNpwvKzQLs8v7x3DGqC8l2Kuzcyt694rIxEZKpU4UIW7ECXnihdZ3lN5BL+sZz/9huvP73ceZ9t49uMSF0jQnhoa/3sDEljyCND5/OHGbudj99WEfeXpvCumNnSc8rsxlNdeJMCUdyilGralbrAp7nBwPRSkOjgcpKyMnxjAaz9dHaRhYptHIR9u2333LdddcxZMgQhg8fDsDmzZvp168fy5Ytc3qupNMibNeuXVx66aWUlZVRWlpKZGQkZ8+eJSgoiJiYGCnCgKM5xbxt6r7+1BV9CA9q4mo4idNM6hvPc6GHyC3WAqJxaueo4Fq3H9ypDT/uyQRgSKc2vHn9oGZZp9O4wit06aWiSvL4cUhLg86dXbI0b2f2+B4czCrmr0M53PXpDiKC/DicXYyfj4qF1yTbzJxMiAxiVPdo/jl6hi+3pfPQxJ6sP36Wr7ed4o+DImU3uFMb+2PNlGinJ4kwlUpEw9LTRUrSG0RYa6yMBIsIKysTP0EunG3rATzyyCPMmzePp59+2ub++fPn88gjjzgtwpx23c6ZM4fJkyeTn59PYGAgmzdv5uTJkwwePJhXXnnF2d15HQaDkf/7di+6KiPje8dwab84dy9JYgeNr5oZ51nmoCZEBtXp2bNuqHrHhc3Ymyk9HW6/Ha6+2tIhvS5cESUJCbGkIdeubfh+WhlqtYpXr02ma3Qw2UUVHM4uJjrUn6/uGs4lfWumHK8fJgqb3l6TwgUv/M3NH27ll31Z6KqM9O8QzrNX1vI/3LRJXJ5/flP9Kk2Dt/nCWms6MjQU/EyBhVbYpiIrK4ubbrqpxv033HADWQ0oPHFahO3evZsHH3wQtVqNj48PWq2WhIQEXnrpJf7zn/84vQBv47MtJ9mZXkCwxoenp8jRRC2Z6ecl4Ocj/j+1mfIVeseHMa5XDBOTYpnQu5l8OCkpQkwtWSL8WZMn25bFV0enE34haHzV3GgxwJw1axq3n1ZGaIAf7980xNzd/tu7RzColqHx43rHEhUiIl3ZRRWEBfhyy4hEfr1/FD/eO5KecXa+3MvKxOQDAFMqxGNQRJi3VEi2xkatIKKarTglOWbMGNatW1fj/vXr1zNq1Cin9+d0OtLPzw+1qWw9JiaG9PR0evfuTXh4OKeUURqtFOvRRI9O6kU7OZqoRRMTGsDk/u1Yses0fdrV3b/NR61iyS1Dm2llJl58UcwI7NsXMjJg/Xq49lrh1fKzk+I+dkx4bkJCoFOnmo87w5gxsGCBFGENoEt0COsevQg/tRp1HcUafj5qHr64Jy+tPML0YR2ZdVE3AjX1VFBv3w56PbRr53kpvQ6mCRUZGe5dh6torZEwECIsK6tVirArrriCRx99lB07dnC+KRq9efNmvvnmG5566il+/PFHm23rw2kRNnDgQLZt20b37t0ZPXo0TzzxBGfPnuXTTz+lb9++zu7OYzEajaw/fpaR3aLM0a6vt5+itLKK5IQIbjivkV+CkmbhqSl9GNipDVMGtHP3UmzJyoKPPxbX335bXE6YAD//DFOmiJmQ1Qc3W5vyG9vfa8QI4Qs7eVL4whITG7e/Voa/r2PtaK4d2pFrhzohppRU5PDhnlcwobxf09Pduw5X0VojYdCqI2H33HMPAG+99RZvvfWW3cdAdJGwN96oOk4fqZ9//nniTaXvzz33HG3atOHf//43Z86c4b333nN2dx6J0Wjk0W/3cuOSrXy62dL1+td9Isw+47yOdZ4BS1oOoQF+3Hh+J8KaepSQs7zzjohqXXABjBwpfr75RkTAfvtN+IGULwGFDRvE5cCBjX/9kBAYaor8SV9Yy0ERYSNGuHcdDUGJ3HmLCGvtkTBolSLMYDA49OOIAAMnRZjRaCQmJsZclhkTE8PKlSspKipix44dJCcnO/8beSAqlco8T/Dpnw6y+UQex3NLOJxdjK9axcQkD+ndI2m5fPeduLz7bst9l18OO3cKb01mJlT3Jfz1l7gcP941a5C+sJaF0QgbN4rrnuYHA+8TYTIS1ipFmKtxKh1pNBrp1q0bBw4coHv37k21Jo/g9lGd2Z9ZyA+7M5n1+U5G9xCDn0d2j7JfVi6ROEpqqqhy9PER7SKs6dtXNFNdsgRWr4ZJk8T9GRlw+LBIQ150kWvWMWaM6BMmRVjL4MQJOHNG9Nsa1EJbpNSFIsJOnRLDrz19JJaMhLVaEbZt2zZWr15Nbm4uBoPB5rGFCxc6tS+nRJharaZ79+7k5eW1ehGmUql4YWp/jueWcCCziBW7TgNwtalTtkTSYBRj56hREBlZ8/GLLhIi7O+/LfcpUbChQyEiwjXruOACIQTT0oQ3rLFmf0njUKJggwd7Zsf5du2E8NLpRMPWhkx0aEnISFirFGHPP/88//3vf+nZsyexsbE2HRAa0g3B6VORF154gYcffpj9+/c7/WLeRqDGh3dvHExUiAY/HxVPXdGHy+yMIJFInEIRYbVV1iiRrp07xRgYgD//FJeuSkWC9IW1NDzZDwai0KOdqQDGGyrpZSRMRGZbGa+99hoffvghhw4dYs2aNaxevdr887f1ibGDOC3CbrrpJrZu3UpycjKBgYFERkba/LQ2OrQJYtXcMWz4v7HcPCKxafqCpaSISIc3HLgkdaPXW75sL7nE/jbt2kGvXsIj9Pff4jm//SYeu/hi165nzBhx+c47IoIhcR+e7AdT8CZfWGsdWwSWCH1+ft3beSFqtZoLLrjAZftzukXFokWLXPbi3oIYS9QE1XVGI/z3v/D88+J2dLQ4eAUEuP61JC2DAwegvBzCwsQQ7tqYPFl4wF5/XRwQ8/PF2amroyR33glvvSWE4RNPiN5hkuanuNgyDcGTRZg3talorWOLQMwChVYpwubMmcPixYtdpoWcEmE6nY61a9fy+OOP01nOk2t6nnjCIsBAhH5XrRLGbIl3snWruBwypG7j8gMPwKJF8M8/8OCD4r7Jk4WHy5V07gzvvy+axL72Gjz9tP1GsZKmZdUqYWbv0sWS0vNEvDES1hpFmOI7dWSUmpfx0EMPcdlll9G1a1eSkpLwq3Y8XLFihVP7cyod6efnx7fffuvUC0gayIkTomM6iFTQrFniulU3XokXsm2buBxaT3f+9u3hllvEdWWg85QpTbOmq6+G8HARoTtwoGleQ1I3338vLpvqf9xcKCLs5Mm6t/MEWrMxX4mEFRaCg/2wvIX777+f1atX06NHD9q2bUt4eLjNj7M4nY688sor+f7775kzZ47TLyZxgsceEx6ciRPhrrvgjz9g8WIhwt5+2/PLuyX2cVSEgYhKHTggvEIxMaKjflOgVov1/PWXiNQNGNA0ryOxj14PP/0krl95pVuX0miU0UWZme5dR2MxGmUkTKGw0H4Vt5fy8ccf8+2333KZizJSTouw7t278/TTT7NhwwYGDx5McHCwzeP333+/SxbWqjl7Fr76SlxXomGjR4sPe3a2+KI+7zz3rU/SNJSXW3w/joiwuDjRJf/oUXE2HhTUdGuzFmF33tl0ryOpybp1cO5c03j+mhsllerpIqyyUohjaJ2RMI1GHG/KykRKshWJsMjISLp27eqy/TktwpYsWUJERAQ7duxgx44dNo+pVCopwlzBX3+JM61+/SxRB39/4QVbtkyMr5EizPvYt0+E9qOja86FrIsePZpuTQrDholLJVInaR4MBpg/X1yfMkW0efBklN5g2dme3bBViYJB6xRhIFKSZWWtzpz/5JNPMn/+fJYuXUqQC058nf5Ep6amNvpFJfXw++/isnq7gWuuESLs66/hpZc89wAmsc/Bg+KyX7+WN5xZEWH790NpKVSLgEuaiA8+EJGw4GB4/HF3r6bxxMWJS71eRPxjYty7noai+MECA11fDOMpRETA6dOtToS9/vrrpKSkEBsbS2JiYg1j/s6dO53an4efVnkhRqPwf0FNEXbJJeKs69Qp2LzZ81MTElsU03tSknvXYY927cRPZqYoBBg50t0r8n4yM+GRR8T1Z5/1jokFfn4i0nvmDGRlea4Ia81+MAXFnN/KKiSvdLEv02ERNnfuXIe2c3ZukqQaBw6Ig29gYM0vusBAkZL4/HPRuuK772S7AG9CEWF9+rh3HbUxbJio0tu6VYqw5uC++4TpeehQcd1baNdOiLDMTEhOdvdqGkZrbtSq0Ep7hc1X7AEuwmERtkspg6+DJukW39pQumKPGGG/Ket99wlP2C+/iPE1s2aJiqnAwGZdpqQJUNKRniDCJE3L99/DihXCA/b++96V8oqPhz17RCTMU2nNjVoVWnGvsIKCApYvX05KSgoPP/wwkZGR7Ny5k9jYWNq3b+/UvhwWYatXr3Z6oZIGsGePuBw40P7j550nDtBTp4rKuA0bxIHgoovEiJnJk6Fbt+ZarcRVlJRYeie1xHQkWCo2pTm/aSkstPQFfPhhz40W1YZSIenJIkxGwlptJGzv3r2MHz+e8PBw0tLSuOOOO4iMjGTFihWkp6fzySefOLU/6exuaSgirK4D76RJcOiQ6KifmCgOCD/+CHPnQt++UK1qVeIBHDokLmNjoW1b966lNoYMEZcnTghTtaRp+OADkarr1s07zPjVUSokPblNhYyEtVoRNnfuXG655RaOHTtGgFW26tJLL+Wff/5xen9ShLUkDAbYu1dcr+/sNzERnnpKDPfeulVUSw4aBFot/Pvfra6LscfT0v1gINIPyjxLGQ1rOr75RlzOmeOdNgNvioS1ZhHWStOR27Zt46677qpxf/v27cnOznZ6f1KEtSTS0sSHW6OBXr0ce47Szfzhh4VPLCxMfEF+/nmTLlXiYhTPZd++7l1HfciUZNOSng5btogWJVOnuns1TYM3RcJkOrLVRcL8/f0pKiqqcf/Ro0eJjo52en9ShLUklFRkUlLDqh7j4sRgZ4Bff619uw0bRMpL6cYvcT+bN4vL88937zrqQ/EqKp39Ja5Fmc07apSlp5a34U2RsNYswlpZJCw9PR2DwcAVV1zB008/jU6nA0RBYnp6Oo8++ijTpk1zer9OiTC9Xs/TTz9NRkaG0y8kcQBH/GD1cdFF4nLDBvuPGwyiwvLcOfjvfy0VeRL3UVFhiYS1dBGmFA3I903TsHy5uLz6aveuoylRImFZWaIvoieiCA/rGYqtjVYWCevcuTNnz57lf//7HyUlJcTExFBeXs7o0aPp1q0boaGhPPfcc07v16lmrb6+vrz88svcdNNNTr+QxAH27xeX/fs3fB/Dholy9owMkdro2NH28a++snzh6/VCkK1a1fDXkzSeXbvEsPaYGOH1a8konrWjR8WaZZ8615GRYWlR04Azao9BifDpdOJksKUWotSFIjxa0czEGrQyEWY0nTCEh4fz559/sn79evbu3UtJSQmDBg1i/PjxDdqv0+nIsWPHsnbt2ga9mKQelAq5xrQoCA62pIyqR8OMRtF5G+COO4T37O+/LakwiXuwTkW29F57HTqIFIxeD8ePu3s13sWKFeLyggssKTtvRKMR3lWAvDz3rqWhnDsnLhUh0hqxTkd6akTTSax7oY4cOZJ77rmHRx55pMECDBowtmjSpEn83//9H/v27WPw4MEEV5shd8UVVzR4Ma0avR6OHRPXe/du3L4uuAC2bxcibPp0y/1//y3SSMHB8PLL4kz0o4/gtddafhrMm1FEmCcMZVepxEnC1q3ivdTY96rEglIV+a9/uXcdzUF0NBQVic75zTGA3tUoIkxGwsT3SFlZq5gn+/jjj9c7tNvZqUFOi7B77rmn1hdSqVRUydYIDSMlRbyZg4IgIaFx+xo5Ugirv/4SZyiKen/jDXF5yy0QHi5M/B99JA7+L78sohyS5mfLFnHpKUJYEWEHDnh32qw5ycy0RK5bw980Kkoc8zy135xMRwrR5ednSSu3ABG2YMECVqxYweHDhwkMDGTEiBG8+OKL9FRa6wAVFRU8+OCDLFu2DK1Wy8UXX8xbb71FbGxsvfvft28fGo2m1scbMjXIaRFmMBicfhGJAyipyF69RNuJxjBxougvdOSIiLIMHy5MsD/9JB6/915xOWCAEGzr1wshNmdO415X4jxZWaJTvkplaf/Q0mlqc/4PP4iikbvvFj3vGvt58ARWrBAnTMOHt46ToagocempIkymI8UxKzIScnLE36OxwQMXsHbtWmbNmsXQoUPR6/X85z//YeLEiRw8eNCctZszZw6//PIL33zzDeHh4dx7771MnTqVDbUVs1nx3XffEePiofOt4OjmISgizBXpnbAwS0rjww/F5fLlojLy/PNte5ApVViKQJM0L0oUrG9fz2n82JQizGgUkx/27xcnC7Nnu/41WiKtoSrSGk8WYQaDjIQpKEUVLcTbt3LlSm655Rb69OlDcnIyH330Eenp6ewwTZEpLCxkyZIlLFy4kLFjxzJ48GCWLl3Kxo0b2VyPN7qpZmM7HQkDKC0tZe3ataSnp1NZWWnz2P333++ShbU6XCnCAGbOhE8+gS+/hBtvhGXLxP3XXmu73eTJ4ovun3/EgaU1n9m5A0/pD2aNIsKOHBFeRt8GHUbss26dGIuk8O67MH++Z1bQOUp2tvj8QesRYUpTyzNn3LuOhlBUZDGit/bjZTOJsOLiYpsGqf7+/vj7+9f7vMLCQgAiTWJ5x44d6HQ6GyN9r1696NixI5s2beL8Oo7DxiYqPnD66Llr1y4uvfRSysrKKC0tJTIykrNnzxIUFERMTIwUYQ3F1SJs1Chh9N6yBUaPFvepVDVNv126iLYDBw7Ab7/B9de75vUljuGJIqxTJ+FdLCsTgsmVxuolS8Tl7beL4pLdu8X0B28+rijezSFDaraU8VY8ORKmpCKDgsABIeDVKJHAJhZhSdU6BsyfP58nn3yyzucYDAZmz57NBRdcQF/TJJLs7Gw0Gg0R1fq7xcbG1jtyaOnSpYSHh/PPP/8wYsQIfKudfOr1ejZu3MiFF17o2C9lwul05Jw5c5g8eTL5+fkEBgayefNmTp48yeDBg3nllVec3Z1E4ehRcenouKL6UKnEwf222yz3jRsH7dvX3FapaP3ll/r3W1xs6RYtaRx6vWX8jyeJMLXacrKgzLx0BYWFlgrBmTOFEAMx0NqbS+B37xaXnvQeaCyeLMJkKtJCM0XCDh48SGFhofln3rx59T5n1qxZ7N+/n2VKFqiR3Hzzzfj7+3PRRRdxThHiVhQWFnKR0izdCZwWYbt37+bBBx9ErVbj4+ODVqslISGBl156if/85z9OL0CCCG8rodZOnVy335AQEVnIyhJm59rmSU6cKC7//rvuL7uiItHNPzYWvv/edetsrRw9KqJJISGuE9/NRVP4wr78EsrLxb7PO09EZf39xYgkV4q9loYrJmV4GooI88R0pDTlW2gmERYaGkpYWJj5p75U5L333svPP//M6tWr6WBV6BIXF0dlZSUF1UYt5eTkEOfgmDCj0WjXH5aXl1ejZZcjOJ2O9PPzQ22qVoqJiSE9PZ3evXsTHh7OqVOnnF6ABNEpG8SHuinKfOPiLNEuewwfLqops7PFl6rSFb06CxZAaqq4PnUqrF0r0p6ShqFMSOjb1/MqAJtChFmnIlUq8XmYMAF+/hm++67lDzdvCEZj6xRhiifMEyNhskeYhRZmzDcajdx333189913rFmzhs6dO9s8PnjwYPz8/Fi1apV5zuORI0dIT09n+PDhde576tSpgDDo33LLLTZCsKqqir179zJixAin1+z0kX/gwIFsM6VQRo8ezRNPPMHnn3/O7NmzzXlXiZMo4tVdpen+/qJVBdQ+wujoUXj1VXG9d2/x5fHVV82zPm9FGYLtiZ8bV4uwgweFB8zPTxSSKFx1lbhUusl7G9nZIhqkVnvm+6CheEM6UkbCWpwImzVrFp999hlffPEFoaGhZGdnk52dTXl5OSBGDs2cOZO5c+eyevVqduzYwa233srw4cPrNOUrzw0PD8doNBIaGmq+HR4eTlxcHHfeeSefffaZ02t2OhL2/PPPU2zyBD333HPcdNNN/Pvf/6Z79+58qLRDkDiHEglzZ5+VcePgzz+Fj6y6CbqwUHwZarViu3vuEQ0l//7bPWv1FqwjYZ6GIsIOH4aqKjGvtDGsWSMux4yxfEGDqN5Vq4VvKjUVqp3ZejyKH6xnTxGNbi0o/+OiIqisFKOMPAUZCbPQwkTY22+/DcCYMWNs7l+6dCm33HILAK+++ipqtZpp06bZNGutj6VLlwKQmJjIQw891KDUoz2cFmFDhgwxX4+JiWHlypUuWUirxt2RMBBpn//7P/jjDzETMD9fzJcsLhbX8/MhPh4+/VREzlQqUdGZlSXulziPJ4uwzp0hIAAqKkSFZPfujdvfpk3isno4PzoaLrxQiLRly8ABQ65H0RpTkSDmDvr4CAF/9qxnzcqUIsxCCxNhjrSRCAgIYPHixSxevLhBrzF//nz0ej1//fUXKSkpXH/99YSGhpKZmUlYWBghISFO7c/DjCheSkuIhA0cCOPHi2jXlCkiIrFnj/iCzc8XrSx+/FEIrshIy5Dw1avdt2ZPpqxMjG0BzxRhPj5i4gJYGs42BkWE2fNl3HSTuPzgA9Eo05torSJMrbZ8gXtaSlKmIy20MBHWHJw8eZJ+/foxZcoUZs2axRlTccmLL77IQw895PT+HIqEDRw40OFusTt37nR6Ea2elhAJU6ngrbegXz+Lz2fiRHjwQZEquPBCW/P4uHGwc6dIScreYs5z6JDw1UVHi2pTT2T4cNHnbONGuOGGhu8nN9ciSO0NMb/mGtFQ+MQJIfrHjWv4a7U0WqsIA5GSzM31PBEmI2EWFBGWny9OkDytwKgBPPDAAwwZMoQ9e/bQ1qqJ9FVXXcUdd9zh9P4cEmFXXnml0zuWOEFLiISBSCl9+aXwhY0fL/w4tXVDHz1aDP1ev7551+gtKKnI2ipRPYERI0SxhhLFaihKw9qkJJGmqk5wMMyYAW+/DW++6T0irLxcTB2A1ivCwPPaVMgWFRYUIWo0QkFBqxCm69atY+PGjTUGeScmJnL69Gmn9+eQCJs/f77TO5Y4QUuIhClcdZWlIq0ulLTRkSMiFO3NY2WaAuXL11UTEtyB4t/au1d4Bxs6+1JJZ9ZVIn7vvfDOO6I/3eefizPua65pfEGAOzlwQEQPoqJap69S+cJW0nuegmzWakGjEZ/74mLxPdAK/iYGg4Gqqqoa92dkZBDagGNgg2OHO3bs4LPPPuOzzz5j165dDdrHggULGDp0KKGhocTExHDllVdyRPlyMlFRUcGsWbNo27YtISEhTJs2jZycnIYuu+VRVGTpQN8SRJijREaKii6wRDIkjnPsmLhsrKHdnbRrJ5oLGwywdWvD96OM7KorGpSUZEl733CDuO7pJn3rVGQTDQdu0SiRJE8SYUYjpKeL655UTNCUNNPoopbCxIkTWbRokfm2SqWipKSE+fPnc+mllzq9P6dFWG5uLmPHjmXo0KHcf//93H///QwePJhx48aZDWqOsnbtWmbNmsXmzZv5888/0el0TJw4kdLSUvM2c+bM4aeffuKbb75h7dq1ZGZmmpumeQVKFKypGrU2JUokpLHpqNaIN4gwsESvNm5s+D6UEy9F1NfG/Pmij5jCyy+LYhFPpTX7wcCSeq7WvbxFk5cnWvaAKFaStDpz/v/+9z82bNhAUlISFRUVXH/99eZU5Isvvuj0/pwWYffddx/FxcUcOHCAc+fOce7cOfbv309RUZHTw7tXrlzJLbfcQp8+fUhOTuajjz4iPT2dHTt2AGIW05IlS1i4cCFjx45l8ODBLF26lI0bN7K5luiLVqul6P/bu++wps63D+DfsGXIEGQ5wL0VFXGLintr1eK2KnW+VWutq2pt6/ppHZVqi1K1DrR174G2WBfLLW5URECRvSE57x+PJwEFWUlOknN/rovrhISc3I8HyZ1n3E9KivwrVdP3OdSU+WBloYw3YDHiOFYGBND+JKy8ibhUqvi3KG4j8Nq12V6b4eHArFnsvlmzWJ0pbST2JEwbe8L439UqVcRV1+1TRJaEValSBbdu3cLChQsxa9YsuLm5YeXKlbhx4wYqV65c6vOVOgk7ffo0fv31V9TPN5elQYMG8PX1xalTp0odQH7J7z9h2Lzv3gwLC0Nubi68vLzkP1OvXj1Uq1YNV4v4o79ixYoClWw/3H1d42jSfLDS4pOw4GD2ZkpKJiYGSE9n85q0vfho/iSsLOUjXrxgSZSxcck+iDRtysqj/PADW1X67BmgjUWiOY7NpQPEm4RpY08Yn4TVqiVsHJqkUiU2nJ6WJnQkKtO8eXMkvv+wsGzZMuTk5GDkyJFYvXo1fv31V0ycOBEVypiUlzoJk8lkMMw/JPCeoaEhZOWo4SOTyTBz5ky0a9dOvv1RbGwsjIyMYPXBiil7e3vExsYWep758+cX2G39vjL3tlMFbe4Jq1+fFexMTweePxc6Gu3BD0W6uGhXpfDCNGkCmJqyN9IHD0r//EeP2LF27dJNsjczAxYuZLdXrPj0xvOaKDaW/Zvp6Wnf5u3Kos09YZSEKfj7A3l5bCcVHRURESGfJvX9998jTYkJZ6kr5nfp0gVfffUV9u7dC6f3ExOjo6Mxa9YsdC3H0vFp06bh7t27+K+cJQ+MjY0LbKyZkpJSrvOpnDb3hOnrsyGk27fZvJ6aNYWOSDvoynwwgM3Rcndnm7lfuaLYzqik+CSsuKHIwkycyOrYvXzJPgRoU68i/+GwVi3WCyhGlITpBhEMyzZr1gzjx49H+/btwXEc1qxZU2Rl/MWLF5fq3KXuCdu0aRNSUlLg4uKCmjVrombNmnB1dUVKSgp++eWX0p4OADB9+nQcP34cFy9eRJV8yYiDgwNycnKQ9EF3dVxcHBwcHMr0WhpHm3vCAMWn+LL0goiVLiVhgGJIsixzA/lJ+WVJwipUUOzcoG2LQ/gkTNOnS6gSDUcSLbF9+3ZUqlQJx48fh0QiwalTp3Do0KGPvg4fPlzqc5e6J6xq1aoIDw/H+fPn8eD9G2/9+vULzNsqKY7jMGPGDBw6dAj//PMPXD/4JNuiRQsYGhoiMDAQQ4YMAQA8fPgQL1++RJtP1RTSJtrcEwZQElYWupaEtWvHjufOlb5qNt8TVtzKyKK0acPmJF69ql07N1ASVvKesPPngZ9+Anr2BL79VvVxfQq/swMlYaJSt25dBAQEAAD09PQQGBhYpkn4hSl1EgawuhjdunVDt27dyvXi06ZNw549e3DkyBFYWFjI53lZWlqiQoUKsLS0xIQJEzB79mzY2NigYsWKmDFjBtq0aYPWrVuX67U1BvWEiY+uJWFduwIVK7Lf5cuXgQ4dSv7c8gxHAiwJ27CBesK0Uf6eMI4rvFbawYPA+w/g+Pdfto+oUIVtk5IUWyxReQrRKs/c98KU+CPr1atXcfz48QL37dy5E66urqhcuTJ8fHyQnZ1dqhffvHkzkpOT4enpCUdHR/nXvn375D+zbt069O3bF0OGDEHHjh3h4OCAgwcPlup1NFZysnYWas2PkrDSkcl0pzwFz8RE8Ua5Z0/Jn5eRoSh8WZ4kDABu3mQLRLQFJWGKnjCZTPF38EMHDihucxywbZvq4yrKvXvs6OhY9t0hiNbbsWMHTpw4If9+7ty5sLKyQtu2bfHixYtSn6/ESdiyZctwj/8lBHDnzh1MmDABXl5emDdvHo4dO4YVK1aU6sU5jiv0a9y4cfKfMTExga+vLxISEpCeno6DBw/q3nwwGxu2wkwb8W+eb9+Kpk5MuURHA1lZbE9OFxeho1Eefihw//6S1+3ik1EbG8U+gqVVtSqrXC6VAqGhZTuHur19y3pUJJKyD8PqAhMTxergooYk+Q93fJL/++/ClcPhd4Vwdxfm9YlGWL58ubwcxdWrV+Hr64vVq1fD1tYWs/j6haVQ4iTs5s2bBVY/BgQEwMPDA35+fpg9ezY2btyI/fv3lzoAUdP2+WAAKxVQrRq7/cGWU6QQ/FCkq2vRm6Nro86dWd2uhAQ2N6wkyjsUCbBEhu8N05YhSX6bJhcX7f3wpQwSiaI3rLDJ+TKZIgn77jtWjyoqCjh0SG0hFsAnYa1aCfP6RCNERUWh1vs5gYcPH8aQIUPg4+ODFStW4NKlS6U+X4mTsMTERNjb28u///fff9GrVy/59+7u7ojikwpSMto+H4zHf5qnIcni6dp8MJ6+PjB8OLu9d2/JnlOelZH5adv2WZGR7EglXT49OT86mg1ZGxiwYdtp09j9q1cXXxcuLw/47DM2gd7Tk304KC8+CfPwKP+5iNYyNzfHu/ejPmfPnpXPjTcxMUFmZmapz1fiJMze3h6R7/945OTkIDw8vMDk+NTU1EKLuJJP0IWeMECRUPDDS6RoujYfLD9+SPLw4ZLNzyrvykhe/p4wbSjayhc21qXh6LL6VJkK/kNdrVqsHt306WwIMySETdL/lOBgNp/s6VP2s9u3ly/O+Hi2OwMAtGxZvnMRrdatWzdMnDgREydOxKNHj+Sbdt+7dw8uZfg/XeIkrHfv3pg3bx4uXbqE+fPnw9TUFB3yrYK6ffs2atInu9KJjmZHZ2dh4ygvPqHge3lI0XS1JwxgwzQ1arAE7Nix4n9eWT1hzZuzuUVv3yreKDUZP3m3enVh49AEn+oJ45MwfvGPnR0wfjy7vXjxpxPuD3tFd+8uX5whIexYt64icSSi5OvrizZt2uDt27c4cOAAKr3fOzMsLAze3t6lPl+Jk7AffvgBBgYG6NSpE/z8/ODn5wejfFuu+Pv7o3v37qUOQNRiYtjx/c4DWouSsJLT5SRMIlEMSRa3gpnjlJeEGRuzRAzQjs3kqSdMoSQ9Yfm3dVqwgF3vS5eAM2eKPi+fhM2Zw4Yzw8PLN13ifY0oGookVlZW2LRpE44cOYKePXvK7//++++xkN9KrRRKnITZ2toiKCgIiYmJSExMxKBBgwo8/tdff2HJkiWlDkDU+CRMqLo3ypJ/OFIbhoOEIpMpij3qYhIGAIMHs+PJk8Cn5ke8e6d441VG4UttmpxPPWEKn+oJ45P0/ElYlSqKuWE//FD4OTlO8XvQrx/Qowe7XdK5ih+6fRv48092m39tImpJSUlYu3atfFhy3bp1SE5OLtO5Sr1tkaWlJfQL2WjXxsamQM8YKYHXr9lR23vCXF3ZxOz0dEViST4WFwdkZ7OK8tq+GKMoLVqwtqWnf3qVJD8frGpV5awQ1JYkTCpVzAWlJEzRE1ZYElZUr/GcOezvzZUrhfduRUWxv60GBmz+Fl/eoqSrdj/0/fcssRs6lFZGEoSGhqJmzZpYt24dEhISkJCQgJ9//hk1a9ZEeHh4qc9X6iSMKEleHvDmDbut7T1hhoaKoRUakiwaPwfQwUG3ylPkJ5EoesM+VUpA2cOyfBJ2+zaQlqacc6pCTAyQm8uuv7Z/+FKGonrCZDLFh9QPP7A4OgLvJ0Pjjz8+PiefiDdtyhL8Ll3Y98HBQEpK6eLLygJOnWK3Fywo3XOJTpo1axb69++P58+f4+DBgzh48CAiIyPRt29fzJw5s9TnoyRMKHFx7NOVvj6bcKrt+CElSsKKpisLMYrDv0EGBRX9M8peJVqlCnuzlskUk6g1ET8UWaWK7ibipfF+UvNHJSTi49kHVYmEfWj5ED9Bf+dO1rucH79ykt/TtHp1Vg5EKmVzyUrj0iU2rO7szJI6InqhoaH49ttvYZDv/6+BgQHmzp2L0DIUjKYkTCj8sJ2DQ+k2PNZUNDm/eGJJwjw82Jvns2eK3t4P8b8nytwIWRuGJGlSfkH8Jsgf/p7w/1cqV2Y97R/q04f1JMbGAps2FXzs4kV27NxZcR9faDwwsHTxnT7Njj16FL63JRGdihUr4iW/3Vo+UVFRsCjDdlY68O6vpXRlUj6PkrDi8cV5tb0uXHEsLYH69dnt69cL/xlV1EvjkzBNXiFJk/IL4kcBikrCivrAYmQE/PQTu71sGStPArC/qw8esISpUyfFz/NDkhculC4+PgnLtwqOiNvw4cMxYcIE7Nu3D1FRUYiKikJAQAAmTpyo2hIVRMl0ZVI+j5Kw4omlJwxQJETXrn38GMeptifs2jXNXaVLPWEF5e8Jy3/N+L+Pn/q/MmYMK02SkgLwK/P/+YcdmzVTzDcDFL1it24pErbiPH7MNlrX0wO8vEr2HKLz1qxZg8GDB2PMmDFwcXFB9erVMW7cOHz22WdYtWpVqc9HSZhQdLUn7MkTNi+HfExMSRi/m0ZhSVh8PHvjlEiUu3WPmxurIfXuneZ+GKCesIL4nrDsbCA1VXE//3/lUx9S9fSAdevY7d9+A+7eVfRc5R+KBFiy17gxu80nasXZuZMdu3cvmNARUTMyMsKGDRuQmJiImzdv4tatW0hISMC6detgbGxc6vPRzFCh8J/0dCUJc3FhE42zstgfUF0twVAeYkzCgoPZhOj8ZW34BKlKFbYNjbIYGbGSBJcvs3lh5S0Cqwp8EkY9YYyZGftKT2c9VBUrsvtL0hMGAB07shIUBw6wIUe+l6tPn49/tmtX4M4dNi9s6NBPn1cmA3bsYLfHjStxc4ju+uKLL0r0c/7+/qU6L/WECUVXquXzDAxYvTCA9pAsiljmhAFsTliFCqxcBL9hNU+VuwZo8uR8jqOesMIUNjm/NB9Y1q1jw9p8Avbtt4o5YPnx95Vkcv7586zemKUlMGBA8T9PdN727dtx8eJFJCUlyYvWF/ZVWtQTJhRdG44E2Jvq48fs68PhALFLSVHUrxJDT5i+PtCgARAWxnof8s/9UuUm5po8Of/NG9ZTLJGIIxEvqcqVWaJeWBJWkg+pVasCN28Cq1cD5uasmGthOnViv5dPngAvXwLVqhX+cxynmGM2erRye2uJ1poyZQr27t2LyMhIjB8/HqNGjYKNjU25z0s9YUKJi2PHwmrgaCuanF80/k3F0pINv4hBo0bsePduwftVMSmfxydhd++WvjCnqvGT8p2d2dApYQrrCSvpcCTPzIxVtv/mm6JLSVSsyIargU+vkjx0iM1lNDWlAq1EztfXFzExMZg7dy6OHTuGqlWrYtiwYThz5gy4ciwEoiRMKO/esSNfrFAXUBJWNDHNB+MVlYSpsifM0ZEN9XEcm4+mSWgosnAflqnIzmaLNwDl/3/h64UVlYRdvqyYAzZrlm6NVJByMzY2hre3N86dO4f79++jYcOGmDp1KlxcXJBWxp06KAkTQmamYnNjXUrCqGp+0XStJElJFJaEqao8RX5t27Kjps0Lo/IUhfuwJ4z/v2JsrPxVifnnhX3Ye/H2LdC3L1ul6elJvWDkk/T09CCRSMBxHKRSadnPo8SYSEnxvWAGBkAZKuxqLL5n4+lTza3TJBT+kz3/hiMGfBL26JFiaxlVlafIT1Mn51NPWOH4/xP8xHr+36lqVeVXqW/bliV3r18rNpHnLVsGJCWx7YlOnFDOxvJEp2RnZ2Pv3r3o1q0b6tSpgzt37mDTpk14+fIlzM3Ny3ROSsKEwO+TZmOjW1th8JON8w8nEIZ/g7G1FTYOdXJ2ZnPg8vIUb3iqKk+RX/6irZpUs456wgr3YU8Yv5pWFUl6hQqKPSW3b1fc/+gRsGULu/3zz5SAkY9MnToVjo6OWLlyJfr27YuoqCj89ddf6N27N/TKsfUgrY4Ugi7OBwPYZGN7e7boICpKNzYmVxY+CRPTv4lEwnrDLl9mKyQbN1ZteQpe06bszTYxEXj4ULGFktCoJ6xwHyZhz56xI1/yRtlGjWJzwlauZMfmzdnfq7w8Vl+ssPIWRPS2bNmCatWqoUaNGvj333/xL79R/AcOHjxYqvNSEiYEXU3CADaEEBfHamI1by50NJpDjEkYwLaPuXwZuHEDGDFCtZPyeYaGbBXcpUtsSFITkjCOU/TwUE9YQUUlYTVqqOb1xo9nSdeSJWzxBr+AQ0+PlbkgpBBjxoyBRAUjV5SECSH/cKSuqVIFCA1lf+SIAj88K6bhSECRiIeHs6OqJ+Xz2rRRJGElrHStUrGxQEYGe6OnJKwge3t2fPsWyM1VJKuqSsIAYPFioHdvtmhk9mzWazpxIqttR0ghtucfvlYiSsKEoOs9YYCiOjxhxNoTlj8J4zg2LAmoticM0LwVkk+fsmO1alQj7EP29mz4ODOTDdmqejiS17Il+2rXDjh5kiVhhKgZTcwXgi4nYfzkfOoJK0isSViDBmx4MCkJOHcOuH+frQru0EG1r8tPzr9/n7220PhhWFWtCNVm+VfK3r6tKGStyp6w/GrXBr76SjxFlIlGoSRMCLo8HEk9YR/LzVUkAmJLwoyM2IR8APjuO3bs3Fn1v/uVK7M3cY4Drl9X7WuVBN8TpuphWG3FJ2Hnz7OjtTVgZSVYOISoCyVhQtDlnjA+CaOeMAX+ekskyi8+qQ34IUl+AvTgwep5XU2qF8YnYdQTVjg+OT17lh1VPRRJiIagJEwIupyE8cORr15RwVYePxRZqRLbQFhs+PlZAEtEBw5Uz+tq0mbeNBz5afy/C5+sqmsokhCBURImBD4J08XhSCcn9kabk6NIPsROrPPBeKNHAzt2AGPGABs3qm/T+vbt2fHKFTYkLCQajvy0D/9dqLwNKYOgoCD069cPTk5OkEgkOHz4cIHHOY7D4sWL4ejoiAoVKsDLywuPBd5mj5IwIfBzwnSxJ4wv2ArQvDAeX55CrEmYgQFLwHbsAKZPV9/rNm7M/o+lpwMhIep73Q8lJir+z1MPT+E+7CEcOlSYOIhWS09PR9OmTeHr61vo46tXr8bGjRuxZcsWXL9+HWZmZujRoweysrLUHKkCJWHqxnG6nYQBNC/sQ2LcskgT6OmxRQAAq4wuFH4o0t4eKOP+cjqvWjXFbTs76jEkZdKrVy/8+OOPGDRo0EePcRyH9evXY9GiRRgwYACaNGmCnTt34vXr1x/1mKkTJWHqlpLCtscAdHM4EqAyFR8S+3CkkPgtaIRMwh4+ZMd69YSLQdMZ5CtZ2bOncHEQjZSamoqUlBT5V3Z2dqnPERkZidjYWHh5ecnvs7S0hIeHB64KuHiHkjB143vBKlRgX7qIylQUREmYcLp2ZccrV1jFeiE8eMCOlIR92ubNrOdy7VqhIyEapkGDBrC0tJR/rVixotTniI2NBQDY89Nl3rO3t5c/JgSqmK9uiYnsqMulCqgnrCCxzwkTUu3arNxBZCRw4ABbJKBulISVzOTJ7IuQD9y/fx/Ozs7y742NjQWMRrmoJ0zdkpPZ0dJS2DhUiXrCCqI5YcKRSBR7R/r5CRMDJWGElIuFhQUqVqwo/ypLEubwflV2HL8jw3txcXHyx4RASZi6iSEJo56wgmg4Uljjx7NJ+pcuASNHAgEBgEymntfOy1NsWk5JGCGCcXV1hYODAwIDA+X3paSk4Pr162jD1xQUACVh6paSwo66nITxPWHR0ep7s9NklIQJy9kZ6N+f3d6zB/D2Zoti7OwUX97eqikuHBnJauaZmBRcAUgIUbq0tDTcvHkTN2/eBMAm49+8eRMvX76ERCLBzJkz8eOPP+Lo0aO4c+cOxowZAycnJwxUVwHpQtCcMHXje8IqVhQ2DlX6sGDrBxMhRYXjFHPCaDhSOL/9Bnh5sd5ZX1/F/0NeQADw+efAgAHKfV1+KLJuXdYbRwhRmdDQUHTmy9IAmD17NgBg7Nix2L59O+bOnYv09HT4+PggKSkJ7du3x+nTp2FiYiJUyJSEqZ0YesIMDVlV9JgYNi9MzElYUhIglbLb1BMmnMqVgWnT2O2FCwsOlW/ZAvzyC7B4MdCvn3KTJT4Jq19feeckhBTK09MT3Cd6tCUSCZYtW4Zly5apMapPo49m6iaGnjCA5oXx+KFICwtAh1b0aDULC6BBA8XX0qXs/+Pt22wFpTK9eMGOtCE1IaQQlISpmxgm5gO0QpJH88E0n40NMGsWu71kiaLnUhn433/+/wMhhORDSZi6iWE4EqCeMB7NB9MOs2ax2n0REWx+mLLwSRj//4EQQvKhJEzdxDIcyW9ULPAO9YKjnjDtYGkJfP01u71+vfLOS0kYIeQTKAlTN7H0hPETke/fFzYOoVESpj18fAAjIyA0FAgPL//5cnIAvjAkJWGEkEJQEqZuYukJa9CAHZ88YW9GYkVbFmkPOztg8GB2+/ffy3++16/Z0ciIhqMJIYWiJEzdxDIx39mZrUKTSsU9JElbFmkXHx923L+//MVbo6PZsUoVVjePEEI+QEmYuollOFIiUQxJRkQIG4uQaDhSu7Rvz0qJJCYCT5+W71w0H4wQUgxKwtRJJlMkYbo+HAkohiTFPC+MkjDtYmgINGvGboeGlu9clIQRQopBSZg6pacrhjh0vScMoJ4wgOaEaaOWLdkxJKR856EkjBBSDErC1ImfD2ZgwDb01XVi7wnjOODNG3ab5oRpD3d3dqSeMEKIilESpk75J+WLYaIu3xP28KFyq5Bri+RkIDOT3XZyEjYWUnJ8T1hYWPl+b/lCxZSEEUKKQEmYOollUj7PxYX1+GVnA5GRQkejfnxPiI0NUKGCsLGQkqtXDzAzY9MHHj4s+3mePGFH2jeSEFIESsLUSSw1wnj6+kDduuy2GOeF0XCUdtLXVwylP3hQtnO8fQu8e8d6vPn/A4QQ8gFKwtRJbD1hgLjnhVESpr1q1mTHspap4D90uLhQLyghpEiUhKmT2HrCANWtkOQ4za/ET0mY9ipvEsb3oNWrp5x4CCE6iZIwdRJLtfz8VNETlpEBeHqyXoa7d5V3XmXLXzGdaJdatdixvD1hlIQRQj6BkjB1EnMSFhFR/m1gAHaOKVOAoCAgJgYYMABISCj/eVWB7wlzdhY2DlJ6yuoJ43uCCSGkEJSEqROfLNjYCBuHOtWqxeqipaUpluyXR2gosHMnmzzt6Ag8ewZ8/jmQl1f+cysbDUdqLz4Je/kSyM0t/fOpJ4wQUgKUhKmTGJMwQ0NFb0B5K5ADQGAgO/bvD5w+DZiaAufOAfPmlf/cykZJmPZydGQT6qVS4MWL0j03I0PxHOoJI4R8AiVh6pSYyI5iSsIAoFMndrx4sfzn+vdfxTmbNAG2b2ffr10L7NpV/vMrS3o6kJTEblMSpn0kEqBGDXa7tEOS/IcNBwfaKYEQ8kmUhKkT3xNmbS1sHOrm6cmO//xTvvPk5QH//cdu84nd0KHAwoXs9sSJiseFxk/KNzcX12pYXVLWeWHnz7Njly7KjYcQonMETcKCgoLQr18/ODk5QSKR4PDhwwUe5zgOixcvhqOjIypUqAAvLy88fvxYmGCVQYzDkYAiYbp3T7GXYlncuMHmlllZAY0bK+5ftoxN0M/OZsOUmlAYlp//RpPytRe/QrK0v0/nzrGjl5dy4yGE6BxBk7D09HQ0bdoUvr6+hT6+evVqbNy4EVu2bMH169dhZmaGHj16ICsrS82RKolYkzBbW0XSFBRU9vPwz+3QgU3M5+npAXv2AK1bsyHfnj2B168/fn5aGnDqFHD2rGJPR1Xh5wS5uKj2dYjq8Bt5X7lS8uckJSmGI7t2VXpIhBDdImgS1qtXL/z4448YNGjQR49xHIf169dj0aJFGDBgAJo0aYKdO3fi9evXH/WYaQWZTDFHSGzDkYBiSJIfqimLW7fY0cPj48dMTYFjx4DatdmKtu7d2bYxvOhooGlToHdvoEcPtpXMqVNlj6U4z5+zIyVh2qtdO3a8dYsl8CURGMj+r9epA1SrprrYCCE6QWPnhEVGRiI2NhZe+br0LS0t4eHhgatXrxb5vOzsbKSkpMi/UlNT1RFu8ZKTFXWyxJiE9ejBjmfOlL1eGF/wla899iFbW3Z+R0c29FmtGlCpEvuqVYuVs7CzYxOmo6KAIUMUyZKyURKm/apWZV9SKRAcXPzPS6XATz+x2/36qTY2QohO0NgkLDY2FgBgb29f4H57e3v5Y4VZsWIFLC0t5V8NinrDVjd+KNLMDDA2FjYWIXh6AkZGLDl59Kj0z5fJSlYA09WV9bY5OrJSAQkJ7Csriz0WEsKSsY4d2ZDkjBnsMWWjJEw38L1hly8X/7M7drB5i5aWwLffqjYuQohO0NgkrKzmz5+P5ORk+dd9Tdk4WqzlKXhmZmwuF8B6q0orKoqVfTA0VKxaK0qDBizRiogo+PXwIVC9Oqv/tHkzKyJ7/DibPM/XH1MWSsJ0Q0mTsLw8tkAEAL77jvW4EkJIMTQ2CXNwcAAAxMXFFbg/Li5O/lhhjI2NUbFiRfmXhYWFSuMsMbGWp8ivZ092PH689M/lV6jVrs0SseKYmLBq5fm/8j+vQQPgjz9YDa+EBGD0aEWiXF45OYoSFZSEaTc+Cbty5dO7Mhw5whZjVKoETJ2qntgIIVpPY5MwV1dXODg4IDBfD0VKSgquX7+ONm3aCBhZGYl1ZWR+AweyIpjnzrFhm9LgkzBlDi+PGsWGRuvUYftQfv21cs776hUbPjUxAT4YTidapmlT9sEpNRUICyv4WE4O+13euJH1fgHA5Mmsp1WN7r+9jxknZ6CdfzsMDBiIreFbwSljn1ZCiMoZCPniaWlpePLkifz7yMhI3Lx5EzY2NqhWrRpmzpyJH3/8EbVr14arqyu+++47ODk5YeDAgcIFXVaUhLHJ8d7erJzE0qWs96Ck+GFlZW8DU6EC4O/Phkr/+AMYPlyxiKCs8g9FSiTljZAISU+PzWc8dAi4cKHgytyJE4E//1R8b2Sk1l6wyMRI/BL8CzYFb0KuTLG/5ZGHRxAYGYjtA7bD2ECE808J0SKC9oSFhobCzc0Nbm5uAIDZs2fDzc0NixcvBgDMnTsXM2bMgI+PD9zd3ZGWlobTp0/DxMREyLDLhh/qEvNwJAAsXsze2I4eZZtxl5QqesJ47dqxCfoA4OPDej3Kg+aD6Ra+8v2FC4r7IiIU22QNHAjMn88WhDg5qTycrLwszD8/H3U21cG6a+uQK8tFn9p9sGvQLizptAQGegYIuBuAuefmqjwWQkj5CNoT5unp+cluc4lEgmXLlmEZP+FVm1FPGFO3LhsG3LkTWLIEOHGi+OdIpcDNm+x2/kr5yrR8OaszFhnJNgMvooBwiVASplv4JOz8edazWbs2K7PCcSwBO3RIbaHEpsViYMBAXI++DgDwquGF2a1no2etnpC873Vt7tgcAwIGYGPwRgysNxCdXTurLT6iflKZFA/iH6CCYQW4WrnKfw+IdhA0CRMVSsIUvvsO2L0bOHkSWLkS4Of4WVuzTbk/dO8eWxlpYcEm2KuCmRng58e2mvn1V2DECMWk7NKiJEy31K/PVuTye0jm3zqNnwumBrfjbqPvnr6ISomCtYk1/Af4Y2C9gR/9XP+6/eHT3Ae/h/+O8UfG486UO7Aw1pAFSkRp7sTdgW+IL/bc2YPUHNZ772juiInNJ+LLFl/CuWLBLdNSs1MRFhOGh/EPIeNk8vsrGFZAM4dmaGLfBHoSjZ0mrrMoCVMXWh2pUKsWG/bbvJkN4+S3bx8wbFjB+66zT/1wdy+4XZGyde0KjB3L6j39+islYYSRSNjm8w8esGv64gUbjnRxAZo3V0sI115dQ7c/uyEtJw11KtXBce/jqF2pdpE/v6b7Gpx9dhbPk55j1plZ2Np/q1riJKqVK83FoQeH4Bvii6AXii3gzI3MkSPNQUxaDH4I+gHLLy1Hh+odULdSXWTkZiAsJgwRbyPAoeiRp1o2tTCy8Uh0rN4Rnap3gr6eCv/WEjlKwtRF7HXCPrRhA9CiBbB1K9vOKT2d1QL77jtg8GBWw4vHJ2GFbVekbJMnsyTsyBFW7NXUtPTnoCRM91Spwr4A9iFCjftC3n1zF71290JaTho6Ve+EQ8MPwbrCpz/MWRhbYPuA7fDc4YltN7ahpnVNzO8w/5PPIZopR5qDQxGHcP7ZeZx4fAIxaTEAAH2JPgbXH4xp7tPQoXoH5EpzcezRMWwK3oR/X/yLf57/g3+e/1PgXNUsq6GJfRMY6ysWbCRmJSI4OhhPEp7g+3+/BwDUsK6BZZ7LMKLxCBreVDEJp+NrmV+9eoWqVasiKioKVfg/okKoX599kg4MVMwxKYG0nDScf3YedqZ2aOHUAiYGHy9KeJv+Fj9d+gkXIi/A0sQSm/tsRqPKjZQZveqlprKK9u/esSRozBjFY40asSHJw4eBAQNUGwfHsaGnyEggIICtliyNnBxWmoLjgNhYKlFByiUxMxEt/VriWeIztK3aFmdHnYWZkVmJn7/2ylrMOTcHADCv3Tz81PUnGnLSEhzHYc+dPfjm3DfyxAsA7M3s8WWLL+HTwuejIUfew/iH+O/lf4hKiYKhniGa2DeBu7M7HMwLr7GZlpOGfXf34eLzizj5+CQSs1inQd86fbFr0C5Ymlgqv4EloDHv3ypESZi6VKrEhiTv3gUaNiz2x3OkOfjf5f9h9ZXVSMlOAQA4mDtgQ88NGNpgqPzTyfFHxzH28FgkZCbIn2tuZI6TI06iQ/UOqmmLqqxaxSbF16zJElYDA7bnprU1S2piYti+j6q2cCGbqN+/f+nKaABs3lCtWiwRy8igEhWkzKQyKfrt7YdTT07BxcoFoZNCUcm0UqnP82PQj/juIpu71qhyI0x3n46RTUbC3Mhc2SETJZFxMnx57EtsvcGGkZ0snDCy8Ui0r9YePWv1hJG+kcpeOyM3A+uvrceyf5chW5qNhnYNccz7GFytXVX2mkXRmPdvFaKPROqQm6uYE1a5crE/fvnlZbj95oZFFxchJTsFLlYusDO1Q2xaLIb/PRz99vbDsYfH8M3Zb9B/b38kZCagiX0T7PtsHzxdPJGWk4Yh+4cgKjlKxQ1TsmnT2CbcT58q6i/t2cMSsDp11JOAAWxSPgCcOlX6KvpUI4woydJ/luLUk1OoYFABh4YfKlMCBgCLOi7CzoE7YWZohrtv7mLyiclw/tkZM0/PxJv0N0qOmpQXx3GYdXoWtt7YCj2JHn7o/AMiv4rE6m6r0b9uf5UmYABgamiKBR0W4L8v/oOjuSPuvb2HVltbISQ6RKWvK1aUhKnD27fsqK/PesQ+YcfNHejwRwfcf3sfdqZ22DVoF57+31NEzYrCkk5LYKRvhBOPT6B/QH+suboGHDh82eJLhE4KxbCGw3BixAk0tW+KtxlvMfzv4ZDKpGpooJKYmwNz39c2WrYMSEsD1q9n30+bpr44GjZkpTByc4EDB0r3XD4Jc1X/p0aiOw4/OIwfL/0IAPDr54dmDs3Kdb7RTUfj1exXWNdjHWrZ1EJKdgo2XN+Alr+3xO2420qImCjLoguLsDF4IwDAv78/FnVcpPLEqzAtnVoieFIw3BzcEJ8Rj+67uuNW7C21x6HrKAlTB37/Szs7Vqi0CIcfHMYXR78ABw4jG4/Eg+kPMLLJSOhJ9GBsYIylnktxa/ItjGs2Dg3tGsKrhhcODz+MzX02w1Cf7YtoamiKQ8MPoaJxRVx9dRVrr65VRwuVZ+pUNgH6+XOgUye2rVDFisD48eqNw9ubHffuLd3zaFI+KadH7x5hzCE2J/Irj68wsslIpZzXysQKM1vPxMPpD3F65GnUqVQHUSlRaLutLY49PKaU1yDlszV8K5b/txwA4NvbF2ObjRU0nioVqyBofBDaVGmDpKwkdNreCeefnRc0Jl1DSZg68EnYJ4Yi7725h1EHR0HGyTDBbQL+HPQnbCp8vJKynm09/DHgD9ydehfnRp/DgHoDPlq94mrtig09NwBgn6rOPT2nvLaompkZsG0bux0ezo4zZ7IaYerEJ2EXLyqGkktCiUlYZm4m/r7/N44+PIr4jPiPHuc4Dk8TniLgbgD239uPZ4nPaM9ALZcny8OYQ2OQmpOKjtU74n/d/qf019CT6KFHrR64NuEavGp4IT03HQMCBmDvnVJ+4CBKdf3VdUw7yXr8v/f8HlPdNWMjeHMjc5wceRJtq7ZFcnYyeu3uheOPjgsdls6gEhXq8Ob9vIsiVsrlSHMw9K+hSM9NRxfXLtjSd0u5lwWPbToWp5+cxr57+zAgYAAODT+EHrXKuSeiunTvzoYj9+5lw5P5V0qqi4sLUL06qwl15w7rlSsJJSVhp5+cxsiDI+ULLgz0DNCndh/Ym9mDA4eolCgERwcXWJABALamthjaYChGNh6J5o7NUcFQvZtJk/JZc2UNrkdfh6WxJXYN2iXv4VYF6wrWODniJCYfnwz/m/4YfWg03ma8xTT3aVQjSs3i0uIwZP8Q5EhzMLDeQCzquEjokAqwMrFC4JhAjD08Fvvv7cdn+z/DyZEn0cW15Cv9SeGoJ0wd+J6wIpKw9dfWIyI+AvZm9ggYEgADvfLnxhKJBDsH7UTv2r2RmZeJ3nt6Y8WlFciV5hb/ZE3w3Xds0+5x4z45hKtS/BZJd++W/DmRkexYvXqZX/ZB/AMM+2sYEjIT4GLlggZ2DZAny8ORh0fwe/jv8Av3w+knp5GQmQAjfSO0cm4Fdyd3GOoZIj4jHptDN6P9H+1RaXUl+BzzwfOk52WOhajPi6QXWPYv26Jtfc/1qGpZVeWvaahvCL/+fhjdZDSknBRfnf4Kbf3b4vqr6wWqqhPVyZXmYtjfwxCdGo16tvWwY+AOjSwjYmJggl2DdmFgvYHIlmaj/97+uBp1VeiwtJ7mXWld9InhyJfJL+V/eFd3Ww07MzulvayRvhEODjuIsU3HQsbJsODCAjTZ0gSbQzYjLSdNaa+js/gk7M6dkv18RgYQHc1u16pVppeUcTJ4H/CWD0c9mv4I96bew40vb2B5l+VY5rkMyzyXwbe3L0ImhSB1fiquT7yO4EnBSJ2fivOjz2N4w+GobFYZmXmZ8Av3Q8NfG+L7f75HXFpcmWIi6jH77Gxk5mWiY/WOGNtUfXOB9CR62D5wO37t/SsqGldEcHQwWm9rDauVVuiyows2XNuA5KxktcUjNt+c+wZBL4JgYWQhn8+rqQz1DREwJADda3ZHem46eu3uhRsxN4QO6yO+vr5wcXGBiYkJPDw8EBwcLHRIReN0XFRUFAeAi4qKEi6I0aPZdr+rVhW4WyqTcl47vTgsBdduWztOJpOp5OVlMhn3x40/OJtVNhyWgsNScBbLLbhl/yzjsnKzPvm8XGmuyuLSeLt3s+vWtm3Jfv7OHfbzVlYcV8Z/s923d3NYCs5yhSUXkxpTpnNwHLt2Qc+DuI5/dJRfc8NlhtyIAyO4Ky+viPeaaqi/7v3FYSk4/e/1uduxtwWLIzolmvP+25sz/clU/nuDpeCsV1pzW8O2crnSXMFi00VHHhyR/xsfjjgsdDgllp6TznXw7yD/W+UX5sdJZVKlv05Z3r8DAgI4IyMjzt/fn7t37x43adIkzsrKiouLi1N6fMpAPWHqUMRw5JbQLTj/7DwqGFSA/wB/lW0PIZFIMK7ZODz7v2fY0HMD6lSqg9ScVCz+ZzHq+dbDiksrcPnlZWTkZgAAniQ8wewzs1FpdSUY/mCIauurYd/dfeKb9J1/OLIkbX/yhB1r1y5TjbAcaY68qObcdnOLrG5dEhKJBB2qd8DFsRexd8hetKnSBrmyXOy5swdt/dui6rqqGLxvMFb+txIXIi8gOy+7zK9FyudN+htMOTEFADCv/Tw0tm8sWCxOFk7YM2QPkucl4/bk29jYcyPq29ZHYlYiJh6biBobauC30N9oqFIJ4jPiMenYJADA122+xoB6Kt4NRIlMDU1xfMRxtKvaDsnZyZh0bBLmnpsrdFgAgJ9//hmTJk3C+PHj0aBBA2zZsgWmpqbw9/cXOrRCUcV8dXBzA27eBE6eBHr1AsASnaZbmiIjNwMbe27EDI8ZagtHxsmw7+4+zDozC3HpiiEqfYk+rCtYF7oSDwB61eqFLX23oJplNXWFKqycHLZaMy+PTdCvVky7//c/tpDA25sVmS2lzSGbMfXkVNib2ePp/z0t1fY0JREeEw7fYF/subsHWXlZBR6rZVMLv/X9jSbaCmD8kfHYfnM7mto3RfCkYEFqQn1KniwPG65twMrLK+V/GzxdPLH/s/1KnT4hJlKZFH329MGZp2fQwK4BwnzCCt2STtPlyfLwy/Vf8NOln3BlwhXUqVRHqefn37/v378PZ2fFFk3GxsYwNjb+6OdzcnJgamqKv//+GwMHDpTfP3bsWCQlJeFIaXdAUQPqCVOHD3rCpDIpxh0eh4zcDHR26YxprdRYiBRsDoh3Y288++oZtvbbiv51+8PB3AFSTor4jHhIIEGf2n1wcsRJvP3mLZZ2WgojfSOcenIKbbe1RWRipFrjFYyREVC3Lrtdknlh+XvCSik9Jx3LgtjcwEUdFyk9AQOA5o7NsW3ANsR/E4+gcUFY020NhjUcBjtTOzxJeIKuO7ti3OFxRSbhRPmuRl3F9pvbAQC/9f1N4xIwgK3M/brt14iaFYV1PdbBzNAM/zz/Bx5bPfAs8ZnQ4WmlBYELcObpGVQwqIA9g/doZQIGsN+NWW1m4cXMF0pPwPJr0KABLC0t5V8rVqwo9Ofi4+MhlUph/8Gok729PWJjY1UWX3lQiQpVk8k+KlGx7to6XI66DAsjC/gP8BdsJYypoSkmNJ+ACc0ngOM4RKdGIzkrGZVMKxUYClviuQTDGw3H4H2DEREfgU7bO2Fb/23oVrObIHGrVb16bPPwp0+L/9nHj9mxDJPyf776M2LTYuFi5QKfFj6lfn5pmBmZoUP1DvK9RZOzkrHwwkL8GvIrdtzageOPjmNdj3UY1WSUyobICfswNv3UdADAF82+gEcVD4Ej+jQTAxPMbD0TPWr2QL+9/fA08Sm67uyKS+MvoUpF3dzXTxVW/bcKq6+sBsB2Q2jq0FTgiMpPFR8a8yusJ0xXUE+YqiUkANL3WwfZ2SHsdRgWXWA1YNb1WAcXKxfhYstHIpGgSsUqaFi5YaFzkerZ1sP5MedRy6YWolKi0H1Xd4w+NBqvU18LEK0a8f/xY2KK/9ky9oS9SHqBFf+xT3bLuyxXe2+IpYklNvXehCsTrqBR5UZ4l/kOYw6PQbc/u+FJwhO1xiImfuF+CI8Jh5WJFVZ4Ff7JXhPVt6uPS+MvoZZNLTxPeo7+e/sjMzdT6LC0gm+wL+YFzgMArOy6Umm7Ieg6CwsLVKxYUf5VVBJma2sLfX19xMUVXAkeFxcHB3XtPVxKlISp2oMH7FilCt7mJmPw/sGsxkrd/vjC7QthYyslJwsnhPuE4/9a/R8kkGDX7V2ovr46hv89HJdeXNLNifuOjuz4uphkMzMTiHq/YXopesI4jsNXp7+Slyb4vNHnZQy0/FpXaY1wn3Cs6LoCJgYmCIwMROPNjbH80nKauK9k7zLeYeGFhQCAHzr/gMpmRe+moYkcLRxxfvR52Jra4kbsDUw+MVk3//8r0fab2+U9n4s6LMK37b8VOCLdY2RkhBYtWiAwMFB+n0wmQ2BgINq0aSNgZEWjJEzVbt4EACS3aIReu3vhZfJL1LapjR0Dd2jlUI+FsQU29NqAaxOvoX219siT5WH/vf3ouL0jmm5piiMPNG/iY7k4ObFjcT1hz97PjbG0LHaT9vx239mNIw+PwEDPAJt6bRL8d8JQ3xDz2s/D3Sl34VXDC1l5WVh4YSGqra+GRRcWISo5StD4dMXCCwuRkJmAJvZNMLnlZKHDKZPqVtWx/7P90JPoYeetnfAN8RU6JI21985eTDg6AQAw02MmlnVeJnBEumv27Nnw8/PDjh07EBERgSlTpiA9PR3j1b3/cAlREqZqN28i3RDo0+wewmLCYGtqi6PeR2FlYiV0ZOXSyrkVLo2/hBtf3sBEt4moYFABd97cwcB9AzF432BEp0QLHaJylLQn7OFDdqxbt8TlKV6lvML0k+yT8ZJOSwQtTfChmjY1cXbUWfw56E84WzjjTfob/HTpJ7hucMWQ/UNwMfIi9XyU0c3Ym/g97HcAwKZem5SyQ4ZQOrt2xmovNr9p1plZOPX4lMARaZZcaS6WXFyCEQdHQMbJMNFtIn7u8bPgH7Z02fDhw7FmzRosXrwYzZo1w82bN3H69OmPJutrCkrCVCz1ThgGeAOXJVGwMrHCudHnUM+2ntBhKU0zh2bw6++H6NnRmNduHgz0DHDowSHU960P32BfSGVSoUMsn5L2hPHDzvVKdm05jsPEoxORnJ0Mdyd3zGs/rxxBqoZEIsGoJqMQ+VUk/h76NzxdPCHlpDgYcRBddnZBo82N4Bvsi9TsVKFD1SoLAheAA4fPG30uXxyhzWa3mY2RjUciT5aHwfsH45/n/wgdkkYIjg5GS7+W8lXPs1rPUsq+wKR406dPx4sXL5CdnY3r16/Dw0NzF71QEqZCL+KfoF3LWwisAZgZmOLUyFNo5tBM6LBUwrqCNVZ4rUC4TzhaV2mN1JxUTD81HTU31sTK/1bibfpboUMsG74nLCEByMoq+udKmYRtvL4RZ56egYmBCXYO2qnRvSGG+oYY0mAILo69iDtT7mBKyykwMzTD/bf3Mf3UdDj/7Iw1V9YgT5YndKgaL+hFEE49OQUDPQP82PlHocNRColEgj8G/IF+dfohKy8L/fb2w/VX14UOSxAcx+Hyy8vwPuCN1ltb43bcbdhUsMGfg/7Ezz1+po3RyUcoCVORf5//i1ZbPXCnMgeHNAkujAlE6yqthQ5L5RrbN8Z/4/+Db29fVKpQCS+SX2B+4HxUXVcVow+Nxq7bu/Do3SPtqbhtbQ3wK3E+VWemFElY0IsgfH32awDAaq/VWtUz2qhyI/za51dEz44usPvCN+e+gbufu2jffEtCKpNi1plZAICJbhNR06amwBEpj6G+IfYP3Y+url2RlpOGnrt74lbsLaHDUqv/Xv6Hln4t0f6P9gi4GwAOHEY3GY0H0x5gVJNRQodHNBRVzC+rpCTghx+AZctYVfX3bsXews/XfsbOWzsBAE1jgWNPWqHqOfG9OWXmZmLfvX3YFLwJYTFhBR5zNHfExOYT4VXDC80dm8PcyFygKEugRg0gMhK4fBlo2/bjxzmOTchPTQXu3wfq1y/yVK9SXqHF7y3wJv0NRjQegV2Ddmn18ISMk2H7ze2Yc3YOErMSAQDtq7XHdPfpGFR/kEYWHxXKb6G/YfKJybA0tsSjGY+0bkVkSaTlpKHHrh64EnUFtqa2OO59XOPrnynD+mvr5Qm2iYEJRjYeiWnu0+Dm6CZwZNpNI3a8UTFKwsqC44AOHYDLl5E5fhTSN63D+Wfn4Rvii/9e/if/sYkJLli35TnMv54PLF+unNfWQhzHITg6GAF3AxD8OhjhMeEFts3Rk+ihgV0DtHJqhVbOrdCvbj84WTgJGPEH2rUDrlwB/v4bGDLk48dfv2b1xPT1gYwMVmm/ENl52ei0vROuR19HU/umuDLhCkwNTVUcvHq8SX+DuefmYved3fJhSQdzB3Sr0Q1mhmZwc3RDK+dWaFS5kUYPvapKdEo0Gm1uhKSsJLVvU6ZuSVlJ8NrphbCYMBjrG+PvYX+jb52+QoelMueenkPP3T0h42T4otkXWNVtFWxNbYUOSydQEqYDVHURV+ychF3XtyLCDuDydWQY6BlgSP0hmNVsMjya9Gb1o8LCgObNlfba2i5HmoODEQfx1/2/EBwdjFcprwo8bqBnAK8aXmjt3BqtnFuhs2tnYbf1GDqUJWAbNwIzCnnzvHAB6NoVqFNHsUqyED7HfOAX7gdrE2uE+oSihnUNFQYtjNepr/F72O/4Lew3xKZ9PHzrYO6A8c3Go1P1TvB08YSxge5Uvi4Kx3HotbsXzjw9A3cnd1yZcEXnE9HU7FSMODgCxx8dh7G+MU6MOIGuNboKHZbSRSZGoqVfSyRkJuCLZl9ga/+tWt2zrWnEkITp9l8CFYqrbIb7+UYTnMwc4dPyS/i08IGjhSPw118sAatRg23gTeSM9I3weaPP5YVJX6e+Rkh0CIKjg3Hh+QVce3UNp5+cxuknpwEAtqa2mOA2AVNaTkF1q+rqD5ifnF/UCsli5oNJZVLMOTsHfuF+kECCPUP26GQCBrCCvks9l2JBhwU49vAYniY+RUJmAkJfhyLkdQhi02Kx4r8VWPHfCtiZ2mFS80mY3HIyqlpWFTp0ldlzZ4/WLMJQFgtjCxwcdhDD/h6Gww8OY8j+IQiZFILalUq/r6qmyszNxOD9g5GQmQB3J3f49vGlBIyUmu7/NVCR8c3Gw6t6Z7h7z4Ht7SfQW/gFJJ5LFD+wfz87fvZZietGiZWThRMG1BuAAfUGAADuvrmLf57/w5KyyAuITo3GqsursPbqWsxtOxfz2s+DhbGFGgN8PzRaVK2w9wV50aDBRw9JZVKMPDgS++7tA8C2qupZq6cKgtQsRvpGGNKg4NBtjjQHhyIO4dijY7gQeQExaTFY/t9y/O/K/7CgwwIs6LBA5+aQpWSnYM65OQBYlXRtWoRRXob6hggYEoAuO7vgStQVDNo3CNcmXtPs+Z+l8N3F73Az9ibsTO1wYNgBrd2EmwiLhiPL6+BBNk/IzIxt8mxvDyQmAg4OQE4OEB5OPWHlkCfLw/FHx7Hx+kZcfH4RAGBhZIEZrWZgYceF6plT9eefwJgxQJcuQL7tMOQaNWKbfB8+DAwYIL87V5qLiccmYuetnTDUM8SuwbswrOEw1cerBXKluTj68Cg2Bm9E0IsgAGxC/6Hhh3RmPg3Hcfji6BfYfnM7atnUwt0pd0Ux/Pqh16mv0eL3FohNi8VnDT7D/s/2l6jHKCU7Bf43/HH04VHkSHNQ3ao6OlbrCO/G3qhoXFENkRftYuRFdN3ZFRw4HPc+jj51+ggaj64Sw3AkJWHlxXGAhwcQEsLmC23cCGzeDEydCjRtquglIeV2KOIQ5gXOw6N3jwAA1SyrYbr7dEx1nwozI7Ninl0OV66wyfnVqwPPnxd8LCmJlbEAgLg4oDIbo07NTsWAgAG4+Pwi9CX62D90PwbXH6y6GLUUx3HYf28/fI77ICU7Ba5Wrjg+4jga2H3cq6httoRuwZQTU6An0cP50efR2bWz0CEJ5vLLy/Dc4Yk8WR4WtF+An7r+9MmfvxN3B/329sOL5BcfPWZqaAoPZw+0cm6FmtY1CyR0tW1qo2P1jiodFgyJDkHXnV2RmpOKL5p9gW0DtqnstcSOkjAdoJaLGBgIeHkBhoZAv37A9etAdDSwbh0wc6ZqXlOkZJwMRx4cwf+d/j/5hP7qltWxousKDGkwRDXDWW/esB5OiYTN8zPO15tx5gzQsyfbtPvxYwBARm4Geu3uhaAXQTA3MkfAkAD6pFyM+2/vo9/efniW+AzmRuZY3mU5prpP1drilg/iH8DtNzdk5WVhldcqzG03V+iQBOcX5gef4z4AgMUdF2Op59JCk6UTj07g8wOfIy0nDS5WLvi6zddwMHfA/bf3sefOHjx8V/TiFwCob1sf09ynYXTT0UrtMeM4DkcfHsWoQ6OQlpMGTxdPnBxxEhUMKyjtNUhBlITpALVdxO7dgXPnFN8bGwMvX8p7RohyZeRmIOBuAJb9u0z+adnB3AFftmCLI5Ra4iJ/HbCIiIIT8JcsYbXiRo8Gdu5Edl42Bu4biNNPTqOicUVcGHMBLZxaKC8WHRafEY+hfw2Vb3vTyrkV1nZfi3ZV22nVhOccaQ7a+7dHyOsQ9KjZA6dGntKq+FVp9eXV+Pb8twCAgfUG4tt238LdyR3RqdEIjg7G3/f/xl/3/4KMk6GzS2f8Pexv2FSwkT+f4zjceXMHwdHBCI4OLrACV8pJEfQiCGk5aQAAcyNzjG06FtPcp6G+XdG1+4pTWL3DLq5dcHj4YfXOTRUhSsJ0gNouYnw8cOgQkJvLvm/ZEmjVSnWvRwAA6TnpWHNlDbaEbZH/QTbQM8Dg+oMx3X062ldrr5w3wObNgRs3gGPHgL75ah516wacPw9s3ow8n4kY9tcwHHpwCKaGpjg76izaVWtX/tcWERknw+9hv+Pb898iJTsFANufdLr7dHg39taKumozTs7AppBNsDS2xN2pd1Glom6+eZTV72G/Y9rJafJ6cgZ6Bh9teTWp+SRs6r2p1D3bKdkp2HlrJ3xDfPEg/oH8/i6uXTDdfTr61e1X4tWpz5OeY3PIZmy7sQ3vMt8BAIz1jTHVfSpWea2Cob5hqWIjpUdJmA4Qw0Ukitpjm4I34XLUZfn9jSs3xvRW0zGy8cjyzRsbNoyVHck/xMxxgJ0d8O4d8oKvYfyrTdh1exeM9I1wYsQJeNXwKl+jROx16mssvrgYu+/slhf2tTaxxhduX2BKyykau+XPzls7MfbwWADAkc+PoH/d/gJHpJnCY8Kx9upaHH14FGk5aTDQM0AT+yZoW6UtJjSfUO49djmOw4XIC9gUsglHHx6Vb5NmamiK5o7N0cqpFdyd3VHZrDKsTazhau2KB/EPkJaThlOPT+HQg0OITIqUn6+aZTVMbTkVE5pP0JmFI9pADO/flIQRnXMz9iZ8g32x+85uZOZlAgAsjS0xvtl4+LTwQT3beqXvHVuwAFixApg2Ddi0id0XFwc4OCDZBBj+mxfORJ6HgZ4BDg47iH51+ym5VeL0LuMd/G/449fQX/E86TkAQAIJutXshk7VO+GzBp+hTqU6wgb5XnhMONr5t0NWXhYWd1yM7zt/L3RIGk8qk+JF8gs4mjuqbG7Vy+SX2BK6BVvDt+JtxtsSP08CCbxqeGF6q+noU7uP1s5P1GZieP+mJIzorMTMRPxx8w/8GvIrniY+ld9va2qLVs6t5NskuTu7F//pdts2YOJEoEcP4DQrIovAQDwd6oX+Y41w3yoHpoam2D14NwbWG6i6RomUVCbFqSensCl4E848PVPgsR41e2Ca+zT0rt1bsDfK+Ix4tPi9BV4mv0Sf2n1w1Pso9CR6gsRCCifjZHj07pF8PllYTBhSs1MRnRqNpKwkOJo7opJpJbhauWJi84noVL0TLE0shQ5b1MTw/k1JGNF5Mk6GM0/OwDfEF2efnkWuLPejn3G1ckXfOn0x1X1q4QU1//0X8PQEatYEnjxBSnYKfls/Gt+nHEW6EeBs4Yyj3kfR3JG2p1K1x+8e49ijYzj/7DxOPzkNDuxPmIuVC6a0nIIJbhNQybSS2uLJkeag1+5euBB5AbVsaiFkUgisTKzU9vqkfDiOQ3J2Ml0zDSSG929KwoioZOVl4VbsLQRHByPkNdsq6cMl711cu8DL1Qvuzu5o6dSS/XGOjkZyzSoIraKHgz9PxM57e+SrsDpw1RDw9VXN2nRcJJ4lPpMPNSVmJQIATAxM4N3IG1Pdp6KFYwuVrkzMk+Xh878/x4GIAzA1NMX1idfRqHIjlb0eIWIihvdvSsKI6CVlJeHyy8vwC/fDsUfH5JN4edUt2X6VHxaOrJdmgrnnszB23l7oDf9cbfGSj/ElSzYFb8KN2Bvy+61NrOHu7I5WTq3QukprdKvZTWm15JKykjDsr2E49+wcjPSNcMz7GLrX7K6UcxNCxPH+TUkYIfm8SHqBAxEH5L1kzxKfFXjcNRFoY90YE0avQ+emAyBJS2dbFhWybyRRP47jcO3VNfiG+OJAxAH5ykqeg7kD+tbuy+YEOrdCw8oNy7Sh9tOEp+i7ty8exD+AqaEp9n22D33r9C3+iYSQEhPD+zclYYR8QnxGPB69ewSO41Dn2GXYTf8W6NqVTdL39gaMjIC0NLZbAtEoOdIc3Im7I0+oTz05VaC4J8BKFgyoOwCjm4yGRxWPAoVBCyPjZNgSugXzzs9Dak4qnC2cccz7GNwcaX9YQpRNDO/flIQRUlL37rHNuvObPRtYu1aYeEip5EhzcObJGVx9dVU+J5AvCMuraV2TrZh1ckcr51Zwc3SDsb4xHsQ/QMjrEPwe9juuvroKAGhTpQ3+HvY3zQUkREXE8P5NSRghJSWTARUqADk57Pthw4A9ewB9qh+kjWScDOEx4fg97Hf88/wfPE54XKLnWRhZYHnX5ZjScgrVjiJEhcTw/l36yRCEiJWeHjBlCrBlC7B0KfDNN5SAaTE9iR5aOrVES6eWAICEzASEvg5FSHQIgl8X3JvQ1NAULZ1aorVza8zwmEFbERFClIJ6wggprexstkE70WkcxyEhMwF5sjxUMq1Upgn8hJCyE8P7N/1VIaS0KAETBYlEotair4QQ8aF9NQghhBBCBEBJGCGEEEKIACgJI4QQQggRACVhhBBCCCECoCSMEEIIIUQAlIQRQgghhAiAkjBCCCGEEAFQEkYIIYQQIgBKwgghhBBCBEBJGCGEEEKIACgJI4QQQggRACVhhBBCCCECoCSMEEIIIUQABkIHoGoymQwAEBMTI3AkhBBCCCkp/n2bfx/XRTqfhMXFxQEAWrVqJXAkhBBCCCmtuLg4VKtWTegwVELCcRwndBCqlJeXhxs3bsDe3h56esobfU1NTUWDBg1w//59WFhYKO282oDaTm2ntosHtZ3aLlTbZTIZ4uLi4ObmBgMD3ewz0vkkTFVSUlJgaWmJ5ORkVKxYUehw1IraTm2ntosHtZ3aLra2qxNNzCeEEEIIEQAlYYQQQgghAqAkrIyMjY2xZMkSGBsbCx2K2lHbqe1iQ22ntouNmNuuTjQnjBBCCCFEANQTRgghhBAiAErCCCGEEEIEQEkYIYQQQogAKAkjhBBCCBEAJWGEEEIIIQLQzX0AlCw+Ph7+/v64evUqYmNjAQAODg5o27Ytxo0bBzs7O4EjVJ2YmBgEBgbCxsYGXl5eMDIykj+Wnp6OtWvXYvHixQJGqDp03em603Wn607XXbevu9CoREUxQkJC0KNHD5iamsLLywv29vYA2IaigYGByMjIwJkzZ9CyZUuBI1W+kJAQdO/eHTKZDLm5uXB2dsbhw4fRsGFDAOzfwMnJCVKpVOBIlY+uO113uu503em66/Z11wgc+SQPDw/Ox8eHk8lkHz0mk8k4Hx8frnXr1gJEpnpeXl7c+PHjOalUyqWkpHBTpkzhKlWqxIWHh3Mcx3GxsbGcnp6ewFGqBl13uu4foutO110Xifm6awJKwophYmLCRUREFPl4REQEZ2JiosaI1Mfa2pp7+PBhgftWrFjBWVtbc8HBwTr9n5OuO133wtB1p+uua8R83TUBzQkrhoODA4KDg1GvXr1CHw8ODpZ3XeuirKysAt/PmzcPBgYG6N69O/z9/QWKSvXoutN1Lwxdd91E112c110TUBJWjDlz5sDHxwdhYWHo2rXrR3MF/Pz8sGbNGoGjVI1GjRrhypUraNKkSYH758yZA5lMBm9vb4EiUz267nTd6bozdN3puhMVErorThsEBARwHh4enIGBASeRSDiJRMIZGBhwHh4e3L59+4QOT2X8/Py4UaNGFfn4ypUrORcXFzVGpF503QtH11030XWn614YXb/uQqPVkaWQm5uL+Ph4AICtrS0MDQ0FjoioA113caLrLk503Yk6URJWStnZ2QAAY2NjgSMh6kTXXZzouosTXXeiLlQxvwTOnTuH3r17w9raGqampjA1NYW1tTV69+6N8+fPCx2eYCIiIlCjRg2hw1AZuu6Fo+suTnTdxUnXr7vQKAkrxo4dO9C7d29YWlpi3bp1OH78OI4fP45169bBysoKvXv3xp9//il0mILIycnBixcvhA5DJei6F42uO113XUPXvWi6fN01AQ1HFqNOnTr46quvMG3atEIf//XXX7Fu3To8fvxYzZGp3uzZsz/5+Nu3b7Fnzx6drKRM171odN3puusauu5F0+XrrgkoCSuGiYkJbt26hbp16xb6+MOHD9GsWTNkZmaqOTLV09fXR7NmzVCxYsVCH09LS0N4eLhO/uek607XvTB03em66xoxX3eNINzCTO3QvHlz7ptvviny8blz53LNmzdXY0TqU6dOHe7PP/8s8vEbN27obCVluu503QtD152uu64R83XXBFSstRhr165F3759cfr06UI3dn327BlOnDghcJSq0bJlS4SFhWHUqFGFPi6RSMDpaEcqXXe67nTdC6LrTtedKB8NR5bA8+fPsXnzZly7dg2xsbEA2DYXbdq0weTJk+Hi4iJsgCoSGxuL7OxsVK9eXehQBEHXna47XXfxoOsuzusuNErCCCGEEEIEQCUqCCGEEEIEQEkYIYQQQogAKAkjhBBCCBEAJWGEEEIIIQKgJIwQQooRGRmJvLw8ocMQhJjbToiqURJWAjExMdi1axdOnjyJnJycAo+lp6dj2bJlAkWmemJu+7lz57BkyRJcuHABABAUFIRevXqhS5cu+OOPPwSOTrXE3PbC1K1bVye3rCkJMbb99evXWLJkCUaOHIk5c+bgwYMHQoekNmJuuxCoREUxQkJC0L17d8hkMuTm5sLZ2RmHDx9Gw4YNAbBifk5OTjq5pYOY275r1y6MHz8eTZo0waNHj/DLL79g1qxZ+OyzzyCTybBr1y7s3r0bn332mdChKp2Y2z548OBC7z9y5Ai6dOkCCwsLAMDBgwfVGZZaiLntpqamePHiBezs7HD//n20bdsWdnZ2cHNzw507d/Dy5UtcvXoVTZo0ETpUpRNz2zUB9YQVY8GCBRg0aBASExMRFxeHbt26oVOnTrhx44bQoamcmNu+du1arF27FmFhYTh8+DCmTp2KxYsXw8/PD9u2bcPy5cuxfv16ocNUCTG3/fDhw0hISIClpWWBLwAwNzcv8L2uEXPbs7Ky5FXhFyxYgI4dOyIiIgL79+/HvXv30L9/fyxcuFDgKFVDzG3XCMLslqQ9rK2tuYcPHxa4b8WKFZy1tTUXHBzMxcbG6uy+WmJuu5mZGffs2TP594aGhtytW7fk30dERHCVKlUSIjSVE3Pb9+7dy1WpUoXz9/cvcL+BgQF37949gaJSDzG3XSKRcHFxcRzHcVzVqlW5oKCgAo+Hh4dzjo6OQoSmcmJuuyagnrASyMrKKvD9vHnzsGDBAnTv3h1XrlwRKCr1EGvbDQ0NC8yBMzY2hrm5eYHvMzMzhQhN5cTc9s8//xyXLl3Ctm3bMGTIECQmJgodktqIue0SiQQSiQQAoKen91GPn5WVlc7+e4i57ZqAkrBiNGrUqNBkY86cOZg/fz68vb0FiEo9xNz2WrVqFZiQGh0dDVdXV/n3T58+RZUqVYQITeXE3HYAcHFxQVBQEBo1aoSmTZvizJkz8jcpXSfWtnMchzp16sDGxgavX7/G7du3Czz+5MkTODg4CBSdaom57ZrAQOgANN2YMWPw77//YvLkyR89NnfuXHAchy1btggQmeqJue0LFiyAtbW1/PuKFSsWeDw0NBTDhg1Td1hqIea28/T09PD999+jW7duGDNmjE4uPimKGNv+4YrfWrVqFfj+2rVrGDRokDpDUhsxt10T0OpIQgj5hLS0NDx9+hT16tWDsbGx0OGolZjbTog6UE9YKSQnJyM2NhYA4ODgoLMrhQpDbae2i7ntLi4uokpCqO30Oy+2tgtGyFUB2sLPz4+rX78+p6enV+Crfv363NatW4UOT6Wo7dR2sbddIpFQ20XYdjH/zoup7UKjnrBi/O9//8PSpUvxf//3f+jRowfs7e0BsEKlZ8+exVdffYXExETMmTNH4EiVj9pObae2U9up7dR2XW27RhA6C9R01apV4/bt21fk4wEBAVzVqlXVGJH6UNup7YWhtlPbdQ21XZxt1wRUoqIYb968QePGjYt8vHHjxoiPj1djROpDbae2F4baTm3XNdR2cbZdE1ASVgx3d3esXLkSeXl5Hz0mlUqxatUquLu7CxCZ6lHbqe0forZT23URtV2cbdcEVKKiGLdv30aPHj2Qm5uLjh07FhgvDwoKgpGREc6ePYtGjRoJHKnyUdup7dR2aju1ndquq23XBJSElUBqaip27dqFa9euFVi+26ZNG4wYMeKjYpa6hNpObae2U9up7dR2ohqUhBFCCCGECIDmhJVBnz59EBMTI3QYgqC2U9vFhtpObRcbMbdd3SgJK4OgoCBkZmYKHYYgqO3UdrGhtlPbxUbMbVc3SsIIIYQQQgRASVgZVK9eHYaGhkKHIQhqO7VdbKjt1HaxEXPb1Y0m5hNCCCGECIB6wopx4MABZGRkCB2GIKjt1HaxobZT28VGzG3XBNQTVgw9PT1YWFhg+PDhmDBhAjw8PIQOSW2o7dR2aju1XQyo7eJsuyagnrASmDNnDkJDQ9GmTRs0atQI69evx7t374QOSy2o7dR2aju1XQyo7eJsu+CE2jlcW0gkEi4uLo7jOI4LDQ3lpkyZwllZWXHGxsbc0KFDubNnzwocoepQ26nt1HZqO7Wd2k5Uh5KwYuT/BeVlZmZyO3fu5Dw9PTk9PT3OxcVFoOhUi9pObedR26nt1HZqO1E+SsKKoaen99EvaH6PHz/mFixYoMaI1IfaTm0vDLWd2q5rqO3ibLsmoIn5xdDT00NsbCwqV64sdChqR22ntosNtZ3aLjZibrsmoIn5xYiMjISdnZ3QYQiC2k5tFxtqO7VdbMTcdk1APWGEEEIIIQIwEDoAbRAfHw9/f39cvXoVsbGxAAAHBwe0bdsW48aN0+lPEdR2aju1ndpObae2E9WgnrBihISEoEePHjA1NYWXlxfs7e0BAHFxcQgMDERGRgbOnDmDli1bChyp8lHbqe3Udmo7tZ3arqtt1whCrgrQBh4eHpyPjw8nk8k+ekwmk3E+Pj5c69atBYhM9ajt1PYPUdup7bqI2i7OtmsCSsKKYWJiwkVERBT5eEREBGdiYqLGiNSH2k5tLwy1ndqua6jt4my7JqDVkcVwcHBAcHBwkY8HBwfLu291DbWd2l4Yaju1XddQ28XZdk1AE/OLMWfOHPj4+CAsLAxdu3b9aLzcz88Pa9asEThK1aC2U9up7dR2aju1XVfbrhGE7orTBgEBAZyHhwdnYGDASSQSTiKRcAYGBpyHhwe3b98+ocNTKWo7tZ3aTm2ntlPbiWrQ6shSyM3NRXx8PADA1tYWhoaGAkekPtR2aju1ndouBtR2cbZdKJSEEUIIIYQIgCbmE0IIIYQIgJIwQgghhBABUBJGCCGEECIASsIIIYQQQgRASRghRKONGzcOAwcOFDoMQghROirWSggRjEQi+eTjS5YswYYNG0CLuAkhuoiSMEKIYGJiYuS39+3bh8WLF+Phw4fy+8zNzWFubi5EaIQQonI0HEkIEYyDg4P8y9LSEhKJpMB95ubmHw1Henp6YsaMGZg5cyasra1hb28PPz8/pKenY/z48bCwsECtWrVw6tSpAq919+5d9OrVC+bm5rC3t8fo0aPlhSkJIUQIlIQRQrTOjh07YGtri+DgYMyYMQNTpkzB0KFD0bZtW4SHh6N79+4YPXo0MjIyAABJSUno0qUL3NzcEBoaitOnTyMuLg7Dhg0TuCWEEDGjJIwQonWaNm2KRYsWoXbt2pg/fz5MTExga2uLSZMmoXbt2li8eDHevXuH27dvAwA2bdoENzc3LF++HPXq1YObmxv8/f1x8eJFPHr0SODWEELEiuaEEUK0TpMmTeS39fX1UalSJTRu3Fh+n729PQDgzZs3AIBbt27h4sWLhc4ve/r0KerUqaPiiAkh5GOUhBFCtM6HGwtLJJIC9/GrLmUyGQAgLS0N/fr1w6pVqz46l6OjowojJYSQolESRgjRec2bN8eBAwfg4uICAwP6s0cI0Qw0J4wQovOmTZuGhIQEeHt7IyQkBE+fPsWZM2cwfvx4SKVSocMjhIgUJWGEEJ3n5OSEy5cvQyqVonv37mjcuDFmzpwJKysr6OnRn0FCiDAkHJWiJoQQQghRO/oISAghhBAiAErCCCGEEEIEQEkYIYQQQogAKAkjhBBCCBEAJWGEEEIIIQKgJIwQQgghRACUhBFCCCGECICSMEIIIYQQAVASRgghhBAiAErCCCGEEEIEQEkYIYQQQogA/h+uHPh7nKGONAAAAABJRU5ErkJggg=="
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 12
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-07T03:57:43.978897Z",
+     "start_time": "2026-01-07T03:57:43.973610Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "len(df_15m)",
+   "id": "14e077d6a70439e3",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "345"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 13
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-07T03:57:44.141479Z",
+     "start_time": "2026-01-07T03:57:44.008504Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plt.plot(minutely_15_dataframe['date'], minutely_15_dataframe['shortwave_radiation_instant'])",
+   "id": "3d07f9ce1c4e6bb4",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1871a105670>]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 14
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-07T03:57:44.169113Z",
+     "start_time": "2026-01-07T03:57:44.161434Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "len(minutely_15_data['date'])",
+   "id": "b9b13efb8846ffb4",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "384"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 15
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-07T03:57:44.244722Z",
+     "start_time": "2026-01-07T03:57:44.226860Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from v4.array_temperature.arrayTemperatureModel import arrayTemperatureModel\n",
+    "def model(u0, u1):\n",
+    "    return arrayTemperatureModel(minutely_15_dataframe['temperature_2m'], minutely_15_dataframe['shortwave_radiation_instant'], 0, u0, u1)\n",
+    "\n"
+   ],
+   "id": "f2db910a0a809a42",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "58.77777777777778\n"
+     ]
+    }
+   ],
+   "execution_count": 16
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-07T03:57:44.288602Z",
+     "start_time": "2026-01-07T03:57:44.274602Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "model2 = model(12, 0.8)\n",
+    "print(model2.calculateArrayTemperature())\n",
+    "faiman_temp = model2.calculateArrayTemperature()"
+   ],
+   "id": "76ddfa495e81e820",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0     -1.734275\n",
+      "1     -1.704018\n",
+      "2     -1.732149\n",
+      "3     -1.660000\n",
+      "4     -1.560000\n",
+      "         ...   \n",
+      "340    3.612377\n",
+      "341    3.189651\n",
+      "342    2.750214\n",
+      "343    2.271817\n",
+      "344    1.921406\n",
+      "Length: 345, dtype: float32\n"
+     ]
+    }
+   ],
+   "execution_count": 17
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-07T03:57:44.394281Z",
+     "start_time": "2026-01-07T03:57:44.307063Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "plt.plot(df_15m.index, df_15m, color = 'red')\n",
+    "plt.plot(df_15m.index, faiman_temp)\n",
+    "plt.xticks(rotation = 90)\n"
+   ],
+   "id": "eb1c544f0f61380f",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([20271. , 20271.5, 20272. , 20272.5, 20273. , 20273.5, 20274. ,\n",
+       "        20274.5]),\n",
+       " [Text(20271.0, 0, '07-02 00'),\n",
+       "  Text(20271.5, 0, '07-02 12'),\n",
+       "  Text(20272.0, 0, '07-03 00'),\n",
+       "  Text(20272.5, 0, '07-03 12'),\n",
+       "  Text(20273.0, 0, '07-04 00'),\n",
+       "  Text(20273.5, 0, '07-04 12'),\n",
+       "  Text(20274.0, 0, '07-05 00'),\n",
+       "  Text(20274.5, 0, '07-05 12')])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 18
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-07T03:57:44.426038Z",
+     "start_time": "2026-01-07T03:57:44.421879Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "",
+   "id": "c15bb0d3e9e1a6fd",
+   "outputs": [],
+   "execution_count": null
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/motor_analysis/faiman_coefficients.ipynb b/motor_analysis/faiman_coefficients.ipynb
new file mode 100644
index 0000000..4b9333c
--- /dev/null
+++ b/motor_analysis/faiman_coefficients.ipynb
@@ -0,0 +1,1055 @@
+{
+ "cells": [
+  {
+   "metadata": {
+    "jupyter": {
+     "is_executing": true
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import matplotlib\n",
+    "\n",
+    "\n",
+    "from data_tools import query\n",
+    "from data_tools.collections import TimeSeries\n",
+    "from datetime import datetime, date, time, timezone\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import dill\n",
+    "import os\n",
+    "\n",
+    "#each 5 seconds\n",
+    "utc_offset_h = 2\n",
+    "start_utc = time(5 , 00, 00) #querying i svancouver time, influxdb gives utc\n",
+    "stop_utc = time(4, 45, 40)\n",
+    "date_start = date(2025, 6, 30)\n",
+    "date_stop = date(2025, 7, 2)\n",
+    "start_time = datetime.combine(date_start, start_utc, tzinfo=timezone.utc)\n",
+    "stop_time = datetime.combine(date_stop, stop_utc, tzinfo=timezone.utc)\n",
+    "\n",
+    "client = query.DBClient()\n",
+    "temp_array_expected_aliter: TimeSeries = client.query_time_series(start_time, stop_time, \"MosfetTemperatureA\")\n"
+   ],
+   "id": "7762d7c5f54c9e9f",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T19:07:28.204093Z",
+     "start_time": "2025-11-29T19:07:18.443470Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "#alternate data:\n",
+    "\n",
+    "from data_tools import query\n",
+    "from data_tools.collections import TimeSeries\n",
+    "from datetime import datetime, date, time, timezone\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import dill\n",
+    "import os\n",
+    "\n",
+    "#each 5 seconds\n",
+    "utc_offset_h = 2\n",
+    "start_utc = time(5 + utc_offset_h , 00, 00) #querying i svancouver time, influxdb gives utc\n",
+    "stop_utc = time(4 +  utc_offset_h, 45, 40)\n",
+    "date_start = date(2025, 7, 1)\n",
+    "date_stop = date(2025, 7, 2)\n",
+    "start_time = datetime.combine(date_start, start_utc, tzinfo=timezone.utc)\n",
+    "stop_time = datetime.combine(date_stop, stop_utc, tzinfo=timezone.utc)\n",
+    "\n",
+    "client = query.DBClient()\n",
+    "temp_array_expected_aliter: TimeSeries = client.query_time_series(start_time, stop_time, \"MosfetTemperatureA\")\n",
+    "speed_kph: TimeSeries = client.query_time_series(start_time, stop_time, \"MotorRotatingSpeed\")\n",
+    "\n",
+    "print(temp_array_expected_aliter)\n",
+    "#this is almost steady state, and expected temperature is roughly 31.8 degrees celsius. note that individual cells do have an upto 5 C difference, which is actually quite a lot.\n",
+    "\n",
+    "\n"
+   ],
+   "id": "a8ea6762121b1311",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[27.46449661 27.46548943 27.46648225 ... 27.29558551 27.29614538\n",
+      " 27.29670525]\n"
+     ]
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T19:07:36.960833Z",
+     "start_time": "2025-11-29T19:07:36.946373Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "#save collected data of 15 minutes\n",
+    "\n",
+    "import os\n",
+    "import dill\n",
+    "\n",
+    "out_dir = os.path.join(\"../../motor_analysis\", \"data\", \"array_temperature_aliter_2025-07-01\")\n",
+    "mosfetA_file_aliter = os.path.join(out_dir, \"mosfetA_aliter.bin\")\n",
+    "\n",
+    "os.makedirs(out_dir, exist_ok=True)\n",
+    "\n",
+    "for filepath, data in zip([mosfetA_file_aliter],\n",
+    "                          [temp_array_expected_aliter]):\n",
+    "    with open(filepath, 'wb') as f:\n",
+    "        dill.dump(data, f)\n",
+    "\n",
+    "\n",
+    "\n",
+    "#time zone matching conventions??"
+   ],
+   "id": "9b566c367639c5e5",
+   "outputs": [],
+   "execution_count": 4
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "alternate data: data over the first 4 days of fsgp 2025",
+   "id": "b59eb1d3085bfd98"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T22:07:18.614759Z",
+     "start_time": "2025-11-29T22:07:18.456316Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from data_tools import query\n",
+    "from data_tools.collections import TimeSeries\n",
+    "from datetime import datetime, date, time, timezone\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import dill\n",
+    "import os\n",
+    "\n",
+    "#each 5 seconds\n",
+    "utc_offset_h = 2\n",
+    "start_utc = time(22 , 00, 00) #querying i svancouver time, influxdb gives utc\n",
+    "stop_utc = time(20, 45, 00)\n",
+    "date_start = date(2025, 7, 1)\n",
+    "date_stop = date(2025, 7, 6)\n",
+    "start_time = datetime.combine(date_start, start_utc, tzinfo=timezone.utc)\n",
+    "stop_time = datetime.combine(date_stop, stop_utc, tzinfo=timezone.utc)\n",
+    "\n",
+    "client = query.DBClient()\n",
+    "temp_array_fsgp: TimeSeries = client.query_time_series(start_time, stop_time, field=  \"MosfetTemperatureA\")\n",
+    "speed_kph: TimeSeries = client.query_time_series(start_time, stop_time, \"MotorRotatingSpeed\")\n",
+    "\n",
+    "#print(temp_array_expected_aliter)\n",
+    "#this is almost steady state, and expected temperature is roughly 31.8 degrees celsius. note that individual cells do have an upto 5 C difference, which is actually quite a lot.\n",
+    "\n",
+    "\n"
+   ],
+   "id": "8b2b4006671bdc31",
+   "outputs": [
+    {
+     "ename": "ApiException",
+     "evalue": "(401)\nReason: Unauthorized\nHTTP response headers: HTTPHeaderDict({'Server': 'nginx/1.27.1', 'Date': 'Sat, 29 Nov 2025 22:07:18 GMT', 'Content-Type': 'application/json; charset=utf-8', 'Content-Length': '55', 'Connection': 'keep-alive', 'X-Influxdb-Build': 'OSS', 'X-Influxdb-Version': 'v2.7.1', 'X-Platform-Error-Code': 'unauthorized'})\nHTTP response body: b'{\"code\":\"unauthorized\",\"message\":\"unauthorized access\"}'\n",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mApiException\u001B[39m                              Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[2]\u001B[39m\u001B[32m, line 20\u001B[39m\n\u001B[32m     17\u001B[39m stop_time = datetime.combine(date_stop, stop_utc, tzinfo=timezone.utc)\n\u001B[32m     19\u001B[39m client = query.DBClient()\n\u001B[32m---> \u001B[39m\u001B[32m20\u001B[39m temp_array_fsgp: TimeSeries = \u001B[43mclient\u001B[49m\u001B[43m.\u001B[49m\u001B[43mquery_time_series\u001B[49m\u001B[43m(\u001B[49m\u001B[43mstart_time\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstop_time\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfield\u001B[49m\u001B[43m=\u001B[49m\u001B[43m  \u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mMosfetTemperatureA\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m     21\u001B[39m speed_kph: TimeSeries = client.query_time_series(start_time, stop_time, \u001B[33m\"\u001B[39m\u001B[33mMotorRotatingSpeed\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m     23\u001B[39m \u001B[38;5;66;03m#print(temp_array_expected_aliter)\u001B[39;00m\n\u001B[32m     24\u001B[39m \u001B[38;5;66;03m#this is almost steady state, and expected temperature is roughly 31.8 degrees celsius. note that individual cells do have an upto 5 C difference, which is actually quite a lot.\u001B[39;00m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\data_tools\\query\\influxdb_query.py:180\u001B[39m, in \u001B[36mDBClient.query_time_series\u001B[39m\u001B[34m(self, start, stop, field, bucket, car, granularity, units, measurement)\u001B[39m\n\u001B[32m    163\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mquery_time_series\u001B[39m(\u001B[38;5;28mself\u001B[39m, start: datetime, stop: datetime, field: \u001B[38;5;28mstr\u001B[39m, bucket: \u001B[38;5;28mstr\u001B[39m = \u001B[33m\"\u001B[39m\u001B[33mCAN_log\u001B[39m\u001B[33m\"\u001B[39m,\n\u001B[32m    164\u001B[39m                       car: \u001B[38;5;28mstr\u001B[39m = \u001B[33m\"\u001B[39m\u001B[33mBrightside\u001B[39m\u001B[33m\"\u001B[39m, granularity: \u001B[38;5;28mfloat\u001B[39m = \u001B[32m0.1\u001B[39m, units: \u001B[38;5;28mstr\u001B[39m = \u001B[33m\"\u001B[39m\u001B[33m\"\u001B[39m,\n\u001B[32m    165\u001B[39m                       measurement: \u001B[38;5;28mstr\u001B[39m = \u001B[38;5;28;01mNone\u001B[39;00m) -> TimeSeries:\n\u001B[32m    166\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    167\u001B[39m \u001B[33;03m    Query the database for a specific field, over a certain time range.\u001B[39;00m\n\u001B[32m    168\u001B[39m \u001B[33;03m    The data will be processed into a TimeSeries, which has homogenous and evenly-spaced (temporally) elements.\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m    178\u001B[39m \u001B[33;03m    :return: a TimeSeries of the resulting time-series data\u001B[39;00m\n\u001B[32m    179\u001B[39m \u001B[33;03m    \"\"\"\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m180\u001B[39m     query_df = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mquery_series\u001B[49m\u001B[43m(\u001B[49m\u001B[43mstart\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstop\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfield\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbucket\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcar\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmeasurement\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    182\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m TimeSeries.from_query_dataframe(query_df, granularity, field, units)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\data_tools\\query\\influxdb_query.py:153\u001B[39m, in \u001B[36mDBClient.query_series\u001B[39m\u001B[34m(self, start, stop, field, bucket, car, measurement)\u001B[39m\n\u001B[32m    151\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m measurement:\n\u001B[32m    152\u001B[39m     query = query.filter(measurement=measurement)\n\u001B[32m--> \u001B[39m\u001B[32m153\u001B[39m query_df = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mquery_dataframe\u001B[49m\u001B[43m(\u001B[49m\u001B[43mquery\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    155\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(query_df, \u001B[38;5;28mlist\u001B[39m):\n\u001B[32m    156\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[33m\"\u001B[39m\u001B[33mQuery returned multiple fields! Please refine your query.\u001B[39m\u001B[33m\"\u001B[39m)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\data_tools\\query\\influxdb_query.py:123\u001B[39m, in \u001B[36mDBClient.query_dataframe\u001B[39m\u001B[34m(self, query)\u001B[39m\n\u001B[32m    120\u001B[39m compiled_query = query.compile_query()\n\u001B[32m    121\u001B[39m compiled_query += \u001B[33m'\u001B[39m\u001B[33m |> pivot(rowKey:[\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m_time\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m], columnKey: [\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m_field\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m], valueColumn: \u001B[39m\u001B[33m\"\u001B[39m\u001B[33m_value\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m) \u001B[39m\u001B[33m'\u001B[39m\n\u001B[32m--> \u001B[39m\u001B[32m123\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_client\u001B[49m\u001B[43m.\u001B[49m\u001B[43mquery_api\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[43mquery_data_frame\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcompiled_query\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\client\\query_api.py:259\u001B[39m, in \u001B[36mQueryApi.query_data_frame\u001B[39m\u001B[34m(self, query, org, data_frame_index, params, use_extension_dtypes)\u001B[39m\n\u001B[32m    225\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mquery_data_frame\u001B[39m(\u001B[38;5;28mself\u001B[39m, query: \u001B[38;5;28mstr\u001B[39m, org=\u001B[38;5;28;01mNone\u001B[39;00m, data_frame_index: List[\u001B[38;5;28mstr\u001B[39m] = \u001B[38;5;28;01mNone\u001B[39;00m, params: \u001B[38;5;28mdict\u001B[39m = \u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[32m    226\u001B[39m                      use_extension_dtypes: \u001B[38;5;28mbool\u001B[39m = \u001B[38;5;28;01mFalse\u001B[39;00m):\n\u001B[32m    227\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    228\u001B[39m \u001B[33;03m    Execute synchronous Flux query and return Pandas DataFrame.\u001B[39;00m\n\u001B[32m    229\u001B[39m \n\u001B[32m   (...)\u001B[39m\u001B[32m    257\u001B[39m \u001B[33;03m            - https://docs.influxdata.com/flux/latest/stdlib/influxdata/influxdb/schema/fieldsascols/\u001B[39;00m\n\u001B[32m    258\u001B[39m \u001B[33;03m    \"\"\"\u001B[39;00m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m259\u001B[39m     _generator = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mquery_data_frame_stream\u001B[49m\u001B[43m(\u001B[49m\u001B[43mquery\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43morg\u001B[49m\u001B[43m=\u001B[49m\u001B[43morg\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata_frame_index\u001B[49m\u001B[43m=\u001B[49m\u001B[43mdata_frame_index\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[43m=\u001B[49m\u001B[43mparams\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    260\u001B[39m \u001B[43m                                              \u001B[49m\u001B[43muse_extension_dtypes\u001B[49m\u001B[43m=\u001B[49m\u001B[43muse_extension_dtypes\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    261\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._to_data_frames(_generator)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\client\\query_api.py:299\u001B[39m, in \u001B[36mQueryApi.query_data_frame_stream\u001B[39m\u001B[34m(self, query, org, data_frame_index, params, use_extension_dtypes)\u001B[39m\n\u001B[32m    265\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    266\u001B[39m \u001B[33;03mExecute synchronous Flux query and return stream of Pandas DataFrame as a :class:`~Generator[DataFrame]`.\u001B[39;00m\n\u001B[32m    267\u001B[39m \n\u001B[32m   (...)\u001B[39m\u001B[32m    295\u001B[39m \u001B[33;03m        - https://docs.influxdata.com/flux/latest/stdlib/influxdata/influxdb/schema/fieldsascols/\u001B[39;00m\n\u001B[32m    296\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m    297\u001B[39m org = \u001B[38;5;28mself\u001B[39m._org_param(org)\n\u001B[32m--> \u001B[39m\u001B[32m299\u001B[39m response = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_query_api\u001B[49m\u001B[43m.\u001B[49m\u001B[43mpost_query\u001B[49m\u001B[43m(\u001B[49m\u001B[43morg\u001B[49m\u001B[43m=\u001B[49m\u001B[43morg\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mquery\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_create_query\u001B[49m\u001B[43m(\u001B[49m\u001B[43mquery\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mdefault_dialect\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    300\u001B[39m \u001B[43m                                                                        \u001B[49m\u001B[43mdataframe_query\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    301\u001B[39m \u001B[43m                                      \u001B[49m\u001B[43masync_req\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_return_http_data_only\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[32m    303\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._to_data_frame_stream(data_frame_index=data_frame_index,\n\u001B[32m    304\u001B[39m                                   response=response,\n\u001B[32m    305\u001B[39m                                   query_options=\u001B[38;5;28mself\u001B[39m._get_query_options(),\n\u001B[32m    306\u001B[39m                                   use_extension_dtypes=use_extension_dtypes)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\service\\query_service.py:285\u001B[39m, in \u001B[36mQueryService.post_query\u001B[39m\u001B[34m(self, **kwargs)\u001B[39m\n\u001B[32m    283\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m.post_query_with_http_info(**kwargs)  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m    284\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m285\u001B[39m     (data) = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mpost_query_with_http_info\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m    286\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m data\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\service\\query_service.py:311\u001B[39m, in \u001B[36mQueryService.post_query_with_http_info\u001B[39m\u001B[34m(self, **kwargs)\u001B[39m\n\u001B[32m    289\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"Query data.\u001B[39;00m\n\u001B[32m    290\u001B[39m \n\u001B[32m    291\u001B[39m \u001B[33;03mRetrieves data from buckets.  Use this endpoint to send a Flux query request and retrieve data from a bucket.  #### Rate limits (with InfluxDB Cloud)  `read` rate limits apply. For more information, see [limits and adjustable quotas](https://docs.influxdata.com/influxdb/cloud/account-management/limits/).  #### Related guides  - [Query with the InfluxDB API](https://docs.influxdata.com/influxdb/latest/query-data/execute-queries/influx-api/) - [Get started with Flux](https://docs.influxdata.com/flux/v0.x/get-started/)\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m    306\u001B[39m \u001B[33;03m         returns the request thread.\u001B[39;00m\n\u001B[32m    307\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m    308\u001B[39m local_var_params, path_params, query_params, header_params, body_params = \\\n\u001B[32m    309\u001B[39m     \u001B[38;5;28mself\u001B[39m._post_query_prepare(**kwargs)  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m311\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mapi_client\u001B[49m\u001B[43m.\u001B[49m\u001B[43mcall_api\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    312\u001B[39m \u001B[43m    \u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m/api/v2/query\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43mPOST\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m    313\u001B[39m \u001B[43m    \u001B[49m\u001B[43mpath_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    314\u001B[39m \u001B[43m    \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    315\u001B[39m \u001B[43m    \u001B[49m\u001B[43mheader_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    316\u001B[39m \u001B[43m    \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m=\u001B[49m\u001B[43mbody_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    317\u001B[39m \u001B[43m    \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43m[\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    318\u001B[39m \u001B[43m    \u001B[49m\u001B[43mfiles\u001B[49m\u001B[43m=\u001B[49m\u001B[43m{\u001B[49m\u001B[43m}\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    319\u001B[39m \u001B[43m    \u001B[49m\u001B[43mresponse_type\u001B[49m\u001B[43m=\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43mstr\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# noqa: E501\u001B[39;49;00m\n\u001B[32m    320\u001B[39m \u001B[43m    \u001B[49m\u001B[43mauth_settings\u001B[49m\u001B[43m=\u001B[49m\u001B[43m[\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    321\u001B[39m \u001B[43m    \u001B[49m\u001B[43masync_req\u001B[49m\u001B[43m=\u001B[49m\u001B[43mlocal_var_params\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43masync_req\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    322\u001B[39m \u001B[43m    \u001B[49m\u001B[43m_return_http_data_only\u001B[49m\u001B[43m=\u001B[49m\u001B[43mlocal_var_params\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m_return_http_data_only\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# noqa: E501\u001B[39;49;00m\n\u001B[32m    323\u001B[39m \u001B[43m    \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m=\u001B[49m\u001B[43mlocal_var_params\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m_preload_content\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    324\u001B[39m \u001B[43m    \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m=\u001B[49m\u001B[43mlocal_var_params\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m_request_timeout\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    325\u001B[39m \u001B[43m    \u001B[49m\u001B[43mcollection_formats\u001B[49m\u001B[43m=\u001B[49m\u001B[43m{\u001B[49m\u001B[43m}\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    326\u001B[39m \u001B[43m    \u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[43m=\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43murlopen_kw\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\api_client.py:343\u001B[39m, in \u001B[36mApiClient.call_api\u001B[39m\u001B[34m(self, resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, async_req, _return_http_data_only, collection_formats, _preload_content, _request_timeout, urlopen_kw)\u001B[39m\n\u001B[32m    304\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"Make the HTTP request (synchronous) and Return deserialized data.\u001B[39;00m\n\u001B[32m    305\u001B[39m \n\u001B[32m    306\u001B[39m \u001B[33;03mTo make an async_req request, set the async_req parameter.\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m    340\u001B[39m \u001B[33;03m    then the method will return the response directly.\u001B[39;00m\n\u001B[32m    341\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    342\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m async_req:\n\u001B[32m--> \u001B[39m\u001B[32m343\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m__call_api\u001B[49m\u001B[43m(\u001B[49m\u001B[43mresource_path\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    344\u001B[39m \u001B[43m                           \u001B[49m\u001B[43mpath_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mheader_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    345\u001B[39m \u001B[43m                           \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfiles\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    346\u001B[39m \u001B[43m                           \u001B[49m\u001B[43mresponse_type\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mauth_settings\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    347\u001B[39m \u001B[43m                           \u001B[49m\u001B[43m_return_http_data_only\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcollection_formats\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    348\u001B[39m \u001B[43m                           \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    349\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m    350\u001B[39m     thread = \u001B[38;5;28mself\u001B[39m.pool.apply_async(\u001B[38;5;28mself\u001B[39m.__call_api, (resource_path,\n\u001B[32m    351\u001B[39m                                    method, path_params, query_params,\n\u001B[32m    352\u001B[39m                                    header_params, body,\n\u001B[32m   (...)\u001B[39m\u001B[32m    356\u001B[39m                                    collection_formats,\n\u001B[32m    357\u001B[39m                                    _preload_content, _request_timeout, urlopen_kw))\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\api_client.py:173\u001B[39m, in \u001B[36mApiClient.__call_api\u001B[39m\u001B[34m(self, resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, _return_http_data_only, collection_formats, _preload_content, _request_timeout, urlopen_kw)\u001B[39m\n\u001B[32m    170\u001B[39m urlopen_kw = urlopen_kw \u001B[38;5;129;01mor\u001B[39;00m {}\n\u001B[32m    172\u001B[39m \u001B[38;5;66;03m# perform request and return response\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m173\u001B[39m response_data = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mrequest\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    174\u001B[39m \u001B[43m    \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m=\u001B[49m\u001B[43mheader_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    175\u001B[39m \u001B[43m    \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m=\u001B[49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    176\u001B[39m \u001B[43m    \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    177\u001B[39m \u001B[43m    \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    179\u001B[39m \u001B[38;5;28mself\u001B[39m.last_response = response_data\n\u001B[32m    181\u001B[39m return_data = response_data\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\api_client.py:388\u001B[39m, in \u001B[36mApiClient.request\u001B[39m\u001B[34m(self, method, url, query_params, headers, post_params, body, _preload_content, _request_timeout, **urlopen_kw)\u001B[39m\n\u001B[32m    379\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m.rest_client.OPTIONS(url,\n\u001B[32m    380\u001B[39m                                     query_params=query_params,\n\u001B[32m    381\u001B[39m                                     headers=headers,\n\u001B[32m   (...)\u001B[39m\u001B[32m    385\u001B[39m                                     body=body,\n\u001B[32m    386\u001B[39m                                     **urlopen_kw)\n\u001B[32m    387\u001B[39m \u001B[38;5;28;01melif\u001B[39;00m method == \u001B[33m\"\u001B[39m\u001B[33mPOST\u001B[39m\u001B[33m\"\u001B[39m:\n\u001B[32m--> \u001B[39m\u001B[32m388\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mrest_client\u001B[49m\u001B[43m.\u001B[49m\u001B[43mPOST\u001B[49m\u001B[43m(\u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    389\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    390\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m=\u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    391\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    392\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    393\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    394\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m=\u001B[49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    395\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    396\u001B[39m \u001B[38;5;28;01melif\u001B[39;00m method == \u001B[33m\"\u001B[39m\u001B[33mPUT\u001B[39m\u001B[33m\"\u001B[39m:\n\u001B[32m    397\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m.rest_client.PUT(url,\n\u001B[32m    398\u001B[39m                                 query_params=query_params,\n\u001B[32m    399\u001B[39m                                 headers=headers,\n\u001B[32m   (...)\u001B[39m\u001B[32m    403\u001B[39m                                 body=body,\n\u001B[32m    404\u001B[39m                                 **urlopen_kw)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\rest.py:311\u001B[39m, in \u001B[36mRESTClientObject.POST\u001B[39m\u001B[34m(self, url, headers, query_params, post_params, body, _preload_content, _request_timeout, **urlopen_kw)\u001B[39m\n\u001B[32m    308\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mPOST\u001B[39m(\u001B[38;5;28mself\u001B[39m, url, headers=\u001B[38;5;28;01mNone\u001B[39;00m, query_params=\u001B[38;5;28;01mNone\u001B[39;00m, post_params=\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[32m    309\u001B[39m          body=\u001B[38;5;28;01mNone\u001B[39;00m, _preload_content=\u001B[38;5;28;01mTrue\u001B[39;00m, _request_timeout=\u001B[38;5;28;01mNone\u001B[39;00m, **urlopen_kw):\n\u001B[32m    310\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"Perform POST HTTP request.\"\"\"\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m311\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mrequest\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mPOST\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    312\u001B[39m \u001B[43m                        \u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m=\u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    313\u001B[39m \u001B[43m                        \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    314\u001B[39m \u001B[43m                        \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    315\u001B[39m \u001B[43m                        \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    316\u001B[39m \u001B[43m                        \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    317\u001B[39m \u001B[43m                        \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m=\u001B[49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    318\u001B[39m \u001B[43m                        \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\rest.py:261\u001B[39m, in \u001B[36mRESTClientObject.request\u001B[39m\u001B[34m(self, method, url, query_params, headers, body, post_params, _preload_content, _request_timeout, **urlopen_kw)\u001B[39m\n\u001B[32m    258\u001B[39m     _BaseRESTClient.log_body(r.data, \u001B[33m'\u001B[39m\u001B[33m<<<\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m    260\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[32m200\u001B[39m <= r.status <= \u001B[32m299\u001B[39m:\n\u001B[32m--> \u001B[39m\u001B[32m261\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m ApiException(http_resp=r)\n\u001B[32m    263\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m r\n",
+      "\u001B[31mApiException\u001B[39m: (401)\nReason: Unauthorized\nHTTP response headers: HTTPHeaderDict({'Server': 'nginx/1.27.1', 'Date': 'Sat, 29 Nov 2025 22:07:18 GMT', 'Content-Type': 'application/json; charset=utf-8', 'Content-Length': '55', 'Connection': 'keep-alive', 'X-Influxdb-Build': 'OSS', 'X-Influxdb-Version': 'v2.7.1', 'X-Platform-Error-Code': 'unauthorized'})\nHTTP response body: b'{\"code\":\"unauthorized\",\"message\":\"unauthorized access\"}'\n"
+     ]
+    }
+   ],
+   "execution_count": 2
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T19:45:37.979942Z",
+     "start_time": "2025-11-29T19:45:37.962502Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "type(temp_array_fsgp)",
+   "id": "52e7b7784852e53d",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "data_tools.collections.time_series.TimeSeries"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 52
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T19:45:39.544344Z",
+     "start_time": "2025-11-29T19:45:39.529624Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df_fsgp = pd.DataFrame(temp_array_fsgp)",
+   "id": "1300423cd026c3ca",
+   "outputs": [],
+   "execution_count": 53
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T19:45:40.816030Z",
+     "start_time": "2025-11-29T19:45:40.810451Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "len(df_fsgp)",
+   "id": "f46c881335aa4926",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2831521"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 54
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T19:45:43.429032Z",
+     "start_time": "2025-11-29T19:45:43.420022Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "len(hourly_data['shortwave_radiation_instant'])",
+   "id": "bbac7524849da8b0",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "120"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 55
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "querying from openmeteo to get solar irradiance data\n",
+    "\n",
+    "- this is hourly irradiance over 4 days\n"
+   ],
+   "id": "bc1ff3d5812338f"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T19:45:50.178966Z",
+     "start_time": "2025-11-29T19:45:50.150527Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import openmeteo_requests\n",
+    "\n",
+    "import pandas as pd\n",
+    "import requests_cache\n",
+    "from retry_requests import retry\n",
+    "\n",
+    "# Setup the Open-Meteo API client with cache and retry on error\n",
+    "cache_session = requests_cache.CachedSession('.cache', expire_after = 3600)\n",
+    "retry_session = retry(cache_session, retries = 5, backoff_factor = 0.2)\n",
+    "openmeteo = openmeteo_requests.Client(session = retry_session)\n",
+    "\n",
+    "# Make sure all required weather variables are listed here\n",
+    "# The order of variables in hourly or daily is important to assign them correctly below\n",
+    "url = \"https://historical-forecast-api.open-meteo.com/v1/forecast\"\n",
+    "params = {\n",
+    "\t\"latitude\": 36.9760,\n",
+    "\t\"longitude\": 86.4491,\n",
+    "\t\"start_date\": \"2025-07-02\",\n",
+    "\t\"end_date\": \"2025-07-06\",\n",
+    "\t\"minutely_15\": [\"temperature_2m\", \"wind_speed_10m\", \"shortwave_radiation_instant\"],\n",
+    "}\n",
+    "responses = openmeteo.weather_api(url, params=params)\n",
+    "\n",
+    "# Process first location. Add a for-loop for multiple locations or weather models\n",
+    "response = responses[0]\n",
+    "print(f\"Coordinates: {response.Latitude()}°N {response.Longitude()}°E\")\n",
+    "print(f\"Elevation: {response.Elevation()} m asl\")\n",
+    "print(f\"Timezone difference to GMT+0: {response.UtcOffsetSeconds()}s\")\n",
+    "\n",
+    "# Process minutely_15 data. The order of variables needs to be the same as requested.\n",
+    "minutely_15 = response.Minutely15()\n",
+    "minutely_15_temperature_2m = minutely_15.Variables(0).ValuesAsNumpy()\n",
+    "minutely_15_shortwave_radiation_instant = minutely_15.Variables(1).ValuesAsNumpy()\n",
+    "minutely_15_wind_speed_10m = minutely_15.Variables(2).ValuesAsNumpy()\n",
+    "\n",
+    "minutely_15_data = {\"date\": pd.date_range(\n",
+    "\tstart = pd.to_datetime(minutely_15.Time(), unit = \"s\", utc = True),\n",
+    "\tend =  pd.to_datetime(minutely_15.TimeEnd(), unit = \"s\", utc = True),\n",
+    "\tfreq = pd.Timedelta(seconds = minutely_15.Interval()),\n",
+    "\tinclusive = \"left\"\n",
+    ")}\n",
+    "\n",
+    "minutely_15_data[\"temperature_2m\"] = minutely_15_temperature_2m\n",
+    "minutely_15_data[\"shortwave_radiation_instant\"] = minutely_15_shortwave_radiation_instant\n",
+    "minutely_15_data[\"wind_speed_10m\"] = minutely_15_wind_speed_10m\n",
+    "\n",
+    "minutely_15_dataframe = pd.DataFrame(data = minutely_15_data)\n",
+    "print(\"\\nMinutely15 data\\n\", minutely_15_dataframe)"
+   ],
+   "id": "f718cf3615289b31",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coordinates: 37.0°N 86.5°E\n",
+      "Elevation: 5139.0 m asl\n",
+      "Timezone difference to GMT+0: 0s\n",
+      "\n",
+      "Minutely15 data\n",
+      "                          date  temperature_2m  shortwave_radiation_instant  \\\n",
+      "0   2025-07-02 00:00:00+00:00           -2.20                     5.588703   \n",
+      "1   2025-07-02 00:15:00+00:00           -2.15                     5.351785   \n",
+      "2   2025-07-02 00:30:00+00:00           -2.15                     5.014219   \n",
+      "3   2025-07-02 00:45:00+00:00           -2.05                     4.680000   \n",
+      "4   2025-07-02 01:00:00+00:00           -1.95                     4.680000   \n",
+      "..                        ...             ...                          ...   \n",
+      "475 2025-07-06 22:45:00+00:00           -1.25                    20.240196   \n",
+      "476 2025-07-06 23:00:00+00:00           -1.05                    19.881649   \n",
+      "477 2025-07-06 23:15:00+00:00           -0.95                    19.917469   \n",
+      "478 2025-07-06 23:30:00+00:00           -0.90                    19.959719   \n",
+      "479 2025-07-06 23:45:00+00:00           -0.80                    20.418695   \n",
+      "\n",
+      "     wind_speed_10m  \n",
+      "0        124.831741  \n",
+      "1        164.539490  \n",
+      "2        205.015503  \n",
+      "3        243.104050  \n",
+      "4        276.808258  \n",
+      "..              ...  \n",
+      "475        0.000000  \n",
+      "476        0.000000  \n",
+      "477       16.716660  \n",
+      "478       48.040825  \n",
+      "479       76.951775  \n",
+      "\n",
+      "[480 rows x 4 columns]\n"
+     ]
+    }
+   ],
+   "execution_count": 56
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "minutely open meteo data starts at 2 july, 12am and goes to 6 july 22 45\n",
+    "therefore on influx i query for 1 july 10  pm to 6 july 20 45\n",
+    "\n",
+    "\n",
+    "utc i s2 hours ahead of vancouver"
+   ],
+   "id": "dd2a5efc6d21b409"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Plot some relevant data. this will further be used to generate the relevant coefficients",
+   "id": "9fe9b268872c73e6"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T21:25:43.719301Z",
+     "start_time": "2025-11-29T21:25:42.242073Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "#this graph shows solar irradiance, array temperature, and ambient temperature over July 2-6 (FSGP 2025)\n",
+    "\n",
+    "fig, ax1 = plt.subplots()\n",
+    "ax_twin = ax1.twinx()\n",
+    "\n",
+    "plt.plot(temp_array_fsgp.datetime_x_axis, temp_array_fsgp, label = \"Array Temperature\")\n",
+    "plt.plot(minutely_15_data['date'],minutely_15_data['temperature_2m'], color = 'green', label = \"Ambient Temperature\")\n",
+    "# plt.plot(minutely_15_data['date'], minutely_15_data['wind_speed_10m'], color = 'blue', label = \"Wind Speed 10m\")\n",
+    "# plt.plot(speed_kph.datetime_x_axis, speed_kph, color = 'yellow', label = \"Speed KPH\")\n",
+    "\n",
+    "ax1.plot(minutely_15_data['date'],minutely_15_data['shortwave_radiation_instant'], color = \"red\", label = \"Solar Irradiance\")\n",
+    "\n",
+    "ax1.set_xlabel(\"Time\")\n",
+    "ax1.set_ylabel(\"Solar Irradiance\")\n",
+    "ax_twin.set_ylabel(\"MosfetTemperatureA\")\n",
+    "\n",
+    "ax1.tick_params(\"x\", rotation  = 90)\n",
+    "\n",
+    "\n",
+    "plt.legend(loc = \"upper left\")\n",
+    "ax1.legend(loc = \"upper left\")\n",
+    "plt.show()"
+   ],
+   "id": "a3fa42c24abb970c",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 64
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T21:43:20.307344Z",
+     "start_time": "2025-11-29T21:43:20.296669Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "print(dir(temp_array_fsgp))\n",
+   "id": "d577cfa934535b4f",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['T', '__abs__', '__add__', '__and__', '__array__', '__array_finalize__', '__array_function__', '__array_interface__', '__array_namespace__', '__array_priority__', '__array_struct__', '__array_ufunc__', '__array_wrap__', '__bool__', '__buffer__', '__class__', '__class_getitem__', '__complex__', '__contains__', '__copy__', '__deepcopy__', '__delattr__', '__delitem__', '__dict__', '__dir__', '__divmod__', '__dlpack__', '__dlpack_device__', '__doc__', '__eq__', '__float__', '__floordiv__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__getstate__', '__gt__', '__hash__', '__iadd__', '__iand__', '__ifloordiv__', '__ilshift__', '__imatmul__', '__imod__', '__imul__', '__index__', '__init__', '__init_subclass__', '__int__', '__invert__', '__ior__', '__ipow__', '__irshift__', '__isub__', '__iter__', '__itruediv__', '__ixor__', '__le__', '__len__', '__lshift__', '__lt__', '__matmul__', '__mod__', '__module__', '__mul__', '__ne__', '__neg__', '__new__', '__or__', '__pos__', '__pow__', '__radd__', '__rand__', '__rdivmod__', '__reduce__', '__reduce_ex__', '__repr__', '__rfloordiv__', '__rlshift__', '__rmatmul__', '__rmod__', '__rmul__', '__ror__', '__rpow__', '__rrshift__', '__rshift__', '__rsub__', '__rtruediv__', '__rxor__', '__setattr__', '__setitem__', '__setstate__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__truediv__', '__xor__', '_length', '_meta', '_period', '_start', '_stop', '_units', 'align', 'all', 'any', 'argmax', 'argmin', 'argpartition', 'argsort', 'astype', 'base', 'byteswap', 'choose', 'clip', 'compress', 'conj', 'conjugate', 'copy', 'ctypes', 'cumprod', 'cumsum', 'data', 'datetime_x_axis', 'device', 'diagonal', 'dot', 'dtype', 'dump', 'dumps', 'fill', 'flags', 'flat', 'flatten', 'from_csv', 'from_query_dataframe', 'getfield', 'granularity', 'imag', 'index_of', 'item', 'itemset', 'itemsize', 'length', 'mT', 'max', 'mean', 'meta', 'min', 'nbytes', 'ndim', 'newbyteorder', 'nonzero', 'partition', 'period', 'plot', 'prod', 'promote', 'ptp', 'put', 'ravel', 'real', 'relative_time', 'repeat', 'reshape', 'resize', 'round', 'searchsorted', 'setfield', 'setflags', 'shape', 'size', 'sort', 'squeeze', 'start', 'std', 'stop', 'strides', 'sum', 'swapaxes', 'take', 'to_device', 'tobytes', 'tofile', 'tolist', 'trace', 'transpose', 'units', 'unix_x_axis', 'var', 'view', 'x_axis']\n"
+     ]
+    }
+   ],
+   "execution_count": 71
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T21:44:22.670216Z",
+     "start_time": "2025-11-29T21:44:21.605928Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "#resampling influx data so that i can fit it with irradiance data (queried every 15 minutes)\n",
+    "\n",
+    "ts = temp_array_fsgp\n",
+    "timestamps = pd.to_datetime(ts.datetime_x_axis, utc=True)\n",
+    "values = ts.data\n",
+    "\n",
+    "df = pd.DataFrame({\"value\": values}, index=timestamps)\n",
+    "df_15m = df.resample(\"15T\").mean()\n",
+    "\n",
+    "print(df_15m.head())\n"
+   ],
+   "id": "203be30192c9302a",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                               value\n",
+      "2025-07-02 07:15:00+00:00  37.371079\n",
+      "2025-07-02 07:30:00+00:00  46.801625\n",
+      "2025-07-02 07:45:00+00:00  29.980004\n",
+      "2025-07-02 08:00:00+00:00  45.573409\n",
+      "2025-07-02 08:15:00+00:00  55.745558\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_30480\\3992666424.py:13: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
+      "  df_15m = df.resample(\"15T\").mean()\n"
+     ]
+    }
+   ],
+   "execution_count": 72
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T21:48:01.173879Z",
+     "start_time": "2025-11-29T21:48:01.131446Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df_15m.index",
+   "id": "9d97681681224101",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatetimeIndex(['2025-07-02 07:15:00+00:00', '2025-07-02 07:30:00+00:00',\n",
+       "               '2025-07-02 07:45:00+00:00', '2025-07-02 08:00:00+00:00',\n",
+       "               '2025-07-02 08:15:00+00:00', '2025-07-02 08:30:00+00:00',\n",
+       "               '2025-07-02 08:45:00+00:00', '2025-07-02 09:00:00+00:00',\n",
+       "               '2025-07-02 09:15:00+00:00', '2025-07-02 09:30:00+00:00',\n",
+       "               ...\n",
+       "               '2025-07-05 11:45:00+00:00', '2025-07-05 12:00:00+00:00',\n",
+       "               '2025-07-05 12:15:00+00:00', '2025-07-05 12:30:00+00:00',\n",
+       "               '2025-07-05 12:45:00+00:00', '2025-07-05 13:00:00+00:00',\n",
+       "               '2025-07-05 13:15:00+00:00', '2025-07-05 13:30:00+00:00',\n",
+       "               '2025-07-05 13:45:00+00:00', '2025-07-05 14:00:00+00:00'],\n",
+       "              dtype='datetime64[ns, UTC]', length=316, freq='15min')"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 73
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-12-03T00:23:39.073811Z",
+     "start_time": "2025-12-03T00:23:38.847947Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "#this graph shows solar irradiance, array temperature, and ambient temperature over July 2-6 (FSGP 2025)\n",
+    "\n",
+    "fig, ax1 = plt.subplots()\n",
+    "ax_twin = ax1.twinx()\n",
+    "\n",
+    "plt.plot(df_15m.index, df_15m, label = \"Array Temperature\")\n",
+    "plt.plot(hourly_data['date'],hourly_data['temperature_2m'], color = 'green', label = \"Ambient Temperature\")\n",
+    "\n",
+    "ax1.plot(hourly_data['date'],hourly_data['shortwave_radiation_instant'], color = \"red\", label = \"Solar Irradiance\")\n",
+    "\n",
+    "ax1.set_xlabel(\"Time\")\n",
+    "ax1.set_ylabel(\"Solar Irradiance\")\n",
+    "ax_twin.set_ylabel(\"MosfetTemperatureA\")\n",
+    "\n",
+    "ax1.tick_params(\"x\", rotation  = 90)\n",
+    "#ax1.set_xticks(minutely_15.timeformat)\n",
+    "\n",
+    "plt.legend(loc = \"upper left\")\n",
+    "ax1.legend(loc = \"upper left\")\n",
+    "plt.show()"
+   ],
+   "id": "7397cf9f7ec92d1e",
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'plt' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mNameError\u001B[39m                                 Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[1]\u001B[39m\u001B[32m, line 3\u001B[39m\n\u001B[32m      1\u001B[39m \u001B[38;5;66;03m#this graph shows solar irradiance, array temperature, and ambient temperature over July 2-6 (FSGP 2025)\u001B[39;00m\n\u001B[32m----> \u001B[39m\u001B[32m3\u001B[39m fig, ax1 = \u001B[43mplt\u001B[49m.subplots()\n\u001B[32m      4\u001B[39m ax_twin = ax1.twinx()\n\u001B[32m      6\u001B[39m plt.plot(df_15m.index, df_15m, label = \u001B[33m\"\u001B[39m\u001B[33mArray Temperature\u001B[39m\u001B[33m\"\u001B[39m)\n",
+      "\u001B[31mNameError\u001B[39m: name 'plt' is not defined"
+     ]
+    }
+   ],
+   "execution_count": 1
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "8848868de645674f"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "import the Physics Array Temperature Model for validation/sanity checks\n",
+   "id": "11a5c363e4b5b953"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T21:48:56.274247Z",
+     "start_time": "2025-11-29T21:48:56.265960Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from v4.array_temperature.arrayTemperatureModel import arrayTemperatureModel\n",
+    "def model(u0, u1):\n",
+    "    return arrayTemperatureModel(hourly_data['temperature_2m'], hourly_data['shortwave_radiation_instant'], 0, u0, u1)\n",
+    "\n",
+    "model = model(22, 0.8)\n",
+    "print(model.calculateArrayTemperature())\n"
+   ],
+   "id": "2099b64dadb66f1a",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 1.8935347e+00  1.0364137e+01  2.1387180e+01  2.9999233e+01\n",
+      "  2.6391014e+01  2.6875341e+01  2.7645386e+01  2.8025963e+01\n",
+      "  2.5642160e+01  2.4427845e+01  1.8177454e+01  1.7554525e+01\n",
+      "  8.0708389e+00  1.9637234e+00 -8.7300003e-01 -1.2230000e+00\n",
+      " -1.4230000e+00 -1.6230000e+00 -2.3230000e+00 -2.8230000e+00\n",
+      " -3.3230000e+00 -3.7229998e+00 -4.0730000e+00 -4.3730001e+00\n",
+      "  1.2934799e+00  1.0832609e+01  2.1798904e+01  3.0714010e+01\n",
+      "  4.0591778e+01  4.3875725e+01  3.3674473e+01  3.8677563e+01\n",
+      "  3.2507423e+01  3.0509079e+01  2.3399044e+01  1.4952563e+01\n",
+      "  7.7067099e+00  1.6932418e+00 -1.2730000e+00 -1.8230001e+00\n",
+      " -2.6229999e+00 -3.2229998e+00 -3.8229997e+00 -4.0730000e+00\n",
+      " -4.5230002e+00 -4.8730001e+00 -5.2730002e+00 -5.5730000e+00\n",
+      " -1.0213566e-01  9.7276592e+00  2.1370445e+01  3.2780659e+01\n",
+      "  4.2558723e+01  4.9203346e+01  5.2815674e+01  5.2470692e+01\n",
+      "  4.7815193e+01  3.2199566e+01  2.7188547e+01  2.1431530e+01\n",
+      "  1.2343058e+01  3.3490453e+00 -1.1730000e+00 -2.4229999e+00\n",
+      " -3.5730000e+00 -3.5730000e+00 -3.0730000e+00 -3.1729999e+00\n",
+      " -3.1229999e+00 -3.5230000e+00 -3.7730000e+00 -3.8229997e+00\n",
+      " -7.6389313e-03  8.4443426e+00  1.7759680e+01  2.5409929e+01\n",
+      "  3.1721752e+01  3.9829716e+01  2.8951809e+01  4.0317398e+01\n",
+      "  3.3866692e+01  2.9610489e+01  2.6545244e+01  2.2101496e+01\n",
+      "  1.3766469e+01  5.0048113e+00  6.7700005e-01  1.7700000e-01\n",
+      " -4.7300002e-01 -1.0230000e+00 -1.7730001e+00 -2.5230000e+00\n",
+      " -3.0230000e+00 -3.2229998e+00 -3.3729999e+00 -3.3230000e+00\n",
+      "  2.0034187e+00  1.2429336e+01  2.4601358e+01  3.6484211e+01\n",
+      "  4.6292747e+01  5.3362427e+01  5.4850025e+01  5.5464657e+01\n",
+      "  5.2211830e+01  4.7174763e+01  3.9110405e+01  2.9210789e+01\n",
+      "  1.8547527e+01  8.4345522e+00  3.3770001e+00  2.2270000e+00\n",
+      "  1.6270000e+00  1.2770000e+00  1.0270000e+00  1.3770000e+00\n",
+      "  9.2700005e-01  1.2700000e-01 -1.4730000e+00 -1.2730000e+00]\n"
+     ]
+    }
+   ],
+   "execution_count": 76
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T21:48:57.548674Z",
+     "start_time": "2025-11-29T21:48:57.541528Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "faiman_temp = model.calculateArrayTemperature()",
+   "id": "492ea4964124380d",
+   "outputs": [],
+   "execution_count": 77
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T21:48:58.404891Z",
+     "start_time": "2025-11-29T21:48:58.394317Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import numpy as np\n",
+    "np.array(faiman_temp)"
+   ],
+   "id": "bee1dddff65cfb38",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.8935347e+00,  1.0364137e+01,  2.1387180e+01,  2.9999233e+01,\n",
+       "        2.6391014e+01,  2.6875341e+01,  2.7645386e+01,  2.8025963e+01,\n",
+       "        2.5642160e+01,  2.4427845e+01,  1.8177454e+01,  1.7554525e+01,\n",
+       "        8.0708389e+00,  1.9637234e+00, -8.7300003e-01, -1.2230000e+00,\n",
+       "       -1.4230000e+00, -1.6230000e+00, -2.3230000e+00, -2.8230000e+00,\n",
+       "       -3.3230000e+00, -3.7229998e+00, -4.0730000e+00, -4.3730001e+00,\n",
+       "        1.2934799e+00,  1.0832609e+01,  2.1798904e+01,  3.0714010e+01,\n",
+       "        4.0591778e+01,  4.3875725e+01,  3.3674473e+01,  3.8677563e+01,\n",
+       "        3.2507423e+01,  3.0509079e+01,  2.3399044e+01,  1.4952563e+01,\n",
+       "        7.7067099e+00,  1.6932418e+00, -1.2730000e+00, -1.8230001e+00,\n",
+       "       -2.6229999e+00, -3.2229998e+00, -3.8229997e+00, -4.0730000e+00,\n",
+       "       -4.5230002e+00, -4.8730001e+00, -5.2730002e+00, -5.5730000e+00,\n",
+       "       -1.0213566e-01,  9.7276592e+00,  2.1370445e+01,  3.2780659e+01,\n",
+       "        4.2558723e+01,  4.9203346e+01,  5.2815674e+01,  5.2470692e+01,\n",
+       "        4.7815193e+01,  3.2199566e+01,  2.7188547e+01,  2.1431530e+01,\n",
+       "        1.2343058e+01,  3.3490453e+00, -1.1730000e+00, -2.4229999e+00,\n",
+       "       -3.5730000e+00, -3.5730000e+00, -3.0730000e+00, -3.1729999e+00,\n",
+       "       -3.1229999e+00, -3.5230000e+00, -3.7730000e+00, -3.8229997e+00,\n",
+       "       -7.6389313e-03,  8.4443426e+00,  1.7759680e+01,  2.5409929e+01,\n",
+       "        3.1721752e+01,  3.9829716e+01,  2.8951809e+01,  4.0317398e+01,\n",
+       "        3.3866692e+01,  2.9610489e+01,  2.6545244e+01,  2.2101496e+01,\n",
+       "        1.3766469e+01,  5.0048113e+00,  6.7700005e-01,  1.7700000e-01,\n",
+       "       -4.7300002e-01, -1.0230000e+00, -1.7730001e+00, -2.5230000e+00,\n",
+       "       -3.0230000e+00, -3.2229998e+00, -3.3729999e+00, -3.3230000e+00,\n",
+       "        2.0034187e+00,  1.2429336e+01,  2.4601358e+01,  3.6484211e+01,\n",
+       "        4.6292747e+01,  5.3362427e+01,  5.4850025e+01,  5.5464657e+01,\n",
+       "        5.2211830e+01,  4.7174763e+01,  3.9110405e+01,  2.9210789e+01,\n",
+       "        1.8547527e+01,  8.4345522e+00,  3.3770001e+00,  2.2270000e+00,\n",
+       "        1.6270000e+00,  1.2770000e+00,  1.0270000e+00,  1.3770000e+00,\n",
+       "        9.2700005e-01,  1.2700000e-01, -1.4730000e+00, -1.2730000e+00],\n",
+       "      dtype=float32)"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 78
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T21:52:30.256333Z",
+     "start_time": "2025-11-29T21:52:30.246409Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "len(faiman_temp)",
+   "id": "53ffac58d50fb409",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "120"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 87
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T21:54:46.173840Z",
+     "start_time": "2025-11-29T21:54:46.151825Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df_15m.head()",
+   "id": "e2789dc942e61cc",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                               value\n",
+       "2025-07-02 07:15:00+00:00  37.371079\n",
+       "2025-07-02 07:30:00+00:00  46.801625\n",
+       "2025-07-02 07:45:00+00:00  29.980004\n",
+       "2025-07-02 08:00:00+00:00  45.573409\n",
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+      ],
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+       "      <th>value</th>\n",
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2025-07-02 07:15:00+00:00</th>\n",
+       "      <td>37.371079</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-02 07:30:00+00:00</th>\n",
+       "      <td>46.801625</td>\n",
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+       "    <tr>\n",
+       "      <th>2025-07-02 07:45:00+00:00</th>\n",
+       "      <td>29.980004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-02 08:00:00+00:00</th>\n",
+       "      <td>45.573409</td>\n",
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+       "      <td>55.745558</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 90
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T21:52:50.120667Z",
+     "start_time": "2025-11-29T21:52:50.111626Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df_15m.tail()",
+   "id": "87c603ad74af1dd1",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                               value\n",
+       "2025-07-05 13:00:00+00:00  38.086209\n",
+       "2025-07-05 13:15:00+00:00  36.774658\n",
+       "2025-07-05 13:30:00+00:00  35.463106\n",
+       "2025-07-05 13:45:00+00:00  34.271664\n",
+       "2025-07-05 14:00:00+00:00  34.174615"
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+       "      <th>2025-07-05 13:00:00+00:00</th>\n",
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+       "      <th>2025-07-05 13:15:00+00:00</th>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-05 13:45:00+00:00</th>\n",
+       "      <td>34.271664</td>\n",
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+       "    <tr>\n",
+       "      <th>2025-07-05 14:00:00+00:00</th>\n",
+       "      <td>34.174615</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 89
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T21:52:04.209818Z",
+     "start_time": "2025-11-29T21:52:03.938367Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df_15m['faiman'] = faiman_temp",
+   "id": "d57cc2a51597298a",
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Length of values (120) does not match length of index (316)",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mValueError\u001B[39m                                Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[86]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[43mdf_15m\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43mfaiman\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m]\u001B[49m = faiman_temp\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:4322\u001B[39m, in \u001B[36mDataFrame.__setitem__\u001B[39m\u001B[34m(self, key, value)\u001B[39m\n\u001B[32m   4319\u001B[39m     \u001B[38;5;28mself\u001B[39m._setitem_array([key], value)\n\u001B[32m   4320\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m   4321\u001B[39m     \u001B[38;5;66;03m# set column\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m4322\u001B[39m     \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_set_item\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mvalue\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:4535\u001B[39m, in \u001B[36mDataFrame._set_item\u001B[39m\u001B[34m(self, key, value)\u001B[39m\n\u001B[32m   4525\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34m_set_item\u001B[39m(\u001B[38;5;28mself\u001B[39m, key, value) -> \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m   4526\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m   4527\u001B[39m \u001B[33;03m    Add series to DataFrame in specified column.\u001B[39;00m\n\u001B[32m   4528\u001B[39m \n\u001B[32m   (...)\u001B[39m\u001B[32m   4533\u001B[39m \u001B[33;03m    ensure homogeneity.\u001B[39;00m\n\u001B[32m   4534\u001B[39m \u001B[33;03m    \"\"\"\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m4535\u001B[39m     value, refs = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_sanitize_column\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvalue\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   4537\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m (\n\u001B[32m   4538\u001B[39m         key \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m.columns\n\u001B[32m   4539\u001B[39m         \u001B[38;5;129;01mand\u001B[39;00m value.ndim == \u001B[32m1\u001B[39m\n\u001B[32m   4540\u001B[39m         \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(value.dtype, ExtensionDtype)\n\u001B[32m   4541\u001B[39m     ):\n\u001B[32m   4542\u001B[39m         \u001B[38;5;66;03m# broadcast across multiple columns if necessary\u001B[39;00m\n\u001B[32m   4543\u001B[39m         \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m.columns.is_unique \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(\u001B[38;5;28mself\u001B[39m.columns, MultiIndex):\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:5288\u001B[39m, in \u001B[36mDataFrame._sanitize_column\u001B[39m\u001B[34m(self, value)\u001B[39m\n\u001B[32m   5285\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m _reindex_for_setitem(value, \u001B[38;5;28mself\u001B[39m.index)\n\u001B[32m   5287\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m is_list_like(value):\n\u001B[32m-> \u001B[39m\u001B[32m5288\u001B[39m     \u001B[43mcom\u001B[49m\u001B[43m.\u001B[49m\u001B[43mrequire_length_match\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvalue\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mindex\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   5289\u001B[39m arr = sanitize_array(value, \u001B[38;5;28mself\u001B[39m.index, copy=\u001B[38;5;28;01mTrue\u001B[39;00m, allow_2d=\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[32m   5290\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m (\n\u001B[32m   5291\u001B[39m     \u001B[38;5;28misinstance\u001B[39m(value, Index)\n\u001B[32m   5292\u001B[39m     \u001B[38;5;129;01mand\u001B[39;00m value.dtype == \u001B[33m\"\u001B[39m\u001B[33mobject\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m   (...)\u001B[39m\u001B[32m   5295\u001B[39m     \u001B[38;5;66;03m# TODO: Remove kludge in sanitize_array for string mode when enforcing\u001B[39;00m\n\u001B[32m   5296\u001B[39m     \u001B[38;5;66;03m# this deprecation\u001B[39;00m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\common.py:573\u001B[39m, in \u001B[36mrequire_length_match\u001B[39m\u001B[34m(data, index)\u001B[39m\n\u001B[32m    569\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    570\u001B[39m \u001B[33;03mCheck the length of data matches the length of the index.\u001B[39;00m\n\u001B[32m    571\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    572\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(data) != \u001B[38;5;28mlen\u001B[39m(index):\n\u001B[32m--> \u001B[39m\u001B[32m573\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[32m    574\u001B[39m         \u001B[33m\"\u001B[39m\u001B[33mLength of values \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    575\u001B[39m         \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m(\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mlen\u001B[39m(data)\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m) \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    576\u001B[39m         \u001B[33m\"\u001B[39m\u001B[33mdoes not match length of index \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    577\u001B[39m         \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m(\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mlen\u001B[39m(index)\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m)\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    578\u001B[39m     )\n",
+      "\u001B[31mValueError\u001B[39m: Length of values (120) does not match length of index (316)"
+     ]
+    }
+   ],
+   "execution_count": 86
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T21:51:34.085612Z",
+     "start_time": "2025-11-29T21:51:33.854519Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "plt.plot(df_15m.index, df_15m, color = 'red')\n",
+    "plt.plot(df_15m.index, faiman_temp)\n",
+    "plt.xticks(rotation = 90)\n"
+   ],
+   "id": "8737baa73f7c5785",
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "x and y must have same first dimension, but have shapes (316,) and (120,)",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mValueError\u001B[39m                                Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[85]\u001B[39m\u001B[32m, line 2\u001B[39m\n\u001B[32m      1\u001B[39m plt.plot(df_15m.index, df_15m, color = \u001B[33m'\u001B[39m\u001B[33mred\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m----> \u001B[39m\u001B[32m2\u001B[39m \u001B[43mplt\u001B[49m\u001B[43m.\u001B[49m\u001B[43mplot\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdf_15m\u001B[49m\u001B[43m.\u001B[49m\u001B[43mindex\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfaiman_temp\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m      3\u001B[39m plt.xticks(rotation = \u001B[32m90\u001B[39m)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\matplotlib\\pyplot.py:3838\u001B[39m, in \u001B[36mplot\u001B[39m\u001B[34m(scalex, scaley, data, *args, **kwargs)\u001B[39m\n\u001B[32m   3830\u001B[39m \u001B[38;5;129m@_copy_docstring_and_deprecators\u001B[39m(Axes.plot)\n\u001B[32m   3831\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mplot\u001B[39m(\n\u001B[32m   3832\u001B[39m     *args: \u001B[38;5;28mfloat\u001B[39m | ArrayLike | \u001B[38;5;28mstr\u001B[39m,\n\u001B[32m   (...)\u001B[39m\u001B[32m   3836\u001B[39m     **kwargs,\n\u001B[32m   3837\u001B[39m ) -> \u001B[38;5;28mlist\u001B[39m[Line2D]:\n\u001B[32m-> \u001B[39m\u001B[32m3838\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mgca\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[43mplot\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m   3839\u001B[39m \u001B[43m        \u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   3840\u001B[39m \u001B[43m        \u001B[49m\u001B[43mscalex\u001B[49m\u001B[43m=\u001B[49m\u001B[43mscalex\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   3841\u001B[39m \u001B[43m        \u001B[49m\u001B[43mscaley\u001B[49m\u001B[43m=\u001B[49m\u001B[43mscaley\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   3842\u001B[39m \u001B[43m        \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43m(\u001B[49m\u001B[43m{\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mdata\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m}\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mif\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mis\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mnot\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01melse\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43m{\u001B[49m\u001B[43m}\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   3843\u001B[39m \u001B[43m        \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   3844\u001B[39m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:1777\u001B[39m, in \u001B[36mAxes.plot\u001B[39m\u001B[34m(self, scalex, scaley, data, *args, **kwargs)\u001B[39m\n\u001B[32m   1534\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m   1535\u001B[39m \u001B[33;03mPlot y versus x as lines and/or markers.\u001B[39;00m\n\u001B[32m   1536\u001B[39m \n\u001B[32m   (...)\u001B[39m\u001B[32m   1774\u001B[39m \u001B[33;03m(``'green'``) or hex strings (``'#008000'``).\u001B[39;00m\n\u001B[32m   1775\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m   1776\u001B[39m kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D)\n\u001B[32m-> \u001B[39m\u001B[32m1777\u001B[39m lines = [*\u001B[38;5;28mself\u001B[39m._get_lines(\u001B[38;5;28mself\u001B[39m, *args, data=data, **kwargs)]\n\u001B[32m   1778\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m line \u001B[38;5;129;01min\u001B[39;00m lines:\n\u001B[32m   1779\u001B[39m     \u001B[38;5;28mself\u001B[39m.add_line(line)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\matplotlib\\axes\\_base.py:297\u001B[39m, in \u001B[36m_process_plot_var_args.__call__\u001B[39m\u001B[34m(self, axes, data, return_kwargs, *args, **kwargs)\u001B[39m\n\u001B[32m    295\u001B[39m     this += args[\u001B[32m0\u001B[39m],\n\u001B[32m    296\u001B[39m     args = args[\u001B[32m1\u001B[39m:]\n\u001B[32m--> \u001B[39m\u001B[32m297\u001B[39m \u001B[38;5;28;01myield from\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_plot_args\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    298\u001B[39m \u001B[43m    \u001B[49m\u001B[43maxes\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mthis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mambiguous_fmt_datakey\u001B[49m\u001B[43m=\u001B[49m\u001B[43mambiguous_fmt_datakey\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    299\u001B[39m \u001B[43m    \u001B[49m\u001B[43mreturn_kwargs\u001B[49m\u001B[43m=\u001B[49m\u001B[43mreturn_kwargs\u001B[49m\n\u001B[32m    300\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\matplotlib\\axes\\_base.py:494\u001B[39m, in \u001B[36m_process_plot_var_args._plot_args\u001B[39m\u001B[34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001B[39m\n\u001B[32m    491\u001B[39m     axes.yaxis.update_units(y)\n\u001B[32m    493\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m x.shape[\u001B[32m0\u001B[39m] != y.shape[\u001B[32m0\u001B[39m]:\n\u001B[32m--> \u001B[39m\u001B[32m494\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mx and y must have same first dimension, but \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    495\u001B[39m                      \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mhave shapes \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mx.shape\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m and \u001B[39m\u001B[38;5;132;01m{\u001B[39;00my.shape\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m\"\u001B[39m)\n\u001B[32m    496\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m x.ndim > \u001B[32m2\u001B[39m \u001B[38;5;129;01mor\u001B[39;00m y.ndim > \u001B[32m2\u001B[39m:\n\u001B[32m    497\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mx and y can be no greater than 2D, but have \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    498\u001B[39m                      \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mshapes \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mx.shape\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m and \u001B[39m\u001B[38;5;132;01m{\u001B[39;00my.shape\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m\"\u001B[39m)\n",
+      "\u001B[31mValueError\u001B[39m: x and y must have same first dimension, but have shapes (316,) and (120,)"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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rPr74QqR7d5Hrr5e4QfGRy/nABuNn6FRHq0N8tGxpL/4F6H6YRdBDxrKdDzwfk/WERKvV9quv/FsvbPlykVmzJG6YLT7cMh/OlQd1h+6384HngwABFB9mEfTCctlzPsCOHaU9JiEk2t0u27ZlCpwYYbb4cMt84CCg6jQI8YHAKWDo1OwdUdDdLo0a1b2PEJKsssu2r8XHzp0SNyg+3CzwIHIfGzfa1wcckCk+6HyYhboQWhbxq8U7u+yCsh5nfRCS7G6XbRQfyXE+ghIfugHqAUE7Xthuaxa5xIc6H+UGTp3lFo5YJyTZzsfWrfY1nY8EZD6CEh/Zky3pfJiJ7iScZZEgyi6AHS+EmFF22bXLXsIjRrDsUinxwVkfZlLI+fCr7OJ8DgZOCakMEAQqNIJYWG6b4xgVs9ApxUe+zIefgdNczsenn/r3HMRc8ZGv7MLAKSGVwfn/HMTCclu/LrvEsPRC8eHmfKh1HaTz0aGDfb12rX/PQeIjPrLLLuXYsTi7yl7VFjBwSkhlcboRQQZOAcVHjKhk5uPAA+3rNWv8ew4SfXQH4afzgR2ZhqcZOCUkmuIjyMwHoPiIODhL1B18mN0u+lwqPjp2tK8//ti/5yBmll2cmQ6no8KyCyHREB/Y79erR/FhdNll5Eh70BfmboQpPnI5H598ErugEImY+HCeRenjOJ+DmQ9CKt/pAoJqtQUxO46YJz7+/nd7MZ53363rRoTd7aIbJHMf5lAo81GK+NDf0bMrhXM+CInOaHUQZLfLTgZOo40Kjr17c2c+NLQXZLcLJlCq+8HSizkUynyUslPSHZzT9XA+B50PQqLlfHzlU7cLxUeM0A8d4qMSZRfnczF0ah5BlF1yrRdD54OQ6IxWBwycGlx2cToflcx8AIZOzSNI8ZHtfDBwSkhyMx+pFJ2PWKEiAB9cJTMfgM6HWWB7051OrvHq2E4gjIuBZRdCzBMfO3Zk7iuY+UhA5sNv8QGhk098MPNhBs6dQ65VbUvZMRVyPjhenZDkiY9tWccnio8EZD78Dpw61SnLLubiFAG5yi6l7Jj051l2ISTa3S5Bio8attpGm0pkPpzJZpZdzEXFB0SCsyVWv6cUm/soVHah80FINAKnuv//6it/Z3wAOh8xdD6CznzkEh8aOMWcj2Lr/CR+6M4hO++hAhjt1+WIj1zdLmy1JaQysOySE7O7XbxkPpDX8Os5s8UHJq3igIPXgYmrxMxOF4DtoNRBYwycEhJNKD5yYp74KGbOB+73o46Wy/nAwQYCBDB0arb4KKfdloFTQqIJxUdOzBIfEBzqZHgJnPoVOnWKj+xaPztezCHXaPVyp5xyzgch5omPrcx8xAdn+SNf5gNiRA8QfuQ+nOUdresrnPVhDrlGq5frfOTqduF4dUIqC7tdcmKW85FLfGS7EX6HTt1mfCiccmoOQZddOF6dkHh0u+zmnA+zxIez/AHxoR0m2WWXMMUHnQ9zKCQ+ggyc+hGcJoSUV3ZRR31XCcsoZMMhYzF1PnBbxYeb89G8uX29eXP5z0vng1Qy8wHh4cfOjhDij/jY4Rg4WG7mo2VL8+Z8TJ48WaqqqmTs2LG13+vXr5/1Pedl9OjREjnnwylE3JwP7UJZty4c54PdLsknqMxHIfEBOOuDkOiIj507/XM+9t/fv8cMEZejoTcWLlwo999/v/Ts2bPOfaNGjZKbb7659usmzp1gJXEKjnwdKH6LApZdSCVabVHGgbDGdg/xsd9+/BwIqWTgVP/3d/ooPtq0EVmxwgznY9u2bTJixAh54IEHZD+XHRrERrt27Wov1dXVEgmcgiNq4gMb0pYt5T8XMS/zkavbBXBxOUKSWXbZsSOz7GLC2i5jxoyRs88+W8444wzX+6dPny5t2rSRHj16yIQJE+TLPJZvTU2NbNmyJeMSCeejQ4f06PMgxQeCrS1a2LfXrCn/uUj8Mx9+dbsAjlgnJDrdLn6WXXZ/fdKhJ/dJL7vMnDlTFi9ebJVd3Bg+fLh07txZOnToIEuXLpXrrrtOli9fLo8//rjrz0+aNEkmTpwoiXY+cs0TcbbbItiK5zriiPKfj8Q78+FX4NT5XMx8EBId52P3bvu44JY39IruJ7Q5Isni46OPPpKrr75ann/+eWmU4+zt8ssvr7191FFHSfv27aV///6yatUq6dKlS52fhzMyfvz42q/hfHTq1EmMyXzoc739Np2PpBN25gOw7EJIdMSH83+/piYzFF6q+NCxEEkWH4sWLZKNGzfKcccdV/u9PXv2yPz58+Wee+6xSij7ZCm53r17W9crV650FR8NGza0LpF1PtavL1+h5lrATuGgMTOohPig80FIdMSH81i3Y4c/4sME5wMOxrJlyzK+d9lll0n37t2t8kq28ABLliyxruGAVJxinI8DDkh3CmzYkM6ABOV8ALbbmp358HvIGKDzQUh0ul1wDMDlq6/KFwsmiY/mzZtbIVInTZs2ldatW1vfR2llxowZMmjQIOt7yHyMGzdO+vbt69qSGzrFOB8QHpj1AUGASxjig4HTZBP2nA/AwCkh0Qmc6snHtm3+iw8Tul1y0aBBA5kzZ44MGDDAckOuueYaGTp0qMyePVsigdP5cIb6cpVD/HIkCokPll3MwGvZpdjAqf68W7cLyy6ERKfs4uesj127zHE+3Jg7d27tbQRF582bJ5Ell/ORvdKs3+22dD5IGK22LLsQEn3x4desj91ZgVMcZ3DJdZIbMcxaWM4t8+FWcqmU8/HJJ7GzzoiPZZcgMh90PgiJpvjY6XPZxfl8McAs8eHmfERBfLRund44/VhLhkQTttoSYhZhi4+d8Sm9mL2qrVfxUW4QtJD4QNmHodPkU0nxwSFjhFS+28XPzMfu3enH02MLxUdCnI+DDrKvP/rIn+fNV4tju23y8Zr54IRTQpLd7eJn5gPlWhU3LLskJPOhk1Y//FAklSr/eb2ID7bbJpegWm3zdbtwzgchyS677Luvv2vGhIRZZZdinQ8NguID/eyz8p83n/hgu23y0R1Drom+QQ4ZY9mFkHDBfn/vXvs2xYfh4sPN+cg3Nh0bTNu2afejVLw8F8suyUfPVHKJD45XJyQ5OEsgQWY+9qXzkbyyi1+5j2KcD5ZdkgnOgHQ7cHMogsp8sOxCSLTEhx+Zjz170lEAp/goN0cSImY5H8WWXZziww/ng4FTc3EKCrdsBqDzQUhycJZPnft+P/IZu3Zl7k9atrRvb9okccEs8eE2Xr2Q+NDQadDOh7PsonVCkhycO4tczoeeHRW7U6LzQUi0w6bOKdp+iI/dWSczbdrYtz/9VOKCWeIjys4HVv3FBoqfxaRTkiycO4tc4kPHJG/f7n+3CwOnhFS+08WvzMduio/kZz6c7bZBig8cODTcytxH8lB3AgIzV/BYxQdWvCzlsTlenZDoiw8/8hm7d2fuT+h8JNj5CLrsAthum1zyCQSladPixQdCZyy7EBI/8bHTB+cDJ60QIBQfCXY+sLKtU7wEIT7YbptcdGeRT3yU4nw4U+90PgiJ9mj1IMQHoPhIoPOBUgg+YIRAIUDCcD5YdjHT+ShFfBQKsmrmA2dhTvFNCAl/tLoz87HDh7ILxUdCh4ypOFFRUGruw8t4dUDnI7moSMjVZusUHzgj8uqyeRUf+riEkGSVXQCdjwQ6H37kPrwKHa7vklyKcT6K6XjJ7vfPxrmODDteCEmm+Gjd2r5mq22CMh9+tNsycEq8ZD6wk1KB6rX0oo8LV805S0DB9q07vxhNPyQk9lTC+di2LTYOJ+d8eBEf5Q4aY+CUeCm7QDwUm/vw4qhw1gch0ZvzsWOHf/uTFi3SJy7lLIIaImaJj1ImnIbpfGjZZetWkS1bSnsuEk28iAQQhPjQnR3LLoSER67/zSCcj3r1Yld6MUt8lJr5KHfQmFfxgQMPFKyOWSdmlV1AkM4Hyy6EJLPsEsPQqVnio9zMR9BlF8DQaTIJ2vnIV86h80FIMsVHA8f+hOIjwc7H558Xv+5GseKDU06TiReRAOh8EJIMgsx87KbzYYbzgVJIdXXp7kcpzgfLLskiqLKLl8dl4JSQ8GHZJS9mlV3cnI9Cszf8yH2U4nxwyqnZZZdi53wwcEpIvMTH7t2lTx2m82GI81Fu7oPOB2HZhRCzKNTt4hQoxULxYUjmo9x2WxU9XlwWOh/JhK22hJhFIeejnNyHm/g44AD7esMGiQPmOh9626v4KGfQGJ0PEnSrbb4gKzMfhIRPLvGB8nv9+uV1vLj937dvb1+vWydxwCzxUSnnoxTxsXFj5rodJN5U0vngnA9CoiM+/Gi33e3ifFB8xMT52Lu3NOcjaPGBXm09kKxdW/xzkXhnPpo29b/bhXM+CDFHfGzeHIuBguY6H0opgdNUKjjxgfU92G6bPDjhlBCzyOdKNi5z1oeb+MBICBU1MSi9mCU+3NqavIoPFQRQqsWOry1GfACGTpNHFMouXNuFkGg4H43LXG/J7WQGJ67qfsTANTdLfLg5H17nfGADateutNBpseKDzkfyYLcLIWaRT3xUV6cXEfXL+YhZ7sMs8VGO81FO7qNU8cFBY8kh6DkfXrpdYlAHJsQo8bGlxNXLKT4MynyUM2is1LILR6wnh6DHq3NhOUKiBcVHXuh8lCI+wnI+KD6SQ1BlFy/ig5kPQswQHx062NcsuyTM+Sh10Fix68gwcJo8Sim7eOmq0m2LZRdC4nPCUc2yC52PMJwPzZoU63wgsazzSIhZZRcIDy8ZDX3cfNsW53wQkiznY1eOkxkGThPufARddsEGhLYpHFg++aS45yLxLrtoicTryrZ0PghJtvhYvlykZ0+RmTPT32Pg1FDnA/U0/fCDEB9Qs23b2reZ+zBLfKA0pwLESxsenQ9Cki0+5swRWbZMZPp07+IDs6givjyHWWWXcp0PrBqIDxulkGKGuBQrPgBDp8nCSzA0e8eEMcl+Oh8cMkZI/MRHTU3dk5Fc+5PWrdPHmfXrJcqYJT7cnA+vIVAVKqWETksRHwydmul8gP32s6+/+MIf50PFB7b/Yhw7QkjlxceuXXV/NleGDMcoHYYZ8Y4Xs8RHuc5HqbkPOh8kKPHhxfnQwCmg+0FI8CAwni9k3rx5MM5HjEKnZomPcjMfpQ4ao/NBvLbaBuF84MwLAWbAKaeEBI/TYSy31XZXHufDbX8Sk1kfZYmPyZMnS1VVlYwdO7b2ezt37pQxY8ZI69atpVmzZjJ06FDZsGGDJMb5KLbdFvkQbZdl5sNcvLbaBuF8QHgw90FIZU503fb71QFlPkxwPhYuXCj333+/9EQLkINx48bJ7NmzZdasWTJv3jxZu3atnH/++ZIY56PYzEehjTAXDJwmi0pmPgBnfRBSmRNdt1xhdXV6mKDbcclNfMC11Mc1VXxs27ZNRowYIQ888IDspztKK5y/WR566CG544475PTTT5devXrJ1KlT5ZVXXpEFCxaIkc5HoY0wFwycJotKZj4AF5cjJHrOh5elFJwts+p+mCo+UFY5++yz5Ywzzsj4/qJFi2T37t0Z3+/evbscdNBB8uqrr0qinA+v4qNc5wMbW6lT8Eg8W23pfBASbwqddDZsmN4XFNq/q/Ph/Nl8GTIVH8WMg6gARRwNbWbOnCmLFy+2yi7ZrF+/Xho0aCAtW7bM+H7btm2t+9yoqamxLsqWIA+0fjofOCuFYtVx2F6esxjxgcdt0cKe9YBBY06lTOJH0GUXr84Hu10ICR7nSafbMaaqyt6nf/ZZYfFB5wMxh4/k6quvlunTp0ujRo18+YwmTZokLVq0qL10UmchinM+ADYYFQJech+lig/A3IeZ4qNVq+LLLoW2LZZdCAkP52Ki2mmWTXW1t0nGbs6Hl7LLxo2F8yQVpKjTfpRVNm7cKMcdd5zUr1/fuiBU+qtf/cq6DYdj165dsmnTpozfQ7dLOx18ksWECROsrIheIHAi7XwU226rz4kNsNjn0twHR6yb2Wr7+ef+OR8MnBISHnrQz3dyW+3oeIGLPmMG2kWLcz7cTmYwiRvHGnRZQoBElKKOhv3795dly5bJkiVLai/HH3+8FT7V2/vuu6+88MILtb+zfPly+fDDD6VPnz6uj9mwYUOprq7OuEQ681Fs7qOUGR/ZzseaNcX/LokWlWy1BXQ+CAkPLyuZVzvEx6RJIiNGiPz85+U7H3hOCJCIh06LOiI2b95cevTokfG9pk2bWjM99PsjR46U8ePHS6tWrSwhcdVVV1nC48QTT5SKU0nnoxzxQecj/jsi3RkVIz7gIOLsJd82ylZbQqJddvEiPl79uiFj3jyRn/wkt/PhRXxo6QU5y6SIDy/ceeedUq9ePWu4GIKkAwcOlHvvvVcigV/ORzHttuWID7bbmjHtMJf4gPCAzYrgsV/OBwOnhETL+di82V61FqCRI/uEw+l8eGm1VfHx5pvJFh9z587N+BpB1ClTpliXyOGX81HMoDE6H8QpPrxkPpDPQCsedjooveQTH16dD5ZdCImm87Fihcinn6adDXzdrVv5zgeIsPjg2i50PkjQOHceXsRHMbkPr84HA6eERNP5+Mc/Mr//+uuZX5fqfER81odZ4iMI5wOrF3p5zmJbep2ZDySWnQcwEi/0s8M24HU78Co+inU+WHYhJFrOx9Kl+cUHnY+YA5GgC7yVKz4gCtA6i7YotcuCKLu0aZPOCETYPiM+TjcNyvlg2YWQaLXatmmT+bWWWrIHeGY7H3hsPZax7BIDcg1bKcWRQD2+bVtvodNyxAcEDtttzRowFpTzwbILIdEqu1xwgcj++6e/vvhi+1rDp7mcDy8Zsg4dIn/Sak7ZJZf4KMX5KKbdtpSzXidst40/QYoPOh+ExLPs0rq1yK9+lf56wIB0aRQr2OZyPryID818oN22UDQgKa22scp7lCM+kPtAbS5I5wOw3dZM8eF1xDqdD0Li6XyA735X5F//skv4vXrZYgW/izVfdN+fz/nItU/RieL4WTxWdoknApgjPuh8kEpRivul4sNrpohzPgiJDl4bDaqqRG66KfP//pNP7KUVVHzkcz5yPT5ECZwVCA+UXiIoPswpu/jtfHgdNFZu2YXru5jpfDht03xwzgch8XU+soFgABANAMFS57ELzodznahci9bFYNaHOeLDb+fD66Axr2emuWDg1Gzxka9PHzumQql3hYFTQsKj1BELrbPER/aIBTgfmIgK8g0fjMGsD3PER6Wdj1IzHwycxp9S3C9Nq+fbcTi3aZZdCIlXq20p4gPZkA0bMkuzuaDzkXDnAwcHZw0uyLKL25wSkkznQ8UHdjS5tl2n+PDaautM0RNCol12qXHkPZQPPrCvKT4MdT6wZDEOJmhjynd2Wq74gHpFXQ+PUyh8SJIjPrB9YduE4MSE23LXjOGEU0LiW3apXx9LyNu3V670Jj4iPuvDnLKLn0PGAA4M6krkK72Um/nA7+lAM7gfxAzxge1SP/dc4rYY58M54TSiff+EJIZynY/PP890PjDYEickYPly+5rOh6HOh9dBY+VmPgBDp/GmVPer0JmLPi6csUIiWssuWjcmhETP+WjVyt35wImLTkPFXBDnz+aCmY+EZj6cuY98zke5ZRfAdtt4o+l0dR/8Cp0WI2yd4oOLyxESr8xHQ4fzsWJF5s96ER8RdDvNKbtU2vkoR3zQ+Yg3urPo0qW43yvUKldMSQ8/oz/H0Ckh8cp8NGiQFh96TPHqfOD/HfNBIoY54iNX65Mf4iPIzAdgu2280Rqtrlrpd9nF69kVZ30QEn/nQykkPuC0VldHNnRqjvjIJQL8KLsEnflg2SXe/Pvf9nXXrv6WXYoVts7QqYKd0pNPRtKWJcTYOR+ff253urk5H17FR8QHjZkjPnRjyO44CNr5YNnFbBDuXL26POfDj8xHLudj1CiR884Tefrp4l4bISS4ssvevXappBznI+KhU3PER5DOB1Ye3bbN/WcYODWbVatsVwH2Z/bOo9wdR6nOh4oPbJsvvmjffuON4l4bIcT/skvDhul5Hii9lOt8RHjWhzniI5fzUeqcD4ADis7Xz1V68TPzASWM2f4knnmPfItAFZpy6jZFt1jnI7vssnhx+ra27xFCKud8ZOc+6HwkgCCcDy/ttn5kPpo1SweHOGjMjLApwJkOBAMs2PffL1/YZpdd/v73uq+TEFI55yN71kcu5wMnMoUWlgMsu0RoY/BbfBRqt/Wj7OIMna5ZU97jkHiETXXb1N/Txyln28ouu7z8cqb44NpBhETX+WjTJv0z++3n7bEpPiIAPrxDDkmLhbCcDz/KLoDttvEDn/1rr5XufIB84kO3rVLKLhAaTvGBYGyhFZoJIcE7H+3apYPmTucDxxCIDq95D0DxEQFOPdUO/z3zTLydD5Zd4sP//Z/Iu+/aJTNsf+WID7eySLHblrPs8t579plVo0Yihx2W+zkIIeG12jpPaOFyO50PoKUXio8Yki024pD5AJxyGi+WLhW55Rb79q9/nV4kLirOh66QjLOso46ybzN0Skjlyy6dHPOjshelLFV8oFlh+3aJEuZ0uwQlPuh8kFyuB0ob558vMmxY6e+Rlmv8yHw4nQ9dbwahte7d7dsUH4RUvuzSsWNafJTrfMB11f/7iLXbmic+stsd/RIfcD7cpkT6nflg4DT6oNTy2GP27ZtuKu+xtCSC+m/2LJlSnQ+ID13rAeJDBQ7LLoREx/lYsybtfJQqPnC8i+isD4qPcsUHRAE+YCjUTz4JLvPBwGl8uOceW4hicqiWNEoFATNdSjvb/Si12wVlF3U+cGZ08MH2bQZOCam889GpU3q+j8510rLLuefaJ7znnOP98SIaOjVTfDjdj3KGjOlGoelkt9CpX5kPteI2bkyrYRJNdDs46yx/Hi9X6aVY5yNX2cXpqnGNF0Iq32rbqJF9W+f7qPMxYIDIBx+InHmm98ej+IgQTrejXOejUOjUL+cDPd4QOjg4REzBkhyfue4wyqVLF/s6e9BYOXM+nGUXtWXh3mFBK0JI5ZyPqqr0ySY6NN0mcxcDxUeCxUe+0KlfmQ9skCy9xAN1psr9zBXtlMku65XqfGSXXXCWpQOM2MpNSGVbbZ0ntCtXln8iQ/ERIeLofACGTuNBdntcuWjmAyU3v5wPZ9kFcNsixD+KPTHIRp0P7Xah85EQwnQ+/Mp8ADofZooPTbjnEh+lzPlwll0Ah9gREj3nQ/HD+UDHXIQwL3AaZ+eDB4h44Odn7hQfucou5cz50AULKWwJiUbg1LmvV8o5kWGrbYSIY+YD0BqPB2E7H+UGTgEXLiQkGoFTkL0GmR/OB8LkWsaJAHQ+/BQfsLX0gKDQ+TCPIMWHsxW2nPHqdD4Iia7z0bt35olxOfsSDCTT31+/XqICxUe5cz40EKhtsNndAkFkPjjlNNroZ+534BQ7tE2b/B+vDlh2iR733Sdy6KEi77xT6VdCwnY+2rQROflkf5wPdErqLKoIjWmg+PDD+cBjOBcDCtr5gMPCYVDmtNpix6PZDGfppRzng2WXaLNwocj//I895+F3v6v0qyFhB07BkCFSS7knMhFst6X48EN85Aud+pn5wAYEFYuDm65KSpJfdskVOi3V+cDrgwBxC5x+8UX6PlK5A9fFF6e/jtABg4TUapstPsr9n6T4SGjgNF/o1E/nA4+hByGWXswUH344H05UfKD8ovdz0FhlWb06c4VhnXJJzHI+unRJH58OP7y810PxYaDz4WfmA7Dd1rzMR65BY6V0u+gkU9C0aXq7dI50prANn88+Sx+wsluqdcolMSdwquB/8ZVXRI48UsoigrM+WHYJ2vnws+wCGDo1L/Phl/ORfQalrofC0GlleP55+/P9yU8yP+PDDqu7uqkbjz9ud0fgcUgyAqdO0dCnj5RNBGd9UHyE5Xz4dSCi8xH9nY7ueKKW+cgWH9rpotD5qAx33SWyd6/IM89kfsYQH+pU5Sq9oGtp1CiR118X+fa3Rf7615BeNAnF+fALll0MyHwELT7ofEQb55yXqGU+CokPOh/hg7kLzz5r30bOA8JVxQdKbWi1zVd6+cUv7OFROMjBcYMQgZAhyXA+/EKdj7iWXe677z7p2bOnVFdXW5c+ffrIM6rWRaRfv35SVVWVcRk9erQYlfnAHAa1SNEO63fmgweI+IiPoMsu5TofLLtUnunT0weqnTtF3nvPu/jAvJY777Rvox0XGR4cXN58M6xXT4IMnPqJuprYf0RkymlRR96OHTvK5MmTZdGiRfLGG2/I6aefLkOGDJG333679mdGjRol69atq73cdtttEjmcgsOvjQM7cj2T1NyH8wwk7LILDkwDB4r88If+PC8pLu8RRuC0XOcju/uFZZdwwcnJb3+b+T0MFNPPuJD4WLxYZNs221IfNkxkwAD7+08/HfQrJ2G02vpJ69bpQWURyX0UJT4GDx4sgwYNksMOO0y6du0qt956qzRr1kwWLFhQ+zNNmjSRdu3a1V7gkBjhfLiFToM4C/ZadnnrLZG//U3kV7+iDVsJ8YHtys+zHr8yH84Fq9Bh4YSuWrgsWSKybJl9UDj7bPt7OJHTzxifuYqPFSvq/v6iRfb18cfb3UrIfACKj8oTNeejqipyJfuSj7x79uyRmTNnyvbt263yizJ9+nRp06aN9OjRQyZMmCBfwhrMQ01NjWzZsiXjElvxkR06DVJ84H3Kl4BHQh5wIFn8Z3w4xQeGy+mOrZROKuf2nl3/1W0LOQR9bBIc6nqcc47It75VV3zA+dAWSwRKMQAul/gAgwbZ12+8Eak1PIwkas5HBJ3Noo+8y5Yts9yOhg0bWnmOJ554Qo444gjrvuHDh8sjjzwiL730kiU8fv/738v3vve9vI83adIkadGiRe2lkx7Ak+Z8+LURNm+ertXnK7047fmIbGxG4HfA2Gmb4uwFVr06FqXmifTnu3XL/H7btvaZGsSNilcSDPjsZsywb19ySVpkoOziFB9HHy3Ss6edB5k2zV189OplX2P9DhUi7HqpLFFzPiLobBZ95O3WrZssWbJEXnvtNbniiivkkksukXe+Xvjo8ssvl4EDB8pRRx0lI0aMkN/97neWOFmVZ0IfRMrmzZtrLx+5LUsfV+fDefbopwL2kvtwHjwoPuLvfGD7weqUTmFZ6gwZHLQuvdReuMwJdpTakheRHVRiweAoiAwIDGSzvj6Bk3ffTdfkcR8EJ9Z4Afi8NEeGrAd+1ik+gJZe/vKXtMh54gk7nErMbbVNgvPRoEEDOfTQQ6VXr16Wa3H00UfLXehTd6E3Bt9YWancE/rgoGj3jF4S53zgwIGdSJgK1ik+whB0JFjx4Zb7KNX5wJn01KkinTtH/uwoseg+8bjj7M/v4IPttXfQiaAHLg0ZjxhhO57IfUyalM6LwAXD56UrlgLNjiDvhce64w6R888X+cEPQv3zjCdqrbZJEB/Z7N2718ptuAGHBLTXs6mo4BQCYWQ+/LbgvQSHWHZJrvgo1/mI0Q4qsbz/vn0N0aFnyCeemL4fQgSts6BZM5Gbb7Zv33CDyD33iLz6al3XQ8UMxAickfnzRf70p3RLb3bAmAQHyy4FKerIixLJ/PnzZfXq1Vb2A1/PnTvXKrGgtHLLLbdYbbi4/89//rNcfPHF0rdvX2s2SKQIw/kIYsaHwrJLdAlKcLqJjyC2Lzof4S0e5xQfoG/f9G0tuShjx6YFyI9/nJ7vceaZmY+L/Zm6Hw8+KLJwoX3bLTNCgoOB04IUdeTduHGjJSiQ++jfv78sXLhQnnvuOTnzzDOtcsycOXNkwIAB0r17d7nmmmtk6NChMnv2bIkcQYkP7Lixw4ATBGs8iDNTfZ5CZ6fMfCTf+QhC6HB8f7jOxze+kVt8ZAPXA52F27fbuRCc7PzXf9X9OZRZwKOPZuYOnJkRYp7z0bFjustNX18FKeqU6aGHHsp5H7pU5s2bJ7EgiCFjesCB5YkdA0ovegDyW3x4OUCw7JI88ZE9aCyIs6uIzQIwpuwCnGUXCIxscGKDfN0JJ9hfwwlp1Kjuz511FsZNi8yda3+NvAf23Qj+z5mTHkZGzHI+2ra1j30QHtiHVDgOwYXl/HQ+sksvQWc+cokPnN1kiw+UgUgyyi7ZgVM/n4tll+BBCUQ7WpziwzlxFuu8uPHNb9oOxoQJIrnGGECk3Htv+sB34YV2Oy/A94mZzkf9+mnBsXx5pV8NxYfv4sMZOg1K/arzgdKKc5aIgoWmnLYadnYMmyWr7IJ2WR06FUQbNwVrcHzwgX2NQCnmtzhBZwu46qrcv4/1sn760/wHNozRf/JJW2zAUbniCvv7KIOz+83MVltwzDHWlZx3nr19VBA6H3F0PrDMNg5ucDPc5vSr67HffukDFm305IiPf/zDHialgtLP7UtXv9yxw14kkQQbNs1uwb//fpFZs2xxUS4InqrogBhBKQau6G9+k679X3mlyIsvlv9cJPqttgCuGcQo/rfhnrmdvIYExUeQzkdQ4gM7LD1IuIkKDZviYKWvh+IjOZmPbPzcwaHFU4eZcdZHeGFTBW7Id75jt9f6jQ4rQxcMhpxBwE6ZgumQtpBFoFW7Y0jyyi4AxwNkM3/0IzuQHER52CMUH3F0PgqFTtX5QMCINfzkZT4UOFunnZa5Uq0fcNZH+G22YXDuuXYgHuu+oLNGXVMEURFCvfVWkZNOst0XkrzAqYITo5/9TOSoo6SSUHzEMfMB8okKp/OhAaOILKOceIJ0Plq2zPwaAheWediBZlIeutyEm/MRJNhORo1Kn5mfcorIkCH214sX29fYZ8EhYS6kdFAO15bmqDkfEYLiIyjnAwd7XU8hSOcjX9kFzoeKj+wVTEn8xIdzW4UQ0QmYfkPxEeyBSaeTHnushA7CqijfnXyyyNNPZ84J6d/fdj5w4ETuhJSGM+wfRecjIlB8+K1M8Y/dsKG9k1F7NQjxke8A8eabaReGzkdyxIcTlFuCgmWXYF0P/M9i+3DO9QgLZMVwIoK6P9bRQrlFy3n/7/+JDBtm3/7jH8N/bUkUH3Q+ckLx4bfzgcfTnfd77wUvPrKdD8yAwKJSAJaqBlNZdol/5gPcfXc6KBgUdD6CQwcxYlAYwr2VAGfjut+DCML+Ai7I6afbYVfc9/rr6WAsKY6gVjNPGBQffq42m116UfERxAaYK3D62GO28sYCU927s+ySNOcDrZHoSAhyOiFHrAcvPk49VSLD0Uen14NBIBUtuYDuR2nQ+fCE2eLDb9cjO3QahvMB8eGcXorVK8Hw4fa1HqSQA4nAPP/EE1bZJUg4Yt0s8ZHNf/6nfU3xURoUH56g+AgCdT401R6E+ICogGuDg92nn9rfQ8AVA6jABRfY16jn4ucQItOx3CQ4kiQ+MPsB03GJP/z733YXHJxQBDujChamw2tcsiQSY7hjXXZh5iMnFB9BOh/abhWE+MDBTYNiWnrRa6wRoa8BOxH9OeY+4p/5CAPMD9E8Aruk/GPGDPv6jDOC61TyA4x8P/NM+zbdj9KdD5z0BeWuJwAz35mwnI+gQ0fZXQkqPnDm6syyaOiUB5LgSYLzgW2HpRd/QWn0kUcy12+JMt/9rn09cyYXpUzSgLEIQfERBOo6KEGdBWd3JTjFhxO224ZHEsQHYMdL+eD/EeVPiI4FC+wyLFxJTBqNOniN2IbffVfkrbcq/WriRVRHq0cMio8g6Nw5XPHh5nw4ofgIjySUXQBnfZTPn/5kd59ddFG6gwTt70Gs2+I3LVqInHWWfZull+Kg8+EJs8VHUMoUOxctdYRRdvHqfLDsEjx0Pkj2Gku6XaD93Y/VasOCpZfSoPPhCbPFR5BhoK5d07dZdjEHig+iaBfa4MEic+aIvPFG+Ou5lANeN4LHKBfp2i+kMBQfnqD4iLP4yBc4dcLAaXgkRXyw7FI+2to+cKC9bkoQAw2DBA7ut7+dDp4Sb7Ds4gmKDxOcD+2+wYwBEixJyXwwcOqf84H1nuKKDhx79NH06ACSHzofnqD4CEN8BJX50APE5s0iW7ak53hkiw+1ejHldMeOYF4LSabzgW2Kk3HLEx9t2khsQegUDghOXNCxQwpD58MTFB9xFh/Nm9srU4J//tPe6JFjwfoM2UOj8LPggw+CeS0kWeKjbVt7W8I25QxOkuLLLnEWH8h8aGswu168QefDE2aKD629Bhk4PfjgumdAQaAux2uvpQ8a2ZY//l5t/129OrjXQpIjPiCYVcRmL15ICoMSBcbTx118OLteZs2iC+YFOh+eMFN8hNHt4jz4BLk0tdrjWALbreSSXXqh+AiWpGQ+AFe3LZ1Nm9IZibiLjwEDRFq2tEtwf/97pV9N9KHz4QmzxUdYE+iCfB4VG1qPLSQ+WHYJlqQ4H4Aj1ktH3U6UReO+LeD1Y7E5wNKLd/HB8ep5MVt8BL3oz9NPi3zrWyK/+EXwZ6cffWRfd+vm/nN0PsIhieJDyy5Yn+Tee+3VTkny8x5upRdMbHWu2krqou8Px6vnheIjSM4+W+TllzPDp36T7XT06eP+cxQf4YqPJJZd/vpXkTFj7HHhJPlttk5OP90WUvi7Xnyx0q8m2rDs4gmKj7hD8RHNzEeSnA8dYvfKK/Y1FhrTM3uS3DZbJyghfOc79m0OHMsPA6eeoPhIytmpdtig28UN7XZZv15k585wXpuJJLnssnBh+j44esQc8eEcOPbEE+ntnNSFzocnKD6S5HzkKrmA1q1Fmja1b3PSaXAkSXw4R6wj74G1SRR2PeRHnaGklF3AySfbi1Sik+dvf6v0q4kudD48QfERd3BmpQe6fOIDsz6Y+wieJLXaqrDdvl3kzTdFvvgifR/Fh3nOBwKUF15o32bpJTd0PjxB8RF30LHTvbt9+7TT8v8sxUewwB1IkvPRpIk9HVetduc6QRAj27ZV7rVFnSSKD2fXy1NPcamGXND58ATFRxLAgQEJ9COPzP9zFB/Bn/FAgCRFfDjdjyeftK/POcfOD+FvffXVir60SJPEsgs48UT784fwRPcTqQudD0+YLT6S0od9yCGFXQ/AQWPB4gzhJU18oMMFnHCCyCmn2LdZenFn61aRd96pGwhPAijfaumFA8fcofjwhNniI+ghY1GD67uEk/dISubD7eDZrx/FRyGmT7edAQz8O/ZYSRxaesEQRZbe6sKyiycMO/oaLj5YdgnP+UiK+HB2U+Fg2qlTWnxgpD9bLjNB2e2+++zbo0enF7FMEscdJ3LooXbmY/bsSr+a6EHnwxOGHX2/xnTxsXatSE1NpV9N8vjyy/RApqRsW07x0b+/fY2AM4KUmBezaFHFXlokQQ5m6VJ7KfpLLpFEAkGl7gdLL3Wh8+GJhOwhi8RU8YEDBjoYnGvBEP/QNU+CHKcfNs6yyxlnpA8+mPkAmPvIRF0PDOTSTqEkogPHnnnGnvtB0tD58IRhR1/DxQdnfQTLP/5hX2MxwaRwwAGZeQ+FoVP39tpZs9IllyTTo4fIEUfYZTe03ZI0XFjOE4YdfQ0XH4C5j+DQkePqCiSlvj9qlMhPf5p5Jq/iA4Jr796KvbxIMW2aXc7Ee/bNb0riUfeDA8cywVA+oBOliSsGHn0pPixWr670p5AsnPmHJDkfEOi/+Y3IhAmZ30cXB3aumHr69tuVenXRAQLs17+2b19xRTKDptlo7mPOnPRQNSKyebP9LrRsyXcjDxQfpsF222DAuiewoLGwH+auJB2EanWc//z5lX41lQcH4FWrRFq0EBk2TIwA2aZjjrHLDI8/XulXEx00A4NtgeTEbPGRlCFjpYiPDz6o9CtJFgjeqethwlkvYO6jbtD04ovNstu19OK16wUzUFCSSrLzSufDE2aLDxMzHxQf/rN4scjPf27f/s53xBic4kPHypsIVv3VeRf//d9iFDrtdO5ckfXrC/88SnhwCZM8H4TOhycMPPpSfFh8/HE6lU1KB2O0hw61p5uef376TNAEeve2h6lhbsz774uxPPig3V7Zt2/h9ZWSxsEH29sBMi+PPeZNqCW91V/FBzMf/omP++67T3r27CnV1dXWpU+fPvKM2s1W5m6njBkzRlq3bi3NmjWToUOHyoYNGyRymOx8IJOAdUews4AAIaWD8dJYaAsWMnIeOKszpeQCMDPm+OPNnvcB0fnAA+mgqYl4HTgGd0z3OUkWHyy7eKKoo2/Hjh1l8uTJsmjRInnjjTfk9NNPlyFDhsjbX6fdx40bJ7Nnz5ZZs2bJvHnzZO3atXI+zgajhsniA38zRmQD5j5KAzvRn/3MXuEVi4ideqrIa6+JtG4txmF67gPlAzg/mIcSxX1dWKUXiG60mucTFZ99lp6s/OGHklhYdvFEUUffwYMHy6BBg+Swww6Trl27yq233mo5HAsWLJDNmzfLQw89JHfccYclSnr16iVTp06VV155xbo/UujZqYniAzD3UTpYz+J73xP58Y9tEYJhUs8/b0+PNRHTxYcGTUeOTM5KxqWM4NfZNjpkLV/JJcnOB/YJLLt4ouSj7549e2TmzJmyfft2q/wCN2T37t1yho5gtpaA6C4HHXSQvIr1DnJQU1MjW7ZsybgEjsnOB6D4KA1Yxqjrz5hht5ree6998EnKInKloN09//63SBRLrEGyYoXdYou///LLxWi8DBxzig+4RUnMnGHej65uzcxHXoo++i5btsxyOxo2bCijR4+WJ554Qo444ghZv369NGjQQFpmveFt27a17svFpEmTpEWLFrWXTloSCJITThBp3ty2y02E4qN44N4h34CkPsorcDtMrfE7wdRTjNp2TnjNR5KWYL//fvv6rLPSk4NNBaFrnMwtXCjy3nuFxQcCuuvWSeJQ1wPvRbNmlX41yRIf3bp1kyVLlshrr70mV1xxhVxyySXyDhL/JTJhwgSrZKOXj8Kw49BnjsmMP/iBGC0+klx39ZPf/tYWqhDRONBiB+tc58R0vJReIN7wnkH0/+53kojy29Sp9m2KUDvIftpp9vvx6KOFxUdSSy/OvIdJ4fMwxAfcjUMPPdTKdMC1OProo+Wuu+6Sdu3aya5du2RT1gqH6HbBfbmAg6LdM3oJBRMHjCl0PryBs7NrrxW59FJ7eum554q88ordXkiKEx/f/rbIvHn27Zdeiv+7h2zD55/b/0twPkjh0ks+8fFf/2W/jxpIjXunC6ebFqTs0MPevXut3AbEyL777isvvPBC7X3Lly+XDz/80MqEkAhx0EFp58Pk4VD5gIjGAfMXv7C//t//FfnTn+wzd+IuPpYsEXHLbH35pd3pkKQzXg2aIuth8omME3T7IAv1z3+K/OtfucWHZqTUecX/2kMPiTz7rMhzz0msYdg0GPGBEsn8+fNl9erVVvYDX8+dO1dGjBhh5TVGjhwp48ePl5deeskKoF522WWW8DgRsxBIdECuBpYgrGPTQoJeWL7cHpyEnWHjxraNPHGiuQFlL90OcIMwO8YtXO4UHkkQHxBZKCPhQPv971f61USHVq1EBgzIPfNDxQdW/XVuB+++m/6ZP/xBYg1nfHimqL3pxo0b5eKLL7ZyH/3795eFCxfKc889J2eeeaZ1/5133inf/va3reFiffv2tcotj3PBoeiBlkAtHbidoZgMBAeEB7o3INKwZPwFF1T6VcW79ILyhBMcdOLsuOnqtTjTz1NSNnrgGEovzs8Yt1V8qBPuJj7+/Of0kvTohnnzTTufFxc44yMY8YE5HnA9UGaBEJkzZ06t8ACNGjWSKVOmyOeff2614EJ45Mt7kApyxBF1//FNBjtHlFjOPts+e0ELKYKlWDqelCc+1PlQwQvHLVuQxAWUlR55xL7NoGldhgxBkM8+qVm2LP19/E+pqFDxoWUXZ8MCSnQQIDhpbd/edkmw3dxxh+2qwamNsnBl2cUz9b3/KEkUhx9ujwcvo1MpUb35GBaGrhYdGIUZHqYOjSpHfGDSK0KDOABliw+UZ9Bq+8kn9llvHCfCQnjgINq9u7mt+vlA0BLB0SeftEsvPXtmuhwozXTrlvk9PQFCxwzEBdrYUfr89FP7fxDC5ZprMsf6YzkDtwtanlEqrRQsu3iG4sNk8QFMdz4wa+C88+yDJoKDd94pcuWVbJMrlq5d7RHjGzfas1DgHGWLD4gNlLJUfBxzjMQKnHFr0BRila2UubteVHz83//Z75O6HAi76ywnbAdwwfQECJOD4T7C4dClH1B2efFFO+yN+SHYbuCOvPWWfXGjQ4dMQdKlS/o2BE6QnxvLLp6h+DC97GKy84GyCtpnMW0Rw7IQLHVM6CVFgB06RmzDLp8/P1N8aIlFnY7Fi+MZOkWbNQ54OLO+5JJKv5rogi4xuBOrVoksWmQP51PxgdZk/K/hfogIZKtUaFx8sS0+NIeGsQtwmLCvwgkBgKuGx4IQyb7g+bDWEv6fcXEbeofPLpdrgvJOua4Jyy6eofgwFfxT65k//mFMGwWMEekor6Dkgp3bU0+JHHpopV9V/EsvEB/IfUyYUNf5gOWOgw6Io/hQ12PYMPP+X4qhaVNbgEDMw/2A+FCBAecDQhXXEBkYTw9HCWsjoUQD1wLCAfTqVbfDDOW8ww6zL9ngcSB0IULcxAm2OTgtWAj168VQ64CcSS5xgvsKuSac8+EZig+Ta7OowWO9EpReTJnFgsFh119vr0oLsJOcPt0+yyL+5D7QIYT3WedfOMsuejCJm/hAiUAXTWPQ1FvpRcUH/tecZReA0gvEh8710DIwpk/jREBvFwOEAbYxXLCERjYYFJjPNUGYGCdjuGAbzqZRo/yuCYQ1nQ/PUHyYDP7hTRIf2LkMHy7yl7/YX+Ps/JZbOCTKL44+2l7PAu8zOh000+EUH7reRdzEB0ap4+CFs3hcSH4QOsVAPnzOmIniLLsAzX3MnWtfH3VU+eKjEAivwt10czjVNXETJrjg9cMlRZk6V6kanZ3aFkxnrCAUHyaDcgNsz6VLJfGsXClyzjm20MIZzMMP2/Y58Q8M3ULWA2ezKL2o+HBmPhBKjZv4wPA0XUQOQVNSGPyPIU/1+9/bMz+cZRfnta4AixJLtuDwW3x4dU3cnhevM59rgnKLcwFVFVkkJxQfJqNncOhOSDIQWBdeaJ+VoNSEJD7PXoMrvaj4uOqqupkPPePFwCkc1OMwNRatnzjIoFSp65cQbwPHID5QfkHZKrvs4kTFB6ZhI/eBi/5sFMBIeHTN4OIG9i2aNdl//9w/R2qh+DAZrYui+wDKXtdcSAqwUu++W2T8eDuDgB2bDi8iwQ8bw/uPM0pn2QUHFXwP2xvacuMwhHDKFPsaHS4IUxJvYAAlOlt0CQeUPdDqmi0+ECLV7jtkr9ABg7xQnFqZ8XeyJFcUMTjtIIGBxDjO5jQBrvzmN3Y2otgVJlHSwEJbqPFWGrz2UaNErr7aFh44cGA1VQqP4AUtDjKwoHEmCHfDWXaBwNXPIA6lFwy7mj3bvv0//1PpVxMvsB1gBL0CwaFOl1N8oMvFeeIDgYeyDUk0FB8mgx2B1jdff92+xgHjv//bXuCpmBUmcSDBjIwHHrAnP8JtQGsiQlphgzOt/v3tlTLxN2I0MwKD3KEFD95j3abgfiB8CgGiZRegdnocxAe2HYC8kE7mJN5xlqmcZRSn+NCSCzEKig/T0QMFBm6BG29M37d6tbfHgL3+ne/Y/floN0NXACaF4kwRYbMwwURE/E1olYOr89e/iowbFy8LN0mlFy25YLtQ8acHnqiLD9TxdeT+tddW+tXEk3797AxEtviAu6FilOLDSCg+TEdzHxgvjtILhm85O0S8gLZKOCeo3eIxHnww3emAAFZYINiGbgsc1HCWitc0cGB4z09yiw/nOi7Z4gM/A9EYNbBdo3yH9UIwvZWU1gGFsenaiu0E7ylKM3ApiXFQfJgO5nsg3IUdLcYbO1eM9Co+sO4CwIEeO2pMDkWbHdCwWZDA1v/JT+x0PfIr//Efdu4E642Q8DnpJNtpwvajWaJ84mPoUPvsF8FnzF7B4DfniqiVQrd/bEd0zkpn0iR7pdrszMxjj9luqa52TIyC4sN0kD6/7DL7Nnb+AOsrlCI+cBBxPm4Y4gNrOSDUhgWs1B7Har0c8lM58N7raqY6MCqX+EDL7bx5tujF5zZ5sj0EDr+PFtdKots/x+6XBxzRwYMzVzoGCJnGcWVj4gsUH8TOeeiOAZMJL7jAvv3++yJffVW4GwBnt7BXsYPJFh9opwwKvD6cZeMAh9f/u9+J/PznnFgapdILznizD+BO8aH3A4SCnWBGRCVB+BpQfBDiOxQfRKRjR3vMOAZw3XqrfY2DOYQH2lPzWeBY7lqDZeh1V3SSZVDOB14XgqVYZRStmzh7vugifppREx9axkPbc7b4gOWOuSu5As568K8UdD4ICQyKD2Lzwx/aFvixx9rtqTqhb8AAkd69c4sIOB9AA6ZhlF3uvdceYISgIgb7oFMHr5FET3wAhICdHQ3YNmC5I6vzwgt1f/e888IPK2cD0aTig9MqCfEdig/ijnOHixAnOhfyiY/scKeKj+3b7YsfoIUXK4qOGWMPDhsxQmT+fNupIdECblT37vbtH/wg8z6IW+dnduSRaacMIDgMMKjMr22nWCBsMaMEQVOsWkoI8RWKD+IOVqR04rbENMAoZJA9gAmrl+pcBz9yH1gbAi7Mr39tHxCwTDcyAY0bl//YJBiwnDrmZGiGyMm2benb+EzhuCkYVqeB4Uq5H+p6oCTJ4XSE+A7FB3EHZY1C4gMzELROn+18QCD4VXrBqrvIdyDXAVGEcdc/+hHbH6MOOlbQvu3WpqodVpgginkPKj7gMqADQp23SosPllwICQSKD+IOShoYFqZj1zEE6ssvM38GBwbU7SEIVGg48aPjBYFEdLRgSW50HWAY2tln81NLwuwHfKaYPgsw2wMiRfMeetAvN3QKh0VXVC2Gd96xr9npQkggUHwQdxAIxLAwBDpRn0fniwoRt7yH29ltOR0vEDU332zPDkHdH04Mnv/ww/mJJQEMtnOO20YoFRkPiBKn+IAAxjbgddR/NmgdxwKKH3/s/XeeeELk9tvt29j+CSG+Q/FB8gNRgQODW+lF8x65JomWWnaB2EDoUNeZGTvWXqPF2cpLkgfEqq5uqiFPrJQM9wuLHTqn73oNjb78ssjmzSLPPON9PRe4frt324uiff/7Rf4RhBAvUHyQwhQSH7lW+yxFfMCKx/Nh9DIORFiZFovUYYgZMYfsrMXf/paeluqVN95I33Zr6XVj1iy7uwsdOI88krnUOyHENyg+iHfx8eqr6eXRi3E+vGY+0M6LYOk//2mfBWOQGM88zQSlEmf3Cxg2TGTixMxt0Kv4wDA8L84JBAdAUBalIUJIIFB8kMJgNUosgb1pUzqI51wYrHNn998rJvPxwAP26pYIBx53nH3gUNFDzAMtrrfdJnLXXSJPPmlnfnbuFLnpJpHp0709BobPKRDAmIabD+RKIIBRahw+vLzXTwjJC8UHKQxKHjpB1Fl6UUfDrdPF+X0ECXOB2vpVV4lcfrl9+8IL7QOAjuAmZk/dxYAyCN/nnrNXvAUow3lxMVR8tGnjrfQCkaNLBUD8EEICg+KDeENdCAT4NBSqrbfO6ZROnAuIuR0sEAj8j/8Quece+2usTDtzpkiTJvxUSCZwI665xh74hbZv3Q5zgXVjcME0VUzFBZiGmw8skJg9Gp4QEggUH6S00Km6Hpgwimmm+cQHRMqnn9bd0Z9wgl2Lx+/jrPP66zk4jOQGw8eQxQC//GXhdllwxBHpgXkLFuR3TNBZA3QsPCEkMCg+iDf69LHPIrGM/bp1afEB18NtxgfAyrgdOqS7WBRMKD3xRHtI2cEH20HWIUP4SZDCXH21fQ2xim3RDQhblGwAAstY1A7hUWy3WDwxF//6l33NWTKEBA7FB/FGdbXIUUel3Q8NkeYquSgaRkWYD2edP/2pLTQwefK00+y6fI8e/BSIN+BkYI0fdLxouS4blFnQLjtwoC1WUMbDqHeACbluwJlDGRBCOlf3FiHENyg+SGmlF6fzkY9vfCO9VgY6CFBagQjByrQIEcJKJ6QYMHQOYAbM1q2Z9y1ZYgeWdUYM3DqggWmUXvKVXCCWmTkiJHAoPkh54iNXp0u28/GLX9hhUnTO3H+/fdbKAU6kFOBowJ3A5FKsmutkyhT7GiPZsSyAgjJfPudDSy7MexASChQfpHjxgW4DXWvDq/OhgVPU4tFWS0ipwM3Q7AfmgOjQMcyI0RkgcNacqPOB+TFo6c6G4oOQUKH4IN7BQmC6yNxf/lJc5kM5/XS+46R80PXSsqVdzsO6PyjljR5tZz0QMM0eUAenBD+PQWVLl9Z9PIZNCQkVig9S2iJzmKFQjPMBUGZB1wwh5YL27FGj0m23M2bYC9ChrIdpudkdWHBL0NqdXXqBaJk2zR7lD1h2ISQUKD5IcWSfURbKfDiXTce6LZhWSYgfXHml3UKLyaW4DbAS8rHHuv+85j40dIrlArBezGWX2Y4Jumg40p+QUKD4IMWRvXMu5Hygc0B/5tRT+W4T/4CwPe+8tJDA6so/+lHun3eGThGaPuYYkT/+0XZLJk2yyzdcTI6QUKD4IKUtMudVfADU4MGgQXy3STBttxo+bdAg989q2QWrMZ98sj347pBDbCHy4x9TeBASIvXDfDKSoEXmMBYddXVdtCsfDz9sT6Nk3oP4zUknifzsZ3aeCC24+cBMmebN07NB4Jog74EBeoSQUKHzQUovvWBnDjFSiHbtKDxIMEAAo9Qybpy3n7/gAvsaJZo//IHCg5AKQfFBigdLjmeHSQmJA1g5+e677dwH1h4ihFQEll1I8WBNlgcfTGc5CIkL7dunO2MIIfFwPiZNmiTf/OY3pXnz5nLAAQfIueeeK8uXL8/4mX79+klVVVXGZTSG/5BkWd0jR9rdAoQQQkiQ4mPevHkyZswYWbBggTz//POye/duGTBggGzfvj3j50aNGiXr1q2rvdx2223Fvi5CCCGEJJSiyi7PPvtsxtfTpk2zHJBFixZJ3759a7/fpEkTaYeQISGEEEKIn4HTzVhVUkRatWqV8f3p06dLmzZtpEePHjJhwgT58ssvcz5GTU2NbNmyJeNCCCGEkORScuB07969MnbsWPnWt75liQxl+PDh0rlzZ+nQoYMsXbpUrrvuOisX8jjWXciRI5k4cWKpL4MQQgghMaMqlcLKSsVzxRVXyDPPPCMvv/yydOzYMefPvfjii9K/f39ZuXKldOnSxdX5wEWB89GpUyfLVanm8B9CCCEkFuD43aJFC0/H75KcjyuvvFKefvppmT9/fl7hAXpjGqZITvHRsGFD60IIIYQQMyhKfMAkueqqq+SJJ56QuXPnysEHH1zwd5YsWWJdt0d/PSGEEEKMpyjxgTbbGTNmyFNPPWXN+li/fr31fdgsjRs3llWrVln3Dxo0SFq3bm1lPsaNG2d1wvTs2dP4N5sQQgghRWY+MDDMjalTp8qll14qH330kXzve9+Tt956y5r9gezGeeedJzfccIPn/EYxNSNCCCGEJDzzUUinQGxgEBkhhBBCSC64sBwhhBBCQoXigxBCCCEUH4QQQghJLiVPOA0KzZVwzDohhBASH/S47aWPJXLiY+vWrbXhVUIIIYTECxzH0fUSyHj1oMCaMWvXrrXmiORq7Y0DOiYe7cdsGeZ7wm2E/zfcl3D/mvTjTSqVsoQH1narV69evJwPvOBCI9vjBDYEig++J9xG+H/DfQn3ryYcb1oUcDwUdrsQQgghJFQoPgghhBASKhQfAYGVem+88Uau2Mv3hNsI/2+4L+H+NVAaxvB4E7nAKSGEEEKSDZ0PQgghhIQKxQchhBBCQoXigxBCCCGhQvFBCCGEkFAxRnxMmTJFvvGNb0ijRo2kd+/e8vrrr9fet3r1amuaqttl1qxZeR936dKlcsopp1iPiwlzt912W8b9DzzwgHX/fvvtZ13OOOOMjOd2Y926dTJ8+HDp2rWrNXRt7NixdX6mlMeNynvy+OOPy/HHHy8tW7aUpk2byjHHHCO///3vC77euXPnynHHHWclug899FCZNm1aUX9Tkt6PpG8jTmbOnGk95rnnnlvxbSRu70nStxN8vtmPiZ81dV8yrYT3I6xtpA4pA5g5c2aqQYMGqYcffjj19ttvp0aNGpVq2bJlasOGDdb9X331VWrdunUZl4kTJ6aaNWuW2rp1a87H3bx5c6pt27apESNGpN56663UH/7wh1Tjxo1T999/f+3PDB8+PDVlypTUm2++mXr33XdTl156aapFixapNWvW5Hzc999/P/WDH/wg9dvf/jZ1zDHHpK6++uo6P1PK40blPXnppZdSjz/+eOqdd95JrVy5MvXLX/4ytc8++6SeffbZnI/73nvvpZo0aZIaP3689Xt33313nd8p9Dcl6f1I+jbi/DsPPPDA1CmnnJIaMmRI3tcb9DYSx/ck6dvJ1KlTU9XV1RmPvX79+ryvN8n7kqklvB9hbCNuGCE+TjjhhNSYMWNqv96zZ0+qQ4cOqUmTJuX8HXwI3//+9/M+7r333pvab7/9UjU1NbXfu+6661LdunXL+TvY8Jo3b2590F449dRTXTeGch83Su8JOPbYY1M33HBDzvt/9KMfpY488siM7333u99NDRw4sKy/Ka7vhwnbCF7vSSedlHrwwQdTl1xyScEDbdDbSBzfk6RvJzjY4iBYDEnel0wt4f0IYxtxI/Fll127dsmiRYssm0iBtYSvX331Vdffwc8vWbJERo4cmfex8ft9+/aVBg0a1H5v4MCBsnz5cvniiy9cf+fLL7+U3bt3S6tWrUr+m8p93Ci9JxDAL7zwgnU/fi/f4zpfrz6uvt5S/qY4vx8mbCM333yzHHDAAQUfL4xtJK7viQnbybZt26Rz585WGWLIkCHy9ttvF3zcJO9LthX5fpSCH8exxIuPTz/9VPbs2SNt27bN+D6+Xr9+vevvPPTQQ3L44YfLSSedlPex8ftuj6v3uXHddddZK/5lb/zlUszjRuE92bx5szRr1sz6Rzr77LPl7rvvljPPPLPox8Vqjjt27Cjpb4rz+5H0beTll1+2Hg+1Zq8EuY3E9T1J+nbSrVs3efjhh+Wpp56SRx55xFoVHY+7Zs0aI/cl3Up4P0rBj+NY4sVHsWDjmzFjRh0VeuSRR1oHB1zOOuuskh578uTJVlDsiSee8BSKqvTjBvmeNG/e3FL7CxculFtvvVXGjx9vhcDiQBzfjzhtI1iS+6KLLrIOsm3atJG4Esf3JE7bCejTp49cfPHFVkj71FNPtcLb+++/v9x///0SB3bE8P3waxupLwkH/6j77LOPbNiwIeP7+Lpdu3Z1fv6xxx6zLCV8gE7++te/WjYTaNy4sXWN33d7XL3Pye233259aHPmzJGePXv69NeV9rhReE9gRSJlDvCP8u6778qkSZOkX79+rq851+Ni+Wg8N/6eYv6muL8fSd5GVq1aZXUEDB48uPZ+nMGB+vXrWzZzly5dQt1G4vqeJHk7cWPfffeVY489VlauXJnzNSd9X1Ls+1EMfh7HEu98wMbu1auXVUd3/tPia6hENwvsnHPOsdSiE9TQcHDA5cADD7S+h9+fP39+7UYCnn/+ecv6QjuSgnaoW265RZ599lmrpdIvSn3cKLwn2eD5a2pqct6Px3W+Xn1cfb3F/k1xfz+SvI10795dli1bZjlBesFjn3baadZt1LLD3kbi+p4keTtxAyUPvE/t27fP+ZpN2pfs8fB+eMX341jKAND61LBhw9S0adOs1qrLL7/can3KbkFasWJFqqqqKvXMM894etxNmzZZrU8XXXSR1fqE50ELl7P1afLkyVbb1WOPPZbR/pSvpQqgpQmXXr16WW1OuI22rXIfNwrvyU9/+tPU3/72t9SqVaus57799ttT9evXTz3wwAMF2+N++MMfWq1eaPtya4/z8jcl4f1I+jaSjZfOjqC3kTi+J0nfTtCi+txzz1n/O4sWLUr953/+Z6pRo0YZf59J+5KJJbwfYWwjbhghPgB6uQ866CDrDUQr1IIFC+r8zIQJE1KdOnWyWqO88s9//jN18sknWxsbeu/xITnp3LkzVg2uc7nxxhvzPq7b7+Cxyn3cKLwn119/ferQQw+1/inQOtanTx/rH6kQmIeBljS83kMOOcRqKyvlb0rK+5HkbaTUA23Q20gc35Mkbydjx46tfV4cmAcNGpRavHixsfuSsSW+H2FsI9lUff3EhBBCCCGhkPjMByGEEEKiBcUHIYQQQkKF4oMQQgghoULxQQghhJBQofgghBBCSKhQfBBCCCEkVCg+CCGEEBIqFB+EEEIICRWKD0IIIYSECsUHIYQQQkKF4oMQQgghoULxQQghhBAJk/8PPcZ2dgeIsekAAAAASUVORK5CYII="
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 85
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-29T19:17:19.791409Z",
+     "start_time": "2025-11-29T19:17:19.683835Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "",
+   "id": "ade5efa1f336b655",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x27b9deece00>]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 23
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "#",
+   "id": "1aa707313aeb5993"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From ee613404a8fc0cd6ca829fd98a1e3309a9018d53 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Sat, 10 Jan 2026 11:26:17 -0800
Subject: [PATCH 07/49] cleaning

---
 array_temp/coefficient_fitting.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/array_temp/coefficient_fitting.ipynb b/array_temp/coefficient_fitting.ipynb
index 8a54043..b620472 100644
--- a/array_temp/coefficient_fitting.ipynb
+++ b/array_temp/coefficient_fitting.ipynb
@@ -599,7 +599,7 @@
    },
    "cell_type": "code",
    "source": [
-    "#this graph shows solar irradiance, array temperature, and ambient temperature over July 2-6 (FSGP 2025)\n",
+    " #this graph shows solar irradiance, array temperature, and ambient temperature over July 2-6 (FSGP 2025)\n",
     "\n",
     "fig, ax1 = plt.subplots()\n",
     "ax_twin = ax1.twinx()\n",

From 1454e4b1cfd96190ecbb2c45fde586b3bd1352cc Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Sat, 10 Jan 2026 16:25:44 -0800
Subject: [PATCH 08/49] cleaning

---
 array_temp/Control_Model.ipynb           |  327 +++++++
 motor_analysis/faiman_coefficients.ipynb | 1055 ----------------------
 2 files changed, 327 insertions(+), 1055 deletions(-)
 create mode 100644 array_temp/Control_Model.ipynb
 delete mode 100644 motor_analysis/faiman_coefficients.ipynb

diff --git a/array_temp/Control_Model.ipynb b/array_temp/Control_Model.ipynb
new file mode 100644
index 0000000..cd8e218
--- /dev/null
+++ b/array_temp/Control_Model.ipynb
@@ -0,0 +1,327 @@
+{
+ "cells": [
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "The overall goal of this project is to find the set of controls (accelerator position, brake pressed, steering) for an optimised speed input (speed, position).\n",
+    "- Steering data is unavailable, so is currently out of scope.\n",
+    "- FSGP data from 2024 July, potentially over like 2-3 days\n"
+   ],
+   "id": "13a4cc26a47d5ad7"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-10T19:40:40.210965Z",
+     "start_time": "2026-01-10T19:39:34.788373Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# query data from influx. looking at timestamps of 2024 FSGP: 14 - 18 overall, but for smaller data response lets try 14 -16\n",
+    "\n",
+    "from data_tools import query\n",
+    "from data_tools.collections import TimeSeries\n",
+    "from datetime import datetime, date, time, timezone\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import dill\n",
+    "import os\n",
+    "import pytz\n",
+    "from datetime import datetime, time, date\n",
+    "\n",
+    "#each 5 seconds\n",
+    "utc_offset_h = 2\n",
+    "start_utc = time(0, 00, 00)  #querying i svancouver time, influxdb gives utc\n",
+    "stop_utc = time(23, 45, 00)\n",
+    "date_start = date(2024, 7, 14)\n",
+    "date_stop = date(2024, 7, 16)\n",
+    "\n",
+    "vancouver = pytz.timezone(\"America/Vancouver\")\n",
+    "\n",
+    "start_local = vancouver.localize(datetime.combine(date_start, start_utc))\n",
+    "stop_local = vancouver.localize(datetime.combine(date_stop, stop_utc))\n",
+    "\n",
+    "start_time = start_local.astimezone(pytz.utc)\n",
+    "stop_time = stop_local.astimezone(pytz.utc)\n",
+    "\n",
+    "client = query.DBClient()\n",
+    "mech_brake_pressed: TimeSeries = client.query_time_series(start_time, stop_time, field=\"MechBrakePressed\")\n",
+    "accel_position: TimeSeries =   client.query_time_series(start_time, stop_time, field=\"AcceleratorPosition\")\n",
+    "speed_kph: TimeSeries = client.query_time_series(start_time, stop_time, \"MotorRotatingSpeed\")\n",
+    "\n"
+   ],
+   "id": "initial_id",
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-10T20:00:38.811023Z",
+     "start_time": "2026-01-10T20:00:38.733064Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# save collected data\n",
+    "\n",
+    "out_dir = os.path.join(\"../../array_temp\", \"data\", \"control_state_fsgp_2024\")\n",
+    "os.makedirs(out_dir, exist_ok=True)\n",
+    "\n",
+    "brake_path = os.path.join(out_dir, \"brake_pressed.bin\")\n",
+    "accel_path = os.path.join(out_dir, \"acceleration.bin\")\n",
+    "speed_path = os.path.join(out_dir, \"speed_kph.bin\")\n",
+    "\n",
+    "filepaths = [brake_path, accel_path, speed_path]\n",
+    "datasets = [mech_brake_pressed, accel_position, speed_kph]\n",
+    "\n",
+    "for filepath, data in zip(filepaths, datasets):\n",
+    "    with open(filepath, \"wb\") as f:\n",
+    "        dill.dump(data, f)\n"
+   ],
+   "id": "9f8652589e173b01",
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-10T21:22:36.496440Z",
+     "start_time": "2026-01-10T21:22:35.443215Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plt.plot(mech_brake_pressed.datetime_x_axis, mech_brake_pressed, color = 'green', label = \"Brake Pressed\")",
+   "id": "d1fc6023bb47051c",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x27786273a70>]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 8
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-10T21:22:56.117895Z",
+     "start_time": "2026-01-10T21:22:55.064872Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plt.plot(accel_position.datetime_x_axis, accel_position, label = \"Accelerator Position\")",
+   "id": "21f820a7985b2d39",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x277890a3a70>]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 9
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-10T21:41:37.944731Z",
+     "start_time": "2026-01-10T21:41:37.913474Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# accel position and brake pressed are in inconsistent units, so i'll probbaly change them to a 0-1 range.\n",
+    "\n",
+    "#using min, max scaling\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "scaled_accel_position = scaler.fit_transform(accel_position.reshape(-1, 1))\n",
+    "\n",
+    "\n"
+   ],
+   "id": "6a0eed52a654e483",
+   "outputs": [],
+   "execution_count": 11
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-10T21:42:21.910654Z",
+     "start_time": "2026-01-10T21:42:20.944632Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plt.plot(accel_position.datetime_x_axis, scaled_accel_position, label = \"Accelerator Position\")",
+   "id": "a7c5e8360bc710de",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x27783d147a0>]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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LI2k6FmEEAABYijACoN14NkfL9h0M/1NBETk0ZkUGYQRAu/HU0paV19RbXQTA9ggjAGCipr0YOMkG/COMAHCESPVNDPdi6FMZOXQQdi7CCAAALSBMRgZhBAAAWIowAqBdSr/da3URghIX9gsxsAueM+JchBEAIWs86tGvni2xuhiAX+HoQkI3lMggjAAIWTTe0hvuk2sOZkDbCCMAHMEOB3Xu1gDMQRgBgCbIG0DkEUYAIEIIOs7DVxYZhBEAAGApwggAALAUYQQAmuBRFUDkEUYAxJS2wwZpBCewNkQGYQSAQ0SmK2G4W0aazo/OkM5DS1lkEEYAwETcQQO0jTACwCGcf4rKQ9MA/wgjABzC+gN5KCWgmT9yzPhTkx8jgzACAIgKBD/nIowAQBOcCaMpVofIIIwAAABLEUYAIEBcBQDMQRgBgCbodwBEXkhhZNasWUpPT1dycrKysrK0du3aVsfft2+fJk2apJSUFCUlJalfv35asWJFSAUGgPaIdNagD0rkxNF25Vgdgp1g8eLFys/P1+zZs5WVlaWZM2cqNzdXW7ZsUc+ePZuN39jYqCuvvFI9e/bUkiVLlJqaqm+//VannXZaOMoPAGHF4QyIvKDDyIwZMzRx4kTl5eVJkmbPnq3ly5dr3rx5uvfee5uNP2/ePO3du1dr1qxRx44dJUnp6entKzUAAIgaQV2maWxsVGlpqXJyck7MID5eOTk5Kikp8TvN66+/ruzsbE2aNEkul0sDBw7U9OnT5Xa7W1xOQ0ODamtrfX4AAIg0WsoiI6gwUlNTI7fbLZfL5TPc5XKpsrLS7zTl5eVasmSJ3G63VqxYoQcffFBPPvmk/vSnP7W4nMLCQnXt2tX7k5aWFkwxAcAUPIHV3gyeCuJYpt9N4/F41LNnTz333HMaOnSoxo0bp/vvv1+zZ89ucZopU6Zo//793p+KigqziwnA5ugICkSvoPqMdO/eXQkJCaqqqvIZXlVVpV69evmdJiUlRR07dlRCQoJ3WP/+/VVZWanGxkYlJiY2myYpKUlJSUnBFA0AEOM2fh/+S/pk4MgIqmUkMTFRQ4cOVVFRkXeYx+NRUVGRsrOz/U4zYsQIbd++XR6Pxzts69atSklJ8RtEAMAfLnegLR99XRP2efKm5cgI+jJNfn6+5syZo/nz52vTpk367W9/q/r6eu/dNePHj9eUKVO84//2t7/V3r17dfvtt2vr1q1avny5pk+frkmTJoWvFgAQoPYcWkI5LnEsA9oW9K2948aNU3V1taZOnarKykplZGRo5cqV3k6tO3fuVHz8iYyTlpamt956S3feeacGDx6s1NRU3X777brnnnvCVwsAlnBiY8WKL3ZbXQSYxIz1MY4muYgIOoxI0uTJkzV58mS/nxUXFzcblp2drY8//jiURQGwMSee9K/eHv6m/EDRSgL4x7tpAMBETU+sufUU8I8wAgBN0CwPRB5hBAAQlbgs5hyEEQBoorV2ES6zAOYgjABwBM5y0RYzLrFx0S4yCCMAYCJCVOQQHJyLMAIAQAvIkpFBGAEQU9o6e27t4BMXwrk3N+cAbSOMAADQAt5NExmEEQAAYCnCCABHCNflDh5qBtgPYQRAyDisAwgHwggAU3HNHUBbCCMAQhaNMSPcT2BtmsXIZeYy4wocl/UigzACAIgKxAbnIowAcIRoaFWIgirYGn9f5yKMAHAEpx5oaOUH2kYYARBTyAaA/RBGAMSUtlpYnNoCg+ZC6XB8MsJrZBBGACBAobybBkDbCCMAYCICTOREQyfnSHr50wrNeGer1cWQJHWwugAAEEmRjgbhuFQA60Tzt/eHVz+XJF01wKWBqV0tLQstIwAAxLDaw0esLgJhBIC5oqnpnFYOe+M2aucijAAIWST3/XY4zkRTsIpGdlhHEBrCCABHIAcA0YswAiC2WHj6TMuKuXipnXMRRgCEjGMr7IQo4lyEEQBoorUDGifeseXtLyu1fc8Bq4sREwgjANCEma093I3jLLf8vdTqIsQMwggAADHMDk8JJowAQITQgRXwjzACIKZE+hyw6Vnn4SPuCC89xlh/go8QEUYAIEChtGw07fT6Wtmu8BUGiCKEEQCIELeH6zSRxGUx5yCMADAVxwNEDCubYxFGADhCuM5yeUonYD+EEQAxxbC47f77fYcsXT5gR4QRACGL6Ft7HdqgcXL24Y4aoDnCCAAAsBRhBACasPoyDtrBoa1nIIwAaAcO27ATOzzWHKEhjESRI26P1UUAACBohJEoMfuDr3Xu/W/q4/IfrC4KYGvtubWXliDAHISRKPHom5slSff98wuLSwKgNVxIAJojjACIKYQBwH4II1GGZmTYTbjuTuEmF7TFYA/oWIQRAIgQbhsG/COMRBl2doC9OPXJsYgddlhHCSMAEKBw7LN5UR/QHGEEAJqgbdG5Tn7oGd+lcxBGogwbHyIpkuf4duicaH0JgOhEGAGACCHMAP4RRqIM/VcRrcL13pG2umy09jHbF2AOwgiAmBLpl6kRYIC2EUYAhCzQ4+zWqjpTy+Ek3EsDNBdSGJk1a5bS09OVnJysrKwsrV27NqDpFi1apLi4OI0dOzaUxSIAdujkB5zs+uc/sboIAGws6DCyePFi5efnq6CgQOvXr9eQIUOUm5urPXv2tDrdjh07dNddd+mSSy4JubBoG03CsBtDUnVdQxjmE6bHyhPYAdsJOozMmDFDEydOVF5engYMGKDZs2erc+fOmjdvXovTuN1uXX/99Zo2bZrOOuusdhUYrfvux0NWFwEALMHz5JwrqDDS2Nio0tJS5eTknJhBfLxycnJUUlLS4nR//OMf1bNnT910000BLaehoUG1tbU+PwAAtIYs4lxBhZGamhq53W65XC6f4S6XS5WVlX6nWb16tebOnas5c+YEvJzCwkJ17drV+5OWlhZMMQHANjhbB9pm6t00dXV1uuGGGzRnzhx179494OmmTJmi/fv3e38qKipMLCUAnGB2jxLCCdBch2BG7t69uxISElRVVeUzvKqqSr169Wo2/tdff60dO3ZozJgx3mEej+fYgjt00JYtW3T22Wc3my4pKUlJSUnBFA0AbIlO5UDbgmoZSUxM1NChQ1VUVOQd5vF4VFRUpOzs7Gbjn3/++friiy9UVlbm/fnlL3+pyy+/XGVlZVx+ARBx7XvoGckCMENQLSOSlJ+frwkTJmjYsGHKzMzUzJkzVV9fr7y8PEnS+PHjlZqaqsLCQiUnJ2vgwIE+05922mmS1Gw4AESClZdJaCUB/As6jIwbN07V1dWaOnWqKisrlZGRoZUrV3o7te7cuVPx8TzYFQAk3/DDM05gR3boxhR0GJGkyZMna/LkyX4/Ky4ubnXaF198MZRFAgAQFIOmKMegCQNATLH6+BTpF/UBTkAYAQBEBW6bdi7CCICQVdUebnMcq1siANgfYQRASL6pqdfPn/zA6mJEWPCn3oSxyOESmHMRRgCE5N2vqtoeCQACQBgBgCZoyQAijzACIKa0r5Nj+5MKnSzNw3NcnIswAgAmInxEDq1azkUYAQAAliKMAAgJZ/wAwoUwAgARwmUEcxGQnYswAsARInUgb+2ARpgAzEEYAQATEWCAthFGAJiK2y1P4C8B+EcYAeAITu0P4NRyA5FEGAEARCVaogITZ4PETBgBgCZa6+NB/w/AHIQRAIggG5yERi3e2utchBEAAGApwgiAmGKH6+MAfBFGAIQk0gd1O/TXaO9tynaoQzQjZzoXYQSApTbtrtVdr2zQ9/sOtToeB3IgenWwugAAYtuopz+UYUjb9hywuigALELLCABLHW/x2FpZZ21BIsKgzwrgB2EEgKmi6fIKt44iGtkhHxNGAITEBvuvkDi13GibHQ6qTmSHEwbCCABbcMIL9ZxQRsCJCCMA4IPA4VR2OMNHaAgjAEIS6f2+U5vgOUACbSOMAICJmoYow6DPipmcGlhBGAEQonDv96O1BYHjI9A2wggABChaA1O04vtyDsIIAEfgwIK20Q7lVISRGGUYhuobjlpdDCDq0Y8hkkisobDDOkoYiVG3/qNUFxS8pa+reR8I7CFSh5HI73htsKcHbI4wEqPe+rJKkrTgk50WlwROZYezKSfi7wY0RxgBAB9hv0/Iz/8ANBXzYcTjYfcAAICVYjqM1Dcc1YjH3lP+4jKriwLANucF4S4I12Uih7+1U8V0GFn+xW7t3n9Y//zse6uLAsABbJOXgCgT02EEQOg4BwUQLoQRADHFyrtZzu15iuKIcbAZO6yRhBEAtmA44CJIKE+BbRp+hvbpFr7CAGFihy2PMAIAQIzYXFlrdRH8IowAiClcJkEs+8XMD60ugl+EEQAIEE9PBcxBGAEQkrgwH5md8FbekPqMNJ1eBBoz8bd1LsIIAFM5IWSYKdyhDUGI8XXPSQgjMY7dJGKNE+7aAWINYSTGcdIG+Ap3S47PZRpyEOAXYQSALXCcBqxhh3NSwgiAkNCqBiBcCCMAQuLUSw7tec5Ie/ubGDJscRYK2A1hBIAjhKvjKS06gP0QRgDYguHUphYA7UYYAeAIZBUgeoUURmbNmqX09HQlJycrKytLa9eubXHcOXPm6JJLLlG3bt3UrVs35eTktDo+AGeIycsd7Q1EBCpTxeIqGS2CDiOLFy9Wfn6+CgoKtH79eg0ZMkS5ubnas2eP3/GLi4t17bXX6v3331dJSYnS0tJ01VVX6fvvv2934QHEDqeGH6eWG9HNbpdFgw4jM2bM0MSJE5WXl6cBAwZo9uzZ6ty5s+bNm+d3/Jdeekm33XabMjIydP755+v555+Xx+NRUVFRuwsPwP4C7Xhqr12jiQgnQDNBhZHGxkaVlpYqJyfnxAzi45WTk6OSkpKA5nHw4EEdOXJEp59+eovjNDQ0qLa21ucHAIDWdEygG2Qo7NB6F9Q3V1NTI7fbLZfL5TPc5XKpsrIyoHncc8896t27t0+gOVlhYaG6du3q/UlLSwummAAQsnC30DTd0cdM649FTuvc0eoiOJIdrthENEY++uijWrRokf71r38pOTm5xfGmTJmi/fv3e38qKipMKc9Rtw2+AYvxRlGEijUHQLh0CGbk7t27KyEhQVVVVT7Dq6qq1KtXr1anfeKJJ/Too4/q3Xff1eDBg1sdNykpSUlJScEULSRvbtxt+jLsjgMK7CJSZ2dtrfNmbhN26zQY7XhDs3ME1TKSmJiooUOH+nQ+Pd4ZNTs7u8XpHn/8cT388MNauXKlhg0bFnppw+xQo9vqIgAIUNiO4+1oDQylCE0fP08WAfwLqmVEkvLz8zVhwgQNGzZMmZmZmjlzpurr65WXlydJGj9+vFJTU1VYWChJeuyxxzR16lQtWLBA6enp3r4lp5xyik455ZQwVgUA2s/MK5eG2vduHCBaBR1Gxo0bp+rqak2dOlWVlZXKyMjQypUrvZ1ad+7cqfj4Ew0uzz77rBobG/Wf//mfPvMpKCjQQw891L7SA0AEhRQjyB4RQ9ALTMNRtxJtdudR0GFEkiZPnqzJkyf7/ay4uNjn9x07doSyCAB2F4Odn7nKgmiwcmOlxgzubXUxfNgrGgGA2drsuNFyyKIDqr3RYTUwnpPWYzucVxBGACBCyDKwAzteziKMADCV0w7AdjhLBMxkx3WcMAIAEWTHAwFgNcIIgJBE+pjKQRyIXoQRALHFwlRDB0vYgR1fA0IYAQAT2W+3j1hnx3WSMBLr7LhWAn6EqyNse1b5kB4Hb8Oz0Ghlx7tE7Cguzn7PzInpMBIfz4rLxgv4MvdFeSbOHAjQyfv9bVUHLCrJCTEdRv59cIrVRQAcKxZP+MMRJmLwzwabOerx+Py+bQ9hxFLJHRKsLgLgWNF6ls9lFUS7qtrDPr/bYVuO6TBCz3YAkcTj5COLP7d/J1+mscOxMKbDCACYjXYW2N0HW6qtLgJhBACaMrUDq4nzBgL1yIpNPr+X19RbVJITCCMATGW3A3Cku4ScvDz6pADNEUYAhIRjKoBwIYwAAKICATlwdutMTRgBEFMi/QRWn+nttf8HbIMwAgBNcHYNRB5hBAAQFWh5ci7CSIzjLBChcup7jSJ9vHLq3wmIJMJIjGM3CUSOHZ50CdgRYQQATNS09ZHLCIB/hBEAMYXWQMB+CCMAAMBShBEAIQm087PdHq5kJf4S5qJDfuDsti4SRgCgidayE8EKMEdMhxH2KwAiiX2OuU5uGOHP7RwxHUYAOEe4DuTteWtuKEXgygHQNsIIAASq3YGIc3XYz08SE6wuAmEEQGg44w8QfyjYXLwNev4SRgAAiGF2aK8jjABAhNCBFXZkh7vECCMAHCFcLcmRbpBu+qI8G+zzAUm+66IdVkvCSIyzwaVCIKrxcrzIYX/mXISRGMfrzQFEi/bcth3L7NBiRxgBYCob7Odsg1YS2JEd1kvCCAA0Ee7dMq2PkUPDSGhoGQGAANlhhwlEIztsWoQRAAiQHZqz0TJaoUJkg9WaMAIgJDSJB4/WHdiRHUI2YQQATERoixz+1qGxQ0gmjACIKRywohdfbeCatobYIIsQRgDATHY464wVJwdNOzzm3Ans8HeK6TBi/Z8fQLRreoBkn2MuOrA6V0yHkWhlh5QL4Jimh0c2TdiRHVZLwkgU8thhzTLJ4SNuq4uA/8VZaPDscNdCNKM/UGjsEJIJI1EomJYRNl44RVSsqzbY6QN2RBiJQsG0jDht/x4fFUek2BKus65Inb2ZeZnTbYdT0CjGi/KcizAShaK5KZh9DawUSpZoeoCM5kuodsDuwbkII1GIky+gZe3p69LebcvDxmkqTlYCZ7dVkTAShey2kiFKseMPSNPLPtzpBvhHGIlCnH0BrbAwRLm5TmMq8rFzEUaiEGEEsA/6jEQOHVidizAShdjfAea4/Pwe7ZqeyzTmIos4F2EkChkeq0sARKfEDu3bZdIyYi6yiHOFtGXNmjVL6enpSk5OVlZWltauXdvq+K+88orOP/98JScna9CgQVqxYkVIhUVgovnWXtgHO/7g0WfEZDSNOFbQYWTx4sXKz89XQUGB1q9fryFDhig3N1d79uzxO/6aNWt07bXX6qabbtJnn32msWPHauzYsdq4cWO7Cw//2N8B9tH0+Eh/LnMRRZwr6DAyY8YMTZw4UXl5eRowYIBmz56tzp07a968eX7H/8tf/qJf/OIXuvvuu9W/f389/PDDuuiii/TXv/613YWHf1yXBuyJTRPwr0MwIzc2Nqq0tFRTpkzxDouPj1dOTo5KSkr8TlNSUqL8/HyfYbm5uVq6dGmLy2loaFBDQ4P399ra2mCKGbB5q7/x/n/aG1+asgwrPPrmZiV2iJfHkDweQx7DkMc4FlLc//v/455+b7vqGo5aV9ggedrZ7BNN37PV1n6zN6DxHlu5udmwUL6H5z4sD3jc1ubvr9xNx99/6EiL0/695Ft9/t3+gMshSbv3Hfb+f/X2Gv35rS0tLhvtU/L1Dz6/P/H2FiV3TLCoNPY2fcUmn9+nvfGlbhzRV2mnd7akPHFGEKfRu3btUmpqqtasWaPs7Gzv8D/84Q/64IMP9MknnzSbJjExUfPnz9e1117rHfbMM89o2rRpqqqq8ruchx56SNOmTWs2fP/+/erSpUugxW3T2fet4BouAACS/nnbcF10ZrewzrO2tlZdu3Zt8/gdVMtIpEyZMsWnNaW2tlZpaWlhX84fr75AzxZ/rbN6nKJBqeELOVao2HtIr2/YpZsv7qvOSR0UH3fspXIJ8XGKO/7/uBP/37anTl98v1+X9mvfrYpWmPX+10FP061zR105wKUepyaZUKLYtXJjpb6urm/x88z00/XTvt10sNGtFz7aIUm6aoBL57pO8Y5Te+io3ty4W6MHpWjn3oN6f0u133lNuvzsFr/7zPTTtXbHsRaPCdl9dEpyy7s2w5CeKT4xn/+4KFUpXZN9xlm4tkJ76xt144i+mvfRiRbUSZef3eJ8W3O83Menn7PqGzW6PRqb0Vup3TqFNE/413QdCfX7Oq6mrlFLy75Xw9HoukXxp+ndlNn3dBVvqdaXu2p166VnKyFecnVJbntikwTVMtLY2KjOnTtryZIlGjt2rHf4hAkTtG/fPr322mvNpjnzzDOVn5+vO+64wzusoKBAS5cu1YYNGwJabqDJCgAA2Eegx++gOrAmJiZq6NChKioq8g7zeDwqKiryuWzTVHZ2ts/4kvTOO++0OD4AAIgtQV+myc/P14QJEzRs2DBlZmZq5syZqq+vV15eniRp/PjxSk1NVWFhoSTp9ttv16WXXqonn3xSo0eP1qJFi7Ru3To999xz4a0JAABwpKDDyLhx41RdXa2pU6eqsrJSGRkZWrlypVwulyRp586dio8/0eAyfPhwLViwQA888IDuu+8+nXvuuVq6dKkGDhwYvloAAADHCqrPiFXoMwIAgPOY0mcEAAAg3AgjAADAUoQRAABgKcIIAACwFGEEAABYijACAAAsRRgBAACWIowAAABLEUYAAIClgn4cvBWOPyS2trbW4pIAAIBAHT9ut/Wwd0eEkbq6OklSWlqaxSUBAADBqqurU9euXVv83BHvpvF4PNq1a5dOPfVUxcXF+XxWW1urtLQ0VVRUOPa9NdTBHqKhDlJ01IM62AN1sAcn18EwDNXV1al3794+L9E9mSNaRuLj43XGGWe0Ok6XLl0c9yWdjDrYQzTUQYqOelAHe6AO9uDUOrTWInIcHVgBAIClCCMAAMBSjg8jSUlJKigoUFJSktVFCRl1sIdoqIMUHfWgDvZAHewhGurQFkd0YAUAANHL8S0jAADA2QgjAADAUoQRAABgKcIIAACwVNjDyKxZs5Senq7k5GRlZWVp7dq13s927NihuLg4vz+vvPJKi/M8fPiw/uu//kuDBg1Shw4dNHbs2FbL8NFHH6lDhw7KyMhos7yff/65LrnkEiUnJystLU2PP/54szoUFRVp0qRJSklJUWJioq3q0NJ8m9ahX79+yszMVI8ePdSlSxddeOGFjqtDVlaW/vjHP2rIkCHq3LmzevToYVodiouL/c63srKy1Xr4W5dOrse5556rPn36KDk5WYMGDdILL7zgiHpYtU2EUge7bRNm1SGS24QkNTQ06P7771efPn2UlJSk9PR0zZs3r8X5StLOnTs1evRode7cWT179tTdd9+tp59+2qcOzz77rC666CIlJSWpT58+jqzD6tWrvfM1+xgRSh1+97vfaejQoUpKSvK7Py4uLtbVV1+tlJQU/eQnP1FGRoZeeumlVucZbmENI4sXL1Z+fr4KCgq0fv16DRkyRLm5udqzZ4+kY++W2b17t8/PtGnTdMopp2jkyJEtztftdqtTp0763e9+p5ycnFbLsG/fPo0fP14///nP2yxvbW2trrrqKvXp00elpaX685//rAceeEB33HGHtw4DBw5Ubm6utmzZoiVLlmjTpk169dVX9e6779qiDv7me/L3kJSUpI0bN+of//iHSktLlZubqw4dOujtt992TB1cLpcKCgp0zTXX6Msvv9SSJUvUr18/jRw50rQ6bNmyxWdd7dmzZ4vj+luXHnroIU2cONFbj7lz5+rrr79WdXW13n33XY0dO1a33HKL3n//fVO3ifbWww7bRDB1sOs2Ee46RHqbuOaaa1RUVKS5c+dqy5YtWrhwoc4777xW5zt69Gg1NjZqzZo1mj9/vv72t7/51OGss87SbbfdpqysLJWVlen3v/+94uPjtWDBAsfUYciQIbriiiu0cuVKzZ07V5s2bdIbb7yh1157zRZ1OO7GG2/UuHHj/H62Zs0aDR48WK+++qo+//xz5eXlafz48Vq2bFmb8w0bI4wyMzONSZMmeX93u91G7969jcLCwhanycjIMG688caAlzFhwgTj6quvbvHzcePGGQ888IBRUFBgDBkypNV5PfPMM0a3bt2MhoYG77CUlBTjtNNO8/4+a9YsIyEhwfjTn/5kyzr4m28g38OAAQOMadOmOaYOjz32mJGQkOBTh6efftpITU0Nex3ef/99Q5Lx448/Bjwvf+vSPffcYyQnJ3vrcc011xijRo3y+S6ysrKM3/zmNz7zsls9rNwmQqmDv/lauU2YVYdIbhNvvvmm0bVrV+OHH34IeF4rVqww4uPjjcrKSu+w9PR0o2PHjt716+677zY6dOjgU4dx48YZubm5jqnD8uXLjbi4OOPBBx9scT5W1qGpYPbHo0aNMvLy8kJaTijC1jLS2Nio0tJSn0QXHx+vnJwclZSU+J2mtLRUZWVluummm8JShhdeeEHl5eUqKCgIaPySkhL927/9mxITEyUdq0NVVZX27dunH3/8UZK0bNkynXnmmZozZ45cLpcGDhyo6dOny+1226IOJ/N4PG1+Dx6PR3V1dTr99NMlOaMOI0aMkMfj0dKlS2UYhqqqqrRkyRKNGjXKlDpIUkZGhlJSUnTllVfqo48+anXck9clSbriiit0+PBh/exnP/OOc+WVV/p8F7m5uT7bh93qYYdtItg6nMwO24QZdYjkNvH6669r2LBhevzxx5Wamqp+/frprrvu0qFDh1qcpqSkRIMGDZLL5ZJ0bF2qqKjQkSNH9OWXX0qSPv74Y5177rk+20DTbcIJdVi2bJlcLpcWLlzod75W1yFU+/fv924PkRC2F+XV1NTI7XZ7v7TjXC6XNm/e7HeauXPnqn///ho+fHi7l79t2zbde++9+vDDD9WhQ2DVqqysVN++fb2/19TUyOPxeD/r1q2bysvLtXPnTnXr1k0rV67U9u3bddttt+nIkSPepncr63CyhoaGNr+HJ554QgcOHNA111wjyfrv4WT+6jBixAiNGTNGy5YtU2Jioo4ePaoxY8Zo1qxZYa9DSkqKZs+erWHDhqmhoUHPP/+8LrvsMn3yySe66KKL/E5z8rokyXtAT0hI8I7jcrl8vguXy+XTd8Bu9bB6mwilDiezepswqw6R3CbKy8u1evVqJScn61//+pdqamp022236YcfftALL7zgd5rj6/txx48Rxz87/m+vXr18tgGXy6Xa2lodOnTIEXUoLy9XdXW1Dh8+rLfeeqvZfK2uQyhefvllffrpp/rb3/4Wtnm2xbK7aQ4dOqQFCxY0S4sXXHCBTjnllDavrzXldrt13XXXadq0aerXr19Yy+nxeNS5c2f17dtXQ4cO1bhx43T//fdr9uzZjqlDUwsWLNC0adP08ssvq2fPno6pw1dffaWioiKlpqaqtLRUK1eu1I4dO3TrrbeGtQ6SdN555+k3v/mNhg4dquHDh2vevHkaPny4nnrqqbDW6WROqUektgkz69CUmduEmXWI5Dbh8XgUFxenl156SZmZmRo1apRmzJih+fPnm3JWLoV/ezCrDsfD+tlnn91svnv37nVEHZp6//33lZeXpzlz5uiCCy4IyzwDEbaWke7duyshIUFVVVU+w6uqqtSrV69m4y9ZskQHDx7U+PHjfYavWLFCR44ckSR16tQpoGXX1dVp3bp1+uyzzzR58mRJx740wzC8ndKuuOKKZtP16tXLp7zdu3dXfHy8PB6Pt8wpKSmqq6tTSkqKd7z+/fursrJSixYtsrwOJ0tKSmrxe3C73br55pv1yiuveJt77fA9BFKHwsJCde/eXUOGDNHgwYM1ePBg/eQnP9Ell1yijIyMsNWhJZmZmVq9enWLn5+8LknHmnQlec+kjo/TdJto+v9wfhfhqoeV20SodTiZlduEmXWI5DaRkpKi1NRUn1fB9+/fX4Zh6LvvvtO5557bbJpevXr53E15/Bjhdru961KvXr1UU1PjM31VVZW6dOmi5cuXO6IOKSkp6tSpk1JTU5vNd+7cuZbXIRgffPCBxowZo6eeeqpZmc0WtpaRxMREDR06VEVFRd5hHo9HRUVFys7Objb+3Llz9ctf/lI9evTwGd6nTx+dc845Ouecc3y+3NZ06dJFX3zxhcrKyrw/t956q8477zyVlZUpKyvL73TZ2dlatWqVd6VITEyUy+XSaaedpm7dukmShg8frurqau91f0naunWrUlJSNH/+fMvrcLL4+Hi/38OyZcu0YcMGLVy4UKNHj/Z+ZofvIZA61NfXa8+ePT7r0vHLHwsXLgxbHVpSVlbmc/A92cnrknTsdrnk5GR98skn3nHeffddn23inXfe8f4/nN9FuOph5TYRah1OZuU2YWYdIrlNjBgxQrt27dKBAwe8w7Zu3ar4+HidccYZfqfJzs7WF1984b2bMjExUWlpaerYsaMGDBggSfrZz36mbdu2+dTh+DYR7u/BrDpkZ2frwIEDPpfcjs/3jTfesLwOgSouLtbo0aP12GOP6ZZbbmnXvEIRtpYRScrPz9eECRM0bNgwZWZmaubMmaqvr1deXp7PeNu3b9eqVau0YsWKgOf91VdfqbGxUXv37lVdXZ3KysokHesUFh8fr4EDB/qM37NnTyUnJzcb3tTxSwo33XST7rnnHm3cuFE//PCDPB6P5s+fr8zMTO3cuVNut1vbt2/X1q1btW3bNk2fPl3XX3+9Zs6caXkd/M33P/7jPzR16lTv9zB58mTt3btXhYWFysrK8l7r3L17ty2+h0DqsHfvXh06dEhxcXEqLy/X7t27dccdd2jw4MH6+OOPw1YHSZo5c6b69u2rCy64QIcPH9bzzz+v9957T2+//XaL8/S3Lv3lL3/Rr3/9a82ZM0fDhg3T1VdfrV//+tdKTk7WxRdfrIceekjr1q3Tc889F/ZtIpz1sGqbCLUO/uZr1TZhZh0iuU1cd911evjhh5WXl6dp06appqZGd999t2688cYWz+yvuuoqDRgwQDfccIMef/xxVVZWetelRYsWKTMzU999952OHj2qiooKbd68We+9955efvllzZkzRzfddJMj6rB27VrFxcVp3bp1+uqrr7zz/dWvfqUlS5ZYXgfp2DH3wIEDqqys1KFDh7zzHTBggBITE/X+++/r3//933X77bfrV7/6lXd7SExMjFwn1nDfnvPf//3fxplnnmkkJiYamZmZxscff9xsnClTphhpaWmG2+0OeL59+vQxJDX7aUmgtzBt2LDBuPjii42kpCQjNTXVePTRR5vVYc6cOUZWVpaRlJRknHXWWcYjjzxi3HPPPbapQ0vzPV6HU0891e/ngwYNckwdMjMzjfz8fGPAgAFGp06djJSUFOP66683Jk+eHPY6PPbYY8bZZ59tJCcnG6effrpx2WWXGe+9916b8/W3LhmG7zZxzjnnGGlpaUZiYqJxwQUXGMuXLzcMw5xtIpz1sGqbCLUOdtomzKpDJLcJwzCMTZs2GTk5OUanTp2MM844w8jPzzcOHjzY6nx37NhhjBw50ujUqZPRvXt34/e//70xc+ZMnzrMmjXLyMjIMBITE42zzjrLeOGFF0w7RphVh0WLFjWb71133WWbOlx66aV+5/vNN98YhnHsVmJ/n1966aUBl7294gzDMAINLgAAAOHGu2kAAIClCCMAAMBShBEAAGApwggAALAUYQQAAFiKMAIAACxFGAEAAJYijAAAAEsRRgAAgKUIIwAAwFKEEQAAYCnCCAAAsNT/D7ZHRZXgMfEDAAAAAElFTkSuQmCC"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 14
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-10T21:42:43.064314Z",
+     "start_time": "2026-01-10T21:42:40.635381Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "#plot relevant data\n",
+    "\n",
+    "fig, ax1 = plt.subplots()\n",
+    "ax_twin = ax1.twinx()\n",
+    "\n",
+    "plt.plot(accel_position.datetime_x_axis, scaled_accel_position, label = \"Accelerator Position\")\n",
+    "plt.plot(mech_brake_pressed.datetime_x_axis, mech_brake_pressed, color = 'green', label = \"Brake Pressed\")\n",
+    "ax1.plot(speed_kph.datetime_x_axis, speed_kph, color = 'red', label = \"Speed KPH\")\n",
+    "\n",
+    "\n",
+    "ax1.set_xlabel(\"Time\")\n",
+    "ax1.set_ylabel(\"Speed\")\n",
+    "ax_twin.set_ylabel(\"position\")\n",
+    "\n",
+    "ax1.tick_params(\"x\", rotation  = 90)\n",
+    "\n",
+    "\n",
+    "plt.legend(loc = \"upper left\")\n",
+    "ax1.legend(loc = \"upper left\")\n",
+    "plt.show()"
+   ],
+   "id": "2bc30605a4cc336c",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 15
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-10T21:20:33.464705Z",
+     "start_time": "2026-01-10T21:20:32.466254Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# position is defined as a percentage\n",
+    "\n",
+    "plt.plot(speed_kph.datetime_x_axis, speed_kph, label = \"Speed KPH\")\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"Speed\")\n",
+    "plt.tick_params(rotation = 90)"
+   ],
+   "id": "8f3d0704205b4567",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 7
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "# in order to get position data, look up miguel's work for localization of fsgp data\n",
+    "\n",
+    "\n",
+    "# this project is essentially constrained in one lap\n",
+    "\n",
+    "\n",
+    "#from miguel's code, we find the number of laps done on one day, so let us"
+   ],
+   "id": "158615364b919eed"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/motor_analysis/faiman_coefficients.ipynb b/motor_analysis/faiman_coefficients.ipynb
deleted file mode 100644
index 4b9333c..0000000
--- a/motor_analysis/faiman_coefficients.ipynb
+++ /dev/null
@@ -1,1055 +0,0 @@
-{
- "cells": [
-  {
-   "metadata": {
-    "jupyter": {
-     "is_executing": true
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "import matplotlib\n",
-    "\n",
-    "\n",
-    "from data_tools import query\n",
-    "from data_tools.collections import TimeSeries\n",
-    "from datetime import datetime, date, time, timezone\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "import dill\n",
-    "import os\n",
-    "\n",
-    "#each 5 seconds\n",
-    "utc_offset_h = 2\n",
-    "start_utc = time(5 , 00, 00) #querying i svancouver time, influxdb gives utc\n",
-    "stop_utc = time(4, 45, 40)\n",
-    "date_start = date(2025, 6, 30)\n",
-    "date_stop = date(2025, 7, 2)\n",
-    "start_time = datetime.combine(date_start, start_utc, tzinfo=timezone.utc)\n",
-    "stop_time = datetime.combine(date_stop, stop_utc, tzinfo=timezone.utc)\n",
-    "\n",
-    "client = query.DBClient()\n",
-    "temp_array_expected_aliter: TimeSeries = client.query_time_series(start_time, stop_time, \"MosfetTemperatureA\")\n"
-   ],
-   "id": "7762d7c5f54c9e9f",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T19:07:28.204093Z",
-     "start_time": "2025-11-29T19:07:18.443470Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "#alternate data:\n",
-    "\n",
-    "from data_tools import query\n",
-    "from data_tools.collections import TimeSeries\n",
-    "from datetime import datetime, date, time, timezone\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "import dill\n",
-    "import os\n",
-    "\n",
-    "#each 5 seconds\n",
-    "utc_offset_h = 2\n",
-    "start_utc = time(5 + utc_offset_h , 00, 00) #querying i svancouver time, influxdb gives utc\n",
-    "stop_utc = time(4 +  utc_offset_h, 45, 40)\n",
-    "date_start = date(2025, 7, 1)\n",
-    "date_stop = date(2025, 7, 2)\n",
-    "start_time = datetime.combine(date_start, start_utc, tzinfo=timezone.utc)\n",
-    "stop_time = datetime.combine(date_stop, stop_utc, tzinfo=timezone.utc)\n",
-    "\n",
-    "client = query.DBClient()\n",
-    "temp_array_expected_aliter: TimeSeries = client.query_time_series(start_time, stop_time, \"MosfetTemperatureA\")\n",
-    "speed_kph: TimeSeries = client.query_time_series(start_time, stop_time, \"MotorRotatingSpeed\")\n",
-    "\n",
-    "print(temp_array_expected_aliter)\n",
-    "#this is almost steady state, and expected temperature is roughly 31.8 degrees celsius. note that individual cells do have an upto 5 C difference, which is actually quite a lot.\n",
-    "\n",
-    "\n"
-   ],
-   "id": "a8ea6762121b1311",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[27.46449661 27.46548943 27.46648225 ... 27.29558551 27.29614538\n",
-      " 27.29670525]\n"
-     ]
-    }
-   ],
-   "execution_count": 3
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T19:07:36.960833Z",
-     "start_time": "2025-11-29T19:07:36.946373Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "#save collected data of 15 minutes\n",
-    "\n",
-    "import os\n",
-    "import dill\n",
-    "\n",
-    "out_dir = os.path.join(\"../../motor_analysis\", \"data\", \"array_temperature_aliter_2025-07-01\")\n",
-    "mosfetA_file_aliter = os.path.join(out_dir, \"mosfetA_aliter.bin\")\n",
-    "\n",
-    "os.makedirs(out_dir, exist_ok=True)\n",
-    "\n",
-    "for filepath, data in zip([mosfetA_file_aliter],\n",
-    "                          [temp_array_expected_aliter]):\n",
-    "    with open(filepath, 'wb') as f:\n",
-    "        dill.dump(data, f)\n",
-    "\n",
-    "\n",
-    "\n",
-    "#time zone matching conventions??"
-   ],
-   "id": "9b566c367639c5e5",
-   "outputs": [],
-   "execution_count": 4
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "alternate data: data over the first 4 days of fsgp 2025",
-   "id": "b59eb1d3085bfd98"
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T22:07:18.614759Z",
-     "start_time": "2025-11-29T22:07:18.456316Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "from data_tools import query\n",
-    "from data_tools.collections import TimeSeries\n",
-    "from datetime import datetime, date, time, timezone\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "import dill\n",
-    "import os\n",
-    "\n",
-    "#each 5 seconds\n",
-    "utc_offset_h = 2\n",
-    "start_utc = time(22 , 00, 00) #querying i svancouver time, influxdb gives utc\n",
-    "stop_utc = time(20, 45, 00)\n",
-    "date_start = date(2025, 7, 1)\n",
-    "date_stop = date(2025, 7, 6)\n",
-    "start_time = datetime.combine(date_start, start_utc, tzinfo=timezone.utc)\n",
-    "stop_time = datetime.combine(date_stop, stop_utc, tzinfo=timezone.utc)\n",
-    "\n",
-    "client = query.DBClient()\n",
-    "temp_array_fsgp: TimeSeries = client.query_time_series(start_time, stop_time, field=  \"MosfetTemperatureA\")\n",
-    "speed_kph: TimeSeries = client.query_time_series(start_time, stop_time, \"MotorRotatingSpeed\")\n",
-    "\n",
-    "#print(temp_array_expected_aliter)\n",
-    "#this is almost steady state, and expected temperature is roughly 31.8 degrees celsius. note that individual cells do have an upto 5 C difference, which is actually quite a lot.\n",
-    "\n",
-    "\n"
-   ],
-   "id": "8b2b4006671bdc31",
-   "outputs": [
-    {
-     "ename": "ApiException",
-     "evalue": "(401)\nReason: Unauthorized\nHTTP response headers: HTTPHeaderDict({'Server': 'nginx/1.27.1', 'Date': 'Sat, 29 Nov 2025 22:07:18 GMT', 'Content-Type': 'application/json; charset=utf-8', 'Content-Length': '55', 'Connection': 'keep-alive', 'X-Influxdb-Build': 'OSS', 'X-Influxdb-Version': 'v2.7.1', 'X-Platform-Error-Code': 'unauthorized'})\nHTTP response body: b'{\"code\":\"unauthorized\",\"message\":\"unauthorized access\"}'\n",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mApiException\u001B[39m                              Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[2]\u001B[39m\u001B[32m, line 20\u001B[39m\n\u001B[32m     17\u001B[39m stop_time = datetime.combine(date_stop, stop_utc, tzinfo=timezone.utc)\n\u001B[32m     19\u001B[39m client = query.DBClient()\n\u001B[32m---> \u001B[39m\u001B[32m20\u001B[39m temp_array_fsgp: TimeSeries = \u001B[43mclient\u001B[49m\u001B[43m.\u001B[49m\u001B[43mquery_time_series\u001B[49m\u001B[43m(\u001B[49m\u001B[43mstart_time\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstop_time\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfield\u001B[49m\u001B[43m=\u001B[49m\u001B[43m  \u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mMosfetTemperatureA\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m     21\u001B[39m speed_kph: TimeSeries = client.query_time_series(start_time, stop_time, \u001B[33m\"\u001B[39m\u001B[33mMotorRotatingSpeed\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m     23\u001B[39m \u001B[38;5;66;03m#print(temp_array_expected_aliter)\u001B[39;00m\n\u001B[32m     24\u001B[39m \u001B[38;5;66;03m#this is almost steady state, and expected temperature is roughly 31.8 degrees celsius. note that individual cells do have an upto 5 C difference, which is actually quite a lot.\u001B[39;00m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\data_tools\\query\\influxdb_query.py:180\u001B[39m, in \u001B[36mDBClient.query_time_series\u001B[39m\u001B[34m(self, start, stop, field, bucket, car, granularity, units, measurement)\u001B[39m\n\u001B[32m    163\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mquery_time_series\u001B[39m(\u001B[38;5;28mself\u001B[39m, start: datetime, stop: datetime, field: \u001B[38;5;28mstr\u001B[39m, bucket: \u001B[38;5;28mstr\u001B[39m = \u001B[33m\"\u001B[39m\u001B[33mCAN_log\u001B[39m\u001B[33m\"\u001B[39m,\n\u001B[32m    164\u001B[39m                       car: \u001B[38;5;28mstr\u001B[39m = \u001B[33m\"\u001B[39m\u001B[33mBrightside\u001B[39m\u001B[33m\"\u001B[39m, granularity: \u001B[38;5;28mfloat\u001B[39m = \u001B[32m0.1\u001B[39m, units: \u001B[38;5;28mstr\u001B[39m = \u001B[33m\"\u001B[39m\u001B[33m\"\u001B[39m,\n\u001B[32m    165\u001B[39m                       measurement: \u001B[38;5;28mstr\u001B[39m = \u001B[38;5;28;01mNone\u001B[39;00m) -> TimeSeries:\n\u001B[32m    166\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    167\u001B[39m \u001B[33;03m    Query the database for a specific field, over a certain time range.\u001B[39;00m\n\u001B[32m    168\u001B[39m \u001B[33;03m    The data will be processed into a TimeSeries, which has homogenous and evenly-spaced (temporally) elements.\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m    178\u001B[39m \u001B[33;03m    :return: a TimeSeries of the resulting time-series data\u001B[39;00m\n\u001B[32m    179\u001B[39m \u001B[33;03m    \"\"\"\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m180\u001B[39m     query_df = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mquery_series\u001B[49m\u001B[43m(\u001B[49m\u001B[43mstart\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstop\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfield\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbucket\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcar\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmeasurement\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    182\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m TimeSeries.from_query_dataframe(query_df, granularity, field, units)\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\data_tools\\query\\influxdb_query.py:153\u001B[39m, in \u001B[36mDBClient.query_series\u001B[39m\u001B[34m(self, start, stop, field, bucket, car, measurement)\u001B[39m\n\u001B[32m    151\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m measurement:\n\u001B[32m    152\u001B[39m     query = query.filter(measurement=measurement)\n\u001B[32m--> \u001B[39m\u001B[32m153\u001B[39m query_df = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mquery_dataframe\u001B[49m\u001B[43m(\u001B[49m\u001B[43mquery\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    155\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(query_df, \u001B[38;5;28mlist\u001B[39m):\n\u001B[32m    156\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[33m\"\u001B[39m\u001B[33mQuery returned multiple fields! Please refine your query.\u001B[39m\u001B[33m\"\u001B[39m)\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\data_tools\\query\\influxdb_query.py:123\u001B[39m, in \u001B[36mDBClient.query_dataframe\u001B[39m\u001B[34m(self, query)\u001B[39m\n\u001B[32m    120\u001B[39m compiled_query = query.compile_query()\n\u001B[32m    121\u001B[39m compiled_query += \u001B[33m'\u001B[39m\u001B[33m |> pivot(rowKey:[\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m_time\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m], columnKey: [\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m_field\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m], valueColumn: \u001B[39m\u001B[33m\"\u001B[39m\u001B[33m_value\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m) \u001B[39m\u001B[33m'\u001B[39m\n\u001B[32m--> \u001B[39m\u001B[32m123\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_client\u001B[49m\u001B[43m.\u001B[49m\u001B[43mquery_api\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[43mquery_data_frame\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcompiled_query\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\client\\query_api.py:259\u001B[39m, in \u001B[36mQueryApi.query_data_frame\u001B[39m\u001B[34m(self, query, org, data_frame_index, params, use_extension_dtypes)\u001B[39m\n\u001B[32m    225\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mquery_data_frame\u001B[39m(\u001B[38;5;28mself\u001B[39m, query: \u001B[38;5;28mstr\u001B[39m, org=\u001B[38;5;28;01mNone\u001B[39;00m, data_frame_index: List[\u001B[38;5;28mstr\u001B[39m] = \u001B[38;5;28;01mNone\u001B[39;00m, params: \u001B[38;5;28mdict\u001B[39m = \u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[32m    226\u001B[39m                      use_extension_dtypes: \u001B[38;5;28mbool\u001B[39m = \u001B[38;5;28;01mFalse\u001B[39;00m):\n\u001B[32m    227\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    228\u001B[39m \u001B[33;03m    Execute synchronous Flux query and return Pandas DataFrame.\u001B[39;00m\n\u001B[32m    229\u001B[39m \n\u001B[32m   (...)\u001B[39m\u001B[32m    257\u001B[39m \u001B[33;03m            - https://docs.influxdata.com/flux/latest/stdlib/influxdata/influxdb/schema/fieldsascols/\u001B[39;00m\n\u001B[32m    258\u001B[39m \u001B[33;03m    \"\"\"\u001B[39;00m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m259\u001B[39m     _generator = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mquery_data_frame_stream\u001B[49m\u001B[43m(\u001B[49m\u001B[43mquery\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43morg\u001B[49m\u001B[43m=\u001B[49m\u001B[43morg\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata_frame_index\u001B[49m\u001B[43m=\u001B[49m\u001B[43mdata_frame_index\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[43m=\u001B[49m\u001B[43mparams\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    260\u001B[39m \u001B[43m                                              \u001B[49m\u001B[43muse_extension_dtypes\u001B[49m\u001B[43m=\u001B[49m\u001B[43muse_extension_dtypes\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    261\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._to_data_frames(_generator)\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\client\\query_api.py:299\u001B[39m, in \u001B[36mQueryApi.query_data_frame_stream\u001B[39m\u001B[34m(self, query, org, data_frame_index, params, use_extension_dtypes)\u001B[39m\n\u001B[32m    265\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    266\u001B[39m \u001B[33;03mExecute synchronous Flux query and return stream of Pandas DataFrame as a :class:`~Generator[DataFrame]`.\u001B[39;00m\n\u001B[32m    267\u001B[39m \n\u001B[32m   (...)\u001B[39m\u001B[32m    295\u001B[39m \u001B[33;03m        - https://docs.influxdata.com/flux/latest/stdlib/influxdata/influxdb/schema/fieldsascols/\u001B[39;00m\n\u001B[32m    296\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m    297\u001B[39m org = \u001B[38;5;28mself\u001B[39m._org_param(org)\n\u001B[32m--> \u001B[39m\u001B[32m299\u001B[39m response = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_query_api\u001B[49m\u001B[43m.\u001B[49m\u001B[43mpost_query\u001B[49m\u001B[43m(\u001B[49m\u001B[43morg\u001B[49m\u001B[43m=\u001B[49m\u001B[43morg\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mquery\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_create_query\u001B[49m\u001B[43m(\u001B[49m\u001B[43mquery\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mdefault_dialect\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    300\u001B[39m \u001B[43m                                                                        \u001B[49m\u001B[43mdataframe_query\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    301\u001B[39m \u001B[43m                                      \u001B[49m\u001B[43masync_req\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_return_http_data_only\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[32m    303\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._to_data_frame_stream(data_frame_index=data_frame_index,\n\u001B[32m    304\u001B[39m                                   response=response,\n\u001B[32m    305\u001B[39m                                   query_options=\u001B[38;5;28mself\u001B[39m._get_query_options(),\n\u001B[32m    306\u001B[39m                                   use_extension_dtypes=use_extension_dtypes)\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\service\\query_service.py:285\u001B[39m, in \u001B[36mQueryService.post_query\u001B[39m\u001B[34m(self, **kwargs)\u001B[39m\n\u001B[32m    283\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m.post_query_with_http_info(**kwargs)  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m    284\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m285\u001B[39m     (data) = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mpost_query_with_http_info\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m    286\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m data\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\service\\query_service.py:311\u001B[39m, in \u001B[36mQueryService.post_query_with_http_info\u001B[39m\u001B[34m(self, **kwargs)\u001B[39m\n\u001B[32m    289\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"Query data.\u001B[39;00m\n\u001B[32m    290\u001B[39m \n\u001B[32m    291\u001B[39m \u001B[33;03mRetrieves data from buckets.  Use this endpoint to send a Flux query request and retrieve data from a bucket.  #### Rate limits (with InfluxDB Cloud)  `read` rate limits apply. For more information, see [limits and adjustable quotas](https://docs.influxdata.com/influxdb/cloud/account-management/limits/).  #### Related guides  - [Query with the InfluxDB API](https://docs.influxdata.com/influxdb/latest/query-data/execute-queries/influx-api/) - [Get started with Flux](https://docs.influxdata.com/flux/v0.x/get-started/)\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m    306\u001B[39m \u001B[33;03m         returns the request thread.\u001B[39;00m\n\u001B[32m    307\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m    308\u001B[39m local_var_params, path_params, query_params, header_params, body_params = \\\n\u001B[32m    309\u001B[39m     \u001B[38;5;28mself\u001B[39m._post_query_prepare(**kwargs)  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m311\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mapi_client\u001B[49m\u001B[43m.\u001B[49m\u001B[43mcall_api\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    312\u001B[39m \u001B[43m    \u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m/api/v2/query\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43mPOST\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m    313\u001B[39m \u001B[43m    \u001B[49m\u001B[43mpath_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    314\u001B[39m \u001B[43m    \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    315\u001B[39m \u001B[43m    \u001B[49m\u001B[43mheader_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    316\u001B[39m \u001B[43m    \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m=\u001B[49m\u001B[43mbody_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    317\u001B[39m \u001B[43m    \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43m[\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    318\u001B[39m \u001B[43m    \u001B[49m\u001B[43mfiles\u001B[49m\u001B[43m=\u001B[49m\u001B[43m{\u001B[49m\u001B[43m}\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    319\u001B[39m \u001B[43m    \u001B[49m\u001B[43mresponse_type\u001B[49m\u001B[43m=\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43mstr\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# noqa: E501\u001B[39;49;00m\n\u001B[32m    320\u001B[39m \u001B[43m    \u001B[49m\u001B[43mauth_settings\u001B[49m\u001B[43m=\u001B[49m\u001B[43m[\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    321\u001B[39m \u001B[43m    \u001B[49m\u001B[43masync_req\u001B[49m\u001B[43m=\u001B[49m\u001B[43mlocal_var_params\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43masync_req\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    322\u001B[39m \u001B[43m    \u001B[49m\u001B[43m_return_http_data_only\u001B[49m\u001B[43m=\u001B[49m\u001B[43mlocal_var_params\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m_return_http_data_only\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# noqa: E501\u001B[39;49;00m\n\u001B[32m    323\u001B[39m \u001B[43m    \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m=\u001B[49m\u001B[43mlocal_var_params\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m_preload_content\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    324\u001B[39m \u001B[43m    \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m=\u001B[49m\u001B[43mlocal_var_params\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m_request_timeout\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    325\u001B[39m \u001B[43m    \u001B[49m\u001B[43mcollection_formats\u001B[49m\u001B[43m=\u001B[49m\u001B[43m{\u001B[49m\u001B[43m}\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    326\u001B[39m \u001B[43m    \u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[43m=\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43murlopen_kw\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\api_client.py:343\u001B[39m, in \u001B[36mApiClient.call_api\u001B[39m\u001B[34m(self, resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, async_req, _return_http_data_only, collection_formats, _preload_content, _request_timeout, urlopen_kw)\u001B[39m\n\u001B[32m    304\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"Make the HTTP request (synchronous) and Return deserialized data.\u001B[39;00m\n\u001B[32m    305\u001B[39m \n\u001B[32m    306\u001B[39m \u001B[33;03mTo make an async_req request, set the async_req parameter.\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m    340\u001B[39m \u001B[33;03m    then the method will return the response directly.\u001B[39;00m\n\u001B[32m    341\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    342\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m async_req:\n\u001B[32m--> \u001B[39m\u001B[32m343\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m__call_api\u001B[49m\u001B[43m(\u001B[49m\u001B[43mresource_path\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    344\u001B[39m \u001B[43m                           \u001B[49m\u001B[43mpath_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mheader_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    345\u001B[39m \u001B[43m                           \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfiles\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    346\u001B[39m \u001B[43m                           \u001B[49m\u001B[43mresponse_type\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mauth_settings\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    347\u001B[39m \u001B[43m                           \u001B[49m\u001B[43m_return_http_data_only\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcollection_formats\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    348\u001B[39m \u001B[43m                           \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    349\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m    350\u001B[39m     thread = \u001B[38;5;28mself\u001B[39m.pool.apply_async(\u001B[38;5;28mself\u001B[39m.__call_api, (resource_path,\n\u001B[32m    351\u001B[39m                                    method, path_params, query_params,\n\u001B[32m    352\u001B[39m                                    header_params, body,\n\u001B[32m   (...)\u001B[39m\u001B[32m    356\u001B[39m                                    collection_formats,\n\u001B[32m    357\u001B[39m                                    _preload_content, _request_timeout, urlopen_kw))\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\api_client.py:173\u001B[39m, in \u001B[36mApiClient.__call_api\u001B[39m\u001B[34m(self, resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, _return_http_data_only, collection_formats, _preload_content, _request_timeout, urlopen_kw)\u001B[39m\n\u001B[32m    170\u001B[39m urlopen_kw = urlopen_kw \u001B[38;5;129;01mor\u001B[39;00m {}\n\u001B[32m    172\u001B[39m \u001B[38;5;66;03m# perform request and return response\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m173\u001B[39m response_data = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mrequest\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    174\u001B[39m \u001B[43m    \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m=\u001B[49m\u001B[43mheader_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    175\u001B[39m \u001B[43m    \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m=\u001B[49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    176\u001B[39m \u001B[43m    \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    177\u001B[39m \u001B[43m    \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    179\u001B[39m \u001B[38;5;28mself\u001B[39m.last_response = response_data\n\u001B[32m    181\u001B[39m return_data = response_data\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\api_client.py:388\u001B[39m, in \u001B[36mApiClient.request\u001B[39m\u001B[34m(self, method, url, query_params, headers, post_params, body, _preload_content, _request_timeout, **urlopen_kw)\u001B[39m\n\u001B[32m    379\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m.rest_client.OPTIONS(url,\n\u001B[32m    380\u001B[39m                                     query_params=query_params,\n\u001B[32m    381\u001B[39m                                     headers=headers,\n\u001B[32m   (...)\u001B[39m\u001B[32m    385\u001B[39m                                     body=body,\n\u001B[32m    386\u001B[39m                                     **urlopen_kw)\n\u001B[32m    387\u001B[39m \u001B[38;5;28;01melif\u001B[39;00m method == \u001B[33m\"\u001B[39m\u001B[33mPOST\u001B[39m\u001B[33m\"\u001B[39m:\n\u001B[32m--> \u001B[39m\u001B[32m388\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mrest_client\u001B[49m\u001B[43m.\u001B[49m\u001B[43mPOST\u001B[49m\u001B[43m(\u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    389\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    390\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m=\u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    391\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    392\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    393\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    394\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m=\u001B[49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    395\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    396\u001B[39m \u001B[38;5;28;01melif\u001B[39;00m method == \u001B[33m\"\u001B[39m\u001B[33mPUT\u001B[39m\u001B[33m\"\u001B[39m:\n\u001B[32m    397\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m.rest_client.PUT(url,\n\u001B[32m    398\u001B[39m                                 query_params=query_params,\n\u001B[32m    399\u001B[39m                                 headers=headers,\n\u001B[32m   (...)\u001B[39m\u001B[32m    403\u001B[39m                                 body=body,\n\u001B[32m    404\u001B[39m                                 **urlopen_kw)\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\rest.py:311\u001B[39m, in \u001B[36mRESTClientObject.POST\u001B[39m\u001B[34m(self, url, headers, query_params, post_params, body, _preload_content, _request_timeout, **urlopen_kw)\u001B[39m\n\u001B[32m    308\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mPOST\u001B[39m(\u001B[38;5;28mself\u001B[39m, url, headers=\u001B[38;5;28;01mNone\u001B[39;00m, query_params=\u001B[38;5;28;01mNone\u001B[39;00m, post_params=\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[32m    309\u001B[39m          body=\u001B[38;5;28;01mNone\u001B[39;00m, _preload_content=\u001B[38;5;28;01mTrue\u001B[39;00m, _request_timeout=\u001B[38;5;28;01mNone\u001B[39;00m, **urlopen_kw):\n\u001B[32m    310\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"Perform POST HTTP request.\"\"\"\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m311\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mrequest\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mPOST\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    312\u001B[39m \u001B[43m                        \u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m=\u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    313\u001B[39m \u001B[43m                        \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    314\u001B[39m \u001B[43m                        \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    315\u001B[39m \u001B[43m                        \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    316\u001B[39m \u001B[43m                        \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    317\u001B[39m \u001B[43m                        \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m=\u001B[49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    318\u001B[39m \u001B[43m                        \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\rest.py:261\u001B[39m, in \u001B[36mRESTClientObject.request\u001B[39m\u001B[34m(self, method, url, query_params, headers, body, post_params, _preload_content, _request_timeout, **urlopen_kw)\u001B[39m\n\u001B[32m    258\u001B[39m     _BaseRESTClient.log_body(r.data, \u001B[33m'\u001B[39m\u001B[33m<<<\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m    260\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[32m200\u001B[39m <= r.status <= \u001B[32m299\u001B[39m:\n\u001B[32m--> \u001B[39m\u001B[32m261\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m ApiException(http_resp=r)\n\u001B[32m    263\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m r\n",
-      "\u001B[31mApiException\u001B[39m: (401)\nReason: Unauthorized\nHTTP response headers: HTTPHeaderDict({'Server': 'nginx/1.27.1', 'Date': 'Sat, 29 Nov 2025 22:07:18 GMT', 'Content-Type': 'application/json; charset=utf-8', 'Content-Length': '55', 'Connection': 'keep-alive', 'X-Influxdb-Build': 'OSS', 'X-Influxdb-Version': 'v2.7.1', 'X-Platform-Error-Code': 'unauthorized'})\nHTTP response body: b'{\"code\":\"unauthorized\",\"message\":\"unauthorized access\"}'\n"
-     ]
-    }
-   ],
-   "execution_count": 2
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T19:45:37.979942Z",
-     "start_time": "2025-11-29T19:45:37.962502Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "type(temp_array_fsgp)",
-   "id": "52e7b7784852e53d",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "data_tools.collections.time_series.TimeSeries"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 52
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T19:45:39.544344Z",
-     "start_time": "2025-11-29T19:45:39.529624Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "df_fsgp = pd.DataFrame(temp_array_fsgp)",
-   "id": "1300423cd026c3ca",
-   "outputs": [],
-   "execution_count": 53
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T19:45:40.816030Z",
-     "start_time": "2025-11-29T19:45:40.810451Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "len(df_fsgp)",
-   "id": "f46c881335aa4926",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2831521"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 54
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T19:45:43.429032Z",
-     "start_time": "2025-11-29T19:45:43.420022Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "len(hourly_data['shortwave_radiation_instant'])",
-   "id": "bbac7524849da8b0",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "120"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 55
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": [
-    "querying from openmeteo to get solar irradiance data\n",
-    "\n",
-    "- this is hourly irradiance over 4 days\n"
-   ],
-   "id": "bc1ff3d5812338f"
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T19:45:50.178966Z",
-     "start_time": "2025-11-29T19:45:50.150527Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "import openmeteo_requests\n",
-    "\n",
-    "import pandas as pd\n",
-    "import requests_cache\n",
-    "from retry_requests import retry\n",
-    "\n",
-    "# Setup the Open-Meteo API client with cache and retry on error\n",
-    "cache_session = requests_cache.CachedSession('.cache', expire_after = 3600)\n",
-    "retry_session = retry(cache_session, retries = 5, backoff_factor = 0.2)\n",
-    "openmeteo = openmeteo_requests.Client(session = retry_session)\n",
-    "\n",
-    "# Make sure all required weather variables are listed here\n",
-    "# The order of variables in hourly or daily is important to assign them correctly below\n",
-    "url = \"https://historical-forecast-api.open-meteo.com/v1/forecast\"\n",
-    "params = {\n",
-    "\t\"latitude\": 36.9760,\n",
-    "\t\"longitude\": 86.4491,\n",
-    "\t\"start_date\": \"2025-07-02\",\n",
-    "\t\"end_date\": \"2025-07-06\",\n",
-    "\t\"minutely_15\": [\"temperature_2m\", \"wind_speed_10m\", \"shortwave_radiation_instant\"],\n",
-    "}\n",
-    "responses = openmeteo.weather_api(url, params=params)\n",
-    "\n",
-    "# Process first location. Add a for-loop for multiple locations or weather models\n",
-    "response = responses[0]\n",
-    "print(f\"Coordinates: {response.Latitude()}°N {response.Longitude()}°E\")\n",
-    "print(f\"Elevation: {response.Elevation()} m asl\")\n",
-    "print(f\"Timezone difference to GMT+0: {response.UtcOffsetSeconds()}s\")\n",
-    "\n",
-    "# Process minutely_15 data. The order of variables needs to be the same as requested.\n",
-    "minutely_15 = response.Minutely15()\n",
-    "minutely_15_temperature_2m = minutely_15.Variables(0).ValuesAsNumpy()\n",
-    "minutely_15_shortwave_radiation_instant = minutely_15.Variables(1).ValuesAsNumpy()\n",
-    "minutely_15_wind_speed_10m = minutely_15.Variables(2).ValuesAsNumpy()\n",
-    "\n",
-    "minutely_15_data = {\"date\": pd.date_range(\n",
-    "\tstart = pd.to_datetime(minutely_15.Time(), unit = \"s\", utc = True),\n",
-    "\tend =  pd.to_datetime(minutely_15.TimeEnd(), unit = \"s\", utc = True),\n",
-    "\tfreq = pd.Timedelta(seconds = minutely_15.Interval()),\n",
-    "\tinclusive = \"left\"\n",
-    ")}\n",
-    "\n",
-    "minutely_15_data[\"temperature_2m\"] = minutely_15_temperature_2m\n",
-    "minutely_15_data[\"shortwave_radiation_instant\"] = minutely_15_shortwave_radiation_instant\n",
-    "minutely_15_data[\"wind_speed_10m\"] = minutely_15_wind_speed_10m\n",
-    "\n",
-    "minutely_15_dataframe = pd.DataFrame(data = minutely_15_data)\n",
-    "print(\"\\nMinutely15 data\\n\", minutely_15_dataframe)"
-   ],
-   "id": "f718cf3615289b31",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Coordinates: 37.0°N 86.5°E\n",
-      "Elevation: 5139.0 m asl\n",
-      "Timezone difference to GMT+0: 0s\n",
-      "\n",
-      "Minutely15 data\n",
-      "                          date  temperature_2m  shortwave_radiation_instant  \\\n",
-      "0   2025-07-02 00:00:00+00:00           -2.20                     5.588703   \n",
-      "1   2025-07-02 00:15:00+00:00           -2.15                     5.351785   \n",
-      "2   2025-07-02 00:30:00+00:00           -2.15                     5.014219   \n",
-      "3   2025-07-02 00:45:00+00:00           -2.05                     4.680000   \n",
-      "4   2025-07-02 01:00:00+00:00           -1.95                     4.680000   \n",
-      "..                        ...             ...                          ...   \n",
-      "475 2025-07-06 22:45:00+00:00           -1.25                    20.240196   \n",
-      "476 2025-07-06 23:00:00+00:00           -1.05                    19.881649   \n",
-      "477 2025-07-06 23:15:00+00:00           -0.95                    19.917469   \n",
-      "478 2025-07-06 23:30:00+00:00           -0.90                    19.959719   \n",
-      "479 2025-07-06 23:45:00+00:00           -0.80                    20.418695   \n",
-      "\n",
-      "     wind_speed_10m  \n",
-      "0        124.831741  \n",
-      "1        164.539490  \n",
-      "2        205.015503  \n",
-      "3        243.104050  \n",
-      "4        276.808258  \n",
-      "..              ...  \n",
-      "475        0.000000  \n",
-      "476        0.000000  \n",
-      "477       16.716660  \n",
-      "478       48.040825  \n",
-      "479       76.951775  \n",
-      "\n",
-      "[480 rows x 4 columns]\n"
-     ]
-    }
-   ],
-   "execution_count": 56
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": [
-    "minutely open meteo data starts at 2 july, 12am and goes to 6 july 22 45\n",
-    "therefore on influx i query for 1 july 10  pm to 6 july 20 45\n",
-    "\n",
-    "\n",
-    "utc i s2 hours ahead of vancouver"
-   ],
-   "id": "dd2a5efc6d21b409"
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "Plot some relevant data. this will further be used to generate the relevant coefficients",
-   "id": "9fe9b268872c73e6"
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T21:25:43.719301Z",
-     "start_time": "2025-11-29T21:25:42.242073Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "#this graph shows solar irradiance, array temperature, and ambient temperature over July 2-6 (FSGP 2025)\n",
-    "\n",
-    "fig, ax1 = plt.subplots()\n",
-    "ax_twin = ax1.twinx()\n",
-    "\n",
-    "plt.plot(temp_array_fsgp.datetime_x_axis, temp_array_fsgp, label = \"Array Temperature\")\n",
-    "plt.plot(minutely_15_data['date'],minutely_15_data['temperature_2m'], color = 'green', label = \"Ambient Temperature\")\n",
-    "# plt.plot(minutely_15_data['date'], minutely_15_data['wind_speed_10m'], color = 'blue', label = \"Wind Speed 10m\")\n",
-    "# plt.plot(speed_kph.datetime_x_axis, speed_kph, color = 'yellow', label = \"Speed KPH\")\n",
-    "\n",
-    "ax1.plot(minutely_15_data['date'],minutely_15_data['shortwave_radiation_instant'], color = \"red\", label = \"Solar Irradiance\")\n",
-    "\n",
-    "ax1.set_xlabel(\"Time\")\n",
-    "ax1.set_ylabel(\"Solar Irradiance\")\n",
-    "ax_twin.set_ylabel(\"MosfetTemperatureA\")\n",
-    "\n",
-    "ax1.tick_params(\"x\", rotation  = 90)\n",
-    "\n",
-    "\n",
-    "plt.legend(loc = \"upper left\")\n",
-    "ax1.legend(loc = \"upper left\")\n",
-    "plt.show()"
-   ],
-   "id": "a3fa42c24abb970c",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 64
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T21:43:20.307344Z",
-     "start_time": "2025-11-29T21:43:20.296669Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "print(dir(temp_array_fsgp))\n",
-   "id": "d577cfa934535b4f",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['T', '__abs__', '__add__', '__and__', '__array__', '__array_finalize__', '__array_function__', '__array_interface__', '__array_namespace__', '__array_priority__', '__array_struct__', '__array_ufunc__', '__array_wrap__', '__bool__', '__buffer__', '__class__', '__class_getitem__', '__complex__', '__contains__', '__copy__', '__deepcopy__', '__delattr__', '__delitem__', '__dict__', '__dir__', '__divmod__', '__dlpack__', '__dlpack_device__', '__doc__', '__eq__', '__float__', '__floordiv__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__getstate__', '__gt__', '__hash__', '__iadd__', '__iand__', '__ifloordiv__', '__ilshift__', '__imatmul__', '__imod__', '__imul__', '__index__', '__init__', '__init_subclass__', '__int__', '__invert__', '__ior__', '__ipow__', '__irshift__', '__isub__', '__iter__', '__itruediv__', '__ixor__', '__le__', '__len__', '__lshift__', '__lt__', '__matmul__', '__mod__', '__module__', '__mul__', '__ne__', '__neg__', '__new__', '__or__', '__pos__', '__pow__', '__radd__', '__rand__', '__rdivmod__', '__reduce__', '__reduce_ex__', '__repr__', '__rfloordiv__', '__rlshift__', '__rmatmul__', '__rmod__', '__rmul__', '__ror__', '__rpow__', '__rrshift__', '__rshift__', '__rsub__', '__rtruediv__', '__rxor__', '__setattr__', '__setitem__', '__setstate__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__truediv__', '__xor__', '_length', '_meta', '_period', '_start', '_stop', '_units', 'align', 'all', 'any', 'argmax', 'argmin', 'argpartition', 'argsort', 'astype', 'base', 'byteswap', 'choose', 'clip', 'compress', 'conj', 'conjugate', 'copy', 'ctypes', 'cumprod', 'cumsum', 'data', 'datetime_x_axis', 'device', 'diagonal', 'dot', 'dtype', 'dump', 'dumps', 'fill', 'flags', 'flat', 'flatten', 'from_csv', 'from_query_dataframe', 'getfield', 'granularity', 'imag', 'index_of', 'item', 'itemset', 'itemsize', 'length', 'mT', 'max', 'mean', 'meta', 'min', 'nbytes', 'ndim', 'newbyteorder', 'nonzero', 'partition', 'period', 'plot', 'prod', 'promote', 'ptp', 'put', 'ravel', 'real', 'relative_time', 'repeat', 'reshape', 'resize', 'round', 'searchsorted', 'setfield', 'setflags', 'shape', 'size', 'sort', 'squeeze', 'start', 'std', 'stop', 'strides', 'sum', 'swapaxes', 'take', 'to_device', 'tobytes', 'tofile', 'tolist', 'trace', 'transpose', 'units', 'unix_x_axis', 'var', 'view', 'x_axis']\n"
-     ]
-    }
-   ],
-   "execution_count": 71
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T21:44:22.670216Z",
-     "start_time": "2025-11-29T21:44:21.605928Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "#resampling influx data so that i can fit it with irradiance data (queried every 15 minutes)\n",
-    "\n",
-    "ts = temp_array_fsgp\n",
-    "timestamps = pd.to_datetime(ts.datetime_x_axis, utc=True)\n",
-    "values = ts.data\n",
-    "\n",
-    "df = pd.DataFrame({\"value\": values}, index=timestamps)\n",
-    "df_15m = df.resample(\"15T\").mean()\n",
-    "\n",
-    "print(df_15m.head())\n"
-   ],
-   "id": "203be30192c9302a",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                               value\n",
-      "2025-07-02 07:15:00+00:00  37.371079\n",
-      "2025-07-02 07:30:00+00:00  46.801625\n",
-      "2025-07-02 07:45:00+00:00  29.980004\n",
-      "2025-07-02 08:00:00+00:00  45.573409\n",
-      "2025-07-02 08:15:00+00:00  55.745558\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_30480\\3992666424.py:13: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
-      "  df_15m = df.resample(\"15T\").mean()\n"
-     ]
-    }
-   ],
-   "execution_count": 72
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T21:48:01.173879Z",
-     "start_time": "2025-11-29T21:48:01.131446Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "df_15m.index",
-   "id": "9d97681681224101",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatetimeIndex(['2025-07-02 07:15:00+00:00', '2025-07-02 07:30:00+00:00',\n",
-       "               '2025-07-02 07:45:00+00:00', '2025-07-02 08:00:00+00:00',\n",
-       "               '2025-07-02 08:15:00+00:00', '2025-07-02 08:30:00+00:00',\n",
-       "               '2025-07-02 08:45:00+00:00', '2025-07-02 09:00:00+00:00',\n",
-       "               '2025-07-02 09:15:00+00:00', '2025-07-02 09:30:00+00:00',\n",
-       "               ...\n",
-       "               '2025-07-05 11:45:00+00:00', '2025-07-05 12:00:00+00:00',\n",
-       "               '2025-07-05 12:15:00+00:00', '2025-07-05 12:30:00+00:00',\n",
-       "               '2025-07-05 12:45:00+00:00', '2025-07-05 13:00:00+00:00',\n",
-       "               '2025-07-05 13:15:00+00:00', '2025-07-05 13:30:00+00:00',\n",
-       "               '2025-07-05 13:45:00+00:00', '2025-07-05 14:00:00+00:00'],\n",
-       "              dtype='datetime64[ns, UTC]', length=316, freq='15min')"
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 73
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-12-03T00:23:39.073811Z",
-     "start_time": "2025-12-03T00:23:38.847947Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "#this graph shows solar irradiance, array temperature, and ambient temperature over July 2-6 (FSGP 2025)\n",
-    "\n",
-    "fig, ax1 = plt.subplots()\n",
-    "ax_twin = ax1.twinx()\n",
-    "\n",
-    "plt.plot(df_15m.index, df_15m, label = \"Array Temperature\")\n",
-    "plt.plot(hourly_data['date'],hourly_data['temperature_2m'], color = 'green', label = \"Ambient Temperature\")\n",
-    "\n",
-    "ax1.plot(hourly_data['date'],hourly_data['shortwave_radiation_instant'], color = \"red\", label = \"Solar Irradiance\")\n",
-    "\n",
-    "ax1.set_xlabel(\"Time\")\n",
-    "ax1.set_ylabel(\"Solar Irradiance\")\n",
-    "ax_twin.set_ylabel(\"MosfetTemperatureA\")\n",
-    "\n",
-    "ax1.tick_params(\"x\", rotation  = 90)\n",
-    "#ax1.set_xticks(minutely_15.timeformat)\n",
-    "\n",
-    "plt.legend(loc = \"upper left\")\n",
-    "ax1.legend(loc = \"upper left\")\n",
-    "plt.show()"
-   ],
-   "id": "7397cf9f7ec92d1e",
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'plt' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mNameError\u001B[39m                                 Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[1]\u001B[39m\u001B[32m, line 3\u001B[39m\n\u001B[32m      1\u001B[39m \u001B[38;5;66;03m#this graph shows solar irradiance, array temperature, and ambient temperature over July 2-6 (FSGP 2025)\u001B[39;00m\n\u001B[32m----> \u001B[39m\u001B[32m3\u001B[39m fig, ax1 = \u001B[43mplt\u001B[49m.subplots()\n\u001B[32m      4\u001B[39m ax_twin = ax1.twinx()\n\u001B[32m      6\u001B[39m plt.plot(df_15m.index, df_15m, label = \u001B[33m\"\u001B[39m\u001B[33mArray Temperature\u001B[39m\u001B[33m\"\u001B[39m)\n",
-      "\u001B[31mNameError\u001B[39m: name 'plt' is not defined"
-     ]
-    }
-   ],
-   "execution_count": 1
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
-   "source": "",
-   "id": "8848868de645674f"
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "import the Physics Array Temperature Model for validation/sanity checks\n",
-   "id": "11a5c363e4b5b953"
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T21:48:56.274247Z",
-     "start_time": "2025-11-29T21:48:56.265960Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "from v4.array_temperature.arrayTemperatureModel import arrayTemperatureModel\n",
-    "def model(u0, u1):\n",
-    "    return arrayTemperatureModel(hourly_data['temperature_2m'], hourly_data['shortwave_radiation_instant'], 0, u0, u1)\n",
-    "\n",
-    "model = model(22, 0.8)\n",
-    "print(model.calculateArrayTemperature())\n"
-   ],
-   "id": "2099b64dadb66f1a",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 1.8935347e+00  1.0364137e+01  2.1387180e+01  2.9999233e+01\n",
-      "  2.6391014e+01  2.6875341e+01  2.7645386e+01  2.8025963e+01\n",
-      "  2.5642160e+01  2.4427845e+01  1.8177454e+01  1.7554525e+01\n",
-      "  8.0708389e+00  1.9637234e+00 -8.7300003e-01 -1.2230000e+00\n",
-      " -1.4230000e+00 -1.6230000e+00 -2.3230000e+00 -2.8230000e+00\n",
-      " -3.3230000e+00 -3.7229998e+00 -4.0730000e+00 -4.3730001e+00\n",
-      "  1.2934799e+00  1.0832609e+01  2.1798904e+01  3.0714010e+01\n",
-      "  4.0591778e+01  4.3875725e+01  3.3674473e+01  3.8677563e+01\n",
-      "  3.2507423e+01  3.0509079e+01  2.3399044e+01  1.4952563e+01\n",
-      "  7.7067099e+00  1.6932418e+00 -1.2730000e+00 -1.8230001e+00\n",
-      " -2.6229999e+00 -3.2229998e+00 -3.8229997e+00 -4.0730000e+00\n",
-      " -4.5230002e+00 -4.8730001e+00 -5.2730002e+00 -5.5730000e+00\n",
-      " -1.0213566e-01  9.7276592e+00  2.1370445e+01  3.2780659e+01\n",
-      "  4.2558723e+01  4.9203346e+01  5.2815674e+01  5.2470692e+01\n",
-      "  4.7815193e+01  3.2199566e+01  2.7188547e+01  2.1431530e+01\n",
-      "  1.2343058e+01  3.3490453e+00 -1.1730000e+00 -2.4229999e+00\n",
-      " -3.5730000e+00 -3.5730000e+00 -3.0730000e+00 -3.1729999e+00\n",
-      " -3.1229999e+00 -3.5230000e+00 -3.7730000e+00 -3.8229997e+00\n",
-      " -7.6389313e-03  8.4443426e+00  1.7759680e+01  2.5409929e+01\n",
-      "  3.1721752e+01  3.9829716e+01  2.8951809e+01  4.0317398e+01\n",
-      "  3.3866692e+01  2.9610489e+01  2.6545244e+01  2.2101496e+01\n",
-      "  1.3766469e+01  5.0048113e+00  6.7700005e-01  1.7700000e-01\n",
-      " -4.7300002e-01 -1.0230000e+00 -1.7730001e+00 -2.5230000e+00\n",
-      " -3.0230000e+00 -3.2229998e+00 -3.3729999e+00 -3.3230000e+00\n",
-      "  2.0034187e+00  1.2429336e+01  2.4601358e+01  3.6484211e+01\n",
-      "  4.6292747e+01  5.3362427e+01  5.4850025e+01  5.5464657e+01\n",
-      "  5.2211830e+01  4.7174763e+01  3.9110405e+01  2.9210789e+01\n",
-      "  1.8547527e+01  8.4345522e+00  3.3770001e+00  2.2270000e+00\n",
-      "  1.6270000e+00  1.2770000e+00  1.0270000e+00  1.3770000e+00\n",
-      "  9.2700005e-01  1.2700000e-01 -1.4730000e+00 -1.2730000e+00]\n"
-     ]
-    }
-   ],
-   "execution_count": 76
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T21:48:57.548674Z",
-     "start_time": "2025-11-29T21:48:57.541528Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "faiman_temp = model.calculateArrayTemperature()",
-   "id": "492ea4964124380d",
-   "outputs": [],
-   "execution_count": 77
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T21:48:58.404891Z",
-     "start_time": "2025-11-29T21:48:58.394317Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "import numpy as np\n",
-    "np.array(faiman_temp)"
-   ],
-   "id": "bee1dddff65cfb38",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1.8935347e+00,  1.0364137e+01,  2.1387180e+01,  2.9999233e+01,\n",
-       "        2.6391014e+01,  2.6875341e+01,  2.7645386e+01,  2.8025963e+01,\n",
-       "        2.5642160e+01,  2.4427845e+01,  1.8177454e+01,  1.7554525e+01,\n",
-       "        8.0708389e+00,  1.9637234e+00, -8.7300003e-01, -1.2230000e+00,\n",
-       "       -1.4230000e+00, -1.6230000e+00, -2.3230000e+00, -2.8230000e+00,\n",
-       "       -3.3230000e+00, -3.7229998e+00, -4.0730000e+00, -4.3730001e+00,\n",
-       "        1.2934799e+00,  1.0832609e+01,  2.1798904e+01,  3.0714010e+01,\n",
-       "        4.0591778e+01,  4.3875725e+01,  3.3674473e+01,  3.8677563e+01,\n",
-       "        3.2507423e+01,  3.0509079e+01,  2.3399044e+01,  1.4952563e+01,\n",
-       "        7.7067099e+00,  1.6932418e+00, -1.2730000e+00, -1.8230001e+00,\n",
-       "       -2.6229999e+00, -3.2229998e+00, -3.8229997e+00, -4.0730000e+00,\n",
-       "       -4.5230002e+00, -4.8730001e+00, -5.2730002e+00, -5.5730000e+00,\n",
-       "       -1.0213566e-01,  9.7276592e+00,  2.1370445e+01,  3.2780659e+01,\n",
-       "        4.2558723e+01,  4.9203346e+01,  5.2815674e+01,  5.2470692e+01,\n",
-       "        4.7815193e+01,  3.2199566e+01,  2.7188547e+01,  2.1431530e+01,\n",
-       "        1.2343058e+01,  3.3490453e+00, -1.1730000e+00, -2.4229999e+00,\n",
-       "       -3.5730000e+00, -3.5730000e+00, -3.0730000e+00, -3.1729999e+00,\n",
-       "       -3.1229999e+00, -3.5230000e+00, -3.7730000e+00, -3.8229997e+00,\n",
-       "       -7.6389313e-03,  8.4443426e+00,  1.7759680e+01,  2.5409929e+01,\n",
-       "        3.1721752e+01,  3.9829716e+01,  2.8951809e+01,  4.0317398e+01,\n",
-       "        3.3866692e+01,  2.9610489e+01,  2.6545244e+01,  2.2101496e+01,\n",
-       "        1.3766469e+01,  5.0048113e+00,  6.7700005e-01,  1.7700000e-01,\n",
-       "       -4.7300002e-01, -1.0230000e+00, -1.7730001e+00, -2.5230000e+00,\n",
-       "       -3.0230000e+00, -3.2229998e+00, -3.3729999e+00, -3.3230000e+00,\n",
-       "        2.0034187e+00,  1.2429336e+01,  2.4601358e+01,  3.6484211e+01,\n",
-       "        4.6292747e+01,  5.3362427e+01,  5.4850025e+01,  5.5464657e+01,\n",
-       "        5.2211830e+01,  4.7174763e+01,  3.9110405e+01,  2.9210789e+01,\n",
-       "        1.8547527e+01,  8.4345522e+00,  3.3770001e+00,  2.2270000e+00,\n",
-       "        1.6270000e+00,  1.2770000e+00,  1.0270000e+00,  1.3770000e+00,\n",
-       "        9.2700005e-01,  1.2700000e-01, -1.4730000e+00, -1.2730000e+00],\n",
-       "      dtype=float32)"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 78
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T21:52:30.256333Z",
-     "start_time": "2025-11-29T21:52:30.246409Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "len(faiman_temp)",
-   "id": "53ffac58d50fb409",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "120"
-      ]
-     },
-     "execution_count": 87,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 87
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-  {
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-    "ExecuteTime": {
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-     "start_time": "2025-11-29T21:54:46.151825Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "df_15m.head()",
-   "id": "e2789dc942e61cc",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "                               value\n",
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-   "cell_type": "code",
-   "source": "df_15m.tail()",
-   "id": "87c603ad74af1dd1",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "                               value\n",
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-       "  </tbody>\n",
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-     },
-     "execution_count": 89,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 89
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T21:52:04.209818Z",
-     "start_time": "2025-11-29T21:52:03.938367Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "df_15m['faiman'] = faiman_temp",
-   "id": "d57cc2a51597298a",
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "Length of values (120) does not match length of index (316)",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mValueError\u001B[39m                                Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[86]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[43mdf_15m\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43mfaiman\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m]\u001B[49m = faiman_temp\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:4322\u001B[39m, in \u001B[36mDataFrame.__setitem__\u001B[39m\u001B[34m(self, key, value)\u001B[39m\n\u001B[32m   4319\u001B[39m     \u001B[38;5;28mself\u001B[39m._setitem_array([key], value)\n\u001B[32m   4320\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m   4321\u001B[39m     \u001B[38;5;66;03m# set column\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m4322\u001B[39m     \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_set_item\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mvalue\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:4535\u001B[39m, in \u001B[36mDataFrame._set_item\u001B[39m\u001B[34m(self, key, value)\u001B[39m\n\u001B[32m   4525\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34m_set_item\u001B[39m(\u001B[38;5;28mself\u001B[39m, key, value) -> \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m   4526\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m   4527\u001B[39m \u001B[33;03m    Add series to DataFrame in specified column.\u001B[39;00m\n\u001B[32m   4528\u001B[39m \n\u001B[32m   (...)\u001B[39m\u001B[32m   4533\u001B[39m \u001B[33;03m    ensure homogeneity.\u001B[39;00m\n\u001B[32m   4534\u001B[39m \u001B[33;03m    \"\"\"\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m4535\u001B[39m     value, refs = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_sanitize_column\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvalue\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   4537\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m (\n\u001B[32m   4538\u001B[39m         key \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m.columns\n\u001B[32m   4539\u001B[39m         \u001B[38;5;129;01mand\u001B[39;00m value.ndim == \u001B[32m1\u001B[39m\n\u001B[32m   4540\u001B[39m         \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(value.dtype, ExtensionDtype)\n\u001B[32m   4541\u001B[39m     ):\n\u001B[32m   4542\u001B[39m         \u001B[38;5;66;03m# broadcast across multiple columns if necessary\u001B[39;00m\n\u001B[32m   4543\u001B[39m         \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m.columns.is_unique \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(\u001B[38;5;28mself\u001B[39m.columns, MultiIndex):\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:5288\u001B[39m, in \u001B[36mDataFrame._sanitize_column\u001B[39m\u001B[34m(self, value)\u001B[39m\n\u001B[32m   5285\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m _reindex_for_setitem(value, \u001B[38;5;28mself\u001B[39m.index)\n\u001B[32m   5287\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m is_list_like(value):\n\u001B[32m-> \u001B[39m\u001B[32m5288\u001B[39m     \u001B[43mcom\u001B[49m\u001B[43m.\u001B[49m\u001B[43mrequire_length_match\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvalue\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mindex\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   5289\u001B[39m arr = sanitize_array(value, \u001B[38;5;28mself\u001B[39m.index, copy=\u001B[38;5;28;01mTrue\u001B[39;00m, allow_2d=\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[32m   5290\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m (\n\u001B[32m   5291\u001B[39m     \u001B[38;5;28misinstance\u001B[39m(value, Index)\n\u001B[32m   5292\u001B[39m     \u001B[38;5;129;01mand\u001B[39;00m value.dtype == \u001B[33m\"\u001B[39m\u001B[33mobject\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m   (...)\u001B[39m\u001B[32m   5295\u001B[39m     \u001B[38;5;66;03m# TODO: Remove kludge in sanitize_array for string mode when enforcing\u001B[39;00m\n\u001B[32m   5296\u001B[39m     \u001B[38;5;66;03m# this deprecation\u001B[39;00m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\common.py:573\u001B[39m, in \u001B[36mrequire_length_match\u001B[39m\u001B[34m(data, index)\u001B[39m\n\u001B[32m    569\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    570\u001B[39m \u001B[33;03mCheck the length of data matches the length of the index.\u001B[39;00m\n\u001B[32m    571\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    572\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(data) != \u001B[38;5;28mlen\u001B[39m(index):\n\u001B[32m--> \u001B[39m\u001B[32m573\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[32m    574\u001B[39m         \u001B[33m\"\u001B[39m\u001B[33mLength of values \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    575\u001B[39m         \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m(\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mlen\u001B[39m(data)\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m) \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    576\u001B[39m         \u001B[33m\"\u001B[39m\u001B[33mdoes not match length of index \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    577\u001B[39m         \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m(\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mlen\u001B[39m(index)\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m)\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    578\u001B[39m     )\n",
-      "\u001B[31mValueError\u001B[39m: Length of values (120) does not match length of index (316)"
-     ]
-    }
-   ],
-   "execution_count": 86
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T21:51:34.085612Z",
-     "start_time": "2025-11-29T21:51:33.854519Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "plt.plot(df_15m.index, df_15m, color = 'red')\n",
-    "plt.plot(df_15m.index, faiman_temp)\n",
-    "plt.xticks(rotation = 90)\n"
-   ],
-   "id": "8737baa73f7c5785",
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "x and y must have same first dimension, but have shapes (316,) and (120,)",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mValueError\u001B[39m                                Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[85]\u001B[39m\u001B[32m, line 2\u001B[39m\n\u001B[32m      1\u001B[39m plt.plot(df_15m.index, df_15m, color = \u001B[33m'\u001B[39m\u001B[33mred\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m----> \u001B[39m\u001B[32m2\u001B[39m \u001B[43mplt\u001B[49m\u001B[43m.\u001B[49m\u001B[43mplot\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdf_15m\u001B[49m\u001B[43m.\u001B[49m\u001B[43mindex\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfaiman_temp\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m      3\u001B[39m plt.xticks(rotation = \u001B[32m90\u001B[39m)\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\matplotlib\\pyplot.py:3838\u001B[39m, in \u001B[36mplot\u001B[39m\u001B[34m(scalex, scaley, data, *args, **kwargs)\u001B[39m\n\u001B[32m   3830\u001B[39m \u001B[38;5;129m@_copy_docstring_and_deprecators\u001B[39m(Axes.plot)\n\u001B[32m   3831\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mplot\u001B[39m(\n\u001B[32m   3832\u001B[39m     *args: \u001B[38;5;28mfloat\u001B[39m | ArrayLike | \u001B[38;5;28mstr\u001B[39m,\n\u001B[32m   (...)\u001B[39m\u001B[32m   3836\u001B[39m     **kwargs,\n\u001B[32m   3837\u001B[39m ) -> \u001B[38;5;28mlist\u001B[39m[Line2D]:\n\u001B[32m-> \u001B[39m\u001B[32m3838\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mgca\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[43mplot\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m   3839\u001B[39m \u001B[43m        \u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   3840\u001B[39m \u001B[43m        \u001B[49m\u001B[43mscalex\u001B[49m\u001B[43m=\u001B[49m\u001B[43mscalex\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   3841\u001B[39m \u001B[43m        \u001B[49m\u001B[43mscaley\u001B[49m\u001B[43m=\u001B[49m\u001B[43mscaley\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   3842\u001B[39m \u001B[43m        \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43m(\u001B[49m\u001B[43m{\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mdata\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m}\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mif\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mis\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mnot\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01melse\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43m{\u001B[49m\u001B[43m}\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   3843\u001B[39m \u001B[43m        \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   3844\u001B[39m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:1777\u001B[39m, in \u001B[36mAxes.plot\u001B[39m\u001B[34m(self, scalex, scaley, data, *args, **kwargs)\u001B[39m\n\u001B[32m   1534\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m   1535\u001B[39m \u001B[33;03mPlot y versus x as lines and/or markers.\u001B[39;00m\n\u001B[32m   1536\u001B[39m \n\u001B[32m   (...)\u001B[39m\u001B[32m   1774\u001B[39m \u001B[33;03m(``'green'``) or hex strings (``'#008000'``).\u001B[39;00m\n\u001B[32m   1775\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m   1776\u001B[39m kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D)\n\u001B[32m-> \u001B[39m\u001B[32m1777\u001B[39m lines = [*\u001B[38;5;28mself\u001B[39m._get_lines(\u001B[38;5;28mself\u001B[39m, *args, data=data, **kwargs)]\n\u001B[32m   1778\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m line \u001B[38;5;129;01min\u001B[39;00m lines:\n\u001B[32m   1779\u001B[39m     \u001B[38;5;28mself\u001B[39m.add_line(line)\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\matplotlib\\axes\\_base.py:297\u001B[39m, in \u001B[36m_process_plot_var_args.__call__\u001B[39m\u001B[34m(self, axes, data, return_kwargs, *args, **kwargs)\u001B[39m\n\u001B[32m    295\u001B[39m     this += args[\u001B[32m0\u001B[39m],\n\u001B[32m    296\u001B[39m     args = args[\u001B[32m1\u001B[39m:]\n\u001B[32m--> \u001B[39m\u001B[32m297\u001B[39m \u001B[38;5;28;01myield from\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_plot_args\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    298\u001B[39m \u001B[43m    \u001B[49m\u001B[43maxes\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mthis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mambiguous_fmt_datakey\u001B[49m\u001B[43m=\u001B[49m\u001B[43mambiguous_fmt_datakey\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    299\u001B[39m \u001B[43m    \u001B[49m\u001B[43mreturn_kwargs\u001B[49m\u001B[43m=\u001B[49m\u001B[43mreturn_kwargs\u001B[49m\n\u001B[32m    300\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\matplotlib\\axes\\_base.py:494\u001B[39m, in \u001B[36m_process_plot_var_args._plot_args\u001B[39m\u001B[34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001B[39m\n\u001B[32m    491\u001B[39m     axes.yaxis.update_units(y)\n\u001B[32m    493\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m x.shape[\u001B[32m0\u001B[39m] != y.shape[\u001B[32m0\u001B[39m]:\n\u001B[32m--> \u001B[39m\u001B[32m494\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mx and y must have same first dimension, but \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    495\u001B[39m                      \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mhave shapes \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mx.shape\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m and \u001B[39m\u001B[38;5;132;01m{\u001B[39;00my.shape\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m\"\u001B[39m)\n\u001B[32m    496\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m x.ndim > \u001B[32m2\u001B[39m \u001B[38;5;129;01mor\u001B[39;00m y.ndim > \u001B[32m2\u001B[39m:\n\u001B[32m    497\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mx and y can be no greater than 2D, but have \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    498\u001B[39m                      \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mshapes \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mx.shape\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m and \u001B[39m\u001B[38;5;132;01m{\u001B[39;00my.shape\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m\"\u001B[39m)\n",
-      "\u001B[31mValueError\u001B[39m: x and y must have same first dimension, but have shapes (316,) and (120,)"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 85
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T19:17:19.791409Z",
-     "start_time": "2025-11-29T19:17:19.683835Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "",
-   "id": "ade5efa1f336b655",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x27b9deece00>]"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 23
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
-   "source": "#",
-   "id": "1aa707313aeb5993"
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From badaeec03d9c58d1bb4e03283bfd8d562bc6954b Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Tue, 13 Jan 2026 10:48:18 -0800
Subject: [PATCH 09/49] cleaning

---
 array_temp/.cache.sqlite             |  Bin 40960 -> 40960 bytes
 array_temp/faiman_coefficients.ipynb | 1870 +++++++++++++++++++-------
 2 files changed, 1398 insertions(+), 472 deletions(-)

diff --git a/array_temp/.cache.sqlite b/array_temp/.cache.sqlite
index be53ebb1f1e66e5045c1af19d58ab7927122f66c..9e98d22ae911df5cc08dfeae6871d35c6d9442c0 100644
GIT binary patch
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diff --git a/array_temp/faiman_coefficients.ipynb b/array_temp/faiman_coefficients.ipynb
index 5a8a122..011a904 100644
--- a/array_temp/faiman_coefficients.ipynb
+++ b/array_temp/faiman_coefficients.ipynb
@@ -1,42 +1,5 @@
 {
  "cells": [
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-07T03:45:51.135307Z",
-     "start_time": "2026-01-07T03:45:46.244586Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "import matplotlib\n",
-    "\n",
-    "\n",
-    "from data_tools import query\n",
-    "from data_tools.collections import TimeSeries\n",
-    "from datetime import datetime, date, time, timezone\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "import dill\n",
-    "import os\n",
-    "\n",
-    "#each 5 seconds\n",
-    "utc_offset_h = 2\n",
-    "start_utc = time(5 , 00, 00) #querying i svancouver time, influxdb gives utc\n",
-    "stop_utc = time(4, 45, 40)\n",
-    "date_start = date(2025, 6, 30)\n",
-    "date_stop = date(2025, 7, 2)\n",
-    "start_time = datetime.combine(date_start, start_utc, tzinfo=timezone.utc)\n",
-    "stop_time = datetime.combine(date_stop, stop_utc, tzinfo=timezone.utc)\n",
-    "\n",
-    "client = query.DBClient()\n",
-    "temp_array_expected_aliter: TimeSeries = client.query_time_series(start_time, stop_time, \"MosfetTemperatureA\")\n"
-   ],
-   "id": "7762d7c5f54c9e9f",
-   "outputs": [],
-   "execution_count": 1
-  },
   {
    "metadata": {},
    "cell_type": "markdown",
@@ -47,108 +10,17 @@
    ],
    "id": "156948c3c78f7e94"
   },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
-   "source": "",
-   "id": "d87b11cfda38ce57"
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-07T03:45:59.783494Z",
-     "start_time": "2026-01-07T03:45:54.175805Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "#alternate data:\n",
-    "\n",
-    "from data_tools import query\n",
-    "from data_tools.collections import TimeSeries\n",
-    "from datetime import datetime, date, time, timezone\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "import dill\n",
-    "import os\n",
-    "\n",
-    "#each 5 seconds\n",
-    "utc_offset_h = 2\n",
-    "start_utc = time(5 + utc_offset_h , 00, 00) #querying i svancouver time, influxdb gives utc\n",
-    "stop_utc = time(4 +  utc_offset_h, 45, 40)\n",
-    "date_start = date(2025, 7, 1)\n",
-    "date_stop = date(2025, 7, 2)\n",
-    "start_time = datetime.combine(date_start, start_utc, tzinfo=timezone.utc)\n",
-    "stop_time = datetime.combine(date_stop, stop_utc, tzinfo=timezone.utc)\n",
-    "\n",
-    "client = query.DBClient()\n",
-    "temp_array_expected_aliter: TimeSeries = client.query_time_series(start_time, stop_time, \"MosfetTemperatureA\")\n",
-    "speed_kph: TimeSeries = client.query_time_series(start_time, stop_time, \"MotorRotatingSpeed\")\n",
-    "\n",
-    "print(temp_array_expected_aliter)\n",
-    "#this is almost steady state, and expected temperature is roughly 31.8 degrees celsius. note that individual cells do have an upto 5 C difference, which is actually quite a lot.\n",
-    "\n",
-    "\n"
-   ],
-   "id": "a8ea6762121b1311",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[27.46449661 27.46548943 27.46648225 ... 27.29558551 27.29614538\n",
-      " 27.29670525]\n"
-     ]
-    }
-   ],
-   "execution_count": 2
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-07T03:49:25.894585Z",
-     "start_time": "2026-01-07T03:49:25.872871Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "#save collected data of 15 minutes\n",
-    "\n",
-    "import os\n",
-    "import dill\n",
-    "\n",
-    "out_dir = os.path.join(\"../../motor_analysis\", \"data\", \"array_temperature_aliter_2025-07-01\")\n",
-    "mosfetA_file_aliter = os.path.join(out_dir, \"mosfetA_aliter.bin\")\n",
-    "\n",
-    "os.makedirs(out_dir, exist_ok=True)\n",
-    "\n",
-    "for filepath, data in zip([mosfetA_file_aliter],\n",
-    "                          [temp_array_expected_aliter]):\n",
-    "    with open(filepath, 'wb') as f:\n",
-    "        dill.dump(data, f)\n",
-    "\n",
-    "\n",
-    "\n",
-    "#time zone matching conventions??"
-   ],
-   "id": "9b566c367639c5e5",
-   "outputs": [],
-   "execution_count": 3
-  },
   {
    "metadata": {},
    "cell_type": "markdown",
-   "source": "alternate data: data over the first 4 days of fsgp 2025",
+   "source": "From InfluxDB, query data over the first 4 days of FSGP 2025.",
    "id": "b59eb1d3085bfd98"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-07T03:49:39.779626Z",
-     "start_time": "2026-01-07T03:49:27.143416Z"
+     "end_time": "2026-01-13T18:11:41.455198Z",
+     "start_time": "2026-01-13T18:11:32.257953Z"
     }
    },
    "cell_type": "code",
@@ -163,32 +35,27 @@
     "import os\n",
     "\n",
     "#each 5 seconds\n",
-    "utc_offset_h = 2\n",
-    "start_utc = time(22 , 00, 00) #querying i svancouver time, influxdb gives utc\n",
-    "stop_utc = time(20, 45, 00)\n",
-    "date_start = date(2025, 7, 1)\n",
+    "utc_offset_h = 7\n",
+    "start_utc = time(12 , 00, 00) #querying is vancouver time, influxdb gives utc\n",
+    "stop_utc = time(00, 00, 00)\n",
+    "date_start = date(2025, 7, 2)\n",
     "date_stop = date(2025, 7, 6)\n",
     "start_time = datetime.combine(date_start, start_utc, tzinfo=timezone.utc)\n",
     "stop_time = datetime.combine(date_stop, stop_utc, tzinfo=timezone.utc)\n",
     "\n",
     "client = query.DBClient()\n",
     "temp_array_fsgp: TimeSeries = client.query_time_series(start_time, stop_time, field=  \"MosfetTemperatureA\")\n",
-    "speed_kph: TimeSeries = client.query_time_series(start_time, stop_time, \"MotorRotatingSpeed\")\n",
-    "\n",
-    "#print(temp_array_expected_aliter)\n",
-    "#this is almost steady state, and expected temperature is roughly 31.8 degrees celsius. note that individual cells do have an upto 5 C difference, which is actually quite a lot.\n",
-    "\n",
-    "\n"
+    "speed_kph: TimeSeries = client.query_time_series(start_time, stop_time, \"MotorRotatingSpeed\")\n"
    ],
    "id": "8b2b4006671bdc31",
    "outputs": [],
-   "execution_count": 4
+   "execution_count": 8
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-07T03:49:55.294234Z",
-     "start_time": "2026-01-07T03:49:55.277751Z"
+     "end_time": "2026-01-13T18:11:42.314915Z",
+     "start_time": "2026-01-13T18:11:42.283529Z"
     }
    },
    "cell_type": "code",
@@ -198,13 +65,13 @@
     "import os\n",
     "import dill\n",
     "\n",
-    "out_dir = os.path.join(\"../../motor_analysis\", \"data\", \"array_temperature_aliter_2025-07-06\")\n",
+    "out_dir = os.path.join(\"../../motor_analysis\", \"data\", \"array_temperature_fsgp_2025-07-06\")\n",
     "mosfetA_file_aliter = os.path.join(out_dir, \"mosfetA_aliter.bin\")\n",
     "\n",
     "os.makedirs(out_dir, exist_ok=True)\n",
     "\n",
     "for filepath, data in zip([mosfetA_file_aliter],\n",
-    "                          [temp_array_expected_aliter]):\n",
+    "                          [temp_array_fsgp]):\n",
     "    with open(filepath, 'wb') as f:\n",
     "        dill.dump(data, f)\n",
     "\n",
@@ -214,13 +81,13 @@
    ],
    "id": "a807d5de05d7c8b0",
    "outputs": [],
-   "execution_count": 5
+   "execution_count": 9
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-07T03:49:56.326119Z",
-     "start_time": "2026-01-07T03:49:56.315594Z"
+     "end_time": "2026-01-13T18:11:43.242897Z",
+     "start_time": "2026-01-13T18:11:43.232987Z"
     }
    },
    "cell_type": "code",
@@ -233,74 +100,28 @@
        "data_tools.collections.time_series.TimeSeries"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 6
+   "execution_count": 10
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-07T03:49:57.060265Z",
-     "start_time": "2026-01-07T03:49:56.982212Z"
+     "end_time": "2026-01-13T18:11:43.971080Z",
+     "start_time": "2026-01-13T18:11:43.962961Z"
     }
    },
    "cell_type": "code",
-   "source": "df_fsgp = pd.DataFrame(temp_array_fsgp)",
+   "source": [
+    "#convert to data frame\n",
+    "df_fsgp = pd.DataFrame(temp_array_fsgp)"
+   ],
    "id": "1300423cd026c3ca",
    "outputs": [],
-   "execution_count": 7
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-07T03:49:57.638445Z",
-     "start_time": "2026-01-07T03:49:57.628343Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "len(df_fsgp)",
-   "id": "f46c881335aa4926",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3422130"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 8
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-07T03:49:58.441874Z",
-     "start_time": "2026-01-07T03:49:58.099405Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "len(hourly_data['shortwave_radiation_instant'])",
-   "id": "bbac7524849da8b0",
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'hourly_data' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
-      "Cell \u001B[1;32mIn[9], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[38;5;28mlen\u001B[39m(\u001B[43mhourly_data\u001B[49m[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mshortwave_radiation_instant\u001B[39m\u001B[38;5;124m'\u001B[39m])\n",
-      "\u001B[1;31mNameError\u001B[0m: name 'hourly_data' is not defined"
-     ]
-    }
-   ],
-   "execution_count": 9
+   "execution_count": 11
   },
   {
    "metadata": {},
@@ -315,8 +136,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-07T03:50:03.609627Z",
-     "start_time": "2026-01-07T03:50:00.071858Z"
+     "end_time": "2026-01-13T18:11:45.163385Z",
+     "start_time": "2026-01-13T18:11:45.142755Z"
     }
    },
    "cell_type": "code",
@@ -382,17 +203,17 @@
       "\n",
       "Minutely15 data\n",
       "                          date  temperature_2m  shortwave_radiation_instant  \\\n",
-      "0   2025-07-02 00:00:00+00:00           -2.20                     5.588703   \n",
-      "1   2025-07-02 00:15:00+00:00           -2.15                     5.351785   \n",
-      "2   2025-07-02 00:30:00+00:00           -2.15                     5.014219   \n",
-      "3   2025-07-02 00:45:00+00:00           -2.05                     4.680000   \n",
-      "4   2025-07-02 01:00:00+00:00           -1.95                     4.680000   \n",
+      "0   2025-07-02 00:00:00+00:00          -2.213                     5.588703   \n",
+      "1   2025-07-02 00:15:00+00:00          -2.163                     5.351785   \n",
+      "2   2025-07-02 00:30:00+00:00          -2.113                     5.014219   \n",
+      "3   2025-07-02 00:45:00+00:00          -2.063                     4.680000   \n",
+      "4   2025-07-02 01:00:00+00:00          -1.963                     4.680000   \n",
       "..                        ...             ...                          ...   \n",
-      "475 2025-07-06 22:45:00+00:00           -1.25                    20.240196   \n",
-      "476 2025-07-06 23:00:00+00:00           -1.05                    19.881649   \n",
-      "477 2025-07-06 23:15:00+00:00           -0.95                    19.917469   \n",
-      "478 2025-07-06 23:30:00+00:00           -0.90                    19.959719   \n",
-      "479 2025-07-06 23:45:00+00:00           -0.80                    20.418695   \n",
+      "475 2025-07-06 22:45:00+00:00          -1.263                    20.240196   \n",
+      "476 2025-07-06 23:00:00+00:00          -1.063                    19.881649   \n",
+      "477 2025-07-06 23:15:00+00:00          -0.963                    19.917469   \n",
+      "478 2025-07-06 23:30:00+00:00          -0.863                    19.959719   \n",
+      "479 2025-07-06 23:45:00+00:00          -0.813                    20.418695   \n",
       "\n",
       "     wind_speed_10m  \n",
       "0        124.831741  \n",
@@ -411,7 +232,7 @@
      ]
     }
    ],
-   "execution_count": 10
+   "execution_count": 12
   },
   {
    "metadata": {},
@@ -434,8 +255,37 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-07T03:50:09.857859Z",
-     "start_time": "2026-01-07T03:50:07.871779Z"
+     "end_time": "2026-01-13T18:17:08.973617Z",
+     "start_time": "2026-01-13T18:17:07.792338Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "plt.plot(temp_array_fsgp.datetime_x_axis, temp_array_fsgp, label = \"Array Temperature\")\n",
+    "plt.ylabel(\"MosfetTemperatureA\")\n",
+    "\n",
+    "plt.tick_params(\"x\", rotation  = 90)"
+   ],
+   "id": "9cde1a3b5eb68331",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 15
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-13T18:11:54.970378Z",
+     "start_time": "2026-01-13T18:11:53.798881Z"
     }
    },
    "cell_type": "code",
@@ -447,9 +297,6 @@
     "\n",
     "plt.plot(temp_array_fsgp.datetime_x_axis, temp_array_fsgp, label = \"Array Temperature\")\n",
     "plt.plot(minutely_15_data['date'],minutely_15_data['temperature_2m'], color = 'green', label = \"Ambient Temperature\")\n",
-    "# plt.plot(minutely_15_data['date'], minutely_15_data['wind_speed_10m'], color = 'blue', label = \"Wind Speed 10m\")\n",
-    "# plt.plot(speed_kph.datetime_x_axis, speed_kph, color = 'yellow', label = \"Speed KPH\")\n",
-    "\n",
     "ax1.plot(minutely_15_data['date'],minutely_15_data['shortwave_radiation_instant'], color = \"red\", label = \"Solar Irradiance\")\n",
     "\n",
     "ax1.set_xlabel(\"Time\")\n",
@@ -470,172 +317,1190 @@
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ],
-      "image/png": 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"
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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 13
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "#there really isn't Influx data for dates after midday 5 July, so I see no reason to incorporate the OpenMeteo data after",
+   "id": "90465ecf61541d72"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-13T18:42:54.102845Z",
+     "start_time": "2026-01-13T18:42:54.097885Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "minutely_15_dataframe.head()\n",
+    "df_15min = minutely_15_dataframe[minutely_15_dataframe['date']<'2025-07-06']"
+   ],
+   "id": "a30bec6abef1672a",
+   "outputs": [],
+   "execution_count": 56
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-13T18:42:55.861090Z",
+     "start_time": "2026-01-13T18:42:55.853919Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "df_15min.tail()\n",
+    "weather_df = df_15min.copy()\n",
+    "\n",
+    "# Set datetime as index\n",
+    "weather_df[\"date\"] = pd.to_datetime(weather_df[\"date\"], utc=True)\n",
+    "weather_df = weather_df.set_index(\"date\")\n",
+    "weather_df = weather_df.sort_index()\n"
+   ],
+   "id": "feced1770070bc8b",
+   "outputs": [],
+   "execution_count": 57
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-13T18:43:01.541534Z",
+     "start_time": "2026-01-13T18:43:01.531001Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "weather_df.head()",
+   "id": "54a41be29ba47ed9",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                           temperature_2m  shortwave_radiation_instant  \\\n",
+       "date                                                                     \n",
+       "2025-07-02 00:00:00+00:00          -2.213                     5.588703   \n",
+       "2025-07-02 00:15:00+00:00          -2.163                     5.351785   \n",
+       "2025-07-02 00:30:00+00:00          -2.113                     5.014219   \n",
+       "2025-07-02 00:45:00+00:00          -2.063                     4.680000   \n",
+       "2025-07-02 01:00:00+00:00          -1.963                     4.680000   \n",
+       "\n",
+       "                           wind_speed_10m  \n",
+       "date                                       \n",
+       "2025-07-02 00:00:00+00:00      124.831741  \n",
+       "2025-07-02 00:15:00+00:00      164.539490  \n",
+       "2025-07-02 00:30:00+00:00      205.015503  \n",
+       "2025-07-02 00:45:00+00:00      243.104050  \n",
+       "2025-07-02 01:00:00+00:00      276.808258  "
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>temperature_2m</th>\n",
+       "      <th>shortwave_radiation_instant</th>\n",
+       "      <th>wind_speed_10m</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2025-07-02 00:00:00+00:00</th>\n",
+       "      <td>-2.213</td>\n",
+       "      <td>5.588703</td>\n",
+       "      <td>124.831741</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-02 00:15:00+00:00</th>\n",
+       "      <td>-2.163</td>\n",
+       "      <td>5.351785</td>\n",
+       "      <td>164.539490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-02 00:30:00+00:00</th>\n",
+       "      <td>-2.113</td>\n",
+       "      <td>5.014219</td>\n",
+       "      <td>205.015503</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-02 00:45:00+00:00</th>\n",
+       "      <td>-2.063</td>\n",
+       "      <td>4.680000</td>\n",
+       "      <td>243.104050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-02 01:00:00+00:00</th>\n",
+       "      <td>-1.963</td>\n",
+       "      <td>4.680000</td>\n",
+       "      <td>276.808258</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 58
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-13T18:43:10.581049Z",
+     "start_time": "2026-01-13T18:43:10.572938Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "print(\"Influx 15min index:\")\n",
+    "print(df_15m.index[:5])\n",
+    "print(df_15m.index[-5:])\n",
+    "\n",
+    "print(\"Weather index:\")\n",
+    "print(weather_df.index[:5])\n",
+    "print(weather_df[-5:])\n"
+   ],
+   "id": "9e3eeb777b7fb7c7",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Influx 15min index:\n",
+      "DatetimeIndex(['2025-07-02 07:15:00+00:00', '2025-07-02 07:30:00+00:00',\n",
+      "               '2025-07-02 07:45:00+00:00', '2025-07-02 08:00:00+00:00',\n",
+      "               '2025-07-02 08:15:00+00:00'],\n",
+      "              dtype='datetime64[ns, UTC]', freq=None)\n",
+      "DatetimeIndex(['2025-07-05 13:00:00+00:00', '2025-07-05 13:15:00+00:00',\n",
+      "               '2025-07-05 13:30:00+00:00', '2025-07-05 13:45:00+00:00',\n",
+      "               '2025-07-05 14:00:00+00:00'],\n",
+      "              dtype='datetime64[ns, UTC]', freq=None)\n",
+      "Weather index:\n",
+      "DatetimeIndex(['2025-07-02 00:00:00+00:00', '2025-07-02 00:15:00+00:00',\n",
+      "               '2025-07-02 00:30:00+00:00', '2025-07-02 00:45:00+00:00',\n",
+      "               '2025-07-02 01:00:00+00:00'],\n",
+      "              dtype='datetime64[ns, UTC]', name='date', freq=None)\n",
+      "                           temperature_2m  shortwave_radiation_instant  \\\n",
+      "date                                                                     \n",
+      "2025-07-05 22:45:00+00:00          -2.013                     8.496305   \n",
+      "2025-07-05 23:00:00+00:00          -1.963                     8.788720   \n",
+      "2025-07-05 23:15:00+00:00          -1.963                     9.085988   \n",
+      "2025-07-05 23:30:00+00:00          -1.913                     9.199390   \n",
+      "2025-07-05 23:45:00+00:00          -1.913                     9.021574   \n",
+      "\n",
+      "                           wind_speed_10m  \n",
+      "date                                       \n",
+      "2025-07-05 22:45:00+00:00        0.000000  \n",
+      "2025-07-05 23:00:00+00:00        0.000000  \n",
+      "2025-07-05 23:15:00+00:00       19.759171  \n",
+      "2025-07-05 23:30:00+00:00       51.912174  \n",
+      "2025-07-05 23:45:00+00:00       86.610596  \n"
+     ]
+    }
+   ],
+   "execution_count": 59
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-13T18:43:13.139726Z",
+     "start_time": "2026-01-13T18:43:13.128886Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Make sure indexes are sorted\n",
+    "df_15m_copy = df_15m.copy().sort_index()\n",
+    "weather_df = weather_df.sort_index()\n",
+    "\n",
+    "# Force both onto exact 15-minute grid\n",
+    "df_15m_copy.index = df_15m_copy.index.floor(\"15T\")\n",
+    "weather_df.index = weather_df.index.floor(\"15T\")\n",
+    "\n",
+    "# Keep only overlapping timestamps\n",
+    "common_index = df_15m_copy.index.intersection(weather_df.index)\n",
+    "\n",
+    "print(\"Overlap start:\", common_index.min())\n",
+    "print(\"Overlap end:\", common_index.max())\n",
+    "print(\"Number of aligned points:\", len(common_index))\n",
+    "\n",
+    "# Merge on common timestamps\n",
+    "merged_df = pd.concat(\n",
+    "    [df_15m_copy.loc[common_index], weather_df.loc[common_index]],\n",
+    "    axis=1\n",
+    ")\n"
+   ],
+   "id": "8d042560a87a9f85",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overlap start: 2025-07-02 07:15:00+00:00\n",
+      "Overlap end: 2025-07-05 14:00:00+00:00\n",
+      "Number of aligned points: 316\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_14188\\439453639.py:6: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
+      "  df_15m_copy.index = df_15m_copy.index.floor(\"15T\")\n",
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_14188\\439453639.py:7: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
+      "  weather_df.index = weather_df.index.floor(\"15T\")\n"
+     ]
+    }
+   ],
+   "execution_count": 60
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-13T18:43:15.243825Z",
+     "start_time": "2026-01-13T18:43:15.234987Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "merged_df.head()",
+   "id": "783fdc2283d6ff2",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                               value  temperature_2m  \\\n",
+       "2025-07-02 07:15:00+00:00  37.371079           1.787   \n",
+       "2025-07-02 07:30:00+00:00  46.801625           1.487   \n",
+       "2025-07-02 07:45:00+00:00  29.980004           1.137   \n",
+       "2025-07-02 08:00:00+00:00  45.573409           0.887   \n",
+       "2025-07-02 08:15:00+00:00  55.745558           0.687   \n",
+       "\n",
+       "                           shortwave_radiation_instant  wind_speed_10m  \n",
+       "2025-07-02 07:15:00+00:00                    23.177401      522.027100  \n",
+       "2025-07-02 07:30:00+00:00                    22.406927      513.199951  \n",
+       "2025-07-02 07:45:00+00:00                    21.945240      500.415741  \n",
+       "2025-07-02 08:00:00+00:00                    21.817902      484.679749  \n",
+       "2025-07-02 08:15:00+00:00                    21.767351      465.017517  "
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "      <th>temperature_2m</th>\n",
+       "      <th>shortwave_radiation_instant</th>\n",
+       "      <th>wind_speed_10m</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2025-07-02 07:15:00+00:00</th>\n",
+       "      <td>37.371079</td>\n",
+       "      <td>1.787</td>\n",
+       "      <td>23.177401</td>\n",
+       "      <td>522.027100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-02 07:30:00+00:00</th>\n",
+       "      <td>46.801625</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-02 07:45:00+00:00</th>\n",
+       "      <td>29.980004</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-02 08:00:00+00:00</th>\n",
+       "      <td>45.573409</td>\n",
+       "      <td>0.887</td>\n",
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+       "      <td>484.679749</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-02 08:15:00+00:00</th>\n",
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+       "      <td>465.017517</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 61
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-13T18:43:39.262303Z",
+     "start_time": "2026-01-13T18:43:39.252536Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "merged_df.dropna()",
+   "id": "8b0082de9f7b7352",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                               value  temperature_2m  \\\n",
+       "2025-07-02 07:15:00+00:00  37.371079           1.787   \n",
+       "2025-07-02 07:30:00+00:00  46.801625           1.487   \n",
+       "2025-07-02 07:45:00+00:00  29.980004           1.137   \n",
+       "2025-07-02 08:00:00+00:00  45.573409           0.887   \n",
+       "2025-07-02 08:15:00+00:00  55.745558           0.687   \n",
+       "...                              ...             ...   \n",
+       "2025-07-05 13:00:00+00:00  38.086209           1.137   \n",
+       "2025-07-05 13:15:00+00:00  36.774658           0.887   \n",
+       "2025-07-05 13:30:00+00:00  35.463106           0.637   \n",
+       "2025-07-05 13:45:00+00:00  34.271664           0.437   \n",
+       "2025-07-05 14:00:00+00:00  34.174615           0.237   \n",
+       "\n",
+       "                           shortwave_radiation_instant  wind_speed_10m  \n",
+       "2025-07-02 07:15:00+00:00                    23.177401      522.027100  \n",
+       "2025-07-02 07:30:00+00:00                    22.406927      513.199951  \n",
+       "2025-07-02 07:45:00+00:00                    21.945240      500.415741  \n",
+       "2025-07-02 08:00:00+00:00                    21.817902      484.679749  \n",
+       "2025-07-02 08:15:00+00:00                    21.767351      465.017517  \n",
+       "...                                                ...             ...  \n",
+       "2025-07-05 13:00:00+00:00                    29.548521       58.205017  \n",
+       "2025-07-05 13:15:00+00:00                    27.475807       22.902834  \n",
+       "2025-07-05 13:30:00+00:00                    25.202570        2.313184  \n",
+       "2025-07-05 13:45:00+00:00                    22.461807        0.000000  \n",
+       "2025-07-05 14:00:00+00:00                    20.056877        0.000000  \n",
+       "\n",
+       "[316 rows x 4 columns]"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "      <th>temperature_2m</th>\n",
+       "      <th>shortwave_radiation_instant</th>\n",
+       "      <th>wind_speed_10m</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2025-07-02 07:15:00+00:00</th>\n",
+       "      <td>37.371079</td>\n",
+       "      <td>1.787</td>\n",
+       "      <td>23.177401</td>\n",
+       "      <td>522.027100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-02 07:30:00+00:00</th>\n",
+       "      <td>46.801625</td>\n",
+       "      <td>1.487</td>\n",
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+       "      <td>513.199951</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-02 07:45:00+00:00</th>\n",
+       "      <td>29.980004</td>\n",
+       "      <td>1.137</td>\n",
+       "      <td>21.945240</td>\n",
+       "      <td>500.415741</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-02 08:00:00+00:00</th>\n",
+       "      <td>45.573409</td>\n",
+       "      <td>0.887</td>\n",
+       "      <td>21.817902</td>\n",
+       "      <td>484.679749</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-02 08:15:00+00:00</th>\n",
+       "      <td>55.745558</td>\n",
+       "      <td>0.687</td>\n",
+       "      <td>21.767351</td>\n",
+       "      <td>465.017517</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-05 13:00:00+00:00</th>\n",
+       "      <td>38.086209</td>\n",
+       "      <td>1.137</td>\n",
+       "      <td>29.548521</td>\n",
+       "      <td>58.205017</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-05 13:15:00+00:00</th>\n",
+       "      <td>36.774658</td>\n",
+       "      <td>0.887</td>\n",
+       "      <td>27.475807</td>\n",
+       "      <td>22.902834</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-05 13:30:00+00:00</th>\n",
+       "      <td>35.463106</td>\n",
+       "      <td>0.637</td>\n",
+       "      <td>25.202570</td>\n",
+       "      <td>2.313184</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-05 13:45:00+00:00</th>\n",
+       "      <td>34.271664</td>\n",
+       "      <td>0.437</td>\n",
+       "      <td>22.461807</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-05 14:00:00+00:00</th>\n",
+       "      <td>34.174615</td>\n",
+       "      <td>0.237</td>\n",
+       "      <td>20.056877</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>316 rows × 4 columns</p>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 62
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-13T18:47:08.107202Z",
+     "start_time": "2026-01-13T18:47:08.102816Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "merged_df = merged_df.rename(columns = {'value':'array_temperature'})",
+   "id": "e0882e697c1613f7",
+   "outputs": [],
+   "execution_count": 70
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-13T18:47:10.162618Z",
+     "start_time": "2026-01-13T18:47:10.154103Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "merged_df.head()",
+   "id": "3d1f52f37c17fa80",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                           array_temperature  temperature_2m  \\\n",
+       "2025-07-02 07:15:00+00:00          37.371079           1.787   \n",
+       "2025-07-02 07:30:00+00:00          46.801625           1.487   \n",
+       "2025-07-02 07:45:00+00:00          29.980004           1.137   \n",
+       "2025-07-02 08:00:00+00:00          45.573409           0.887   \n",
+       "2025-07-02 08:15:00+00:00          55.745558           0.687   \n",
+       "\n",
+       "                           shortwave_radiation_instant  wind_speed_10m  \n",
+       "2025-07-02 07:15:00+00:00                    23.177401      522.027100  \n",
+       "2025-07-02 07:30:00+00:00                    22.406927      513.199951  \n",
+       "2025-07-02 07:45:00+00:00                    21.945240      500.415741  \n",
+       "2025-07-02 08:00:00+00:00                    21.817902      484.679749  \n",
+       "2025-07-02 08:15:00+00:00                    21.767351      465.017517  "
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>array_temperature</th>\n",
+       "      <th>temperature_2m</th>\n",
+       "      <th>shortwave_radiation_instant</th>\n",
+       "      <th>wind_speed_10m</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2025-07-02 07:15:00+00:00</th>\n",
+       "      <td>37.371079</td>\n",
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+       "      <td>522.027100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-02 07:30:00+00:00</th>\n",
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+       "      <th>2025-07-02 07:45:00+00:00</th>\n",
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+       "      <td>500.415741</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-02 08:00:00+00:00</th>\n",
+       "      <td>45.573409</td>\n",
+       "      <td>0.887</td>\n",
+       "      <td>21.817902</td>\n",
+       "      <td>484.679749</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-07-02 08:15:00+00:00</th>\n",
+       "      <td>55.745558</td>\n",
+       "      <td>0.687</td>\n",
+       "      <td>21.767351</td>\n",
+       "      <td>465.017517</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 71
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-13T18:47:31.441983Z",
+     "start_time": "2026-01-13T18:47:31.322607Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "#this graph shows solar irradiance, array temperature, and ambient temperature over July 2-6 (FSGP 2025)\n",
+    "\n",
+    "\n",
+    "\n",
+    "plt.plot(merged_df, merged_df['array_temperature'], label = \"Array Temperature\")\n",
+    "plt.plot(merged_df,merged_df['temperature_2m'], color = 'green', label = \"Ambient Temperature\")\n",
+    "plt.plot(merged_df,merged_df['shortwave_radiation_instant'], color = \"red\", label = \"Solar Irradiance\")\n",
+    "\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"Solar Irradiance\")\n",
+    "ax_twin.set_ylabel(\"MosfetTemperatureA\")\n",
+    "\n",
+    "ax1.tick_params(\"x\", rotation  = 90)\n",
+    "\n",
+    "\n",
+    "plt.legend(loc = \"upper left\")\n",
+    "ax1.legend(loc = \"upper left\")\n",
+    "plt.show()"
+   ],
+   "id": "1624b6be9409b4fe",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 73
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "#for easier access, im going to convert everything to a data frame after resampling.",
+   "id": "ddf6f6b4bafc6f88"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-13T18:31:48.434426Z",
+     "start_time": "2026-01-13T18:31:47.531595Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "#resampling influx data so that i can fit it with irradiance data (queried every 15 minutes)\n",
+    "\n",
+    "ts = temp_array_fsgp\n",
+    "timestamps = pd.to_datetime(ts.datetime_x_axis, utc=True)\n",
+    "values = ts.data\n",
+    "\n",
+    "df = pd.DataFrame({\"value\": values}, index=timestamps)\n",
+    "df_15m = df.resample(\"15T\").mean()\n",
+    "\n",
+    "print(df_15m.head())\n"
+   ],
+   "id": "203be30192c9302a",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                               value\n",
+      "2025-07-02 07:15:00+00:00  37.371079\n",
+      "2025-07-02 07:30:00+00:00  46.801625\n",
+      "2025-07-02 07:45:00+00:00  29.980004\n",
+      "2025-07-02 08:00:00+00:00  45.573409\n",
+      "2025-07-02 08:15:00+00:00  55.745558\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_14188\\1903960078.py:10: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
+      "  df_15m = df.resample(\"15T\").mean()\n"
+     ]
+    }
+   ],
+   "execution_count": 38
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-13T18:32:05.414754Z",
+     "start_time": "2026-01-13T18:32:05.405747Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "merged_df = df_15m.join(df_15min, how=\"inner\")\n",
+   "id": "d6a34dc28db1adc1",
+   "outputs": [],
+   "execution_count": 39
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-13T18:32:13.384358Z",
+     "start_time": "2026-01-13T18:32:13.377851Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "merged_df.head()",
+   "id": "15fc332acae2aa61",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [value, date, temperature_2m, shortwave_radiation_instant, wind_speed_10m, array_temperature]\n",
+       "Index: []"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "      <th>date</th>\n",
+       "      <th>temperature_2m</th>\n",
+       "      <th>shortwave_radiation_instant</th>\n",
+       "      <th>wind_speed_10m</th>\n",
+       "      <th>array_temperature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
      },
+     "execution_count": 40,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
-   "execution_count": 11
+   "execution_count": 40
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-11-29T21:43:20.307344Z",
-     "start_time": "2025-11-29T21:43:20.296669Z"
+     "end_time": "2026-01-13T18:27:49.490562Z",
+     "start_time": "2026-01-13T18:27:49.484557Z"
     }
    },
    "cell_type": "code",
-   "source": "print(dir(temp_array_fsgp))\n",
-   "id": "d577cfa934535b4f",
+   "source": "len(df_15m)",
+   "id": "fe74f6c4ed193e41",
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['T', '__abs__', '__add__', '__and__', '__array__', '__array_finalize__', '__array_function__', '__array_interface__', '__array_namespace__', '__array_priority__', '__array_struct__', '__array_ufunc__', '__array_wrap__', '__bool__', '__buffer__', '__class__', '__class_getitem__', '__complex__', '__contains__', '__copy__', '__deepcopy__', '__delattr__', '__delitem__', '__dict__', '__dir__', '__divmod__', '__dlpack__', '__dlpack_device__', '__doc__', '__eq__', '__float__', '__floordiv__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__getstate__', '__gt__', '__hash__', '__iadd__', '__iand__', '__ifloordiv__', '__ilshift__', '__imatmul__', '__imod__', '__imul__', '__index__', '__init__', '__init_subclass__', '__int__', '__invert__', '__ior__', '__ipow__', '__irshift__', '__isub__', '__iter__', '__itruediv__', '__ixor__', '__le__', '__len__', '__lshift__', '__lt__', '__matmul__', '__mod__', '__module__', '__mul__', '__ne__', '__neg__', '__new__', '__or__', '__pos__', '__pow__', '__radd__', '__rand__', '__rdivmod__', '__reduce__', '__reduce_ex__', '__repr__', '__rfloordiv__', '__rlshift__', '__rmatmul__', '__rmod__', '__rmul__', '__ror__', '__rpow__', '__rrshift__', '__rshift__', '__rsub__', '__rtruediv__', '__rxor__', '__setattr__', '__setitem__', '__setstate__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__truediv__', '__xor__', '_length', '_meta', '_period', '_start', '_stop', '_units', 'align', 'all', 'any', 'argmax', 'argmin', 'argpartition', 'argsort', 'astype', 'base', 'byteswap', 'choose', 'clip', 'compress', 'conj', 'conjugate', 'copy', 'ctypes', 'cumprod', 'cumsum', 'data', 'datetime_x_axis', 'device', 'diagonal', 'dot', 'dtype', 'dump', 'dumps', 'fill', 'flags', 'flat', 'flatten', 'from_csv', 'from_query_dataframe', 'getfield', 'granularity', 'imag', 'index_of', 'item', 'itemset', 'itemsize', 'length', 'mT', 'max', 'mean', 'meta', 'min', 'nbytes', 'ndim', 'newbyteorder', 'nonzero', 'partition', 'period', 'plot', 'prod', 'promote', 'ptp', 'put', 'ravel', 'real', 'relative_time', 'repeat', 'reshape', 'resize', 'round', 'searchsorted', 'setfield', 'setflags', 'shape', 'size', 'sort', 'squeeze', 'start', 'std', 'stop', 'strides', 'sum', 'swapaxes', 'take', 'to_device', 'tobytes', 'tofile', 'tolist', 'trace', 'transpose', 'units', 'unix_x_axis', 'var', 'view', 'x_axis']\n"
-     ]
+     "data": {
+      "text/plain": [
+       "316"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
-   "execution_count": 71
+   "execution_count": 35
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-07T03:50:21.450203Z",
-     "start_time": "2026-01-07T03:50:20.047261Z"
+     "end_time": "2026-01-13T18:27:23.915402Z",
+     "start_time": "2026-01-13T18:27:23.902788Z"
     }
    },
    "cell_type": "code",
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "#resampling influx data so that i can fit it with irradiance data (queried every 15 minutes)\n",
-    "\n",
-    "ts = temp_array_fsgp\n",
-    "timestamps = pd.to_datetime(ts.datetime_x_axis, utc=True)\n",
-    "values = ts.data\n",
-    "\n",
-    "df = pd.DataFrame({\"value\": values}, index=timestamps)\n",
-    "df_15m = df.resample(\"15T\").mean()\n",
-    "\n",
-    "print(df_15m.head())\n"
-   ],
-   "id": "203be30192c9302a",
+   "source": "df_15min.head(20)",
+   "id": "9d97681681224101",
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                               value\n",
-      "2025-07-01 15:00:00+00:00  31.502800\n",
-      "2025-07-01 15:15:00+00:00  32.612457\n",
-      "2025-07-01 15:30:00+00:00  32.363079\n",
-      "2025-07-01 15:45:00+00:00  31.748017\n",
-      "2025-07-01 16:00:00+00:00  31.132956\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_13384\\1903960078.py:10: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
-      "  df_15m = df.resample(\"15T\").mean()\n"
-     ]
+     "data": {
+      "text/plain": [
+       "                        date  temperature_2m  shortwave_radiation_instant  \\\n",
+       "0  2025-07-02 00:00:00+00:00          -2.213                     5.588703   \n",
+       "1  2025-07-02 00:15:00+00:00          -2.163                     5.351785   \n",
+       "2  2025-07-02 00:30:00+00:00          -2.113                     5.014219   \n",
+       "3  2025-07-02 00:45:00+00:00          -2.063                     4.680000   \n",
+       "4  2025-07-02 01:00:00+00:00          -1.963                     4.680000   \n",
+       "5  2025-07-02 01:15:00+00:00          -1.863                     4.680000   \n",
+       "6  2025-07-02 01:30:00+00:00          -1.713                     5.154415   \n",
+       "7  2025-07-02 01:45:00+00:00          -1.513                     5.692100   \n",
+       "8  2025-07-02 02:00:00+00:00          -1.313                     6.638072   \n",
+       "9  2025-07-02 02:15:00+00:00          -1.063                     8.647496   \n",
+       "10 2025-07-02 02:30:00+00:00          -0.763                    11.212135   \n",
+       "11 2025-07-02 02:45:00+00:00          -0.463                    14.332341   \n",
+       "12 2025-07-02 03:00:00+00:00          -0.163                    16.766108   \n",
+       "13 2025-07-02 03:15:00+00:00           0.187                    18.556595   \n",
+       "14 2025-07-02 03:30:00+00:00           0.587                    20.188908   \n",
+       "15 2025-07-02 03:45:00+00:00           0.937                    21.749481   \n",
+       "16 2025-07-02 04:00:00+00:00           1.237                    22.862123   \n",
+       "17 2025-07-02 04:15:00+00:00           1.487                    23.828083   \n",
+       "18 2025-07-02 04:30:00+00:00           1.687                    24.472057   \n",
+       "19 2025-07-02 04:45:00+00:00           1.837                    24.795612   \n",
+       "\n",
+       "    wind_speed_10m  array_temperature  \n",
+       "0       124.831741                NaN  \n",
+       "1       164.539490                NaN  \n",
+       "2       205.015503                NaN  \n",
+       "3       243.104050                NaN  \n",
+       "4       276.808258                NaN  \n",
+       "5       309.431335                NaN  \n",
+       "6       344.148895                NaN  \n",
+       "7       386.145966                NaN  \n",
+       "8       440.518890                NaN  \n",
+       "9       500.905579                NaN  \n",
+       "10      554.864990                NaN  \n",
+       "11      590.127563                NaN  \n",
+       "12      601.736938                NaN  \n",
+       "13      599.094421                NaN  \n",
+       "14      596.590515                NaN  \n",
+       "15      603.370850                NaN  \n",
+       "16      622.409363                NaN  \n",
+       "17      643.468811                NaN  \n",
+       "18      653.364685                NaN  \n",
+       "19      643.052490                NaN  "
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>temperature_2m</th>\n",
+       "      <th>shortwave_radiation_instant</th>\n",
+       "      <th>wind_speed_10m</th>\n",
+       "      <th>array_temperature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2025-07-02 00:00:00+00:00</td>\n",
+       "      <td>-2.213</td>\n",
+       "      <td>5.588703</td>\n",
+       "      <td>124.831741</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2025-07-02 00:15:00+00:00</td>\n",
+       "      <td>-2.163</td>\n",
+       "      <td>5.351785</td>\n",
+       "      <td>164.539490</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2025-07-02 00:30:00+00:00</td>\n",
+       "      <td>-2.113</td>\n",
+       "      <td>5.014219</td>\n",
+       "      <td>205.015503</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2025-07-02 00:45:00+00:00</td>\n",
+       "      <td>-2.063</td>\n",
+       "      <td>4.680000</td>\n",
+       "      <td>243.104050</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2025-07-02 01:00:00+00:00</td>\n",
+       "      <td>-1.963</td>\n",
+       "      <td>4.680000</td>\n",
+       "      <td>276.808258</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2025-07-02 01:15:00+00:00</td>\n",
+       "      <td>-1.863</td>\n",
+       "      <td>4.680000</td>\n",
+       "      <td>309.431335</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2025-07-02 01:30:00+00:00</td>\n",
+       "      <td>-1.713</td>\n",
+       "      <td>5.154415</td>\n",
+       "      <td>344.148895</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2025-07-02 01:45:00+00:00</td>\n",
+       "      <td>-1.513</td>\n",
+       "      <td>5.692100</td>\n",
+       "      <td>386.145966</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2025-07-02 02:00:00+00:00</td>\n",
+       "      <td>-1.313</td>\n",
+       "      <td>6.638072</td>\n",
+       "      <td>440.518890</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2025-07-02 02:15:00+00:00</td>\n",
+       "      <td>-1.063</td>\n",
+       "      <td>8.647496</td>\n",
+       "      <td>500.905579</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2025-07-02 02:30:00+00:00</td>\n",
+       "      <td>-0.763</td>\n",
+       "      <td>11.212135</td>\n",
+       "      <td>554.864990</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>2025-07-02 02:45:00+00:00</td>\n",
+       "      <td>-0.463</td>\n",
+       "      <td>14.332341</td>\n",
+       "      <td>590.127563</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>2025-07-02 03:00:00+00:00</td>\n",
+       "      <td>-0.163</td>\n",
+       "      <td>16.766108</td>\n",
+       "      <td>601.736938</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2025-07-02 03:15:00+00:00</td>\n",
+       "      <td>0.187</td>\n",
+       "      <td>18.556595</td>\n",
+       "      <td>599.094421</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>2025-07-02 03:30:00+00:00</td>\n",
+       "      <td>0.587</td>\n",
+       "      <td>20.188908</td>\n",
+       "      <td>596.590515</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>2025-07-02 03:45:00+00:00</td>\n",
+       "      <td>0.937</td>\n",
+       "      <td>21.749481</td>\n",
+       "      <td>603.370850</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>2025-07-02 04:00:00+00:00</td>\n",
+       "      <td>1.237</td>\n",
+       "      <td>22.862123</td>\n",
+       "      <td>622.409363</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>2025-07-02 04:15:00+00:00</td>\n",
+       "      <td>1.487</td>\n",
+       "      <td>23.828083</td>\n",
+       "      <td>643.468811</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>2025-07-02 04:30:00+00:00</td>\n",
+       "      <td>1.687</td>\n",
+       "      <td>24.472057</td>\n",
+       "      <td>653.364685</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>2025-07-02 04:45:00+00:00</td>\n",
+       "      <td>1.837</td>\n",
+       "      <td>24.795612</td>\n",
+       "      <td>643.052490</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
-   "execution_count": 12
+   "execution_count": 34
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-07T03:50:23.625393Z",
-     "start_time": "2026-01-07T03:50:23.615067Z"
+     "end_time": "2026-01-13T18:29:35.228638Z",
+     "start_time": "2026-01-13T18:29:35.221164Z"
     }
    },
    "cell_type": "code",
-   "source": "df_15m.index",
-   "id": "9d97681681224101",
+   "source": "df_15min['array_temperature']",
+   "id": "b2ed3e842258cf69",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "DatetimeIndex(['2025-07-01 15:00:00+00:00', '2025-07-01 15:15:00+00:00',\n",
-       "               '2025-07-01 15:30:00+00:00', '2025-07-01 15:45:00+00:00',\n",
-       "               '2025-07-01 16:00:00+00:00', '2025-07-01 16:15:00+00:00',\n",
-       "               '2025-07-01 16:30:00+00:00', '2025-07-01 16:45:00+00:00',\n",
-       "               '2025-07-01 17:00:00+00:00', '2025-07-01 17:15:00+00:00',\n",
-       "               ...\n",
-       "               '2025-07-05 11:45:00+00:00', '2025-07-05 12:00:00+00:00',\n",
-       "               '2025-07-05 12:15:00+00:00', '2025-07-05 12:30:00+00:00',\n",
-       "               '2025-07-05 12:45:00+00:00', '2025-07-05 13:00:00+00:00',\n",
-       "               '2025-07-05 13:15:00+00:00', '2025-07-05 13:30:00+00:00',\n",
-       "               '2025-07-05 13:45:00+00:00', '2025-07-05 14:00:00+00:00'],\n",
-       "              dtype='datetime64[ns, UTC]', length=381, freq='15min')"
+       "0     NaN\n",
+       "1     NaN\n",
+       "2     NaN\n",
+       "3     NaN\n",
+       "4     NaN\n",
+       "       ..\n",
+       "379   NaN\n",
+       "380   NaN\n",
+       "381   NaN\n",
+       "382   NaN\n",
+       "383   NaN\n",
+       "Name: array_temperature, Length: 384, dtype: float64"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 13
+   "execution_count": 37
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-07T03:50:24.895375Z",
-     "start_time": "2026-01-07T03:50:24.699623Z"
+     "end_time": "2026-01-13T18:29:02.678456Z",
+     "start_time": "2026-01-13T18:29:02.670655Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "#this graph shows solar irradiance, array temperature, and ambient temperature over July 2-6 (FSGP 2025)\n",
-    "\n",
-    "fig, ax1 = plt.subplots()\n",
-    "ax_twin = ax1.twinx()\n",
-    "\n",
-    "plt.plot(df_15m.index, df_15m, label = \"Array Temperature\")\n",
-    "plt.plot(hourly_data['date'],hourly_data['temperature_2m'], color = 'green', label = \"Ambient Temperature\")\n",
-    "\n",
-    "ax1.plot(hourly_data['date'],hourly_data['shortwave_radiation_instant'], color = \"red\", label = \"Solar Irradiance\")\n",
-    "\n",
-    "ax1.set_xlabel(\"Time\")\n",
-    "ax1.set_ylabel(\"Solar Irradiance\")\n",
-    "ax_twin.set_ylabel(\"MosfetTemperatureA\")\n",
-    "\n",
-    "ax1.tick_params(\"x\", rotation  = 90)\n",
-    "#ax1.set_xticks(minutely_15.timeformat)\n",
-    "\n",
-    "plt.legend(loc = \"upper left\")\n",
-    "ax1.legend(loc = \"upper left\")\n",
-    "plt.show()"
+    "#drop all NaN values\n",
+    "df_15min.dropna()"
    ],
-   "id": "7397cf9f7ec92d1e",
+   "id": "ee4434e4e652bd72",
    "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'hourly_data' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
-      "Cell \u001B[1;32mIn[14], line 7\u001B[0m\n\u001B[0;32m      4\u001B[0m ax_twin \u001B[38;5;241m=\u001B[39m ax1\u001B[38;5;241m.\u001B[39mtwinx()\n\u001B[0;32m      6\u001B[0m plt\u001B[38;5;241m.\u001B[39mplot(df_15m\u001B[38;5;241m.\u001B[39mindex, df_15m, label \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mArray Temperature\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m----> 7\u001B[0m plt\u001B[38;5;241m.\u001B[39mplot(\u001B[43mhourly_data\u001B[49m[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mdate\u001B[39m\u001B[38;5;124m'\u001B[39m],hourly_data[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtemperature_2m\u001B[39m\u001B[38;5;124m'\u001B[39m], color \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mgreen\u001B[39m\u001B[38;5;124m'\u001B[39m, label \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mAmbient Temperature\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m      9\u001B[0m ax1\u001B[38;5;241m.\u001B[39mplot(hourly_data[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mdate\u001B[39m\u001B[38;5;124m'\u001B[39m],hourly_data[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mshortwave_radiation_instant\u001B[39m\u001B[38;5;124m'\u001B[39m], color \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mred\u001B[39m\u001B[38;5;124m\"\u001B[39m, label \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mSolar Irradiance\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m     11\u001B[0m ax1\u001B[38;5;241m.\u001B[39mset_xlabel(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mTime\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n",
-      "\u001B[1;31mNameError\u001B[0m: name 'hourly_data' is not defined"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
+       "Empty DataFrame\n",
+       "Columns: [date, temperature_2m, shortwave_radiation_instant, wind_speed_10m, array_temperature]\n",
+       "Index: []"
       ],
-      "image/png": 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"
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>temperature_2m</th>\n",
+       "      <th>shortwave_radiation_instant</th>\n",
+       "      <th>wind_speed_10m</th>\n",
+       "      <th>array_temperature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
      },
+     "execution_count": 36,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
-   "execution_count": 14
+   "execution_count": 36
   },
   {
    "metadata": {
@@ -659,15 +1524,15 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-11-29T21:48:56.274247Z",
-     "start_time": "2025-11-29T21:48:56.265960Z"
+     "end_time": "2026-01-13T09:28:39.288902Z",
+     "start_time": "2026-01-13T09:28:39.191300Z"
     }
    },
    "cell_type": "code",
    "source": [
     "from v4.array_temperature.arrayTemperatureModel import arrayTemperatureModel\n",
     "def model(u0, u1):\n",
-    "    return arrayTemperatureModel(hourly_data['temperature_2m'], hourly_data['shortwave_radiation_instant'], 0, u0, u1)\n",
+    "    return arrayTemperatureModel(minutely_15_dataframe['temperature_2m'], minutely_15_dataframe['shortwave_radiation_instant'], 0, u0, u1)\n",
     "\n",
     "model = model(22, 0.8)\n",
     "print(model.calculateArrayTemperature())\n"
@@ -678,59 +1543,42 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 1.8935347e+00  1.0364137e+01  2.1387180e+01  2.9999233e+01\n",
-      "  2.6391014e+01  2.6875341e+01  2.7645386e+01  2.8025963e+01\n",
-      "  2.5642160e+01  2.4427845e+01  1.8177454e+01  1.7554525e+01\n",
-      "  8.0708389e+00  1.9637234e+00 -8.7300003e-01 -1.2230000e+00\n",
-      " -1.4230000e+00 -1.6230000e+00 -2.3230000e+00 -2.8230000e+00\n",
-      " -3.3230000e+00 -3.7229998e+00 -4.0730000e+00 -4.3730001e+00\n",
-      "  1.2934799e+00  1.0832609e+01  2.1798904e+01  3.0714010e+01\n",
-      "  4.0591778e+01  4.3875725e+01  3.3674473e+01  3.8677563e+01\n",
-      "  3.2507423e+01  3.0509079e+01  2.3399044e+01  1.4952563e+01\n",
-      "  7.7067099e+00  1.6932418e+00 -1.2730000e+00 -1.8230001e+00\n",
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-      "  1.6270000e+00  1.2770000e+00  1.0270000e+00  1.3770000e+00\n",
-      "  9.2700005e-01  1.2700000e-01 -1.4730000e+00 -1.2730000e+00]\n"
+      "58.77777777777778\n",
+      "0     -1.958968\n",
+      "1     -1.919737\n",
+      "2     -1.885081\n",
+      "3     -1.850273\n",
+      "4     -1.750273\n",
+      "         ...   \n",
+      "475   -0.342991\n",
+      "476   -0.159289\n",
+      "477   -0.057661\n",
+      "478    0.044260\n",
+      "479    0.115122\n",
+      "Length: 480, dtype: float32\n"
      ]
     }
    ],
-   "execution_count": 76
+   "execution_count": 9
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-11-29T21:48:57.548674Z",
-     "start_time": "2025-11-29T21:48:57.541528Z"
+     "end_time": "2026-01-13T09:28:44.395145Z",
+     "start_time": "2026-01-13T09:28:44.384842Z"
     }
    },
    "cell_type": "code",
    "source": "faiman_temp = model.calculateArrayTemperature()",
    "id": "492ea4964124380d",
    "outputs": [],
-   "execution_count": 77
+   "execution_count": 10
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-11-29T21:48:58.404891Z",
-     "start_time": "2025-11-29T21:48:58.394317Z"
+     "end_time": "2026-01-13T09:28:45.147985Z",
+     "start_time": "2026-01-13T09:28:45.123933Z"
     }
    },
    "cell_type": "code",
@@ -743,45 +1591,135 @@
     {
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+       "        7.2911944e+00,  7.0280943e+00,  6.7791138e+00,  6.4803033e+00,\n",
+       "        6.1214867e+00,  5.6999063e+00,  5.2411556e+00,  4.7198000e+00,\n",
+       "        4.2776542e+00,  3.8282909e+00,  3.4217005e+00,  3.0256128e+00,\n",
+       "        2.7524867e+00,  2.5912068e+00,  2.4849684e+00,  2.4039991e+00,\n",
+       "        2.3798802e+00,  2.3262670e+00,  2.3240905e+00,  2.2899051e+00,\n",
+       "        2.2899051e+00,  2.2735453e+00,  2.1908267e+00,  2.1250515e+00,\n",
+       "        2.1273825e+00,  2.1822195e+00,  2.2213850e+00,  2.3156993e+00,\n",
+       "        2.3417325e+00,  2.3324814e+00,  2.2635233e+00,  2.1344621e+00,\n",
+       "        2.0554879e+00,  1.9708241e+00,  1.8361888e+00,  1.6806998e+00,\n",
+       "        1.4203515e+00,  9.6086144e-01,  3.8238627e-01, -1.9434059e-01,\n",
+       "       -5.6918478e-01, -6.5627074e-01, -4.9077415e-01, -3.4299111e-01,\n",
+       "       -1.5928864e-01, -5.7660520e-02,  4.4259906e-02,  1.1512250e-01],\n",
        "      dtype=float32)"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 78
+   "execution_count": 11
   },
   {
    "metadata": {
@@ -959,78 +1897,6 @@
    ],
    "execution_count": 89
   },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T21:52:04.209818Z",
-     "start_time": "2025-11-29T21:52:03.938367Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "df_15m['faiman'] = faiman_temp",
-   "id": "d57cc2a51597298a",
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "Length of values (120) does not match length of index (316)",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mValueError\u001B[39m                                Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[86]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[43mdf_15m\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43mfaiman\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m]\u001B[49m = faiman_temp\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:4322\u001B[39m, in \u001B[36mDataFrame.__setitem__\u001B[39m\u001B[34m(self, key, value)\u001B[39m\n\u001B[32m   4319\u001B[39m     \u001B[38;5;28mself\u001B[39m._setitem_array([key], value)\n\u001B[32m   4320\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m   4321\u001B[39m     \u001B[38;5;66;03m# set column\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m4322\u001B[39m     \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_set_item\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mvalue\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:4535\u001B[39m, in \u001B[36mDataFrame._set_item\u001B[39m\u001B[34m(self, key, value)\u001B[39m\n\u001B[32m   4525\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34m_set_item\u001B[39m(\u001B[38;5;28mself\u001B[39m, key, value) -> \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m   4526\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m   4527\u001B[39m \u001B[33;03m    Add series to DataFrame in specified column.\u001B[39;00m\n\u001B[32m   4528\u001B[39m \n\u001B[32m   (...)\u001B[39m\u001B[32m   4533\u001B[39m \u001B[33;03m    ensure homogeneity.\u001B[39;00m\n\u001B[32m   4534\u001B[39m \u001B[33;03m    \"\"\"\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m4535\u001B[39m     value, refs = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_sanitize_column\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvalue\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   4537\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m (\n\u001B[32m   4538\u001B[39m         key \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m.columns\n\u001B[32m   4539\u001B[39m         \u001B[38;5;129;01mand\u001B[39;00m value.ndim == \u001B[32m1\u001B[39m\n\u001B[32m   4540\u001B[39m         \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(value.dtype, ExtensionDtype)\n\u001B[32m   4541\u001B[39m     ):\n\u001B[32m   4542\u001B[39m         \u001B[38;5;66;03m# broadcast across multiple columns if necessary\u001B[39;00m\n\u001B[32m   4543\u001B[39m         \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m.columns.is_unique \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(\u001B[38;5;28mself\u001B[39m.columns, MultiIndex):\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:5288\u001B[39m, in \u001B[36mDataFrame._sanitize_column\u001B[39m\u001B[34m(self, value)\u001B[39m\n\u001B[32m   5285\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m _reindex_for_setitem(value, \u001B[38;5;28mself\u001B[39m.index)\n\u001B[32m   5287\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m is_list_like(value):\n\u001B[32m-> \u001B[39m\u001B[32m5288\u001B[39m     \u001B[43mcom\u001B[49m\u001B[43m.\u001B[49m\u001B[43mrequire_length_match\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvalue\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mindex\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   5289\u001B[39m arr = sanitize_array(value, \u001B[38;5;28mself\u001B[39m.index, copy=\u001B[38;5;28;01mTrue\u001B[39;00m, allow_2d=\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[32m   5290\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m (\n\u001B[32m   5291\u001B[39m     \u001B[38;5;28misinstance\u001B[39m(value, Index)\n\u001B[32m   5292\u001B[39m     \u001B[38;5;129;01mand\u001B[39;00m value.dtype == \u001B[33m\"\u001B[39m\u001B[33mobject\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m   (...)\u001B[39m\u001B[32m   5295\u001B[39m     \u001B[38;5;66;03m# TODO: Remove kludge in sanitize_array for string mode when enforcing\u001B[39;00m\n\u001B[32m   5296\u001B[39m     \u001B[38;5;66;03m# this deprecation\u001B[39;00m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\common.py:573\u001B[39m, in \u001B[36mrequire_length_match\u001B[39m\u001B[34m(data, index)\u001B[39m\n\u001B[32m    569\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    570\u001B[39m \u001B[33;03mCheck the length of data matches the length of the index.\u001B[39;00m\n\u001B[32m    571\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    572\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(data) != \u001B[38;5;28mlen\u001B[39m(index):\n\u001B[32m--> \u001B[39m\u001B[32m573\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[32m    574\u001B[39m         \u001B[33m\"\u001B[39m\u001B[33mLength of values \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    575\u001B[39m         \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m(\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mlen\u001B[39m(data)\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m) \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    576\u001B[39m         \u001B[33m\"\u001B[39m\u001B[33mdoes not match length of index \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    577\u001B[39m         \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m(\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mlen\u001B[39m(index)\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m)\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    578\u001B[39m     )\n",
-      "\u001B[31mValueError\u001B[39m: Length of values (120) does not match length of index (316)"
-     ]
-    }
-   ],
-   "execution_count": 86
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T21:51:34.085612Z",
-     "start_time": "2025-11-29T21:51:33.854519Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "plt.plot(df_15m.index, df_15m, color = 'red')\n",
-    "plt.plot(df_15m.index, faiman_temp)\n",
-    "plt.xticks(rotation = 90)\n"
-   ],
-   "id": "8737baa73f7c5785",
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "x and y must have same first dimension, but have shapes (316,) and (120,)",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mValueError\u001B[39m                                Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[85]\u001B[39m\u001B[32m, line 2\u001B[39m\n\u001B[32m      1\u001B[39m plt.plot(df_15m.index, df_15m, color = \u001B[33m'\u001B[39m\u001B[33mred\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m----> \u001B[39m\u001B[32m2\u001B[39m \u001B[43mplt\u001B[49m\u001B[43m.\u001B[49m\u001B[43mplot\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdf_15m\u001B[49m\u001B[43m.\u001B[49m\u001B[43mindex\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfaiman_temp\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m      3\u001B[39m plt.xticks(rotation = \u001B[32m90\u001B[39m)\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\matplotlib\\pyplot.py:3838\u001B[39m, in \u001B[36mplot\u001B[39m\u001B[34m(scalex, scaley, data, *args, **kwargs)\u001B[39m\n\u001B[32m   3830\u001B[39m \u001B[38;5;129m@_copy_docstring_and_deprecators\u001B[39m(Axes.plot)\n\u001B[32m   3831\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mplot\u001B[39m(\n\u001B[32m   3832\u001B[39m     *args: \u001B[38;5;28mfloat\u001B[39m | ArrayLike | \u001B[38;5;28mstr\u001B[39m,\n\u001B[32m   (...)\u001B[39m\u001B[32m   3836\u001B[39m     **kwargs,\n\u001B[32m   3837\u001B[39m ) -> \u001B[38;5;28mlist\u001B[39m[Line2D]:\n\u001B[32m-> \u001B[39m\u001B[32m3838\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mgca\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[43mplot\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m   3839\u001B[39m \u001B[43m        \u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   3840\u001B[39m \u001B[43m        \u001B[49m\u001B[43mscalex\u001B[49m\u001B[43m=\u001B[49m\u001B[43mscalex\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   3841\u001B[39m \u001B[43m        \u001B[49m\u001B[43mscaley\u001B[49m\u001B[43m=\u001B[49m\u001B[43mscaley\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   3842\u001B[39m \u001B[43m        \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43m(\u001B[49m\u001B[43m{\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mdata\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m}\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mif\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mis\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mnot\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01melse\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43m{\u001B[49m\u001B[43m}\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   3843\u001B[39m \u001B[43m        \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   3844\u001B[39m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:1777\u001B[39m, in \u001B[36mAxes.plot\u001B[39m\u001B[34m(self, scalex, scaley, data, *args, **kwargs)\u001B[39m\n\u001B[32m   1534\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m   1535\u001B[39m \u001B[33;03mPlot y versus x as lines and/or markers.\u001B[39;00m\n\u001B[32m   1536\u001B[39m \n\u001B[32m   (...)\u001B[39m\u001B[32m   1774\u001B[39m \u001B[33;03m(``'green'``) or hex strings (``'#008000'``).\u001B[39;00m\n\u001B[32m   1775\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m   1776\u001B[39m kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D)\n\u001B[32m-> \u001B[39m\u001B[32m1777\u001B[39m lines = [*\u001B[38;5;28mself\u001B[39m._get_lines(\u001B[38;5;28mself\u001B[39m, *args, data=data, **kwargs)]\n\u001B[32m   1778\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m line \u001B[38;5;129;01min\u001B[39;00m lines:\n\u001B[32m   1779\u001B[39m     \u001B[38;5;28mself\u001B[39m.add_line(line)\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\matplotlib\\axes\\_base.py:297\u001B[39m, in \u001B[36m_process_plot_var_args.__call__\u001B[39m\u001B[34m(self, axes, data, return_kwargs, *args, **kwargs)\u001B[39m\n\u001B[32m    295\u001B[39m     this += args[\u001B[32m0\u001B[39m],\n\u001B[32m    296\u001B[39m     args = args[\u001B[32m1\u001B[39m:]\n\u001B[32m--> \u001B[39m\u001B[32m297\u001B[39m \u001B[38;5;28;01myield from\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_plot_args\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    298\u001B[39m \u001B[43m    \u001B[49m\u001B[43maxes\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mthis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mambiguous_fmt_datakey\u001B[49m\u001B[43m=\u001B[49m\u001B[43mambiguous_fmt_datakey\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    299\u001B[39m \u001B[43m    \u001B[49m\u001B[43mreturn_kwargs\u001B[49m\u001B[43m=\u001B[49m\u001B[43mreturn_kwargs\u001B[49m\n\u001B[32m    300\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\matplotlib\\axes\\_base.py:494\u001B[39m, in \u001B[36m_process_plot_var_args._plot_args\u001B[39m\u001B[34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001B[39m\n\u001B[32m    491\u001B[39m     axes.yaxis.update_units(y)\n\u001B[32m    493\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m x.shape[\u001B[32m0\u001B[39m] != y.shape[\u001B[32m0\u001B[39m]:\n\u001B[32m--> \u001B[39m\u001B[32m494\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mx and y must have same first dimension, but \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    495\u001B[39m                      \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mhave shapes \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mx.shape\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m and \u001B[39m\u001B[38;5;132;01m{\u001B[39;00my.shape\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m\"\u001B[39m)\n\u001B[32m    496\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m x.ndim > \u001B[32m2\u001B[39m \u001B[38;5;129;01mor\u001B[39;00m y.ndim > \u001B[32m2\u001B[39m:\n\u001B[32m    497\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mx and y can be no greater than 2D, but have \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    498\u001B[39m                      \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mshapes \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mx.shape\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m and \u001B[39m\u001B[38;5;132;01m{\u001B[39;00my.shape\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m\"\u001B[39m)\n",
-      "\u001B[31mValueError\u001B[39m: x and y must have same first dimension, but have shapes (316,) and (120,)"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 85
-  },
   {
    "metadata": {
     "ExecuteTime": {
@@ -1065,13 +1931,73 @@
    ],
    "execution_count": 23
   },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-13T09:29:45.958671Z",
+     "start_time": "2026-01-13T09:29:36.083027Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import numpy as np\n",
+    "from scipy.optimize import curve_fit\n",
+    "\n",
+    "# Faiman model\n",
+    "def faiman_model(G, Ta, v, u0, u1):\n",
+    "    return Ta + G / (u0 + u1 * v)\n",
+    "\n",
+    "def fit_faiman(G, Ta, v, T_array):\n",
+    "    popt, _ = curve_fit(\n",
+    "        lambda X, u0, u1: faiman_model(X[0], X[1], X[2], u0, u1),\n",
+    "        (G, Ta, v),\n",
+    "        T_array\n",
+    "    )\n",
+    "    return popt  # u0, u1\n"
+   ],
+   "id": "1aa707313aeb5993",
+   "outputs": [],
+   "execution_count": 13
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-13T09:29:49.398396Z",
+     "start_time": "2026-01-13T09:29:49.391333Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def plot_comparison(df):\n",
+    "    fig, ax1 = plt.subplots()\n",
+    "    ax2 = ax1.twinx()\n",
+    "\n",
+    "    ax1.plot(df.index, df[\"irradiance\"], label=\"Solar Irradiance\", color=\"red\")\n",
+    "    ax2.plot(df.index, df[\"value\"], label=\"Array Temp\")\n",
+    "\n",
+    "    ax1.set_xlabel(\"Time\")\n",
+    "    ax1.set_ylabel(\"Irradiance (W/m²)\")\n",
+    "    ax2.set_ylabel(\"Array Temperature (°C)\")\n",
+    "\n",
+    "    ax1.legend(loc=\"upper left\")\n",
+    "    ax2.legend(loc=\"upper right\")\n",
+    "\n",
+    "    plt.xticks(rotation=90)\n",
+    "    plt.show()\n"
+   ],
+   "id": "5fc38f6262d9e38e",
+   "outputs": [],
+   "execution_count": 14
+  },
   {
    "metadata": {},
    "cell_type": "code",
    "outputs": [],
    "execution_count": null,
-   "source": "#",
-   "id": "1aa707313aeb5993"
+   "source": "",
+   "id": "ba3e4aad2d9800f4"
   }
  ],
  "metadata": {

From 1cab91bb573ed3d554b8f9110e07e3de85132b87 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Tue, 13 Jan 2026 15:11:28 -0800
Subject: [PATCH 10/49] cleaning

---
 array_temp/.cache.sqlite             |  Bin 40960 -> 49152 bytes
 array_temp/coefficient_fitting.ipynb |   43 +-
 array_temp/faiman_coefficients.ipynb | 1563 +++++++-------------------
 3 files changed, 433 insertions(+), 1173 deletions(-)

diff --git a/array_temp/.cache.sqlite b/array_temp/.cache.sqlite
index 9e98d22ae911df5cc08dfeae6871d35c6d9442c0..1aa1fdbdc6b86f8d885c7441ebfdea77888a789a 100644
GIT binary patch
delta 2019
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delta 83
zcmZo@U~V|TG(nn`oq>UYYodZZBm2gLCGs3h{D&C$uk#<;ENHNefAS%HC!okP2LAW_
k&o&D>+~VK-%wEochmoa`0f<0a**6O$6tiq@ZmE?90Obf63IG5A

diff --git a/array_temp/coefficient_fitting.ipynb b/array_temp/coefficient_fitting.ipynb
index b620472..009826e 100644
--- a/array_temp/coefficient_fitting.ipynb
+++ b/array_temp/coefficient_fitting.ipynb
@@ -637,30 +637,6 @@
    ],
    "execution_count": 12
   },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-07T03:57:43.978897Z",
-     "start_time": "2026-01-07T03:57:43.973610Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "len(df_15m)",
-   "id": "14e077d6a70439e3",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "345"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 13
-  },
   {
    "metadata": {
     "ExecuteTime": {
@@ -695,6 +671,25 @@
    ],
    "execution_count": 14
   },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "345"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 13,
+   "source": "len(df_15m)",
+   "id": "14e077d6a70439e3"
+  },
   {
    "metadata": {
     "ExecuteTime": {
diff --git a/array_temp/faiman_coefficients.ipynb b/array_temp/faiman_coefficients.ipynb
index 011a904..daa6c4a 100644
--- a/array_temp/faiman_coefficients.ipynb
+++ b/array_temp/faiman_coefficients.ipynb
@@ -36,7 +36,7 @@
     "\n",
     "#each 5 seconds\n",
     "utc_offset_h = 7\n",
-    "start_utc = time(12 , 00, 00) #querying is vancouver time, influxdb gives utc\n",
+    "start_utc = time(12, 00, 00)  #querying is vancouver time, influxdb gives utc\n",
     "stop_utc = time(00, 00, 00)\n",
     "date_start = date(2025, 7, 2)\n",
     "date_stop = date(2025, 7, 6)\n",
@@ -44,7 +44,7 @@
     "stop_time = datetime.combine(date_stop, stop_utc, tzinfo=timezone.utc)\n",
     "\n",
     "client = query.DBClient()\n",
-    "temp_array_fsgp: TimeSeries = client.query_time_series(start_time, stop_time, field=  \"MosfetTemperatureA\")\n",
+    "temp_array_fsgp: TimeSeries = client.query_time_series(start_time, stop_time, field=\"MosfetTemperatureA\")\n",
     "speed_kph: TimeSeries = client.query_time_series(start_time, stop_time, \"MotorRotatingSpeed\")\n"
    ],
    "id": "8b2b4006671bdc31",
@@ -136,8 +136,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T18:11:45.163385Z",
-     "start_time": "2026-01-13T18:11:45.142755Z"
+     "end_time": "2026-01-13T21:57:09.918259Z",
+     "start_time": "2026-01-13T21:57:07.406593Z"
     }
    },
    "cell_type": "code",
@@ -157,11 +157,12 @@
     "# The order of variables in hourly or daily is important to assign them correctly below\n",
     "url = \"https://historical-forecast-api.open-meteo.com/v1/forecast\"\n",
     "params = {\n",
-    "\t\"latitude\": 36.9760,\n",
+    "\t\"latitude\": 36.976,\n",
     "\t\"longitude\": 86.4491,\n",
     "\t\"start_date\": \"2025-07-02\",\n",
-    "\t\"end_date\": \"2025-07-06\",\n",
-    "\t\"minutely_15\": [\"temperature_2m\", \"wind_speed_10m\", \"shortwave_radiation_instant\"],\n",
+    "\t\"end_date\": \"2025-07-07\",\n",
+    "\t\"minutely_15\": [\"temperature_2m\", \"shortwave_radiation_instant\", \"wind_speed_10m\"],\n",
+    "\t\"timezone\": \"America/Chicago\",\n",
     "}\n",
     "responses = openmeteo.weather_api(url, params=params)\n",
     "\n",
@@ -169,6 +170,7 @@
     "response = responses[0]\n",
     "print(f\"Coordinates: {response.Latitude()}°N {response.Longitude()}°E\")\n",
     "print(f\"Elevation: {response.Elevation()} m asl\")\n",
+    "print(f\"Timezone: {response.Timezone()}{response.TimezoneAbbreviation()}\")\n",
     "print(f\"Timezone difference to GMT+0: {response.UtcOffsetSeconds()}s\")\n",
     "\n",
     "# Process minutely_15 data. The order of variables needs to be the same as requested.\n",
@@ -199,40 +201,41 @@
      "text": [
       "Coordinates: 37.0°N 86.5°E\n",
       "Elevation: 5139.0 m asl\n",
-      "Timezone difference to GMT+0: 0s\n",
+      "Timezone: b'America/Chicago'b'GMT-6'\n",
+      "Timezone difference to GMT+0: -21600s\n",
       "\n",
       "Minutely15 data\n",
       "                          date  temperature_2m  shortwave_radiation_instant  \\\n",
-      "0   2025-07-02 00:00:00+00:00          -2.213                     5.588703   \n",
-      "1   2025-07-02 00:15:00+00:00          -2.163                     5.351785   \n",
-      "2   2025-07-02 00:30:00+00:00          -2.113                     5.014219   \n",
-      "3   2025-07-02 00:45:00+00:00          -2.063                     4.680000   \n",
-      "4   2025-07-02 01:00:00+00:00          -1.963                     4.680000   \n",
+      "0   2025-07-02 06:00:00+00:00           1.487                   505.264526   \n",
+      "1   2025-07-02 06:15:00+00:00           1.587                   516.453796   \n",
+      "2   2025-07-02 06:30:00+00:00           1.787                   527.607666   \n",
+      "3   2025-07-02 06:45:00+00:00           1.937                   528.744202   \n",
+      "4   2025-07-02 07:00:00+00:00           1.987                   526.880981   \n",
       "..                        ...             ...                          ...   \n",
-      "475 2025-07-06 22:45:00+00:00          -1.263                    20.240196   \n",
-      "476 2025-07-06 23:00:00+00:00          -1.063                    19.881649   \n",
-      "477 2025-07-06 23:15:00+00:00          -0.963                    19.917469   \n",
-      "478 2025-07-06 23:30:00+00:00          -0.863                    19.959719   \n",
-      "479 2025-07-06 23:45:00+00:00          -0.813                    20.418695   \n",
+      "571 2025-07-08 04:45:00+00:00           4.137                   525.869263   \n",
+      "572 2025-07-08 05:00:00+00:00           4.237                   537.033020   \n",
+      "573 2025-07-08 05:15:00+00:00           4.387                   552.215698   \n",
+      "574 2025-07-08 05:30:00+00:00           4.487                   567.368408   \n",
+      "575 2025-07-08 05:45:00+00:00           4.587                   573.449036   \n",
       "\n",
       "     wind_speed_10m  \n",
-      "0        124.831741  \n",
-      "1        164.539490  \n",
-      "2        205.015503  \n",
-      "3        243.104050  \n",
-      "4        276.808258  \n",
+      "0         23.688984  \n",
+      "1         23.617756  \n",
+      "2         23.557316  \n",
+      "3         23.507751  \n",
+      "4         23.469128  \n",
       "..              ...  \n",
-      "475        0.000000  \n",
-      "476        0.000000  \n",
-      "477       16.716660  \n",
-      "478       48.040825  \n",
-      "479       76.951775  \n",
+      "571       30.466295  \n",
+      "572       29.964457  \n",
+      "573       29.462870  \n",
+      "574       28.666941  \n",
+      "575       28.373846  \n",
       "\n",
-      "[480 rows x 4 columns]\n"
+      "[576 rows x 4 columns]\n"
      ]
     }
    ],
-   "execution_count": 12
+   "execution_count": 101
   },
   {
    "metadata": {},
@@ -242,10 +245,86 @@
     "therefore on influx i query for 1 july 10  pm to 6 july 20 45\n",
     "\n",
     "\n",
-    "utc i s2 hours ahead of vancouver"
+    "utc is 2 hours ahead of vancouver\n",
+    "\n",
+    "- some preprocessing, including:\n",
+    "- merging everything into a dataframe, with resampling influx data to match openmeteo's frequency"
    ],
    "id": "dd2a5efc6d21b409"
   },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-13T21:58:39.932854Z",
+     "start_time": "2026-01-13T21:58:39.926640Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "print(merged_df['shortwave_radiation_instant'].describe())\n",
+   "id": "255c40a73c01f500",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "count    316.000000\n",
+      "mean      22.459007\n",
+      "std       10.592769\n",
+      "min        3.096837\n",
+      "25%       14.742397\n",
+      "50%       20.974435\n",
+      "75%       28.357857\n",
+      "max       48.654636\n",
+      "Name: shortwave_radiation_instant, dtype: float64\n"
+     ]
+    }
+   ],
+   "execution_count": 104
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-13T21:58:09.542421Z",
+     "start_time": "2026-01-13T21:58:09.536271Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "minutely_15_dataframe['date'].head(20)\n",
+    "print(minutely_15_dataframe['shortwave_radiation_instant'].head(20))\n"
+   ],
+   "id": "573cc20871481ce0",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0     505.264526\n",
+      "1     516.453796\n",
+      "2     527.607666\n",
+      "3     528.744202\n",
+      "4     526.880981\n",
+      "5     522.027100\n",
+      "6     513.199951\n",
+      "7     500.415741\n",
+      "8     484.679749\n",
+      "9     465.017517\n",
+      "10    445.389191\n",
+      "11    424.808441\n",
+      "12    401.317627\n",
+      "13    379.828491\n",
+      "14    362.278351\n",
+      "15    352.529755\n",
+      "16    353.420837\n",
+      "17    354.186615\n",
+      "18    346.120880\n",
+      "19    323.533844\n",
+      "Name: shortwave_radiation_instant, dtype: float32\n"
+     ]
+    }
+   ],
+   "execution_count": 103
+  },
   {
    "metadata": {},
    "cell_type": "markdown",
@@ -261,10 +340,10 @@
    },
    "cell_type": "code",
    "source": [
-    "plt.plot(temp_array_fsgp.datetime_x_axis, temp_array_fsgp, label = \"Array Temperature\")\n",
+    "plt.plot(temp_array_fsgp.datetime_x_axis, temp_array_fsgp, label=\"Array Temperature\")\n",
     "plt.ylabel(\"MosfetTemperatureA\")\n",
     "\n",
-    "plt.tick_params(\"x\", rotation  = 90)"
+    "plt.tick_params(\"x\", rotation=90)"
    ],
    "id": "9cde1a3b5eb68331",
    "outputs": [
@@ -284,8 +363,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T18:11:54.970378Z",
-     "start_time": "2026-01-13T18:11:53.798881Z"
+     "end_time": "2026-01-13T21:59:01.139683Z",
+     "start_time": "2026-01-13T21:58:59.840291Z"
     }
    },
    "cell_type": "code",
@@ -295,19 +374,19 @@
     "fig, ax1 = plt.subplots()\n",
     "ax_twin = ax1.twinx()\n",
     "\n",
-    "plt.plot(temp_array_fsgp.datetime_x_axis, temp_array_fsgp, label = \"Array Temperature\")\n",
-    "plt.plot(minutely_15_data['date'],minutely_15_data['temperature_2m'], color = 'green', label = \"Ambient Temperature\")\n",
-    "ax1.plot(minutely_15_data['date'],minutely_15_data['shortwave_radiation_instant'], color = \"red\", label = \"Solar Irradiance\")\n",
+    "plt.plot(temp_array_fsgp.datetime_x_axis, temp_array_fsgp, label=\"Array Temperature\")\n",
+    "plt.plot(minutely_15_data['date'], minutely_15_data['temperature_2m'], color='green', label=\"Ambient Temperature\")\n",
+    "ax1.plot(minutely_15_data['date'], minutely_15_data['shortwave_radiation_instant'], color=\"red\",\n",
+    "         label=\"Solar Irradiance\")\n",
     "\n",
     "ax1.set_xlabel(\"Time\")\n",
     "ax1.set_ylabel(\"Solar Irradiance\")\n",
     "ax_twin.set_ylabel(\"MosfetTemperatureA\")\n",
     "\n",
-    "ax1.tick_params(\"x\", rotation  = 90)\n",
-    "\n",
+    "ax1.tick_params(\"x\", rotation=90)\n",
     "\n",
-    "plt.legend(loc = \"upper left\")\n",
-    "ax1.legend(loc = \"upper left\")\n",
+    "plt.legend(loc=\"upper left\")\n",
+    "ax1.legend(loc=\"upper left\")\n",
     "plt.show()"
    ],
    "id": "a3fa42c24abb970c",
@@ -317,43 +396,96 @@
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 13
+   "execution_count": 105
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-13T21:59:17.741087Z",
+     "start_time": "2026-01-13T21:59:17.736661Z"
+    }
+   },
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": "#there really isn't Influx data for dates after midday 5 July, so I see no reason to incorporate the OpenMeteo data after",
-   "id": "90465ecf61541d72"
+   "id": "90465ecf61541d72",
+   "outputs": [],
+   "execution_count": 106
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-13T21:59:19.716357Z",
+     "start_time": "2026-01-13T21:59:18.790075Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "#for easier access, im going to convert everything to a data frame after resampling.\n",
+    "import pandas as pd\n",
+    "\n",
+    "#resampling influx data so that i can fit it with irradiance data (queried every 15 minutes)\n",
+    "\n",
+    "ts = temp_array_fsgp\n",
+    "timestamps = pd.to_datetime(ts.datetime_x_axis, utc=True)\n",
+    "values = ts.data\n",
+    "\n",
+    "df = pd.DataFrame({\"value\": values}, index=timestamps)\n",
+    "df_15m = df.resample(\"15T\").mean()\n",
+    "\n",
+    "print(df_15m.head())\n"
+   ],
+   "id": "203be30192c9302a",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                               value\n",
+      "2025-07-02 07:15:00+00:00  37.371079\n",
+      "2025-07-02 07:30:00+00:00  46.801625\n",
+      "2025-07-02 07:45:00+00:00  29.980004\n",
+      "2025-07-02 08:00:00+00:00  45.573409\n",
+      "2025-07-02 08:15:00+00:00  55.745558\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_14188\\1837817124.py:11: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
+      "  df_15m = df.resample(\"15T\").mean()\n"
+     ]
+    }
+   ],
+   "execution_count": 107
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T18:42:54.102845Z",
-     "start_time": "2026-01-13T18:42:54.097885Z"
+     "end_time": "2026-01-13T21:59:22.599682Z",
+     "start_time": "2026-01-13T21:59:22.588459Z"
     }
    },
    "cell_type": "code",
    "source": [
     "minutely_15_dataframe.head()\n",
-    "df_15min = minutely_15_dataframe[minutely_15_dataframe['date']<'2025-07-06']"
+    "df_15min = minutely_15_dataframe[minutely_15_dataframe['date'] < '2025-07-06']"
    ],
    "id": "a30bec6abef1672a",
    "outputs": [],
-   "execution_count": 56
+   "execution_count": 108
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T18:42:55.861090Z",
-     "start_time": "2026-01-13T18:42:55.853919Z"
+     "end_time": "2026-01-13T21:59:23.329227Z",
+     "start_time": "2026-01-13T21:59:23.295121Z"
     }
    },
    "cell_type": "code",
@@ -361,20 +493,20 @@
     "df_15min.tail()\n",
     "weather_df = df_15min.copy()\n",
     "\n",
-    "# Set datetime as index\n",
+    "#set datetime as index\n",
     "weather_df[\"date\"] = pd.to_datetime(weather_df[\"date\"], utc=True)\n",
     "weather_df = weather_df.set_index(\"date\")\n",
     "weather_df = weather_df.sort_index()\n"
    ],
    "id": "feced1770070bc8b",
    "outputs": [],
-   "execution_count": 57
+   "execution_count": 109
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T18:43:01.541534Z",
-     "start_time": "2026-01-13T18:43:01.531001Z"
+     "end_time": "2026-01-13T21:59:27.182855Z",
+     "start_time": "2026-01-13T21:59:27.172396Z"
     }
    },
    "cell_type": "code",
@@ -386,19 +518,19 @@
       "text/plain": [
        "                           temperature_2m  shortwave_radiation_instant  \\\n",
        "date                                                                     \n",
-       "2025-07-02 00:00:00+00:00          -2.213                     5.588703   \n",
-       "2025-07-02 00:15:00+00:00          -2.163                     5.351785   \n",
-       "2025-07-02 00:30:00+00:00          -2.113                     5.014219   \n",
-       "2025-07-02 00:45:00+00:00          -2.063                     4.680000   \n",
-       "2025-07-02 01:00:00+00:00          -1.963                     4.680000   \n",
+       "2025-07-02 06:00:00+00:00           1.487                   505.264526   \n",
+       "2025-07-02 06:15:00+00:00           1.587                   516.453796   \n",
+       "2025-07-02 06:30:00+00:00           1.787                   527.607666   \n",
+       "2025-07-02 06:45:00+00:00           1.937                   528.744202   \n",
+       "2025-07-02 07:00:00+00:00           1.987                   526.880981   \n",
        "\n",
        "                           wind_speed_10m  \n",
        "date                                       \n",
-       "2025-07-02 00:00:00+00:00      124.831741  \n",
-       "2025-07-02 00:15:00+00:00      164.539490  \n",
-       "2025-07-02 00:30:00+00:00      205.015503  \n",
-       "2025-07-02 00:45:00+00:00      243.104050  \n",
-       "2025-07-02 01:00:00+00:00      276.808258  "
+       "2025-07-02 06:00:00+00:00       23.688984  \n",
+       "2025-07-02 06:15:00+00:00       23.617756  \n",
+       "2025-07-02 06:30:00+00:00       23.557316  \n",
+       "2025-07-02 06:45:00+00:00       23.507751  \n",
+       "2025-07-02 07:00:00+00:00       23.469128  "
       ],
       "text/html": [
        "<div>\n",
@@ -432,52 +564,52 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>2025-07-02 00:00:00+00:00</th>\n",
-       "      <td>-2.213</td>\n",
-       "      <td>5.588703</td>\n",
-       "      <td>124.831741</td>\n",
+       "      <th>2025-07-02 06:00:00+00:00</th>\n",
+       "      <td>1.487</td>\n",
+       "      <td>505.264526</td>\n",
+       "      <td>23.688984</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-02 00:15:00+00:00</th>\n",
-       "      <td>-2.163</td>\n",
-       "      <td>5.351785</td>\n",
-       "      <td>164.539490</td>\n",
+       "      <th>2025-07-02 06:15:00+00:00</th>\n",
+       "      <td>1.587</td>\n",
+       "      <td>516.453796</td>\n",
+       "      <td>23.617756</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-02 00:30:00+00:00</th>\n",
-       "      <td>-2.113</td>\n",
-       "      <td>5.014219</td>\n",
-       "      <td>205.015503</td>\n",
+       "      <th>2025-07-02 06:30:00+00:00</th>\n",
+       "      <td>1.787</td>\n",
+       "      <td>527.607666</td>\n",
+       "      <td>23.557316</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-02 00:45:00+00:00</th>\n",
-       "      <td>-2.063</td>\n",
-       "      <td>4.680000</td>\n",
-       "      <td>243.104050</td>\n",
+       "      <th>2025-07-02 06:45:00+00:00</th>\n",
+       "      <td>1.937</td>\n",
+       "      <td>528.744202</td>\n",
+       "      <td>23.507751</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-02 01:00:00+00:00</th>\n",
-       "      <td>-1.963</td>\n",
-       "      <td>4.680000</td>\n",
-       "      <td>276.808258</td>\n",
+       "      <th>2025-07-02 07:00:00+00:00</th>\n",
+       "      <td>1.987</td>\n",
+       "      <td>526.880981</td>\n",
+       "      <td>23.469128</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 58
+   "execution_count": 110
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T18:43:10.581049Z",
-     "start_time": "2026-01-13T18:43:10.572938Z"
+     "end_time": "2026-01-13T21:59:27.990777Z",
+     "start_time": "2026-01-13T21:59:27.982711Z"
     }
    },
    "cell_type": "code",
@@ -500,61 +632,60 @@
       "DatetimeIndex(['2025-07-02 07:15:00+00:00', '2025-07-02 07:30:00+00:00',\n",
       "               '2025-07-02 07:45:00+00:00', '2025-07-02 08:00:00+00:00',\n",
       "               '2025-07-02 08:15:00+00:00'],\n",
-      "              dtype='datetime64[ns, UTC]', freq=None)\n",
+      "              dtype='datetime64[ns, UTC]', freq='15min')\n",
       "DatetimeIndex(['2025-07-05 13:00:00+00:00', '2025-07-05 13:15:00+00:00',\n",
       "               '2025-07-05 13:30:00+00:00', '2025-07-05 13:45:00+00:00',\n",
       "               '2025-07-05 14:00:00+00:00'],\n",
-      "              dtype='datetime64[ns, UTC]', freq=None)\n",
+      "              dtype='datetime64[ns, UTC]', freq='15min')\n",
       "Weather index:\n",
-      "DatetimeIndex(['2025-07-02 00:00:00+00:00', '2025-07-02 00:15:00+00:00',\n",
-      "               '2025-07-02 00:30:00+00:00', '2025-07-02 00:45:00+00:00',\n",
-      "               '2025-07-02 01:00:00+00:00'],\n",
+      "DatetimeIndex(['2025-07-02 06:00:00+00:00', '2025-07-02 06:15:00+00:00',\n",
+      "               '2025-07-02 06:30:00+00:00', '2025-07-02 06:45:00+00:00',\n",
+      "               '2025-07-02 07:00:00+00:00'],\n",
       "              dtype='datetime64[ns, UTC]', name='date', freq=None)\n",
       "                           temperature_2m  shortwave_radiation_instant  \\\n",
       "date                                                                     \n",
-      "2025-07-05 22:45:00+00:00          -2.013                     8.496305   \n",
-      "2025-07-05 23:00:00+00:00          -1.963                     8.788720   \n",
-      "2025-07-05 23:15:00+00:00          -1.963                     9.085988   \n",
-      "2025-07-05 23:30:00+00:00          -1.913                     9.199390   \n",
-      "2025-07-05 23:45:00+00:00          -1.913                     9.021574   \n",
+      "2025-07-05 22:45:00+00:00          -2.013                     0.000000   \n",
+      "2025-07-05 23:00:00+00:00          -1.963                     0.000000   \n",
+      "2025-07-05 23:15:00+00:00          -1.963                    19.759171   \n",
+      "2025-07-05 23:30:00+00:00          -1.913                    51.912174   \n",
+      "2025-07-05 23:45:00+00:00          -1.913                    86.610596   \n",
       "\n",
       "                           wind_speed_10m  \n",
       "date                                       \n",
-      "2025-07-05 22:45:00+00:00        0.000000  \n",
-      "2025-07-05 23:00:00+00:00        0.000000  \n",
-      "2025-07-05 23:15:00+00:00       19.759171  \n",
-      "2025-07-05 23:30:00+00:00       51.912174  \n",
-      "2025-07-05 23:45:00+00:00       86.610596  \n"
+      "2025-07-05 22:45:00+00:00        8.496305  \n",
+      "2025-07-05 23:00:00+00:00        8.788720  \n",
+      "2025-07-05 23:15:00+00:00        9.085988  \n",
+      "2025-07-05 23:30:00+00:00        9.199390  \n",
+      "2025-07-05 23:45:00+00:00        9.021574  \n"
      ]
     }
    ],
-   "execution_count": 59
+   "execution_count": 111
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T18:43:13.139726Z",
-     "start_time": "2026-01-13T18:43:13.128886Z"
+     "end_time": "2026-01-13T21:59:29.502734Z",
+     "start_time": "2026-01-13T21:59:29.469182Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "# Make sure indexes are sorted\n",
+    "#sort indices\n",
     "df_15m_copy = df_15m.copy().sort_index()\n",
     "weather_df = weather_df.sort_index()\n",
     "\n",
-    "# Force both onto exact 15-minute grid\n",
+    "#resampling for 15minute range\n",
     "df_15m_copy.index = df_15m_copy.index.floor(\"15T\")\n",
     "weather_df.index = weather_df.index.floor(\"15T\")\n",
-    "\n",
-    "# Keep only overlapping timestamps\n",
+    "#ensure common timestamps\n",
     "common_index = df_15m_copy.index.intersection(weather_df.index)\n",
     "\n",
-    "print(\"Overlap start:\", common_index.min())\n",
-    "print(\"Overlap end:\", common_index.max())\n",
-    "print(\"Number of aligned points:\", len(common_index))\n",
+    "print(\"overlap start:\", common_index.min())\n",
+    "print(\"overlap end:\", common_index.max())\n",
+    "print(\"no of aligned points:\", len(common_index))\n",
     "\n",
-    "# Merge on common timestamps\n",
+    "#merging common timestamps\n",
     "merged_df = pd.concat(\n",
     "    [df_15m_copy.loc[common_index], weather_df.loc[common_index]],\n",
     "    axis=1\n",
@@ -582,114 +713,13 @@
      ]
     }
    ],
-   "execution_count": 60
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-13T18:43:15.243825Z",
-     "start_time": "2026-01-13T18:43:15.234987Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "merged_df.head()",
-   "id": "783fdc2283d6ff2",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "                               value  temperature_2m  \\\n",
-       "2025-07-02 07:15:00+00:00  37.371079           1.787   \n",
-       "2025-07-02 07:30:00+00:00  46.801625           1.487   \n",
-       "2025-07-02 07:45:00+00:00  29.980004           1.137   \n",
-       "2025-07-02 08:00:00+00:00  45.573409           0.887   \n",
-       "2025-07-02 08:15:00+00:00  55.745558           0.687   \n",
-       "\n",
-       "                           shortwave_radiation_instant  wind_speed_10m  \n",
-       "2025-07-02 07:15:00+00:00                    23.177401      522.027100  \n",
-       "2025-07-02 07:30:00+00:00                    22.406927      513.199951  \n",
-       "2025-07-02 07:45:00+00:00                    21.945240      500.415741  \n",
-       "2025-07-02 08:00:00+00:00                    21.817902      484.679749  \n",
-       "2025-07-02 08:15:00+00:00                    21.767351      465.017517  "
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>value</th>\n",
-       "      <th>temperature_2m</th>\n",
-       "      <th>shortwave_radiation_instant</th>\n",
-       "      <th>wind_speed_10m</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2025-07-02 07:15:00+00:00</th>\n",
-       "      <td>37.371079</td>\n",
-       "      <td>1.787</td>\n",
-       "      <td>23.177401</td>\n",
-       "      <td>522.027100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-07-02 07:30:00+00:00</th>\n",
-       "      <td>46.801625</td>\n",
-       "      <td>1.487</td>\n",
-       "      <td>22.406927</td>\n",
-       "      <td>513.199951</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-07-02 07:45:00+00:00</th>\n",
-       "      <td>29.980004</td>\n",
-       "      <td>1.137</td>\n",
-       "      <td>21.945240</td>\n",
-       "      <td>500.415741</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-07-02 08:00:00+00:00</th>\n",
-       "      <td>45.573409</td>\n",
-       "      <td>0.887</td>\n",
-       "      <td>21.817902</td>\n",
-       "      <td>484.679749</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-07-02 08:15:00+00:00</th>\n",
-       "      <td>55.745558</td>\n",
-       "      <td>0.687</td>\n",
-       "      <td>21.767351</td>\n",
-       "      <td>465.017517</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 61
+   "execution_count": 112
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T18:43:39.262303Z",
-     "start_time": "2026-01-13T18:43:39.252536Z"
+     "end_time": "2026-01-13T21:59:37.315273Z",
+     "start_time": "2026-01-13T21:59:37.303549Z"
     }
    },
    "cell_type": "code",
@@ -713,17 +743,17 @@
        "2025-07-05 14:00:00+00:00  34.174615           0.237   \n",
        "\n",
        "                           shortwave_radiation_instant  wind_speed_10m  \n",
-       "2025-07-02 07:15:00+00:00                    23.177401      522.027100  \n",
-       "2025-07-02 07:30:00+00:00                    22.406927      513.199951  \n",
-       "2025-07-02 07:45:00+00:00                    21.945240      500.415741  \n",
-       "2025-07-02 08:00:00+00:00                    21.817902      484.679749  \n",
-       "2025-07-02 08:15:00+00:00                    21.767351      465.017517  \n",
+       "2025-07-02 07:15:00+00:00                   522.027100       23.177401  \n",
+       "2025-07-02 07:30:00+00:00                   513.199951       22.406927  \n",
+       "2025-07-02 07:45:00+00:00                   500.415741       21.945240  \n",
+       "2025-07-02 08:00:00+00:00                   484.679749       21.817902  \n",
+       "2025-07-02 08:15:00+00:00                   465.017517       21.767351  \n",
        "...                                                ...             ...  \n",
-       "2025-07-05 13:00:00+00:00                    29.548521       58.205017  \n",
-       "2025-07-05 13:15:00+00:00                    27.475807       22.902834  \n",
-       "2025-07-05 13:30:00+00:00                    25.202570        2.313184  \n",
-       "2025-07-05 13:45:00+00:00                    22.461807        0.000000  \n",
-       "2025-07-05 14:00:00+00:00                    20.056877        0.000000  \n",
+       "2025-07-05 13:00:00+00:00                    58.205017       29.548521  \n",
+       "2025-07-05 13:15:00+00:00                    22.902834       27.475807  \n",
+       "2025-07-05 13:30:00+00:00                     2.313184       25.202570  \n",
+       "2025-07-05 13:45:00+00:00                     0.000000       22.461807  \n",
+       "2025-07-05 14:00:00+00:00                     0.000000       20.056877  \n",
        "\n",
        "[316 rows x 4 columns]"
       ],
@@ -757,36 +787,36 @@
        "      <th>2025-07-02 07:15:00+00:00</th>\n",
        "      <td>37.371079</td>\n",
        "      <td>1.787</td>\n",
-       "      <td>23.177401</td>\n",
        "      <td>522.027100</td>\n",
+       "      <td>23.177401</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2025-07-02 07:30:00+00:00</th>\n",
        "      <td>46.801625</td>\n",
        "      <td>1.487</td>\n",
-       "      <td>22.406927</td>\n",
        "      <td>513.199951</td>\n",
+       "      <td>22.406927</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2025-07-02 07:45:00+00:00</th>\n",
        "      <td>29.980004</td>\n",
        "      <td>1.137</td>\n",
-       "      <td>21.945240</td>\n",
        "      <td>500.415741</td>\n",
+       "      <td>21.945240</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2025-07-02 08:00:00+00:00</th>\n",
        "      <td>45.573409</td>\n",
        "      <td>0.887</td>\n",
-       "      <td>21.817902</td>\n",
        "      <td>484.679749</td>\n",
+       "      <td>21.817902</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2025-07-02 08:15:00+00:00</th>\n",
        "      <td>55.745558</td>\n",
        "      <td>0.687</td>\n",
-       "      <td>21.767351</td>\n",
        "      <td>465.017517</td>\n",
+       "      <td>21.767351</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -799,36 +829,36 @@
        "      <th>2025-07-05 13:00:00+00:00</th>\n",
        "      <td>38.086209</td>\n",
        "      <td>1.137</td>\n",
-       "      <td>29.548521</td>\n",
        "      <td>58.205017</td>\n",
+       "      <td>29.548521</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2025-07-05 13:15:00+00:00</th>\n",
        "      <td>36.774658</td>\n",
        "      <td>0.887</td>\n",
-       "      <td>27.475807</td>\n",
        "      <td>22.902834</td>\n",
+       "      <td>27.475807</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2025-07-05 13:30:00+00:00</th>\n",
        "      <td>35.463106</td>\n",
        "      <td>0.637</td>\n",
-       "      <td>25.202570</td>\n",
        "      <td>2.313184</td>\n",
+       "      <td>25.202570</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2025-07-05 13:45:00+00:00</th>\n",
        "      <td>34.271664</td>\n",
        "      <td>0.437</td>\n",
-       "      <td>22.461807</td>\n",
        "      <td>0.000000</td>\n",
+       "      <td>22.461807</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2025-07-05 14:00:00+00:00</th>\n",
        "      <td>34.174615</td>\n",
        "      <td>0.237</td>\n",
-       "      <td>20.056877</td>\n",
        "      <td>0.000000</td>\n",
+       "      <td>20.056877</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -836,31 +866,31 @@
        "</div>"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 114,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 62
+   "execution_count": 114
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T18:47:08.107202Z",
-     "start_time": "2026-01-13T18:47:08.102816Z"
+     "end_time": "2026-01-13T21:59:39.073453Z",
+     "start_time": "2026-01-13T21:59:39.062767Z"
     }
    },
    "cell_type": "code",
-   "source": "merged_df = merged_df.rename(columns = {'value':'array_temperature'})",
+   "source": "merged_df = merged_df.rename(columns={'value': 'array_temperature'})",
    "id": "e0882e697c1613f7",
    "outputs": [],
-   "execution_count": 70
+   "execution_count": 115
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T18:47:10.162618Z",
-     "start_time": "2026-01-13T18:47:10.154103Z"
+     "end_time": "2026-01-13T21:59:40.158800Z",
+     "start_time": "2026-01-13T21:59:40.150764Z"
     }
    },
    "cell_type": "code",
@@ -878,11 +908,11 @@
        "2025-07-02 08:15:00+00:00          55.745558           0.687   \n",
        "\n",
        "                           shortwave_radiation_instant  wind_speed_10m  \n",
-       "2025-07-02 07:15:00+00:00                    23.177401      522.027100  \n",
-       "2025-07-02 07:30:00+00:00                    22.406927      513.199951  \n",
-       "2025-07-02 07:45:00+00:00                    21.945240      500.415741  \n",
-       "2025-07-02 08:00:00+00:00                    21.817902      484.679749  \n",
-       "2025-07-02 08:15:00+00:00                    21.767351      465.017517  "
+       "2025-07-02 07:15:00+00:00                   522.027100       23.177401  \n",
+       "2025-07-02 07:30:00+00:00                   513.199951       22.406927  \n",
+       "2025-07-02 07:45:00+00:00                   500.415741       21.945240  \n",
+       "2025-07-02 08:00:00+00:00                   484.679749       21.817902  \n",
+       "2025-07-02 08:15:00+00:00                   465.017517       21.767351  "
       ],
       "text/html": [
        "<div>\n",
@@ -914,75 +944,75 @@
        "      <th>2025-07-02 07:15:00+00:00</th>\n",
        "      <td>37.371079</td>\n",
        "      <td>1.787</td>\n",
-       "      <td>23.177401</td>\n",
        "      <td>522.027100</td>\n",
+       "      <td>23.177401</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2025-07-02 07:30:00+00:00</th>\n",
        "      <td>46.801625</td>\n",
        "      <td>1.487</td>\n",
-       "      <td>22.406927</td>\n",
        "      <td>513.199951</td>\n",
+       "      <td>22.406927</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2025-07-02 07:45:00+00:00</th>\n",
        "      <td>29.980004</td>\n",
        "      <td>1.137</td>\n",
-       "      <td>21.945240</td>\n",
        "      <td>500.415741</td>\n",
+       "      <td>21.945240</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2025-07-02 08:00:00+00:00</th>\n",
        "      <td>45.573409</td>\n",
        "      <td>0.887</td>\n",
-       "      <td>21.817902</td>\n",
        "      <td>484.679749</td>\n",
+       "      <td>21.817902</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2025-07-02 08:15:00+00:00</th>\n",
        "      <td>55.745558</td>\n",
        "      <td>0.687</td>\n",
-       "      <td>21.767351</td>\n",
        "      <td>465.017517</td>\n",
+       "      <td>21.767351</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 116,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 71
+   "execution_count": 116
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T18:47:31.441983Z",
-     "start_time": "2026-01-13T18:47:31.322607Z"
+     "end_time": "2026-01-13T21:59:44.805394Z",
+     "start_time": "2026-01-13T21:59:44.690753Z"
     }
    },
    "cell_type": "code",
    "source": [
     "#this graph shows solar irradiance, array temperature, and ambient temperature over July 2-6 (FSGP 2025)\n",
     "\n",
+    "fig, ax1 = plt.subplots()\n",
+    "ax_twin = ax1.twinx()\n",
     "\n",
+    "ax1.plot(merged_df['array_temperature'], label=\"Array Temperature\")\n",
+    "ax1.plot(merged_df['temperature_2m'], color='green', label=\"Ambient Temperature\")\n",
+    "plt.plot(merged_df['shortwave_radiation_instant'], color=\"red\", label=\"Solar Irradiance\")\n",
     "\n",
-    "plt.plot(merged_df, merged_df['array_temperature'], label = \"Array Temperature\")\n",
-    "plt.plot(merged_df,merged_df['temperature_2m'], color = 'green', label = \"Ambient Temperature\")\n",
-    "plt.plot(merged_df,merged_df['shortwave_radiation_instant'], color = \"red\", label = \"Solar Irradiance\")\n",
-    "\n",
-    "plt.xlabel(\"Time\")\n",
-    "plt.ylabel(\"Solar Irradiance\")\n",
+    "ax1.set_xlabel(\"Time\")\n",
+    "ax1.set_ylabel(\"Solar Irradiance\")\n",
     "ax_twin.set_ylabel(\"MosfetTemperatureA\")\n",
     "\n",
-    "ax1.tick_params(\"x\", rotation  = 90)\n",
+    "ax1.tick_params(\"x\", rotation=90)\n",
     "\n",
-    "\n",
-    "plt.legend(loc = \"upper left\")\n",
-    "ax1.legend(loc = \"upper left\")\n",
+    "plt.legend(loc=\"upper left\")\n",
+    "ax1.legend(loc=\"upper left\")\n",
     "plt.show()"
    ],
    "id": "1624b6be9409b4fe",
@@ -992,1004 +1022,239 @@
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 73
+   "execution_count": 117
   },
   {
    "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
-   "source": "#for easier access, im going to convert everything to a data frame after resampling.",
-   "id": "ddf6f6b4bafc6f88"
+   "cell_type": "markdown",
+   "source": "import the Physics Array Temperature Model for validation/sanity checks\n",
+   "id": "11a5c363e4b5b953"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T18:31:48.434426Z",
-     "start_time": "2026-01-13T18:31:47.531595Z"
+     "end_time": "2026-01-13T23:03:53.927558Z",
+     "start_time": "2026-01-13T23:03:53.922317Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "import pandas as pd\n",
+    "from v4.array_temperature.arrayTemperatureModel import arrayTemperatureModel\n",
+    "import numpy as np\n",
+    "from scipy.optimize import curve_fit\n",
     "\n",
-    "#resampling influx data so that i can fit it with irradiance data (queried every 15 minutes)\n",
     "\n",
-    "ts = temp_array_fsgp\n",
-    "timestamps = pd.to_datetime(ts.datetime_x_axis, utc=True)\n",
-    "values = ts.data\n",
+    "def faiman_model(xdata, u0, u1):\n",
+    "    irradiance, ambient_temp, wind_speed = xdata\n",
+    "    model = arrayTemperatureModel(ambient_temperature=ambient_temp, irradiance=irradiance, wind_speed=wind_speed,\n",
+    "                                  thermal_loss_coefficient=u0, convective_loss_coefficient=u1)\n",
+    "    return model.calculateArrayTemperature()\n",
     "\n",
-    "df = pd.DataFrame({\"value\": values}, index=timestamps)\n",
-    "df_15m = df.resample(\"15T\").mean()\n",
     "\n",
-    "print(df_15m.head())\n"
-   ],
-   "id": "203be30192c9302a",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                               value\n",
-      "2025-07-02 07:15:00+00:00  37.371079\n",
-      "2025-07-02 07:30:00+00:00  46.801625\n",
-      "2025-07-02 07:45:00+00:00  29.980004\n",
-      "2025-07-02 08:00:00+00:00  45.573409\n",
-      "2025-07-02 08:15:00+00:00  55.745558\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_14188\\1903960078.py:10: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
-      "  df_15m = df.resample(\"15T\").mean()\n"
-     ]
-    }
+    "#xdata, ydata\n",
+    "def fit_faiman(model, xdata, ydata, params):\n",
+    "    popt, _ = curve_fit(model, xdata, ydata, p0=params)\n",
+    "\n",
+    "    return popt  # u0, u1\n"
    ],
-   "execution_count": 38
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-13T18:32:05.414754Z",
-     "start_time": "2026-01-13T18:32:05.405747Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "merged_df = df_15m.join(df_15min, how=\"inner\")\n",
-   "id": "d6a34dc28db1adc1",
+   "id": "1aa707313aeb5993",
    "outputs": [],
-   "execution_count": 39
+   "execution_count": 140
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T18:32:13.384358Z",
-     "start_time": "2026-01-13T18:32:13.377851Z"
+     "end_time": "2026-01-13T23:03:55.231396Z",
+     "start_time": "2026-01-13T23:03:55.225474Z"
     }
    },
    "cell_type": "code",
-   "source": "merged_df.head()",
-   "id": "15fc332acae2aa61",
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-       "Empty DataFrame\n",
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-   "cell_type": "code",
-   "source": "len(df_15m)",
-   "id": "fe74f6c4ed193e41",
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-       "316"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
+   "source": [
+    "xdata = np.stack([merged_df['shortwave_radiation_instant'], merged_df['temperature_2m'], merged_df['wind_speed_10m']])\n",
+    "faiman_temp = np.array(faiman_model(xdata, 22,0.8))"
    ],
-   "execution_count": 35
+   "id": "492ea4964124380d",
+   "outputs": [],
+   "execution_count": 141
   },
   {
    "metadata": {
     "ExecuteTime": {
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    "cell_type": "code",
-   "source": "df_15min.head(20)",
-   "id": "9d97681681224101",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "                        date  temperature_2m  shortwave_radiation_instant  \\\n",
-       "0  2025-07-02 00:00:00+00:00          -2.213                     5.588703   \n",
-       "1  2025-07-02 00:15:00+00:00          -2.163                     5.351785   \n",
-       "2  2025-07-02 00:30:00+00:00          -2.113                     5.014219   \n",
-       "3  2025-07-02 00:45:00+00:00          -2.063                     4.680000   \n",
-       "4  2025-07-02 01:00:00+00:00          -1.963                     4.680000   \n",
-       "5  2025-07-02 01:15:00+00:00          -1.863                     4.680000   \n",
-       "6  2025-07-02 01:30:00+00:00          -1.713                     5.154415   \n",
-       "7  2025-07-02 01:45:00+00:00          -1.513                     5.692100   \n",
-       "8  2025-07-02 02:00:00+00:00          -1.313                     6.638072   \n",
-       "9  2025-07-02 02:15:00+00:00          -1.063                     8.647496   \n",
-       "10 2025-07-02 02:30:00+00:00          -0.763                    11.212135   \n",
-       "11 2025-07-02 02:45:00+00:00          -0.463                    14.332341   \n",
-       "12 2025-07-02 03:00:00+00:00          -0.163                    16.766108   \n",
-       "13 2025-07-02 03:15:00+00:00           0.187                    18.556595   \n",
-       "14 2025-07-02 03:30:00+00:00           0.587                    20.188908   \n",
-       "15 2025-07-02 03:45:00+00:00           0.937                    21.749481   \n",
-       "16 2025-07-02 04:00:00+00:00           1.237                    22.862123   \n",
-       "17 2025-07-02 04:15:00+00:00           1.487                    23.828083   \n",
-       "18 2025-07-02 04:30:00+00:00           1.687                    24.472057   \n",
-       "19 2025-07-02 04:45:00+00:00           1.837                    24.795612   \n",
-       "\n",
-       "    wind_speed_10m  array_temperature  \n",
-       "0       124.831741                NaN  \n",
-       "1       164.539490                NaN  \n",
-       "2       205.015503                NaN  \n",
-       "3       243.104050                NaN  \n",
-       "4       276.808258                NaN  \n",
-       "5       309.431335                NaN  \n",
-       "6       344.148895                NaN  \n",
-       "7       386.145966                NaN  \n",
-       "8       440.518890                NaN  \n",
-       "9       500.905579                NaN  \n",
-       "10      554.864990                NaN  \n",
-       "11      590.127563                NaN  \n",
-       "12      601.736938                NaN  \n",
-       "13      599.094421                NaN  \n",
-       "14      596.590515                NaN  \n",
-       "15      603.370850                NaN  \n",
-       "16      622.409363                NaN  \n",
-       "17      643.468811                NaN  \n",
-       "18      653.364685                NaN  \n",
-       "19      643.052490                NaN  "
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-       "      <th></th>\n",
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-       "      <th>temperature_2m</th>\n",
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-       "      <td>2025-07-02 00:00:00+00:00</td>\n",
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-       "      <td>2025-07-02 00:15:00+00:00</td>\n",
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-       "      <td>2025-07-02 00:30:00+00:00</td>\n",
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-       "      <td>2025-07-02 00:45:00+00:00</td>\n",
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-       "      <td>2025-07-02 01:15:00+00:00</td>\n",
-       "      <td>-1.863</td>\n",
-       "      <td>4.680000</td>\n",
-       "      <td>309.431335</td>\n",
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-       "      <td>2025-07-02 01:30:00+00:00</td>\n",
-       "      <td>-1.713</td>\n",
-       "      <td>5.154415</td>\n",
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-       "      <th>7</th>\n",
-       "      <td>2025-07-02 01:45:00+00:00</td>\n",
-       "      <td>-1.513</td>\n",
-       "      <td>5.692100</td>\n",
-       "      <td>386.145966</td>\n",
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-       "      <th>8</th>\n",
-       "      <td>2025-07-02 02:00:00+00:00</td>\n",
-       "      <td>-1.313</td>\n",
-       "      <td>6.638072</td>\n",
-       "      <td>440.518890</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <th>9</th>\n",
-       "      <td>2025-07-02 02:15:00+00:00</td>\n",
-       "      <td>-1.063</td>\n",
-       "      <td>8.647496</td>\n",
-       "      <td>500.905579</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <th>10</th>\n",
-       "      <td>2025-07-02 02:30:00+00:00</td>\n",
-       "      <td>-0.763</td>\n",
-       "      <td>11.212135</td>\n",
-       "      <td>554.864990</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <th>11</th>\n",
-       "      <td>2025-07-02 02:45:00+00:00</td>\n",
-       "      <td>-0.463</td>\n",
-       "      <td>14.332341</td>\n",
-       "      <td>590.127563</td>\n",
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-       "      <th>12</th>\n",
-       "      <td>2025-07-02 03:00:00+00:00</td>\n",
-       "      <td>-0.163</td>\n",
-       "      <td>16.766108</td>\n",
-       "      <td>601.736938</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <th>13</th>\n",
-       "      <td>2025-07-02 03:15:00+00:00</td>\n",
-       "      <td>0.187</td>\n",
-       "      <td>18.556595</td>\n",
-       "      <td>599.094421</td>\n",
-       "      <td>NaN</td>\n",
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-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>2025-07-02 03:30:00+00:00</td>\n",
-       "      <td>0.587</td>\n",
-       "      <td>20.188908</td>\n",
-       "      <td>596.590515</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>2025-07-02 03:45:00+00:00</td>\n",
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-       "      <th>16</th>\n",
-       "      <td>2025-07-02 04:00:00+00:00</td>\n",
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-       "      <td>22.862123</td>\n",
-       "      <td>622.409363</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <th>17</th>\n",
-       "      <td>2025-07-02 04:15:00+00:00</td>\n",
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-       "      <td>23.828083</td>\n",
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-       "    <tr>\n",
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-       "      <td>NaN</td>\n",
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-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>2025-07-02 04:45:00+00:00</td>\n",
-       "      <td>1.837</td>\n",
-       "      <td>24.795612</td>\n",
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-       "  </tbody>\n",
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-       "</div>"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
+   "source": [
+    "#comparing immediate, unfit results\n",
+    "plt.plot(np.array(merged_df['array_temperature']), label=\"Array Temperature\")\n",
+    "plt.plot(faiman_temp, color=\"green\", label=\"Modelled Temperature\")"
    ],
-   "execution_count": 34
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-13T18:29:35.228638Z",
-     "start_time": "2026-01-13T18:29:35.221164Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "df_15min['array_temperature']",
-   "id": "b2ed3e842258cf69",
+   "id": "5e9c99851878c7d3",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0     NaN\n",
-       "1     NaN\n",
-       "2     NaN\n",
-       "3     NaN\n",
-       "4     NaN\n",
-       "       ..\n",
-       "379   NaN\n",
-       "380   NaN\n",
-       "381   NaN\n",
-       "382   NaN\n",
-       "383   NaN\n",
-       "Name: array_temperature, Length: 384, dtype: float64"
+       "[<matplotlib.lines.Line2D at 0x1b48c63b5f0>]"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 37
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-13T18:29:02.678456Z",
-     "start_time": "2026-01-13T18:29:02.670655Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "#drop all NaN values\n",
-    "df_15min.dropna()"
-   ],
-   "id": "ee4434e4e652bd72",
-   "outputs": [
+    },
     {
      "data": {
       "text/plain": [
-       "Empty DataFrame\n",
-       "Columns: [date, temperature_2m, shortwave_radiation_instant, wind_speed_10m, array_temperature]\n",
-       "Index: []"
+       "<Figure size 640x480 with 1 Axes>"
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"
      },
-     "execution_count": 36,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
-   "execution_count": 36
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-07T03:50:25.378205Z",
-     "start_time": "2026-01-07T03:50:25.369392Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "",
-   "id": "8848868de645674f",
-   "outputs": [],
-   "execution_count": null
+   "execution_count": 142
   },
   {
    "metadata": {},
    "cell_type": "markdown",
-   "source": "import the Physics Array Temperature Model for validation/sanity checks\n",
-   "id": "11a5c363e4b5b953"
+   "source": "clearly, the faiman model peaks too early - which is due to its steady state nature. Also, to note that the MPPT sensors might have a ~5 C discrepancy, but that is iirelevant here as the model is currently, still quite close to ambient temperature. This indicates either an offset change or coefficients. Curve fitting might help for a more decisive result.",
+   "id": "80032b76b8bf2a13"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T09:28:39.288902Z",
-     "start_time": "2026-01-13T09:28:39.191300Z"
+     "end_time": "2026-01-13T23:05:28.357166Z",
+     "start_time": "2026-01-13T23:05:28.350125Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "from v4.array_temperature.arrayTemperatureModel import arrayTemperatureModel\n",
-    "def model(u0, u1):\n",
-    "    return arrayTemperatureModel(minutely_15_dataframe['temperature_2m'], minutely_15_dataframe['shortwave_radiation_instant'], 0, u0, u1)\n",
-    "\n",
-    "model = model(22, 0.8)\n",
-    "print(model.calculateArrayTemperature())\n"
+    "params = [22, 0.8]\n",
+    "xdata = np.stack([merged_df['shortwave_radiation_instant'], merged_df['temperature_2m'], merged_df['wind_speed_10m']])\n",
+    "fit_faiman(faiman_model,\n",
+    "           xdata,\n",
+    "           merged_df['array_temperature'], params)"
    ],
-   "id": "2099b64dadb66f1a",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "58.77777777777778\n",
-      "0     -1.958968\n",
-      "1     -1.919737\n",
-      "2     -1.885081\n",
-      "3     -1.850273\n",
-      "4     -1.750273\n",
-      "         ...   \n",
-      "475   -0.342991\n",
-      "476   -0.159289\n",
-      "477   -0.057661\n",
-      "478    0.044260\n",
-      "479    0.115122\n",
-      "Length: 480, dtype: float32\n"
-     ]
-    }
-   ],
-   "execution_count": 9
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-13T09:28:44.395145Z",
-     "start_time": "2026-01-13T09:28:44.384842Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "faiman_temp = model.calculateArrayTemperature()",
-   "id": "492ea4964124380d",
-   "outputs": [],
-   "execution_count": 10
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-13T09:28:45.147985Z",
-     "start_time": "2026-01-13T09:28:45.123933Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "import numpy as np\n",
-    "np.array(faiman_temp)"
-   ],
-   "id": "bee1dddff65cfb38",
+   "id": "9839f97f4cfe5300",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([-1.9589682e+00, -1.9197371e+00, -1.8850808e+00, -1.8502727e+00,\n",
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-       "       -1.0112695e+00, -6.6993195e-01, -2.5335747e-01,  1.8847007e-01,\n",
-       "        5.9909582e-01,  1.0304816e+00,  1.5046777e+00,  1.9256127e+00,\n",
-       "        2.2761874e+00,  2.5700946e+00,  2.7993662e+00,  2.9640732e+00,\n",
-       "        3.0212963e+00,  2.9718862e+00,  2.7955081e+00,  2.6217003e+00,\n",
-       "        2.5637720e+00,  2.6605344e+00,  2.8577871e+00,  3.0055342e+00,\n",
-       "        3.0537786e+00,  2.8405180e+00,  2.5054965e+00,  2.1345110e+00,\n",
-       "        1.8787228e+00,  1.6764250e+00,  1.6000929e+00,  1.4689474e+00,\n",
-       "        1.3838892e+00,  1.2705032e+00,  1.1520731e+00,  1.0254774e+00,\n",
-       "        9.4465721e-01,  9.8156601e-01,  1.0339725e+00,  1.1411557e+00,\n",
-       "        1.1396110e+00,  1.0429782e+00,  8.5300910e-01,  6.6731703e-01,\n",
-       "        4.6032685e-01,  2.7901125e-01,  1.4293087e-01, -8.7931752e-03,\n",
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-       "       -1.1874874e+00, -8.3239919e-01, -4.7569072e-01, -7.8866422e-02,\n",
-       "        3.0759770e-01,  7.4508339e-01,  1.2935739e+00,  1.7324544e+00,\n",
-       "        2.0588007e+00,  2.1761534e+00,  2.1306484e+00,  2.0717545e+00,\n",
-       "        2.0130208e+00,  2.0580006e+00,  2.0764778e+00,  2.1095500e+00,\n",
-       "        2.1064448e+00,  2.0824544e+00,  2.0626793e+00,  2.0581484e+00,\n",
-       "        1.9832172e+00,  1.8884705e+00,  1.7964685e+00,  1.6511371e+00,\n",
-       "        1.5550499e+00,  1.5151457e+00,  1.4704767e+00,  1.4259934e+00,\n",
-       "        1.3259935e+00,  1.1606334e+00,  9.8964667e-01,  8.1873727e-01,\n",
-       "        6.4358920e-01,  4.6017116e-01,  3.2870823e-01,  1.6798645e-01,\n",
-       "        3.0918479e-02, -1.0910058e-01, -2.5826740e-01, -4.0716201e-01,\n",
-       "       -5.4299104e-01, -7.2875792e-01, -8.5176903e-01, -1.0247729e+00,\n",
-       "       -1.1500715e+00, -1.2369771e+00, -1.3339453e+00, -1.4255325e+00,\n",
-       "       -1.4776783e+00, -1.5025468e+00, -1.5437350e+00, -1.5894361e+00,\n",
-       "       -1.6333120e+00, -1.7315655e+00, -1.8953291e+00, -2.0745225e+00,\n",
-       "       -2.2036142e+00, -2.3590670e+00, -2.4798193e+00, -2.6053429e+00,\n",
-       "       -2.7256908e+00, -2.8413916e+00, -2.9366100e+00, -2.9970748e+00,\n",
-       "       -3.1125541e+00, -3.2488203e+00, -3.3850582e+00, -3.5212641e+00,\n",
-       "       -3.6574323e+00, -3.7435579e+00, -3.8796346e+00, -3.9609137e+00,\n",
-       "       -4.0313554e+00, -4.0813551e+00, -4.1858225e+00, -4.2358222e+00,\n",
-       "       -4.2858224e+00, -4.3358221e+00, -4.4516044e+00, -4.5173545e+00,\n",
-       "       -4.5874743e+00, -4.6769814e+00, -4.7859221e+00, -4.8211818e+00,\n",
-       "       -4.8026018e+00, -4.7009740e+00, -4.6026020e+00, -4.4535818e+00,\n",
-       "       -4.2372313e+00, -4.0362883e+00, -3.7847226e+00, -3.4663596e+00,\n",
-       "       -3.1521957e+00, -2.8218181e+00, -2.4207733e+00, -1.9968052e+00,\n",
-       "       -1.5952914e+00, -1.1367053e+00, -6.7143303e-01, -2.0503968e-01,\n",
-       "        2.4426001e-01,  6.8693691e-01,  1.1320921e+00,  1.5562851e+00,\n",
-       "        1.9306870e+00,  2.3055341e+00,  2.6181149e+00,  2.9807959e+00,\n",
-       "        3.2435739e+00,  3.5194063e+00,  3.7553682e+00,  3.9913681e+00,\n",
-       "        4.1871862e+00,  4.4059739e+00,  4.5619349e+00,  4.6911554e+00,\n",
-       "        4.7081141e+00,  4.5633545e+00,  4.2575784e+00,  3.9533610e+00,\n",
-       "        3.7379525e+00,  3.6110935e+00,  3.5226257e+00,  3.4227800e+00,\n",
-       "        3.2727799e+00,  3.0727801e+00,  2.8614044e+00,  2.6382673e+00,\n",
-       "        2.4382672e+00,  2.3382673e+00,  2.2151306e+00,  2.1533613e+00,\n",
-       "        2.0648115e+00,  1.9996405e+00,  1.9082832e+00,  1.8066040e+00,\n",
-       "        1.6204576e+00,  1.3493049e+00,  1.0054842e+00,  6.8913281e-01,\n",
-       "        3.3323383e-01,  1.4457822e-02, -3.0425847e-01, -5.8607590e-01,\n",
-       "       -9.1431487e-01, -1.2876858e+00, -1.6730586e+00, -1.9924792e+00,\n",
-       "       -2.2738125e+00, -2.4758337e+00, -2.6504009e+00, -2.6952350e+00,\n",
-       "       -2.7033606e+00, -2.5792427e+00, -2.3177805e+00, -2.0400863e+00,\n",
-       "       -1.8883488e+00, -1.8833120e+00, -1.9530134e+00, -2.0706306e+00,\n",
-       "       -2.1360290e+00, -2.1571844e+00, -2.1714330e+00, -2.1855981e+00,\n",
-       "       -2.2411289e+00, -2.3323328e+00, -2.4005656e+00, -2.4642966e+00,\n",
-       "       -2.5642967e+00, -2.6870012e+00, -2.7685626e+00, -2.8996756e+00,\n",
-       "       -2.9802818e+00, -3.0381813e+00, -3.0381813e+00, -3.0457947e+00,\n",
-       "       -3.0311959e+00, -2.9665241e+00, -2.9086738e+00, -2.8437350e+00,\n",
-       "       -2.7221098e+00, -2.5933471e+00, -2.4784546e+00, -2.3401875e+00,\n",
-       "       -2.0825722e+00, -1.7152160e+00, -1.1975664e+00, -6.8563271e-01,\n",
-       "       -3.1816614e-01, -9.8838329e-02,  4.6386600e-02,  1.6885722e-01,\n",
-       "        3.4167039e-01,  5.7634234e-01,  7.8795254e-01,  1.0110936e+00,\n",
-       "        1.2726257e+00,  1.5959195e+00,  1.9082832e+00,  2.2103984e+00,\n",
-       "        2.5473833e+00,  2.9079728e+00,  3.2690179e+00,  3.6304777e+00,\n",
-       "        3.8923144e+00,  4.0947990e+00,  4.2520761e+00,  4.3246536e+00,\n",
-       "        4.4485741e+00,  4.5372853e+00,  4.6128712e+00,  4.6748824e+00,\n",
-       "        4.6729622e+00,  4.5788512e+00,  4.4132099e+00,  4.2476950e+00,\n",
-       "        4.1467042e+00,  4.0879664e+00,  4.0869913e+00,  4.0782433e+00,\n",
-       "        4.0695133e+00,  4.0108042e+00,  3.9666281e+00,  3.9225802e+00,\n",
-       "        3.8506083e+00,  3.8080897e+00,  3.7376442e+00,  3.6524706e+00,\n",
-       "        3.5105767e+00,  3.2889819e+00,  3.0459352e+00,  2.8030930e+00,\n",
-       "        2.4801145e+00,  2.1359003e+00,  1.7825713e+00,  1.4579911e+00,\n",
-       "        1.1486763e+00,  8.7415725e-01,  7.0551008e-01,  5.0219721e-01,\n",
-       "        3.4896487e-01,  2.1760847e-01,  1.1631182e-01,  2.3246735e-02,\n",
-       "       -6.7545488e-02, -1.8158327e-01, -2.9043049e-01, -3.9345881e-01,\n",
-       "       -4.8508096e-01, -5.4043049e-01, -6.2870842e-01, -6.8158323e-01,\n",
-       "       -7.3332542e-01, -8.3390903e-01, -9.3332541e-01, -9.8158330e-01,\n",
-       "       -1.0747418e+00, -1.1137565e+00, -1.2042682e+00, -1.2466193e+00,\n",
-       "       -1.2734977e+00, -1.3112695e+00, -1.4003732e+00, -1.4890165e+00,\n",
-       "       -1.5272464e+00, -1.5772463e+00, -1.6658750e+00, -1.6658750e+00,\n",
-       "       -1.7041174e+00, -1.6920106e+00, -1.6295878e+00, -1.6268044e+00,\n",
-       "       -1.5635128e+00, -1.5500007e+00, -1.4948459e+00, -1.5029285e+00,\n",
-       "       -1.3884906e+00, -1.2384906e+00, -1.0384907e+00, -7.8067786e-01,\n",
-       "       -5.3067780e-01, -2.3067778e-01,  1.4166179e-01,  4.9166182e-01,\n",
-       "        9.0012270e-01,  1.2454740e+00,  1.6291211e+00,  2.0266919e+00,\n",
-       "        2.5021420e+00,  3.0816903e+00,  3.7608957e+00,  4.4428158e+00,\n",
-       "        5.0874457e+00,  5.7061915e+00,  6.3684282e+00,  7.0056076e+00,\n",
-       "        7.5404782e+00,  7.9071598e+00,  8.1676960e+00,  8.4134922e+00,\n",
-       "        8.6157990e+00,  8.8698740e+00,  9.0673285e+00,  9.2457600e+00,\n",
-       "        9.3843632e+00,  9.4622803e+00,  9.4172163e+00,  9.3776941e+00,\n",
-       "        9.3341284e+00,  9.2315254e+00,  9.1070824e+00,  8.9953880e+00,\n",
-       "        8.8722477e+00,  8.8225851e+00,  8.8242722e+00,  8.7793255e+00,\n",
-       "        8.7444725e+00,  8.6702518e+00,  8.5332336e+00,  8.4093761e+00,\n",
-       "        8.2322760e+00,  8.0551815e+00,  7.8314743e+00,  7.5411944e+00,\n",
-       "        7.2911944e+00,  7.0280943e+00,  6.7791138e+00,  6.4803033e+00,\n",
-       "        6.1214867e+00,  5.6999063e+00,  5.2411556e+00,  4.7198000e+00,\n",
-       "        4.2776542e+00,  3.8282909e+00,  3.4217005e+00,  3.0256128e+00,\n",
-       "        2.7524867e+00,  2.5912068e+00,  2.4849684e+00,  2.4039991e+00,\n",
-       "        2.3798802e+00,  2.3262670e+00,  2.3240905e+00,  2.2899051e+00,\n",
-       "        2.2899051e+00,  2.2735453e+00,  2.1908267e+00,  2.1250515e+00,\n",
-       "        2.1273825e+00,  2.1822195e+00,  2.2213850e+00,  2.3156993e+00,\n",
-       "        2.3417325e+00,  2.3324814e+00,  2.2635233e+00,  2.1344621e+00,\n",
-       "        2.0554879e+00,  1.9708241e+00,  1.8361888e+00,  1.6806998e+00,\n",
-       "        1.4203515e+00,  9.6086144e-01,  3.8238627e-01, -1.9434059e-01,\n",
-       "       -5.6918478e-01, -6.5627074e-01, -4.9077415e-01, -3.4299111e-01,\n",
-       "       -1.5928864e-01, -5.7660520e-02,  4.4259906e-02,  1.1512250e-01],\n",
-       "      dtype=float32)"
+       "array([19.30085889,  0.07811406])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 146,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 11
+   "execution_count": 146
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-11-29T21:52:30.256333Z",
-     "start_time": "2025-11-29T21:52:30.246409Z"
+     "end_time": "2026-01-13T23:08:48.978947Z",
+     "start_time": "2026-01-13T23:08:48.853587Z"
     }
    },
    "cell_type": "code",
-   "source": "len(faiman_temp)",
-   "id": "53ffac58d50fb409",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "120"
-      ]
-     },
-     "execution_count": 87,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
+   "source": [
+    "#this graph shows solar irradiance, array temperature, and ambient temperature over July 2-6 (FSGP 2025)\n",
+    "\n",
+    "fig, ax1 = plt.subplots()\n",
+    "ax_twin = ax1.twinx()\n",
+    "\n",
+    "ax1.plot(merged_df['array_temperature'], label=\"Array Temperature\")\n",
+    "ax1.plot(merged_df['temperature_2m'], color='green', label=\"Ambient Temperature\")\n",
+    "ax1.plot(merged_df.index,faiman_temp, label = \"Faiman_Fitted\")\n",
+    "plt.plot(merged_df['shortwave_radiation_instant'], color=\"red\", label=\"Solar Irradiance\")\n",
+    "\n",
+    "ax1.set_xlabel(\"Time\")\n",
+    "ax1.set_ylabel(\"Solar Irradiance\")\n",
+    "ax_twin.set_ylabel(\"MosfetTemperatureA\")\n",
+    "\n",
+    "ax1.tick_params(\"x\", rotation=90)\n",
+    "\n",
+    "plt.legend(loc=\"upper left\")\n",
+    "ax1.legend(loc=\"upper left\")\n",
+    "plt.show()"
    ],
-   "execution_count": 87
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T21:54:46.173840Z",
-     "start_time": "2025-11-29T21:54:46.151825Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "df_15m.head()",
-   "id": "e2789dc942e61cc",
+   "id": "16245939d2e22766",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "                               value\n",
-       "2025-07-02 07:15:00+00:00  37.371079\n",
-       "2025-07-02 07:30:00+00:00  46.801625\n",
-       "2025-07-02 07:45:00+00:00  29.980004\n",
-       "2025-07-02 08:00:00+00:00  45.573409\n",
-       "2025-07-02 08:15:00+00:00  55.745558"
+       "<Figure size 640x480 with 2 Axes>"
       ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2025-07-02 07:15:00+00:00</th>\n",
-       "      <td>37.371079</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-07-02 07:30:00+00:00</th>\n",
-       "      <td>46.801625</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-07-02 07:45:00+00:00</th>\n",
-       "      <td>29.980004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-07-02 08:00:00+00:00</th>\n",
-       "      <td>45.573409</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-07-02 08:15:00+00:00</th>\n",
-       "      <td>55.745558</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ]
+      "image/png": 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"
      },
-     "execution_count": 90,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
-   "execution_count": 90
+   "execution_count": 152
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-29T21:52:50.120667Z",
-     "start_time": "2025-11-29T21:52:50.111626Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
-   "source": "df_15m.tail()",
-   "id": "87c603ad74af1dd1",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "                               value\n",
-       "2025-07-05 13:00:00+00:00  38.086209\n",
-       "2025-07-05 13:15:00+00:00  36.774658\n",
-       "2025-07-05 13:30:00+00:00  35.463106\n",
-       "2025-07-05 13:45:00+00:00  34.271664\n",
-       "2025-07-05 14:00:00+00:00  34.174615"
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2025-07-05 13:00:00+00:00</th>\n",
-       "      <td>38.086209</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-07-05 13:15:00+00:00</th>\n",
-       "      <td>36.774658</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-07-05 13:30:00+00:00</th>\n",
-       "      <td>35.463106</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-07-05 13:45:00+00:00</th>\n",
-       "      <td>34.271664</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-07-05 14:00:00+00:00</th>\n",
-       "      <td>34.174615</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ]
-     },
-     "execution_count": 89,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 89
+   "outputs": [],
+   "execution_count": null,
+   "source": "#trying an offset:",
+   "id": "512015ec2bff8ec0"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-11-29T19:17:19.791409Z",
-     "start_time": "2025-11-29T19:17:19.683835Z"
+     "end_time": "2026-01-13T22:43:24.236521Z",
+     "start_time": "2026-01-13T22:43:24.152472Z"
     }
    },
    "cell_type": "code",
-   "source": "",
-   "id": "ade5efa1f336b655",
+   "source": [
+    "plt.plot(np.array(merged_df['array_temperature']), label=\"Mosfet Temperature\")\n",
+    "plt.plot(faiman_model(xdata, *params) + 27, color = 'green', label = 'offset_faiman_temperature')\n",
+    "plt.plot(faiman_model(xdata, *params), color  ='red', label = 'faiman_predicted')\n",
+    "plt.title(\"Comparison of Faiman Model\")\n",
+    "plt.legend(loc=\"upper left\")\n",
+    "plt.show()"
+   ],
+   "id": "2374477e05f92f37",
    "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x27b9deece00>]"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
     {
      "data": {
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 23
+   "execution_count": 134
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-13T09:29:45.958671Z",
-     "start_time": "2026-01-13T09:29:36.083027Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
-   "source": [
-    "import numpy as np\n",
-    "from scipy.optimize import curve_fit\n",
-    "\n",
-    "# Faiman model\n",
-    "def faiman_model(G, Ta, v, u0, u1):\n",
-    "    return Ta + G / (u0 + u1 * v)\n",
-    "\n",
-    "def fit_faiman(G, Ta, v, T_array):\n",
-    "    popt, _ = curve_fit(\n",
-    "        lambda X, u0, u1: faiman_model(X[0], X[1], X[2], u0, u1),\n",
-    "        (G, Ta, v),\n",
-    "        T_array\n",
-    "    )\n",
-    "    return popt  # u0, u1\n"
-   ],
-   "id": "1aa707313aeb5993",
    "outputs": [],
-   "execution_count": 13
+   "execution_count": null,
+   "source": "#the faiman model doesn't seem to take into account the thermal mass i.e. the time it takes for the arrays themselves to heat up. following the appraoch in the paper linked, i used the thermal capacitance approach, namely consideringthe differential equation:\n",
+   "id": "af79779a9cf6cc6b"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-13T09:29:49.398396Z",
-     "start_time": "2026-01-13T09:29:49.391333Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "def plot_comparison(df):\n",
-    "    fig, ax1 = plt.subplots()\n",
-    "    ax2 = ax1.twinx()\n",
-    "\n",
-    "    ax1.plot(df.index, df[\"irradiance\"], label=\"Solar Irradiance\", color=\"red\")\n",
-    "    ax2.plot(df.index, df[\"value\"], label=\"Array Temp\")\n",
-    "\n",
-    "    ax1.set_xlabel(\"Time\")\n",
-    "    ax1.set_ylabel(\"Irradiance (W/m²)\")\n",
-    "    ax2.set_ylabel(\"Array Temperature (°C)\")\n",
-    "\n",
-    "    ax1.legend(loc=\"upper left\")\n",
-    "    ax2.legend(loc=\"upper right\")\n",
-    "\n",
-    "    plt.xticks(rotation=90)\n",
-    "    plt.show()\n"
-   ],
-   "id": "5fc38f6262d9e38e",
    "outputs": [],
-   "execution_count": 14
+   "execution_count": null,
+   "source": "",
+   "id": "db7f7a7c2a6a49ab"
   },
   {
    "metadata": {},

From 1b9cd591a818174f7fb71f9d773d274c61300657 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Tue, 13 Jan 2026 20:01:26 -0800
Subject: [PATCH 11/49] cleaning

---
 array_temp/.cache.sqlite                      | Bin 49152 -> 57344 bytes
 array_temp/coefficient_fitting.ipynb          | 287 ++-----
 array_temp/faiman_coefficients.ipynb          | 766 +++++++++++-------
 v4/array_temperature/arrayTemperatureModel.py |   2 -
 4 files changed, 532 insertions(+), 523 deletions(-)

diff --git a/array_temp/.cache.sqlite b/array_temp/.cache.sqlite
index 1aa1fdbdc6b86f8d885c7441ebfdea77888a789a..681cff879f6265ea877813f7947168a6a22d2520 100644
GIT binary patch
delta 1219
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delta 1863
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zt*_>i+&AV%UB>9ezsnEXxM*=MTe|MDx*e~@m=C_+YBt7T2ceHZtI!E(4f<8+*MWZn
z_%py}5i20JfY?RE-i2L3tctTs@Rtz34F7%DHP}~hz5@T6)hy!|ITYml1K7*rQnRrL
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zufcD@{{;SL@IS}h@^u8i5TfhXMUX)Dg}NAv{eeZ;I^w5*IR+Bi3EGnfsL&GCqkFI`
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wmM~7N1z!H@qt&18O+GE35H5uGU#PBKbq`34=g;3WUs*rtknw+Kv$`z(3j=cGJOBUy

diff --git a/array_temp/coefficient_fitting.ipynb b/array_temp/coefficient_fitting.ipynb
index 009826e..67fc9b0 100644
--- a/array_temp/coefficient_fitting.ipynb
+++ b/array_temp/coefficient_fitting.ipynb
@@ -1,15 +1,13 @@
 {
  "cells": [
   {
-   "cell_type": "code",
-   "id": "initial_id",
    "metadata": {
-    "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2026-01-07T03:57:28.810983Z",
-     "start_time": "2026-01-07T03:57:09.262265Z"
+     "end_time": "2026-01-14T03:48:36.765143Z",
+     "start_time": "2026-01-14T03:48:25.630944Z"
     }
    },
+   "cell_type": "code",
    "source": [
     "from data_tools import query\n",
     "from data_tools.collections import TimeSeries\n",
@@ -23,9 +21,9 @@
     "from datetime import datetime, time, date\n",
     "\n",
     "#each 5 seconds\n",
-    "utc_offset_h = 2\n",
-    "start_utc = time(0, 00, 00)  #querying i svancouver time, influxdb gives utc\n",
-    "stop_utc = time(23, 45, 00)\n",
+    "utc_offset_h =\n",
+    "start_utc = time(00+utc_offset_h, 00, 00)  #querying i svancouver time, influxdb gives utc\n",
+    "stop_utc = time(00+utc_offset_h, 00, 00)\n",
     "date_start = date(2025, 7, 2)\n",
     "date_stop = date(2025, 7, 6)\n",
     "\n",
@@ -46,17 +44,15 @@
     "\n",
     "\n"
    ],
+   "id": "initial_id",
    "outputs": [],
-   "execution_count": 1
+   "execution_count": 59
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-07T03:59:14.746195Z",
-     "start_time": "2026-01-07T03:59:14.675268Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
+   "outputs": [],
+   "execution_count": 19,
    "source": [
     "#save collected data of 15 minutes\n",
     "\n",
@@ -77,15 +73,13 @@
     "\n",
     "#time zone matching conventions??"
    ],
-   "id": "98caafc58a09e0c8",
-   "outputs": [],
-   "execution_count": 19
+   "id": "98caafc58a09e0c8"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-07T03:59:42.337364Z",
-     "start_time": "2026-01-07T03:59:30.836156Z"
+     "end_time": "2026-01-14T03:52:16.302993Z",
+     "start_time": "2026-01-14T03:52:03.032092Z"
     }
    },
    "cell_type": "code",
@@ -99,7 +93,7 @@
     "date_stop  = date(2025, 7, 6)\n",
     "\n",
     "# Local start/end\n",
-    "start_local = vancouver.localize(datetime.combine(date_start, time(23,45,0)))\n",
+    "start_local = vancouver.localize(datetime.combine(date_start, time(0,0,0)))\n",
     "stop_local  = vancouver.localize(datetime.combine(date_stop, time(0,0,0)))\n",
     "\n",
     "# Convert to UTC\n",
@@ -110,7 +104,7 @@
     "print(\"Stop UTC:\", stop_utc)    # 2025-07-07 06:45:00+00:00\n",
     "\n",
     "client = query.DBClient()\n",
-    "temp_array_fsgp_aliter = client.query_time_series(start_utc, stop_utc, field=\"MosfetTemperatureA\")\n",
+    "temp_array_fsgp = client.query_time_series(start_utc, stop_utc, field=\"MosfetTemperatureA\")\n",
     "speed_kph_aliter       = client.query_time_series(start_utc, stop_utc, \"MotorRotatingSpeed\")\n"
    ],
    "id": "856e8e887bd73795",
@@ -119,21 +113,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Start UTC: 2025-07-02 06:45:00+00:00\n",
+      "Start UTC: 2025-07-01 07:00:00+00:00\n",
       "Stop UTC: 2025-07-06 07:00:00+00:00\n"
      ]
     }
    ],
-   "execution_count": 20
+   "execution_count": 70
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-07T03:59:46.674302Z",
-     "start_time": "2026-01-07T03:59:46.595670Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
+   "outputs": [],
+   "execution_count": 21,
    "source": [
     "#save collected data of 15 minutes\n",
     "\n",
@@ -154,15 +145,13 @@
     "\n",
     "#time zone matching conventions??"
    ],
-   "id": "5f382e13f6628392",
-   "outputs": [],
-   "execution_count": 21
+   "id": "5f382e13f6628392"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-07T03:57:39.894705Z",
-     "start_time": "2026-01-07T03:57:38.856358Z"
+     "end_time": "2026-01-14T03:52:21.645631Z",
+     "start_time": "2026-01-14T03:52:20.313563Z"
     }
    },
    "cell_type": "code",
@@ -188,14 +177,14 @@
      "output_type": "stream",
      "text": [
       "                               value\n",
-      "2025-07-01 23:45:00+00:00  26.032398\n",
-      "2025-07-02 00:00:00+00:00  25.083505\n",
-      "2025-07-02 00:15:00+00:00  24.787958\n",
-      "2025-07-02 00:30:00+00:00  24.492412\n",
-      "2025-07-02 00:45:00+00:00  24.675365\n",
+      "2025-07-01 04:45:00+00:00  27.896234\n",
+      "2025-07-01 05:00:00+00:00  29.866226\n",
+      "2025-07-01 05:15:00+00:00  31.134633\n",
+      "2025-07-01 05:30:00+00:00  31.028468\n",
+      "2025-07-01 05:45:00+00:00  30.719860\n",
       "                               value\n",
       "2025-07-05 13:00:00+00:00  38.086209\n",
-      "2025-07-05 13:15:00+00:00  36.774657\n",
+      "2025-07-05 13:15:00+00:00  36.774658\n",
       "2025-07-05 13:30:00+00:00  35.463106\n",
       "2025-07-05 13:45:00+00:00  34.271664\n",
       "2025-07-05 14:00:00+00:00  34.174615\n"
@@ -205,12 +194,12 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_8744\\2898644639.py:10: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_24984\\2898644639.py:10: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
       "  df_15m = df.resample(\"15T\").mean()\n"
      ]
     }
    ],
-   "execution_count": 3
+   "execution_count": 71
   },
   {
    "metadata": {},
@@ -225,8 +214,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-07T03:57:43.206073Z",
-     "start_time": "2026-01-07T03:57:39.923337Z"
+     "end_time": "2026-01-14T03:52:24.802595Z",
+     "start_time": "2026-01-14T03:52:24.782459Z"
     }
    },
    "cell_type": "code",
@@ -292,17 +281,17 @@
       "\n",
       "Minutely15 data\n",
       "                          date  temperature_2m  shortwave_radiation_instant  \\\n",
-      "0   2025-07-02 00:00:00+00:00           -2.20                     5.588703   \n",
-      "1   2025-07-02 00:15:00+00:00           -2.15                     5.351785   \n",
-      "2   2025-07-02 00:30:00+00:00           -2.15                     5.014219   \n",
-      "3   2025-07-02 00:45:00+00:00           -2.05                     4.680000   \n",
-      "4   2025-07-02 01:00:00+00:00           -1.95                     4.680000   \n",
+      "0   2025-07-02 00:00:00+00:00          -2.213                     5.588703   \n",
+      "1   2025-07-02 00:15:00+00:00          -2.163                     5.351785   \n",
+      "2   2025-07-02 00:30:00+00:00          -2.113                     5.014219   \n",
+      "3   2025-07-02 00:45:00+00:00          -2.063                     4.680000   \n",
+      "4   2025-07-02 01:00:00+00:00          -1.963                     4.680000   \n",
       "..                        ...             ...                          ...   \n",
-      "379 2025-07-05 22:45:00+00:00           -2.00                     8.496305   \n",
-      "380 2025-07-05 23:00:00+00:00           -1.95                     8.788720   \n",
-      "381 2025-07-05 23:15:00+00:00           -1.95                     9.085988   \n",
-      "382 2025-07-05 23:30:00+00:00           -1.90                     9.199390   \n",
-      "383 2025-07-05 23:45:00+00:00           -1.85                     9.021574   \n",
+      "379 2025-07-05 22:45:00+00:00          -2.013                     8.496305   \n",
+      "380 2025-07-05 23:00:00+00:00          -1.963                     8.788720   \n",
+      "381 2025-07-05 23:15:00+00:00          -1.913                     9.085988   \n",
+      "382 2025-07-05 23:30:00+00:00          -1.863                     9.199390   \n",
+      "383 2025-07-05 23:45:00+00:00          -1.863                     9.021574   \n",
       "\n",
       "     wind_speed_10m  \n",
       "0        124.831741  \n",
@@ -321,26 +310,26 @@
      ]
     }
    ],
-   "execution_count": 4
+   "execution_count": 72
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-07T03:57:43.289544Z",
-     "start_time": "2026-01-07T03:57:43.266224Z"
+     "end_time": "2026-01-14T03:52:25.458959Z",
+     "start_time": "2026-01-14T03:52:25.443206Z"
     }
    },
    "cell_type": "code",
    "source": "minutely_15_dataframe = minutely_15_dataframe[minutely_15_dataframe['date']<'2025-07-05 14:15:00']",
    "id": "1f24e675de2a0cdd",
    "outputs": [],
-   "execution_count": 5
+   "execution_count": 73
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-07T03:57:43.322372Z",
-     "start_time": "2026-01-07T03:57:43.307373Z"
+     "end_time": "2026-01-14T03:52:26.159701Z",
+     "start_time": "2026-01-14T03:52:26.147935Z"
     }
    },
    "cell_type": "code",
@@ -351,13 +340,13 @@
    ],
    "id": "cea74153df653c65",
    "outputs": [],
-   "execution_count": 6
+   "execution_count": 74
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-07T03:57:43.365624Z",
-     "start_time": "2026-01-07T03:57:43.343293Z"
+     "end_time": "2026-01-14T03:52:26.874448Z",
+     "start_time": "2026-01-14T03:52:26.856107Z"
     }
    },
    "cell_type": "code",
@@ -368,11 +357,11 @@
      "data": {
       "text/plain": [
        "                         date  temperature_2m  shortwave_radiation_instant  \\\n",
-       "340 2025-07-05 13:00:00+00:00            1.15                    29.548521   \n",
-       "341 2025-07-05 13:15:00+00:00            0.90                    27.475807   \n",
-       "342 2025-07-05 13:30:00+00:00            0.65                    25.202570   \n",
-       "343 2025-07-05 13:45:00+00:00            0.40                    22.461807   \n",
-       "344 2025-07-05 14:00:00+00:00            0.25                    20.056877   \n",
+       "340 2025-07-05 13:00:00+00:00           1.137                    29.548521   \n",
+       "341 2025-07-05 13:15:00+00:00           0.887                    27.475807   \n",
+       "342 2025-07-05 13:30:00+00:00           0.637                    25.202570   \n",
+       "343 2025-07-05 13:45:00+00:00           0.437                    22.461807   \n",
+       "344 2025-07-05 14:00:00+00:00           0.237                    20.056877   \n",
        "\n",
        "     wind_speed_10m  \n",
        "340       58.205017  \n",
@@ -410,35 +399,35 @@
        "    <tr>\n",
        "      <th>340</th>\n",
        "      <td>2025-07-05 13:00:00+00:00</td>\n",
-       "      <td>1.15</td>\n",
+       "      <td>1.137</td>\n",
        "      <td>29.548521</td>\n",
        "      <td>58.205017</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>341</th>\n",
        "      <td>2025-07-05 13:15:00+00:00</td>\n",
-       "      <td>0.90</td>\n",
+       "      <td>0.887</td>\n",
        "      <td>27.475807</td>\n",
        "      <td>22.902834</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342</th>\n",
        "      <td>2025-07-05 13:30:00+00:00</td>\n",
-       "      <td>0.65</td>\n",
+       "      <td>0.637</td>\n",
        "      <td>25.202570</td>\n",
        "      <td>2.313184</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>343</th>\n",
        "      <td>2025-07-05 13:45:00+00:00</td>\n",
-       "      <td>0.40</td>\n",
+       "      <td>0.437</td>\n",
        "      <td>22.461807</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>344</th>\n",
        "      <td>2025-07-05 14:00:00+00:00</td>\n",
-       "      <td>0.25</td>\n",
+       "      <td>0.237</td>\n",
        "      <td>20.056877</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
@@ -447,132 +436,12 @@
        "</div>"
       ]
      },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 7
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-07T03:57:43.447030Z",
-     "start_time": "2026-01-07T03:57:43.436435Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "len(minutely_15_dataframe)",
-   "id": "592396d2c42fe633",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "345"
-      ]
-     },
-     "execution_count": 8,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 8
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-07T03:57:43.501909Z",
-     "start_time": "2026-01-07T03:57:43.481229Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "print(\"\\nMinutely15 data\\n\", minutely_15_dataframe)\n",
-   "id": "b02b2e521e671912",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Minutely15 data\n",
-      "                          date  temperature_2m  shortwave_radiation_instant  \\\n",
-      "0   2025-07-02 00:00:00+00:00           -2.20                     5.588703   \n",
-      "1   2025-07-02 00:15:00+00:00           -2.15                     5.351785   \n",
-      "2   2025-07-02 00:30:00+00:00           -2.15                     5.014219   \n",
-      "3   2025-07-02 00:45:00+00:00           -2.05                     4.680000   \n",
-      "4   2025-07-02 01:00:00+00:00           -1.95                     4.680000   \n",
-      "..                        ...             ...                          ...   \n",
-      "340 2025-07-05 13:00:00+00:00            1.15                    29.548521   \n",
-      "341 2025-07-05 13:15:00+00:00            0.90                    27.475807   \n",
-      "342 2025-07-05 13:30:00+00:00            0.65                    25.202570   \n",
-      "343 2025-07-05 13:45:00+00:00            0.40                    22.461807   \n",
-      "344 2025-07-05 14:00:00+00:00            0.25                    20.056877   \n",
-      "\n",
-      "     wind_speed_10m  \n",
-      "0        124.831741  \n",
-      "1        164.539490  \n",
-      "2        205.015503  \n",
-      "3        243.104050  \n",
-      "4        276.808258  \n",
-      "..              ...  \n",
-      "340       58.205017  \n",
-      "341       22.902834  \n",
-      "342        2.313184  \n",
-      "343        0.000000  \n",
-      "344        0.000000  \n",
-      "\n",
-      "[345 rows x 4 columns]\n"
-     ]
-    }
-   ],
-   "execution_count": 9
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-07T03:57:43.540177Z",
-     "start_time": "2026-01-07T03:57:43.528157Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "print(df_15m)",
-   "id": "58bd4131e7093a9d",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                               value\n",
-      "2025-07-01 23:45:00+00:00  26.032398\n",
-      "2025-07-02 00:00:00+00:00  25.083505\n",
-      "2025-07-02 00:15:00+00:00  24.787958\n",
-      "2025-07-02 00:30:00+00:00  24.492412\n",
-      "2025-07-02 00:45:00+00:00  24.675365\n",
-      "...                              ...\n",
-      "2025-07-05 13:00:00+00:00  38.086209\n",
-      "2025-07-05 13:15:00+00:00  36.774657\n",
-      "2025-07-05 13:30:00+00:00  35.463106\n",
-      "2025-07-05 13:45:00+00:00  34.271664\n",
-      "2025-07-05 14:00:00+00:00  34.174615\n",
-      "\n",
-      "[346 rows x 1 columns]\n"
-     ]
-    }
-   ],
-   "execution_count": 10
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-07T03:57:43.693592Z",
-     "start_time": "2026-01-07T03:57:43.689899Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "df_15m = df_15m.iloc[1:]",
-   "id": "a7bbd11d36fdb821",
-   "outputs": [],
-   "execution_count": 11
+   "execution_count": 75
   },
   {
    "metadata": {},
@@ -593,8 +462,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-07T03:57:43.948394Z",
-     "start_time": "2026-01-07T03:57:43.792528Z"
+     "end_time": "2026-01-14T03:52:29.773018Z",
+     "start_time": "2026-01-14T03:52:29.412010Z"
     }
    },
    "cell_type": "code",
@@ -606,8 +475,7 @@
     "\n",
     "plt.plot(df_15m.index, df_15m, label=\"Array Temperature\")\n",
     "plt.plot(minutely_15_dataframe['date'], minutely_15_dataframe['temperature_2m'], color='green', label=\"Ambient Temperature\")\n",
-    "# plt.plot(minutely_15_data['date'], minutely_15_data['wind_speed_10m'], color = 'blue', label = \"Wind Speed 10m\")\n",
-    "# plt.plot(speed_kph.datetime_x_axis, speed_kph, color = 'yellow', label = \"Speed KPH\")\n",
+    "\n",
     "\n",
     "ax1.plot(minutely_15_dataframe['date'], minutely_15_dataframe['shortwave_radiation_instant'], color=\"red\",\n",
     "         label=\"Solar Irradiance\")\n",
@@ -629,29 +497,22 @@
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 12
+   "execution_count": 76
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-07T03:57:44.141479Z",
-     "start_time": "2026-01-07T03:57:44.008504Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
-   "source": "plt.plot(minutely_15_dataframe['date'], minutely_15_dataframe['shortwave_radiation_instant'])",
-   "id": "3d07f9ce1c4e6bb4",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1871a105670>]"
+       "[<matplotlib.lines.Line2D at 0x17bc3f5a630>]"
       ]
      },
      "execution_count": 14,
@@ -669,7 +530,9 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 14
+   "execution_count": 14,
+   "source": "plt.plot(minutely_15_dataframe['date'], minutely_15_dataframe['shortwave_radiation_instant'])",
+   "id": "3d07f9ce1c4e6bb4"
   },
   {
    "metadata": {},
diff --git a/array_temp/faiman_coefficients.ipynb b/array_temp/faiman_coefficients.ipynb
index daa6c4a..5e3593d 100644
--- a/array_temp/faiman_coefficients.ipynb
+++ b/array_temp/faiman_coefficients.ipynb
@@ -19,8 +19,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T18:11:41.455198Z",
-     "start_time": "2026-01-13T18:11:32.257953Z"
+     "end_time": "2026-01-14T04:00:52.158338Z",
+     "start_time": "2026-01-14T04:00:40.315802Z"
     }
    },
    "cell_type": "code",
@@ -34,10 +34,10 @@
     "import dill\n",
     "import os\n",
     "\n",
-    "#each 5 seconds\n",
+    "\n",
     "utc_offset_h = 7\n",
-    "start_utc = time(12, 00, 00)  #querying is vancouver time, influxdb gives utc\n",
-    "stop_utc = time(00, 00, 00)\n",
+    "start_utc = time(00 + utc_offset_h, 00, 00)  #querying is vancouver time, influxdb gives utc\n",
+    "stop_utc = time(00 + utc_offset_h, 00, 00)\n",
     "date_start = date(2025, 7, 2)\n",
     "date_stop = date(2025, 7, 6)\n",
     "start_time = datetime.combine(date_start, start_utc, tzinfo=timezone.utc)\n",
@@ -49,13 +49,13 @@
    ],
    "id": "8b2b4006671bdc31",
    "outputs": [],
-   "execution_count": 8
+   "execution_count": 288
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T18:11:42.314915Z",
-     "start_time": "2026-01-13T18:11:42.283529Z"
+     "end_time": "2026-01-14T03:36:08.886968Z",
+     "start_time": "2026-01-14T03:36:08.838530Z"
     }
    },
    "cell_type": "code",
@@ -81,13 +81,13 @@
    ],
    "id": "a807d5de05d7c8b0",
    "outputs": [],
-   "execution_count": 9
+   "execution_count": 239
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T18:11:43.242897Z",
-     "start_time": "2026-01-13T18:11:43.232987Z"
+     "end_time": "2026-01-14T03:36:09.360790Z",
+     "start_time": "2026-01-14T03:36:09.352530Z"
     }
    },
    "cell_type": "code",
@@ -100,18 +100,18 @@
        "data_tools.collections.time_series.TimeSeries"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 240,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 10
+   "execution_count": 240
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T18:11:43.971080Z",
-     "start_time": "2026-01-13T18:11:43.962961Z"
+     "end_time": "2026-01-14T03:36:09.848404Z",
+     "start_time": "2026-01-14T03:36:09.844180Z"
     }
    },
    "cell_type": "code",
@@ -121,7 +121,7 @@
    ],
    "id": "1300423cd026c3ca",
    "outputs": [],
-   "execution_count": 11
+   "execution_count": 241
   },
   {
    "metadata": {},
@@ -136,8 +136,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T21:57:09.918259Z",
-     "start_time": "2026-01-13T21:57:07.406593Z"
+     "end_time": "2026-01-14T03:57:04.589766Z",
+     "start_time": "2026-01-14T03:57:03.552991Z"
     }
    },
    "cell_type": "code",
@@ -162,7 +162,7 @@
     "\t\"start_date\": \"2025-07-02\",\n",
     "\t\"end_date\": \"2025-07-07\",\n",
     "\t\"minutely_15\": [\"temperature_2m\", \"shortwave_radiation_instant\", \"wind_speed_10m\"],\n",
-    "\t\"timezone\": \"America/Chicago\",\n",
+    "\t\"timezone\": \"auto\",\n",
     "}\n",
     "responses = openmeteo.weather_api(url, params=params)\n",
     "\n",
@@ -191,7 +191,7 @@
     "minutely_15_data[\"wind_speed_10m\"] = minutely_15_wind_speed_10m\n",
     "\n",
     "minutely_15_dataframe = pd.DataFrame(data = minutely_15_data)\n",
-    "print(\"\\nMinutely15 data\\n\", minutely_15_dataframe)"
+    "print(\"\\nMinutely15 data\\n\", minutely_15_dataframe)\n"
    ],
    "id": "f718cf3615289b31",
    "outputs": [
@@ -201,41 +201,41 @@
      "text": [
       "Coordinates: 37.0°N 86.5°E\n",
       "Elevation: 5139.0 m asl\n",
-      "Timezone: b'America/Chicago'b'GMT-6'\n",
-      "Timezone difference to GMT+0: -21600s\n",
+      "Timezone: b'Asia/Shanghai'b'GMT+8'\n",
+      "Timezone difference to GMT+0: 28800s\n",
       "\n",
       "Minutely15 data\n",
       "                          date  temperature_2m  shortwave_radiation_instant  \\\n",
-      "0   2025-07-02 06:00:00+00:00           1.487                   505.264526   \n",
-      "1   2025-07-02 06:15:00+00:00           1.587                   516.453796   \n",
-      "2   2025-07-02 06:30:00+00:00           1.787                   527.607666   \n",
-      "3   2025-07-02 06:45:00+00:00           1.937                   528.744202   \n",
-      "4   2025-07-02 07:00:00+00:00           1.987                   526.880981   \n",
+      "0   2025-07-01 16:00:00+00:00          -0.013                          0.0   \n",
+      "1   2025-07-01 16:15:00+00:00          -0.113                          0.0   \n",
+      "2   2025-07-01 16:30:00+00:00          -0.213                          0.0   \n",
+      "3   2025-07-01 16:45:00+00:00          -0.363                          0.0   \n",
+      "4   2025-07-01 17:00:00+00:00          -0.463                          0.0   \n",
       "..                        ...             ...                          ...   \n",
-      "571 2025-07-08 04:45:00+00:00           4.137                   525.869263   \n",
-      "572 2025-07-08 05:00:00+00:00           4.237                   537.033020   \n",
-      "573 2025-07-08 05:15:00+00:00           4.387                   552.215698   \n",
-      "574 2025-07-08 05:30:00+00:00           4.487                   567.368408   \n",
-      "575 2025-07-08 05:45:00+00:00           4.587                   573.449036   \n",
+      "571 2025-07-07 14:45:00+00:00           1.587                          0.0   \n",
+      "572 2025-07-07 15:00:00+00:00           1.437                          0.0   \n",
+      "573 2025-07-07 15:15:00+00:00           1.287                          0.0   \n",
+      "574 2025-07-07 15:30:00+00:00           1.187                          0.0   \n",
+      "575 2025-07-07 15:45:00+00:00           1.087                          0.0   \n",
       "\n",
       "     wind_speed_10m  \n",
-      "0         23.688984  \n",
-      "1         23.617756  \n",
-      "2         23.557316  \n",
-      "3         23.507751  \n",
-      "4         23.469128  \n",
+      "0          8.825508  \n",
+      "1         10.594036  \n",
+      "2         13.138765  \n",
+      "3         15.391840  \n",
+      "4         16.873980  \n",
       "..              ...  \n",
-      "571       30.466295  \n",
-      "572       29.964457  \n",
-      "573       29.462870  \n",
-      "574       28.666941  \n",
-      "575       28.373846  \n",
+      "571       15.580141  \n",
+      "572       13.237038  \n",
+      "573       11.753876  \n",
+      "574       10.685391  \n",
+      "575       10.464797  \n",
       "\n",
       "[576 rows x 4 columns]\n"
      ]
     }
    ],
-   "execution_count": 101
+   "execution_count": 274
   },
   {
    "metadata": {},
@@ -253,118 +253,83 @@
    "id": "dd2a5efc6d21b409"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-13T21:58:39.932854Z",
-     "start_time": "2026-01-13T21:58:39.926640Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "print(merged_df['shortwave_radiation_instant'].describe())\n",
-   "id": "255c40a73c01f500",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "count    316.000000\n",
-      "mean      22.459007\n",
-      "std       10.592769\n",
-      "min        3.096837\n",
-      "25%       14.742397\n",
-      "50%       20.974435\n",
-      "75%       28.357857\n",
-      "max       48.654636\n",
-      "Name: shortwave_radiation_instant, dtype: float64\n"
-     ]
-    }
-   ],
-   "execution_count": 104
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Plot some relevant data. this will further be used to generate the relevant coefficients",
+   "id": "9fe9b268872c73e6"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T21:58:09.542421Z",
-     "start_time": "2026-01-13T21:58:09.536271Z"
+     "end_time": "2026-01-14T03:36:16.895778Z",
+     "start_time": "2026-01-14T03:36:15.774130Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "minutely_15_dataframe['date'].head(20)\n",
-    "print(minutely_15_dataframe['shortwave_radiation_instant'].head(20))\n"
+    "plt.plot(temp_array_fsgp.datetime_x_axis, temp_array_fsgp, label=\"Array Temperature\")\n",
+    "plt.ylabel(\"MosfetTemperatureA\")\n",
+    "\n",
+    "plt.tick_params(\"x\", rotation=90)"
    ],
-   "id": "573cc20871481ce0",
+   "id": "9cde1a3b5eb68331",
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0     505.264526\n",
-      "1     516.453796\n",
-      "2     527.607666\n",
-      "3     528.744202\n",
-      "4     526.880981\n",
-      "5     522.027100\n",
-      "6     513.199951\n",
-      "7     500.415741\n",
-      "8     484.679749\n",
-      "9     465.017517\n",
-      "10    445.389191\n",
-      "11    424.808441\n",
-      "12    401.317627\n",
-      "13    379.828491\n",
-      "14    362.278351\n",
-      "15    352.529755\n",
-      "16    353.420837\n",
-      "17    354.186615\n",
-      "18    346.120880\n",
-      "19    323.533844\n",
-      "Name: shortwave_radiation_instant, dtype: float32\n"
-     ]
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
-   "execution_count": 103
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "Plot some relevant data. this will further be used to generate the relevant coefficients",
-   "id": "9fe9b268872c73e6"
+   "execution_count": 245
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T18:17:08.973617Z",
-     "start_time": "2026-01-13T18:17:07.792338Z"
+     "end_time": "2026-01-14T03:36:19.982255Z",
+     "start_time": "2026-01-14T03:36:17.851Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "plt.plot(temp_array_fsgp.datetime_x_axis, temp_array_fsgp, label=\"Array Temperature\")\n",
-    "plt.ylabel(\"MosfetTemperatureA\")\n",
     "\n",
-    "plt.tick_params(\"x\", rotation=90)"
+    "plt.plot(temp_array_fsgp.datetime_x_axis, temp_array_fsgp, label=\"Array Temperature\")\n",
+    "plt.plot(speed_kph.datetime_x_axis, speed_kph, label=\"Speed (kph)\")"
    ],
-   "id": "9cde1a3b5eb68331",
+   "id": "c5ccc00464a923b0",
    "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1b48cb7a060>]"
+      ]
+     },
+     "execution_count": 246,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
     {
      "data": {
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 15
+   "execution_count": 246
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T21:59:01.139683Z",
-     "start_time": "2026-01-13T21:58:59.840291Z"
+     "end_time": "2026-01-14T04:00:56.194770Z",
+     "start_time": "2026-01-14T04:00:53.505402Z"
     }
    },
    "cell_type": "code",
@@ -375,6 +340,7 @@
     "ax_twin = ax1.twinx()\n",
     "\n",
     "plt.plot(temp_array_fsgp.datetime_x_axis, temp_array_fsgp, label=\"Array Temperature\")\n",
+    "plt.plot(speed_kph.datetime_x_axis, speed_kph, label=\"Speed (kph)\")\n",
     "plt.plot(minutely_15_data['date'], minutely_15_data['temperature_2m'], color='green', label=\"Ambient Temperature\")\n",
     "ax1.plot(minutely_15_data['date'], minutely_15_data['shortwave_radiation_instant'], color=\"red\",\n",
     "         label=\"Solar Irradiance\")\n",
@@ -396,13 +362,13 @@
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ],
-      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 105
+   "execution_count": 289
   },
   {
    "metadata": {
@@ -420,8 +386,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T21:59:19.716357Z",
-     "start_time": "2026-01-13T21:59:18.790075Z"
+     "end_time": "2026-01-14T03:57:37.180216Z",
+     "start_time": "2026-01-14T03:57:35.991597Z"
     }
    },
    "cell_type": "code",
@@ -447,11 +413,11 @@
      "output_type": "stream",
      "text": [
       "                               value\n",
-      "2025-07-02 07:15:00+00:00  37.371079\n",
-      "2025-07-02 07:30:00+00:00  46.801625\n",
-      "2025-07-02 07:45:00+00:00  29.980004\n",
-      "2025-07-02 08:00:00+00:00  45.573409\n",
-      "2025-07-02 08:15:00+00:00  55.745558\n"
+      "2025-07-02 00:45:00+00:00  25.031385\n",
+      "2025-07-02 01:00:00+00:00  25.388523\n",
+      "2025-07-02 01:15:00+00:00  25.761938\n",
+      "2025-07-02 01:30:00+00:00  26.135353\n",
+      "2025-07-02 01:45:00+00:00  26.508768\n"
      ]
     },
     {
@@ -463,13 +429,13 @@
      ]
     }
    ],
-   "execution_count": 107
+   "execution_count": 276
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T21:59:22.599682Z",
-     "start_time": "2026-01-13T21:59:22.588459Z"
+     "end_time": "2026-01-14T03:57:37.373487Z",
+     "start_time": "2026-01-14T03:57:37.367422Z"
     }
    },
    "cell_type": "code",
@@ -479,13 +445,13 @@
    ],
    "id": "a30bec6abef1672a",
    "outputs": [],
-   "execution_count": 108
+   "execution_count": 277
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T21:59:23.329227Z",
-     "start_time": "2026-01-13T21:59:23.295121Z"
+     "end_time": "2026-01-14T03:57:37.441412Z",
+     "start_time": "2026-01-14T03:57:37.436456Z"
     }
    },
    "cell_type": "code",
@@ -500,13 +466,13 @@
    ],
    "id": "feced1770070bc8b",
    "outputs": [],
-   "execution_count": 109
+   "execution_count": 278
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T21:59:27.182855Z",
-     "start_time": "2026-01-13T21:59:27.172396Z"
+     "end_time": "2026-01-14T03:57:38.503173Z",
+     "start_time": "2026-01-14T03:57:38.487933Z"
     }
    },
    "cell_type": "code",
@@ -518,19 +484,19 @@
       "text/plain": [
        "                           temperature_2m  shortwave_radiation_instant  \\\n",
        "date                                                                     \n",
-       "2025-07-02 06:00:00+00:00           1.487                   505.264526   \n",
-       "2025-07-02 06:15:00+00:00           1.587                   516.453796   \n",
-       "2025-07-02 06:30:00+00:00           1.787                   527.607666   \n",
-       "2025-07-02 06:45:00+00:00           1.937                   528.744202   \n",
-       "2025-07-02 07:00:00+00:00           1.987                   526.880981   \n",
+       "2025-07-01 16:00:00+00:00          -0.013                          0.0   \n",
+       "2025-07-01 16:15:00+00:00          -0.113                          0.0   \n",
+       "2025-07-01 16:30:00+00:00          -0.213                          0.0   \n",
+       "2025-07-01 16:45:00+00:00          -0.363                          0.0   \n",
+       "2025-07-01 17:00:00+00:00          -0.463                          0.0   \n",
        "\n",
        "                           wind_speed_10m  \n",
        "date                                       \n",
-       "2025-07-02 06:00:00+00:00       23.688984  \n",
-       "2025-07-02 06:15:00+00:00       23.617756  \n",
-       "2025-07-02 06:30:00+00:00       23.557316  \n",
-       "2025-07-02 06:45:00+00:00       23.507751  \n",
-       "2025-07-02 07:00:00+00:00       23.469128  "
+       "2025-07-01 16:00:00+00:00        8.825508  \n",
+       "2025-07-01 16:15:00+00:00       10.594036  \n",
+       "2025-07-01 16:30:00+00:00       13.138765  \n",
+       "2025-07-01 16:45:00+00:00       15.391840  \n",
+       "2025-07-01 17:00:00+00:00       16.873980  "
       ],
       "text/html": [
        "<div>\n",
@@ -564,52 +530,52 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>2025-07-02 06:00:00+00:00</th>\n",
-       "      <td>1.487</td>\n",
-       "      <td>505.264526</td>\n",
-       "      <td>23.688984</td>\n",
+       "      <th>2025-07-01 16:00:00+00:00</th>\n",
+       "      <td>-0.013</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.825508</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-02 06:15:00+00:00</th>\n",
-       "      <td>1.587</td>\n",
-       "      <td>516.453796</td>\n",
-       "      <td>23.617756</td>\n",
+       "      <th>2025-07-01 16:15:00+00:00</th>\n",
+       "      <td>-0.113</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.594036</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-02 06:30:00+00:00</th>\n",
-       "      <td>1.787</td>\n",
-       "      <td>527.607666</td>\n",
-       "      <td>23.557316</td>\n",
+       "      <th>2025-07-01 16:30:00+00:00</th>\n",
+       "      <td>-0.213</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13.138765</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-02 06:45:00+00:00</th>\n",
-       "      <td>1.937</td>\n",
-       "      <td>528.744202</td>\n",
-       "      <td>23.507751</td>\n",
+       "      <th>2025-07-01 16:45:00+00:00</th>\n",
+       "      <td>-0.363</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.391840</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-02 07:00:00+00:00</th>\n",
-       "      <td>1.987</td>\n",
-       "      <td>526.880981</td>\n",
-       "      <td>23.469128</td>\n",
+       "      <th>2025-07-01 17:00:00+00:00</th>\n",
+       "      <td>-0.463</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>16.873980</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ]
      },
-     "execution_count": 110,
+     "execution_count": 279,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 110
+   "execution_count": 279
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T21:59:27.990777Z",
-     "start_time": "2026-01-13T21:59:27.982711Z"
+     "end_time": "2026-01-14T03:57:39.127974Z",
+     "start_time": "2026-01-14T03:57:39.109258Z"
     }
    },
    "cell_type": "code",
@@ -629,18 +595,18 @@
      "output_type": "stream",
      "text": [
       "Influx 15min index:\n",
-      "DatetimeIndex(['2025-07-02 07:15:00+00:00', '2025-07-02 07:30:00+00:00',\n",
-      "               '2025-07-02 07:45:00+00:00', '2025-07-02 08:00:00+00:00',\n",
-      "               '2025-07-02 08:15:00+00:00'],\n",
+      "DatetimeIndex(['2025-07-02 00:45:00+00:00', '2025-07-02 01:00:00+00:00',\n",
+      "               '2025-07-02 01:15:00+00:00', '2025-07-02 01:30:00+00:00',\n",
+      "               '2025-07-02 01:45:00+00:00'],\n",
       "              dtype='datetime64[ns, UTC]', freq='15min')\n",
       "DatetimeIndex(['2025-07-05 13:00:00+00:00', '2025-07-05 13:15:00+00:00',\n",
       "               '2025-07-05 13:30:00+00:00', '2025-07-05 13:45:00+00:00',\n",
       "               '2025-07-05 14:00:00+00:00'],\n",
       "              dtype='datetime64[ns, UTC]', freq='15min')\n",
       "Weather index:\n",
-      "DatetimeIndex(['2025-07-02 06:00:00+00:00', '2025-07-02 06:15:00+00:00',\n",
-      "               '2025-07-02 06:30:00+00:00', '2025-07-02 06:45:00+00:00',\n",
-      "               '2025-07-02 07:00:00+00:00'],\n",
+      "DatetimeIndex(['2025-07-01 16:00:00+00:00', '2025-07-01 16:15:00+00:00',\n",
+      "               '2025-07-01 16:30:00+00:00', '2025-07-01 16:45:00+00:00',\n",
+      "               '2025-07-01 17:00:00+00:00'],\n",
       "              dtype='datetime64[ns, UTC]', name='date', freq=None)\n",
       "                           temperature_2m  shortwave_radiation_instant  \\\n",
       "date                                                                     \n",
@@ -660,13 +626,13 @@
      ]
     }
    ],
-   "execution_count": 111
+   "execution_count": 280
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T21:59:29.502734Z",
-     "start_time": "2026-01-13T21:59:29.469182Z"
+     "end_time": "2026-01-14T03:57:39.709507Z",
+     "start_time": "2026-01-14T03:57:39.693995Z"
     }
    },
    "cell_type": "code",
@@ -697,29 +663,29 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Overlap start: 2025-07-02 07:15:00+00:00\n",
-      "Overlap end: 2025-07-05 14:00:00+00:00\n",
-      "Number of aligned points: 316\n"
+      "overlap start: 2025-07-02 00:45:00+00:00\n",
+      "overlap end: 2025-07-05 14:00:00+00:00\n",
+      "no of aligned points: 342\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_14188\\439453639.py:6: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_14188\\4230900981.py:6: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
       "  df_15m_copy.index = df_15m_copy.index.floor(\"15T\")\n",
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_14188\\439453639.py:7: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_14188\\4230900981.py:7: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
       "  weather_df.index = weather_df.index.floor(\"15T\")\n"
      ]
     }
    ],
-   "execution_count": 112
+   "execution_count": 281
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T21:59:37.315273Z",
-     "start_time": "2026-01-13T21:59:37.303549Z"
+     "end_time": "2026-01-14T03:57:40.479854Z",
+     "start_time": "2026-01-14T03:57:40.468932Z"
     }
    },
    "cell_type": "code",
@@ -730,11 +696,11 @@
      "data": {
       "text/plain": [
        "                               value  temperature_2m  \\\n",
-       "2025-07-02 07:15:00+00:00  37.371079           1.787   \n",
-       "2025-07-02 07:30:00+00:00  46.801625           1.487   \n",
-       "2025-07-02 07:45:00+00:00  29.980004           1.137   \n",
-       "2025-07-02 08:00:00+00:00  45.573409           0.887   \n",
-       "2025-07-02 08:15:00+00:00  55.745558           0.687   \n",
+       "2025-07-02 00:45:00+00:00  25.031385          -2.063   \n",
+       "2025-07-02 01:00:00+00:00  25.388523          -1.963   \n",
+       "2025-07-02 01:15:00+00:00  25.761938          -1.863   \n",
+       "2025-07-02 01:30:00+00:00  26.135353          -1.713   \n",
+       "2025-07-02 01:45:00+00:00  26.508768          -1.513   \n",
        "...                              ...             ...   \n",
        "2025-07-05 13:00:00+00:00  38.086209           1.137   \n",
        "2025-07-05 13:15:00+00:00  36.774658           0.887   \n",
@@ -743,11 +709,11 @@
        "2025-07-05 14:00:00+00:00  34.174615           0.237   \n",
        "\n",
        "                           shortwave_radiation_instant  wind_speed_10m  \n",
-       "2025-07-02 07:15:00+00:00                   522.027100       23.177401  \n",
-       "2025-07-02 07:30:00+00:00                   513.199951       22.406927  \n",
-       "2025-07-02 07:45:00+00:00                   500.415741       21.945240  \n",
-       "2025-07-02 08:00:00+00:00                   484.679749       21.817902  \n",
-       "2025-07-02 08:15:00+00:00                   465.017517       21.767351  \n",
+       "2025-07-02 00:45:00+00:00                   243.104050        4.680000  \n",
+       "2025-07-02 01:00:00+00:00                   276.808258        4.680000  \n",
+       "2025-07-02 01:15:00+00:00                   309.431335        4.680000  \n",
+       "2025-07-02 01:30:00+00:00                   344.148895        5.154415  \n",
+       "2025-07-02 01:45:00+00:00                   386.145966        5.692100  \n",
        "...                                                ...             ...  \n",
        "2025-07-05 13:00:00+00:00                    58.205017       29.548521  \n",
        "2025-07-05 13:15:00+00:00                    22.902834       27.475807  \n",
@@ -755,7 +721,7 @@
        "2025-07-05 13:45:00+00:00                     0.000000       22.461807  \n",
        "2025-07-05 14:00:00+00:00                     0.000000       20.056877  \n",
        "\n",
-       "[316 rows x 4 columns]"
+       "[342 rows x 4 columns]"
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       "text/html": [
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@@ -784,39 +750,39 @@
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-       "      <th>2025-07-02 07:15:00+00:00</th>\n",
-       "      <td>37.371079</td>\n",
-       "      <td>1.787</td>\n",
-       "      <td>522.027100</td>\n",
-       "      <td>23.177401</td>\n",
+       "      <th>2025-07-02 00:45:00+00:00</th>\n",
+       "      <td>25.031385</td>\n",
+       "      <td>-2.063</td>\n",
+       "      <td>243.104050</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-02 07:30:00+00:00</th>\n",
-       "      <td>46.801625</td>\n",
-       "      <td>1.487</td>\n",
-       "      <td>513.199951</td>\n",
-       "      <td>22.406927</td>\n",
+       "      <th>2025-07-02 01:00:00+00:00</th>\n",
+       "      <td>25.388523</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-02 07:45:00+00:00</th>\n",
-       "      <td>29.980004</td>\n",
-       "      <td>1.137</td>\n",
-       "      <td>500.415741</td>\n",
-       "      <td>21.945240</td>\n",
+       "      <th>2025-07-02 01:15:00+00:00</th>\n",
+       "      <td>25.761938</td>\n",
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+       "      <td>309.431335</td>\n",
+       "      <td>4.680000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-02 08:00:00+00:00</th>\n",
-       "      <td>45.573409</td>\n",
-       "      <td>0.887</td>\n",
-       "      <td>484.679749</td>\n",
-       "      <td>21.817902</td>\n",
+       "      <th>2025-07-02 01:30:00+00:00</th>\n",
+       "      <td>26.135353</td>\n",
+       "      <td>-1.713</td>\n",
+       "      <td>344.148895</td>\n",
+       "      <td>5.154415</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-02 08:15:00+00:00</th>\n",
-       "      <td>55.745558</td>\n",
-       "      <td>0.687</td>\n",
-       "      <td>465.017517</td>\n",
-       "      <td>21.767351</td>\n",
+       "      <th>2025-07-02 01:45:00+00:00</th>\n",
+       "      <td>26.508768</td>\n",
+       "      <td>-1.513</td>\n",
+       "      <td>386.145966</td>\n",
+       "      <td>5.692100</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -862,35 +828,35 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>316 rows × 4 columns</p>\n",
+       "<p>342 rows × 4 columns</p>\n",
        "</div>"
       ]
      },
-     "execution_count": 114,
+     "execution_count": 282,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 114
+   "execution_count": 282
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T21:59:39.073453Z",
-     "start_time": "2026-01-13T21:59:39.062767Z"
+     "end_time": "2026-01-14T03:57:40.919962Z",
+     "start_time": "2026-01-14T03:57:40.913562Z"
     }
    },
    "cell_type": "code",
    "source": "merged_df = merged_df.rename(columns={'value': 'array_temperature'})",
    "id": "e0882e697c1613f7",
    "outputs": [],
-   "execution_count": 115
+   "execution_count": 283
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T21:59:40.158800Z",
-     "start_time": "2026-01-13T21:59:40.150764Z"
+     "end_time": "2026-01-14T03:57:41.596702Z",
+     "start_time": "2026-01-14T03:57:41.571484Z"
     }
    },
    "cell_type": "code",
@@ -901,18 +867,18 @@
      "data": {
       "text/plain": [
        "                           array_temperature  temperature_2m  \\\n",
-       "2025-07-02 07:15:00+00:00          37.371079           1.787   \n",
-       "2025-07-02 07:30:00+00:00          46.801625           1.487   \n",
-       "2025-07-02 07:45:00+00:00          29.980004           1.137   \n",
-       "2025-07-02 08:00:00+00:00          45.573409           0.887   \n",
-       "2025-07-02 08:15:00+00:00          55.745558           0.687   \n",
+       "2025-07-02 00:45:00+00:00          25.031385          -2.063   \n",
+       "2025-07-02 01:00:00+00:00          25.388523          -1.963   \n",
+       "2025-07-02 01:15:00+00:00          25.761938          -1.863   \n",
+       "2025-07-02 01:30:00+00:00          26.135353          -1.713   \n",
+       "2025-07-02 01:45:00+00:00          26.508768          -1.513   \n",
        "\n",
        "                           shortwave_radiation_instant  wind_speed_10m  \n",
-       "2025-07-02 07:15:00+00:00                   522.027100       23.177401  \n",
-       "2025-07-02 07:30:00+00:00                   513.199951       22.406927  \n",
-       "2025-07-02 07:45:00+00:00                   500.415741       21.945240  \n",
-       "2025-07-02 08:00:00+00:00                   484.679749       21.817902  \n",
-       "2025-07-02 08:15:00+00:00                   465.017517       21.767351  "
+       "2025-07-02 00:45:00+00:00                   243.104050        4.680000  \n",
+       "2025-07-02 01:00:00+00:00                   276.808258        4.680000  \n",
+       "2025-07-02 01:15:00+00:00                   309.431335        4.680000  \n",
+       "2025-07-02 01:30:00+00:00                   344.148895        5.154415  \n",
+       "2025-07-02 01:45:00+00:00                   386.145966        5.692100  "
       ],
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@@ -941,57 +907,57 @@
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        "    <tr>\n",
-       "      <th>2025-07-02 07:15:00+00:00</th>\n",
-       "      <td>37.371079</td>\n",
-       "      <td>1.787</td>\n",
-       "      <td>522.027100</td>\n",
-       "      <td>23.177401</td>\n",
+       "      <th>2025-07-02 00:45:00+00:00</th>\n",
+       "      <td>25.031385</td>\n",
+       "      <td>-2.063</td>\n",
+       "      <td>243.104050</td>\n",
+       "      <td>4.680000</td>\n",
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        "    <tr>\n",
-       "      <th>2025-07-02 07:30:00+00:00</th>\n",
-       "      <td>46.801625</td>\n",
-       "      <td>1.487</td>\n",
-       "      <td>513.199951</td>\n",
-       "      <td>22.406927</td>\n",
+       "      <th>2025-07-02 01:00:00+00:00</th>\n",
+       "      <td>25.388523</td>\n",
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+       "      <td>276.808258</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-02 07:45:00+00:00</th>\n",
-       "      <td>29.980004</td>\n",
-       "      <td>1.137</td>\n",
-       "      <td>500.415741</td>\n",
-       "      <td>21.945240</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-02 08:00:00+00:00</th>\n",
-       "      <td>45.573409</td>\n",
-       "      <td>0.887</td>\n",
-       "      <td>484.679749</td>\n",
-       "      <td>21.817902</td>\n",
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        "    <tr>\n",
-       "      <th>2025-07-02 08:15:00+00:00</th>\n",
-       "      <td>55.745558</td>\n",
-       "      <td>0.687</td>\n",
-       "      <td>465.017517</td>\n",
-       "      <td>21.767351</td>\n",
+       "      <th>2025-07-02 01:45:00+00:00</th>\n",
+       "      <td>26.508768</td>\n",
+       "      <td>-1.513</td>\n",
+       "      <td>386.145966</td>\n",
+       "      <td>5.692100</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ]
      },
-     "execution_count": 116,
+     "execution_count": 284,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 116
+   "execution_count": 284
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T21:59:44.805394Z",
-     "start_time": "2026-01-13T21:59:44.690753Z"
+     "end_time": "2026-01-14T03:57:42.692177Z",
+     "start_time": "2026-01-14T03:57:42.530435Z"
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    "cell_type": "code",
@@ -1022,13 +988,13 @@
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 117
+   "execution_count": 285
   },
   {
    "metadata": {},
@@ -1039,8 +1005,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T23:03:53.927558Z",
-     "start_time": "2026-01-13T23:03:53.922317Z"
+     "end_time": "2026-01-13T23:45:51.145870Z",
+     "start_time": "2026-01-13T23:45:51.135169Z"
     }
    },
    "cell_type": "code",
@@ -1065,29 +1031,29 @@
    ],
    "id": "1aa707313aeb5993",
    "outputs": [],
-   "execution_count": 140
+   "execution_count": 175
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T23:03:55.231396Z",
-     "start_time": "2026-01-13T23:03:55.225474Z"
+     "end_time": "2026-01-13T23:51:18.519525Z",
+     "start_time": "2026-01-13T23:51:18.489052Z"
     }
    },
    "cell_type": "code",
    "source": [
     "xdata = np.stack([merged_df['shortwave_radiation_instant'], merged_df['temperature_2m'], merged_df['wind_speed_10m']])\n",
-    "faiman_temp = np.array(faiman_model(xdata, 22,0.8))"
+    "faiman_temp = np.array(faiman_model(xdata, 22, 0.8))"
    ],
    "id": "492ea4964124380d",
    "outputs": [],
-   "execution_count": 141
+   "execution_count": 187
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T23:04:02.066289Z",
-     "start_time": "2026-01-13T23:04:01.992472Z"
+     "end_time": "2026-01-13T23:51:19.650057Z",
+     "start_time": "2026-01-13T23:51:19.560043Z"
     }
    },
    "cell_type": "code",
@@ -1101,10 +1067,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1b48c63b5f0>]"
+       "[<matplotlib.lines.Line2D at 0x1b48c73d760>]"
       ]
      },
-     "execution_count": 142,
+     "execution_count": 188,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1119,7 +1085,7 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 142
+   "execution_count": 188
   },
   {
    "metadata": {},
@@ -1130,38 +1096,51 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T23:05:28.357166Z",
-     "start_time": "2026-01-13T23:05:28.350125Z"
+     "end_time": "2026-01-14T00:12:25.620050Z",
+     "start_time": "2026-01-14T00:12:25.611179Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "params = [22, 0.8]\n",
+    "params = [22, 5.8]\n",
     "xdata = np.stack([merged_df['shortwave_radiation_instant'], merged_df['temperature_2m'], merged_df['wind_speed_10m']])\n",
-    "fit_faiman(faiman_model,\n",
-    "           xdata,\n",
-    "           merged_df['array_temperature'], params)"
+    "popt = fit_faiman(faiman_model,\n",
+    "                  xdata,\n",
+    "                  merged_df['array_temperature'], params)"
    ],
    "id": "9839f97f4cfe5300",
+   "outputs": [],
+   "execution_count": 213
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-14T00:12:26.520024Z",
+     "start_time": "2026-01-14T00:12:26.509310Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "popt\n",
+   "id": "fd0a96ba439544cc",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([19.30085889,  0.07811406])"
+       "array([19.30077583,  0.07811705])"
       ]
      },
-     "execution_count": 146,
+     "execution_count": 214,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 146
+   "execution_count": 214
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T23:08:48.978947Z",
-     "start_time": "2026-01-13T23:08:48.853587Z"
+     "end_time": "2026-01-14T00:12:27.655687Z",
+     "start_time": "2026-01-14T00:12:27.518223Z"
     }
    },
    "cell_type": "code",
@@ -1173,7 +1152,7 @@
     "\n",
     "ax1.plot(merged_df['array_temperature'], label=\"Array Temperature\")\n",
     "ax1.plot(merged_df['temperature_2m'], color='green', label=\"Ambient Temperature\")\n",
-    "ax1.plot(merged_df.index,faiman_temp, label = \"Faiman_Fitted\")\n",
+    "ax1.plot(merged_df.index, faiman_model(xdata, *popt), label=\"Faiman_Fitted\")\n",
     "plt.plot(merged_df['shortwave_radiation_instant'], color=\"red\", label=\"Solar Irradiance\")\n",
     "\n",
     "ax1.set_xlabel(\"Time\")\n",
@@ -1193,34 +1172,135 @@
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 152
+   "execution_count": 215
   },
   {
    "metadata": {},
    "cell_type": "code",
    "outputs": [],
    "execution_count": null,
+   "source": "",
+   "id": "a48e41b4efacee1c"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "including an offset and these particular parameters, closer to the curve fit seems to yield pretty consistent results with MPPT temperature. i will probably stick with these coefficients, and move onto the transient faiman model (parametrisation and a further curve fit)",
+   "id": "cf5b1688eced04d5"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-14T02:19:38.156260Z",
+     "start_time": "2026-01-14T02:19:38.032125Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "plt.plot(merged_df['array_temperature'], label=\"Array Temperature\")\n",
+    "plt.plot(merged_df['temperature_2m'], color='green', label=\"Ambient Temperature\")\n",
+    "plt.plot(merged_df.index, 27 + (faiman_model(xdata, *[22, 0.8])), label=\"Faiman_Fitted\")\n",
+    "plt.title(\"offset_faiman_temperature\")"
+   ],
+   "id": "a0e596c81dc17f5",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'offset_faiman_temperature')"
+      ]
+     },
+     "execution_count": 220,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 220
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-14T02:59:15.584641Z",
+     "start_time": "2026-01-14T02:59:15.427704Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "slope, intercept = popt\n",
+    "x = merged_df['temperature_2m']\n",
+    "y_pred = slope * x + intercept\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.scatter(merged_df['array_temperature'], faiman_model(xdata, 22, 0.8) + 27, label = \"measured vs predicted\")\n",
+    "#plt.plot(merged_df['array_temperature'], y_pred, linewidth=2)\n",
+    "\n",
+    "plt.xlabel(\"Array temperature\")\n",
+    "plt.ylabel(\"Faiman model output\")\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "# #plt.scatter(merged_df['array_temperature'],faiman_model(xdata, *[22, 0.8])+27)\n",
+    "#\n",
+    "# plt.plot(merged_df['array_temperature'], faiman_model(xdata, *[22, 0.8]) + 27,\n",
+    "#          label=f'Fitted Line: y = {slope:.2f}x + {intercept:.2f}', color='red', linewidth=2)"
+   ],
+   "id": "4d9168ed6f08c511",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 233
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-13T23:46:15.666706Z",
+     "start_time": "2026-01-13T23:46:15.659369Z"
+    }
+   },
+   "cell_type": "code",
    "source": "#trying an offset:",
-   "id": "512015ec2bff8ec0"
+   "id": "512015ec2bff8ec0",
+   "outputs": [],
+   "execution_count": 180
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T22:43:24.236521Z",
-     "start_time": "2026-01-13T22:43:24.152472Z"
+     "end_time": "2026-01-14T02:03:19.946547Z",
+     "start_time": "2026-01-14T02:03:19.839201Z"
     }
    },
    "cell_type": "code",
    "source": [
+    "# initialparams, sourced from literature\n",
     "plt.plot(np.array(merged_df['array_temperature']), label=\"Mosfet Temperature\")\n",
-    "plt.plot(faiman_model(xdata, *params) + 27, color = 'green', label = 'offset_faiman_temperature')\n",
-    "plt.plot(faiman_model(xdata, *params), color  ='red', label = 'faiman_predicted')\n",
+    "plt.plot(faiman_model(xdata, *params) + 33, color='green', label='offset_faiman_temperature')\n",
+    "plt.plot(faiman_model(xdata, *params), color='red', label='faiman_predicted')\n",
     "plt.title(\"Comparison of Faiman Model\")\n",
     "plt.legend(loc=\"upper left\")\n",
     "plt.show()"
@@ -1232,37 +1312,105 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 134
+   "execution_count": 217
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    " the faiman model doesn't seem to take into account the thermal mass i.e. the time it takes for the arrays themselves to heat up. following the appraoch in the paper linked, i used the thermal capacitance approach, namely considering the differential equation:\n",
+    "\n",
+    "capacitance * dT/dt = G - (u0 + (u1*w) ) * (T_array - T_ambient)\n",
+    " where capacitance is in terms of heat capacity per area (J/m^2 *K)\n",
+    "\n",
+    "the analytical solution to this is:\n",
+    "T_array = T_steady-state - C(exp) * [-t *(u0/Ca + u1*w/Ca)]\n",
+    "and T_steady-state is just the output of the steady-state Faiman model.\n",
+    "\n",
+    "in order to determine the capacitative parameter, there needs to be a recursive model. So assuming steady state condition at t - delt time, then compute the temperature for the next time step and integrate (a summation). Linearising the above differential yields:\n",
+    "\n",
+    "t_array(i+1) = t_array(i) + del(t)/Ca * [G(i+0.5) - (u0 + u1*w(i+0.5) * (t_array(i) - t_array(i+0.5))]\n"
+   ],
+   "id": "fc1bbf9c64684bcb"
   },
   {
    "metadata": {},
    "cell_type": "code",
    "outputs": [],
    "execution_count": null,
-   "source": "#the faiman model doesn't seem to take into account the thermal mass i.e. the time it takes for the arrays themselves to heat up. following the appraoch in the paper linked, i used the thermal capacitance approach, namely consideringthe differential equation:\n",
-   "id": "af79779a9cf6cc6b"
+   "source": [
+    "T_a[0] = T_measured[0]  # start from measured temperature\n",
+    "for i in range(1, len(T_a)):\n",
+    "    dt = (t[i] - t[i - 1]).total_seconds()\n",
+    "    T_ss = faiman_model_single(G[i - 1], T_ambient[i - 1], v[i - 1], u0, u1)  # steady-state\n",
+    "    tau = 1800  # time constant in seconds (example)\n",
+    "    T_a[i] = T_a[i - 1] + (T_ss - T_a[i - 1]) * dt / tau\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "def faiman_dynamic(G, T_amb, v, u0, u1, dt_seconds, T_prev, tau):\n",
+    "    # steady-state Faiman\n",
+    "    T_ss = T_amb + G / (u0 + u1 * v)\n",
+    "    # first-order dynamics\n",
+    "    T_new = T_prev + (T_ss - T_prev) * dt_seconds / tau\n",
+    "    return T_new\n",
+    "\n",
+    "\n",
+    "# initialize\n",
+    "T_model = np.zeros_like(G)\n",
+    "T_model[0] = T_measured[0]  # start from sensor reading\n",
+    "tau = 3600  # 1 hour time constant\n",
+    "\n",
+    "for i in range(1, len(G)):\n",
+    "    dt = (t[i] - t[i - 1]).total_seconds()\n",
+    "    T_model[i] = faiman_dynamic(G[i - 1], T_amb[i - 1], v[i - 1], u0, u1, dt, T_model[i - 1], tau)\n",
+    "\n",
+    "# optional MPPT offset\n",
+    "T_model += 5\n"
+   ],
+   "id": "f87c5e6e2955929f"
   },
   {
    "metadata": {},
    "cell_type": "code",
    "outputs": [],
-   "execution_count": null,
    "source": "",
-   "id": "db7f7a7c2a6a49ab"
+   "id": "af79779a9cf6cc6b",
+   "execution_count": null
   },
   {
    "metadata": {},
    "cell_type": "code",
    "outputs": [],
-   "execution_count": null,
    "source": "",
+   "id": "db7f7a7c2a6a49ab",
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "# to dos - vector addition of car speed, and cleaning influx data for that. as of now, i am focusing on seeing if adding the thermal capacitance term might allow the faiman model's peaks to align with what we queried from influx.\n",
+    "\n",
+    "\n"
+   ],
    "id": "ba3e4aad2d9800f4"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "#some questions - does the temperature of the panels ever reach ambient temperature, even at night? if no, does that mean the car was always stored in room temperature competitions?",
+   "id": "8c77bceec3ac9fb6"
   }
  ],
  "metadata": {
diff --git a/v4/array_temperature/arrayTemperatureModel.py b/v4/array_temperature/arrayTemperatureModel.py
index a8f131e..8bc62fb 100644
--- a/v4/array_temperature/arrayTemperatureModel.py
+++ b/v4/array_temperature/arrayTemperatureModel.py
@@ -76,5 +76,3 @@ def model():
                                  0.8)
 
 
-model = model()
-print(model.calculateArrayTemperature())

From 4f7571875328995035513b8752c581dbc3818663 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Wed, 14 Jan 2026 22:20:26 -0800
Subject: [PATCH 12/49] cleaning

---
 array_temp/faiman_coefficients.ipynb | 760 +++++++++++----------------
 1 file changed, 294 insertions(+), 466 deletions(-)

diff --git a/array_temp/faiman_coefficients.ipynb b/array_temp/faiman_coefficients.ipynb
index 5e3593d..457efbb 100644
--- a/array_temp/faiman_coefficients.ipynb
+++ b/array_temp/faiman_coefficients.ipynb
@@ -19,8 +19,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-14T04:00:52.158338Z",
-     "start_time": "2026-01-14T04:00:40.315802Z"
+     "end_time": "2026-01-15T02:39:38.054262Z",
+     "start_time": "2026-01-15T02:39:27.049603Z"
     }
    },
    "cell_type": "code",
@@ -35,7 +35,7 @@
     "import os\n",
     "\n",
     "\n",
-    "utc_offset_h = 7\n",
+    "utc_offset_h = 5\n",
     "start_utc = time(00 + utc_offset_h, 00, 00)  #querying is vancouver time, influxdb gives utc\n",
     "stop_utc = time(00 + utc_offset_h, 00, 00)\n",
     "date_start = date(2025, 7, 2)\n",
@@ -49,13 +49,50 @@
    ],
    "id": "8b2b4006671bdc31",
    "outputs": [],
-   "execution_count": 288
+   "execution_count": 30
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "alternate timezone calculations, by converting everything to vancouver time first, seems to yield better results but there is still a lag",
+   "id": "cd65ecb41856dcb0"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "from datetime import datetime, date, time\n",
+    "import pytz\n",
+    "\n",
+    "vancouver = pytz.timezone(\"America/Vancouver\")\n",
+    "\n",
+    "date_start = date(2025, 7, 1)\n",
+    "date_stop  = date(2025, 7, 6)\n",
+    "\n",
+    "\n",
+    "start_local = vancouver.localize(datetime.combine(date_start, time(0,0,0)))\n",
+    "stop_local  = vancouver.localize(datetime.combine(date_stop, time(0,0,0)))\n",
+    "\n",
+    "#convert to utc\n",
+    "start_utc = start_local.astimezone(pytz.utc)\n",
+    "stop_utc  = stop_local.astimezone(pytz.utc)\n",
+    "\n",
+    "print(\"start UTC:\", start_utc)\n",
+    "print(\"stop UTC:\", stop_utc)\n",
+    "\n",
+    "client = query.DBClient()\n",
+    "temp_array_fsgp = client.query_time_series(start_utc, stop_utc, field=\"MosfetTemperatureA\")\n",
+    "speed_kph_aliter       = client.query_time_series(start_utc, stop_utc, \"MotorRotatingSpeed\")\n"
+   ],
+   "id": "5e563f4ef0e494b6"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-14T03:36:08.886968Z",
-     "start_time": "2026-01-14T03:36:08.838530Z"
+     "end_time": "2026-01-15T02:36:04.152139Z",
+     "start_time": "2026-01-15T02:36:04.107665Z"
     }
    },
    "cell_type": "code",
@@ -81,63 +118,13 @@
    ],
    "id": "a807d5de05d7c8b0",
    "outputs": [],
-   "execution_count": 239
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-14T03:36:09.360790Z",
-     "start_time": "2026-01-14T03:36:09.352530Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "type(temp_array_fsgp)",
-   "id": "52e7b7784852e53d",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "data_tools.collections.time_series.TimeSeries"
-      ]
-     },
-     "execution_count": 240,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 240
+   "execution_count": 2
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-14T03:36:09.848404Z",
-     "start_time": "2026-01-14T03:36:09.844180Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "#convert to data frame\n",
-    "df_fsgp = pd.DataFrame(temp_array_fsgp)"
-   ],
-   "id": "1300423cd026c3ca",
-   "outputs": [],
-   "execution_count": 241
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": [
-    "querying from openmeteo to get solar irradiance data\n",
-    "\n",
-    "- this is hourly irradiance over 4 days\n"
-   ],
-   "id": "bc1ff3d5812338f"
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-14T03:57:04.589766Z",
-     "start_time": "2026-01-14T03:57:03.552991Z"
+     "end_time": "2026-01-15T02:36:07.109367Z",
+     "start_time": "2026-01-15T02:36:05.220372Z"
     }
    },
    "cell_type": "code",
@@ -235,17 +222,13 @@
      ]
     }
    ],
-   "execution_count": 274
+   "execution_count": 4
   },
   {
    "metadata": {},
    "cell_type": "markdown",
    "source": [
-    "minutely open meteo data starts at 2 july, 12am and goes to 6 july 22 45\n",
-    "therefore on influx i query for 1 july 10  pm to 6 july 20 45\n",
-    "\n",
-    "\n",
-    "utc is 2 hours ahead of vancouver\n",
+    "kentucky is 3 hours ahead of vancouver\n",
     "\n",
     "- some preprocessing, including:\n",
     "- merging everything into a dataframe, with resampling influx data to match openmeteo's frequency"
@@ -261,8 +244,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-14T03:36:16.895778Z",
-     "start_time": "2026-01-14T03:36:15.774130Z"
+     "end_time": "2026-01-15T02:36:08.906658Z",
+     "start_time": "2026-01-15T02:36:07.160122Z"
     }
    },
    "cell_type": "code",
@@ -279,19 +262,19 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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6dMG+ffuwatUqXHTRRcEPQK9HTk6Ox/1VVVV488038eGHH2L8+PEAgMWLF6Nv377YuHEjRo4cGcrQ24yWkQy5aYsLxXPLD6Cooh4AkNjSGI8ZGaLoY5b5g5Hz1FJUd/Z95pln0L9/f1x//fVIS0vD2rVrsXv3bmg0GmRkZIQ0gIMHD6JTp07o1q0bpk+fjqKiIgDA1q1b0dzcjIkTJ4rP7dOnD7p06YINGzb4fD2TyYTq6mqXLyUwkCF33lYC9MxKxPkFabL9jhdWHhS/X11o37eJNTJE0SecU0tqy84EFcjMmzcP11xzDY4fP45//etfGDx4cKt++YgRI/D222/j+++/xyuvvIKjR4/ioosuQk1NDUpKSmAwGJCamuryM9nZ2SgpKfH5mgsXLkRKSor4lZeX16oxhqqirkmR30uRy1smePkDF+PTu0eH5ffFGXQAuGqJKBrJXezrPFUV1YHM3//+d3z66afo2rUr5s2bh19//bVVv/yyyy7Db37zGwwaNAhTpkzBt99+i8rKSnzyySchv+b8+fNRVVUlfhUXF7dqjKFqaOabB7lqq86+gtT4GADMyBBFI7mvJyv2qmMhjTdBBTLz58/HgQMH8N5776GkpAQjRozA4MGDYbPZcO7cuVYPJjU1Fb169cKhQ4eQk5ODpqYmVFZWujyntLTUa02NwGg0Ijk52eWLKBKcrGxwuT2ia3pYf59Q7NvYbG3zIIqIwqtZ5qmleqcPPGq7WoS0auniiy/GO++8g5KSEtx7770YNmwYLr74YowePRrPPvtsyIOpra3F4cOH0bFjRwwbNgwxMTFYuXKl+HhhYSGKioowatSokH+H3Goam5UeAqlUj6zEsL5+SlyM+H09szJEUUX+Yl/1atXy66SkJPzhD3/AL7/8gu3bt+OCCy7AE088Ifnn58yZgzVr1uDYsWNYv349rr32Wuh0Otx4441ISUnBHXfcgQceeACrVq3C1q1bMWPGDIwaNSqiVizdt2SH0kMglYjRuRaAh7sePClWL26VwZVLROrVZPYMWuQu9nXW7rYoaGxsBAAMHDgQzz//PE6ePCn5Z0+cOIEbb7wRvXv3xg033ICMjAxs3LgRHTp0AAA899xzuOKKKzBt2jSMHTsWOTk5WLp0aWuHLKsf959RegikEvEG390OLh/YEQAwpX+2bL/PoNMivqXgl71kiNSr1svfr+ydfdUVu7gIKZCxWq34+9//js6dOyMxMRFHjhwBADz88MN47733JL/OkiVLcOrUKZhMJpw4cQJLlixB9+7dxcdjY2Px0ksvoaKiAnV1dVi6dKnf+hiiSOa+15LGqf/z4LwUAECCn2AnWDF6rfh6dVy5RKRa3vbuk7vurd3tfv2Pf/wDb7/9Np566ikYDAbx/v79+2PRokWyDY4omlj9XHj0WvufYrOMF6cYnRbxxpaMDGtkiNTLyzS0nNcKoB31kRG8++67eP311zF9+nTodDrx/sGDB2P//v2yDY4omljcMzJOFyehfsYsY7rYoNOIGRkW+xKpl7eaFTmvFVJ+XyQLKZA5efIkevTo4XG/1WpFczNX8RB54/4ByvlDlq4lIyPn/ikGvRYJQkaGU0tEquXtsmC1+c/ytobc+ziFW0iBTL9+/bBu3TqP+//73/9iyJAhrR4UUTTyO7UUhoxMjE7LjAxRFHCvrxPIFXBYrDbsOeXYzkfupd3hFlJl4cMPP4xbb70VJ0+ehNVqxdKlS1FYWIh3330XX3/9tdxjJIoKHsW+TnNL4tRSiBemxmYLHvhkh8t99hoZFvsSqZ3vQMYKQ+sXH+OhL3bjs20nxNtyN9sLt5COwNVXX42vvvoKK1asQEJCAh5++GHs27cPX331FSZNmiT3GFXpop6ZSg+BIoy/GEUs9g0xI/P2+mP4drfrHmT2jEzLfkvMyBCpl49rh1wBx0ebXLfyUVsn8KAzMmazGY8//jhuv/12LF++PBxjigrc/ZqC4Sj2De0CUl5r8rjPoNeIvWvq2BCPSLXcFwoIwlXwG85C4nAIOiOj1+vx1FNPwWzmJzx/fKUCiQTOsW5rl1//crTC4z6d1lHsW8+GeESq1WwOb42Mx+9rD1NLEyZMwJo1a+QeS1RhIEPBaG2x764TVZ6vqWVGhigaNFm8//2GK5CJ+qklALjsssvw4IMPYvfu3Rg2bBgSEhJcHr/qqqtkGZyaqe1EoLbn3Nk3Rtey/FrGT0IaDRwZGdbIEKnWsj2lXu8P1xRQc3tYtXTvvfcCgNedrjUaDSw+osf2hHEMBUPfsrmjnBeQ7h0ScarSvhcaVy0RqdfRsjqv94drCiicG1KGQ8h7Lfn6YhBjV9PIT8Dkn0uNTCuLfb0xcNUSUVS4YlBHr/eHq99Lu2iIR4EVV9Srrs0ztS3ndW1Csa+cU5I2gH1kiKKAr7eScGVO1LZqKaSppccee8zv4w8//HBIg4kmtSYzKuqakJFoVHoopAJCRibUPjK+CBkZbhpJpF6+MiThypyoLSMTUiDz+eefu9xubm7G0aNHodfr0b17dwYyLY5X1DOQIZ9cN42Uf68lm83mWLXEjAyRavnK1Iavj0w7CGS2b9/ucV91dTVuu+02XHvtta0eVLQoKq/H0C5pSg+DVEAs9g3xwjSkSyq2F1W63Jcab0BtS/8Y1sgQqZevQCZsxb4qW7UkW41McnIyHn30Ufztb3+T6yVV73h5vdJDoAjmHHi0dvl1dlKsy+0XbxwCnVaDBKOwaaQlbDvlElF4+ezsG6aAo100xPOlqqoKVVWejbnaq+MV3pfMEQHAluPnxO/FVUshXpjcp6SEDI+w+zUANDRzeolIjSw+rgvha4inroxMSFNLL774osttm82G06dP47333sNll10my8CiQREzMiSRY9NIG2w2m8vO2FK4d5IWbsfGaKHR2Fc91DWZxQwNEamHrxnn8K1aUldGJqSr2nPPPedyW6vVokOHDrj11lsxf/58WQYWDY5XMJAhh7z0OBRXNHh9TNg0ErDPh+t1wQUy7nPowm2NRoMEgx61JjPqTRYgKchBE5Hiiny8l4Svs287CGSOHj0q9ziiilZj7+x7tsaEhiYL4lqWwFL75jzN406ndQQuZqsN+iBPGfeMjPPNeIMOtSYzl2ATqdSLKw96vT9cAYfappZCqpG5/fbbUVNT43F/XV0dbr/99lYPSu3iDXokx9rftHxF0tT++NtIVCj2BeSZ93b+Xc4Fv0SkfsLMc7gCjnZR7PvOO++gocEzRd7Q0IB333231YNSuyazFfkZ9o00j5ez4Jfs/MUneueMjAzp4in9c8Tv44WmeCZmZIjUpsHLBxBHuwZ29gWCnFqqrq6GzWYvRqypqUFsrGPJp8ViwbfffousrCzZB6k2TRYrumTEY/fJKmZkSOQvI+M8tSTHxcm5qFeY0mJGhkh9vthx0uM+vVaLZoslfMW+0Vwjk5qaCo1GA41Gg169enk8rtFo8Oijj8o2ODXLT48HwF4y5OBv6y2NRoMYnQbNFpvsvSHijczIEKmVt4yM8MEnbJtGqmxqKahAZtWqVbDZbBg/fjw+++wzpKeni48ZDAbk5+ejU6dOsg9SjfIzWgIZZmSoRaANIVvzKctfkMSMDJF6eev2rQv31JLKin2DCmQuvvhiAPZVS3l5edBquXm2L13S7TUyRayRoRb+ppYA+xLshubQtimwwfdrCzUytczIEKmOyex5PdCGudg3qqeWBPn5+QCA+vp6FBUVoampyeXxQYMGtX5kKidkZE6ca4DZYoVex6CvvXOPY7RurWIMekdTvNa+tjPHqiUGMkRq8+zyAx73DS9Ix/K9pWEs9m0HgczZs2cxY8YMfPfdd14ft1iYws5OjoVBp0WTxYrTVY3Ia6mZofbLPSOz+f9NdLktLMEOKSPj57rjWLXEv0siNXvo8r64flgunlpWCCB8AUeom9cqJaQ0wf3334/Kykr88ssviIuLw/fff4933nkHPXv2xJdffin3GFVJp9UgNz0OAAt+yc492MhINLrcFgKZphAuIv6mrZiRIYoOsTE6pMYbEBPmYt9A9XyRJqSMzI8//oj//e9/GD58OLRaLfLz8zFp0iQkJydj4cKFuPzyy+Uepyrlp8fjyNk6HK+ow4XIVHo4pDB/dSyAY+PIZi9z4oFf27cEISPDYl8iVROKfIVShXDVsqitRiakjExdXZ3YLyYtLQ1nz54FAAwcOBDbtm2Tb3QqJzTF4+aRBPif/gEAg641NTJ+in2FjAyLfYlUTairExrihW2vpfYwtdS7d28UFtrn6AYPHozXXnsNJ0+exKuvvoqOHTvKOkA168JeMuTEOdS4bkhnj8dbUyPj7wOUsPyaGRki9Yl32qtPoxEyMuFdft0uppbuu+8+nD59GgCwYMECXHrppfjggw9gMBjw9ttvyzk+VUky6lHj9KlXWLnE7r7k7OOZI3FB13SP+4UdsEOpkfGfkbFfCFkjQ6Q+3Tok4NeT1QAAnRDIaIWpJe61BIQYyNx8883i98OGDcPx48exf/9+dOnSBZmZ7bcWJCU+xmcgY7PZxGia2ich1kiOi/F6LggZmVBWIkjJyNRz1RKR6jjHKkLrNuFDT7hWLUX97tfNzc3o3r079u3bJ94XHx+PoUOHtiqIeeKJJ6DRaHD//feL940bN07cEkH4uvvuu0P+HeFm1Lsezty0eGg09kZkFXVNPn6K2g/7RcdXPOvoIyNvsa+4/JoZGSLVcV6RqG25eOi0LPZ1FnRGJiYmBo2NjbIOYvPmzXjttde8NtK766678Nhjj4m34+Mjtx+LQa9zuR0bo0NOcixOVzXieEW9x3Jbap808B7JtGb5tb+pJXH5NTMyRKrjLZBxZGS4/BoIsdh31qxZePLJJ2E2t/4TXm1tLaZPn45FixYhLS3N4/H4+Hjk5OSIX8nJya3+neEinFzOhIJfrlyiQKuWYsQCPpn7yDhlZPwFPEQUeZxjCq1YI9NyrZAh4DhZ2eBxn9oyMiEFMps3b8bSpUvRpUsXTJkyBdddd53LVzBmzZqFyy+/HBMnTvT6+AcffIDMzEwMGDAA8+fPR3195AYEMV62IRA3j2Qg0+4JlwZfU0vn6poBAJX1zcG/tr/Ovi0ZGavN+74tRBS5nD+kCG8xYh8ZGTIyp70EMmrLyIRU7Juamopp06a1+pcvWbIE27Ztw+bNm70+ftNNN4k7au/atQvz5s1DYWEhli5d6vM1TSYTTCaTeLu6urrV45RqfJ8sbD1+DkmxjsMq9JI5XsHNI8nOV8n3pmMVAICl205g1iU9gnpNf9eduBjHlGedyYzYGJ3vJxNRRHH+kKJxy8jIEXBo3Td9a3ldNS1QCSmQWbx4cat/cXFxMe677z4sX74csbGxXp8zc+ZM8fuBAweiY8eOmDBhAg4fPozu3bt7/ZmFCxfi0UcfbfX4QjFzbDfkJMdiVPcM8T5OLZEg0LTOuN4dsLrwLIbney7Nbs1r67QaxMXo0NBsQZ3JgozEoF+eiBTiHKyIU0utaJ7pTu8lkBF+r95LuUQkCnlLZrPZjBUrVuC1115DTU0NAODUqVOora2V9PNbt27FmTNnMHToUOj1euj1eqxZswYvvvgi9Hq9140nR4wYAQA4dOiQz9edP38+qqqqxK/i4uIQ/u9CE6PTYtqwXHRKjRPvE6eW2Eum3Qs0tTSocwoAwBgT/J9loNKXBCNXLhGpnc59+bUMy6S1Pi5IaqqTCSkjc/z4cVx66aUoKiqCyWTCpEmTkJSUhCeffBImkwmvvvpqwNeYMGECdu/e7XLfjBkz0KdPH8ybNw86nWf6e8eOHQDgt3uw0WiE0Rg5q4OEjMzZGhPqm8yIN4R0yCkKOIIN7xcOoZYllF2qnefRB3T2LIi3n3dNbIpHpDLOe7Rp3BriyZGR0fnJyKhFyJ19hw8fjp07dyIjwzGNcu211+Kuu+6S9BpJSUkYMGCAy30JCQnIyMjAgAEDcPjwYXz44YeYOnUqMjIysGvXLsyePRtjx471ukw7UqXGG5Acq0d1oxlFFfXokxO5q66obfjKyAjF4rtOVAb9ms6XnLsu6ubxuNhLhkuwiVTFOduqc9uiQI5iX2+rbYF2kJFZt24d1q9fD4PB4HJ/QUEBTp48KcvADAYDVqxYgeeffx51dXXIy8vDtGnT8NBDD8ny+m0pPyMBu09W4Xg5A5n2LFCNTGm1vT/TwTPSpmed+Vt+DQCJQi8ZZmSIVMXmZ/m1HFmT4grPVUtyvXZbCSmQsVqtXmtYTpw4gaSkpJAHs3r1avH7vLw8rFmzJuTXiiRdMuKx+2QVC37bObFGxsfjg3NTAQCp8THBv3aAa05rpq2IKDKIu1/LWOz7j2/2er0/XPs4hUNIxb6TJ0/G888/L97WaDSora3FggULMHXqVLnGFjXy07l5JDn4WtKYkWjPcKYnGLw+7o9ztsfgpZ+R0BSPGRki9RKWSsdo5Sv29ZV4ifqMzDPPPIMpU6agX79+aGxsxE033YSDBw8iMzMTH330kdxjVD2uXCIA/jdEApAca8/E1DQGH2wI15ykWD0m9cv2eFwoMq9rYkaGSK3cl1/LsWmkr4AlXBtShkNIgUxubi527tyJJUuWYNeuXaitrcUdd9yB6dOnIy4uLvALtDNd0u1N8YrK2RSvPQs0tSQ0UqxuCKGzb8urv3P7BeJFzpmw/LrexIwMkZrYXPZasv9XJ2ZkWh9s9MlJ8jpbEPUZGQDQ6/W4+eab5RxL1BIyMifONcBssXp9o6H2w9eqpeQ4e0bGZLbCZLbAqJfegVfIMPvqCcGMDJH62Gw2nKpybNIsTi3JuGppVPcM/LC31OP+qF+1BACFhYX497//jX379gEA+vbtiz/+8Y/o06ePbIOLdIUlNZKel5McC4NeiyazFaerGpGXHrk7eFP4BFq1JKwsAuzTS8ZE6YGM8Nq+sj2skSFSn90nq1xua937yMgQbPjKvKgpIxNSauCzzz7DgAEDsHXrVgwePBiDBw/Gtm3bMHDgQHz22WdyjzFi/ep2kvmi1WqQl2afcuPmke2XY2rJe7ih02qQ1BLMBFsnI7y2z4wMVy0RqY77Jq9CHxk5MzLurRuEVZNqWrUUUkZm7ty5mD9/Ph577DGX+xcsWIC5c+fKsqGkGjifAPMv85+Jys9IwOGzdTheUYcLkRnuoVEEEk4Xf/uw1bTUsARbJyOci75emxkZIvXTuC2/lqMg13kK6aWbhmLWh9sAAE1m9QQyIWVkTp8+jVtuucXj/ptvvhmnT59u9aDUwvkUmjrQ97YJADePpOBsPX4uqOdbAwRJzMgQqV9dywcdvYzFvtaW1/jt8DxcPsjxPvbehuOtfu22ElIgM27cOKxbt87j/p9++gkXXXRRqwelFs41D4F2OxeXYDOQabdsgdZfO+mQFNx+YcKp6GtqScjIcNNIIvVwL6sTbupl3DRSmJ3SuW1VcNqpyDjShTS1dNVVV2HevHnYunUrRo4cCQDYuHEjPv30Uzz66KP48ssvXZ4brZxPMl9NzgTsJUNSppYu7tUBaw6cRWNzcJkTW4CpJXHVEpdfE6nWBQXpAFw3jbTZbAHff/yxtARDQv3N1IE5+HZ3CbpnJbRytG0npEDm3nvvBQC8/PLLePnll70+Btjf3L1tZRAtnIPlQKeRY2qprtUnHqmbv3/7xJZeMrVBBhyBin3FPjJcfk2kGu4rHd2XXwP21UV6Hxs/SmFp+R1Cb5qLe3XAt7tLcOKc9z2YIlFIU0tWq1XSVzQHMYBrsW+guCQ3LR4ajb2PR3ldU5hHRpFIysRSokHY3FH6347NZkNFyznl6zRkRoZI3V69eaj4vRB0AMHVyZytMeFYmWtjVuHnhdfMTXP0PVMLdmZrBZeppQA5mdgYHXKSYwGwTqbdEqaW/DwlviVzEkzAUe20VPtsrcnrcxy7X1sC9rMhosjg/JfqnG2NcWqquq3oHKobpa1yPP+fKzDu6dU4U+OofxGKffViIGNvFXLiXL1qrhUhN8TbvHkzVq1ahTNnzsDqVnD07LPPtnpgauAytSQhs9clPR6nqxpRXFGPYflpYRsXRZ4zNY1oaqmq83euJISQkXHWK9v77vNCgGS22tBksQbVNZiIlOc8Ja13ysjctOgXdE6Nw88Pjpf8WntPVSOrt/2DtZCREaatOqbEQaMBGputKK9rQmZicAsPlBBSIPP444/joYceQu/evZGdne1ygNtT7YfLqiUJz8/PiMcvRyuYkWmHpr7wk6TnhZKRcT4PU1u2OfB43RhH4FJvCm77AyKKLM5TSwBwsjK4aSDnRnvuGRmDXouc5FicrmrEiXMN0RvIvPDCC3jrrbdw2223yTwcdbFaXeaWAsrPsFeBH6/g5pHtTZnTlI+/aUgh4AhmmbRz9tdXsa9ep4VRr4XJbEVdkxlpCQbJr09EyvA1s9PahIHzqkgxI+P0mrlpcS2BTD3Oy0tt1e9qCyHVyGi1WowZM0busaiO66qlwCcWm+IR4H9q6dDZWgDAt7tLJL+e1KLzBGPrpq2ISDlyznW4ZGRsrhkZQH0FvyEFMrNnz8ZLL70k91hUx7WPTODns5dM++GvD4y/U0UoCA+GVWI/o3hD8NNWRBQdnKegnQMZYZsDrdY1IwPYC37VIKSppTlz5uDyyy9H9+7d0a9fP8TEuM7LL126VJbBRTprsDUy6fappbM1JtQ3mcUlsRRdVuwtxZ3vbsFDl/fFnRd1C+pnD56pDfr3CR2DtQFOwtYWEhNR2wqmG3jA13J6KZPTBy33PjKAcyATxRmZP//5z1i1ahV69eqFjIwMpKSkuHy1F64p/cChTEp8DFJaijGLmJWJWrM/3gEA+Mc3+7w/wc+pYmoOvuW4o2Ow/3MwlEJiIlKQjKufrT4yMhar+qeWQkoJvPPOO/jss89w+eWXyz0eVXHeQV3q/GV+Rjx2najC8fJ69MlJDsu4SFmWAL0X/NVT9cxOxPd7gvt9wgWKGRki8sX5uuQ89W3xUewLOHrJRPpq5JAyMunp6ejevbvcY1GdYDr7CljwG/0srdiRtkdWYtA/I2ZkAoTT8dw4kkhVfDW4DIXL1JK3Yl+nbQ7ce8lEupACmUceeQQLFixAfX37fjN27SMjLZJxFPxyCXa0sgbKyPg5Vfp3cmTpGiRmTsTfFigjY+Q2BURq8tAXv8r2Wi5TS87Lry2eGRmhlwygjumlkKaWXnzxRRw+fBjZ2dkoKCjwKPbdtm2bLIOLdFbX9deSCAW/bIoXvQJlZPydKgUZjh1nG5otiDMEblxnkzi15Fi1xKklIjWocdp+pLWzO86XpcZm/8uvAUcvmeKKyO8lE1Igc80118g8DHUKZWopT5haYrFv1GrFzBL0Oi1iY7RobLaittGMdAmN66ROLTn6yDAjQ6Q2rQ1knD9gmcxeGuJ5BDLx2HzsXPRmZBYsWCD3OFQpyMa+ABxTSyfPNcBssUKv476d7U2gwjnh01JRRT26tJwv/jhWLfl/nqNGhhkZovbGVx8Zb6uWAHX1kgn5XbSyshJvvPEG5s+fj4qKCgD2KaWTJ0/KNrhI57xFgdSq7pzkWBj0WpitNpyuagz8AxR1pAa9D3yyQ9LzhF4TgV5X3AGbNTJE7Y7VR7GvEMi479+kpl4yIWVkdu3ahYkTJyIlJQXHjh3DXXfdhfT0dCxduhRFRUV499135R5nRHJuViT1zUmr1SAvLQ6Hz9bheHm9ONVE7YfUFPGZGmkrFiT3kWlZfs2MDFH74zy15LyQwHcgI/SSidKMzAMPPIDbbrsNBw8eRGyso6X61KlTsXbtWtkGF+mC3aJAwM0jSU7CaRjoHExoaYjHGhmi9sd5aqneSx8ZncZ3RsYWYCWm0kIKZDZv3ow//OEPHvd37twZJSXSN7tTu2A3jRSwl0z7Fsy5IoVQdB7oVcWMDFctEalOa68bzu9XjU2eWxS4F/sKvWRMZivKaiO7l0xIgYzRaER1dbXH/QcOHECHDh1aPSi1CD0j09JLhoFM+yRzk0ypU0sJBmZkiNor5/er+mbHNcBXsa9Br0VHsZdMZL9XBRXIFBUVwWq14qqrrsJjjz2G5uZmAPYLaFFREebNm4dp06aFZaCRyBbC8muAu2CT3FoyMoFWLRmZkSFqr5xrOhuaPIt93TMygHr2XAoqkOnatSvKysrwzDPPoLa2FllZWWhoaMDFF1+MHj16ICkpCf/85z/DNdaIYw2hsy8AdGlpildUXhfxc48kP7m3LXH0kfGPGRmi6PbF9pP43w7vK4ed32oanK4BQg2w1suFSS0rl4JatSS86aakpGD58uX46aefsGvXLtTW1mLo0KGYOHFiWAYZqZxPjEBdVZ3lpdvnHuuaLCiva0JmolH+wVHECnSq9MpOxIHSWsmvJ5yG3i5EzsSMDFctEUWdA6U1uP/jHQCA8/JSxUUlAuePzPXNFnEzSH+dwdXSSyboGhnnefgLL7wQ9957L+bOndvughjArSFeEB+zjXqdOPfIOhly9/erBwCA5ABXLPYNuPu1PSPTZLai2XnrdiJSnffuuMDl9iebi8XvL/7Xao/nO2f/bTZHLxl/ncHVMrUUdB+Zv/3tb4iP99/75Nlnnw15QGoSSh8ZQZeMeJyqakRRRR2G5afJOzBSTE1jc8DnBAp6hW0JqiW8FuCcGZTWRwYA6pssSIljV2ki1XD7876op+vCmkAfTtyrGBqaLIiN0aGwtMb+8u0pI7N7925s377d59eOHTtCGsgTTzwBjUaD+++/X7yvsbERs2bNQkZGBhITEzFt2jSUlpaG9PrhEOqqJYCbR0arN386GvA5gU6VlHj7JqxNZqukHbClblFg0GsRo7M/iTtgE6lMgHJKISARrD9c5nK7vM51CXVDs+u1xf35gGtGJpLrOYPOyHz++efIysqSdRCbN2/Ga6+9hkGDBrncP3v2bHzzzTf49NNPkZKSgj/+8Y+47rrr8PPPP8v6+0PlumopuEhG2EOHvWSii5TAI9Cpkhbv2CiyvM6EXIP/DKjULQoAe1amqqGZBb9EUWbjkQqX2zct+gXHnrhcvJ3W8gFJUO92raqo88wA56TEQuvUS6ZDUmTWcwaVkQn2zVqK2tpaTJ8+HYsWLUJammOKpaqqCm+++SaeffZZjB8/HsOGDcPixYuxfv16bNy4UfZxhKI1uxwLTfG4BJvcxei0SGopzK1uCBxw2PysOnAn1MlwCTaRutgCpWS8cJ5usri9Ybl/6PJW7GvQa5Gjgl4yQQUy4UgtzZo1C5dffrlHsfDWrVvR3Nzscn+fPn3QpUsXbNiwwefrmUwmVFdXu3yFi7UVx4NN8aKTlDNCylL9mpapn/K6wPstSZ1aApxXLjEjQxTtnIMX9w/e7lNL7nstCdRQ8BtUILN48WKkpKRg7dq1MJs9L4RmszmovZaWLFmCbdu2YeHChR6PlZSUwGAwIDU11eX+7Oxsv9sgLFy4ECkpKeJXXl6e5PEEqzVhnVAjU1ZrYpq/nQkmsfnhL0UBnxPM1FKCuAM2MzJEkczampR/i2Pljv383D94u7/v+MroqqGXTFCBzK233gqj0YhLLrkEFRUVHo9XVVXhkksukfRaxcXFuO+++/DBBx+4bDzZWvPnz0dVVZX4VVxcHPiHQtSaDFVKfAxS4uxzlkWcXiIfhhekB3yO1C0KAKepJQbPRBHN4vb+EsrbjfP0kXsg09jsPrUUKJCJ3PepkNZfCo103JWXlyMhIcHLT3jaunUrzpw5g6FDh0Kv10Ov12PNmjV48cUXodfrkZ2djaamJlRWVrr8XGlpKXJycny+rtFoRHJysstXuLR2po3TS9FH7iqyZXsCb8IazGkoLMF2L/QjosjiXtMSyvtNs8XxQ+6v534N8NXUVQ1TS0GtWrruuusA2D/53XbbbTAaHRXMFosFu3btwujRoyW91oQJE7B7926X+2bMmIE+ffpg3rx5yMvLQ0xMDFauXCnu31RYWIiioiKMGjUqmGGHTXJcTOAn+dElPR67TlRx5VI7E8zU0uEzgTv8ip05JXwsSTAKxb7MyBBFMjlKUp2Lfd1fzyOQ8VkjE/kZmaACmZSUFAD2C2dSUhLi4uLExwwGA0aOHIm77rpL0mslJSVhwIABLvclJCQgIyNDvP+OO+7AAw88gPT0dCQnJ+NPf/oTRo0ahZEjRwYz7LDplZ3Uqp93bB5ZF+CZ1N7ceEEePtpU7NH7wRvhg5aUImJmZIjUwWNqKYTXKKs1iTMogaaWfH3Acu8lE47Vy60VVCCzePFiAEBBQQHmzJkjeRopVM899xy0Wi2mTZsGk8mEKVOm4OWXXw7r7wzFuN4dAj/JCzbFi0IS/salLJM+VdkYxC+VtkUBwBoZIrXwnFoKPpS5b8kOfP9rCV65eVjAqaWBnVO8voZzL5mztSZkJclX0yqXkGpkFixYAKPRiBUrVuC1115DTY29o+CpU6dQWyt9szt3q1evxvPPPy/ejo2NxUsvvYSKigrU1dVh6dKlfutj2lprl6OLTfFY7BvVzlS7BiVSApnrh+VKfn2pu18DjuXXXLVEFNmkrFqS8iH6u1/tdXaBll+f72NhgWsvmciskwkpkDl+/DgGDhyIq6++GrNmzcLZs2cBAE8++STmzJkj6wDVINREmzC1dPJcA8zcxC9qjXriR5fbUs6XxFjpyVLh+sRVS0TRQ8rU0gu/GyL59dynloQVTcJlw9/lI9ILfkMKZO677z4MHz4c586dc6mTufbaa7Fy5UrZBhfpWluLlZ0UC4NeC7PVFuRUAkUqb3Uq7ildKVNA5+Wmit+bzP6zJ6E0xGNGhiiyuQce3iYAUoJYcOKe4alvMsNms0nqDB7pBb8hBTLr1q3DQw89BIPB4HJ/QUEBTp48KcvAVCGI/h3eaLUap60KWPAbDY6cdZ1aXfjtPo/nSDlfUp32RTlY6n+6VrjgSWqIx4wMkSpYZU7Su2d4GpqtLsGRtEAmijIyVqsVFovnJ7oTJ04gKal1K3nUqDU13Pnp7CUTTbp2cC2Af23tkZBexznY8bYrrbNgGuIJq5a4/JoosrkHHq2dA3B/uYYms0vWx1cfGSBKp5YmT57sUpSr0WhQW1uLBQsWYOrUqXKNLeKFsomXOxb8RpdYvU7213x59WG/jwe3RYF9fFx+TRTZmsyuKZnW9pXxtmrJ+S5/H4QifWopqOXXgmeeeQZTpkxBv3790NjYiJtuugkHDx5EZmYmPvroI7nHGLGCqU3wRZxaKufUEnlXWd/s/wnB1MgYuGkkkRp8ueOUrK/nUezbbJGckclLdyxMicReMiEFMrm5udi5cyc+/vhj7Ny5E7W1tbjjjjswffp0l+Lf9iP0f9R8MSMTmSk7Co7ZaWI7LT4G5wIFITIQLkVSlnUnstiXSBW2FZ1zud3a/L+3VUs2iRmZSO8lI3lqaejQoTh3zn5gH3vsMTQ1NWH69Ol46qmn8PLLL+POO+9sd0GMDB2k0aWlKV5ReV2r+9KQ8nrnOPb2Msuwe60U7hcof+JZ7EukClcO7uRyO5i3h+uGdPa4z714OJiMTIxOi44pkVvwKzmQ2bdvH+rq7NMfjz76aKsa30Wb1mTZ8tLjoNEAdU0WSe3oKbLpnE6GmsbWBQtSz6ugdr9uycg0Nls95syJKHIkGqXV22UlGV1uTx2Yg6H5aR7PE4KW2Bj72757nVygLU46R/DKJclTS+eddx5mzJiBCy+8EDabDU8//TQSExO9Pvfhhx+WbYCRTI4EilGvQ8fkWJyqasTx8npkJhoD/xBFrEAF4G/cMlzyay36/XDc+e4WCb/Tzt8nKoGQkQHsfSSSYlu38SkRhYd7j1Rf15YfZo/FeY8tF2//6/rB+L6lm68zIZBJNOrR2Nxkn1oKYjy5aXHYdDQyC34lBzJvv/02FixYgK+//hoajQbfffcd9HrPH9doNO0nkAlitYg/XTLicaqqEUUVdRjmJZIm9QgU3OqkRBstnM+FxmYLYmO8f0IT+8hIeGmjXgudVgOL1Yb6JgsDGaII5bn82rvUeNd+bglGPeIMrteKX09WiSuUEox6lNU2oaHZ4lLOEOj6EclLsCUHMr1798aSJUsAAFqtFitXrkRWVlbYBqYmrS3gzk9PwMYjFewlQy5S42OQHKtHdaMZRRX1vndbl9CZU6DRaBBv0KGm0cxeMkQRzL0TbzAzAHFuH3pOnKsXp5ITWlYuWqw2jyXe/kRyU7yQG+IxiJFnaglw6iXDQEb1Ap0SzUHsqaXRaFCQaS8GP1bme3l+MJ19AceFzHmO/PtfT6PgwW9Q8OA3ksdHROHjXsM2pkem5J91z96azFbxOpFgdJ5edlwDAmdkIreXTEiBzDvvvINvvnFc8ObOnYvU1FSMHj0ax48fl21wahGoSCoQYQn2cTbFU71AK8+CXcmUn2EPZPxl66xBFPsCQHzLhcw5I3P3+9vE7w+dYSE/kdKEqaWLembi10enID3BEOAngN4tWVv3qSWTU+bFoNdC3zLF7RLIBHgfy0tz7SUTSUIKZB5//HFxqfWGDRvw0ksv4amnnkJmZiZmz54t6wAjmVz/lPnpgd+sKDqcqw9uZVpBS5B7zE/DROGiIrX8xj0j454lem/DsaDGSETyE/6uDTqt2P8pkEv62GdK3KeWzBabywyCEOjUB9GGwb2XTCQJqSFecXExevToAQD44osvMG3aNMycORNjxozBuHHj5BxfZAuiyNIfYWqprNaEOpNZXCJL0eeCgvSgnh+WjIxbL5n3NrhmUeu4fQGR4oTPF9ogFggIwY97INNktiDOYM9baOCokwtmaknoJXOysgEnzjVEVFO8kDIyiYmJKC8vBwD88MMPmDRpEgAgNjYWDQ2RVwgUbq0NZFLiYsTdjrnnkrr5y7h+/acL0dNXwa4PQkbmp0P+No4MMiNjdN048rGv97o8bgqiAJCIwkOYWtIF8QYj9ooxuL61O/9NazSOQMd1aimwSO0lE1IgM2nSJNx555248847ceDAAXGjyD179qCgoEDO8UU0OWcJuQt29BvQOSXon8lOdnzq2Xuq2utzxIyMxFotMSPjY5uC05WRdZEiao+EVUvBtGwYmJsKwDMjs/C7/W5TS/YPM+ecmrBKyehGasFvSIHMSy+9hFGjRuHs2bP47LPPkJGRAQDYunUrbrzxRlkHGMlsQb6B+NOlZQqhqIKbR6qZHDuiOxMuHAAw9cV13n9nkJuXOmpkvM+Ps+icSHnCqiUpf9fLZ4/FU9cPwpWDOgLwXLUEuGaLTc32DzFzP9sl3ifl8iH0kimOsL0BQyrGSE1NxX/+8x+P+x999NFWD6i96pJuf8Pi1JK6yV3ML+VTUjAN8QCnVUtutTBDuqRie1ElztaYUN9kFnfKJqK2J/xdS8nI9MxOcpm2jtH5zlFoNBoc8dPOwZ9IzciEfKWqrKzEm2++iX379gEA+vfvj9tvvx0pKcGnz9XK5kjJtBpXLkW3xbedH7bXDmb3a8B5B2wzKp1WUT10eT/MWLxJbMDXx2kDTCJqW9YQamT8CfQZS8qvEQKZk9FQI7NlyxZ0794dzz33HCoqKlBRUYFnn30W3bt3x7Zt2wK/QJSR4zQTm+IxI6NqvjIywrLIULwyfaj4fVV9s5ffGWRGpiXTUtdkwYbD5eL9g3NTxFVSbM5IpKxQVi1J4evVpGR/hV4yJyobPDoPKymkQGb27Nm46qqrcOzYMSxduhRLly7F0aNHccUVV+D++++XeYiRS9Zi3wxHsyFzEN1fKbJ4Oyf+MqlXq15zSv8c8ftVhWc8f2cQWxQAjs6e9U1mdHDaOVev0zKgJooQsmdkZJj3FnrJNJmtKIugXjIhZ2TmzZvnsmmkXq/H3LlzsWVL4N16o4UtyP4d/mQnxcKg18JsteFUZWOrX48iR3pi4I6c/mi1GuS0rF5qaPZcaWQN8gIlZmRMFnH5ZZ8c+/x6F66eI4oIQrGvXBkZ4SrRmrcroZcMABRH0PRSSIFMcnIyioqKPO4vLi5GUlJwfTKigRynmVarcbyJcOWSann71CPHqrbxfe1TU96WRluDzcg4dfWsbrRPVSXH2fsYiW0AmJEhUpQYyMg7swQNgOuGdg755ztHYMFvSIHMb3/7W9xxxx34+OOPUVxcjOLiYixZsgR33nln+1p+LfPrsZeM+nk7J7p1SGj163ZKsWdkTlV5ZuuC3aIg3ujIyFQ32JdgJ8faAxnHBqYMpomUFMyqJSmctyJ5ctqgkF8nEnfBDmnV0tNPPw2NRoNbbrkFZrMZNpsNBoMB99xzD5544gm5xxjxZJrCZH1ClPK3FFKqnJZ07pkaz3npYKc4nTMyVQ32jEyKkJFpKfY90VKrpZdh7BQ+vxwpx29f34jVc8aJO6VTdLCKH1DkeYNpaJlG1mg0iNFpMblfNn7YWxr064gFvxEUyIR0lTIYDHjhhRdw7tw57NixAzt37kRFRQWee+45GI3GwC8QJeTeAdSRkeGnYdXyckoYZAgGslqKcs9Ue8nIBLlFgfOqJWFqKSnWfl9OciwMOnut1mkv2R+KLL99fSMAYNzTq5UdCMlOSKDIlZERA5mW26/fMhyPXtU/6NeJxF4yQWVkbr/9dknPe+utt0IajFrJNYUpZYNAimzeOvvG6GUoBm8p9i31Esg4VkEGuWrJZBYvbsJ9Oq0GuelxOHK2DsfL65HXElwTUduSe2pJ+NDi7MKemUG/jtDdN5J6yQQVyLz99tvIz8/HkCFDZM9GqJkcq5YA16klm80m2+uSsuSYWspOtmdkztU3w2S2wKh3tCB3LL+W9lrOGRmT2R7IGHSO18tPj7cHMhV1uBDBX+iobThn54T9syh6BLNFgRSL1h31eL3uHRKxeMb5SDJKDwXEjExLLxm5+9yEIqhA5p577sFHH32Eo0ePYsaMGbj55puRnp4errFFPLljudy0OGg09h1Jy2qbXHp8kDoI54RRrxV3nJVjaiklLgYGvRZNZivOVJtcMiXBblEgZF8A4KNNxfYx6h1jtGcGz7JWK4LUmezdlvt2dHRb3uO0iWh9kwUVdU1IT2jdUn+KHEIgI1cfGQfX17ukd3DNOjumxEKn1Yi9ZLKcNrZVSlBX2JdeegmnT5/G3Llz8dVXXyEvLw833HADli1b1q4zNHKdZka9Dp1ShD2XpNXJHC+vQ8GD3+CV1YdlGgW1hvBX4PwJWY6MjEajEbMyZ2pcp5eC3aIgVu/56d05kBHaALC7b+Tov2AZLnthHZ79oVC875ejFS7P2V/ifXd0gcVqw//7fDeWbPJsnUGRp7VTSzPGFMg4Gge9Tiv2tYqUXjJBX2GNRiNuvPFGLF++HHv37kX//v1x7733oqCgALW1teEYY8SSe6djwOlNROKn4Yv/tRoA8OT3+2UfC4Uuzmn3WecgoTWyk4Q6GdeVS8FuUeAtFWx0yciwDUCkWrz+mPj9q2tcP7wUltT4/dlfjpbjg1+K8ODS3S57bFFkau2qpb9O7Yv/3j3K435hOrk1Iq3gt1VXWK1WC41GA5vNBoul9QdHbcQklIyZv2A6qx4+274CRzUQzokYp8BAr5PnBPFV8CtHh2mDl0BGqNUiZTm/WWT4mTryF8i8se4Iblr0i3j7q52n5BkchU1rVy3F6LQYXpCOv1/tujJpYOfWb+ycG2FLsIPuI2MymbB06VK89dZb+Omnn3DFFVfgP//5Dy699FJote2z54QcnVsFjoZkgQOZ9zYcl+33kjyELJ1zXYxepmK4rJapJfeMjFgj04rXdh6vcJGqNZlRUdeEjETWaiml2WLFrA+3i7fLaps8Cix/d34elmwuxn4/gcw/vtnncvu/W0/g96MKZB8vyUfYlLG1q5Z+P6oA3TskwqDXomtmgix1VJHWFC+oQObee+/FkiVLkJeXh9tvvx0fffQRMjPb76qGcHxWFdP6EqaW5HqDJPkJfVkA+QLdrJapJfdeMsFuUeCN8xtjbIwOOcmxKKluxPGKegYyCvr3yoPYWVwp3q41mXGkrNal2FsoAD5QWiN5FcnOE1U4UFqDXtntb0sZtbAEOWXsz+ge8r5Pq3pq6dVXX0VycjK6deuGNWvWYObMmbjuuus8vqR65ZVXMGjQICQnJyM5ORmjRo3Cd999Jz4+btw4aDQal6+77747mCGHlSOlL99r5qdL7yUT77ZkjrtmK084JzokGfGn8T1w/8SeiJNpaayj2Nd7jUwwca173Y77zwaTGaTwefOnox737SiuwsFSx7Ty1IEdYdBrUd9k8foJuc5k9vra/916Qr6BkuysYVu11HqR1ksmqIzMLbfcImtvk9zcXDzxxBPo2bMnbDYb3nnnHVx99dXYvn07+ve3z+vdddddeOyxx8SfiY+PvAZdcp5mwhtIWa0JdSYzEvys77daXXNCTWwprzjnf5G/TO4t62vLWSMTF6NDk9kR+Lpnc/LT47HpaAULfhVW1+RZe7jrRKXLjucdkozo0SERe09XY39JtXgNESxad8Tray/ddhL/N6W3LKvqSH4WmRviySnSeskE3RBPTldeeaXL7X/+85945ZVXsHHjRjGQiY+PR05Ojqy/Vy7hWLWUEheD1PgYVNY3e/SNcGd2D2TMVsSzjUREkLNuSpAt1si4L78OPgUdF6MT91kCvAQyGdyJXWkWt7/vnlmJOHimFjuLKz3+vfrkJGHv6WoUltRgcn/X62WzW6a2d3YSyutMKKs1Ye2Bs5jQNzs8/wPUKo7dryMvkHHuJfP/vtiNQbmpuPGCLoqNJ2JCcYvFgiVLlqCurg6jRjmWjH3wwQfIzMzEgAEDMH/+fNTX+/+EaDKZUF1d7fIVbnKfZ1J3wbbaPAMZUlgYV/kIjaeqGx1bCwCOGplggif36S7PqSX7FGcxm+Ipxr3+YHwfe+OyfadrsN2pbgYAeufYa10+337S5dwAgJdWOZZpT+ybjS//NAbXnNcZAPDpFvv0ktVqw8p9pS7BLSlLuJREYkbGuZfMR5uKMX/pbkXHo3ggs3v3biQmJsJoNOLuu+/G559/jn79+gEAbrrpJrz//vtYtWoV5s+fj/feew8333yz39dbuHAhUlJSxK+8vLywjT1c71nCm0igpnj7TrsGaSYGMhEjHB+ikox6sT+Nc1O8YLcoAOwFvc68TS0B7CWjpF0nqlxuD85LRVp8DJosVpcCYMARyBwpq8PF/1qFxmZ7MLNq/xmX571x63AY9TpcPzwXALByfykq6pqwqvAM7nhnC8Y+tYpL7iOEIyOj8EB8EKaXBEqeN4oHMr1798aOHTvwyy+/4J577sGtt96KvXv3AgBmzpyJKVOmYODAgZg+fTreffddfP755zh82HcX2/nz56Oqqkr8Ki4uDvv/g9zTCFLfRNYdLHO53cRiX8WJrYXCcPFx7u7rvAQ72C0KACAuxq3Y1+1KIEwtnakxeXzCp7bhXoybEheDQbmpXp/bJ8cxBX2mxoTHv7Uvt57x9mafzx/YOQXNFhv+t+Mkdp+0B01VDc340S34IWUINTJK15/4IhT8CpT8IK14IGMwGNCjRw8MGzYMCxcuxODBg/HCCy94fe6IESMAAIcOHfL5ekajUVwFJXyFm9xvWs6bR/rTOdU1IubUkvLC/aEky6ngd+vxcy7t5oOZS/ecWnL92dR4A5JblpBzzyVl5LsV7Rr0WgzOS/X6XCHAFby74TiW7Slxue+BSb1cbl8/zJ6V+e/WEy51V/9aVuixkIDaXiSvWgI8MzJKfpBWPJBxZ7VaYTKZvD62Y8cOAEDHjh3bcES+hSuVJiUjs/tEFU5Wui59YyATOcJR7AsAWUmOgt9pr6zHg0t3Y82Bs/bfGczUkt5/IAMIm0fa9/OitpfmVrmvATA413tXVm8r1ub+dxd6ZCWKt/88oafL41cN7gSDTos9p6rFzUMBYH9JDb7a5bvzr8Vqw/99uhOfbA5/trs9i/yMTOR8kFY0kJk/fz7Wrl2LY8eOYffu3Zg/fz5Wr16N6dOn4/Dhw/j73/+OrVu34tixY/jyyy9xyy23YOzYsRg0aJCSw/Yge7FvyxvIycoGjxUHgulvbPS4j1NLyhOD2zBde7wtwd7UsnlgclyM5NeJDZCRAYLf94vkVdPo2v8lOznW59SSu0G5KahqaMahM/Z+M49c2c/jOWkJBkzs533n42eXHxCvPc0WK15bc1isyftm92l8uvUE5n62S+r/CoVgdaH9A4rwbxhpOLXU4syZM7jlllvQu3dvTJgwAZs3b8ayZcswadIkGAwGrFixApMnT0afPn3wl7/8BdOmTcNXX32l5JBdhGsaISvJCKNeC4vVhlOV3hsOVTtd5DJbOq8yI6O8cCfkhSkEbxe3PjnSu7QmBFi1BDimOFnw2/Yq65tQ3WhfQTS6ewb+feMQ5KXHo0OS0WNKWbDoluEAgIev6IcXfzfE5d/YGOO9KeNvhrkuhtBogMxEA46X1+OTLfaMy6Nf7cHC7/bjshfWwWazcWVTG3MvzI8U7hkZU7NytXRB77UkpzfffNPnY3l5eVizZk0bjiZ4jjcteT9+a7UadEmPx8EztSiqqBczNL5kJBhQVmtiIBNBwpUMFjIye055thVIDaKJUO8c19oxb+lrcYqTGZk2VVhSgynPrxVvXzogB1cO7iTeHpyXIk4rP3hZH/H+Sf2ysfexKYg32C/r/7x2IO7/eAcA3ytfLuqZibgYHRpa3oT+MLY7spONePSrvXhx5UFMG5qL9zc66rBWHzjrUj9z5GwtunVI9Hhdar0xPTLw86FydO/g//qvlI4psS63WSOjcuGoxfK3C7Z7o6wEoz1i5/Jr5cmxE7U/4n5LNZ51ZPFBfHK7ZVQ+Lh/oqDXzOrXUkpFhL5m29fZ6120JEt26eztPL6W4TScKQQwAXDOkM+64sCtS42NwQdcMr79Lr9NiqtN5MHtST9w0ogs6p8ahtNqEdzccc3n+G+uOoNopI/PeRm5cGy6RvgrevYu8qZmBjCqF80Tzt3LpD+9tdbkt7JvDGhnltdXUkjfOb2KBxOi0ePzageJts9Xz3BEygSfO1XsEzxQ+7g3QkmJdg5XBToGMUe//Ev63K/ph20OT0DXT96f6W0fnO72eDka9DvdNtBcGv7z6MK4+z5EN+vlQOSqdApn/bjmBWh97OVHrhPtDkRySnTbHZUZGpcTW8GF4bcfKJc8VIyv2lbrcNrSsQOHUUuQI16VHWH7tTYw+uN+a6HQRqjN5zm/nJMfCoNOi2eK7Vovk577c1nkndQAY6LRyyb0g2JtAq14G5aZiycyR+OqPF4r3XTekM7p3SEBlfTP+t8N1BdP3vzqWddeYzPjvFq5eCodwvr/IpZNTvRYzMioXjoDZsfTVNSPjLZVraEnxMZBRni2E5nTBSDTqPaYaBN0yg6tVcP7kX2vyLODUaTXITbdfqLhyqW28se4Ivtnt2v/FPZBx/vd338U8VCO7ZbgESHqdFnPcNj2d2LInk3vbh7fXH2PfmTBwZGSUHYc/zoXnb68/ptg4GMi0QltNLQlvjmdqGvG3L371eK6QXm4yswNre5DlZXrptd8Pa9WbWmqc90JhblXQdo6V1eEf3+xDWa1r/VOS0XNZ/ft3jMBtowvEPZPC4dIBORjY2RHcDM5NwQUF6eLtzqlxSI7V41h5PVYVshuw3MQu4RGck3HOyFi8TE+3FQYyMgjHiZabFgeNBqhvsqCstgkAUFnvfdkja2QiTzgvPUJTPGdDfHR8DWTxjPPxwKReGNe7g9fHxcwgd8EOm7JaE8wWK+p9bAXhnpEBgAt7ZuKRq/p7dGiWk0ajwf9NcWRlTlc34s6Lujo9DvyuZcfjt34+6vHz1Eoh7KHW1pwD3d8MD9++hoEwkGmFcCZTjXodOqUIaX37m4i3gsvP7hkNfcuZ/jE7bSquLVYaCH2DAGBkt3T88tcJfmtn/Lmkdxb+PKGnz4JCsSkeMzJhcehMDc7/5wrM+nAbGpq917vEG5XrI3JRz0zx+26ZCeL0EgCcONeAW0blQ6uxFwEXltQoMcSoFcoeam1t2rBc/HZ4Hv5wcTdcNiBHsXEwkGmNMJ9o7kuwvf2enJRYrG5pUX/4LD81K00s0Avj1cd5ye2VgzuJvWXCwV8bAGq9RWuPwmYDlu0pRa2XguupA3Ng1CsXyGg0Gmx5aCIevqIffnt+HrRaDW4fY8/KpMbHIDctHlP629/AFjMrI6tw9SmTk06rwZPXD8L8y/oqurqKgYwMwvXPly+hs2qMToNaCSsXqG2F80/aOZD5bZjTuflearVIPh87rfh5ZbXrZrj/uWkIXp4+rK2H5CEz0YjbL+wqLgP/69Q++H9T++Kt284HANx+oT2w+Xz7SVTUNSk2zmgT7oUD0YSBTCuE+7Lu3kvG29SSQad1mUOtaWT7cCW1xXu9855K7k2p5JbXkpGpNZlxzkeNFslj45EKl9vxYax/aQ29Tou7xnbD0C5pAIDh+WkY2DkFJrMVHzntxk6t4yj2pUAYyMggXCm1/HTX3YebLZ7vkjE6rUuR718+2RmWsZA04r9QGK8+afHSN4dsrdgYHXJapq64C7a8GgPsTaPklFIwNBoNZowpAAC8u+GYz41uKThqaIgXKRjItEK4P33nu2VkvF0g9DoNzndaEvnD3lKP51DbcWx+Hb6Lz0U97SuMknz0k5Gbvy7TFLryANMwanr7unxQR3RIMqK02oRvd59WejhRgRkZ6RjItIItzJNLwhtIWW0T6kxmr4FMjFYrW1Msaj3hnAjnkslOqXFYN/cS/PTg+PD9EifsJRMex8r8Z7jU1E7BqNfh5hH2rQ4W/3xM2cFEC9bISMZ3QBmE60RLjo0RpxGKKuq9Ti1ptRpx+bWARZnKaatunHnp8R4bBoaLlKJzCp77363g4l4dcM15nXBhj0yvj0eq6SO7wKDTYkdxJbYVnVN6OKonZmQYyATEQKYV2iJecF7++tPBs16fo9e6/jNy5YByhKxZuItw21KXlqZ4RWyKJyths8X+nZJd7n9y2iA8/7shqjuHMhONuKplg8m3fuJS7NZijYx06vpLiTDC3kbhrIcQ3kS+2nUKi9Z5vzjEuE0tHQmQsqbwMbdkzQwqexPyh1NL4SFs+JjqVrzdwUvnZrUQin6/+7UEp6u40WhriA3xFB6HGkTP1baNfb3rFN5o+dQRzoBZeBP5ZpfvAjr3N80jZ2vDNyDyS8jIxOii5/IjTC2dqTGhwUcbfQpeTUtGxn0TUF0k96QPoH+nFIzomg6L1YZ3N3hucEvSMSMjHQOZED3kZfPGcBAKfv1xX47LDr/KeW3tEQDRNbWUGm9Acst+P8LKpW1F5zD3vzu51LYVth2315EkGmNwScteV5HaOyYYQoO8jzYVMfBtBa5aki56rrZtzLkQb0dxZdh+j5CRcTa0SyqGdkkVW4X/aXxPnF+Qhj45SQCYkVFKlVPDuHpTdHVbdl+Cfd3L6/HJlhO45/1tSg5L1XLT7Hup7TtdjWdvOA83XtAFS2aOVHhUrTexbzby0uNQWd+Mz7efVHo4qsXOvtIxkAmR8zx2ODfLEnYfdmaxAUvvHYOHr+wHAEiJj8Gnd4/G366w3z7CjIwizE7b2EfbujHn5ozOq+JW7GPfolCZWmrsLuyZibQEAxZeNxCDclOVHZQMdFoNbh1VAMC+/xJXUbZOOGswowUDmRAJf5sXFKTj5pH5Yfs9WUlGGN2KeRuavH/a794hEQBwvKJeLESmtmOJ4gu2c0bGvTEet8UIjamls6/733c0uOH8PCQYdDh4phY/HSpTejiq1FatHKJB9P0FtRGhonxk9wzExoRvXlur1YhLsAV1XnbJBYDsZCMSDDpYrDZ2YVWA815Y3vbFUjPnlUsHS12nLtcd5BtVKISMTDQGMsmxMfhNy4ambJAXGqG5JuOYwKLvL6iNCIFMWywwyHcr+PW1R4tGo0HXDvYpANbJtD2zU8PCKItjXDIyd767xeWxFdwWIySOQEb9Bb7e3Dq6ABoN8OP+M7wehYCrlqRjIBMi4Y1K2wYnWZd01zqZej8rAYTpJa5cantmp+jFGmWRjFCrdeKcZ6bvx8IzMHP1UtBM5pappZjovAx3zUzA+N5ZAIB31h9TdjAqZGWxr2TR+RfUBnLT4jA4L1XcGTic3DMyDX52ze2WaQ9k+Amo7Vmcin2jrV4mJzkWBp3WY5uMlLgYVNY3Y+txtqQPlqk5eqeWBMJS7E+3nkBVA2upgsHl19JF719QmN07rgf+N2sMbjg/L+y/S0ovGUE3YWqJ3X3bnPObfLTVyOi0GuSmx3ncP76P/RM3Vy8Fp7qxGVtagr+spPB/GFLK6O4Z6J2dhPomCz7ZXKz0cNSFU0uSMZBRAW+9ZHxxTC0xI9PWnIOXaGwU5+08nNg3GwCwYt+Zth6Oqr2x7iiqGprRIysRY3t1UHo4YaPRaMRtC95ef4xTkEHgppHSMZBRgdw06YFM10x7RqayvpmbR7ahJrMVG4+Ui7eFFRvRxFtPo7G9MhGj0+BoWR2DZ4nKa014c529A/RfJvVS9ZYEUlwzpDPS4mNwsrKBmbsg2LjXkmQMZFTAEMQcepxBh86p9ikA1sm0nWd+KMQ/vtkn3r44Cj9l57llZAZ0TkZSbAxGdssAwNVLUr265jDqmiwY0DkZl4axmWakiI3R4aYRXQAAb3EptmTMyEjHQCYKCXUy/ITcdoQ9lgCgewfPzEU0cJ9aum9CLwDApH7C9BIDmUBKqxvFzRT/Mrl3u6l/+P3IAui1Gmw6WoFfT1YpPRxVcKwXaB/nSGswkFGJoV1Sxe//fs0Av88V6mS4VUHb6Z2dJH4frVMF7qvnhA0OJ7TUyWw9fg7ltaY2H5ea/PvHgzCZrTi/IA3jojBr50tOSiymDuwIgA3ypBIa4kXp5URWDGRUYvGMC/DK9KH49dEp+H2ALREcGRkGMm2ld44jkInW4+4+tSRMeXZOjUO/jsmw2oBVhWeVGJoqFJXXY8km+8qdOe0oGyMQin6/2nkKZ2sY8AYidHNob+dJKBjIqERKXAwuG9gRiUZ9wOeyl0zbc77WRNvSa0FsjM6lb5JB57h8TOzbsgybdTI+Pb/yAMxWGy7qmYkRLXVF7cmQLmkY0iUVTRYrPvjleFA/e9viTSh48BusLmx/q+MYxgTGQCYKdc+yZ2SKKuqjchlwJIrW4MWdc08j5460E1vqZNYePOtzC4327NCZGnyx/SQAezamvbp9jL1B3vsbj4udjQOpqGvC6pZM322LN4dtbJHGxs6+kjGQiUI5ybGIN+hg5uaRbeLw2VrsPVUt3p45tpuCowkv54Jf54zMgE4pyE42or7J4rIMneyeXX4AVhswpX82BuelKj0cxVw6IAc5ybEoq23C1ztPS/qZoX9fLn4fjasBfXF09mUkE4iigcwrr7yCQYMGITk5GcnJyRg1ahS+++478fHGxkbMmjULGRkZSExMxLRp01BaytR1IBqNRuwnw4Lf8Gm2WPHSqkO47IV1OFJWh3iDDl/MGoO/Tu2r9NDCxrng17ktgFarEYt+uXrJ1a8nq/Dt7hJoNPaVSu1ZjE6LW0bba/ze+vmomHWQKivJGI5hRSTHppHKjkMNFA1kcnNz8cQTT2Dr1q3YsmULxo8fj6uvvhp79uwBAMyePRtfffUVPv30U6xZswanTp3Cddddp+SQVYMdfsNr94kqXPWfn/GvZYVoMlsxtlcH/DB7LM6L8k/bXZya4rnv2jxJCGT2npH0BjX74x0oePAbHC+P7mD76R8KAQBXD+6EXk6r29qrG8/vgtgYLfacqsbmY/736HJfBddobj9T5Ta0j+lqOSgayFx55ZWYOnUqevbsiV69euGf//wnEhMTsXHjRlRVVeHNN9/Es88+i/Hjx2PYsGFYvHgx1q9fj40bNyo5bFUQ91xiICOrhiYLFn67D1e/9BP2na5GanwMnr1hMN6ZcX5QHZjVqnOqU7GvW6PGUd0zEBejQ0l1I/Y4TbX58nlLzcjD/9sj7yAjyJZjFVhdeBY6rQb3T+yl9HAiQlqCAdcOyQUAvPXTUb/PPVXZ6HLbW/3ViXP1qG6Mvg0pmZGRLmJqZCwWC5YsWYK6ujqMGjUKW7duRXNzMyZOnCg+p0+fPujSpQs2bNig4EjVoRt7ychu/aEyXPrCWry29gisNuDKwZ2w4oGLcd3Q3HazRLJLunNGxvXyERujw9hemQCA5QFWL+0srhS/z0g0yDfACGKz2fDUMns25obhuSjIjM5GiaEQlmL/sLcExX7q+E5XNbjcdg9kvtl1Ghc+uQqDHvlB9jFGCtbIBBZ4LW+Y7d69G6NGjUJjYyMSExPx+eefo1+/ftixYwcMBgNSU1Ndnp+dnY2SkhKfr2cymWAyOdKR1dWBPxlGo+7s7iubqoZmPP7NPny8xd4DJCc5Fv+8doBYE9KedEgy4u9X94fVZg9c3E3sm41le0qxYl8pZk/ynYF48vv94vfVDdH3aRoA1h0sw6ajFTDotfjT+J5KDyei9MpOwkU9M7HuYBne3XAM/+/yfl6fd6rSNZAxNbtOLc36cFvYxqg0TixJp3hGpnfv3tixYwd++eUX3HPPPbj11luxd+/ekF9v4cKFSElJEb/y8qJv8z4phGLfc/XNOMfNI0P2/a+nMfHZNWIQc/PILlj+wNh2GcQIfj+qALeOLvD62Pg+WdBogD2nqj3ehJytP+xY2XSsPPpW1tlsNrE25uYR+ejUsv8ZOQhZmSWbi1FnMnt9zqkq+9RSQUuReaOfJdvRtuyfU0vSKR7IGAwG9OjRA8OGDcPChQsxePBgvPDCC8jJyUFTUxMqKytdnl9aWoqcHN8brc2fPx9VVVXiV3FxcZj/DyJTvEGPTin2eoYjZczKBOtMTSPueX8r7n5/G87WmNAtMwGf/GEU/nHNQCTFxig9vIiVkWjEsC5pAICVElcvFZXXR10fnh/2lmLXiSrEG3S495LuSg8nIo3rlYWumQmoaTTjs20nvD5HCIaFxQvOwcr2ItdC4cr66MzsMZAJTPFAxp3VaoXJZMKwYcMQExODlStXio8VFhaiqKgIo0aN8vnzRqNRXM4tfLVX3bNaVi6dYZ2MVDabDZ9sLsbEZ9bgu19LoNdqMOuS7vj2votwQdd0pYenCo5l2NK6sDZZrCipbgz8RJWwWG149ocDAOxZh8zE9rNkOBharQa3tWT2Fv98DFYvwawQyAiLFxqdppb++vmvLs+tiropyugK7sNJ0UBm/vz5WLt2LY4dO4bdu3dj/vz5WL16NaZPn46UlBTccccdeOCBB7Bq1Sps3boVM2bMwKhRozBy5Eglh60a3Vqmlw4zIyNJUXk9bn7zF8z9bBeqG80Y2DkFX/7xQvzflD5e60HIu0n97NsVbDhcjlovUwYNTZ5TAMfLoifY/mrnKRSW1iA5Vo+ZFzEb48/1w3KRFKvH0bI6rDnguU/XtqJKAI7FC84ZmX2nXesfK+vtU+gnztXjzne2BN2jJtKIU0ss9g1I0WLfM2fO4JZbbsHp06eRkpKCQYMGYdmyZZg0aRIA4LnnnoNWq8W0adNgMpkwZcoUvPzyy0oOWVW4ckkas8WKxT8fwzPLC9HYbEVsjBYPTOqF28d0hV4XcUnLiNe9QyIKMuJxrLwe6w6cxWUtux4Lvtx5UvxeKPg8Vl6P0T3aeqTya7ZY8dwKezbmDxd3R0o8pyH9STDq8bvz87Bo3VG89fNRXNIny+vzhBojIZB5vuUYO1vw5R7sL6kRb3ed/y2OPXF5GEbdtji1FJiigcybb77p9/HY2Fi89NJLeOmll9poRNHFX1O8ZosVm49WYGh+WrvONuw7XY15n+3CrhNVAIBR3TLwxLSByM/gUtlQaTQaTOybjTd+Oorl+0o9Apk4g+Oy0yMrEesOlkVNU7z/bj2B4+X1yEw0iNMm5N8towrw5k9Hse5gGQ6U1ohNA5ucmt8JHX2rG81oMlvx/IqD4mNGvRYms9UliBEUPPgNLuiajpzkWOSkxCIryYiclFjkJMciOzkWWclGj8aOkULd+aS2pfjyawofYV65qNy+eWSMU3bhmpd+xp5T1eiamYBVc8YpNELlNDZb8J8fD+HVNYdhttqQFKvHQ5f3xQ3D89pNT5hwmtjPHsis2n8GZovVJbPV0GSfbhrbqwMKWgLGY1EQyDQ2W/DiSvsb7D3jeiBBwk71BOSlx2Nyvxx8v6cEi38+hoXXDQQA1Dc5piUzEhy9hr7edcrl58/LS8UvRyt8vv4mP48BQHqCAdnJschONooBTk6K/XZ2sj3oSYs3QKtt2+uCuGlkm/5WdeJfWhTLSY5FXIwODc0WFFXUixkaAGLn1aNRVJsg1eZjFZj32S5xyu3S/jl47Or+yEqODfCTJNXw/DSkxMXgXH0zthVVuhRKn62x93nqmBwr7t10rEz9S7A/+KUIp6sa0TElFtNHdFF6OKoyY0wBvt9Tgs+3n8DcKb2RlmBAXUstlUGvdQkK3Tclzc+I9whkji6cisNna1FR14yS6kaUVjXa/9vyZf/ehCazFRV1Taioa8I+P3tYxug0yEqKFbM5WS1Bjz3L47g/ziB/doefqwJjIBPFtFoNGlrmlB/8bBc+vXu0wiNSVk1jM576vhDvbTwOwN7c7bGr+ntMfVDr6XVajO+Thc+3n8SKfaVeA5kOSUYxI3O8og5Wq63NP/XKpc5kxsurDgEA/jyhZ7uerg3FBV3T0b9TMvacqsZHm4tw77geqG8pFE8w6FyOp3vNX47TBxCNBjj0z6nQaDTokeV/XyubzYbK+mbXAKfKhJLqRpypdgQ+ZbVNaLbYcLKyASf99EYCgKRYvRjguGd5hExPZqIROgnnOaeWpGMg00742pxNpe8bQftxfyn+3+e/4nRLg60bhufi/03tx2LMMJrYN1sMZJx3BD9b6whkOqfFQafVoLHZijM1JuSkqDMr9vb6Yyiva0J+RjyuH5ar9HBUR6PRYMaYrpjz6U68u/447rqom5iRiTfoodNqMGNMARb/fAyFbrUwKfGOaafUuBhJQYLwO9MSDEhLMKBvR99tOprMVpytNaGkyi2jU2XP6gi365ssqGk0o6axFgfP+F4pqtNq0CHR6Ji6SnEKdJJjkZNiRFZyLByLrtrJRboVGMhEuQcv64MnvrO3g29stnh8UoyyPmQeymtNePSrvfhyp31evUt6PBZeNxBjemQqPLLoN7ZXJmJ0Ghw5W4fDZ2vFqU0hI5OZaESMTovctDgcL6/HsfI6VQYyVfXNeG3NYQDA7Im9XGrRSLorB3fEE9/tQ0l1I77/tUTcgyu+Zbpm6sCOWPzzMdS4LelPjXN8GKkzyd/d16DXonNqHDr76c5ss9lQazK7ZHUcWZ5GlNaYUFrViLO1JlisNpS0BD9AVcDfz6mlwBjIRLk/jO2Gt38+hpLqRmw9fq7dvIHbbDZ8seMkHvtqL87VN0OrAe68qBtmT+wVlnls8pQUG4OR3TKw7mAZVu4rFQMZoQNrWks2LD8jAcfL63G8vA4ju2XAZrPhrne3oM5kwQd3jkBZrQn//vEQpo/sgj45kdfg8vV1h1HdaEav7ERcObiT0sNRLaNeh+kj8vHCyoN46+ejuG+CfX8qYZf1Xj6milKcApkmi9Xrc8JNo9EgKTYGSbExfqe0LFYbympNjgCnpVbHPfCpbrQHa0lGvbhii3xjIBPlNBoNRvfIwNJtJ/HzoTLExujw3W7XqjZvmZpAzBYrtBpNRNY0nDhXj4e++BWrC+0NtvrkJOGp6wdhUG6qsgNrhyb2zca6g2VYsfcMZo61N4erablIJ7e8ARVkxGMtHHsuVTeaxa7Ax8rr8PyKg/hy5yl8uKkIhx+f2vb/E16cqWnEXz7ZicsG2LMEAPCXyb0lT2uQd9NHdsErqw9je1ElNh+zF/AKGa6U+BhkJxtRWm1y+ZlUp+lhQ4Rnw3RajTiNNMjPDGR9kxml1SakJxi4JYoEDGTagTHdM8VA5uXVhz0eL6lqREGm9L4pD32xG+9vLPL6mJINqKxWG97dcAxPLStEfZMFBp0W903siZljuzHdr5AJfbOw4Ms92HK8AhV1TUhPMKC60Z6RSYq1X36Enj1CL5nyWscb1Z5T1eK0YCTtx/Tkd4VYd7AM6w6WAQAG56Zgcr/2u5GoXLKSYnHF4I5Yuu0kXl97BIBrcNIrO8lvIBMt24jEG/Tomsm3Z6l4dW8HhOmk3Se9z8cGu8+NryBGSQdLa3D9q+vxyFd7Ud9kwfkFafj2vosw65IeDGIUlJsWj74dk2G1Aav2n4HNZhO7s8a1ZAELnJZgf7XzFP671bGB4J8+2u7yeiY/ux+3pbJa1zfTv0zuzf5DMrl9TFcAQLPFHrjG6B3HtaeXaZuUOEexr17Hf4P2iCFfO5CTEotuHRJ8blVQUiU9kJm/dJdcw5JFk9mKV1YfxkurDqHJYkWCQYcHL+uD6SPyI3Laqz2a1DcL+05XY8W+Ulx1XiexwFzoqCpkZPaervYIXNwdOVvnd4VJW3EOji/omo6LeraP2rO2MKBzCi7omi42sotxycgkujz3sgE5rjUyZmVqZEhZ/KjaTozp7vtCu/bgWby48qDLhmy+fLSpWPz+iJd6BW8bAobL9qJzuPLfP+G5FQfQZLFifJ8sLH/gYvx+VAGDmAgysWXKZe2Bs2J9DAAYY+yXn7z0OMkrMw6U2pfevrHuCD7ZUhzg2eFjcMoSsBu0/G4fUyB+7zyl2CvHkZG5bEAOXrppqFgMDEDSNYyiDzMy7cSYHhliIzh3S7fZN/E7ea4BT14/yOdruBcJa7UaHHvicthsNvT+2/doMltRXmdCriFevoF7Ud9kxtPLDmDx+qOw2ewtxhdc2Q9XDe7EN5QINKBTilikuebAGfF+ofbBqNehU0pcwGZjAFBYUoPiinr845t9AIDrh+YqErQ6ZwkSjVwFJ7dJ/XLE74U6JADomeXIyMTF6Dz+7RuamZFpj5iRaSdGdssI+Kn34wCfcO/5YJv4/fa/TRK/12g0yGzZC6W8tin0QUqw7uBZTH5uLd762R7EXDukM1Y8cDGuPq8zg5gIpdVqMKGvPSvzza4SAPaW785vQgWZ0oLfwpIaVDU0i7drm8x+nh0+znXHEVSDHDV0Wg3+cHE3AMDMsd3E+51X8Bz1sj8XMzLtEzMy7URqvAEDO6eIuzz7Umcye93szmZzvVqnOW3iBgAZiUacqmpEeZ1rEaRcKuub8Pev9+GzbfZC0M6pcfjHtQNwSe+ssPw+ktekvtn48JcirNhXCgAeOw7nZyTg50Pl3n7URWFpjUuvkJpGM5IVWJ7qPIUaCTU70ejBS/tgUt9sn8f3mJd94hjItE/MyLQjo/3UyQh+OlTm9f53NzimpWY4zV8LhC6cZTJnZGw2G77ZdRoTn12Dz7adgEYD3Da6AMtmj2UQoyKjumeIq5QAwKh3vfQIK5cCOXGuAaVOxenVTtmZYPx8qAz3LdmOirrQzldh9dSMMQXoGkTrApJOo9FgeEG6xwcr4frzl8m9PX6mvg1r9ChyMCPTjozpkYFX13j2kXH2474zmNI/x+P+BV/ucXx/ZX+PxzMS7N0n5ZxaKqlqxN/+9yuW77V/iu+RlYgnpw3CsPw02X4HtY3YGB0u6pmJH/YKGRnXQEZYuSTF1uOOfcOci4eDMf2NXwDY6yyemOa7LswXU8vqmOH50dG3RE0eurwfbh6Zj25eAkgGle0TA5l2RMpFd1WhvdeHe71JXIxO3Enbm8xEoUam9VNLVqsNSzYXY+G3+1BjMiNGp8E943pg1iXdPaYkSD0m9ssWAxmDR0Ym8BtQWnwMztU3Y4tTILP5WIXkJmg2mw3/+GYf8tIce+Yc8TI9IYWwzNc9IKPw02k14nYXgm/+fCFeX3sEf5nkmaWh6MdAph2JM+jwzu0XoKSqAYfO1GJsrw74/ZubXJ5zpsaEPaeqMaBzisv9QhDz9G8Ge31tYWqpPMRUveBoWR0e/GwXfmnpITE4LxVPTRuE3jm+9y8hdRjfxzEV6D6l0yU98NTSgM4pWHewDDuKK8X7/rWsELMu6SHp928rOoc3fzrqcp/QqyQQm82GDYfLkZsWjy4Z8WJGxj0gI2X075SCF343ROlhkEIYyLQzF/fq4POxzqn2JbA/7j/jEsiYnYor3RtSCYSpJfeOp1I1W6xYtO4Inl9xEE1mK+JidJgzpTduG13A/WuiRGaiY/O7arcpISkbeXbNTHBZihss99b2wdh5ogo3tUxH3XhBHopaVswwI0OkPP4VkmhU9wwAwI/7z7jcf6rSUVw5oJNrpkYgZmRCqJH59WQVrnnpZzz1fSGazFZ7LcXssbjjwq4MYqLMuN6+A+lARrecn+6e/H6/pJ/31axRSvDt3P36o03FqGt5LWOQm60SkfwYyLRzS2aOBABkJRlx/8SeAICdJypdLu6Hy2rF7301HxM+bQez/Lqx2YInvtuPq1/6GXtOVSMlLgZP/2Yw3r39AuRJmGog9Xn6N4MxqlsGnr3B+xSlM+cg9t83DsFYH9nEV1YfxqEzNQFfr9HHPk0/+1ip50xYpWTQa3FBgaMmJy2eOxMTKY1TS+3cyG4ZLjtW9++UjD2nqrG68CyuH2bfZ37/6cBvEs4ZGW/Fwu42HinH/KW7cbSl2PLygR3xyFX90SHJ6PfnSN0yE434qCV4dnf7mK5462dHDYvzGTS5fzaMeh26pMejqKLe42cXfrsfb952vt/f7Ssj8/OhMlx9Xme/Pyv0J7mwRybevHU41h8uR1VDc1CrrYgoPJiRIRdCQeYqp+mlmsbAvTrSWxrkma02VDf4XhJb3diM+Ut343evb8TRsjpkJxvx+u+H4aXpQxnEtHMPXtYHi2c4gpHLBnYUvxe2M+iV7b3oe+X+M1gfILPiq8fITwfLPBo+umtsaX0fG6OFRqPBmB6ZmOo0PiJSDgMZcnFJSyCz9sBZNLcU+aa2pM+vGtzJ588Z9TokxdoTfGU+ppd+2FOCSc+uwUebigAAN43oguUPXIzJXvrWUPtj0GtxSe8srH9wPF743Xl4ctpAdEmPx7D8NDHD1zvHe7E5APzjm30uGwwKymtNKK6o99r11aDT4lRVY8Bl2MLPxnL5P1HE4dQSuRicm4r0BAMq6pqw5dg5jOqegYYme0AjBCq+ZCYaUdNoRnltE7o7lTOcrTHhkS/34JuWTSe7ZiZg4XUDMbKb9+JNat86pcaJUz0//uVil2nK3jne29Unxeqx93Q1Pt9+UpwSFUx4dg0q65txxSDPDMrwgjSsP1yOnw+VefQmcSZkZFjcSxR5mJEhFzqtRlxZsqrQPr0k9JCJC3ARz0hwbYpns9nw6ZZiTHx2Db7ZfRo6rQb3jOuO7+67iEEMSaLXaV2Kfns7TS39ZVIv3DA8F1/98UL8saWXzNPLCj1qYSrr7VOj3/9a4nL/E9cNxJge9m07Ai3rFgqFY2N4ySSKNPyrJA9CnczKlg3+xLR6gEBGqJMpq2tCcUU9bnlrE/7vv7tQ1dCM/p2S8b9ZYzDv0j4BX4fIF+cW9BoN8NT1gzEwNwW3ji5A59Q4lFQ3YtG6I+JzrE5TTWa3aaf+nVJwUU97ILPxcLlLvyR3Uv8GiKjtMZAhDxf17ACdVoPDZ+tQVO6oLQjUtCyjZQn2sz8UYvJza7HuYBmMei3mXdoH/5s1xqNbMFGwnDvpCtM9gD3AmHdZHwDAq2sO40yNve9Lk5/gxBijRf9OKUiNj0GNyYydfnaGF4t9WSNDFHEYyJCHlLgYDG/ZmPHH/aXi1FKgT6PCfkvn6pvR0GzBiK7p+P7+sbhnXHfodTzVSB5PThuIYflpuGV0vsv9Vw7qiPPyUlHfZMFzyw+gocmC37y6wefrGFqmrYRGez/5mV4yNXNqiShS8a+SvBKml34sPCvWHEitkQGA/Ix4fHTXSO5GS7L77fld8Nk9o5GVFOtyv0ajwUOX9wUAfLy5GE9+vx+7T/rOsgjZnQt72GvCfjp01uM5jc0WvLTqEL7fY6+viTdyfQRRpGEgQ14JgczGI+XiBn9xBv+nS4bTXjoJBr3PLsBE4TK8IB2XDciB1Qa8vf6Y3+daW3rHCHUy24sqsfdUNRqbLbDZbFi+txRTnl+Lfy0rRH2TBcPy03ApWwUQRRx+vCCvemQlIi89DsUVDdhy/BwACRmZREdGhil4UsqDl/XBin2laLb4bnLXIysROcn2jE5eerzYMXjqi+tg1GtxQdd0cSVTdrIR8y/ri6vP6xSwYzURtT2+25BXGo0G43tnudwXqIeG8+7GRhZFkkLyMxJwy6gCn4+vnjMO3993kUvd1oUtWRkAMJmtWHewDAadFveO644f/zIO1wzpzCCGKEIxkCGfhC6/gmBqZPQ6XvRJOX8a38PnYwa91qP4/MIemS63J/bNxg+zx2LupX2QwLoYoojGQIZ8GtktwyV4CRTIpMY7Ahlf+9oQtYXUeAPuuLArAMCo1+LNW4eLj2m9ZFaElUuCN24djgIWqhOpAj9qkE+xMTqM6ZGBFfvsHX4D9ZFx7sBa2+h740iitvDQ5X0xqlsGBuamICvJiDE9MlBZ3+x1c1LnIJyI1EXRjMzChQtx/vnnIykpCVlZWbjmmmtQWFjo8pxx48ZBo9G4fN19990Kjbj9Gd8nW/w+UEbGWa2JgQwpS6PRYGK/bGQnx0Kj0eCDO0fi6z9d6BJwO2OrACJ1UjSQWbNmDWbNmoWNGzdi+fLlaG5uxuTJk1FX57oT7V133YXTp0+LX0899ZRCI25/Lunj2P0xmPbsDGQoEvkr2H3ppqEw6LX4w9hubTgiImotRaeWvv/+e5fbb7/9NrKysrB161aMHTtWvD8+Ph45OezfoISOKXGYM7kXzvlIyftS09gcxlERya9fp2TsfmQyDOxCTaQqEfUXW1Vl78KZnp7ucv8HH3yAzMxMDBgwAPPnz0d9fb3P1zCZTKiurnb5otb54/ie+NsV/SQ9t0t6PABgSJe0cA6JKCyMeh2XWROpTMQU+1qtVtx///0YM2YMBgwYIN5/0003IT8/H506dcKuXbswb948FBYWYunSpV5fZ+HChXj00Ufbatjk5sO7RuCd9ccwY0xXpYdCRETtgMZms/luf9mG7rnnHnz33Xf46aefkJub6/N5P/74IyZMmIBDhw6he/fuHo+bTCaYTCbxdnV1NfLy8lBVVYXk5OSwjJ2IiIjkVV1djZSUlIDv3xGRkfnjH/+Ir7/+GmvXrvUbxADAiBEjAMBnIGM0GmE0Sq/lICIiIvVSNJCx2Wz405/+hM8//xyrV69G166BpyN27NgBAOjYsWOYR0dERESRTtFAZtasWfjwww/xv//9D0lJSSgpKQEApKSkIC4uDocPH8aHH36IqVOnIiMjA7t27cLs2bMxduxYDBo0SMmhExERUQRQtEbG1+qAxYsX47bbbkNxcTFuvvlm/Prrr6irq0NeXh6uvfZaPPTQQ5LrXaTOsREREVHkUEWNTKAYKi8vD2vWrGmj0RAREZHaRFQfGSIiIqJgMJAhIiIi1WIgQ0RERKrFQIaIiIhUi4EMERERqRYDGSIiIlItBjJERESkWgxkiIiISLUiYtPIcBKa7lVXVys8EiIiIpJKeN8O1Dw36gOZmpoaAPYuwURERKQuNTU1SElJ8fm4onsttQWr1YpTp04hKSnJ595OwaiurkZeXh6Ki4u5d5MPPEbS8DgFxmMkDY9TYDxG0kTScbLZbKipqUGnTp2g1fquhIn6jIxWq0Vubq7sr5ucnKz4P3Kk4zGShscpMB4jaXicAuMxkiZSjpO/TIyAxb5ERESkWgxkiIiISLUYyATJaDRiwYIFMBqNSg8lYvEYScPjFBiPkTQ8ToHxGEmjxuMU9cW+REREFL2YkSEiIiLVYiBDREREqsVAhoiIiFSLgQwRERGpFgMZIiIiUq2o7+zbGqdPn8bKlSuRnp6OiRMnwmAwiI/V1dXhmWeewcMPP6zgCJVXVlaGt956Cxs2bEBJSQkAICcnB6NHj8Ztt92GDh06KDzCyMBzKTCeS9LwXAqM55I00XIucfm1D5s3b8bkyZNhtVrR3NyMzp0744svvkD//v0BAKWlpejUqRMsFovCI1XO5s2bMWXKFMTHx2PixInIzs4GYD82K1euRH19PZYtW4bhw4crPFJl8VwKjOeSNDyXAuO5JE1UnUs28mrixIm2GTNm2CwWi626utp2zz332DIyMmzbtm2z2Ww2W0lJiU2r1So8SmWNGDHCNnPmTJvVavV4zGq12mbOnGkbOXKkAiOLLDyXAuO5JA3PpcB4LkkTTecSAxkf0tLSbIWFhS73LVy40JaWlmbbtGmTqv6RwyU2Nta2b98+n4/v27fPFhsb24Yjikw8lwLjuSQNz6XAeC5JE03nEmtk/GhsbHS5/eCDD0Kv12Py5Ml46623FBpV5MjJycGmTZvQp08fr49v2rRJTOu2dzyX/OO5JB3PJf94LkkXLecSAxkfBgwYgPXr12PQoEEu98+ZMwdWqxU33nijQiOLHHPmzMHMmTOxdetWTJgwwWMuetGiRXj66acVHqXyeC4FxnNJGp5LgfFckiaqziWlU0KRatGiRbabb77Z5+NPPPGEraCgoA1HFJmWLFliGzFihE2v19s0Go1No9HY9Hq9bcSIEbaPP/5Y6eFFBJ5L0vBcCoznkjQ8lwKLpnOJq5ZIFs3NzSgrKwMAZGZmIiYmRuERkVrxXCK58FxqHxjIkGxMJhMAqGr7d4pMPJdILjyXoh87+4Zo37596Natm9LDUNzy5csxdepUpKWlIT4+HvHx8UhLS8PUqVOxYsUKpYenCjyX7HgutR7PJTueS62npnOJgUyImpqacPz4caWHoah33nkHU6dORUpKCp577jl8/fXX+Prrr/Hcc88hNTUVU6dOxXvvvaf0MCMezyWeS3LhucRzSS5qOpc4teTDAw884Pfxs2fP4sMPP1RH18Mw6dWrF+677z7MmjXL6+Mvv/wynnvuORw8eLCNRxZZeC4FxnNJGp5LgfFckiaaziUGMj7odDqcd955SE5O9vp4bW0ttm3bpop/5HCJjY3Fzp070bt3b6+PFxYW4rzzzkNDQ0Mbjyyy8FwKjOeSNDyXAuO5JE1UnUvKLZiKbL169bK99957Ph/fvn27aroehsvQoUNt//d//+fz8blz59qGDh3ahiOKTDyXAuO5JA3PpcB4LkkTTecSG+L5MHz4cGzduhU333yz18c1Gg1s7TyZ9cwzz+CKK67A999/73VztiNHjuCbb75ReJTK47kUGM8laXguBcZzSZpoOpc4teRDSUkJTCYT8vPzlR5KRDt27BheeeUVbNy4ESUlJQDsLcJHjRqFu+++GwUFBcoOMALwXJKG51JgPJek4bkUWDSdSwxkiIiISLW4/JqIiIhUi4EMERERqRYDGSIiIlItBjJERESkWgxkiChiHT16FGazWelhRDweJ2rPGMj4cfr0abz//vv49ttv0dTU5PJYXV0dHnvsMYVGFjmWL1+OBQsW4McffwQArF27FpdddhnGjx+PxYsXKzy6yMHjFJrevXu3+1byUvA4+Xbq1CksWLAA06dPx5w5c7B//36lhxSR1HycuPzah82bN2Py5MmwWq1obm5G586d8cUXX6B///4A7M2VOnXqpI72zWHy/vvvY8aMGRg0aBAOHDiAf//735g9ezauv/56WK1WvP/++/jggw9w/fXXKz1URfE4BXbdddd5vf9///sfxo8fj6SkJADA0qVL23JYEYfHKbD4+HgcP34cHTp0wN69ezF69Gh06NABQ4YMwe7du1FUVIQNGzZg0KBBSg9VUdF0nBjI+DBp0iTk5eXhjTfeQF1dHebNm4dPPvkEy5cvx5AhQxjIABgyZAhmzJiBP//5z1i5ciWuvPJK/POf/8Ts2bMB2Dtsfv755/jpp58UHqmyeJwC02q1GDt2LLp27epy/7vvvourrroKqampANDus1c8ToFptVqUlJQgKysL11xzDaxWK5YuXQq9Xg+r1Yrp06ejtrYWX331ldJDVVRUHSdldkaIfGlpabbCwkKX+xYuXGhLS0uzbdq0yVZSUqKafSjCJSEhwXbkyBHxdkxMjG3nzp3i7X379tkyMjKUGFpE4XEK7KOPPrLl5uba3nrrLZf79Xq9bc+ePQqNKvLwOAWm0WhspaWlNpvNZsvLy7OtXbvW5fFt27bZOnbsqMTQIko0HSfWyPjR2NjocvvBBx/EX//6V0yePBnr169XaFSRIyYmxqV2yGg0IjEx0eV2e99hFuBxkuJ3v/sd1q1bhzfffBPTpk3DuXPnlB5SROJxCkyj0UCj0QCwZx1SUlJcHk9NTeVxQ3QdJwYyPgwYMMBrsDJnzhzMnz8fN954owKjiiw9evRwKQg7efKkS8r78OHDyM3NVWJoEYXHSZqCggKsXbsWAwYMwODBg7Fs2TLxQksOPE7+2Ww29OrVC+np6Th16hR27drl8vihQ4eQk5Oj0OgiRzQdJ+5+7cMtt9yCNWvW4O677/Z4bO7cubDZbHj11VcVGFnk+Otf/4q0tDTxdnJyssvjW7ZswQ033NDWw4o4PE7SabVaPProo5g0aRJuueWWdl2D5g+Pk2/u9UE9evRwub1x40Zce+21bTmkiBRNx4nFvkQUkWpra3H48GH06dMHRqNR6eFELB4nau+YkZGgqqrKZSt497lE4jGSiscpMOdjVFBQwDdnH3icAuPfmzSqP06KlhpHuEWLFtn69u1r02q1Ll99+/a1vfHGG0oPLyLwGEnD4xSY+zHSaDQ8Rl7wOAXGvzdpouU4MSPjw7/+9S888sgj+POf/4wpU6YgOzsbgL0R3g8//ID77rsP586dw5w5cxQeqXJ4jKThcQqMx0gaHqfAeIykiarjpHQkFam6dOli+/jjj30+vmTJElteXl4bjijy8BhJw+MUGI+RNDxOgfEYSRNNx4nLr304c+YMBg4c6PPxgQMHoqysrA1HFHl4jKThcQqMx0gaHqfAeIykiabjxEDGh/PPPx9PPPGE1x1lLRYLnnzySZx//vkKjCxy8BhJw+MUGI+RNDxOgfEYSRNNx4nLr33YtWsXpkyZgubmZowdO9Zl/nDt2rUwGAz44YcfMGDAAIVHqhweI2l4nALjMZKGxykwHiNpouk4MZDxo6amBu+//z42btzosjRt1KhRuOmmmzwam7VHPEbS8DgFxmMkDY9TYDxG0kTLcWIgQ0RERKrFGpkgXH755Th9+rTSw4hoPEbS8DgFxmMkDY9TYDxG0qj1ODGQCcLatWvb/S7FgfAYScPjFBiPkTQ8ToHxGEmj1uPEQIaIiIhUi4FMEPLz8xETE6P0MCIaj5E0PE6B8RhJw+MUGI+RNGo9Tiz2JSIiItViRsaHzz77DPX19UoPI6LxGEnD4xQYj5E0PE6B8RhJE03HiRkZH7RaLZKSkvDb3/4Wd9xxB0aMGKH0kCIOj5E0PE6B8RhJw+MUGI+RNNF0nJiR8WPOnDnYsmULRo0ahQEDBuD5559HeXm50sOKKDxG0vA4BcZjJA2PU2A8RtJEzXFSarfKSKfRaGylpaU2m81m27Jli+2ee+6xpaam2oxGo+03v/mN7YcfflB4hMrjMZKGxykwHiNpeJwC4zGSJpqOEwMZH5z/kQUNDQ22d9991zZu3DibVqu1FRQUKDS6yMBjJA2PU2A8RtLwOAXGYyRNNB0nBjI+aLVaj39kZwcPHrT99a9/bcMRRR4eI2l4nALjMZKGxykwHiNpouk4sdjXB61Wi5KSEmRlZSk9lIjFYyQNj1NgPEbS8DgFxmMkTTQdJxb7+nD06FF06NBB6WFENB4jaXicAuMxkobHKTAeI2mi6TgxI0NERESqpVd6AJGsrKwMb731FjZs2ICSkhIAQE5ODkaPHo3bbrstaqLZ1uAxkobHKTAeI2l4nALjMZImWo4TMzI+bN68GVOmTEF8fDwmTpyI7OxsAEBpaSlWrlyJ+vp6LFu2DMOHD1d4pMrhMZKGxykwHiNpeJwC4zGSJpqOEwMZH0aOHInBgwfj1VdfhUajcXnMZrPh7rvvxq5du7BhwwaFRqg8HiNpeJwC4zGShscpMB4jaaLpODGQ8SEuLg7bt29Hnz59vD6+f/9+DBkyBA0NDW08ssjBYyQNj1NgPEbS8DgFxmMkTTQdJ65a8iEnJwebNm3y+fimTZvEVFx7xWMkDY9TYDxG0vA4BcZjJE00HScW+/owZ84czJw5E1u3bsWECRM85g8XLVqEp59+WuFRKovHSBoep8B4jKThcQqMx0iaqDpObd6CT0WWLFliGzFihE2v19s0Go1No9HY9Hq9bcSIEbaPP/5Y6eFFBB4jaXicAuMxkobHKTAeI2mi5TixRkaC5uZmlJWVAQAyMzMRExOj8IgiD4+RNDxOgfEYScPjFBiPkTRqP04MZIiIiEi1WOxLREREqsVAhoiIiFSLgQwRERGpFgMZIiIiUi0GMkRERKRaDGSIiIhItRjIEBERkWoxkCEiIiLV+v+3jsB7wNj9GwAAAABJRU5ErkJggg=="
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 245
+   "execution_count": 5
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-14T03:36:19.982255Z",
-     "start_time": "2026-01-14T03:36:17.851Z"
+     "end_time": "2026-01-15T02:36:11.975924Z",
+     "start_time": "2026-01-15T02:36:08.943450Z"
     }
    },
    "cell_type": "code",
@@ -305,10 +288,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1b48cb7a060>]"
+       "[<matplotlib.lines.Line2D at 0x1a6aba44c50>]"
       ]
      },
-     "execution_count": 246,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -317,19 +300,19 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 246
+   "execution_count": 6
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-14T04:00:56.194770Z",
-     "start_time": "2026-01-14T04:00:53.505402Z"
+     "end_time": "2026-01-15T02:39:47.695586Z",
+     "start_time": "2026-01-15T02:39:44.969217Z"
     }
    },
    "cell_type": "code",
@@ -362,32 +345,32 @@
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 289
+   "execution_count": 31
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T21:59:17.741087Z",
-     "start_time": "2026-01-13T21:59:17.736661Z"
+     "end_time": "2026-01-15T02:36:17.034746Z",
+     "start_time": "2026-01-15T02:36:17.020021Z"
     }
    },
    "cell_type": "code",
    "source": "#there really isn't Influx data for dates after midday 5 July, so I see no reason to incorporate the OpenMeteo data after",
    "id": "90465ecf61541d72",
    "outputs": [],
-   "execution_count": 106
+   "execution_count": 8
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-14T03:57:37.180216Z",
-     "start_time": "2026-01-14T03:57:35.991597Z"
+     "end_time": "2026-01-15T02:46:18.773765Z",
+     "start_time": "2026-01-15T02:46:17.610839Z"
     }
    },
    "cell_type": "code",
@@ -404,6 +387,8 @@
     "df = pd.DataFrame({\"value\": values}, index=timestamps)\n",
     "df_15m = df.resample(\"15T\").mean()\n",
     "\n",
+    "df_15m[\"value_shifted\"] = df_15m[\"value\"].shift(-8)\n",
+    "\n",
     "print(df_15m.head())\n"
    ],
    "id": "203be30192c9302a",
@@ -412,30 +397,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                               value\n",
-      "2025-07-02 00:45:00+00:00  25.031385\n",
-      "2025-07-02 01:00:00+00:00  25.388523\n",
-      "2025-07-02 01:15:00+00:00  25.761938\n",
-      "2025-07-02 01:30:00+00:00  26.135353\n",
-      "2025-07-02 01:45:00+00:00  26.508768\n"
+      "                               value  value_shifted\n",
+      "2025-07-01 22:30:00+00:00  25.815057      24.492416\n",
+      "2025-07-01 22:45:00+00:00  27.457032      24.675352\n",
+      "2025-07-01 23:00:00+00:00  27.983232      25.388531\n",
+      "2025-07-01 23:15:00+00:00  27.667362      25.761946\n",
+      "2025-07-01 23:30:00+00:00  27.437863      26.135361\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_14188\\1837817124.py:11: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_12116\\3978562244.py:11: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
       "  df_15m = df.resample(\"15T\").mean()\n"
      ]
     }
    ],
-   "execution_count": 276
+   "execution_count": 40
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-14T03:57:37.373487Z",
-     "start_time": "2026-01-14T03:57:37.367422Z"
+     "end_time": "2026-01-15T02:40:28.212873Z",
+     "start_time": "2026-01-15T02:40:28.206714Z"
     }
    },
    "cell_type": "code",
@@ -445,13 +430,13 @@
    ],
    "id": "a30bec6abef1672a",
    "outputs": [],
-   "execution_count": 277
+   "execution_count": 33
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-14T03:57:37.441412Z",
-     "start_time": "2026-01-14T03:57:37.436456Z"
+     "end_time": "2026-01-15T02:40:28.758150Z",
+     "start_time": "2026-01-15T02:40:28.750901Z"
     }
    },
    "cell_type": "code",
@@ -466,179 +451,22 @@
    ],
    "id": "feced1770070bc8b",
    "outputs": [],
-   "execution_count": 278
+   "execution_count": 34
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-14T03:57:38.503173Z",
-     "start_time": "2026-01-14T03:57:38.487933Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "weather_df.head()",
-   "id": "54a41be29ba47ed9",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "                           temperature_2m  shortwave_radiation_instant  \\\n",
-       "date                                                                     \n",
-       "2025-07-01 16:00:00+00:00          -0.013                          0.0   \n",
-       "2025-07-01 16:15:00+00:00          -0.113                          0.0   \n",
-       "2025-07-01 16:30:00+00:00          -0.213                          0.0   \n",
-       "2025-07-01 16:45:00+00:00          -0.363                          0.0   \n",
-       "2025-07-01 17:00:00+00:00          -0.463                          0.0   \n",
-       "\n",
-       "                           wind_speed_10m  \n",
-       "date                                       \n",
-       "2025-07-01 16:00:00+00:00        8.825508  \n",
-       "2025-07-01 16:15:00+00:00       10.594036  \n",
-       "2025-07-01 16:30:00+00:00       13.138765  \n",
-       "2025-07-01 16:45:00+00:00       15.391840  \n",
-       "2025-07-01 17:00:00+00:00       16.873980  "
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>temperature_2m</th>\n",
-       "      <th>shortwave_radiation_instant</th>\n",
-       "      <th>wind_speed_10m</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>date</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2025-07-01 16:00:00+00:00</th>\n",
-       "      <td>-0.013</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8.825508</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-07-01 16:15:00+00:00</th>\n",
-       "      <td>-0.113</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>10.594036</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-07-01 16:30:00+00:00</th>\n",
-       "      <td>-0.213</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>13.138765</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-07-01 16:45:00+00:00</th>\n",
-       "      <td>-0.363</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>15.391840</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2025-07-01 17:00:00+00:00</th>\n",
-       "      <td>-0.463</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>16.873980</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ]
-     },
-     "execution_count": 279,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 279
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-14T03:57:39.127974Z",
-     "start_time": "2026-01-14T03:57:39.109258Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "print(\"Influx 15min index:\")\n",
-    "print(df_15m.index[:5])\n",
-    "print(df_15m.index[-5:])\n",
-    "\n",
-    "print(\"Weather index:\")\n",
-    "print(weather_df.index[:5])\n",
-    "print(weather_df[-5:])\n"
-   ],
-   "id": "9e3eeb777b7fb7c7",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Influx 15min index:\n",
-      "DatetimeIndex(['2025-07-02 00:45:00+00:00', '2025-07-02 01:00:00+00:00',\n",
-      "               '2025-07-02 01:15:00+00:00', '2025-07-02 01:30:00+00:00',\n",
-      "               '2025-07-02 01:45:00+00:00'],\n",
-      "              dtype='datetime64[ns, UTC]', freq='15min')\n",
-      "DatetimeIndex(['2025-07-05 13:00:00+00:00', '2025-07-05 13:15:00+00:00',\n",
-      "               '2025-07-05 13:30:00+00:00', '2025-07-05 13:45:00+00:00',\n",
-      "               '2025-07-05 14:00:00+00:00'],\n",
-      "              dtype='datetime64[ns, UTC]', freq='15min')\n",
-      "Weather index:\n",
-      "DatetimeIndex(['2025-07-01 16:00:00+00:00', '2025-07-01 16:15:00+00:00',\n",
-      "               '2025-07-01 16:30:00+00:00', '2025-07-01 16:45:00+00:00',\n",
-      "               '2025-07-01 17:00:00+00:00'],\n",
-      "              dtype='datetime64[ns, UTC]', name='date', freq=None)\n",
-      "                           temperature_2m  shortwave_radiation_instant  \\\n",
-      "date                                                                     \n",
-      "2025-07-05 22:45:00+00:00          -2.013                     0.000000   \n",
-      "2025-07-05 23:00:00+00:00          -1.963                     0.000000   \n",
-      "2025-07-05 23:15:00+00:00          -1.963                    19.759171   \n",
-      "2025-07-05 23:30:00+00:00          -1.913                    51.912174   \n",
-      "2025-07-05 23:45:00+00:00          -1.913                    86.610596   \n",
-      "\n",
-      "                           wind_speed_10m  \n",
-      "date                                       \n",
-      "2025-07-05 22:45:00+00:00        8.496305  \n",
-      "2025-07-05 23:00:00+00:00        8.788720  \n",
-      "2025-07-05 23:15:00+00:00        9.085988  \n",
-      "2025-07-05 23:30:00+00:00        9.199390  \n",
-      "2025-07-05 23:45:00+00:00        9.021574  \n"
-     ]
-    }
-   ],
-   "execution_count": 280
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-14T03:57:39.709507Z",
-     "start_time": "2026-01-14T03:57:39.693995Z"
+     "end_time": "2026-01-15T02:47:02.603520Z",
+     "start_time": "2026-01-15T02:47:02.581502Z"
     }
    },
    "cell_type": "code",
    "source": [
     "#sort indices\n",
     "df_15m_copy = df_15m.copy().sort_index()\n",
+    "df_15m_copy[\"array_temperature\"] = df_15m_copy[\"value_shifted\"]\n",
+    "df_15m_copy = df_15m_copy.drop(columns=[\"value\", \"value_shifted\"], errors=\"ignore\")\n",
+    "\n",
     "weather_df = weather_df.sort_index()\n",
     "\n",
     "#resampling for 15minute range\n",
@@ -663,29 +491,61 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "overlap start: 2025-07-02 00:45:00+00:00\n",
+      "overlap start: 2025-07-01 22:30:00+00:00\n",
       "overlap end: 2025-07-05 14:00:00+00:00\n",
-      "no of aligned points: 342\n"
+      "no of aligned points: 351\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_14188\\4230900981.py:6: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_12116\\1287101057.py:9: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
       "  df_15m_copy.index = df_15m_copy.index.floor(\"15T\")\n",
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_14188\\4230900981.py:7: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_12116\\1287101057.py:10: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
       "  weather_df.index = weather_df.index.floor(\"15T\")\n"
      ]
     }
    ],
-   "execution_count": 281
+   "execution_count": 41
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-15T02:48:41.847113Z",
+     "start_time": "2026-01-15T02:48:41.694559Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "fig, ax1 = plt.subplots()\n",
+    "ax_twin = ax1.twinx()\n",
+    "plt.plot(merged_df[\"array_temperature\"], label=\"Shifted Array Temp\")\n",
+    "ax1.plot(merged_df[\"shortwave_radiation_instant\"], label=\"Solar Irradiance\", color = 'green')\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ],
+   "id": "aec609113ac3d228",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 48
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-14T03:57:40.479854Z",
-     "start_time": "2026-01-14T03:57:40.468932Z"
+     "end_time": "2026-01-15T02:47:23.995002Z",
+     "start_time": "2026-01-15T02:47:23.980510Z"
     }
    },
    "cell_type": "code",
@@ -695,33 +555,33 @@
     {
      "data": {
       "text/plain": [
-       "                               value  temperature_2m  \\\n",
-       "2025-07-02 00:45:00+00:00  25.031385          -2.063   \n",
-       "2025-07-02 01:00:00+00:00  25.388523          -1.963   \n",
-       "2025-07-02 01:15:00+00:00  25.761938          -1.863   \n",
-       "2025-07-02 01:30:00+00:00  26.135353          -1.713   \n",
-       "2025-07-02 01:45:00+00:00  26.508768          -1.513   \n",
-       "...                              ...             ...   \n",
-       "2025-07-05 13:00:00+00:00  38.086209           1.137   \n",
-       "2025-07-05 13:15:00+00:00  36.774658           0.887   \n",
-       "2025-07-05 13:30:00+00:00  35.463106           0.637   \n",
-       "2025-07-05 13:45:00+00:00  34.271664           0.437   \n",
-       "2025-07-05 14:00:00+00:00  34.174615           0.237   \n",
+       "                           array_temperature  temperature_2m  \\\n",
+       "2025-07-01 22:30:00+00:00          24.492416          -2.163   \n",
+       "2025-07-01 22:45:00+00:00          24.675352          -2.213   \n",
+       "2025-07-01 23:00:00+00:00          25.388531          -2.263   \n",
+       "2025-07-01 23:15:00+00:00          25.761946          -2.263   \n",
+       "2025-07-01 23:30:00+00:00          26.135361          -2.263   \n",
+       "...                                      ...             ...   \n",
+       "2025-07-05 11:00:00+00:00          38.086209           2.387   \n",
+       "2025-07-05 11:15:00+00:00          36.774658           2.337   \n",
+       "2025-07-05 11:30:00+00:00          35.463106           2.237   \n",
+       "2025-07-05 11:45:00+00:00          34.271664           2.137   \n",
+       "2025-07-05 12:00:00+00:00          34.174615           1.987   \n",
        "\n",
        "                           shortwave_radiation_instant  wind_speed_10m  \n",
-       "2025-07-02 00:45:00+00:00                   243.104050        4.680000  \n",
-       "2025-07-02 01:00:00+00:00                   276.808258        4.680000  \n",
-       "2025-07-02 01:15:00+00:00                   309.431335        4.680000  \n",
-       "2025-07-02 01:30:00+00:00                   344.148895        5.154415  \n",
-       "2025-07-02 01:45:00+00:00                   386.145966        5.692100  \n",
+       "2025-07-01 22:30:00+00:00                     0.000000        7.342588  \n",
+       "2025-07-01 22:45:00+00:00                     0.000000        7.280550  \n",
+       "2025-07-01 23:00:00+00:00                     0.000000        6.877790  \n",
+       "2025-07-01 23:15:00+00:00                    25.322779        6.489992  \n",
+       "2025-07-01 23:30:00+00:00                    55.886383        6.130579  \n",
        "...                                                ...             ...  \n",
-       "2025-07-05 13:00:00+00:00                    58.205017       29.548521  \n",
-       "2025-07-05 13:15:00+00:00                    22.902834       27.475807  \n",
-       "2025-07-05 13:30:00+00:00                     2.313184       25.202570  \n",
-       "2025-07-05 13:45:00+00:00                     0.000000       22.461807  \n",
-       "2025-07-05 14:00:00+00:00                     0.000000       20.056877  \n",
+       "2025-07-05 11:00:00+00:00                   443.232941       32.199379  \n",
+       "2025-07-05 11:15:00+00:00                   395.066681       32.363979  \n",
+       "2025-07-05 11:30:00+00:00                   346.080292       33.014175  \n",
+       "2025-07-05 11:45:00+00:00                   297.251648       33.340351  \n",
+       "2025-07-05 12:00:00+00:00                   248.622940       33.518684  \n",
        "\n",
-       "[342 rows x 4 columns]"
+       "[343 rows x 4 columns]"
       ],
       "text/html": [
        "<div>\n",
@@ -742,7 +602,7 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>value</th>\n",
+       "      <th>array_temperature</th>\n",
        "      <th>temperature_2m</th>\n",
        "      <th>shortwave_radiation_instant</th>\n",
        "      <th>wind_speed_10m</th>\n",
@@ -750,39 +610,39 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>2025-07-02 00:45:00+00:00</th>\n",
-       "      <td>25.031385</td>\n",
-       "      <td>-2.063</td>\n",
-       "      <td>243.104050</td>\n",
-       "      <td>4.680000</td>\n",
+       "      <th>2025-07-01 22:30:00+00:00</th>\n",
+       "      <td>24.492416</td>\n",
+       "      <td>-2.163</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.342588</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-02 01:00:00+00:00</th>\n",
-       "      <td>25.388523</td>\n",
-       "      <td>-1.963</td>\n",
-       "      <td>276.808258</td>\n",
-       "      <td>4.680000</td>\n",
+       "      <th>2025-07-01 22:45:00+00:00</th>\n",
+       "      <td>24.675352</td>\n",
+       "      <td>-2.213</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.280550</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-02 01:15:00+00:00</th>\n",
-       "      <td>25.761938</td>\n",
-       "      <td>-1.863</td>\n",
-       "      <td>309.431335</td>\n",
-       "      <td>4.680000</td>\n",
+       "      <th>2025-07-01 23:00:00+00:00</th>\n",
+       "      <td>25.388531</td>\n",
+       "      <td>-2.263</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>6.877790</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-02 01:30:00+00:00</th>\n",
-       "      <td>26.135353</td>\n",
-       "      <td>-1.713</td>\n",
-       "      <td>344.148895</td>\n",
-       "      <td>5.154415</td>\n",
+       "      <th>2025-07-01 23:15:00+00:00</th>\n",
+       "      <td>25.761946</td>\n",
+       "      <td>-2.263</td>\n",
+       "      <td>25.322779</td>\n",
+       "      <td>6.489992</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-02 01:45:00+00:00</th>\n",
-       "      <td>26.508768</td>\n",
-       "      <td>-1.513</td>\n",
-       "      <td>386.145966</td>\n",
-       "      <td>5.692100</td>\n",
+       "      <th>2025-07-01 23:30:00+00:00</th>\n",
+       "      <td>26.135361</td>\n",
+       "      <td>-2.263</td>\n",
+       "      <td>55.886383</td>\n",
+       "      <td>6.130579</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -792,71 +652,71 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-05 13:00:00+00:00</th>\n",
+       "      <th>2025-07-05 11:00:00+00:00</th>\n",
        "      <td>38.086209</td>\n",
-       "      <td>1.137</td>\n",
-       "      <td>58.205017</td>\n",
-       "      <td>29.548521</td>\n",
+       "      <td>2.387</td>\n",
+       "      <td>443.232941</td>\n",
+       "      <td>32.199379</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-05 13:15:00+00:00</th>\n",
+       "      <th>2025-07-05 11:15:00+00:00</th>\n",
        "      <td>36.774658</td>\n",
-       "      <td>0.887</td>\n",
-       "      <td>22.902834</td>\n",
-       "      <td>27.475807</td>\n",
+       "      <td>2.337</td>\n",
+       "      <td>395.066681</td>\n",
+       "      <td>32.363979</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-05 13:30:00+00:00</th>\n",
+       "      <th>2025-07-05 11:30:00+00:00</th>\n",
        "      <td>35.463106</td>\n",
-       "      <td>0.637</td>\n",
-       "      <td>2.313184</td>\n",
-       "      <td>25.202570</td>\n",
+       "      <td>2.237</td>\n",
+       "      <td>346.080292</td>\n",
+       "      <td>33.014175</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-05 13:45:00+00:00</th>\n",
+       "      <th>2025-07-05 11:45:00+00:00</th>\n",
        "      <td>34.271664</td>\n",
-       "      <td>0.437</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>22.461807</td>\n",
+       "      <td>2.137</td>\n",
+       "      <td>297.251648</td>\n",
+       "      <td>33.340351</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-05 14:00:00+00:00</th>\n",
+       "      <th>2025-07-05 12:00:00+00:00</th>\n",
        "      <td>34.174615</td>\n",
-       "      <td>0.237</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>20.056877</td>\n",
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+       "      <td>248.622940</td>\n",
+       "      <td>33.518684</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>342 rows × 4 columns</p>\n",
+       "<p>343 rows × 4 columns</p>\n",
        "</div>"
       ]
      },
-     "execution_count": 282,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 282
+   "execution_count": 43
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-14T03:57:40.919962Z",
-     "start_time": "2026-01-14T03:57:40.913562Z"
+     "end_time": "2026-01-15T02:47:24.833809Z",
+     "start_time": "2026-01-15T02:47:24.825277Z"
     }
    },
    "cell_type": "code",
    "source": "merged_df = merged_df.rename(columns={'value': 'array_temperature'})",
    "id": "e0882e697c1613f7",
    "outputs": [],
-   "execution_count": 283
+   "execution_count": 44
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-14T03:57:41.596702Z",
-     "start_time": "2026-01-14T03:57:41.571484Z"
+     "end_time": "2026-01-15T02:47:25.680913Z",
+     "start_time": "2026-01-15T02:47:25.663703Z"
     }
    },
    "cell_type": "code",
@@ -867,18 +727,18 @@
      "data": {
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-       "2025-07-02 01:00:00+00:00          25.388523          -1.963   \n",
-       "2025-07-02 01:15:00+00:00          25.761938          -1.863   \n",
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-       "2025-07-02 01:45:00+00:00          26.508768          -1.513   \n",
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+       "2025-07-01 23:30:00+00:00          26.135361          -2.263   \n",
        "\n",
        "                           shortwave_radiation_instant  wind_speed_10m  \n",
-       "2025-07-02 00:45:00+00:00                   243.104050        4.680000  \n",
-       "2025-07-02 01:00:00+00:00                   276.808258        4.680000  \n",
-       "2025-07-02 01:15:00+00:00                   309.431335        4.680000  \n",
-       "2025-07-02 01:30:00+00:00                   344.148895        5.154415  \n",
-       "2025-07-02 01:45:00+00:00                   386.145966        5.692100  "
+       "2025-07-01 22:30:00+00:00                     0.000000        7.342588  \n",
+       "2025-07-01 22:45:00+00:00                     0.000000        7.280550  \n",
+       "2025-07-01 23:00:00+00:00                     0.000000        6.877790  \n",
+       "2025-07-01 23:15:00+00:00                    25.322779        6.489992  \n",
+       "2025-07-01 23:30:00+00:00                    55.886383        6.130579  "
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       "text/html": [
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@@ -907,57 +767,57 @@
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-       "      <th>2025-07-02 00:45:00+00:00</th>\n",
-       "      <td>25.031385</td>\n",
-       "      <td>-2.063</td>\n",
-       "      <td>243.104050</td>\n",
-       "      <td>4.680000</td>\n",
+       "      <th>2025-07-01 22:30:00+00:00</th>\n",
+       "      <td>24.492416</td>\n",
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+       "      <td>0.000000</td>\n",
+       "      <td>7.342588</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-02 01:00:00+00:00</th>\n",
-       "      <td>25.388523</td>\n",
-       "      <td>-1.963</td>\n",
-       "      <td>276.808258</td>\n",
-       "      <td>4.680000</td>\n",
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-       "      <td>309.431335</td>\n",
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-       "      <td>-1.713</td>\n",
-       "      <td>344.148895</td>\n",
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-       "      <th>2025-07-02 01:45:00+00:00</th>\n",
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-       "      <td>-1.513</td>\n",
-       "      <td>386.145966</td>\n",
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        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ]
      },
-     "execution_count": 284,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 284
+   "execution_count": 45
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-14T03:57:42.692177Z",
-     "start_time": "2026-01-14T03:57:42.530435Z"
+     "end_time": "2026-01-15T02:47:26.624775Z",
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    "cell_type": "code",
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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 285
+   "execution_count": 46
   },
   {
    "metadata": {},
@@ -1005,8 +865,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T23:45:51.145870Z",
-     "start_time": "2026-01-13T23:45:51.135169Z"
+     "end_time": "2026-01-15T02:36:24.291013Z",
+     "start_time": "2026-01-15T02:36:19.722347Z"
     }
    },
    "cell_type": "code",
@@ -1031,13 +891,13 @@
    ],
    "id": "1aa707313aeb5993",
    "outputs": [],
-   "execution_count": 175
+   "execution_count": 19
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T23:51:18.519525Z",
-     "start_time": "2026-01-13T23:51:18.489052Z"
+     "end_time": "2026-01-15T02:36:24.347055Z",
+     "start_time": "2026-01-15T02:36:24.335999Z"
     }
    },
    "cell_type": "code",
@@ -1047,13 +907,13 @@
    ],
    "id": "492ea4964124380d",
    "outputs": [],
-   "execution_count": 187
+   "execution_count": 20
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T23:51:19.650057Z",
-     "start_time": "2026-01-13T23:51:19.560043Z"
+     "end_time": "2026-01-15T02:36:24.478949Z",
+     "start_time": "2026-01-15T02:36:24.375680Z"
     }
    },
    "cell_type": "code",
@@ -1067,10 +927,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1b48c73d760>]"
+       "[<matplotlib.lines.Line2D at 0x1a6abc3fdd0>]"
       ]
      },
-     "execution_count": 188,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1079,13 +939,13 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 188
+   "execution_count": 21
   },
   {
    "metadata": {},
@@ -1096,8 +956,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-14T00:12:25.620050Z",
-     "start_time": "2026-01-14T00:12:25.611179Z"
+     "end_time": "2026-01-15T02:36:24.518883Z",
+     "start_time": "2026-01-15T02:36:24.504640Z"
     }
    },
    "cell_type": "code",
@@ -1110,13 +970,13 @@
    ],
    "id": "9839f97f4cfe5300",
    "outputs": [],
-   "execution_count": 213
+   "execution_count": 22
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-14T00:12:26.520024Z",
-     "start_time": "2026-01-14T00:12:26.509310Z"
+     "end_time": "2026-01-15T02:36:24.565473Z",
+     "start_time": "2026-01-15T02:36:24.554267Z"
     }
    },
    "cell_type": "code",
@@ -1126,21 +986,21 @@
     {
      "data": {
       "text/plain": [
-       "array([19.30077583,  0.07811705])"
+       "array([17.38793587,  0.13933319])"
       ]
      },
-     "execution_count": 214,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 214
+   "execution_count": 23
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-14T00:12:27.655687Z",
-     "start_time": "2026-01-14T00:12:27.518223Z"
+     "end_time": "2026-01-15T02:36:24.995288Z",
+     "start_time": "2026-01-15T02:36:24.596951Z"
     }
    },
    "cell_type": "code",
@@ -1172,21 +1032,26 @@
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 215
+   "execution_count": 24
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-15T02:36:25.037948Z",
+     "start_time": "2026-01-15T02:36:25.033124Z"
+    }
+   },
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": "",
-   "id": "a48e41b4efacee1c"
+   "id": "a48e41b4efacee1c",
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -1197,8 +1062,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-14T02:19:38.156260Z",
-     "start_time": "2026-01-14T02:19:38.032125Z"
+     "end_time": "2026-01-15T02:36:25.224065Z",
+     "start_time": "2026-01-15T02:36:25.069515Z"
     }
    },
    "cell_type": "code",
@@ -1216,7 +1081,7 @@
        "Text(0.5, 1.0, 'offset_faiman_temperature')"
       ]
      },
-     "execution_count": 220,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1225,19 +1090,19 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 220
+   "execution_count": 25
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-14T02:59:15.584641Z",
-     "start_time": "2026-01-14T02:59:15.427704Z"
+     "end_time": "2026-01-15T02:36:25.324516Z",
+     "start_time": "2026-01-15T02:36:25.233184Z"
     }
    },
    "cell_type": "code",
@@ -1267,32 +1132,32 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 233
+   "execution_count": 26
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-13T23:46:15.666706Z",
-     "start_time": "2026-01-13T23:46:15.659369Z"
+     "end_time": "2026-01-15T02:36:25.395683Z",
+     "start_time": "2026-01-15T02:36:25.387426Z"
     }
    },
    "cell_type": "code",
    "source": "#trying an offset:",
    "id": "512015ec2bff8ec0",
    "outputs": [],
-   "execution_count": 180
+   "execution_count": 27
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-14T02:03:19.946547Z",
-     "start_time": "2026-01-14T02:03:19.839201Z"
+     "end_time": "2026-01-15T02:36:25.519458Z",
+     "start_time": "2026-01-15T02:36:25.405067Z"
     }
    },
    "cell_type": "code",
@@ -1312,13 +1177,13 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 217
+   "execution_count": 28
   },
   {
    "metadata": {},
@@ -1339,43 +1204,6 @@
    ],
    "id": "fc1bbf9c64684bcb"
   },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
-   "source": [
-    "T_a[0] = T_measured[0]  # start from measured temperature\n",
-    "for i in range(1, len(T_a)):\n",
-    "    dt = (t[i] - t[i - 1]).total_seconds()\n",
-    "    T_ss = faiman_model_single(G[i - 1], T_ambient[i - 1], v[i - 1], u0, u1)  # steady-state\n",
-    "    tau = 1800  # time constant in seconds (example)\n",
-    "    T_a[i] = T_a[i - 1] + (T_ss - T_a[i - 1]) * dt / tau\n",
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "def faiman_dynamic(G, T_amb, v, u0, u1, dt_seconds, T_prev, tau):\n",
-    "    # steady-state Faiman\n",
-    "    T_ss = T_amb + G / (u0 + u1 * v)\n",
-    "    # first-order dynamics\n",
-    "    T_new = T_prev + (T_ss - T_prev) * dt_seconds / tau\n",
-    "    return T_new\n",
-    "\n",
-    "\n",
-    "# initialize\n",
-    "T_model = np.zeros_like(G)\n",
-    "T_model[0] = T_measured[0]  # start from sensor reading\n",
-    "tau = 3600  # 1 hour time constant\n",
-    "\n",
-    "for i in range(1, len(G)):\n",
-    "    dt = (t[i] - t[i - 1]).total_seconds()\n",
-    "    T_model[i] = faiman_dynamic(G[i - 1], T_amb[i - 1], v[i - 1], u0, u1, dt, T_model[i - 1], tau)\n",
-    "\n",
-    "# optional MPPT offset\n",
-    "T_model += 5\n"
-   ],
-   "id": "f87c5e6e2955929f"
-  },
   {
    "metadata": {},
    "cell_type": "code",

From 301ea8ccb01e92f5a2a0d7296c42f397c18c70bc Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Tue, 27 Jan 2026 19:09:30 -0800
Subject: [PATCH 13/49] control model data acquisition and preprocessing

---
 array_temp/.cache.sqlite             | Bin 57344 -> 57344 bytes
 array_temp/Control_Model.ipynb       | 299 +++++++++++++++++++++------
 array_temp/coefficient_fitting.ipynb |  87 +++++++-
 array_temp/faiman_coefficients.ipynb | 264 ++++++++++++++---------
 4 files changed, 488 insertions(+), 162 deletions(-)

diff --git a/array_temp/.cache.sqlite b/array_temp/.cache.sqlite
index 681cff879f6265ea877813f7947168a6a22d2520..519e171f5ec6c577efd51fd6d2482c8ec1a19c88 100644
GIT binary patch
delta 216
zcmZoTz}#?vd4e<}-$WT_M!t;+KKhK@o7d{=2(Xl@pX1(q*WS#Wk$bX8c`0-MZpY0%
z<t2=ae4807ncOF~C``6re{l1Y3+<diMkXPKmR5$wRtAQ8mL^7~2Bz8u237_JQ+n7_
za}o=RQ&Xn6+kZOJ{W@CL7bsLwkXe*kJf%m$$iT=<*T7I0sNcu{MgQc+oI1(i#1b6^
yBNGL$#5}N41*j!PlO=L*Z`Qw~!^8}9<-|tE&HMMJFf#IPPUxM(w0Xgy1YQ8Yu14zs

delta 169
zcmZoTz}#?vd4e<}_e2?IM(&LXKKhIto7d{=2(Y9pDROMSYj0-G$T3-@yp-8#v;F3t
z@)AZy-p!1aOzw<)n-hBHFl}CNDS>z51m(&0>kn>za-f}avVIPmnyI;!fr*}}p}C2P
zv9^JMm4U&O9`@9n#De0~lqv4^?+khOvnIct9GxS@gRFFNW6mU2u)t)A+}oS=&*(5Q
L1D&z4aS<Z`7Q{CS

diff --git a/array_temp/Control_Model.ipynb b/array_temp/Control_Model.ipynb
index cd8e218..05f0f65 100644
--- a/array_temp/Control_Model.ipynb
+++ b/array_temp/Control_Model.ipynb
@@ -6,15 +6,21 @@
    "source": [
     "The overall goal of this project is to find the set of controls (accelerator position, brake pressed, steering) for an optimised speed input (speed, position).\n",
     "- Steering data is unavailable, so is currently out of scope.\n",
-    "- FSGP data from 2024 July, potentially over like 2-3 days\n"
+    "- FSGP data from 2024 July, potentially over like 2-3 days (16-18 July)\n",
+    "\n",
+    "current ideas for a workflow:\n",
+    "\n",
+    "- using a RNN for a state to control model.\n",
+    "- we are mapping state inputs (velocity, position) to control (accel, brake pressed, steering)\n",
+    "- a question is - position as a dependent of speed, but i feel like it makes sense to use it anyways because otherwise the model wouldn't be able to pinpoint that particular part of the track with corresponding cornering/acceleration changes.\n"
    ],
    "id": "13a4cc26a47d5ad7"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-10T19:40:40.210965Z",
-     "start_time": "2026-01-10T19:39:34.788373Z"
+     "end_time": "2026-01-28T02:40:44.183402Z",
+     "start_time": "2026-01-28T02:40:24.411979Z"
     }
    },
    "cell_type": "code",
@@ -33,7 +39,7 @@
     "from datetime import datetime, time, date\n",
     "\n",
     "#each 5 seconds\n",
-    "utc_offset_h = 2\n",
+    "utc_offset_h = 7\n",
     "start_utc = time(0, 00, 00)  #querying i svancouver time, influxdb gives utc\n",
     "stop_utc = time(23, 45, 00)\n",
     "date_start = date(2024, 7, 14)\n",
@@ -50,18 +56,26 @@
     "client = query.DBClient()\n",
     "mech_brake_pressed: TimeSeries = client.query_time_series(start_time, stop_time, field=\"MechBrakePressed\")\n",
     "accel_position: TimeSeries =   client.query_time_series(start_time, stop_time, field=\"AcceleratorPosition\")\n",
-    "speed_kph: TimeSeries = client.query_time_series(start_time, stop_time, \"MotorRotatingSpeed\")\n",
+    "speed_kph: TimeSeries = client.query_time_series(start_time, stop_time, \"VehicleVelocity\")\n",
     "\n"
    ],
    "id": "initial_id",
    "outputs": [],
-   "execution_count": 1
+   "execution_count": 11
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "# speed is most likely in m/s",
+   "id": "5d30f3a75f7e9149"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-10T20:00:38.811023Z",
-     "start_time": "2026-01-10T20:00:38.733064Z"
+     "end_time": "2026-01-28T02:40:56.314986Z",
+     "start_time": "2026-01-28T02:40:56.276921Z"
     }
    },
    "cell_type": "code",
@@ -84,13 +98,13 @@
    ],
    "id": "9f8652589e173b01",
    "outputs": [],
-   "execution_count": 2
+   "execution_count": 12
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-10T21:22:36.496440Z",
-     "start_time": "2026-01-10T21:22:35.443215Z"
+     "end_time": "2026-01-28T02:40:57.677460Z",
+     "start_time": "2026-01-28T02:40:56.838799Z"
     }
    },
    "cell_type": "code",
@@ -100,10 +114,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x27786273a70>]"
+       "[<matplotlib.lines.Line2D at 0x1c0220a58e0>]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -118,26 +132,26 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 8
+   "execution_count": 13
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-10T21:22:56.117895Z",
-     "start_time": "2026-01-10T21:22:55.064872Z"
+     "end_time": "2026-01-28T02:25:45.551404Z",
+     "start_time": "2026-01-28T02:25:44.624153Z"
     }
    },
    "cell_type": "code",
-   "source": "plt.plot(accel_position.datetime_x_axis, accel_position, label = \"Accelerator Position\")",
+   "source": "plt.plot(accel_position.datetime_x_axis, accel_position, label = \"Accelerator Position\")\n",
    "id": "21f820a7985b2d39",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x277890a3a70>]"
+       "[<matplotlib.lines.Line2D at 0x1c01cd9b1d0>]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -152,13 +166,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 9
+   "execution_count": 4
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-10T21:41:37.944731Z",
-     "start_time": "2026-01-10T21:41:37.913474Z"
+     "end_time": "2026-01-28T02:25:56.201426Z",
+     "start_time": "2026-01-28T02:25:46.539316Z"
     }
    },
    "cell_type": "code",
@@ -174,26 +188,73 @@
    ],
    "id": "6a0eed52a654e483",
    "outputs": [],
-   "execution_count": 11
+   "execution_count": 5
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-28T02:46:27.816995Z",
+     "start_time": "2026-01-28T02:46:27.807935Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "start_utc = time(0, 00, 00)  #querying i svancouver time, influxdb gives utc\n",
+    "stop_utc = time(10, 45, 00)\n",
+    "date_start = date(2024, 7, 14)\n",
+    "date_stop = date(2024, 7, 14)\n",
+    "\n",
+    "vancouver = pytz.timezone(\"America/Vancouver\")\n",
+    "\n",
+    "start_local = vancouver.localize(datetime.combine(date_start, start_utc))\n",
+    "stop_local = vancouver.localize(datetime.combine(date_stop, stop_utc))\n",
+    "\n",
+    "start_time = start_local.astimezone(pytz.utc)\n",
+    "stop_time = stop_local.astimezone(pytz.utc)"
+   ],
+   "id": "ea55f2bf89a84139",
+   "outputs": [],
+   "execution_count": 18
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-10T21:42:21.910654Z",
-     "start_time": "2026-01-10T21:42:20.944632Z"
+     "end_time": "2026-01-28T02:42:51.137465Z",
+     "start_time": "2026-01-28T02:42:48.923530Z"
     }
    },
    "cell_type": "code",
-   "source": "plt.plot(accel_position.datetime_x_axis, scaled_accel_position, label = \"Accelerator Position\")",
+   "source": [
+    "plt.figure(1)\n",
+    "plt.plot(accel_position.datetime_x_axis, scaled_accel_position, label = \"Accelerator Position\")\n",
+    "plt.xlim(start_time, stop_time)\n",
+    "plt.title(\"accelerator position\")\n",
+    "plt.tick_params(rotation = 90)\n",
+    "\n",
+    "plt.figure(2)\n",
+    "plt.plot(mech_brake_pressed.datetime_x_axis, mech_brake_pressed, color = 'green', label = \"Brake Pressed\")\n",
+    "plt.title(\"brake position\")\n",
+    "plt.tick_params(rotation = 90)\n",
+    "plt.xlim(start_time, stop_time)\n",
+    "plt.figure(3)\n",
+    "# position is defined as a percentage\n",
+    "\n",
+    "plt.plot(speed_kph.datetime_x_axis, speed_kph, label = \"Speed KPH\")\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"Speed\")\n",
+    "plt.tick_params(rotation = 90)\n",
+    "plt.xlim(start_time, stop_time)\n",
+    "plt.title(\"speed kph\")\n"
+   ],
    "id": "a7c5e8360bc710de",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x27783d147a0>]"
+       "Text(0.5, 1.0, 'speed kph')"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -202,64 +263,110 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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LI2k6FmEEAABYijACoN14NkfL9h0M/1NBETk0ZkUGYQRAu/HU0paV19RbXQTA9ggjAGCipr0YOMkG/COMAHCESPVNDPdi6FMZOXQQdi7CCAAALSBMRgZhBAAAWIowAqBdSr/da3URghIX9gsxsAueM+JchBEAIWs86tGvni2xuhiAX+HoQkI3lMggjAAIWTTe0hvuk2sOZkDbCCMAHMEOB3Xu1gDMQRgBgCbIG0DkEUYAIEIIOs7DVxYZhBEAAGApwggAALAUYQQAmuBRFUDkEUYAxJS2wwZpBCewNkQGYQSAQ0SmK2G4W0aazo/OkM5DS1lkEEYAwETcQQO0jTACwCGcf4rKQ9MA/wgjABzC+gN5KCWgmT9yzPhTkx8jgzACAIgKBD/nIowAQBOcCaMpVofIIIwAAABLEUYAIEBcBQDMQRgBgCbodwBEXkhhZNasWUpPT1dycrKysrK0du3aVsfft2+fJk2apJSUFCUlJalfv35asWJFSAUGgPaIdNagD0rkxNF25Vgdgp1g8eLFys/P1+zZs5WVlaWZM2cqNzdXW7ZsUc+ePZuN39jYqCuvvFI9e/bUkiVLlJqaqm+//VannXZaOMoPAGHF4QyIvKDDyIwZMzRx4kTl5eVJkmbPnq3ly5dr3rx5uvfee5uNP2/ePO3du1dr1qxRx44dJUnp6entKzUAAIgaQV2maWxsVGlpqXJyck7MID5eOTk5Kikp8TvN66+/ruzsbE2aNEkul0sDBw7U9OnT5Xa7W1xOQ0ODamtrfX4AAIg0WsoiI6gwUlNTI7fbLZfL5TPc5XKpsrLS7zTl5eVasmSJ3G63VqxYoQcffFBPPvmk/vSnP7W4nMLCQnXt2tX7k5aWFkwxAcAUPIHV3gyeCuJYpt9N4/F41LNnTz333HMaOnSoxo0bp/vvv1+zZ89ucZopU6Zo//793p+KigqziwnA5ugICkSvoPqMdO/eXQkJCaqqqvIZXlVVpV69evmdJiUlRR07dlRCQoJ3WP/+/VVZWanGxkYlJiY2myYpKUlJSUnBFA0AEOM2fh/+S/pk4MgIqmUkMTFRQ4cOVVFRkXeYx+NRUVGRsrOz/U4zYsQIbd++XR6Pxzts69atSklJ8RtEAMAfLnegLR99XRP2efKm5cgI+jJNfn6+5syZo/nz52vTpk367W9/q/r6eu/dNePHj9eUKVO84//2t7/V3r17dfvtt2vr1q1avny5pk+frkmTJoWvFgAQoPYcWkI5LnEsA9oW9K2948aNU3V1taZOnarKykplZGRo5cqV3k6tO3fuVHz8iYyTlpamt956S3feeacGDx6s1NRU3X777brnnnvCVwsAlnBiY8WKL3ZbXQSYxIz1MY4muYgIOoxI0uTJkzV58mS/nxUXFzcblp2drY8//jiURQGwMSee9K/eHv6m/EDRSgL4x7tpAMBETU+sufUU8I8wAgBN0CwPRB5hBAAQlbgs5hyEEQBoorV2ES6zAOYgjABwBM5y0RYzLrFx0S4yCCMAYCJCVOQQHJyLMAIAQAvIkpFBGAEQU9o6e27t4BMXwrk3N+cAbSOMAADQAt5NExmEEQAAYCnCCABHCNflDh5qBtgPYQRAyDisAwgHwggAU3HNHUBbCCMAQhaNMSPcT2BtmsXIZeYy4wocl/UigzACAIgKxAbnIowAcIRoaFWIgirYGn9f5yKMAHAEpx5oaOUH2kYYARBTyAaA/RBGAMSUtlpYnNoCg+ZC6XB8MsJrZBBGACBAobybBkDbCCMAYCICTOREQyfnSHr50wrNeGer1cWQJHWwugAAEEmRjgbhuFQA60Tzt/eHVz+XJF01wKWBqV0tLQstIwAAxLDaw0esLgJhBIC5oqnpnFYOe+M2aucijAAIWST3/XY4zkRTsIpGdlhHEBrCCABHIAcA0YswAiC2WHj6TMuKuXipnXMRRgCEjGMr7IQo4lyEEQBoorUDGifeseXtLyu1fc8Bq4sREwgjANCEma093I3jLLf8vdTqIsQMwggAADHMDk8JJowAQITQgRXwjzACIKZE+hyw6Vnn4SPuCC89xlh/go8QEUYAIEChtGw07fT6Wtmu8BUGiCKEEQCIELeH6zSRxGUx5yCMADAVxwNEDCubYxFGADhCuM5yeUonYD+EEQAxxbC47f77fYcsXT5gR4QRACGL6Ft7HdqgcXL24Y4aoDnCCAAAsBRhBACasPoyDtrBoa1nIIwAaAcO27ATOzzWHKEhjESRI26P1UUAACBohJEoMfuDr3Xu/W/q4/IfrC4KYGvtubWXliDAHISRKPHom5slSff98wuLSwKgNVxIAJojjACIKYQBwH4II1GGZmTYTbjuTuEmF7TFYA/oWIQRAIgQbhsG/COMRBl2doC9OPXJsYgddlhHCSMAEKBw7LN5UR/QHGEEAJqgbdG5Tn7oGd+lcxBGogwbHyIpkuf4duicaH0JgOhEGAGACCHMAP4RRqIM/VcRrcL13pG2umy09jHbF2AOwgiAmBLpl6kRYIC2EUYAhCzQ4+zWqjpTy+Ek3EsDNBdSGJk1a5bS09OVnJysrKwsrV27NqDpFi1apLi4OI0dOzaUxSIAdujkB5zs+uc/sboIAGws6DCyePFi5efnq6CgQOvXr9eQIUOUm5urPXv2tDrdjh07dNddd+mSSy4JubBoG03CsBtDUnVdQxjmE6bHyhPYAdsJOozMmDFDEydOVF5engYMGKDZs2erc+fOmjdvXovTuN1uXX/99Zo2bZrOOuusdhUYrfvux0NWFwEALMHz5JwrqDDS2Nio0tJS5eTknJhBfLxycnJUUlLS4nR//OMf1bNnT910000BLaehoUG1tbU+PwAAtIYs4lxBhZGamhq53W65XC6f4S6XS5WVlX6nWb16tebOnas5c+YEvJzCwkJ17drV+5OWlhZMMQHANjhbB9pm6t00dXV1uuGGGzRnzhx179494OmmTJmi/fv3e38qKipMLCUAnGB2jxLCCdBch2BG7t69uxISElRVVeUzvKqqSr169Wo2/tdff60dO3ZozJgx3mEej+fYgjt00JYtW3T22Wc3my4pKUlJSUnBFA0AbIlO5UDbgmoZSUxM1NChQ1VUVOQd5vF4VFRUpOzs7Gbjn3/++friiy9UVlbm/fnlL3+pyy+/XGVlZVx+ARBx7XvoGckCMENQLSOSlJ+frwkTJmjYsGHKzMzUzJkzVV9fr7y8PEnS+PHjlZqaqsLCQiUnJ2vgwIE+05922mmS1Gw4AESClZdJaCUB/As6jIwbN07V1dWaOnWqKisrlZGRoZUrV3o7te7cuVPx8TzYFQAk3/DDM05gR3boxhR0GJGkyZMna/LkyX4/Ky4ubnXaF198MZRFAgAQFIOmKMegCQNATLH6+BTpF/UBTkAYAQBEBW6bdi7CCICQVdUebnMcq1siANgfYQRASL6pqdfPn/zA6mJEWPCn3oSxyOESmHMRRgCE5N2vqtoeCQACQBgBgCZoyQAijzACIKa0r5Nj+5MKnSzNw3NcnIswAgAmInxEDq1azkUYAQAAliKMAAgJZ/wAwoUwAgARwmUEcxGQnYswAsARInUgb+2ARpgAzEEYAQATEWCAthFGAJiK2y1P4C8B+EcYAeAITu0P4NRyA5FEGAEARCVaogITZ4PETBgBgCZa6+NB/w/AHIQRAIggG5yERi3e2utchBEAAGApwgiAmGKH6+MAfBFGAIQk0gd1O/TXaO9tynaoQzQjZzoXYQSApTbtrtVdr2zQ9/sOtToeB3IgenWwugAAYtuopz+UYUjb9hywuigALELLCABLHW/x2FpZZ21BIsKgzwrgB2EEgKmi6fIKt44iGtkhHxNGAITEBvuvkDi13GibHQ6qTmSHEwbCCABbcMIL9ZxQRsCJCCMA4IPA4VR2OMNHaAgjAEIS6f2+U5vgOUACbSOMAICJmoYow6DPipmcGlhBGAEQonDv96O1BYHjI9A2wggABChaA1O04vtyDsIIAEfgwIK20Q7lVISRGGUYhuobjlpdDCDq0Y8hkkisobDDOkoYiVG3/qNUFxS8pa+reR8I7CFSh5HI73htsKcHbI4wEqPe+rJKkrTgk50WlwROZYezKSfi7wY0RxgBAB9hv0/Iz/8ANBXzYcTjYfcAAICVYjqM1Dcc1YjH3lP+4jKriwLANucF4S4I12Uih7+1U8V0GFn+xW7t3n9Y//zse6uLAsABbJOXgCgT02EEQOg4BwUQLoQRADHFyrtZzu15iuKIcbAZO6yRhBEAtmA44CJIKE+BbRp+hvbpFr7CAGFihy2PMAIAQIzYXFlrdRH8IowAiClcJkEs+8XMD60ugl+EEQAIEE9PBcxBGAEQkrgwH5md8FbekPqMNJ1eBBoz8bd1LsIIAFM5IWSYKdyhDUGI8XXPSQgjMY7dJGKNE+7aAWINYSTGcdIG+Ap3S47PZRpyEOAXYQSALXCcBqxhh3NSwgiAkNCqBiBcCCMAQuLUSw7tec5Ie/ubGDJscRYK2A1hBIAjhKvjKS06gP0QRgDYguHUphYA7UYYAeAIZBUgeoUURmbNmqX09HQlJycrKytLa9eubXHcOXPm6JJLLlG3bt3UrVs35eTktDo+AGeIycsd7Q1EBCpTxeIqGS2CDiOLFy9Wfn6+CgoKtH79eg0ZMkS5ubnas2eP3/GLi4t17bXX6v3331dJSYnS0tJ01VVX6fvvv2934QHEDqeGH6eWG9HNbpdFgw4jM2bM0MSJE5WXl6cBAwZo9uzZ6ty5s+bNm+d3/Jdeekm33XabMjIydP755+v555+Xx+NRUVFRuwsPwP4C7Xhqr12jiQgnQDNBhZHGxkaVlpYqJyfnxAzi45WTk6OSkpKA5nHw4EEdOXJEp59+eovjNDQ0qLa21ucHAIDWdEygG2Qo7NB6F9Q3V1NTI7fbLZfL5TPc5XKpsrIyoHncc8896t27t0+gOVlhYaG6du3q/UlLSwummAAQsnC30DTd0cdM649FTuvc0eoiOJIdrthENEY++uijWrRokf71r38pOTm5xfGmTJmi/fv3e38qKipMKc9Rtw2+AYvxRlGEijUHQLh0CGbk7t27KyEhQVVVVT7Dq6qq1KtXr1anfeKJJ/Too4/q3Xff1eDBg1sdNykpSUlJScEULSRvbtxt+jLsjgMK7CJSZ2dtrfNmbhN26zQY7XhDs3ME1TKSmJiooUOH+nQ+Pd4ZNTs7u8XpHn/8cT388MNauXKlhg0bFnppw+xQo9vqIgAIUNiO4+1oDQylCE0fP08WAfwLqmVEkvLz8zVhwgQNGzZMmZmZmjlzpurr65WXlydJGj9+vFJTU1VYWChJeuyxxzR16lQtWLBA6enp3r4lp5xyik455ZQwVgUA2s/MK5eG2vduHCBaBR1Gxo0bp+rqak2dOlWVlZXKyMjQypUrvZ1ad+7cqfj4Ew0uzz77rBobG/Wf//mfPvMpKCjQQw891L7SA0AEhRQjyB4RQ9ALTMNRtxJtdudR0GFEkiZPnqzJkyf7/ay4uNjn9x07doSyCAB2F4Odn7nKgmiwcmOlxgzubXUxfNgrGgGA2drsuNFyyKIDqr3RYTUwnpPWYzucVxBGACBCyDKwAzteziKMADCV0w7AdjhLBMxkx3WcMAIAEWTHAwFgNcIIgJBE+pjKQRyIXoQRALHFwlRDB0vYgR1fA0IYAQAT2W+3j1hnx3WSMBLr7LhWAn6EqyNse1b5kB4Hb8Oz0Ghlx7tE7Cguzn7PzInpMBIfz4rLxgv4MvdFeSbOHAjQyfv9bVUHLCrJCTEdRv59cIrVRQAcKxZP+MMRJmLwzwabOerx+Py+bQ9hxFLJHRKsLgLgWNF6ls9lFUS7qtrDPr/bYVuO6TBCz3YAkcTj5COLP7d/J1+mscOxMKbDCACYjXYW2N0HW6qtLgJhBACaMrUDq4nzBgL1yIpNPr+X19RbVJITCCMATGW3A3Cku4ScvDz6pADNEUYAhIRjKoBwIYwAAKICATlwdutMTRgBEFMi/QRWn+nttf8HbIMwAgBNcHYNRB5hBAAQFWh5ci7CSIzjLBChcup7jSJ9vHLq3wmIJMJIjGM3CUSOHZ50CdgRYQQATNS09ZHLCIB/hBEAMYXWQMB+CCMAAMBShBEAIQm087PdHq5kJf4S5qJDfuDsti4SRgCgidayE8EKMEdMhxH2KwAiiX2OuU5uGOHP7RwxHUYAOEe4DuTteWtuKEXgygHQNsIIAASq3YGIc3XYz08SE6wuAmEEQGg44w8QfyjYXLwNev4SRgAAiGF2aK8jjABAhNCBFXZkh7vECCMAHCFcLcmRbpBu+qI8G+zzAUm+66IdVkvCSIyzwaVCIKrxcrzIYX/mXISRGMfrzQFEi/bcth3L7NBiRxgBYCob7Odsg1YS2JEd1kvCCAA0Ee7dMq2PkUPDSGhoGQGAANlhhwlEIztsWoQRAAiQHZqz0TJaoUJkg9WaMAIgJDSJB4/WHdiRHUI2YQQATERoixz+1qGxQ0gmjACIKRywohdfbeCatobYIIsQRgDATHY464wVJwdNOzzm3Ans8HeK6TBi/Z8fQLRreoBkn2MuOrA6V0yHkWhlh5QL4Jimh0c2TdiRHVZLwkgU8thhzTLJ4SNuq4uA/8VZaPDscNdCNKM/UGjsEJIJI1EomJYRNl44RVSsqzbY6QN2RBiJQsG0jDht/x4fFUek2BKus65Inb2ZeZnTbYdT0CjGi/KcizAShaK5KZh9DawUSpZoeoCM5kuodsDuwbkII1GIky+gZe3p69LebcvDxmkqTlYCZ7dVkTAShey2kiFKseMPSNPLPtzpBvhHGIlCnH0BrbAwRLm5TmMq8rFzEUaiEGEEsA/6jEQOHVidizAShdjfAea4/Pwe7ZqeyzTmIos4F2EkChkeq0sARKfEDu3bZdIyYi6yiHOFtGXNmjVL6enpSk5OVlZWltauXdvq+K+88orOP/98JScna9CgQVqxYkVIhUVgovnWXtgHO/7g0WfEZDSNOFbQYWTx4sXKz89XQUGB1q9fryFDhig3N1d79uzxO/6aNWt07bXX6qabbtJnn32msWPHauzYsdq4cWO7Cw//2N8B9tH0+Eh/LnMRRZwr6DAyY8YMTZw4UXl5eRowYIBmz56tzp07a968eX7H/8tf/qJf/OIXuvvuu9W/f389/PDDuuiii/TXv/613YWHf1yXBuyJTRPwr0MwIzc2Nqq0tFRTpkzxDouPj1dOTo5KSkr8TlNSUqL8/HyfYbm5uVq6dGmLy2loaFBDQ4P399ra2mCKGbB5q7/x/n/aG1+asgwrPPrmZiV2iJfHkDweQx7DkMc4FlLc//v/455+b7vqGo5aV9ggedrZ7BNN37PV1n6zN6DxHlu5udmwUL6H5z4sD3jc1ubvr9xNx99/6EiL0/695Ft9/t3+gMshSbv3Hfb+f/X2Gv35rS0tLhvtU/L1Dz6/P/H2FiV3TLCoNPY2fcUmn9+nvfGlbhzRV2mnd7akPHFGEKfRu3btUmpqqtasWaPs7Gzv8D/84Q/64IMP9MknnzSbJjExUfPnz9e1117rHfbMM89o2rRpqqqq8ruchx56SNOmTWs2fP/+/erSpUugxW3T2fet4BouAACS/nnbcF10ZrewzrO2tlZdu3Zt8/gdVMtIpEyZMsWnNaW2tlZpaWlhX84fr75AzxZ/rbN6nKJBqeELOVao2HtIr2/YpZsv7qvOSR0UH3fspXIJ8XGKO/7/uBP/37anTl98v1+X9mvfrYpWmPX+10FP061zR105wKUepyaZUKLYtXJjpb6urm/x88z00/XTvt10sNGtFz7aIUm6aoBL57pO8Y5Te+io3ty4W6MHpWjn3oN6f0u133lNuvzsFr/7zPTTtXbHsRaPCdl9dEpyy7s2w5CeKT4xn/+4KFUpXZN9xlm4tkJ76xt144i+mvfRiRbUSZef3eJ8W3O83Menn7PqGzW6PRqb0Vup3TqFNE/413QdCfX7Oq6mrlFLy75Xw9HoukXxp+ndlNn3dBVvqdaXu2p166VnKyFecnVJbntikwTVMtLY2KjOnTtryZIlGjt2rHf4hAkTtG/fPr322mvNpjnzzDOVn5+vO+64wzusoKBAS5cu1YYNGwJabqDJCgAA2Eegx++gOrAmJiZq6NChKioq8g7zeDwqKiryuWzTVHZ2ts/4kvTOO++0OD4AAIgtQV+myc/P14QJEzRs2DBlZmZq5syZqq+vV15eniRp/PjxSk1NVWFhoSTp9ttv16WXXqonn3xSo0eP1qJFi7Ru3To999xz4a0JAABwpKDDyLhx41RdXa2pU6eqsrJSGRkZWrlypVwulyRp586dio8/0eAyfPhwLViwQA888IDuu+8+nXvuuVq6dKkGDhwYvloAAADHCqrPiFXoMwIAgPOY0mcEAAAg3AgjAADAUoQRAABgKcIIAACwFGEEAABYijACAAAsRRgBAACWIowAAABLEUYAAIClgn4cvBWOPyS2trbW4pIAAIBAHT9ut/Wwd0eEkbq6OklSWlqaxSUBAADBqqurU9euXVv83BHvpvF4PNq1a5dOPfVUxcXF+XxWW1urtLQ0VVRUOPa9NdTBHqKhDlJ01IM62AN1sAcn18EwDNXV1al3794+L9E9mSNaRuLj43XGGWe0Ok6XLl0c9yWdjDrYQzTUQYqOelAHe6AO9uDUOrTWInIcHVgBAIClCCMAAMBSjg8jSUlJKigoUFJSktVFCRl1sIdoqIMUHfWgDvZAHewhGurQFkd0YAUAANHL8S0jAADA2QgjAADAUoQRAABgKcIIAACwVNjDyKxZs5Senq7k5GRlZWVp7dq13s927NihuLg4vz+vvPJKi/M8fPiw/uu//kuDBg1Shw4dNHbs2FbL8NFHH6lDhw7KyMhos7yff/65LrnkEiUnJystLU2PP/54szoUFRVp0qRJSklJUWJioq3q0NJ8m9ahX79+yszMVI8ePdSlSxddeOGFjqtDVlaW/vjHP2rIkCHq3LmzevToYVodiouL/c63srKy1Xr4W5dOrse5556rPn36KDk5WYMGDdILL7zgiHpYtU2EUge7bRNm1SGS24QkNTQ06P7771efPn2UlJSk9PR0zZs3r8X5StLOnTs1evRode7cWT179tTdd9+tp59+2qcOzz77rC666CIlJSWpT58+jqzD6tWrvfM1+xgRSh1+97vfaejQoUpKSvK7Py4uLtbVV1+tlJQU/eQnP1FGRoZeeumlVucZbmENI4sXL1Z+fr4KCgq0fv16DRkyRLm5udqzZ4+kY++W2b17t8/PtGnTdMopp2jkyJEtztftdqtTp0763e9+p5ycnFbLsG/fPo0fP14///nP2yxvbW2trrrqKvXp00elpaX685//rAceeEB33HGHtw4DBw5Ubm6utmzZoiVLlmjTpk169dVX9e6779qiDv7me/L3kJSUpI0bN+of//iHSktLlZubqw4dOujtt992TB1cLpcKCgp0zTXX6Msvv9SSJUvUr18/jRw50rQ6bNmyxWdd7dmzZ4vj+luXHnroIU2cONFbj7lz5+rrr79WdXW13n33XY0dO1a33HKL3n//fVO3ifbWww7bRDB1sOs2Ee46RHqbuOaaa1RUVKS5c+dqy5YtWrhwoc4777xW5zt69Gg1NjZqzZo1mj9/vv72t7/51OGss87SbbfdpqysLJWVlen3v/+94uPjtWDBAsfUYciQIbriiiu0cuVKzZ07V5s2bdIbb7yh1157zRZ1OO7GG2/UuHHj/H62Zs0aDR48WK+++qo+//xz5eXlafz48Vq2bFmb8w0bI4wyMzONSZMmeX93u91G7969jcLCwhanycjIMG688caAlzFhwgTj6quvbvHzcePGGQ888IBRUFBgDBkypNV5PfPMM0a3bt2MhoYG77CUlBTjtNNO8/4+a9YsIyEhwfjTn/5kyzr4m28g38OAAQOMadOmOaYOjz32mJGQkOBTh6efftpITU0Nex3ef/99Q5Lx448/Bjwvf+vSPffcYyQnJ3vrcc011xijRo3y+S6ysrKM3/zmNz7zsls9rNwmQqmDv/lauU2YVYdIbhNvvvmm0bVrV+OHH34IeF4rVqww4uPjjcrKSu+w9PR0o2PHjt716+677zY6dOjgU4dx48YZubm5jqnD8uXLjbi4OOPBBx9scT5W1qGpYPbHo0aNMvLy8kJaTijC1jLS2Nio0tJSn0QXHx+vnJwclZSU+J2mtLRUZWVluummm8JShhdeeEHl5eUqKCgIaPySkhL927/9mxITEyUdq0NVVZX27dunH3/8UZK0bNkynXnmmZozZ45cLpcGDhyo6dOny+1226IOJ/N4PG1+Dx6PR3V1dTr99NMlOaMOI0aMkMfj0dKlS2UYhqqqqrRkyRKNGjXKlDpIUkZGhlJSUnTllVfqo48+anXck9clSbriiit0+PBh/exnP/OOc+WVV/p8F7m5uT7bh93qYYdtItg6nMwO24QZdYjkNvH6669r2LBhevzxx5Wamqp+/frprrvu0qFDh1qcpqSkRIMGDZLL5ZJ0bF2qqKjQkSNH9OWXX0qSPv74Y5177rk+20DTbcIJdVi2bJlcLpcWLlzod75W1yFU+/fv924PkRC2F+XV1NTI7XZ7v7TjXC6XNm/e7HeauXPnqn///ho+fHi7l79t2zbde++9+vDDD9WhQ2DVqqysVN++fb2/19TUyOPxeD/r1q2bysvLtXPnTnXr1k0rV67U9u3bddttt+nIkSPepncr63CyhoaGNr+HJ554QgcOHNA111wjyfrv4WT+6jBixAiNGTNGy5YtU2Jioo4ePaoxY8Zo1qxZYa9DSkqKZs+erWHDhqmhoUHPP/+8LrvsMn3yySe66KKL/E5z8rokyXtAT0hI8I7jcrl8vguXy+XTd8Bu9bB6mwilDiezepswqw6R3CbKy8u1evVqJScn61//+pdqamp022236YcfftALL7zgd5rj6/txx48Rxz87/m+vXr18tgGXy6Xa2lodOnTIEXUoLy9XdXW1Dh8+rLfeeqvZfK2uQyhefvllffrpp/rb3/4Wtnm2xbK7aQ4dOqQFCxY0S4sXXHCBTjnllDavrzXldrt13XXXadq0aerXr19Yy+nxeNS5c2f17dtXQ4cO1bhx43T//fdr9uzZjqlDUwsWLNC0adP08ssvq2fPno6pw1dffaWioiKlpqaqtLRUK1eu1I4dO3TrrbeGtQ6SdN555+k3v/mNhg4dquHDh2vevHkaPny4nnrqqbDW6WROqUektgkz69CUmduEmXWI5Dbh8XgUFxenl156SZmZmRo1apRmzJih+fPnm3JWLoV/ezCrDsfD+tlnn91svnv37nVEHZp6//33lZeXpzlz5uiCCy4IyzwDEbaWke7duyshIUFVVVU+w6uqqtSrV69m4y9ZskQHDx7U+PHjfYavWLFCR44ckSR16tQpoGXX1dVp3bp1+uyzzzR58mRJx740wzC8ndKuuOKKZtP16tXLp7zdu3dXfHy8PB6Pt8wpKSmqq6tTSkqKd7z+/fursrJSixYtsrwOJ0tKSmrxe3C73br55pv1yiuveJt77fA9BFKHwsJCde/eXUOGDNHgwYM1ePBg/eQnP9Ell1yijIyMsNWhJZmZmVq9enWLn5+8LknHmnQlec+kjo/TdJto+v9wfhfhqoeV20SodTiZlduEmXWI5DaRkpKi1NRUn1fB9+/fX4Zh6LvvvtO5557bbJpevXr53E15/Bjhdru961KvXr1UU1PjM31VVZW6dOmi5cuXO6IOKSkp6tSpk1JTU5vNd+7cuZbXIRgffPCBxowZo6eeeqpZmc0WtpaRxMREDR06VEVFRd5hHo9HRUVFys7Objb+3Llz9ctf/lI9evTwGd6nTx+dc845Ouecc3y+3NZ06dJFX3zxhcrKyrw/t956q8477zyVlZUpKyvL73TZ2dlatWqVd6VITEyUy+XSaaedpm7dukmShg8frurqau91f0naunWrUlJSNH/+fMvrcLL4+Hi/38OyZcu0YcMGLVy4UKNHj/Z+ZofvIZA61NfXa8+ePT7r0vHLHwsXLgxbHVpSVlbmc/A92cnrknTsdrnk5GR98skn3nHeffddn23inXfe8f4/nN9FuOph5TYRah1OZuU2YWYdIrlNjBgxQrt27dKBAwe8w7Zu3ar4+HidccYZfqfJzs7WF1984b2bMjExUWlpaerYsaMGDBggSfrZz36mbdu2+dTh+DYR7u/BrDpkZ2frwIEDPpfcjs/3jTfesLwOgSouLtbo0aP12GOP6ZZbbmnXvEIRtpYRScrPz9eECRM0bNgwZWZmaubMmaqvr1deXp7PeNu3b9eqVau0YsWKgOf91VdfqbGxUXv37lVdXZ3KysokHesUFh8fr4EDB/qM37NnTyUnJzcb3tTxSwo33XST7rnnHm3cuFE//PCDPB6P5s+fr8zMTO3cuVNut1vbt2/X1q1btW3bNk2fPl3XX3+9Zs6caXkd/M33P/7jPzR16lTv9zB58mTt3btXhYWFysrK8l7r3L17ty2+h0DqsHfvXh06dEhxcXEqLy/X7t27dccdd2jw4MH6+OOPw1YHSZo5c6b69u2rCy64QIcPH9bzzz+v9957T2+//XaL8/S3Lv3lL3/Rr3/9a82ZM0fDhg3T1VdfrV//+tdKTk7WxRdfrIceekjr1q3Tc889F/ZtIpz1sGqbCLUO/uZr1TZhZh0iuU1cd911evjhh5WXl6dp06appqZGd999t2688cYWz+yvuuoqDRgwQDfccIMef/xxVVZWetelRYsWKTMzU999952OHj2qiooKbd68We+9955efvllzZkzRzfddJMj6rB27VrFxcVp3bp1+uqrr7zz/dWvfqUlS5ZYXgfp2DH3wIEDqqys1KFDh7zzHTBggBITE/X+++/r3//933X77bfrV7/6lXd7SExMjFwn1nDfnvPf//3fxplnnmkkJiYamZmZxscff9xsnClTphhpaWmG2+0OeL59+vQxJDX7aUmgtzBt2LDBuPjii42kpCQjNTXVePTRR5vVYc6cOUZWVpaRlJRknHXWWcYjjzxi3HPPPbapQ0vzPV6HU0891e/ngwYNckwdMjMzjfz8fGPAgAFGp06djJSUFOP66683Jk+eHPY6PPbYY8bZZ59tJCcnG6effrpx2WWXGe+9916b8/W3LhmG7zZxzjnnGGlpaUZiYqJxwQUXGMuXLzcMw5xtIpz1sGqbCLUOdtomzKpDJLcJwzCMTZs2GTk5OUanTp2MM844w8jPzzcOHjzY6nx37NhhjBw50ujUqZPRvXt34/e//70xc+ZMnzrMmjXLyMjIMBITE42zzjrLeOGFF0w7RphVh0WLFjWb71133WWbOlx66aV+5/vNN98YhnHsVmJ/n1966aUBl7294gzDMAINLgAAAOHGu2kAAIClCCMAAMBShBEAAGApwggAALAUYQQAAFiKMAIAACxFGAEAAJYijAAAAEsRRgAAgKUIIwAAwFKEEQAAYCnCCAAAsNT/D7ZHRZXgMfEDAAAAAElFTkSuQmCC"
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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 14
+   "execution_count": 16
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "649310b8f7176c67"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "# why does brake pressed look more continous then acceleartor - accelerator seems like it is either 100% or none\n",
+    "# a good place where this lines up is"
+   ],
+   "id": "54a0ee4a260b394"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-10T21:42:43.064314Z",
-     "start_time": "2026-01-10T21:42:40.635381Z"
+     "end_time": "2026-01-28T02:46:32.657931Z",
+     "start_time": "2026-01-28T02:46:31.918654Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "#plot relevant data\n",
-    "\n",
-    "fig, ax1 = plt.subplots()\n",
-    "ax_twin = ax1.twinx()\n",
-    "\n",
-    "plt.plot(accel_position.datetime_x_axis, scaled_accel_position, label = \"Accelerator Position\")\n",
-    "plt.plot(mech_brake_pressed.datetime_x_axis, mech_brake_pressed, color = 'green', label = \"Brake Pressed\")\n",
-    "ax1.plot(speed_kph.datetime_x_axis, speed_kph, color = 'red', label = \"Speed KPH\")\n",
-    "\n",
-    "\n",
-    "ax1.set_xlabel(\"Time\")\n",
-    "ax1.set_ylabel(\"Speed\")\n",
-    "ax_twin.set_ylabel(\"position\")\n",
-    "\n",
-    "ax1.tick_params(\"x\", rotation  = 90)\n",
-    "\n",
+    "plt.plot(speed_kph.datetime_x_axis, speed_kph, label = \"Speed KPH\")\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"Speed\")\n",
+    "plt.tick_params(rotation = 90)\n",
+    "plt.xlim(start_time, stop_time)\n",
+    "plt.title(\"speed kph\")\n",
     "\n",
-    "plt.legend(loc = \"upper left\")\n",
-    "ax1.legend(loc = \"upper left\")\n",
-    "plt.show()"
+    "# what is up with the whack units\n",
+    "\n"
    ],
-   "id": "2bc30605a4cc336c",
+   "id": "d41febe9473c8559",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
+       "Text(0.5, 1.0, 'speed kph')"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 15
+   "execution_count": 19
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "#clearly speed units are whack. from looking at lap data, i see that out average speed was around 16 miles per hour. so i think due to hwo influx registers small numbers in different units, we are probably looking at",
+   "id": "1ef57bddd9f5a956"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-10T21:20:33.464705Z",
-     "start_time": "2026-01-10T21:20:32.466254Z"
+     "end_time": "2026-01-24T19:55:00.999144Z",
+     "start_time": "2026-01-24T19:55:00.035354Z"
     }
    },
    "cell_type": "code",
@@ -284,13 +391,71 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 7
+   "execution_count": 9
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-28T03:08:57.191958Z",
+     "start_time": "2026-01-28T03:08:55.447614Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "#plot relevant data\n",
+    "\n",
+    "fig, ax1 = plt.subplots()\n",
+    "ax_twin = ax1.twinx()\n",
+    "\n",
+    "plt.plot(accel_position.datetime_x_axis, scaled_accel_position, label = \"Accelerator Position\")\n",
+    "plt.xlim(start_time, stop_time)\n",
+    "plt.plot(mech_brake_pressed.datetime_x_axis, mech_brake_pressed, color = 'green', label = \"Brake Pressed\")\n",
+    "plt.xlim(start_time, stop_time)\n",
+    "#ax1.plot(speed_kph.datetime_x_axis, speed_kph, color = 'red', label = \"Speed KPH\")\n",
+    "\n",
+    "\n",
+    "ax1.set_xlabel(\"Time\")\n",
+    "ax1.set_ylabel(\"Speed\")\n",
+    "ax_twin.set_ylabel(\"position\")\n",
+    "\n",
+    "ax1.tick_params(\"x\", rotation  = 90)\n",
+    "\n",
+    "\n",
+    "plt.legend(loc = \"upper left\")\n",
+    "ax1.legend(loc = \"upper left\")\n",
+    "plt.show()"
+   ],
+   "id": "2bc30605a4cc336c",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_5852\\1203527545.py:21: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
+      "  ax1.legend(loc = \"upper left\")\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 21
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-24T19:55:01.036961Z",
+     "start_time": "2026-01-24T19:55:01.033707Z"
+    }
+   },
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
     "# in order to get position data, look up miguel's work for localization of fsgp data\n",
     "\n",
@@ -300,7 +465,17 @@
     "\n",
     "#from miguel's code, we find the number of laps done on one day, so let us"
    ],
-   "id": "158615364b919eed"
+   "id": "158615364b919eed",
+   "outputs": [],
+   "execution_count": 10
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "#preprocessing - convert everything to pandas dataframes.",
+   "id": "a6707eebb9ccd8c9"
   }
  ],
  "metadata": {
diff --git a/array_temp/coefficient_fitting.ipynb b/array_temp/coefficient_fitting.ipynb
index 67fc9b0..67e8126 100644
--- a/array_temp/coefficient_fitting.ipynb
+++ b/array_temp/coefficient_fitting.ipynb
@@ -686,6 +686,62 @@
    ],
    "execution_count": 18
   },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def transient_faiman(\n",
+    "    irradiance,      # G [W/m^2]\n",
+    "    ambient_temp,    # Ta [°C]\n",
+    "    wind_speed,      # w [m/s]\n",
+    "    dt,              # timestep [seconds]\n",
+    "    Ca,              # thermal capacitance [J/m^2 K]\n",
+    "    u0, u1,          # Faiman coefficients\n",
+    "    T0=None          # initial temperature\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Transient Faiman thermal RC model\n",
+    "    \"\"\"\n",
+    "\n",
+    "    N = len(irradiance)\n",
+    "    T = np.zeros(N)\n",
+    "\n",
+    "    # Initial condition\n",
+    "    if T0 is None:\n",
+    "        T[0] = ambient_temp[0]\n",
+    "    else:\n",
+    "        T[0] = T0\n",
+    "\n",
+    "    for i in range(N-1):\n",
+    "        h = u0 + u1 * wind_speed[i]\n",
+    "        dTdt = (irradiance[i] - h * (T[i] - ambient_temp[i])) / Ca\n",
+    "        T[i+1] = T[i] + dt * dTdt\n",
+    "\n",
+    "    return T\n",
+    "G  = merged_df[\"shortwave_radiation_instant\"].values   # W/m^2\n",
+    "Ta = merged_df[\"temperature_2m\"].values               # °C\n",
+    "w  = merged_df[\"wind_speed_10m\"].values               # m/s\n",
+    "\n",
+    "# 15-minute timestep\n",
+    "dt = 15 * 60  # seconds\n",
+    "\n",
+    "# Fitted Faiman parameters\n",
+    "u0, u1 = popt   # from your curve fit\n",
+    "\n",
+    "# Initial temperature from measurement\n",
+    "T0 = merged_df[\"array_temperature\"].iloc[0]\n",
+    "\n",
+    "# Trial capacitance (typical PV value)\n",
+    "Ca = 20000  # J/m^2 K\n",
+    "\n",
+    "T_transient = transient_faiman(G, Ta, w, dt, Ca, u0, u1, T0)\n"
+   ],
+   "id": "4d2af51c791357f2"
+  },
   {
    "metadata": {
     "ExecuteTime": {
@@ -694,7 +750,36 @@
     }
    },
    "cell_type": "code",
-   "source": "",
+   "source": [
+    "T_a[0] = T_measured[0]  # start from measured temperature\n",
+    "for i in range(1, len(T_a)):\n",
+    "    dt = (t[i] - t[i - 1]).total_seconds()\n",
+    "    T_ss = faiman_model_single(G[i - 1], T_ambient[i - 1], v[i - 1], u0, u1)  # steady-state\n",
+    "    tau = 1800  # time constant in seconds (example)\n",
+    "    T_a[i] = T_a[i - 1] + (T_ss - T_a[i - 1]) * dt / tau\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "def faiman_dynamic(G, T_amb, v, u0, u1, dt_seconds, T_prev, tau):\n",
+    "    # steady-state Faiman\n",
+    "    T_ss = T_amb + G / (u0 + u1 * v)\n",
+    "    # first-order dynamics\n",
+    "    T_new = T_prev + (T_ss - T_prev) * dt_seconds / tau\n",
+    "    return T_new\n",
+    "\n",
+    "\n",
+    "# initialize\n",
+    "T_model = np.zeros_like(G)\n",
+    "T_model[0] = T_measured[0]  # start from sensor reading\n",
+    "tau = 3600  # 1 hour time constant\n",
+    "\n",
+    "for i in range(1, len(G)):\n",
+    "    dt = (t[i] - t[i - 1]).total_seconds()\n",
+    "    T_model[i] = faiman_dynamic(G[i - 1], T_amb[i - 1], v[i - 1], u0, u1, dt, T_model[i - 1], tau)\n",
+    "\n",
+    "# optional MPPT offset\n",
+    "T_model += 5\n"
+   ],
    "id": "c15bb0d3e9e1a6fd",
    "outputs": [],
    "execution_count": null
diff --git a/array_temp/faiman_coefficients.ipynb b/array_temp/faiman_coefficients.ipynb
index 457efbb..0ea8832 100644
--- a/array_temp/faiman_coefficients.ipynb
+++ b/array_temp/faiman_coefficients.ipynb
@@ -19,15 +19,15 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-15T02:39:38.054262Z",
-     "start_time": "2026-01-15T02:39:27.049603Z"
+     "end_time": "2026-01-24T19:03:02.470160Z",
+     "start_time": "2026-01-24T19:02:49.353246Z"
     }
    },
    "cell_type": "code",
    "source": [
     "from data_tools import query\n",
     "from data_tools.collections import TimeSeries\n",
-    "from datetime import datetime, date, time, timezone\n",
+    "from datetime import datetime, date, time, timezone, tzinfo\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
@@ -49,7 +49,7 @@
    ],
    "id": "8b2b4006671bdc31",
    "outputs": [],
-   "execution_count": 30
+   "execution_count": 3
   },
   {
    "metadata": {},
@@ -58,26 +58,29 @@
    "id": "cd65ecb41856dcb0"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-24T19:09:59.449997Z",
+     "start_time": "2026-01-24T19:09:59.331692Z"
+    }
+   },
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
-    "from datetime import datetime, date, time\n",
+    "from datetime import datetime, date, time, timedelta, timezone\n",
     "import pytz\n",
     "\n",
-    "vancouver = pytz.timezone(\"America/Vancouver\")\n",
+    "utc = pytz.timezone('UTC')\n",
     "\n",
     "date_start = date(2025, 7, 1)\n",
     "date_stop  = date(2025, 7, 6)\n",
     "\n",
     "\n",
-    "start_local = vancouver.localize(datetime.combine(date_start, time(0,0,0)))\n",
-    "stop_local  = vancouver.localize(datetime.combine(date_stop, time(0,0,0)))\n",
+    "start_local = utc.localize(datetime.combine(date_start, time(0,0,0)))\n",
+    "stop_local  = utc.localize(datetime.combine(date_stop, time(0,0,0)))\n",
     "\n",
     "#convert to utc\n",
-    "start_utc = start_local.astimezone(pytz.utc)\n",
-    "stop_utc  = stop_local.astimezone(pytz.utc)\n",
+    "# start_utc = start_local.astimezone(pytz.utc)\n",
+    "# stop_utc  = stop_local.astimezone(pytz.utc)\n",
     "\n",
     "print(\"start UTC:\", start_utc)\n",
     "print(\"stop UTC:\", stop_utc)\n",
@@ -86,13 +89,46 @@
     "temp_array_fsgp = client.query_time_series(start_utc, stop_utc, field=\"MosfetTemperatureA\")\n",
     "speed_kph_aliter       = client.query_time_series(start_utc, stop_utc, \"MotorRotatingSpeed\")\n"
    ],
-   "id": "5e563f4ef0e494b6"
+   "id": "5e563f4ef0e494b6",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "start UTC: 05:00:00\n",
+      "stop UTC: 05:00:00\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "Datetime object must be timezone-aware.",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[1;31mValueError\u001B[0m                                Traceback (most recent call last)",
+      "Cell \u001B[1;32mIn[4], line 21\u001B[0m\n\u001B[0;32m     18\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mstop UTC:\u001B[39m\u001B[38;5;124m\"\u001B[39m, stop_utc)\n\u001B[0;32m     20\u001B[0m client \u001B[38;5;241m=\u001B[39m query\u001B[38;5;241m.\u001B[39mDBClient()\n\u001B[1;32m---> 21\u001B[0m temp_array_fsgp \u001B[38;5;241m=\u001B[39m \u001B[43mclient\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mquery_time_series\u001B[49m\u001B[43m(\u001B[49m\u001B[43mstart_utc\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstop_utc\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfield\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mMosfetTemperatureA\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m     22\u001B[0m speed_kph_aliter       \u001B[38;5;241m=\u001B[39m client\u001B[38;5;241m.\u001B[39mquery_time_series(start_utc, stop_utc, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mMotorRotatingSpeed\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n",
+      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\data_tools\\query\\influxdb_query.py:180\u001B[0m, in \u001B[0;36mDBClient.query_time_series\u001B[1;34m(self, start, stop, field, bucket, car, granularity, units, measurement)\u001B[0m\n\u001B[0;32m    163\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mquery_time_series\u001B[39m(\u001B[38;5;28mself\u001B[39m, start: datetime, stop: datetime, field: \u001B[38;5;28mstr\u001B[39m, bucket: \u001B[38;5;28mstr\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mCAN_log\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m    164\u001B[0m                       car: \u001B[38;5;28mstr\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mBrightside\u001B[39m\u001B[38;5;124m\"\u001B[39m, granularity: \u001B[38;5;28mfloat\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m0.1\u001B[39m, units: \u001B[38;5;28mstr\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m    165\u001B[0m                       measurement: \u001B[38;5;28mstr\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m TimeSeries:\n\u001B[0;32m    166\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m    167\u001B[0m \u001B[38;5;124;03m    Query the database for a specific field, over a certain time range.\u001B[39;00m\n\u001B[0;32m    168\u001B[0m \u001B[38;5;124;03m    The data will be processed into a TimeSeries, which has homogenous and evenly-spaced (temporally) elements.\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    178\u001B[0m \u001B[38;5;124;03m    :return: a TimeSeries of the resulting time-series data\u001B[39;00m\n\u001B[0;32m    179\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[1;32m--> 180\u001B[0m     query_df \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mquery_series\u001B[49m\u001B[43m(\u001B[49m\u001B[43mstart\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstop\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfield\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbucket\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcar\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmeasurement\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    182\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m TimeSeries\u001B[38;5;241m.\u001B[39mfrom_query_dataframe(query_df, granularity, field, units)\n",
+      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\data_tools\\query\\influxdb_query.py:141\u001B[0m, in \u001B[0;36mDBClient.query_series\u001B[1;34m(self, start, stop, field, bucket, car, measurement)\u001B[0m\n\u001B[0;32m    127\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m    128\u001B[0m \u001B[38;5;124;03mQuery the database for a specific field, over a certain time range.\u001B[39;00m\n\u001B[0;32m    129\u001B[0m \u001B[38;5;124;03mThe data will be returned as a DataFrame.\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    136\u001B[0m \u001B[38;5;124;03m:return: a TimeSeries of the resulting time-series data\u001B[39;00m\n\u001B[0;32m    137\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m    138\u001B[0m \u001B[38;5;66;03m# InfluxDB has an issue where PST timestamps were interpreted as UTC. So, we need to mutate\u001B[39;00m\n\u001B[0;32m    139\u001B[0m \u001B[38;5;66;03m# the timestamps to represent a time -7 hours to compensate for the UTC offset of +7.\u001B[39;00m\n\u001B[1;32m--> 141\u001B[0m utc_start \u001B[38;5;241m=\u001B[39m \u001B[43mensure_utc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mstart\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;241m-\u001B[39m timedelta(hours\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m7\u001B[39m)\n\u001B[0;32m    142\u001B[0m utc_end \u001B[38;5;241m=\u001B[39m ensure_utc(stop) \u001B[38;5;241m-\u001B[39m timedelta(hours\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m7\u001B[39m)\n\u001B[0;32m    144\u001B[0m \u001B[38;5;66;03m# Make the query\u001B[39;00m\n",
+      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\data_tools\\utils\\times.py:14\u001B[0m, in \u001B[0;36mensure_utc\u001B[1;34m(dt)\u001B[0m\n\u001B[0;32m     12\u001B[0m \u001B[38;5;66;03m# Check if ``dt`` is naive (not localized to a timezone), in that case we cannot safely proceed.\u001B[39;00m\n\u001B[0;32m     13\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m dt\u001B[38;5;241m.\u001B[39mtzinfo \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m---> 14\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mDatetime object must be timezone-aware.\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m     16\u001B[0m \u001B[38;5;66;03m# Otherwise, we can re-localize the ``dt`` to UTC if it isn't already\u001B[39;00m\n\u001B[0;32m     17\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m dt\u001B[38;5;241m.\u001B[39mtzinfo \u001B[38;5;241m!=\u001B[39m timezone\u001B[38;5;241m.\u001B[39mutc:\n",
+      "\u001B[1;31mValueError\u001B[0m: Datetime object must be timezone-aware."
+     ]
+    }
+   ],
+   "execution_count": 4
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "temp_array_fsgp._start = temp_array_fsgp._start + timedelta(hours = 7)",
+   "id": "a629eda4d93cbba8"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-15T02:36:04.152139Z",
-     "start_time": "2026-01-15T02:36:04.107665Z"
+     "end_time": "2026-01-21T02:58:12.363515Z",
+     "start_time": "2026-01-21T02:58:12.288999Z"
     }
    },
    "cell_type": "code",
@@ -114,17 +150,17 @@
     "\n",
     "\n",
     "\n",
-    "#time zone matching conventions??"
+    "#time zone matching conventions?? - add factor of 8 to open meteo maybe?"
    ],
    "id": "a807d5de05d7c8b0",
    "outputs": [],
-   "execution_count": 2
+   "execution_count": 3
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-15T02:36:07.109367Z",
-     "start_time": "2026-01-15T02:36:05.220372Z"
+     "end_time": "2026-01-24T19:13:01.978445Z",
+     "start_time": "2026-01-24T19:12:59.360895Z"
     }
    },
    "cell_type": "code",
@@ -222,7 +258,34 @@
      ]
     }
    ],
-   "execution_count": 4
+   "execution_count": 5
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-24T19:14:11.950227Z",
+     "start_time": "2026-01-24T19:14:11.837834Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "plt.plot(minutely_15_dataframe[\"date\"],minutely_15_dataframe[\"shortwave_radiation_instant\"])\n",
+    "plt.show()"
+   ],
+   "id": "e2873933fba61490",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 9
   },
   {
    "metadata": {},
@@ -244,8 +307,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-15T02:36:08.906658Z",
-     "start_time": "2026-01-15T02:36:07.160122Z"
+     "end_time": "2026-01-21T02:58:18.041202Z",
+     "start_time": "2026-01-21T02:58:16.220050Z"
     }
    },
    "cell_type": "code",
@@ -262,7 +325,7 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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Zkgx3xfZM+mJffUZGijdLfbFvPoeWiCQjdQ2kFMt1NAYGMiSZa/llzm4CNQBF9diSr5SBjL+uRoZDS0SOEQQBSrNVN6TOyFQwI0Nyo9+BmzxLjYyMBN/6uLIvUf1UagWYT1aVPJBxk1muDGRIMieuMpDxRGKxr5fu7aJKK6CyHm9wgT5eXNmXqJ4s1a9UagVJd6x2l+U6GMh4qE4xIYgO8QMAvH5v/ffKsQUzMp4pp1ADwJCRAer3Bvfd9IFQVwcymkotytxkiieRK9FY+buRMvhgjQw1GktFtiqlAj8/NwhrJvfBI31bNEo7Tqbns+DXA12+XgLALJCxI4XtqzJ9m2ndNAjBvip4VQ/ws06GyH7WZhRJOdOIQ0vUaCyt6qtUKhAe6IPB7ZqKHxgN7XpxOQt+PUDHZqYbrQrQXV8qpUIsLrQnkLEU2ioUCoT46RYW5/ASkf32XLxu8XEp62QYyFCjqbQQyNi6iaTUOLzk/nxUlt8WFAqFYVE8SaZgc+YSkaNmrT9m8XEph5akLh5uKAxkPICl0ZzGysLoNQ/1B8CCX0+gNb+gjO7qC37terO0MtoozlzifktEkpEy+LD0JdkVMZDxAFUWIpnGDmS6xakBMCPjCcyHKo3v++hX97VraMnymyFX9yWSnqRDS8zIUGOxVCPT6IFMbCgAFvx6AvPLqUVkoHjbV9KhJa7uS+SI2pY/YI0MuSVLgUNjBzKdYtTwUipY8OsBzK+nh/vEibcd2abAWnY6lBtHEjlk54Vck/sP9I5FbJhueL+8SrrlDMo5/Zoai6WMjKqWQKYhMib+Pl5oGxUMgMNL7s788lB5Gd4mHNmmwNL1CXB1XyJHma8h89Z93eDnrRv25fRrckuWamQUtcxasvbBUh9eSgW6NNdN22XBr3uzVtMCGG9TUP9vferqWUucfk1kH+MiXKXZytscWiK3ZCnBUtvAUk6RRvI2eCkU6NKcBb+eQH89JUYG4tfnbzV5Tso3Sw4tETkmNixAvK3PxEi5O70eAxlqNJYyLOYJGeOamZPpBZK3obxKi87VgQwLft2b/sy9ObYL2kUHmzwn7ToyDGSIHGH8nq9/b5dyU1e98kr3eB9nIOMBLAYyZjkZby/D/aNpNyVvg6aiCh2ahbDg1wPog1BLo5NSfuszrCPDQIbIHpYCGUfq14xZ2mySGRlqNBaHlsw+hLyVhlN9NC1P8jZUaAX4eXuhTdMgABxecmf6y8nS8OSfZ3MAAH9l1D+rx4wMkWOMAxn9xI76DvuWWNiEkoEMNRrLxb6m972Nlp0/lpYvWcHv2B7N0TYqCP1ahgMAusYahpfITVVfGrUVjB9JrX9WT+2vK/YtKKtokAJ0Ik9lPHQv1dCSpXIABjLUaGwZWnphRFvxdpGmEhdziiT53cse7I5fn78VvtUrvuoLfo9z5pLbEjMytVSMO7oa76A2keJt/dCSIACFZczKENnK+C1fv69efYd9LX2OVHAdGWoslVoLF67Zh9CEvi2w/Z+345YEXebkaGqeZL/f+Js7C37dn1gjY+G5v/VrAQDoWr2Ss71WTOgp3vZRKRHgowuAObxEZDvj/dCUZkNLjhbil1XU/DlmZKjRfLznco3HLH0ItYgIRI8WoQCAIw1QJwOABb8eoLaMTNvqWUzmC3LZKsTP2+R+KAt+iexmHMiozIeWHAxkNh65WuMxBjLUaL47mlHjMWv1DT3iwgA4XuNQV5aFBb/uz3CKa15DkYG6upbrxbavxlvbdhkh3DiSyG7G78JKs0DG0eCjoLSyxmPcooAaTUl5zW/H1j46esSHAgDOZRWiWFPzwq2LLTWZLPh1b/qVfS3FwhFBvgDsC1JD/FQAgPcn9qrxHGcuEdnveJrh70+qGhn9+7Yx7n5NLikqxA/N1H7QCo5lTPZevF7na1jw6970GRlLwXB9No1sExVU47nQ6plL+SXcb4nIVsu2nBNvi+vIeNVv1lJY9ZYhxji0RE5V24wTfVbmiAMFv8euGn5mXI/mFl/Dgl/3JgYyFi6iwOriXABIzyu16Xj6hbaUFo6nz8iwRobIMfp9l+qbkbG0x9r5bGlmtzY0BjIe4JbE8BqP1bbXUve4UACOrfBrHKHPu7ujxdew4NczWLqGWjc1ZFUu2TiFX1+YaKlURs0aGaJ6uVAdbEg5/fqDvxmGgUstlC64GgYyHuDOLs1qPFbbYmbdqwt+HVnh1/hi168dY44Fv+6tti0KjK8rfRBSl8paMjJq1sgQ1UvHZiEAjKZfOzgcpP+S2jVWjcHtmoqPn80qrGcLG57kgUxVletHb57G0vBNbRmZLs3V8FIqkFWgwbV824YH9Iy3j69t+IoFv+7LsEWB5RPcvnoK9k0bhoOqtIK4roXSQkpGXyPDoSUix3z1934AAJ/qL5aaCi00lXV/Dq/ccQmf7bsi3tdvEOntpYSPSikuXnn6mvSbDEtNskDm3LlzmD17NmJjY6U6JNnIYhVKLUGGv4+X+GFkb51MlY2BjL7glxkZ95JbpKlzODC8egr2TRumYKfkGoafVBYCGX1WJ7+Uxb5EjgiuXptJP7S05XQW2r28GXm1FNDnFmnwxs+n8fKmkyirXhNKXySs32C4Q3Wmx+MDmZKSEqxZswaDBg1Cx44dsWPHDsycOVOqtpGNLE2JtvZtWs9QJ5Nn1++qNFpXwNJQgZ6+4PfEVRb8upOnPksWb1s7vfop2LlFmjqPpzLarDS4ehq2MU6/JpKGj8r043zr6WyrrzXe6bqwTLcMh36qtT6z06GZ7suuOwQyNd9ZbLBv3z589NFHWL9+PeLj43H69Gls27YNgwYNkrp9ZAOLQ0u1xzHoHheKz/en2r1VgfGKkrX9CvOC35hQf7t+DzUeQRDE2peDl+suAI8M0mVkcmwIZAKMZjkF+NR8u1FzZV8iSehrZPRUXnV8CFQrLKtAk2BfMSPjY5aROXOt0OQ9whXZlZFZsmQJOnXqhPvuuw9hYWHYsWMHTpw4AYVCgYiIiIZqI9XBUsKjrktOPwX7eHoeKu0oDjPe16m2C5sFv+7h64Np6PH67xZXerZ2eiP1GZnCuoeD9F/8vK28qXLWEpE0fM0yMsbZUHNVgoWMjD6QqT5OqyZB8PFSolBTias37aulbGx2BTIvvvgixowZgytXruA///kPunXr1lDtIjtYmv9fV/DcMjIIwX4qlFVocSbT9qp045inrmCpS3MW/Lq62RuOI6+kAtO/OFLjOWvDk03sGFrSijOgLB9LP7RUXqkVx+qJyH7mQ0u1/T0Z1zoWVO88r5+27V2d2fH2UorLLbj68JJdgczrr7+O9evXIzExES+++CJOnjzZUO0iO1jOyNQeZiiVCofqZLQ2FvsChplLzMi4J6sZmWDd0JI9gYy17ZaCfFXiyqQcXiJynHkg8/2xmnvw6Rkl1sWMjGFoyXAcQ8Gva0/BtiuQmTNnDs6dO4dPP/0UmZmZ6Nu3L7p16wZBEHDzpmObEFL9WSz2tWE405FAxjglWdeYKQt+3VudQ0s2BDL6026tMFyhUBh2wObMJSKHmdfI1LJXq8n7eEH1sG6Ffvq1yjiQcY+CX4dmLd122234+OOPkZmZiaeffhq9evXCbbfdhv79+2Pp0qVSt5Hq4MjQEuBYIKO1IyDhCr/uzVpWTx/IXC8qN8nQWWLIyFi/IMUp2MzIEDnMPCNT267zVRZmLZVXrwFnHBDpF9s7nemBgYxecHAw/v73v2P//v04cuQIbrnlFixatEiqtpGNLMcWdUcy+kDmQnaRzdNf7UmssODXfVhexdfyayOqZy1VagVxfN0a/ftlbYG1fnVfFvwSOc48kKnty4Nxhlz/N1xRZbpnE2AYWrpyvQRFmkrJ2iq1ei+IV1am+6bdpUsXLF++HOnp6fVuFNnH0WGbiCBfxIcHAACOG20GWRt7MjIAC37dmbW3QV+VF0Kq14Spa3jJloxMKDMyRPVWc2iployMhVlL+mJf4+OEBfogOsQPAHDWhbMyDgUyWq0Wr7/+Opo3b46goCBcunQJADBv3jx8+umnkjaQ6max2NfGKf/i8JKN68lU1TGUYI4Fv+6rtmsoMlg3vJRTxxRsoY5iX8B4CjZrZIjqklNo+cuD+VCSrUNL+hoZw8q+pmFB++o6mVMuXPDrUCDz73//G2vXrsVbb70FHx8f8fFOnTph5cqVkjWObGMptLB16SJ762TsTf6w4NedWb+KbC341dZR7AsAoQG69xCu7ktUt4U/n7b4uPkCeAoFsPlkJnZfyK3xWuNZSwVmK/t6q0yP4w5bFTgUyHzyySf48MMPMWHCBHh5GVbu7NatG86cOeNQQxYtWgSFQoHnn39efKysrAzTpk1DREQEgoKCMH78eGRlZTl0fE9mabjH1oyMfmG8I2l5NgUa9g4tseDXPdhTIwPYvpZMXevIAFzdl8geG49YLt/wVXmZ3D91rQD/+CwZEz7ajx/MpmJXWaiR0X/p8FLIJJBJT09H69atazyu1WpRUWH/m9HBgwfxwQcfoGvXriaPz5gxAz/88APWr1+P7du3IyMjA+PGjXOkyR7NkXVk9DrGhMDHS4kbxeVIu1H36o32Di2x4Nd1WUtR69V2BV3M0W0G+fn+1FqPof/mZ9vQEgMZIkep/b3FmkQAuJRTLN5+5kvTBS+rjFIy+hoZwUo9W8fqoaWzmYV1zlJ0FocCmY4dO2Lnzp01Hv/mm2/Qo0cPu45VVFSECRMmYOXKlQgLCxMfz8/Px6pVq7B06VIMGTIEvXr1wpo1a7Bnzx7s27fPkWZ7rIy8mgGIrRkZX5UXOsToIu4jaXWvBeTIdcyCX9e05Lez4m3LdVbWLyL9atAXsousvgawsdi3etZSAQMZonqZM6q91eeM/96NV2jX/90ZsqemP5cQEQhflRIl5VW4cqNEusZKyKFAZt68eZg+fToWL14MrVaLjRs34sknn8Qbb7yBefPm2XWsadOm4c4778SwYcNMHk9OTkZFRYXJ4+3bt0d8fDz27t3rSLM91vrkqzUes2d7rx7VdTJHbCr4tT+SYcGvazKeOm0p0KjtGnp+WBubfodhQTzrr9EHMhxaIqqfqlqG/v/vjwvibeM988yHlszfC1ReSrSLdu2F8RwKZO6991788MMP2LJlCwIDAzFv3jycPn0aP/zwA4YPH27zcdatW4fDhw9j4cKFNZ7LzMyEj48PQkNDTR6PiopCZmam1WNqNBoUFBSY/JMje3Yq1dfJ2FLwa+/QEmAo+D2ZzoJfV2VvjUy/lrpNYhMjA2s9rm01MrpiX85aIqofW9+fjYt9izSV0GoFMUixVAfZIdq162TsDmQqKyuxYMECJCYm4vfff0d2djZKSkqwa9cujBgxwubjpKWl4bnnnsPnn38OPz8/e5th1cKFC6FWq8V/cXFxkh3bU+lnLp3KKICmsvaN+xwZWtIX/OYWseDXVV25XjNlXFudVdPq6dcpucW1Bqfi0FIt7zRc2ZdIGiHVf0t1Mc7cCAJQVF6J89XDxGt2X67xelffqsDuQEalUuGtt95CZWX9VvlLTk5GdnY2evbsCZVKBZVKhe3bt+Odd96BSqVCVFQUysvLkZeXZ/JzWVlZiI6OtnrcOXPmID8/X/yXlpZWr3a6KzsSMogPD0B4oA/Kq7R1bg4WFeJrd1tY8OuebFlHBgAyaglObZt+XV0jU1bpUMaPiHTMF8UzV1y9Oq951kVf8AsA6RZqLl1980iHhpaGDh2K7du31+sXDx06FCdOnMDRo0fFf71798aECRPE297e3ti6dav4M2fPnkVqaiqSkpKsHtfX1xchISEm/+TI1llLgC7t312sk6m94Ld3QrhD7WHBr+ux5xoxF+Jn+OaXaiGbo2dtJoQxtdG3SBb8EjmutkXwAKDT/F+RkVdaI4tq/HentpDVaV8dyKTnlbpk5lTlyA+NGjUKL730Ek6cOIFevXohMNB0nPyee+6p8xjBwcHo3LmzyWOBgYGIiIgQH58yZQpmzpyJ8PBwhISE4JlnnkFSUhL69evnSLNlxZ6MDKAbXvrjTHbddTLV1/+Q9k3tOn6XWDXWJ19lRsaFbTuTbXK/rmuof6sI7Ll43eI3OD1b9lry9lIi0McLxeVVyC+tQFigj/UXE5FVcdVbztTmu6MZYoZcr7CsEj4qJcortXj5zg41fkbt743mof5IzyvF6cwCsUbOVTgUyDz99NMAYHGna4VCgaqq2ussbLVs2TIolUqMHz8eGo0GI0eOxLvvvivJsT2dvd+1bV3hV7/Ttr3H72JW8GtPMTI1DPNTMHntQbPnaz9HLZsEYs/F67iUY30Kti3TrwHd6r7F5aVcS4aoHoJ8VfBSKmodotUKQo2hpYLSCnRsFoKjaXkIC7D8RaJDsxBdIHPNQwIZrXHJs4T+/PNPk/t+fn5YsWIFVqxY0SC/z5PZGyd0qw5krlwvwY3icoRb+VYs2PAN2xLzgt+YUH/7DkCSq6saRVVHmrplpO5bnfHCW+bEWUt1/C61vzfS80qRV8KZS0T10TzUH6m1rPdSpRVqTNoo1FSIw03W3ts7NgvGltNZLlnwW+/dr8kzqP290aqJbojwaC0L4xmuf/siGRb8uh5NRe2Z07qyKC2rr5eUXOuBjGBDsS9gNHOJGRmiejHfc8mcVhAs1MhUiu/t1v5WXbng16GMzIIFC2p93t5F8Uh6jgzddI8Lw8WcYhxNzcOQ9lEWX+NoRgbQDS+dySzEyfR8jOxkfeYZNY7SOgKZus6xPiOTcr0YVVrBYqGhYfp1XUNLDGSIpFBXJlVrKSNTVmEYbrLy4/pA5mxWISqrtFDVMUOqMTkUyHz77bcm9ysqKpCSkgKVSoVWrVoxkGlkfRLCcPDyTUQE+uB6sS4170gFSvf4UGw4fBVHaqmTcbRGBmDBr6u5p1sMdl+4bvV545lJljQP8xcLBDPySi0WGhqmX9feFq7uS2QbfdGtNYPbN8W5LNO6tZZNAsUhYE2ltmaNTFllndnT+PAAsSg/JbcYbaKC69ELaTkUUh05csTk38mTJ3Ht2jUMHToUM2bMkLqNVAf9yqhNjNb2cCTS0G9VcCwtz+rmYPXNyABc4ddV1HUKfFS1vz14KRUor9TVyx28fMPia2wt9hVX92UgQ1Srzs1rX1JkxrC2Jvf3zx2KGLWhJvGDHZcsFvvW9aVDqVSIWxWccrE6GclyQyEhIXjttdfwyiuvSHVIspGltTocWSOkXXQw/LyVKCirxCUrdQ/6y9+R4xsX/GYWcIVfZ5MylJz59THLv0O8Nmv/eUs1Mncs34GEl37CzvM50jSSyANU1THXxs/by+R+VIgfgnxNB1/Mv8QUllUain1reW931ToZSQe59KvpUuMSi7SMzqYjGRNvL6WYNbE6DbuOyvbaGBf8Hr/K68TZGiMppp/gWFfNlqFGRjc0WlpeJe6w/bdVBxqugURuxjib8t20ATb9TLCfaSBTc2ipwqYNXg2BjGtlZByqkXnnnXdM7guCgGvXruHTTz/FqFGjJGkY2c5S+t7RVVq6x4Xi4OWbOJp2E/f1iq3xvJiRcfAXsODXdQgS5GTG9WyOjYfTdcezsD5QlY0ZmVB/0xqZDvM2mzyfX1phccVRIrmprB4Devv+buKyGdboC/CDzerdzCsHCsoq6yz2BTwskFm2bJnJfaVSiSZNmuCxxx7DnDlzJGkY2c5Qt2IUyDgYaHSPCwOQYjUjY7jWHfsFLPh1HbVlZE6+NtKmY7wwop0YyEz5+BBWT+pj9jt0v6SupdPrmn594mo+BraJtKlNRJ5MX79oy6Qh/eJ2QeYZmepj6It3j6XlITZMV0dTWz1b++hgKBRAdqEG14s0iAiyf++9huBQIJOSkiJ1O6geLC065mig0SM+FIBuDLS0vAr+PqbjrYINUXttuMKv67AWx7x9f7caY+rWNDda2PAPsy0OAOMtCuoIZPSzlqwFMukMZIgAiKv21hZwfP5EX7z582ksHNcFABBiFsicztRlVPQragPA1ZuldR430FeFFuEBuHy9BGcyCzGgtWsEMg7VyDz++OMoLKxZ7FNcXIzHH3+83o0ixxh/6XU0Pmim9kPTYF9UaQWczKiZNTEU+zqGBb8uxEpKZmQny2sIOUJr69BS9TfH/JIKZBtdF7e1bQKAm40S6emHa1VK6x/fA1pH4qdnB6FrbCiAmjUy+sxniIXh2ro+O1xxeMmhQObjjz9GaWnNeeylpaX45JNP6t0oso+YkZGgRsZ4J+yjqXk1nrc0jGUPFvy6DkthzP65Q2uMp9dl/9yh4u00s6XRDVM6bRtaKq/S4v3tl8THp97aEgBwPD3PrjYReSp7hpb0gnxN/6YLyyoBGGrTjNX1pUMfyLjSFGy7ApmCggLk5+uGBAoLC1FQUCD+u3nzJn7++Wc0bWrfrshUfxarzesxZNMjPgxA7RtI1mdAyHh4iZzHUkImKsTP7uNEhfihb2I4gJrDS1ob0uCAbqxevyLp6t2GoevOMbprJe0G92EiAowL6G1/FzbPyBRUZ2TMH9ep/biuOAXbrkAmNDQU4eHhUCgUaNu2LcLCwsR/kZGRePzxxzFt2rSGaitZobUw/78+gYY+I3MkteaeS1LM2O0Sq/twYsGvc0m5KGHvBF3wey7L9M1Na+N0fYVCIU7BDjCqy1IHeKNFhG7F4JPprvMN0NPlFGrw4/EMZzeDLDBkZBwPZPQZGUv7MtWdkdEtinchu1BcENPZ7Cr23bZtGwRBwJAhQ7BhwwaEh4eLz/n4+KBFixaIiYmRvJFUO0NBpeGx+tTQdo1VQ6kAMvLLkF1QhqZG39Lr2iHVFp1Z8OsSpFxGJlG/75LZQor6a9OWN121vzdyi8rRPS4Uey5ex7iezQHorpcr10tY8NuI+ryxBQBwPqsIM4a3rePV1JiOVQ/Jl1XYHkTUCGQ0uoyMUqHAD9MH4u7/7RKfq+u4zUP9EeKnQkFZJS7mFIkZGmeyK5C57bbbAOhmLcXFxUFZS7ERNSIb6xBsFeirQtuoYJzJLMSRtDyL673U5zd1NCv4bWa0fDY1Hiu7UDgkMdLyTti2blEAGOpkTlS/UUdWT+3s0lyNn45f41BkI9lzMVe8/e6fFxjIuKhKrT2BjGktTNoNwwylLrFqpCwcjcQ5PwMwnYloiUKhQPtmITiQcgOnrxW4XyCj16JFCwBASUkJUlNTUV5uOnbdtWvX+reMbGbYYdjwmKPTr/V6xIfqAplU00CmvsW+gKHg90xmIY5fza8zkDmfVYireaUY3I71V1KScmipZXUgcy2/DCXllQjwUZn8Dluy4PqZS4UaXdpbPwVcX1PFoUjpCYIgfoBdXnQnAOCRlfvF5/UBqjVnMwtxvUiD/q2ZKWtsLSJqPzfGAq0sp6D/u1QoFNj14mBk5pchPqLm5q/mOhoFMq7AoZRKTk4O7rrrLgQHB6NTp07o0aOHyT9qXOIWBRIsiKcnzlxKM62Tqc/u18ZsLfjNKdRg+LIdmLzmIC7lFNX6WnKesEAfscblcq5h5lKVjVsUADVnUOjffPUFv6k3SpDPTSUltf2cYR8r8xlnAHAuqwg5hRqLP7vrfC5GLt+BRz7aj62nsxqsjWTZ8at5Nr820McLtySE13jc+O8yNiwAvS28xhJ9nYyrFPw6FMg8//zzyMvLw/79++Hv74/Nmzfj448/Rps2bfD9999L3Uaqg/m+GUD9Aw3dCr+6NH+V0RiE+Kvq+QtsLfh94IO94m39gk0kjSopx5Zg+PZ++bpheMnWdWSAmmta+Hnr3p7UAd6ID9d9S2RWRjo3issxac1B8b61YGTvpes1HjualoeJqwyZm3nf/SUWoVLj0K8RYwuFQoGv/t4PG5/ub/K4PiCxl/FaMlJmdh3lUCDzxx9/YOnSpejduzeUSiVatGiBiRMn4q233sLChQulbiPVQbBQI1PfjEzrpkHi8tXnsw1Rd312vzZmXvBrjXHNxVcH0+r1O8lUlcRvQJbqZCztzG6NPqOjpzKKfji8JL2er/9ucv/301nQVFaJ9+/uppu4sdeoZkbvrc1nTO6n55Xiu2PpDdBKsqZJsH2r6ioUCvSMD8OBfw3FbzNuxdF5w/HkoJYO/e62UcFQKoDrxeVWM3aNyaFApri4WFwvJiwsDDk5uvRkly5dcPjwYelaRzbRF30Zzwyp70wgL6VC3JDsiNHCeIYamXodvkbBry1+OnGtfr+UTOi/QY/sFIVJ/RPw4zMD63U8fZ3MpRzjjIzuv0obUjLmQ0teRkVf+gweC34bzv5LN3AuUzd86+2lwJjuukBmz8WaGRlLj/1n81mUVVTVeJwahqNTn5sG+6FtVDBCA3zqtbCp/ovLtrPZJqtxO4NDgUy7du1w9uxZAEC3bt3wwQcfID09He+//z6aNWsmaQOpbvo1Acz306gvSyv8SlUjY7zC7wmu8OsU+vqViCBfvHpPJzFL5ijDFGxDLZNds5aYkWk0mfk1P3gqtQLWHUwFoKuXuCUxHF5KBa5cL8Fn+66Ir0u9XrOWppnaDxn5ZViz+zIA4Ne/MvHdUWZoGlKg2T54jU0/vPTihhO45c2tqKxy3poyDgUyzz33HK5d0307nj9/Pn755RfEx8fjnXfewZtvvilpA6l2giAgr6TmvhlSzMQ2FPzmGf0+6Y7PDyfn0g8teUk0bd/S0JJhi4K6fz7YbBl14ywOC36ltfT3syb3763Ovqw/dBUAEBbgjWA/b3StzoS9vOkk5n13EoIg4Nb/bBN/bnjHKJxecAdmjWgHAHh32wVcyy/F3z9NxnPrjuJnZlEbjLPX3zKfdl1UPdvQGRwKZCZOnIhJkyYBAHr16oUrV67g4MGDSEtLw4MPPihl+6gO3yRfRX5pBXxVSpNpzPWtYQGA7tU7YZ/LLqxxkUpx/LoKfn/9K9Pkfv9WEfX+nWTgyAqhtUmI1BXk3iypwHdH05Hw0k/irBhbMjJBZhlF44yMccGvpc1MyT5fVwcseg/2jgOg2+sKAKqqA9Ckloa/uU/2XsHcb0+Y/Ny/RneAv48XxvZojo7NQlCoqcRkowLi2d8cx2WztYX0tFoBuy/kMjC1g3E9oUR/tg7raBbI2LNAn9TsDmQqKirQqlUrnD59WnwsICAAPXv2RGQk1xJoTDmFGvz7J915mDG8rckHgRTBetNgPzQP9YcgAMerszJSrOyrV1fB798/TTa5HxboU/9fSiJH9mypTYCPCtHVq0A/t+4oAGBHdSDjbWEpdHMhfuY1MqY/o8/gcbPR+mtqVijar2UEIoMMf1/Hqv/e9fuu6X15wFBw7+etREJ1Fk6pVOBfd3YAAJzJNEwOKNJU4h+fJaO0XFc7s/lkJj7aeQlarYCvDqVhwkf7Mebd3dJ1zMMZTwyT6u/WUeYZmVIn1kfZHch4e3ujrMy5hT2k8+oPfyG/tAKdYkLwxMBEk+ekusT1WZkjYiBTfXwJfoG9Bb9l5SwklJIju+jWxdoCaubf3iwJ8beekQFMA19y3Gs//IVso5kmM4a1hVKpwND2UTVeO6B1BPy9vRAX7o//PdLDJCD95blbzV4bicHtmpg8FhnkgzOZhXjlu5Oo0gr4x2fJ+PdPp/H+jotYVz0LMSW32CWm8LoDrUlGxrmBTFSIaTBcUu5mQ0vTpk3D4sWLUVnpvIbL3e+nsvDT8WvwUiqweHxXqMw+jaS6xnuY1ckY/ozq/wtqK/jdc6HmlE9nRvyeSL+OjC0zimyV2MRyINPUhl21m6n9xW0JgJqzMlhTVX83i8vFglwAGNejOZ4b1gaArt5F79khrQHosmwH/jUUPz87CHd1jcFHj/UBoMvGWJr+O2d0B5P77zzcA0qFbgh8sdGU7f/8elbM+gDAt0dYGGwL40BG4eQdghQKhckXl1InftF06H/FwYMHsXHjRsTHx2PkyJEYN26cyT9qWIVlFXhl00kAwBODEi3ONpGihgUwLfgVBEHSjAxg+cOpSivgkY/213htCTMykpK62BcwTME252/DDAsvpQL75w4V7980q53QXyss+HXcLrMvCCM7G7YfGWC0zUCq0Sq/wX7e4l49t7Vtgj0vDcGPzwwSt5Aw1jYqGJP6JwAA1k3th/6tIjFrpK4Q+MMdl8TXmSdgXv/xFK4XOX89Eldn/P/NFbbabRdlWFDPmdsVOBTIhIaGYvz48Rg5ciRiYmKgVqtN/lHDWrz5DDILytAiIgAzhjXshm6dm6uhUiqQU6hBel6pZNOv9SwV/Laa+7PF13KNCmnpMzLmQzj1YW1oqWdcmMXHzRnXxRSWmQYrLPitn1W7UvDMl0dMHjPOgPn7eCGiug7NvDbGWEyoP1pXZ1ItmX93RxybPwL9qguF/3FrKwzrYLpPmq/K9KPnZkkFXv/xlG0dkTHBhWpkAKBttCGQ+e2U87apcGjhkTVr1kjdDrLRwcs38Nk+3VoPC8d1gZ+35W+6Ul3jft5e6NAsBCfS802mYUvFvOC3timFxkWEVH8NMrRkIZB5Y2znGmvE1GbKwET8cCwD43vF1niuS3M1Um+U4ER6vkkGgSwrq6jCvO9OYnzPWIuBgo/ZkPQvzw3C9nM54qq+jlAoFOJO5oDu+lpyf3d0W/Cb+NjM4W2x8BfdUJNKqYBWELDpaAbG9GiO27k5rFWuVCMDwGT/JuPNhRubw6NslZWV2LJlCz744AMUFuo+YDIyMlBUxI39GkpZRRVe3HAcAPBQnzj0b9U4b+TGC+NJPbRkS8HvF0/0FW9bqp0hx2gbYGgpLtx059y1k/uIU3tt9cpdHbF/7lCTbIGePvDlIoq2Gbh4G74+dBUPfrjP4vMxoaa1S01D/HB/7zirX5AcpQ7wxvfTByDYT4VBbSIxxWhyQs8WYZjUX3f/X9+eRLET1yNxdSY1Ms6PYzCgdQQeS2qBUZ2j8fAt8U5rh0OBzJUrV9ClSxfce++9mDZtmrhFweLFizFr1ixJG0gGK7ZdwKWcYjQJ9sWcUR1qPG98XUu5WFKP6plLR9PyJNtrSc+84NfS6pDRasOb7ZLfz0nye6lhMjLeZt/wb2/XtEYhui2sXb8s+LVPbi11J98+3R8RFoLFhtI1NhR75wzF2sm3QOWlxJ+zbseoztGYNaIdXhjRFs1D/ZGeV4ql/Bu3ypWmXwO6v9PX7u2M9yb2kmw9Kkc4vLJv7969cfPmTfj7GxZhGzt2LLZu3SpZ48jgTGYB3vvzIgBgwT2d6kzVS3lJ6TMyJ9LzUVEdaEj5N9TFaHipzML+IQE+hhHQ5Cs3pfvFMqePGRvqDahPgm11Mfbo3Fw3jZsFv/UTFeJbax1MQwnyVYnXW0JkIN6b2Au3JIYj0FeFN8Z2BgCs2Z1iMqOJjJgEMs5rhqtxKJDZuXMnXn75Zfj4mC5QlpCQgPR0TqOTWpVWwIsbTqBSK2BkpyiM6lL3flZSBhqJkYFQ+3tDU6nFmerKdCn/hvQFv8fT8y0W9Pqbpbkd3SyNTDXE0BIAvDuhJ1pGBmL+3Z0kPS4AhAb4sODXRrUN0ViaceRst7drinu7x0ArAC9uOC5+aSIDV6uRcRUOBTJarRZVVTU/cK5evYrg4GALP0H1sXbPZRxLy0OwrwoL7u1s089IeYkrFIadsA9XbyAp5dCVccHvbgs1MH4+ppfpKSdO8/MkDTG0BACjuzTDH7Nur/cmlNZweMk21oaVbkkIx5IHujduY2w0766OCAvwxpnMQpPp2qTjajUyrsKhQGbEiBFYvny5eF+hUKCoqAjz58/H6NGjpWobAUi7UYK3f9Vt8DZndAdE2bCwGCD9hmL6hfHyS6VP5xsX/OqXtjdmPrOCw0vSMKwj4+SG2KkzAxmb5FSv3htitofV1/9IEoeLXU1EkC9evrMjAOC/W8+bbEBKpjUyzt400pU4FMgsWbIEu3fvRseOHVFWVoZHHnlEHFZavHix1G2ULUEQMPfbEyitqELfxHA81Mf22R9SX+P6rQr0CsqkC2iMC34tMf+DPZzKQKa+BEHAT8d1OxO72xtiF7OtCvZcyMXYd3dz6wIz+kDGeM2XF4Y37LpTUhjXszkGtYlEeaUWczYe5/YFRgRxfzQnN8TFOBTIxMbG4tixY5g7dy5mzJiBHj16YNGiRThy5AiaNuUaAFL59kg6dp7PhY9KiYXjutg1BCD1dd49NtTkvtRj7F0sDEPckhCO18fohtJeGN5WXKzrMDMy9VZRZfhwcLdvvfqC3yvXdQW/z647giOpefjbqpqrQctZbnE5AN2id59N6YvxPWPxt6QWTm5V3RQKBd4Y0wV+3krsu3QDXx9Kq/uHZEL/V8v6GFMOfxqpVCpMnDhRyraQkdwiDRZUL2D13NA2aNnEesbCIokvdPOdpx+rXoZcKl1i1ViffNXksa//kSTefmZoGzw+MBFdX/sN1/LLkJFXiphQf/PDkI00lYYaN3f7xhsa4IO4cH+k3SjFyYx85BbpPrBvllTg9LWCGrvyypW+2DfIT4WBbSIxsI37LCAYHxGAF4a3wxs/n8YbP53G4PZN0TTYtmF1T6aVeMd6T+Hwgnhnz57F9OnTMXToUAwdOhTTp0/HmTNn6v5BssmCH04hr6QCHZqFYOqtLW36GeNru6Ev81b2BlZ1sKUwNNBXhQ7NdMXkHF6qH+OMTJWbBTKAIYNnvtngRztTnNEcl1RSHcgE+rjeDCVbTB6QgC7N1Sgoq8Rr33P7AsBQI8M4xpRDgcyGDRvQuXNnJCcno1u3bujWrRsOHz6MLl26YMOGDVK3UXb+OJOF749lQKkAFo/vUmORMVs0xIX+z+rN3+yp1bFVRxu/RfesXvuCBb/1Yzy11R2ns3dpHgoA2H4ux+Tx74+lI9vKCtFyU1CmC2QCbNiw0xWpvHRD6l5KBX46cQ2/O3EvH1ehrY5kGMiYciiQmT17NubMmYO9e/di6dKlWLp0Kfbs2YO5c+di9uzZUrdRVoo0lXj5W93O1lMGJqKrWW2KraRaedfYU7e1wvp/JOHVe6RfH8TWJdF7tdAFMvpp4OQY4+BF45aBjC4joy9oTWoZgV4twlBRJeCTvVec2TSXcC6rEF8d1NWWtKqlkN7VdW6uxhODdNsXvLLpZI2NROVGnzzl0JIphwKZa9eu4dFHH63x+MSJE3Ht2rV6N0rO/rP5DDLyyxAfHoCZw9s5uzkmlEoF+iSES74Pi15CRECdr9FnZP6ysnge1a2grAKT1hwQ7w9p734F+vqCX729l67jyeoPvM/2X0FpuXyvjWJNJZ76LBmlFVUY2DoS43vW3HzTnTw/tC1aRAQgs6AM/6leikKuBLBGxhKHApnbb78dO3furPH4rl27MGjQoHo3Sq6Sr9zAJ/t03ybfHNsF/vVICbvjdd4xpu7hpdgwfzQJ9kWlVuA6Ig76+mAaLuYYZirdU4+djp1FX/CrlxARgOEdoxEX7o+8kgpsOHy1lp/2XIIgYM7GE7iYU4yoEF8sf6i7U/fAkYK/jxfeHNsFAPDpvitIvnLDyS1yHtbIWOZQIHPPPffgxRdfxPTp0/HZZ5/hs88+w/Tp0/HSSy9h7Nix+P7778V/ZBtNZRVe3HACggDc1yu23jMM3PE6/8dtrcTbjw9ItPgahUKBXqyTqZcAs+JPd1tHRs94yv7IztHwUirE62b1rhSxnkBOPtufiu+PZcBLqcD/HulpcQdxdzSgdSTu7xULQQBe3HDCZNZdXYo8aDdtzlqyzKFA5umnn0Zubi7effddPProo3j00Ufx7rvvIicnB08//TTGjBmDMWPGYOzYsbUe57333kPXrl0REhKCkJAQJCUl4ZdffhGfLysrw7Rp0xAREYGgoCCMHz8eWVmeWfD17raLuJBdhMggH7x8Z82dre3ljtd519hQ7HpxME4vuAPz7u5o9XX6OhkGMo5xoHbcJRnPdAvw1gVn9/eOQ7CfCpdyi7HtbLazmuYUJ67m4/UfdLN7XryjHfokhDu5RdL6150dEBnkgwvZReIGurURBAEJL/2EzvN/xbTPDzdCCxseF8SzzOG9lmz5Z2k/JmOxsbFYtGgRkpOTcejQIQwZMgT33nsv/vrrLwDAjBkz8MMPP2D9+vXYvn07MjIyMG7cOEea7NLOZRXi3T8vAABevacTQgN86viJujVEsW9jiA0LqHNIrWeLUADAkdSbbrcGijNpKquw/lAaVhpNUbZ1tpgrMs7I6GfmBPmq8Mgt8QCAlTvls1dPfkkFnv4iGeVVWgzvGIUnB9m2ZIM7CQ3wETci1X3xK6z19YlzfhZv/3TCM2o3DUNL7vn+3lCc+t3s7rvvxujRo9GmTRu0bdsWb7zxBoKCgrBv3z7k5+dj1apVWLp0KYYMGYJevXphzZo12LNnD/bt2+fMZktKt7P1cVRUCRjWoSnutGFna2tMghcPvs47xajh46VEblE5Um+UOLs5Li+3SIPlW85hwKI/8M9vjuNCdhH8vb0weUACPnqst7Ob5zDjQKZCa5h59Vj/BKiUCuy7dKPObQue+PgQRv13p1vvtCwIAmZ9cwxpN0oRF+6Pt+/v5rEfdHd1bYYh7ZuivEqLlzacsHn4cJLEC3g6i/57m2eeXcc5vFLSwYMHsW3bNmRnZ0OrNX0TWLp0qd3Hq6qqwvr161FcXIykpCQkJyejoqICw4YNE1/Tvn17xMfHY+/evejXr5+jTXcpn+69jCOpeQjyVeH1MZ0lewPy5Avdz9sLnZqH4EhqHpKv3ESLiEBnN8klnckswOpdKdh0NEOcbt1M7YfH+ifg4T7xUAd4O7mF9WOcubyWZ1g7JibUH3d2bYbvjmZg1a4ULHuwu8WfP5VRgC2ndUPVv5/Kwuh6fIlwppU7L+H3U1nw8VLi3Ud6Qe3v3ue1NgqFAq+P6Yz9S7fj0JWb+PxAKv7Wr+a2C2cyC0zuG9fJlFVUof0rmwEAlxfd2bANlph+1pKHxqkOcyiQefPNN/Hyyy+jXbt2iIqKMvnwtfeD+MSJE0hKSkJZWRmCgoLw7bffomPHjjh69Ch8fHwQGhpq8vqoqChkZmZaPZ5Go4FGY9i+vqCgwOprnS09rxRvVU8nfHFUezRTS7fkvqd+I9PrFR+GI6l5OJx6E+PcfHqplLRaAdvP5WDVrhTsupArPt4tLhRTBiZiVOdohxZYdFVPDkrEJ3uv4JG+8SaPTxmYiO+OZuCHYxl48Y72iFbXXN5+xldHxduXr7vXflN6By/fwOLNuveQV+7uiC6xda+Q7e6ah/rjnyPb4dUfTmHxL2cwvENUjfObet00U1tUZghk9EEMAJSUV9YofndlhpF0z35/t5dDZ/C///0vVq9ejUmTJtW7Ae3atcPRo0eRn5+Pb775Bo899hi2b9/u8PEWLlyI1157rd7tamiCIOBf355ASXkV+iSEYcIt8XX/kB08/TLv1SIMH+1KQfKVPGc3xSWUlFdiw+F0rNmdgkvVU6uVCmBU52Z4fGACesaHeWRw+687O+KFEe1qrG3UNTYUtySG40DKDXy89zJevKN9jZ89m2WosTie5n5T+XOLNJj+xWFUaQXc0y0GE/tK+x7iyv6WlIDvjmXgSGoeXvnuJD78Wy+T63t/iukU7eJyXSBjPhSVXaBBQqT7BTIe+KdcLw59NVMqlRgwYIAkDfDx8UHr1q3Rq1cvLFy4EN26dcN///tfREdHo7y8HHl5eSavz8rKQnR0tNXjzZkzB/n5+eK/tDTX3Dn1+2MZ+PNsDny8lFg4rqtdO1vbwtMv9J7VM5fOZhZ41PRKe2Xml2Hx5jNIWvgHXtl0EpdyihHsq8KTgxKxY/ZgrJjQE71ahHtkEKNnbYHGJwbqpmJ/vu+KuIGiNcev5kndrAZVpRXw/LqjyCrQoFWTQCwc18Wjz7E5L6UCi8Z1hbeXAr+fysLmk6ZZ+iPVe7Hp/5fo3yPu/2CvyeuyqrezGLDoD/x3y/kGbnX9iUNLTm6Hq3EoFJ0xYwZWrFiB5cuXS9wc3YwojUaDXr16wdvbG1u3bsX48eMB6DaqTE1NRVJSktWf9/X1ha+va6+dcKO4HK9VT5N8ZkhrtG6AJcQ9/T0tKsQPzUP9kZ5XimNpeRjQ2rDuTllFFQrKKjx6t9xjaXlYtSsFP5+4hsrqb5nx4QGYPCAB9/eOQ5Cv+3zLbChDO0QhISIAl6+X4JvkqyY7tpsvdZ+RX4acQg2aBLv2e4feO1vPY9eFXPh7e+G9ib0QKMPz3S46GE/d1grv/HEB877/C/1bRYp1X/rg9paEcOxPuYEjqXnYfi6nxpIND35omDiybMs5LNtyDi+Nao9Qf2+EBnhD7e+DsEBvhPr7IDTAu8FWNbeXp7+/28uhq3/WrFm488470apVK3Ts2BHe3qbFZRs3brTpOHPmzMGoUaMQHx+PwsJCfPHFF/jzzz/x66+/Qq1WY8qUKZg5cybCw8MREhKCZ555BklJSW5f6Pv6j6dwo7gc7aKC8XejReCk5K7Tr+3Rq0UY0vNKkXzlpkkgox8D3zLzVrRuGuys5kmuSivgt78ysWpXCg4ZvSH3TQzHlIGJGNohyu1XcZWSl1KBxwcmYt53f2H17hRM7NdC/P9zpbqGIjLIB2EBPjifXYTjV/MwtEOUM5tskx3ncvDOH7rswRtjO6NtlOdc4/Z6enBr/HjiGi7lFGPR5tNYOK4rAEMgExFkKAh/bPUBi8cwt+iXM1af81UpERbgUx3k6IKdUH8fhBoFO7ogqPp29fP1WaXdGFebsMyhQObZZ5/Ftm3bMHjwYERERDic0szOzsajjz6Ka9euQa1Wo2vXrvj1118xfPhwAMCyZcugVCoxfvx4aDQajBw5Eu+++65Dv8tV/Hk2G98eSYdCASy+ryt8VNIVXhqfBjlE7D3iQ/H9sQws/f0cnhnSGgqFwmRdmXnf/YUvnnTvoBfQ7Y309cE0rN1zGVdvlgIAvL0UuLtrDB4fmGiyMByZuq9XLJb8dg5Xrpdgy+ksjOykG5a+lKurI0qMDER8eCDOZxfh2NV8lw9kMvPL8PxXRyEIwMO3xMm+0N3P2wuLxnXFAx/sxZcH0nBv9+bo1zJCLO61lpUN8PFCiYX9uIJ8VRjZKRr5peW4WVKBvJJy5JdWIK+kApVaAZpKLTILypBp5w7rviqlGNSoq4MdMSCqfjzM6LY+CPL39rL4+SqHL6r2cCiQ+fjjj7FhwwbceWf9pq6tWrWq1uf9/PywYsUKrFixol6/x1UUayrxr+qdrSf3T0T3uFDnNsjNtWxiGJI7djUf3eNCcaO4XHxsz8XrzmiWZFKvl2DNnhSsP3RVHOMPC/DGhL4t8LekFogK8dyhM6kE+KgwoW883v3zIlbtTBEDmZQcQyDTKUaNDYev4oRRnYxWK0ChcK3ZfxVVWkz/4jBuFJejY7MQcXE4ubslMRyP9I3HF/tTMWfjCfzy3CAUVA8dDm7fFGv3XK7xM+2jg3E4NQ8A0DM+FBufrr3mUxAEFGkqkVdSIQY2N0vKkVdagfyS8ur7Fcgv1d3OK9UFQcYBUFaBBlkFmlp/jzkflVIc5gr198HVm7pMogtdli7BoUAmPDwcrVo1zJCIJ3v7t7NIzytFbJg/Zo1s6+zmuL1BRsNJXx9KQ5umQdjt5sGLIAg4ePkmVu3SrQ2in2TRumkQHh+QiHE9m7vMOL27eKx/AlbuvIQDl2/gWFoeusWFIiW3CACQGBmErtVTlo9fzYcgCKioEjD6nZ1oEuSLL6f2w2s//AVNpRZvSLjOkz0u5RShSFOJH49fw6ErNxHsq8J7E3vyOjDy0qj22HIqCym5xfi/P86LgX+Inwpv398Ns9YfM3l9m6aGQKZZaN3LXigUCgT7eSPYzxtxdrRLEAQUl1eJQY0uyCkXA6KbxeXVQU+FURZId7uiSkB5pRbZhRpkF5oGQJ68VpAjHApkXn31VcyfPx9r1qxBQECA1G3ySEdSb4rfDN4Y26XB1y5wpW+SDUWpVOCLJ/vikZX78cPRDBxMuYHz2UUOHSvhpZ8sPt5YC2aVV2rx04kMrN512WRX71vbNsGUgYm4tU2kLM5pQ4gK8cPdXWOw8Ug6Vu1KwTsP90BKdY1MYmQAOjQLgUqpwPXicqTnlSK/tAIXsotwIbsIy34/hzW7LwMAJvSNR6eYxh/GG7LEdDmK/9zflYtAmgnx88aCezvjH58l44Ptl8QC+GA/Ffom1txzyrg4OqwBF4ZUKBQI8lUhyFeF2DDbf04QBJSUV+myPmZZoIKyCtzetmmDtdkdOfRp+s477+DixYuIiopCQkJCjWLfw4c9Y4MuqZRX6pbTFgRgXI/muK1tkwb/nXL5yOuXGIH48ACk3ihBoVkQkxBhW5BtLYhpDDeLy/HFgVR8sveymHb2VSkxrmdzPD4gEW1kXMgppSmDErHxSDp+OnENL41qj7wS3RBkk2Bf+Hl7oX2zYJxML8DAxdtMfu6/Ww1Tcg+m3Gj0QKbUrI5jysBE3NHZPVcgbmh3dI7GHZ2isfkvw1TsIF9vRKsNMxz12jcz/F0FuuCCeAqFAoG+KgTaGQDJlUNncMyYMRI3w7O9v/0izmYVIiLQB6/cZX1XZynJ5cu7UqnA/b1iseT3czWeu3y9BPe9twfPDm2DW60Ej8Y1NZZoKqvgq5I+hX8huxCrd1/GxsNXUVah2z6gabAvHk1qgUf6tkB4YP03DiWDTjFq9G8VgT0Xr2Ptnstioad/9a7ZXWNDcTK99lXA9166jnG9YvG3j/ZjRKdoTBvcusHbfbPE9Pq0tLAfGbx2byf8cSYb5dV7ZwX56c5v35bh2Hg4HQDQNVaNUZ2jMfub4wAATgRyfw4FMvPnz5e6HR7rQnYh/veHbmfreXd3RFgjfUDJqar9vt6xWLrlnMWpiYeu3MSjqw/g0pujLS462PP138XbW2beisTIIKTdKMGo/+5EaUUVMvLKkBgpTRpfEATsPJ+LVbtSsP1cjvh4p5gQTBmYiLu6xkg6i41MPTEoEXsuXseX+1NRWF1DEeirC1K7Nlfjizp+fn/KDXy27wqOXc3Hsav5jRLImAfavD5qFxXih3+ObIc3fj4NAAioriPqlxghBjIrH+2NYD/DKIKcF9T0FA7n1PLy8vDNN9/g4sWL+Oc//4nw8HAcPnwYUVFRaN68uZRtdFtarYCXNpxAeZUWg9s1wT3dYhrtd8slIwMAzdT+uLVNE5PgwNz2czkY3N50XHnPxVyT+/o1ZxIiAxEb5o/z2UW4erOk3oFMWUUVNh1Jx+rdKTiXpRv+UiiAYR2iMGVgIvomevbKu67i9rZN0bJJoLiFAwBxfY+usaF1/nxeSQUOmi19b48Xvj6GZmo/zBrZzuafKTTaI+j5YW0c/t1yMmVgIorLK9Ek2Ff88tIrwTA+Y76mi/H/Y3JPDgUyx48fx7Bhw6BWq3H58mU8+eSTCA8Px8aNG5GamopPPvlE6na6pc/3X8GhKzcR6OOFf49t+CXEFVZuy8GDfeJqDWTW7LlcI5B5ZOV+8fZysx2SDYFMKRyVXViGT/dewef7U8Vv1oE+Xri/dxwm9U9AgkSZHrKNUqnAlIGJ4hIIgKE+om2UbatrG0/pLyyrMPlmb832czkoq6jChsNXAQAvjGhr83tBSbnhQ/aZIQxkbKFUKvD8MNNZoS0jAzGpfwIqtVqEmJ2zZhY2FCX34lAgM3PmTEyaNAlvvfUWgoMNRVOjR4/GI488Ilnj3FlGXqm4K+3sO9qjuQ1T/KQkty/4wzpEoUmwL3IKLa/TsONcDi5kF1ndDmJMD9MsYly4rlA47UaJpZfX6q+MfKzalYIfjmWgoko33tU81B+T+ifggT5xnDrpRON6xJoEMv7VQw8qLyUig3yQW1RXzZRWvP3Ml0ewdvIttb7+xNX8GivKXswpqnXF6YoqLd778yIignzE6ff9WoZz1eZ6UCgUePUe03V3vpraD5uOpjfKECE1LIcCmYMHD+KDDz6o8Xjz5s2RmZlp4SfkRRAEvLLpJIo0legZH4qJ/Vo4oRXyetPzUSnx56zboVAA6TdL8cL6Yzh+1XRH44/3XMbrYzoDMN0F9/Mn+tY4XmyYLvC0NSNTpRXwx5lsrNp1CfsuGYYfesaHYsrAlhjZKQoqL9Y3OJu/jxfG9miOb4/o6iWM66baNA1GbpHt6xD9eTYHaTdKxKDXkgOXaw5FfbzningdWvL7qSwsNSted8WZNe6ub8sI9G0Z4exmkAQcemf19fVFQUHNCv9z586hSZOGn1rs6n48fg1bz2TD20uBxeO7OuWblNwyMoBubYgAHxXaRAXjvYm9xMefHKTbBXnD4avIL9Wt+JlbbMjc3GJhnYnYMN2Hk34lTWuKNZVYuzsFQ5b8iSc/OYR9l27AS6nA3d1i8O3T/bHx6QG4s2szBjEuZM6o9ogO8cOgNpEmjxtPyTX34d96IdjCxoyD3tpW687aBaUVNR7bdjbbJJA2l21h+fvGmiRA5I7sCvNTU1MRGxuLe+65BwsWLMDXX38NQJe2S01NxYsvvijuVC1XN4vL8er3fwEApg1u7bR1QGQYx5hoHuqPM6/fgbySCkSF+GLHuVyczSrE+kNpeGJQS2Tm6z4sokP84G0hyKgrI5OeV4qP91zGlwdSxWLBED8VHu4bj8eSEhDTyEOJZLumIX7485+3w9dsBtC0wa3x84lrFpeRH9EpGn0S0/DHmewaz73w9TG8O6FnjVlxgiCgtKLmfj5Xb5Zi98VcDGpj+UuffhbNQ33iMGlAAnadz8WoLlw7hsgau74mJiYmIjc3F0uWLEFRURGaNm2K0tJS3HbbbWjdujWCg4PxxhtvNFRb3cK/fzqN68XlaNM0CE/d7rxtHDgLRrehXLTaDwqFApMGJAAAPtl7BVVaAdf0gYyVQj99Ria7UIMyow+j5Cs3Me3zw7j1rW34cMclFJZVIjEyEAvu7YS9c4ZizqgODGLcgJ+Fzfgig3yxb85QbHy6Pyb1T8CaSX0Q6OOFN8bqhoGSrAxDbP4rU9yNWm/9oTR0fe037L9keahq3YE0q20rrl7jJsBHhfbRIXhiUMtGr7Ejcid2ZWT0Owur1Wr8/vvv2LVrF44fP46ioiL07NkTw4YNa5BGuoud53Ow4fBVKBTAovFdG2QhNVsxjDE1pntzLPrlDFJvlOCPM9kmGRlLwgK8EejjheLyKqTeKMHZzEKs2pWCo2l54mv6t4rAlIGJGNyuqcU1asj9KBQK9IwPQ8943XTdE6+OFM9tUivTQCYqxBePD0jEwl/OYPmW82gfHSyuuvvP6sXWjpnVab03oSee+vwwfjuVietFGkQE+dZoQ4nZGjdEVDu7K8iMv8UMHDgQAwcOlLRB7qqkvBJzvz0BAHgsKQG9Wjh3XWkmZEz5+3jhoVvi8MH2S1i7JwVdmocCsJ6RUSgUiA0LwNmsQoxYtkN83MdLiXu6x+DxAYnoGBPSGE0nJzIOUDs0M5zvpQ90w7iesQCAzIIyrNl9GTO+OoYWEYG1rso8qkszdItV49jVfGw8nI4nb21Z4zXGGRkiqpvdfymvvPJKnRtFLl261OEGuaulv51D2o1SxNi54JWUGLzU7m/9WmDljkvYfeG6uC1AWID1D53YMH+czSoEAKiUCjw9uDUm9otH02CuOyFHXkoFXr6zA7aezsbwjlHi4/8a3QHns4qw60IunvzkUJ0z3R7sE49jV0/gy4OpeGJQovjlMO1GCdYdTMXvp7IAAAE+zMgQ2cLuQObEiRPw8bH+5i/H2oxjaXlYvTsFAPDGuC4IsjC7obHJ8DTUKTYsACM66jaVS75yE0DtHxb6gl8A+NedHTB5QGKDt5Fc2xODWuKJQaZZFJWXEv97pAfuXbEbV67Xve7QPd1j8O+fTuFSTjES5/yM+3vFIqdIg+3ncsRtNizNqiIiy+z+xP3222/RtCm3ENerqNLixQ3HoRWAe7vHYHA71/h/I6e9luwxeUCCye64frUEMsbrg4QGcBE7si40wAcrH+2NsSt2i0NDxpqH+uO56i0GgnxVuKtrM3x9SLfS7/rkq+LrBrWJxCO3xGNYxyiLs+mIqCa7/lLkmG2py4c7LuFMZiHCArwxr5F2trYJT5VFtySGm9Q6+NWyCZ9xRka/SzKRNW2jgrH8oR4Wn/vw0V54oHeceP+hW+JNnv/7rS3x56zb8emUvhjVpRmDGCI72PXXIljaXljGLuYU4b9bddMu593d0eIMBGdhHGOZQqHA5P4J4v3adhPWT8EGOIOEbDO8YxSWP9gdD/SOxaGXh6F/qwhEhfiiZaTp1hg94kJN7s8Z3YF7bxE5yK5AZs2aNVCr1dixYwcqK2uuZllZWYkdO3ZY+EnPU1JeiaFLtqO8Uotb2zbBmO6uteM3s2fW3dPdsAu5+aJoxowzMl78/0k2GtOjOd66rxsig3zx+RN9sevFITV2XFYoFJg1Qrex4TNDuNcPUX3YlS9/7LHHAACDBw/GtWvXatTK5OfnY/DgwaiqqjlG7GnWHzKMa78xprPLBQ6u1RrX4ufthU+n3IJtZ3IwtEOU1dcZb+5YxWwkOUChUMDby/Jf4/QhbTCkfRRaNmEmhqg+HBr4FwTB4gf39evXERgojz/KjHzdFMuwAO9aN41rTMYFvi4WV7mcQW2aWF0iXk+hUGDeXR1x7Goe+rfiDBKSHtciIqo/uwKZcePGAdC9wU+aNAm+voaakKqqKhw/fhz9+/eXtoUuSr9q711dY+p4pXNw1pI0Hh/IKddERK7MrkBGrVYD0GVkgoOD4e9vqCHw8fFBv3798OSTT0rbQhelqdQNn9VWY+FMzMgQEZEc2BXIrFmzBgCQkJCAWbNmyWYYyZLySt3KsL7eLhrIOLsBREREjcChT+H58+fD19cXW7ZswQcffIDCQt0y7hkZGSgqKpK0ga5Kow9knLgxZK0YyRARkQw4VOx75coV3HHHHUhNTYVGo8Hw4cMRHByMxYsXQ6PR4P3335e6nS5nUOtIBPmqxF1yXQ1rZIiISA4cysg899xz6N27N27evGlSJzN27Fhs3bpVssa5slFdmmHu6A4Y6KL7obBGhoiI5MChjMzOnTuxZ8+eGptHJiQkID09XZKGEREREdXFoYyMVqu1uOjd1atXERwcXO9GkYMUFm8SERF5LIcCmREjRmD58uXifYVCgaKiIsyfPx+jR4+Wqm1UD6620jAREVFDcGhoacmSJRg5ciQ6duyIsrIyPPLIIzh//jwiIyPx5ZdfSt1GcgDjGCIikgOHApnY2FgcO3YMX331FY4dO4aioiJMmTIFEyZMMCn+JedhHENERHJgcyDTs2dPbN26FWFhYViwYAFmzZqFCRMmYMKECQ3ZPnIQMzJERCQHNtfInD59GsXFxQCA1157TTYL37kvRjJEROT5bM7IdO/eHZMnT8bAgQMhCALefvttBAUFWXztvHnzJGsgOYYZGSIikgObA5m1a9di/vz5+PHHH6FQKPDLL79Apar54wqFgoGMC2AcQ0REcmBzINOuXTusW7cOAKBUKrF161Y0bdq0wRpG9jMOXjj9moiI5MChWUtarVbqdpDEGMYQEZEcOLQg3scff4yffvpJvD979myEhoaif//+uHLlimSNI8cxIUNERHLgUCDz5ptviuvF7N27FytWrMBbb72FyMhIzJgxQ9IGkmO4+zUREcmBQ0NLaWlpaN26NQBg06ZNGD9+PKZOnYoBAwbg9ttvl7J9RERERFY5lJEJCgrC9evXAQC//fYbhg8fDgDw8/NDaWmpdK0jh3FoiYiI5MChjMzw4cPxxBNPoEePHjh37py4UeRff/2FhIQEKdtHREREZJVDGZkVK1YgKSkJOTk52LBhAyIiIgAAycnJePjhh20+zsKFC9GnTx8EBwejadOmGDNmDM6ePWvymrKyMkybNg0REREICgrC+PHjkZWV5UizPZ7xlGtmZIiISA4UgiAIzvrld9xxBx566CH06dMHlZWVmDt3Lk6ePIlTp04hMDAQAPDUU0/hp59+wtq1a6FWqzF9+nQolUrs3r3bpt9RUFAAtVqN/Px8hISENGR3nO6b5KuYtf4YAODnZwehY4xn95eIiDyXrZ/fDg0tAUBeXh5WrVqF06dPAwA6deqExx9/HGq12uZjbN682eT+2rVr0bRpUyQnJ+PWW29Ffn4+Vq1ahS+++AJDhgwBAKxZswYdOnTAvn370K9fP0eb7/GYkSEiIjlwaGjp0KFDaNWqFZYtW4YbN27gxo0bWLp0KVq1aoXDhw873Jj8/HwAQHh4OADdUFVFRQWGDRsmvqZ9+/aIj4/H3r17Hf49csBAhoiI5MChjMyMGTNwzz33YOXKleJ+S5WVlXjiiSfw/PPPY8eOHXYfU6vV4vnnn8eAAQPQuXNnAEBmZiZ8fHwQGhpq8tqoqChkZmZaPI5Go4FGoxHvFxQU2N0WT8B1ZIiISA4czsi8+OKLJptGqlQqzJ49G4cOHXKoIdOmTcPJkyfF/ZwctXDhQqjVavFfXFxcvY7nrpiRISIiOXAokAkJCUFqamqNx9PS0hAcHGz38aZPn44ff/wR27ZtQ2xsrPh4dHQ0ysvLkZeXZ/L6rKwsREdHWzzWnDlzkJ+fL/5LS0uzuz2egHEMERHJgUOBzIMPPogpU6bgq6++QlpaGtLS0rBu3To88cQTdk2/FgQB06dPx7fffos//vgDiYmJJs/36tUL3t7e2Lp1q/jY2bNnkZqaiqSkJIvH9PX1RUhIiMk/OWJGhoiI5MChGpm3334bCoUCjz76KCorKyEIAnx8fPDUU09h0aJFNh9n2rRp+OKLL/Ddd98hODhYrHtRq9Xw9/eHWq3GlClTMHPmTISHhyMkJATPPPMMkpKSOGPJAkUt94iIiDxRvdaRKSkpwcWLFwEArVq1QkBAgH2/3EraYM2aNZg0aRIA3YJ4L7zwAr788ktoNBqMHDkS7777rtWhJXNyWkdmQ/JVvFC9jszWF25DqyZBTm4RERGRYxpkHZnHH3/cptetXr3aptfZEkP5+flhxYoVWLFihU3HJB3mY4iISA7sCmTWrl2LFi1aoEePHjYFIUREREQNya5A5qmnnsKXX36JlJQUTJ48GRMnThQXryPXYm3YjoiIyJPYNWtpxYoVuHbtGmbPno0ffvgBcXFxeOCBB/Drr78yQ+NiGMYQEZEc2D392tfXFw8//DB+//13nDp1Cp06dcLTTz+NhIQEFBUVNUQbyQFMyBARkRw4tI6M+MNKJRQKBQRBQFVVlVRtIglwiwIiIpIDuwMZjUaDL7/8EsOHD0fbtm1x4sQJ/O9//0NqaiqCgjjd15mMszDMyBARkRzYVez79NNPY926dYiLi8Pjjz+OL7/8EpGRkQ3VNiIiIqJa2RXIvP/++4iPj0fLli2xfft2bN++3eLrNm7cKEnjyHHMyBARkRzYFcg8+uijnNbrJnieiIhIDuxeEI/cA8MYIiKSg3rNWiLXxYQMERHJAQMZD8Xp10REJAcMZDwIp18TEZHcMJDxUIxjiIhIDhjIEBERkdtiIOOpmJIhIiIZYCDjoVjsS0REcsBAxkOx2JeIiOSAgYwHEQTDbcYxREQkBwxkPBS3KCAiIjlgIONBmJEhIiK5YSDjQYziGNbIEBGRLDCQ8SCCUUqGs5aIiEgOGMh4EOOMDOMYIiKSAwYynsS4RoaBDBERyQADGQ8iwHhoiYiIyPMxkPEgJrOWmJIhIiIZYCDjoRjGEBGRHDCQISIiIrfFQMaDcB0ZIiKSGwYyHsR0ZV9GMkRE5PkYyHgQk1lLjGOIiEgGGMh4EEGo+zVERESehIGMB2GNDBERyQ0DGQ/FGhkiIpIDBjKeRGCNDBERyQsDGQ9iMrTktFYQERE1HgYyHoRbFBARkdwwkPEggsBNI4mISF4YyHgQzloiIiK5YSDjQTi0REREcsNAhoiIiNwWAxkiIiJyWwxkPAh3KCAiIrlxaiCzY8cO3H333YiJiYFCocCmTZtMnhcEAfPmzUOzZs3g7++PYcOG4fz5885prBsQuNkSERHJjFMDmeLiYnTr1g0rVqyw+Pxbb72Fd955B++//z7279+PwMBAjBw5EmVlZY3cUiIiInJFKmf+8lGjRmHUqFEWnxMEAcuXL8fLL7+Me++9FwDwySefICoqCps2bcJDDz3UmE11C0zIEBGR3LhsjUxKSgoyMzMxbNgw8TG1Wo2+ffti7969TmyZ6xJYJUNERDLj1IxMbTIzMwEAUVFRJo9HRUWJz1mi0Wig0WjE+wUFBQ3TQBfEjAwREcmNy2ZkHLVw4UKo1WrxX1xcnLOb1GgYxxARkdy4bCATHR0NAMjKyjJ5PCsrS3zOkjlz5iA/P1/8l5aW1qDtJCIiIudx2UAmMTER0dHR2Lp1q/hYQUEB9u/fj6SkJKs/5+vri5CQEJN/csGhJSIikhun1sgUFRXhwoUL4v2UlBQcPXoU4eHhiI+Px/PPP49///vfaNOmDRITE/HKK68gJiYGY8aMcV6jXRiLfYmISG6cGsgcOnQIgwcPFu/PnDkTAPDYY49h7dq1mD17NoqLizF16lTk5eVh4MCB2Lx5M/z8/JzVZJfGjAwREcmNUwOZ22+/vdbVaBUKBRYsWIAFCxY0YquIiIjIXbhsjQwRERFRXRjIeBDutURERHLDQIaIiIjcFgMZD8KEDBERyQ0DGQ/COIaIiOSGgYwHYUaGiIjkhoGMB+GCeEREJDcMZDwIMzJERCQ3DGQ8COMYIiKSGwYynoQpGSIikhkGMkREROS2GMh4EOZjiIhIbhjIeBCOLBERkdwwkPEgnH5NRERyw0CGiIiI3BYDGQ/CoSUiIpIbBjIehHEMERHJDQMZIiIiclsMZDwIh5aIiEhuGMh4EM5aIiIiuWEg40kYxxARkcwwkPEgjGOIiEhuGMh4EIFFMkREJDMMZDwI4xgiIpIbBjIehHEMERHJDQMZIiIiclsMZDwIh5aIiEhuGMgQERGR22Ig40G4IB4REckNAxkPwqElIiKSGwYyRERE5LYYyHgQLohHRERyw0DGgzCMISIiuWEgQ0RERG6LgYwH4cgSERHJDQMZD8Lp10REJDcMZDwIMzJERCQ3DGQ8COMYIiKSGwYyHoQZGSIikhsGMh6FkQwREckLAxkiIiJyWwxkiIiIyG0xkPEgrJEhIiK5YSDjQRjIEBGR3LhFILNixQokJCTAz88Pffv2xYEDB5zdJJfEBfGIiEhuXD6Q+eqrrzBz5kzMnz8fhw8fRrdu3TBy5EhkZ2c7u2kuhxkZIiKSG5cPZJYuXYonn3wSkydPRseOHfH+++8jICAAq1evdnbTXA7jGCIikhuXDmTKy8uRnJyMYcOGiY8plUoMGzYMe/fudWLLXBMzMkREJDcqZzegNrm5uaiqqkJUVJTJ41FRUThz5ozFn9FoNNBoNOL9goKCBm2jK2kTFeTsJhARETUqlw5kHLFw4UK89tprzm6GU0zqn4DCsgoMbtfU2U0hIiJqFC49tBQZGQkvLy9kZWWZPJ6VlYXo6GiLPzNnzhzk5+eL/9LS0hqjqS7Bz9sL/xzZHr0Twp3dFCIiokbh0oGMj48PevXqha1bt4qPabVabN26FUlJSRZ/xtfXFyEhISb/iIiIyDO5/NDSzJkz8dhjj6F379645ZZbsHz5chQXF2Py5MnObhoRERE5mcsHMg8++CBycnIwb948ZGZmonv37ti8eXONAmAiIiKSH4UgePak3YKCAqjVauTn53OYiYiIyE3Y+vnt0jUyRERERLVhIENERERui4EMERERuS0GMkREROS2GMgQERGR22IgQ0RERG6LgQwRERG5LQYyRERE5LYYyBAREZHbcvktCupLv3BxQUGBk1tCREREttJ/bte1AYHHBzKFhYUAgLi4OCe3hIiIiOxVWFgItVpt9XmP32tJq9UiIyMDwcHBUCgUDh+noKAAcXFxSEtLk92eTXLuOyDv/su574C8+y/nvgPy7r+r9F0QBBQWFiImJgZKpfVKGI/PyCiVSsTGxkp2vJCQENld1Hpy7jsg7/7Lue+AvPsv574D8u6/K/S9tkyMHot9iYiIyG0xkCEiIiK3xUDGRr6+vpg/fz58fX2d3ZRGJ+e+A/Luv5z7Dsi7/3LuOyDv/rtb3z2+2JeIiIg8FzMyRERE5LYYyBAREZHbYiBDREREbouBDBEREbktBjJERETktjx+ZV9H5ObmYvXq1di7dy8yMzMBANHR0ejfvz8mTZqEJk2aOLmFDefatWvYunUrwsPDMWzYMPj4+IjPFRcXY8mSJZg3b54TW9iweO557nnuee4BnnvAfc49p1+bOXjwIEaOHImAgAAMGzYMUVFRAICsrCxs3boVJSUl+PXXX9G7d28nt1R6Bw8exIgRI6DValFRUYHmzZtj06ZN6NSpEwDd/4OYmBhUVVU5uaUNg+ee557nnuee594Nz71AJvr27StMnTpV0Gq1NZ7TarXC1KlThX79+jmhZQ1v2LBhwuTJk4WqqiqhoKBAeOqpp4SIiAjh8OHDgiAIQmZmpqBUKp3cyobDc89zz3PPc2+M5949zj0DGTN+fn7C6dOnrT5/+vRpwc/PrxFb1HjCwsKEs2fPmjy2cOFCISwsTDhw4IDbXNSO4rnnubeG557n3hN5yrlnjYyZ6OhoHDhwAO3bt7f4/IEDB8TUoycqKyszuf/SSy9BpVJhxIgRWL16tZNa1Th47nnuee51eO4NeO5dHwMZM7NmzcLUqVORnJyMoUOH1hgvXblyJd5++20nt7JhdO7cGXv27EHXrl1NHp81axa0Wi0efvhhJ7WscfDc89zz3Bvw3PPcu825d3ZKyBWtW7dO6Nu3r6BSqQSFQiEoFApBpVIJffv2Fb766itnN6/BrFy5Upg4caLV5xctWiQkJCQ0YosaH8+9ZTz3PPeejOfeMnc595y1VIuKigrk5uYCACIjI+Ht7e3kFlFj4bmXL557+eK5d08MZOqg0WgAwG22Myfp8NzLF8+9fPHcux+u7GvB77//jtGjRyMsLAwBAQEICAhAWFgYRo8ejS1btji7eU5z+vRptGzZ0tnNaFA895bx3PPcezKee8vc5dwzkDHz8ccfY/To0VCr1Vi2bBl+/PFH/Pjjj1i2bBlCQ0MxevRofPrpp85uplOUl5fjypUrzm5Gg+G5t47nnufeU/HcW+cu555DS2batm2L5557DtOmTbP4/Lvvvotly5bh/Pnzjdyyhjdz5sxan8/JycEXX3zh+qs8Oojn3jqee557nnuee1fFQMaMn58fjh07hnbt2ll8/uzZs+jevTtKS0sbuWUNz8vLC927d0dISIjF54uKinD48GGXv6gdxXPPc89zXxPPPc+9y597502Yck09e/YU/vnPf1p9fvbs2ULPnj0bsUWNp23btsKnn35q9fkjR464xSqPjuK557m3huee594Tecq554J4ZpYsWYK77roLmzdvtriB2KVLl/DTTz85uZUNo3fv3khOTsbEiRMtPq9QKCB4cAKP557nnue+Jp57nntXx6ElCy5fvoz33nsP+/btM9nSPSkpCf/4xz+QkJDg3AY2kMzMTGg0GrRo0cLZTXEannuee557+eG5d+9zz0CGiIiI3BanXxMREZHbYiBDREREbouBDBEREbktBjJERETkthjIEBFZkZKSgsrKSmc3wynk3HdyLwxkLLh27Ro+++wz/PzzzygvLzd5rri4GAsWLHBSyxqenPsO6DaPmz9/Pv744w8AwI4dOzBq1CgMGTIEa9ascXLrGpac+25Nu3btPHJpelvIte8ZGRmYP38+JkyYgFmzZuHMmTPOblKjcde+c/q1mYMHD2LEiBHQarWoqKhA8+bNsWnTJnTq1AmAbpGkmJgY11+y2QFy7jsAfPbZZ5g8eTK6du2Kc+fO4f/+7/8wY8YM3HfffdBqtfjss8/w+eef47777nN2UyUn574DwLhx4yw+/t1332HIkCEIDg4GAGzcuLExm9Uo5Nx3AAgICMCVK1fQpEkTnDp1Cv3790eTJk3Qo0cPnDhxAqmpqdi7dy+6du3q7KZKzlP6zoyMmblz52Ls2LG4efMmsrKyMHz4cNx22204cuSIs5vW4OTcd0C3wueSJUuQnJyMTZs24emnn8a8efOwcuVKrFq1Cm+++SaWL1/u7GY2CDn3HQA2bdqEGzduQK1Wm/wDgKCgIJP7nkbOfQeAsrIycfXauXPn4tZbb8Xp06fx9ddf46+//sI999yDf/3rX05uZcPwmL47Z2cE1xUWFiacPXvW5LGFCxcKYWFhwoEDB4TMzEy32HvCEXLuuyAIQmBgoHDp0iXxvre3t3Ds2DHx/unTp4WIiAhnNK3BybnvgiAIX375pRAbGyusXr3a5HGVSiX89ddfTmpV45Bz3wVBEBQKhZCVlSUIgiDExcUJO3bsMHn+8OHDQrNmzZzRtAbnKX1nRsaCsrIyk/svvfQS5s6dixEjRmDPnj1OalXjkHPfvb29TeqCfH19ERQUZHLfE3fABeTddwB46KGHsHPnTqxatQrjx4/HzZs3nd2kRiPnvgO6/YQUCgUAQKlU1sg+hYaGeuz/E0/pOwMZM507d7b4gT1r1izMmTMHDz/8sBNa1Tjk3HcAaN26tUlxW3p6OhITE8X7Fy9eRGxsrDOa1uDk3He9hIQE7NixA507d0a3bt3w66+/im/ynk7OfRcEAW3btkV4eDgyMjJw/Phxk+cvXLiA6OhoJ7WuYXlK37n7tZlHH30U27dvxz/+8Y8az82ePRuCIOD99993Qssanpz7DujGiMPCwsT7ISEhJs8fOnQIDzzwQGM3q1HIue/GlEolXnvtNQwfPhyPPvqoxxa2WyLXvpvPyGvdurXJ/X379mHs2LGN2aRG4yl956wlIiILioqKcPHiRbRv3x6+vr7Obk6jknPfyf0wI1OL/Px8ky3dPbly35yc+w7Iu/9y7jtg2v+EhARZfZDLue+AvK99t+67MyuNXdXKlSuFDh06CEql0uRfhw4dhI8++sjZzWtQcu67IMi7/3LuuyDU7L9CoZBN/+Xcd0GQ97XvCX1nRsbMf/7zH7z66qt49tlnMXLkSERFRQHQLQb322+/4bnnnsPNmzcxa9YsJ7dUenLuOyDv/su574C8+y/nvgPy7r/H9N3ZkZSriY+PF7766iurz69bt06Ii4trxBY1Hjn3XRDk3X85910Q5N1/OfddEOTdf0/pO6dfm8nOzkaXLl2sPt+lSxfk5uY2Yosaj5z7Dsi7/3LuOyDv/su574C8++8pfWcgY6ZPnz5YtGiRxV1fq6qqsHjxYvTp08cJLWt4cu47IO/+y7nvgLz7L+e+A/Luv6f0ndOvzRw/fhwjR45ERUUFbr31VpMxwx07dsDHxwe//fYbOnfu7OSWSk/OfQfk3X859x2Qd//l3HdA3v33lL4zkLGgsLAQn332Gfbt22cyHS0pKQmPPPJIjcXCPImc+w7Iu/9y7jsg7/7Lue+AvPvvCX1nIENERERuizUyNrjzzjtx7do1ZzfDKeTcd0De/Zdz3wF591/OfQfk3X937DsDGRvs2LHDo3f+rY2c+w7Iu/9y7jsg7/7Lue+AvPvvjn1nIENERERui4GMDVq0aAFvb29nN8Mp5Nx3QN79l3PfAXn3X859B+Tdf3fsO4t9iYiIyG0xI2Nmw4YNKCkpcXYznELOfQfk3X859x2Qd//l3HdA3v33lL4zI2NGqVQiODgYDz74IKZMmYK+ffs6u0mNRs59B+Tdfzn3HZB3/+Xcd0De/feUvjMjY8GsWbNw6NAhJCUloXPnzli+fDmuX7/u7GY1Cjn3HZB3/+Xcd0De/Zdz3wF5998j+u6s3SpdlUKhELKysgRBEIRDhw4JTz31lBAaGir4+voK999/v/Dbb785uYUNR859FwR591/OfRcEefdfzn0XBHn331P6zkDGjPGJ1SstLRU++eQT4fbbbxeUSqWQkJDgpNY1LDn3XRDk3X85910Q5N1/OfddEOTdf0/pOwMZM0qlssaJNXb+/Hlh7ty5jdiixiPnvguCvPsv574Lgrz7L+e+C4K8++8pfWexrxmlUonMzEw0bdrU2U1pdHLuOyDv/su574C8+y/nvgPy7r+n9J3FvmZSUlLQpEkTZzfDKeTcd0De/Zdz3wF591/OfQfk3X9P6TszMkREROS2VM5ugCvKzc3F6tWrsXfvXmRmZgIAoqOj0b9/f0yaNMkjIlhr5Nx3QN79l3PfAXn3X859B+Tdf0/oOzMyZg4ePIiRI0ciICAAw4YNQ1RUFAAgKysLW7duRUlJCX799Vf07t3byS2Vnpz7Dsi7/3LuOyDv/su574C8++8xfXdmpbEr6tu3rzB16lRBq9XWeE6r1QpTp04V+vXr54SWNTw5910Q5N1/OfddEOTdfzn3XRDk3X9P6TsDGTN+fn7C6dOnrT5/+vRpwc/PrxFb1Hjk3HdBkHf/5dx3QZB3/+Xcd0GQd/89pe+ctWQmOjoaBw4csPr8gQMHxPSbp5Fz3wF591/OfQfk3X859x2Qd/89pe8s9jUza9YsTJ06FcnJyRg6dGiNMcOVK1fi7bffdnIrG4ac+w7Iu/9y7jsg7/7Lue+AvPvvMX13dkrIFa1bt07o27evoFKpBIVCISgUCkGlUgl9+/YVvvrqK2c3r0HJue+CIO/+y7nvgiDv/su574Ig7/57Qt85a6kWFRUVyM3NBQBERkbC29vbyS1qPHLuOyDv/su574C8+y/nvgPy7r87952BDBEREbktFvsSERGR22IgQ0RERG6LgQwRERG5LQYyRERE5LYYyBAREZHbYiBDREREbouBDBEREbktBjJERETktv4f2TFNirdKcmQAAAAASUVORK5CYII="
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -273,8 +336,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-15T02:36:11.975924Z",
-     "start_time": "2026-01-15T02:36:08.943450Z"
+     "end_time": "2026-01-21T02:58:22.078880Z",
+     "start_time": "2026-01-21T02:58:19.236043Z"
     }
    },
    "cell_type": "code",
@@ -288,7 +351,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1a6aba44c50>]"
+       "[<matplotlib.lines.Line2D at 0x1b99b24ce00>]"
       ]
      },
      "execution_count": 6,
@@ -300,7 +363,7 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -311,8 +374,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-15T02:39:47.695586Z",
-     "start_time": "2026-01-15T02:39:44.969217Z"
+     "end_time": "2026-01-21T02:58:26.671686Z",
+     "start_time": "2026-01-21T02:58:23.552776Z"
     }
    },
    "cell_type": "code",
@@ -351,13 +414,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 31
+   "execution_count": 7
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-15T02:36:17.034746Z",
-     "start_time": "2026-01-15T02:36:17.020021Z"
+     "end_time": "2026-01-21T02:58:27.819007Z",
+     "start_time": "2026-01-21T02:58:27.799131Z"
     }
    },
    "cell_type": "code",
@@ -369,8 +432,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-15T02:46:18.773765Z",
-     "start_time": "2026-01-15T02:46:17.610839Z"
+     "end_time": "2026-01-21T02:58:29.706550Z",
+     "start_time": "2026-01-21T02:58:28.379122Z"
     }
    },
    "cell_type": "code",
@@ -409,18 +472,18 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_12116\\3978562244.py:11: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_32752\\3978562244.py:11: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
       "  df_15m = df.resample(\"15T\").mean()\n"
      ]
     }
    ],
-   "execution_count": 40
+   "execution_count": 9
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-15T02:40:28.212873Z",
-     "start_time": "2026-01-15T02:40:28.206714Z"
+     "end_time": "2026-01-21T02:58:30.476336Z",
+     "start_time": "2026-01-21T02:58:30.465127Z"
     }
    },
    "cell_type": "code",
@@ -430,13 +493,13 @@
    ],
    "id": "a30bec6abef1672a",
    "outputs": [],
-   "execution_count": 33
+   "execution_count": 10
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-15T02:40:28.758150Z",
-     "start_time": "2026-01-15T02:40:28.750901Z"
+     "end_time": "2026-01-21T02:58:31.033231Z",
+     "start_time": "2026-01-21T02:58:31.020598Z"
     }
    },
    "cell_type": "code",
@@ -451,13 +514,13 @@
    ],
    "id": "feced1770070bc8b",
    "outputs": [],
-   "execution_count": 34
+   "execution_count": 11
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-15T02:47:02.603520Z",
-     "start_time": "2026-01-15T02:47:02.581502Z"
+     "end_time": "2026-01-21T02:58:31.679645Z",
+     "start_time": "2026-01-21T02:58:31.655283Z"
     }
    },
    "cell_type": "code",
@@ -500,20 +563,20 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_12116\\1287101057.py:9: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_32752\\1287101057.py:9: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
       "  df_15m_copy.index = df_15m_copy.index.floor(\"15T\")\n",
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_12116\\1287101057.py:10: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_32752\\1287101057.py:10: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
       "  weather_df.index = weather_df.index.floor(\"15T\")\n"
      ]
     }
    ],
-   "execution_count": 41
+   "execution_count": 12
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-15T02:48:41.847113Z",
-     "start_time": "2026-01-15T02:48:41.694559Z"
+     "end_time": "2026-01-21T02:58:32.365442Z",
+     "start_time": "2026-01-21T02:58:32.236108Z"
     }
    },
    "cell_type": "code",
@@ -539,13 +602,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 48
+   "execution_count": 13
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-15T02:47:23.995002Z",
-     "start_time": "2026-01-15T02:47:23.980510Z"
+     "end_time": "2026-01-21T03:00:16.483077Z",
+     "start_time": "2026-01-21T03:00:16.473106Z"
     }
    },
    "cell_type": "code",
@@ -692,31 +755,31 @@
        "</div>"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 43
+   "execution_count": 27
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-15T02:47:24.833809Z",
-     "start_time": "2026-01-15T02:47:24.825277Z"
+     "end_time": "2026-01-21T02:58:33.464606Z",
+     "start_time": "2026-01-21T02:58:33.445256Z"
     }
    },
    "cell_type": "code",
    "source": "merged_df = merged_df.rename(columns={'value': 'array_temperature'})",
    "id": "e0882e697c1613f7",
    "outputs": [],
-   "execution_count": 44
+   "execution_count": 15
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-15T02:47:25.680913Z",
-     "start_time": "2026-01-15T02:47:25.663703Z"
+     "end_time": "2026-01-21T02:58:33.965162Z",
+     "start_time": "2026-01-21T02:58:33.952450Z"
     }
    },
    "cell_type": "code",
@@ -806,18 +869,18 @@
        "</div>"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 45
+   "execution_count": 16
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-15T02:47:26.624775Z",
-     "start_time": "2026-01-15T02:47:26.486746Z"
+     "end_time": "2026-01-21T02:58:34.606Z",
+     "start_time": "2026-01-21T02:58:34.458522Z"
     }
    },
    "cell_type": "code",
@@ -854,7 +917,7 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 46
+   "execution_count": 17
   },
   {
    "metadata": {},
@@ -865,8 +928,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-15T02:36:24.291013Z",
-     "start_time": "2026-01-15T02:36:19.722347Z"
+     "end_time": "2026-01-21T02:59:31.545204Z",
+     "start_time": "2026-01-21T02:59:31.540211Z"
     }
    },
    "cell_type": "code",
@@ -891,13 +954,13 @@
    ],
    "id": "1aa707313aeb5993",
    "outputs": [],
-   "execution_count": 19
+   "execution_count": 23
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-15T02:36:24.347055Z",
-     "start_time": "2026-01-15T02:36:24.335999Z"
+     "end_time": "2026-01-21T02:59:32.133227Z",
+     "start_time": "2026-01-21T02:59:32.101296Z"
     }
    },
    "cell_type": "code",
@@ -907,13 +970,13 @@
    ],
    "id": "492ea4964124380d",
    "outputs": [],
-   "execution_count": 20
+   "execution_count": 24
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-15T02:36:24.478949Z",
-     "start_time": "2026-01-15T02:36:24.375680Z"
+     "end_time": "2026-01-21T02:59:33.009661Z",
+     "start_time": "2026-01-21T02:59:32.920197Z"
     }
    },
    "cell_type": "code",
@@ -927,10 +990,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1a6abc3fdd0>]"
+       "[<matplotlib.lines.Line2D at 0x1b99b1daed0>]"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -939,13 +1002,13 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 21
+   "execution_count": 25
   },
   {
    "metadata": {},
@@ -956,8 +1019,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-15T02:36:24.518883Z",
-     "start_time": "2026-01-15T02:36:24.504640Z"
+     "end_time": "2026-01-21T02:59:35.003065Z",
+     "start_time": "2026-01-21T02:59:34.925750Z"
     }
    },
    "cell_type": "code",
@@ -969,13 +1032,28 @@
     "                  merged_df['array_temperature'], params)"
    ],
    "id": "9839f97f4cfe5300",
-   "outputs": [],
-   "execution_count": 22
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "array must not contain infs or NaNs",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[1;31mValueError\u001B[0m                                Traceback (most recent call last)",
+      "Cell \u001B[1;32mIn[26], line 3\u001B[0m\n\u001B[0;32m      1\u001B[0m params \u001B[38;5;241m=\u001B[39m [\u001B[38;5;241m22\u001B[39m, \u001B[38;5;241m5.8\u001B[39m]\n\u001B[0;32m      2\u001B[0m xdata \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39mstack([merged_df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mshortwave_radiation_instant\u001B[39m\u001B[38;5;124m'\u001B[39m], merged_df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtemperature_2m\u001B[39m\u001B[38;5;124m'\u001B[39m], merged_df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mwind_speed_10m\u001B[39m\u001B[38;5;124m'\u001B[39m]])\n\u001B[1;32m----> 3\u001B[0m popt \u001B[38;5;241m=\u001B[39m \u001B[43mfit_faiman\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfaiman_model\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m      4\u001B[0m \u001B[43m                  \u001B[49m\u001B[43mxdata\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m      5\u001B[0m \u001B[43m                  \u001B[49m\u001B[43mmerged_df\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43marray_temperature\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[43m)\u001B[49m\n",
+      "Cell \u001B[1;32mIn[23], line 15\u001B[0m, in \u001B[0;36mfit_faiman\u001B[1;34m(model, xdata, ydata, params)\u001B[0m\n\u001B[0;32m     14\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mfit_faiman\u001B[39m(model, xdata, ydata, params):\n\u001B[1;32m---> 15\u001B[0m     popt, _ \u001B[38;5;241m=\u001B[39m \u001B[43mcurve_fit\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mxdata\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mydata\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mp0\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mparams\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     17\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m popt\n",
+      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\scipy\\optimize\\_minpack_py.py:932\u001B[0m, in \u001B[0;36mcurve_fit\u001B[1;34m(f, xdata, ydata, p0, sigma, absolute_sigma, check_finite, bounds, method, jac, full_output, nan_policy, **kwargs)\u001B[0m\n\u001B[0;32m    930\u001B[0m \u001B[38;5;66;03m# optimization may produce garbage for float32 inputs, cast them to float64\u001B[39;00m\n\u001B[0;32m    931\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m check_finite:\n\u001B[1;32m--> 932\u001B[0m     ydata \u001B[38;5;241m=\u001B[39m \u001B[43mnp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43masarray_chkfinite\u001B[49m\u001B[43m(\u001B[49m\u001B[43mydata\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mfloat\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m    933\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m    934\u001B[0m     ydata \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39masarray(ydata, \u001B[38;5;28mfloat\u001B[39m)\n",
+      "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\numpy\\lib\\_function_base_impl.py:649\u001B[0m, in \u001B[0;36masarray_chkfinite\u001B[1;34m(a, dtype, order)\u001B[0m\n\u001B[0;32m    647\u001B[0m a \u001B[38;5;241m=\u001B[39m asarray(a, dtype\u001B[38;5;241m=\u001B[39mdtype, order\u001B[38;5;241m=\u001B[39morder)\n\u001B[0;32m    648\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m a\u001B[38;5;241m.\u001B[39mdtype\u001B[38;5;241m.\u001B[39mchar \u001B[38;5;129;01min\u001B[39;00m typecodes[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mAllFloat\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m np\u001B[38;5;241m.\u001B[39misfinite(a)\u001B[38;5;241m.\u001B[39mall():\n\u001B[1;32m--> 649\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[0;32m    650\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124marray must not contain infs or NaNs\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m    651\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m a\n",
+      "\u001B[1;31mValueError\u001B[0m: array must not contain infs or NaNs"
+     ]
+    }
+   ],
+   "execution_count": 26
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-15T02:36:24.565473Z",
+     "end_time": "2026-01-21T02:58:48.126078200Z",
      "start_time": "2026-01-15T02:36:24.554267Z"
     }
    },
@@ -1200,26 +1278,14 @@
     "\n",
     "in order to determine the capacitative parameter, there needs to be a recursive model. So assuming steady state condition at t - delt time, then compute the temperature for the next time step and integrate (a summation). Linearising the above differential yields:\n",
     "\n",
-    "t_array(i+1) = t_array(i) + del(t)/Ca * [G(i+0.5) - (u0 + u1*w(i+0.5) * (t_array(i) - t_array(i+0.5))]\n"
+    "t_array(i+1) = t_array(i) + del(t)/Ca * [G(i+0.5) - (u0 + u1*w(i+0.5) * (t_array(i) - t_array(i+0.5))]\n",
+    "\n",
+    "\n",
+    "T[i+1] = T[i] + dt/Ca * ( G[i] - (u0 + u1*w[i]) * (T[i] - Ta[i]) )\n",
+    "\n"
    ],
    "id": "fc1bbf9c64684bcb"
   },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "source": "",
-   "id": "af79779a9cf6cc6b",
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "source": "",
-   "id": "db7f7a7c2a6a49ab",
-   "execution_count": null
-  },
   {
    "metadata": {},
    "cell_type": "code",

From b17656edadfb6959a40d63b1affabf8c3fc49c45 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Tue, 3 Feb 2026 18:46:08 -0800
Subject: [PATCH 14/49] control model data acquisition and preprocessing

---
 array_temp/.cache.sqlite             | Bin 57344 -> 57344 bytes
 array_temp/Control_Model.ipynb       | 433 +++++++++++++++++++++------
 array_temp/coefficient_fitting.ipynb |   2 +-
 array_temp/data_preprocessing.py     |  56 ++++
 array_temp/faiman_coefficients.ipynb | 142 ++++-----
 5 files changed, 477 insertions(+), 156 deletions(-)
 create mode 100644 array_temp/data_preprocessing.py

diff --git a/array_temp/.cache.sqlite b/array_temp/.cache.sqlite
index 519e171f5ec6c577efd51fd6d2482c8ec1a19c88..f968b26676411f8a0c7176a476b6458756847d4d 100644
GIT binary patch
delta 213
zcmZoTz}#?vd4e<}|3n#QM*fWnKKhJ2o7d{=2(UEm-O981uDzK#BhO@!@)BmjAFDQZ
zmzOd!@@{6VWO8TZ+nmrlhiUVIO9{LaCn!(0Uw?4(lLM`sAx63eCLsn!R>nqF2Bvz(
zW~K(lM%o4jRt5%Bde~EQ5(|n`Q>M7vzY_h?f6~kzC{$69S(I8lrANWYz{pJ30HWU*
sMgQc+oMsUN69u=_B(N$4nE8_>a&K?eKcmCM40P_qM#s(j_a-s|0Krm5w*UYD

delta 218
zcmZoTz}#?vd4e<}-$WT_M!t;+KKhK@o7d{=2(Xl@pX1(q*WS#Wk$bX8c?t7^{@t6q
z%S#y<`8G3FGPzG|QJ8GM{@~^(7g{+(40Vl6LJTdf42`V}4D~Ecj7$wowG9lc3=F39
zu&3rE78IwZOmVmWbfo)rw5~5usG=aVD7AP>kAjhbk(sUmSig}0ivG!sIdzi3i6uG;
yMkWegiFsh93Q$XoCQIbr-mHH|hlv^J&WVkVoA>WcVPxdpoX|UmY4d_Z3A_Mi=0~Rh

diff --git a/array_temp/Control_Model.ipynb b/array_temp/Control_Model.ipynb
index 05f0f65..4b7bab9 100644
--- a/array_temp/Control_Model.ipynb
+++ b/array_temp/Control_Model.ipynb
@@ -19,8 +19,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-28T02:40:44.183402Z",
-     "start_time": "2026-01-28T02:40:24.411979Z"
+     "end_time": "2026-01-31T19:34:34.910245Z",
+     "start_time": "2026-01-31T19:34:11.849855Z"
     }
    },
    "cell_type": "code",
@@ -42,8 +42,8 @@
     "utc_offset_h = 7\n",
     "start_utc = time(0, 00, 00)  #querying i svancouver time, influxdb gives utc\n",
     "stop_utc = time(23, 45, 00)\n",
-    "date_start = date(2024, 7, 14)\n",
-    "date_stop = date(2024, 7, 16)\n",
+    "date_start = date(2024, 7, 16)\n",
+    "date_stop = date(2024, 7, 18)\n",
     "\n",
     "vancouver = pytz.timezone(\"America/Vancouver\")\n",
     "\n",
@@ -61,21 +61,26 @@
    ],
    "id": "initial_id",
    "outputs": [],
-   "execution_count": 11
+   "execution_count": 24
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-31T19:27:46.052990Z",
+     "start_time": "2026-01-31T19:27:46.047232Z"
+    }
+   },
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": "# speed is most likely in m/s",
-   "id": "5d30f3a75f7e9149"
+   "id": "5d30f3a75f7e9149",
+   "outputs": [],
+   "execution_count": 2
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-28T02:40:56.314986Z",
-     "start_time": "2026-01-28T02:40:56.276921Z"
+     "end_time": "2026-01-31T19:27:51.064530Z",
+     "start_time": "2026-01-31T19:27:51.021122Z"
     }
    },
    "cell_type": "code",
@@ -98,13 +103,13 @@
    ],
    "id": "9f8652589e173b01",
    "outputs": [],
-   "execution_count": 12
+   "execution_count": 3
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-28T02:40:57.677460Z",
-     "start_time": "2026-01-28T02:40:56.838799Z"
+     "end_time": "2026-01-31T19:27:52.868417Z",
+     "start_time": "2026-01-31T19:27:51.901459Z"
     }
    },
    "cell_type": "code",
@@ -114,10 +119,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1c0220a58e0>]"
+       "[<matplotlib.lines.Line2D at 0x1f08b002d20>]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -126,19 +131,19 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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nos1DwhkkcBigdhKPFzhuxOD4HVchMkG34FAMTmrIgiMnrGNwKKDYX0jgMEDXgmPDRSVbmrgRXrqo9rTuCeRiRhac1FCQsZyINg8JZ5DAYYBuDI4Lhf5EdD+ZwQtrQ0l+CcYVjIMCBTubdrp+fJ7QmxdDelGluMYhuNCqQbDYhyCK3iAg2jwknEEChwF6LirVgkOtGobilXALipvKKMjYT0HM2845basGisGREt7mIeEtJHB8pqe/R2vyqBeD4yRNnGJwrBGUzuJUB8c6ZMGRE7LgBAsSOIP4tZtVqxhnRbIwMnek9robLipZ8WoxDroFx094EzhpWzXYjMFxw51HeAdv85DwFhI4PhMffxP/kLXTiyowAscj8ak23dzVsgtdfV2enINXgi5w0kEWHH5w8/5nnUWVjnQCmbKwrEECx2f04m+A0xYcKwtBchaV6EHGRuP3yoIztmAsxgwbg6gSxdtH3vbkHDxAdXCs09nXKfz9JAtuinG/5qHhs4zmlK8EWuCwMCfrpYgDcTE45KLyjVAoFIjGmzzUweF955yMAgVd/cGy6vGKm3OVxxgcK+sQCSRrBFrgsEAvRRxwx0Ula5Cxk5s63WfVisYyx+HwZsERZZ6Sm4oPRLTgEHxAAsdn0rmoLBX6S86iklTde7kgaoHGTRILHAoytkReZh4AKvbHC0GKwSHchQSOzxi5qGzVwYkFw0XlpXCrGjsQaPx289uBcvmRwDEmPysfgHsWHFk3Hn7h5gZHpHlIOIcEjs9oLqqCRBeVnTo4QVqQ7ZIu6+DMojMxImcE+mJ9ePfIuz6Nyl+oDo41hmUOA+BesT+/xaRsuHn9eIzBIbyDBI7PGFpw3HBRmVi0RIl/iMfLMScEGkta8M+MiyqVlcGN1FShBE7WgMAxa8FJNz9FvOd4wk0LmEjzkHAOCRwf6envwbGuYwCMXVRkwRmK1yZ+2Qv+8RBkLNLOWXVRuRWDQxYcZ7iaRUUxOIGCBI6PqAHG2ZFsjMgZkfA7O3VwqNmmO6gF/2QVOHoog//zi/h5zUO131T3iuqiohgcPqAsqtOQNdAaJHB8JD7+Jtnsb6cOTnKhP8IeqgVnZ/NO4R+AelAWlTU0Cw7F4HCBq1lUAlkSCeeQwBnED2V8uF0/RRwgF1UqvN4Bnz3ybBRkFaC7vxu7W3Z7ei4W8OCi4k3gpIorUmNw7Lio9I5Lu25nUBYVYRcSOD5iFGAMULPNVHi9QIRDYcwsmwlATjcVWXCskZ9pLU08ncuNLDjOcDWLivMYHOo15S4kcHzEqIoxYLMOjmSVjI3G74e1QeaKxpQmbg3NguOSi4picJwhYhaV6M9iWQi0wPFbLR86ZWzBsVMHJzlNnLCPWvBP5orG8fhtVeB95xyP00J/yRYdWuycIX0vKrLaeIYtgbN69WpMmDABOTk5mDNnDrZv357y/Y888ggmT56M3NxclJeX4+tf/zq6u7ttDVhkUsbguOCiknWn6McCoQYaNxxukM6loNtsM+AxOKmwWugv3fyUbT75DWVREXaxLHA2bNiA5cuXo7a2Fjt27EBFRQXmzZuHI0eO6L7/qaeewre//W3U1tZi9+7deOKJJ7BhwwZ85zvfcTx40TDrojK7+AQmBseHxXjyqMnIzchFR18H9h7d6/n5/IQ3FxXvFg1q1cAX1IuKsItlgfPQQw9hyZIlqKmpwbRp07BmzRrk5eVh3bp1uu9/7bXXcOGFF+JLX/oSJkyYgCuvvBLXX399WquPjJgJMgaAqBI1dbzkNHHeFw67+PG9IuEIKksrAcgXh0NBxtZwkkWlB1lwnEEWHMIulgROb28v6uvrUV1dffoA4TCqq6uxbds23c9ccMEFqK+v1wTNvn37sGnTJlx99dWG5+np6UFbW1vCj+h093fjePdxAKljcADzuwyy4LhLkAr+JV9TL0WkoigJop13i4brhf4k3Xj4hVvXT1EU5jE4NBf8JSP9W07T2tqKaDSKkpKShNdLSkrw3nvv6X7mS1/6ElpbW3HRRRdBURT09/fjq1/9akoX1apVq7By5UorQ+MeNf4mJyMHw3OGD/m96qICBuJwcpGb9phBicHxC1lbNuilMftpVTBrkeQFKvTHF2491+jvEDw8z6LasmULfvCDH+DRRx/Fjh078D//8z/4wx/+gO9973uGn1mxYgVOnjyp/Rw8eNDrYXpOfPyNXtR8vIvKjAVHUZTgWHB82vXECxyZxKKRi8qv78hj3EPKVg0uu6hkmksscEuYsLbeuAHNJWtYsuAUFxcjEomgubk54fXm5maUlpbqfua73/0ubrjhBtxyyy0AgBkzZqCjowO33nor7r77boTDQzVWdnY2srOzrQyNe9Q+VHruKQCIhCLaf5vxE+vdrKa6iQt4g/g15mmjpyErkoWTPSex7/g+nD3ybF/O6zWsg4xFi3twO8iYLAfOcGuuijYPCedYsuBkZWWhqqoKdXV12muxWAx1dXWYO3eu7mc6OzuHiJhIZGAxF3GxtUuqAGNgoBaClX5UQbHeAP4txpmRTJxXch4AudxUrIOMeVxYUrZqsJgmnnBcHXcgxV04w611gkdLolVoLlnDsotq+fLlWLt2LX75y19i9+7d+NrXvoaOjg7U1NQAABYvXowVK1Zo71+wYAEee+wxrF+/Hh999BFefvllfPe738WCBQs0oRMEUqWIq1jpR0VF/rxBxkBj1r2oeBQ4qSALDl+4df1Em4eEcyy5qABg0aJFaGlpwb333oumpiZUVlZi8+bNWuDxgQMHEiw299xzD0KhEO655x588sknGD16NBYsWID777/fvW/hAl4/8NNZcIABC0JXfxdZcJLwczFW43DqD9f7dk4WkIvKGDUGpz/Wj95oL7IiWY6OFyRLtRe4NVdFiMFJ19cs3e+JRCwLHABYtmwZli1bpvu7LVu2JJ4gIwO1tbWora21cyppSBeDA1jrRyWjwDFaCPxcjJMDjWUoo87aRSXCwhKP6qICBqw4I3NHOjoeWXCc4ZZA9FNok6jlg2D3ovJRDZux4FjpR5Vc5A+Q96by83tNHzMdGeEMHO06ioNt4mfvAeSiskpmJFOz2riRSUVxE85wLYtKghgcwhqBFjh+osXgFKSIwbHQj0pGCw4P5GTkYPqY6QDkicNhbcFJFjgiWMXcLPYn68bDL2TPoiK3k3eQwPGBrr4unOg+AcCci8rMTkMVOOHQ6T+hrDtFJ9/LzsNjVqmcBf/iCXoWVTrcLPZHLipnuJZFJZirlHAOCRwfUONvcjNyUZRdZPg+1YJjJQYnPgBS1p2ik+9lRxzJFmhspg6Ol3MneT6LME/dLPYn68bDLyiLirALCRwfiHdPpTLPW6mDo6aJO83wEAG/FwjZWjboXT+KwUmNm6niZMFxhuwuKsI7SOD4gJkAY8CeiyoIAsdvKkorEA6F0XSqSeshJjKsKxmLGNxppdhfOrEogsWKZyjImLALCRwfUBfJtALHhosqO3K6pYWspnC/F4i8zDxMLZ4KQA4rjmEvKp/mC48753Rziiw4/CBimrhXyPqM9woSOD6gWXDyTVpwzLioouSi8hLZ4nCSoSDj1FAMDj8EqdBfOsgaaA0SOD5w6FT6FHHAWh0cCjL2FpnicKgOzlDSparnZ9qz4Ogdlyw4zqAgY8IuJHB8wKqLykodnOwMubqu84JUAoezOjgioFlwLKaJsxaTMkLNNgm7kMAZxEszstUgYzMLgl4WlaymcBbfa2bpTADAwbaDaOlo8f38bsI6yFhIgWOh0F86axBZcJwRpCyqdHNJhCKZPEECxwfMdBIH4iw4lEWVAIsdcEF2ASaNmgQAaGhq8P38bsLagiNi7INW6I9icJjjWhaVgPOQcAYJHI/p7OvEyZ6TANJbcKzUwdHNohLcFG60ELBaILRA40PyBRoHPQYnHXZdVHqIfl+yRsQsKhK1fBBogeOHuU+Nv8nLzENhdmHK91qpg6NmUalWH8J9tJYNTWLH4ZCLyjpW0sTTLcDkonKGa1lUnMbgkNvJOwItcPwgPv4m3UR22qpBVljtgGUJNDbjovJS8IgocKwU+ksH7eadQVlUhF1I4HiM2fgbwFodHD0XlaywdlHtO74Px7uOMxmDG7B2kQzpRSXAgk+F/viBmm0SdiGB4zFqo8108TeAtTo4gcqiYrRAj8gdgbOGnwVA/EBjloi4c3a10B/F4DiCLDinoblkDRI4HmM2RRywZ8EJgouKJTK4qVgLX15jH1JBFhx+kD0Gh/AOEjgeY8lF5bQXlaTqnuUCLYXAMZgXfs0XEXfOTmJwkmPtWAtM0RExi8oraC5ZgwSOx9iy4FjIogqCBYelcKsqqwIguMBh/FAUcWGxa8GhVg3uQ3VwCLuQwPEYWzE4Nl1UrBcyr2D5vWaWDVQ03nt0L9p72pmNwwmsLXvJAicE/tNi1Ric7v5uRGPRlO9NNz9ZX3/RCVIlY8JdSOB4jCULjo1KxtSLylvGDBuD8YXjoUBBY1Mj6+EIiYgLi+qiAgaKdZpFT8yQBccZ1IuKsAsJnEG82GV19HagracNQPpO4oC1XlTUTdw/RI/DYW3ZE1Hg5GTkIBwaeDymc1Ols0ixvv6iE6QsqnRzSQTrJ0+QwPEQ1T01LHMYCrIK0r7fiovKbgyOiA9bJ2N244EgekVj1gJRxNiHUCjkWrE/1tdfdFzLohJwHhLOIIHjIVaqGANxLqqApomzzvYxomqs2IHGrEWtCDtnPdxKFScXlTNEzKJi/cwiBgi0wPHa3KeliJtwTwHWsqiCVMmYNaqLalfLLkvxGLxg5mHr5QNZVIHjVrE/1gJTdFzLouIgBoeEj78EWuB4jdpo00yAMWCtDo5eJWNZYb1AlOWXoWRYCWJKDG81v8V0LCIiYqsGgCw4vCB7FpWVjbYo9w4vkMDxEM1FlW9O4NhJE4/PopJ18rPe9YRCIaEDjVnPC14XlnRQDA4fUC8qwi4kcDzk0Cl/XVSyPkidLNBuLe4iF/xjPS+S5zPr8ZjFrAUn3RwjC44zgpRFRbgLCRwPsVIDB7AWZKyXRcV6py4zqgWn/nA945FYx2he+DVfeFxYzIgsisHhA8qiOo0omwNeIIHjIZZjcBzWwZEVHm5qVeC8c+QdLf6JMEe/wp/AMYNbLiqy4DiDLDiEXUjgeIhVC44Wg2PBRRUIgcPBDviMojMwMnck+mP9eOfIO6yHYwnWApHHhcVM2QY7QcZ6x2V9/UWHKhkTdiGB4xGnek+hvXegd5GZTuKARRdVkLKoOFggRA40Zi0QeRQ4ZtAsOA5dVGTBcYbsWVSEd5DA8QjVPZWflY+C7PRVjAF7QcZBaNXAC6IGGrOeF6IuLHYsOHrXmrXAFB3qJk7YhQTOIG4/hKy6pwBrdXCC1GyTlwVC1EBj1tdPVNeAFmScJgYnXR0TsuA4Q8RKxnZJ5zo141olTkMCxyO0KsYm3VOA+To40VgUUSUKIBhZVKwtECqqwHmr+S1hF20WiLCw6OFWoT9e5q+ouJZFRfds4CCB4xG2LDgmXVSq9QYISAwOJ8Jt4oiJKMwuRE+0B7tbd7MejmlYL7CiChyzWVTp5icv81dUKIuKsAsJHI9QO4nbcVGls+DECxyrhf5YL3apMKzXwsmYw6GwkIHGZhZYLxdhURcWatXAByJWMvbqfuLlWSgKgRY4XvoznVhw0i0I8QJHFUWEP8wqHYzDOSROHA7rh6KoAse1Qn+0KDlC9iwqiqvxjkALHC9xFIOTxkWlVjHODGcmBDjKagrn6XtpFpwmuSw4XpK8sIjyQFddVGTBYYtM3cQJfyGB4xF+uKiCEH8D8LUDVgVOY1MjorEo49GIgajpuaqLynGzTY4EuogEKYuKcBcSOB7hR5Bxcoo4T0LATXhaICaNmoRhmcPQ2deJvUf3sh6OKYzmhV/zJXlh4WGe+tmLiiw4zqBeVKfh6VkoAiRwPKC9p10za5vtJA6Yr4MTpCrGvBEJR1BZWglAnEBj1g9FUXfO8UHGTkQZD4JOZGTKomJ9LwYNEjgeoFpvCrIKtIekGczWwSEXFVtEK/jH+vrxsLAkYyYOSI3BUaCgu7/b9nHJguMM6kVF2IUEjgfYib8BErOoUt3UmosqIn8VY4C/XY+IqeIs4VHgmCEvM0/771SBxukWYN7mr2jIZMEh/IUEjgfYib8BElO+1UrFeqhZVMkWHFkfpE52cOnK6NtBFTgNTQ1C7M5ZzwtRF5ZIOILcjFwA5gON9eaqCHOEZygGh7ALCZxB3DTjayniFuJvgNMWHCC1OTVoLiremDZ6GnIyctDW04Z9x/exHk5aDIOMfRI+IrsGzBT7S+fuYu0iFJ0gZVGl25B5sWGTGRI4HqB2Eh+bb82Co8bgAKl3G5RFxZaMcAbOKzkPgBgF/1hXiBZhYTHCjUwq3uavaJCL6jQ0l6xBAscDDp1y7qJKtesNWhYVj8JNrWgsQhyO0fWLXzi8vMYiLyxuFPsjF5UzqNkmYRcSOB5g10UVCUW0/061KMS7qGRS9KxdKVYQsaJxMn5dV5EFjp1if8luBB4FukiI6KKivzkfBFrgeOXP1FxUFi04oVDodLE/My6qgGRR8Uh8JhXvDzMjIeOXZUFkgeOGi4osOM5w4/pFY1EuN0oAxdV4SaAFjhcoimI7iwow14/KKIvK1Pg4vclTwaOAmD5mOjLDmTjWdQwHTh5gPZyUmHFReYnIAseNjuIi3nM84cb1E3kOEvYhgeMy7b3tmjnbSqNNFTP9qIyyqGR9kPL4vbIzsjF9zHQA/Bf8Y23B4TE916xoVmNwUrmo0h2LLDjOcGODw8scdGqt4XGzxzMkcFxGtd4UZhdq5m0rxBf7M8Ioi0pWeL2pRSn4x9KCoyiK0LtnVyw4nM5fUXBjnoo8Bwn7kMBxGbvxNyqaBcdMFlU4yYJDD9IheGn9EUXgGOHHfNFbnHiwyJlp1QDEWXBMxuBQqwb3cWO+UAZVMCGB4zJO4m8Ac/2oyEXFB1VlVQAGXFQ8i0uWLirRd84Ug8MeN+4tdR7GZ6oS8mNL4KxevRoTJkxATk4O5syZg+3bt6d8/4kTJ7B06VKUlZUhOzsbkyZNwqZNm2wNmHe0FHEb8TfAaReVmUrGgSn0x+n3Oq/kPERCERzpOKL93XmEpYtKdIGjZVFZSBNPhiw4znDj+qkbxvhiqoT8WBY4GzZswPLly1FbW4sdO3agoqIC8+bNw5EjR3Tf39vbiyuuuAL79+/Hxo0bsWfPHqxduxbjxo1zPHgesdtoU0V1UZmtgxMEeN0B52bmYuroqQD4dlMZVjL24brqWSJFSou1asHRE5O8CnRRcDOLKr6YKiE/lgXOQw89hCVLlqCmpgbTpk3DmjVrkJeXh3Xr1um+f926dTh27Biee+45XHjhhZgwYQIuueQSVFRUOB68m7j1sHfqojJTBydozTZ5RuQ4HLLgpMdMFlXaXlR0XzrCFQtOVAwLjtnYMMIclgROb28v6uvrUV1dffoA4TCqq6uxbds23c+88MILmDt3LpYuXYqSkhJMnz4dP/jBDxCNpuiW3dODtra2hB9RcC0Gh5ptavC8A9ZaNghY0Tj+unq1CKsCJxw6/agRacGnQn/scTMGJ76hMQtEmvsyYEngtLa2IhqNoqSkJOH1kpISNDU16X5m37592LhxI6LRKDZt2oTvfve7ePDBB/H973/f8DyrVq1CUVGR9lNeXm5lmExxHINjoQ5OdiQ7wdzPsxBwAs8PhaqxA4HGZMHRR11YeN85G2HGRZXuvpP1vvQLV7KoKAYnkHieRRWLxTBmzBg8/vjjqKqqwqJFi3D33XdjzZo1hp9ZsWIFTp48qf0cPHjQ62G6gqIozmNwTNTBcVLJmGdYd722Q0VJBUII4eO2j3GkQz8OjTU8BBmLurCYcVGlgyw4znCzDo5fMTg8b8qChCWBU1xcjEgkgubm5oTXm5ubUVpaqvuZsrIyTJo0CZHI6fS8qVOnoqmpCb29vbqfyc7ORmFhYcKPCLT1tKGzrxOA9UabKmbq4AQtTZxnCrILMGnUJAD8WnFYBhnz4hqwC6WJs8eVSsaCxOAQ7mJJ4GRlZaGqqgp1dXXaa7FYDHV1dZg7d67uZy688EJ88MEHiMVOq/C9e/eirKwMWVlsLRBuB3Sp7qmi7CLkZebZOoaVOjiBqWTM+QIhaqCxH5YF0RcWOzE4yfOVLDjOcDWLikOhTYHF3mHZRbV8+XKsXbsWv/zlL7F792587WtfQ0dHB2pqagAAixcvxooVK7T3f+1rX8OxY8dw5513Yu/evfjDH/6AH/zgB1i6dKl734ITnLqnAHN1cLRKxpK5qIzg2UUFnC74x6vAIRfVUMzOKWrVwB7datgWr6ksMTi8b/Z4w/Jfe9GiRWhpacG9996LpqYmVFZWYvPmzVrg8YEDBxAOn9ZN5eXlePHFF/H1r38d5513HsaNG4c777wT3/rWt9z7FpzgNIMK8L4OjogPW95vatWCw2vTTR4qGYu6sKgxOH2xPvRGe23dc2TBcYZubSEoluopUR2cYGLrqbNs2TIsW7ZM93dbtmwZ8trcuXPx+uuv2zmVUGgZVDbjbwBzdXDis6h4X/yDwMyymQCA/Sf241jXMYzMHcl4RImQBWcopntRxTXM7ejtQFZuaoGjd1y6R52hd/0URYGVepGiu0oJe1AvKhfRLDj59i04ZurgyJpFZYQTq5MfVXOH5wzHxBETAQANhxs8P59VWGan6QkckayIWZEsbdNhN5OKLDjOcKNhK88xOFYQ6d7hARI4LuJKDI6FOjhDsqgknfwi7IBFDDT204IjsmsgXaBxuvkp633pF260v5AlBoewBgkcF3ElBsdEHZzAZVEJsEDEdxbnDZYuKhkWFiuBxnrXmiw4zhCxDg7BByRwBnFjEXU1BsdGFpUIlg5Z4dmCY6YOjlciktcYHCukK/aXzg1K96UzdGNwLF5TUWJw0s0lSim3Bgkcl1AUBYfbnbuorNTBCUwMjgALxMzSgUDj94+9j7YevnqnUZCxM5ymiotggeQZN1xUMsxDwjokcFziZM9JdPV3AbDfhwqwVsk4O5LoopL1QSrC9xo9bDTKCwd6pjU2NbIdjElYBRmLhtOGm+SicoYbQcbqhlH0IGPCGiRwXEJ1Tw3PGY7czFzbxwlyLyojRLDgAPw23qQ6OM5wbMERZP7yimGauAVkmIeEdUjguIQbAcaAsywq0RHBUpOKWaV8FvzjQeCIvHN22nCTLDjOMCr0ZwXVIu5bs03Bn2WyEGiB42aNFDfib4D0dXAURTHMopJ1pyjKw4LXQGOj6+fHfBEluDMVFIPDFjezqHich37U6goqgRY4buKaBSdNJeN415VsFhwjRBFuqsB5r/U92/EaXsCDBSd+YREtE0Sz4Nisg0MWHGe44aLiJQbHqdglsWwNEjguoaWIOwgwBtL3olLjb4AAFfoT5HuVFZShNL8UMSWGt5rfYj2ctFAMjjm0IGMTLipq1eA+brioZJiHAM0lq5DAcQk3qhgD6S04qnsKCI4FRyR4LPhHaeLOcOqiIguOM1zpJh7lw4JD+AsJHJdwy0WVLgZHFTjhUHjIomFG3Yu4AxBpzDzG4fDgohK5gqzVIOPkmApRLJC84kahPxmENmEdEjgu4baLysiCY1TFGJD3Qerke/ktjrgUOEZBxtRs0xRkwWGLm72oRBbahHVI4LiAoiiuu6iMYnBSpYiLZOmwgkjfSxU477a8i+7+bsajGcCMBcerayxDLyorhf7ciBchEpE9i4rwDhI4gzh5CJ3oPqEtZk76UAHpKxkbVTGWGZF2/OWF5SjOK0Z/rB/vHHmH9XBS4sfCqy0sIXEXlnQWnLS9qASavzziZi8q3mNw0mUYUkq5NUjguIDqnhqZOxI5GTmOjpWuF1XQqhiLRigU0qw49Yf4CDSmIGNnWI3BSV58yUXlDDeCjGWYh4R1SOC4gFvxN4B1F1UQdoeimfjVisa8xOHwEGQs8sKSzoKTbn6KNn95w5VKxhSDE0hI4LiAW/E3gAUXVQa5qHhFCzRu4kTgcBBkLPLCQs022UK9qAi7kMBxAbdSxIH0dXBSZVGJjtGuTLQdsCpw3mp+K2VXeNb4sfDK1KrBbi8q0QQ6b4jYTVy0Z5asBFrguFUy3k0Xldk6OEFKExeNiSMmoii7CL3RXrzb8i7r4ZCLSgcr94oag9PZ12nrmsVAFhwnuPFc43UeWoWEkzUCLXDcwgsXVboYHMqi4pf4QGMe4nAoyNgZqosKGBA5VhFt/vKGmy4qHl2lVjbaNJesQQLHBXx1UaXIopJV3Tv5XqzSKrkSOGTBGYKVRSU3I1ebR+mK/ekdl2JwnOGKi0oCVylhHRI4LuCqwDEZZCxjDI4RIu5auBI4RkHGftbBEXhhCYVCjgKNZd14+IWrFhzO6+AQ7kICxyGKopyOwXFY5A9IXwcnVRaViEJAVtSmm41NjYjGooxHo4+vvagEX1ictGsgC44z3EwTF1loE9YhgeOQ493HNdHBog6OVUQUQSLugM8ZdQ7ys/LR1d+F91rfYzoWU60aPJoXsiwsqYr9pbt2It5zPOFmqwbWMTgiPstEhgSOQ1TrzajcUa7UpknnokrZbFPSm0fEBSIcCqOytBIAezcVuaicY9aCQ72o3McNFxXF4AQTEjiD2F1E3XRPAeZdVKrAsZrq7lZqvJ+IukDwUtGYgoydkyoGJ909RS4qZ7ghGnlxlaZLekj7ewGf3ywhgeOQw+3upYgDcVlUNpptimjpkBleKhrzUMlYdIHjJAaH7ktnuNGLShZXKWENEjgOcTODCkhfByeIzTZFXSCqxg4EGjccbuByF08WHPNYbbgZD49/e5Fwo5s4LzE4hL+QwHGIJnDyXbbgmHRRxSOqKycdon6vKcVTkJORg/bednxw7ANm4+DBRSX6wkJp4uxwxYJDMTiBhASOQw6d8iYGpz/Wr3sTUyVjccgIZ6CipAIA2zgclkHGsrgG8jMpTZwVbrZqYB2D4xRRn4WsIIHjENdjcOJ2unpuKqmbbUp48/JQ8I8HC47oAkez4NhwUck4r/3EDReV30Lbq785WQOtEWiB40YZf9djcMKpBU4Qm22KfFOrBf/qD9czHslQSOCYJ1WQcfx9R60a3McNFxXPrlJW7WSCQKAFjlMURdEabbpR5A9IvAH14nB6Y8aVjGVFZOEWb8Fh9T2C1mzTi+vsJMhYZIHOA65UMqYYnEBCAscBx7qOaRaV0vxSV44ZfwPqpYonu6hEXvzNIvICce6Yc5EZzsSJ7hPYf2I/kzEEzUXlxXyhVg3soF5UhF1I4DhAdU8V5xW7ZlGJhCLaf+tacALYbFNksiJZmFEyAwC7OBwe6uD4ubB4YsGxkEWVfP4gbEK8xJVu4pIEuxPWIIHjALfjb4ABH36qflSURSUerCsam+pF5ZGVjIVrgCw4cuFqFhWHMTiEd5DAcYDb8TcqqfpRBbLQn8AuKuB0wT/eAo2pF5V5zMbgUC8q96FeVIRdSOAMYuch5IUFB0jdj8ppoT8RrSFOxszD4sI60Ji3IGOv/yZ+u6jS9QcS8Z7jCTdcVKII7XRziTKurEECxwFeCZxU/ag0F5VOzA89SPlkxpgZiIQiaOlswSftn/h+fgoydg65qNihaxWz8KyLxqLanKAg42BBAscBWidxj1xUVgv98WCt8ALRv1duZi6mjZ4GgE0cDhdBxj7GPvidJp7ufKLPX9Y4teDEP0d5t+Ckg+aSNUjgOECNwfHMgkNZVADksExpcTiH/I/DIQuOc+ItOFbnI1lwnOE0Bif+Oco6yJgEir+QwHGA5zE4qVxUOllUMggBPZw8FHjxWWuZVE3sWjYk48fCyyI918x9YPVeUWNwYkpMC/Q3IjmOQtb70i+cXj+pLDg0lyxBAscmiqJofajcarSpomVRBcyCI/PuhmVPKt6CjEVEdVEB1uNwyILjDKcuqviNom+9qCR+lolEoAVOuoj1VBztOqoJELeqGKukqoOTKk1c1ptKhl1LRWkFQgjhUPshNJ1q8vXc5KIaitV7PxKOICcjB0DqYn96x5X1vvQLpy4qdQ6GQ2GEQ4Fe8gIH/bVtorqnRueNdt2akqoODjXbFJP8rHxMLp4MAGg43ODruQ2DjBnVwfHabejVfWA3k4osOM5wWltI3YjymkHlZKNNpIYEjk28ir8BzNXB0U0Tl0AI6CGLcOOts7ifFhxfWzV4dB/Ybbgpy/xlhdNu4rK4SQnrkMCxiZYi7nL8DZC6Dk6qNHGCb1jF4Zhq1eDRIqyeg7cgYzuoFhwz/ajiIQuOM3RdVDZicFhnUBH+QwLHJmqA8dh89y04RnVworEookoUALmoRISZwGFYB0dFht2zmkmV7KJKNz9lmb+scCuLSoY5SFiDBI5NvHRRGdXBif83NdsUj5mlMwEAfz/5dxztPOrbeVkGGavwFmRsBzMuKqdVd4mhOHVR8R6DQ3gHCZxBrD6EDp3yzkVlVAdHdU8BAcuikuR7FeUU4VMjPwXAXysOyyBjFZlcVMkWnHRB0+SicoZTF5VIFhxe6nbJAgkcm2guKi8sOAZ1cNQA4/j3EGLBsh5OMqwsOJ432/TKgpOi4WYqZBHorHBqFaMYnOBCAscmfriokmNwVIGTGc7U6jlYfXiK+LCVycTPoqIxaxdVCCFEwhFfzgV4aMHJpDRxFrjVi0oECw7hLiRwbBBTYp71oQKM6+CkKvInMyKKMiNYWHBYBxn7vbB4bsGhNHFfcasXFcXgBA8SODZo7WxFf6wfIYRQMqzE9eMb1cGRuU1DKmRaIFSB88GxD3Cy+6Qv52RtwZFl50yF/tjg9PrJZMGRabPnB7YEzurVqzFhwgTk5ORgzpw52L59u6nPrV+/HqFQCAsXLrRzWm5Q429GDxvtiV83nYtKr8ifDMgkZIwYlTcKZxadCQBoaPKnojHLXlQAAwuOR/NIy6JK06oh+fy0KDnDcSVjBjE4QXiWiYBlgbNhwwYsX74ctbW12LFjByoqKjBv3jwcOXIk5ef279+Pb3zjG7j44ottD5YXvIy/AYwL/QW1yJ9sCwQvgcbSChyP5otW6C/JRZXufGTBcYZbvahksOAQ1rAscB566CEsWbIENTU1mDZtGtasWYO8vDysW7fO8DPRaBRf/vKXsXLlSkycONHRgHlAq2Kc736KOJA+i8pI4Mi6a5Dte/ktcILmovLMgmNQ6C8dss1fv3HcTZzzGBxKDfcOSwKnt7cX9fX1qK6uPn2AcBjV1dXYtm2b4ef+/d//HWPGjMHNN99s6jw9PT1oa2tL+OEJLwOMAeM6OJqLKkBF/gCy4DjFTB0cL6+x3+m5vPWiIguOM5ymifNkwSGx6y+WBE5rayui0ShKShIDa0tKStDU1KT7ma1bt+KJJ57A2rVrTZ9n1apVKCoq0n7Ky8utDNNz/HJRGcXgGFpwJBMCKrI9FNSmm++1vmfZGuAmrCw4Xv89eesmLut96RfUi4qwi6dZVO3t7bjhhhuwdu1aFBcXm/7cihUrcPLkSe3n4MGDHo7SOp4LHAMXlV6aOJk3xaMkvwRjC8ZCgYKdTTs9P18opD9HZHVReYXtQn+SCXS/oSwqwi6W/uLFxcWIRCJobm5OeL25uRmlpaVD3v/hhx9i//79WLBggfZaLDbYXTgjA3v27MHZZ5895HPZ2dnIzubXDeN5DI5BkHFgY3Ak3AHPKpuFQ+2HsOPwDlx4xoWenovq4LiDGQuO3jUlF5UzHFcy5jwGh/AOSxacrKwsVFVVoa6uTnstFouhrq4Oc+fOHfL+KVOm4O2330ZjY6P2c8011+Cyyy5DY2Mjd64ns/gWg2MQZCxrmrgRMgo3PysaU5CxOxjF4KSzosoo0P0kUL2oDKythD0s/8WXL1+OG2+8Eeeffz5mz56NRx55BB0dHaipqQEALF68GOPGjcOqVauQk5OD6dOnJ3x++PDhADDkddaYvWFiSszTPlTAaRdVcgxOujRxWR+kMn4vPwONWTfbDJIFRw+y4DjDaTdx9TlKMTjBw/KTZ9GiRWhpacG9996LpqYmVFZWYvPmzVrg8YEDBxAOy1sguaWjBVElOlDFON/9KsZAnIuK0sSlpWrsQKDxu0feRVdfF3Izcz07F2sLjt+uAa/TxHujveiP9WvCLZ2govvSGW4V+hPBgkO4i62/+LJly7Bs2TLd323ZsiXlZ3/xi1/YOSU3qO6pMcPGeHbDGPWiSpcmLqOlA5BzgRhXMA6j80ajpbMFbx95G7PHzfZ9DNK6qDy24AADgcZFOUWmPkcWHGe4VeiPYnCCh7ymFo/wOoMKMI7BcaPZpohiQUbhFgqFfHNTBa1Vg1dkRbK072LFTSXj/PUTp/NUfY7KMg8J85DAsYgfAsd2HRwBxYsZZP1evgkcCjJ2jXTF/vSCRMmC4wynLiqy4AQXEjgWUQOMvUoRB+y7qEQnaDtdteBf/eF6T89DaeLuYSfQWFaB7hdOg4xZxOAYzUHKkvIXEjgW8dOCM8RFpZNFFQRRIOt3VC04bze/rYlXLzBjwfFyEfa9VYOXFhwbxf7IguMMt9LEKYsqeJDAscihUz7G4Fgs9Ccrsu6AJwyfgOE5w9EX68O7R971/fzSuqh4s+BIKtD9wq1Cf7zG4JBVxztI4FhEq2Jc4L2LyigGx6jQn6wPUlm/l1+BxoGrg8MwBkcPsuA4w2k3cYrBCS4kcCzidZE/IIWLyoUsKoIv1IrGXsbhBC3I2Ev0LDjJgip5Ry6rBdIvnApxqoMTXEjgWCAai6Lp1EDXdE8FTpog48BlUUlqwQFOF/xjYsGhIGPLpIvBcZrxQwzFqYtKpFYNhLuQwLFAS+dAFeNwKIwxw8Z4dp60vagkzaIyQlbhBpwONN7ZvHOIS9JrZLXgeDlfVAtOvIsqncWGXFTOcOqi0pptChBknK6vGWENEjiDmHkoqu4pL6sYA8Z1cILqopJ5B/ypkZ9CflY+uvu78V7re56cg7WLyvdWDV5acAZjcChN3D/cqmTMgwWH5oK/kMCxgB8p4oB9FxUhHuFQGDNLZwLwzk1FQcbuoVlwKE3cNxz3olItOBRkHDhI4FjAN4GTptlm4LKoJN/1aAX/DnkTaGw0L6KxqCfnSyZZ4Hg9T7mz4Eh6X/qFW93EebDgEP5CAscCWoq4h1WMAaqDk4zsC4SWKt7krwVH1hgcq1iJe9CCjKlVg2+4lUUlQgwO4S4kcCygdhL3y0U1JAZHp5JxPLJaOmT9XiqqwGk43ODrYkguKutQqwb/casODu9Cm3AfEjgW4MVF5aibOMfWkKAuBJOLJyM3IxcdfR14/+j7rh/fzN/cy3khVZq4TqG/dPOWLDjOcKuSsZ8xOEF9lvEGCRwL8BJkbJQmzrN4cYKs30slI5yBitIKAN4U/GP9sPU9i4o3C47k89dr3OpFRRac4EECxwKqi8q3GByLlYxZL2ReIev3ikcNNPYik4r1Apu8sHhd64NloT89yILjDLe6ifMag0O1b7yDBI5J/KpiDBjXwaEgY3nxsicVa4EoYwyOlV5UrK+/6Di9fmTBCS4kcExypOMIYkrM8yrGQGKQcfzNHdQ08SAQL3BkWxBlWlgoTdx/nLqoqA5OcCGBYxI1/qZkWAki4Yin54q/EeOtOOmyqGRFtgVfj2mjpyErkoWTPSfx0YmPXD026wVWpiDjdIX+9OYquaicQXVwCLuQwDGJXyniQOKNGB+HQ802raNXl4RHsiJZOK/kPADuF/xjPS9kclHF18FRF14zc4z130BkHFcy5jwGJx5RnleiQAJnkHQ3jF8ZVEDijRhvwdHLogrCgzMI3xEAZpV6E4fD2oLj98LiR5o4AHT1dZn+HOu/gcjI1IuK8BcSOCbxq4oxkOiiUncfiqIEttlmUPC6ojErZLLg5GbmalkvahyOmfMFRaR7gWvdxCkGJ3CQwDGJ2kncDwtOJBzRHqLqzRlvyQmawAnK7terQGPWi6tMMTjhUBh5mXkArGVSURyOfSiLirALCRyTHDrln4sKGNqPSnVPAcHLonLygGO9uFthRskMZIQz0NrZioNtB107Lut54XezTatYjXtIVezP6Fi8fWeRcOqiEikGh3AXEjgm8TMGBxjaj0p1TwEUZCwrORk5OHf0uQDcjcNh7UKRyUUFULE/v5GpF1VQnmW8QALHJKqLqqzA+xgcYGg/KtWCE0IIkZB+mjrdPOLjRcE/1vNiiAXHYwHi9felhpv+ImIvKoIPSOCYoD/Wj+aOZgD+W3CSXVTZGdkJZnCZ0gqNFqYgLQ5eVjRmhUy9qAD9hpvpIAuOfUTsRcV6U0EMQALHBGoV40gogtF5o305p3ozai4ql4r8iSgWgvSw8MSCQ0HGrpK22J/DBZk4jdHclSkGR6ZNKm+QwDGBVsU43/sqxipGLqqgZVAB7BdoP6koqUA4FMbhU4c1t6hTWC+ussbgWHFRkQXHHm7MXZ5icAh/IYFjAj9TxFWMXFSpBE6QhICsDMsahinFUwC4Z8VhPS9kW1iSXVRmFmHWfwNRMRKGZoVPNBbV3ksxOMGDBI4J/M6gAuLSxJMsOPFVjIMCawuE37jtpmJ9/WR1UelZcAxdKgGbw27h1EUVXz9MNqFNpIcEjgk0gZPvowUnrJ8mntKCI+lDNGi7X61lgyQVjX1v1eBXkPFgDI5alDMV5KKyh2HigclnXXwvP15jcOIxM5cI85DAGSTVQ1FttOlXijhgz0UlK7IKNyNUC45bTTdZC8QgWXCMYP03EBVDFxVZcAgTkMAxAQsXlVGQsVEVY4AeorIws2wmAOBg20G0dLQ4Ph5rgchjkLGTeyW+o7hZyIJjD6cuP3WDCJDACSIkcEzANAZn8AZ1K01cRIIm3AqzC3HOyHMAAA1NDY6Px/r6JS8soqfF2rLgBMwK6RZOr5tqwQmHwgiHaLkLGvQXN4GfncRVkls1mMqikvQhKuv3SoVMBf94dFElFMu0GPeQqtCfkXgjC449nLqoqIpxsCGBk4b+WD+OdBwBwImLKohZVAGz4ABxcTiHncfhmEpj9lBE+t6qwePjpyv0pyeYgjiH3cCpi4pq4AQbEjhpaD7VDAXKQBXjYf5UMQaGBhmbyqKS9CEaRAtOVVkVAHcsOKznhe+tGjyeL8mF/sycjyw49nDavoX3KsaEt5DASYPmnioo89WHa1QHJ1ngsF683ESm7+IUNdB43/F9ON513NGxWAtEHoOMnaBZcCwEGbP+G4iK00J/rCw49CzjAxI4adBSxH2MvwGG1sExk0UlevCmEUF8WIzMHYkJwycAABqbGh0di/X14zEGxwlqDA61avAetwr9UQxOMCGBkwYWGVSAjovKRBYV64XMK5wsWCKLPlkCjWWLf9DSxA1icPSQ9d70GqfCULWA8zwHqbifd5DASQMzgWPUbDPssJu4gKbyoC4OakVjp4HGrP/msrkH4tPEzZ6L9d9AVJxWMuYtyDiozzJWkMBJA4sUcWBoHRxThf7oISoVVWPdCTRm/VCV1UUVVaLafamd2+Bak4vKHk5dVBRkHGxI4KRBjcFhZcGx0ovKDCKaQ4Mq3GaWDgQa7z26F+097baPw/r6SdeLatBFBQxYcczcU6xFpqiIGmRsRDqXucgudR4hgTOI0Q3DPAYnTRZVPLI+RGX9XukoyS/BuIJxUKBgZ/NO28dhff1ks+BkhDO0elRmM6nIgmMPx2niVOgv0JDASQPzGJxkF1UQC/0F1IIDuN94kwWyxeAAicX+WBdSlBkq9Ec4gQROCvqifVqzQz87iQPm6+DEQw9R+dAK/jXZj8NhPS8ioQjT86fDjlsgudhfumORBccejrOoKAYn0JDASUFzx0AV44xwBorzin09d3IvKrdicESEtYuFJW6kinvdXTsVkVDE97gCPwRdqmJ/eucP8hx2glMXFVlwgg0JnBTEZ1D53YnWyEUVSIHjYMESfWFRBc6ull3o7Ou0dQyWFhy9hcXr8fjxN7da7I8sOPZw6qKiGJxgQwInBfFtGvzGKMg4ZZq44Iu5EbJ+LzOMLRiLMcPGIKbE8Hbz26yHYxkWrgE/BJ3VYn+s3YSi4rSbOFlwgg0JnBQcbmeTIg4MrYNjqpKx4A9R0cfvBaFQyHFncZYCkcXC4meQMVlwvMVpoT+KwQk2JHBSoGVQ5fsvcLQ6OEpiLypyUQUPp53FeXNReY0vFpxBF5XZNPEgWyGd4FYvKtlKFRDmIIGTAlYp4sDQXlSBThMP+OLgNNA4aBYcP7BqwaEFzx5u9aLiOQaHivt5BwmcFGidxFnE4CT1ogp0FlXAFwdV4Lxz5B3NVWkF3iw4XlfT9jPIuKO3w9T5yEVlD9l6URH+QgInBSwtOEa9qIIocILOmUVnYkTOCPTF+vDOkXdYD8cSsrqojNLEnbpUiEScBhlTDE6wsSVwVq9ejQkTJiAnJwdz5szB9u3bDd+7du1aXHzxxRgxYgRGjBiB6urqlO/nCR5cVOoOxCiLyurDXERrSNAXh1Ao5KjxJsvrx8I14IsFJ67QnxkXA1lw7BG0SsYi9grkGcsCZ8OGDVi+fDlqa2uxY8cOVFRUYN68eThy5Iju+7ds2YLrr78er7zyCrZt24by8nJceeWV+OSTTxwP3k2Sb6S+aB9aOgerGPvcSRzQcVGZyKKSFRFFmdvMKrUfh8Obi8prTLVOcCiCUhX6szsmYijUi4pwgmWB89BDD2HJkiWoqanBtGnTsGbNGuTl5WHdunW673/yySdx++23o7KyElOmTMHPfvYzxGIx1NXVOR68lzSdagIwcGOMyhvl+/mNgowDKXACbsEB4gKNbbRsCFqQMRX6kwfZuokT/mJJ4PT29qK+vh7V1dWnDxAOo7q6Gtu2bTN1jM7OTvT19WHkyJGG7+np6UFbW1vCj9/EF/nzu4oxYNyLKohZVMRpgbOzaacmes3CshkkrwtLvFvJjlvAqNCfkbuKRLo9nF43LQaHEwsOzQN/sbRyt7a2IhqNoqSkJOH1kpISNDU1mTrGt771LYwdOzZBJCWzatUqFBUVaT/l5eVWhukKLONvgLg6ONSLisz7AM4eeTYKsgrQE+3B7tbdlj7L0q/PpFWDj0HGZMHxFupFRTjBV9PED3/4Q6xfvx7PPvsscnJyDN+3YsUKnDx5Uvs5ePCgj6McQEsRZxB/A5h3UQUhKI12PUA4FHal8abfMGnV4GeaOMXgeIpTF5UWg0NZVIHEksApLi5GJBJBc3NzwuvNzc0oLS1N+dkf//jH+OEPf4iXXnoJ5513Xsr3Zmdno7CwMOHHb3ix4FjpRSUrtDgMYFfg8BZk7LUA8duCQ3VwvEPUSsYEH1gSOFlZWaiqqkoIEFYDhufOnWv4uR/96Ef43ve+h82bN+P888+3P1ofYS1w1BuyP9aPmBLTbtQguqicIFOVUNsCh4KMXcdys02yQtrCtV5UnMTgEP5i+emzfPly3HjjjTj//PMxe/ZsPPLII+jo6EBNTQ0AYPHixRg3bhxWrVoFAHjggQdw77334qmnnsKECRO0WJ38/Hzk5+e7+FXchScXlWq9AeQWOFQkLTWqwGloakA0FkUkHGE8ovTwmibuFKtp4mTBsYeo3cTpmcUHlv/qixYtQktLC+699140NTWhsrISmzdv1gKPDxw4gHD4tGHoscceQ29vL77whS8kHKe2thb33Xefs9F7CGsLTryLKl7gBDGLilxUA0weNRl5mXno7OvE3qN7MXX0VFOf481FJQNqDE53fzeiSjTt+2kO28NxFhXF4AQaW0+fZcuWYdmyZbq/27JlS8K/9+/fb+cUzGEucAwsOEG8UWk3NEAkHEFlaSVeO/gadhzeYV7gkIvKdVQLDmDOTUVz2B5BqIMThEQRVlAvKh16o71o7WwFwKbRJpAYg6NWMc4IZzCpyUPwg52KxiytB0xaNfjwfbMiWYiEBlyE8W4qRVF0FyxyUdmDKhkTTqDVUoeEKsa5/lcxVs8NJLqoZI6/SQWZ90/jpKIxC2S14IRCodNxOEkWHL35SnPYHoHrRSVRUgQPkMAZJP6GiXdPsZpwei6qIMbfAGTejyc+k0oEq4CsQcZAYsPNdIjwt+IR6iZOOIEEjg6s42+ARAuOm1WMRRQLtPs9zbTR05AdyUZbTxv2Hd/n2nG9mhciWHDsbmL0+lFRqwZ3cXrvi2bBIdyFBI4Oh9sHU8QZxd8AiTE4QXdREafJjGTivJKBQpkiVDSWeWGx0q6BLDj2cOqiohicYEMCRwfNgpPP0IITOd2LSg0yDmIVY4B2v8mI1LJBT+B47fb120VlphYOWSHtIWodHIIPSODocOgUPy4qAOjs6wQQXAsOLQ6JqAKn/nA945Gkh0kWlU+C2EqxP7Lg2MO1SsYUgxNISODooFpwWLqo4m9I1QSuJ3CCsPiTBSeRqrIqAAMWHN6vjdRBxjoxOEbw/nfiFepFRTiBBI4OagwOSwtO/A2pWnCCmkXlBBkXluljpiMjnIFjXcdw4OQB1sNJCZNmm35bcEwU+iMLjj3c6iZOAieYkMDRgacsKuC0CVx2F5VTc3RQyM7IxvQx0wHwH4dDFpwBaA7bw60sKr9dpfQs4wMSOEn09PfgaNdRAGwFTiQc0SqipnJRBQEZrTBOUSsa8x6HI/PO2UoWFc1hezh1UakxODLPQ8IYEjhJqFWMsyPZGJEzgulY1Dgc1QQe2Cwq2vUMQZRMKhHq4NiFCv15j1u9qHgOMqbqxd5BAieJ+ABj1hNPXRyC4qIizFM1diDQuP5wPdfWARYLi98uquReVCzHJBtu9aIiC04wIYGTBA/xNyqq31i14ARV4PC8gLPivJLzEA6FcaTjCA6fOsx6OIbIbMGhIGPvcc2CI0ihP+os7i4kcAZRH4rqYlGWzy5FXEVzUQ3uEIOaRUW736HkZeZhavFUAO64qby6xlIHGQ+6qFQrQSpIpNuD0sQJJ5DASYJLC07AXVS0OOijFfw7xG+gsQgWHLu7ZtWCk3AsA7c2WXDs4VSsUqG/YEMCJwmeBI4WgxNwFxWhj1bwr4nfQGOZd85qDI4ZyAppD7dcVDLPQ8IYEjhJ8CRwkl1UrnQTF/BBK+KY/UCETCqZXVR6FhwjyIJjD8dp4tRsM9CQwEmCqxicpCDjwMbgOHBRsc6E85LK0koAwMdtH+NIxxG2gzFA5l5UagyOGcjNag+nBfPIghNsSOAkIbsFR0TIgqNPQXYBJo2aBIBfKw5ZcAYgC449nHYT5y0Gh7Kk/IUEThzd/d041nUMAB8Ch2JwBqDdrzG8u6lECDK2i1EMjt75SaTbw0ldoZgS095HFpxgQgInjvgqxsNzhrMdDIZmUQW1kjFhTHxncR7RbbYpyWJPLirvcTJXVOsNQDE4QYUEThzx7ikeYjeSWzXIbsGhKrDWIQvOUPyaL3mZeabfSy4qezhxUanxN4D/85AELR+QwImDp/gb4PSuo6u/C4D8AscIelgYM7N0JgDgoxMf4XjXccajGQqTVg0+zZdwKGxa5JBIt4eTTU98AUZeYnAIfyGBEwdvAid51xHYLCpaHAwZkTsCZw0/CwCfVhy9nbPXgZZ+zpfkQGOjBZksOPZw0ouKpQXHChR47B0kcOI43M5PijgwdNehZ8Eh6wahNt50InC8mkcyBxkD5ov90X1qDyeF/tQYnHAojHCIj6Uu3bhJ7LgLH391DlCg4NApviw4yYFx5KIi9JhVOhiHw2FFYxZBxlaP7yTeLtmCQ60a3MVJoT+qgUOQwImDNxdVsgVHL4uKh2BoryEXVWp4DjSWfXExm0lFc9geToQhVTEmSODEobmoCvhwUSUvDmTBIfSYWTYQaLz36F609bQxHk0isruozBb7IwuOPZxUMiYLDkECJw7uLDjkoiJMMGbYGIwvHA8AaGxqZDuYJJi0avDRWkIxON7ixEXFWxVjwn9I4AzS1deF490DabbcCJxkFxVlUREG8Frwjyw4A5AFxx5OgozJgkOQwBlEbbKZm5GLouwixqMZICPkvotKxJ2kiGP2G6M4HKNr51e2hsyF/gB9C45eXByJdHs4SROnGByCBM4g8fE3vATumkkTDwK0OKTHUOAYXDu/0mZlt+CYDTImC449nBT6IwsOQQJnEN7ib4ChOw/qRUUYoQqc3a27tdYegPECIbPA8ROzLiqyQtrDURYVxeAEHhI4g3ApcAJmwXFijjY8ZkAWlrEFY1GaX4qYEsNbzW9pr/NowfH6b8LaRaUHWSHt4UYlY9ndpIQxJHAGUWNweKliDFCauAo9LMxhpR6OX25YmXtRARRk7DVu9KKiGJzgQgJnEC4tOJQmDiA4VhinaBWN4wROEF1UvlpwkmJwnKQ1E0Nxo5s4725SXmI+ZYQEziBcChxKEycsoFpw6g/Xa68ZLfbJWVReiQLZg4zJguMtTgr9iRiDQ2LHXUjgDKIKHJ5cVGTBGYBcVOZQBc67Le+iu78bAFlwzOAkZT45BsdogaI5bI8gWHAI7yCBM4jqr+XJgkMxOAM42ZEHaUd0RtEZGJU7Cv2xfrxz5J2U75VZ4PgJWXC8xcm9TzE4BAmcJHgSOPGm1RBC0i8WRtDu1xyhUGhIoDGPWVRew7oOjt75KQbHHtSLinACCZw48jLzUJhdyHoYGvE7j6xIVqCsEYQ9tDicQwNxOOSi8hazaeJkwbGHExeViDE4hLuQwImjLJ+fKsZA4o1p5J4Kws4wCN/RLTQLTlNqC45f81xPSHl9bh6DjMkKaQ/ZKhnTs8xfSODEwZN7Cki8MYNcxZgWB/OoTTffan4LfdE+5hYcFrBMEzeCLDj2cKMXFU8Ch/AXeZ9yNuBN4CS7qIIK7XrMM3HERBRlF6E32otdLbsM3ye1wPEzBsdsJWOaw7Zwo5s4BRkHF3mfcjbgKUUcMOeisgpZQ+QmFAphZtlMAAOBxmbr4BD2MBvfQfedPRxlUUXJghN0SODEwbMFJ8hF/mhxsIZa0bj+cD25qDiBXFT2cKMXFc9BxmTZ8xZ5n3I24E3gxO88guCiojL37hCfKs46TVwPz5ttcjhfeByTCDhxUWkxOCG5SxUQxpDAiYM3geOFi0pEeNyR80zV2IFA48amRm0Xm0yywJHpgczjfCELjj2cbHpEsOAQ3kICJ46yAs5icOJdVAHOoiKscc7IczAscxi6+ruwp3WP7nukdlFZFGt+pMzzKLpEwI1eVLzH4MTHw1FsnLvI+5SzAVlw+EQm64IfRMIRVJZWAkhsvBkPT/We3IZHMUEWHHu40YuKsqiCCwmcQbIiWSjIKmA9jASCFoNjBI8LFu+ocThvHnpT9/cyW3B4hES6PdzoRcW7BYfwDnrKDcJbFWOA6uCo0OJgHbXgn6EFR2JTuN/zJTcjN+17yIJjD1fq4FAMTmAhgTMIb+4pIPHGDHKaOGEd1YJj1FVcZguO3xY/M+0ayAppjyDE4BDeIe9TziJcChyy4ACgxcEOU0dPRU5GjuHvI+GIj6PxF78tOGbaNZAFxx6uZFFRDE5gIYEzCI8Ch2JwBiAXlXUywhk4r+Q8w99L7aLi0YJDc9gWrtTBIQtOYLElcFavXo0JEyYgJycHc+bMwfbt21O+/5lnnsGUKVOQk5ODGTNmYNOmTbYG6yW8tWkAyEWlQhYce6gVjfWQ2kXltwXHRD8qmsP2cHLdKAaHsPyU27BhA5YvX47a2lrs2LEDFRUVmDdvHo4cOaL7/tdeew3XX389br75ZjQ0NGDhwoVYuHAh3nlHPzaAFTxacMhFRThBLfinh8wCx2/MWHDIRWUPN1xUZMEJLpafcg899BCWLFmCmpoaTJs2DWvWrEFeXh7WrVun+/6f/OQn+Id/+Ad885vfxNSpU/G9730Ps2bNwn/91385HrybcClwqA4OADLv20UNNNaDt4xBN/HbWmImBofmsD3ccFFRDE5wsSRte3t7UV9fjxUrVmivhcNhVFdXY9u2bbqf2bZtG5YvX57w2rx58/Dcc88Znqenpwc9PT3av9va2qwM0zSPvP6I9t/jC8d7cg4n2InBuWvzXSl//3Hbx6bf6zc//OsP8b97/xfRWBRRJYqYEkNUiaI32st6aEJy7uhzDX+XbMF5etfT2HNUv+pxNBZ1dVxusfQPS7U5ElNi2o9RarxXmLHgbD24FXf88Q4AiWInfqFOFkFGv/PidT/OZ+czbx95G3ps/2Q7bv/D7QN//6Tnhfra6x+/DsA9C46V5+VP3viJ7utPNDwx5Ji7W3dr//7VW7+yfU5XPveZuzBh+ARbn+URS3/51tZWRKNRlJSUJLxeUlKC9957T/czTU1Nuu9vamoyPM+qVauwcuVKK0OzxQt7XtD+e9KoSZ6fzyrxD87Rw0brvmd4zvCEfxvdWHpYea8fbD2wFVsPbHX1mBeMv8DV44lEdkY2KkoqsLN555DflQxLvCe37N+CLfu3ODrfp8d+Gn879Le07/vHSf+Il/e97OhcAPDom4/a/uxF5RfhVzt/hQnDJ6CnvwedfZ22j3XpmZfiqbefAjBgCc7LzBvynl0tu7CrZZftcxCJ7D26F3uP7jX13tL8UlfOaeV5aSSyWzpbEv790+0/Tfh38mbO7jPa7ue+OP2LwRU4frFixYoEq09bWxvKy8tdP89tVbfhwMkDuHTCpVymzeZl5uGlf3kJH534CIsrFuu+Z0TuCKy4aAVWbV2F71z0nbTH7O7vxpNvP4kFkxZgzLAxbg/ZFm8efhNNp5qwcPJCRMIRREIR7f/DoTAi4Qj2tO7Bmvo1up/XW8S/UvkVzB0/FzWVNX58BW556p+fwgt7XsCull34uO1jVE+sRkVJBSYXT8Zv3voNPm77GIqimFoEQqEQ7v/L/UNev3bytfja+V/D5WddjqzvD1gai/OKsf2WxOSDN5e8iV0tu3BDxQ04cPIAHtz24JBjNf2/JhzvPo6pq6cO+d2Tn38SK19dib1H9+LLM76Ms0ecjUh4YI6oP+qcyc3MxT1/vgfHu48DAFq+mbiw3Fh5I/pj/bj4zItxvOs4vvPn72Drga34v6/8X9rrkMzNs27Gn/f/GROKJmgtMu6YfQd+uv2n2LNsD17Y8wJOdJ/Q3p/Qeyik34fI6HU7n2H1ulvHys/Kx7ljzsWn134aAPD1z3wdwzKHDXlGxP/91X+X5pfi6nOuThhT3eI6XPPba9DR1wEzZEWy8C8z/kX3Hvmk/RO8+OGLuOiMi7Bx10YAwNTiqfinKf+E3a278dKHL2nn+WrVVzEydyQeeeMRdPZ14tzR5+LaydcCAH7//u8xZtgYfHrsp7F2x1q0drZizrg5+NxZn0s4X2+0Fz/e9mMAwJTiKXiv9bRhYWrxVOxu3Y3PnfU5zBk3x9R3S4bHUA0nhBQLzuHe3l7k5eVh48aNWLhwofb6jTfeiBMnTuD5558f8pkzzjgDy5cvx1133aW9Vltbi+eeew47dw7dWerR1taGoqIinDx5EoWFhWaHSxAEQRAEQ1iu35aCjLOyslBVVYW6ujrttVgshrq6OsydO1f3M3Pnzk14PwC8/PLLhu8nCIIgCIJwimUX1fLly3HjjTfi/PPPx+zZs/HII4+go6MDNTUDroDFixdj3LhxWLVqFQDgzjvvxCWXXIIHH3wQ8+fPx/r16/Hmm2/i8ccfd/ebEARBEARBDGJZ4CxatAgtLS2499570dTUhMrKSmzevFkLJD5w4ADC4dOGoQsuuABPPfUU7rnnHnznO9/BOeecg+eeew7Tp09371sQBEEQBEHEYSkGhxUUg0MQBEEQ4iFMDA5BEARBEIQIkMAhCIIgCEI6SOAQBEEQBCEdJHAIgiAIgpAOEjgEQRAEQUgHCRyCIAiCIKSDBA5BEARBENJBAocgCIIgCOkggUMQBEEQhHRYbtXAArXYcltbG+OREARBEARhFnXdZtE0QQiB097eDgAoLy9nPBKCIAiCIKzS3t6OoqIiX88pRC+qWCyGQ4cOoaCgAKFQKOF3bW1tKC8vx8GDB4XsU0XjZwuNnx0ijx2g8bNG5PGLPHbA2vgVRUF7ezvGjh2b0IjbD4Sw4ITDYYwfPz7lewoLC4WcKCo0frbQ+Nkh8tgBGj9rRB6/yGMHzI/fb8uNCgUZEwRBEAQhHSRwCIIgCIKQDuEFTnZ2Nmpra5Gdnc16KLag8bOFxs8OkccO0PhZI/L4RR47IM74hQgyJgiCIAiCsILwFhyCIAiCIIhkSOAQBEEQBCEdJHAIgiAIgpAOEjgEQRAEQUiH6wJn9erVmDBhAnJycjBnzhxs375d+93+/fsRCoV0f5555hnDY3Z3d+MrX/kKZsyYgYyMDCxcuDDlGP76178iIyMDlZWVacf71ltv4eKLL0ZOTg7Ky8uxcOHCIeM/ceIEli5ditGjR3M1fr3jJl//Bx54AFdccQVGjx6N/Px84ca/fft2PPnkk6ioqEBOTo5n49+yZYvucZuamlJ+BzPz55lnnsGUKVOQnZ3N9fhHjBiBkSNHDrl3T5w4gRtuuIGrsfM0970av19zHwB6enpw991348wzz0R2djYmTJiAdevWGR4XAA4cOID58+cjLy8PY8aMQXV19ZDxb9myBbNmzUJWVhbX4y8oKEBRUdGQud/T04OlS5dyN/Y77rgDVVVVyM7ORmVl5ZC589hjj+Haa69FWVkZcnNzuR+/Hi+++CI+85nPoKCgAKNHj8Y///M/Y//+/SmPm4yrAmfDhg1Yvnw5amtrsWPHDlRUVGDevHk4cuQIgIFeUocPH074WblyJfLz83HVVVcZHjcajSI3Nxd33HEHqqurU47hxIkTWLx4MT73uc+lHW9bWxuuvPJKnHnmmaivr8fnP/95PP/887jkkksSxn/ppZdi//79+N3vfoft27fjd7/7Hf70pz8xH3/ycT/55JMh17+2thZz587Fpk2b8Oabb+Jf//VfkZGRgZdeekmI8X/uc5/DDTfcgJtvvhnvvPMOnnvuOUyaNAlXXXWVJ+Pfs2dPwvwcM2aM4XvNzJ/q6mp88YtfxM0334z6+nrcddddyMjIwCuvvMLV+L///e/j1KlTOHXqFL797W9rc//jjz/GFVdcgaNHj+L5559PmP8sx87j3Hd7/H7O/euuuw51dXV44oknsGfPHvz2t7/F5MmTUx53/vz56O3txWuvvYabbroJdXV1mD59ujb+K664AldffTUuu+wy7NixA/fffz/C4TCeeuoprsZfW1uL7u5uxGIxLF68OGHduu666/Dmm29iw4YN2L59O/73f/8Xzz//PNOxq9x0001YtGgRjh8/PmTuLF++HGeffTZ+97vfYefOnfj3f/93hEIh/OpXv2J+7ZPHr8dHH32Ea6+9FpdffjkaGxvx4osvorW1FZ///OfTHjcBxUVmz56tLF26VPt3NBpVxo4dq6xatcrwM5WVlcpNN91k+hw33nijcu211xr+ftGiRco999yj1NbWKhUVFSmP9eijjyojRoxQenp6tPHPmjVLmTx5sjb+oqIiZeTIkUpvby93408+7vDhw01d/2nTpikrV64UYvyFhYXKyJEjE97705/+VBk3bpyr43/llVcUAMrx48dNH8vM/MnNzdX+rTJnzhzltttu42r86r37rW99S5k8ebI2dxYuXKhMnDhRd/6zHHvycVnOfa/G79fc/+Mf/6gUFRUpR48eNX2sTZs2KeFwWGlqalIUZWDuX3LJJUphYaHS09OjRKNRJT8/XxkzZkzC5xYtWqTMmzePq/Grc/+xxx5TCgsLla6uLmXs2LFKTU2N4XFZjj2e2tpaJS8vz9Tcv/rqq5Wamhruxq+3TjzzzDNKRkaGEo1GtddeeOEFJRQKGa7Ferhmwent7UV9fX2C0guHw6iursa2bdt0P1NfX4/GxkbcfPPNrozh5z//Ofbt24fa2lpT79+2bRs++9nPIisrSxv/woULsWfPHhw/fhzhcBj5+fnIy8vD0qVLUVJSgunTp+MHP/gBotEo8/HHE41GcfLkybTXPxaLob29HSNHjhRi/BdccAGOHz+OTZs2QVEUNDc3Y+PGjbj66qtdHz8AVFZWoqysDFdccQX++te/pnyvmfkTCoWQmZmZ8Ll58+Zh27Zt3IwfgHbvzps3D3v27NH+Fq+99hrmzp07ZP5v376d6djj4WHuezF+v+b+Cy+8gPPPPx8/+tGPMG7cOEyaNAnf+MY30NXVZfiZbdu2YcaMGSgpKdHm/vXXX4+2tja8++67CIfDyMvLQ35+fsLnvJj7TsY/YsSIhLnf1taG3bt3o7q6Gq+88orucf/6178yHXs80WgUnZ2dptbdkydPuj73nY7fiKqqKoTDYfz85z/X7o9f//rXqK6uHvI8TYVrzTZbW1sRjUZRUlKS8HpJSQnee+893c888cQTmDp1Ki644ALH53///ffx7W9/G3/5y1+QkWHuazU1NeGss84CcHr8Z599tva7ESNGoLOzEydOnEA0GsWmTZvwwQcf4Pbbb0dfXx+am5uZjj+enp4eKIqS9vr/+Mc/xqlTp3Ddddfhvvvu4378M2bMwAcffIBFixahu7sb/f39WLBgAVavXo0777zTtfGXlZVhzZo1OP/889HT04Of/exnuPTSS/HGG29g1qxZup8xM3+6u7uH3OwlJSVoampydf47GX/8vas2xWtqakJJSQna2tqwceNGfPnLX06Y/88++yzTscfDeu57NX6/5v6+ffuwdetW5OTk4Nlnn0Vraytuv/12HD16FD//+c91P6POD+D03J80aZL2OwDo6+tDb29vwufUOfXf//3fXIw/fu6r30f93fHjx3WP+/vf/57p2OPp7OwEgLRz/+mnn8bf/vY3/Pd//zdWr17NzfiNOOuss/DSSy/huuuuw2233YZoNKq5m63ALIuqq6sLTz311BAVee655yI/Pz+tfzCeaDSKL33pS1i5cqV2k7lJZmYmHn/8cVRVVWHRokW4++678dhjjwkzfpWnnnoKK1euxNNPP42CggIhxt/a2oq///3vuPfee1FfX4/Nmzdj//79uOWWW1wbPwBMnjwZt912G6qqqnDBBRdg3bp1uOCCC/Dwww+7+n1UFEURYvyKomDMmDEJ8//f/u3fUF9fz/3Y4/Fq7ns5fr/mfiwWQygUwpNPPonZs2fj6quvxkMPPYRf/vKXjnfiRjz99NPcj19RlCHHXbVqFfbs2YPFixdzPfZ4XnnlFdTU1GDt2rWYOHGiEHOnqakJS5YswY033oi//e1vePXVV5GVlYUvfOELUCw0X3DNglNcXIxIJILm5uaE15ubm1FaWjrk/Rs3bkRnZ+eQibJp0yb09fUBAHJzc02du729HW+++SYaGhqwbNkyAAMXXlEULajw8ssvH/K50tJSbbzq+D/88EPtd8CAuCksLEQkEtE+N3XqVDQ3NyMzM5Pp+ONRM3SMrv/69etxyy234JlnnkF1dTV+/etfM7/+Zsb/6quvori4GN/85jcBAOeddx6GDRuGiy++2LXrb8Ts2bOxdetWw9+bmT85OTlDxtHc3IycnBwtIJv1+OPv3e7u7oTf5efnY9KkSQnzv6WlBYqi4Itf/CKzscfDcu57OX6/5n5ZWRnGjRunWe+AgWecoij4+OOPcc455wz5TGlpqZZppM6fvXv3ar8DBp6dWVlZCZ9T576b19/J+OPnvnr91blfWFiI7OzshOMeOHAAAHDZZZcxG3s8eXl5AGA491999VUsWLAADz/8MBYvXuz63Hc6fiNWr16NoqIi/OhHP9Je+81vfoPy8nK88cYb+MxnPmPqOK4JnKysLFRVVaGurk5LJ4vFYqirq9MWvXieeOIJXHPNNRg9enTC62eeeablcxcWFuLtt99OeO3RRx/Fn//8Z2zcuFFzIyQzd+5c3H333ejr69PG//zzz2Py5MkYMWIEYrEYurq6EIlEEIvFEA4PGLz27t2LrKwsLFiwgOn444lEIigqKtK9/hdffDFqamqwfv16zJ8/HwAf19/M+A8dOjTkJlEXWzUF2On4jWhsbERZWZnh783MH0VR0N/fn/C5l19+Gf39/a5df6fjD4VC2r1bUFCAyZMna38L1U0SP/+fe+455OTkYNy4cczGHg/Lue/l+P2a+xdeeCGeeeYZnDp1SouZ2bt3L8LhMMaPH6/7mblz5+L+++/HkSNHMGbMGFRVVWH9+vUoLCzEtGnTEIvF0NnZiVAolPC5l19+Gbm5ubj88su5GP+JEye0uX/kyBEUFhZiypQpqKurw+zZs7F58+aE4/76178GMCA2WY09nkgkgry8PN25f/XVV2P+/Pl44IEHcOuttwJwf+47Hb8RnZ2d2vNGRZ37sVjM/IEshz2nYP369Up2drbyi1/8Qtm1a5dy6623KsOHD9ci7VXef/99JRQKKX/84x9NH/vdd99VGhoalAULFiiXXnqp0tDQoDQ0NBi+30wWz4kTJ5SSkhLlhhtuUN555x3ljjvuUAAoX/nKV7TxFxYWKsOGDVOWLVum7NmzR/n973+vjBw5kovxJx932rRpSlZWlrJy5Upt/Hl5eUokElFWr16tHD58WDl8+LDy2muvCTf+Rx99VPnwww+VrVu3KtOnT1cAuDr+hx9+WHnuueeU999/X3n77beVO++8UwmHw8qf/vQnw2OamT8FBQVKJBJRfvzjHyu7d+9WamtrlYyMDNevv9Px/8d//IeSmZmpZGVlKffdd59279bX1ysFBQXa/H/88ccVAMrixYuZjj35uCznvtfj93rut7e3K+PHj1e+8IUvKO+++67y6quvKuecc45yyy23GB6zv79fmT59unLllVcqjY2NyooVKxQAyvz58xOenbm5uco3v/lNZffu3crq1auVcDjs+vV3Ov4HHnhAyczMVAoKCpQlS5Zoc//DDz9MOO6TTz6pANCywFiNXVEG1tCGhgbltttuU8rKyrS509jYqNx6661Kfn6+kpubq6xYscLTue/G+CdNmqQdV81IraurU0KhkLJy5Upl7969Sn19vTJv3jzlzDPPVDo7O02P31WBoyiK8p//+Z/KGWecoWRlZSmzZ89WXn/99SHvWbFihVJeXp6QApaOM888UwEw5McIswvszp07lYsuukjJzs5Wxo0bp1xzzTVDxv/aa68pc+bMUbKzs5WJEycql1xyCTfjNzquOv6ZM2fq/n7YsGFCjP/1119XfvrTnyrTpk1TcnNzlbKyMuXcc89Vxo4d6+r4H3jgAeXss89WcnJylJEjRyqXXnqp8uc//zntcc3Mn6efflqZNGmSkpWVpZx77rnKdddd5/r8cWP8RUVFyvDhw4fcu/Hzf/jw4UpRUZGlVE2vxs7L3Pdq/H7NfUVRlN27dyvV1dVKbm6uMn78eGX58uVpF5L9+/crV111lZKbm6sUFxcrl19++ZC5/8orryiVlZVKVlaWMnHiRGX+/PmePDudjn/YsGFKQUHBkLkff9yCggKloKBAOXXqFPOxX3LJJSnnztVXX637++zsbC6uvdH4P/roI+09v/3tb5WZM2cqw4YNU0aPHq1cc801yu7du02PXVEUJaQoFiJ2CIIgCIIgBIB6UREEQRAEIR0kcAiCIAiCkA4SOARBEARBSAcJHIIgCIIgpIMEDkEQBEEQ0kEChyAIgiAI6SCBQxAEQRCEdJDAIQiCIAhCOkjgEARBEAQhHSRwCIIgCIKQDhI4BEEQBEFIBwkcgiAIgiCk4/8HMvQFEyd9r+AAAAAASUVORK5CYII="
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 13
+   "execution_count": 4
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-28T02:25:45.551404Z",
-     "start_time": "2026-01-28T02:25:44.624153Z"
+     "end_time": "2026-01-31T19:34:07.117300Z",
+     "start_time": "2026-01-31T19:34:06.083522Z"
     }
    },
    "cell_type": "code",
@@ -148,10 +153,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1c01cd9b1d0>]"
+       "[<matplotlib.lines.Line2D at 0x1f08a7f8b00>]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -160,19 +165,19 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 4
+   "execution_count": 23
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-28T02:25:56.201426Z",
-     "start_time": "2026-01-28T02:25:46.539316Z"
+     "end_time": "2026-01-31T19:34:44.016581Z",
+     "start_time": "2026-01-31T19:34:43.992027Z"
     }
    },
    "cell_type": "code",
@@ -188,39 +193,13 @@
    ],
    "id": "6a0eed52a654e483",
    "outputs": [],
-   "execution_count": 5
+   "execution_count": 25
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-28T02:46:27.816995Z",
-     "start_time": "2026-01-28T02:46:27.807935Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "start_utc = time(0, 00, 00)  #querying i svancouver time, influxdb gives utc\n",
-    "stop_utc = time(10, 45, 00)\n",
-    "date_start = date(2024, 7, 14)\n",
-    "date_stop = date(2024, 7, 14)\n",
-    "\n",
-    "vancouver = pytz.timezone(\"America/Vancouver\")\n",
-    "\n",
-    "start_local = vancouver.localize(datetime.combine(date_start, start_utc))\n",
-    "stop_local = vancouver.localize(datetime.combine(date_stop, stop_utc))\n",
-    "\n",
-    "start_time = start_local.astimezone(pytz.utc)\n",
-    "stop_time = stop_local.astimezone(pytz.utc)"
-   ],
-   "id": "ea55f2bf89a84139",
-   "outputs": [],
-   "execution_count": 18
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-28T02:42:51.137465Z",
-     "start_time": "2026-01-28T02:42:48.923530Z"
+     "end_time": "2026-01-31T19:34:47.287669Z",
+     "start_time": "2026-01-31T19:34:44.540200Z"
     }
    },
    "cell_type": "code",
@@ -254,7 +233,7 @@
        "Text(0.5, 1.0, 'speed kph')"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -263,7 +242,7 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -273,7 +252,7 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -283,38 +262,48 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 16
+   "execution_count": 26
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-31T19:28:09.046566Z",
+     "start_time": "2026-01-31T19:28:09.041415Z"
+    }
+   },
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": "",
-   "id": "649310b8f7176c67"
+   "id": "649310b8f7176c67",
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-31T19:28:22.900645Z",
+     "start_time": "2026-01-31T19:28:22.894594Z"
+    }
+   },
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
     "# why does brake pressed look more continous then acceleartor - accelerator seems like it is either 100% or none\n",
     "# a good place where this lines up is"
    ],
-   "id": "54a0ee4a260b394"
+   "id": "54a0ee4a260b394",
+   "outputs": [],
+   "execution_count": 10
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-28T02:46:32.657931Z",
-     "start_time": "2026-01-28T02:46:31.918654Z"
+     "end_time": "2026-01-31T19:34:56.251992Z",
+     "start_time": "2026-01-31T19:34:55.381927Z"
     }
    },
    "cell_type": "code",
@@ -337,7 +326,7 @@
        "Text(0.5, 1.0, 'speed kph')"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -346,13 +335,13 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 19
+   "execution_count": 27
   },
   {
    "metadata": {},
@@ -365,8 +354,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-24T19:55:00.999144Z",
-     "start_time": "2026-01-24T19:55:00.035354Z"
+     "end_time": "2026-01-31T19:35:15.420190Z",
+     "start_time": "2026-01-31T19:35:14.477156Z"
     }
    },
    "cell_type": "code",
@@ -385,19 +374,19 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 9
+   "execution_count": 30
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-28T03:08:57.191958Z",
-     "start_time": "2026-01-28T03:08:55.447614Z"
+     "end_time": "2026-01-31T19:35:21.803280Z",
+     "start_time": "2026-01-31T19:35:20.212216Z"
     }
    },
    "cell_type": "code",
@@ -431,7 +420,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_5852\\1203527545.py:21: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_13952\\1203527545.py:21: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
       "  ax1.legend(loc = \"upper left\")\n"
      ]
     },
@@ -440,13 +429,13 @@
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 21
+   "execution_count": 31
   },
   {
    "metadata": {
@@ -469,13 +458,289 @@
    "outputs": [],
    "execution_count": 10
   },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-31T19:35:47.170052Z",
+     "start_time": "2026-01-31T19:35:47.163555Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "#preprocessing - convert everything to pandas dataframes.\n",
+    "\n",
+    "\n",
+    "df_mech_brake_pressed = pd.DataFrame(mech_brake_pressed)\n",
+    "df_accel_position = pd.DataFrame(scaled_accel_position)\n",
+    "df_speed_kph = pd.DataFrame(speed_kph)\n",
+    "\n",
+    "\n"
+   ],
+   "id": "a6707eebb9ccd8c9",
+   "outputs": [],
+   "execution_count": 32
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-31T19:41:06.493573Z",
+     "start_time": "2026-01-31T19:41:06.480312Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df_speed_kph.head(100)",
+   "id": "59a233a5c0817c8e",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "      0\n",
+       "0   0.0\n",
+       "1   0.0\n",
+       "2   0.0\n",
+       "3   0.0\n",
+       "4   0.0\n",
+       "..  ...\n",
+       "95  0.0\n",
+       "96  0.0\n",
+       "97  0.0\n",
+       "98  0.0\n",
+       "99  0.0\n",
+       "\n",
+       "[100 rows x 1 columns]"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 1 columns</p>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 34
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-31T19:41:21.451485Z",
+     "start_time": "2026-01-31T19:41:21.121306Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plt.plot(df_speed_kph)",
+   "id": "5f804fb7b1f09099",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1f089faeea0>]"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 35
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-31T19:59:44.616950Z",
+     "start_time": "2026-01-31T19:59:44.610437Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# combine all dfs and resample, then feed to scaler.\n",
+    "# states = velocity, position\n",
+    "# control = mbrake pressed, accelerator position\n",
+    "def combine_dfs(telemetry_names, index_common, all_dfs):\n",
+    "        combined_df = pd.DataFrame(index=index_common)\n",
+    "\n",
+    "        for name, df in zip(telemetry_names, all_dfs):\n",
+    "            #df_interp = self.resample(df, index_common)\n",
+    "            combined_df[name] = df\n",
+    "\n",
+    "        return combined_df\n"
+   ],
+   "id": "3e63d74e16a13193",
+   "outputs": [],
+   "execution_count": 40
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-31T20:00:02.846028Z",
+     "start_time": "2026-01-31T20:00:02.683528Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "all_dfs = [df_mech_brake_pressed, df_accel_position, df_speed_kph]\n",
+    "combined_df = combine_dfs([\"mech_brake_pressed\", \"accel_position\", \"speed_kph\"], df_mech_brake_pressed.index, all_dfs)\n"
+   ],
+   "id": "6dea8ee6d0965889",
+   "outputs": [],
+   "execution_count": 42
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-31T20:00:14.613514Z",
+     "start_time": "2026-01-31T20:00:14.278469Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plt.plot(combined_df[\"mech_brake_pressed\"])",
+   "id": "42e9fe006fc1b367",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1f0824277a0>]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 43
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "# i dont really think there is much of a need to resample right now, because on querying from influx they will all be at the same frequency.\n",
+    "# preprocessing pipeline: dataset - rescaling - sequences - tensors - RNN"
+   ],
+   "id": "e4585f807a9c575a"
+  },
   {
    "metadata": {},
    "cell_type": "code",
    "outputs": [],
    "execution_count": null,
-   "source": "#preprocessing - convert everything to pandas dataframes.",
-   "id": "a6707eebb9ccd8c9"
+   "source": [
+    "# testing training split - 70 - 30\n",
+    "\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "\n",
+    "def\n",
+    "\n",
+    "# Choose columns to scale (all states + controls)\n",
+    "cols_to_scale = state + control\n",
+    "\n",
+    "# Fit scaler on the training portion only\n",
+    "train_len = int(0.8 * len(final_df_car13))\n",
+    "df_train_raw = final_df_car13.iloc[:train_len].reset_index(drop=True)\n",
+    "df_test_raw  = final_df_car13.iloc[train_len:].reset_index(drop=True)\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "scaler.fit(df_train_raw[cols_to_scale])  # fit only on train\n",
+    "\n",
+    "# Transform both train and test\n",
+    "df_train = df_train_raw.copy()\n",
+    "df_train[cols_to_scale] = scaler.transform(df_train_raw[cols_to_scale])\n",
+    "\n",
+    "df_test = df_test_raw.copy()\n",
+    "df_test[cols_to_scale] = scaler.transform(df_test_raw[cols_to_scale])\n"
+   ],
+   "id": "ab4848af3022daef"
   }
  ],
  "metadata": {
diff --git a/array_temp/coefficient_fitting.ipynb b/array_temp/coefficient_fitting.ipynb
index 67e8126..04e1181 100644
--- a/array_temp/coefficient_fitting.ipynb
+++ b/array_temp/coefficient_fitting.ipynb
@@ -21,7 +21,7 @@
     "from datetime import datetime, time, date\n",
     "\n",
     "#each 5 seconds\n",
-    "utc_offset_h =\n",
+    "utc_offset_h = 7\n",
     "start_utc = time(00+utc_offset_h, 00, 00)  #querying i svancouver time, influxdb gives utc\n",
     "stop_utc = time(00+utc_offset_h, 00, 00)\n",
     "date_start = date(2025, 7, 2)\n",
diff --git a/array_temp/data_preprocessing.py b/array_temp/data_preprocessing.py
new file mode 100644
index 0000000..c2cc226
--- /dev/null
+++ b/array_temp/data_preprocessing.py
@@ -0,0 +1,56 @@
+#helper class to clean data from influx and organise further
+
+
+
+
+from os import rename
+import pandas
+from sqlalchemy.testing.util import total_size
+from data_tools import query
+from data_tools.collections import TimeSeries
+from datetime import datetime, date, time, timezone
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import dill
+import os
+import pytz
+from datetime import datetime, time, date
+
+def data_preprocessing():
+
+    def __init__(self, influx):
+        self.influx = influx
+
+    # #queries brake_pressed, accelerator position and speed
+    # def query_data(self, db, start_time, stop_time):
+    #     utc_offset_h = 7
+    #     start_utc = time(start_time)
+    #     stop_utc = time(stop_time)
+    #
+    #     date_start = date(2024, 7, 14)
+    #     date_stop = date(2024, 7, 16)
+    #
+    #     vancouver = pytz.timezone("America/Vancouver")
+    #
+    #     start_local = vancouver.localize(datetime.combine(date_start, start_utc))
+    #     stop_local = vancouver.localize(datetime.combine(date_stop, stop_utc))
+    #
+    #     start_time = start_local.astimezone(pytz.utc)
+    #     stop_time = stop_local.astimezone(pytz.utc)
+    #
+    #     client = query.DBClient()
+    #     mech_brake_pressed: TimeSeries = client.query_time_series(start_time, stop_time, field="MechBrakePressed")
+    #     accel_position: TimeSeries = client.query_time_series(start_time, stop_time, field="AcceleratorPosition")
+    #     speed_kph: TimeSeries = client.query_time_series(start_time, stop_time, "VehicleVelocity")
+
+
+    def combine_dfs(self, telemetry_names, index_common, all_dfs):
+        combined_df = pd.DataFrame(index=index_common)
+
+        for name, df in zip(telemetry_names, all_dfs):
+            #df_interp = self.resample(df, index_common)
+            combined_df[name] = pd.to_numeric(df).values
+
+        return combined_df
+
diff --git a/array_temp/faiman_coefficients.ipynb b/array_temp/faiman_coefficients.ipynb
index 0ea8832..11757ef 100644
--- a/array_temp/faiman_coefficients.ipynb
+++ b/array_temp/faiman_coefficients.ipynb
@@ -19,8 +19,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-24T19:03:02.470160Z",
-     "start_time": "2026-01-24T19:02:49.353246Z"
+     "end_time": "2026-02-04T02:31:54.601284Z",
+     "start_time": "2026-02-04T02:31:35.114318Z"
     }
    },
    "cell_type": "code",
@@ -49,7 +49,7 @@
    ],
    "id": "8b2b4006671bdc31",
    "outputs": [],
-   "execution_count": 3
+   "execution_count": 1
   },
   {
    "metadata": {},
@@ -127,8 +127,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-21T02:58:12.363515Z",
-     "start_time": "2026-01-21T02:58:12.288999Z"
+     "end_time": "2026-02-04T02:32:02.997314Z",
+     "start_time": "2026-02-04T02:32:02.955553Z"
     }
    },
    "cell_type": "code",
@@ -154,13 +154,13 @@
    ],
    "id": "a807d5de05d7c8b0",
    "outputs": [],
-   "execution_count": 3
+   "execution_count": 2
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-24T19:13:01.978445Z",
-     "start_time": "2026-01-24T19:12:59.360895Z"
+     "end_time": "2026-02-04T02:32:05.455099Z",
+     "start_time": "2026-02-04T02:32:03.785220Z"
     }
    },
    "cell_type": "code",
@@ -258,13 +258,13 @@
      ]
     }
    ],
-   "execution_count": 5
+   "execution_count": 3
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-24T19:14:11.950227Z",
-     "start_time": "2026-01-24T19:14:11.837834Z"
+     "end_time": "2026-02-04T02:32:06.668745Z",
+     "start_time": "2026-02-04T02:32:06.290283Z"
     }
    },
    "cell_type": "code",
@@ -285,7 +285,7 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 9
+   "execution_count": 4
   },
   {
    "metadata": {},
@@ -307,8 +307,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-21T02:58:18.041202Z",
-     "start_time": "2026-01-21T02:58:16.220050Z"
+     "end_time": "2026-02-04T02:32:10.234465Z",
+     "start_time": "2026-02-04T02:32:08.835881Z"
     }
    },
    "cell_type": "code",
@@ -336,8 +336,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-21T02:58:22.078880Z",
-     "start_time": "2026-01-21T02:58:19.236043Z"
+     "end_time": "2026-02-04T02:32:14.288541Z",
+     "start_time": "2026-02-04T02:32:11.446668Z"
     }
    },
    "cell_type": "code",
@@ -351,7 +351,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1b99b24ce00>]"
+       "[<matplotlib.lines.Line2D at 0x2903a1cb080>]"
       ]
      },
      "execution_count": 6,
@@ -374,8 +374,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-21T02:58:26.671686Z",
-     "start_time": "2026-01-21T02:58:23.552776Z"
+     "end_time": "2026-02-04T02:32:17.585480Z",
+     "start_time": "2026-02-04T02:32:14.346223Z"
     }
    },
    "cell_type": "code",
@@ -419,8 +419,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-21T02:58:27.819007Z",
-     "start_time": "2026-01-21T02:58:27.799131Z"
+     "end_time": "2026-02-04T02:32:17.653384Z",
+     "start_time": "2026-02-04T02:32:17.649414Z"
     }
    },
    "cell_type": "code",
@@ -432,8 +432,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-21T02:58:29.706550Z",
-     "start_time": "2026-01-21T02:58:28.379122Z"
+     "end_time": "2026-02-04T02:32:19.088024Z",
+     "start_time": "2026-02-04T02:32:17.739079Z"
     }
    },
    "cell_type": "code",
@@ -456,6 +456,14 @@
    ],
    "id": "203be30192c9302a",
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_23588\\3978562244.py:11: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
+      "  df_15m = df.resample(\"15T\").mean()\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -467,14 +475,6 @@
       "2025-07-01 23:15:00+00:00  27.667362      25.761946\n",
       "2025-07-01 23:30:00+00:00  27.437863      26.135361\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_32752\\3978562244.py:11: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
-      "  df_15m = df.resample(\"15T\").mean()\n"
-     ]
     }
    ],
    "execution_count": 9
@@ -482,8 +482,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-21T02:58:30.476336Z",
-     "start_time": "2026-01-21T02:58:30.465127Z"
+     "end_time": "2026-02-04T02:32:27.134739Z",
+     "start_time": "2026-02-04T02:32:27.122976Z"
     }
    },
    "cell_type": "code",
@@ -498,8 +498,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-21T02:58:31.033231Z",
-     "start_time": "2026-01-21T02:58:31.020598Z"
+     "end_time": "2026-02-04T02:32:34.653131Z",
+     "start_time": "2026-02-04T02:32:34.641704Z"
     }
    },
    "cell_type": "code",
@@ -514,13 +514,13 @@
    ],
    "id": "feced1770070bc8b",
    "outputs": [],
-   "execution_count": 11
+   "execution_count": 12
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-21T02:58:31.679645Z",
-     "start_time": "2026-01-21T02:58:31.655283Z"
+     "end_time": "2026-02-04T02:32:35.156144Z",
+     "start_time": "2026-02-04T02:32:35.131346Z"
     }
    },
    "cell_type": "code",
@@ -563,20 +563,20 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_32752\\1287101057.py:9: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_23588\\1287101057.py:9: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
       "  df_15m_copy.index = df_15m_copy.index.floor(\"15T\")\n",
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_32752\\1287101057.py:10: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_23588\\1287101057.py:10: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
       "  weather_df.index = weather_df.index.floor(\"15T\")\n"
      ]
     }
    ],
-   "execution_count": 12
+   "execution_count": 13
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-21T02:58:32.365442Z",
-     "start_time": "2026-01-21T02:58:32.236108Z"
+     "end_time": "2026-02-04T02:32:35.941404Z",
+     "start_time": "2026-02-04T02:32:35.790454Z"
     }
    },
    "cell_type": "code",
@@ -602,13 +602,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 13
+   "execution_count": 14
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-21T03:00:16.483077Z",
-     "start_time": "2026-01-21T03:00:16.473106Z"
+     "end_time": "2026-02-04T02:32:44.400501Z",
+     "start_time": "2026-02-04T02:32:44.375421Z"
     }
    },
    "cell_type": "code",
@@ -755,31 +755,31 @@
        "</div>"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 27
+   "execution_count": 15
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-21T02:58:33.464606Z",
-     "start_time": "2026-01-21T02:58:33.445256Z"
+     "end_time": "2026-02-04T02:32:45.475105Z",
+     "start_time": "2026-02-04T02:32:45.469354Z"
     }
    },
    "cell_type": "code",
    "source": "merged_df = merged_df.rename(columns={'value': 'array_temperature'})",
    "id": "e0882e697c1613f7",
    "outputs": [],
-   "execution_count": 15
+   "execution_count": 16
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-21T02:58:33.965162Z",
-     "start_time": "2026-01-21T02:58:33.952450Z"
+     "end_time": "2026-02-04T02:32:46.110191Z",
+     "start_time": "2026-02-04T02:32:46.095120Z"
     }
    },
    "cell_type": "code",
@@ -869,18 +869,18 @@
        "</div>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 16
+   "execution_count": 17
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-21T02:58:34.606Z",
-     "start_time": "2026-01-21T02:58:34.458522Z"
+     "end_time": "2026-02-04T02:32:47.122329Z",
+     "start_time": "2026-02-04T02:32:46.971398Z"
     }
    },
    "cell_type": "code",
@@ -917,7 +917,7 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 17
+   "execution_count": 18
   },
   {
    "metadata": {},
@@ -928,8 +928,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-21T02:59:31.545204Z",
-     "start_time": "2026-01-21T02:59:31.540211Z"
+     "end_time": "2026-02-04T02:32:55.965069Z",
+     "start_time": "2026-02-04T02:32:49.257526Z"
     }
    },
    "cell_type": "code",
@@ -954,13 +954,13 @@
    ],
    "id": "1aa707313aeb5993",
    "outputs": [],
-   "execution_count": 23
+   "execution_count": 19
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-21T02:59:32.133227Z",
-     "start_time": "2026-01-21T02:59:32.101296Z"
+     "end_time": "2026-02-04T02:32:57.234994Z",
+     "start_time": "2026-02-04T02:32:57.214043Z"
     }
    },
    "cell_type": "code",
@@ -970,30 +970,30 @@
    ],
    "id": "492ea4964124380d",
    "outputs": [],
-   "execution_count": 24
+   "execution_count": 20
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-21T02:59:33.009661Z",
-     "start_time": "2026-01-21T02:59:32.920197Z"
+     "end_time": "2026-02-04T02:33:18.348547Z",
+     "start_time": "2026-02-04T02:33:17.969422Z"
     }
    },
    "cell_type": "code",
    "source": [
     "#comparing immediate, unfit results\n",
     "plt.plot(np.array(merged_df['array_temperature']), label=\"Array Temperature\")\n",
-    "plt.plot(faiman_temp, color=\"green\", label=\"Modelled Temperature\")"
+    "plt.plot(faiman_temp+27, color=\"green\", label=\"Modelled Temperature\")"
    ],
    "id": "5e9c99851878c7d3",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1b99b1daed0>]"
+       "[<matplotlib.lines.Line2D at 0x2903a7f70e0>]"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1002,13 +1002,13 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 25
+   "execution_count": 22
   },
   {
    "metadata": {},

From 8c1e20e44615551142c5d9ff7e8842806ae37839 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Sat, 7 Feb 2026 12:28:38 -0800
Subject: [PATCH 15/49] localisation code

---
 array_temp/Control_Model.ipynb       |  71 +++-
 array_temp/faiman_coefficients.ipynb | 505 +++++++++++++++++----------
 2 files changed, 385 insertions(+), 191 deletions(-)

diff --git a/array_temp/Control_Model.ipynb b/array_temp/Control_Model.ipynb
index 4b7bab9..2f2114a 100644
--- a/array_temp/Control_Model.ipynb
+++ b/array_temp/Control_Model.ipynb
@@ -16,11 +16,21 @@
    ],
    "id": "13a4cc26a47d5ad7"
   },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "#necessary imports\n",
+    "import sklearn as sk"
+   ],
+   "id": "8672b9d1bf8a74aa"
+  },
   {
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-31T19:34:34.910245Z",
-     "start_time": "2026-01-31T19:34:11.849855Z"
+    "jupyter": {
+     "is_executing": true
     }
    },
    "cell_type": "code",
@@ -40,8 +50,8 @@
     "\n",
     "#each 5 seconds\n",
     "utc_offset_h = 7\n",
-    "start_utc = time(0, 00, 00)  #querying i svancouver time, influxdb gives utc\n",
-    "stop_utc = time(23, 45, 00)\n",
+    "start_utc = time(0+utc_offset_h, 00, 00)  #querying i svancouver time, influxdb gives utc\n",
+    "stop_utc = time(23+utc_offset_h, 45, 00)\n",
     "date_start = date(2024, 7, 16)\n",
     "date_stop = date(2024, 7, 18)\n",
     "\n",
@@ -61,20 +71,20 @@
    ],
    "id": "initial_id",
    "outputs": [],
-   "execution_count": 24
+   "execution_count": null
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-31T19:27:46.052990Z",
-     "start_time": "2026-01-31T19:27:46.047232Z"
+     "end_time": "2026-02-06T00:27:15.513885Z",
+     "start_time": "2026-02-06T00:27:15.504931Z"
     }
    },
    "cell_type": "code",
    "source": "# speed is most likely in m/s",
    "id": "5d30f3a75f7e9149",
    "outputs": [],
-   "execution_count": 2
+   "execution_count": 1
   },
   {
    "metadata": {
@@ -85,6 +95,7 @@
    },
    "cell_type": "code",
    "source": [
+    "\n",
     "# save collected data\n",
     "\n",
     "out_dir = os.path.join(\"../../array_temp\", \"data\", \"control_state_fsgp_2024\")\n",
@@ -458,6 +469,48 @@
    "outputs": [],
    "execution_count": 10
   },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "localisation code is primarily for day1 and day3 of fsgp.\n",
+    "- integrate the velocity curve up to a point and then manual mapping from distance covered to found coordinates.\n",
+    "- labs before and after we pitted will have nan values\n",
+    "- lap7 day1 - might have diverging velocities\n",
+    "- he dip happens after lap 28 and lap 32 which is when we pitted.\n",
+    "- used MDI vehicle velocity\n",
+    "-"
+   ],
+   "id": "73256b66933551d3"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "from data_tools import lap_tools\n",
+    "from physics.environment.gis.gis import GIS\n",
+    "from physics.environment.gis.gis import calculate_path_distances\n",
+    "\n",
+    "# Defining dictionary \"route_data\"\n",
+    "\n",
+    "route_data = {\n",
+    "    \"path\" : np.array(coords),\n",
+    "    \"elevations\" : np.zeros(len(coords)),\n",
+    "    \"time_zones\" : np.zeros(len(coords)),\n",
+    "    \"num_unique_coords\" : (len(coords) - 1) }\n",
+    "\n",
+    "# Creating GIS object\n",
+    "\n",
+    "starting_coords = [37.00107373, -86.36854755]\n",
+    "\n",
+    "gis = GIS(route_data, starting_coords, current_coord = starting_coords)\n",
+    "\n",
+    "lap_length = np.cumsum(calculate_path_distances(gis.path[:gis.num_unique_coords]))[-1] # TOTAL LAP LENGTH"
+   ],
+   "id": "88a4ae7fb0d75eed"
+  },
   {
    "metadata": {
     "ExecuteTime": {
diff --git a/array_temp/faiman_coefficients.ipynb b/array_temp/faiman_coefficients.ipynb
index 11757ef..6946d41 100644
--- a/array_temp/faiman_coefficients.ipynb
+++ b/array_temp/faiman_coefficients.ipynb
@@ -19,8 +19,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-04T02:31:54.601284Z",
-     "start_time": "2026-02-04T02:31:35.114318Z"
+     "end_time": "2026-02-04T02:59:06.874190Z",
+     "start_time": "2026-02-04T02:58:53.887072Z"
     }
    },
    "cell_type": "code",
@@ -35,7 +35,7 @@
     "import os\n",
     "\n",
     "\n",
-    "utc_offset_h = 5\n",
+    "utc_offset_h = 7\n",
     "start_utc = time(00 + utc_offset_h, 00, 00)  #querying is vancouver time, influxdb gives utc\n",
     "stop_utc = time(00 + utc_offset_h, 00, 00)\n",
     "date_start = date(2025, 7, 2)\n",
@@ -159,8 +159,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-04T02:32:05.455099Z",
-     "start_time": "2026-02-04T02:32:03.785220Z"
+     "end_time": "2026-02-04T02:59:08.603709Z",
+     "start_time": "2026-02-04T02:59:08.182291Z"
     }
    },
    "cell_type": "code",
@@ -258,13 +258,13 @@
      ]
     }
    ],
-   "execution_count": 3
+   "execution_count": 2
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-04T02:32:06.668745Z",
-     "start_time": "2026-02-04T02:32:06.290283Z"
+     "end_time": "2026-02-04T02:59:09.779360Z",
+     "start_time": "2026-02-04T02:59:09.575856Z"
     }
    },
    "cell_type": "code",
@@ -285,7 +285,7 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 4
+   "execution_count": 3
   },
   {
    "metadata": {},
@@ -307,8 +307,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-04T02:32:10.234465Z",
-     "start_time": "2026-02-04T02:32:08.835881Z"
+     "end_time": "2026-02-04T03:54:19.443686Z",
+     "start_time": "2026-02-04T03:54:17.120886Z"
     }
    },
    "cell_type": "code",
@@ -325,19 +325,19 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 5
+   "execution_count": 8
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-04T02:32:14.288541Z",
-     "start_time": "2026-02-04T02:32:11.446668Z"
+     "end_time": "2026-02-04T03:54:23.281676Z",
+     "start_time": "2026-02-04T03:54:20.710545Z"
     }
    },
    "cell_type": "code",
@@ -351,10 +351,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x2903a1cb080>]"
+       "[<matplotlib.lines.Line2D at 0x1c531538320>]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -363,19 +363,19 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 6
+   "execution_count": 9
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-04T02:32:17.585480Z",
-     "start_time": "2026-02-04T02:32:14.346223Z"
+     "end_time": "2026-02-04T03:54:26.094168Z",
+     "start_time": "2026-02-04T03:54:23.316319Z"
     }
    },
    "cell_type": "code",
@@ -408,32 +408,32 @@
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 7
+   "execution_count": 10
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-04T02:32:17.653384Z",
-     "start_time": "2026-02-04T02:32:17.649414Z"
+     "end_time": "2026-02-04T03:54:26.884924Z",
+     "start_time": "2026-02-04T03:54:26.877214Z"
     }
    },
    "cell_type": "code",
    "source": "#there really isn't Influx data for dates after midday 5 July, so I see no reason to incorporate the OpenMeteo data after",
    "id": "90465ecf61541d72",
    "outputs": [],
-   "execution_count": 8
+   "execution_count": 11
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-04T02:32:19.088024Z",
-     "start_time": "2026-02-04T02:32:17.739079Z"
+     "end_time": "2026-02-04T03:54:28.834441Z",
+     "start_time": "2026-02-04T03:54:27.570631Z"
     }
    },
    "cell_type": "code",
@@ -457,33 +457,33 @@
    "id": "203be30192c9302a",
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_23588\\3978562244.py:11: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
-      "  df_15m = df.resample(\"15T\").mean()\n"
+      "                               value  value_shifted\n",
+      "2025-07-02 00:45:00+00:00  25.031385      28.002427\n",
+      "2025-07-02 01:00:00+00:00  25.388523      28.375842\n",
+      "2025-07-02 01:15:00+00:00  25.761938      28.749256\n",
+      "2025-07-02 01:30:00+00:00  26.135353      29.122671\n",
+      "2025-07-02 01:45:00+00:00  26.508768      29.496086\n"
      ]
     },
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "                               value  value_shifted\n",
-      "2025-07-01 22:30:00+00:00  25.815057      24.492416\n",
-      "2025-07-01 22:45:00+00:00  27.457032      24.675352\n",
-      "2025-07-01 23:00:00+00:00  27.983232      25.388531\n",
-      "2025-07-01 23:15:00+00:00  27.667362      25.761946\n",
-      "2025-07-01 23:30:00+00:00  27.437863      26.135361\n"
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_648\\3978562244.py:11: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
+      "  df_15m = df.resample(\"15T\").mean()\n"
      ]
     }
    ],
-   "execution_count": 9
+   "execution_count": 12
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-04T02:32:27.134739Z",
-     "start_time": "2026-02-04T02:32:27.122976Z"
+     "end_time": "2026-02-04T03:54:28.996598Z",
+     "start_time": "2026-02-04T03:54:28.963950Z"
     }
    },
    "cell_type": "code",
@@ -493,13 +493,13 @@
    ],
    "id": "a30bec6abef1672a",
    "outputs": [],
-   "execution_count": 10
+   "execution_count": 13
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-04T02:32:34.653131Z",
-     "start_time": "2026-02-04T02:32:34.641704Z"
+     "end_time": "2026-02-04T03:54:29.099264Z",
+     "start_time": "2026-02-04T03:54:29.079366Z"
     }
    },
    "cell_type": "code",
@@ -514,13 +514,13 @@
    ],
    "id": "feced1770070bc8b",
    "outputs": [],
-   "execution_count": 12
+   "execution_count": 14
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-04T02:32:35.156144Z",
-     "start_time": "2026-02-04T02:32:35.131346Z"
+     "end_time": "2026-02-04T03:54:29.810420Z",
+     "start_time": "2026-02-04T03:54:29.733653Z"
     }
    },
    "cell_type": "code",
@@ -554,29 +554,29 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "overlap start: 2025-07-01 22:30:00+00:00\n",
+      "overlap start: 2025-07-02 00:45:00+00:00\n",
       "overlap end: 2025-07-05 14:00:00+00:00\n",
-      "no of aligned points: 351\n"
+      "no of aligned points: 342\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_23588\\1287101057.py:9: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_648\\1287101057.py:9: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
       "  df_15m_copy.index = df_15m_copy.index.floor(\"15T\")\n",
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_23588\\1287101057.py:10: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_648\\1287101057.py:10: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
       "  weather_df.index = weather_df.index.floor(\"15T\")\n"
      ]
     }
    ],
-   "execution_count": 13
+   "execution_count": 15
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-04T02:32:35.941404Z",
-     "start_time": "2026-02-04T02:32:35.790454Z"
+     "end_time": "2026-02-04T03:54:30.494999Z",
+     "start_time": "2026-02-04T03:54:30.364340Z"
     }
    },
    "cell_type": "code",
@@ -596,19 +596,19 @@
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 14
+   "execution_count": 16
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-04T02:32:44.400501Z",
-     "start_time": "2026-02-04T02:32:44.375421Z"
+     "end_time": "2026-02-04T03:54:31.023413Z",
+     "start_time": "2026-02-04T03:54:30.991150Z"
     }
    },
    "cell_type": "code",
@@ -619,11 +619,11 @@
      "data": {
       "text/plain": [
        "                           array_temperature  temperature_2m  \\\n",
-       "2025-07-01 22:30:00+00:00          24.492416          -2.163   \n",
-       "2025-07-01 22:45:00+00:00          24.675352          -2.213   \n",
-       "2025-07-01 23:00:00+00:00          25.388531          -2.263   \n",
-       "2025-07-01 23:15:00+00:00          25.761946          -2.263   \n",
-       "2025-07-01 23:30:00+00:00          26.135361          -2.263   \n",
+       "2025-07-02 00:45:00+00:00          28.002427          -2.063   \n",
+       "2025-07-02 01:00:00+00:00          28.375842          -1.963   \n",
+       "2025-07-02 01:15:00+00:00          28.749256          -1.863   \n",
+       "2025-07-02 01:30:00+00:00          29.122671          -1.713   \n",
+       "2025-07-02 01:45:00+00:00          29.496086          -1.513   \n",
        "...                                      ...             ...   \n",
        "2025-07-05 11:00:00+00:00          38.086209           2.387   \n",
        "2025-07-05 11:15:00+00:00          36.774658           2.337   \n",
@@ -632,11 +632,11 @@
        "2025-07-05 12:00:00+00:00          34.174615           1.987   \n",
        "\n",
        "                           shortwave_radiation_instant  wind_speed_10m  \n",
-       "2025-07-01 22:30:00+00:00                     0.000000        7.342588  \n",
-       "2025-07-01 22:45:00+00:00                     0.000000        7.280550  \n",
-       "2025-07-01 23:00:00+00:00                     0.000000        6.877790  \n",
-       "2025-07-01 23:15:00+00:00                    25.322779        6.489992  \n",
-       "2025-07-01 23:30:00+00:00                    55.886383        6.130579  \n",
+       "2025-07-02 00:45:00+00:00                   243.104050        4.680000  \n",
+       "2025-07-02 01:00:00+00:00                   276.808258        4.680000  \n",
+       "2025-07-02 01:15:00+00:00                   309.431335        4.680000  \n",
+       "2025-07-02 01:30:00+00:00                   344.148895        5.154415  \n",
+       "2025-07-02 01:45:00+00:00                   386.145966        5.692100  \n",
        "...                                                ...             ...  \n",
        "2025-07-05 11:00:00+00:00                   443.232941       32.199379  \n",
        "2025-07-05 11:15:00+00:00                   395.066681       32.363979  \n",
@@ -644,7 +644,7 @@
        "2025-07-05 11:45:00+00:00                   297.251648       33.340351  \n",
        "2025-07-05 12:00:00+00:00                   248.622940       33.518684  \n",
        "\n",
-       "[343 rows x 4 columns]"
+       "[334 rows x 4 columns]"
       ],
       "text/html": [
        "<div>\n",
@@ -673,39 +673,39 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>2025-07-01 22:30:00+00:00</th>\n",
-       "      <td>24.492416</td>\n",
-       "      <td>-2.163</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>7.342588</td>\n",
+       "      <th>2025-07-02 00:45:00+00:00</th>\n",
+       "      <td>28.002427</td>\n",
+       "      <td>-2.063</td>\n",
+       "      <td>243.104050</td>\n",
+       "      <td>4.680000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-01 22:45:00+00:00</th>\n",
-       "      <td>24.675352</td>\n",
-       "      <td>-2.213</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>7.280550</td>\n",
+       "      <th>2025-07-02 01:00:00+00:00</th>\n",
+       "      <td>28.375842</td>\n",
+       "      <td>-1.963</td>\n",
+       "      <td>276.808258</td>\n",
+       "      <td>4.680000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-01 23:00:00+00:00</th>\n",
-       "      <td>25.388531</td>\n",
-       "      <td>-2.263</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>6.877790</td>\n",
+       "      <th>2025-07-02 01:15:00+00:00</th>\n",
+       "      <td>28.749256</td>\n",
+       "      <td>-1.863</td>\n",
+       "      <td>309.431335</td>\n",
+       "      <td>4.680000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-01 23:15:00+00:00</th>\n",
-       "      <td>25.761946</td>\n",
-       "      <td>-2.263</td>\n",
-       "      <td>25.322779</td>\n",
-       "      <td>6.489992</td>\n",
+       "      <th>2025-07-02 01:30:00+00:00</th>\n",
+       "      <td>29.122671</td>\n",
+       "      <td>-1.713</td>\n",
+       "      <td>344.148895</td>\n",
+       "      <td>5.154415</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-01 23:30:00+00:00</th>\n",
-       "      <td>26.135361</td>\n",
-       "      <td>-2.263</td>\n",
-       "      <td>55.886383</td>\n",
-       "      <td>6.130579</td>\n",
+       "      <th>2025-07-02 01:45:00+00:00</th>\n",
+       "      <td>29.496086</td>\n",
+       "      <td>-1.513</td>\n",
+       "      <td>386.145966</td>\n",
+       "      <td>5.692100</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -751,35 +751,35 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>343 rows × 4 columns</p>\n",
+       "<p>334 rows × 4 columns</p>\n",
        "</div>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 15
+   "execution_count": 17
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-04T02:32:45.475105Z",
-     "start_time": "2026-02-04T02:32:45.469354Z"
+     "end_time": "2026-02-04T03:54:31.675444Z",
+     "start_time": "2026-02-04T03:54:31.666097Z"
     }
    },
    "cell_type": "code",
    "source": "merged_df = merged_df.rename(columns={'value': 'array_temperature'})",
    "id": "e0882e697c1613f7",
    "outputs": [],
-   "execution_count": 16
+   "execution_count": 18
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-04T02:32:46.110191Z",
-     "start_time": "2026-02-04T02:32:46.095120Z"
+     "end_time": "2026-02-04T03:54:32.075553Z",
+     "start_time": "2026-02-04T03:54:32.059616Z"
     }
    },
    "cell_type": "code",
@@ -790,18 +790,18 @@
      "data": {
       "text/plain": [
        "                           array_temperature  temperature_2m  \\\n",
-       "2025-07-01 22:30:00+00:00          24.492416          -2.163   \n",
-       "2025-07-01 22:45:00+00:00          24.675352          -2.213   \n",
-       "2025-07-01 23:00:00+00:00          25.388531          -2.263   \n",
-       "2025-07-01 23:15:00+00:00          25.761946          -2.263   \n",
-       "2025-07-01 23:30:00+00:00          26.135361          -2.263   \n",
+       "2025-07-02 00:45:00+00:00          28.002427          -2.063   \n",
+       "2025-07-02 01:00:00+00:00          28.375842          -1.963   \n",
+       "2025-07-02 01:15:00+00:00          28.749256          -1.863   \n",
+       "2025-07-02 01:30:00+00:00          29.122671          -1.713   \n",
+       "2025-07-02 01:45:00+00:00          29.496086          -1.513   \n",
        "\n",
        "                           shortwave_radiation_instant  wind_speed_10m  \n",
-       "2025-07-01 22:30:00+00:00                     0.000000        7.342588  \n",
-       "2025-07-01 22:45:00+00:00                     0.000000        7.280550  \n",
-       "2025-07-01 23:00:00+00:00                     0.000000        6.877790  \n",
-       "2025-07-01 23:15:00+00:00                    25.322779        6.489992  \n",
-       "2025-07-01 23:30:00+00:00                    55.886383        6.130579  "
+       "2025-07-02 00:45:00+00:00                   243.104050        4.680000  \n",
+       "2025-07-02 01:00:00+00:00                   276.808258        4.680000  \n",
+       "2025-07-02 01:15:00+00:00                   309.431335        4.680000  \n",
+       "2025-07-02 01:30:00+00:00                   344.148895        5.154415  \n",
+       "2025-07-02 01:45:00+00:00                   386.145966        5.692100  "
       ],
       "text/html": [
        "<div>\n",
@@ -830,57 +830,57 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>2025-07-01 22:30:00+00:00</th>\n",
-       "      <td>24.492416</td>\n",
-       "      <td>-2.163</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>7.342588</td>\n",
+       "      <th>2025-07-02 00:45:00+00:00</th>\n",
+       "      <td>28.002427</td>\n",
+       "      <td>-2.063</td>\n",
+       "      <td>243.104050</td>\n",
+       "      <td>4.680000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-01 22:45:00+00:00</th>\n",
-       "      <td>24.675352</td>\n",
-       "      <td>-2.213</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>7.280550</td>\n",
+       "      <th>2025-07-02 01:00:00+00:00</th>\n",
+       "      <td>28.375842</td>\n",
+       "      <td>-1.963</td>\n",
+       "      <td>276.808258</td>\n",
+       "      <td>4.680000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-01 23:00:00+00:00</th>\n",
-       "      <td>25.388531</td>\n",
-       "      <td>-2.263</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>6.877790</td>\n",
+       "      <th>2025-07-02 01:15:00+00:00</th>\n",
+       "      <td>28.749256</td>\n",
+       "      <td>-1.863</td>\n",
+       "      <td>309.431335</td>\n",
+       "      <td>4.680000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-01 23:15:00+00:00</th>\n",
-       "      <td>25.761946</td>\n",
-       "      <td>-2.263</td>\n",
-       "      <td>25.322779</td>\n",
-       "      <td>6.489992</td>\n",
+       "      <th>2025-07-02 01:30:00+00:00</th>\n",
+       "      <td>29.122671</td>\n",
+       "      <td>-1.713</td>\n",
+       "      <td>344.148895</td>\n",
+       "      <td>5.154415</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2025-07-01 23:30:00+00:00</th>\n",
-       "      <td>26.135361</td>\n",
-       "      <td>-2.263</td>\n",
-       "      <td>55.886383</td>\n",
-       "      <td>6.130579</td>\n",
+       "      <th>2025-07-02 01:45:00+00:00</th>\n",
+       "      <td>29.496086</td>\n",
+       "      <td>-1.513</td>\n",
+       "      <td>386.145966</td>\n",
+       "      <td>5.692100</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 17
+   "execution_count": 19
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-04T02:32:47.122329Z",
-     "start_time": "2026-02-04T02:32:46.971398Z"
+     "end_time": "2026-02-04T03:54:32.696232Z",
+     "start_time": "2026-02-04T03:54:32.557803Z"
     }
    },
    "cell_type": "code",
@@ -911,13 +911,13 @@
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ],
-      "image/png": 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"
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 18
+   "execution_count": 20
   },
   {
    "metadata": {},
@@ -928,8 +928,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-04T02:32:55.965069Z",
-     "start_time": "2026-02-04T02:32:49.257526Z"
+     "end_time": "2026-02-04T03:54:34.499123Z",
+     "start_time": "2026-02-04T03:54:33.722289Z"
     }
    },
    "cell_type": "code",
@@ -954,13 +954,13 @@
    ],
    "id": "1aa707313aeb5993",
    "outputs": [],
-   "execution_count": 19
+   "execution_count": 21
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-04T02:32:57.234994Z",
-     "start_time": "2026-02-04T02:32:57.214043Z"
+     "end_time": "2026-02-04T03:54:34.528212Z",
+     "start_time": "2026-02-04T03:54:34.523803Z"
     }
    },
    "cell_type": "code",
@@ -970,13 +970,13 @@
    ],
    "id": "492ea4964124380d",
    "outputs": [],
-   "execution_count": 20
+   "execution_count": 22
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-04T02:33:18.348547Z",
-     "start_time": "2026-02-04T02:33:17.969422Z"
+     "end_time": "2026-02-04T03:54:34.972812Z",
+     "start_time": "2026-02-04T03:54:34.881477Z"
     }
    },
    "cell_type": "code",
@@ -990,10 +990,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x2903a7f70e0>]"
+       "[<matplotlib.lines.Line2D at 0x1c53176c350>]"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1002,13 +1002,13 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 22
+   "execution_count": 23
   },
   {
    "metadata": {},
@@ -1019,8 +1019,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-21T02:59:35.003065Z",
-     "start_time": "2026-01-21T02:59:34.925750Z"
+     "end_time": "2026-02-04T03:54:36.513943Z",
+     "start_time": "2026-02-04T03:54:36.056474Z"
     }
    },
    "cell_type": "code",
@@ -1040,21 +1040,21 @@
      "traceback": [
       "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
       "\u001B[1;31mValueError\u001B[0m                                Traceback (most recent call last)",
-      "Cell \u001B[1;32mIn[26], line 3\u001B[0m\n\u001B[0;32m      1\u001B[0m params \u001B[38;5;241m=\u001B[39m [\u001B[38;5;241m22\u001B[39m, \u001B[38;5;241m5.8\u001B[39m]\n\u001B[0;32m      2\u001B[0m xdata \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39mstack([merged_df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mshortwave_radiation_instant\u001B[39m\u001B[38;5;124m'\u001B[39m], merged_df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtemperature_2m\u001B[39m\u001B[38;5;124m'\u001B[39m], merged_df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mwind_speed_10m\u001B[39m\u001B[38;5;124m'\u001B[39m]])\n\u001B[1;32m----> 3\u001B[0m popt \u001B[38;5;241m=\u001B[39m \u001B[43mfit_faiman\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfaiman_model\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m      4\u001B[0m \u001B[43m                  \u001B[49m\u001B[43mxdata\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m      5\u001B[0m \u001B[43m                  \u001B[49m\u001B[43mmerged_df\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43marray_temperature\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[43m)\u001B[49m\n",
-      "Cell \u001B[1;32mIn[23], line 15\u001B[0m, in \u001B[0;36mfit_faiman\u001B[1;34m(model, xdata, ydata, params)\u001B[0m\n\u001B[0;32m     14\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mfit_faiman\u001B[39m(model, xdata, ydata, params):\n\u001B[1;32m---> 15\u001B[0m     popt, _ \u001B[38;5;241m=\u001B[39m \u001B[43mcurve_fit\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mxdata\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mydata\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mp0\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mparams\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     17\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m popt\n",
+      "Cell \u001B[1;32mIn[24], line 3\u001B[0m\n\u001B[0;32m      1\u001B[0m params \u001B[38;5;241m=\u001B[39m [\u001B[38;5;241m22\u001B[39m, \u001B[38;5;241m5.8\u001B[39m]\n\u001B[0;32m      2\u001B[0m xdata \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39mstack([merged_df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mshortwave_radiation_instant\u001B[39m\u001B[38;5;124m'\u001B[39m], merged_df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtemperature_2m\u001B[39m\u001B[38;5;124m'\u001B[39m], merged_df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mwind_speed_10m\u001B[39m\u001B[38;5;124m'\u001B[39m]])\n\u001B[1;32m----> 3\u001B[0m popt \u001B[38;5;241m=\u001B[39m \u001B[43mfit_faiman\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfaiman_model\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m      4\u001B[0m \u001B[43m                  \u001B[49m\u001B[43mxdata\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m      5\u001B[0m \u001B[43m                  \u001B[49m\u001B[43mmerged_df\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43marray_temperature\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[43m)\u001B[49m\n",
+      "Cell \u001B[1;32mIn[21], line 15\u001B[0m, in \u001B[0;36mfit_faiman\u001B[1;34m(model, xdata, ydata, params)\u001B[0m\n\u001B[0;32m     14\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mfit_faiman\u001B[39m(model, xdata, ydata, params):\n\u001B[1;32m---> 15\u001B[0m     popt, _ \u001B[38;5;241m=\u001B[39m \u001B[43mcurve_fit\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mxdata\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mydata\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mp0\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mparams\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     17\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m popt\n",
       "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\scipy\\optimize\\_minpack_py.py:932\u001B[0m, in \u001B[0;36mcurve_fit\u001B[1;34m(f, xdata, ydata, p0, sigma, absolute_sigma, check_finite, bounds, method, jac, full_output, nan_policy, **kwargs)\u001B[0m\n\u001B[0;32m    930\u001B[0m \u001B[38;5;66;03m# optimization may produce garbage for float32 inputs, cast them to float64\u001B[39;00m\n\u001B[0;32m    931\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m check_finite:\n\u001B[1;32m--> 932\u001B[0m     ydata \u001B[38;5;241m=\u001B[39m \u001B[43mnp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43masarray_chkfinite\u001B[49m\u001B[43m(\u001B[49m\u001B[43mydata\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mfloat\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m    933\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m    934\u001B[0m     ydata \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39masarray(ydata, \u001B[38;5;28mfloat\u001B[39m)\n",
       "File \u001B[1;32m~\\PyCharmMiscProject\\.venv\\Lib\\site-packages\\numpy\\lib\\_function_base_impl.py:649\u001B[0m, in \u001B[0;36masarray_chkfinite\u001B[1;34m(a, dtype, order)\u001B[0m\n\u001B[0;32m    647\u001B[0m a \u001B[38;5;241m=\u001B[39m asarray(a, dtype\u001B[38;5;241m=\u001B[39mdtype, order\u001B[38;5;241m=\u001B[39morder)\n\u001B[0;32m    648\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m a\u001B[38;5;241m.\u001B[39mdtype\u001B[38;5;241m.\u001B[39mchar \u001B[38;5;129;01min\u001B[39;00m typecodes[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mAllFloat\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m np\u001B[38;5;241m.\u001B[39misfinite(a)\u001B[38;5;241m.\u001B[39mall():\n\u001B[1;32m--> 649\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[0;32m    650\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124marray must not contain infs or NaNs\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m    651\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m a\n",
       "\u001B[1;31mValueError\u001B[0m: array must not contain infs or NaNs"
      ]
     }
    ],
-   "execution_count": 26
+   "execution_count": 24
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-21T02:58:48.126078200Z",
-     "start_time": "2026-01-15T02:36:24.554267Z"
+     "end_time": "2026-02-04T03:54:37.402308Z",
+     "start_time": "2026-02-04T03:54:37.386806Z"
     }
    },
    "cell_type": "code",
@@ -1062,23 +1062,24 @@
    "id": "fd0a96ba439544cc",
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([17.38793587,  0.13933319])"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "NameError",
+     "evalue": "name 'popt' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
+      "Cell \u001B[1;32mIn[25], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mpopt\u001B[49m\n",
+      "\u001B[1;31mNameError\u001B[0m: name 'popt' is not defined"
+     ]
     }
    ],
-   "execution_count": 23
+   "execution_count": 25
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-15T02:36:24.995288Z",
-     "start_time": "2026-01-15T02:36:24.596951Z"
+     "end_time": "2026-02-04T03:54:38.203268Z",
+     "start_time": "2026-02-04T03:54:38.001412Z"
     }
    },
    "cell_type": "code",
@@ -1105,24 +1106,35 @@
    ],
    "id": "16245939d2e22766",
    "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'popt' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
+      "Cell \u001B[1;32mIn[26], line 8\u001B[0m\n\u001B[0;32m      6\u001B[0m ax1\u001B[38;5;241m.\u001B[39mplot(merged_df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124marray_temperature\u001B[39m\u001B[38;5;124m'\u001B[39m], label\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mArray Temperature\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m      7\u001B[0m ax1\u001B[38;5;241m.\u001B[39mplot(merged_df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtemperature_2m\u001B[39m\u001B[38;5;124m'\u001B[39m], color\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mgreen\u001B[39m\u001B[38;5;124m'\u001B[39m, label\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mAmbient Temperature\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m----> 8\u001B[0m ax1\u001B[38;5;241m.\u001B[39mplot(merged_df\u001B[38;5;241m.\u001B[39mindex, faiman_model(xdata, \u001B[38;5;241m*\u001B[39m\u001B[43mpopt\u001B[49m), label\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mFaiman_Fitted\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m      9\u001B[0m plt\u001B[38;5;241m.\u001B[39mplot(merged_df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mshortwave_radiation_instant\u001B[39m\u001B[38;5;124m'\u001B[39m], color\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mred\u001B[39m\u001B[38;5;124m\"\u001B[39m, label\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mSolar Irradiance\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m     11\u001B[0m ax1\u001B[38;5;241m.\u001B[39mset_xlabel(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mTime\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n",
+      "\u001B[1;31mNameError\u001B[0m: name 'popt' is not defined"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 24
+   "execution_count": 26
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-15T02:36:25.037948Z",
-     "start_time": "2026-01-15T02:36:25.033124Z"
+     "end_time": "2026-02-04T03:54:38.567073Z",
+     "start_time": "2026-02-04T03:54:38.559323Z"
     }
    },
    "cell_type": "code",
@@ -1140,8 +1152,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-15T02:36:25.224065Z",
-     "start_time": "2026-01-15T02:36:25.069515Z"
+     "end_time": "2026-02-04T03:54:39.771515Z",
+     "start_time": "2026-02-04T03:54:39.524900Z"
     }
    },
    "cell_type": "code",
@@ -1159,7 +1171,7 @@
        "Text(0.5, 1.0, 'offset_faiman_temperature')"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1168,13 +1180,142 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 25
+   "execution_count": 27
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-04T03:54:40.570497Z",
+     "start_time": "2026-02-04T03:54:40.381690Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "plt.figure(figsize=(10, 5))\n",
+    "\n",
+    "plt.plot(\n",
+    "    merged_df.index,\n",
+    "    merged_df['array_temperature'],\n",
+    "    label=\"Panel Temperature\",\n",
+    "    linewidth=2.5\n",
+    ")\n",
+    "\n",
+    "plt.plot(\n",
+    "    merged_df.index,\n",
+    "    merged_df['temperature_2m'],\n",
+    "    label=\"Ambient Temperature\",\n",
+    "    linewidth=2,\n",
+    "    linestyle='--'\n",
+    ")\n",
+    "\n",
+    "plt.plot(\n",
+    "    merged_df.index,\n",
+    "    27 + faiman_model(xdata, 22, 0.8),\n",
+    "    label=\"Faiman Model Prediction\",\n",
+    "    linewidth=2.5,\n",
+    "    alpha=0.85\n",
+    ")\n",
+    "\n",
+    "plt.title(\n",
+    "    \"Open Waters Solar Panel Temperature vs Ambient\",\n",
+    "    fontsize=14,\n",
+    "    fontweight=\"bold\"\n",
+    ")\n",
+    "\n",
+    "plt.xlabel(\"Time\", fontsize=12)\n",
+    "plt.ylabel(\"Temperature (°C)\", fontsize=12)\n",
+    "\n",
+    "plt.legend(fontsize=11)\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ],
+   "id": "581598d51b9fc68b",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 28
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-04T03:57:31.821518Z",
+     "start_time": "2026-02-04T03:57:31.720975Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "ax.axis(\"off\")\n",
+    "\n",
+    "# Helper function to draw boxes\n",
+    "def draw_box(x, y, w, h, text):\n",
+    "    rect = plt.Rectangle((x, y), w, h, fill=False, linewidth=2)\n",
+    "    ax.add_patch(rect)\n",
+    "    ax.text(x + w/2, y + h/2, text, ha=\"center\", va=\"center\", fontsize=10)\n",
+    "\n",
+    "# Helper function to draw arrows\n",
+    "def draw_arrow(x1, y1, x2, y2):\n",
+    "    ax.annotate(\n",
+    "        \"\",\n",
+    "        xy=(x2, y2),\n",
+    "        xytext=(x1, y1),\n",
+    "        arrowprops=dict(arrowstyle=\"->\", linewidth=2)\n",
+    "    )\n",
+    "\n",
+    "# Boxes\n",
+    "draw_box(0.05, 0.55, 0.25, 0.15, \"Desired State\\n(Target Velocity,\\nTarget Position)\")\n",
+    "draw_box(0.40, 0.55, 0.25, 0.15, \"Feasibility Check\\n(Is target achievable?)\")\n",
+    "draw_box(0.40, 0.30, 0.25, 0.15, \"Recursive Neural Network\\n(Control → State)\")\n",
+    "#draw_box(0.05, 0.05, 0.25, 0.15, \"Vehicle + Environment\\n(Dynamics Model)\")\n",
+    "draw_box(0.75, 0.30, 0.20, 0.15, \"Control Outputs\\nAccelerator %\\nBrake %\")\n",
+    "\n",
+    "# Arrows\n",
+    "draw_arrow(0.30, 0.625, 0.40, 0.625)\n",
+    "draw_arrow(0.525, 0.55, 0.525, 0.45)\n",
+    "draw_arrow(0.65, 0.375, 0.75, 0.375)\n",
+    "draw_arrow(0.525, 0.30, 0.175, 0.20)\n",
+    "draw_arrow(0.175, 0.20, 0.175, 0.55)\n",
+    "\n",
+    "# Feedback label\n",
+    "ax.text(0.02, 0.30, \"Feedback:\\nSpeed → Position\\n(Non-independent)\",\n",
+    "        fontsize=9, va=\"center\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ],
+   "id": "d7695cde6434094",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 30
   },
   {
    "metadata": {

From 7403bdcb8b51f3f0529baa6e40eff79253890807 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Thu, 12 Feb 2026 18:48:02 -0800
Subject: [PATCH 16/49] localisation code

---
 array_temp/Control_Model.ipynb | 212 ++++++++++++++++++++++-----------
 1 file changed, 145 insertions(+), 67 deletions(-)

diff --git a/array_temp/Control_Model.ipynb b/array_temp/Control_Model.ipynb
index 2f2114a..d9b04e0 100644
--- a/array_temp/Control_Model.ipynb
+++ b/array_temp/Control_Model.ipynb
@@ -16,21 +16,47 @@
    ],
    "id": "13a4cc26a47d5ad7"
   },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "other references:\n",
+    "https://docs.google.com/spreadsheets/d/1yjyuKODt6wtIB31OLfhpwe0kKQ4CeoXJ/edit?gid=159364596#gid=159364596\n",
+    "- this document has lap timings\n",
+    "- i also used Miguel's monday updates to find which laps were ignored/had issues\n",
+    "- for now the RNN does not need to distinguish between laps, but  i wonder if that could be a possible input"
+   ],
+   "id": "3d1eb59ff0e6ef7a"
+  },
   {
    "metadata": {},
    "cell_type": "code",
    "outputs": [],
    "execution_count": null,
+   "source": "#other references :\n",
+   "id": "b191dbc392da20f6"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-13T02:36:08.615007Z",
+     "start_time": "2026-02-13T02:36:05.471408Z"
+    }
+   },
+   "cell_type": "code",
    "source": [
     "#necessary imports\n",
     "import sklearn as sk"
    ],
-   "id": "8672b9d1bf8a74aa"
+   "id": "8672b9d1bf8a74aa",
+   "outputs": [],
+   "execution_count": 2
   },
   {
    "metadata": {
-    "jupyter": {
-     "is_executing": true
+    "ExecuteTime": {
+     "end_time": "2026-02-13T02:39:18.682853Z",
+     "start_time": "2026-02-13T02:38:42.472473Z"
     }
    },
    "cell_type": "code",
@@ -51,7 +77,7 @@
     "#each 5 seconds\n",
     "utc_offset_h = 7\n",
     "start_utc = time(0+utc_offset_h, 00, 00)  #querying i svancouver time, influxdb gives utc\n",
-    "stop_utc = time(23+utc_offset_h, 45, 00)\n",
+    "stop_utc = time(16+utc_offset_h, 45, 00)\n",
     "date_start = date(2024, 7, 16)\n",
     "date_stop = date(2024, 7, 18)\n",
     "\n",
@@ -71,12 +97,12 @@
    ],
    "id": "initial_id",
    "outputs": [],
-   "execution_count": null
+   "execution_count": 3
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-06T00:27:15.513885Z",
+     "end_time": "2026-02-13T02:36:05.432169Z",
      "start_time": "2026-02-06T00:27:15.504931Z"
     }
    },
@@ -89,8 +115,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-31T19:27:51.064530Z",
-     "start_time": "2026-01-31T19:27:51.021122Z"
+     "end_time": "2026-02-13T02:44:52.680566Z",
+     "start_time": "2026-02-13T02:44:52.600491Z"
     }
    },
    "cell_type": "code",
@@ -114,13 +140,13 @@
    ],
    "id": "9f8652589e173b01",
    "outputs": [],
-   "execution_count": 3
+   "execution_count": 4
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-31T19:27:52.868417Z",
-     "start_time": "2026-01-31T19:27:51.901459Z"
+     "end_time": "2026-02-13T02:44:54.615627Z",
+     "start_time": "2026-02-13T02:44:53.297419Z"
     }
    },
    "cell_type": "code",
@@ -130,10 +156,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1f08b002d20>]"
+       "[<matplotlib.lines.Line2D at 0x1ef6774aa20>]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -142,19 +168,19 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 4
+   "execution_count": 5
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-31T19:34:07.117300Z",
-     "start_time": "2026-01-31T19:34:06.083522Z"
+     "end_time": "2026-02-13T02:44:56.052998Z",
+     "start_time": "2026-02-13T02:44:54.639505Z"
     }
    },
    "cell_type": "code",
@@ -164,10 +190,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1f08a7f8b00>]"
+       "[<matplotlib.lines.Line2D at 0x1ef02ae9970>]"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -176,19 +202,19 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 23
+   "execution_count": 6
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-31T19:34:44.016581Z",
-     "start_time": "2026-01-31T19:34:43.992027Z"
+     "end_time": "2026-02-13T02:44:56.170260Z",
+     "start_time": "2026-02-13T02:44:56.090581Z"
     }
    },
    "cell_type": "code",
@@ -204,13 +230,13 @@
    ],
    "id": "6a0eed52a654e483",
    "outputs": [],
-   "execution_count": 25
+   "execution_count": 7
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-31T19:34:47.287669Z",
-     "start_time": "2026-01-31T19:34:44.540200Z"
+     "end_time": "2026-02-13T02:45:07.658524Z",
+     "start_time": "2026-02-13T02:45:04.327714Z"
     }
    },
    "cell_type": "code",
@@ -244,7 +270,7 @@
        "Text(0.5, 1.0, 'speed kph')"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -253,7 +279,7 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -263,7 +289,7 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -273,19 +299,19 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 26
+   "execution_count": 9
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-31T19:28:09.046566Z",
-     "start_time": "2026-01-31T19:28:09.041415Z"
+     "end_time": "2026-02-13T02:45:07.689335Z",
+     "start_time": "2026-02-13T02:45:07.685010Z"
     }
    },
    "cell_type": "code",
@@ -297,8 +323,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-31T19:28:22.900645Z",
-     "start_time": "2026-01-31T19:28:22.894594Z"
+     "end_time": "2026-02-13T02:45:07.770742Z",
+     "start_time": "2026-02-13T02:45:07.766204Z"
     }
    },
    "cell_type": "code",
@@ -313,8 +339,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-31T19:34:56.251992Z",
-     "start_time": "2026-01-31T19:34:55.381927Z"
+     "end_time": "2026-02-13T02:45:08.923828Z",
+     "start_time": "2026-02-13T02:45:07.859326Z"
     }
    },
    "cell_type": "code",
@@ -337,7 +363,7 @@
        "Text(0.5, 1.0, 'speed kph')"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -346,27 +372,32 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 27
+   "execution_count": 11
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-13T02:45:08.965455Z",
+     "start_time": "2026-02-13T02:45:08.958465Z"
+    }
+   },
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": "#clearly speed units are whack. from looking at lap data, i see that out average speed was around 16 miles per hour. so i think due to hwo influx registers small numbers in different units, we are probably looking at",
-   "id": "1ef57bddd9f5a956"
+   "id": "1ef57bddd9f5a956",
+   "outputs": [],
+   "execution_count": 12
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-31T19:35:15.420190Z",
-     "start_time": "2026-01-31T19:35:14.477156Z"
+     "end_time": "2026-02-13T02:45:10.221086Z",
+     "start_time": "2026-02-13T02:45:09.004450Z"
     }
    },
    "cell_type": "code",
@@ -385,19 +416,19 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 30
+   "execution_count": 13
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-31T19:35:21.803280Z",
-     "start_time": "2026-01-31T19:35:20.212216Z"
+     "end_time": "2026-02-13T02:45:12.064997Z",
+     "start_time": "2026-02-13T02:45:10.245402Z"
     }
    },
    "cell_type": "code",
@@ -431,7 +462,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_13952\\1203527545.py:21: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_27256\\1203527545.py:21: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
       "  ax1.legend(loc = \"upper left\")\n"
      ]
     },
@@ -440,19 +471,19 @@
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 31
+   "execution_count": 14
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-24T19:55:01.036961Z",
-     "start_time": "2026-01-24T19:55:01.033707Z"
+     "end_time": "2026-02-13T02:45:12.148036Z",
+     "start_time": "2026-02-13T02:45:12.127837Z"
     }
    },
    "cell_type": "code",
@@ -467,7 +498,7 @@
    ],
    "id": "158615364b919eed",
    "outputs": [],
-   "execution_count": 10
+   "execution_count": 15
   },
   {
    "metadata": {},
@@ -488,6 +519,17 @@
    "cell_type": "code",
    "outputs": [],
    "execution_count": null,
+   "source": "",
+   "id": "2741c957d09edb03"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-13T02:45:31.551205Z",
+     "start_time": "2026-02-13T02:45:31.520030Z"
+    }
+   },
+   "cell_type": "code",
    "source": [
     "from data_tools import lap_tools\n",
     "from physics.environment.gis.gis import GIS\n",
@@ -509,12 +551,26 @@
     "\n",
     "lap_length = np.cumsum(calculate_path_distances(gis.path[:gis.num_unique_coords]))[-1] # TOTAL LAP LENGTH"
    ],
-   "id": "88a4ae7fb0d75eed"
+   "id": "88a4ae7fb0d75eed",
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'coords' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mNameError\u001B[39m                                 Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[18]\u001B[39m\u001B[32m, line 8\u001B[39m\n\u001B[32m      3\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[34;01mphysics\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01menvironment\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01mgis\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01mgis\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m calculate_path_distances\n\u001B[32m      5\u001B[39m \u001B[38;5;66;03m# Defining dictionary \"route_data\"\u001B[39;00m\n\u001B[32m      7\u001B[39m route_data = {\n\u001B[32m----> \u001B[39m\u001B[32m8\u001B[39m     \u001B[33m\"\u001B[39m\u001B[33mpath\u001B[39m\u001B[33m\"\u001B[39m : np.array(\u001B[43mcoords\u001B[49m),\n\u001B[32m      9\u001B[39m     \u001B[33m\"\u001B[39m\u001B[33melevations\u001B[39m\u001B[33m\"\u001B[39m : np.zeros(\u001B[38;5;28mlen\u001B[39m(coords)),\n\u001B[32m     10\u001B[39m     \u001B[33m\"\u001B[39m\u001B[33mtime_zones\u001B[39m\u001B[33m\"\u001B[39m : np.zeros(\u001B[38;5;28mlen\u001B[39m(coords)),\n\u001B[32m     11\u001B[39m     \u001B[33m\"\u001B[39m\u001B[33mnum_unique_coords\u001B[39m\u001B[33m\"\u001B[39m : (\u001B[38;5;28mlen\u001B[39m(coords) - \u001B[32m1\u001B[39m) }\n\u001B[32m     13\u001B[39m \u001B[38;5;66;03m# Creating GIS object\u001B[39;00m\n\u001B[32m     15\u001B[39m starting_coords = [\u001B[32m37.00107373\u001B[39m, -\u001B[32m86.36854755\u001B[39m]\n",
+      "\u001B[31mNameError\u001B[39m: name 'coords' is not defined"
+     ]
+    }
+   ],
+   "execution_count": 18
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-31T19:35:47.170052Z",
+     "end_time": "2026-02-13T02:45:14.470779400Z",
      "start_time": "2026-01-31T19:35:47.163555Z"
     }
    },
@@ -536,7 +592,7 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-31T19:41:06.493573Z",
+     "end_time": "2026-02-13T02:45:14.470779400Z",
      "start_time": "2026-01-31T19:41:06.480312Z"
     }
    },
@@ -645,7 +701,7 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-31T19:41:21.451485Z",
+     "end_time": "2026-02-13T02:45:14.504701700Z",
      "start_time": "2026-01-31T19:41:21.121306Z"
     }
    },
@@ -679,7 +735,7 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-31T19:59:44.616950Z",
+     "end_time": "2026-02-13T02:45:14.511796600Z",
      "start_time": "2026-01-31T19:59:44.610437Z"
     }
    },
@@ -704,7 +760,7 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-31T20:00:02.846028Z",
+     "end_time": "2026-02-13T02:45:14.512728600Z",
      "start_time": "2026-01-31T20:00:02.683528Z"
     }
    },
@@ -720,7 +776,7 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-31T20:00:14.613514Z",
+     "end_time": "2026-02-13T02:45:14.513514Z",
      "start_time": "2026-01-31T20:00:14.278469Z"
     }
    },
@@ -763,10 +819,13 @@
    "id": "e4585f807a9c575a"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-13T02:45:15.053828Z",
+     "start_time": "2026-02-13T02:45:15.043379Z"
+    }
+   },
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
     "# testing training split - 70 - 30\n",
     "\n",
@@ -793,7 +852,26 @@
     "df_test = df_test_raw.copy()\n",
     "df_test[cols_to_scale] = scaler.transform(df_test_raw[cols_to_scale])\n"
    ],
-   "id": "ab4848af3022daef"
+   "id": "ab4848af3022daef",
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (1177843209.py, line 6)",
+     "output_type": "error",
+     "traceback": [
+      "  \u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[17]\u001B[39m\u001B[32m, line 6\u001B[39m\n\u001B[31m    \u001B[39m\u001B[31mdef\u001B[39m\n       ^\n\u001B[31mSyntaxError\u001B[39m\u001B[31m:\u001B[39m invalid syntax\n"
+     ]
+    }
+   ],
+   "execution_count": 17
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "20dfb9bc00550510"
   }
  ],
  "metadata": {

From b2d36923e4e7036955d9b48e1d9b743c8d1bdb0f Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Mon, 16 Feb 2026 22:27:58 -0800
Subject: [PATCH 17/49] rnn architecture + training

---
 array_temp/Control_Model.ipynb  | 1270 ++++++++++++++++++++++++++-----
 array_temp/DataPreprocessing.py |   70 ++
 array_temp/RNN.py               |   51 ++
 array_temp/RNN_Dataset.py       |   42 +
 4 files changed, 1233 insertions(+), 200 deletions(-)
 create mode 100644 array_temp/DataPreprocessing.py
 create mode 100644 array_temp/RNN.py
 create mode 100644 array_temp/RNN_Dataset.py

diff --git a/array_temp/Control_Model.ipynb b/array_temp/Control_Model.ipynb
index d9b04e0..c1b6bcb 100644
--- a/array_temp/Control_Model.ipynb
+++ b/array_temp/Control_Model.ipynb
@@ -29,24 +29,34 @@
    "id": "3d1eb59ff0e6ef7a"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-17T06:17:01.847821Z",
+     "start_time": "2026-02-17T06:17:01.842168Z"
+    }
+   },
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": "#other references :\n",
-   "id": "b191dbc392da20f6"
+   "id": "b191dbc392da20f6",
+   "outputs": [],
+   "execution_count": 1
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-13T02:36:08.615007Z",
-     "start_time": "2026-02-13T02:36:05.471408Z"
+     "end_time": "2026-02-17T06:17:14.923191Z",
+     "start_time": "2026-02-17T06:17:01.897347Z"
     }
    },
    "cell_type": "code",
    "source": [
     "#necessary imports\n",
-    "import sklearn as sk"
+    "import sklearn as sk\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from array_temp.DataPreprocessing import make_sequence_datasets"
    ],
    "id": "8672b9d1bf8a74aa",
    "outputs": [],
@@ -55,8 +65,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-13T02:39:18.682853Z",
-     "start_time": "2026-02-13T02:38:42.472473Z"
+     "end_time": "2026-02-17T06:18:10.286001Z",
+     "start_time": "2026-02-17T06:17:15.821940Z"
     }
    },
    "cell_type": "code",
@@ -102,21 +112,21 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-13T02:36:05.432169Z",
-     "start_time": "2026-02-06T00:27:15.504931Z"
+     "end_time": "2026-02-17T06:18:10.706635Z",
+     "start_time": "2026-02-17T06:18:10.676957Z"
     }
    },
    "cell_type": "code",
    "source": "# speed is most likely in m/s",
    "id": "5d30f3a75f7e9149",
    "outputs": [],
-   "execution_count": 1
+   "execution_count": 4
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-13T02:44:52.680566Z",
-     "start_time": "2026-02-13T02:44:52.600491Z"
+     "end_time": "2026-02-17T06:18:11.011266Z",
+     "start_time": "2026-02-17T06:18:10.824351Z"
     }
    },
    "cell_type": "code",
@@ -140,13 +150,48 @@
    ],
    "id": "9f8652589e173b01",
    "outputs": [],
-   "execution_count": 4
+   "execution_count": 5
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-13T02:44:54.615627Z",
-     "start_time": "2026-02-13T02:44:53.297419Z"
+     "end_time": "2026-02-17T06:18:11.748146Z",
+     "start_time": "2026-02-17T06:18:11.716013Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "mech_brake_pressed.granularity\n",
+    "#granularity is at 0.1 seconds"
+   ],
+   "id": "467ef53567154e4a",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_30512\\4023827949.py:1: DeprecationWarning: Please use TimeSeries.period instead of TimeSeries.granularity\n",
+      "  mech_brake_pressed.granularity\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.10000002186928031)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 6
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-17T06:18:17.292467Z",
+     "start_time": "2026-02-17T06:18:12.477752Z"
     }
    },
    "cell_type": "code",
@@ -156,10 +201,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1ef6774aa20>]"
+       "[<matplotlib.lines.Line2D at 0x1a1e128ed50>]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -168,19 +213,19 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 5
+   "execution_count": 7
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-13T02:44:56.052998Z",
-     "start_time": "2026-02-13T02:44:54.639505Z"
+     "end_time": "2026-02-17T06:18:23.419245Z",
+     "start_time": "2026-02-17T06:18:17.432286Z"
     }
    },
    "cell_type": "code",
@@ -190,10 +235,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1ef02ae9970>]"
+       "[<matplotlib.lines.Line2D at 0x1a1f9659ca0>]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -202,19 +247,19 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 6
+   "execution_count": 8
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-13T02:44:56.170260Z",
-     "start_time": "2026-02-13T02:44:56.090581Z"
+     "end_time": "2026-02-17T06:18:23.941583Z",
+     "start_time": "2026-02-17T06:18:23.610541Z"
     }
    },
    "cell_type": "code",
@@ -230,13 +275,13 @@
    ],
    "id": "6a0eed52a654e483",
    "outputs": [],
-   "execution_count": 7
+   "execution_count": 9
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-13T02:45:07.658524Z",
-     "start_time": "2026-02-13T02:45:04.327714Z"
+     "end_time": "2026-02-17T06:18:38.237021Z",
+     "start_time": "2026-02-17T06:18:24.012680Z"
     }
    },
    "cell_type": "code",
@@ -270,7 +315,7 @@
        "Text(0.5, 1.0, 'speed kph')"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -279,7 +324,7 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -289,7 +334,7 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -299,19 +344,19 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 9
+   "execution_count": 10
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-13T02:45:07.689335Z",
-     "start_time": "2026-02-13T02:45:07.685010Z"
+     "end_time": "2026-02-17T06:18:38.330745Z",
+     "start_time": "2026-02-17T06:18:38.307363Z"
     }
    },
    "cell_type": "code",
@@ -323,8 +368,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-13T02:45:07.770742Z",
-     "start_time": "2026-02-13T02:45:07.766204Z"
+     "end_time": "2026-02-17T06:18:38.486438Z",
+     "start_time": "2026-02-17T06:18:38.460068Z"
     }
    },
    "cell_type": "code",
@@ -334,13 +379,13 @@
    ],
    "id": "54a0ee4a260b394",
    "outputs": [],
-   "execution_count": 10
+   "execution_count": 11
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-13T02:45:08.923828Z",
-     "start_time": "2026-02-13T02:45:07.859326Z"
+     "end_time": "2026-02-17T06:18:43.661600Z",
+     "start_time": "2026-02-17T06:18:38.657715Z"
     }
    },
    "cell_type": "code",
@@ -363,7 +408,7 @@
        "Text(0.5, 1.0, 'speed kph')"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -372,32 +417,32 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 11
+   "execution_count": 12
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-13T02:45:08.965455Z",
-     "start_time": "2026-02-13T02:45:08.958465Z"
+     "end_time": "2026-02-17T06:18:43.729129Z",
+     "start_time": "2026-02-17T06:18:43.716025Z"
     }
    },
    "cell_type": "code",
-   "source": "#clearly speed units are whack. from looking at lap data, i see that out average speed was around 16 miles per hour. so i think due to hwo influx registers small numbers in different units, we are probably looking at",
+   "source": "#clearly speed units are whack. from looking at lap data, i see that out average speed was around 16 miles per hour. so i think due to hwo influx registers small numbers in different units",
    "id": "1ef57bddd9f5a956",
    "outputs": [],
-   "execution_count": 12
+   "execution_count": 13
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-13T02:45:10.221086Z",
-     "start_time": "2026-02-13T02:45:09.004450Z"
+     "end_time": "2026-02-17T06:18:48.167187Z",
+     "start_time": "2026-02-17T06:18:43.934851Z"
     }
    },
    "cell_type": "code",
@@ -416,19 +461,19 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 13
+   "execution_count": 14
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-13T02:45:12.064997Z",
-     "start_time": "2026-02-13T02:45:10.245402Z"
+     "end_time": "2026-02-17T06:18:55.696032Z",
+     "start_time": "2026-02-17T06:18:48.424383Z"
     }
    },
    "cell_type": "code",
@@ -462,7 +507,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_27256\\1203527545.py:21: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_30512\\1203527545.py:21: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
       "  ax1.legend(loc = \"upper left\")\n"
      ]
     },
@@ -471,19 +516,19 @@
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 14
+   "execution_count": 15
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-13T02:45:12.148036Z",
-     "start_time": "2026-02-13T02:45:12.127837Z"
+     "end_time": "2026-02-17T06:18:55.839231Z",
+     "start_time": "2026-02-17T06:18:55.832074Z"
     }
    },
    "cell_type": "code",
@@ -498,7 +543,7 @@
    ],
    "id": "158615364b919eed",
    "outputs": [],
-   "execution_count": 15
+   "execution_count": 16
   },
   {
    "metadata": {},
@@ -515,18 +560,318 @@
    "id": "73256b66933551d3"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-17T06:18:56.082772Z",
+     "start_time": "2026-02-17T06:18:56.012522Z"
+    }
+   },
    "cell_type": "code",
+   "source": [
+    "reverse_coords = [\n",
+    "    [ 37.0011529 , -86.36837867],\n",
+    "    [ 37.00122817, -86.3682181 ],\n",
+    "    [ 37.00133071, -86.36801267],\n",
+    "    [ 37.00143614, -86.36779264],\n",
+    "    [ 37.00152389, -86.3675912 ],\n",
+    "    [ 37.00160574, -86.36740819],\n",
+    "    [ 37.00167596, -86.36725066],\n",
+    "    [ 37.00175285, -86.36709064],\n",
+    "    [ 37.00183166, -86.36691875],\n",
+    "    [ 37.00192538, -86.36670617],\n",
+    "    [ 37.00200136, -86.36653034],\n",
+    "    [ 37.00208623, -86.36635086],\n",
+    "    [ 37.00215644, -86.36619701],\n",
+    "    [ 37.00222549, -86.36603626],\n",
+    "    [ 37.00229839, -86.3658645 ],\n",
+    "    [ 37.00237732, -86.36569622],\n",
+    "    [ 37.00245038, -86.36553914],\n",
+    "    [ 37.00252912, -86.36537128],\n",
+    "    [ 37.00259904, -86.36521818],\n",
+    "    [ 37.00266755, -86.36507091],\n",
+    "    [ 37.00274639, -86.36490341],\n",
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+    "    [ 37.0003852 , -86.37123775],\n",
+    "    [ 37.00042033, -86.37099884],\n",
+    "    [ 37.00038811, -86.3707825 ],\n",
+    "    [ 37.00031195, -86.37062837],\n",
+    "    [ 37.00026491, -86.37041498],\n",
+    "    [ 37.00030254, -86.37020247],\n",
+    "    [ 37.00038472, -86.3700261 ],\n",
+    "    [ 37.0004699 , -86.36984594],\n",
+    "    [ 37.00056383, -86.36964375],\n",
+    "    [ 37.00064015, -86.3694857 ],\n",
+    "    [ 37.00070993, -86.36934481],\n",
+    "    [ 37.0008098 , -86.36912411],\n",
+    "    [ 37.00090662, -86.36891836],\n",
+    "    [ 37.00098578, -86.36874579],\n",
+    "    [ 37.00107373, -86.36854755]\n",
+    "  ]\n",
+    "\n",
+    "coords = reverse_coords[::-1] # coordinates in the correct order; starting coordinate goes first"
+   ],
+   "id": "2741c957d09edb03",
    "outputs": [],
-   "execution_count": null,
-   "source": "",
-   "id": "2741c957d09edb03"
+   "execution_count": 17
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-13T02:45:31.551205Z",
-     "start_time": "2026-02-13T02:45:31.520030Z"
+     "end_time": "2026-02-17T06:18:59.845981Z",
+     "start_time": "2026-02-17T06:18:56.253672Z"
     }
    },
    "cell_type": "code",
@@ -552,26 +897,294 @@
     "lap_length = np.cumsum(calculate_path_distances(gis.path[:gis.num_unique_coords]))[-1] # TOTAL LAP LENGTH"
    ],
    "id": "88a4ae7fb0d75eed",
+   "outputs": [],
+   "execution_count": 18
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-17T06:19:00.287595Z",
+     "start_time": "2026-02-17T06:18:59.919178Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "plt.plot(calculate_path_distances(gis.path[:gis.num_unique_coords]))\n",
+    "\n",
+    "# Obtain the distance between each coordinate by approximating the spline between them\n",
+    "#     as a straight line, and use the Haversine formula (https://en.wikipedia.org/wiki/Haversine_formula)\n",
+    "#     to calculate distance between coordinates on a sphere.\n"
+   ],
+   "id": "4d4053447813cbbe",
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'coords' is not defined",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1a1e2c8f8f0>]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 19
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    " \"\"\"\n",
+    "        Given the original, lap-averaged `speeds_kmh` and an array of speed deviations in km/h for each track index,\n",
+    "        compute the position and actual speed as simulation-time arrays.\n",
+    "\n",
+    "        :param speeds_kmh: Lap-averaged speeds in km/h.\n",
+    "        :param track_speeds: A speed deviation in km/h for each track index. Expects the mean to be at 0.\n",
+    "        :param dt:\n",
+    "        :return:\n",
+    "        \"\"\"\n",
+    "\n",
+    "so, to actually compute the position, we are using the calculate_speeds_and_position from physics_rs\n",
+    "but this is still not time aligned."
+   ],
+   "id": "756fa51c0541bbfa"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-17T06:19:00.390099Z",
+     "start_time": "2026-02-17T06:19:00.372296Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "",
+   "id": "251896367b559590",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-17T06:19:07.717596Z",
+     "start_time": "2026-02-17T06:19:00.596853Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "distances = calculate_path_distances(gis.path[:gis.num_unique_coords])\n",
+    "state_array = gis.calculate_speeds_and_position(speed_kph, np.zeros_like(speed_kph),dt = 0.1)"
+   ],
+   "id": "40d6a78a4e43784f",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating closest GIS indices: 100%|██████████| 1985282/1985282 [00:06<00:00, 305603.32it/s]\n"
+     ]
+    }
+   ],
+   "execution_count": 20
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-17T06:19:07.871306Z",
+     "start_time": "2026-02-17T06:19:07.797795Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "calculated_speeds, calculated_position = state_array",
+   "id": "f4fb71b02da66f3e",
+   "outputs": [],
+   "execution_count": 21
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-17T06:19:08.233365Z",
+     "start_time": "2026-02-17T06:19:07.969313Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "speed_kph = pd.DataFrame(speed_kph).sort_index()\n",
+    "calculated_position = pd.DataFrame(calculated_position).sort_index()\n",
+    "\n",
+    "merged_df = pd.merge_asof(\n",
+    "    speed_kph,\n",
+    "    calculated_position,\n",
+    "    left_index=True,\n",
+    "    right_index=True,\n",
+    "    direction='nearest'\n",
+    ")"
+   ],
+   "id": "a46a29e38c162456",
+   "outputs": [],
+   "execution_count": 22
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-17T06:19:09.869678Z",
+     "start_time": "2026-02-17T06:19:08.372346Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plt.plot(merged_df['0_y'])",
+   "id": "f9bda40d7ce7546c",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1a18a5dda00>]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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yePBg97QBAwZIZWWlbNkS+kP5AACwqvzSSrn4X8tlxLPfR3tTrFnFM27cOJ/TVemJatD5r3/9S/773/9KixYt5Oabb/aq7nE5duyYUU3Upk2bExsSH2985+BB/w1GI9HjK9Tlub5n1vYEs5xg12n2tuqINNqD3Y+j3dMXK2k0g9o92blFJ947orwxtbbDzOMY6DJM62a8fft2I0Dp1q2b3HDDDbJq1Sr561//arRBueCCC7zmLSs7/oClhIQEr+nqvWp8G4yMjFQxW0VVTdDfycw8vh1JSd5pCpVreWbPG+59pxvSaA92P452T59d0lhYVhm2Zbds1VwKnY5GX9fN0KxZgt/tiORxNC1AueKKK4z2JKoURDnllFNk586d8uabb9YJUBITE42/tYMR9d5fmxV/8vIKTX3IkorsUtObB/293NxC429ZmTktvF3LM3teVxpVJjN73+mENNqD3Y+j3dNntzR+uy0vbMs+cqRYCo6VhnxdN1NJSUWd7TDzOLqWFbEARZWeuIITF1Wasnz58jrzqvlUkJKbmyvdu3c3plVVVUl+fr7RsDYYakfpkOlVw6b/91mWbM878STixggmTaGmX5d9F06k0R7sfhztnj67pNEZ5keeiEcJijOa+8pj3bW3I5LH0bSB2v75z3/KhAkTvKapBq8qSKmz0iZNpG/fvrJ69Wr3tHXr1hntUFTJixU9/dU2+c+2PNmTf7z6CgCAoIMUmB+gqOod1e7k5ZdfNrogv/HGG/L+++/LLbfc4m53kpOT49XYVs37xRdfyIYNG+SRRx4xui8HW8UTDs4Q4uRcBu8BADTCne/8pMX+c2oylKxpVTxqLBNVivLMM88Yfzt27Cj/+7//a4wSq3z88cfGYGxbt2413l922WWyb98+Y2A21fbkwgsvlPvvv9+szQEAABbWqADFFWy4jBw50nj5MnbsWOPl6bbbbjNeAAAAnnhYoE2UVVZHexMAwNbKQxiCIlANNT9xOp0xd50nQDHJ6j0FEi0zl2XL8Ge+k00HjkVtGwDAztbszZcHlv4csfW9s26/fLz5kPv9w//ealznd+aZ+0BanRGg2MDb6/Ybf//1/a5obwoA2NIz/9kR0fU9sSxb/vbvrVJd4zSGsfjk58PG9NnfRnY7bNFIFgAAhOa9Db4f83Lfkk3y7fYj7vdfZ+fJyl1HZcjJLW2/qylBsRM9eoYBAII0f9Uen9O/9QhOXP5v1d6Y2L8EKAAAQDsEKDayfNfRsLYyB4BYpHrQbDoYvWfjxCoCFB90GG344LGykIfcBwCY56vs8D0kUEdODe6BCgGKDztNeuBfY3y25cRjAYLx7oYDpm8LAMSy5TvrtgNB+BGg+FCjQS3Js9/ETlcyAABqI0ABAKAeDnHE1P6p0aSOhwBF4yc5AgAQae/6GZMl0ghQNHbNaz9GexMAADEmv7RSdECAorEdMfTMBQDQlS5VHrGGAMWHWKtvBAD49uPufHn/Jz2qPGINAYoN26DsLwhtDBUAgLepH2zWclDOsspqsTsCFBsa/dLKaG8CANhCYXmV6OiJZdlidwQoAABYzEebDondEaAAAADtEKD4QINtAACiiwAFAACLcYr9EaAAAADtEKAAAADtEKAAAGBBTps3mCRAAQDAgu59f5PYGQEKAAAW9M32I2JnBCgAAEA7BCgAAFjU3O93SlWNPduiEKAA0FZVdY0sXb9fcosror0pgJZe/GG3fPDTAbGjkAOUiooKGTVqlKxYscI9bd26dXLdddfJwIED5aKLLpJFixbVu4zBgwdL7969vV7FxcWhbhIAm5m/aq9MfnOt/GHe6mhvCqCtPfn2fIJ9fChfKi8vl3vvvVeysrLc03JycuTWW2+V66+/Xv7+97/Lpk2bZNq0adK6dWsZMWJEnWUcOnRICgsL5YsvvpCkpCT39GbNmkm02bznFmAZ32zLM/7mlVRGe1MA6B6gZGdnG8FJ7f7XKtDIzMyUe+65x3jfpUsXo3Rl6dKlPgOUbdu2GcFL586dG7P9AADAhoIOUFauXClDhw6Vu+++WwYMGOCePnz4cOnTp0+d+YuKivwGOl27dhUdOWPiKQcAANgoQBk3bpzP6Z06dTJeLnl5efLRRx/J5MmTfc6vSlBKS0tl/PjxsmPHDiO4efDBB4MOWhyOIBMQ4eVFS33pcH1ml7T6Qhrtx475lXwKU/KRhO/8qJ1HzVhPoMsIqQ1KQ8rKyozARFX5XHvttT7n2b59uxQUFBhVQikpKfLiiy/KhAkTjKBGvQ9URkaqmG3f3nyxuszM1KjsO92QRmuLbxoXVJ62KvIpGqNZs4SwnR+1lxvJvGp6gKJ64dxxxx2yc+dOeeONNyQ5OdnnfC+//LJUVlZK8+bNjfdPPfWUnHPOOfLVV1/J5ZdfHvD68vIKTW3Uapdfabm5hfWmUWUys/edTkijPVRVVgeUp62KfAozFBWXS07OMXH4uIHVOJ2/lrCEdnNznXdm5lXXsiIaoKj2JhMnTpTdu3fLvHnzjIay/iQkJBgvl8TERKOKSPXuCYbaUXa9yTZGIPskFvYdabQPO+dV8ika4/XV+2RfQZnMHH2a1/TqGqeMm79akpvGyavjBoQUpNQ+7yKZV00bqK2mpkbuuusu2bt3r/zf//2f9OzZ0++8qgfQyJEj5d1333VPKykpkV27dkm3bt3M2iQAAGLC19nHu+R7OnCsTLbnlcimg4VSUW29CN+0EpR33nnH6Fb8wgsvSFpamjEuitK0aVNp0aKFMbCbanPSqlUriYuLM7oeP/vss9KxY0dj2j//+U9p166dUc0DAADMU15VLYnxTdyFBOVVNZLk0cbL1gHKp59+apSiTJo0yWv6kCFDjBKVtWvXyo033ijLli0zqnLuv/9+iY+PN8ZUUVVDZ511lsydO9cIXqLNzkXJAIDYc/7sH+T+83rINQM7yN3vbZLvdhyRpbcOkXZpJwZKtVWAsnXrVq9Gr/VRY6d4zq/anDzwwAPGCwAAhNfML7ONAEUFJ8qHmw7JxLNP1na3h6WbMQAAiI5lv+RIZvMEyWh+oiOKFRGgAABgAxVVNZKVWywPLP3ZeP/+xDPrzFNQap3nWhGg+EATFACA1Vw6Z7kUlFXVO8/I538QqzCtmzEAAIieggaCE6shQAGgrVBHvwRgfQQoPqg+4gAAxIryqhrZm18qOqENCgAAPpR5PAvKihzGU3gC84f5q2XX0VJ55foBogtKUHygWBnQA6WZiKbc4oqYOQC7jh4vPfls6/FR4HVAgAIAgA/ZOcUxt1+ycopEFwQoPvCrDQDw+uq9MbcTVu8pEF0QoAAA4MOBY+XslygiQPGhaRy7BQBindU7uTssngDuxD6kJzeN/JEAAMBEDovvTQIUAAB8OFho7yoep+iNAAUAAGiHAAUAAGiHAMUHRroHACC6CFAAAIB2CFAAaIvHTgCxiwDFB6f2bZsBALA3AhQAAKAdAhQAAGKwitQheiNAAQAA2iFA8YFuxgAARBcBCgAA0A4BCgAA0A4Big90Mgb04KS+FQiZ7o1gG0KAAkBbe/LLor0JAKKEAAWAtvJLK6O9CYBlORzWri0gQAEAAPYJUCoqKmTUqFGyYsUK97Q9e/bIhAkTZMCAAXLppZfKt99+W+8yPvzwQxk5cqT0799f7rzzTjly5IjogHpvQD/Ld+pxfQCgcYBSXl4u99xzj2RlZXnd1FWQkZmZKYsXL5bRo0fLXXfdJfv37/e5jA0bNshDDz1kzLNw4UI5duyYTJs2LfSUALC1yYs3RnsTAEtxSIwFKNnZ2XLNNdfI7t27vaYvX77cKEF57LHHpHv37jJp0iSjJEUFK74sWLBALrnkErniiivklFNOkSeffFL+85//GMuwijdvHCS6OnCMxoUAgBgKUFauXClDhw41Sj08rV+/Xk499VRp1qyZe9qgQYNk3bp1Ppej5h88eLD7ffv27aVDhw7G9GgLtOFQ29RE0dU1r/4Y7U0AACBk8cF+Ydy4cT6n5+TkSJs2bbymZWRkyMGDB33Of/jw4aDmD7WVcrCM5QUQobwybkDI6764T2v55OecOtP/dU0/uf3tDWKGsqoav9vnmm72vtMJabQnu+VZ8ikikb/8cQQyT637hRnnYKDLCDpA8ae0tFQSEhK8pqn3qjGtL2VlZUHN709GRqqYLT+nqMF5OrdNk1apSSEtf/b4M6W4okr6PfKZ1/TBvdrIpN91kzn/3S5myMxMjfi+0w1ptJeG8rRVkU8RDq1apdT7ebNmiQ2eU7U/j2ReNS1ASUxMlPz8fK9pKthISkryO3/tYES9T05ODmq9eXmFpj7c73hk13B4d+RosTSpCG2MBrXNTRwOmX/DQLlxwdoTy8wrEkd1tZglN7fQbxpVJjN73+mENNqTvzxtVeRThNORo8X1fl5SUt7gOeX63My86lpWxAKUtm3bGg1oPeXm5tapxvGcX31ee/7WrVsHtV61o8y+yQayvJpGrNfYZrXzm4S3vLqh7QvHvtMNabQXu+ZX8inCla/q4wzwPlH7faTOQ9MGalNjmWzatMmounFZvXq1Md3f/OpzlwMHDhgvf/Nr59cgozEclu8EBgCA5gHKkCFDjJ44aiwTNT7K3LlzjbFOrrrqKnf1jWpIW/1rFcb1118vS5YskUWLFsmWLVtk6tSpMmLECOncubNYgTNMgwQTtAAiWTlF8q/vdrIrgBhmWoASFxcnzz//vBGEjB07Vj744AOZPXu20XVYWbt2rQwbNswoJVEGDhxojJmi5lHBSnp6usyYMUP0EKHyKwpQAJ/GzV8jLy/3HmsJQGxpVBuUrVu3er0/+eSTjQHYfFFjp9SeXwUy6mVFRh1cGOIYu3WjBABEh8PiO56HBYaoU4vkRlfztEnx7mYNAACOI0AJUbOEOGmstKSmXu9pfwIAwHEEKD4E2oXKpj0eAQA24LB4HQ8BSiPYdUwGAACijQBFMxYPeAEAARrQMS2s+8ph8SNBgBKghy/qJef1zPSalp5k2kC8AADAAwGKD75qbtqnJcnZXVp6TYuPY/cBABAO3GEDNLBTetBFaINPahH0dxwBtGpq1cy79w8AAHZDgBKA2Vf1lbgAH+yXEOcIvf4vwC/cMLhTsEsGAGjG6m1Ewo0AJYDeOfUVatTXkSeUTj5kWACAGbblloiVEaCEE/2QAQBR8vTX2yy97wlQGun+83oE3YYEAIBwc1p8rC4ClEa6ZmAH+fekoV7T+rY/3rd9dN/2QS+P+AYAYkSYL/jZucViZQzk4UOwDwHMTEn0ej/n2n5yqLBcmpvwvB4AgHWd3j5VNh4oFB3N/X6X3Dz0JIkPsBNIpFGCEoZisqZxTYynHQcr0CySGE/gAwBWUO+wEhJ97284ILoiQAmjcD2d+Pentw3LcgEAsWVPfqnoigClkd2MI61rq2aS1JQSFACAvRGgaIZeQAAAEKCEVzhKXjQqzQEA1K9/R9+PSdGpdL6yukZ0RAlKECKRmTTJr0DUOK0+eAPgYdJvTva7P1IT9ehIe+1rP4qOCFB84PoIADBD7TaDnp0c7juvh7ROSYj6jt6TXyY6IkAxOXBpTAmILsV9AIDw+H7HUff/26YmyovX9WdX+0GAohmCFMQ6KnhgZ4M6p0dkOAo7IEAJYCTZUDOQv2/RUwcA7O933TPqTGOYiMARoJjkwt6tjb/jz+xk1iIBABZz9YAO7v/H+RhCvvYUSs3906MJsQ2ezfPoJb3lD4M7ySltU8K9MUBM9/ChBBI6u/+87rJo3X7j/5o+4sYyKEEJQn2RbnxcEzm1Xao08ZiJyBgIHr3oYGWeAbQjgN+YgcYwQ05qIbGGAEWjC6Rq69Lgr0MicgCwCPMu2Of/2owglhCghBGtswEgdplZin7ZqW1j7j5FgKKZhrKKvlkJMEd9BZg0wYKumifEyQ2DG99JYsrvuvqcnhjfJGptLG3RSPbdd9+VadOm1Zmuqi22bNlSZ/rvf/972bp1q9e0pUuXSq9evUQnrgiTunEg/NbtLWA3w3LWPHyBFOaXeN0nQvlBmanByLK2DFAuvfRSGT58uPt9VVWV3HTTTTJixIg681ZXV8vOnTtlwYIF0qVLF/f0li1bil2Eo5GsK+/fOayLzP52p/krAKJs2S850d4EIGiJ8XFS+Ov/O6Qlyv5j5XJBAO1Garc75IdwmAKUpKQk4+UyZ84co1vgfffdV2fevXv3SmVlpfTr108SExPFjkLJaIHGNBOGnkSAAlvafbS0/nOKek5oQFW5lFf5fgrw6zcOkl1HSoyenWaI+7W/8nt/PFPGvLxKzBSTbVDy8/PlxRdflHvvvVcSEuoWWWVnZ0v79u0tFZzQbRgIv5W789nNsLSUxHg5rX1aQGP2BBIePH91X+NvpxbJEkvCNlDbm2++KW3atJGLL77Y5+fbtm2Tpk2byqRJk2Tjxo3StWtXmTp1qlGiEs2gQS2vdsmHmmasxxHcepv4Cf/8fdeYP4BexoGm2d98rul2DrhIoz25z0WbIJ9al69s6C9v/v3yPvLA0p/rfM9XfvZ8P6ZfO8krrpQzOqWHMd87g8qjZmxHoMsIS4CiqnUWLVokEydO9DvPjh07pKCgQK6++mqZMmWKvP3220Z7lY8//tgoWQlURoY5RWie9tdqpJeWniyZmamSmnLiKZTqfUMSyip9Tvf33VYZKZKaUn8Dwfj4JgGtO5BtDMe+0w1ptBeVp30NH2515FPr8VU64u84di86cS84q2dref+ng+78XNW0qde8qaknmknMGjdIwu311fuCuo9EMq+GJUD56aef5NChQ3LZZZf5nWf69OlSVlYmKSnHh4Z/5JFHZM2aNbJkyRK5/fbbA15XXl6hqY2KfEV2pUVlkptbKIVFZe5p6n1Disqr3P+fOfpUuX/J5nq/eySvSIo81qE8MLKHUax31zs/Ge+rqmoCWnd961FpVJnM7H2nE9JoTzm5hRJvowCFfGpd6od4bf6uqQX5Je7/n9ulhfzlwp4yoGO6cY0+WlTuNW9hYXD3mXBzbYOZedW1rKgEKN98840MHjxY0tPT/a84Pt4dnLii0W7duhmBTTDUjjL7Jlu7X3jf9mnH1+ExOZB1es7jeVH1993j070vviN6ZNbp/x5oehuaLxz7Tjek0V7sejztmi47p9HXj1l/aWyTeqKtpXocyui+7f3O7/neqcH+8rV9kdqusDSS3bBhg5xxxhn1zjN+/Hh57rnn3O9ramqMMVFUkKITVQdoxsPJGnNA7VTnDgB2EEzvl/ZpSTJrzGny0nX9fSwHES1BycrKMgZhqz3uyZEjR4xSFdWr57zzzpPZs2dLnz59jAay8+fPl8LCQhkzZozopGtG85C/G47GRFPOORHAtUlJkMNFFY1fCQAgrNf3Yd0y2MM6lKDk5uZKWlqa17QDBw7IsGHDZO3atcb7CRMmGI1oH3/8cRk9erTR7fjVV1/1qvbRwdX9A2+wG4lM3z2jmfv/SyYOCf8GATrRocwbCONFv2Uz70azsSw+XFU8tXXq1MlrWHtVbaIawwbTIDbS18B2qYkSH2dODBeOy2ow21ZcUSXNmsaZUl0FAAiPzi2S5cELekrLZAIVHhYYRqGM0BeO8GHt3gIZ8ez38sSy7DAsHQBi24xRfUL+rq9r/ph+7WVEz0yJdQQo9dChsMGMTZjz/fFn9ixef8CEpQHRQwUPoml033Y+7w8XnNLwM3cQPAKUMF4EdQhwAAD2MbZfZNtFRhMBimYIagBAT56/OR++qLfx967hXSP6Q/b+83tItAeks/yzeOygsQUgTT0GZ0tNjI9acEJBDgCY69yemfLNlN9KUtO4iO7aeBuNpNwQApQwNnpVvWxeuX6AVNU4JSUxsEzsaw2JHr110kNo2V1S6fuR4IDV0MsY0VT7R6QZwYnu4YYzittIgBLmIq2+HY6PB5OVUxRyUKQCnXdvOVOqa5ySHMIJsflg9J/nAACwHmcUIxQClEY8oyfcXY49dW6Z3KjvAwD009h7g53RSFa3Fqu0kgX8opsxoikWgwlnFNdNgGKTi2AgjXABAHoL5Ddq84TINsyNFgIUzQQbn7/3xzPlqdGnydldWoZpi4DI6t1Gr+dxAS7NwhEYhFAoc+3ADhILLdMJUOrh0KH4sIGN6NQiWc7pkUHNEGyjfVpitDcB8OnmoZ1lQMc0mTYysmORRLMpQDRrFKgXAKCV+h5oGc1Bo4C0pKby4nUDTN0RsdeqJXCUoPjg7xrYmAZSHdKTfE7v2z611jqA2KaeIg5AD9H8TUAJSoRK0VTd5Wd/Okuaegy6pmQ0Twj7r04AgJ5qX7oD+SHskMgpqaiWhPjolGUQoERQy2YJIWRWAABOaJ1izg/bQFTVRG8kcqp4fHKaPlBbuHWgYSEAxITRp7eL2Lqqo3jbI0DRSGNqaSYMPUn6tKV7JqxP358BgPlCadsYH9ckYmOhRLNhOgFKPSJRvVK77UioDXHVM3oeuqCXSVsF6GlbbnG0NwGIKTWUoABAwya8sY7dBFup0+5Qs4aHNZSg2L+bsT91ltiIzKpbxgYAWFsNJSh6slrXXYttLgAgRIkR6vpLCUoMMzOoiMUnbcJ+GC0WaFikfkBHc6A2Gslq1ovAYeKXV+0+2sitAQCEU6hxRkc/o5ObrZo2KFAaGw83qbWAL3/JZccCgA3vA49fdopEAt2MLSISA7WFMuwxACC2tE+LTAlKFAeSpYon2swMP5rUWhoDXgGA3nT/EVoTxTsJbVCi3M3YVJpvHgDAWmpoJBvL93tzRpKtuyQAgO50Hx6ipsYmJSiff/659O7d2+s1ZcoUn/N+//33MmrUKOnfv7/ceOONsmfPHjM3JSYzbxPdczoAwFJqotiLJ97MhWVnZ8u5554r06dPd09LTEysM9/+/fvlzjvvlMmTJ8vw4cNl9uzZcscdd8gHH3ygxeBo0Wy13Jjka7DrAABB0P2y7bRLFc+2bdukV69e0rp1a/crLS2tznyLFi2S008/XW655Rbp2bOnzJgxQ/bt2ycrV640c3MA2MCp7eteQwBEhm0ayaoApUuXLg3Ot379ehk8eLD7fXJyspx22mmybp1eDwKLRImEqSPJ6h6KAyH8Yru0bzv2GxAl0exmHG9mtciOHTvk22+/lTlz5kh1dbVcfPHFRhuUhIQEr3lzcnKkTZs2XtMyMjLk4MGDUb8p1/tkSUd4gwG1zNqDralpga7LVxsUX+lxTbNzQEMaLaxOnq3VkNxG+ZZ8ai3+8l5jjmN912hdxv8y+74R6DJMC1BUu5LS0lIjGPnHP/4he/fulccff1zKysrkL3/5i9e8rvk8qfcVFRVBrzcjI1XMllWQZ/yNi2simZknlp+aku/+v+f0xkhKauq1zLRDxV6fZ2SkSKrHPPUpj4+vs+za2+n5Phz7Tjek0XqSG8jvZp17OiGfWkNDeS+U41hWWe31vlWrFMlskRzQd/t1SpcNewsknFJSk6N23zAtQOnYsaOsWLFC0tPTjV88ffr0kZqaGrn//vtl2rRpEhcX59VwtnYwot77aq/SkLy8QlMb8XhGdjXVTsnNLXS/Lywqc//fc3pjlJdXei2zsPDEOpS8vCIpTwzsMB095v3dsrLKOtup3qs0qkxm9r7TCWm0rtKyE+eEr19bZp17OiCfWou/vNeY41he5V2HcvRosSRUVQX03VZJpvZz8Sm/oMT0+4ZrWQ0xNXUtWrTwet+9e3cpLy+XgoICadWqlXt627ZtJTfX+zkx6r0KaoKldlQ4b7Jey/b4v2nrrL1MZ+jpaxrXpMHver4P977TAWm0oAbypB3zLPnUGhrKe6EcR1/XaJ3yeE1N9O4bpjWS/eabb2To0KFG9Y3Lzz//bAQtnsGJosY+Wb16tfu9+s7mzZuN6TqIVuZQJU+Nqd9LD7AqCNCZ02ojOAONoHvurrbD04wHDhxoVN2o9ibbt2+X//znP/Lkk0/KxIkTjQazqmGsq1rnyiuvlDVr1sjcuXMlKyvLqALq1KmTEeDEGjMbQ8XVbmEL2IBODQYBnXRID/8DA23xNOOUlBR5+eWX5ciRI0YA8tBDD8m1115rBCgHDhyQYcOGydq1a415VTDy7LPPyuLFi+Wqq66S/Px8Y7A2HQZp86LB5ui2SwAAerjtNyfb+lk8prZBUYOuvfrqq3Wmq4Bk69atXtPOOecc4wVv2gVpQJRxRsDO6nQzDuK7zRPi5fozOsqba/ZJuNiiBMVOVL9vn7hSAgA0MqybdxtPs1XbZah7u3FYcJ2DT/LuSQVYTe1fbBQqws4icZ9JDXCoCl8oQbEKZ/gzat3ivuCyL4U8sBudulwCVnTTkM6x3YsHeuBaDgAWonkRoZMqnthV5zkjkWhLA1iI5tdvQHuORny3hhIUmHU15loOu2GgNtiZI0IBeUZz7+ffWeFpxlTxRBkBBeCNcj8gcIGeLy9fH9pI7ZSgWIVD/2iagAd2QxUPELiUxBMP5vXUMT2wJyTXFsUCFEpQdPsJR4AB+Hd6+8g96h2wRADu9H77xOWnmroOuhlrKtK/3PilCNQvpRHjOQCx4ORWzUxdXjVtUGJXnXFPKEIBgJhRp1q/kctLT4oPaEDPjycF9nBeSlDgoZG9eAhwACBmxIXwFPvuGc2kdUqi9gO1UV4aDGfkG8USbwCAfTX2AbEDO6UHvLx5fxgoX2zNkT+efVLAy2egNgAIQEVVNPsUAHqWoNwU4FD2p7ZLlSnndDOeghwouhlbRRS6GQOxzvMX4Zq9BVHdFkB3DpOXV8NQ93qJ1vHw9bBAAEAM0ewmUMNQ9zE8xLbJmZFhwWF1PL0YCJ2vW0pj2rnQBgVhCYq40AOA/vQqM/FGCUoMc5idU3XO6UAIojkOA2A1jiDmPa9nZoPzEKAAABCjotXsJDu3uMF5qmkkG7vMHkUQABDDHHXvIv7uKz1bN29wcYwkC795q7GD+ABW44zm0zoBC3KE8Xt0M8avuYVgBABijU5X/qZxDm1KUBjq3odotskzs40sv0MBILY4Qpz7Lxf2lPN6tpaCskr55OfDMuf7XcZ0nmYcwwUadZ+9Y+JKiVAAQGL9ZuNwNDw9KT5OUpPipVOLZJl49slaVLk2idqaAQCA1rFODb14YledEhMTn2ZMY0NYEcOeQAdlldW2b4PiCGAeevEgqAxTb4bTqbUVAFhUXkmFWJEjmHkDmLk6ikUoVPEEwWGx5fNLFHZAPobd6fzDsoYqHoQjo9JGFnZAVSUQOLPHzrLNUPeHDh2SKVOmyJAhQ2T48OEyY8YMKS8v9znvn/70J+ndu7fX66uvvhKdL4hhOUwN9OLRObIGAJhLt0u+M4q/dE0bB0U1pFHBSVpamrz++utSUFAgDz74oDRp0kT+/Oc/15l/27ZtMnPmTDn77LPd09LT0yXWEZAg1tW+HlLFA7uLWiNZh6PBe080S1BMC1C2b98u69atk++++04yM48/IVEFLE888USdAKWiokL27t0rffv2ldatW0ssMztjepbAUMUDAHD4iT4csVLFowKNl156yR2cuBQVFfkMZtQO69y5s1mrB2BTBNqwO52fuVZjhyoeVbWj2p241NTUyIIFC+Sss87yGaCkpKTI1KlTZeXKldKuXTuZPHmynHPOOUGv1+zj6rk8h4/3Zq/Xa/kOkSZ1HhYY+rq+3Z4nV7y00u/yND4nGo00WldD2dJO+ZZ8qi9HCCOympE3Q7nmO2rdR+p87m+6103N9zyqTabZ941AlxG2Z/Go9iWbN2+Wd955x2eAUlZWJsOGDZPbbrtNPv/8c6PR7MKFC41qn2BkZKSK6Y6UGX/i4uMkM/PE8lNT893/95zeGElJTd3/b52ZKulFlV6fq/U0jQu8oCshIc79/4LSKuNVe3lh3XeaIY3W43lOKLVLmM0693RCPtVPaZMT19JA816ox9Hzht2qVYpkpiYG9f3k5IR6t7F580Sf0z3PtdTUZJ/zNE1oGrX7Rny4gpN58+bJrFmzpFevXnU+v+OOO2T8+PHuRrGnnHKKbNq0Sd5+++2gA5S8vEJTG9F5ZpTqqmrJzS10vy8qOh64KJ7TG6Os7ERAkptXKMeOlXp9npdbKPFBBCiVDYx+qLZbpVFlMrP3nU5Io3WVepwTvph17umAfKqvvHzva3F9ea+xx9HzO0eOFImjPLhB4kpLK+rdxpKScp/Tyz3OtcLCUp/zqGWbfd9wLSviAcr06dPlzTffNIKUiy66yOc8qmdP7R473bp1k+zs7KDXp3ZUuG6yarGey/b3f9PWZ6yw7jQz11U7DXYNUFxIowXVPgdqTbBjniWf6sdXPmso75lxHENZhrOBe5PfZXr+IvczT3Wt6ZHMq6aOg/Lcc8/JW2+9JU8//bRcdtllfud74IEHZNq0aV7TtmzZYgQpOojkBdDUpxcDAEwRybZOOt8FnHboxaPGNXn++efl1ltvlUGDBklOTo77pai/qt2Jct5558nSpUvl/fffl127dhmBzerVq+WGG24QndTONJE4THZqAAiYwY4lJtBfLOQ7R6z04lm2bJlUV1fLCy+8YLw8bd261WgQq0aWHTt2rFx44YXyt7/9zZhv//790rNnT6OLcqdOnczaHPsgYgEAWwv3Zd4R5HTbDdSmeuOolz8qSPF09dVXGy+cQOEJILItr5jdAGjCFgO1xQJHBCJnnQfsAcJt5a6jsvFAoTZ14ICd2yI6AlgtTzPGiQzT2AzHvoSFfbb1eJs1AHoERrZoJIvwIOBArKMABWicxhTMU4KimWgWKFPDAwAx1s04ao8zlgbRBkVT0WgPQokJ4I0WKIilkrtoBSsOPyuuiWIRClU8MRYQXfnKKjlUWB729QAAIsPhsOc4KAQoUVYngzQySGkoyNl9tFQe+8S7yzegM9qggHzXWL7vC/TiQaOyUTgKVVbsOvFUZgBAdNte6Nz20BnFSlZKUKKsdomHzhkVCLeC0vqfZAwgst2Mq2mDAgAiX2fnsRsQc50kGKjNN0pQdKvSMXl5/uzOK2nkmgD7FzEDduBoxI2Fgdo0E80DEqlIOqeInjwAgPpF8+cBJSj1cFhwGwKNlGnrAqugFw/sTofrscPPdNqgoOFcYvLFXIPzAQgIFTxAeAQ2Dgq9eGI2yq3zNGOJjCY6hOwAgKj9YHQEcB9goDaLsEJRM1U8sB0rnHiATQMgJyUo8FuiEqaSjmh1awMA6MERwDyUoCDigQM1PLAKyk+A8AhsqHvaoAAAAM1UM5KsXiIZL9apwolQzcs3WbmRWRHQSDRBgd153gfCcQtwWLRGn3FQNDuokVplaUVVhNYEBK5P2xR2FxCNwMjhex7aoMRyN2Ozl2fVUBkQkbapiXX2A0Pdw+50vmrX0AYFLpGKL2h4CACxzRHAPLRBiWF1m6BEJkKhXh868pUvyauwu3D/MHWINdEGJUZREwQdRbM4GYg1joC6GUvUEKAEIRLXTocNol4gVL5OMUIWIHpog6KbaF4RHZH5Oj9UYRXkVUQn3xEaK7RBiWHRGnKenhHQEfcEIAz81OV43n/83YkoQbGISLTboFoHsczXxZBgGtEQySEbojU8hCOA1UazHMnUNijl5eXy4IMPyuDBg2XYsGHyyiuv+J138+bNcvXVV0v//v3lyiuvlI0bN0osMvvhgIFmJn6pQkcUqgN6qbFLI9knn3zSCDTmzZsnf/vb3+S5556TTz75pM58JSUlcttttxmBzLvvvisDBw6USZMmGdNjnVcj2TAG1cXljCQLDdFKFjHYBsWh89OMa2zwsEAVXCxatEgeeughOe200+SCCy6QiRMnyuuvv15n3o8//lgSExNl6tSp0r17d+M7zZs39xnM2F20Mua8H3ZJbnFFlNYO+EZ1DnTRNK6Jbe4jjkYs2xZVPFu2bJGqqiqjNMRl0KBBsn79eqmpqfGaV01Tn7mqM9TfM844Q9atWyexLpJVkV/zwEBoxtePNap9EA2xMFaUQ/M0xpu1oJycHGnZsqUkJCS4p2VmZhrtUvLz86VVq1Ze8/bo0cPr+xkZGZKVlRX1Hbzp4DH54+vHAyVHreWHo/rFa/kOX21SJGyeXJYtX9o0SFG7rWnTeKmsrLLtDc6OaVy1O7/OtKO1SvrufGeD2IUdj6Fd0lhe5f3Dur7rsWt6qNfrhu4DDar1/Tof+1mmV5vHetbr+X0z7kmBLsO0AKW0tNQrOFFc7ysqKgKat/Z8gcjISBUz/bBqn/tXXIdWzSQz88TyR5zeROSTX6RbZnOv6Y0xoGuGzF+11/i/WmbT5knuz9qkJgW9njGDO8tXAQYdKp0rd9W9IQA6GXhSS1m0+vg5opBnES0NXY9DvR+1b5EsOUUV0sQh0rl9C0lqGhfU94ef0k5eWb5HOrdKNraxX6d02bC3wP15t/bpPrf95DYnpvXo2MLnPOOGnuQ13ex7bkQCFNWmpHaA4XqflJQU0Ly15wtEXl6hqT1SbhjQTgZ0TpejBSVy5kktJTe30P2ZehD8gvEDJaN5gtf0xvhtpzR59JLeMqhzunuZb950hmzLLZG+7VODXs/ZHVLkLxf2lJ1HSqV7ZjPZerhIurRqJl9n50nvNikyb+UeaZ2SIGPP6CSdU72DRLtJTU2SwsIysTM7pvGddfula0YzGTeok2TnFst1QzpLr5aJ8vSX2+TCU1pLvIXaBsTqMbRLGnfmlUh+aaVcfno7aZuW6Pd6rEoE1I071PvRk6NOkbV7j0nvtilSVFAiRUF+//SMJPnXNf2kc8tkYxvnXN1XXv5ht7RLS5T26UnSN/P49Nou6dlKWo45TZolxMlJzeK95vng1iHy2ZYcuXFIJ2N6Y9PoybWsiAUobdu2laNHjxrtUOLj491VOSroSEtLqzNvbq73r3z1vk2bNkGvV+0oMwMUFblefHp744D4WnbvXyNOs9bZxOGQS09t67XMHpkpxiu09ThkdN/27nejTjv+98r+HYy/dw3vamQOFRG70mhHpNG6LjrlxHVABSqqGFoF2f+8sq/YDfnUWhq6XoZ6P8ponigje7cOaB2+OWRQ5xbu78c3aSKTftvFaw5fy1XzDeuW4XOe9mlJctOQznWmm33PrY9pP0X69OljBCaeDV1Xr14tffv2lSZNvFejxj5Zu3atuxuX+rtmzRpjOgAAgGkBSnJyslxxxRXyyCOPyIYNG+SLL74wBmq78cYb3aUpZWXHi/guvvhiOXbsmPzP//yPZGdnG39Vu5RLLrmEIwIAAMwdqG3atGnGGCg33XSTPProozJ58mS58MILjc/UyLJq/BMlJSVF5syZY5SwjB071uh2PHfuXGnWrBmHBAAAiMNp8Uc2mt2Ogjphe+A42oPdj6Pd06eQRntwmJhXXctqiL2awwMAAFsgQAEAANohQAEAANohQAEAANohQAEAANohQAEAANohQAEAANohQAEAANohQAEAANox7WnG0aJGpAvH8sxerk5Ioz1wHK2PY2gPHMfgBHp/tfxQ9wAAwH6o4gEAANohQAEAANohQAEAANohQAEAANohQAEAANohQAEAANohQAEAANohQAEAANohQAEAANqJiQClvLxcHnzwQRk8eLAMGzZMXnnlFb/zbt68Wa6++mrp37+/XHnllbJx40avzz/88EMZOXKk8fmdd94pR44cEaul8euvv5bRo0fLwIED5fLLL5dly5Z5fa6W0bt3b69XcXGxWCmNf/rTn+qk4auvvnJ//tprr8nw4cONfaCWWVpaKjoINI3jx4+vkz71mjZtmvF5QUFBnc+GDh0qOqmoqJBRo0bJihUrbHc+Bpo+q56LwaTRqudioGm08rl46NAhmTJligwZMsQ4BjNmzDCuQdqci84Y8Nhjjzkvv/xy58aNG52fffaZc+DAgc5///vfdeYrLi52/va3v3X+/e9/d2ZnZzunT5/u/M1vfmNMV9avX+/s16+f87333nP+/PPPzhtuuMF52223Oa2URrXdp512mnPevHnOnTt3OhcsWGC8V9OVgwcPOnv16uXcvXu38/Dhw+5XTU2N0yppVC644ALnkiVLvNJQXl5ufPbJJ584Bw0a5Pzyyy+NY3rppZc6H330UacOAk3j0aNHvdL2+eefG8dxw4YNxuc//vijc8iQIV7z5ObmOnVRVlbmvPPOO428tnz5cp/zWPl8DCR9Vj4XA02jlc/FQNNo1XOxpqbGec011zgnTpzo/OWXX5yrVq0yjpU633Q5F20foKgd2LdvX6/MNXv2bGMH1rZo0SLneeed574AqL/qgC1evNh4f//99zv//Oc/u+ffv3+/s3fv3sYFxCppnDlzpvOPf/yj17RbbrnF+fTTTxv//+6774yMqJtg0qgufn369HFu377d57LGjRvnfOaZZ9zv1YmpTq6SkhKnVdLoqaqqyriwz5o1yz3t7bffdl577bVOHWVlZTl///vfG4FYfRd+q56PgabPqudiMGm06rkYTBqtei5mZ2cb6crJyXFPW7p0qXPYsGHanIu2r+LZsmWLVFVVGcWHLoMGDZL169dLTU2N17xqmvrM8eujFtXfM844Q9atW+f+XBW5urRv3146dOhgTLdKGseMGSP33XdfnWUUFhYaf7Ozs6Vr166im2DSuH37duPYde7cuc5yqqur5aeffvI6jgMGDJDKykpjHVZJo6d3333XKEa+9dZb3dPUcezSpYvoaOXKlUYR98KFC+udz6rnY6Dps+q5GEwarXouBpNGq56LrVu3lpdeekkyMzO9phcVFWlzLsaLzeXk5EjLli0lISHBPU0dEFXPlp+fL61atfKat0ePHl7fz8jIkKysLOP/hw8fljZt2tT5/ODBg2KVNHbv3t3ruyptP/zwg1x33XXG+23bthl1wKpedceOHdKnTx+jXjjaF8pg0qguiikpKTJ16lTjItOuXTuZPHmynHPOOXLs2DHjO57HMT4+Xlq0aGGp4+iiSkHVRebGG2+U5s2bu6er46iCnauuusqoZ1YXD1UnXjv/RsO4ceMCms+q52Og6bPquRhMGq16LgaTRquei2lpaUa7Exf1I2jBggVy1llnaXMu2r4ERZ3gnhd8xfVeNX4KZF7XfGVlZfV+boU0elKNmNTFQkXC559/vvuCon4BqIZtzz//vCQlJcmECRN8RtW6plGlQR0r1chUXTDUxVClR/1aU9M9v2v146ga7qmLwDXXXFNnH6hjpi6Es2bNMi4gt99+u/Gr1Sqsej6GwkrnYjCsei6Gwurn4syZM42GsHfffbc256LtS1ASExPr7CTXe3XCBzKvaz5/nycnJ4tV0uiSm5srN998sxH1P/PMM9KkyfFY9eWXXzaKWF2/AJ566injoqJa3ateBlZI4x133GH86kxPTzfen3LKKbJp0yZ5++233SefXY7jp59+Kr/73e+MX52ePvroI6MY1vU9dYzVTUIVuaqboBVY9XwMltXOxWBY9VwMhZXPxZkzZ8q8efOMAKpXr17anIu2L0Fp27atHD161Chi8yyuUjtWFXHVnlddLDyp966iK3+fq7o8q6RRUcWMf/jDH4wMNH/+fK+qAxX1ehZPqozXqVMn4ztWSaO6wLsuiC7dunUz0qAuHipNnsdRLVNVoVjtOCrffPON+xe3J3Vh8AxqVHGrSnu0j2MwrHo+BsOK52IwrHouhsKq5+L06dPl1VdfNYKUiy66SKtz0fYBiqq3VfWarsY8yurVq6Vv377uXyouqv/22rVrjV8yivq7Zs0aY7rrc/VdlwMHDhgv1+dWSGNJSYlMnDjRmK7qG1XGclHpVf3YVUMvz/l37dplXFSsksYHHnjAPQaBi2p0p9Kg5lXf8TyOaplq2erXnVXS6KoW2LNnj9F4zZMqTj7zzDNl+fLl7mnqYqiCn2gfx2BY9XwMlFXPxWBY9VwMllXPxeeee07eeustefrpp+Wyyy7T71x0xoC//vWvzssuu8zoq636qJ9xxhnOTz/91PhM9UkvLS01/l9YWOg866yzjD7eqouZ+qu6+bn6eq9Zs8bo3666jbn6ek+aNMlppTSqLoyqG5+az7Nf/rFjx4zPVZpHjBhhdKlTfePVGACjRo0yus9ZJY1qmjpOqk++Gl/i2WefNdK8Z88e4/MPP/zQ+K5ahlqWWqZKtw4CTaOijpHqluxrXAyVL1UXSbUcNabK9ddfb4x3oJva3Tftcj4Gkj4rn4uBptHK52KgabTquZidnW10AVddoj3zn3rpci7GRICi+tRPnTrVOWDAAKOP96uvvuqV8Vx9uRWVia644gojs1111VXOTZs2eS1LzXvOOecYy1IXjCNHjjitlMaLLrrIeF/75erDrgYmmjFjhpH5+vfvb2Qy1afdasdRnSgXXnih8/TTT3eOGTPGuXLlSq9lzZkzx3n22Wcbg0RNmzbNSLfV0vjRRx/5HScjPz/f+cADDziHDh1qDPZ23333GdN0v/Db5XwMJH1WPheDOYZWPReDSaMVz8U5c+b4zH/qpcu56FD/NK4MBgAAwFy2b4MCAACshwAFAABohwAFAABohwAFAABohwAFAABohwAFAABohwAFAADUSz2OYdSoUcZDEQOlnmA9evRoY0RZ9RBFNYpwMAhQAACAX+Xl5XLPPfdIVlaWBEoN/X/rrbfKBRdcIEuWLJHevXsbD48M5gnHBCgAAMCn7Oxso/Rj9+7dEgz1fKl+/frJXXfdJV26dJEHH3zQeP7S9u3bA14GAQoAAPBbTTN06FBZuHBhnc9+/PFHGTt2rBGIXH755fLpp596fe/CCy/0eqrzF198EdSDIOMDnhMAAMSUcePG+Zyek5MjkyZNkrvvvluGDx9uPIlaPb06IyNDBg8ebFTxJCUlyZQpU4xApkePHvLwww8bfwNFCQoAAAjK66+/Lr/5zW/khhtukJNPPtloDHvttdfKvHnzjM9LSkrkqaeekjPPPFNefPFFad++vUyYMEGKi4sDXgclKAAAICiqLclXX30lAwcOdE+rrKyUrl27Gv+Pi4uT8847T8aPH2+8nz59uowYMUK+/PJLozooEAQoAAAgKFVVVUagcfvtt3tNj48/Hla0bt3aHawoCQkJ0rFjRzlw4EDA66CKBwAABEUFH7t27TKqd1yvZcuWydKlS43PBwwYIFu3bnXPr7oXq3YpnTp1CngdBCgAACDoxrMbN26UWbNmyc6dO43A5Omnn5YOHToYn990001Gr5433njD+Pyxxx6TxMREo5onUA6n0+kMbrMAAECs6d27t8yfP9/odqx8//33RkPYX375Rdq2bSs333yz0WjWRXUrVp/v27dPTj/9dCNI6dmzZ8DrI0ABAADaoYoHAABohwAFAABohwAFAABohwAFAABohwAFAABohwAFAABohwAFAABohwAFAABohwAFAABohwAFAABohwAFAABohwAFAACIbv5/zNwqL8TpMScAAAAASUVORK5CYII="
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 23
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-17T06:19:11.279947Z",
+     "start_time": "2026-02-17T06:19:10.101292Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "#plt.plot(calculated_position)\n",
+    "plt.plot(calculated_speeds, color = 'red') # this seems not right."
+   ],
+   "id": "d543d0eca7887c24",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1a18a61e000>]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 24
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-17T06:19:12.512529Z",
+     "start_time": "2026-02-17T06:19:11.440668Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "plt.xticks(rotation = 90)\n",
+    "plt.plot(speed_kph.datetime_x_axis, speed_kph)"
+   ],
+   "id": "d86c58fb82b41f22",
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'DataFrame' object has no attribute 'datetime_x_axis'",
      "output_type": "error",
      "traceback": [
       "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mNameError\u001B[39m                                 Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[18]\u001B[39m\u001B[32m, line 8\u001B[39m\n\u001B[32m      3\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[34;01mphysics\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01menvironment\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01mgis\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01mgis\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m calculate_path_distances\n\u001B[32m      5\u001B[39m \u001B[38;5;66;03m# Defining dictionary \"route_data\"\u001B[39;00m\n\u001B[32m      7\u001B[39m route_data = {\n\u001B[32m----> \u001B[39m\u001B[32m8\u001B[39m     \u001B[33m\"\u001B[39m\u001B[33mpath\u001B[39m\u001B[33m\"\u001B[39m : np.array(\u001B[43mcoords\u001B[49m),\n\u001B[32m      9\u001B[39m     \u001B[33m\"\u001B[39m\u001B[33melevations\u001B[39m\u001B[33m\"\u001B[39m : np.zeros(\u001B[38;5;28mlen\u001B[39m(coords)),\n\u001B[32m     10\u001B[39m     \u001B[33m\"\u001B[39m\u001B[33mtime_zones\u001B[39m\u001B[33m\"\u001B[39m : np.zeros(\u001B[38;5;28mlen\u001B[39m(coords)),\n\u001B[32m     11\u001B[39m     \u001B[33m\"\u001B[39m\u001B[33mnum_unique_coords\u001B[39m\u001B[33m\"\u001B[39m : (\u001B[38;5;28mlen\u001B[39m(coords) - \u001B[32m1\u001B[39m) }\n\u001B[32m     13\u001B[39m \u001B[38;5;66;03m# Creating GIS object\u001B[39;00m\n\u001B[32m     15\u001B[39m starting_coords = [\u001B[32m37.00107373\u001B[39m, -\u001B[32m86.36854755\u001B[39m]\n",
-      "\u001B[31mNameError\u001B[39m: name 'coords' is not defined"
+      "\u001B[31mAttributeError\u001B[39m                            Traceback (most recent call last)",
+      "\u001B[32m~\\AppData\\Local\\Temp\\ipykernel_30512\\3802325483.py\u001B[39m in \u001B[36m?\u001B[39m\u001B[34m()\u001B[39m\n\u001B[32m      1\u001B[39m plt.xticks(rotation = \u001B[32m90\u001B[39m)\n\u001B[32m----> \u001B[39m\u001B[32m2\u001B[39m plt.plot(speed_kph.datetime_x_axis, speed_kph)\n",
+      "\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\generic.py\u001B[39m in \u001B[36m?\u001B[39m\u001B[34m(self, name)\u001B[39m\n\u001B[32m   6317\u001B[39m             \u001B[38;5;28;01mand\u001B[39;00m name \u001B[38;5;28;01mnot\u001B[39;00m \u001B[38;5;28;01min\u001B[39;00m self._accessors\n\u001B[32m   6318\u001B[39m             \u001B[38;5;28;01mand\u001B[39;00m self._info_axis._can_hold_identifiers_and_holds_name(name)\n\u001B[32m   6319\u001B[39m         ):\n\u001B[32m   6320\u001B[39m             \u001B[38;5;28;01mreturn\u001B[39;00m self[name]\n\u001B[32m-> \u001B[39m\u001B[32m6321\u001B[39m         \u001B[38;5;28;01mreturn\u001B[39;00m object.__getattribute__(self, name)\n",
+      "\u001B[31mAttributeError\u001B[39m: 'DataFrame' object has no attribute 'datetime_x_axis'"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": "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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
-   "execution_count": 18
+   "execution_count": 25
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-17T06:19:12.642285500Z",
+     "start_time": "2026-02-14T18:40:00.061991Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "#how do i align this with time?",
+   "id": "75a36cec8ab0ffb9",
+   "outputs": [],
+   "execution_count": 23
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-17T06:19:12.681558Z",
+     "start_time": "2026-02-14T18:40:00.328201Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "len(calculated_position)",
+   "id": "cd71ca48061bb80b",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1985282"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 24
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-13T02:45:14.470779400Z",
-     "start_time": "2026-01-31T19:35:47.163555Z"
+     "end_time": "2026-02-17T06:24:04.097100Z",
+     "start_time": "2026-02-17T06:24:04.084122Z"
     }
    },
    "cell_type": "code",
@@ -581,42 +1194,88 @@
     "\n",
     "df_mech_brake_pressed = pd.DataFrame(mech_brake_pressed)\n",
     "df_accel_position = pd.DataFrame(scaled_accel_position)\n",
-    "df_speed_kph = pd.DataFrame(speed_kph)\n",
+    "#df_speed_kph = pd.DataFrame(speed_kph)\n",
     "\n",
     "\n"
    ],
    "id": "a6707eebb9ccd8c9",
    "outputs": [],
-   "execution_count": 32
+   "execution_count": 26
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-17T06:24:04.621577Z",
+     "start_time": "2026-02-17T06:24:04.611485Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# combine all dfs and resample, then feed to scaler.\n",
+    "# states = velocity, position\n",
+    "# control = mbrake pressed, accelerator position\n",
+    "def combine_dfs(telemetry_names, index_common, all_dfs):\n",
+    "        combined_df = pd.DataFrame(index=index_common)\n",
+    "\n",
+    "        for name, df in zip(telemetry_names, all_dfs):\n",
+    "            #df_interp = self.resample(df, index_common)\n",
+    "            combined_df[name] = df\n",
+    "\n",
+    "        return combined_df\n"
+   ],
+   "id": "3e63d74e16a13193",
+   "outputs": [],
+   "execution_count": 27
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-17T06:24:05.269547Z",
+     "start_time": "2026-02-17T06:24:05.211588Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "all_dfs = [df_mech_brake_pressed, df_accel_position]\n",
+    "combined_df = combine_dfs([\"mech_brake_pressed\", \"accel_position\"], df_mech_brake_pressed.index, all_dfs)\n"
+   ],
+   "id": "6dea8ee6d0965889",
+   "outputs": [],
+   "execution_count": 28
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-13T02:45:14.470779400Z",
-     "start_time": "2026-01-31T19:41:06.480312Z"
+     "end_time": "2026-02-17T06:24:05.919214Z",
+     "start_time": "2026-02-17T06:24:05.750770Z"
     }
    },
    "cell_type": "code",
-   "source": "df_speed_kph.head(100)",
-   "id": "59a233a5c0817c8e",
+   "source": "final_df = pd.concat([combined_df, merged_df], axis = 1)",
+   "id": "830ffd454d7d6e0d",
+   "outputs": [],
+   "execution_count": 29
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-17T06:24:06.386224Z",
+     "start_time": "2026-02-17T06:24:06.364514Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "final_df.head()",
+   "id": "b1208bd2410e08fc",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "      0\n",
-       "0   0.0\n",
-       "1   0.0\n",
-       "2   0.0\n",
-       "3   0.0\n",
-       "4   0.0\n",
-       "..  ...\n",
-       "95  0.0\n",
-       "96  0.0\n",
-       "97  0.0\n",
-       "98  0.0\n",
-       "99  0.0\n",
-       "\n",
-       "[100 rows x 1 columns]"
+       "   mech_brake_pressed  accel_position  0_x  0_y\n",
+       "0                 0.0             0.0  0.0  0.0\n",
+       "1                 0.0             0.0  0.0  0.0\n",
+       "2                 0.0             0.0  0.0  0.0\n",
+       "3                 0.0             0.0  0.0  0.0\n",
+       "4                 0.0             0.0  0.0  0.0"
       ],
       "text/html": [
        "<div>\n",
@@ -637,85 +1296,78 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>0</th>\n",
+       "      <th>mech_brake_pressed</th>\n",
+       "      <th>accel_position</th>\n",
+       "      <th>0_x</th>\n",
+       "      <th>0_y</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
        "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>95</th>\n",
        "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>96</th>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>97</th>\n",
+       "      <th>4</th>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98</th>\n",
        "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>99</th>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>100 rows × 1 columns</p>\n",
        "</div>"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 34
+   "execution_count": 30
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-13T02:45:14.504701700Z",
-     "start_time": "2026-01-31T19:41:21.121306Z"
+     "end_time": "2026-02-17T06:24:07.505036Z",
+     "start_time": "2026-02-17T06:24:06.923575Z"
     }
    },
    "cell_type": "code",
-   "source": "plt.plot(df_speed_kph)",
-   "id": "5f804fb7b1f09099",
+   "source": "plt.plot(final_df[\"mech_brake_pressed\"])",
+   "id": "42e9fe006fc1b367",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1f089faeea0>]"
+       "[<matplotlib.lines.Line2D at 0x1a18a666000>]"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -724,146 +1376,364 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 35
+   "execution_count": 31
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-13T02:45:14.511796600Z",
-     "start_time": "2026-01-31T19:59:44.610437Z"
+     "end_time": "2026-02-17T06:24:08.034008Z",
+     "start_time": "2026-02-17T06:24:08.028740Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "# combine all dfs and resample, then feed to scaler.\n",
-    "# states = velocity, position\n",
-    "# control = mbrake pressed, accelerator position\n",
-    "def combine_dfs(telemetry_names, index_common, all_dfs):\n",
-    "        combined_df = pd.DataFrame(index=index_common)\n",
+    "# i dont really think there is much of a need to resample right now, because on querying from influx they will all be at the same frequency - granularity is 1s.\n",
+    "# preprocessing pipeline: dataset - rescaling - sequences - tensors - RNN"
+   ],
+   "id": "e4585f807a9c575a",
+   "outputs": [],
+   "execution_count": 32
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-17T06:24:09.139437Z",
+     "start_time": "2026-02-17T06:24:08.736408Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import DataPreprocessing\n",
+    "train_dataset, test_dataset, train_loader, test_loader, scaler = DataPreprocessing.make_sequence_datasets(final_df, state_cols = ['0_x', '0_y'], control_cols = ['mech_brake_pressed', 'accel_position'], seq_len = 10, train_frac=0.7, batch_size = 64)"
+   ],
+   "id": "caa83b2458af2b80",
+   "outputs": [],
+   "execution_count": 33
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "so we are feeding a certain \"seq\" worth of data into the RNN. This includes the controls and state ( brake, accel pos, speed and position). when we are using the model, we feed in the last n seconds of these inputs. the model then outputs the exact control that the driver will be using next.\n",
+    "this does not make sense\n",
     "\n",
-    "        for name, df in zip(telemetry_names, all_dfs):\n",
-    "            #df_interp = self.resample(df, index_common)\n",
-    "            combined_df[name] = df\n",
+    "what we really want to do is given an optimised speed and position input, this would probably be a singular point\n",
+    "we want the RNN to predict the steps to reach there - or at the least atleast a timestep before this predicted control, as otherwise this would not be useful for the driver.\n",
+    "what would be more useful for the driver is to probably give them the change in these controls over this given sequence of time.\n",
+    "so what the model really takes in would be the current state and control at this given time, then we give them a future \"state\" - position and time, and we want to tell the driver how they reach these optimised speed.\n",
+    "the inverse model maps the given state to controls. this can be done by preliminary RNN.\n",
     "\n",
-    "        return combined_df\n"
+    "so, is our model predicting a list of controls from current state to optimised state\n",
+    "this means we might need to predict the states trajectory up to this optimised state, maybe indirectly? this needs a defined sequence. granularity of current data is 0.1 seconds. we would need at least 1 second to see meaningful change, so a seq length of 10 is feasible.\n",
+    "further, does the model evaluate if this state is physcially possible. more ver if the given state oc control changes is physcially feasible.\n",
+    "\n",
+    "Input: Current state + target state (position, speed, time)+ current controls for context\n",
+    "Output: Sequence of control changes to reach the target\n",
+    "\n",
+    "\n",
+    "\n"
    ],
-   "id": "3e63d74e16a13193",
+   "id": "573dc63af8846209"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-17T06:24:10.629691Z",
+     "start_time": "2026-02-17T06:24:10.616511Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# now we define the training loop. we are pretending a trajectory of controls.\n",
+    "\n",
+    "#build the model\n",
+    "\n",
+    "\n",
+    "def train_model(model, train_loader, test_loader, epochs):\n",
+    "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "    model = model.to(device)\n",
+    "    criterion = nn.MSELoss()\n",
+    "    optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=1e-5)\n",
+    "    train_losses = []\n",
+    "    test_losses = []\n",
+    "    for epoch in range(epochs):\n",
+    "\n",
+    "        #training loop\n",
+    "        model.train()\n",
+    "        train_loss = 0\n",
+    "        for x_batch, y_batch in train_loader:\n",
+    "            x_batch = x_batch.to(device)\n",
+    "            y_batch = y_batch.to(device)\n",
+    "            optimizer.zero_grad() #\n",
+    "\n",
+    "            outputs = model(x_batch)\n",
+    "            loss = criterion(outputs, y_batch)\n",
+    "            loss.backward()\n",
+    "            optimizer.step()\n",
+    "\n",
+    "            train_loss += loss.item()\n",
+    "        print(f\"Epoch {epoch + 1}/{epochs}, Train Loss: {train_loss:.4f}\")\n",
+    "        train_losses.append(train_loss)\n",
+    "\n",
+    "    # testing loop\n",
+    "    model.eval()\n",
+    "    test_loss = 0\n",
+    "    with torch.no_grad():\n",
+    "        for x_batch, y_batch in test_loader:\n",
+    "            x_batch = x_batch.to(device)\n",
+    "            y_batch = y_batch.to(device)\n",
+    "            optimizer.zero_grad()\n",
+    "            predictions = model(x_batch)\n",
+    "            loss = criterion(predictions, y_batch)\n",
+    "            test_loss += loss.item()\n",
+    "        print(f\"Epoch {epoch + 1}/{epochs}, Test Loss: {test_loss:.4f}\")\n",
+    "        test_losses.append(test_loss)\n",
+    "\n",
+    "    return train_losses, test_losses\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ],
+   "id": "329e1f0797e61e90",
    "outputs": [],
-   "execution_count": 40
+   "execution_count": 34
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-13T02:45:14.512728600Z",
-     "start_time": "2026-01-31T20:00:02.683528Z"
+     "end_time": "2026-02-17T06:24:11.518937Z",
+     "start_time": "2026-02-17T06:24:11.509573Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "all_dfs = [df_mech_brake_pressed, df_accel_position, df_speed_kph]\n",
-    "combined_df = combine_dfs([\"mech_brake_pressed\", \"accel_position\", \"speed_kph\"], df_mech_brake_pressed.index, all_dfs)\n"
+    "from RNN import *\n",
+    "state   = [\"0_x\", \"0_y\"]\n",
+    "control = [\"mech_brake_pressed\", \"accel_position\"]\n",
+    "seq_length = 10\n",
+    "input_size = 6\n",
+    "output_size = len(control)\n",
+    "\n",
+    "\n",
+    "hidden_size = 128      # was 256\n",
+    "num_layers  = 2\n",
+    "model = RNN(input_size, hidden_size, num_layers, seq_length, output_size).to(device)\n",
+    "\n"
    ],
-   "id": "6dea8ee6d0965889",
+   "id": "f9712a97111555d4",
    "outputs": [],
-   "execution_count": 42
+   "execution_count": 35
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-13T02:45:14.513514Z",
-     "start_time": "2026-01-31T20:00:14.278469Z"
+     "end_time": "2026-02-17T06:25:23.289173Z",
+     "start_time": "2026-02-17T06:24:37.334999Z"
     }
    },
    "cell_type": "code",
-   "source": "plt.plot(combined_df[\"mech_brake_pressed\"])",
-   "id": "42e9fe006fc1b367",
+   "source": "train_model(model, train_loader, test_loader, epochs = 20)",
+   "id": "395f2702e2f5034d",
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20, Train Loss: 21.5501\n",
+      "Epoch 2/20, Train Loss: 21.8018\n",
+      "Epoch 3/20, Train Loss: 20.8826\n",
+      "Epoch 4/20, Train Loss: 20.4658\n",
+      "Epoch 5/20, Train Loss: 20.3852\n",
+      "Epoch 6/20, Train Loss: 20.7814\n",
+      "Epoch 7/20, Train Loss: 20.3128\n",
+      "Epoch 8/20, Train Loss: 20.2736\n",
+      "Epoch 9/20, Train Loss: 19.9926\n",
+      "Epoch 10/20, Train Loss: 20.0709\n",
+      "Epoch 11/20, Train Loss: 20.0546\n",
+      "Epoch 12/20, Train Loss: 20.0973\n",
+      "Epoch 13/20, Train Loss: 19.8260\n",
+      "Epoch 14/20, Train Loss: 19.9745\n",
+      "Epoch 15/20, Train Loss: 19.9951\n",
+      "Epoch 16/20, Train Loss: 20.2109\n",
+      "Epoch 17/20, Train Loss: 19.7599\n",
+      "Epoch 18/20, Train Loss: 19.6065\n",
+      "Epoch 19/20, Train Loss: 19.6601\n",
+      "Epoch 20/20, Train Loss: 19.6907\n",
+      "Epoch 20/20, Test Loss: nan\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1f0824277a0>]"
+       "([21.550057827826095,\n",
+       "  21.801832769531757,\n",
+       "  20.882633866023752,\n",
+       "  20.4657924415078,\n",
+       "  20.385249996785205,\n",
+       "  20.781431681334652,\n",
+       "  20.312828877853462,\n",
+       "  20.273624309396837,\n",
+       "  19.992607293650508,\n",
+       "  20.07088219281286,\n",
+       "  20.054594984045252,\n",
+       "  20.097335380502045,\n",
+       "  19.825971096754074,\n",
+       "  19.974464091472328,\n",
+       "  19.995050944889954,\n",
+       "  20.21087316039484,\n",
+       "  19.759864646941423,\n",
+       "  19.6065372582525,\n",
+       "  19.660053336338024,\n",
+       "  19.690653511759592],\n",
+       " [nan])"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
-    },
+    }
+   ],
+   "execution_count": 38
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-17T06:25:26.366109Z",
+     "start_time": "2026-02-17T06:25:26.357735Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "def plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx=0):\n",
+    "    model.eval()\n",
+    "    x_input, y_target = test_dataset[sample_idx]\n",
+    "\n",
+    "    # Run inference (add batch dimension)\n",
+    "    with torch.no_grad():\n",
+    "        # Move to same device as model\n",
+    "        device = next(model.parameters()).device\n",
+    "        x_input = x_input.to(device).unsqueeze(0)\n",
+    "        y_pred = model(x_input).squeeze(0).cpu().numpy()\n",
+    "\n",
+    "    y_target = y_target.numpy()\n",
+    "\n",
+    "    # Plotting\n",
+    "    fig, ax = plt.subplots(len(control_cols), 1, figsize=(10, 6), sharex=True)\n",
+    "    time_steps = np.arange(0, len(y_target) * 0.1, 0.1) # 0.1s granularity\n",
+    "\n",
+    "    for i, col in enumerate(control_cols):\n",
+    "        ax[i].plot(time_steps, y_target[:, i], 'g-', label='Actual (Driver)')\n",
+    "        ax[i].plot(time_steps, y_pred[:, i], 'r--', label='Predicted (RNN)')\n",
+    "        ax[i].set_ylabel(f'Scaled {col}')\n",
+    "        ax[i].legend()\n",
+    "        ax[i].grid(True)\n",
+    "\n",
+    "    ax[-1].set_xlabel(\"Time (seconds)\")\n",
+    "    plt.suptitle(f\"Control Sequence for Sample {sample_idx}\")\n",
+    "    plt.show()"
+   ],
+   "id": "20dfb9bc00550510",
+   "outputs": [],
+   "execution_count": 39
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-17T06:27:11.356106Z",
+     "start_time": "2026-02-17T06:27:11.055977Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plot_control_trajectory(model, test_dataset,scaler, state_cols = ['0_x', '0_y'], control_cols = ['mech_brake_pressed', 'accel_position'], sample_idx = 0)",
+   "id": "ee12fb52648c9f04",
+   "outputs": [
     {
      "data": {
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 1000x600 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 43
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
-   "source": [
-    "# i dont really think there is much of a need to resample right now, because on querying from influx they will all be at the same frequency.\n",
-    "# preprocessing pipeline: dataset - rescaling - sequences - tensors - RNN"
-   ],
-   "id": "e4585f807a9c575a"
+   "execution_count": 41
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-13T02:45:15.053828Z",
-     "start_time": "2026-02-13T02:45:15.043379Z"
+     "end_time": "2026-02-17T06:25:27.565019Z",
+     "start_time": "2026-02-17T06:25:27.553092Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "# testing training split - 70 - 30\n",
+    "import seaborn as sns\n",
     "\n",
-    "from sklearn.preprocessing import StandardScaler\n",
+    "def plot_error_heatmap(model, loader, device):\n",
+    "    all_errors = []\n",
+    "    all_speeds = []\n",
+    "    all_targets = []\n",
     "\n",
+    "    model.eval()\n",
+    "    with torch.no_grad():\n",
+    "        for x_batch, y_batch in loader:\n",
+    "            preds = model(x_batch.to(device)).cpu()\n",
+    "            # Calculate Mean Absolute Error for the whole 10-step sequence\n",
+    "            error = torch.mean(torch.abs(preds - y_batch), dim=(1, 2))\n",
     "\n",
-    "def\n",
+    "            # Extract Current Speed and Target Speed from x_batch\n",
+    "            # Assuming Speed is index 0 and Target Speed is index 2 in your cat()\n",
+    "            speeds = x_batch[:, 0]\n",
+    "            targets = x_batch[:, 2]\n",
     "\n",
-    "# Choose columns to scale (all states + controls)\n",
-    "cols_to_scale = state + control\n",
+    "            all_errors.extend(error.numpy())\n",
+    "            all_speeds.extend(speeds.numpy())\n",
+    "            all_targets.extend(targets.numpy())\n",
     "\n",
-    "# Fit scaler on the training portion only\n",
-    "train_len = int(0.8 * len(final_df_car13))\n",
-    "df_train_raw = final_df_car13.iloc[:train_len].reset_index(drop=True)\n",
-    "df_test_raw  = final_df_car13.iloc[train_len:].reset_index(drop=True)\n",
+    "    # Create a DataFrame for plotting\n",
+    "    import pandas as pd\n",
+    "    df_err = pd.DataFrame({'Current Speed': all_speeds, 'Target Speed': all_targets, 'MAE': all_errors})\n",
     "\n",
-    "scaler = StandardScaler()\n",
-    "scaler.fit(df_train_raw[cols_to_scale])  # fit only on train\n",
+    "    # Pivot for heatmap (binning speeds)\n",
+    "    df_err['Speed Bin'] = pd.cut(df_err['Current Speed'], bins=10)\n",
+    "    df_err['Target Bin'] = pd.cut(df_err['Target Speed'], bins=10)\n",
+    "    pivot_table = df_err.pivot_table(index='Speed Bin', columns='Target Bin', values='MAE', aggfunc='mean')\n",
     "\n",
-    "# Transform both train and test\n",
-    "df_train = df_train_raw.copy()\n",
-    "df_train[cols_to_scale] = scaler.transform(df_train_raw[cols_to_scale])\n",
-    "\n",
-    "df_test = df_test_raw.copy()\n",
-    "df_test[cols_to_scale] = scaler.transform(df_test_raw[cols_to_scale])\n"
+    "    plt.figure(figsize=(10, 8))\n",
+    "    sns.heatmap(pivot_table, annot=True, cmap='YlOrRd')\n",
+    "    plt.title(\"Control Prediction Error (MAE) across State Space\")\n",
+    "    plt.show()"
    ],
-   "id": "ab4848af3022daef",
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "invalid syntax (1177843209.py, line 6)",
-     "output_type": "error",
-     "traceback": [
-      "  \u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[17]\u001B[39m\u001B[32m, line 6\u001B[39m\n\u001B[31m    \u001B[39m\u001B[31mdef\u001B[39m\n       ^\n\u001B[31mSyntaxError\u001B[39m\u001B[31m:\u001B[39m invalid syntax\n"
-     ]
+   "id": "9e3d7d9c0f207973",
+   "outputs": [],
+   "execution_count": 40
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-17T06:25:29.139937Z",
+     "start_time": "2026-02-17T06:25:29.135927Z"
     }
-   ],
-   "execution_count": 17
+   },
+   "cell_type": "code",
+   "source": "",
+   "id": "4c0e45388dee0647",
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -871,7 +1741,7 @@
    "outputs": [],
    "execution_count": null,
    "source": "",
-   "id": "20dfb9bc00550510"
+   "id": "6c0f3bbbc80cd9e"
   }
  ],
  "metadata": {
diff --git a/array_temp/DataPreprocessing.py b/array_temp/DataPreprocessing.py
new file mode 100644
index 0000000..257dc8e
--- /dev/null
+++ b/array_temp/DataPreprocessing.py
@@ -0,0 +1,70 @@
+import pandas as pd
+import torch
+from torch import nn
+from torch.utils.data import DataLoader
+from sklearn.preprocessing import StandardScaler
+from RNN_Dataset import RNN_Dataset
+
+
+def make_sequence_datasets(
+    df_xy,
+    state_cols,
+    control_cols,
+    seq_len,
+    stride=100,
+    train_frac=0.8,
+    batch_size=64,
+):
+
+
+    cols_to_scale = state_cols + control_cols
+
+    # Train/test split (time-series safe)
+    n_total = len(df_xy)
+    train_len = int(train_frac * n_total)
+
+    df_train_raw = df_xy.iloc[:train_len].reset_index(drop=True)
+    df_test_raw  = df_xy.iloc[train_len:].reset_index(drop=True)
+
+    # Fit scaler ONLY on training data
+    scaler = StandardScaler()
+    scaler.fit(df_train_raw[cols_to_scale])
+
+    # Apply scaling
+    df_train = df_train_raw.copy()
+    df_test  = df_test_raw.copy()
+
+    df_train[cols_to_scale] = scaler.transform(df_train_raw[cols_to_scale])
+    df_test[cols_to_scale]  = scaler.transform(df_test_raw[cols_to_scale])
+
+    # Create datasets
+    train_dataset = RNN_Dataset(
+        df_train,
+        state_cols,
+        control_cols,
+        seq_len,
+        stride
+    )
+
+    test_dataset = RNN_Dataset(
+        df_test,
+        state_cols,
+        control_cols,
+        seq_len,
+        stride
+    )
+
+    # DataLoaders
+    train_loader = DataLoader(
+        train_dataset,
+        batch_size=batch_size,
+        shuffle=True
+    )
+
+    test_loader = DataLoader(
+        test_dataset,
+        batch_size=batch_size,
+        shuffle=False
+    )
+
+    return train_dataset, test_dataset, train_loader, test_loader, scaler
diff --git a/array_temp/RNN.py b/array_temp/RNN.py
new file mode 100644
index 0000000..bf26cfd
--- /dev/null
+++ b/array_temp/RNN.py
@@ -0,0 +1,51 @@
+import os
+import torch
+from torch import nn
+from torch.utils.data import TensorDataset, DataLoader
+#model class to declare RNN and defining a forward pass of the model
+
+device = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else "cpu"
+
+
+class RNN(nn.Module):
+
+    def __init__(self, input_size, hidden_size, num_layers, seq_length, output_size):
+        #inherits from nn.Module
+        super(RNN, self).__init__()
+        self.hidden_size = hidden_size  #dim of memory inside lstm
+        self.num_layers = num_layers  #stacked lstm layers
+
+        #lstm: long short term memory - looks at long term dependencies in sequential data
+
+
+        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)  #correspond to input data shape
+        self.seq_length = seq_length  #no of timestamps to look at to predict the next control output
+
+        #num classes is the no of outputs predicted by the model
+        #to convert memory vector to outputs (shaping constraints)
+        self.fc = nn.Linear(hidden_size, output_size)
+
+
+    def forward(self, x):
+
+
+        #x shape : [batch size, input size]
+        # associate the state array (start + goal state) with timeseries dependency
+        # this is done by repeating the input vector seq_len times
+        x_repeated = x.unsqueeze(1).repeat(1, self.seq_length, 1)
+
+        #inital hidden, cell states - these are internal memory vectors
+        #hidden = short term memory, current output of LSTM at a given time
+        hidden_state = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
+
+        #cell state = long term memory, stores trends (remmebers info over many time steps)
+
+        cell_states = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
+
+        #forward propagate lstm
+        out, _ = self.lstm(x_repeated, (hidden_state, cell_states)) #out; tensor of shape(batch_soze, seq_length, hidden_size) - at the final time step
+        #decode the hidden state of t
+        # predicted is a series of controls
+        out = self.fc(out)
+        return out
+
diff --git a/array_temp/RNN_Dataset.py b/array_temp/RNN_Dataset.py
new file mode 100644
index 0000000..f5afb4c
--- /dev/null
+++ b/array_temp/RNN_Dataset.py
@@ -0,0 +1,42 @@
+import os
+import torch
+from torch import nn
+from torch.utils.data import Dataset, TensorDataset, DataLoader
+
+#This class creates a dataset for the Neural Network, specifically a Seq2Seq model. We encode an input sequence and generate a corresponding output sequence.
+# Sequence generation via sliding window  ( sequence of consecutive timesteps as input, the target is the value following the window).
+#create sequences from data (seq_len timestamps as input - the next timestamp is the target)
+
+class RNN_Dataset(torch.utils.data.Dataset):
+    #stride as an argument is used to control the overlap between input windows.
+    def __init__(self, df, state_cols, control_cols, seq_len, stride):
+        self.seq_len = seq_len #length of input sequences
+        # Convert directly to tensors
+        self.states = torch.tensor(df[state_cols].values, dtype=torch.float32)
+        self.controls = torch.tensor(df[control_cols].values, dtype=torch.float32)
+        self.stride = stride #the step between the start o consecutive sequences - to reduce overlapping between sequences being fed to the network.
+        self.total_size = self.states.size(0) #total number of timestamps
+
+        #compute all possible start indices
+        self.indices = list(range(0, self.total_size - self.seq_len, self.stride)) #first seq starts at t0, second at t0+stride, next at t0 + 2*stride, etc
+
+        #the target timestamp is: i+seq_len, so the input is from i:i+seq_len, so i<total_size - seq_len
+    def __len__(self):
+        return len(self.indices)
+
+    def __getitem__(self, index):
+        idx = self.indices[index] #index of the first timestep of seq
+
+        #input: current states + controls
+
+        current_state = self.states[idx]
+        target_state = self.states[idx +  self.seq_len] # state at t + seq_len
+        current_control = self.controls[idx]
+        x_seq = torch.cat([current_state, target_state, current_control], dim=0) #concatenate along feature dimension
+
+        #output: sequence of next controls to get from current to target
+        y_seq = self.controls[idx:idx + self.seq_len]
+        return x_seq, y_seq
+
+
+

From 58364760cd5c5cdf3cb26727c8387b74beb6954a Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Tue, 17 Feb 2026 22:33:15 -0800
Subject: [PATCH 18/49] rnn architecture + training

---
 array_temp/Control_Model.ipynb | 462 ++++++++++++++++-----------------
 array_temp/RNN.py              |  11 +-
 2 files changed, 223 insertions(+), 250 deletions(-)

diff --git a/array_temp/Control_Model.ipynb b/array_temp/Control_Model.ipynb
index c1b6bcb..e02d3a0 100644
--- a/array_temp/Control_Model.ipynb
+++ b/array_temp/Control_Model.ipynb
@@ -30,22 +30,21 @@
   },
   {
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-17T06:17:01.847821Z",
-     "start_time": "2026-02-17T06:17:01.842168Z"
+    "jupyter": {
+     "is_executing": true
     }
    },
    "cell_type": "code",
    "source": "#other references :\n",
    "id": "b191dbc392da20f6",
    "outputs": [],
-   "execution_count": 1
+   "execution_count": null
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:17:14.923191Z",
-     "start_time": "2026-02-17T06:17:01.897347Z"
+     "end_time": "2026-02-18T02:31:48.942639Z",
+     "start_time": "2026-02-18T02:31:38.627098Z"
     }
    },
    "cell_type": "code",
@@ -60,13 +59,13 @@
    ],
    "id": "8672b9d1bf8a74aa",
    "outputs": [],
-   "execution_count": 2
+   "execution_count": 1
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:18:10.286001Z",
-     "start_time": "2026-02-17T06:17:15.821940Z"
+     "end_time": "2026-02-18T02:32:34.688067Z",
+     "start_time": "2026-02-18T02:32:10.172546Z"
     }
    },
    "cell_type": "code",
@@ -107,13 +106,13 @@
    ],
    "id": "initial_id",
    "outputs": [],
-   "execution_count": 3
+   "execution_count": 2
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:18:10.706635Z",
-     "start_time": "2026-02-17T06:18:10.676957Z"
+     "end_time": "2026-02-17T18:57:24.314769Z",
+     "start_time": "2026-02-17T18:57:24.304217Z"
     }
    },
    "cell_type": "code",
@@ -125,8 +124,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:18:11.011266Z",
-     "start_time": "2026-02-17T06:18:10.824351Z"
+     "end_time": "2026-02-17T18:57:24.771122Z",
+     "start_time": "2026-02-17T18:57:24.717797Z"
     }
    },
    "cell_type": "code",
@@ -155,8 +154,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:18:11.748146Z",
-     "start_time": "2026-02-17T06:18:11.716013Z"
+     "end_time": "2026-02-17T18:57:25.261719Z",
+     "start_time": "2026-02-17T18:57:25.245325Z"
     }
    },
    "cell_type": "code",
@@ -170,7 +169,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_30512\\4023827949.py:1: DeprecationWarning: Please use TimeSeries.period instead of TimeSeries.granularity\n",
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_24340\\4023827949.py:1: DeprecationWarning: Please use TimeSeries.period instead of TimeSeries.granularity\n",
       "  mech_brake_pressed.granularity\n"
      ]
     },
@@ -190,8 +189,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:18:17.292467Z",
-     "start_time": "2026-02-17T06:18:12.477752Z"
+     "end_time": "2026-02-17T18:57:26.912402Z",
+     "start_time": "2026-02-17T18:57:25.702431Z"
     }
    },
    "cell_type": "code",
@@ -201,7 +200,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1a1e128ed50>]"
+       "[<matplotlib.lines.Line2D at 0x21386f58770>]"
       ]
      },
      "execution_count": 7,
@@ -224,18 +223,21 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:18:23.419245Z",
-     "start_time": "2026-02-17T06:18:17.432286Z"
+     "end_time": "2026-02-17T18:57:27.988803Z",
+     "start_time": "2026-02-17T18:57:26.930907Z"
     }
    },
    "cell_type": "code",
-   "source": "plt.plot(accel_position.datetime_x_axis, accel_position, label = \"Accelerator Position\")\n",
+   "source": [
+    "\n",
+    "plt.plot(accel_position.datetime_x_axis, accel_position, label = \"Accelerator Position\")\n"
+   ],
    "id": "21f820a7985b2d39",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1a1f9659ca0>]"
+       "[<matplotlib.lines.Line2D at 0x21390ea0b60>]"
       ]
      },
      "execution_count": 8,
@@ -258,8 +260,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:18:23.941583Z",
-     "start_time": "2026-02-17T06:18:23.610541Z"
+     "end_time": "2026-02-18T02:32:43.877757Z",
+     "start_time": "2026-02-18T02:32:43.849574Z"
     }
    },
    "cell_type": "code",
@@ -275,13 +277,13 @@
    ],
    "id": "6a0eed52a654e483",
    "outputs": [],
-   "execution_count": 9
+   "execution_count": 3
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:18:38.237021Z",
-     "start_time": "2026-02-17T06:18:24.012680Z"
+     "end_time": "2026-02-17T17:37:58.934642Z",
+     "start_time": "2026-02-17T17:37:56.003596Z"
     }
    },
    "cell_type": "code",
@@ -315,7 +317,7 @@
        "Text(0.5, 1.0, 'speed kph')"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -350,12 +352,12 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 10
+   "execution_count": 8
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:18:38.330745Z",
+     "end_time": "2026-02-17T17:31:51.542391100Z",
      "start_time": "2026-02-17T06:18:38.307363Z"
     }
    },
@@ -368,7 +370,7 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:18:38.486438Z",
+     "end_time": "2026-02-17T17:31:51.542391100Z",
      "start_time": "2026-02-17T06:18:38.460068Z"
     }
    },
@@ -384,7 +386,7 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:18:43.661600Z",
+     "end_time": "2026-02-17T17:31:51.551036900Z",
      "start_time": "2026-02-17T06:18:38.657715Z"
     }
    },
@@ -428,7 +430,7 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:18:43.729129Z",
+     "end_time": "2026-02-17T17:31:51.551642500Z",
      "start_time": "2026-02-17T06:18:43.716025Z"
     }
    },
@@ -441,7 +443,7 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:18:48.167187Z",
+     "end_time": "2026-02-17T17:31:51.551642500Z",
      "start_time": "2026-02-17T06:18:43.934851Z"
     }
    },
@@ -472,8 +474,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:18:55.696032Z",
-     "start_time": "2026-02-17T06:18:48.424383Z"
+     "end_time": "2026-02-17T17:38:06.233438Z",
+     "start_time": "2026-02-17T17:38:04.509937Z"
     }
    },
    "cell_type": "code",
@@ -507,7 +509,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_30512\\1203527545.py:21: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_30724\\1203527545.py:21: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
       "  ax1.legend(loc = \"upper left\")\n"
      ]
     },
@@ -522,13 +524,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 15
+   "execution_count": 9
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:18:55.839231Z",
-     "start_time": "2026-02-17T06:18:55.832074Z"
+     "end_time": "2026-02-17T17:38:07.368594Z",
+     "start_time": "2026-02-17T17:38:07.364655Z"
     }
    },
    "cell_type": "code",
@@ -543,7 +545,7 @@
    ],
    "id": "158615364b919eed",
    "outputs": [],
-   "execution_count": 16
+   "execution_count": 10
   },
   {
    "metadata": {},
@@ -562,8 +564,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:18:56.082772Z",
-     "start_time": "2026-02-17T06:18:56.012522Z"
+     "end_time": "2026-02-18T02:32:57.854672Z",
+     "start_time": "2026-02-18T02:32:57.825274Z"
     }
    },
    "cell_type": "code",
@@ -865,13 +867,13 @@
    ],
    "id": "2741c957d09edb03",
    "outputs": [],
-   "execution_count": 17
+   "execution_count": 4
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:18:59.845981Z",
-     "start_time": "2026-02-17T06:18:56.253672Z"
+     "end_time": "2026-02-18T02:32:59.789367Z",
+     "start_time": "2026-02-18T02:32:58.575995Z"
     }
    },
    "cell_type": "code",
@@ -898,13 +900,13 @@
    ],
    "id": "88a4ae7fb0d75eed",
    "outputs": [],
-   "execution_count": 18
+   "execution_count": 5
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:19:00.287595Z",
-     "start_time": "2026-02-17T06:18:59.919178Z"
+     "end_time": "2026-02-18T02:33:00.825285Z",
+     "start_time": "2026-02-18T02:33:00.674927Z"
     }
    },
    "cell_type": "code",
@@ -920,10 +922,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1a1e2c8f8f0>]"
+       "[<matplotlib.lines.Line2D at 0x283939977d0>]"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -938,7 +940,7 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 19
+   "execution_count": 6
   },
   {
    "metadata": {},
@@ -962,8 +964,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:19:00.390099Z",
-     "start_time": "2026-02-17T06:19:00.372296Z"
+     "end_time": "2026-02-18T02:33:02.922060Z",
+     "start_time": "2026-02-18T02:33:02.918007Z"
     }
    },
    "cell_type": "code",
@@ -975,8 +977,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:19:07.717596Z",
-     "start_time": "2026-02-17T06:19:00.596853Z"
+     "end_time": "2026-02-18T02:33:05.227426Z",
+     "start_time": "2026-02-18T02:33:03.513714Z"
     }
    },
    "cell_type": "code",
@@ -990,30 +992,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Calculating closest GIS indices: 100%|██████████| 1985282/1985282 [00:06<00:00, 305603.32it/s]\n"
+      "Calculating closest GIS indices: 100%|██████████| 1985282/1985282 [00:01<00:00, 1309236.48it/s]\n"
      ]
     }
    ],
-   "execution_count": 20
+   "execution_count": 7
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:19:07.871306Z",
-     "start_time": "2026-02-17T06:19:07.797795Z"
+     "end_time": "2026-02-18T02:33:05.996932Z",
+     "start_time": "2026-02-18T02:33:05.990327Z"
     }
    },
    "cell_type": "code",
    "source": "calculated_speeds, calculated_position = state_array",
    "id": "f4fb71b02da66f3e",
    "outputs": [],
-   "execution_count": 21
+   "execution_count": 8
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:19:08.233365Z",
-     "start_time": "2026-02-17T06:19:07.969313Z"
+     "end_time": "2026-02-18T02:33:08.386829Z",
+     "start_time": "2026-02-18T02:33:08.260692Z"
     }
    },
    "cell_type": "code",
@@ -1031,26 +1033,29 @@
    ],
    "id": "a46a29e38c162456",
    "outputs": [],
-   "execution_count": 22
+   "execution_count": 9
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:19:09.869678Z",
-     "start_time": "2026-02-17T06:19:08.372346Z"
+     "end_time": "2026-02-18T02:35:38.127874Z",
+     "start_time": "2026-02-18T02:35:37.776865Z"
     }
    },
    "cell_type": "code",
-   "source": "plt.plot(merged_df['0_y'])",
+   "source": [
+    "#plt.plot(merged_df['0_y'])\n",
+    "plt.plot(merged_df['0_x'],merged_df['0_y'], color = 'red')"
+   ],
    "id": "f9bda40d7ce7546c",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1a18a5dda00>]"
+       "[<matplotlib.lines.Line2D at 0x28394f2eb70>]"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1059,24 +1064,24 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 23
+   "execution_count": 18
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:19:11.279947Z",
-     "start_time": "2026-02-17T06:19:10.101292Z"
+     "end_time": "2026-02-18T02:35:23.130482Z",
+     "start_time": "2026-02-18T02:35:22.370794Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "#plt.plot(calculated_position)\n",
+    "plt.plot(calculated_position)\n",
     "plt.plot(calculated_speeds, color = 'red') # this seems not right."
    ],
    "id": "d543d0eca7887c24",
@@ -1084,10 +1089,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1a18a61e000>]"
+       "[<matplotlib.lines.Line2D at 0x28394b97bf0>]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1096,57 +1101,18 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 24
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-17T06:19:12.512529Z",
-     "start_time": "2026-02-17T06:19:11.440668Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "plt.xticks(rotation = 90)\n",
-    "plt.plot(speed_kph.datetime_x_axis, speed_kph)"
-   ],
-   "id": "d86c58fb82b41f22",
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'DataFrame' object has no attribute 'datetime_x_axis'",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mAttributeError\u001B[39m                            Traceback (most recent call last)",
-      "\u001B[32m~\\AppData\\Local\\Temp\\ipykernel_30512\\3802325483.py\u001B[39m in \u001B[36m?\u001B[39m\u001B[34m()\u001B[39m\n\u001B[32m      1\u001B[39m plt.xticks(rotation = \u001B[32m90\u001B[39m)\n\u001B[32m----> \u001B[39m\u001B[32m2\u001B[39m plt.plot(speed_kph.datetime_x_axis, speed_kph)\n",
-      "\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\generic.py\u001B[39m in \u001B[36m?\u001B[39m\u001B[34m(self, name)\u001B[39m\n\u001B[32m   6317\u001B[39m             \u001B[38;5;28;01mand\u001B[39;00m name \u001B[38;5;28;01mnot\u001B[39;00m \u001B[38;5;28;01min\u001B[39;00m self._accessors\n\u001B[32m   6318\u001B[39m             \u001B[38;5;28;01mand\u001B[39;00m self._info_axis._can_hold_identifiers_and_holds_name(name)\n\u001B[32m   6319\u001B[39m         ):\n\u001B[32m   6320\u001B[39m             \u001B[38;5;28;01mreturn\u001B[39;00m self[name]\n\u001B[32m-> \u001B[39m\u001B[32m6321\u001B[39m         \u001B[38;5;28;01mreturn\u001B[39;00m object.__getattribute__(self, name)\n",
-      "\u001B[31mAttributeError\u001B[39m: 'DataFrame' object has no attribute 'datetime_x_axis'"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": "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"
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 25
+   "execution_count": 17
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:19:12.642285500Z",
+     "end_time": "2026-02-17T17:31:51.560686Z",
      "start_time": "2026-02-14T18:40:00.061991Z"
     }
    },
@@ -1159,8 +1125,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:19:12.681558Z",
-     "start_time": "2026-02-14T18:40:00.328201Z"
+     "end_time": "2026-02-17T17:38:36.072424Z",
+     "start_time": "2026-02-17T17:38:36.052305Z"
     }
    },
    "cell_type": "code",
@@ -1173,18 +1139,18 @@
        "1985282"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 24
+   "execution_count": 18
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:24:04.097100Z",
-     "start_time": "2026-02-17T06:24:04.084122Z"
+     "end_time": "2026-02-18T02:36:02.075663Z",
+     "start_time": "2026-02-18T02:36:02.069953Z"
     }
    },
    "cell_type": "code",
@@ -1200,13 +1166,13 @@
    ],
    "id": "a6707eebb9ccd8c9",
    "outputs": [],
-   "execution_count": 26
+   "execution_count": 19
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:24:04.621577Z",
-     "start_time": "2026-02-17T06:24:04.611485Z"
+     "end_time": "2026-02-18T02:36:02.754455Z",
+     "start_time": "2026-02-18T02:36:02.734513Z"
     }
    },
    "cell_type": "code",
@@ -1225,13 +1191,13 @@
    ],
    "id": "3e63d74e16a13193",
    "outputs": [],
-   "execution_count": 27
+   "execution_count": 20
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:24:05.269547Z",
-     "start_time": "2026-02-17T06:24:05.211588Z"
+     "end_time": "2026-02-18T02:36:56.645308Z",
+     "start_time": "2026-02-18T02:36:56.539056Z"
     }
    },
    "cell_type": "code",
@@ -1241,26 +1207,26 @@
    ],
    "id": "6dea8ee6d0965889",
    "outputs": [],
-   "execution_count": 28
+   "execution_count": 21
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:24:05.919214Z",
-     "start_time": "2026-02-17T06:24:05.750770Z"
+     "end_time": "2026-02-18T02:38:01.056863Z",
+     "start_time": "2026-02-18T02:38:00.798459Z"
     }
    },
    "cell_type": "code",
    "source": "final_df = pd.concat([combined_df, merged_df], axis = 1)",
    "id": "830ffd454d7d6e0d",
    "outputs": [],
-   "execution_count": 29
+   "execution_count": 22
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:24:06.386224Z",
-     "start_time": "2026-02-17T06:24:06.364514Z"
+     "end_time": "2026-02-17T18:57:51.715457Z",
+     "start_time": "2026-02-17T18:57:51.684247Z"
     }
    },
    "cell_type": "code",
@@ -1343,18 +1309,18 @@
        "</div>"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 30
+   "execution_count": 22
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:24:07.505036Z",
-     "start_time": "2026-02-17T06:24:06.923575Z"
+     "end_time": "2026-02-17T17:38:57.248658Z",
+     "start_time": "2026-02-17T17:38:56.777579Z"
     }
    },
    "cell_type": "code",
@@ -1364,10 +1330,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1a18a666000>]"
+       "[<matplotlib.lines.Line2D at 0x218aff809e0>]"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1382,39 +1348,39 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 31
+   "execution_count": 24
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:24:08.034008Z",
-     "start_time": "2026-02-17T06:24:08.028740Z"
+     "end_time": "2026-02-17T17:38:58.095084Z",
+     "start_time": "2026-02-17T17:38:58.082431Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "# i dont really think there is much of a need to resample right now, because on querying from influx they will all be at the same frequency - granularity is 1s.\n",
+    "# i dont really think there is much of a need to resample right now, because on querying from influx they will all be at the same frequency - granularity is 0.1s.\n",
     "# preprocessing pipeline: dataset - rescaling - sequences - tensors - RNN"
    ],
    "id": "e4585f807a9c575a",
    "outputs": [],
-   "execution_count": 32
+   "execution_count": 25
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:24:09.139437Z",
-     "start_time": "2026-02-17T06:24:08.736408Z"
+     "end_time": "2026-02-18T03:33:17.481456Z",
+     "start_time": "2026-02-18T03:33:16.869895Z"
     }
    },
    "cell_type": "code",
    "source": [
     "import DataPreprocessing\n",
-    "train_dataset, test_dataset, train_loader, test_loader, scaler = DataPreprocessing.make_sequence_datasets(final_df, state_cols = ['0_x', '0_y'], control_cols = ['mech_brake_pressed', 'accel_position'], seq_len = 10, train_frac=0.7, batch_size = 64)"
+    "train_dataset, test_dataset, train_loader, test_loader, scaler = DataPreprocessing.make_sequence_datasets(final_df, state_cols = ['0_x', '0_y'], control_cols = ['mech_brake_pressed', 'accel_position'], seq_len = 100, train_frac=0.7, batch_size = 64)"
    ],
    "id": "caa83b2458af2b80",
    "outputs": [],
-   "execution_count": 33
+   "execution_count": 85
   },
   {
    "metadata": {},
@@ -1444,8 +1410,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:24:10.629691Z",
-     "start_time": "2026-02-17T06:24:10.616511Z"
+     "end_time": "2026-02-18T03:34:48.610827Z",
+     "start_time": "2026-02-18T03:34:48.594505Z"
     }
    },
    "cell_type": "code",
@@ -1470,14 +1436,14 @@
     "        for x_batch, y_batch in train_loader:\n",
     "            x_batch = x_batch.to(device)\n",
     "            y_batch = y_batch.to(device)\n",
-    "            optimizer.zero_grad() #\n",
+    "            optimizer.zero_grad() #resets the gradients to zero\n",
     "\n",
     "            outputs = model(x_batch)\n",
     "            loss = criterion(outputs, y_batch)\n",
     "            loss.backward()\n",
     "            optimizer.step()\n",
     "\n",
-    "            train_loss += loss.item()\n",
+    "            train_loss += loss.item() #convert tensor to float\n",
     "        print(f\"Epoch {epoch + 1}/{epochs}, Train Loss: {train_loss:.4f}\")\n",
     "        train_losses.append(train_loss)\n",
     "\n",
@@ -1495,23 +1461,17 @@
     "        print(f\"Epoch {epoch + 1}/{epochs}, Test Loss: {test_loss:.4f}\")\n",
     "        test_losses.append(test_loss)\n",
     "\n",
-    "    return train_losses, test_losses\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
+    "    return train_losses, test_losses"
    ],
    "id": "329e1f0797e61e90",
    "outputs": [],
-   "execution_count": 34
+   "execution_count": 86
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:24:11.518937Z",
-     "start_time": "2026-02-17T06:24:11.509573Z"
+     "end_time": "2026-02-18T03:35:09.065139Z",
+     "start_time": "2026-02-18T03:35:09.048487Z"
     }
    },
    "cell_type": "code",
@@ -1519,96 +1479,63 @@
     "from RNN import *\n",
     "state   = [\"0_x\", \"0_y\"]\n",
     "control = [\"mech_brake_pressed\", \"accel_position\"]\n",
-    "seq_length = 10\n",
+    "seq_length = 100\n",
     "input_size = 6\n",
     "output_size = len(control)\n",
     "\n",
     "\n",
-    "hidden_size = 128      # was 256\n",
+    "hidden_size = 32      # was 256\n",
     "num_layers  = 2\n",
     "model = RNN(input_size, hidden_size, num_layers, seq_length, output_size).to(device)\n",
     "\n"
    ],
    "id": "f9712a97111555d4",
    "outputs": [],
-   "execution_count": 35
+   "execution_count": 89
   },
   {
    "metadata": {
+    "jupyter": {
+     "is_executing": true
+    },
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:25:23.289173Z",
-     "start_time": "2026-02-17T06:24:37.334999Z"
+     "start_time": "2026-02-18T03:35:09.622503Z"
     }
    },
    "cell_type": "code",
-   "source": "train_model(model, train_loader, test_loader, epochs = 20)",
+   "source": "train_model(model, train_loader, test_loader, epochs = 50)",
    "id": "395f2702e2f5034d",
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/20, Train Loss: 21.5501\n",
-      "Epoch 2/20, Train Loss: 21.8018\n",
-      "Epoch 3/20, Train Loss: 20.8826\n",
-      "Epoch 4/20, Train Loss: 20.4658\n",
-      "Epoch 5/20, Train Loss: 20.3852\n",
-      "Epoch 6/20, Train Loss: 20.7814\n",
-      "Epoch 7/20, Train Loss: 20.3128\n",
-      "Epoch 8/20, Train Loss: 20.2736\n",
-      "Epoch 9/20, Train Loss: 19.9926\n",
-      "Epoch 10/20, Train Loss: 20.0709\n",
-      "Epoch 11/20, Train Loss: 20.0546\n",
-      "Epoch 12/20, Train Loss: 20.0973\n",
-      "Epoch 13/20, Train Loss: 19.8260\n",
-      "Epoch 14/20, Train Loss: 19.9745\n",
-      "Epoch 15/20, Train Loss: 19.9951\n",
-      "Epoch 16/20, Train Loss: 20.2109\n",
-      "Epoch 17/20, Train Loss: 19.7599\n",
-      "Epoch 18/20, Train Loss: 19.6065\n",
-      "Epoch 19/20, Train Loss: 19.6601\n",
-      "Epoch 20/20, Train Loss: 19.6907\n",
-      "Epoch 20/20, Test Loss: nan\n"
+      "Epoch 1/50, Train Loss: 71.0480\n",
+      "Epoch 2/50, Train Loss: 59.8627\n",
+      "Epoch 3/50, Train Loss: 58.4473\n",
+      "Epoch 4/50, Train Loss: 57.3073\n",
+      "Epoch 5/50, Train Loss: 56.6901\n",
+      "Epoch 6/50, Train Loss: 56.0367\n",
+      "Epoch 7/50, Train Loss: 55.4903\n",
+      "Epoch 8/50, Train Loss: 55.6834\n",
+      "Epoch 9/50, Train Loss: 55.4839\n",
+      "Epoch 10/50, Train Loss: 55.2013\n",
+      "Epoch 11/50, Train Loss: 54.9586\n",
+      "Epoch 12/50, Train Loss: 55.8387\n",
+      "Epoch 13/50, Train Loss: 54.8928\n",
+      "Epoch 14/50, Train Loss: 54.4527\n",
+      "Epoch 15/50, Train Loss: 54.9433\n",
+      "Epoch 16/50, Train Loss: 55.9105\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "([21.550057827826095,\n",
-       "  21.801832769531757,\n",
-       "  20.882633866023752,\n",
-       "  20.4657924415078,\n",
-       "  20.385249996785205,\n",
-       "  20.781431681334652,\n",
-       "  20.312828877853462,\n",
-       "  20.273624309396837,\n",
-       "  19.992607293650508,\n",
-       "  20.07088219281286,\n",
-       "  20.054594984045252,\n",
-       "  20.097335380502045,\n",
-       "  19.825971096754074,\n",
-       "  19.974464091472328,\n",
-       "  19.995050944889954,\n",
-       "  20.21087316039484,\n",
-       "  19.759864646941423,\n",
-       "  19.6065372582525,\n",
-       "  19.660053336338024,\n",
-       "  19.690653511759592],\n",
-       " [nan])"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
-   "execution_count": 38
+   "execution_count": null
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:25:26.366109Z",
-     "start_time": "2026-02-17T06:25:26.357735Z"
+     "end_time": "2026-02-18T03:20:31.932299Z",
+     "start_time": "2026-02-18T03:20:31.919336Z"
     }
    },
    "cell_type": "code",
@@ -1616,7 +1543,7 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "\n",
-    "def plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx=0):\n",
+    "def plot_control_trajectory(model, test_dataset, state_cols, control_cols, sample_idx):\n",
     "    model.eval()\n",
     "    x_input, y_target = test_dataset[sample_idx]\n",
     "\n",
@@ -1628,6 +1555,7 @@
     "        y_pred = model(x_input).squeeze(0).cpu().numpy()\n",
     "\n",
     "    y_target = y_target.numpy()\n",
+    "    x_input = x_input.to(device).squeeze(0).numpy()\n",
     "\n",
     "    # Plotting\n",
     "    fig, ax = plt.subplots(len(control_cols), 1, figsize=(10, 6), sharex=True)\n",
@@ -1636,27 +1564,34 @@
     "    for i, col in enumerate(control_cols):\n",
     "        ax[i].plot(time_steps, y_target[:, i], 'g-', label='Actual (Driver)')\n",
     "        ax[i].plot(time_steps, y_pred[:, i], 'r--', label='Predicted (RNN)')\n",
+    "\n",
     "        ax[i].set_ylabel(f'Scaled {col}')\n",
     "        ax[i].legend()\n",
     "        ax[i].grid(True)\n",
     "\n",
+    "    fig1, ax1 = plt.subplots(len(state_cols), 1, figsize=(10, 6), sharex=True)\n",
+    "    time_steps = np.arange(0, len(x_input) * 0.1, 0.1) # 0.1s granularity\n",
+    "\n",
+    "    for i, col in enumerate(state_cols):\n",
+    "         ax1[i].plot(time_steps, test_dataset[:, i], 'b-', label='input')\n",
+    "\n",
     "    ax[-1].set_xlabel(\"Time (seconds)\")\n",
     "    plt.suptitle(f\"Control Sequence for Sample {sample_idx}\")\n",
     "    plt.show()"
    ],
    "id": "20dfb9bc00550510",
    "outputs": [],
-   "execution_count": 39
+   "execution_count": 77
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:27:11.356106Z",
-     "start_time": "2026-02-17T06:27:11.055977Z"
+     "end_time": "2026-02-18T03:26:16.735571Z",
+     "start_time": "2026-02-18T03:26:16.452676Z"
     }
    },
    "cell_type": "code",
-   "source": "plot_control_trajectory(model, test_dataset,scaler, state_cols = ['0_x', '0_y'], control_cols = ['mech_brake_pressed', 'accel_position'], sample_idx = 0)",
+   "source": "plot_control_trajectory(model, test_dataset,state_cols = ['0_x', '0_y'], control_cols = ['mech_brake_pressed', 'accel_position'], sample_idx = 10)",
    "id": "ee12fb52648c9f04",
    "outputs": [
     {
@@ -1664,19 +1599,41 @@
       "text/plain": [
        "<Figure size 1000x600 with 2 Axes>"
       ],
-      "image/png": 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"
+      "image/png": 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SuEh3QWmnOQMpAYiMAZRAU4IqCYAkY2QOOJ/n85N9SEZO3qOcOzmGFAKRDN/zkmtNutzKeZDgRIqHyLmU61I+BykOI6SNUrZfpgGQAE66IUoGVAJFCWik4Ih1gZXkkuBR/i1IO6RIyddff62uFVtS6vOzRc6B7Eeud5leQfYtGTU55/HH1RER2YNGSkLa5chERETPyTwhtgSYlP5I10UJus6fP2/vphARpQscs0ZEREREROSAGKwRERERERE5IHaDJCIiIiIickDMrBERERERETkgBmtEREREREQOiMEaERERERGRA2KwRkRERERE5IAYrBERERERETkgBmtEREREREQOiMEaERERERGRA2KwRkRERERE5IAYrBERERERETkgBmtEREREREQOiMEaERERERGRA2KwRkRERERE5IAYrBERERERETkgBmtEREREREQOiMEaERERERGRA2KwRkRERERE5IAYrBERERERETkgBmtEREREREQOiMEaERERERGRA2KwRkRERERE5IAYrBERERERETkgBmtEREREREQOiMEaERERERGRA2KwRkRERERE5IAYrBERERERETkgBmtEREREREQOiMEaERERERGRA2KwRkRERERE5IAYrBERERERETkgBmtEREREREQOiMEaERERERGRA2KwRkRERERE5IAYrBERERERETkgBmtEREREREQOiMEaERERERGRA2KwRkRERERE5IAYrBERERERETkgBmtEREREREQOiMEaERERERGRA3LoYM1oNGL27NmoUaMGqlWrhpkzZ8JgMDzzddevX0e5cuUSLP/666/RqFEjVKpUCb1798bNmzfV8sOHD6NkyZI2b3fu3FHbTJ48OcG69evXp8K7JiIiIiIiApwd+SSsWrUKO3bswMKFCxEdHY3hw4fDz89PBVqJuXv3Lvr164fIyMg4yw8ePIhZs2Zhzpw5KFKkCObOnYv+/fvjhx9+QMWKFfH777/H2X7w4MHImjUr8uXLp55fvnwZQ4cORdu2bS3beHt7p/h7JiIiIiIicvjM2tq1azFo0CBUqVJFZdeGDRuGDRs2JLr93r178dZbb8HV1TXBugMHDqBOnTpo2LAhihYtigEDBuD8+fMIDAxU2+fMmdNyk0zbhQsXMGnSJMvrJVgrU6ZMnO08PDxS7b0TEREREVHm5rDB2v3791WWrGrVqpZllStXxu3bt/HgwQObr9m/fz8+/PBDjB49OsE6yZIdPXpUBV2Spdu2bRvy58+PLFmyxNlOp9Phs88+w/vvv4/s2bOrZSEhIao9kpEjIiIiIiLK1N0gHz58qO5z5cplWZYjRw51f+/evTjLzWRcmZDMWHzvvvsu/vrrLzRv3hxOTk4qKyZZOnlsbdeuXQgODkaXLl0syyTA02g0+PLLL/Hbb7+pwK9nz55xukQSERERERFlmGAtIiJCZaxsCQsLU/fWXRrNj6Oiop77WJKNk3FsUrCkcOHC+OKLL9QYuM2bN8PNzc2y3caNG9GuXTu4u7tbll25ckUFa8WKFUPXrl1Vhm7s2LFqzNobb7zx3G0hIiIiIiJy6GDt9OnT6Natm811EkiZAzNzMGUO0pIzVmz8+PFo3LgxWrZsqZ5LoZEGDRrgl19+Udk2ERAQgGPHjqlAzFqbNm3UWDfJqIlSpUrh2rVrqrrk8wRrgYHBMBphVxoNkD27j0O0hTIHXnPEa44yMn7HEa85Su73hsMHa9WrV1dFPmyRjJtUb5TukAUKFIjTNVKKezyvf/75R41DM/Py8lIZNhkDZ10xUo4lZfmtSVbNHKiZSZbt0KFDz9UGmXXA3gGSXByO0hbKHHjNEa85ysj4HUe85ii53xvpusBI7ty5Vdn848ePW5bJY1lma7zas8hrZOyZmWTpbt26ZQkExZkzZ9QcbPHNnz8fPXr0iLPsv//+UwEbERERERFRpiowIjp16qTGmOXJk8fSdbFXr16W9VJ2X7pISpbsWd555x1VIEQqOkpGbcmSJep1r732mmWbixcvom7dugleK10gly5dihUrVqhujzInm1STlKkFiIiIiIiIMl2wJpNfyzgymRNNqjZK4Q/rDJc8l4qMAwcOTNK+zBUjHz16pCbCXr16dZziIv7+/vD19U3w2nLlyqns2oIFC9S9lPyXwFH2QURERERElBo0RiNHLqUVf//Ei3rIx2Aw6GGQwWSp3Ec2WzYvBAWFcsxaBqfVaqHVOqkxl/Ykh8+Rw+ep1z8RrzlKr/gdR7zmKLnfG+k+s5ZZREfr8PhxIHS6iDQ5XmCgNtWDQnIMrq7u8PXNDmdnF3s3hYiIiIieE4M1O5OMWkDAPZUFyZIlB5ycnFM9E+LkpIFezxRHRr+u9PpohIQ8UtdXrlwF7J5hI6I0FBkJp0sXoQkJgSbUfAuFJiRY3UeXrwhdA9OYbc39+/D98H9quZQKNjo7y38UgNYJcNIiqtEbCO/7gWm/YWHw+ai/aZ2zM4yynZNsr1Wv0VWohMgOnU3b6vXwnDXNtC8nJ9N+1T5N+9UXLYaoN5pamuy2+VvTA/N+Y44h2xpy5ER0hdgCYM4nj5tKGst+zfuM2dbo6QVD3nyWbTVBgaY/YyfY1un5SrIREdkBgzUHyKoZjQZkyZJTZUHSgrOzFtHRzKxlfG5qrGdg4H11nbm4xE4wT0QOIioK2gf34wRSlgArJATR5SsgulIVtan2xnV4TZ0Ysz7Uso3pcSjC+/4PYSNGqW2dblxH9oa1Ej1sWN//xQZrMMJ1395Et9UXKGh5rImMgPvW7xLdVvN2+9hgLToaXnNnJrptZLMWcYI1n4HvQ6PX2z5N9Rvi8abvLc+ztGsNbfATm9vqqlbHo50/W55nq1cDTvfv2d721fJ49MtBy/OsjerC6daN2MDOHNQ5OUFfpCgeb9wW2973e8Hp2lXTenc3+Bo1pkBTbjlyInjBYsu2HvPnqM9E7dfFxRTgurjA6OwEo5c3wgcNsWzr+uMOaO/dNe1LbesU+9jFFVFNTXPDCqf/zkHz5AngHLON9KKIaYPcGwoWig1Iw8NN8/bIflTg7LAFwSmjketO/m1HR5uuR/eY37sGg+lal+XR0dDIdubH+mgYfLPAUDSm8rrBAJf9+6AxyH70lm3M+zXkyQtdvQaWQ3osWwxE6QB9NDTRMdu5uCBs8DCkNwzWHIRGwy9N4nVF5LAMhtjsVJwgKSaoeqUc9C+XUJs6XbkEjy8XxQmkNKExgVhoKMIGDEZEz/fUts5nTyNbs0aJHjZ06MeWYE0TEQH3LZsT3VZjFbwYfXygz5UbRi8vFQwYvb3jPDbvU721rNnwZMFitV5lswz62B83ej30RYvH7tfdAyGTpwP6mB9Vsq35R5hBj+gyr1g1SIPwXn1M2xrMP65M+5QfUNEVKsdpv/qhJT/CZFt9zH7VjzED9MVfivtxFCgAY0iIpY2xP9r0pvdhfV4SCQDNbbSmffwI2qAgm5uqYMmK87//wPm/c5bn1n8O0+fLH2dbt1074HIidiqiOO8lW7Y4wZrH8i/h+vtvttvg5gb/m6Y5Z4XX5PFw+2k3EvPwbpAp2JPgcvAHcQJtowRrMUGdBHmBJ/+B0cdUZM1r8gS47vg+Zr0ElXJvyqLKeXiych2M2f3Utu4b1sLl11/irFfBqASi8uP4oxEw+pm2dfltP1xOHINRrY/ZxuoziHzrHct+nU8cg/PJE3HfkPW2LVrDGDOVk9PZM3A5fjTRbaOaNFM/5tW2/52Dy5FDiW/bsBEMMX+gkH/LLn/9mfi2tevCULiI5Y8pLn/+nuhnoatWA4Zipn9L2ju34RL/M7bab3TFytC/9LJp8YMHcP3t1zjbqU09XeEWFAJd+YrQlylr2u+9u3D7blNsgGL+NxoT3MgfPXQNG1na4DljSsy/ddO/M8vj6GhEtmyDyE5dLdv69n5X/fuy3kY9NhgQ0a4Dwj4ZY2lv9hoVTf8mzYGX1YD18M7vIuSzRaZtQ0PgV6F0oucsou3bCF6yyvTEaETWjm8lum1k46ZxgjWvT8dBExkZZxsJ/hisERER2Zv8MAgPh+bhQ1PAZBVY6UuVhiHmh7TTxQtw2/RNbEYrNBRaq+xW6EfDENWqrdrW5cCvyNrB9NiWkIlTER4TrGkCAuCxekWi22r9Y39sq+DJzS0mmLIKqGLuzT/YhCFPHoRMmhY3+PL2ibn3hiHmR65p27wI/Pti0s6XmxsiO3ZJ2rYeHrFdIp/F1RUh0+ckbVsAj7/dmuRtgw7E+7H9FAH/XIr9q74EdjHBoAoMtXGDtUffbYcmKip2W3MQKDfXuL0TQmbMVVkt2cbX0wVPgoIBnekHrHym1sJ7vIeoJs0BXcxf+nUxP2Rl/25xe9XoataGMVv22G3NP7rlJgGOFUOu3CrjZ35/mpjXqB/Vcm+VPVM/2K2oLIa816goyFlQAVQM+dHvfCV2btoEDLE/vp1Pn4T7D4l/dnK9mIM1yeB6frEg0W11depDH3Mdu+79CV6zpye6bXTFSoiOCdZcD/wK70/HJrrto5detgRrLn8chM/IxLMrjzdsRFRMsOZ85DB8PhqQ+LbL1yAqJlhzPnUCvoP+l+i28geRyJhgzfmfs/Ad0C/RbYOnzbb823e+eB6+H/SxuZ2UpwgZNwnh5mDtzm14TzQFTbYYPT0twZpcux5fr090W33J0rCEOtHRcDl+LNFttYEBVk+06ns0Mep6NrdHgnv5dyUZ55gA39QNOuaPCFmzxdlvdNlX4/7hQGW+Tc/lD2bWItu2U/8uzFlmdQwPT6RHzKxRivjxx+2YOnUiPvlkDFq0aJOk19y+fQs3blxHzZq1X/j4U6ZMUPejR5vubVmyZBHy5cuPli3bYMCAvjh1KvYvdjKFQ+HCRdCxY1c0btws0X2cOHEMgwa9j99/T/xLK7mkfXnz5kOrmB+HRPQc3Wtish5uG7+Gz5CB6gdobOgSK/izRYjo/K567HTjGrw+m53orp3u3rE8lgBJ3Wu1cQIkc8BkyJ07tkmFCiN02CexQZd1IObtDX3+2G6FEjxaZ0qexuibBeH9+idpW7IiaQjzDzY5j085OeYsSVJIUGXePXL4IOopFW+THAxLF9XhI5O8bcjcz5O87ZPFy4H5X8RkPCRDoovT5czSNU0yukNGILxrD9Nync7yGvNjydxa3lvbdoguWSomqJSgUWd6rLbVw5gli2VbXaXKCO/a3RRUmoNVK9b7jS5VGhFW/x9aZ2eEIUtWy2N9seKIfLOV1Y7ibWv9h4zChRHZ9E3ro8bdNkfO2Mf58iOySbPE95srT+yqXLkR+XrjxLeNCRbVY78cKoOX6Lb5C8R5n1H1GsZZL2129XBDlHz9FS4cZ78R7TvFBjEyhtMS0DirLsKxbc+FkDETY8d6Wm0nY0b1pcvEOSeP131r2s6SFXWO6SrspP5oYGlZtmwIOHwqtvuueXsZ16q681r90cPTE/63/JEkGg2Cfv0jadvKd71VN+T0jqX705Ct0uU6XRQCAu7Czy9vmo0pSo0xa0OGDFDBV86cubBw4dIkvWbgwH6oUKESevdO/K9LKRWs3bhxDSNHDsOaNd/A2dlZBWulSpVBp05d1WcSGhqCgwf3Y9myxfj44zFo3rylzf3odDo8efIYfn45kNKCg4PRq1dXLF++Blms/hN6Efa4vuJjWWtK0evp/n3VjUoKTEi3Mvlrdsi0WYh8p6Na73zoL2Rr1STOX5LNgZLB2wdhg4dasmXSxcl9xVIbWS3TY33JUrE/mmJ+fKoftCxKQfyOIzvi/6vpH0v3U5oKCgrE8eNHMXLkOBU03blzW2WwniUtp/hbv34NmjZtoQI1Mw8PD0vQlSNHDhQu3APh4eH44osFaNSocZwJ081cXFxSJVATPj4+qF69BrZu3YwePUzjWYgI0F69Au/JE9QYFqfbtxKcEgnYzMGadI0KPHwS2UsXh3+Y3vSX30Toi72E0CmJF8CIe5DYzAwREVFaYVULemH79u2Ft7e36j6YI0dO7N6907JOgp+ZM6egefNG6jZjxhRERkaqoE66Ia5atUxlue7evYM6daqoe7MVK5aodWbbt29D585vo0GDGnjzzUaYM2cG9E8bOG6Vsfrll59Qz2rgaWKkC+KjR0E4c+aUet6uXUsVvLVu3QQ9e3ZW3SClnWL8+JGYPHl8nNdPmDAa06dPUo/v37+Hjz/+CI0a1Vb7WblyqaW90m30f//rpbJ9TZrUx08/7VLLa9euh++/38J58CjziY6G099n4b52Fbw/GqDuLTw94bZ9mwrUpBhBdOkyapB68KzPEPjL7widMCV2Wzc30yB+6U4VU1iBiIgoveKfCR2UZJ3CosNSZd/OxoTdID2dPZM9D5cEQjVr1lFzxUmwIcFaz5591P4kcLl8+RKmT58DNzd3TJo0VnU1/PDDYbh58wZeeaUcunXriVCZ3+cpTp48js8+m4Vx4yahRIlS+O+/fzFp0jhUqVIV9eubyk8n5tSp46pboYxJe5bcufPAw8MT165dRdWYvt0//7wbc+cuUgFUsFW1tUaNmmDatE8RHR2tMnZRUVH488/fMWXKTPX5jR49Ai+99DJWrdoAf39/zJo1VZ0jc9bs7Nkz6NatF/r164+sMYNoK1WqgsDAAFy5clm9lijD0ung9uN2OEtXRunSeOYUNGGx33mR9+4ioltP9diQOw+Cp86EvnRZVcpexogRERFlBgzWHJD80G+xtTGO3jucZseslqcGtrfd89wBm2SPzp49jQ4dTIOn69dviG3bNqvMVNGixbF//y+YN28RypWroNYPHz4KFy+eV5k4CXCkK6Kvb5ZnBmsSQH3yyVhLYCaFOL75ZgOuXr3yzGDtwoXzSQrUzKRtYWGx7ZGMYfGYstGSWTOrUaOWmiNPllWrVgNHjhxSXScl4JJuoffu3cXSpatVgFaoUBH07z9YFWExB2tyrrt376WCWDN5fd68+XHhwn8M1ijD0DwKUiW4NeHhiGrewrRQq4X34AHQhoZYtjP4+KqJj6MrVUZUvMJDEe+9n9bNJiIisjsGaw5KowrpOj7Jqrm6uqJ69ZrqecWKleHj44tdu3agdeu3VLe/UqVi59AoX76iuj0v2YcEMtI18urVyypbd+vWTRUkPUtQUNBzFeyQQM3Lap6evHljKzhZk/ddt24DHDiwT7VD7hs0aKQmor5+/aoqRCJdHM0kMyddQB8/fqSeZ8uWPU6gZpYlSxY1DpAoXYqMVGWpZXyZKgBy8jicL19Sq6KLvxQbrDk5IbJdB/myg65SFTXvlypVzYl6iYiILBisOSDJuEiWK9W6QTqnXDfIvXv3qADEOiiRAO3XX/eiRYvWSd6PrWNbj0c7fPgvNb6radPmKqPVs2dfzJkzPcn7lkApKWTMnGT5ihWLnYDV1TVhoREzKUQi2TLp1vn7779h2rTZlrZLNk26f8bnFVMCXII9W6StnCSd0gWDQc3rY55AVmR98w3VpTE+mQtKsmaqzH7MWLKQWfPStLlERETpDYM1ByUBhpdLbHYnxYM1zYuX7pc50qSL4eDBw1TXPzPpmjh+/Cg1Jk2yTBcvXkT58qZukFIeX4qKrFy5IU6A5uxsmiMpzGrMilSVNNu+fSvefLMVhg79WD2XcWIyVUDlylWf2c7s2bOrtibFzp0/wM/Pz9Jt81mqVKkGg0GPb7/dAHd3d0vWsGDBwqqLqIxFk26V4ujRQ/jxxx0YI/OaPIVk3qQNRI5G8+ABXCRTduJoTNn8k9BERsD/8m3LPGfRr5aD060bpmxZxcqqS6OuYmUYreY5IiIioqRhsEbJJlk1GW/WqtVbcbJEkpVatWq5KszRtOmbmD9/FoYNG6nGbi1Z8oVlEmwZryZdGaXLnwRUuXLlxldfrUWvXn1x+vRJ/PXX73j55ZJqWznO33+fVt0fJchbv341AgL8VVGPZ5F9bNmyKcFyqVQp+xAhISEqG7hhwxo1Ns66xP/TyHYyZm7t2lVo2bK1JQCVbpF58uTBp5+OVQVEQkKCMXPmVBXcSQD7tC6YMtZNiqgQOQqPRQvgsXIpnG7eSLDO6O4Op2tXoX+5hHoeMmWmaaJezkVGRET0wli6n15ovJoU37DVna9t27dx7NgRVRXypZdK4KOP+mPYsEGoVKky+vT5n9qmRYs2OHToTwwdOlAFciNHjsW5c//g3Xfbq8BJKiWa9erVT43x6tevh9qXHLNNm3aqWMmzSNZPgqX42bVvvlmP1q2bqtv//tdbtXfSpBlo0qT5c50H6QoZHh6mqkOaSUA2ffpcVYCkb9/uqjJkjRq1VRbyaaRCpEwsXrRosedqA9EL0evh9M/fcF+/Bt5DByFbg1rQWgdm0ToVqKmy+SVLIbxTVwTPnIegvb+prJo5UFM8PRmoERERpRCNMS1nJs7k/P2DEf9s63RRCAi4Cz+/vHBxsT2GKS3GrGV0Mq+bTNQtwaMjk/Fv0s6UmhTbHtdXfJJgyZHDx+b1T/aj5jTb/K2pbP5pKZsftyLr4+VrENWqrXqsvX4NTjeuI7pCRRh9fOHoeM0RrzfKyPgdl3E+w6RgN0jKFDp37oaRI4fi3Xd7JrmLY1qTsWpHjx7G6tVf2bsplIFoHj9SZfNlrFnkG02hf+VVtdz58kV4frHAsp3ByxvRFaVsfhU1xkxXs1bsusJF1I2IiIjSlrMjzzU2Z84cbN68WVXHa9euHYYNG6a6yz3N9evX0bJlS5w5cybO8m3btmHx4sV4+PAhatasiQkTJiBnzpyW9atXr8aKFSvU2KVmzZph7NixakyVkGqHEydOxE8//aSKSPTq1UvdKP2QboX16jVUBURkSgFH9PXX69W8a88zzQBRHFFRMWXzj8cUAjkG50sXLauNTk4IjwnWdFWrI7x7b+gqVVaFQFRXxqeMpyQiIqK057DB2qpVq7Bjxw4sXLhQVf4bPny4qpDXu3fvRF9z9+5d9OvXTwVX1g4ePIhRo0apmwRqX375Jfr06YMtW7ao4G/Pnj3qOLNmzVLHGDlypHo8btw49fqZM2fi77//xpo1a3Dnzh18/PHHyJcvH5o2bZrq54FSzgcfDHLo0/n++wPs3QRKT4xGOF29DCM0MBQrrhY5n/sH2Zo0TLCpvnARFZTpS8bOeWjIl5+l84mIiBycwwZra9euxaBBg1CliqkkvGTV5s+fn2iwtnfvXpUNs86Wma1fv15l27p27aqeT5o0CfXr18cff/yBunXrqmN1794dDRuafuRIFk2OIwGiZPg2bdqEZcuWoWzZsuompeg3bNjAYI2I0ozm4UO4nDwWmzU7eRzaR48Q3rW7qfqi1AEp8wr0efNBX7pMzETTlaGrUBnGHDn4SREREaVDDhms3b9/X2XJqlaNnUOrcuXKuH37Nh48eIBcuXIleM3+/fvx4YcfomjRoujWrVucdTdv3kS9evUsz6UrY6FChXDq1CnUqlULZ8+exYABsVmNChUqQKfT4b///lPBmmT2KlasGKctkp2T7pnP6pZJRPRCIiKQvU41ON24lmCV0c0NGuvpK1xcEHjqHKsxEhERZRAOGazJuDJhHZTliPnL8L1792wGa5MnT1b3hw8fTrBOujZKkGcmQZYEhEFBQXjy5InqNmm9TylAkTVrVnUsCcayZcsWpzy9tEVe8+jRIzU/WFLZmnaIUxFRWpDrzF7Xmvm4vNaTRnvjuioIEtXaVIkRHu4wupi+qqNLlDRNNF25iqlbY+mygKsr4ny0PNG85ihN8TuO0hqvufTvef6rtluwFhERoQImW8LCwtS9dYBkfpyUSZDja968OebNm4cGDRrglVdeUV0aAwICVPZM2hH/WObncizJrNlal5y2+PklLNEpxw8M1MLJSaNK6qeVtDwW2Y/BoIn5g4OXyijbk63rn6zcuCFzTAArV8o/UKD5G0DevKZ1328DChSAc5YsjvkXNgfFa454vVFGxu+4zMFu/++fPn06QXdFMxkrZg6G3NzcLI+FuULj82jfvj0uXLiALl26qOdNmjRR3SK9vb0T7N9Mnsux9Hq9zXXieX/8BgTYnmdNMn16vTHN5j7LjPOsZVZyXcn1FRQUChcXnd3+eiT/odi6/gnQ3rkNj89mw339Wmh0ps8oqk5dhNy4B4OLt+kU5S4EyCr/YJ4yXnPkYPgdR7zmKLnfGw4drFWvXh3nz5+3uU4yblKNUbpDFihQIE7XSFsFRJ7FyckJ48ePx4gRI1T3ReniKFMB1K5dWz2WgM3f3x/Fi5sqqskYNeniKMeSzJp0l5Rl5vm5pC0SqPn6Pt/ksPJDNf6PVf54pbRg69rLjG1wJBp/f3jNmQ73dast486i6tZH6PBRiK5R07QRz9cL4TVHaYnXG6U1XnOZg0P2hcudO7cqjX/8+HHLMnksy2yNV3sWmUNt6dKlKlMmwZmMXzt37hyqVaumuoi9+uqrcY4lhUckMCtVqhRKly6tHssy67bIazJ7cZF27VqiTp0qllv9+tXRufPb2LgxZSd1HjCgL1asWKIeT5kyQd2eRbq4/vDD1mQf88cft6v39zRHjx7Cp5+OVY+lfdbnQm6NGtVG9+4dceDAPstrZLsGDWrgypXLCfYnx5PjPs928h6XLFmU7PdJ9qOJ1sF9w1oVqEXVrI1HW3fi8XfbYwM1IiIiyvQcdvhDp06dMHv2bOTJk0c9lwmyrSeiDgwMVBkxLy+vZ+5LsnMyd1q5cuVUsREp8S+l+0uUKKHWd+7cWc2pJs8lGJQJs6XrpLnLZZs2bdSyqVOnqkBv5cqVmDZtWqq99/Rk0KChaNToDfVYso8nThzD9OmT4OPji2bNWqT48T78cFiSttu7dw/Wrl2JVq1iijSkMAkGP/tsNmbMmGdZ9sor5TBlykzL88ePH2HDhjUYP34U1q/fhAIFClrO09y5M7Bw4dKnHiMp2zVv3lIFhHKuCxUqnCLvjVKH5sEDuO35ERHv9lDPDXnyImTCFDUZta5OPRYGISIiovQTrMk8Z1IERErqSzdG6bbYo4fpR46Q523btsXAgQOfua/XX38dly9fVnO1STdIeT569GjL+jfffFNNCyABm4xHa9y4sWXcnJBAT4I1mYtNxrnJMWUbgjoffn6xczhJ0PDzz3vw22+/pkqwJsdLCum+mpokGMydO68lABOSgbU+F/L4k0/G4cCBX/Hnn7+jfftOannOnLlw9uxp7Nq146nnKCnbyTFlnQSFI0eaJnEnx+vu6LloPjxWLoUmPBzRZcoiurJpWpKIXn3s3TwiIiJyYA4brEmAJkGS3GzZty+2a1lSxsL169dP3RLTt29fdbNFMmwzZsxQN3o2Z2cnODu7WLowFi/+Ev788w/o9dFYv34jgoODVcbo2LEjyJYte0x2qLf6zIUEN4sXfw5//wdqnRTIMDN3gRw92nS/Z8+PWLNmBe7fv4eXXy6JIUNGICQkBFOnTlTrpTvipk0/IE+evGq7rVs3IzIyAuXKVcSQIR9bMrf+/g8xbdoknD59QmWoatas89T3uG3bd2jW7M1nngvpKisBlfm9ifz5C+C1117HF18sQJ069eHjY3uAaVK3k3W9e3fFgAEfJboNpT1NYAA8v/gcHsuXQBMWqpZJuX0iIiKipMrcg64cXWho4reYKQeStG14+LO3TQHSbU/GZx05cgh169a3LJfxVePGfYqpU2fDw8MTo0ePUEHaqlUbMGrUePz8826sW7dKbXv16hWMG/cJ2rZ9GytWrFf7PHMmdrygtcOH/8K0aZ+qjNWaNd+gVKnSGDHiI7z6annVPTNXrtz4/vvd6v67777FTz/twvjxk7FkyWo1P96QIf3V/sWYMR/DYNBj6dI16NKlOzZu/DrR9ylz8/3779+oWrXGU89HeHg4li//ElFROtSqFTf46927nwrilixZ+NR9JGW7IkWKwtc3iwo0yQGEhsJz+iRkr/wqPBfMVYGarnxFPN6wEY927bNk1YiIiIjSbWaNgJxFY+ZYsiHy9cZ48tVmy/McZYtDEzM/XXxRterg8bYfLc+zVCgDbUBAnG0ePniSrFM+e/Y0zJtnGqclXUzd3NzRvn1nNG7czLKNBCoSQAnJpt27dxdLl65WWadChYqgf//BKhPWo8d7KrCrUKESOnQwTbMg2S/pQmjL999vwRtvNEWbNu3Uc9mPZPSePHmsukvK/s3dEr/6ap3aV6VKVdTz4cNHoXXrpjh06E/ky5cff/99Bps371CZtmLFiuP8+XPYt2+vzeNeunQBLi4uyJs3X5zlElS+8UZdSzdM6VJbokQpzJ49P8G2np5eGDhwCCZMGKWyh2XKvGLzWEndTgK28+f/U1k2sjNnZ7h/+zW0oSHQvVIOYSNGIapJM45JIyIioufGYI1eiGR+6td/zTJZuARH1l3+RJ48sYHK9etXVTDVpElsUCHdHCXQk4Ic165dwUsvlYi9QJ2d8fLLsc+t3bhxHW3avGV5LgHUgAGDbU6y/uDBfYwfPzJOBU855s2bN1RQJZkpc5dIUapU2USDNZnKQQqoxK8GWrJkaZW5k/cjWb8VK75Ehw6dLQFifNLFcceO7zF79nQsW7bG5jZJ3U7aL+2itKd58hjuX69HeO9+psms3dwQMmm6Cs6ipKtsJq8aS0RERMnHYM2BPbx6N/GV8QIi/38Slni3iPdj8fGpf1NsUmzpzmhdZMMWCeLMZJJxyaZNnz4nwXZeXubiIXGLg5jHv8VnnvfuWeSYYtKkGQkqJspceceOHU1QkMTFJfF9azSaOOPozKQ6qflcyHEiIiIwefJ45MtXAGXL2s6IyRi7bt06YuvWTU99D8/aTtqv1Wqeug9KWZqQYDUezeOLBdA+egRD1myI7NBZrYtq0Yqnm4iIiNIuWNu2bVuSdyql7ikFJGFagmRvm0LB2vMqWLCwKgaSNWs2S2VHma/sxx93YMyYiShatLjqkmgmQdGlSxfx0ksvJ9iXBEayzjoo69ixLcaO/VQFVGZSdEOCysBAf8vYMSm9LyX1O3d+V3V7DA5+glu3blqCrQsXbE/YLmS8m2wvAZL1ceKTfe/b9xNmzpyMlSs3JMg4mt9D167d1dg2jSbxDMyztpOspLwPSgMhIfBYuQyeX8yHNjBQLYouURJGPz+efiIiIrJPsLZgwYI4z+/evasyJgULFlTdz65fv666lclE0gzWKDHVqtVQ3Q1lMul+/fojJCQYM2dORZUq1VQwI/Oibd78rarc2LBhIzUu7f592xnGdu06YMiQAShfvqIaE7d58zcquCtZshQCAvxVQCXdHGW8mHRHXLp0MbJmzY7ChYtg9erlqix+oUJjVXatcuVqqljJRx+NwN27t/Hddxvh6elp87jFi7+sArVr166iaNFiib5XeT+yvw8+eE9lxNq162hzu65de6jiJxIsPs3Ttrt8+RI6djSN86NUotfDY8kX8Fw4D1p/f7UouvhLCBv2CSLbvJ0g201ERET0opI8mEJK5Ztv77zzDho2bIgDBw5gx44d2Lp1K3777Tc0adIElSuzNDXhqQHM9OlzYTQa0Ldvd1UZskaN2hg8eJglgzRjxhw1j1mPHl3g7++v1tsihUikaMiqVcvUxNAXL17AzJmfqSInlStXRf78BdVyKQjSqdO7aNGiNWbNmoKePTur7N7cuZ+rQE18+ulUZMmSFe+/3xNLlizCO+/YDqzMmboyZcomWqXSWrlyFdCkSTMsX74k0TFl8kcPeR/Pkth2N25cU+PyKla0PTaOUohWC9c9P6pALbpoMTxZuARBB48g8u32DNSIiIgoVWiMyZg9uEqVKvj2229RvHjcbldXrlxRk1WfOMES4rb4+wcj/tnW6aIQEHAXfn554eISO7YrNTk7a1NszFpmJVUrd+/eiQULvrR3U7By5VJVQOWTT8YmWGeP6ys+6SmaI4ePzevfoUVEwH39akS+9Q6M2U1dHJ2PHobT5UuIbNfBVEyEHFK6veYoXeL1RrzmKLnfG0mRrDJlkln4999/Eyw/fvy4Gs9DlNHJlAEyBYFktexJ5omTicElc0gpJDIS7iuXIXv1CvAZNQKei2PnuIuuWh2R0t2UgRoRERGlgWT9abhfv34YPXo0Dh8+jNKlS6vxO2fPnsWuXbswbdq0lG8lkYORcZoyHm3lymWYMGGK3dohJf0bNGikxuHRC4qKUiX4PT+bDafbt9Qifb78alwaERERUbrpBikOHjyIzZs34/JlU8n4l19+GV26dFFdJMk2doOktMZukEnj9s0GeM2aBqebN9RzfZ68CBs8DBFduql50yh9Ybc04vVGGRm/4zJXN8hkD7qoW7euuhERpXcuMhbt5g3oc+VG2OChiOjaA3B3t3eziIiIKJNLdrD2ww8/YPXq1bhx44aqBrlu3TrkyJEDffv2TdkWEhGlpOhouH23EdHlKkBfuoxaJFk0/cslEN69N+DhwfNNREREDiFZBUa++uorzJw5E2+99ZaaXFiULVsWK1aswMKFsYPxKemS2RuViNdVUun1cNv8LbLVqQrfge/Da0bsWENDwUIIf38AAzUiIiJK/8GaZNEmT56Mrl27Qqs17aJ169YqgNu0aVNKtzHDzzsmoqIi7d0UyoDM15WTk3PmDtK2bka2etXh+0EfOF+5DEP27NBVrip/JbF364iIiIgSlaxfcHfu3Ekwx5ooWLAgHj16lJxdZlparRM8PLwREmKaMNnV1Q0aGXWYigwGDfR6/kjN6JlaCdTkupLry/xHlczG9efd8Jo0Hs7/nVPPDVmzIqz/h4jo3RdG76QN7CUiIiJKV8Fa+fLlsW3bNgwcODDOj8OVK1eiXLlyKdm+TMHX1zQ3nTlgS23yw91g4KTYmYEEaubrKzNyunRJBWqGLFkR/r8BCO/zPow+vvZuFhEREVHqBWtjxoxRhUT279+PqKgoTJw4EdeuXUN4eDiWL1+enF1mapJJy5LFDz4+2aDXR6fysYBs2bwQFBTKHmAZnHR9zFQZNaMRrnt2wejhAV39hmpRePdeQFQkInr0hjFLVnu3kIiIiCht5lmLjIzE9u3b1Txrer0eRYsWRatWreDl5YWUIk2bM2eOms9NMkHt2rXDsGHDnvkD9Pr162jZsiXOnDkTZ7lkAxcvXoyHDx+iZs2amDBhAnLmzKnWSdA5b9487Ny5UwWd1apVw9ixY5EnTx61/ueff8aAAQPi7K9JkyZYsGDBC82zltY4NwdluGtOgrS9e+A5cxpcTp9EdKnSCNr/l6SQU+FglB7we454vVFGxu+49C9N5llzc3NT1SAlcHrw4AGOHz+u7iVoSymrVq3Cjh07VIXJ6OhoDB8+HH5+fujdu3eir7l79y769eungsn4k3iPGjVK3SRQ+/LLL9GnTx9s2bJFvQcJuvbu3YvZs2cje/bsmDVrlgrOpGCKZL4uXbqEhg0bYtKkSXHOARHZidEIl1/3wmvmVLicOG5a5OmFqMbN5K9JrOxIRERE6V6y/vQsgZlMiH3kyBEVoEnQNm7cOJXN2rVrV4o1bu3atRg0aBCqVKmCGjVqqKzahg0bEt1egi1pi6ura4J169evV+2TCpZSHEWCLgns/vjjD7Ve5or76KOPVEbtpZdeUuvPnj2rsnRCMoglSpRQmTjzzdeXY1+I7MH56GFkbf46snZ8WwVqRk9PhA0YjIBjZxE6ZgIDNSIiIsq8wdq0adPQvHlzVWhk48aNKsMkQY8EOM/TLfBp7t+/r4KpqlWrWpZVrlwZt2/fVgGiLTKG7sMPP8To0aMTrLt582ac4ifu7u4oVKgQTp06pbpYSiatVq1aCV4XHBxsCdaKFCmSIu+NiF6M9lEQXI4fhdHdHWH/G4iAo2cROu5TGHPk4KklIiKiDCNZ3SAvXLiggjIPDw/s27cPjRs3VtksyUrJOLCUIOPKRK5cuSzLcsT8ELt3716c5WYy95s4fPhwgnXSfdI6yJMATQLCoKAg1Q0yfqAmWb1s2bKhZMmSauzc1atX8fvvv2PJkiVqjF7Tpk1V1s9WFi8xqVyR/7na4AhtocwhJa455z9+h9O9O4h8u716rnujCUInTEbE2+1hjBlXykuaUvKaI0oqXm+U1njNpX/P8/9TsoI1CZpkDFdYWBj+/fdffPLJJ2r5n3/+ibx58yZ5PxERESpgskX2LayDIfNjKQbyvCQTKAVEGjRogFdeeQXLli1DQEAAdDqdze6UMg2BVLmUY0o2T4qOyOPPPvsMt27dUoGhtF8qYyaVn5/jzOvkSG2hzCFZ19zvvwPjxwP79gFZs8KnYzsgSxbTuvGjkXLljCgj4vcc8XqjjIzfcZlDsoK1Hj16oH///ioj9eqrr6qMmhTskEIg0kUyqU6fPo1u3brZXCfFRMyBmbmQhzlIk4ze82rfvr3KCHbp0sVSybFevXrw9vZOEKgNHjxYjW1755131LL8+fOrbF2WLFlUsZHSpUurzJy0ceTIkXByckpSGwICHKMapPzjdoS2UOaQnGtOxqR5zpwK1/2/qudGFxdEtH0bYfeCYNSxyiOl/DVHlFy83iit8ZrLOJ9hqgVrEmBJ0Y87d+6gTp06apkUAJGsValSpZK8n+rVq+P8+fM210nGTcaRSXfIAgUKxOkaaS63/zwkoBo/fjxGjBihKkVmzZpVTQVQu3ZtyzZStl/Wd+zYUVWNtCbbW5MiJbKfx48fq+qRSSE/Ghzlh4MjtYUyh6Rcc04XL8B77Cdw3bfX9BpnZ0R0ehdhg4fCULBQzI7SoLGUIfB7jni9UUbG77jMIdl/opYASipCSqGO//77D8eOHVPjv1JK7ty5kS9fPlV50kweyzJb49WeZfXq1Vi6dKnKykngJePXzp07p7KC4q+//lKBmmTeZH61+GX/JbCUrpBm8lrZT1IDNSJKAq0GLvv3wejkhPAu3RD41wmEzJkfG6gRERERZSLJCtakq6B0IZTgSUrbS4Ajpe8/+OADVSI/pXTq1EnNeyZdEOUmE2Rbd5sMDAxEaGhokoNLGad26NAhXLx4URUHqV+/virHL3O4SSZNKk/K3GuSwTPfpOtlxYoVVVdMGZ925coVHDhwADNnzsR7772XYu+VKDNyPnsaHku/sDzXF38ZIbPnI/DP4wiZtxCGwqzASkRERJlXsrpBSpENCXakgqIEU1JURCav/vXXX1X5fhnvlRJk8mspAiKTU0s3Rum2KOPlzOR527ZtMXDgwGfu6/XXX1fl92WuNum+KM/NJf7//vtv1aXTulundVVIyaqtWLECU6dOxdtvvw0vLy/VVZLBGlHyOP3zN7xmTYPbj9th1GoR1aAR9CVKqnURXbvztBIRERHJ+Daj1KV/TjJf2Z49e1SQ1qxZM1XGXuY3kyqJLVq0UHOXUUL+/vYf7C4DGnPk8HGItlDmYH3Naf/9F16zp8Nt+za1zqjRILJtO4SOGgdDocL2biplEPyeI15vlJHxOy7jfIapllmTcWPSLVHGlcn8Y6+99ppavn37dk4cTUQJ3b0Ln/4D4bpti/yFyBSktW6LsKGfQF8y6UWJiIiIiDKTZAVr0gVSinHI5NBSAVLK98+YMQPffPONKt9PRBSHlxdcfv1FBWqRLdsgdNgn0Jcuw5NERERElNLdIM3FPaS8vsw5JqTwhq+vr5owm2xzhK6HTJ2Tva65J2u+QnShItC/8io/BEqTa84RvnMp4+P1RrzmKDW7QSa7dL9MEC3BmpTEf/LkCYKDgy2TVxMRua9dBdc9uywnIurNlgzUiIiIiFK7G+Tdu3fRq1cvNSG03Bo1aoTly5fj5MmTqmpiyZKmqm5ElDm57PsZ3iM+UjN2Pt6zD3ijgb2bRERERJTuJCuz9umnn6JKlSpqsmhXV1e1bO7cuaqU/+TJk1O6jUSUjjj9dw6+fXpCYzAgskNnRFeoZO8mEREREWWeYO3YsWMqsyZzn5m5uLioSbFlzjIiypw0/v7I0rU9tMFPEFWzNoJnzzd1zCYiIiKitAnW3N3d1WTV8UkZf29v7+TskojSu8hIZOnRGU43rkNfuAierFwPxGTeiYiIiCiNgrWOHTti3Lhx2L9/vyVI++677zB27Fi0a9cuObskovTMaITPkIFwOXIIBt8seLxhE4x+fvZuFREREVHmKzDSv39/VaZ/woQJCA8PR9++feHn54cePXqgd+/eKd9KInJsBgOM3t4wOjnhyfI10JdgkSEiIiIiuwRrO3bsQMuWLfHuu+8iLCxMTY7t45O0uQKIKANyckLIjLkI7/EeJ7smIiIismc3yIkTJ6pJsYWnpycDNaJMSnv1CqDTWZ7rS5exa3uIiIiIkNmDterVq6vsWlRUVMq3iIjSBe2d28jaqimydGoHzeNH9m4OERERUYaTrG6QUgnyiy++wJdffons2bPDzc0tzvpffvklpdpHRI4oJAS+XTvA6f49GLNnB7TJ+rsPEREREaV0sNa+fXt1I6JMyGCA7wd94PL3GRhy5MDjdd/C6ONr71YRERERZTjJCtbatm2r7kNCQnDt2jVotVoULVoUHh4eKd0+InIwXpMnwG33Thjd3PB49dcwFCps7yYRERERZUjJCtakXL/Ms7Zr1y5ER0erZa6uriqIGzNmDFxcXFK6nUTkANy/WgfPhZ+px8GfLUJ0ter2bhIRERFRhpWsgSYy+fV///2HFStW4Pjx4zh69Kgav3bs2DFMmzYt5VtJRHanCX4Cr4lj1OPQoR8j8m12hSYiIiJyuGBt3759mD59uqoK6eXlpUr316pVC1OnTlVVIlOK0WjE7NmzUaNGDVSrVg0zZ86EwWB45uuuX7+OcuXKJVi+bds2NGnSBJUqVVITez98+NCy7vHjxyhZsmScm7w/s6CgIAwcOBAVK1bEa6+9hu+//z7F3idReiDj0h5t/RFh/T5A2PCR9m4OERERUYaXrG6Qfn5+qiJkfFLK39vbGyll1apVKvhbuHCh6m45fPhwdezevXsn+pq7d++iX79+iIyMjLP84MGDGDVqlLrVrFlTZQL79OmDLVu2qDF3ly5dQtasWeMEm7LcbOTIkYiIiMC3336L06dPq+6eMk7PVlBIlFHpy5RF6KTp9m4GERERUaaQrGBNgqHRo0ere8k0OTs749y5c1iwYIEatybdIs2qVq2a7MatXbsWgwYNQpUqVdTzYcOGYf78+YkGa3v37lVdNHPmzJlg3fr169GyZUt07dpVPZ80aRLq16+PP/74A3Xr1sWVK1dU8GXrtTdu3MCvv/6qpiQoUKAASpQogVOnTuGrr75isEYZm04HnwF9Ed6zL6Jr1LR3a4iIiIgylWQFa5JVEpMnT06wbtGiReomNBqNCuKS4/79+ypLZh3sVa5cGbdv38aDBw+QK1euBK/Zv38/PvzwQxV0devWLc66mzdvol69epbn7u7uKFSokAq6JFiTzFqRIkVstkUyaXnz5lWBmnVblixZkqz3RpQuGI3wHjkc7lu/g+uBXxF47CyM3j72bhURERFRppGsYE2KiySFFByRrpFSKfJ5mceTWQdlOXLkUPf37t2zGayZg8fDhw8nWCfdJyXIM5OxbxIQylg0cfnyZdXVsl27dmq5ZPOk66McR9oS/3iyP9nueWg0sDtzGxyhLeTY3JcuhsfalTBqNAhZsBjw8UFyLhtec5TWeM0RrzfKyPgdl/49z+/wZAVrSdW3b19ViKNgwYI218sYsMQCnrCwMHVvHeiZH0sA+LyaN2+OefPmoUGDBnjllVewbNkyNe5Op9Op9dINMnv27CpAk8Imsu3777+PTZs2qakK4gec8vx52+Hn5zhZCUdqCzmgnTuBcaPUQ82sWfDt8uKVH3nNUVrjNUe83igj43dc5pCqwZoEPU8j3Qvjd1c0k2IiQgIiNzc3y2ORnMm327dvjwsXLqBLly7quVSFlG6R5oIoO3fuVN02pXukkPF3derUUW2U48cPzOS5edukCggIlp5ldo/k5R+3I7SFHJPTv/8gS4eO0BoMiOjaHSHd+gD+wcneH685Smu85ojXG2Vk/I7LOJ+h3YO1Z5HS+OfPn7e5TjJus2bNUl0QzWPFzF0jbRUBeRYnJyeMHz8eI0aMUJUipfKjdHmsXbu2zQBQujnKNtKO3Llzw9/fP856ef687ZDgyFECJEdqCzkOzcOHKoumDQ1BVJ16CJ4+R5YCKXCt8JqjtMZrjni9UUbG77jMIVnzrKUFCZDy5cunJt02k8eyzNZ4tWdZvXo1li5dqoIyCcJk/JoUP5H520JCQlQhk0OHDlm2N49nK1asGCpUqKAKm8hYOeu2yHKijMTo7Q1d5aqILlYcT1aslf6+9m4SERERUaZl18zas3Tq1ElNip0nTx71fM6cOejVq5dlfWBgoOqiKBNzP4tk52Q8msyLJlkzKfEvpfulDL+5uuO0adNUSX/Jwk2ZMkVViZTJsYV0iZSumTJlwdmzZ9V8bDIdAFGG4uGB4CUroQkIgDFbdnu3hoiIiChTc+hgTeZTkyIgAwYMUAGUdFvs0aOHZb08l3ndBg4c+Mx9vf7666rio8zVJt0g5bkEXmYzZszA9OnTVVEUGY/WqFEjyxQFYubMmWp7Gfsm3R+nTp3KOdYow3D54yB0NWvLTPDqZkxGV2MiIiIiSlka47OqgLwAmTD7hx9+SLQaZGbj72//oh4yoDFHDh+HaAs5Bretm+HbrxciWr+F4C9XyADPFN0/rzlKa7zmiNcbZWT8jss4n6Hdx6zJ5NQuLi6peQgiegHOx47AZ9D/1GNDvvwpHqgRERERkR26Qd68eRNfffUVrl+/jgkTJuC3335DkSJF1GTSZlu2bHmBphFRatLeuoks3TtDExmJyCbNEDruU55wIiIiIgeSrMza0aNH0apVK1Uh8eDBg2oMmEwqLePJfvrpp5RvJRGlKE1IMLJ07QDtwweILvsqnixO+e6PRERERGSHYE3mPxs6dKiaONrZ2ZSck/nLpHiHLCMiB6bXw+f93nD+928YcubC43XfADGTwxMRERFROg/WLly4oMrexycVFG/cuJES7SKiVOJ85hRc9++D0d0dj9d+DUMBFgAiIiIiyjBj1vLnz6/mGotf5XH//v1qHRE5ruiKlfFo83Zo/R8iunJVezeHiIiIiFIyWBs8eDA++eQTFbDp9Xps27YNt27dws6dO9V8ZETkgGSuBqkVKwFbjZr2bg0RERERpUY3yDfeeAMbNmxQE1a//PLL+OWXX9RE0rKsefPmydklEaUip8sXkbVRXTj98zfPMxEREVFGzqyFhISgVKlSNrNoe/fuxeuvv54SbSOiFKAJDIBv53fgfPUKvCeOweON23heiYiIiDJqZu3dd99FYGBggnnX+vTpo7pIEpGDiIqCb+9uKlDTFyyEJ4uW2btFRERERJSawZoUFunUqRPu3Lmjuj9+9tlnePPNNxEREYHvvvsuObskopRmNML74yFw/eMgDN4+eLx+I4w5c/I8ExEREWXkbpDz58/HlClT0LFjR7i6usJgMGDGjBlo1qxZyreQiJLFY/FCeGxYC6NWi+ClK6EvXYZnkoiIiCijB2sajQZjxoxBnjx5VFZt+fLlqFGjRsq3joiSxWXfXnhNHKMeh346FVGvN+GZJCIiIsqowdprr72mgjRb+vXrhxw5clieS3VIIrKf6MpVoKvXAPoixRDe53/8KIiIiIgycrA2cODA1G0JEaUYY5asePzVZtOTRP7IQkREREQZJFhr27ZtkrbT6XQv0h4iSq7wcLj9uB2Rb71jCtBcXHguiYiIiDLbmDV/f38sWbIEly5dgl6vV8uMRqMK1C5fvoyjR4+mdDuJ6GkMBvgMfB/uP2xF2Ll/ETpmAs8XERERUWYs3T9q1CgcPHgQr776Kk6cOIHy5csje/bsOHPmDLtLEtmB58ypKlAzurggqtEb/AyIiIiIMmuwJpmzadOmYciQIShZsiQaNGigyvnLhNi//fZbijVOsnWzZ89WlSarVauGmTNnqmkCnuX69esoV65cguXbtm1DkyZNUKlSJfTv3x8PHz5Uy2/duqXeh62bOUu4evXqBOtkugIie3Pb/C285s5Uj4Nnz4euZm17N4mIiIiI7NUNUoKo3Llzq8cvvfQS/v33X1SuXFnNs7ZixQqklFWrVmHHjh1YuHAhoqOjMXz4cPj5+aF3796Jvubu3buqOmVkZGSc5ZIJlIyg3GrWrIkvv/wSffr0wZYtW5A3b178/vvvcbafPn26CvoqVKignkuXz86dO+ODDz6wbOPh4ZFi75UoOZwPH4LP4P7qcdjAjxDZqStPJBEREVFmzqyVKVMG33//vXpcunRp/PHHH5YMVUpau3YtBg0ahCpVqqjs2rBhw7Bhw4ZEt9+7dy/eeustNVF3fOvXr0fLli3RtWtXFC9eHJMmTVKBnbTdyckJOXPmtNxu3ryJPXv2qMyZS0yRBhmLV6pUqTjbeXt7p+j7JXoe2uvXkKVnZ2iiohDZrAVCR4/nCSQiIiLK7MHa0KFDsXLlStU1sHXr1vj7779VIDRgwAA0b948RRp2//59FUxVrVrVskyyd7dv38aDBw9svmb//v348MMPMXr06ATrJACz7hrp7u6OQoUK4dSpUwm2nTNnDtq3b6+COrMrV66gSJEiKfDOiFKGy+G/oAkIgO7V8njyxTJAm6x/zkRERESUkbpBSjbt119/RUREBLJly4bvvvtOZbWyZs2qukKmBPN4sly5clmWmSfevnfvXpzlZpMnT1b3hw8fTrBOuk9aB3ky9k0CwqCgoDjbHT9+XAVwc+fOjVP98tGjR9i6dStGjhwJNzc3tGvXDr169Up0onBbHGG6K3MbHKEt9GKiOnTCEz8/6MuUhcbby2FPJ6854jVHGRm/44jXHD2v5/kdnqxgrUWLFmocmXSHFDJ+rUuXLs+9Hwn2JGCyJSwsTN1bd2k0P46KinruY0nGb968eaoYyiuvvIJly5YhQLIS8eaF27hxI9544w3LmDxzVs0c8C1evBjnzp1TgaF0n+zRo0eS2+Dn5wNH4UhtoecUESGpYdPjjm+nm9PHa454zVFGxu844jVHqSFZwZpWq02Rya9Pnz6Nbt262VwnxUTMgZlkssyPk1vYQ7o1XrhwwRJUSlXIevXqxRl3JkVMfvnlF1V10ppUojx06JDKIgqpBBkYGIivv/76uYK1gIBgGI2weyQv/6E4Qlvo+bmvWg73FUvxZMNGGAqnj265vOaI1xxlZPyOI15zlNzvjVQL1iQ71bNnTzRs2BD58+dPUNBDxq4lRfXq1XH+/Hmb6yTjNmvWLNUdskCBAnG6Rkpxj+clWbDx48djxIgRqlKkdNmUroy1a8eWOZfujxKwWS8zMwdqZjKeLbGsYGIkOHKUAMmR2kJJ47J/H7xGDodGr4frzh0I/1/S/p05Cl5zxGuOMjJ+xxGvOUoNyQrWJMAqW7asGgMWv9jH84zhehrphpgvXz41hswcrMljWWZrvNqzSDEUycz17dtXZeak3dKdcerUqXEyffK+zJk8s02bNmH58uXYvXu35f3Ja4sVK/bC75MoKZwunIfve91VoBbRvhPC3zeV6yciIiKijCtZwdq6deuQFjp16qQmxc6TJ4+lSqMU9TCTrogSWHl5Pbu4ggR8UhxEKkLK2LOxY8eifv36KFGihGWbixcvxqkAaVarVi01CbiU8pc2SfVLGfMm5f+JUptUfMzS5R1onzyGrnpNBM9ZwAoxRERERJlAsoI1cyl8yTjJ/GMyF5kEOR07dkxWF8XEyOTXUgREulVKN0bptmg9Rkyet23bFgMHDnzmvl5//XXVVpmrTbpByvP4Jf6l6qNUuoxPunouXbpUdcuUcWoS7Ml+UmqaAqJERUbCt2cXOF2/Bn2hIni8+isgXuaXiIiIiDImjdH4/COXdu3apYIVmfdMug1KGfwzZ86oroFSLbFmzZqp09p0zt/f/kU9pBdnjhw+DtEWejav8aPhufhzGHx88ejHvdCXLJXuThuvOeI1RxkZv+OI1xwl93sj1TJrUgJfJsa27pIoFi1apEra79y5Mzm7JaJ4wj8YCJejhxE67JN0GagRERERUfJpk/MiKc4hlSDja9q0KW7duvUCzSEia4bcefBox0/QvfY6TwwRERFRJqNN7qTYq1atgl6vj7NcxnPJhNJElHzOZ07Bbcsmq3+lyfpnSkRERETpXJK7Qb777ruWsvUyIfbJkydx4MABVZBDJsmWSoq3b99WFRaJKHm0d+/At2sHON27iycGAyLbdeCpJCIiIsqkkhysyQTW1uJPHF2mTJmUaxVRZhQaCt93O6pALbpkKUQ1bmrvFhERERFRegjWpHz+85IJqKXgSHImsSbKVAwG+PbvC5czp2Dw88Pj9Rth9M1i71YRERERkR2l6mCYo0ePqjnNiOjpvKZ+Crcft8Po6orHq7+GoXARnjIiIiKiTI6VC4jszO2bDfBcMFc9Dp77OaKr17B3k4iIiIjIATBYI7Izp+vX1H3oR8MQ2b6TvZtDRERERA4iWZNiE1HKCft4NHS16kBXuy5PKxERERFZMLNGZAeaJ4+BiAjLc13d+pxPjYiIiIjiYLBGlNZ0Ovj27oasb7WA5sEDnn8iIiIisondIInSktEI79Ej4HrgVxg9PaG9fw96Tm1BRERERGmdWZsxYwZy5MiRmocgSlfcVyyBx+oVMGo0ePLFcuhfLWfvJhERERFRRsqs3b17F7Nnz8Z///2n5lEzGo1x1v/yyy/qvnHjxinTSqIMwPWXn+A95hP1OHTsp4hq3sLeTSIiIiKijBasjRgxAo8fP0aHDh3g4+OT8q0iymCczv0Lnz49oTEYEN75XYT3H2TvJhERERFRRgzWTp8+je+++w4vv/xyyreIKKMxGuEz6H/QhgQjqlYdhMycB2g09m4VEREREWXEMWuFCxdWmTUiSgIZn7ZkJSLfaIInK9cBrq48bURERESUcsHa0aNHLbdmzZqprpCbNm3CoUOH4qyTW0qQcXAyLq5GjRqoVq0aZs6cCYPBkOj2p06dQseOHVGxYkU0adJEtc3an3/+iRYtWqB8+fLo1q0bbt68GWf96tWrUbduXfX6UaNGITw83LJOxuXJsipVqqBOnTpYuXJlirxHyjwMxYrjyYZNMGb3s3dTiIiIiCijdYN89913EywbO3ZsgmUajQbnzp174YatWrUKO3bswMKFCxEdHY3hw4fDz88PvXv3TrDtw4cP0adPH3Tq1AnTp0/HP//8g5EjRyJnzpxo0KAB7ty5g/79+2PgwIEqIFu0aBE++OAD/PDDD6q9e/bsUceZNWuWOoa8Vh6PGzdO7V8Cxb///htr1qxR+/r444+RL18+NG3a9IXfJ2VcHosWILp0aehee8PeTSEiIiKijBysSeXHtLR27VoMGjRIZbPEsGHDMH/+fJvB2t69e9UUAUOGDFHPixQpgsOHD2P79u0qWJMs2yuvvIJevXqp9dOmTUPt2rVx5MgRVK9eXR2re/fuaNiwoVo/ceJEdRwJECXDJ69ftmwZypYtq24XL17Ehg0bGKxRoty+3wLviWNg1GoRdOAQ9CVL8WwRERERUeqPWYuKilLZJglYzN566y3VbVGn0+FF3b9/X00PULVqVcuyypUr4/bt23jw4EGC7SVbJgFYfCEhIZaCKOagT3h4eKigS7pO6vV6nD17Ns76ChUqqPchAarcJLMn3SOt2yL7fFq3TMq8nE8cg8/A99Xj8D7/Y6BGRERERGkXrE2ePBkHDhxAqVKx2QLpVrh//341EfaLkm6NIleuXJZl5sm17927l2D7AgUKqADLLCAgADt37kTNmjUt+7Pel5DujrKvJ0+eqDFp1uudnZ2RNWtWtV5emy1bNrhaFYWQtshrHj169MLvlTIW7e1byPJuR2giIlRBkdAJk+3dJCIiIiLKTKX7f/rpJzWmrHTp0pZlr7/+OnLnzo1+/fphzJgxz9xHRESEyqDZEhYWpu6tAyTzY8nqPWu/MjZNAiqZB05IsRDrfZn3J/uS7eMfy3q9dIO0tS4pbYnPEaq1m9vgCG3JcEJCkKVrB2gfPkB0mbIIWboSGmcnZHa85ojXHGVk/I4jXnP0vJ7nd3iygjUJYCSzZGt5UrtBSjdCqcpoi4wVMwdDbm5ulsfmLoyJCQ0NVRm+a9eu4auvvrJsK/uIH1jJc19f3wT7t14vr5dukrbWCXd3dzwPPz/HmUDckdqSIej1QO+uwD9nJSUM5x93wq9wPnu3yqHwmiNec5SR8TuOeM1RakhWsCal8aUS5Pjx41GmTBm1TMZ2SffIN95IWuU7Kexx/vx5m+sk4ybVGKULonRxtO4aKRUebZHxae+99x5u3LihqjZKkREzyfj5+/vH2V6eS2ZQujtKwCbPixcvrtbJGDXp4ijHkgA0KChILZPukea2SKAmwd7zCAgIlvmR7R7Jy38ojtCWDEWng3eW7OpaerzmK0R7ZQf8g+3dKofAa454zVFGxu844jVHyf3eSLVgTUrbjx49WlVQNBfZ0Gq1aNOmjZqP7EVJcCWl8Y8fP24J1uSxLIs/9kxIGwYMGIBbt25h3bp1lqDLTOZWk9ebSbfIf//9V71G2v3qq6+q9RJACik8IoGZeUyePJZl5iIksq28Rl77PCQ4cpQAyZHakiE4uyB49nyEvT8A+pdLADy3CfCao7TGa454vVFGxu+4zCFZwZp0D5w7d64qznH9+nW4uLiooMrb2zvFGiZzpkl1yTx58qjnc+bMsZTeF4GBgSqL4eXlhc2bN6tS/YsXL1bZLnMWTtolmbO3334bK1aswNKlS1V5fplnTdprDs46d+6s5lQrUaKECgYnTJiA9u3bW7pRShAqy6ZOnaqqUcqk2LaqT1Lm43TpIvRFikpEr/5MogI1IiIiIqIUoDFKP79kCA4OVpNKX716VY0TkzFoktEqVKhQSrRLjRWT6QG2bNkCJycntGvXDkOHDlWTWIvXXnsNbdu2VcVEZE6033//PcE+qlWrpjJtQqpXSrAlFR6lDP+kSZNQsGBBy7YSyK1evVqNR2vcuLHq4mkezyaZOAnWpLCKBKRyvB49ejz3e/L3t3/XQzl9OXL4OERb0junK5eQtVkjRFesjCfL18DozXGAtvCao7TGa454vVFGxu+4jPMZplqwduHCBdUFMm/evOrxrl278MUXX2D37t1YsmSJCpIoIUcIkPgPPIXO46MgFag5X74EXeWqeLR1p1ScSaG9Zyy85ojXHGVk/I4jXnOUmsFasudZk26KkvWSroZCugVKd0LJhhFlaDodfHt3U4GavkBBPF7zNQM1IiIiIkpxyQrWzp49q8ZxxdexY0dcunQpJdpF5JiMRnh/MgyuBw/A4OWNx+s3wmij6A0RERERkV0KjGTPnl2NVYs/Pu3EiRPw8/N74UZR6pFer6FRoQjVhdq9S2Z65Lt0CTzWrYJRq8XDLxYj/OUigC7U3s1y+FS/R5SW1xzxmqMMid9xxGsu/fB09rTUv8jQwVqfPn0wZswYvP/++6ps/l9//YWtW7eqAh1DhgxJ+VZSigVqLbY2xtF7h3lGk8EvFLgy3/T4ozcMmH/1XWAZTyURERFRelAtTw1sb7snXQVsyQrWpLujzIW2fPlyVd5eSuwXLVoUU6ZMQbNmzVK+lZRi0tPF6WgCvID6PYG3zgHza9i7NURERESU0SWrGmRYWBg2bdqEy5cvQ6fTJVjPOcgctxqkzNbsmcUJ/gGO0BbHpgkNhc/aNdAGB+PRiI/t3Zz0XfHIz4fXHPGaowyJ33HEay798HSQbpDPUw0yWZk16ep48uRJ1KpVC+4sV56uyAXq5eqFcBcDg7XEztHjR/BYsRQeSxZBGxQEo6sr9L3ehyFvvrT9sDII+ULiNUe85iij4ncc8Zqj1JSsYO3w4cNYuXKlmlyaKKPQBATAY9kX8Fi2BNrgJ2pZdLHiCBs8DIYcOe3dPCIiIiLKZJIVrBUrVgwREREp3xoiO3HZ9zOy9OoGTZipsmN0qdIqSIts/Rbg5MTPhYiIiIjSR7A2ffp0DBgwAC1btkS+fPmg1cadrs3WHGxEDsdgAGKu3ehykiU2QvdqeYR9NBxRzVtY1hERERERpZtgbePGjbh+/Tq+/vpruLm5JRgTxWCNHJn22lV4fj4PTrdu4vG3W9UyY44cCPr5N+hfetk0AIGIiIiIKD0Ga5s3b8bcuXPRvHnzlG8RUSpxungBnvPnwO27jdDo9aZlf5+F/pVX1WP9yyV47omIiIgofQdr2bJlw0svvZTyrSFKBU7//gPPz2bB7futMleFWhbVsJHq7mgO1IiIiIiIMkSwNn78eHz66afo378/ChQoAKd4BRhkHBuRI3D5bT+ytmtleR7Z9E2EfTQM0RUr27VdRERERESpEqz169dP3ffs2TPOxHIyv7Y8P3fuXHJ2S5RiJfiNfn7qsa5mbeiLFIWufEWEfTiUmTQiIiIiytjB2i+//JLyLSF6EUYjXH7/DZ7zZsHp2lUEHj4FuLioW+CvfwJeXjy/RERERJTxg7X8+fOnfEuIksNohOu+n+E5dxZcjh42LXJxgfPxY4iuUdO0DQM1IiIiIsoswRqR3RkMcN21U2XSXM6cUouM7u4I79od4f0/hCF/AXu3kIiIiIjohTj0rL8yBm727NmoUaMGqlWrhpkzZ8IgExkn4tSpU+jYsSMqVqyIJk2aYNOmTXHW//nnn2jRogXKly+Pbt264ebNm5Z1UVFRmDFjBurVq4eqVauq4in37t2zrP/5559RsmTJOLdBgwal0junZ3E+expZenZRgZrR0wthHwxCwNGzCJ06i4EaEREREWUIDh2srVq1Cjt27MDChQuxYMECbN++XS2z5eHDh+jTp48K6rZu3aoCqUmTJmH//v1q/Z07d1QA9tZbb6l54rJnz44PPvhABYRC9r93714VHMpk39HR0RgwYIBl/aVLl9CwYUP8/vvvltvkyZPT8GxkcjodnE8cszyNLl8RkS1aI/SjYQg4/jdCJ0yGMXduuzaRiIiIiCjTdINcu3atCrqqVKming8bNgzz589H7969E2wrgVaOHDkwZMgQ9bxIkSI4fPiwCvAaNGigsmyvvPIKevXqpdZPmzYNtWvXxpEjR1C9enUV4I0ePVoFe0ICvbp16+L69etqX5cvX0aJEiWQM2fOND0HmV5kJNy/2QDPz+dB++A+Ao79DWOuXOq0PFm5LtOfHiIiIiLKuBw2WLt//z7u3r2ruiSaVa5cGbdv38aDBw+QK+YHu5kEVqVLl06wn5CQEHV/+vRpS9AnPDw8ULZsWdV1Uo4xa9YslClTJsHrg4OD1b0Ea7Vq1UrR90hPERYGjw1r4LFwPpzu3lGLDDlywvnieejiffZERERERBmRwwZr0q1RWAdlkjkTMpYsfrAmk3PLzSwgIAA7d+7EwIEDLfuL/xo/Pz+1L61WmyAQk6xetmzZ1Ng06Qp59epV1fVxyZIl0Ov1aNq0qcr6ubq6psK7z8RCQuCxajk8F38Orb/pGtDnzYfwAR8ivEt3wNPT3i0kIiIiIsr4wVpERITKoNkSFham7q2DIfNjKQbyrP1KkCbBXYcOHdSy8PDwBIGVPLe1L+lSuXLlSkycOFFtI9k88+s/++wz3Lp1S41Xk+OMGTMmye/Xav5wuzG3wRHaYosmNAReM6dAExkJfaHCCB/0ESI6dgHc3OCgTaZ0fs1RxsNrjni9UUbG77j073l+E9k1WJOuiVKV0Zbhw4erewmm3NzcLI/NXRgTExoaqgqHXLt2DV999ZVlW9lH/MBMnvv6+iYI1AYPHoyuXbvinXfescwrJ+PfsmTJAo1Go7pbSlVKaePIkSPh5OSUpPfr5+cDR+EwbfH3B374AYgZS4gcPsDEiUCePHDq3BneLi7wtncbKWNdc5Rp8JojXm+UkfE7LnOwa7AmhT3Onz9vc51k3GQcmXRfNHdvNHeNTKzIh4xPe++993Djxg2sWbNGFQYxy507N/wlMLAiz63HuUm3yREjRqjy/6NGjYqzbdasWeM8L168OCIjI/H48WNVWTIpAgKCZQ5nu0fy8o/b3m3R3LsHj8Wfw2P1CmjCwvCoYDFEV6xsWtn7A9P94wjJk9qvkZShrjnKPHjNEa83ysj4HZdxPsN0PWZNgqt8+fLh+PHjlmBNHsuy+GPPhGS6pNS+dFFct26dCqasydxq8noz6db477//qteIv/76SwVqXbp0SRCoHTx4UFWilGkAzJm6c+fOqQAuqYGakB+qjvJj1V5t0d6+Bc+Fn8F9/RrV1VHoylWAMSraYc4NpQ5Huv4pc+A1R7zeKCPjd1zm4LDBmujUqZOa9yxPnjzq+Zw5cyyl90VgYKDq3ujl5aXmTpOuiosXL1ZdG81ZOBcXFxVUvf3221ixYgWWLl2q5ktbtGiRCgIluydzqkmAJlUhZa4282uFdH2USbblODI+TeZqk8m0ZYJuyeJR0mgeBcHr03Fw//YraHQ6tUxXpRrCho5A1GtvcEATEREREVE8GqN51mcHJFUXJSjasmWLGhfWrl07DB06VI0bE6+99hratm2rionI3GtSrTE+mTdNMm3iwIEDmDp1qqoAKQGYzKVWsGBBVb7fXIgkPqkKKQHdxYsX1WtlWwkOpaukBG7mtiSFv7/9u4FJc3Pk8En7tkRGInv1CnC6cxtRdeoh7KPh0NWpxyAtE7DbNUeZFq854vVGGRm/4zLOZ5jug7WMxhF+rKbVP3Cnf/6Gx/rVCJk0HXA2JXBd9+yCIWs2RFevkXoHJofD/1SI1xxlZPyOI15zlJrBmkN3g6T0x/nkcXjOmwW33T+q57rKVRHZzpS1jGrSzM6tIyIiIiJKPxisUcpcSIf+gte8mXD99Rf13KjRILJ1W0S/Uo5nmIiIiIgoGRis0QvRhATDt2sHuP5pGi9odHJC5NvtEfbhUOhfLsGzS0RERESUTAzW6IUYvX1k3gQYXVwQ0bELwgZ+BEORojyrREREREQviMEaJZ3BANddO+Gx9As8WbkeRj8/tThk5jwYfXxgyG+aD4+IiIiIiF6cNgX2QRmdXg+3LZuQrUFNZOnZBa5//QGPZYtjV5cqzUCNiIiIiCiFMbNGidPp4PbdRnh+NhvOVy6rRQYfX4T36Yfwvv/jmSMiIiIiSkUM1si2qChkq18DzpcvqaeG7NkR3q8/wnv1gTFLVp41IiIiIqJUxmCNYkVFAa6upseurtDVqgPtkycI+2AQwrv3Ary9ebaIiIiIiNIIx6yRKr/vsWAe/CqUhtPZM5YzEjp6PAKOnUV4/0EM1IiIiIiI0hiDtUxM8ygInrOnI3ulsvCePB5a/4fwWLfKst6Y3Q/w8LBrG4mIiIiIMit2g8yMHj6E55QZcF+xFNqQYLUouvhLCBs8DJFvvWPv1hEREREREYO1TMhgAGrVhOdlU3XH6NJlEPbRcES2bAM4Odm7dUREREREFIOZtcxGqwX+9z/o1m1A2JARiGrSzLSMiIiIiIgcCoO1zOjDD/G4Wx8YobF3S4iIiIiIKBFMqWRGzs6AhoEaEREREZEjY7BGRERERETkgBisEREREREROSAGa0RERERERA6IwRoREREREZEDYrBGRERERETkgBisEREREREROSDOs5aGHKFavrkNjtAWyhx4zRGvOcrI+B1HvOboeT3P73CN0Wg0PvcRiIiIiIiIKFWxGyQREREREZEDYrBGRERERETkgBisEREREREROSAGa0RERERERA6IwRoREREREZEDYrBGRERERETkgBisEREREREROSAGa0RERERERA6IwRoREREREZEDYrBGRERERETkgBisEREREREROSAGa0RERERERA6IwRoREREREZEDYrBGRERERETkgBisEREREREROSAGa0RERERERA6IwRoREREREZEDYrBGRERERETkgBisEREREREROSAGa0RERERERA6IwRoREREREZEDYrBGRERERETkgBisEREREREROSAGa0RERERERA6IwRoREREREZEDYrBGRERERETkgBisEREREREROSAGa0RERERERA6IwRoREREREZEDYrBGRERERETkgBisEREREREROSAGa0RERERERA6IwRoREREREZEDYrBGRERERETkgBisEREREREROSAGa0RERERERA6IwRoREREREZEDYrBGRERERETkgBisEREREREROSAGa0RERERERA6IwRoREREREZEDYrBGRERERETkgJzt3YDMJCAgGEajfdug0QB+fj4O0RbKHHjNEa85ysj4HUe85ii53xtJwWAtDUlw5CgBkiO1hTIHXnPEa44yMn7HEa85Sg3sBklEREREROSAGKwRERERERE5IAZrREREREREDohj1hyE0WiEwaCHwWBI9QGNERER0OmiOGYtg9NqtdBqnaCRD52IiIiI0h0Gaw4gOlqHx48DodNFpMnxAgO1qR4UkmNwdXWHr292ODu72LspRERERPScGKw5QEYtIOCeyoJkyZIDTk7OqZ4JcXLSQK9nKciMfl3p9dEICXmkrq9cuQoww0ZEmYf8QTIyEpqoSCAyCprICECng6FYccsmzmdPQ3v/nmm92k62jwKi5F6H8A8GWrZ1/2odnE8cj9kuAprImO3Mr92/z7Kt07//QBMcDEOhQjDkziPdHNL87RNRxsFgzQGyakajAVmy5FRZkLTg7KxFdDQzaxmfG5ycnBAYeF9dZy4urvZuEBFlZBESxERYgh9LoCT3Wi2iXy1v2dRl38/QPnwITWRk3KAnMhJGT0+EDxpi2dZz+mQ4Xzgfs505qDK9xujujke7frFs69vpbbge+BWa6OgEzTO6ucH/5sPY/c6YArefdif6dsL7/k/+wzS198A+uG/9LvH3Lu2KKQPgsWQRPL5ebzqmiwsM+QtAX7Aw9BK8FSyk9mv0Ttr8SkREDNYchEbDv7wRrysisgODAc4njsFtzy4YvL0R/uFQyyqf93tBe/dunMBLE2EKsPTFX8LjrTst22arXwPOV6/YPIS+SFEEHjltee41eSJc/j5je9tcueMEa66//waXI4dsbmv09IrzXGMwJBqoGd09TJOhxfRe0RctDl25CtJfXAV96t7VTW0rj1V2LkZkizbQv1xSrYdbzHYxr4GbG3zlsS7KdKwsWaEvVATa2zeh0engdO2qupmF/S82Y+c9/CO4HtgXJ5jTq1thU2Yubz5Le4koc2KwRkRElBkDtKNH4LZjG9y2fw+nO7fVYl2FinGCNZejR+B084bNXRh94mWHJMixDo6sAhvVHdBKdNVqMObMabVdbJBkyJo1zrZh/T6A9q13TNtIQKW2i9legiQrTxZ8CY1BH+fYcHGxGfCETpqW5NMV1bK1utmidi1tCTYFa6GfTlU3REdDe++uOn/aG9fVvSYwAPDwsLzW6cqlBMGctYfX71u2d/tmg/qc4gRzcl6dnJL8Pogo/WGwRkRElIl4zpwK93Wr4STjtWIYvH0Q1bgJdHXqx9k2ZPIMU8ZIgqOYLJLl3ts7zrZBu/aZug3K+mdkg0JmzE1ye6NatknytsbcueEwI7KdnWEoUFDdULO2zU2CFy6B0/VrpmDuxnVob95QQZ3TjRtAtC5OYOe+6Ru4HjwQ5/XW3Swff7vF0m1Te+WyCmQZzBGlfwzWKEX8+ON2TJ06EZ98MgYtWiTtP9bbt2/hxo3rqJnIf2LPY8qUCep+9GjTvS1LlixCvnz50bJlGwwY0BenTp2wrHNzc0PhwkXQsWNXNG7cLNF9nDhxDIMGvY/ffz/2wm221b68efOhVau2Kb5vIsqkoqNVF0JdjVqWQhfa+/dVoGbw8UVU0+aIbNkGUQ1eS5ClElHN3kz6sbzidkmkZ5Nujqqro3w+CVbGHVse2bwl9AUKWoI5626WmtBQS6AmfKR75cH9NsfMSWYusl0Hdq8kSicYrFGK2Lt3D/LnL4Ddu39McrA2ffokVKhQKUWCtWe5ceMafvvtV6xZ841lmQRmnTp1VUMYQkNDcPDgfhX0RUdHo3nzljb38+qr5fH994kPSH8RnTt3Q69eXVG/fkNkyRK3GxARUZLpdHD546Dq3ui2azu0/v4I2vkzoqtWV6vDe/dFVNNmiKrXME7XRXIw8apIRvTuG3e9VTdLTUhw3HXSFdTJyeaYOUPOXIh8p6Pluc/A96G9cyc2mCtUOG43S1azJLIrBmv0woKCAnH8+FGMHDlOBTt37txWGayklJdPK+vXr0HTpi3gbPWXRw8PD/j55VCPc+TIgcKFeyA8PBxffLEAjRo1Vtm2+FxcXCyvSWk+Pj6oXr0Gtm7djB493kuVYxBRBg7QDu6PCdB2QBsYaFllyJYNTrdvWYI1fZmy6kbpnHU3y3hU4RcbY+akm2X8DKrL4b8SHTOnz5cfgafOWZ67bTL9wZNj5ojSDoM1ByWBTFh0WKrs29mYsHS/p7Nnsufh2rdvL7y9vVX3QenKt3v3TvTqZfoLoAQ/n38+F/tj5qCpX/81DB48DLNnT1PdEOV28uRx1X3xnXdaYdOmH1RXQLFixRK1buHCper59u3b8PXX61Qw6OXlhddea6z2JeXpnyY4OBi//PITVq7c8Mz3Il0Q16xZgTNnTqFq1epo164lXnvtDezZsxPZs/th4MAhlm6Q48ePVOXwx4yZaHn9hAmj4e7ujk8+GYv79+9h7twZOHbsCLJly66ydd2791btlW6j27dvRdas2XHixFEMHfqJOn+1a9fDzJlT0K1bLzX3HhFRUjgfP4asHd+2PDf4+SGyeStEtmwNXe26piIblLkkYcycePL5Ejhdu2IJ5qy7WRryxC0M4zl7epyKn9bdLKNfeRWhE6dY1kkxFamMyQIoRJk0WJNgZs6cOdi8eTMMBgPatWuHYcOGJfoD9+DBg5g1axauXbuGIkWKYOjQoahfP+5AanH69Gl07NgRP//8MwoUKKCWPXnyBDNmzMCvv/6qjtWgQQOMGjUKvr6+qfbeWmxtjKP3DiOtVMtTA9vb7klWwCaBUM2addS5l2BDgrWePfuofUlXx8uXL2H69Dlwc3PHpEljsWzZYnz44TDcvHkDr7xSDt269USo9Ld/CgnaPvtsFsaNm4QSJUrhv//+xaRJ41ClSlUVAD7NqVPHVbdCGZP2LLlz54GHhyeuXbuqgjXx88+7MXfuIvXZBwc/sWzbqFETTJv2qeo2KRm7qKgo/Pnn75gyZab6DEePHoGXXnoZq1ZtgL+/P2bNmqrOkTlrdvbsGRWU9evXH1mzZlPLKlWqgsDAAFy5clm9logojogIuO7fB7ft22DIkxehY01/LIquVh26V8sjunIVNQZNJz/OrXoSECUmunoNdUu4IhqaJ4/jLNLVbaCCs/hj5tS4uZDY/x9F1uavq8DP1pg5mfYhulIVfihESZBuv8lXrVqFHTt2YOHCherH8vDhw+Hn54fevXsn2Pb69esYMGAAPvroIzRq1Ah79+5F//79sXv3bktAJnQ6HcaMGaN+lFsbP348bty4gaVLl6oAZMKECWq7BQsWpNr70yB9zKsi2aOzZ0+jQ4cu6rmMt9q2bbPKTBUtWhz79/+CefMWoZzMZQNg+PBRuHjxvMrESYAjXRF9fbM8M1iTAEqyVebATLJv33yzAVevXnlmsHbhwvkkBWpm0rawsNj2SMarePGXLAVGzGrUqKUmNJdl1arVwJEjh1TXSQm4pFvovXt3sXTpahWgFSpUBP37D1ZFWMzBmlxL3bv3UkGsmbw+b978uHDhPwZrRGQSHg7XfXtVgOb6025oY8Ynydij0FHjTJkLrRaPfjnIM0Ypx9kZxux+cRaFzP4s9ol1N8ubN2C0qlwpg8GlkI2tMXPmKSIe/RRb2dJ7yECV/dUXLqrm5NMXLqJuiFdxlCgzSrfB2tq1azFo0CBUqWL6y4xk1ebPn28zWLt37x7at2+PHj16qOc9e/bE4sWLcebMmTjB2vLly9UPdWthYWHYs2cPvv76a7zyyitqmWTVunTpgsjISJvjml6U/IiXLFeqdYN0TrlukJJVc3V1RfXqNdXzihUrw8fHF7t27UDr1m9Br9ejVKnSlu3Ll6+obs9L9iHnWrpGXr16WWXrbt26qYKkZwkKCnqugh0SqEk3S7O8efPa3E7ed926DXDgwD7VDrlv0KCR6uZ4/fpVPHnyGE2axGZv5Y8Acs08fvxIPZeukdaBmlmWLFnUOEAiIq9xo+CxdhU0Vn9A0ufNp7o3ykTNnDCZHLKbpUaDgIs3bI6Zk3t9yVKx2xoMaloCTWRkgkMYcuRE1OuNEbxgcexhT51QhU9Y/IQyi3QZrN2/fx93795F1apVLcsqV66M27dv48GDB8iVK1ec7atXr65u5uzZtm3bVJe1cuXKWba5evUqNmzYgEWLFqnAzkyyIl9++SVKl44NOIQEIZINSo1gTUjg5OXilXrBmiZusPYiVSAlALEOSuTc/PrrXrRoYXsCUVtsBYqyH7PDh//CyJHD0LRpc5XR6tmzL+bMmZ7kfcfPlibm7t076nMtVsyUSROuMqlqIqQQiWTLpFvn77//hmnTZlvaLtk06f4Zn5eXtyXYs0XaqtFwvBpRphMSAjf5TpVqtObvB61WBWpSsj2yRWtEtmpj6j7GMa2UQcbMSYYuZPocaK9fM42dU/dXoQ0Kgtb/ofwFNXZbgwFZWzZRgZ3R3d1UuTImC2coUhTRr5Y3dQEmykDSZbD28OFDdW8dlEk1P3MWLX6wZt0dslmzZuqHtIxZM2fVZHzRuHHjMHDgQNWV0poUi6hXr16CrF7JkiWRPXv252q3rcRVMmt6OASZI026GEqRD+n6ZyZdE8ePH6XGpEmW6eLFiyhf3tQNUsrjr1q1TBX7sA7QnJ1dLJlMMykkYibFON58sxWGDv1YPZeurzJPW+XKsQF7YuRzkrYmxc6dP6hrwNxt81mqVKkGg0GPb7/doK4Vc9awYMHCqouojEUzZ2uPHj2EH3/cEacgiS2SeYt/Hb4oOdX2utbMx03P1zqlL+npmtMEP4Hrnl1w3f696uqoiYhQkxvrXntdrY/o9R6i2rRFdIVKljeUDt5WppKerjeH5OaKyK7dEizWPH4E7fXrKuiznOPHQTDkzQvtzZvq34rzhfPqZhb5ZitE14oJ1oxG+L7dSs1jpy9iCuZUN8vCRWCU34np+APjNZf+Pc/l57DBWkREhMqg2WL+QW+dmTA/lozZ0360S0GSkydPYvr06ShcuDCaNGmilknGTTJqkp17mvXr12PXrl2qy+Tz8vPzsfk+AwO1cHLSqIxXWkmJY+3b95Mab/bWW+3ifBYlSpTA6tXLsXfvbjRv3gLz58/Gxx+PUlnKpUu/QK1atdXxPT09VMD15Mkj5MqVQxX3+OabdXjvvX44efIE/vrrD5QoUVJtmzVrVvzzzxlcu3ZZ7WfNmpUICPBHdLROrTcHfrbeV6lSpbBly6Y462T7yMgIPH4caKkYKVUtN2xYg1GjxsHdPfb9aLWxn42Tk+ne/NzZ2RUNGzbCunWr0KpVG7i4mCpT1qpVS3WflIIq//vfAISEBGPmzKmqaImbm4vap632SlZPxrqVKVMmRT4jg0Gjzle2bF4qmLQnW9c/Uaa85oKDga1bgc2bgT175D+u2HUvvYQszkYgR0zbc7xqt2ZSBrne0iv5N1C8YMJlV6+qbBxu3ACuXDHdLl9W925168LN/G/n7l3gYOy4uDg8PYH33gPmzzc9l6mEdu8GihUDihRJN/MP8prLHBw2WJOqjN26JfxLi5BiIubAzNwN0RykScGKp81jJT+C5Xb58mUVeFWqVAnz5s3D6tWrnzlmS7pJTp48GSNHjkSdOnWe+z0FBASr7wNrOl2U6vam1xsTjCNLLbbGrCXHzz/vUcU3tFrnBPtr0+ZtzJ8/Bxs3fo+VK5di0KD/qTnKpAx+797/U9u/+WYbVU3x2rUrKtP2ySdjMG/eLHTq1E5lzKRKpARssm2PHn0xdeoEvPded9WNUCbSbtOmHc6f/0+tN8/ZZut9lS9fWQVLV65cRaFChdUy2f6rr9apm5Cgs1ix4pg0aQbq1KkXZz8GQ+xno9eb7q3XN2z4BrZu/Q4NGza2Wq7BtGlzVAXL3r27qQIpDRu+jgEDPlTbyD5ttffUqVPImTMXChYskiKfkVxXcn0FBYXCxUUHe5B/VvIfiq3rnyjTXHPSkJj/Y5z++RvZune3rIp+6WVEtWqjqjjqy75i2s4/3iTH5LAc8nrLDHxzAhXkZhrmYmH+txMJuC5dFdutUu6vX4P29i1owsIQFm1EWMy2mvv34de8uXpslKET+fLDENO9Um66WnUQXbMWHAWvufTP/BkmaVtjWs5MnEIk4yZdE3/55RdLV8abN2/i9ddfVyX643eDlG54jx8/thQjMQdeUjRECpJIwRBz1kFOh8wNJkHf+++/r25ixYoVmDlzJkaMGGGziElS+PvbDtYCAu7Czy+vmrMrPQVr6YlM1i0TdcuUAo5Mxr9JO1NqUmx7XF+2vpBy5PCxef0TZeRrThMQoCaodvthqyqzbymSIN2zunVU42tUgCZFmNJxl6zMzlGuN0qiqCg43boBo5u7mlZAOF26CN/3upumILAq5mMW9v4AhH46VT3W+PsjS/s2cYI5cwVLNT4vkfHoKYnXXPpn/gzTdWbtaXLnzo18+fLh+PHjlmBNHssyW+PVZH60LVu2qO6L5uzZP//8g2LFiuGNN95Q2TXrQPDdd99VZfqlO5/YunWrCtQko2auKEnpS+fO3TBy5FC8+25PNWWAI5KxakePHsbq1V/ZuylElEyahw/h9uN2uP2wDS5/HoQmplCSwdsHmD3f9ENOo8GTdd/yHBPZg6sr9FZFxIT+pZcRtP9P9YcUCcacrl+1ZOTkXhdT8VpIERSXv88AcovHqNUibMgIhI0YpZ5rQoLhsm+vJbAzxsypSvQ8HPNXaxJ06tQJs2fPRp48edRzmSC7V69elvWBgYGqi6SUYG/VqhWWLFmitn/nnXfwxx9/4IcffsC3336rij9Yl+uXghhCAj8ZJ/Xo0SN8+umnaNu2Ld58801LcRPzGDjz9uTYihYthnr1GqoCIjKlgCP6+uv1at6155lmgIgch/ewwXBfvxoaq+qzMlF1lJTZb9k6Tf7iTkQvQKOBMWdORMutSjWbm+hfLoHHGzZaulWaAzq5acLDYbAqPud0/j9keS+2y7Mha1ZLkRMJ4CKbNFcT2hNlyGBNuiIGBASoya4lYGrXrl2crJc8lwBLKjxKQCfdGKdOnarGqeXPn1/NyVa2bNlnHkcCOyloItk1uVmz7oZJju+DDwbBkb3//gB7N4GIkkh79w5cd/6AiI5dLRP3SpU6CdRkwl+ZA00CNEPRYjynRBmIMUtWRL3R1MYKI7QP7sNoXZxEvg+qVjcFdg/uQ/voEbSPTsLl9EnT6jx5LMGa8+mT8O3TI6ZLZdG43SuLFIHRxzfN3iM5lnQ5Zi294pg1Smscs0aZUWqN55DCBG47vjd1cTx6WC17vHwNolq1NR334UNowsNgiClkRJkDxw9RkoSGwkkmB7eaTy6iXQdEx0xB5LbtO/j27Znoy4OnzUZE777qsbw++18HEOzqCUPWbKZbtuwwZsumgknOw+j4MvyYNSIiorSgCQyA+9cb4LZjG1yOH4uzTlethqkEeAzpPsW/fhKRTV5e0Jcuo262RDVshEff74JWdau8Gtu9UipZBgRYiqEI51MngQ8/hK2f+lLNMnj+F4js2MW07dnT8Pjic9U9U8bMWe4lsMuWXWXv5J4cF4M1IiIiaxERQEyFYE1wMLwnjrH8CNLVqKW6N0a9aZpsl4goJUhGTFezNiC3eDTBT2C0quhsyJ0HaN8eUfceQBMUBO2jIGgCA6ENDZEuczBKQaMYTpcvwf27jYkeN3ju54joahpX5/LXH/AeMtBmUCfZO13d+tCXKGl6YVQUNJERpmOxmm2qYrBGRESZntPli3Db/j1ct38PQ758lmqNUgQgvHtvRJcug6g3W5p+JBERpaH449Wia9UGWjXFk/hdvSWAevQIRqvCedFlX0XIhCnQBgWaAju5fxQEbaDp3pAztoq69v49OF++lGg7gucttARrLn8cRNYObWF0clLdL1U3TKvgLqJ9JxXcCU1QIJxPn4Ixuynok3ujlzeDvCRisEZERJmS04XzcNu+TY1Bcz73j2W5UX6shIcDHh7qeciseXZsJRFRErm6whhvCiupXhn+smkqqmeJqtdAdcW0BHXmrF1QILRBQdAXj53yQPv4kbqX6UlkugOtv3+cfUkvBJ1Vt00J7KwZXVwswV3YR8MR+XZ7035v3YT75m9N4/As2b3spoBQpj7w8sp0QR6DNUq2du1a4t69u5bnUpUzf/4CaNPmbbRv3znFzuyAAX1RsWJl9O7dT01uLUaPNt0nRqfTYdeuHWgVM/D/ef3443asXLkUmzdvT3Sbo0cPYdeunRg3bhJWrFiCVauWxVkvU0cUKFAQvXr1Rf36r6llst26dauwcuUGFCtWPMH5lG2bN2+Z5O1++GEr7t69g379+ifrfRJlVj79+8J90zeW50ZnZ+jqNVCTVEc2fdMSqBERZRbG7H6mrphJENnmbTxs0jwmmIsX3AUGQlepSuzGTk6qd4Jlu8hIaHQ6aB4+gPbhA2ik67l504sX4DX100SPGzJhCsI/GBi77aTxsV01493L/HmGPHmR3jFYoxcyaNBQNGr0hnocHR2NEyeOYfr0SfDx8UWzZi1S/Ox++OGwJG23d+8erF27MtnB2rNIMPjZZ7MxY0bsX9xfeaUcpkyZGWeS6w0b1mD8+FFYv36TCtzM52nu3BlYuHDpU4+RlO0kYOvevaM614VYgY4oIaMRTv/8rQqEhPf5AEY/P9O/r/IVYNz2HaLqN0Rkq7aIatKMg+yJiJ6HhwcM8oetvPmgf8pm8oewoAOHLN/J0nPBOrizztgZcuREeOd3Ld00tebxeLK9Tgdj1ti5aLU3b8Bt985EjxsyfjLC+5umbXI+cwq+3Tsj8MQ/6S4zx2CNXohMKO7nl8PyXIKGn3/eg99++zVVgjXrCcyfJrVnpJBgMHfuvJYATDg7O8c5F/L4k0/G4cCBX/Hnn7+jfftOannOnLlw9uxplfl72jlKynZyTFknQeHIkeNS9D0SpVvy7//ECXiu/QquP2yF89UrarGhQCHLQPqITl0R0aGzqcw1ERGlDQmUPD1hkEq6+QskCPL0r5ZDyGeLbH+vh4XFmZZAxs8Fz/os4Xi8IFPXTRl/bDmsvz80EeHpLlATDNYo5S8qZyc4O7tYujAWL/4S/vzzD+j10Vi/fiOCg4NVxujYsSPIli17THaot+pGKSS4Wbz4c/j7P1DrDAaDZd/xu0Hu2fMj1qxZgfv37+Hll0tiyJARCAkJwdSpE9X6OnWqYNOmH5AnT1613datmxEZGYFy5SpiyJCP1YTpwt//IaZNm4TTp0+oDFXNmnWe+h63bfsOzZq9+cxzodVqVUBlfm9Cuoq+9trr+OKLBahTpz58fGzPs5HU7WRd795dMWDAR4luQ5QpGAxw2/g1vObOAK5dg7movtHdHVGvvaFKVJtxglkionREozGNV7NiKFAQEd17JenlMtXKo60/Ij2KDU/J8YSGJn6z6t/7zG1loPyztk0B0m3vwIF9OHLkEOrGVAAyj/8aN+5TTJ06Gx4enhg9eoQK0lat2oBRo8bj5593q/FZ4urVKxg37hO0bfs2VqxYr/Z55swpm8c7fPgvTJv2qcpYrVnzDUqVKo0RIz7Cq6+WV90zc+XKje+/363uv/vuW/z00y6MHz8ZS5asRvbs2TFkSH+1fzFmzMcwGPRYunQNunTpjo0bv070fT558gT//vs3qlat8dTzER4ejuXLv0RUlA61asUN/mT8nQRxS5YsfOo+krJdkSJF4eubRQWaRJmZjF/wGdwfTteuqe45Mv7sydJV8P/3Cp6s3gBdnXr2biIREdmDtzf0pUqny3PPzJoDy1k08UGRka83xpOvNlue5yhbHBpJD9sQVasOHm+L/WtClgpl1ASL1h4+eJKsNs6ePQ3z5pnGaUVGRsLNzV0VF2ncuJllGwlUJIASkk2ToiRLl65WWadChYqgf//BKhPWo8d7KrCrUKESOnQwTeYo2S/pQmjL999vwRtvNEWbNu3Uc9mPZPSePHmsukvK/s3dEr/6ap3aV6WYAa/Dh49C69ZNcejQn8iXLz/+/vsMNm/eoTJtUtDj/Plz2Ldvr83jXrp0AS4uLsgbb44lCSrfeKOupRtmVFQUSpQohdmz5yfY1tPTCwMHDsGECaNU9rBMmVdsHiup20nAdv78fyrLRpSpyB9cnE3/lelLlkJ4/w9V1TCvEUMQHG6IW9aaiIgonWGwRi9EMj/mSoeurq4qOLLu8ify5IkNVK5fv6qCqSZNYoMK6eYogZ4U5Lh27Qpeeim2xKxklV5OpOTsjRvX0abNW5bnEkANGDA4wXZhYWF48OA+xo8fqQI4MznmzZs3VFAlmSlzl0hRqlTZRIO1oKAgVUDFel+iZMnSKnMn70eyfitWfIkOHTpbAsT4pIvjjh3fY/bs6Vi2bI3NbZK6nbRf2kWUaRiNcPt+C7wmT8DjrzZb5v4JHTsxpreMFxAebO9WEhERvRAGaw7s4dXYsvgJxAuI/P+5nPi28YKKx6f+RXR07DiwFyHdGa2LbNgiQZyZXq9X2bTp0+ck2M5LJkhU4v4p3Dz+LT4J5JJCjikmTZqRoGKir68vjh07mqAgiYtL4vvWaDRxxtHFL9Uv5DgRERGYPHk88uUrgLJlbWfEZIxdt24dsXXrpqe+h2dtJ+3XatPfoFmi5HC6dBHenwyD62+/queen89D8Odf8mQSEVGGwzFrjkz+MpzYzd096dvGny/I1jZppGDBwqoYSNas2VRgI7e7d2+recUkCCpatDjOnfvXsr0ERZcuXbS5L3mt9ToJyt55p5Xqjij7MpOiGxJUBgb6W46ZO3ceVbhDsnPS7TE4+Alu3bppec2FC+cTfQ8y3k22f1bFyc6d31X7njlzsiVgtPUeunbtrsa2hT5l7OCztpOsZPbsppLkRBlWWBg8p32KbPVrqEDN6OaG0OEjVTUwIiKijIjBGqWpatVqqO6Gn346FpcvX8Lp0ycxc+ZUuLu7q+6TMi/af/+dU5Ubb9y4hkWLPsP9+7YzjO3adVBFQ6S0vQRan38+VwV3JUuWUvuTgEq6OUoREemOuHTpYvz++29qmcwFJ2XxJcsn470qV66mipVI8Hfw4H58993GRN9D8eIvq0Dt2rWrT32v8n4++miEep9Py5x17dpDBa/S3qd52nZyDHnfRBmV6949yF63GrzmzVZz7ci43cDfDiNs+MiEf7wiIiLKIBisUZqSAGb69LkwGg3o27e7qgxZo0ZtDB48zJJBmjFjjprHrEePLvD391frbZFCJFI0ZNWqZWpi6IsXL2DmzM9UkZPKlasif/6CarkUBOnU6V20aNEas2ZNQc+enVV2b+7cz1U3SPHpp1ORJUtWvP9+TyxZsgjvvNMx0fcgmboyZcomWqXSWrlyFdCkSTMsX74k0TFl0k1U3sezJLadBLUyLq9iRdtj44gyAqf//oPTzRvQ5y+Ax6s24MmGTTAULWbvZhEREaUqjTG1Zw8mC3//4ASVyXS6KAQE3IWfX164uMSO7UpNzs7aFBuzlllJ1crdu3diwQL7j5NZuXKpKqDyySdjE6yzx/UVn/RIzZHDx+b1T5SoyEho79yODch0Ongs+QLhPd97ZtdtXnOUlni9UVrjNZdxPsOkYGaNKBlkygCZgkCyWvYkXTxlYnDJHBJlFC779yFbg5rI0rU9EBUVs9AF4QM+TNMxtkRERPbGYI0oGWSaABmPtnLlMruePynp36BBIxQuXMSu7SBKCdq7d+DTpweytm8D58uXoH30CE6XL/HkEhFRpsXS/UTJVLNmbXWzpzZt3rbr8YlShHRxXL4EnjOnQhsaAqNWi/DefRH28WgYfbPwJBMRUabFYI2IiOxGExCArG+9CeeYKTt0lasieOY86F8tx0+FiIgyPQZrRERkN8bs2WHImRuG+/cQOvZTRHTqCmjZQ5+IiEgwWHMQLMpJvK4oU9Dr4f7VOkS2aAVjtuyqJFbw/EUwenjAyIndiYiI4uCfLx1g3jERFRVp76ZQBmS+rpyc+HcZsj/nUyeQtdlr8Bk6CF5TJ1mWG/IXYKBGRERkA3/B2ZlW6wQPD2+EhJgmTHZ1dYNGJl9IRQaDBno9J7zK6JlaCdTkupLrS8tuZWRHmkdB8Jr6KdzXrJTJPWHw8UV0qdL8TIiIiJ6BwZoD8PXNru7NAVtqkx/uBgMnxc4MJFAzX19Eac5ohNu3X8H707HQ+vurRRHtOiBk/GQYc+fmB0JERPQMDNYcgGTSsmTxg49PNuj10al8LCBbNi8EBYXK7yjKwKTrIzNqZE8en8+D9+QJ6nF0yVIImT4Hutp1+aEQERElEYM1ByI/rLVa11QP1tzd3eHiomOwRkSpKqJLd3isXoHwnn0Q3u8D6efNM05ERPQcGKwREVHKdHn8YStc9u9DyNzP1V+GjH5+CDx0kkEaERFRMjFYIyKiF+J0+SK8Px4G199+Vc+jmr2JqMbNTCuZTSMiIko2BmtERJQ8YWHwXDAHngvnQxMVBaObG8IGDUFUvYY8o0RERCmAwRoRET031z274D16BJxuXFfPo157HcHTZsNQtBjPJhERUQphsEZERM8nKgreYz9RgZo+X36ETJ6BqDdbmioYERERUYrRIh1P+jt79mzUqFED1apVw8yZM586d9jBgwfRqlUrlCtXTt0fOHDA5nanT59G6dKlcevWLZvrJ06ciHfffTfF3gcRUboQFQVEx0wt4uqK4OlzEDZgMAJ/P4qoFq0YqBEREaWCdJtZW7VqFXbs2IGFCxciOjoaw4cPh5+fH3r37p1g2+vXr2PAgAH46KOP0KhRI+zduxf9+/fH7t27UaBAAct2Op0OY8aMSTToO3HiBL7++mtUrVoV6TnIDY0KRaiO86xR2pBki0eUltdcOuZ+8CD8Ro/Ek+49ENz7PdPCujVNN6ELhSPhNUe83igj43dc8nk6e6r5jdOTdBusrV27FoMGDUKVKlXU82HDhmH+/Pk2g7V79+6hffv26NGjh3res2dPLF68GGfOnIkTrC1fvhze3t42jxcVFYVx48ahQoUKSM+BWoutjXH03mF7N4WI0oG8T4C5e4CO/5ie+88fizLRY2FIt30yiIgoM6uWpwa2t92TrgK2VAnW/v33X0yePBlnz55VWa/4zp0790L7v3//Pu7evRsnw1W5cmXcvn0bDx48QK5cueJsX716dXUzZ8+2bdumgi/pEml29epVbNiwAYsWLVKBXXxLly5FyZIlUaRIERw5cgTpVXq6OInIPpz0wMAjwMRfAd8oQK8BFlUFxjUEAzUiIqL0HqyNGjUKPj4+KtOVWKbqRTx8+FDdWwdlOXLksGTR4gdr1t0hmzVrBr1ej6FDh1qyapJxkqzZwIEDVVfK+C5fvqy6P37//ffqPrnsHSdJoLbzrT3w8HVCQGCwzGFLlAbXHeCX3YfXXDrheuYM/IZ8BNd//1XPIytVQuDUaWhdrhxaI33gNUe83igj43dc+u8G+TxNSJVg7cqVK9i+fTsKFy6c7H1ERESoDJotYWFh6t7VarJV82PJmCUme/bs2Lx5M06ePInp06er9jVp0kQtk4ybZNQkO2fNOpAzB4TJ5efnA0fhlcfL3k2gTIbXXDrx8AFw/rx8YQLTp8Otd2/k1abPfo+85ojXG2Vk/I7LHFIlWJNqipKNepFgTaoyduvWzeY6KSZiDszc3Nwsj4WHh0ei+5RsX5kyZdRN2rd+/XpUqlQJ8+bNw+rVq21G2t9++63KxHXo0AEvKiDA/tks9dcYPx+HaAtlDrzmHJxeD+cTxxBd1dRVHPmKwnXJSujq1INRehoEOlbxkKTgNUe83igj43dcxvkM7RastW7dWlVVfOutt1TA5uLiEmd9mzZtnrkPGWN2Xv66a4Nk3GbNmqW6Q5q7Mpq7RubMmTPB9hcvXsTjx48txUhE8eLF1diz33//HUFBQZZgTDJpokWLFnj//ffxxx9/4O+//1ZBnZAMnARvFStWxM6dO5EvX74knxfZtaMESI7UFsoceM05HufTJ+E94iM4nzmNoF9+h75MWbU8slVb0wbp/DuC1xzxeqOMjN9xmUOqBGtSVdHd3R0//vhjgnWSvUpKsPY0uXPnVkHS8ePHLcGaPJZltsar/frrr9iyZQt27dplyZ79888/KFasGN544w1LIGYOBGUeNSkoUqJECbRt21Z1yTRbt26dyvrJHG+JjY0jInJkmkdB8Jo2Ce6rV0BjNMLg7QOny5cswRoRERFl4GBt3759SG2dOnVSAVOePHnU8zlz5qBXr16W9YGBgaqLpJeXl5oEe8mSJWr7d955R2XLfvjhB9XFUQqgWBdBcXJyUvcS+GXNmjXBcbNkyaIC0Rfp4klEZBdGI9w2fg3viWOg9fdXiyLeegehE6fAkNv0XUpERESZYJ41KaEvpfBlbJh0G5QslgRKUvo+Jch8agEBAWqyawmw2rVrZ5lHTchzyYpJYRAJ6FasWIGpU6eqcWr58+dXlSrLluVfkYkokzAa4ftuB7j9tFs9jS5REiHT56ixaUREROSYNEbzIK0UdOzYMfTp00fNSyaTSEuwJl0HZQzaypUr1ZxomZG/v/2Lekgv0Bw5fByiLZQ58JpzHB5Lv4DX1E8ROuRjhL/fX8roIiPiNUe83igj43dcxvkM7RasSVarZs2aai4za9INUQK5b775BpmRIwRI/AdOvOYyCaMRrtu3wZjdLzZ7Fh0N7f17MOQ3jfXNqPg9R7zeKCPjd1zmCtZSZfIcqb749ttv2wzizp07lxqHJCKiGE5XLiFLh7bI8l53eA8dJBNXmlY4O2f4QI2IiCgjSZVgTcaEnTlzJsFy6Qr5ohNLExFRIsLD4Tl9MrLVqwHX/ftgdHND5Fvv8HQRERGlU6lSYOS9997D+PHjceXKFZQrV84SqEnZ+yFDhqTGIYmIMjXXn3fDe+QION24pp5HvfY6gqfOgqFYcXs3jYiIiBwpWJPJsIVUXly1apUqoV+0aFFMmTIFzZo1S41DEhFlWs5HDiNLl/bqsT5ffoRMnoGoN1uaOsUTERFRupVqpfslYDMHbURElHqiq1ZDZNM3oS/+EkKHfgxYzR1JRERE6VeKBWsLFy5Uc595eHiox08jc6MREVHyuPy2H14zpuDx2m9g9PNTGbQnqzcA2lQZhkxERETpPVg7fPgwunXrpoI1eZwYDbvlEBEli/beXXiNHwX3rd+p557zZiJ08oyYlQzUiIiIMpoUC9akeIjZjBkzkCdPHmjj/XiQybH/+++/lDokEVHmEB0NjxVL4DljKrQhwTBqtYjo+R7Cho+0d8uIiIgoFaXKn2IbNWqER48eJVh+69YtdO7cOTUOSUSUYYuHZHu9HrzHjlSBmq5SZTz6aT9Cps2GMUtWezePiIiI0kNmbdOmTfjyyy/VY6PRqCbFjp9Ze/LkCYoXZxlpIqKkct/0DZz//RuGrFkROmYiIrp2Z5dHIiKiTCLFgrU2bdrAxcUFBoMBo0aNQs+ePeHj4xNnrJqMZ6tRo0ZKHZKIKMPR+PtD8+SxZX600FFjYXRxRtiQj2HMkcPezSMiIqL0GKxJoCYBmyhQoAAqVaoEZ+dUmxmAiCjDBWmeXyyAx8pl0FWuisff/aCWG7NlR+jUWfZuHhEREWWU0v1HjhxRt8SwdD8RkYnm4UNTkLZqGTRhYaZlTx5DE/wERh9fniYiIqJMjKX7iYjsFaQtmg+P1cstQZquYiWEDfsEUa83UXOnERERUeaWKqX7rR8TEVFCbj/tUhk1IRUeVZDWqDGDNCIiIkrd0v0yn9rXX3+NO3fuqOfz58/Hm2++ieHDh9ss6U9ElNFp7t+H87HY7uER7TshskVrPP56Mx7t2sdsGhEREaVNsDZt2jR88cUXqlT/3r17sWzZMrRu3Rp3797FpEmTUuOQREQOG6R5jR0Jv6qvwvf93kBUlGmFiwuerFzHbBoRERElKlXKNf74448qWCtVqpQK1OrUqYO+ffuiYcOG6NixY2ockojIoWjv34PHws/gsWYlNBERapkhZy5oHz6AIX8BezePiIiIMmuwFh4eDj8/P0RHR+O3337DsGHD1HKZg43l/Ikoo2fSPD+fC4+1qyxBmq5KNYQOHwldg9c4Jo2IiIjsG6zJHGuzZs2Ct7e3Ctxef/11/Pfff6oLJCfFJqKMzOnaVXguXawe66pWNwVp9RsySCMiIiLHGLM2efJk6HQ6/PPPP2r8mmTZdu3ape7Hjx+fGockIrIL7b27cN210/I8unoNhP1vIB5t+h6PdvzEbBoRERElm8ZoNBqT/3J6Hv7+wbD32Zapm3Lk8HGItlDmkFGvOe3dO/D4fB481q0GtFoEHPsbxpw57d0sysDXHDkmXm/Ea46S+71ht26QQqpALl++HFeuXFGl/IsWLYquXbuiTZs2qXVIIqI0CdI8F8yF+/o10ERGqmW66jWhfRQEPYM1IiIiSkGpEqx98803mDFjhgrOpAqkFBY5ceIEJk6cqLpHvvPOO6lxWCKiVKN5+BBec6abgrSY8vtRNWohTMak1anHMWlERESUPoI1yajJ2DTrLJoUGXn55Zfx5ZdfMlgjonRHE62zBGpRNWubgrTadRmkERERUfoK1gICAlChQoUEyytWrKgmxiYicnTaWzfh+tNuRPTqo54b8uZDyMSp0JcqbQrSiIiIiNJjNcjSpUtj27ZtCZZv3boVL730UmockogoxYI07+EfIXv1CvD5ZCicT52wrIvo3ZeBGhEREaXvzNrw4cPRo0cPHD58GOXLl1fLTp06peZak26QRESORnvzBjznz4X71+ug0enUsijJoDk52btpRERElEmlSrAm3R23bNmCjRs34vLly3Bzc0PVqlUxb9485M2bNzUOSUSULJpHQfCaNAHu36yPDdLq1EPYsE+gq1WHZ5WIiIjsJtVK9xcvXhwDBw7EtWvXoNVqVel+Dw+P1DocEVGyGN094PrzbhWoRdWtbwrSatbm2SQiIqKMOWYtLCxMdYWsUaOGqvz49ttvo3r16qpCpJTuTwkyl/fs2bPVMapVq4aZM2eqKQISc/DgQbRq1QrlypVT9wcOHLC53enTp9WYu1u3bsU51oIFC1CrVi11rLFjxyIyZn4lIkpftNevwWvKRECvNy1wd0fIzHl49MNuPP5uOwM1IiIiytjB2rhx49T4tBUrVuDYsWM4cuSIGqsmj6dNm5Yix1i1ahV27NiBhQsXqkBq+/btapkt169fx4ABA/DWW29h586daNu2Lfr37x8nIBMSSI4ZMyZB0Lds2TJ89dVXmDNnjpqW4NChQ+q4RJS+gjTvjwYge81K8Jw/B25bN1vWRTVtDl2NWnZtHxEREVGaBGv79u3D9OnTVTbNy8sLPj4+Kis1depUFWClhLVr12LQoEGoUqWKyq4NGzYMGzZssLntvXv30L59e1X0pGDBgujZsyc8PT1x5syZONtJIObt7R1nmV6vV0Hgxx9/jJo1a6rMnHTv/Oeff1LkfRBR6tJeuwrvwf1VkOaxYS000dGIqt8Q+uKsTEtERESZcMyan5+fmmstvqioqATBUHLcv39fzdcmRUvMKleujNu3b+PBgwfIlStXnO0laJSbOXsm0wpIWyTwMrt69aoK9hYtWqQCO7OLFy8iKChITeptJt0o5UZEDiwiAt6fDIX7t19BE9PlMarBawgdNhLR1UzfB0RERESZLljr168fRo8ere6lMqSzszPOnTunuitKF8SjR49atrUOuJLq4cOH6t46KMuRI4clixY/WLPuDtmsWTOVLRs6dCgKFChgGZMmXTclYyaBpjXpKpklSxacOHFCVbOUwK1x48ZqTJ6rq+tzt52I0oibG5wvXVSBWlTDRggd9gmiqzJIIyIiokwerMm4LzF58uQE6yRzJTeh0WhUEGdLRESEyqAlVsBEWAdL5seSMUtM9uzZsXnzZpw8eVJ10yxcuDCaNGmilknGTTJqkp2zFhoaqtoi49VGjhypxrNJoRS5l0Ijz0Pz//buBLypKm3g+JumpRQopbTsyFL2VWQTFR0+kGEXUEDKKpuCCo6sjiuKC6sOCuOAsoOyy+IIgyioMCoKCAJSWlZBQEqhLRTaJs33vAcSWihOwaZNk//vea5J7r3JPT09XPvmnPMei+Q6Zxk8oSzwDTnV5vwOHZQC06bKxZfGiiO0qLngxTfGi9hsYmvY+EoZ3FsEeAjuc6C9wZtxj8v7buVvIrcEa5pcJCs04YgGV5n1UGlWxj59+mT6Pu3VUvpeXcPN+Vz90fIAOneuZs2aZtP13xYuXCj169c3PWZz5841weP1tFdQgzUNQDUTpHruuedk+PDhpvdQlyXIqrCwYPEUnlSWPCE2Vrtvr73eskVk506RmjWvbCVLEgHnVpuLjtZvhkR0zqrdLvnLlxUZN+7KsQf/4p5rIk/gPgfaG7wZ9zjf4LZ11rLi8ccfl9WrV5ukH9fTOWZRUVGZvk973CZNmmSGQzqHMjqHRhYrVuyG83XeWXx8vElGkn4dOM1SuWXLFjO08dFHH3UNiVTt27eXwYMHm2BORUREuN6ra8Zp6v64uDjX8MusOHs2Ua5+fK7ReFT/cXtCWfIK6+5dEtKpnSSNfk4uD37a7Cu44CMJmvm+65y0kCJir1rNbLZq1SW5e48rvTtwW5vzOxgtBd6eJIHLl4rlagbXlBYtJen+5mKLTaTmfRj3OdDe4M24x3nP79DjgzVnYHSrSpQoIaVLl5bt27e7gjV9rvsym6+2adMmWblypaxbt87Ve6bZHDUAa9mypSsgcwaCvXv3lpkzZ0rVqlVNz1pAQIDpLWzatKk5R3vlNMtlkSJFbvHnvbJ5Ak8qiyfz+/WYFI7sIn6JCZJvw3/k0oDB2t0qqbXqiF/rdmI9sF+sRw6LX/x58fvhewn44XvzvsuPPOqq3/yzZkrAzu1iM8FcdfOYVr6CiNUqviTb2pzDYVLw51+8yBWkJT/4V7OYta3+1S9kaNvgPoccxv9XQZuDO+RqsPZnREZGmkWxS+rwMxEzp6x///6u49rrpUMkNajSzI0zZsww5+si3Vu3bpU1a9bIkiVLTHbK9BkqrVf/gNbAzxmM6Vy2cePGyYQJE1yLcevnaCAH72U5f05CenQR6++nxVajpiTMWWgCNZUc2ctsxuXLYj0YI/7RUWKN2i/WX4+JI12Pa75NGyVww/oMn+0IDBR7pSpiq1pVEt+ZLlKw4NUDDoZT/s9fzJUvXDRQS27Z6kqQdleDbPzNAwAAeIY8G20MGDDALA+gi11rgNWlSxezjpqTvtbMk5rhUQM6XaBb13nTeWplypSRqVOnSq1atbJ0LZ2jpsMuddimBmsa/Gk2SXix5GQp/FhP8Y/aL/ZSpSX+o+XiKByS+bn584u9Vm2zZebSwMFia9DoSiAXfcAEdZbLl8V/3x7xO3ZUpEAB17nBT/QT/317xV5Fh1Ne7YmrUk3slavohEzxRdaYaCkwZYIkDX1W7DWv/JvVAO3yYwPEVu9arzgAAIC3sThudyxiNtC0/trDldmcNW8UG5v788S0UyI8PNgjyuKx0tIk+MmBkn/lckkrFCzn1/7npoHYbbHbzfBK/wP7xRIXJ8nde7oOhd7XUPyjD9zwFofFIvbqNeTc5m9dPUu62LP24DkKeXaymNttcxrYapAWuGrFlV60Dp0kYdZ8dxYVXoL7HGhv8Gbc47znd+jVPWuAu+T7YoMJ1Bz+/pIwe0H2BmrKapW0ChUlpULFGw7FL1tteuA0kLMeOHD1cb/4nTsnDqt/hiGSIf16if/en8VepqzYq1Q1iU1cPXHVquXZBCfWA1FS4O0JEvjJCv02yexLbt1Okp4ZnttFAwAAyFEEa8B1Ulq2lgtvTJC04MKS2qx5jtZPWukyZkv9vxbXdjocYomNFb9zcRn3JSaYp9YTx82Wb/OXrsOaxOTclmuLzweuXilpYeFiq1pdHJox1UMX2iv4/CgJmjXzWpDWpr0kjRwjtjp35nbRAAAAfCtY0xT4mmkR8DSXBg0Rj2GxmADLnn5ZCotF4n78WSzn4tL1wEW5Hu3Valw71+GQ4GeeEkvSRfMyLTT06py46qZHLrVeA7E1uUc8QVrpsiZQS27bQS6OGCP2OnVzu0gAAAB5f87aqlWrsnxup06dxBd5wjwxxjlnzn/ndikwebwk/vMDcYTc2pIMHsludy0NYLmQKMFDBppkKX5Hj7h6rZySW7WRhAVLrqXFHz1c7OXKm6GUOqQyrVz5P7XMwM3anHX/L2ZOWnKnRySlXYcrOy9eFOuhgwRp+FO4zyEn0d6Q02hzeV+uzFl79913s3SernPmq8EaPJMm6gjp2U38Ys9I2lvj5ML4KZLnpQuuNAGJKxi7dOnKMgOunrgoSb27ietcy5kzEjRvVoaPcuTPL7bKVc2C3ymt2khy5y5/rmi/7LuSOGTtKhM4anlS2ra/cucqWJBADQAAILuDtS+/vDZfBsgrLHFnJSTyEROopdauKxdfHCteLShI7LXrmC1Tfn5yccwLJqmJ/4EDYo05YJYZCNiz22xpxYq5gjVdh65Ih1ZXk5pUFXs1XfC7utgrVTbLGVzPum+vFJh8JUhzSm7f0Qx39NQ5dAAAAF45Zy0xMdGk5T9y5IgMGTJEdu3aJZUrV/aZNP3IAy5dkpA+keJ/MEbsZe+QhI+WeXwafHfTpQCSNHhKv8zA0SNmOQHNUmlr0NB1yBoVZYZW6haY/jP8/MRevoJcGjLUrIVmvPSShL7+uuscTcN/cfjo7M+0CQAA4EXcEqwdOHBA+vbtK6VKlTLP+/TpIxs2bJDhw4fLjBkzpHHjxu64LJB1aWlS+OknJGDbd5JWOMQsep1WshQ1mNkyAxGVJCWikkirNhkO2WvUkPOLV2YYUqkBnV/8efE/fOjKvDmnq//mLz/UWZI0SLu6uDUAAAByOFh7/fXXJTIyUoYNG2YWvlZvvfWWFC1aVCZOnCjLly93x2WBLCsw4XUzHM+RL58kzPvILDiNW+MoHCKpzR8027WdDrH8/rv4R0eJvWLEtf3t28u5b7eLrVIVqhkAACCL/MQNfv7550yTiHTv3l1iYmLccUngliR37ir2O8pJ4tR/Sup991N72bnMQIkSktr0AUkrUzbDfntlAjUAAIBc71nTHrTDhw9LuXLlMuzfsWOHhIWFueOSwC3RnrS4b7aJFChAzQEAAMB3grVBgwbJiy++KIMHDxZdxu27776TTz75RObOnWvmrQG5wf+H78WSnGx6fQwCNQAAAPhasKbDHYsXLy6zZs2S/Pnzm3lqFStWNHPZ2rZt645LAn/IejBaQno/KpbERIlfvFJS7/8LNQYAAADfTN3/wAMPSN26dSU8PNy83rlzp9SqRQY45Dxd6Dmk+yPiFxcnqXfVl9T619LPAwAAAD6VYOSXX36RFi1ayOzZs137Ro4cKa1bt5bo6Gh3XBLIXFKShPTuJtajR8ReroLEL1gqUrAgtQUAAADfDNZee+01admypTz77LOufZ9//rk0b97cHANyhN0uhQcPkIAd2yUtNFTiF68QR/HiVD4AAAB8u2dNF8UOCAi4diE/P7M49p49e9xxSSAjh0MKvTBaAtf/WxyBgRI/fwmp4wEAAJCnuCVYK1WqlHz77bc37NfU/c45bIBb2e1iiY8Xh8UiCf/8QGx3N6HCAQAAkKe4JcGIpux/4YUXTFKR2rVrm3379++XNWvWyCuvvOKOSwIZ+ftL4vSZcqnvALE1uYfaAQAAQJ7jlmCtY8eOZmHspUuXyscffyz+/v5Svnx5k8q/YUMy8cF9rIdixF4hQsfdmo1ADQAAAHmV21L316xZ0yyAreurqc8++8wEbIC7WA9ESZF2Lc0aagnTZ4oEBVHZAAAAyLPcMmdN56tpNsi1a9e69s2fP98siL19+3Z3XBI+zu/0KQmJfET84s+L36mTuV0cAAAAwDODtQkTJph5a8OGDXPtW7x4sQwcOFDefPNNd1wSvuzCBSncs5tYfz0mtohKEr9gCb1qAAAAyPPcEqwdOXLELIB9vTZt2khMTIw7LglfZbNJ4UF9JWD3T5IWHi7xH68QR1hYbpcKAAAA8MxgLSIiQtatW3fD/i+//FLKlSvnjkvCV9dSGzNcAr/4XBxBQaZHLa1iRG6XCgAAAPDcBCN/+9vf5Mknn5StW7dKrVq1XKn7f/zxR5k2bZo7LgkfZI2JlvzLFovDz08S/jVbbA0a5XaRAAAAAM8O1h544AFZtWqVLF++XA4dOiQBAQEmO+Rrr70mJUuWdMcl4YPsVarK+eVrxf/Afklp0y63iwMAAAB4frAWGxsrS5YsMfPT7Ha7JCUlye7du00myIMHD8oPP/zgjsvCV9jtIlareWprfLfZAAAAAG/jljlrzz//vHzzzTdSp04d2bFjh9SrV0/CwsJMwDZ06FB3XBI+wrpvr4T+pYlYf96d20UBAAAA8l6wpj1nb731llkUu1q1atKsWTP5xz/+Yeayff311+64JHyA328nJKRHF/E/ECUFx4/L7eIAAAAAeS9YczgcUqJECfO8cuXKsm/fPlfq/p9//tkdl4SXsyQmSEiPrmL97YTYqlSVxGkzcrtIAAAAQN4L1jSZyOrVq83zGjVqmKyQ6vjx4+64HLxdSooU7tdb/PftEXvxElfWUgstmtulAgAAAPJesDZixAiZPXu2zJ07Vzp27Ch79uyRDh06yNNPPy1t27bNtt67yZMnS5MmTaRx48YyceJESUtLu+n5OofuoYcekrp165rHr776KtPzdu3aZQLM9IFlfHy8jBw50lzn/vvvlylTpvzhtZCNHA4JHjFM8n29SRwFCkrCoqWSVq48VQwAAACv55ZskA0aNJBNmzbJ5cuXJTQ0VFasWCEbN26UIkWKmKGQ2WHOnDny6aefmnXbbDabjBo1yiQxGTBgwA3nHj161ASKzz77rLRo0cKU5amnnpL169dL2bJlXeelpqbKiy++eEMg9uqrr5oMl4sWLZKzZ8+awE2v9dhjj2XLz4Kbyz/nQ8m/5CNxWK2S8OFcsd15F9UFAAAAn+CWnjVVqFAhCQ8PN891/lrPnj2lXbt24ueXPZecP3++DBs2TBo2bGh61zSA0mAqM6dOnZJu3bqZ4OqOO+6Qfv36SYECBUx2yvQ+/PBDU+7raS+cvqdKlSrmWu3bt5dvv/02W34O/LHkro9KSrPmcmHiO5LyYCuqCwAAAD7DbcGaO50+fVpOnjwpjRo1ytCbd+LECfn9999vOP/uu++WF154wdV7tmzZMklJSTFDIp0OHz5sgr3nnnvuhvdrj+CaNWvk0qVL5to6pFKHSsL9HMGFJX7xSrncm15MAAAA+Ba3DIN0tzNnzpjH4sWLu/Y5e/G0Fy39/uuHQ+owTF2oW+fVOYdA6vy3l19+2awBp8Mbr/fKK6/I6NGjpX79+maI5L333muGVd4qi0VynbMMnlCWm9E11AK2fC2XBz91paDWPPmdAvJQm4N3oc2B9gZvxj0u77uVv4k8NljT+W7ai5WZpKQk85gvXz7XPudz7TG7maJFi8ry5ctl586dMn78eClfvry0atXK7NMeNx0qqb1z19Net9q1a5sATQNFncP2wQcfyJAhQ27pZwoLCxZP4UllyeDYMZGeXUVOnpRCRQuLPPVUbpcI3t7m4LVoc6C9wZtxj/MNHhusaVbGPn36ZHpMk4k4A7PAwEDXcxUUFHTTzwwODjbLCuh28OBBWbhwoekte+edd0zmSksmYe6RI0dkwoQJsnnzZlePnQ6HHDt2rAwaNEj8/bNehWfPJmpyw1ylP6L+4/aEslzPEn9eQtq1Ev+TJ8VWo6bEt+kojtjE3C4WvLjNwTvR5kB7gzfjHuc9v8M8HazpPLOoqKhMj2mP26RJk0wvl3Moo3NoZLFixW44Pzo62qTf12QkTpUqVZJt27bJli1b5Ny5c/Loo4+6hkQqTSIyePBgKVeunMlomX5opQZ7Fy9eNJ+Z2bDJm9GP9pQ/Vj2pLEZyshTu21P8o/aLvVRpif9ouaQFh4h4UhnhXW0OXo82B9obvBn3ON/gscHaH9HskqVLl5bt27e7gjV9rvsym6+mywisXLlS1q1b5+o927t3r0REREjLli1N71r6QLB3794yc+ZMqVq1qsTExJhgTlP2OwOzQ4cOmWySOqwS2SAtTYKfeVLybf1G0goFXwnUylxbUgEAAADwRXkyWFORkZFmUeySJUua17pQdf/+/V3H4+LizBDJggULmkWwZ8yYYc7v2rWrbN261WR3XLJkiUnVnz5dv9VqNY8a+GkWyHr16pleOE0wopkiNXDTBbh79eqV6bBJ3LqCb42T/CuXicPfXxJmLxB7rdpUIwAAAHxeng3WdPFr7e3SpB8aYHXp0iXDItX6unPnzibDowZ0s2bNkjfffNPMUytTpoxMnTpVatWq9T+vo3PSNJnIG2+8YdaK0x61jh073lY2SGTOXrqMWfQ68e33JLVZc6oJAAAA0PltDuckLbhdbGzuJ1jQzsDw8GCPKEt61kMxYo+onNvFgA+1OXgv2hxob/Bm3OO853eYFSxghVxh3bdXLOfiXK8J1AAAAICMCNaQ4/yOHpEiXR6SIu1ait+J4/wGAAAAgEwQrCFHWeLOSkjkI+IXe0Yc+YPEERLCbwAAAADIBMEacs7lyxLSt4f4x0SLvUxZSfhomTgKZW28LgAAAOBrCNaQc2upDR0sAd9/K2mFQyT+4xWSVrIUtQ8AAADcBMEackTBV1+S/KtXiiMgQBLmLhJ79RrUPAAAAPAHCNbgdpb48xL47zXmeeK770tq0weodQAAAMBbF8VG3uEIKSLnPvtC8n35uSQ/0i23iwMAAADkCfSswX2SklxPHcWLS3L3ntQ2AAAAkEUEa3ALv0MHpWiTuyRwyUfUMAAAAHAbCNaQ7SyxsVKk+8NiPXVSgmbPFLHZqGUAAADgFhGsIXslJUlI725iPXJY7OUqSPyCpSL+TI0EAAAAbhXBGrKP3S6FhwyUgO0/SlpoqMQvXmHmqgEAAAC4dQRryB4OhxR86TkJXPepOAIDJX7eYrFXrkLtAgAAALeJYA3ZQtPyF/hwhjgsFkmYPlNsTe6hZgEAAIA/gclEyBYpzVvKxRFjxFE4RFIe6kytAgAAAH8SwRqyh8UiSWNeoDYBAACAbMIwSNw2a/QBCR46OMPi1wAAAACyBz1ruC2W06clJPIRsR47Ko4CBeTChLepSQAAACAb0bOGW3fhgoT06mYCNVvFCLk46nlqEQAAAMhmBGu4NTabFH78MQnYtVPSwsIk/uMV4ggPpxYBAACAbEawhqxzOKTQmBESuHGDOIKCJH7BEkmLqEQNAgAAAG5AsIYsC5o2VYIWzLmyltr7s8TWsDG1BwAAALgJCUaQZan33idp4eFycfhoSWnbnpoDAAAA3IhgDVlma9BI4rb8II6iYdQaAAAA4GYMg8Qfsv6yT/x37XS9JlADAAAAcgbBGm7eOE7+ZtZSK9KxrQR8919qCgAAAMhBBGvIlCUxQUJ6dBXrbyfEXrq02KpVp6YAAACAHESwhhulpkrh/r3Ff+/Pklas+JW11EKLUlMAAABADiJYQ0YOhwSPGCb5vtokjgIFJH7RUkkrX4FaAgAAAHIYwRoyKDB5vORfvEgcfn6S8MFcsdWrTw0BAAAAuYBgDdfY7eL/0w7z9MKEtyWlZWtqBwAAAMgleTZYczgcMnnyZGnSpIk0btxYJk6cKGlpaTc9/5tvvpGHHnpI6tatax6/+uqrDMd1X7Vq1TJsBw4cuK1r5VlWqyTM+1ji534kl/v2z+3SAAAAAD4tzy6KPWfOHPn0009l2rRpYrPZZNSoURIWFiYDBgy44dyjR4/K008/Lc8++6y0aNFCNm7cKE899ZSsX79eypYtK3a7XY4cOSILFy6UChWuzc8KDQ295WvlRZbTp8VRvLiIxSLi7y8pbdvndpEAAAAAn5dne9bmz58vw4YNk4YNG5oer5EjR8qiRYsyPffUqVPSrVs3eeyxx+SOO+6Qfv36SYECBWT37t3m+PHjxyU1NdX0uhUrVsy1+fv73/K18hq/479K6IP3S6GRz4jYbLldHAAAAAB5uWft9OnTcvLkSWnUqJFrX4MGDeTEiRPy+++/S3HtJUrn7rvvNpvSoGzVqlWSkpJigjMVExMjpUqVksDAwD99rbzEEn9eQnp0EevpUxLww/diSboojsIhuV0sAAAAAHk1WDtz5ox5TB8ohYeHu3rRbhZA6XDINm3amGGPI0aMMEMg1cGDByUgIECeeOIJ2bNnj1SsWFFGjx5tgrnbvVZmdJRhbnOWwZKSLMH9eon//l/EXrKUJHy8XCQkRDygiPAyrjZH4wJtDl6Iexxoc7hVt/I3kccGa5cvXza9WplJSkoyj/ny5XPtcz7XHrObKVq0qCxfvlx27twp48ePl/Lly0urVq3k8OHDEh8fL127djXDHZcuXSp9+/aVzz77zJTjdq6VmbCwYPEIDoeEjf6byJavRQoVEuu6z6RovZq5XSp4OY9p//AZtDnQ3uDNuMf5Bo8N1nbt2iV9+vTJ9Jgm+HAGS86hi87AKSgo6KafGRwcLDVr1jSb9qZpQhEN1saNG2eCskKFCpnzxo4dKzt27JDVq1fLvffee1vXyszZs4kaJ+V6JB825S2RRYvE4e8vCbMXSGrZSiKxiblbMHgt0+bCgj2i/cM30OZAe4M34x7nPb/DPB2s6RyzqKioTI9pj9ukSZPMEEXnUEbncEVNDHK96Oho03OmCUKcKlWqJNu2bTPPNZGIM1BTFotFIiIizHVKlCjh+vysXOuP6B+quf3HqjUmWmTiRPM8ccq7ktKshQh/QCMHeEL7h2+hzYH2Bm/GPc435MlskBpAlS5dWrZv3+7ap891X2ZzyDZt2iQvvviiWS/Nae/evSYgU7179zZp+Z10DTUNFPX4rV7L09krVxFZu1YuvjhWkiN75XZxAAAAAOS1nrX/JTIy0ixUXbJkSfN6ypQp0r//tYWc4+LizLDFggULmgWvZ8yYYc7XeWlbt26VNWvWyJIlS8y5zZs3l+nTp0uNGjVMchFN1Z+YmCidO3fO0rXynDZt5FKjpvSoAQAAAB4szwZruiD12bNnzWLXVqtVunTpYtZRc9LXGmwNHTrUBFmzZs2SN99808xTK1OmjEydOlVq1aplztX3JScny+uvvy6xsbFy5513moWwnUMj/9e1AAAAACC7WRzpxwbCrWJjcz/Bgk5oDA8P9oiywDfQ5kCbgzfjHgfaHG73vuG1c9YAAAAAwNsRrAEAAACAByJYAwAAAAAPRLAGAAAAAB6IYA0AAAAAPFCeTd2fVzO/eEoZPKEs8A20OdDm4M24x4E2h1t1K3+Hk7ofAAAAADwQwyABAAAAwAMRrAEAAACAByJYAwAAAAAPRLAGAAAAAB6IYA0AAAAAPBDBGgAAAAB4III1AAAAAPBABGsAAAAA4IEI1gAAAADAAxGseaHk5GR5/vnnpWHDhtK0aVOZPXv2Tc/dt2+fdO3aVe6880555JFHZM+ePTlaVvhem9u8ebN07NhR7rrrLunQoYN88cUXOVpW+F6bczp+/Lhpd99//32OlBG+2d6ioqIkMjJS6tata+5x3333XY6WFb7X5j7//HNp06aNub9p29u7d2+OlhXuRbDmhSZOnGiCrnnz5skrr7wi06ZNk/Xr199wXlJSkjz++OPmRrBy5Urzj/yJJ54w+wF3tLn9+/fL008/bb4YWLVqlXTv3l2eeeYZsx9wR5tLb+zYsdzf4Nb2lpiYKP3795fKlSvL2rVrpWXLluaed/bsWWoebmlz0dHRMmLECPP32+rVq6VGjRrm+aVLl6hxb+GAV7l48aKjTp06ju+++861b/r06Y5evXrdcO6yZcsczZs3d6SlpZnX+tiyZUvHihUrcrTM8J02N2nSJMeAAQMy7Ovfv7/j7bffzpGywvfanNPq1asd3bt3d1StWjXD+4DsbG/z5s1zPPjggw6bzeba9/DDDzs2b95MRcMtbW7OnDmOzp07u14nJiaa+9zu3bupcS9Bz5qX0R4Km81mesmcGjRoILt27ZK0tLQM5+o+PWaxWMxrfaxfv7789NNPOV5u+Eab69y5s4wcOTLTb6MBd7Q5de7cOZk0aZK89tprVDLc2t62bdsmLVq0EKvV6tq3YsUK+ctf/kLNwy1trkiRIhITEyPbt283x3SkVKFChaRcuXLUuJcgWPMyZ86ckdDQUMmXL59rX3h4uBn7fP78+RvOLV68eIZ9YWFhcurUqRwrL3yrzVWqVEmqV6+eYfjGt99+K/fcc0+Olhm+0+bU+PHjzRcFVapUyeGSwtfa26+//ipFixaVl156Se677z7p1q2b+SMacFeba9u2rTRr1kx69OghtWvXNsMn3333XQkJCaHSvQTBmpfRMcrp/3Er5+uUlJQsnXv9eUB2tbn04uLiZOjQoaY3V7+JBtzR5v773/+aP5affPJJKhhub28653vmzJlSrFgx+eCDD6RRo0YyYMAAOXnyJLUPt7Q5HTmgwd3LL78sS5cuNQm8/v73vzNP0osQrHmZwMDAG/4hO1/nz58/S+defx6QXW3OKTY2Vvr27atzZs03gH5+3IqQ/W3u8uXL5g8YnZzPfQ05cY/T4Y+a4GHYsGFSs2ZNGTVqlFSoUMEkfgDc0eYmT54sVatWlZ49e5qetXHjxklQUJAZfgvvwF9IXqZEiRLmWxYd6+yk37joP+7ChQvfcK7+0Zyevr5+aCSQXW1OnT592vxPRf/HM3/+fDNkCHBHm9u9e7cZlqZ/OOvcD+f8j0GDBpkgDsjO9qa0Ry0iIiLDPg3W6FmDO+5xStP0p59eoF9+6uvffvuNSvcSBGteRr/R8/f3z5AkRIcA1alT54beC11bbefOnaZ3Q+njjh07zH7AHW1OhwgNHDjQ7F+4cKH5HxLgrvucrnO1YcMGs0yEc1Ovv/66WTICyO57XL169cw6a+kdOnRIypQpQ2Uj2+9xSr9gP3jwYIZ9hw8flrJly1LjXoJgzcto13enTp3MekL6rfLGjRvNQop9+vRxfTOjQ4NU69atJSEhQd544w2TSUgfdZy0LqwIuKPNzZgxQ44dOyYTJkxwHdONbJBwx31Ov4UuX758hk3plwSaTAnI7nucrh2pwdp7770nR48elalTp5reXZ1HBGT3PU5pEhudq6ZfRmmb02GR2qumSZXgJXJ77QBkv6SkJMfo0aMd9erVczRt2tSsweGka2+kX0dt165djk6dOpn1PLp06eLYu3cvvxK4rc21atXKvL5+GzNmDLUOt93n0mOdNbi7vf34449m3avatWs7Onbs6Ni2bRuVDre2uaVLlzpat25tzo2MjHTs2bOHGvciFv1PbgeMAAAAAICMGAYJAAAAAB6IYA0AAAAAPBDBGgAAAAB4III1AAAAAPBABGsAAAAA4IEI1gAAAADAAxGsAQAAAIAHIlgDAOQpzz33nFSrVu2m28qVK83j8ePHc6Q8ulxp79695eDBg5KX6lC3/2Xp0qXyzjvv5EiZAAA38s9kHwAAHuuFF16QESNGmOefffaZzJ49W5YvX+46HhISIvfff78ULVo0R8rzySefSOnSpaVSpUribR5++GHp0KGDdOrUSSpWrJjbxQEAn0PPGgAgTwkODpZixYqZTZ9brVbXa93y5ctnHnV/TvSqvf/++xIZGSneyN/fXzp37iwffPBBbhcFAHwSwRoAwKvo8Mf0wyD1+bp166RNmzZy5513yvDhw+XXX3+VPn36mNc9evSQ06dPu97/+eefS9u2bc2xLl26yLZt2256rS1btsilS5fMuU5vv/22NG3aVOrWrWuGR0ZHR7uO/fjjj6a3So9pj9V//vOfDJ83Z84cad68udx1110yYMAAU06VlpYmH374obRo0cL1uVFRUa736c+4evVqad++vdSuXdv8TM73Oq+rvWP63meeecaU2SkhIUGGDh0qDRs2lEaNGsnIkSPlwoULruN6zX//+9/mPABAziJYAwB4vXfffVfGjx8vM2bMkA0bNpieMN0WL14sZ86ccfUc7d+/X8aMGSNDhgyRNWvWyEMPPSSDBg2So0ePZvq533zzjdxzzz1isVhcgd6SJUvkH//4h3z66acSHh4uf//7380xvc4TTzxhgrW1a9fKwIEDzbwxDaSUlmXatGkmWNKhlQULFjSBlZo+fboZ7vn888+bY2XKlDHvT0pKcpXlvffeM0NEdc7euXPnTBlUXFycue69994rq1atksqVK8v69esz1I2W7eOPP5b58+ebOvjnP//pOq7DO3Vo6Q8//OCG3wwA4I8wZw0A4PUee+wxV+9XjRo1zPwr7WlTf/3rX02AombNmiXdunUzvV5Ke980SNFAJrOEHPv27TO9aE4nTpyQgIAAM4dNt5deekkOHTpkji1atMgETL169TKvy5cvL7/88ovMmzfP9GppkKfl1F499fLLL5vyXL58WRYuXGh6BLWXS40bN05atmxpAsru3bubff369TOBo9JAVK+ntFdR5++NGjXKBJXai/bVV19lKLMGhmXLlpWgoCCZOnXqDT+nBnj6szqvDwDIGQRrAACvd8cdd7ie58+f3/RMpX+dkpJinmtGRw1uNHBySk1NzRCQpae9VqGhoa7X7dq1M4GVBjX16tWTBx980AylVBq0bdq0yQxxTP/ZzsQdhw8fllq1armOaa+c9vLFxsbK+fPnMwy11IBQhzumz0CpwZ9ToUKFzGermJgYqV69uqv3T9WpU8c1FFID0ieffNIEerq1atXKFaw6FSlSRM6ePfs/6xkAkL0I1gAAXu/6ZCN+fpnPArDb7WbYo87vSk8DusxoAKTvcdLEJhrsbd261QRm2jOm6e91+KHNZjNB0ODBg29I4pH+8XqBgYE3LavOZUsfwP1RIpT09FxnsKYBmva0ffHFF7J582bTo6dz8SZPnuw6X69zszoDALgPd14AAK7SXi5NTKK9VM5Ne9m+/vrrTOsoLCzM9Ho5abCzbNkyadasmbz66qsm6ceRI0fkwIED5rN17lv6z9YASeevKX3tHI6pdN5ZkyZNJD4+3vSy/fTTT65j2mu2d+/eLKXTr1KlihnCmD6o1OGXTnPnzjWfpVkfdQjkW2+9Zeb1padl0TIAAHIWwRoAAFfpnDFdu00TbRw7dswEMrpVqFAh0zqqWbNmhqyM2gM1ceJEk2hEgz5N9qHzwPT9mqFxz549ZpFpDeA0SNPMkTq3TWmGR52/tnHjRjMk8pVXXjHzyHTTcmkikC+//NIMfdS5cMnJya75bX9Eh2ZqL9obb7xhhmJqVsnt27e7jp86dUpee+01EwxquTRDpf5c6WmwmX6IJgAgZzAMEgCAq3SemQZbmllRH8uVKydTpkwxKe0zo4tva+IRHWaoQyI17f6wYcNM75RmWIyIiDCZFTWbom7/+te/zPBCHR5ZokQJ817NOKk6duxolhDQHjlNnd+4cWMToKn+/fubfRqk6aPOe1uwYEGWFv7W62qANnbsWHMN/Vn00Tk0UjNOJiYmmgyYml1Sj0+aNMn1fg3wLl68aMoDAMhZFsf1A9kBAECW6NBCTcihwdnNArq8TpcTOHnypOmZAwDkLIZBAgDwJxKXPP7442aNNG+kc+N03p327AEAch7BGgAAf4Km5v/tt98ypNH3FitWrDA9h7owNgAg5zEMEgAAAAA8ED1rAAAAAOCBCNYAAAAAwAMRrAEAAACAByJYAwAAAAAPRLAGAAAAAB6IYA0AAAAAPBDBGgAAAAB4III1AAAAAPBABGsAAAAAIJ7n/wFLTmwSZdSrWQAAAABJRU5ErkJggg=="
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "ename": "IndexError",
+     "evalue": "too many indices for array: array is 1-dimensional, but 2 were indexed",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mIndexError\u001B[39m                                Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[83]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[43mplot_control_trajectory\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtest_dataset\u001B[49m\u001B[43m,\u001B[49m\u001B[43mstate_cols\u001B[49m\u001B[43m \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m0_x\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m0_y\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcontrol_cols\u001B[49m\u001B[43m \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43mmech_brake_pressed\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43maccel_position\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msample_idx\u001B[49m\u001B[43m \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[32;43m10\u001B[39;49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[82]\u001B[39m\u001B[32m, line 48\u001B[39m, in \u001B[36mplot_control_trajectory\u001B[39m\u001B[34m(model, test_dataset, state_cols, control_cols, sample_idx)\u001B[39m\n\u001B[32m     44\u001B[39m position_idx = state_cols.index(\u001B[33m\"\u001B[39m\u001B[33m0_y\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m     46\u001B[39m time_steps_input = np.arange(\u001B[38;5;28mlen\u001B[39m(x_input)) * dt\n\u001B[32m---> \u001B[39m\u001B[32m48\u001B[39m ax2[\u001B[32m0\u001B[39m].plot(time_steps_input, \u001B[43mx_input\u001B[49m\u001B[43m[\u001B[49m\u001B[43m:\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mspeed_idx\u001B[49m\u001B[43m]\u001B[49m, \u001B[33m'\u001B[39m\u001B[33mb-\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m     49\u001B[39m ax2[\u001B[32m0\u001B[39m].set_ylabel(\u001B[33m\"\u001B[39m\u001B[33mSpeed\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m     50\u001B[39m ax2[\u001B[32m0\u001B[39m].grid(\u001B[38;5;28;01mTrue\u001B[39;00m)\n",
+      "\u001B[31mIndexError\u001B[39m: too many indices for array: array is 1-dimensional, but 2 were indexed"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ],
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 41
+   "execution_count": 83
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:25:27.565019Z",
-     "start_time": "2026-02-17T06:25:27.553092Z"
+     "end_time": "2026-02-18T03:13:50.268505Z",
+     "start_time": "2026-02-18T03:13:50.246914Z"
     }
    },
    "cell_type": "code",
@@ -1720,20 +1677,39 @@
    ],
    "id": "9e3d7d9c0f207973",
    "outputs": [],
-   "execution_count": 40
+   "execution_count": 67
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T06:25:29.139937Z",
-     "start_time": "2026-02-17T06:25:29.135927Z"
+     "end_time": "2026-02-18T03:13:52.267870Z",
+     "start_time": "2026-02-18T03:13:51.085685Z"
     }
    },
    "cell_type": "code",
-   "source": "",
+   "source": "plot_error_heatmap(model, test_loader, device)",
    "id": "4c0e45388dee0647",
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_19904\\252557315.py:31: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n",
+      "  pivot_table = df_err.pivot_table(index='Speed Bin', columns='Target Bin', values='MAE', aggfunc='mean')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 68
   },
   {
    "metadata": {},
@@ -1741,7 +1717,7 @@
    "outputs": [],
    "execution_count": null,
    "source": "",
-   "id": "6c0f3bbbc80cd9e"
+   "id": "c08265494dbc609e"
   }
  ],
  "metadata": {
diff --git a/array_temp/RNN.py b/array_temp/RNN.py
index bf26cfd..c0a0e7b 100644
--- a/array_temp/RNN.py
+++ b/array_temp/RNN.py
@@ -1,12 +1,12 @@
-import os
 import torch
 from torch import nn
-from torch.utils.data import TensorDataset, DataLoader
+
+
+
 #model class to declare RNN and defining a forward pass of the model
 
 device = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else "cpu"
 
-
 class RNN(nn.Module):
 
     def __init__(self, input_size, hidden_size, num_layers, seq_length, output_size):
@@ -16,8 +16,6 @@ def __init__(self, input_size, hidden_size, num_layers, seq_length, output_size)
         self.num_layers = num_layers  #stacked lstm layers
 
         #lstm: long short term memory - looks at long term dependencies in sequential data
-
-
         self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)  #correspond to input data shape
         self.seq_length = seq_length  #no of timestamps to look at to predict the next control output
 
@@ -28,7 +26,6 @@ def __init__(self, input_size, hidden_size, num_layers, seq_length, output_size)
 
     def forward(self, x):
 
-
         #x shape : [batch size, input size]
         # associate the state array (start + goal state) with timeseries dependency
         # this is done by repeating the input vector seq_len times
@@ -43,7 +40,7 @@ def forward(self, x):
         cell_states = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
 
         #forward propagate lstm
-        out, _ = self.lstm(x_repeated, (hidden_state, cell_states)) #out; tensor of shape(batch_soze, seq_length, hidden_size) - at the final time step
+        out, _ = self.lstm(x_repeated, (hidden_state, cell_states)) #out; tensor of shape(batch_size, seq_length, hidden_size) - at the final time step
         #decode the hidden state of t
         # predicted is a series of controls
         out = self.fc(out)

From 8537047c5f3687e1760f6fdd96c7dd2fe37645a8 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Sat, 28 Feb 2026 00:30:55 -0800
Subject: [PATCH 19/49] rnn architecture + training

---
 array_temp/Control_Model.ipynb | 874 +++++++++++++++++++++++++++------
 array_temp/RNN.py              |   1 +
 array_temp/RNN_Dataset.py      |  17 +-
 3 files changed, 745 insertions(+), 147 deletions(-)

diff --git a/array_temp/Control_Model.ipynb b/array_temp/Control_Model.ipynb
index e02d3a0..9063325 100644
--- a/array_temp/Control_Model.ipynb
+++ b/array_temp/Control_Model.ipynb
@@ -30,21 +30,22 @@
   },
   {
    "metadata": {
-    "jupyter": {
-     "is_executing": true
+    "ExecuteTime": {
+     "end_time": "2026-02-21T18:43:13.687189Z",
+     "start_time": "2026-02-21T18:43:13.680673Z"
     }
    },
    "cell_type": "code",
    "source": "#other references :\n",
    "id": "b191dbc392da20f6",
    "outputs": [],
-   "execution_count": null
+   "execution_count": 1
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-18T02:31:48.942639Z",
-     "start_time": "2026-02-18T02:31:38.627098Z"
+     "end_time": "2026-02-28T07:35:45.226560Z",
+     "start_time": "2026-02-28T07:35:40.607802Z"
     }
    },
    "cell_type": "code",
@@ -64,8 +65,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-18T02:32:34.688067Z",
-     "start_time": "2026-02-18T02:32:10.172546Z"
+     "end_time": "2026-02-28T07:36:11.380337Z",
+     "start_time": "2026-02-28T07:35:47.946377Z"
     }
    },
    "cell_type": "code",
@@ -124,8 +125,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T18:57:24.771122Z",
-     "start_time": "2026-02-17T18:57:24.717797Z"
+     "end_time": "2026-02-21T18:45:30.183672Z",
+     "start_time": "2026-02-21T18:45:30.050528Z"
     }
    },
    "cell_type": "code",
@@ -149,7 +150,7 @@
    ],
    "id": "9f8652589e173b01",
    "outputs": [],
-   "execution_count": 5
+   "execution_count": 4
   },
   {
    "metadata": {
@@ -260,8 +261,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-18T02:32:43.877757Z",
-     "start_time": "2026-02-18T02:32:43.849574Z"
+     "end_time": "2026-02-28T07:36:19.074137Z",
+     "start_time": "2026-02-28T07:36:19.053304Z"
     }
    },
    "cell_type": "code",
@@ -282,8 +283,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T17:37:58.934642Z",
-     "start_time": "2026-02-17T17:37:56.003596Z"
+     "end_time": "2026-02-28T07:36:22.364062Z",
+     "start_time": "2026-02-28T07:36:19.684565Z"
     }
    },
    "cell_type": "code",
@@ -317,7 +318,7 @@
        "Text(0.5, 1.0, 'speed kph')"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -352,7 +353,7 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 8
+   "execution_count": 4
   },
   {
    "metadata": {
@@ -564,8 +565,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-18T02:32:57.854672Z",
-     "start_time": "2026-02-18T02:32:57.825274Z"
+     "end_time": "2026-02-28T07:36:28.520336Z",
+     "start_time": "2026-02-28T07:36:28.503139Z"
     }
    },
    "cell_type": "code",
@@ -867,13 +868,13 @@
    ],
    "id": "2741c957d09edb03",
    "outputs": [],
-   "execution_count": 4
+   "execution_count": 5
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-18T02:32:59.789367Z",
-     "start_time": "2026-02-18T02:32:58.575995Z"
+     "end_time": "2026-02-28T07:36:30.912417Z",
+     "start_time": "2026-02-28T07:36:29.730320Z"
     }
    },
    "cell_type": "code",
@@ -900,13 +901,13 @@
    ],
    "id": "88a4ae7fb0d75eed",
    "outputs": [],
-   "execution_count": 5
+   "execution_count": 6
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-18T02:33:00.825285Z",
-     "start_time": "2026-02-18T02:33:00.674927Z"
+     "end_time": "2026-02-28T07:36:31.569352Z",
+     "start_time": "2026-02-28T07:36:31.479252Z"
     }
    },
    "cell_type": "code",
@@ -922,10 +923,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x283939977d0>]"
+       "[<matplotlib.lines.Line2D at 0x1fc484111c0>]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -940,7 +941,7 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 6
+   "execution_count": 7
   },
   {
    "metadata": {},
@@ -964,8 +965,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-18T02:33:02.922060Z",
-     "start_time": "2026-02-18T02:33:02.918007Z"
+     "end_time": "2026-02-21T18:45:55.003466Z",
+     "start_time": "2026-02-21T18:45:54.997016Z"
     }
    },
    "cell_type": "code",
@@ -977,8 +978,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-18T02:33:05.227426Z",
-     "start_time": "2026-02-18T02:33:03.513714Z"
+     "end_time": "2026-02-28T07:36:37.414928Z",
+     "start_time": "2026-02-28T07:36:35.888218Z"
     }
    },
    "cell_type": "code",
@@ -992,30 +993,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Calculating closest GIS indices: 100%|██████████| 1985282/1985282 [00:01<00:00, 1309236.48it/s]\n"
+      "Calculating closest GIS indices: 100%|██████████| 1985282/1985282 [00:01<00:00, 1454244.28it/s]\n"
      ]
     }
    ],
-   "execution_count": 7
+   "execution_count": 8
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-18T02:33:05.996932Z",
-     "start_time": "2026-02-18T02:33:05.990327Z"
+     "end_time": "2026-02-28T07:36:38.046061Z",
+     "start_time": "2026-02-28T07:36:38.042437Z"
     }
    },
    "cell_type": "code",
    "source": "calculated_speeds, calculated_position = state_array",
    "id": "f4fb71b02da66f3e",
    "outputs": [],
-   "execution_count": 8
+   "execution_count": 9
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-18T02:33:08.386829Z",
-     "start_time": "2026-02-18T02:33:08.260692Z"
+     "end_time": "2026-02-28T07:36:38.911570Z",
+     "start_time": "2026-02-28T07:36:38.792263Z"
     }
    },
    "cell_type": "code",
@@ -1033,13 +1034,13 @@
    ],
    "id": "a46a29e38c162456",
    "outputs": [],
-   "execution_count": 9
+   "execution_count": 10
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-18T02:35:38.127874Z",
-     "start_time": "2026-02-18T02:35:37.776865Z"
+     "end_time": "2026-02-21T18:46:00.078727Z",
+     "start_time": "2026-02-21T18:45:59.741957Z"
     }
    },
    "cell_type": "code",
@@ -1052,10 +1053,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x28394f2eb70>]"
+       "[<matplotlib.lines.Line2D at 0x1c902dc7fe0>]"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1070,13 +1071,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 18
+   "execution_count": 12
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-18T02:35:23.130482Z",
-     "start_time": "2026-02-18T02:35:22.370794Z"
+     "end_time": "2026-02-21T18:46:01.113524Z",
+     "start_time": "2026-02-21T18:46:00.402175Z"
     }
    },
    "cell_type": "code",
@@ -1089,10 +1090,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x28394b97bf0>]"
+       "[<matplotlib.lines.Line2D at 0x1c902e82600>]"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1107,7 +1108,7 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 17
+   "execution_count": 13
   },
   {
    "metadata": {
@@ -1149,8 +1150,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-18T02:36:02.075663Z",
-     "start_time": "2026-02-18T02:36:02.069953Z"
+     "end_time": "2026-02-28T07:36:46.801270Z",
+     "start_time": "2026-02-28T07:36:46.797614Z"
     }
    },
    "cell_type": "code",
@@ -1166,13 +1167,13 @@
    ],
    "id": "a6707eebb9ccd8c9",
    "outputs": [],
-   "execution_count": 19
+   "execution_count": 11
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-18T02:36:02.754455Z",
-     "start_time": "2026-02-18T02:36:02.734513Z"
+     "end_time": "2026-02-28T07:36:47.355129Z",
+     "start_time": "2026-02-28T07:36:47.350804Z"
     }
    },
    "cell_type": "code",
@@ -1191,13 +1192,13 @@
    ],
    "id": "3e63d74e16a13193",
    "outputs": [],
-   "execution_count": 20
+   "execution_count": 12
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-18T02:36:56.645308Z",
-     "start_time": "2026-02-18T02:36:56.539056Z"
+     "end_time": "2026-02-28T07:36:48.166270Z",
+     "start_time": "2026-02-28T07:36:48.101508Z"
     }
    },
    "cell_type": "code",
@@ -1207,26 +1208,26 @@
    ],
    "id": "6dea8ee6d0965889",
    "outputs": [],
-   "execution_count": 21
+   "execution_count": 13
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-18T02:38:01.056863Z",
-     "start_time": "2026-02-18T02:38:00.798459Z"
+     "end_time": "2026-02-28T07:36:48.935577Z",
+     "start_time": "2026-02-28T07:36:48.739747Z"
     }
    },
    "cell_type": "code",
    "source": "final_df = pd.concat([combined_df, merged_df], axis = 1)",
    "id": "830ffd454d7d6e0d",
    "outputs": [],
-   "execution_count": 22
+   "execution_count": 14
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T18:57:51.715457Z",
-     "start_time": "2026-02-17T18:57:51.684247Z"
+     "end_time": "2026-02-28T07:36:49.707248Z",
+     "start_time": "2026-02-28T07:36:49.688278Z"
     }
    },
    "cell_type": "code",
@@ -1309,12 +1310,12 @@
        "</div>"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 22
+   "execution_count": 15
   },
   {
    "metadata": {
@@ -1369,8 +1370,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-18T03:33:17.481456Z",
-     "start_time": "2026-02-18T03:33:16.869895Z"
+     "end_time": "2026-02-28T07:36:56.729594Z",
+     "start_time": "2026-02-28T07:36:56.383260Z"
     }
    },
    "cell_type": "code",
@@ -1380,7 +1381,7 @@
    ],
    "id": "caa83b2458af2b80",
    "outputs": [],
-   "execution_count": 85
+   "execution_count": 16
   },
   {
    "metadata": {},
@@ -1410,8 +1411,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-18T03:34:48.610827Z",
-     "start_time": "2026-02-18T03:34:48.594505Z"
+     "end_time": "2026-02-28T08:19:32.385979Z",
+     "start_time": "2026-02-28T08:19:32.371927Z"
     }
    },
    "cell_type": "code",
@@ -1444,20 +1445,22 @@
     "            optimizer.step()\n",
     "\n",
     "            train_loss += loss.item() #convert tensor to float\n",
+    "        train_loss/=len(train_loader) #average losses over batches\n",
     "        print(f\"Epoch {epoch + 1}/{epochs}, Train Loss: {train_loss:.4f}\")\n",
     "        train_losses.append(train_loss)\n",
     "\n",
-    "    # testing loop\n",
-    "    model.eval()\n",
-    "    test_loss = 0\n",
-    "    with torch.no_grad():\n",
-    "        for x_batch, y_batch in test_loader:\n",
-    "            x_batch = x_batch.to(device)\n",
-    "            y_batch = y_batch.to(device)\n",
-    "            optimizer.zero_grad()\n",
-    "            predictions = model(x_batch)\n",
-    "            loss = criterion(predictions, y_batch)\n",
-    "            test_loss += loss.item()\n",
+    "        # testing loop\n",
+    "        model.eval()\n",
+    "        test_loss = 0\n",
+    "        with torch.no_grad():\n",
+    "            for x_batch, y_batch in test_loader:\n",
+    "                x_batch = x_batch.to(device)\n",
+    "                y_batch = y_batch.to(device)\n",
+    "                optimizer.zero_grad()\n",
+    "                predictions = model(x_batch)\n",
+    "                loss = criterion(predictions, y_batch)\n",
+    "                test_loss += loss.item()\n",
+    "        test_loss/=len(test_loader)\n",
     "        print(f\"Epoch {epoch + 1}/{epochs}, Test Loss: {test_loss:.4f}\")\n",
     "        test_losses.append(test_loss)\n",
     "\n",
@@ -1465,13 +1468,73 @@
    ],
    "id": "329e1f0797e61e90",
    "outputs": [],
-   "execution_count": 86
+   "execution_count": 49
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "realised that brake pressed although told to be continous is actually not (either 0 or 1). this makes it harder for the rnn to truly capture they dynamics.",
+   "id": "90141b7cb2d4e96a"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-28T08:19:33.429440Z",
+     "start_time": "2026-02-28T08:19:33.398783Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "state_cols = ['0_x', '0_y']\n",
+    "control_cols = ['mech_brake_pressed', 'accel_position']\n",
+    "print(train_dataset[1].isna().sum())"
+   ],
+   "id": "1a2bef66c43d7650",
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'tuple' object has no attribute 'isna'",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mAttributeError\u001B[39m                            Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[50]\u001B[39m\u001B[32m, line 3\u001B[39m\n\u001B[32m      1\u001B[39m state_cols = [\u001B[33m'\u001B[39m\u001B[33m0_x\u001B[39m\u001B[33m'\u001B[39m, \u001B[33m'\u001B[39m\u001B[33m0_y\u001B[39m\u001B[33m'\u001B[39m]\n\u001B[32m      2\u001B[39m control_cols = [\u001B[33m'\u001B[39m\u001B[33mmech_brake_pressed\u001B[39m\u001B[33m'\u001B[39m, \u001B[33m'\u001B[39m\u001B[33maccel_position\u001B[39m\u001B[33m'\u001B[39m]\n\u001B[32m----> \u001B[39m\u001B[32m3\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[43mtrain_dataset\u001B[49m\u001B[43m[\u001B[49m\u001B[32;43m1\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m.\u001B[49m\u001B[43misna\u001B[49m().sum())\n",
+      "\u001B[31mAttributeError\u001B[39m: 'tuple' object has no attribute 'isna'"
+     ]
+    }
+   ],
+   "execution_count": 50
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-28T08:19:35.145940Z",
+     "start_time": "2026-02-28T08:19:35.117103Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "print(train_dataset[0].describe())  # should be roughly mean=0, std=1 after scaling",
+   "id": "167dd9f1f422fdfc",
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'tuple' object has no attribute 'describe'",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mAttributeError\u001B[39m                            Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[51]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[43mtrain_dataset\u001B[49m\u001B[43m[\u001B[49m\u001B[32;43m0\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m.\u001B[49m\u001B[43mdescribe\u001B[49m())  \u001B[38;5;66;03m# should be roughly mean=0, std=1 after scaling\u001B[39;00m\n",
+      "\u001B[31mAttributeError\u001B[39m: 'tuple' object has no attribute 'describe'"
+     ]
+    }
+   ],
+   "execution_count": 51
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-18T03:35:09.065139Z",
-     "start_time": "2026-02-18T03:35:09.048487Z"
+     "end_time": "2026-02-28T08:19:36.009757Z",
+     "start_time": "2026-02-28T08:19:36.003612Z"
     }
    },
    "cell_type": "code",
@@ -1480,62 +1543,257 @@
     "state   = [\"0_x\", \"0_y\"]\n",
     "control = [\"mech_brake_pressed\", \"accel_position\"]\n",
     "seq_length = 100\n",
-    "input_size = 6\n",
+    "input_size = len(state)\n",
     "output_size = len(control)\n",
     "\n",
     "\n",
-    "hidden_size = 32      # was 256\n",
+    "hidden_size = 64     # was 256\n",
     "num_layers  = 2\n",
     "model = RNN(input_size, hidden_size, num_layers, seq_length, output_size).to(device)\n",
     "\n"
    ],
    "id": "f9712a97111555d4",
    "outputs": [],
-   "execution_count": 89
+   "execution_count": 52
   },
   {
    "metadata": {
-    "jupyter": {
-     "is_executing": true
-    },
     "ExecuteTime": {
-     "start_time": "2026-02-18T03:35:09.622503Z"
+     "end_time": "2026-02-28T08:23:45.296250Z",
+     "start_time": "2026-02-28T08:19:39.348922Z"
     }
    },
    "cell_type": "code",
-   "source": "train_model(model, train_loader, test_loader, epochs = 50)",
-   "id": "395f2702e2f5034d",
+   "source": "train_model(model, train_loader, test_loader, epochs = 30)",
+   "id": "83eb0a191001c256",
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/50, Train Loss: 71.0480\n",
-      "Epoch 2/50, Train Loss: 59.8627\n",
-      "Epoch 3/50, Train Loss: 58.4473\n",
-      "Epoch 4/50, Train Loss: 57.3073\n",
-      "Epoch 5/50, Train Loss: 56.6901\n",
-      "Epoch 6/50, Train Loss: 56.0367\n",
-      "Epoch 7/50, Train Loss: 55.4903\n",
-      "Epoch 8/50, Train Loss: 55.6834\n",
-      "Epoch 9/50, Train Loss: 55.4839\n",
-      "Epoch 10/50, Train Loss: 55.2013\n",
-      "Epoch 11/50, Train Loss: 54.9586\n",
-      "Epoch 12/50, Train Loss: 55.8387\n",
-      "Epoch 13/50, Train Loss: 54.8928\n",
-      "Epoch 14/50, Train Loss: 54.4527\n",
-      "Epoch 15/50, Train Loss: 54.9433\n",
-      "Epoch 16/50, Train Loss: 55.9105\n"
+      "Epoch 1/30, Train Loss: 0.7140\n",
+      "Epoch 1/30, Test Loss: nan\n",
+      "Epoch 2/30, Train Loss: 0.6909\n",
+      "Epoch 2/30, Test Loss: nan\n",
+      "Epoch 3/30, Train Loss: 0.6881\n",
+      "Epoch 3/30, Test Loss: nan\n",
+      "Epoch 4/30, Train Loss: 0.6860\n",
+      "Epoch 4/30, Test Loss: nan\n",
+      "Epoch 5/30, Train Loss: 0.6841\n",
+      "Epoch 5/30, Test Loss: nan\n",
+      "Epoch 6/30, Train Loss: 0.6846\n",
+      "Epoch 6/30, Test Loss: nan\n",
+      "Epoch 7/30, Train Loss: 0.6843\n",
+      "Epoch 7/30, Test Loss: nan\n",
+      "Epoch 8/30, Train Loss: 0.6831\n",
+      "Epoch 8/30, Test Loss: nan\n",
+      "Epoch 9/30, Train Loss: 0.6834\n",
+      "Epoch 9/30, Test Loss: nan\n",
+      "Epoch 10/30, Train Loss: 0.6828\n",
+      "Epoch 10/30, Test Loss: nan\n",
+      "Epoch 11/30, Train Loss: 0.6823\n",
+      "Epoch 11/30, Test Loss: nan\n",
+      "Epoch 12/30, Train Loss: 0.6829\n",
+      "Epoch 12/30, Test Loss: nan\n",
+      "Epoch 13/30, Train Loss: 0.6830\n",
+      "Epoch 13/30, Test Loss: nan\n",
+      "Epoch 14/30, Train Loss: 0.6824\n",
+      "Epoch 14/30, Test Loss: nan\n",
+      "Epoch 15/30, Train Loss: 0.6822\n",
+      "Epoch 15/30, Test Loss: nan\n",
+      "Epoch 16/30, Train Loss: 0.7040\n",
+      "Epoch 16/30, Test Loss: nan\n",
+      "Epoch 17/30, Train Loss: 0.6826\n",
+      "Epoch 17/30, Test Loss: nan\n",
+      "Epoch 18/30, Train Loss: 0.6832\n",
+      "Epoch 18/30, Test Loss: nan\n",
+      "Epoch 19/30, Train Loss: 0.6821\n",
+      "Epoch 19/30, Test Loss: nan\n",
+      "Epoch 20/30, Train Loss: 0.7053\n",
+      "Epoch 20/30, Test Loss: nan\n",
+      "Epoch 21/30, Train Loss: 0.6885\n",
+      "Epoch 21/30, Test Loss: nan\n",
+      "Epoch 22/30, Train Loss: 0.6993\n",
+      "Epoch 22/30, Test Loss: nan\n",
+      "Epoch 23/30, Train Loss: 0.6861\n",
+      "Epoch 23/30, Test Loss: nan\n",
+      "Epoch 24/30, Train Loss: 0.6858\n",
+      "Epoch 24/30, Test Loss: nan\n",
+      "Epoch 25/30, Train Loss: 0.6806\n",
+      "Epoch 25/30, Test Loss: nan\n",
+      "Epoch 26/30, Train Loss: 0.6857\n",
+      "Epoch 26/30, Test Loss: nan\n",
+      "Epoch 27/30, Train Loss: 0.6816\n",
+      "Epoch 27/30, Test Loss: nan\n",
+      "Epoch 28/30, Train Loss: 0.6811\n",
+      "Epoch 28/30, Test Loss: nan\n",
+      "Epoch 29/30, Train Loss: 0.6846\n",
+      "Epoch 29/30, Test Loss: nan\n",
+      "Epoch 30/30, Train Loss: 0.6816\n",
+      "Epoch 30/30, Test Loss: nan\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
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+       "  nan])"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
-   "execution_count": null
+   "execution_count": 53
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-18T03:20:31.932299Z",
-     "start_time": "2026-02-18T03:20:31.919336Z"
+     "end_time": "2026-02-28T07:42:10.906806Z",
+     "start_time": "2026-02-28T07:42:10.896753Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "\n",
+    "def plot_control_trajectory(model, test_dataset, state_cols, control_cols, sample_idx, scaler):\n",
+    "    model.eval()\n",
+    "\n",
+    "    # 1. Get the single sample for inference\n",
+    "    x_input, y_target = test_dataset[sample_idx]\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        device = next(model.parameters()).device\n",
+    "        x_input_device = x_input.to(device).unsqueeze(0)\n",
+    "        y_pred = model(x_input_device).squeeze(0).cpu().numpy()\n",
+    "\n",
+    "    y_target = y_target.numpy()\n",
+    "\n",
+    "    # 2. Extract the full sequence of input states directly from the dataset\n",
+    "    # We need this because x_input only holds the start and end states!\n",
+    "    raw_idx = test_dataset.indices[sample_idx]\n",
+    "    seq_len = test_dataset.seq_len\n",
+    "    states_seq = test_dataset.states[raw_idx : raw_idx + seq_len].numpy()\n",
+    "\n",
+    "    # 3. Manually Unscale the Data\n",
+    "    # The scaler was fit on [states + controls]. We slice the means and scales to match.\n",
+    "    num_states = len(state_cols)\n",
+    "\n",
+    "    state_means = scaler.mean_[:num_states]\n",
+    "    state_scales = scaler.scale_[:num_states]\n",
+    "    control_means = scaler.mean_[num_states:]\n",
+    "    control_scales = scaler.scale_[num_states:]\n",
+    "\n",
+    "    # Apply inverse transform: original = scaled * scale + mean\n",
+    "    y_target_unscaled = y_target * control_scales + control_means\n",
+    "    y_pred_unscaled = y_pred * control_scales + control_means\n",
+    "    states_seq_unscaled = states_seq * state_scales + state_means\n",
+    "\n",
+    "    # 4. Plotting\n",
+    "    time_steps = np.arange(0, seq_len * 0.1, 0.1) # 0.1s granularity\n",
+    "\n",
+    "    # --- Plot Controls ---\n",
+    "    fig, ax = plt.subplots(len(control_cols), 1, figsize=(10, 6), sharex=True)\n",
+    "    if len(control_cols) == 1: ax = [ax] # Handle single control edge case\n",
+    "\n",
+    "    for i, col in enumerate(control_cols):\n",
+    "        ax[i].plot(time_steps, y_target_unscaled[:, i], 'g-', label='Actual (Driver)')\n",
+    "        ax[i].plot(time_steps, y_pred_unscaled[:, i], 'r--', label='Predicted (RNN)')\n",
+    "        ax[i].set_ylabel(f'{col}')\n",
+    "        ax[i].legend()\n",
+    "        ax[i].grid(True)\n",
+    "\n",
+    "    ax[-1].set_xlabel(\"Time (seconds)\")\n",
+    "    plt.suptitle(f\"Control Sequence for Sample {sample_idx}\")\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "    # --- Plot States (e.g., Speed) ---\n",
+    "    fig1, ax1 = plt.subplots(len(state_cols), 1, figsize=(10, 6), sharex=True)\n",
+    "    if len(state_cols) == 1: ax1 = [ax1] # Handle single state edge case\n",
+    "\n",
+    "    for i, col in enumerate(state_cols):\n",
+    "        ax1[i].plot(time_steps, states_seq_unscaled[:, i], 'b-', label=f'Input Trajectory')\n",
+    "        ax1[i].set_ylabel(f'{col}')\n",
+    "        ax1[i].legend()\n",
+    "        ax1[i].grid(True)\n",
+    "\n",
+    "    ax1[-1].set_xlabel(\"Time (seconds)\")\n",
+    "    plt.suptitle(f\"Input States Sequence for Sample {sample_idx}\")\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ],
+   "id": "ed7e024db3ba2a13",
+   "outputs": [],
+   "execution_count": 20
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-28T07:42:12.166847Z",
+     "start_time": "2026-02-28T07:42:12.160614Z"
     }
    },
    "cell_type": "code",
@@ -1581,59 +1839,397 @@
    ],
    "id": "20dfb9bc00550510",
    "outputs": [],
-   "execution_count": 77
+   "execution_count": 21
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-18T03:26:16.735571Z",
-     "start_time": "2026-02-18T03:26:16.452676Z"
+     "end_time": "2026-02-28T07:47:26.758332Z",
+     "start_time": "2026-02-28T07:47:26.750820Z"
     }
    },
    "cell_type": "code",
-   "source": "plot_control_trajectory(model, test_dataset,state_cols = ['0_x', '0_y'], control_cols = ['mech_brake_pressed', 'accel_position'], sample_idx = 10)",
-   "id": "ee12fb52648c9f04",
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import torch\n",
+    "import numpy as np\n",
+    "\n",
+    "def visualise_predictions(model, dataset, scaler, state_cols, control_cols, sample_idx=0, device=\"cpu\"):\n",
+    "    model.eval()\n",
+    "\n",
+    "    x, y_true = dataset[sample_idx]\n",
+    "    x_input = x.unsqueeze(0).to(device)  # add batch dim\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        y_pred = model(x_input).squeeze(0).cpu().numpy()  # [seq_len, n_controls]\n",
+    "\n",
+    "    y_true = y_true.numpy()  # [seq_len, n_controls]\n",
+    "\n",
+    "    # x is [seq_len, n_states] — extract state sequence for plotting\n",
+    "    x_np = x.numpy()  # [seq_len, n_states]\n",
+    "\n",
+    "    # unscale states for interpretable axis values\n",
+    "    # scaler was fit on state_cols + control_cols so we need to inverse correctly\n",
+    "    n_states = len(state_cols)\n",
+    "    n_controls = len(control_cols)\n",
+    "    seq_len = y_true.shape[0]\n",
+    "    timesteps = np.arange(seq_len) * 0.1  # 0.1s granularity\n",
+    "\n",
+    "    fig, axes = plt.subplots(2 + n_controls, 1, figsize=(12, 4 * (2 + n_controls)), sharex=True)\n",
+    "\n",
+    "    # plot states (speed, position)\n",
+    "    state_labels = state_cols\n",
+    "    for i, label in enumerate(state_labels):\n",
+    "        axes[i].plot(timesteps, x_np[:, i], label=label, color=\"steelblue\")\n",
+    "        axes[i].set_ylabel(label)\n",
+    "        axes[i].legend()\n",
+    "        axes[i].grid(True)\n",
+    "\n",
+    "    # plot controls — predicted vs real\n",
+    "    control_labels = control_cols\n",
+    "    colors_pred = [\"tomato\", \"darkorange\"]\n",
+    "    colors_true = [\"steelblue\", \"seagreen\"]\n",
+    "\n",
+    "    for i, label in enumerate(control_labels):\n",
+    "        ax = axes[n_states + i]\n",
+    "        ax.plot(timesteps, y_true[:, i], label=f\"{label} (actual)\", color=colors_true[i], linewidth=2)\n",
+    "        ax.plot(timesteps, y_pred[:, i], label=f\"{label} (predicted)\", color=colors_pred[i], linestyle=\"--\", linewidth=2)\n",
+    "        ax.set_ylabel(label)\n",
+    "        ax.legend()\n",
+    "        ax.grid(True)\n",
+    "\n",
+    "    axes[-1].set_xlabel(\"Time (s)\")\n",
+    "    fig.suptitle(f\"Predicted vs Actual Controls — Sample {sample_idx}\", fontsize=14)\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ],
+   "id": "14c5d4347da416ef",
+   "outputs": [],
+   "execution_count": 30
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-28T08:08:43.185547Z",
+     "start_time": "2026-02-28T08:08:43.179918Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# inverse transform requires dummy array of full feature width\n",
+    "def inverse_scale_controls(scaler, control_array, state_cols, control_cols):\n",
+    "    n_states = len(state_cols)\n",
+    "    dummy = np.zeros((len(control_array), len(state_cols) + len(control_cols)))\n",
+    "    dummy[:, n_states:] = control_array\n",
+    "    return scaler.inverse_transform(dummy)[:, n_states:]\n"
+   ],
+   "id": "ff93632196839e77",
+   "outputs": [],
+   "execution_count": 35
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-28T08:30:05.184764Z",
+     "start_time": "2026-02-28T08:30:05.166507Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "def plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx):\n",
+    "    model.eval()\n",
+    "    x_input, y_target = test_dataset[sample_idx]\n",
+    "\n",
+    "# x_input should be [seq_len, n_states]\n",
+    "    print(x_input.shape)  # confirm this first\n",
+    "\n",
+    "    x_np = x_input.numpy()  # [seq_len, n_states]\n",
+    "\n",
+    "    # derive time axes from actual array shapes, not seq_len variable\n",
+    "    time_controls = np.arange(y_target.shape[0]) * 0.1\n",
+    "    time_states   = np.arange(x_np.shape[0]) * 0.1\n",
+    "    x_input, y_target = test_dataset[sample_idx]\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        device = next(model.parameters()).device\n",
+    "        x_tensor = x_input.to(device).unsqueeze(0)\n",
+    "        y_pred = model(x_tensor).squeeze(0).cpu().numpy()\n",
+    "\n",
+    "    y_target = y_target.numpy()\n",
+    "    x_np = x_input.numpy()  # [seq_len, n_states]\n",
+    "\n",
+    "    # inverse transform controls\n",
+    "    # scaler was fit on state_cols + control_cols so controls start at index n_states\n",
+    "    n_states = len(state_cols)\n",
+    "    n_controls = len(control_cols)\n",
+    "    seq_len = y_target.shape[0]\n",
+    "\n",
+    "    def unscale_controls(arr):\n",
+    "        # arr: [seq_len, n_controls]\n",
+    "        dummy = np.zeros((seq_len, n_states + n_controls))\n",
+    "        dummy[:, n_states:] = arr\n",
+    "        return scaler.inverse_transform(dummy)[:, n_states:]\n",
+    "\n",
+    "    def unscale_states(arr):\n",
+    "        n = arr.shape[0]\n",
+    "        dummy = np.zeros((n, n_states + n_controls))\n",
+    "        dummy[:, :n_states] = arr\n",
+    "        return scaler.inverse_transform(dummy)[:, :n_states]\n",
+    "\n",
+    "    # def unscale_states(arr):\n",
+    "    #     # arr: [seq_len, n_states]\n",
+    "    #     dummy = np.zeros((seq_len, n_states + n_controls))\n",
+    "    #     dummy[:, :n_states] = arr\n",
+    "    #     return scaler.inverse_transform(dummy)[:, :n_states]\n",
+    "\n",
+    "    y_target_unscaled = unscale_controls(y_target)\n",
+    "    y_pred_unscaled   = unscale_controls(y_pred)\n",
+    "    x_unscaled        = unscale_states(x_np)\n",
+    "\n",
+    "    time_controls = np.arange(seq_len) * 0.1\n",
+    "    time_states   = np.arange(x_np.shape[0]) * 0.1\n",
+    "\n",
+    "    # plot controls\n",
+    "    fig, axes = plt.subplots(n_controls, 1, figsize=(10, 4 * n_controls), sharex=True)\n",
+    "    if n_controls == 1:\n",
+    "        axes = [axes]\n",
+    "\n",
+    "    for i, col in enumerate(control_cols):\n",
+    "        axes[i].plot(time_controls, y_target_unscaled[:, i], 'g-', label='Actual (Driver)')\n",
+    "        axes[i].plot(time_controls, y_pred_unscaled[:, i], 'r--', label='Predicted (RNN)')\n",
+    "        axes[i].set_ylabel(col)\n",
+    "        axes[i].legend()\n",
+    "        axes[i].grid(True)\n",
+    "\n",
+    "    axes[-1].set_xlabel(\"Time (seconds)\")\n",
+    "    plt.suptitle(f\"Control Trajectory — Sample {sample_idx}\")\n",
+    "    plt.tight_layout()\n",
+    "\n",
+    "    # plot states\n",
+    "    fig2, axes2 = plt.subplots(n_states, 1, figsize=(10, 4 * n_states), sharex=True)\n",
+    "    if n_states == 1:\n",
+    "        axes2 = [axes2]\n",
+    "    print(x_np.shape)        # should be [seq_len, n_states]\n",
+    "    print(x_unscaled.shape)  # should match\n",
+    "    print(time_states.shape)\n",
+    "    for i, col in enumerate(state_cols):\n",
+    "        axes2[i].plot(time_states, x_unscaled[:, i], 'b-', label=col)\n",
+    "        axes2[i].set_ylabel(col)\n",
+    "        axes2[i].legend()\n",
+    "        axes2[i].grid(True)\n",
+    "\n",
+    "    axes2[-1].set_xlabel(\"Time (seconds)\")\n",
+    "    plt.suptitle(f\"Input State Sequence — Sample {sample_idx}\")\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ],
+   "id": "55763d08b5868893",
+   "outputs": [],
+   "execution_count": 56
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-28T08:30:06.758367Z",
+     "start_time": "2026-02-28T08:30:06.091164Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx=10)\n",
+    "plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx=150)"
+   ],
+   "id": "3460de89ee1321c7",
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2])\n",
+      "(2,)\n",
+      "(2, 2)\n",
+      "(2,)\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "<Figure size 1000x600 with 2 Axes>"
+       "<Figure size 1000x800 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "ename": "IndexError",
-     "evalue": "too many indices for array: array is 1-dimensional, but 2 were indexed",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mIndexError\u001B[39m                                Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[83]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[43mplot_control_trajectory\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtest_dataset\u001B[49m\u001B[43m,\u001B[49m\u001B[43mstate_cols\u001B[49m\u001B[43m \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m0_x\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m0_y\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcontrol_cols\u001B[49m\u001B[43m \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43mmech_brake_pressed\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43maccel_position\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msample_idx\u001B[49m\u001B[43m \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[32;43m10\u001B[39;49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[82]\u001B[39m\u001B[32m, line 48\u001B[39m, in \u001B[36mplot_control_trajectory\u001B[39m\u001B[34m(model, test_dataset, state_cols, control_cols, sample_idx)\u001B[39m\n\u001B[32m     44\u001B[39m position_idx = state_cols.index(\u001B[33m\"\u001B[39m\u001B[33m0_y\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m     46\u001B[39m time_steps_input = np.arange(\u001B[38;5;28mlen\u001B[39m(x_input)) * dt\n\u001B[32m---> \u001B[39m\u001B[32m48\u001B[39m ax2[\u001B[32m0\u001B[39m].plot(time_steps_input, \u001B[43mx_input\u001B[49m\u001B[43m[\u001B[49m\u001B[43m:\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mspeed_idx\u001B[49m\u001B[43m]\u001B[49m, \u001B[33m'\u001B[39m\u001B[33mb-\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m     49\u001B[39m ax2[\u001B[32m0\u001B[39m].set_ylabel(\u001B[33m\"\u001B[39m\u001B[33mSpeed\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m     50\u001B[39m ax2[\u001B[32m0\u001B[39m].grid(\u001B[38;5;28;01mTrue\u001B[39;00m)\n",
-      "\u001B[31mIndexError\u001B[39m: too many indices for array: array is 1-dimensional, but 2 were indexed"
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2])\n",
+      "(2,)\n",
+      "(2, 2)\n",
+      "(2,)\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<Figure size 1000x600 with 2 Axes>"
+       "<Figure size 1000x800 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 83
+   "execution_count": 57
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-28T08:29:53.033620Z",
+     "start_time": "2026-02-28T08:29:53.017147Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "def plot_control_trajectory_extended(model, test_dataset, scaler, state_cols, control_cols, n_samples=10, start_idx=0):\n",
+    "    model.eval()\n",
+    "    device = next(model.parameters()).device\n",
+    "\n",
+    "    all_states   = []\n",
+    "    all_y_true   = []\n",
+    "    all_y_pred   = []\n",
+    "\n",
+    "    n_states   = len(state_cols)\n",
+    "    n_controls = len(control_cols)\n",
+    "\n",
+    "    def unscale_controls(arr):\n",
+    "        n = arr.shape[0]\n",
+    "        dummy = np.zeros((n, n_states + n_controls))\n",
+    "        dummy[:, n_states:] = arr\n",
+    "        return scaler.inverse_transform(dummy)[:, n_states:]\n",
+    "\n",
+    "    def unscale_states(arr):\n",
+    "        n = arr.shape[0]\n",
+    "        dummy = np.zeros((n, n_states + n_controls))\n",
+    "        dummy[:, :n_states] = arr\n",
+    "        return scaler.inverse_transform(dummy)[:, :n_states]\n",
+    "\n",
+    "    for i in range(start_idx, start_idx + n_samples):\n",
+    "        x_input, y_target = test_dataset[i]\n",
+    "\n",
+    "        with torch.no_grad():\n",
+    "            x_tensor = x_input.unsqueeze(0).to(device)\n",
+    "            y_pred = model(x_tensor).squeeze(0).cpu().numpy()\n",
+    "\n",
+    "        all_states.append(x_input.numpy())\n",
+    "        all_y_true.append(y_target.numpy())\n",
+    "        all_y_pred.append(y_pred)\n",
+    "\n",
+    "    # concatenate along time axis\n",
+    "    all_states = np.concatenate(all_states, axis=0)\n",
+    "    all_y_true = np.concatenate(all_y_true, axis=0)\n",
+    "    all_y_pred = np.concatenate(all_y_pred, axis=0)\n",
+    "\n",
+    "    all_states = unscale_states(all_states)\n",
+    "    all_y_true = unscale_controls(all_y_true)\n",
+    "    all_y_pred = unscale_controls(all_y_pred)\n",
+    "\n",
+    "    time_axis = np.arange(len(all_y_true)) * 0.1\n",
+    "\n",
+    "    # plot controls\n",
+    "    fig, axes = plt.subplots(n_controls, 1, figsize=(14, 4 * n_controls), sharex=True)\n",
+    "    if n_controls == 1:\n",
+    "        axes = [axes]\n",
+    "\n",
+    "    for i, col in enumerate(control_cols):\n",
+    "        axes[i].plot(time_axis, all_y_true[:, i], 'g-', label='Actual', linewidth=1)\n",
+    "        axes[i].plot(time_axis, all_y_pred[:, i], 'r--', label='Predicted', linewidth=1)\n",
+    "        axes[i].set_ylabel(col)\n",
+    "        axes[i].legend()\n",
+    "        axes[i].grid(True)\n",
+    "\n",
+    "    axes[-1].set_xlabel(\"Time (seconds)\")\n",
+    "    plt.suptitle(f\"Control Trajectory — {n_samples} consecutive samples\")\n",
+    "    plt.tight_layout()\n",
+    "\n",
+    "    # plot states\n",
+    "    time_states = np.arange(len(all_states)) * 0.1\n",
+    "    fig2, axes2 = plt.subplots(n_states, 1, figsize=(14, 4 * n_states), sharex=True)\n",
+    "    if n_states == 1:\n",
+    "        axes2 = [axes2]\n",
+    "\n",
+    "    for i, col in enumerate(state_cols):\n",
+    "        axes2[i].plot(time_states, all_states[:, i], 'b-', label=col, linewidth=1)\n",
+    "        axes2[i].set_ylabel(col)\n",
+    "        axes2[i].legend()\n",
+    "        axes2[i].grid(True)\n",
+    "\n",
+    "    axes2[-1].set_xlabel(\"Time (seconds)\")\n",
+    "    plt.suptitle(f\"Input State Sequence — {n_samples} consecutive samples\")\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ],
+   "id": "81629f965d2585a1",
+   "outputs": [],
+   "execution_count": 54
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-28T08:29:54.168280Z",
+     "start_time": "2026-02-28T08:29:53.762499Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plot_control_trajectory_extended(model, test_dataset, scaler, state_cols, control_cols, n_samples=50, start_idx=0)",
+   "id": "d3204b6b0778da19",
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "could not broadcast input array from shape (100,) into shape (100,2)",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mValueError\u001B[39m                                Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[55]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[43mplot_control_trajectory_extended\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtest_dataset\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mscaler\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstate_cols\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcontrol_cols\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mn_samples\u001B[49m\u001B[43m=\u001B[49m\u001B[32;43m50\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstart_idx\u001B[49m\u001B[43m=\u001B[49m\u001B[32;43m0\u001B[39;49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[54]\u001B[39m\u001B[32m, line 40\u001B[39m, in \u001B[36mplot_control_trajectory_extended\u001B[39m\u001B[34m(model, test_dataset, scaler, state_cols, control_cols, n_samples, start_idx)\u001B[39m\n\u001B[32m     37\u001B[39m all_y_true = np.concatenate(all_y_true, axis=\u001B[32m0\u001B[39m)\n\u001B[32m     38\u001B[39m all_y_pred = np.concatenate(all_y_pred, axis=\u001B[32m0\u001B[39m)\n\u001B[32m---> \u001B[39m\u001B[32m40\u001B[39m all_states = \u001B[43munscale_states\u001B[49m\u001B[43m(\u001B[49m\u001B[43mall_states\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m     41\u001B[39m all_y_true = unscale_controls(all_y_true)\n\u001B[32m     42\u001B[39m all_y_pred = unscale_controls(all_y_pred)\n",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[54]\u001B[39m\u001B[32m, line 21\u001B[39m, in \u001B[36mplot_control_trajectory_extended.<locals>.unscale_states\u001B[39m\u001B[34m(arr)\u001B[39m\n\u001B[32m     19\u001B[39m n = arr.shape[\u001B[32m0\u001B[39m]\n\u001B[32m     20\u001B[39m dummy = np.zeros((n, n_states + n_controls))\n\u001B[32m---> \u001B[39m\u001B[32m21\u001B[39m \u001B[43mdummy\u001B[49m\u001B[43m[\u001B[49m\u001B[43m:\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m:\u001B[49m\u001B[43mn_states\u001B[49m\u001B[43m]\u001B[49m = arr\n\u001B[32m     22\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m scaler.inverse_transform(dummy)[:, :n_states]\n",
+      "\u001B[31mValueError\u001B[39m: could not broadcast input array from shape (100,) into shape (100,2)"
+     ]
+    }
+   ],
+   "execution_count": 55
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-18T03:13:50.268505Z",
-     "start_time": "2026-02-18T03:13:50.246914Z"
+     "end_time": "2026-02-28T07:42:34.096055Z",
+     "start_time": "2026-02-28T07:42:34.086115Z"
     }
    },
    "cell_type": "code",
@@ -1677,13 +2273,13 @@
    ],
    "id": "9e3d7d9c0f207973",
    "outputs": [],
-   "execution_count": 67
+   "execution_count": 23
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-18T03:13:52.267870Z",
-     "start_time": "2026-02-18T03:13:51.085685Z"
+     "end_time": "2026-02-21T18:58:56.168643Z",
+     "start_time": "2026-02-21T18:58:54.532791Z"
     }
    },
    "cell_type": "code",
@@ -1694,7 +2290,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_19904\\252557315.py:31: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n",
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_20608\\252557315.py:31: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n",
       "  pivot_table = df_err.pivot_table(index='Speed Bin', columns='Target Bin', values='MAE', aggfunc='mean')\n"
      ]
     },
@@ -1703,13 +2299,13 @@
       "text/plain": [
        "<Figure size 1000x800 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 68
+   "execution_count": 25
   },
   {
    "metadata": {},
diff --git a/array_temp/RNN.py b/array_temp/RNN.py
index c0a0e7b..21dd9fb 100644
--- a/array_temp/RNN.py
+++ b/array_temp/RNN.py
@@ -44,5 +44,6 @@ def forward(self, x):
         #decode the hidden state of t
         # predicted is a series of controls
         out = self.fc(out)
+        #out shape: [batch_size, seq_length, hidden_size] - one control per timestamp
         return out
 
diff --git a/array_temp/RNN_Dataset.py b/array_temp/RNN_Dataset.py
index f5afb4c..2dc9221 100644
--- a/array_temp/RNN_Dataset.py
+++ b/array_temp/RNN_Dataset.py
@@ -27,15 +27,16 @@ def __len__(self):
     def __getitem__(self, index):
         idx = self.indices[index] #index of the first timestep of seq
 
-        #input: current states + controls
-
-        current_state = self.states[idx]
-        target_state = self.states[idx +  self.seq_len] # state at t + seq_len
-        current_control = self.controls[idx]
-        x_seq = torch.cat([current_state, target_state, current_control], dim=0) #concatenate along feature dimension
-
-        #output: sequence of next controls to get from current to target
+        #input: current states +  target state
+
+        current_state = self.states[idx:idx+self.seq_len] # speed+ position
+        #target_state = self.states[idx +  self.seq_len] # state at t + seq_len
+        #current_control = self.controls[idx] #mech_brake + accelerator position
+        x_seq = torch.cat([current_state], dim=0) #concatenate along feature dimension
+        #x_seq = torch.cat([current_state])
+        #output: next controls to get from current to target (singular control at time of target state
         y_seq = self.controls[idx:idx + self.seq_len]
+
         return x_seq, y_seq
 
 

From 1db9cb088b9b8ed6cdb76cc2d074f84426462f3f Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Sat, 28 Feb 2026 08:12:18 -0800
Subject: [PATCH 20/49] rnn architecture + training

---
 array_temp/Control_Model.ipynb  | 1078 ++++++++++++++++---------------
 array_temp/DataPreprocessing.py |    1 +
 array_temp/RNN.py               |    4 +-
 3 files changed, 569 insertions(+), 514 deletions(-)

diff --git a/array_temp/Control_Model.ipynb b/array_temp/Control_Model.ipynb
index 9063325..554b4b4 100644
--- a/array_temp/Control_Model.ipynb
+++ b/array_temp/Control_Model.ipynb
@@ -31,8 +31,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-21T18:43:13.687189Z",
-     "start_time": "2026-02-21T18:43:13.680673Z"
+     "end_time": "2026-02-28T14:59:46.360584Z",
+     "start_time": "2026-02-28T14:59:46.339368Z"
     }
    },
    "cell_type": "code",
@@ -44,8 +44,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T07:35:45.226560Z",
-     "start_time": "2026-02-28T07:35:40.607802Z"
+     "end_time": "2026-02-28T15:33:26.313268Z",
+     "start_time": "2026-02-28T15:33:26.107174Z"
     }
    },
    "cell_type": "code",
@@ -60,7 +60,7 @@
    ],
    "id": "8672b9d1bf8a74aa",
    "outputs": [],
-   "execution_count": 1
+   "execution_count": 3
   },
   {
    "metadata": {
@@ -125,8 +125,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-21T18:45:30.183672Z",
-     "start_time": "2026-02-21T18:45:30.050528Z"
+     "end_time": "2026-02-28T15:33:29.842089Z",
+     "start_time": "2026-02-28T15:33:29.790741Z"
     }
    },
    "cell_type": "code",
@@ -149,14 +149,61 @@
     "        dill.dump(data, f)\n"
    ],
    "id": "9f8652589e173b01",
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'os' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mNameError\u001B[39m                                 Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[4]\u001B[39m\u001B[32m, line 3\u001B[39m\n\u001B[32m      1\u001B[39m \u001B[38;5;66;03m# save collected data\u001B[39;00m\n\u001B[32m----> \u001B[39m\u001B[32m3\u001B[39m out_dir = \u001B[43mos\u001B[49m.path.join(\u001B[33m\"\u001B[39m\u001B[33m../../array_temp\u001B[39m\u001B[33m\"\u001B[39m, \u001B[33m\"\u001B[39m\u001B[33mdata\u001B[39m\u001B[33m\"\u001B[39m, \u001B[33m\"\u001B[39m\u001B[33mcontrol_state_fsgp_2024\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m      4\u001B[39m os.makedirs(out_dir, exist_ok=\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[32m      6\u001B[39m brake_path = os.path.join(out_dir, \u001B[33m\"\u001B[39m\u001B[33mbrake_pressed.bin\u001B[39m\u001B[33m\"\u001B[39m)\n",
+      "\u001B[31mNameError\u001B[39m: name 'os' is not defined"
+     ]
+    }
+   ],
    "execution_count": 4
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T18:57:25.261719Z",
-     "start_time": "2026-02-17T18:57:25.245325Z"
+     "end_time": "2026-02-28T15:33:33.579953Z",
+     "start_time": "2026-02-28T15:33:32.205995Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import os\n",
+    "import dill\n",
+    "\n",
+    "# same directory you used for saving\n",
+    "out_dir = os.path.join(\"../../array_temp\", \"data\", \"control_state_fsgp_2024\")\n",
+    "\n",
+    "brake_path = os.path.join(out_dir, \"brake_pressed.bin\")\n",
+    "accel_path = os.path.join(out_dir, \"acceleration.bin\")\n",
+    "speed_path = os.path.join(out_dir, \"speed_kph.bin\")\n",
+    "\n",
+    "filepaths = [brake_path, accel_path, speed_path]\n",
+    "\n",
+    "loaded_datasets = []\n",
+    "\n",
+    "for filepath in filepaths:\n",
+    "    with open(filepath, \"rb\") as f:\n",
+    "        data = dill.load(f)\n",
+    "        loaded_datasets.append(data)\n",
+    "\n",
+    "# unpack them\n",
+    "mech_brake_pressed, accel_position, speed_kph = loaded_datasets"
+   ],
+   "id": "31de4e6237f9a41e",
+   "outputs": [],
+   "execution_count": 5
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-28T15:33:34.709051Z",
+     "start_time": "2026-02-28T15:33:34.702770Z"
     }
    },
    "cell_type": "code",
@@ -170,7 +217,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_24340\\4023827949.py:1: DeprecationWarning: Please use TimeSeries.period instead of TimeSeries.granularity\n",
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_23684\\4023827949.py:1: DeprecationWarning: Please use TimeSeries.period instead of TimeSeries.granularity\n",
       "  mech_brake_pressed.granularity\n"
      ]
     },
@@ -190,8 +237,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T18:57:26.912402Z",
-     "start_time": "2026-02-17T18:57:25.702431Z"
+     "end_time": "2026-02-28T15:33:36.643098Z",
+     "start_time": "2026-02-28T15:33:35.679992Z"
     }
    },
    "cell_type": "code",
@@ -201,7 +248,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x21386f58770>]"
+       "[<matplotlib.lines.Line2D at 0x1d53049e120>]"
       ]
      },
      "execution_count": 7,
@@ -261,8 +308,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T07:36:19.074137Z",
-     "start_time": "2026-02-28T07:36:19.053304Z"
+     "end_time": "2026-02-28T15:34:12.079935Z",
+     "start_time": "2026-02-28T15:34:12.051910Z"
     }
    },
    "cell_type": "code",
@@ -278,7 +325,7 @@
    ],
    "id": "6a0eed52a654e483",
    "outputs": [],
-   "execution_count": 3
+   "execution_count": 16
   },
   {
    "metadata": {
@@ -565,8 +612,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T07:36:28.520336Z",
-     "start_time": "2026-02-28T07:36:28.503139Z"
+     "end_time": "2026-02-28T15:33:47.938459Z",
+     "start_time": "2026-02-28T15:33:47.907308Z"
     }
    },
    "cell_type": "code",
@@ -868,13 +915,13 @@
    ],
    "id": "2741c957d09edb03",
    "outputs": [],
-   "execution_count": 5
+   "execution_count": 8
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T07:36:30.912417Z",
-     "start_time": "2026-02-28T07:36:29.730320Z"
+     "end_time": "2026-02-28T15:33:49.834096Z",
+     "start_time": "2026-02-28T15:33:48.778100Z"
     }
    },
    "cell_type": "code",
@@ -901,13 +948,13 @@
    ],
    "id": "88a4ae7fb0d75eed",
    "outputs": [],
-   "execution_count": 6
+   "execution_count": 9
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T07:36:31.569352Z",
-     "start_time": "2026-02-28T07:36:31.479252Z"
+     "end_time": "2026-02-28T15:33:51.608513Z",
+     "start_time": "2026-02-28T15:33:51.498626Z"
     }
    },
    "cell_type": "code",
@@ -923,10 +970,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1fc484111c0>]"
+       "[<matplotlib.lines.Line2D at 0x1d53598a240>]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -941,7 +988,7 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 7
+   "execution_count": 10
   },
   {
    "metadata": {},
@@ -965,21 +1012,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-21T18:45:55.003466Z",
-     "start_time": "2026-02-21T18:45:54.997016Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "",
-   "id": "251896367b559590",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-28T07:36:37.414928Z",
-     "start_time": "2026-02-28T07:36:35.888218Z"
+     "end_time": "2026-02-28T15:33:55.157558Z",
+     "start_time": "2026-02-28T15:33:53.238615Z"
     }
    },
    "cell_type": "code",
@@ -993,30 +1027,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Calculating closest GIS indices: 100%|██████████| 1985282/1985282 [00:01<00:00, 1454244.28it/s]\n"
+      "Calculating closest GIS indices: 100%|██████████| 1985282/1985282 [00:01<00:00, 1184076.21it/s]\n"
      ]
     }
    ],
-   "execution_count": 8
+   "execution_count": 11
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T07:36:38.046061Z",
-     "start_time": "2026-02-28T07:36:38.042437Z"
+     "end_time": "2026-02-28T15:33:55.849286Z",
+     "start_time": "2026-02-28T15:33:55.823426Z"
     }
    },
    "cell_type": "code",
    "source": "calculated_speeds, calculated_position = state_array",
    "id": "f4fb71b02da66f3e",
    "outputs": [],
-   "execution_count": 9
+   "execution_count": 12
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T07:36:38.911570Z",
-     "start_time": "2026-02-28T07:36:38.792263Z"
+     "end_time": "2026-02-28T15:33:56.820829Z",
+     "start_time": "2026-02-28T15:33:56.691725Z"
     }
    },
    "cell_type": "code",
@@ -1034,13 +1068,13 @@
    ],
    "id": "a46a29e38c162456",
    "outputs": [],
-   "execution_count": 10
+   "execution_count": 13
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-21T18:46:00.078727Z",
-     "start_time": "2026-02-21T18:45:59.741957Z"
+     "end_time": "2026-02-28T15:33:57.819677Z",
+     "start_time": "2026-02-28T15:33:57.545145Z"
     }
    },
    "cell_type": "code",
@@ -1053,10 +1087,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1c902dc7fe0>]"
+       "[<matplotlib.lines.Line2D at 0x1d5359fd8e0>]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1071,13 +1105,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 12
+   "execution_count": 14
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-21T18:46:01.113524Z",
-     "start_time": "2026-02-21T18:46:00.402175Z"
+     "end_time": "2026-02-28T15:08:39.502040Z",
+     "start_time": "2026-02-28T15:08:38.742067Z"
     }
    },
    "cell_type": "code",
@@ -1090,10 +1124,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1c902e82600>]"
+       "[<matplotlib.lines.Line2D at 0x285ff381220>]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1108,26 +1142,26 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 13
+   "execution_count": 14
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T17:31:51.560686Z",
-     "start_time": "2026-02-14T18:40:00.061991Z"
+     "end_time": "2026-02-28T15:08:42.735959Z",
+     "start_time": "2026-02-28T15:08:42.726601Z"
     }
    },
    "cell_type": "code",
    "source": "#how do i align this with time?",
    "id": "75a36cec8ab0ffb9",
    "outputs": [],
-   "execution_count": 23
+   "execution_count": 15
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T17:38:36.072424Z",
-     "start_time": "2026-02-17T17:38:36.052305Z"
+     "end_time": "2026-02-28T15:08:43.888446Z",
+     "start_time": "2026-02-28T15:08:43.860250Z"
     }
    },
    "cell_type": "code",
@@ -1140,18 +1174,18 @@
        "1985282"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 18
+   "execution_count": 16
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T07:36:46.801270Z",
-     "start_time": "2026-02-28T07:36:46.797614Z"
+     "end_time": "2026-02-28T15:34:17.658043Z",
+     "start_time": "2026-02-28T15:34:17.634871Z"
     }
    },
    "cell_type": "code",
@@ -1167,13 +1201,13 @@
    ],
    "id": "a6707eebb9ccd8c9",
    "outputs": [],
-   "execution_count": 11
+   "execution_count": 17
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T07:36:47.355129Z",
-     "start_time": "2026-02-28T07:36:47.350804Z"
+     "end_time": "2026-02-28T15:34:18.313095Z",
+     "start_time": "2026-02-28T15:34:18.295440Z"
     }
    },
    "cell_type": "code",
@@ -1192,42 +1226,43 @@
    ],
    "id": "3e63d74e16a13193",
    "outputs": [],
-   "execution_count": 12
+   "execution_count": 18
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T07:36:48.166270Z",
-     "start_time": "2026-02-28T07:36:48.101508Z"
+     "end_time": "2026-02-28T15:34:19.043390Z",
+     "start_time": "2026-02-28T15:34:18.968047Z"
     }
    },
    "cell_type": "code",
    "source": [
+    "\n",
     "all_dfs = [df_mech_brake_pressed, df_accel_position]\n",
     "combined_df = combine_dfs([\"mech_brake_pressed\", \"accel_position\"], df_mech_brake_pressed.index, all_dfs)\n"
    ],
    "id": "6dea8ee6d0965889",
    "outputs": [],
-   "execution_count": 13
+   "execution_count": 19
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T07:36:48.935577Z",
-     "start_time": "2026-02-28T07:36:48.739747Z"
+     "end_time": "2026-02-28T15:34:19.801157Z",
+     "start_time": "2026-02-28T15:34:19.572071Z"
     }
    },
    "cell_type": "code",
    "source": "final_df = pd.concat([combined_df, merged_df], axis = 1)",
    "id": "830ffd454d7d6e0d",
    "outputs": [],
-   "execution_count": 14
+   "execution_count": 20
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T07:36:49.707248Z",
-     "start_time": "2026-02-28T07:36:49.688278Z"
+     "end_time": "2026-02-28T15:34:20.538903Z",
+     "start_time": "2026-02-28T15:34:20.516755Z"
     }
    },
    "cell_type": "code",
@@ -1310,28 +1345,31 @@
        "</div>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 15
+   "execution_count": 21
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T17:38:57.248658Z",
-     "start_time": "2026-02-17T17:38:56.777579Z"
+     "end_time": "2026-02-28T15:09:24.850777Z",
+     "start_time": "2026-02-28T15:09:23.754206Z"
     }
    },
    "cell_type": "code",
-   "source": "plt.plot(final_df[\"mech_brake_pressed\"])",
+   "source": [
+    "plt.plot(final_df[\"0_y\"])\n",
+    "plt.plot(final_df[\"0_x\"])"
+   ],
    "id": "42e9fe006fc1b367",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x218aff809e0>]"
+       "[<matplotlib.lines.Line2D at 0x28580f51910>]"
       ]
      },
      "execution_count": 24,
@@ -1343,7 +1381,7 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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UI488Ujp06OB/ZWRkhFzOnDlz5Oqrr5ZTTjlFjj32WHnggQfknXfecUUpCgAATemcoYbQP1RKKU2TApQlS5bIqFGjZN68eQHTS0tLZe/evdKrV69Gl1FbWyurV6+WESNG+KcNHTpUqqurZf369dFuEgAAjnfwwD7pP2+U9Jx9tN2b4swqnsmTJ4ecrkpPPB6P/O1vf5Ovv/5a2rZtK9dcc01AdY/PwYMHzWqijh07Ht6QlBTzO3v27LG1x1dTl+f7nlXbE81yol2n1duqI9LoDm4/jm5PX6Kk0Qpq9xRsXiZ9RYP95Ql//KzYrkiXYVkvnk2bNpkBSp8+feSKK66Q77//Xu677z6zDcrpp58eMG9FRYX5b2pqasB09V41vo1GdnZrsVpV5c/bF42cnJ+3Iy/Zmp7bvuVZPW+s951uSKM7uP04uj19bkljSfH+mC07q22G1JSkNfu6boUWLZLDbkc8j6NlAcrEiRPN9iSqFEQZNGiQbNmyRd544416AUpa2s8HITgYUe/DtVkJp7CwRKwcgE9Fdq0zW0T9vYKCEvPf2lqvJdvhW15OFPNGk0aVyazedzohje7g9uPo9vS5LY25Cz+UE2K07KKiQ1JWVtnk67qVqqtr622HlcfRt6y4BSiq9MQXnPio0pRFixbVm1fNp4KUgoIC6dv35wKtmpoaKSoqMhvWRkPtKB0yfVHBLin+x2/liEprHr8dTZqamn5d9l0skUZ3cPtxdHv63JLGWG6+YQ7UdrgE3rBzX9VZd/B2xPM4WjaS7F//+leZMmVKwDTV4FUFKfVWmpQkxxxzjCxdevj5MytWrDDboaiSFycq/OBuGV6xUHoYu+3eFACAAxnG4ZILWBigqOod1e7kpZdeMrsgv/766/KPf/xDrr32Wn+7k/z8/IDGtmrezz77TFatWiX333+/2X052iqe2EWy0WlVxXiuAICmG73oek12n1d0YFkVjxrLRJWiPPHEE+a/3bp1k//93/81R4lVPv74Y3Mwtg0bNpjvzz33XNm5c6c5MJtqe3LGGWfIXXfdZdXmAAAAB2tWgOILNnxOO+008xXKpEmTzFddN9xwg/kCAACoi6cZu0RleandmwAAruatjt1I54a34ZanhtebcNd5AhSLHF29Wuzy02tTpfvLg2Tbmm9s2wYAcLPNKz6TMaumxW196/75hKz/4pXD659zjXmd37PZvntNvBGguMBJRX83/03+bpbdmwIArpS16P/FdX0nb3pExq67T2rVEBwFO2VU2efm9MovHpJEQYACAIDN9i98OeT0Xa9eZj6fx2dExbeycek/JREQoLiIJ6bDCAEAYmVs/mshpx9XubjetIxlzybEgSBAAQAA2iFAcZEhVcuksuKQ3ZsBAK6ietAMrP3J7s1IOAQoIRgaPDCicPemJn1v29t3Wr4tAJDIfvpuviQUQ7RAgBLC7k0/it32LZnXpO+dWPyB5dsCAIksacuXdm9CQiJACcHbyIA58XDyjqft3gQAAGxDgAIAQIM8CbV/PJo8VZkAJZQmPM0YAAA3OGrfz4N/2o0ARWNlz5xg9yYAABJMeykRHRCgaKyXsd3uTQAAUKpuCwKUUDzsFgCAyKZl/5YTD37ErrABd2IXRsv5O/Ps3gQAcIXB300VLQflLC8VtyNAcaEj/zHe7k0AAFdo4ykTHe2Y/1txOwIUAAAcZuTBf4vbEaAAAADtEKBo+iweAAASGQEKAAAOk+Rx/w9pAhQAAKAdApSQ3B+ZAgCgMwIUAAAcyPA6e8yuxhCgAADgQDtevkTcjAAFAAAHOq5ysbgZAQoAANAOAQoAAA617p37pKamWtyIAAWAtmqqKmXpRy/Kgfyddm8KoKWT97wiuZ89J27U5AClqqpKJkyYIIsXH64DW7Fihfzyl7+UYcOGyZlnninz589vcBkjRoyQgQMHBrzKyvR8MBOA+Pvpo0dk+Pd3Srt5Z7D7gTA8xVvEjVKa8qXKykq58847JTc31z8tPz9frr/+ern88svlz3/+s6xZs0amT58uHTp0kPHj6z9dd+/evVJSUiKfffaZpKen+6e3bNlS7MZQ94AeOu75j/lvjhRLvt0bA0DvACUvL88MToJv4irQyMnJkTvuuMN836tXL7N05YMPPggZoGzcuNEMXnr06NGc7QcAAC4UdYCyZMkSGTVqlNx+++0ydOhQ//SxY8fK4MGD681fWloaNtDp3bu3aImHBQIA4KwAZfLkySGnd+/e3Xz5FBYWykcffSRTp04NOb8qQSkvL5crr7xSNm/ebAY399xzT9RBi8cTZQLivDy7NJQO32duSWsopNF93JhfyaewJB9J7M6P4DxqxXoiXUaT2qA0pqKiwgxMVJXPZZddFnKeTZs2SXFxsVkllJmZKS+88IJMmTLFDGrU+0hlZ7cWq+3fJo6Xk9Paln2nG9LobIV1rmSR5GmnIp+iOVJSkmN2fgQvN5551fIARfXCufnmm2XLli3y+uuvS0ZGRsj5XnrpJamurpZWrVqZ7x999FEZN26cfPnll3LeeedFvL7CwhJLa2Tc8iutoKCkwTSqTGb1vtMJaXSHum3dGsrTTkU+1V+O6K+mulry9xWLJ6l+x1yvt1Y86r8Qn0WSZt95Z2Ve9S0rrgGKam9y3XXXybZt22T27NlmQ9lwUlNTzZdPWlqaWUWkevdEQ+0ot95kmyOSfZII+440uoeb8yr5FM0xpvAtWfryDjni128FTK+tqZHy50+WqqR0aXfDZ1EFKXXzpl151bKB2rxer9x6662yY8cO+b//+z/p379/g7+KTjvtNHn33Xf90w4dOiRbt26VPn36WLVJAAAkhOEV39WbVrh7o/Q2tsnA2p+kqqpCnMayEpS3337b7Fb87LPPSps2bcxxUZQWLVpI27ZtzYHdVJuT9u3bS3Jystn1+Mknn5Ru3bqZ0/76179K586dzWoeAABgnerKQ5KW/vM4Y4bXK1XqfUbk7T0dHaB88sknZinKjTfeGDB95MiRZonK8uXL5aqrrpLPP//crMq56667JCUlxRxTRVUNjR49Wp5//nkzeLEbA7UBANyk99yh8lXvu+TIc34rO1++SIZVfi/rJ30t2V36uDNA2bBhQ0Cj14aosVPqzq/anNx9993mCwAAxNa4zbMkX35rBifKvoVzJHvS/dru9ph0MwYAAPbYsOB1ScvqIi3bdXX0ISBAAQDABaoqy2XPxuUyZtU08/3aiV/Um6ekKF86iDMQoITk4v6MAABXavXiMBkuoR8v49PntWHiFJZ1MwYAAPZp20hw4jQEKAC0ZbhlaGcAUSNACUH1EQcAIFFUVhySfdsP97TVAW1QAAAIobK8LGHKIGpfPkWOMnbKD+Pe0KYRLSUoITTleQUArOdx8wN4oL3igu2SKHoaO81/K1a/I7rgTgwAQAhF21Yl3H5pW6JPNQ8BSgi0QQEAtPvxxYTbCUdXrxZdEKAAABBC+5p97BcbEaCEkNwiNf5HAgCgFae3gPI4vJs+AUoILdvkxP9IAABgIQ8BCgAA7tNFCsTdDNEZJSgAAEA7BCgAAEA7BCghGAwOBQCArQhQAACAdghQAGiLpxkDiYsAJSSeZgwAgJ0IUAAAgHYIUAAAcCNPY7d4vUeaJUABAADaIUAJwfDqPboeAABuR4ACAAC0Q4ACAAC0Q4ASgqH5A5SAROFhVGegyXiaMQDESMfaXexbIEFRggJAW+2lxO5NAFxcgmKIzghQAACAewKUqqoqmTBhgixevNg/bfv27TJlyhQZOnSonHPOOfLNN980uIwPP/xQTjvtNBkyZIjccsstsn//ftGCwVD3gG42fv+h3ZsAQPcApbKyUu644w7Jzc31TzMMwwwycnJy5J133pELLrhAbr31Vtm1K3Qd8qpVq+Tee+8155k3b54cPHhQpk+f3vSUAHC10UtusnsTAEfxaD5SrOUBSl5enlx66aWybdu2gOmLFi0yS1AefPBB6du3r9x4441mSYoKVkKZO3eunH322TJx4kQZNGiQPPLII/LVV1+Zy3CKlWe8L7oq2LXR7k0AACB+AcqSJUtk1KhRZqlHXStXrpQjjzxSWrZs6Z82fPhwWbFiRcjlqPlHjBjhf9+lSxfp2rWrOd1uqjQoElkde4quer57ht2bAABAk6VE+4XJkyeHnJ6fny8dO3YMmJadnS179uwJOf++ffuimj+cRhspRynS5f0w7nXp0MR1L2l1qows+6L+9BNflpHfXStWaOmplEOehtNo9b7TCWl0J7flWfIpYpvBJOI82NjnVubVSJcRdYASTnl5uaSmpgZMU+9VY9pQKioqopo/nOzs1mK1ssLG5+nQubO0bdeqScsffvt8OVhaLG3+0idget8ho2TRpl/J6D2viRVyclrHfd/phjS6S2N52qnIp4iFdm0bvke1aJHS6DkV/Hk886plAUpaWpoUFRUFTFPBRnp6etj5g4MR9T4jIyOq9RYWloiVg01GGtkVHzwk3hYlktWEdRTuL5WkpFRZdup8Oe6LS/zTiw6USVXS4Sqy5iooKAmbRpXJrN53OiGN7pATYZ52KvKps/Kf0xwoLm8wDdXVNf5zKtx8vs+tzKu+ZcUtQOnUqZPZgLaugoKCetU4dedXnwfP36FDh6jWq3aU1TfZSJbn9RpNXq9vm5NSUmNaft3Y9sVi3+mGNLqLW/Mr+RSxyldW3CeC38frPLRsoDY1lsmaNWvMqhufpUuXmtPDza8+99m9e7f5Cje/js8IibQxrVu7gAEAoH2AMnLkSLMnjhrLRI2P8vzzz5tjnVx88cX+6hvVkLa2ttZ8f/nll8t7770n8+fPl/Xr18u0adNk/Pjx0qNHD0nsBwoStAA7c5fKurf/wI4AEphlAUpycrI888wzZhAyadIkef/99+Xpp582uw4ry5cvlzFjxpilJMqwYcPMMVPUPCpYycrKkpkzZ0oijSRruK1LAmCRof++QE7e+yr7E0hgzWqDsmHDhoD3PXv2NAdgC0WNnRI8vwpk1MuJjFhVxBG0AAAs4PTbCQ8LbKLsbgNUU9lm7fysDs6ozgIAIN4IUJooo1WbZu/8zKzgjl0cDgAAuCOGE2HVTXN78YRcJo1kAQAW8Di8joef7M0QiwAFAAAQoAAAYIu1KUfGdPkeSlASw9cD/yQ/ZIwJmJbZxukDIQMAoCeqeCIchC2tfU851H1cwLSU1LTYHRkAABIYAUqEeh57asjpXiN8I6TVLYZEPcS9x9P4ISls0iMKAQBwDgKUCCwa/YIkp0Q2pl2l0aLpPXIirC9c0+OK6JYLANAOvTYbRoASQe+chhoaJXnC9+TxNOl5Pc7uFgYA0EPBltXiZAQoMUU3ZACAPXIWz3D0ridAaaavet8V8J6QBACgA08zH8diNwKUZjrynN9K7iULA6atSx5k/lvU/7LoF0gNDwAkiNhe8Pt4t4qTEaCEYkQXdbbtGPjQv6xrP5Q1538uXYec0ayDAwBwtvXJA0VXJ+9+SWpqqkVXBCgxGNK+RWq6dOwRfaZUjXEjadVtpLSMetkAAL3o0Isn99NnRVcEKDHNcLHZvX1PuS4mywUAJBZP8RbRFQFKCMEFJtE8z6BpXYsjt8XTXdIyWsV0HQAA2I0ARTcOf7gTAABWIECJZRVPTIINAhgAcIr8dsO0v5xXV1WIjghQohKP3KRJjgVsYnidPXYDUFfv8x8Iu0MqkttosbNqXxwrOiJAsaDXDgAAoQS3GVzY+iz/35nnPiz7pL3tO66HsVt0RIASFSOm3caiaYwLAHCefiWL/X9nd+4lBRPftnV7dEaAohmCFCQ6gwdGwMU2txoaNIXbcDjsmYhGkk2yNNjwJLHbAcDtlqWPrjetJjnDlm1xIu6UFlnScrz57w9dJjt+ZEEAQNN823ai/2+vJyXEHIHXeErNwyNAiUjjvQp6/OolWXrqfBl44f0SWzTgReKihw901//yJ+q84wdncxCgRCV8ZktJTZMjBp8gSUnJh+em0SsQNcNLEA7nqluFb4S8BwTm70jvE6taBLddcT8CFI26GXtUANRoZiUiBwBnsO56XdTzbEk0BCixDjgAAAnJyjaFfcZdI7Gh732KAEU7DWcWGtEikbsZ0wUZuioz0uWb7MuavZyve9wScnpaekuJDX2rVEM1MW6yd999V6ZPn15vuqpjW79+fb3p559/vmzYsCFg2gcffCADBgwQexlh4jh9DyTgFttWfSld7N4IIEop0zfLoNJaaW4LAU/rzuz7WAQo55xzjowde3hM/5qaGrn66qtl/Pifu+DWVVtbK1u2bJG5c+dKr169/NPbtWsnrpFkfdGZ579B0lfdbpJxO/9m+fIBu9Wse8/uTQCipko4SkpLzL93SUfpKvukesD5jX/RE1SRwe/g2FTxpKenS4cOHfyv999/32xw+rvf/a7evDt27JDq6mo59thjA76TkmJpzGSvJvRGiLTdypET/9CEDQL017psa9jP6OEDXZQbqWE/q7rqc1k6/k3pf8LFlqyr2vi5d+ia8z8T6yVgG5SioiJ54YUX5M4775TU1PoHMi8vT7p06SJpaWniHPoeSMAtjq1eYfcmAM3SqnU7OeKoMRGNGh5JN+PlY14y/+3YY1BCHZmYFVe88cYb0rFjRznrrMNPbqxr48aN0qJFC7nxxhvlxx9/lN69e8u0adPMEpVoWD3UiFpecDdjNc1cjye69XqSwwx172lg/kaWqxrJRprmsOv573Q3D9NCGt3Jfy66BPnUuUJ1WAiXN7859hEZs2raz/MEzV/vK3UmLMw6V9IqC6X3kFNjlu99zQbCfh50v7BiOyJdRkwCFHWDnz9/vlx33XVh59m8ebMUFxfLJZdcIrfddpu89dZbZnuVjz/+2CxZiVR2dmux2v7tge9btkqVnJzWkp7Wwj9NvW9MakpVyOnhvtuuXabsqbOOcAc2knVHso2x2He6IY3uovJ0spuqgf+LfOru45jdrZfIqp//Tu45UmT1x/787K1sFTBvRvrhe8AJt78usTam8K2o7iPxzKsxOdNXr14te/fulXPPPTfsPDNmzJCKigrJzMw0399///2ybNkyee+99+Smm26KeF2FhSXNbjXdWGRXUSlSUFAiFZXV/mnqfWPKSsqkzX///nboY3LSijvqfTenzvwHDpRKZWVNwDK+7jtNUnP6yujFN5rvVVp936/73VDCbaNKo8pkVu87nZBG52ooX+cXHJSUlIaDeCchn+qtsWtssHDX1NLSCv/fPU+aIgsqqqXdgDHSuaBEiosPScc685ZXNHyfyZH48m2DlXnVtyxbApQFCxbIiBEjJCsrK/yKU1L8wYmvHq5Pnz5mYBMNtaMsv8kGLfCIo07+eVKdyZGss+48nuQWjX7XTEtQgV+XERdJi7SWIoujW3ck88Vk32mGNLqLW4+nW9Pl5jQaUaQxM6en/++k5BQZdMZvws9fZ4Khwf4K3oZ4HseYNJJdtWqVHHfccQ3Oc+WVV8pTTz3lf+/1es0xUVSQopPvsiZE1NCpUc04ojzTBwB0E3ljjJyufeW74U/K92PnhliKixpVWSwmJSi5ubnmIGzB457s37/fLFVRvXpOPfVUefrpp2Xw4MFmA9k5c+ZISUmJXHjhhaKT6nb9bQ0sgpexe/jvxbdFe6W9dJL9zV4HACA60Y7q3X+0Xvc2J4hJCUpBQYG0aeNrffGz3bt3y5gxY2T58uXm+ylTppiNaB966CG54IILzG7Hr7zySkC1jw4GnH5rHNfWeBeF9kcc4//be/33cdgmQCNer91bAFjKCBqoLaV1B/ZwLEtQVBVPsO7duwcMa69KBlRj2GgaxMaLr5vxbsmRlFSrxmmxvtIumm07VFokGS3bWFNdBQAJLlbNMNp07i9f97tHkjNzxO6HvtiNu1UMeYKHMI7kOzHYjs0rv5Ces4+W3DfiWRoEAInhm2MebvJ3QxWaDz7zZhlw0qWS6AhQGmR/4yUr2rG0XDzL/Pekovct2CLAPjzNGHb6rs05IdugDBr3K5u2yN0IUGJYeEfvGwCAlb7LOi9hdigBim7cNI43ALjK4evz6qEPmf9+3f3m5i2x3jW/4XtA718+IfFk2Ngw3X1jRtvYjSxYSsrhhySmtGxnW4lLc9MBAAg0YMylsmP4OTI4I749T1NcNJJyYwhQohFlAKF62fww7g3x1lZLasusSFdSb0pqaob/71ZZ0XdBS/WWR/0dQEfBD/IE7LwHpFkQnOjeFMCw8ScuAUqMi7R6Hj3W/Hdn7tImLiHJDHTWnPeZGeh0bhn9g5oG1uY2cd0AgERmeA3bGoMQoMRruPpmxqAdjxjUrO8DAHREU9Bw2DMNsKVgS/PiPsBOVPHAXol3fTZiNiRd4whQXDLWwkGjld2bAABoJk9S40FQiXG4XaKbEaA4PEJfc/7n8u2wv8r6VsfHbIuAeMpN0uuJ5oBPbYtWWpSar+h0icSNjd2MCVB0Hxq/kczbscdAGXDiRVQNwTUOpna2exOAkLqfdbesTTlSvu57t617yIhjUwA7axRoJAtA66e7Bn7ovOpXuEdmVo5k3vhvsfJ5w7p3M7YTJSjRXASbkZHade0Xcvr65IGWrIPB2OAWFRld7N4EABo0TKcEJU7FaBmt2sjGy7+X5BZpUrcWs7RFjkjtBgvWQBQOAE5TvwQlSavrfUXZQUlNs6dRLgFKHLVp3yXqjEfxHwAgQGYnkb0SF96aKrELVTyhhCvS0rj+u6pVN7s3AYgDfc9BIF76n3pD3NZV660VuxCgaKQ5pSW9zp0uPyWHbucCOAtBCBJHU0YZT0lNi99YKAbdjLUUl4anwUFJE4OU9JatpWz8I9ZsE6CpvZtW2L0JQELxMg4KADRu+H9+yW6CuwSNHBtpSbpH4sOgisf93YwjFrSOqKp9kqixAwBYx6AEBVagxw8AOE3TfvhWeFIlHgyDRrIJzGNrYytAOxr3lgP04YnLWihB0Yy9TzP2WDaw3KZl/7JgewAAupV856fEZ8RlL21QYEUVTVJQG5SadR+wYwHAjfeB8/4mcUE3Y4d0M45D0XNw5qTaBgAQLKdrX4kHo5Y2KAnLyuf9eOo9BZa6fADQWf3rtl4MYaA2Zzy9UffHYgeXvhCfwIE8BNaANmgkm8gjydZbR3PWqXkABQBw1PAQhlsayX766acycODAgNdtt90Wct7vvvtOJkyYIEOGDJGrrrpKtm/fbuWmuHaUwQZnTUqO6aYAABKLYWOAkmLlwvLy8uSUU06RGTNm+KelpaXVm2/Xrl1yyy23yNSpU2Xs2LHy9NNPy8033yzvv/++HtGkbeMweJpXjaTBrgMARE6Le14i9OLZuHGjDBgwQDp06OB/tWnTpt588+fPl6OPPlquvfZa6d+/v8ycOVN27twpS5YssXJzEpDmGR1ogo3JfdhvgE0MNwUovXr1anS+lStXyogRI/zvMzIy5KijjpIVKxLvSaX127k0PcjwRFEdBGgrqARzX48zbdsUINEZNj6LJ8XKni+bN2+Wb775Rp577jmpra2Vs846y2yDkpoa+MyA/Px86dixY8C07Oxs2bNnT9Trtbp0LHh54ZYfk1K5EDU86n2k6wrVXa2h5elestgcpNHBgvNs0O8oN+Vb8qmzNHY/aFLerPeAWM3yuFFr+X0j0mVYFqCodiXl5eVmMPKXv/xFduzYIQ899JBUVFTIH/7wh4B5ffPVpd5XVVVFvd7s7NZitT2+X3Aej+TkHF5+RnoL/991pzfH1uTDF9+c7EzZkxHYZqd9+9bSOiuyddUcygx4n5ySVG87676Pxb7TDWl0no11zgnFCLqYWXXu6YR86gyN5b2mHMeKQ4EZPKttq4jzeG5Kf+lfkyux1DIjxbb7hmUBSrdu3WTx4sWSlZVlNvoZPHiweL1eueuuu2T69OmSnJwc0HA2OBhR70O1V2lMYWGJpW1a60Z2arkFBSX+9+UV1f6/605vjto6xWcFhaVSUWcdyv79pVJZfTgwakhx8SHpXHfZNV5zO3PqTFPvVRpVJrN63+mENDpXTW1wkXLgBdyqc08H5FO91b12NpT3mnMcKysOSbc674uLyyUlI7I8fjClg0iMA5TS0nLL7xu+ZcW1F0/btm0D3vft21cqKyuluLhY2rdv75/eqVMnKSgoCJhXvVdBTbTUjorlTTbcsi1bpxH0d9DPxWjSl9wivd7Cg79b932s950OSKMDNZIn3ZhnyafO0Fjea8pxDHWN1iqPewPvI/HcPssayS5YsEBGjRplVt/4rFu3zgxa6gYnihr7ZOnSpf736jtr1641p2vBrtzRzMrHzKzAdj2AMwWdf5oPBQ64uZuxYdTYtm7Lzvxhw4aZVTeqvcmmTZvkq6++kkceeUSuu+46s8Gsahjrq9a56KKLZNmyZfL8889Lbm6uWQXUvXt3M8BJOBZmzuQUSwvEAC3offkG7HOoVfeYr8MV3YwzMzPlpZdekv3795sByL333iuXXXaZGaDs3r1bxowZI8uXLzfnVcHIk08+Ke+8845cfPHFUlRUZA7Wpl8kacP21Ot1o9s+AQDo4IgJf4z9Srz21TdZ+pNbDbr2yiuv1JuuApINGzYETBs3bpz5SnT1xkGhOBsIOicI0uFewT9Co/lR2jKzrXzT/hIZs3++xIzhkmfxuEXYx0tzoQQAaCRlwBkxXb4rqnjcKD5PMw7WvHWubqFJQ2PAskaylKDAvTxxuM8cNFo1/cs2jiRLgKJR7x6zaK9eG5QkBwRVQAxp1ecScJ4VPa5O7F480IOnsUEkAAD6SNL8NmzYd0/RfM8kgqAGUpaWgBCswAWo4gGaqRn3FdqgwJ8XmtmLhyoeuA/VlnCv4B+lsRpaokACR3qPlOGlF08C4+ILBKLkD7C6CmbfxHebuHwayTpDHIqa4xVNA47BOQBE7KDRMuT0Dt0GSJMQoGjGzl4DxCNAWOuTB7J34CqeJI+l96u1J/w1xEqas3xKULQU9GDhmKO0BGhYRXImuwhoQNtug8VKBuOgJLDgIIjibABIGPWq9ZtZjJ6Z1SGiAT1/uuS7iJbnoQQFhzWzFw8BDhzOQxtZIGKe5BYSreLMftKu4xERzWvwLJ7EbZsS3C04OL6g2gegYRbcy9PMgdp6Hntq0AI9Ye8zS0+dLwtyLpce5z8Q+QpsLEGx9GnGABBLKd4qdjBQR3JKiizo8CsZm/+aNOaIwSeIqFc0qOJxiLhUn/BrEQh33h1ds5qdAzTA6lJ32qDoxqZuxmbjKNqQAEDCau5o4lYzKEHRVRxKMxoNSKLdBkpg4HS0kgUsLUFpxg9fSlBwODNY+fxGHlMPANrzxnvQrWhQgpLIAjNmc/MpDwuE6xBoAxGLZhyVHzLGND4T3YwBAEhMdlVqdqrY2PhMlKAkrvrjoGhc1AcA0FtSqGYCoe8re9P7Nbo42qCgoZHa2DtIMDSSBaLjid3I45SgwNTMEQUBAM6jU9vBKiNo/FYCFL3Y2SavuQ+KCoyk+SUKAInEE9U95PC8X/f/g2y5YqXkXvCJfN3lusOzEKDoyZ6o1rp16hOTAwDsutd4wlTl1F1vUouW0iorWzr2GCiDJ91/+Lti37N4qFMAAMAlPEke11QpEKDYLXhYY4+FvXpsLJoDrLsgUlWJ+KssL4vbunTO4R6qeHA4NzQz+qVeBwCa7eD+ne5vg+KJpBdPrdiFEpSoxLiekOgCqI+RZOF6Hm3buHgoQYE/M1g47olH64JDIFLkYyBSniiGq4gocHFLgLJ371657bbbZOTIkTJ27FiZOXOmVFZWhpz3N7/5jQwcODDg9eWXX4oevHG7UBqNHBJGlgWAxKHb2JweG38gBI3I0nSGYZjBSZs2beS1116T4uJiueeeeyQpKUl+//vf15t/48aNMmvWLDnhhBP807KyskQv8c8pumVOIP6CLohU8cDl7BuozVPnT492JSiWBSibNm2SFStWyLfffis5OTnmNBWwPPzww/UClKqqKtmxY4ccc8wx0qFDB0lsnhhmdIrGASDheTxNvv943NBIVgUaL774oj848SktLQ0ZzKiqix49eli1etfQachjQAuUoMDldP4p6bHx/LOsBEVV7ah2Jz5er1fmzp0ro0ePDhmgZGZmyrRp02TJkiXSuXNnmTp1qowbN872KpGGntXnicF6A5bvEUmqNw5K09c1sGShHHp6eNjlubk6iTQ6V2PZ0k35lnyqL08Uec/K49ika76n/rYEfxxymQE3tXDf9Vp+34h0GZYFKMFU+5K1a9fK22+/HTJAqaiokDFjxsgNN9wgn376qdlodt68eWa1TzSys1uL1Xb5/vCI5OQcXn5aeqr/77rTm2NT8uFCrA4d2khhZnrA5zk5baRFalrEy9tZZxTBdlJivgKX1zqm+043pNF56p4TSvDvN6vOPZ2QT/VTVdKq3rTG8l5Tj2N5nUihbdtWUefxlJRk/9+hvpuamhJy+ubkw99Lz2gRch51Otp130iJVXAye/Zsefzxx2XAgAH1Pr/55pvlyiuv9DeKHTRokKxZs0beeuutqAOUwsISS0uAAyI7Q6Sg4PANvrKi2v933enNUVN7eOMLCkukrKwq4PPCghJJSQ2c1hBvI+2Z1HarNKpMZvW+0wlpdK7a2oYzsVXnng7Ip/oqKiqVrhHmveYex7qhUFFRmUhKdHm8pqY2YBsDG1qIVFXXhtz2uudaRXl1wDy+ZXhrayy/b/iWFfcAZcaMGfLGG2+YQcqZZ54Zch7Vsye4x06fPn0kLy8v6vWpHRXLm2zdZdddTSzWGWqZVqcvID0x3nc6II0uENSLwI15lnyqn3DX48a+09z82aRlGIHfj3iZdX+Rh5lHDdRm133D0nFQnnrqKXnzzTflsccek3PPPTfsfHfffbdMnz49YNr69evNIEULcb0CuqhCHQBcwvKH7jm0c4THDQO1qXFNnnnmGbn++utl+PDhkp+f738p6l/V7kQ59dRT5YMPPpB//OMfsnXrVjOwWbp0qVxxxRWik/phShwCFze1AAQs4cIiE2jP8CZCvvOE/DNwDheMg/L5559LbW2tPPvss+arrg0bNpgNYtXIspMmTZIzzjhD/vSnP5nz7dq1S/r37292Ue7evbtVm+NgQbkkimGLAQDOE+tQyBMm+ohsqHsXdDNWvXHUKxwVpNR1ySWXmC9Y+7BAnYsKgUi0K9sY8N6TCD9kAU0liQsGaksMsbj5Bw+8wiFB4tq49J8yqDbwx4xhYxEzEB8a/7A07PuFwN1QMzwcEInMu/bvdm8CkDg8jYcAdrZBIUCxmeGJfbUP4Ghu7FcMxJPH48ih7glQwjyZ2S504gEA+yVCN2Mjgnlog6IpezINJSYAkKjdjG2r5vckuXccFDSfJw5diiueOV4K92yJ+XoAAJFpdijkaWpQ0/j3PDaOQ0SAYjuLe/E0klF7GLul4v2pzVsHEE+0QYEdXJTvjLCjsEUQoFCCgnAZJhb1oEOqlrPDAaABXm/8xv/QefwqDyUoiSwoIKGVLBJYWlWR3ZsAJBBPo3PQSBYARGR4xXfsB2jBE9dBM+0qQfE0PgdVPInMY0tR4c5N6yxdLxA77mkLADhuHBShkaxe7BwHJU7tlksKd8ZlPQAAZw4UqhCgaMv+hkvRjyQb4fy0dYFTuKg3BdDUQCH2Qm9EElU8OJwbmptTI7uYM4Q+nIMABYgNGsm6Rkx62NRbZnxCaU9SclzWAwDQtZuxJ4I5aIPiCHY+oydykWV0ujPDKex8WBngBp5mfZcAJWEFR87BgUPMhr+nDQoAaEHnEpQk2qDgcHbxxCWjx7ePP9B0Bm1QANt+qCYJDwsEAACaSSJASeBfbEERrBGnqpf8lf+Ky3qAZqMNClzOiHtnjGhK3GmDoqV4BQt2NF71VJXFZT1ANH5K7scOA+LFU+d+E+bWk0SA4gyxCR4sXiaNX+FgB1t0CjGVXjxwOy1GaguJKh7EvQSFhofQkZ1dGoHE42l0DgKUROaxKZKmXh9aChGgkFfhcrHvZuxpxjdpg4J4oyoIGrLz0e5AwvE0PkuSjT8QGAzD5pFkGxqozavHE6QAm1HtA9hVckMVj27sLFKOV8kGxebQUMjiZOIT2MCIY2mezlk8iXFQEpldpSQ6nxJIXORLwHJhf/h6Gh3FPJkAxRni08OGah0krtAPBqRdCuIvvo8Dsem670mgpxlXVlbKPffcIyNGjJAxY8bIyy+/HHbetWvXyiWXXCJDhgyRiy66SH788UdJSMEPB4zXSUEVD7RECQqgE49bApRHHnnEDDRmz54tf/rTn+Spp56Sf/2r/pDqhw4dkhtuuMEMZN59910ZNmyY3Hjjjeb0RFe3lCaW2SKpqjSGSwesuxiGLlUB3NQGRd+nGSe7oYpHBRfz58+Xe++9V4466ig5/fTT5brrrpPXXnut3rwff/yxpKWlybRp06Rv377md1q1ahUymHE/ezLmqIJ35ED+TlvWDYRHMAI9JLdIE/fcRzxNXnKSxwUlKOvXr5eamhqzNMRn+PDhsnLlSvF6AyMwNU195istUP8ed9xxsmLFCqs2x7niGK/s/uGd+K0MiAClJUi0Ub0RXopYJD8/X9q1ayepqan+aTk5OWa7lKKiImnfvn3AvP36BT4ULDs7W3Jzc6Ner9V5aNvab2TIVzf4A4Vwy4/VAyeDT4pYniMn5c2SNS/8W9xqt8cTk7FrdOK2NB5bXf9HSlLFgYD3u1+YIG7itmPoljSmeKvqTWvsftDU67U3oDdNE5bjqb8twZ+Hnp7U+Dz+e1MDy49SpMuwLEApLy8PCE4U3/uqqqqI5g2eLxLZ2a3FSrnrPpbk/xZpHUrrIANyDi+/94jTRdbfL9s9XaVHnenNkd7jWJGCN8y/c3JaS4vkXv7PCj3tpGOU69ly9ESR77+JaF6VzmOrKLWC3oxuI0SKPva/J8/CLuoaHYv7UW5KtnSs2S+1hke69ugq6S0zo/p+m4FjRfbOkV2eTtI1p7XkpvSX/jWHf/C36dwz5La3aNdVJP+/296td8h5FmdfIKPqTLf6nhuXAEW1KQkOMHzv09PTI5o3eL5IFBaWWNoh5Yizp8vydSPkUGmZdDn2TCkoKPF/lt62pyw/7W3JbN81YHqz1jfqcllQUS0djxwv2eYy02Tlme9JyY4fJbv/CZIU5Xp6Hn+pLCg6IJ79eWLkDJKk/B+ltl0/ydr+qRzMGixj81+TfdJeNnU+W2qyjxQ3S09vIRUV1eJmbkxj+w2vSVGrPtL6pJulePtqOenCG+THXidIzaf3SUmfCeJJbiFu4sZj6JY0GgW5kly5X9KHXSGtO/aUdmGux6pEQN24m3w/mjRHvln3tbTrdZx0OmRI6aHorvtdjz5TltS8LFldB5j3psxr/ykL3ntIpE03aZHdU/ocE3gv8+k5/kZZ2LqHJKe3lt69RgbMk3/hfyT/+7dk0Pm/N6c3O411+JbV6HyGReVuy5YtkyuuuEJWrVolKSk/xz2LFi0ye+csX75ckpIOFyXdd999Ul1dLX/+85/9037/+9+bgcuDDz4Y1XrVjrMyQFE7TkWRVi9XJ6TRHTiOzscxdAeOY9P2V9wayQ4ePNgMTOo2dF26dKkcc8wxAcGJosY+UUGLLzZS/6oAR00HAACwLEDJyMiQiRMnyv3332+Wonz22WfmQG1XXXWVv2FsRUWF+fdZZ50lBw8elP/5n/+RvLw881/VLuXss8/miAAAAGsHaps+fbo5BsrVV18tDzzwgEydOlXOOOMM8zM1sqwa/0TJzMyU5557zixhmTRpktnt+Pnnn5eWLVtySAAAgHVtUOxCG5ToUV/qDhxH5+MYugPHUfM2KAAAAFYhQAEAANohQAEAANohQAEAANohQAEAANohQAEAANohQAEAANohQAEAANohQAEAANr5+bHDDqZGpIvF8qxerk5IoztwHJ2PY+gOHMfoRHp/dfxQ9wAAwH2o4gEAANohQAEAANohQAEAANohQAEAANohQAEAANohQAEAANohQAEAANohQAEAANohQAEAANpJiAClsrJS7rnnHhkxYoSMGTNGXn755bDzrl27Vi655BIZMmSIXHTRRfLjjz8GfP7hhx/KaaedZn5+yy23yP79+8VpafzPf/4jF1xwgQwbNkzOO+88+fzzzwM+V8sYOHBgwKusrEyclMbf/OY39dLw5Zdf+j9/9dVXZezYseY+UMssLy8XHUSaxiuvvLJe+tRr+vTp5ufFxcX1Phs1apTopKqqSiZMmCCLFy923fkYafqcei5Gk0annouRptHJ5+LevXvltttuk5EjR5rHYObMmeY1SJtz0UgADz74oHHeeecZP/74o/Hvf//bGDZsmPHPf/6z3nxlZWXGSSedZPz5z3828vLyjBkzZhgnnniiOV1ZuXKlceyxxxp///vfjXXr1hlXXHGFccMNNxhOSqPa7qOOOsqYPXu2sWXLFmPu3LnmezVd2bNnjzFgwABj27Ztxr59+/wvr9drOCWNyumnn2689957AWmorKw0P/vXv/5lDB8+3Pjiiy/MY3rOOecYDzzwgKGDSNN44MCBgLR9+umn5nFctWqV+fkPP/xgjBw5MmCegoICQxcVFRXGLbfcYua1RYsWhZzHyedjJOlz8rkYaRqdfC5Gmkannoter9e49NJLjeuuu8746aefjO+//948Vup80+VcdH2AonbgMcccE5C5nn76aXMHBps/f75x6qmn+i8A6l91wN555x3z/V133WX8/ve/98+/a9cuY+DAgeYFxClpnDVrlvHrX/86YNq1115rPPbYY+bf3377rZkRdRNNGtXFb/DgwcamTZtCLmvy5MnGE0884X+vTkx1ch06dMhwShrrqqmpMS/sjz/+uH/aW2+9ZVx22WWGjnJzc43zzz/fDMQauvA79XyMNH1OPRejSaNTz8Vo0ujUczEvL89MV35+vn/aBx98YIwZM0abc9H1VTzr16+Xmpoas/jQZ/jw4bJy5Urxer0B86pp6jPPfx+1qP497rjjZMWKFf7PVZGrT5cuXaRr167mdKek8cILL5Tf/e539ZZRUlJi/puXlye9e/cW3USTxk2bNpnHrkePHvWWU1tbK6tXrw44jkOHDpXq6mpzHU5JY13vvvuuWYx8/fXX+6ep49irVy/R0ZIlS8wi7nnz5jU4n1PPx0jT59RzMZo0OvVcjCaNTj0XO3ToIC+++KLk5OQETC8tLdXmXEwRl8vPz5d27dpJamqqf5o6IKqeraioSNq3bx8wb79+/QK+n52dLbm5uebf+/btk44dO9b7fM+ePeKUNPbt2zfguyptCxculF/+8pfm+40bN5p1wKpedfPmzTJ48GCzXtjuC2U0aVQXxczMTJk2bZp5kencubNMnTpVxo0bJwcPHjS/U/c4pqSkSNu2bR11HH1UKai6yFx11VXSqlUr/3R1HFWwc/HFF5v1zOrioerEg/OvHSZPnhzRfE49HyNNn1PPxWjS6NRzMZo0OvVcbNOmjdnuxEf9CJo7d66MHj1am3PR9SUo6gSve8FXfO9V46dI5vXNV1FR0eDnTkhjXaoRk7pYqEj4F7/4hf+Con4BqIZtzzzzjKSnp8uUKVNCRtW6plGlQR0r1chUXTDUxVClR/1aU9Prftfpx1E13FMXgUsvvbTePlDHTF0IH3/8cfMCctNNN5m/Wp3CqedjUzjpXIyGU8/FpnD6uThr1iyzIeztt9+uzbno+hKUtLS0ejvJ916d8JHM65sv3OcZGRnilDT6FBQUyDXXXGNG/U888YQkJf0cq7700ktmEavvF8Cjjz5qXlRUq3vVy8AJabz55pvNX51ZWVnm+0GDBsmaNWvkrbfe8p98bjmOn3zyiZx88snmr866PvroI7MY1vc9dYzVTUIVuaqboBM49XyMltPOxWg49VxsCiefi7NmzZLZs2ebAdSAAQO0ORddX4LSqVMnOXDggFnEVre4Su1YVcQVPK+6WNSl3vuKrsJ9rurynJJGRRUz/upXvzIz0Jw5cwKqDlTUW7d4UmW87t27m99xShrVBd53QfTp06ePmQZ18VBpqnsc1TJVFYrTjqOyYMEC/y/uutSFoW5Qo4pbVdrtPo7RcOr5GA0nnovRcOq52BROPRdnzJghr7zyihmknHnmmVqdi64PUFS9rarX9DXmUZYuXSrHHHOM/5eKj+q/vXz5cvOXjKL+XbZsmTnd97n6rs/u3bvNl+9zJ6Tx0KFDct1115nTVX2jylg+Kr2qH7tq6FV3/q1bt5oXFaek8e677/aPQeCjGt2pNKh51XfqHke1TLVs9evOKWn0VQts377dbLxWlypOPv7442XRokX+aepiqIIfu49jNJx6PkbKqediNJx6LkbLqefiU089JW+++aY89thjcu655+p3LhoJ4L777jPOPfdcs6+26qN+3HHHGZ988on5meqTXl5ebv5dUlJijB492uzjrbqYqX9VNz9fX+9ly5aZ/dtVtzFfX+8bb7zRcFIaVRdG1Y1PzVe3X/7BgwfNz1Wax48fb3apU33j1RgAEyZMMLvPOSWNapo6TqpPvhpf4sknnzTTvH37dvPzDz/80PyuWoZallqmSrcOIk2joo6R6pYcalwMlS9VF0m1HDWmyuWXX26Od6Cb4O6bbjkfI0mfk8/FSNPo5HMx0jQ69VzMy8szu4CrLtF185966XIuJkSAovrUT5s2zRg6dKjZx/uVV14JyHi+vtyKykQTJ040M9vFF19srFmzJmBZat5x48aZy1IXjP379xtOSuOZZ55pvg9++fqwq4GJZs6caWa+IUOGmJlM9Wl32nFUJ8oZZ5xhHH300caFF15oLFmyJGBZzz33nHHCCSeYg0RNnz7dTLfT0vjRRx+FHSejqKjIuPvuu41Ro0aZg7397ne/M6fpfuF3y/kYSfqcfC5Gcwydei5Gk0YnnovPPfdcyPynXrqcix71v+aVwQAAAFjL9W1QAACA8xCgAAAA7RCgAAAA7RCgAAAA7RCgAAAA7RCgAAAA7RCgAACABqnHMUyYMMF8KGKk1BOsL7jgAnNEWfUQRTWKcDQIUAAAQFiVlZVyxx13SG5urkRKDf1//fXXy+mnny7vvfeeDBw40Hx4ZDRPOCZAAQAAIeXl5ZmlH9u2bZNoqOdLHXvssXLrrbdKr1695J577jGfv7Rp06aIl0GAAgAAwlbTjBo1SubNm1fvsx9++EEmTZpkBiLnnXeefPLJJwHfO+OMMwKe6vzZZ59F9SDIlIjnBAAACWXy5Mkhp+fn58uNN94ot99+u4wdO9Z8ErV6enV2draMGDHCrOJJT0+X2267zQxk+vXrJ3/84x/NfyNFCQoAAIjKa6+9JieeeKJcccUV0rNnT7Mx7GWXXSazZ882Pz906JA8+uijcvzxx8sLL7wgXbp0kSlTpkhZWVnE66AEBQAAREW1Jfnyyy9l2LBh/mnV1dXSu3dv8+/k5GQ59dRT5corrzTfz5gxQ8aPHy9ffPGFWR0UCQIUAAAQlZqaGjPQuOmmmwKmp6T8HFZ06NDBH6woqamp0q1bN9m9e3fE66CKBwAAREUFH1u3bjWrd3yvzz//XD744APz86FDh8qGDRv886vuxapdSvfu3SNeBwEKAACIuvHsjz/+KI8//rhs2bLFDEwee+wx6dq1q/n51Vdfbfbqef31183PH3zwQUlLSzOreSLlMQzDiG6zAABAohk4cKDMmTPH7HasfPfdd2ZD2J9++kk6deok11xzjdlo1kd1K1af79y5U44++mgzSOnfv3/E6yNAAQAA2qGKBwAAaIcABQAAaIcABQAAaIcABQAAaIcABQAAaIcABQAAaIcABQAAaIcABQAAaIcABQAAaIcABQAAaIcABQAAaIcABQAAiG7+P8XLIVjGKM/IAAAAAElFTkSuQmCC"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -1370,18 +1408,105 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T07:36:56.729594Z",
-     "start_time": "2026-02-28T07:36:56.383260Z"
+     "end_time": "2026-02-28T15:53:05.418746Z",
+     "start_time": "2026-02-28T15:53:05.395106Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import pandas as pd\n",
+    "import torch\n",
+    "from torch import nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from RNN_Dataset import RNN_Dataset\n",
+    "\n",
+    "\n",
+    "def make_sequence_datasets(\n",
+    "    df_xy,\n",
+    "    state_cols,\n",
+    "    control_cols,\n",
+    "    seq_len,\n",
+    "    stride=100,\n",
+    "    train_frac=0.8,\n",
+    "    batch_size=64,\n",
+    "):\n",
+    "\n",
+    "\n",
+    "    cols_to_scale = state_cols + control_cols\n",
+    "\n",
+    "    # Train/test split (time-series safe)\n",
+    "    n_total = len(df_xy)\n",
+    "    train_len = int(train_frac * n_total)\n",
+    "    df_xy = df_xy.dropna(subset=state_cols + control_cols).reset_index(drop=True)\n",
+    "\n",
+    "    df_train_raw = df_xy.iloc[:train_len].reset_index(drop=True)\n",
+    "    df_test_raw  = df_xy.iloc[train_len:].reset_index(drop=True)\n",
+    "\n",
+    "    # Fit scaler ONLY on training data\n",
+    "    scaler = StandardScaler()\n",
+    "    scaler.fit(df_train_raw[cols_to_scale])\n",
+    "\n",
+    "    # Apply scaling\n",
+    "    df_train = df_train_raw.copy()\n",
+    "    df_test  = df_test_raw.copy()\n",
+    "    df_train.dropna()\n",
+    "    df_test = df_test.dropna()\n",
+    "\n",
+    "    df_train[cols_to_scale] = scaler.transform(df_train_raw[cols_to_scale])\n",
+    "    df_test[cols_to_scale]  = scaler.transform(df_test_raw[cols_to_scale])\n",
+    "\n",
+    "    # Create datasets\n",
+    "    train_dataset = RNN_Dataset(\n",
+    "        df_train,\n",
+    "        state_cols,\n",
+    "        control_cols,\n",
+    "        seq_len,\n",
+    "        stride\n",
+    "    )\n",
+    "\n",
+    "    test_dataset = RNN_Dataset(\n",
+    "        df_test,\n",
+    "        state_cols,\n",
+    "        control_cols,\n",
+    "        seq_len,\n",
+    "        stride\n",
+    "    )\n",
+    "\n",
+    "    # DataLoaders\n",
+    "    train_loader = DataLoader(\n",
+    "        train_dataset,\n",
+    "        batch_size=batch_size,\n",
+    "        shuffle=True\n",
+    "    )\n",
+    "\n",
+    "    test_loader = DataLoader(\n",
+    "        test_dataset,\n",
+    "        batch_size=batch_size,\n",
+    "        shuffle=False\n",
+    "    )\n",
+    "\n",
+    "    return train_dataset, test_dataset, train_loader, test_loader, scaler\n"
+   ],
+   "id": "ac176284a9de95e8",
+   "outputs": [],
+   "execution_count": 58
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-28T15:53:06.498322Z",
+     "start_time": "2026-02-28T15:53:06.128102Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "import DataPreprocessing\n",
+    "#import DataPreprocessing\n",
     "train_dataset, test_dataset, train_loader, test_loader, scaler = DataPreprocessing.make_sequence_datasets(final_df, state_cols = ['0_x', '0_y'], control_cols = ['mech_brake_pressed', 'accel_position'], seq_len = 100, train_frac=0.7, batch_size = 64)"
    ],
    "id": "caa83b2458af2b80",
    "outputs": [],
-   "execution_count": 16
+   "execution_count": 59
   },
   {
    "metadata": {},
@@ -1408,14 +1533,43 @@
    ],
    "id": "573dc63af8846209"
   },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "realised that brake pressed although told to be continous is actually not (either 0 or 1). this makes it harder for the rnn to truly capture they dynamics.",
+   "id": "90141b7cb2d4e96a"
+  },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T08:19:32.385979Z",
-     "start_time": "2026-02-28T08:19:32.371927Z"
+     "end_time": "2026-02-28T15:53:47.574120Z",
+     "start_time": "2026-02-28T15:53:47.554479Z"
     }
    },
    "cell_type": "code",
+   "source": [
+    "\n",
+    "from RNN import *\n",
+    "state   = [\"0_x\", \"0_y\"]\n",
+    "control = [\"mech_brake_pressed\", \"accel_position\"]\n",
+    "seq_length = 100\n",
+    "input_size = len(state)\n",
+    "output_size = len(control)\n",
+    "\n",
+    "\n",
+    "hidden_size = 64     # was 256\n",
+    "num_layers  = 2\n",
+    "model = RNN(input_size, hidden_size, num_layers, seq_length, output_size).to(device)"
+   ],
+   "id": "f9712a97111555d4",
+   "outputs": [],
+   "execution_count": 64
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": 63,
    "source": [
     "# now we define the training loop. we are pretending a trajectory of controls.\n",
     "\n",
@@ -1429,6 +1583,8 @@
     "    optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=1e-5)\n",
     "    train_losses = []\n",
     "    test_losses = []\n",
+    "    print(\"NaNs in Train Loader:\", any(torch.isnan(x).any() for x, y in train_loader))\n",
+    "    print(\"NaNs in Test Loader:\", any(torch.isnan(x).any() for x, y in test_loader))\n",
     "    for epoch in range(epochs):\n",
     "\n",
     "        #training loop\n",
@@ -1440,8 +1596,22 @@
     "            optimizer.zero_grad() #resets the gradients to zero\n",
     "\n",
     "            outputs = model(x_batch)\n",
+    "\n",
+    "            if torch.isnan(outputs).any():\n",
+    "                print(\"NaN in model outputs\")\n",
+    "                print(\"x_batch min/max:\", x_batch.min().item(), x_batch.max().item())\n",
+    "                break\n",
+    "\n",
+    "            loss = criterion(outputs, y_batch)\n",
+    "\n",
+    "            if torch.isnan(loss):\n",
+    "                print(\"NaN in loss\")\n",
+    "                print(\"outputs min/max:\", outputs.min().item(), outputs.max().item())\n",
+    "                print(\"y_batch min/max:\", y_batch.min().item(), y_batch.max().item())\n",
+    "                break\n",
     "            loss = criterion(outputs, y_batch)\n",
     "            loss.backward()\n",
+    "            torch.nn.utils.clip_grad_norm_(model.parameters(), 1)\n",
     "            optimizer.step()\n",
     "\n",
     "            train_loss += loss.item() #convert tensor to float\n",
@@ -1461,211 +1631,94 @@
     "                loss = criterion(predictions, y_batch)\n",
     "                test_loss += loss.item()\n",
     "        test_loss/=len(test_loader)\n",
-    "        print(f\"Epoch {epoch + 1}/{epochs}, Test Loss: {test_loss:.4f}\")\n",
     "        test_losses.append(test_loss)\n",
     "\n",
     "    return train_losses, test_losses"
    ],
-   "id": "329e1f0797e61e90",
-   "outputs": [],
-   "execution_count": 49
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "realised that brake pressed although told to be continous is actually not (either 0 or 1). this makes it harder for the rnn to truly capture they dynamics.",
-   "id": "90141b7cb2d4e96a"
+   "id": "329e1f0797e61e90"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T08:19:33.429440Z",
-     "start_time": "2026-02-28T08:19:33.398783Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "state_cols = ['0_x', '0_y']\n",
-    "control_cols = ['mech_brake_pressed', 'accel_position']\n",
-    "print(train_dataset[1].isna().sum())"
-   ],
-   "id": "1a2bef66c43d7650",
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'tuple' object has no attribute 'isna'",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mAttributeError\u001B[39m                            Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[50]\u001B[39m\u001B[32m, line 3\u001B[39m\n\u001B[32m      1\u001B[39m state_cols = [\u001B[33m'\u001B[39m\u001B[33m0_x\u001B[39m\u001B[33m'\u001B[39m, \u001B[33m'\u001B[39m\u001B[33m0_y\u001B[39m\u001B[33m'\u001B[39m]\n\u001B[32m      2\u001B[39m control_cols = [\u001B[33m'\u001B[39m\u001B[33mmech_brake_pressed\u001B[39m\u001B[33m'\u001B[39m, \u001B[33m'\u001B[39m\u001B[33maccel_position\u001B[39m\u001B[33m'\u001B[39m]\n\u001B[32m----> \u001B[39m\u001B[32m3\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[43mtrain_dataset\u001B[49m\u001B[43m[\u001B[49m\u001B[32;43m1\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m.\u001B[49m\u001B[43misna\u001B[49m().sum())\n",
-      "\u001B[31mAttributeError\u001B[39m: 'tuple' object has no attribute 'isna'"
-     ]
-    }
-   ],
-   "execution_count": 50
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-28T08:19:35.145940Z",
-     "start_time": "2026-02-28T08:19:35.117103Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "print(train_dataset[0].describe())  # should be roughly mean=0, std=1 after scaling",
-   "id": "167dd9f1f422fdfc",
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'tuple' object has no attribute 'describe'",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mAttributeError\u001B[39m                            Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[51]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[43mtrain_dataset\u001B[49m\u001B[43m[\u001B[49m\u001B[32;43m0\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m.\u001B[49m\u001B[43mdescribe\u001B[49m())  \u001B[38;5;66;03m# should be roughly mean=0, std=1 after scaling\u001B[39;00m\n",
-      "\u001B[31mAttributeError\u001B[39m: 'tuple' object has no attribute 'describe'"
-     ]
-    }
-   ],
-   "execution_count": 51
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-28T08:19:36.009757Z",
-     "start_time": "2026-02-28T08:19:36.003612Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "from RNN import *\n",
-    "state   = [\"0_x\", \"0_y\"]\n",
-    "control = [\"mech_brake_pressed\", \"accel_position\"]\n",
-    "seq_length = 100\n",
-    "input_size = len(state)\n",
-    "output_size = len(control)\n",
-    "\n",
-    "\n",
-    "hidden_size = 64     # was 256\n",
-    "num_layers  = 2\n",
-    "model = RNN(input_size, hidden_size, num_layers, seq_length, output_size).to(device)\n",
-    "\n"
-   ],
-   "id": "f9712a97111555d4",
-   "outputs": [],
-   "execution_count": 52
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-28T08:23:45.296250Z",
-     "start_time": "2026-02-28T08:19:39.348922Z"
+     "end_time": "2026-02-28T15:58:49.960377Z",
+     "start_time": "2026-02-28T15:53:48.499547Z"
     }
    },
    "cell_type": "code",
    "source": "train_model(model, train_loader, test_loader, epochs = 30)",
-   "id": "83eb0a191001c256",
+   "id": "205d60e11f150705",
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/30, Train Loss: 0.7140\n",
-      "Epoch 1/30, Test Loss: nan\n",
-      "Epoch 2/30, Train Loss: 0.6909\n",
-      "Epoch 2/30, Test Loss: nan\n",
-      "Epoch 3/30, Train Loss: 0.6881\n",
-      "Epoch 3/30, Test Loss: nan\n",
-      "Epoch 4/30, Train Loss: 0.6860\n",
-      "Epoch 4/30, Test Loss: nan\n",
-      "Epoch 5/30, Train Loss: 0.6841\n",
-      "Epoch 5/30, Test Loss: nan\n",
-      "Epoch 6/30, Train Loss: 0.6846\n",
-      "Epoch 6/30, Test Loss: nan\n",
-      "Epoch 7/30, Train Loss: 0.6843\n",
-      "Epoch 7/30, Test Loss: nan\n",
-      "Epoch 8/30, Train Loss: 0.6831\n",
-      "Epoch 8/30, Test Loss: nan\n",
-      "Epoch 9/30, Train Loss: 0.6834\n",
-      "Epoch 9/30, Test Loss: nan\n",
-      "Epoch 10/30, Train Loss: 0.6828\n",
-      "Epoch 10/30, Test Loss: nan\n",
-      "Epoch 11/30, Train Loss: 0.6823\n",
-      "Epoch 11/30, Test Loss: nan\n",
-      "Epoch 12/30, Train Loss: 0.6829\n",
-      "Epoch 12/30, Test Loss: nan\n",
-      "Epoch 13/30, Train Loss: 0.6830\n",
-      "Epoch 13/30, Test Loss: nan\n",
-      "Epoch 14/30, Train Loss: 0.6824\n",
-      "Epoch 14/30, Test Loss: nan\n",
-      "Epoch 15/30, Train Loss: 0.6822\n",
-      "Epoch 15/30, Test Loss: nan\n",
-      "Epoch 16/30, Train Loss: 0.7040\n",
-      "Epoch 16/30, Test Loss: nan\n",
-      "Epoch 17/30, Train Loss: 0.6826\n",
-      "Epoch 17/30, Test Loss: nan\n",
-      "Epoch 18/30, Train Loss: 0.6832\n",
-      "Epoch 18/30, Test Loss: nan\n",
-      "Epoch 19/30, Train Loss: 0.6821\n",
-      "Epoch 19/30, Test Loss: nan\n",
-      "Epoch 20/30, Train Loss: 0.7053\n",
-      "Epoch 20/30, Test Loss: nan\n",
-      "Epoch 21/30, Train Loss: 0.6885\n",
-      "Epoch 21/30, Test Loss: nan\n",
-      "Epoch 22/30, Train Loss: 0.6993\n",
-      "Epoch 22/30, Test Loss: nan\n",
-      "Epoch 23/30, Train Loss: 0.6861\n",
-      "Epoch 23/30, Test Loss: nan\n",
-      "Epoch 24/30, Train Loss: 0.6858\n",
-      "Epoch 24/30, Test Loss: nan\n",
-      "Epoch 25/30, Train Loss: 0.6806\n",
-      "Epoch 25/30, Test Loss: nan\n",
-      "Epoch 26/30, Train Loss: 0.6857\n",
-      "Epoch 26/30, Test Loss: nan\n",
-      "Epoch 27/30, Train Loss: 0.6816\n",
-      "Epoch 27/30, Test Loss: nan\n",
-      "Epoch 28/30, Train Loss: 0.6811\n",
-      "Epoch 28/30, Test Loss: nan\n",
-      "Epoch 29/30, Train Loss: 0.6846\n",
-      "Epoch 29/30, Test Loss: nan\n",
-      "Epoch 30/30, Train Loss: 0.6816\n",
-      "Epoch 30/30, Test Loss: nan\n"
+      "NaNs in Train Loader: False\n",
+      "NaNs in Test Loader: True\n",
+      "Epoch 1/30, Train Loss: 0.7202\n",
+      "Epoch 2/30, Train Loss: 0.6946\n",
+      "Epoch 3/30, Train Loss: 0.6908\n",
+      "Epoch 4/30, Train Loss: 0.6893\n",
+      "Epoch 5/30, Train Loss: 0.6881\n",
+      "Epoch 6/30, Train Loss: 0.6860\n",
+      "Epoch 7/30, Train Loss: 0.7056\n",
+      "Epoch 8/30, Train Loss: 0.6852\n",
+      "Epoch 9/30, Train Loss: 0.6828\n",
+      "Epoch 10/30, Train Loss: 0.6787\n",
+      "Epoch 11/30, Train Loss: 0.6755\n",
+      "Epoch 12/30, Train Loss: 0.6640\n",
+      "Epoch 13/30, Train Loss: 0.6529\n",
+      "Epoch 14/30, Train Loss: 0.6456\n",
+      "Epoch 15/30, Train Loss: 0.6390\n",
+      "Epoch 16/30, Train Loss: 0.6422\n",
+      "Epoch 17/30, Train Loss: 0.6275\n",
+      "Epoch 18/30, Train Loss: 0.6235\n",
+      "Epoch 19/30, Train Loss: 0.6190\n",
+      "Epoch 20/30, Train Loss: 0.6327\n",
+      "Epoch 21/30, Train Loss: 0.6134\n",
+      "Epoch 22/30, Train Loss: 0.6102\n",
+      "Epoch 23/30, Train Loss: 0.6085\n",
+      "Epoch 24/30, Train Loss: 0.6042\n",
+      "Epoch 25/30, Train Loss: 0.6016\n",
+      "Epoch 26/30, Train Loss: 0.6069\n",
+      "Epoch 27/30, Train Loss: 0.5994\n",
+      "Epoch 28/30, Train Loss: 0.5971\n",
+      "Epoch 29/30, Train Loss: 0.5976\n",
+      "Epoch 30/30, Train Loss: 0.6187\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "([0.7140238776600738,\n",
-       "  0.6908602790793243,\n",
-       "  0.6880908035820695,\n",
-       "  0.6860014968235636,\n",
-       "  0.6840919272224019,\n",
-       "  0.6845910328006313,\n",
-       "  0.684310179570378,\n",
-       "  0.6831428177140619,\n",
-       "  0.6833995667947835,\n",
-       "  0.6827774052824235,\n",
-       "  0.6823315585086889,\n",
-       "  0.6829381807753605,\n",
-       "  0.6830271740288082,\n",
-       "  0.6824388360747924,\n",
-       "  0.6822162142710347,\n",
-       "  0.7039632424543616,\n",
-       "  0.6825905680589007,\n",
-       "  0.6831549356156345,\n",
-       "  0.682105971222145,\n",
-       "  0.7052697260185604,\n",
-       "  0.6885104945005335,\n",
-       "  0.6993319677416555,\n",
-       "  0.6860525134099622,\n",
-       "  0.6858327123778978,\n",
-       "  0.6805644514570139,\n",
-       "  0.685663951400718,\n",
-       "  0.6816361314360035,\n",
-       "  0.6810958826434019,\n",
-       "  0.6845737903366261,\n",
-       "  0.6815792499910788],\n",
+       "([0.7201533619381858,\n",
+       "  0.6946240679879264,\n",
+       "  0.6907852335108183,\n",
+       "  0.6892980485606719,\n",
+       "  0.6881164071188052,\n",
+       "  0.6860182614284841,\n",
+       "  0.7055505071492756,\n",
+       "  0.6851833204810436,\n",
+       "  0.6827870494774564,\n",
+       "  0.6787244300421209,\n",
+       "  0.6754942850938817,\n",
+       "  0.6640038222571776,\n",
+       "  0.6528504782470104,\n",
+       "  0.6455738350614164,\n",
+       "  0.6390082142126169,\n",
+       "  0.6422068501961716,\n",
+       "  0.6275138414351109,\n",
+       "  0.623540780696664,\n",
+       "  0.6189669531925973,\n",
+       "  0.6326697754559878,\n",
+       "  0.613436116150062,\n",
+       "  0.6101548756004999,\n",
+       "  0.6085371648623056,\n",
+       "  0.6041843240987931,\n",
+       "  0.6015912443378358,\n",
+       "  0.6068879433009959,\n",
+       "  0.5993611881408284,\n",
+       "  0.5971358119767176,\n",
+       "  0.5976458289302312,\n",
+       "  0.6187297694418765],\n",
        " [nan,\n",
        "  nan,\n",
        "  nan,\n",
@@ -1698,214 +1751,12 @@
        "  nan])"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 53
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-28T07:42:10.906806Z",
-     "start_time": "2026-02-28T07:42:10.896753Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "\n",
-    "def plot_control_trajectory(model, test_dataset, state_cols, control_cols, sample_idx, scaler):\n",
-    "    model.eval()\n",
-    "\n",
-    "    # 1. Get the single sample for inference\n",
-    "    x_input, y_target = test_dataset[sample_idx]\n",
-    "\n",
-    "    with torch.no_grad():\n",
-    "        device = next(model.parameters()).device\n",
-    "        x_input_device = x_input.to(device).unsqueeze(0)\n",
-    "        y_pred = model(x_input_device).squeeze(0).cpu().numpy()\n",
-    "\n",
-    "    y_target = y_target.numpy()\n",
-    "\n",
-    "    # 2. Extract the full sequence of input states directly from the dataset\n",
-    "    # We need this because x_input only holds the start and end states!\n",
-    "    raw_idx = test_dataset.indices[sample_idx]\n",
-    "    seq_len = test_dataset.seq_len\n",
-    "    states_seq = test_dataset.states[raw_idx : raw_idx + seq_len].numpy()\n",
-    "\n",
-    "    # 3. Manually Unscale the Data\n",
-    "    # The scaler was fit on [states + controls]. We slice the means and scales to match.\n",
-    "    num_states = len(state_cols)\n",
-    "\n",
-    "    state_means = scaler.mean_[:num_states]\n",
-    "    state_scales = scaler.scale_[:num_states]\n",
-    "    control_means = scaler.mean_[num_states:]\n",
-    "    control_scales = scaler.scale_[num_states:]\n",
-    "\n",
-    "    # Apply inverse transform: original = scaled * scale + mean\n",
-    "    y_target_unscaled = y_target * control_scales + control_means\n",
-    "    y_pred_unscaled = y_pred * control_scales + control_means\n",
-    "    states_seq_unscaled = states_seq * state_scales + state_means\n",
-    "\n",
-    "    # 4. Plotting\n",
-    "    time_steps = np.arange(0, seq_len * 0.1, 0.1) # 0.1s granularity\n",
-    "\n",
-    "    # --- Plot Controls ---\n",
-    "    fig, ax = plt.subplots(len(control_cols), 1, figsize=(10, 6), sharex=True)\n",
-    "    if len(control_cols) == 1: ax = [ax] # Handle single control edge case\n",
-    "\n",
-    "    for i, col in enumerate(control_cols):\n",
-    "        ax[i].plot(time_steps, y_target_unscaled[:, i], 'g-', label='Actual (Driver)')\n",
-    "        ax[i].plot(time_steps, y_pred_unscaled[:, i], 'r--', label='Predicted (RNN)')\n",
-    "        ax[i].set_ylabel(f'{col}')\n",
-    "        ax[i].legend()\n",
-    "        ax[i].grid(True)\n",
-    "\n",
-    "    ax[-1].set_xlabel(\"Time (seconds)\")\n",
-    "    plt.suptitle(f\"Control Sequence for Sample {sample_idx}\")\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()\n",
-    "\n",
-    "    # --- Plot States (e.g., Speed) ---\n",
-    "    fig1, ax1 = plt.subplots(len(state_cols), 1, figsize=(10, 6), sharex=True)\n",
-    "    if len(state_cols) == 1: ax1 = [ax1] # Handle single state edge case\n",
-    "\n",
-    "    for i, col in enumerate(state_cols):\n",
-    "        ax1[i].plot(time_steps, states_seq_unscaled[:, i], 'b-', label=f'Input Trajectory')\n",
-    "        ax1[i].set_ylabel(f'{col}')\n",
-    "        ax1[i].legend()\n",
-    "        ax1[i].grid(True)\n",
-    "\n",
-    "    ax1[-1].set_xlabel(\"Time (seconds)\")\n",
-    "    plt.suptitle(f\"Input States Sequence for Sample {sample_idx}\")\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()"
-   ],
-   "id": "ed7e024db3ba2a13",
-   "outputs": [],
-   "execution_count": 20
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-28T07:42:12.166847Z",
-     "start_time": "2026-02-28T07:42:12.160614Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "\n",
-    "def plot_control_trajectory(model, test_dataset, state_cols, control_cols, sample_idx):\n",
-    "    model.eval()\n",
-    "    x_input, y_target = test_dataset[sample_idx]\n",
-    "\n",
-    "    # Run inference (add batch dimension)\n",
-    "    with torch.no_grad():\n",
-    "        # Move to same device as model\n",
-    "        device = next(model.parameters()).device\n",
-    "        x_input = x_input.to(device).unsqueeze(0)\n",
-    "        y_pred = model(x_input).squeeze(0).cpu().numpy()\n",
-    "\n",
-    "    y_target = y_target.numpy()\n",
-    "    x_input = x_input.to(device).squeeze(0).numpy()\n",
-    "\n",
-    "    # Plotting\n",
-    "    fig, ax = plt.subplots(len(control_cols), 1, figsize=(10, 6), sharex=True)\n",
-    "    time_steps = np.arange(0, len(y_target) * 0.1, 0.1) # 0.1s granularity\n",
-    "\n",
-    "    for i, col in enumerate(control_cols):\n",
-    "        ax[i].plot(time_steps, y_target[:, i], 'g-', label='Actual (Driver)')\n",
-    "        ax[i].plot(time_steps, y_pred[:, i], 'r--', label='Predicted (RNN)')\n",
-    "\n",
-    "        ax[i].set_ylabel(f'Scaled {col}')\n",
-    "        ax[i].legend()\n",
-    "        ax[i].grid(True)\n",
-    "\n",
-    "    fig1, ax1 = plt.subplots(len(state_cols), 1, figsize=(10, 6), sharex=True)\n",
-    "    time_steps = np.arange(0, len(x_input) * 0.1, 0.1) # 0.1s granularity\n",
-    "\n",
-    "    for i, col in enumerate(state_cols):\n",
-    "         ax1[i].plot(time_steps, test_dataset[:, i], 'b-', label='input')\n",
-    "\n",
-    "    ax[-1].set_xlabel(\"Time (seconds)\")\n",
-    "    plt.suptitle(f\"Control Sequence for Sample {sample_idx}\")\n",
-    "    plt.show()"
-   ],
-   "id": "20dfb9bc00550510",
-   "outputs": [],
-   "execution_count": 21
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-28T07:47:26.758332Z",
-     "start_time": "2026-02-28T07:47:26.750820Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import torch\n",
-    "import numpy as np\n",
-    "\n",
-    "def visualise_predictions(model, dataset, scaler, state_cols, control_cols, sample_idx=0, device=\"cpu\"):\n",
-    "    model.eval()\n",
-    "\n",
-    "    x, y_true = dataset[sample_idx]\n",
-    "    x_input = x.unsqueeze(0).to(device)  # add batch dim\n",
-    "\n",
-    "    with torch.no_grad():\n",
-    "        y_pred = model(x_input).squeeze(0).cpu().numpy()  # [seq_len, n_controls]\n",
-    "\n",
-    "    y_true = y_true.numpy()  # [seq_len, n_controls]\n",
-    "\n",
-    "    # x is [seq_len, n_states] — extract state sequence for plotting\n",
-    "    x_np = x.numpy()  # [seq_len, n_states]\n",
-    "\n",
-    "    # unscale states for interpretable axis values\n",
-    "    # scaler was fit on state_cols + control_cols so we need to inverse correctly\n",
-    "    n_states = len(state_cols)\n",
-    "    n_controls = len(control_cols)\n",
-    "    seq_len = y_true.shape[0]\n",
-    "    timesteps = np.arange(seq_len) * 0.1  # 0.1s granularity\n",
-    "\n",
-    "    fig, axes = plt.subplots(2 + n_controls, 1, figsize=(12, 4 * (2 + n_controls)), sharex=True)\n",
-    "\n",
-    "    # plot states (speed, position)\n",
-    "    state_labels = state_cols\n",
-    "    for i, label in enumerate(state_labels):\n",
-    "        axes[i].plot(timesteps, x_np[:, i], label=label, color=\"steelblue\")\n",
-    "        axes[i].set_ylabel(label)\n",
-    "        axes[i].legend()\n",
-    "        axes[i].grid(True)\n",
-    "\n",
-    "    # plot controls — predicted vs real\n",
-    "    control_labels = control_cols\n",
-    "    colors_pred = [\"tomato\", \"darkorange\"]\n",
-    "    colors_true = [\"steelblue\", \"seagreen\"]\n",
-    "\n",
-    "    for i, label in enumerate(control_labels):\n",
-    "        ax = axes[n_states + i]\n",
-    "        ax.plot(timesteps, y_true[:, i], label=f\"{label} (actual)\", color=colors_true[i], linewidth=2)\n",
-    "        ax.plot(timesteps, y_pred[:, i], label=f\"{label} (predicted)\", color=colors_pred[i], linestyle=\"--\", linewidth=2)\n",
-    "        ax.set_ylabel(label)\n",
-    "        ax.legend()\n",
-    "        ax.grid(True)\n",
-    "\n",
-    "    axes[-1].set_xlabel(\"Time (s)\")\n",
-    "    fig.suptitle(f\"Predicted vs Actual Controls — Sample {sample_idx}\", fontsize=14)\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()"
-   ],
-   "id": "14c5d4347da416ef",
-   "outputs": [],
-   "execution_count": 30
+   "execution_count": 65
   },
   {
    "metadata": {
@@ -1930,8 +1781,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T08:30:05.184764Z",
-     "start_time": "2026-02-28T08:30:05.166507Z"
+     "end_time": "2026-02-28T15:40:13.814713Z",
+     "start_time": "2026-02-28T15:40:13.798153Z"
     }
    },
    "cell_type": "code",
@@ -1966,24 +1817,17 @@
     "    n_states = len(state_cols)\n",
     "    n_controls = len(control_cols)\n",
     "    seq_len = y_target.shape[0]\n",
-    "\n",
+    "    def unscale_states(arr):\n",
+    "        state_mean = scaler.mean_[:n_states]\n",
+    "        state_std  = scaler.scale_[:n_states]\n",
+    "        return arr * state_std + state_mean\n",
     "    def unscale_controls(arr):\n",
     "        # arr: [seq_len, n_controls]\n",
     "        dummy = np.zeros((seq_len, n_states + n_controls))\n",
     "        dummy[:, n_states:] = arr\n",
     "        return scaler.inverse_transform(dummy)[:, n_states:]\n",
     "\n",
-    "    def unscale_states(arr):\n",
-    "        n = arr.shape[0]\n",
-    "        dummy = np.zeros((n, n_states + n_controls))\n",
-    "        dummy[:, :n_states] = arr\n",
-    "        return scaler.inverse_transform(dummy)[:, :n_states]\n",
     "\n",
-    "    # def unscale_states(arr):\n",
-    "    #     # arr: [seq_len, n_states]\n",
-    "    #     dummy = np.zeros((seq_len, n_states + n_controls))\n",
-    "    #     dummy[:, :n_states] = arr\n",
-    "    #     return scaler.inverse_transform(dummy)[:, :n_states]\n",
     "\n",
     "    y_target_unscaled = unscale_controls(y_target)\n",
     "    y_pred_unscaled   = unscale_controls(y_pred)\n",
@@ -2028,18 +1872,75 @@
    ],
    "id": "55763d08b5868893",
    "outputs": [],
-   "execution_count": 56
+   "execution_count": 30
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-28T15:43:14.698464Z",
+     "start_time": "2026-02-28T15:43:14.636621Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# find a sample with nonzero controls\n",
+    "for i in range(len(test_dataset)):\n",
+    "    x, y = test_dataset[i]\n",
+    "    if y.abs().mean() > 0.1:\n",
+    "        print(f\"sample {i} has nonzero controls\")\n",
+    "        break"
+   ],
+   "id": "2ce209bf8aab8f07",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sample 0 has nonzero controls\n"
+     ]
+    }
+   ],
+   "execution_count": 35
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T08:30:06.758367Z",
-     "start_time": "2026-02-28T08:30:06.091164Z"
+     "end_time": "2026-02-28T16:11:22.021757Z",
+     "start_time": "2026-02-28T16:11:21.805444Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx=10)\n",
+    "for i in range(len(test_dataset)):\n",
+    "    x, y = test_dataset[i]\n",
+    "    if y.abs().mean() >= 0.88:\n",
+    "        print(f\"use sample_idx={i}\")\n",
+    "        break"
+   ],
+   "id": "f66c02f2a3933818",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "use sample_idx=3004\n"
+     ]
+    }
+   ],
+   "execution_count": 68
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-28T16:11:31.061286Z",
+     "start_time": "2026-02-28T16:11:29.966789Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "state_cols = ['0_x', '0_y']\n",
+    "control_cols = ['mech_brake_pressed', 'accel_position']\n",
+    "plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx=3004)\n",
     "plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx=150)"
    ],
    "id": "3460de89ee1321c7",
@@ -2048,10 +1949,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "torch.Size([2])\n",
-      "(2,)\n",
-      "(2, 2)\n",
-      "(2,)\n"
+      "torch.Size([100, 2])\n",
+      "(100, 2)\n",
+      "(100, 2)\n",
+      "(100,)\n"
      ]
     },
     {
@@ -2059,7 +1960,7 @@
       "text/plain": [
        "<Figure size 1000x800 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -2069,7 +1970,7 @@
       "text/plain": [
        "<Figure size 1000x800 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -2078,10 +1979,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "torch.Size([2])\n",
-      "(2,)\n",
-      "(2, 2)\n",
-      "(2,)\n"
+      "torch.Size([100, 2])\n",
+      "(100, 2)\n",
+      "(100, 2)\n",
+      "(100,)\n"
      ]
     },
     {
@@ -2089,7 +1990,7 @@
       "text/plain": [
        "<Figure size 1000x800 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -2099,13 +2000,166 @@
       "text/plain": [
        "<Figure size 1000x800 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 57
+   "execution_count": 69
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-28T15:46:49.290498Z",
+     "start_time": "2026-02-28T15:46:49.250970Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "plt.plot(train_losses, label='train')\n",
+    "plt.plot(test_losses, label='test')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ],
+   "id": "8466fb8dd15e5c5",
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'train_losses' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mNameError\u001B[39m                                 Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[41]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m plt.plot(\u001B[43mtrain_losses\u001B[49m, label=\u001B[33m'\u001B[39m\u001B[33mtrain\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m      2\u001B[39m plt.plot(test_losses, label=\u001B[33m'\u001B[39m\u001B[33mtest\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m      3\u001B[39m plt.legend()\n",
+      "\u001B[31mNameError\u001B[39m: name 'train_losses' is not defined"
+     ]
+    }
+   ],
+   "execution_count": 41
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-02-28T15:38:32.564480Z",
+     "start_time": "2026-02-28T15:38:32.457710Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "x, y = test_dataset[10]\n",
+    "print(y)  # should not be all zeros"
+   ],
+   "id": "83c67c533b9c1721",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439],\n",
+      "        [-0.2009, -0.3439]])\n"
+     ]
+    }
+   ],
+   "execution_count": 29
   },
   {
    "metadata": {
diff --git a/array_temp/DataPreprocessing.py b/array_temp/DataPreprocessing.py
index 257dc8e..4238920 100644
--- a/array_temp/DataPreprocessing.py
+++ b/array_temp/DataPreprocessing.py
@@ -22,6 +22,7 @@ def make_sequence_datasets(
     # Train/test split (time-series safe)
     n_total = len(df_xy)
     train_len = int(train_frac * n_total)
+    df_xy = df_xy.dropna(subset=state_cols + control_cols).reset_index(drop=True)
 
     df_train_raw = df_xy.iloc[:train_len].reset_index(drop=True)
     df_test_raw  = df_xy.iloc[train_len:].reset_index(drop=True)
diff --git a/array_temp/RNN.py b/array_temp/RNN.py
index 21dd9fb..70b2672 100644
--- a/array_temp/RNN.py
+++ b/array_temp/RNN.py
@@ -29,7 +29,7 @@ def forward(self, x):
         #x shape : [batch size, input size]
         # associate the state array (start + goal state) with timeseries dependency
         # this is done by repeating the input vector seq_len times
-        x_repeated = x.unsqueeze(1).repeat(1, self.seq_length, 1)
+        #x_repeated = x.unsqueeze(1).repeat(1, self.seq_length, 1)
 
         #inital hidden, cell states - these are internal memory vectors
         #hidden = short term memory, current output of LSTM at a given time
@@ -40,7 +40,7 @@ def forward(self, x):
         cell_states = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
 
         #forward propagate lstm
-        out, _ = self.lstm(x_repeated, (hidden_state, cell_states)) #out; tensor of shape(batch_size, seq_length, hidden_size) - at the final time step
+        out, _ = self.lstm(x, (hidden_state, cell_states)) #out; tensor of shape(batch_size, seq_length, hidden_size) - at the final time step
         #decode the hidden state of t
         # predicted is a series of controls
         out = self.fc(out)

From b00f8474f7a2a0c98bbabdca82be0d3596665435 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Tue, 3 Mar 2026 17:09:00 -0800
Subject: [PATCH 21/49] changing localisation code

---
 array_temp/Control_Model.ipynb | 875 ++++++++++++++++-----------------
 1 file changed, 416 insertions(+), 459 deletions(-)

diff --git a/array_temp/Control_Model.ipynb b/array_temp/Control_Model.ipynb
index 554b4b4..bd93804 100644
--- a/array_temp/Control_Model.ipynb
+++ b/array_temp/Control_Model.ipynb
@@ -24,28 +24,15 @@
     "https://docs.google.com/spreadsheets/d/1yjyuKODt6wtIB31OLfhpwe0kKQ4CeoXJ/edit?gid=159364596#gid=159364596\n",
     "- this document has lap timings\n",
     "- i also used Miguel's monday updates to find which laps were ignored/had issues\n",
-    "- for now the RNN does not need to distinguish between laps, but  i wonder if that could be a possible input"
+    "- for now the RNN does not need to distinguish between laps"
    ],
    "id": "3d1eb59ff0e6ef7a"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T14:59:46.360584Z",
-     "start_time": "2026-02-28T14:59:46.339368Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "#other references :\n",
-   "id": "b191dbc392da20f6",
-   "outputs": [],
-   "execution_count": 1
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-28T15:33:26.313268Z",
-     "start_time": "2026-02-28T15:33:26.107174Z"
+     "end_time": "2026-03-03T23:38:22.231190Z",
+     "start_time": "2026-03-03T23:38:10.483891Z"
     }
    },
    "cell_type": "code",
@@ -60,13 +47,19 @@
    ],
    "id": "8672b9d1bf8a74aa",
    "outputs": [],
-   "execution_count": 3
+   "execution_count": 1
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Querying data from Influx - 14-16 July, FSGP 2024. Chosen as this is also the same code Miguel worked on.",
+   "id": "5bc7173b832222e8"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T07:36:11.380337Z",
-     "start_time": "2026-02-28T07:35:47.946377Z"
+     "end_time": "2026-03-04T00:56:10.688576Z",
+     "start_time": "2026-03-04T00:55:34.811655Z"
     }
    },
    "cell_type": "code",
@@ -107,7 +100,7 @@
    ],
    "id": "initial_id",
    "outputs": [],
-   "execution_count": 2
+   "execution_count": 58
   },
   {
    "metadata": {
@@ -167,16 +160,15 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T15:33:33.579953Z",
-     "start_time": "2026-02-28T15:33:32.205995Z"
+     "end_time": "2026-03-03T23:38:31.469949Z",
+     "start_time": "2026-03-03T23:38:29.653254Z"
     }
    },
    "cell_type": "code",
    "source": [
+    "#loading data\n",
     "import os\n",
     "import dill\n",
-    "\n",
-    "# same directory you used for saving\n",
     "out_dir = os.path.join(\"../../array_temp\", \"data\", \"control_state_fsgp_2024\")\n",
     "\n",
     "brake_path = os.path.join(out_dir, \"brake_pressed.bin\")\n",
@@ -192,18 +184,18 @@
     "        data = dill.load(f)\n",
     "        loaded_datasets.append(data)\n",
     "\n",
-    "# unpack them\n",
+    "#unnpack\n",
     "mech_brake_pressed, accel_position, speed_kph = loaded_datasets"
    ],
    "id": "31de4e6237f9a41e",
    "outputs": [],
-   "execution_count": 5
+   "execution_count": 2
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T15:33:34.709051Z",
-     "start_time": "2026-02-28T15:33:34.702770Z"
+     "end_time": "2026-03-03T23:38:32.348528Z",
+     "start_time": "2026-03-03T23:38:32.313694Z"
     }
    },
    "cell_type": "code",
@@ -217,7 +209,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_23684\\4023827949.py:1: DeprecationWarning: Please use TimeSeries.period instead of TimeSeries.granularity\n",
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_11732\\4023827949.py:1: DeprecationWarning: Please use TimeSeries.period instead of TimeSeries.granularity\n",
       "  mech_brake_pressed.granularity\n"
      ]
     },
@@ -227,12 +219,12 @@
        "np.float64(0.10000002186928031)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 6
+   "execution_count": 3
   },
   {
    "metadata": {
@@ -242,7 +234,10 @@
     }
    },
    "cell_type": "code",
-   "source": "plt.plot(mech_brake_pressed.datetime_x_axis, mech_brake_pressed, color = 'green', label = \"Brake Pressed\")",
+   "source": [
+    "plt.plot(mech_brake_pressed.datetime_x_axis, mech_brake_pressed, color = 'green', label = \"Brake Pressed\")\n",
+    "# note that assumed continuity of brake is not real. assumed to be brake pressure but this clearly is just checking if brake has been pressed or not as a 0 or 1 value. checked this in the bay, threshold seems extremely low."
+   ],
    "id": "d1fc6023bb47051c",
    "outputs": [
     {
@@ -313,19 +308,19 @@
     }
    },
    "cell_type": "code",
-   "source": [
-    "# accel position and brake pressed are in inconsistent units, so i'll probbaly change them to a 0-1 range.\n",
-    "\n",
-    "#using min, max scaling\n",
-    "from sklearn.preprocessing import MinMaxScaler\n",
-    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
-    "scaled_accel_position = scaler.fit_transform(accel_position.reshape(-1, 1))\n",
-    "\n",
-    "\n"
-   ],
-   "id": "6a0eed52a654e483",
    "outputs": [],
-   "execution_count": 16
+   "execution_count": 16,
+   "source": [
+    "# # accel position and brake pressed are in inconsistent units, so i'll probbaly change them to a 0-1 range.\n",
+    "#\n",
+    "# #using min, max scaling\n",
+    "# from sklearn.preprocessing import MinMaxScaler\n",
+    "# scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "# scaled_accel_position = scaler.fit_transform(accel_position.reshape(-1, 1))\n",
+    "# # remove this\n",
+    "#\n"
+   ],
+   "id": "6a0eed52a654e483"
   },
   {
    "metadata": {
@@ -403,17 +398,10 @@
    "execution_count": 4
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-17T17:31:51.542391100Z",
-     "start_time": "2026-02-17T06:18:38.307363Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "",
-   "id": "649310b8f7176c67",
-   "outputs": [],
-   "execution_count": null
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "A little weird how mech brake and accelerator seem to be 100% at the same time, but considering that brake pressed seems to not be continous this doesn't seem that bad.",
+   "id": "7ec4163e070c9bfc"
   },
   {
    "metadata": {
@@ -497,8 +485,6 @@
    },
    "cell_type": "code",
    "source": [
-    "# position is defined as a percentage\n",
-    "\n",
     "plt.plot(speed_kph.datetime_x_axis, speed_kph, label = \"Speed KPH\")\n",
     "plt.xlabel(\"Time\")\n",
     "plt.ylabel(\"Speed\")\n",
@@ -612,8 +598,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T15:33:47.938459Z",
-     "start_time": "2026-02-28T15:33:47.907308Z"
+     "end_time": "2026-03-03T23:38:50.567632Z",
+     "start_time": "2026-03-03T23:38:50.519647Z"
     }
    },
    "cell_type": "code",
@@ -915,13 +901,13 @@
    ],
    "id": "2741c957d09edb03",
    "outputs": [],
-   "execution_count": 8
+   "execution_count": 4
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T15:33:49.834096Z",
-     "start_time": "2026-02-28T15:33:48.778100Z"
+     "end_time": "2026-03-03T23:38:53.779642Z",
+     "start_time": "2026-03-03T23:38:51.696897Z"
     }
    },
    "cell_type": "code",
@@ -948,13 +934,13 @@
    ],
    "id": "88a4ae7fb0d75eed",
    "outputs": [],
-   "execution_count": 9
+   "execution_count": 5
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T15:33:51.608513Z",
-     "start_time": "2026-02-28T15:33:51.498626Z"
+     "end_time": "2026-03-03T23:38:55.404881Z",
+     "start_time": "2026-03-03T23:38:55.040724Z"
     }
    },
    "cell_type": "code",
@@ -970,10 +956,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1d53598a240>]"
+       "[<matplotlib.lines.Line2D at 0x26651988b90>]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -988,7 +974,15 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 10
+   "execution_count": 6
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "#initially used this function, but this seems wrong because on plotting we get a linear y = x relationship between position and speed, which doesn't seem real.",
+   "id": "1e4b479b6d424b34"
   },
   {
    "metadata": {},
@@ -1012,8 +1006,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T15:33:55.157558Z",
-     "start_time": "2026-02-28T15:33:53.238615Z"
+     "end_time": "2026-03-04T00:59:12.891102Z",
+     "start_time": "2026-03-04T00:59:08.571632Z"
     }
    },
    "cell_type": "code",
@@ -1027,24 +1021,354 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Calculating closest GIS indices: 100%|██████████| 1985282/1985282 [00:01<00:00, 1184076.21it/s]\n"
+      "Calculating closest GIS indices: 100%|██████████| 1985282/1985282 [00:03<00:00, 525880.98it/s]\n"
      ]
     }
    ],
-   "execution_count": 11
+   "execution_count": 70
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T15:33:55.849286Z",
-     "start_time": "2026-02-28T15:33:55.823426Z"
+     "end_time": "2026-03-04T00:59:15.558216Z",
+     "start_time": "2026-03-04T00:59:15.550851Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from data_tools.collections.time_series import TimeSeries\n",
+    "from data_tools.query.influxdb_query import DBClient\n",
+    "# from data_tools.query.postgresql_query import PostgresClient\n",
+    "from datetime import datetime, timezone\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ],
+   "id": "2b08c51e67a4bdcc",
+   "outputs": [],
+   "execution_count": 71
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "since this function from physics_rs didn't make sense, i found an alternative in this notebook - https://github.com/UBC-Solar/data_analysis/blob/localization_with_acceleration/v3/Locate%20FSGP.ipynb\n",
+    "note that becuase position is only relative to the track ie distance covered along the lap, it will reset to zero everytime we finish a lap and start a new one."
+   ],
+   "id": "bdd1535e01ef7321"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T00:59:17.315478Z",
+     "start_time": "2026-03-04T00:59:17.305833Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from data_tools import *\n",
+    "#from data_tools.fsgp_2024_laps import FSGPDayLaps"
+   ],
+   "id": "84e31e6326fbd121",
+   "outputs": [],
+   "execution_count": 72
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T00:59:17.916729Z",
+     "start_time": "2026-03-04T00:59:17.905907Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "from datetime import *",
+   "id": "8bfb296ce8b6d5f6",
+   "outputs": [],
+   "execution_count": 73
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T01:06:30.434334Z",
+     "start_time": "2026-03-04T01:06:30.394911Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from scipy import integrate as intg\n",
+    "import datetime\n",
+    "from datetime import datetime\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "day_num = 1\n",
+    "day = FSGPDayLaps(day_num)\n",
+    "client = query.DBClient()\n",
+    "\n",
+    "# Calculates the distance in a specified lap number\n",
+    "def distance_covered_lap(lap_num: int):\n",
+    "    start = day.get_start_utc(lap_num)\n",
+    "    stop = day.get_finish_utc(lap_num)\n",
+    "    # start_time = datetime.strptime(start, \"%Y-%m-%dT%H:%M:%SZ\").replace(tzinfo=timezone.utc)\n",
+    "    # end_time = datetime.strptime(stop, \"%Y-%m-%dT%H:%M:%SZ\").replace(tzinfo=timezone.utc)\n",
+    "    vel: TimeSeries = client.query_time_series(start, stop, \"VehicleVelocity\", granularity = 1,  units=\"m/s\")\n",
+    "#    vel = speed_kph[start:stop]\n",
+    "    dist_m = intg.simpson(vel) # distance in meters\n",
+    "    return dist_m\n",
+    "\n",
+    "# Once you already have a lap number and a velocity array, it calculates the distance you have covered\n",
+    "def distance_so_far(start: datetime, stop: datetime, vel_lap: TimeSeries):\n",
+    "    difference = int(stop.timestamp() - start.timestamp())\n",
+    "    vel = vel_lap[: difference]\n",
+    "    dist_m = intg.simpson(vel)\n",
+    "    return dist_m\n",
+    "\n",
+    "# This assumes start and stop time stamps are in the same lap\n",
+    "def return_coords(start: datetime, stop: datetime, lap: int):\n",
+    "    distance = distance_so_far(start, stop, vel)\n",
+    "    total_lap_distance = distance_covered_lap(lap, save = True)\n",
+    "    total_track_length = 5033.62413471853\n",
+    "    index = gis._python_calculate_closest_gis_indices(np.array([(distance / total_lap_distance) * total_track_length]), gis.get_path_distances())\n",
+    "    return route_data.get(\"path\")[index[0]]"
+   ],
+   "id": "59db5061a42a09db",
+   "outputs": [],
+   "execution_count": 79
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T00:59:30.585520Z",
+     "start_time": "2026-03-04T00:59:19.122Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "pos = []\n",
+    "for day_num in range(1, 4):  # days 1,2,3\n",
+    "    day = FSGPDayLaps(day_num)\n",
+    "    num_laps = day.get_lap_count()\n",
+    "    for lap_num in range(1, num_laps + 1):\n",
+    "        pos.append(distance_covered_lap(lap_num))"
+   ],
+   "id": "29b3a4b6fac893d5",
+   "outputs": [],
+   "execution_count": 75
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T00:59:35.273125Z",
+     "start_time": "2026-03-04T00:59:35.082002Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plt.plot(pos)",
+   "id": "50b7ed46a96a8b21",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2665ba238f0>]"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 76
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T01:06:35.493162Z",
+     "start_time": "2026-03-04T01:06:34.991311Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "pos = []\n",
+    "for day_num in range(1, 4):\n",
+    "    day = FSGPDayLaps(day_num)\n",
+    "    num_laps = day.get_lap_count()\n",
+    "\n",
+    "    for lap_num in range(1, num_laps + 1):\n",
+    "        start_time = day.get_start_utc(lap_num)\n",
+    "        end_time   = day.get_finish_utc(lap_num)\n",
+    "\n",
+    "        lap_duration_s = int((end_time - start_time).total_seconds())\n",
+    "\n",
+    "        # Fetch velocity once per lap\n",
+    "        vel_lap = client.query_time_series(start_time, end_time, \"VehicleVelocity\", granularity=1, units=\"m/s\")\n",
+    "\n",
+    "        # Sample distance_so_far at each second — resets to 0 at start of every lap\n",
+    "        for t in range(lap_duration_s):\n",
+    "            current_time = start_time +timedelta(seconds=t)\n",
+    "            dist = distance_so_far(start_time, current_time, vel_lap)\n",
+    "            pos.append({\n",
+    "                \"day\":        day_num,\n",
+    "                \"lap\":        lap_num,\n",
+    "                \"t\":          t,\n",
+    "                \"distance_m\": dist\n",
+    "            })\n",
+    "\n",
+    "pos_df = pd.DataFrame(pos)"
+   ],
+   "id": "25a88d24f23e2b2d",
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "tuple index out of range",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mIndexError\u001B[39m                                Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[80]\u001B[39m\u001B[32m, line 18\u001B[39m\n\u001B[32m     16\u001B[39m         \u001B[38;5;28;01mfor\u001B[39;00m t \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(lap_duration_s):\n\u001B[32m     17\u001B[39m             current_time = start_time + timedelta(seconds=t)\n\u001B[32m---> \u001B[39m\u001B[32m18\u001B[39m             dist = \u001B[43mdistance_so_far\u001B[49m\u001B[43m(\u001B[49m\u001B[43mstart_time\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcurrent_time\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mvel_lap\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m     19\u001B[39m             pos.append({\n\u001B[32m     20\u001B[39m                 \u001B[33m\"\u001B[39m\u001B[33mday\u001B[39m\u001B[33m\"\u001B[39m:        day_num,\n\u001B[32m     21\u001B[39m                 \u001B[33m\"\u001B[39m\u001B[33mlap\u001B[39m\u001B[33m\"\u001B[39m:        lap_num,\n\u001B[32m     22\u001B[39m                 \u001B[33m\"\u001B[39m\u001B[33mt\u001B[39m\u001B[33m\"\u001B[39m:          t,\n\u001B[32m     23\u001B[39m                 \u001B[33m\"\u001B[39m\u001B[33mdistance_m\u001B[39m\u001B[33m\"\u001B[39m: dist\n\u001B[32m     24\u001B[39m             })\n\u001B[32m     26\u001B[39m pos_df = pd.DataFrame(pos)\n",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[79]\u001B[39m\u001B[32m, line 25\u001B[39m, in \u001B[36mdistance_so_far\u001B[39m\u001B[34m(start, stop, vel_lap)\u001B[39m\n\u001B[32m     23\u001B[39m difference = \u001B[38;5;28mint\u001B[39m(stop.timestamp() - start.timestamp())\n\u001B[32m     24\u001B[39m vel = vel_lap[difference]\n\u001B[32m---> \u001B[39m\u001B[32m25\u001B[39m dist_m = \u001B[43mintg\u001B[49m\u001B[43m.\u001B[49m\u001B[43msimpson\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvel\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m     26\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m dist_m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\scipy\\integrate\\_quadrature.py:445\u001B[39m, in \u001B[36msimpson\u001B[39m\u001B[34m(y, x, dx, axis)\u001B[39m\n\u001B[32m    443\u001B[39m y = np.asarray(y)\n\u001B[32m    444\u001B[39m nd = \u001B[38;5;28mlen\u001B[39m(y.shape)\n\u001B[32m--> \u001B[39m\u001B[32m445\u001B[39m N = \u001B[43my\u001B[49m\u001B[43m.\u001B[49m\u001B[43mshape\u001B[49m\u001B[43m[\u001B[49m\u001B[43maxis\u001B[49m\u001B[43m]\u001B[49m\n\u001B[32m    446\u001B[39m last_dx = dx\n\u001B[32m    447\u001B[39m returnshape = \u001B[32m0\u001B[39m\n",
+      "\u001B[31mIndexError\u001B[39m: tuple index out of range"
+     ]
+    }
+   ],
+   "execution_count": 80
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T00:52:48.689445Z",
+     "start_time": "2026-03-04T00:52:48.667951Z"
+    }
+   },
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": 51,
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from scipy import integrate as intg\n",
+    "import datetime\n",
+    "from datetime import timezone, datetime\n",
+    "\n",
+    "def get_position_dataframe(lap_num: int, vel_lap_timeseries=None) -> pd.DataFrame:\n",
+    "    \"\"\"\n",
+    "    Returns a DataFrame of (lat, lon, relative_position) time-aligned to the\n",
+    "    velocity TimeSeries for a given lap.\n",
+    "\n",
+    "    Parameters:\n",
+    "        lap_num: The lap number to query\n",
+    "        vel_lap_timeseries: Optional pre-fetched velocity TimeSeries.\n",
+    "                            If None, fetches it internally.\n",
+    "\n",
+    "    Returns:\n",
+    "        pd.DataFrame with columns: ['timestamp', 'lat', 'lon', 'relative_position']\n",
+    "        where relative_position ∈ [0, 1] (fraction of lap completed)\n",
+    "    \"\"\"\n",
+    "    # --- 1. Get lap time bounds ---\n",
+    "    start_str = day.get_start_utc(lap_num)\n",
+    "    stop_str  = day.get_finish_utc(lap_num)\n",
+    "    # start_time = datetime.strptime(start_str).replace(tzinfo=timezone.utc)\n",
+    "    # end_time   = datetime.strptime(stop_str).replace(tzinfo=timezone.utc)\n",
+    "    start_time = start_str\n",
+    "    end_time = stop_str\n",
+    "    # --- 2. Fetch velocity timeseries if not provided (granularity=1 → 1 sample/sec) ---\n",
+    "    if vel_lap_timeseries is None:\n",
+    "        vel_lap_timeseries = client.query_time_series(\n",
+    "            start_time, end_time, \"VehicleVelocity\", granularity=1, units=\"m/s\"\n",
+    "        )\n",
+    "\n",
+    "    vel_array = np.array(vel_lap_timeseries)  # shape: (N,)\n",
+    "    n_samples = len(vel_array)\n",
+    "\n",
+    "    # --- 3. Total lap distance (denominator for normalization) ---\n",
+    "    total_lap_distance = intg.simpson(vel_array)  # metres\n",
+    "    total_track_length = 5033.62413471853         # metres (full circuit)\n",
+    "\n",
+    "    # --- 4. Build cumulative distance at each timestep ---\n",
+    "    # Use cumulative trapezoid so every index has a running distance\n",
+    "    cumulative_dist = intg.cumulative_trapezoid(vel_array, dx=1.0, initial=0.0)\n",
+    "    # shape: (N,)  — same length as vel_array, starts at 0\n",
+    "\n",
+    "    # --- 5. Relative position along the lap ∈ [0, 1] ---\n",
+    "    relative_position = cumulative_dist / total_lap_distance  # normalised to lap\n",
+    "    track_distances   = relative_position * total_track_length  # map onto GIS path\n",
+    "\n",
+    "    # --- 6. Vectorised GIS lookup ---\n",
+    "    path_distances = gis.get_path_distances()\n",
+    "    gis_indices = gis._python_calculate_closest_gis_indices(\n",
+    "        track_distances, path_distances\n",
+    "    )                                               # shape: (N,)\n",
+    "\n",
+    "    path = route_data.get(\"path\")                  # list/array of (lat, lon)\n",
+    "    coords = np.array([path[i] for i in gis_indices])  # shape: (N, 2)\n",
+    "\n",
+    "    # --- 7. Build timestamp index aligned to velocity series ---\n",
+    "    timestamps = [\n",
+    "        start_time + datetime.timedelta(seconds=i)\n",
+    "        for i in range(n_samples)\n",
+    "    ]\n",
+    "\n",
+    "    # --- 8. Assemble DataFrame ---\n",
+    "    df = pd.DataFrame({\n",
+    "        \"timestamp\":         timestamps,\n",
+    "        \"lat\":               coords[:, 0],\n",
+    "        \"lon\":               coords[:, 1],\n",
+    "        \"relative_position\": relative_position,   # ∈ [0, 1]\n",
+    "        \"cumulative_dist_m\": cumulative_dist,      # raw metres, useful for debug\n",
+    "    })\n",
+    "    df.set_index(\"timestamp\", inplace=True)\n",
+    "\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def get_position_dataframe_multi_lap(lap_nums: list[int]) -> pd.DataFrame:\n",
+    "    \"\"\"\n",
+    "    Concatenates position DataFrames across multiple laps.\n",
+    "    Relative position resets to 0 at the start of each lap (lap-relative).\n",
+    "    \"\"\"\n",
+    "    frames = []\n",
+    "    for lap in lap_nums:\n",
+    "        df = get_position_dataframe(lap)\n",
+    "        df[\"lap\"] = lap\n",
+    "        frames.append(df)\n",
+    "    return pd.concat(frames)"
+   ],
+   "id": "228ebd05c7402928"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T00:58:52.314771Z",
+     "start_time": "2026-03-04T00:58:52.243817Z"
     }
    },
    "cell_type": "code",
    "source": "calculated_speeds, calculated_position = state_array",
    "id": "f4fb71b02da66f3e",
-   "outputs": [],
-   "execution_count": 12
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'state_array' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mNameError\u001B[39m                                 Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[69]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m calculated_speeds, calculated_position = \u001B[43mstate_array\u001B[49m\n",
+      "\u001B[31mNameError\u001B[39m: name 'state_array' is not defined"
+     ]
+    }
+   ],
+   "execution_count": 69
   },
   {
    "metadata": {
@@ -1078,10 +1402,7 @@
     }
    },
    "cell_type": "code",
-   "source": [
-    "#plt.plot(merged_df['0_y'])\n",
-    "plt.plot(merged_df['0_x'],merged_df['0_y'], color = 'red')"
-   ],
+   "source": "plt.plot(merged_df['0_x'],merged_df['0_y'], color = 'red')",
    "id": "f9bda40d7ce7546c",
    "outputs": [
     {
@@ -1107,6 +1428,20 @@
    ],
    "execution_count": 14
   },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "clearly there is something wrong here. why is position and speed having a y= x relationship. need to fix this first.",
+   "id": "edd233437ab6b46c"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "29b817f0d25f5b56"
+  },
   {
    "metadata": {
     "ExecuteTime": {
@@ -1405,93 +1740,6 @@
    "outputs": [],
    "execution_count": 25
   },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-28T15:53:05.418746Z",
-     "start_time": "2026-02-28T15:53:05.395106Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "import pandas as pd\n",
-    "import torch\n",
-    "from torch import nn\n",
-    "from torch.utils.data import DataLoader\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from RNN_Dataset import RNN_Dataset\n",
-    "\n",
-    "\n",
-    "def make_sequence_datasets(\n",
-    "    df_xy,\n",
-    "    state_cols,\n",
-    "    control_cols,\n",
-    "    seq_len,\n",
-    "    stride=100,\n",
-    "    train_frac=0.8,\n",
-    "    batch_size=64,\n",
-    "):\n",
-    "\n",
-    "\n",
-    "    cols_to_scale = state_cols + control_cols\n",
-    "\n",
-    "    # Train/test split (time-series safe)\n",
-    "    n_total = len(df_xy)\n",
-    "    train_len = int(train_frac * n_total)\n",
-    "    df_xy = df_xy.dropna(subset=state_cols + control_cols).reset_index(drop=True)\n",
-    "\n",
-    "    df_train_raw = df_xy.iloc[:train_len].reset_index(drop=True)\n",
-    "    df_test_raw  = df_xy.iloc[train_len:].reset_index(drop=True)\n",
-    "\n",
-    "    # Fit scaler ONLY on training data\n",
-    "    scaler = StandardScaler()\n",
-    "    scaler.fit(df_train_raw[cols_to_scale])\n",
-    "\n",
-    "    # Apply scaling\n",
-    "    df_train = df_train_raw.copy()\n",
-    "    df_test  = df_test_raw.copy()\n",
-    "    df_train.dropna()\n",
-    "    df_test = df_test.dropna()\n",
-    "\n",
-    "    df_train[cols_to_scale] = scaler.transform(df_train_raw[cols_to_scale])\n",
-    "    df_test[cols_to_scale]  = scaler.transform(df_test_raw[cols_to_scale])\n",
-    "\n",
-    "    # Create datasets\n",
-    "    train_dataset = RNN_Dataset(\n",
-    "        df_train,\n",
-    "        state_cols,\n",
-    "        control_cols,\n",
-    "        seq_len,\n",
-    "        stride\n",
-    "    )\n",
-    "\n",
-    "    test_dataset = RNN_Dataset(\n",
-    "        df_test,\n",
-    "        state_cols,\n",
-    "        control_cols,\n",
-    "        seq_len,\n",
-    "        stride\n",
-    "    )\n",
-    "\n",
-    "    # DataLoaders\n",
-    "    train_loader = DataLoader(\n",
-    "        train_dataset,\n",
-    "        batch_size=batch_size,\n",
-    "        shuffle=True\n",
-    "    )\n",
-    "\n",
-    "    test_loader = DataLoader(\n",
-    "        test_dataset,\n",
-    "        batch_size=batch_size,\n",
-    "        shuffle=False\n",
-    "    )\n",
-    "\n",
-    "    return train_dataset, test_dataset, train_loader, test_loader, scaler\n"
-   ],
-   "id": "ac176284a9de95e8",
-   "outputs": [],
-   "execution_count": 58
-  },
   {
    "metadata": {
     "ExecuteTime": {
@@ -1536,7 +1784,7 @@
   {
    "metadata": {},
    "cell_type": "markdown",
-   "source": "realised that brake pressed although told to be continous is actually not (either 0 or 1). this makes it harder for the rnn to truly capture they dynamics.",
+   "source": "realised that brake pressed although told to be continous is actually not (either 0 or 1). this might make it harder for the rnn to truly capture they dynamics, and will probably skew some loss computations.",
    "id": "90141b7cb2d4e96a"
   },
   {
@@ -1758,26 +2006,6 @@
    ],
    "execution_count": 65
   },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-28T08:08:43.185547Z",
-     "start_time": "2026-02-28T08:08:43.179918Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "# inverse transform requires dummy array of full feature width\n",
-    "def inverse_scale_controls(scaler, control_array, state_cols, control_cols):\n",
-    "    n_states = len(state_cols)\n",
-    "    dummy = np.zeros((len(control_array), len(state_cols) + len(control_cols)))\n",
-    "    dummy[:, n_states:] = control_array\n",
-    "    return scaler.inverse_transform(dummy)[:, n_states:]\n"
-   ],
-   "id": "ff93632196839e77",
-   "outputs": [],
-   "execution_count": 35
-  },
   {
    "metadata": {
     "ExecuteTime": {
@@ -2008,277 +2236,6 @@
    ],
    "execution_count": 69
   },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-28T15:46:49.290498Z",
-     "start_time": "2026-02-28T15:46:49.250970Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "plt.plot(train_losses, label='train')\n",
-    "plt.plot(test_losses, label='test')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ],
-   "id": "8466fb8dd15e5c5",
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'train_losses' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mNameError\u001B[39m                                 Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[41]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m plt.plot(\u001B[43mtrain_losses\u001B[49m, label=\u001B[33m'\u001B[39m\u001B[33mtrain\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m      2\u001B[39m plt.plot(test_losses, label=\u001B[33m'\u001B[39m\u001B[33mtest\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m      3\u001B[39m plt.legend()\n",
-      "\u001B[31mNameError\u001B[39m: name 'train_losses' is not defined"
-     ]
-    }
-   ],
-   "execution_count": 41
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-28T15:38:32.564480Z",
-     "start_time": "2026-02-28T15:38:32.457710Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "x, y = test_dataset[10]\n",
-    "print(y)  # should not be all zeros"
-   ],
-   "id": "83c67c533b9c1721",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tensor([[-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439],\n",
-      "        [-0.2009, -0.3439]])\n"
-     ]
-    }
-   ],
-   "execution_count": 29
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-28T08:29:53.033620Z",
-     "start_time": "2026-02-28T08:29:53.017147Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "def plot_control_trajectory_extended(model, test_dataset, scaler, state_cols, control_cols, n_samples=10, start_idx=0):\n",
-    "    model.eval()\n",
-    "    device = next(model.parameters()).device\n",
-    "\n",
-    "    all_states   = []\n",
-    "    all_y_true   = []\n",
-    "    all_y_pred   = []\n",
-    "\n",
-    "    n_states   = len(state_cols)\n",
-    "    n_controls = len(control_cols)\n",
-    "\n",
-    "    def unscale_controls(arr):\n",
-    "        n = arr.shape[0]\n",
-    "        dummy = np.zeros((n, n_states + n_controls))\n",
-    "        dummy[:, n_states:] = arr\n",
-    "        return scaler.inverse_transform(dummy)[:, n_states:]\n",
-    "\n",
-    "    def unscale_states(arr):\n",
-    "        n = arr.shape[0]\n",
-    "        dummy = np.zeros((n, n_states + n_controls))\n",
-    "        dummy[:, :n_states] = arr\n",
-    "        return scaler.inverse_transform(dummy)[:, :n_states]\n",
-    "\n",
-    "    for i in range(start_idx, start_idx + n_samples):\n",
-    "        x_input, y_target = test_dataset[i]\n",
-    "\n",
-    "        with torch.no_grad():\n",
-    "            x_tensor = x_input.unsqueeze(0).to(device)\n",
-    "            y_pred = model(x_tensor).squeeze(0).cpu().numpy()\n",
-    "\n",
-    "        all_states.append(x_input.numpy())\n",
-    "        all_y_true.append(y_target.numpy())\n",
-    "        all_y_pred.append(y_pred)\n",
-    "\n",
-    "    # concatenate along time axis\n",
-    "    all_states = np.concatenate(all_states, axis=0)\n",
-    "    all_y_true = np.concatenate(all_y_true, axis=0)\n",
-    "    all_y_pred = np.concatenate(all_y_pred, axis=0)\n",
-    "\n",
-    "    all_states = unscale_states(all_states)\n",
-    "    all_y_true = unscale_controls(all_y_true)\n",
-    "    all_y_pred = unscale_controls(all_y_pred)\n",
-    "\n",
-    "    time_axis = np.arange(len(all_y_true)) * 0.1\n",
-    "\n",
-    "    # plot controls\n",
-    "    fig, axes = plt.subplots(n_controls, 1, figsize=(14, 4 * n_controls), sharex=True)\n",
-    "    if n_controls == 1:\n",
-    "        axes = [axes]\n",
-    "\n",
-    "    for i, col in enumerate(control_cols):\n",
-    "        axes[i].plot(time_axis, all_y_true[:, i], 'g-', label='Actual', linewidth=1)\n",
-    "        axes[i].plot(time_axis, all_y_pred[:, i], 'r--', label='Predicted', linewidth=1)\n",
-    "        axes[i].set_ylabel(col)\n",
-    "        axes[i].legend()\n",
-    "        axes[i].grid(True)\n",
-    "\n",
-    "    axes[-1].set_xlabel(\"Time (seconds)\")\n",
-    "    plt.suptitle(f\"Control Trajectory — {n_samples} consecutive samples\")\n",
-    "    plt.tight_layout()\n",
-    "\n",
-    "    # plot states\n",
-    "    time_states = np.arange(len(all_states)) * 0.1\n",
-    "    fig2, axes2 = plt.subplots(n_states, 1, figsize=(14, 4 * n_states), sharex=True)\n",
-    "    if n_states == 1:\n",
-    "        axes2 = [axes2]\n",
-    "\n",
-    "    for i, col in enumerate(state_cols):\n",
-    "        axes2[i].plot(time_states, all_states[:, i], 'b-', label=col, linewidth=1)\n",
-    "        axes2[i].set_ylabel(col)\n",
-    "        axes2[i].legend()\n",
-    "        axes2[i].grid(True)\n",
-    "\n",
-    "    axes2[-1].set_xlabel(\"Time (seconds)\")\n",
-    "    plt.suptitle(f\"Input State Sequence — {n_samples} consecutive samples\")\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()"
-   ],
-   "id": "81629f965d2585a1",
-   "outputs": [],
-   "execution_count": 54
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-28T08:29:54.168280Z",
-     "start_time": "2026-02-28T08:29:53.762499Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "plot_control_trajectory_extended(model, test_dataset, scaler, state_cols, control_cols, n_samples=50, start_idx=0)",
-   "id": "d3204b6b0778da19",
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "could not broadcast input array from shape (100,) into shape (100,2)",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mValueError\u001B[39m                                Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[55]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[43mplot_control_trajectory_extended\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtest_dataset\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mscaler\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstate_cols\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcontrol_cols\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mn_samples\u001B[49m\u001B[43m=\u001B[49m\u001B[32;43m50\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstart_idx\u001B[49m\u001B[43m=\u001B[49m\u001B[32;43m0\u001B[39;49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[54]\u001B[39m\u001B[32m, line 40\u001B[39m, in \u001B[36mplot_control_trajectory_extended\u001B[39m\u001B[34m(model, test_dataset, scaler, state_cols, control_cols, n_samples, start_idx)\u001B[39m\n\u001B[32m     37\u001B[39m all_y_true = np.concatenate(all_y_true, axis=\u001B[32m0\u001B[39m)\n\u001B[32m     38\u001B[39m all_y_pred = np.concatenate(all_y_pred, axis=\u001B[32m0\u001B[39m)\n\u001B[32m---> \u001B[39m\u001B[32m40\u001B[39m all_states = \u001B[43munscale_states\u001B[49m\u001B[43m(\u001B[49m\u001B[43mall_states\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m     41\u001B[39m all_y_true = unscale_controls(all_y_true)\n\u001B[32m     42\u001B[39m all_y_pred = unscale_controls(all_y_pred)\n",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[54]\u001B[39m\u001B[32m, line 21\u001B[39m, in \u001B[36mplot_control_trajectory_extended.<locals>.unscale_states\u001B[39m\u001B[34m(arr)\u001B[39m\n\u001B[32m     19\u001B[39m n = arr.shape[\u001B[32m0\u001B[39m]\n\u001B[32m     20\u001B[39m dummy = np.zeros((n, n_states + n_controls))\n\u001B[32m---> \u001B[39m\u001B[32m21\u001B[39m \u001B[43mdummy\u001B[49m\u001B[43m[\u001B[49m\u001B[43m:\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m:\u001B[49m\u001B[43mn_states\u001B[49m\u001B[43m]\u001B[49m = arr\n\u001B[32m     22\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m scaler.inverse_transform(dummy)[:, :n_states]\n",
-      "\u001B[31mValueError\u001B[39m: could not broadcast input array from shape (100,) into shape (100,2)"
-     ]
-    }
-   ],
-   "execution_count": 55
-  },
   {
    "metadata": {
     "ExecuteTime": {

From ba0df26dbc7cd65546467966443422686d774a08 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Tue, 3 Mar 2026 20:01:37 -0800
Subject: [PATCH 22/49] changing localisation code

---
 array_temp/Control_Model.ipynb | 885 +++++++++++++++++++++++++--------
 array_temp/RNN.py              |   4 +-
 array_temp/RNN_Dataset.py      |   6 +-
 3 files changed, 670 insertions(+), 225 deletions(-)

diff --git a/array_temp/Control_Model.ipynb b/array_temp/Control_Model.ipynb
index bd93804..e28dd4d 100644
--- a/array_temp/Control_Model.ipynb
+++ b/array_temp/Control_Model.ipynb
@@ -31,8 +31,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-03T23:38:22.231190Z",
-     "start_time": "2026-03-03T23:38:10.483891Z"
+     "end_time": "2026-03-04T01:57:09.810487Z",
+     "start_time": "2026-03-04T01:57:02.564998Z"
     }
    },
    "cell_type": "code",
@@ -58,8 +58,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T00:56:10.688576Z",
-     "start_time": "2026-03-04T00:55:34.811655Z"
+     "end_time": "2026-03-04T01:58:58.276527Z",
+     "start_time": "2026-03-04T01:58:36.507195Z"
     }
    },
    "cell_type": "code",
@@ -100,7 +100,7 @@
    ],
    "id": "initial_id",
    "outputs": [],
-   "execution_count": 58
+   "execution_count": 15
   },
   {
    "metadata": {
@@ -160,8 +160,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-03T23:38:31.469949Z",
-     "start_time": "2026-03-03T23:38:29.653254Z"
+     "end_time": "2026-03-04T01:57:34.483171Z",
+     "start_time": "2026-03-04T01:57:33.108477Z"
     }
    },
    "cell_type": "code",
@@ -194,8 +194,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-03T23:38:32.348528Z",
-     "start_time": "2026-03-03T23:38:32.313694Z"
+     "end_time": "2026-03-04T01:57:35.189309Z",
+     "start_time": "2026-03-04T01:57:35.173239Z"
     }
    },
    "cell_type": "code",
@@ -209,7 +209,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_11732\\4023827949.py:1: DeprecationWarning: Please use TimeSeries.period instead of TimeSeries.granularity\n",
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_33888\\4023827949.py:1: DeprecationWarning: Please use TimeSeries.period instead of TimeSeries.granularity\n",
       "  mech_brake_pressed.granularity\n"
      ]
     },
@@ -563,8 +563,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T17:38:07.368594Z",
-     "start_time": "2026-02-17T17:38:07.364655Z"
+     "end_time": "2026-03-04T01:57:45.139755Z",
+     "start_time": "2026-03-04T01:57:45.136276Z"
     }
    },
    "cell_type": "code",
@@ -579,7 +579,7 @@
    ],
    "id": "158615364b919eed",
    "outputs": [],
-   "execution_count": 10
+   "execution_count": 4
   },
   {
    "metadata": {},
@@ -598,8 +598,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-03T23:38:50.567632Z",
-     "start_time": "2026-03-03T23:38:50.519647Z"
+     "end_time": "2026-03-04T01:57:46.542718Z",
+     "start_time": "2026-03-04T01:57:46.525500Z"
     }
    },
    "cell_type": "code",
@@ -901,13 +901,13 @@
    ],
    "id": "2741c957d09edb03",
    "outputs": [],
-   "execution_count": 4
+   "execution_count": 5
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-03T23:38:53.779642Z",
-     "start_time": "2026-03-03T23:38:51.696897Z"
+     "end_time": "2026-03-04T02:34:08.877159Z",
+     "start_time": "2026-03-04T02:34:08.863443Z"
     }
    },
    "cell_type": "code",
@@ -934,13 +934,13 @@
    ],
    "id": "88a4ae7fb0d75eed",
    "outputs": [],
-   "execution_count": 5
+   "execution_count": 28
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-03T23:38:55.404881Z",
-     "start_time": "2026-03-03T23:38:55.040724Z"
+     "end_time": "2026-03-04T01:57:49.206066Z",
+     "start_time": "2026-03-04T01:57:49.063379Z"
     }
    },
    "cell_type": "code",
@@ -956,10 +956,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x26651988b90>]"
+       "[<matplotlib.lines.Line2D at 0x1fb81916960>]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -974,15 +974,20 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 6
+   "execution_count": 7
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T01:57:50.550208Z",
+     "start_time": "2026-03-04T01:57:50.545800Z"
+    }
+   },
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": "#initially used this function, but this seems wrong because on plotting we get a linear y = x relationship between position and speed, which doesn't seem real.",
-   "id": "1e4b479b6d424b34"
+   "id": "1e4b479b6d424b34",
+   "outputs": [],
+   "execution_count": 8
   },
   {
    "metadata": {},
@@ -1006,8 +1011,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T00:59:12.891102Z",
-     "start_time": "2026-03-04T00:59:08.571632Z"
+     "end_time": "2026-03-04T01:57:53.317540Z",
+     "start_time": "2026-03-04T01:57:51.780954Z"
     }
    },
    "cell_type": "code",
@@ -1021,32 +1026,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Calculating closest GIS indices: 100%|██████████| 1985282/1985282 [00:03<00:00, 525880.98it/s]\n"
+      "Calculating closest GIS indices: 100%|██████████| 1985282/1985282 [00:01<00:00, 1444437.80it/s]\n"
      ]
     }
    ],
-   "execution_count": 70
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T00:59:15.558216Z",
-     "start_time": "2026-03-04T00:59:15.550851Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "from data_tools.collections.time_series import TimeSeries\n",
-    "from data_tools.query.influxdb_query import DBClient\n",
-    "# from data_tools.query.postgresql_query import PostgresClient\n",
-    "from datetime import datetime, timezone\n",
-    "\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
-   ],
-   "id": "2b08c51e67a4bdcc",
-   "outputs": [],
-   "execution_count": 71
+   "execution_count": 9
   },
   {
    "metadata": {},
@@ -1060,37 +1044,41 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T00:59:17.315478Z",
-     "start_time": "2026-03-04T00:59:17.305833Z"
+     "end_time": "2026-03-04T02:34:34.405549Z",
+     "start_time": "2026-03-04T02:34:34.399193Z"
     }
    },
    "cell_type": "code",
    "source": [
     "from data_tools import *\n",
+    "from data_tools.collections.time_series import TimeSeries\n",
+    "from data_tools.query.influxdb_query import DBClient\n",
+    "# from data_tools.query.postgresql_query import PostgresClient\n",
+    "from datetime import datetime, timezone\n",
     "#from data_tools.fsgp_2024_laps import FSGPDayLaps"
    ],
    "id": "84e31e6326fbd121",
    "outputs": [],
-   "execution_count": 72
+   "execution_count": 29
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T00:59:17.916729Z",
-     "start_time": "2026-03-04T00:59:17.905907Z"
+     "end_time": "2026-03-04T01:57:55.628957Z",
+     "start_time": "2026-03-04T01:57:55.625334Z"
     }
    },
    "cell_type": "code",
    "source": "from datetime import *",
    "id": "8bfb296ce8b6d5f6",
    "outputs": [],
-   "execution_count": 73
+   "execution_count": 12
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T01:06:30.434334Z",
-     "start_time": "2026-03-04T01:06:30.394911Z"
+     "end_time": "2026-03-04T01:59:01.011369Z",
+     "start_time": "2026-03-04T01:59:00.995009Z"
     }
    },
    "cell_type": "code",
@@ -1132,7 +1120,53 @@
    ],
    "id": "59db5061a42a09db",
    "outputs": [],
-   "execution_count": 79
+   "execution_count": 16
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "62f1a76206e8d1d8"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T02:01:11.045670Z",
+     "start_time": "2026-03-04T02:01:10.930673Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import pandas as pd\n",
+    "plt.plot(all_positions)\n",
+    "#plt.plot(pd.DataFrame(speed_kph))"
+   ],
+   "id": "73543cfa882ed428",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1fb848d2720>]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 24
   },
   {
    "metadata": {
@@ -1216,12 +1250,9 @@
     "        for t in range(lap_duration_s):\n",
     "            current_time = start_time +timedelta(seconds=t)\n",
     "            dist = distance_so_far(start_time, current_time, vel_lap)\n",
-    "            pos.append({\n",
-    "                \"day\":        day_num,\n",
-    "                \"lap\":        lap_num,\n",
-    "                \"t\":          t,\n",
-    "                \"distance_m\": dist\n",
-    "            })\n",
+    "            pos.append(\n",
+    "               dist\n",
+    "            )\n",
     "\n",
     "pos_df = pd.DataFrame(pos)"
    ],
@@ -1246,145 +1277,375 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T00:52:48.689445Z",
-     "start_time": "2026-03-04T00:52:48.667951Z"
+     "end_time": "2026-03-04T02:02:58.235081Z",
+     "start_time": "2026-03-04T02:02:58.229568Z"
     }
    },
    "cell_type": "code",
+   "source": "calculated_speeds, calculated_position = state_array",
+   "id": "f4fb71b02da66f3e",
    "outputs": [],
-   "execution_count": 51,
+   "execution_count": 25
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T03:59:58.341628Z",
+     "start_time": "2026-03-04T03:59:55.695352Z"
+    }
+   },
+   "cell_type": "code",
    "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from scipy import integrate as intg\n",
-    "import datetime\n",
-    "from datetime import timezone, datetime\n",
-    "\n",
-    "def get_position_dataframe(lap_num: int, vel_lap_timeseries=None) -> pd.DataFrame:\n",
-    "    \"\"\"\n",
-    "    Returns a DataFrame of (lat, lon, relative_position) time-aligned to the\n",
-    "    velocity TimeSeries for a given lap.\n",
-    "\n",
-    "    Parameters:\n",
-    "        lap_num: The lap number to query\n",
-    "        vel_lap_timeseries: Optional pre-fetched velocity TimeSeries.\n",
-    "                            If None, fetches it internally.\n",
-    "\n",
-    "    Returns:\n",
-    "        pd.DataFrame with columns: ['timestamp', 'lat', 'lon', 'relative_position']\n",
-    "        where relative_position ∈ [0, 1] (fraction of lap completed)\n",
-    "    \"\"\"\n",
-    "    # --- 1. Get lap time bounds ---\n",
-    "    start_str = day.get_start_utc(lap_num)\n",
-    "    stop_str  = day.get_finish_utc(lap_num)\n",
-    "    # start_time = datetime.strptime(start_str).replace(tzinfo=timezone.utc)\n",
-    "    # end_time   = datetime.strptime(stop_str).replace(tzinfo=timezone.utc)\n",
-    "    start_time = start_str\n",
-    "    end_time = stop_str\n",
-    "    # --- 2. Fetch velocity timeseries if not provided (granularity=1 → 1 sample/sec) ---\n",
-    "    if vel_lap_timeseries is None:\n",
-    "        vel_lap_timeseries = client.query_time_series(\n",
-    "            start_time, end_time, \"VehicleVelocity\", granularity=1, units=\"m/s\"\n",
-    "        )\n",
-    "\n",
-    "    vel_array = np.array(vel_lap_timeseries)  # shape: (N,)\n",
-    "    n_samples = len(vel_array)\n",
-    "\n",
-    "    # --- 3. Total lap distance (denominator for normalization) ---\n",
-    "    total_lap_distance = intg.simpson(vel_array)  # metres\n",
-    "    total_track_length = 5033.62413471853         # metres (full circuit)\n",
-    "\n",
-    "    # --- 4. Build cumulative distance at each timestep ---\n",
-    "    # Use cumulative trapezoid so every index has a running distance\n",
-    "    cumulative_dist = intg.cumulative_trapezoid(vel_array, dx=1.0, initial=0.0)\n",
-    "    # shape: (N,)  — same length as vel_array, starts at 0\n",
-    "\n",
-    "    # --- 5. Relative position along the lap ∈ [0, 1] ---\n",
-    "    relative_position = cumulative_dist / total_lap_distance  # normalised to lap\n",
-    "    track_distances   = relative_position * total_track_length  # map onto GIS path\n",
-    "\n",
-    "    # --- 6. Vectorised GIS lookup ---\n",
-    "    path_distances = gis.get_path_distances()\n",
-    "    gis_indices = gis._python_calculate_closest_gis_indices(\n",
-    "        track_distances, path_distances\n",
-    "    )                                               # shape: (N,)\n",
-    "\n",
-    "    path = route_data.get(\"path\")                  # list/array of (lat, lon)\n",
-    "    coords = np.array([path[i] for i in gis_indices])  # shape: (N, 2)\n",
-    "\n",
-    "    # --- 7. Build timestamp index aligned to velocity series ---\n",
-    "    timestamps = [\n",
-    "        start_time + datetime.timedelta(seconds=i)\n",
-    "        for i in range(n_samples)\n",
-    "    ]\n",
-    "\n",
-    "    # --- 8. Assemble DataFrame ---\n",
-    "    df = pd.DataFrame({\n",
-    "        \"timestamp\":         timestamps,\n",
-    "        \"lat\":               coords[:, 0],\n",
-    "        \"lon\":               coords[:, 1],\n",
-    "        \"relative_position\": relative_position,   # ∈ [0, 1]\n",
-    "        \"cumulative_dist_m\": cumulative_dist,      # raw metres, useful for debug\n",
-    "    })\n",
-    "    df.set_index(\"timestamp\", inplace=True)\n",
-    "\n",
-    "    return df\n",
-    "\n",
-    "\n",
-    "def get_position_dataframe_multi_lap(lap_nums: list[int]) -> pd.DataFrame:\n",
-    "    \"\"\"\n",
-    "    Concatenates position DataFrames across multiple laps.\n",
-    "    Relative position resets to 0 at the start of each lap (lap-relative).\n",
-    "    \"\"\"\n",
-    "    frames = []\n",
-    "    for lap in lap_nums:\n",
-    "        df = get_position_dataframe(lap)\n",
-    "        df[\"lap\"] = lap\n",
-    "        frames.append(df)\n",
-    "    return pd.concat(frames)"
-   ],
-   "id": "228ebd05c7402928"
+    "# use sunbeam instead to save yourself a headache\n",
+    "from data_tools import *\n",
+    "client = query.SunbeamClient()\n",
+    "pos_array = pd.DataFrame(client.get_file(origin = \"production\", event = \"FSGP_2024_Day_1\", source = \"localization\", name = \"TrackIndex\" ).unwrap().data)\n",
+    "pos_arr = pd.DataFrame(client.get_file(origin = \"production\", event = \"FSGP_2024_Day_2\", source = \"localization\", name = \"TrackIndex\" ).unwrap().data)\n",
+    "pos_arr3 = pd.DataFrame(client.get_file(origin = \"production\", event = \"FSGP_2024_Day_3\", source = \"localization\", name = \"TrackIndex\" ).unwrap().data)\n",
+    "\n",
+    "\n"
+   ],
+   "id": "34a49f9470cefbc4",
+   "outputs": [],
+   "execution_count": 67
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T00:58:52.314771Z",
-     "start_time": "2026-03-04T00:58:52.243817Z"
+     "end_time": "2026-03-04T03:59:38.948248Z",
+     "start_time": "2026-03-04T03:59:38.930843Z"
     }
    },
    "cell_type": "code",
-   "source": "calculated_speeds, calculated_position = state_array",
-   "id": "f4fb71b02da66f3e",
+   "source": "",
+   "id": "e972a8973285d19f",
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'state_array' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mNameError\u001B[39m                                 Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[69]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m calculated_speeds, calculated_position = \u001B[43mstate_array\u001B[49m\n",
-      "\u001B[31mNameError\u001B[39m: name 'state_array' is not defined"
-     ]
+     "data": {
+      "text/plain": [
+       "                0                                                  1\n",
+       "0            data  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
+       "1       file_type                                         TimeSeries\n",
+       "2  canonical_path  production/FSGP_2024_Day_2/localization/TrackI...\n",
+       "3        metadata                                                 {}\n",
+       "4     description  The best available TrackIndex data for the eve..."
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>data</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>file_type</td>\n",
+       "      <td>TimeSeries</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>canonical_path</td>\n",
+       "      <td>production/FSGP_2024_Day_2/localization/TrackI...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>metadata</td>\n",
+       "      <td>{}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>description</td>\n",
+       "      <td>The best available TrackIndex data for the eve...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 66
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "# re query everything from sunbeam\n",
+    "# look into increasing sequence length, maybe over each lap\n"
+   ],
+   "id": "c6edb2a9f27ba9ba"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T03:57:47.647670Z",
+     "start_time": "2026-03-04T03:57:46.795483Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "speed_kph = pd.merge_asof(pd.DataFrame(client.get_file(origin = \"production\", event = \"FSGP_2024_Day_1\", source = \"ingress\", name = \"VehicleVelocity\" ).unwrap()), pd.DataFrame(client.get_file(origin = \"production\", event = \"FSGP_2024_Day_2\", source = \"ingress\", name = \"VehicleVelocity\").unwrap()),     left_index=True,\n",
+    "    right_index=True,\n",
+    "    direction='nearest')\n"
+   ],
+   "id": "7f5ffca5c4284012",
+   "outputs": [],
+   "execution_count": 61
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T03:58:28.377554Z",
+     "start_time": "2026-03-04T03:58:27.924815Z"
     }
+   },
+   "cell_type": "code",
+   "source": [
+    "speed_kph = pd.merge_asof(speed_kph, pd.DataFrame(client.get_file(origin = \"production\", event = \"FSGP_2024_Day_3\", source = \"ingress\", name = \"VehicleVelocity\" ).unwrap()),left_index=True,\n",
+    "    right_index=True,\n",
+    "    direction='nearest')\n"
    ],
+   "id": "5f8e9f86e0dd0af0",
+   "outputs": [],
+   "execution_count": 62
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T04:00:03.690019Z",
+     "start_time": "2026-03-04T04:00:03.640576Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "final = pd.merge_asof(\n",
+    "    pos_array,\n",
+    "    pos_arr,\n",
+    "    left_index=True,\n",
+    "    right_index=True,\n",
+    "    direction='nearest'\n",
+    ")\n"
+   ],
+   "id": "f971ea1b473f30d3",
+   "outputs": [],
+   "execution_count": 68
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T04:00:04.442149Z",
+     "start_time": "2026-03-04T04:00:04.360187Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "final = pd.merge_asof(final, pos_arr3, left_index=True,\n",
+    "    right_index=True,\n",
+    "    direction='nearest')"
+   ],
+   "id": "d3b1ffcdcac5bbd1",
+   "outputs": [],
    "execution_count": 69
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T15:33:56.820829Z",
-     "start_time": "2026-02-28T15:33:56.691725Z"
+     "end_time": "2026-03-04T04:00:47.124729Z",
+     "start_time": "2026-03-04T04:00:47.100026Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "final.head()",
+   "id": "c36c30c15e6866a2",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "   TrackIndexSpreadsheet_x  TrackIndexSpreadsheet_y  TrackIndexSpreadsheet\n",
+       "0                      0.0                      0.0                    0.0\n",
+       "1                      0.0                      0.0                    0.0\n",
+       "2                      0.0                      0.0                    0.0\n",
+       "3                      0.0                      0.0                    0.0\n",
+       "4                      0.0                      0.0                    0.0"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>TrackIndexSpreadsheet_x</th>\n",
+       "      <th>TrackIndexSpreadsheet_y</th>\n",
+       "      <th>TrackIndexSpreadsheet</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 74
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T04:01:02.116894Z",
+     "start_time": "2026-03-04T04:01:01.691418Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plt.plot(final['TrackIndexSpreadsheet_x'])",
+   "id": "31214f0019d28a58",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1fb8dbabd10>]"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 75
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T02:50:50.015029Z",
+     "start_time": "2026-03-04T02:50:49.816065Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plt.plot(pos_array)",
+   "id": "403a468cb250a6e5",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1fb8602c440>]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 47
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T03:58:57.132542Z",
+     "start_time": "2026-03-04T03:58:57.108523Z"
     }
    },
    "cell_type": "code",
    "source": [
     "speed_kph = pd.DataFrame(speed_kph).sort_index()\n",
-    "calculated_position = pd.DataFrame(calculated_position).sort_index()\n",
+    "#calculated_position = pd.DataFrame(calculated_position).sort_index()\n",
     "\n",
     "merged_df = pd.merge_asof(\n",
     "    speed_kph,\n",
-    "    calculated_position,\n",
+    "    final,\n",
     "    left_index=True,\n",
     "    right_index=True,\n",
     "    direction='nearest'\n",
@@ -1392,13 +1653,190 @@
    ],
    "id": "a46a29e38c162456",
    "outputs": [],
-   "execution_count": 13
+   "execution_count": 63
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T03:59:07.624607Z",
+     "start_time": "2026-03-04T03:59:07.578876Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "merged_df.head()",
+   "id": "1d8ad291f461ae37",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "            0_x_x                                              1_x_x  \\\n",
+       "0            data  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
+       "1       file_type                                         TimeSeries   \n",
+       "2  canonical_path  production/FSGP_2024_Day_1/ingress/VehicleVelo...   \n",
+       "3        metadata                                                 {}   \n",
+       "4     description                                Speed of Brightside   \n",
+       "\n",
+       "            0_y_x                                              1_y_x  \\\n",
+       "0            data  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
+       "1       file_type                                         TimeSeries   \n",
+       "2  canonical_path  production/FSGP_2024_Day_2/ingress/VehicleVelo...   \n",
+       "3        metadata                                                 {}   \n",
+       "4     description                                Speed of Brightside   \n",
+       "\n",
+       "              0_x                                                1_x  \\\n",
+       "0            data  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
+       "1       file_type                                         TimeSeries   \n",
+       "2  canonical_path  production/FSGP_2024_Day_3/ingress/VehicleVelo...   \n",
+       "3        metadata                                                 {}   \n",
+       "4     description                                Speed of Brightside   \n",
+       "\n",
+       "            0_x_y                                              1_x_y  \\\n",
+       "0            data  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
+       "1       file_type                                         TimeSeries   \n",
+       "2  canonical_path  production/FSGP_2024_Day_1/localization/TrackI...   \n",
+       "3        metadata                                                 {}   \n",
+       "4     description  The best available TrackIndex data for the eve...   \n",
+       "\n",
+       "            0_y_y                                              1_y_y  \\\n",
+       "0            data  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
+       "1       file_type                                         TimeSeries   \n",
+       "2  canonical_path  production/FSGP_2024_Day_2/localization/TrackI...   \n",
+       "3        metadata                                                 {}   \n",
+       "4     description  The best available TrackIndex data for the eve...   \n",
+       "\n",
+       "              0_y                                                1_y  \n",
+       "0            data  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
+       "1       file_type                                         TimeSeries  \n",
+       "2  canonical_path  production/FSGP_2024_Day_3/localization/TrackI...  \n",
+       "3        metadata                                                 {}  \n",
+       "4     description  The best available TrackIndex data for the eve...  "
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0_x_x</th>\n",
+       "      <th>1_x_x</th>\n",
+       "      <th>0_y_x</th>\n",
+       "      <th>1_y_x</th>\n",
+       "      <th>0_x</th>\n",
+       "      <th>1_x</th>\n",
+       "      <th>0_x_y</th>\n",
+       "      <th>1_x_y</th>\n",
+       "      <th>0_y_y</th>\n",
+       "      <th>1_y_y</th>\n",
+       "      <th>0_y</th>\n",
+       "      <th>1_y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>data</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "      <td>data</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "      <td>data</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "      <td>data</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "      <td>data</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "      <td>data</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>file_type</td>\n",
+       "      <td>TimeSeries</td>\n",
+       "      <td>file_type</td>\n",
+       "      <td>TimeSeries</td>\n",
+       "      <td>file_type</td>\n",
+       "      <td>TimeSeries</td>\n",
+       "      <td>file_type</td>\n",
+       "      <td>TimeSeries</td>\n",
+       "      <td>file_type</td>\n",
+       "      <td>TimeSeries</td>\n",
+       "      <td>file_type</td>\n",
+       "      <td>TimeSeries</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>canonical_path</td>\n",
+       "      <td>production/FSGP_2024_Day_1/ingress/VehicleVelo...</td>\n",
+       "      <td>canonical_path</td>\n",
+       "      <td>production/FSGP_2024_Day_2/ingress/VehicleVelo...</td>\n",
+       "      <td>canonical_path</td>\n",
+       "      <td>production/FSGP_2024_Day_3/ingress/VehicleVelo...</td>\n",
+       "      <td>canonical_path</td>\n",
+       "      <td>production/FSGP_2024_Day_1/localization/TrackI...</td>\n",
+       "      <td>canonical_path</td>\n",
+       "      <td>production/FSGP_2024_Day_2/localization/TrackI...</td>\n",
+       "      <td>canonical_path</td>\n",
+       "      <td>production/FSGP_2024_Day_3/localization/TrackI...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>metadata</td>\n",
+       "      <td>{}</td>\n",
+       "      <td>metadata</td>\n",
+       "      <td>{}</td>\n",
+       "      <td>metadata</td>\n",
+       "      <td>{}</td>\n",
+       "      <td>metadata</td>\n",
+       "      <td>{}</td>\n",
+       "      <td>metadata</td>\n",
+       "      <td>{}</td>\n",
+       "      <td>metadata</td>\n",
+       "      <td>{}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>description</td>\n",
+       "      <td>Speed of Brightside</td>\n",
+       "      <td>description</td>\n",
+       "      <td>Speed of Brightside</td>\n",
+       "      <td>description</td>\n",
+       "      <td>Speed of Brightside</td>\n",
+       "      <td>description</td>\n",
+       "      <td>The best available TrackIndex data for the eve...</td>\n",
+       "      <td>description</td>\n",
+       "      <td>The best available TrackIndex data for the eve...</td>\n",
+       "      <td>description</td>\n",
+       "      <td>The best available TrackIndex data for the eve...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 64
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T15:33:57.819677Z",
-     "start_time": "2026-02-28T15:33:57.545145Z"
+     "end_time": "2026-03-04T02:03:00.365672Z",
+     "start_time": "2026-03-04T02:03:00.091522Z"
     }
    },
    "cell_type": "code",
@@ -1408,10 +1846,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1d5359fd8e0>]"
+       "[<matplotlib.lines.Line2D at 0x1fb848c6f00>]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1426,7 +1864,7 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 14
+   "execution_count": 27
   },
   {
    "metadata": {},
@@ -1434,14 +1872,6 @@
    "source": "clearly there is something wrong here. why is position and speed having a y= x relationship. need to fix this first.",
    "id": "edd233437ab6b46c"
   },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
-   "source": "",
-   "id": "29b817f0d25f5b56"
-  },
   {
    "metadata": {
     "ExecuteTime": {
@@ -1529,7 +1959,7 @@
     "\n",
     "\n",
     "df_mech_brake_pressed = pd.DataFrame(mech_brake_pressed)\n",
-    "df_accel_position = pd.DataFrame(scaled_accel_position)\n",
+    "df_accel_position = pd.DataFrame(accel_position)\n",
     "#df_speed_kph = pd.DataFrame(speed_kph)\n",
     "\n",
     "\n"
@@ -1743,18 +2173,30 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T15:53:06.498322Z",
-     "start_time": "2026-02-28T15:53:06.128102Z"
+     "end_time": "2026-03-04T03:28:54.818190Z",
+     "start_time": "2026-03-04T03:28:54.773514Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "#import DataPreprocessing\n",
+    "import DataPreprocessing\n",
     "train_dataset, test_dataset, train_loader, test_loader, scaler = DataPreprocessing.make_sequence_datasets(final_df, state_cols = ['0_x', '0_y'], control_cols = ['mech_brake_pressed', 'accel_position'], seq_len = 100, train_frac=0.7, batch_size = 64)"
    ],
    "id": "caa83b2458af2b80",
-   "outputs": [],
-   "execution_count": 59
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'final_df' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mNameError\u001B[39m                                 Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[56]\u001B[39m\u001B[32m, line 2\u001B[39m\n\u001B[32m      1\u001B[39m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[34;01mDataPreprocessing\u001B[39;00m\n\u001B[32m----> \u001B[39m\u001B[32m2\u001B[39m train_dataset, test_dataset, train_loader, test_loader, scaler = DataPreprocessing.make_sequence_datasets(\u001B[43mfinal_df\u001B[49m, state_cols = [\u001B[33m'\u001B[39m\u001B[33m0_x\u001B[39m\u001B[33m'\u001B[39m, \u001B[33m'\u001B[39m\u001B[33m0_y\u001B[39m\u001B[33m'\u001B[39m], control_cols = [\u001B[33m'\u001B[39m\u001B[33mmech_brake_pressed\u001B[39m\u001B[33m'\u001B[39m, \u001B[33m'\u001B[39m\u001B[33maccel_position\u001B[39m\u001B[33m'\u001B[39m], seq_len = \u001B[32m100\u001B[39m, train_frac=\u001B[32m0.7\u001B[39m, batch_size = \u001B[32m64\u001B[39m)\n",
+      "\u001B[31mNameError\u001B[39m: name 'final_df' is not defined"
+     ]
+    }
+   ],
+   "execution_count": 56
   },
   {
    "metadata": {},
@@ -2009,8 +2451,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T15:40:13.814713Z",
-     "start_time": "2026-02-28T15:40:13.798153Z"
+     "end_time": "2026-03-04T03:28:30.187590Z",
+     "start_time": "2026-03-04T03:28:30.174323Z"
     }
    },
    "cell_type": "code",
@@ -2023,7 +2465,7 @@
     "    x_input, y_target = test_dataset[sample_idx]\n",
     "\n",
     "# x_input should be [seq_len, n_states]\n",
-    "    print(x_input.shape)  # confirm this first\n",
+    "    print(x_input.shape)\n",
     "\n",
     "    x_np = x_input.numpy()  # [seq_len, n_states]\n",
     "\n",
@@ -2044,13 +2486,12 @@
     "    # scaler was fit on state_cols + control_cols so controls start at index n_states\n",
     "    n_states = len(state_cols)\n",
     "    n_controls = len(control_cols)\n",
-    "    seq_len = y_target.shape[0]\n",
+    "    seq_len = 1000\n",
     "    def unscale_states(arr):\n",
     "        state_mean = scaler.mean_[:n_states]\n",
     "        state_std  = scaler.scale_[:n_states]\n",
     "        return arr * state_std + state_mean\n",
     "    def unscale_controls(arr):\n",
-    "        # arr: [seq_len, n_controls]\n",
     "        dummy = np.zeros((seq_len, n_states + n_controls))\n",
     "        dummy[:, n_states:] = arr\n",
     "        return scaler.inverse_transform(dummy)[:, n_states:]\n",
@@ -2100,13 +2541,13 @@
    ],
    "id": "55763d08b5868893",
    "outputs": [],
-   "execution_count": 30
+   "execution_count": 53
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T15:43:14.698464Z",
-     "start_time": "2026-02-28T15:43:14.636621Z"
+     "end_time": "2026-03-04T03:28:31.168398Z",
+     "start_time": "2026-03-04T03:28:31.098439Z"
     }
    },
    "cell_type": "code",
@@ -2121,20 +2562,24 @@
    "id": "2ce209bf8aab8f07",
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "sample 0 has nonzero controls\n"
+     "ename": "NameError",
+     "evalue": "name 'test_dataset' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mNameError\u001B[39m                                 Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[54]\u001B[39m\u001B[32m, line 2\u001B[39m\n\u001B[32m      1\u001B[39m \u001B[38;5;66;03m# find a sample with nonzero controls\u001B[39;00m\n\u001B[32m----> \u001B[39m\u001B[32m2\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(\u001B[38;5;28mlen\u001B[39m(\u001B[43mtest_dataset\u001B[49m)):\n\u001B[32m      3\u001B[39m     x, y = test_dataset[i]\n\u001B[32m      4\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m y.abs().mean() > \u001B[32m0.1\u001B[39m:\n",
+      "\u001B[31mNameError\u001B[39m: name 'test_dataset' is not defined"
      ]
     }
    ],
-   "execution_count": 35
+   "execution_count": 54
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T16:11:22.021757Z",
-     "start_time": "2026-02-28T16:11:21.805444Z"
+     "end_time": "2026-03-04T03:28:46.299991Z",
+     "start_time": "2026-03-04T03:28:46.245373Z"
     }
    },
    "cell_type": "code",
@@ -2148,14 +2593,18 @@
    "id": "f66c02f2a3933818",
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "use sample_idx=3004\n"
+     "ename": "NameError",
+     "evalue": "name 'test_dataset' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mNameError\u001B[39m                                 Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[55]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(\u001B[38;5;28mlen\u001B[39m(\u001B[43mtest_dataset\u001B[49m)):\n\u001B[32m      2\u001B[39m     x, y = test_dataset[i]\n\u001B[32m      3\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m y.abs().mean() >= \u001B[32m0.88\u001B[39m:\n",
+      "\u001B[31mNameError\u001B[39m: name 'test_dataset' is not defined"
      ]
     }
    ],
-   "execution_count": 68
+   "execution_count": 55
   },
   {
    "metadata": {
diff --git a/array_temp/RNN.py b/array_temp/RNN.py
index 70b2672..536852d 100644
--- a/array_temp/RNN.py
+++ b/array_temp/RNN.py
@@ -28,8 +28,6 @@ def forward(self, x):
 
         #x shape : [batch size, input size]
         # associate the state array (start + goal state) with timeseries dependency
-        # this is done by repeating the input vector seq_len times
-        #x_repeated = x.unsqueeze(1).repeat(1, self.seq_length, 1)
 
         #inital hidden, cell states - these are internal memory vectors
         #hidden = short term memory, current output of LSTM at a given time
@@ -44,6 +42,6 @@ def forward(self, x):
         #decode the hidden state of t
         # predicted is a series of controls
         out = self.fc(out)
-        #out shape: [batch_size, seq_length, hidden_size] - one control per timestamp
+        #out shape: [batch_size, seq_length, hidden_size]
         return out
 
diff --git a/array_temp/RNN_Dataset.py b/array_temp/RNN_Dataset.py
index 2dc9221..1a143d1 100644
--- a/array_temp/RNN_Dataset.py
+++ b/array_temp/RNN_Dataset.py
@@ -27,14 +27,12 @@ def __len__(self):
     def __getitem__(self, index):
         idx = self.indices[index] #index of the first timestep of seq
 
-        #input: current states +  target state
-
+        #input: current states
         current_state = self.states[idx:idx+self.seq_len] # speed+ position
         #target_state = self.states[idx +  self.seq_len] # state at t + seq_len
         #current_control = self.controls[idx] #mech_brake + accelerator position
         x_seq = torch.cat([current_state], dim=0) #concatenate along feature dimension
-        #x_seq = torch.cat([current_state])
-        #output: next controls to get from current to target (singular control at time of target state
+        #output: controls
         y_seq = self.controls[idx:idx + self.seq_len]
 
         return x_seq, y_seq

From b233bc90a8e8638e56b9048a512c4b3163ca0203 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Wed, 4 Mar 2026 10:06:54 -0800
Subject: [PATCH 23/49] changing localisation code

---
 array_temp/Control_Model.ipynb | 868 +++++++++++++--------------------
 1 file changed, 336 insertions(+), 532 deletions(-)

diff --git a/array_temp/Control_Model.ipynb b/array_temp/Control_Model.ipynb
index e28dd4d..5c65397 100644
--- a/array_temp/Control_Model.ipynb
+++ b/array_temp/Control_Model.ipynb
@@ -24,15 +24,15 @@
     "https://docs.google.com/spreadsheets/d/1yjyuKODt6wtIB31OLfhpwe0kKQ4CeoXJ/edit?gid=159364596#gid=159364596\n",
     "- this document has lap timings\n",
     "- i also used Miguel's monday updates to find which laps were ignored/had issues\n",
-    "- for now the RNN does not need to distinguish between laps"
+    "- the RNN does not need to distinguish between laps"
    ],
    "id": "3d1eb59ff0e6ef7a"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T01:57:09.810487Z",
-     "start_time": "2026-03-04T01:57:02.564998Z"
+     "end_time": "2026-03-04T17:24:30.228937Z",
+     "start_time": "2026-03-04T17:24:26.190575Z"
     }
    },
    "cell_type": "code",
@@ -43,7 +43,19 @@
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "from array_temp.DataPreprocessing import make_sequence_datasets"
+    "from array_temp.DataPreprocessing import make_sequence_datasets\n",
+    "\n",
+    "\n",
+    "from data_tools import query\n",
+    "from data_tools.collections import TimeSeries\n",
+    "from datetime import datetime, date, time, timezone\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import dill\n",
+    "import os\n",
+    "import pytz\n",
+    "from datetime import datetime, time, date"
    ],
    "id": "8672b9d1bf8a74aa",
    "outputs": [],
@@ -58,25 +70,14 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T01:58:58.276527Z",
-     "start_time": "2026-03-04T01:58:36.507195Z"
+     "end_time": "2026-03-04T06:04:21.897790Z",
+     "start_time": "2026-03-04T06:03:56.662249Z"
     }
    },
    "cell_type": "code",
    "source": [
     "# query data from influx. looking at timestamps of 2024 FSGP: 14 - 18 overall, but for smaller data response lets try 14 -16\n",
     "\n",
-    "from data_tools import query\n",
-    "from data_tools.collections import TimeSeries\n",
-    "from datetime import datetime, date, time, timezone\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "import dill\n",
-    "import os\n",
-    "import pytz\n",
-    "from datetime import datetime, time, date\n",
-    "\n",
     "#each 5 seconds\n",
     "utc_offset_h = 7\n",
     "start_utc = time(0+utc_offset_h, 00, 00)  #querying i svancouver time, influxdb gives utc\n",
@@ -100,13 +101,13 @@
    ],
    "id": "initial_id",
    "outputs": [],
-   "execution_count": 15
+   "execution_count": 3
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-17T18:57:24.314769Z",
-     "start_time": "2026-02-17T18:57:24.304217Z"
+     "end_time": "2026-03-04T06:04:29.408291Z",
+     "start_time": "2026-03-04T06:04:29.392184Z"
     }
    },
    "cell_type": "code",
@@ -160,8 +161,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T01:57:34.483171Z",
-     "start_time": "2026-03-04T01:57:33.108477Z"
+     "end_time": "2026-03-04T18:00:34.902333Z",
+     "start_time": "2026-03-04T18:00:34.850894Z"
     }
    },
    "cell_type": "code",
@@ -189,7 +190,7 @@
    ],
    "id": "31de4e6237f9a41e",
    "outputs": [],
-   "execution_count": 2
+   "execution_count": 60
   },
   {
    "metadata": {
@@ -598,8 +599,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T01:57:46.542718Z",
-     "start_time": "2026-03-04T01:57:46.525500Z"
+     "end_time": "2026-03-04T06:04:38.193513Z",
+     "start_time": "2026-03-04T06:04:38.171982Z"
     }
    },
    "cell_type": "code",
@@ -1044,8 +1045,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T02:34:34.405549Z",
-     "start_time": "2026-03-04T02:34:34.399193Z"
+     "end_time": "2026-03-04T17:24:38.250033Z",
+     "start_time": "2026-03-04T17:24:38.240610Z"
     }
    },
    "cell_type": "code",
@@ -1059,20 +1060,20 @@
    ],
    "id": "84e31e6326fbd121",
    "outputs": [],
-   "execution_count": 29
+   "execution_count": 2
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T01:57:55.628957Z",
-     "start_time": "2026-03-04T01:57:55.625334Z"
+     "end_time": "2026-03-04T17:24:40.966989Z",
+     "start_time": "2026-03-04T17:24:40.960108Z"
     }
    },
    "cell_type": "code",
    "source": "from datetime import *",
    "id": "8bfb296ce8b6d5f6",
    "outputs": [],
-   "execution_count": 12
+   "execution_count": 3
   },
   {
    "metadata": {
@@ -1290,331 +1291,157 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T03:59:58.341628Z",
-     "start_time": "2026-03-04T03:59:55.695352Z"
+     "end_time": "2026-03-04T17:24:50.057608Z",
+     "start_time": "2026-03-04T17:24:49.536482Z"
     }
    },
    "cell_type": "code",
    "source": [
     "# use sunbeam instead to save yourself a headache\n",
     "from data_tools import *\n",
+    "import numpy as np\n",
     "client = query.SunbeamClient()\n",
-    "pos_array = pd.DataFrame(client.get_file(origin = \"production\", event = \"FSGP_2024_Day_1\", source = \"localization\", name = \"TrackIndex\" ).unwrap().data)\n",
-    "pos_arr = pd.DataFrame(client.get_file(origin = \"production\", event = \"FSGP_2024_Day_2\", source = \"localization\", name = \"TrackIndex\" ).unwrap().data)\n",
-    "pos_arr3 = pd.DataFrame(client.get_file(origin = \"production\", event = \"FSGP_2024_Day_3\", source = \"localization\", name = \"TrackIndex\" ).unwrap().data)\n",
+    "pos_array = np.array(client.get_file(origin = \"production\", event = \"FSGP_2024_Day_1\", source = \"localization\", name = \"TrackIndex\" ).unwrap().data)\n",
+    "pos_arr = np.array(client.get_file(origin = \"production\", event = \"FSGP_2024_Day_2\", source = \"localization\", name = \"TrackIndex\" ).unwrap().data)\n",
+    "pos_arr3 = np.array(client.get_file(origin = \"production\", event = \"FSGP_2024_Day_3\", source = \"localization\", name = \"TrackIndex\" ).unwrap().data)\n",
     "\n",
     "\n"
    ],
    "id": "34a49f9470cefbc4",
    "outputs": [],
-   "execution_count": 67
+   "execution_count": 4
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T03:59:38.948248Z",
-     "start_time": "2026-03-04T03:59:38.930843Z"
+     "end_time": "2026-03-04T17:31:16.422443Z",
+     "start_time": "2026-03-04T17:31:16.224308Z"
     }
    },
    "cell_type": "code",
-   "source": "",
-   "id": "e972a8973285d19f",
+   "source": "file = client.get_file(origin = \"production\", event = event, source = \"ingress\", name = \"VehicleVelocity\" ).unwrap().data",
+   "id": "636774902401e518",
+   "outputs": [],
+   "execution_count": 25
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T17:32:02.062089Z",
+     "start_time": "2026-03-04T17:32:01.888794Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plt.plot(file.datetime_x_axis, file)",
+   "id": "90dbd2d15d06f417",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "                0                                                  1\n",
-       "0            data  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
-       "1       file_type                                         TimeSeries\n",
-       "2  canonical_path  production/FSGP_2024_Day_2/localization/TrackI...\n",
-       "3        metadata                                                 {}\n",
-       "4     description  The best available TrackIndex data for the eve..."
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "      <th>1</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>data</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>file_type</td>\n",
-       "      <td>TimeSeries</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>canonical_path</td>\n",
-       "      <td>production/FSGP_2024_Day_2/localization/TrackI...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>metadata</td>\n",
-       "      <td>{}</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>description</td>\n",
-       "      <td>The best available TrackIndex data for the eve...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+       "[<matplotlib.lines.Line2D at 0x238c18d8350>]"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
-   "execution_count": 66
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
-   "source": [
-    "# re query everything from sunbeam\n",
-    "# look into increasing sequence length, maybe over each lap\n"
-   ],
-   "id": "c6edb2a9f27ba9ba"
+   "execution_count": 27
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T03:57:47.647670Z",
-     "start_time": "2026-03-04T03:57:46.795483Z"
+     "end_time": "2026-03-04T17:40:12.883282Z",
+     "start_time": "2026-03-04T17:40:11.071314Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "\n",
-    "speed_kph = pd.merge_asof(pd.DataFrame(client.get_file(origin = \"production\", event = \"FSGP_2024_Day_1\", source = \"ingress\", name = \"VehicleVelocity\" ).unwrap()), pd.DataFrame(client.get_file(origin = \"production\", event = \"FSGP_2024_Day_2\", source = \"ingress\", name = \"VehicleVelocity\").unwrap()),     left_index=True,\n",
-    "    right_index=True,\n",
-    "    direction='nearest')\n"
+    "speed_arr = []\n",
+    "for event in [\"FSGP_2024_Day_1\", \"FSGP_2024_Day_2\", \"FSGP_2024_Day_3\"]:\n",
+    "    speed_arr.append(pd.DataFrame(data = client.get_file(origin = \"production\", event = event, source = \"ingress\", name = \"VehicleVelocity\" ).unwrap().data, index = client.get_file(origin = \"production\", event = event, source = \"ingress\", name = \"VehicleVelocity\" ).unwrap().data.datetime_x_axis))"
    ],
-   "id": "7f5ffca5c4284012",
+   "id": "81328e7e1792137d",
    "outputs": [],
-   "execution_count": 61
+   "execution_count": 37
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T03:58:28.377554Z",
-     "start_time": "2026-03-04T03:58:27.924815Z"
+     "end_time": "2026-03-04T17:58:05.475471Z",
+     "start_time": "2026-03-04T17:58:05.466062Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "speed_kph = pd.merge_asof(speed_kph, pd.DataFrame(client.get_file(origin = \"production\", event = \"FSGP_2024_Day_3\", source = \"ingress\", name = \"VehicleVelocity\" ).unwrap()),left_index=True,\n",
-    "    right_index=True,\n",
-    "    direction='nearest')\n"
+    "def make_df(source, name):\n",
+    "    dfs = []\n",
+    "\n",
+    "    for event in [\"FSGP_2024_Day_1\", \"FSGP_2024_Day_2\", \"FSGP_2024_Day_3\"]:\n",
+    "        file = client.get_file(\n",
+    "            origin=\"production\",\n",
+    "            event=event,\n",
+    "            source=source,\n",
+    "            name=name\n",
+    "        ).unwrap()\n",
+    "\n",
+    "        dfs.append(\n",
+    "            pd.DataFrame(\n",
+    "                data=file.data,\n",
+    "                index=file.data.datetime_x_axis\n",
+    "            )\n",
+    "        )\n",
+    "\n",
+    "    return pd.concat(dfs).sort_index()"
    ],
-   "id": "5f8e9f86e0dd0af0",
+   "id": "878293d5e3c2267b",
    "outputs": [],
-   "execution_count": 62
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T04:00:03.690019Z",
-     "start_time": "2026-03-04T04:00:03.640576Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "final = pd.merge_asof(\n",
-    "    pos_array,\n",
-    "    pos_arr,\n",
-    "    left_index=True,\n",
-    "    right_index=True,\n",
-    "    direction='nearest'\n",
-    ")\n"
-   ],
-   "id": "f971ea1b473f30d3",
-   "outputs": [],
-   "execution_count": 68
+   "execution_count": 54
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T04:00:04.442149Z",
-     "start_time": "2026-03-04T04:00:04.360187Z"
+     "end_time": "2026-03-04T17:58:22.105287Z",
+     "start_time": "2026-03-04T17:58:21.425774Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "final = pd.merge_asof(final, pos_arr3, left_index=True,\n",
-    "    right_index=True,\n",
-    "    direction='nearest')"
+    "pos = []\n",
+    "pos_df = make_df(source = \"localization\", name = \"TrackIndex\")"
    ],
-   "id": "d3b1ffcdcac5bbd1",
+   "id": "198c0fb7383fad4a",
    "outputs": [],
-   "execution_count": 69
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T04:00:47.124729Z",
-     "start_time": "2026-03-04T04:00:47.100026Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "final.head()",
-   "id": "c36c30c15e6866a2",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "   TrackIndexSpreadsheet_x  TrackIndexSpreadsheet_y  TrackIndexSpreadsheet\n",
-       "0                      0.0                      0.0                    0.0\n",
-       "1                      0.0                      0.0                    0.0\n",
-       "2                      0.0                      0.0                    0.0\n",
-       "3                      0.0                      0.0                    0.0\n",
-       "4                      0.0                      0.0                    0.0"
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>TrackIndexSpreadsheet_x</th>\n",
-       "      <th>TrackIndexSpreadsheet_y</th>\n",
-       "      <th>TrackIndexSpreadsheet</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 74
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T04:01:02.116894Z",
-     "start_time": "2026-03-04T04:01:01.691418Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "plt.plot(final['TrackIndexSpreadsheet_x'])",
-   "id": "31214f0019d28a58",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1fb8dbabd10>]"
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 75
+   "execution_count": 55
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T02:50:50.015029Z",
-     "start_time": "2026-03-04T02:50:49.816065Z"
+     "end_time": "2026-03-04T17:58:33.019158Z",
+     "start_time": "2026-03-04T17:58:32.863763Z"
     }
    },
    "cell_type": "code",
-   "source": "plt.plot(pos_array)",
-   "id": "403a468cb250a6e5",
+   "source": "plt.plot(pos_df)",
+   "id": "18aceaa5d2fb1956",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1fb8602c440>]"
+       "[<matplotlib.lines.Line2D at 0x238c912a150>]"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1623,233 +1450,37 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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wQTTMoSqaCxSxOm7UutRXSLaZtCAqJoo75pZoaeDSLS5WGdg90JvwNV02kbcTNkbDInaDMmmA7HyHtyHpOxhllLG0sIGbjMIVO6Y6CtIiobSAmBWU9pvD9qhL0mHDKHDWQRVrmeuFtDGEh+qwYKlLwRdNI5Il5nIWk/0SqwTHZCSGgfoDBpNawJV1LkdXiID1raC+QrLNpN08GG085pZoWVy0xxJGQozwskyy1vdayKaphE6vylisuVwT8lYAH6uMhv1o72PGPkEE7VI8S7RhAoSlQNEqYiwHkgVpLEEqreQuYw5Ra740K2sq0mLVCoG5PcaPMW1sl00EP/R4V2GshDEJADGu8JHdvDyLjbeLtT5vFvUVkm0mzYzObDANZrl8A5swori1y1rMBpb8ptYmsbT0WAyIrVZIYje15kKjBDihjBZMwoo/O5UikeLgic0sYWiD2EsSf/UKhlbIghJ4kVEqrdiI8xGZdjHEZnGFckpi6RT8lTLGjIm56tWiVYwL4V02FvXKh1sFU5GC+grJNlPdQ8lK8ws3qRVDoqV7WcKCwRmwBKuVFRMbUFYn9a0KVTf1IVo364WJPwh9ydr6U8BoRJsLhFnZwahv9Y1NynIJSVLYrNpEEhWzoAaSuAB6jTXrhdgsLkbZYAKZrV5MC0mMW7nHywojgwp4SsbaxPD5Mz3yIUObxUOx1FdItpm0Q4IxxcaYMc0YkhrWmnATSopF7RtQ5IE/GokLItHxPXI2TK8+XivrhUmfTakkMbevFy7bQGQMngmD68LA1KeOJ5KyC8JsqJjAyQIKK4ECK9FZI9sulBM1oIb0WAgyqFWiFMOyLjQokcBQr4i7jEISY8lgLhWbjTHj6Hml4OlWUl8h2WZi0+kkMrE9IsyH2lis20cRRdGOtO8tqDVOQWORaS3LDI8p00jmrmOU0VhU1LrEZC2lcuvsJUHrUt6IZZdNb8rhfJCOK8VWMKn4rmCdRlol4zoU4npogd3MARyTkivRghHEPGrEWDBW3BRxSzGXCSbjbCtihXp1YaWmvkKyzVT35mCnytrBkYxCYuFLhNaLXpUsywLCpL3FKGCs4sJmMTBxCgWhqMwTI6hSHf4sMcBMzPuYWyYzl6lJUlrYEgNqqmuNA86a980ytocXAM31wPBFCswYSzG0hhEOwcIHqcO7MQBmMYHZrxGFC3uF/O8HtfYpSTEsy+QX3nLqCK7rBhZDuFF7zWO3bgBMamoxXZYsXmcoLmwKHSNz2ZgeRtlghKO1bjHE+Oip9M7E2WMMSe4YFqelF+Ky5LbmMO+FVte496ZSQi3aZVyoTLdyIBusPS9tTWYPxBzuMXFn6814JYdVSGIhCzaL+haSzZ7gxua6bCyFJEaYaQh9VjwGcxu0yEKmZVJBrYrBVWRZjlJGtxcUJjJzoY7gSxmrliJFlXa9RfAt84nSPivGeVS3YYV6L26KFFk2m2XKZ919qbBn6loeLGtDuF+sy4rEL9NEzE/Mt8YoLyMkH4aXUJZ3T89sjtsvlvoKySaTxRB1FRIrlTcVkmtMDMlmFXVihHcvMROWotOrv9lSSDTeYG5i528sb6kpllE2Xr5iB76eIYRfDAgeVwZeUEgCvh0RrJW9KqSclat+ls2zm5R9cX2JuzVTSlUCK4/Vh6WQFNKTDbkruXQseRv7rWcjFIFlkg+XAoWEVWIOjG9fQb2Q+grJJpN1S69TX4bLzd9IMlYTnCkCNXMzgY9YM3NIKcGLLCoUCKuZPjtJZLSkHHsIUV4nE8Aq8R57yFIBqwQ2jqSQ94rRcIM40K2Cckza+RGlJtJW0NXFleQZaJshH2KsuJJcZpSNKwTKcExWWoxM6NVitpkYJzHUV0g2mar8ngW/dU0LibUJ11NZSIx+YqLFN+sGxBZ320wLiEWsxUpLCc772iLffcxti4qJINrcvq+60F0M3xbjQ6QDxnbxxRwaITUIZxKTuXTCSDvfLOh4VpmV4OJDmhjhY+WYeji9yIcUspC54LGxa7EW3WHS2hHC0bP0xLkb2U6gvkKyyVQl5IqBjfYSaOdl3VvDBhnUd/b64qYffNZmpyLcyXGE7WIESF1izbmp4jWYmjAhqW4kKgAzTdBfHAy4jbAqrXFqXInYvlO0ee7i7LYqJJRiHGGpk5RHq+r2azPpZJR0v0odLxNjwVwm+TC25lfsBWAzqa+QbDJVadqMmbjQR40D09LA2XTdsd1GafCIjSXdRK2NzBQ5Y4UBG1yamtgbUaqU3ljDVR0rWXtMadrECPVxwa8f9tErWJ+zsvQeQ1I/PoQ58A8kqmrdq7s39SEtXdKsWi4WXkuM+5rhqRRxWykKN6awmG1FgUaG+grJZk9wxe0t3BhMDEkdt46V9slUlXVkDSFF8KyFM8K8gj3www3YaxZFT3gupDmVEWrzRF+xrFO3/g+TIs2kb8cIdTmDxobNnwvAy3qd4zo3Va5mkWXlzDaF6Bt5YouXtFZWNpwVqBrjypDGEGawpLBQsPOyVmqnXTR6AfLrZ9l8hZDtsmls6iERU9xJe4+1yWJuH9KYLhtBk2NEtUv2IAu/O3UdhxQxKYygYgIFY4Na646dKWN+mYD4jjs8bOuHtMTWNJcPoGYElGGKDBomYLyo4CUMYiah2FNhz5jxGwlizOryFMO7Md/KZsEsRcTGMcHnO5X6FpJNpipBWGRY+wCoE/caFXne2LzS4EzGw4GxodrvYIVoSkj1cgCllq3EvvdFom6MlSbN3tYKY6+pnDHuAybL5rphLWMCw1PEvNTJPrhApGUzNURenJ7flkqtktuiTKev2amrMRcWEZTM6MNM+02gFO0jShq8do1HVKUtuusRF74eUGhr5lYko75CsslUxRzRVWtroLmmSgu2+mFuxmbGQ4KgVnauGNNrTP2LEHFxfGiwtsXiMJE+y9wIY+MY6hYkTBVPYNUlKVgZhXVh1tgaC+NekmjSiGlgMmiY4o6bFUPCoogyCubLEcUSh4U9bOmGKeSHLaPsPmLWg53j+WXeQhKrRLtv2oZKDZXUV0g2maoOsdhsEaZSae8luzlOfMaI5LdS/4oHe28ZDymB0dgLW5jTP6YoO6Ew1Nbr4iwHhJQqgJTBueilIOF2Z9kwbk+uHtDGpgX7Mby4nsSKk6YydJlWyW9neNXCp2FqPNW2kBi/v5HM6saf7k+fv5FcyXjNyDbaqXVsHPUVkk2mKvM9d2vlYjssSnVruG2vnhZmCovgNdKBnQQYLbEJtAA3rfh7i5lTcjs2oyeWR+pV6I0vSCj3lcZCYvUTKgoMLHyvpRfquGy2KuNoLuL2HEOvkq6H5AqmIO8sK0sMUmuvPEXxboR76qQhV3tx2eyJxM3ZKRk2jvoKySaQ5d/mynXHBb5KNBNRFK9OuxhQIunAtt7B3PbDGJJm4jodWnxFeIvVXLg0DgnRjqlCywnQZrIA35cI6HhmHS1isHxSYOPUcdlcuMFA5NcLfN3Mw0SrbRUbpxSjLEjyznKFvHJ1sVagdDgG0YqbKJW701+nbbOZxooRG8jOuq+3gvoKySZQscx1BVIr0QeTssiQxcjzRtojbWmJiFWRvueiIcAZAckeQinxQFrtEil2MfgETIQ+V2dkI1ksDRM3Ma7E2LBUmMfG5uG0WBaUusTUI9KUop0A+T2zUL/gHIOH1KhZkypGIZHGcJX41msRYIRsDN9yxDrHBuynLC9Rl/oKySZQeBuoTPslGKZYj6D3TJwQY6OK7QbI2ry2whEhcIQDzdp0THwDGwNxLRAs4WHb680kFHbqTSeYp2bNtFfG/RPewKX3LQZppdLYWTA/BtMlBRAd5SZI4A6pk33AfGddC0eMeyCWirf3Zq0ifLbLpr67xAJOs9ZjjbAUzhEWsxgXGqsMXCMUoV7rim0mD8VSXyHZBAo3l4VDIvYRqbWOCEGlKeIymNLfMe+RNvs+M+2XcXVxm4sFQ2M3N4uBwCijjvYQ6YWpAl+ZsYe3Li2AmQrYThBEx+wPBo/BUmzqWCCYMTLrrNFmumzYg4ri1Yh5luRD3bTeGBwTyXPJpNrvGR1Mbs3YHaHEv0CkktctSLpZ1FdINoHCQ6BXhYQRBlzxsHqR52w/Z4zI7nDjNTYxAJe9cbCCwFLE8veSCslLZPojZwGw2zx9fjYJr7ElBqhAzgSxHUwRRc6CZMUt9Y7xwcWH1FMoeikmuR21bJ42qjenSBe3L02WfEmDOBvjAnn24lzSjCdHR6dsyICQZggL11ZRXyHZBLICIYtM3ez5dmXFqsQe8tp+fjyoBlnVLgw4axq3j+YmVutkDzv2VsAeGKzl4xCJURAt+ITFY4pmca6N8AarjSku6K9q2Mw6F1qIbibbFWWBijFAWKkyaHpR0zbT//8sWbSPkSF37B/T+wh5QpiJuvLB2lOMNTTWDWjdZ07utXFoYuNCdlJMSCz1FZJNIOtwuk7U+6DKuBOl01PhkNxzkBcom5XeyBzSFqolW724TjbOVt06mZLh1C2dasONm6n98uVL+q0wVVVhph9L4SjGcsWR9Z0xiux2YEjcvm+MHEN9F/QaYYlM6Tau7D8RqN9mZMScu87hF/WSXdbHIbnFyQ6UayTxDTNR4dYN8AaZ1ljX9UOZQ413MBDZRyY4C8QoiejKIiSytVdYF9nzBHT8bYmsH2FQ61bcug4baxQKSBlGPJXSorc5VUpjjqmefHxqpHZRM+uQLK9LbHVnvW+OV5lLgIlTRPDXC8aesObq0uxKArfRRrJLEfvOGHnVZRlMiBa7FdS3kGwCWUzGFOhiDjhGIbEYma7oGSkYY587umAUixsnYM1TBemy1WDjFRLuvQz8tJb5EGPZWCTGnhKroJkAFJA5LGcJZdLiyzrItQyPWSjMdtDt5sWQsAGzDBBXTJZNr9YsKyXXqqzO7M2zhKViaoR387EWsvUITTPGmuJovgbWTmrqKySbQEmKPBHphsWbZPUvWBgmEkxz7MaxgutizYJVShuDx8IHtXLt2LRoq/R5TNogW+SO+darRKYJM3YGhp1pQ6GwhlV6iTYS3UiQjlon7Tc80KW3aKi+rT6MfXNlfvMCEhnMDXZfFmRiszdlIMS/6cUSFI6z2aNS1CAuljG4UVYRyV6yqWKnZidVB+4rJJtASSDQCQZM4fNkb+yWL/204U6ZIeJmLAsIM1Ya8Gxt8yDmNTpjmOhj3svMB1MunZkLyoXIxscYgp+ZS6aSrgWUxbyrjjmbsT5a8TvW++tC/Wt0gay7xFjhrO9gAOgsfteqbDOKMMO/DDjjZsRkrETw4URkgUwWAmEraOeM5BYi6xac6mDdqkBRRmBYxbOsmyCzkZmxPn6WK1b1LBFwyIwp9gbDVPFl38u0sdYlJa+xuBW2hcTuZ2yIAB0jxmzF6sSiXob0pFE4zbnc7GBwPV2+zvgsOjTB8SpzkD9jpP0yilXdoNQUsAKp2sS0c/TlS1zGUy9K9E7KyukrJJsxqYa5nWHEi0Y8BY0fkUghsbRoO8ak/qHHjPXO/VyxquNkrj6LQ8G2YxWcS4RlgzH3MjxiYcg4ukrAYbNKmWU5YAKJucwOezwn9uiBp4+duZ71Sncaqa7uE5o1Y5M28zBh4xsYHrv7wNimZ+rU/X0GDTUVQnC7XQy2yAjdNtZC08+yucXJWmDLveFonKnbkkAo00KnZj+WwuJui80kiKJpY0j2kjgULDoju/nHiKh6xlrPKAlMEB5TcTpVpV9mZZh3MdkO1nrcZRykdfpm9p7F84ws6ZXYPfLS9ELtdadgDmpCnD9vIJiOGtDz7THY43yZLB4Zhy2yQbdlMvRCOkWCNe5IC8nFixezH/zBH8ze/va3Z1/zNV+TffSjH82Wl9u3+Z/4iZ/IXve61xX+fPKTn8x/94/+6I+yb/zGb8wefvjh7IMf/GB29erV7FYka3MxUemMX5/xUVvMyRT3cpvQ2od1o+hTXfRSx3yk7u85EpnReq87qJg5YywtnMtmY8v64crZbyRxOYSHblWgZB0LhHWgM0Bc1lxMRpaajyHGSstmhKVQzqwUaYuOGVYGC7CPjQHbT8DLx7pWViLg3a20+l7Wb0cqJO4W65SRxcXF7Ld+67eyn/3Zn83+9E//NPvYxz7Wev7SSy9lP/zDP5x97nOfy/+8733vaz174oknso985CPZhz70oex3f/d3sxs3bmQf/vCHs1uRLMsFk43ACHcm1fSgwWxMfjtzQFgl5WcN98KSkaXDupZeJrV9BtMkTnFpJoshcfxh3ijJcXGZSamUDYKv19K8i8kuoQ45gw/qxGhYBzrTt8VXdd0YGrHhsilA6uYIWcZUkq4zBsb1lMqKEpNx5+i1a7wV4ysGGO3UqVPZY4891rKK3Hfffdnb3va2loLiLB9QSB588MHs0KFD+Z/R0bZP31lK3v3ud2ff9m3flr3+9a/PfvInfzL77Gc/m50+fTq71cgSNIzgZhAvGTwTK3uUyC7NlombXMPo6PriWi1rDxufYOE6xKTVMuOOSTFl0xWpg50UIgxWCYPXwYCnJbO0EGs9T+DDMNYhK5jywo16t/L6ComVhbM5hwnjQo3hVyvbi8HC0LYsVVYigVxuEGoaK6tiFIFdEanEsTwxTeyTHamQOAXj137t17KDBw8Wfj43N9f649w5d955Z+XvPv744y0FBnTs2LHs+PHjrZ/famTldTP+UkYQMrdya3Mwm2KJAF+yLC1WaXA7/ZJUSMgUNrYdCzHPuL5YxYUBu2IUBHdTYi7QDK9dIsz3zLcxUOxMqrJU3TpW0FrAZ3XSfuumoTLBxJulkMS4qlIU+GNSTzVZRskxY8+kyn5kyw3EVJIeLs2PNtI5El061jW3FRRlA5uammrFjYA2NjZalo+/9bf+Vss64m7Jv/Irv5L92Z/9WbZ3797sH/2jf5R9+7d/e6vtpUuXssOHDxf6O3DgQHbhwoWoAZMX2576TNX3WHj4Nrr7XS+ZBqveG5b0ds+r2hQ2odgmAJmqaFOowiv0Ud6EVe3K6YnlOa3qo/D7JWFQfkc5ZkAaayh0wnGowkDoy9G+saLLiy1LLrUr39qZ+c6I+az6hrIbTJqPcOzSvIb4C1KbqjFrfCK1KSuvlXwd8oMwnr1jg1l2VW+zbPClw8YJrZXst7eUwbCcfUXfZStPVZuywlR+/lRFNecUcmyxpBRL3122TJTbuX87a4v7UVPli+5+yqTJqrK7per3nw1SZ6vm+uWwTEDFGMuu1Ko+ykqNxi+X5pbptssl5UU6s6rCASx+KMsu5ndiie2vllPup37qp7Jnnnkm+73f+73s6aefbikkd999d/bd3/3d2Re/+MXsX//rf51NTExk73rXu7KlpaVsaKgo3N3/V1bizEUHDkzWGfKW9D04PJ3/e3hoMDt4sNjvS0GK6OjYUNdzR43gBjg4OFDZZu7Zy/m/d+2qbhNGfE9MjHS1efFKZywjI7sr+3htoSg49+0bzw4eHC/8LBRfExPD+Vzi793DHabfvXtX13vOLxU33IH9k9meYKNcbxY5ev+Bia7sECccQ4EwJsxtufBZ1RqBmsEtV5rjFgU4K9J6lQ84aXzzjeIhtGfPWFe7kfFigN7eveNdbVZL1p2pqe719x9mjn3XUEdUDAxUtxm+XPRzu7UfLYE0lde5xUul9NjBkU6wu5MpleMJLHJV/ORoPTCvjwxX83Y5yLgsA8qKX2u8B4q8X0WzJWtR1d6bnCu22bd/PDtYKmi3+9Ug7biRdfVx1+GJQpFFSZ7E0lIpZmF8vJp3yt85NTXazYfrG4Ub/d593fy8O9jLQxX70e3t8Kzfv38iOxikbG+UQNyqxnr3oYnsS6/N5DKq3ObEwQl1DGUFvzWGEu/eIOYjf3Z+ztznoGdLfOreXcWvCyUlV5VZnta9tuCs3K5ExPh499xsFQ3WUUZ+8zd/sxXYev/997diSr7+67++ZRlx5OJEXnnllex3fud3WgrJ8PBwl/Lh/o8YE5auXJlNWkDKkVsPt7Cp+n7itWv5v5dX1rLp6eIt5kgQaLq4sNL13NHfvNwRymtrG5VtstUO862vV7dxaatASZ2bW+pqc3RiKHvS/3tpabWyj+krxc1w7dp8NpEVBfWpIJtnbm65NZfhnD7+amdOVlfXu94zfbX4jitXZ7PVhY6QulBK2bt6ZS5bKQW5lYPEFoS5RWT5Fe9aq1oj0F+9dMWcY0d/HbQT16sk1KTxvVYCkbp+fSGbnh4q8OqrF4r4GDMz89n0aOnwLwVs3rjRvf6OvviyPfYvvXK1YBmtavP8mbawB7m1Hym58l4+P9PFS6MbRUH/1Kudd7nbddW7HgnGU8VPjp4IMESWlqt522UkhFarUAa4Q7B8c3fjHW8ybqfi7bdq710s76ur89nIemkuXgsyEZtZVx+znodHBgdargJJnsTShVJw+Px8Ne9gD4Fu3FgstHP8OjJRlPEz1xay6cHiBePJ4DtXKvZjOYD/6tW5bHC1c/ifD7B0XM9VY50P0KKdjCq3uR4oNVVjKFdpd2Mo8+60MR8hXb42X7HPZyvbHpkcyi4GxQHdu48f2dN1Zs2UsFQ0mQX6G7+XhgYbmfvE+fnuualLkFubAoz24z/+49knPvGJllLyTd/0Tf6FjVwZATlriYsrcXTkyJFserpjOXDk/u/iUmLITf5m/EnZ994wFa/iednHWNXHsQC4S3pPwYwntulsmKrn4QFJvUdoFxa+ctNZntNyKlr3OPR3rBBjKMdVYBxVfwr9KWt5cm9RmMrtihaLqjYulbvsmmfmu9yuMruiop9yoKA0HyE4mPR9YbaW1KYcl1PVpuymr2qzp2T5qh5zsC7CeAo81wNvhwBtyFaSvt3k54rfLce4VLWZCCxTVe+HBQdzr/F8zJ9yUL3Urlx00dxrQpuD48Xss+751N+zXEqLrXpHCIFQNU+F1Noe17RcqFKb4/PXi0prM4afhDOrqgimtda3o2p4+PnN9H8YilZIfvEXfzH71Kc+lf3Mz/xM9i3f8i35z3/u534ue//7319o+9xzz7WUEkcOe+SRRx7Jn50/f771x/38VqPy5ijTSwRgExM8ZQVSutulFfzIjGWGyDSpmw0wa0TZM/MRU/nUel/+3oQBajNkIS2mL2Y+2Awh5hvLsRa99rOV72LmUUO7Dfkpturv0hbNBUCtGOC6GGKrV5dv5L2mt1pzkSILz0obtgLYqT1O1OzazCJ8sz1U7gXKNFsqYDMpiotd4OrHP/7x7B//43+cvfWtb80uX76c/3HuGhc38uu//uvZa6+9lv32b/929vu///vZBz7wgdbv/oN/8A+yP/iDP8g+/elPtxSVH/mRH8m+7uu+LrvtttuyW42m55drR5QzADzWBmGircs+/l5hw600ZasYmrWRGYHDVj61ME96yf8/TcCvs+9lMmiYNEkWj4ABnGJKmjPCkEnXLbsBeoW7t4S4lZkBpcK5Q2KD/CzcHbakvAVnPu73b0w1WIaYzDqWx5iD/IKR6WHV0mLGa6UGW1lR14ksspjsJLaa8noErggDtifBAjDFKHdUDMmf/MmfZOvr69kv//Ivt/6E9OUvf7llJfn5n//51t8nTpzIfvqnfzp7y1ve0nru/v6xH/ux1vPr169n73znO1uun1uRrhiMxmjGzA3F2kCM8GfwOMzaPMSN1jqsrClhUtNYaOmYNEVWvqS6sTu6Yii0js4SCgJrMaoy85bpKqEkMOm6THo0U1PE4ifmoLS+GwdGL2FlDC8yh4d1GYAsceigvdyO66aCMpYgRhG/UhMqgRmvte8tucwoBjFWWvays0Baq6oyd2Jk0qFIhNdtV0i+7/u+r/VHIgcL7/5I9N73vrf151YnyzTIbGLXh4VnUtfMyY7F6ofpwyrbbbm5GNyQlO6VEDiLufW44E3LSsTcmtvvtL+1HCxaRa+SaLSO16yxtzOa9P4mhnclqdHD9DM1MqjeMJlD0OIXHECOXwYjb4/l+IAqOhWmmfa4b7D3Ut9urUuIFLzbq1vHqgVlHfSDxp5hLk0p5BxzUQCxa3Ytwg1UNrpZRrgrgcLrLIHbTds/gluQLEHHwJszmrZV3pzxOzIQ6lacSQqlpoABUEGMUmC5hWIVA3fLZt7rzJ4Msi5jomcVJgYICub8rXpfqtgXCvDPaMOMxYIrxzdblWqraJBADGZqiCyTsiQ2xiVVwTXmvYxuY+2zq4Y71lI4mLikMH26V1dizDqwoHuLERZd8INT2BmaWwoxdjYB5CuS+grJJtBjZ6/XLq53jjhcj5vFouoHejH1cBiTorXZq8B5Yk3wLPoqC9nMxGnEWFzYdsxhwNWEaSa7gTF1PpjCgczcW7zC9MPwpFnwzfNcLwGjHES+vT5PGZcOKDUDEQGSDLEHWqrgXWt/W+4Us04NMdd3INukVsVr3vr6WJCWzvS5h1gTxARaMTfl2JtyluB2UV8h2QTK06hqHK4UlLJZmyHNzTfFe6zNbhcR6+FbBIsEe1CX+2vWhO9mzNuOwhTqOlY2Zl2YWjdsifkwVV2ilwMgvl73DxOvQmXqGHyAW3tMNgTbd7t/e4y3ldLOpX3BWsNYYpXns0RwMdOXtb/ryqAUljlOVvIRR12QApnUZzG1m7Gs31sCrrTitVJb2HqlnTGKW4ws8yOjkKRQJpjNwY2l/ka1C1tt1A6YZIUBq0Dw/XHtWJMoM74ypH2vmShsJg7jXmDmgSnNziiflsLO8KT1ntnlVaoAX6/KZ4iivB1WHI1eJcbGxjJdX7T3bhmJNFYGmfF2W3TxYt3GMQGwC97ax6zxqz6Fl7nQhvwTM+7NpL5CsglUl7EdkzDnhJWuWQbpqbodMwfSeaP4Wgqfv6VwMGc5W3GXvf2xigtrpmXT95j3VtWs6EUBYr8xRTXVVj/EuFMo0oybwHI3IP06jMViAZ4YFcYqSBlza98dALelIPpAI9aK2W9h7a4qsjKILP4so6z2Ek/HVMXmayQ7/A9O6Zvz72Vi37DlGeyXcN7u7CFOajOor5BsAmlVRss1GeoccJa53bo1sLdjK2OBGa81Fitwi0lPZtJF2+1WksaQsGl5dOwK0R+jSDBzxvjWW+3W04BCMYqCxStOGbMOICYY2aoGjLR6RnHoRRlkSsrb1X5hIdmegMQ5AlqAmotGPZ6w5pIRdY0E/M1mJ8VQI0JJxPsPT3IgZysR8SlbQX2FZBNIy3dPla3gaNwISLUYONV7YnPfK99h+MCZWzPrR2fN26zCxtwSY1w7DHpuitgfR/NBPaS6SheTscWMycpmYJQahiet2BAH9e/oOBEbs1mZS2cVK2j4+1baayyxsuHFAI5d7IvIErEUSCtd3NpbDN9Zco5JWY+BFBj0BTktPlz2Y7+NCDztBMDartGwfT+G5BYmjXGp1MiuNs2e+iqDCTV7NNdbBy7DzNZl0I67SYOpUtWuWVO4lFEipf5eKBUIlAxcR0q3m6p2j5/tIROloqNuC0mz0iLBqFLHjKwvR8+UCgdWUbkuUtYD31IZERvc/uklPiOsJl1HsTqqKEMhsFrqoNbHCP5iY4uePT9bfy0sN3eC+A8rpoOJ+XjkdLF4pGbdxsXVWrvXvKLPBLVCjrpCeQy97GNOtsvCVqa+hWQTSBOYKS0kFpw3bnh1gcTq1qlhYmKsPpiU0qcJwefoFGGBCG/Z1lZl8AmY9OmYdWEyURilhTncWThqpq/biHHb/JYmnsXqB9g4vdwemYyjx4n0Zm2M4SHOuOdiiOEvVlYdnLT53ipzYa3V85f0PR1WdJYUBGsMVuCto+NBoUp23iyAtD3encKArgGbilWiofzHALptJvUVksTkbpOaqZ8RpoxflkHWe9WAJmbrLlgAbFYcAnM4PGkIZ2aja7fJkFhzJg6iZrLsGTZeo37mk6M79o9umeuH7SuFK4O5qTJKnZVJctAXGztrBHVXvp9YHwZwTZuLMC05degC3mvhkViYS46WDYWWcY0+X7IulunYHn3vW0ols15MXAZrpQ1B+SyFZMWvxYNHJs1+79jf5qmwUjXT90NH7b63gvoKSWKyDgqq5glxI3UavSXcrdRQ1hJzx76xWnUkmG8+YeAtcKnFTSrADZvQjlvZoHBBMLZGoqBWytwfMR916/qw40bVUI2eMqxYjq+tw4Gx2DAxJNb6w8L4ukMTZl9dv8tYnoh9oQFcwY25GeZ28JcV38C46Z6/OFtbwbSsi5YsS+GOYeTls4RLshxYbwXCrkQELoNnGQtdq30ExslW0M4YxS1EVqotE6nNYoM0a8Z+MNUr2+MxNmKjftbIhpXGSaUWI0CLU0gssyYrCFjgIi1AMaSDRJErDhiNsMYRNynWQjK6e6D2wcJUOmZQKJkMFmt6Ousaf+CfJgJ8rTW0sug6FpL0YhxAeLZloX5KbwqrmRVQbVXWZcbAYLMwMTWhjGfqNl30Fz7GDZPLNlLBiOl7K2hnjOIWIis4izHaM5ucqW8QA2Osj0cfteW/phQsY96YOA20sYQoDjRLcUG6s6Vo4MZuKS5sLRsmdZCplcIEAjMgpIzS0u6LqRyd1bamMSG2zFisQEjwSS/CmlGIzOqyTS7zKabWSez4rc9gALUstmfcC2yclkQWhgcLtZ6KoEy2i1ZyFvNlYm9AwbBkGwjp82w84WZTXyFJTOVbadM4JKo2K+WeMDJoGPjssgWlSeJxNC2I9WYv39yMEkhVfbDl4i/4mIAmGTRsCdTzXihb7dhMCKpwXimTq+rVzEHFKIssvHy5rybVphmtRHXzbbMn3rZ4Lnf79AA2xiCYlvFNuvYNmwVHpqfHEKxdew23bxmwrGok80ZMXBdPNOOBE614OgtLhgH+Y/BooPh0LidNCtCOkRvDwTdKexIW+AYFzdfps59lc4uSdZgwjG/FZLDCyspMWSMPGqtqcFk56uWbLaF6iHBjoI1lsUGwonXj2OtNzZabba8vnW65pvCNlmLCHDBMGyamI8XaxPRltWHexViamGKA1rvgXurFAgE5oJ03lvLFlqA4QWZ2xFCePmrctFEKQLMIPWkEvjIKuOXGNBFtyRTvui5QFKq0rLQxkP+rfuyMDITLyAqUBS37hIRDXiZuN/UtJIlpLYHAZZiJ2RwWkA4L6GXFBlj9MN9spTDH1NyxMgOgzJnVkv077z4wTr3XLKpIFmtjlE1mPpjANkr5IfiELUOQYn8w42HaPGcEW0J5CrO3WFsETODagWPPhZWd0h14GgNdrgYW+/FbgGvg/WGl3V1GobcUyoBdK6uZYM9t0BYHC5QSqNJM4cbVPJaNxyGxLEagx89djyoVsNm0M0ZxC5FVQyCVME0h/NeJDUZhABgpecz3TBooiUwfEEpWfEUeDGi0W6P726DiBiDQrHbnCL98zPprQH2peI0tcppEISHavOARRLWlK1dbld7TS7Xf5/37NVO4NfeWBQ+/3+andJk2ThHDyCwLCbPnzAtLguKcFkbIVvEdvsU64DGvpzzWDfPeQUp5KQbYWyPGHmDTlTeb+gpJYrKYlsmyWSUYkElTs24FVuQ5WzfEuokzqaUpTPm5GdTIOoAJ1PLf5jcTq13+XiNIkRQslnIW9lVX8bro4yS0IVFuHTLewQoeZMz3MItrBKuGtgXYQ6qXLJajHrNC8i4x6LfW/oXrqheFSaMw9sxyPTCyylpTJgvPWivLGgyQO4mYYnRQcrV9giWz1gSXvDed2EPDAOwmLCSYSzYQG2vTS3mEzaC+QpKYrI3DABjlAU/KQcJgMaTIsgmFYqPHA4sZK2ueZlL/rAAtpFta5mi4kazNjSBZq90pvJe0pGi3LAgerav89qo0wjNtCcvQ+HVreGhEAToRMSTgSa1omDU/uYutB5yPNeP9jGXK2ldIkd7MOjYaDzrXDsao8b6JvEvwjlUzx5qr/UZwLnPhsVJ6wz5sVxcHUeDoiI8dsZSmsAI4Eywb6w7aCtoZo7iFyFICmOhnRsvdyOoHxzLKURjQJwknFvG1jkCwYkyKB0uDyoCwDlHMzwKJnGtlEyCY1S4EhoDCAVP4ajzC8BFmShOMVlAzGwcQCkyJKE5iqhjj5k7EcEgKLKxHLkg6Fgm1c6kYMJW8hmHJk57P+XVhS82zhH3h9ruGQhzysXYJOENgslg0acSFMfy32RRemiyFACnbMWUJDhtIsaEcHiYwgRydve7r5PQVkluTrMOGuelDkdC0fqZsvJn1wYCNBYe2dFO0s2w2zIOq3EX57GKA0aAwTY7oQcEI+LIQWDv96e1GBtvvGxuyqi/vKmTvWC4ySciHayLFh4RpgVrMCg5lDQ+BSQmkggKpeJWNNHEvhHUIh4KkNCDdncWPqVLicgybUh8hP0s4N53ba/sbyqPY7QHbGEUvhm4srlHp3iukJaVsAelO9eb3dqcP+TBu9pBaHMNTUh+hJdiyOJzzVlVGj1ohAofLl2FWwbCAPLea+haSxATzvUSMNQFmXi3dkAFP24eUvEbvtwq8R8u0qR/hzs8JZwbl0CVHjFsExo20Xqs/pEBa7TSgtdBqI31HmMIoKQuhMquuHREDgPXRTNYQ+JrqEo5beh0T98Io9eXDXA98bahmfjZjARQe5LI7yDbZW1YWrMttRnBuLGF+bUtecFlR4mwgh2qlaNcsyWHKqARp4uE7LGC+CR8nxlhfV0j01fD9rAsGODOWBWqrqK+QJCbtZscrAe02d/pCSVV0majVgU06LgRJxtSH0Rj8S2eu18QAqC8MwhooVhBiB2OBU1yszc0qQozfGOiwGhBTWMVZOvAKN3DNbUHEK3GBi7YCECqm0pyGBeOsfjTLzxO+WKPWz0kfCClZGHLcGCLIuGp8Wgo/EzA74y0VolJTIwtIoxUyjT3k50YtZYEI0DZkhBU4bysTTJC4EQvj+2AAENHXPUZKdKg4W/EmmAPHDyxLYByW9WWraGeM4hYiS+F4adpO82IOOAYJ0rphMWNBXIR2KN9lVC09f31502tZhALHqj2C4FcLEh6F4CxF42lfUMtybaAEuh4A2DlspUtW4UAWGoW3V+19AM8brKm04KanfptfZzdsSWACHVXrB6mSmp/+Tl/pWKuNg3ncL1h+mOBy24LFuWOqCL8qxfDkCkniw6RTE2qAOgBthV2XiWeJ+DCtD8blY1UTP0PUmWItRoy7JCaY9LAParWsLpCjMci9OV5OD/WaNoP6Cklisg5OK9rb0fNEmhfyxjVXBvAsJM364Lid6gXe1mpJ5JYY4WZgAZWFB5Vk/bCEWhjYZ1mpEDtibUEcatbtCzdJK2YHN1nN385YUXIhQqRaum608xTpsZrF7bQX1lo6NYSgNgcQmJq8hHtEC8gGgqqGnYDDA+m3vRwgHctQnJgM47ukNUJQq6ZMgOellNbNspDMeDgASxGDG0Vb87blUg/OZdyx2qWFsfRalybLLcug9SIeg1FgkTXEZNmskICP4DsG0RWEPdQPar1FCXVN6mwewPhq1XjzIkrK4QYFQdKsMRbG3IrDubofXTBCmEiVLa3bgnvcjLi9WMBjnfEMUu2OGNHtaKeVYnduAQY6nnET3fB8oR3IOCTsirbtBvcdmhDbjPkYlAuz8i0Sa4jbXPW7dD5o99Nuc/+hcXPM2rowfncrTbyT9h134K+EyqDAi0hv1hT9DuhY9TdMe4tbaoUEGXxWlh6zPwoxUcJahFk9lhuz17Rh8x3+uTST4RikmCK8A5ZQjfaP76axoFbzujeki5nkh+I3cVDzm019C0liMiPTI2DBNR8u3oNMAa0f6QBkgixhHdAKS716bUHt58rCSiHLRBqHJPgtgRRuWgvmPvwmC00ReCVWUCNqxuiWDzJV1x9UWion1lX7VhamnrHIYOiav7sTKGwrGxov5SnPNa1IuVtOaYP5kyIgMHVM5eAq/tKUQWTuaAoclEq2enUqAu/cYcSQwMqjWSRDpVnaR1DKLGXBURU7M5e8V/0e7XUMUDacjJL4AcHADMgY5tiy3BTS9w23ChvzVjWvbO2bzaa+QpKYcCNq1EAlvOoPcO0gBD6ApGw4haVzK9illpXXboAw5WspyHgk9QIfuKSs5bd54R0MrDHrvw1vbFYcDkz21oHEWJrYlDwEWKr4D/4b4LrQkHwt8y3WRhNiGLt28DDxFlA2tHd1BL/cBsqaitNDpMJiXSWLDZS50FTOVD6OKXOgZS6h3om0L5CJlPowwX6zKv2iAq821+BVJxclaxG+U+onLFRY1ccCAby4buwrKHXSl0BWassP3rUA1EK0YUsGrYexcYZsg7WFTVPHeB2fW1blraK+QpKYrBTJV/ytWyMEm2pMsuw3oQSWEwbzSemtCA7UquNC6GtxFBDoewR8jQkvMCXBCiEvBSAiqLJBQPJbykPoC7YEOcZtpS2OE+1CRVQ7tJcpF5l9aF/w5nbLQvLq1QWTB/KsJEVBhqtSm/9rXtHWPCCv5OOxLVfa/pjwmDB7Ruw1qboVh3FLsSiWUAa1kgpM39i3Umoo9oVTOmOB2zTKXVkG74C35pRLFg56jQ+tfWjNFWMhwR6V4jDwXFKKsKZawCjmjXGhIYbEhpjfyP+tWRbD/TBLWsw6iv3OUEYc9RWSxNS5qVcvshWP4OiwjyHRGBBpjZLWHKKGSv1gLNomwwF5vxJjwPpnTwh4CXh+l5DmzJjxoexYdU4wVrd5Bw0pjnFZLhv0qd12QkVIs34g40C7DZ2dQeyDvX2hmEjE8COyiDThiTm6pL6vYRYPBJaLto5QwrXYgc6aCBgeAc9X8W1MgbkyXSSyqZj4FHzD3YKrzEL07ZUm/QXjqrGXsD9ef3jCdOtoQal4dmLPqJHpp8eoMXNpuWSkCuBQuDUXC/Y/E0MCHraUgeXgkma5mKE0vU5Zj2L7DTMTbaupr5Akpse9oiAJIzCBBqLFtEF2xKwQa4A+nJ9cYvl8E+4ZMW/Q0mZwgj0vQS60AU6J9Py8F87Sc3yjXmyw/S0PHOlsxqYi3Cwlo+Dy8oJGMtfD7aEJDNaci1uOloaIeTjjFRPtfV91Ui/ehXaaawdCWotVQD9vODZlKusPH58y+9GCbKGInBCyT9w6deKEdqkorGFwc4ggGlNgrmIArb9OK5DpiF/R1hkp2RJfPXeRS0uPpSd9uvsDxsGG72P4/g7F4mfJByhecJvExn84foA1TAreLI+hvNNhOdFKWOBb36jwd5mHcfmsfGkWplbLsSugM9d0OSpdVqSL4HZQXyFJTCf84S4pCl86q2++kFEkYRoy/73C7QmHh9bHo4YgCE3eUsT99eAWJVWpxYEmwRTDFw6Tp2Sa1uJvcCsx62/4ebNieUIX1bgGY15ARJXbwYxq33La43vb7XvFNoCofuttcpuz/qDT1j8EXdJ5bd28Hb7o3Yza9zEHGA5Drc0jOd9Wjznce5KJPreUNaoV3TBYvMVTRA2qsuXgv9PW0Ld58wn58ILyimyaMiGrCzFnqeiAvwjdMGrkYG7PK+jUV+dXzfWEYikFr4P/pCB/7EBJYQktAFK8UD4GwZKDoNi3K2sKdzyjELxsXPTKfMoAt437bwMsvUUbXnadVi42W019hSQxWWZWHM4aFgNuyRqzPnX+BmVVQGxFFcHHrQlblL5+8OikaS6VfPrWgYY+3npb9W0eQkLLOoLl84KRdn3FC0gLhRIHUsMoVBUqWTB1a9aBi4awsKxFjl4jhBlcFboLpSOgh5UIfrhstPeN+5gNAJtVEczT2gGGQF3NDw6lX7JYFarVGpZKSalhC8xVEXhQXcM8C0izLqxTe0+SNb3SY2dvdFkbtVg3zSqWpxAr+7JjRRlTEWstd4uUFYR51C4N6EOSC7g0TStp2oBf11K5O+MYoKxbs16OM67V5/3l4k2EhSZ0D7/jjn3ZTqG+QpKYEHAnCUKgOEoM5qwBiIfQgHDwTIr/QKCXlrrXwXOQzfXYiBIwUMc1JB/Gp/ycSDEg1i1KCz4EQVBY/lMIC8tCgoh1N0PacRRixWgxAzhg71VcEY5w+Glpv3AtAI+kimDRep1xqODmtU/JqMh5Tbmlgd8eEg7PUClj3Dr3ajgkBv5F6K6UCIq6FKwNJdgKJNS+QXNxwfwuWVJDQDpp3+S37MQuGyizVqwovkEDDUMGjbYvc2h04Ttv+LUKrbGF534fcCm71WM4Na0r+XDVSMph6EK935BBTpGG1caqfzXreYgBZgNpUBBVe9YCftxK6iskqSe0UV3RshwcJTF+CBPN1ETAbbFMYHgt88PyvTqCviMV8IJA1Q4rK6UMh7pklpz2t27tHRB81rs2CPyH0HxsRcGzguKyn2uzHoWPcWHQFrVbE9ZFe1sxNdwWBVqsEQ547QDHAa3xNSw6IzVcmjlPKmZIPBP3of8eJoPDivPSSAPTgwVI4vv8eeJqv5g2LdMrlFUaX0DGMJkn0taAgUqKv8Lel8p2gDc1t4cF1yDB91fKAmM9rnuLDwXOuL5RyPjTCPxwx77A0qSMBeO1ZOFWUl8hSUzDRml7WD+kgzM84HYpyHzwl0qZHdDoGXOzClHvxyNVg4QfUtOyMSd7hTnBN0hDxfi0AlswfbL1HqSI/rLiiAJsFhaDZVKFQLbqTMCfK/FPGHmv4ZBgzjSTMJMaXuQ1JfPH85t27iCGRONJWHY4vhUAqvzBox1AsLJYrjsGN0gOFB4295V2YcC+QWxAmaD8abzSCyHg17IOQcZMKYdqrogoywnlU1I4YMmVlDfMkwQrwNR2QR8SdEG+Ft41qe25SSXV3NFckAFp4ZC86q1gRxXFtRxbJn1DmRBQzeCmbBX1FZLEZKWAQogCJ0H6fenwLrsopBs3cvslf2a4eTUcgGd8/IB0sFnBtWEb6daLW6h008Lzuw/Y77h9n75xX/LYK1YBPshRKBwSYRZZmG3L34911wp5PeUDoxmAsWPK7bWAyaIIRvCa9j4oR5rwRxE7zaqEfgBXX6YwbkQKosa3a9kDF40ifuC5+xTXkVWLSrOGPUNkyFiyAHNt1W6KJQSIW/ENe70ypemOHVydsdo4I6ICaiBbw4WmKZ+WpRBj0NzbnUy63RzwHKE4jPm1dVYgS0dHrBebpr7H1+9hXTxbQX2FJDGF2R4a2JJ0IMJ9ETJVs4egKGygQmBaIMxDU7R20GATS5D3OIg1kz/iMcS0PqPAUwdvgQm8NArr+VgJS4HAOr1RiXdotUOq37HAt9yUUzhDpaypCLXQMlN2/530a6IFLKO66bDyvqKgb4hjgosF61PVBvxxZFJWgJi4JrSRbpkMBD/jisS3SKm5vYKiha6aK0qdEljotFghBJdahyTivFJRvt4h7zQVcEbFEvSskbrMoCwDRExai7zYpCQLfdE5qX8nl+Hmky5N1juKWYu7qOKFjJt01StbDxy1sUVQhHGRQLZmAnm3g/oKSWICQ1f5TBlhCsuFU2w0jRiHtGRWDTdQVT+hgqFWljWQZ1FfAcG82o1WGiuiwyWBgXQ6TRggY0g7pEMFohVkp86vDbZUbKdvJdyanCKk9fh4cAhJ68/c+G7bFxx4QkchsJbOa3aQKFel2D7kLeWT4VusGYI+tX7eeHxSvXWX0UqZaA18g2YVwPslLJVwfaVsonyMwXymCCcJ11vj1SeR6afwBaotS5basOiktNcge6TsrJcQNC/8/tVFWPgaprVYsmx3rA/ytyLmRiuzET63LkUFpZOIwWGKTob0dJ5ivzPq2DjqKySJCZurirHDGAgJlhlKixaV7jYxmFqqZopYBMvqYJVXt5Bn4a9/88kpYrNXvweuGhThKxP87IhT0ObtTqNYFW4cljkagZMaOmx7zLBoDVB88eAROUrf0UkfPAx/cK8HO7BbpKDndj+2MsVCqOMmrM3X85dtVwbcQ1KF2zDAUzbxe+uWkv6I+ZHWDTD3vcBqd/pu9Pz+kGf2jg6pMQBujKlsJDGQ+Z30a7kNZJ5UmDG8pElzgXl4QNg7CMqUUv7RL8oSSP1rVgtY9UIEbOlbLJcN5sRKq5aUzjqXgipZM7tsVxzeKuorJAnJbWYwUJVGGwYRSoKbYariJpZiSPSc+BA/Q7sdw/Ihuoby79UPB60NlBbpxo/nGkS1pTiVYdctBQL9WDEkeN3lef22k1ffNcaHwlhahsZL+cFuWxrUysJeuGpWpVBYa+/DeDWIcBwcEoCVI0yPdCMEFLkjSY9meAGKpDg/flMwMOBlgmVGm/uXfJopo1RKPMNk4cUSu97hvtRiIaA8W67lVhsFDVrjCTyXlJ5cfggKDSOjwNaa9aEju/V5i4H8v0jWpArBCVtWcbO1Hbu3HdRXSDZpM1cJGgiY1sQLWgBT7TQE+pHaoXcpqNEyK4Ig1KWsB3yTdJMsWIWkNoZZEoe0dphj41qbHNlNVrou7pwwOWstrYyXcB4swcKYaHGLC+HNy3TBm7cZoacdbKGCoVkLMF8qroLnIakgZNhGCtRkMiYQpKdmGPk2Eh8MlFBLY2i3P9Q05QzWQu17ckuFwAuQIb1gpdRd79b4DEUhBMqDa1ezeIkKhyUfjHFYpS1CGSXJmLV8/w6YmXmWNQPyW7JuV30bE3iKPcNUpI5RoLaSds5IbjWFpGJz4Lnmi8eNTGOqYiyKfmuQMmjw3ALmgetJSu3DWGRh0hmrVFE2vwEZZnpN8AHjwjqvIJRGzQqjGyY4XSioNGAxVtFs9UdVgW2Pfa8ytrxcuhL1gLXTbn2Mi6Tdl/19uZtCEMQFV6ThItyjfDvcbRovQCGweHKvsa69pLm33j9op+wCul2a084hmS6olbFY5O8n1hzWN0mcQRlwyz1gyofenue8acgoN4/STOZyjnCHWMuBUgCM4jDi14AJgLVSl8uEKs29BG5vFu2ckdwCFN4uqhgXG0cD78JtVYvQD4PApC2UCytxk3tBYOwey9WAWBVJmCDFzb1Gcg3lh7BhktUELxQrq2hexy9v+Xl1JSlvRyhLIQS/hZPCWFKwdhqGAeJutJTumHe55ZcO73Zf9jzEKK+iC5DgWyiRGpIt+EByjeV71X9zDHq8dRlot7GDpqGcSZ/K7ItYgjvB9WnxKhP4vd8rdObeJmJpZHcK59KRDl5gKGnTaKUeh7LHsliBL5B9qNGaH7uGaVNuyyoYQMi1zoCtpL5CkpBCl0zV5uiYFu3Np+EfMGb9/F1GZLklzKxb2EEjAh7v0W6rj3lcjTrmUsybhXzIKhDWraqrnXFzAtKt5SpCoKImoJHGqCsSPrhaiaBnghdZxYy5LVtzxZjOmTHj2zXcms6tWI9r6iWlltmf1iEa3p4lxYbdwzEUY8ZHTBzznVPCBQD7QesDyrylyErPcWmyXD4a9s+rPj2ckUGWQoD33EfUIFqNWGNWtoEALSBdWreD+gpJQgJDihkp1C2S2OQRQtm6NdgKCdePBIyG55qJHUX3gGsgZXBohwP7PawCAXArq10enKgIqnB8VkwKTPjcbc0+/LX5ACS/9o2skIOrUesL6d3SXDGuSOB27K7JC7QboIfDHocIN0ZBIQrOxqr5cu4tKPnaIRlLQKa1+B4lGFrvV+UQF/+hIeIiw0UK0H/mwg11HmClEauJ+zFoiKWwwErTYiU0VEEPMDEkaxFYM6xsK2fZSSjc20FRnHzx4sXsB3/wB7O3v/3t2dd8zddkH/3oR7Pl5bZQO336dPb+978/e/Ob35x98zd/c/a5z32u8Lt/+Zd/mb3nPe/JHn744ex7vud7Wu1vNbKEDFw6IcM2RReJFtRqHzaWOQ4CoDDWpiz0wn7C21v1rSB8XmVib0aZJWFdCE2c5dgIdjMCoMxSIADVbGVZHPbIjVbJbyv4t2wl0NA3T+XR9OGaxN/W4PKBUtVranDoaiyscrP6RgaeKLfBuxxJ7iH8GJakqvdUuRmbke5TFErrRSFBZprGYxhjYV+F+2bdcP+G7q2EwGiYF6t2S/j+QnE/aS2MC01Yu0laKwmzBVWCpWwx8JtUaFLCnKlqg+xF6bm1Lx29eg38Ya/b8wYoXJV73JJt3dbGm9BC4g4hp4wsLi5mv/Vbv5X97M/+bPanf/qn2cc+9rHWsw9+8IPZwYMHs8985jPZt37rt2Yf+tCHsnPnzrV+1/3tnr/3ve/Nfu/3fi/bv39/9v3f//10NPDNQqEgrIrtQHS1C7prWFHpCrPCzOkOaKkVAhWlCpm4mWgHaZiJ4xSXqm+yAsKKAqk395EFDR1GuFuuBaS4zRuuE8vyU/6++w2IcdyatfpEYX+aAEKMhDyjnSBf9Zae43VM1koNLgRsa3ET/n0OhbWSl4gYq9ClacZPOZ4UugJCq8RzUx4pFgGyMYTbtCbdrvn5FPdNYe81dPdWSpeNP9SOG4UBywewGB9m7O1w70tfwfYhpa+a8oWwQrCKlQZGCZoc3k1BCjByvEqBZbNmwIM7KaiVttWcOnUqe+yxx7K/+Iu/aCkejpyC8u///b/P/s7f+Tsti8enPvWpbGxsLLvnnnuyv/qrv2opJz/wAz+QffrTn87e8IY3ZB/4wAdav+csK+985zuzL3zhC9k73vGO7FYh1mTHVHLVypJveFHHFNVyBalOVaCoYvtoLgQmBTAX/mYEPOFeMmNI5Hl1T1wvlmyGqfnQ+FC+ISvfSd4emO8rg1jV9Rlj7ax4GasfzKsmwHBnMLON8pgbwsduCHUtCw3roo0Z1V+1b8c6yNDtTbXKtUY4mxhTuKR8mQoJmf0USwhUtYq+hdk42jwD96dOBp3t3qrnnuZcfLqcC93N1npABt1hVFN2hEu7VhW6DAGgFcGsopsy7ffQoUPZr/3ar+XKCGhubi57/PHHswcffLCljIDe+ta3thQYR+752972tvzZ6Oho9tBDD+XPbxWyDk4wvqZIgJk1bAhsYs3nmQtuA2xIqzYaKiRS2pnlPory5xtBa9rNOS9aaBzSnTgfK+YjHW5I2C70vVcJHyvtlbGiMFDY7THxa6PxmnsfzkftlmnVXunMJRFkq4wZSoZW7Rf8fEBI6+0oR7uSB4a6dcboJYsS3u/aDSgQAu5RyiyJvNaWlV0W8Jj2fjST+N5SNmIsJKJyZwQwpxhDjMUK/MEURVw15Hi5HaughhaVzQDY23QLydTUVCtuBLSxsZF98pOfzP7W3/pb2eXLl7PDhw8X2h84cCC7cOFC69/W8xjajIBg9Fm3b+AGtPoJ+kK/ncj9gfx5q2mjm1kPTgwVfh7+u5D2K7TBBikIliD1tpN2GJgXS6m5YWpiKBQbQTugUpaB0/BfQHCHv9M11oqDKmyPG9Bu4bkT8Dn6oasBUzGOsgLlihtq7cJsjpA/yu1CBUf6PlgYXNxOK91YWPtQAQwFUPhe93d+4AVgU2GbMEajVRNH+M4wzkTmtSD2RWiD9WnPQ/WYQh93yJPFcdt8HY5H4lukdK8F4+raZ/5dzooRouyiTedgLo2FkBP4zuHB6jG6rptV81V4/0ZnfrIqWVLcvxrvxdCF2bYlz+33ghwrvQPuZycWCgpTeS3GdrcsVq4yc95VYS6CvS/sC3yrJB+qLKwFnjHkB4JlpXkMY+mkPrBeLvW33EeXbIHM8G3zx43utjnqcEle5XNV2vNueOFcludCsqhIfJaK2P56Dq/9qZ/6qeyZZ55pxYT8h//wH7KhoeJNw/1/ZaW90C7uRHseQwcO6LVA6lDdvvfNtAXbxdmVbHKibWIbGhrMDh5s9zt6oR2gNDo8mI35+hSjY0P5c0cDu9tLsndyNJuabJv0Bgd3FdqMXmz3MzI8mO3Z27ZK7do1UGgDXO19e8daY3A0MTGStxkZbafajo7szsbG2m6bkeHdhT5mvQHNFV9yPwdT7ds3nh082A4Qu/vwZKsg3GqjkQ1736h7D+Zy/97x3GUxNeW/Z3fxe+BLPbi/E3R24MBEDkrVwLfs6VjgDuyfzPZ4604YDHns0FQ24g+ksdLchpH2Bw9MZDM+jCRcI9DTvjz8vr2j2V7/DQMDpTl2QbI+6GzP1Ei2x49v12B3O5+pmx05OJmNvurnvjS+Kz5+yNHtx/Zmg7vaN5c9U2N5u42NTjT/4YOTrXVvtdnbaXM9CP49dmSqNd+O3PyH77vg242PDcm8dqmtcI4ODWZ7Pa+5AyhsE+J9HD40VVjDcW+xckojFK5DBydyXtq/byI76LOsBm/4sveDu7J9AS8UeHL9Uns8I7uzSV9ZeGh3cf0aPtX5nqNT+SXA8WZhn/l527dnNBuZ74wffDvob41Tk+09A4V7b8D7lpXr8KHJyr23FMQvHT08lfe9f/9EdtAHaGLvua72H+ieizn/3Ckk7meO1x053i/zXgwd3Deex7K5fpxMaI1/fLjQ78xGIx+f+7njG0d7SjyWB60emMj3sdtPaLPs98Lw0K58PXcPFXkQVY+dLGsfy81s//7x7KCvmIx+3bvL8+TopavtPT85MZwNL7YvjRMTne85tH8ul1FuncoyIVwvx99VvIv5GPaybbffc5Ol+XD0rM/g2+ef7cLc7elu+5QvgHdg33h2IOA7J/9CfgW/uL3j+ti76BXG8rngaT2IHTxxdE+LB4d93NR4aa23kgZ7VUZ+8zd/sxXYev/992fDw8PZzMxMoY1TNkZG2ovrnpeVD/d/Z3WJpStXZpNUtAzJMZhb2Lp9T1+bzwvjzc61F3xlZS2bnm4z1bWZtnBvrm9kC74C5eLCSv7c0SMvX2n9vbq8mt2YbW+ktbX1QpuXzrU3cWOjmV33fa6vbxT7efVa6++l+aXWGBzNzS3lbZ47037eXF/PFhbaB+HS8mqhj9O+wqUzQbqfY26uXZvPphvt/8x5C8iB4V3ZaV+kyb3HzaWb06sz7Tl547HJ7MYN/z2rxe9xJkNnPZjzzx1duTKXrS20N8jf+G9ZDG6yV67OZqv+eRgHMHt9PlvysSELpblF+rHLIFicW8rmZrvXCHT73tFWmfDLVxeyQ0P+cNgozrGjk1PD2cvT89mFK/PZ8bH2dlpf62637IXawuxitrhUvfZh3YqrV+eytfX271y/sZC3C333s9cXWuveajOzkE3791/y/TgROeP68e928x++bwJVcS/NZjdu31PJay+e8/u62cxmPK85pShsczVIx5y/vlBYw0V/sIcm4qW5pZyXrl6by8aafnz+d13cwbWrbaHtKHzXgL+Zv3hxNpvF+q0W1w88ubq0mgeAL5d4e94rUUsLy9mSX4/2mNt8/ugrbZ5bWWr/Hnz5M473B2QhER5eCzcWK/deyK/Xr83nfbs1H91o//5Fr+g69+7VK91zccYrzE6vcj9zvN56/2LxO2Ppql/jh45OtvpxMqE15vnlQr+XpudyV577ueOb1veUeAz8Oj+7mDX9LX5mZj6b3t3mvVU/9y9cnMvXc3WlyIMusNxlDTr5iUykq1fns92r7Xlc9nPs1rI8T46O+IvL6ctz2bKf+7m5zvfgm52McutUlglhQOmi+44K3j2dpx63ZeWq54PZ0nw4OjoxnD1/cS47f3Wu9Wwdc3e9u60rYOiKijrZeMXPOeTfyaN7c34Fvzh+d324Oa46F0BngxRot09b8+j3xHxprVOesRZFR7P8+I//ePaJT3yipZR80zd9U+tnR44cyaanpwvt3P/hppGeu7iUWHKTvxl/UvRdSKMKZBaeI4Om5evEO0vvRaS42wShchS2AVCSC1aV2tzlb1oFP3rw3GU6OHr1ameDZcL3uAwL6T0SgBjah8BaVh9hMFb4/G5/CwkP4/D5fFCszbkDwuOivEYrgf9Wbedv867uisYf6M+tm/R97g+yXlrmVGHt8c7WNwh9LQupsVVtEIUgfSfG7hRo6X2IoTjlUoONMZXjXorvCnzcga+++P2dDBppPJijh45OiXz7rD+sC+mXpX5Qet3FDVS9q5NqvqKuazffhO6roO+Kb2itYQkCoLw27huq1g9xEdcX14rjqynH8jRvzIswxx1ebRTeL8kQx6tNhQcfdpWZhX2BfR+iK4fPkRor7Qe8404vE8vPQ5dx1VqDdxvKO7Aezv1j8Qvm7o59Y+2fCXPXDMbuvr2gBpdkEvagy8oprJswBgThuoyw/OcVfJbyD0NRCskv/uIvtjJpfuZnfib7lm/5lvznDlvk6aefzpaWOmagRx55pPVzPHf/BzkXjnP34PmtQp1Mg4YaSIeUwyrCBkZufRWBoZ1Gb9dY2a2O1d2E5LFgI4/2nGViFQgrtuktABebthUvYcJdc0F7z3mzqgVFz/QXBvQVMBtKJB3ssbVGMB9WZgwzdrzv4RNTPa9fedzS93UOYbmf0KcuEZR6raowUsgl5FzsHy3VXBufXrG400YsueAPDCsoHdguqQjp89r8hiit2loxQdoUWrDRBustBTGHsUk9yR8CQwjrjsuTRlCYmfo0q8T8hMX3mD5b7Vfj2m8V0aN56aWXso9//OPZP/7H/7iVQeMCVfHHAaUdO3Ys+/CHP5y98MIL2a/+6q9mTzzxRPYd3/Edrd993/velz366KOtn7vnrt3JkydvqZTfEGwMfv0yITBKg4XvlE7XhLIded0pBqULNGaTaWOx8BxgntYi5CFG5IJqekopvtVSHsLibZZCkmN9GMFYrwAEixCorTEqkfWYKw2Uiqk1Amh5S9gwFYEZpYWpaWKlsYbKgYR0HILjafO9btSpCdu44HFtvOC5RnSGjQsK1Q9A5oBxFhBmfKkI02rhzkDWafgYRdiA3uWQ1QYuLynDzgKbzGWUAV3A7HHmgIelh6m4PuPn15JX+AZYYi3CurHtt4pobv6TP/mTbH19PfvlX/7l7Ku/+qsLf3bt2tVSVpxy4sDP/vAP/zD7pV/6pez48eOt33XKxy/8wi+0cEmckuLiTdxz6zZ7sxGYR4JAZ1IJnYnYEsrwUzPQ8VaFS/U2mmekyG1wKIY34CrGl0qxF/EMehNazM06vHUyaXT4rj3etWVZP7R03oJVg8D80IQaU2skdj7CiPvuNjaUOIRaaB3oHrc33beyN6r7QkFJiVfa72q3Cd0e0ruYA0SaR6uopIUfpNVFqXJBdJF/rVX1OCVKa+hCsqpXYwl1Xm3Whg0IY6JMaAFhLkIQOm3NZBnVuQRIhD0UgwmjpdKXyUrtxnpYl7Ku9j2kte+IoNbv+77va/2R6I477milAUv0tV/7ta0/tzJB0VjyQUq9IAKiNLV2NT9zXXb5dBWtMgqZqYKAQBLErUSCVB73P5cOq1BoSWOFeVFSWGRwqyIhVZEBDwrrxUjKFr7fWTRc8GGY/iqa8QmLlAtkk0ir+QG6LNT8KNPE8C5TiLmAOpZgxq9+Zo8b4whdGhK/aXcZ7D9NierwVEM9YGJRUIOMa5GYMgdATJV4YTMq/bbH5t2jVjXsIHZKojD9XBonrHQaYVdJiqwFrma9A3JbUkiAVqwR3sGsBvYwY9Fl5RUU4GPKelS112TNdtDOciDd5AQhEQZPxQoR1HPRSr2jWJTLAjHdR0I/TOlpAPcg+0MTOhLIz6rhiw8BhcLbRXi0IxbAuiVMlw7isn6ATbgrorS6ZqlqjT+PaRgwze/WexlQKpincaBWEdb8oiGI8b79yk0NwGFaqXTMwT0HZf85zNOamRrjQUC21kZDrmQKHoKnpRvtM0GGWQyBn4+V3EXhV8Mlp8VVWVYuFrgvltiaUIwVLiwFECoT4b6E4qPBqOeghwGAV3U9reoxH/HpxMWpanZ9sxUvpLlSodRcDlL3LTlVnrtmV32uYoB0oW1TdhUyBIWXbb9V1FdIEpJVi4NBBMRGnxyxETbvVg4ADEFyD0Gga4IATP76wxNErEpvQWsFU77wDph7pXdgzhCbI/XDblon7ArAaMq9h/GB4+Zl+YwhzBhXg3azwWH1Jpe5QByeasDxhh2sl1e3JYrJaWUTCiBsNQ5MKANaeAWqT7fr3XT3he/VXC+9uosw3ed8AKlmmWoB7lU8d1hHjlKitMbEppz1pRA05bl8AauaZ/DOPUrNqDAGpKEqZ7ryts9jP5VppbDX5TWFwlJ1pwBfPqAkCYBQGgGXGEteaWMrF4PM+c64cKGG2U6qY+NoZ43mJqfcJSMICVQBVaG6iRgBawOGyIKijzzmoCHGYta6kSCXjcykVh8BCmJ1H1ygaidokjN/Mm0ZAQ6BetioYQTl8eWrbetVL/Dr7TZ6EF95XHUyHIr92IqNvs72uJn5xpgnBDdi2A9S6KXnTL2oWMsFxqcp+g7ZNKwMW6bxIbtScy/0vM8us2JTUHxQGh/LX1YGSxj0LgdDWzJGvzS9cHlOvahQLjYyGyZsa5UlWCZqiZXXA8CPFsH1ybbfKuorJAnJcskgiEmrsItbU50DgClNHlPUStsMr3gUREk5euWK/tyKYC/USRHG2olEN8zMfoNbpdWZ1FpQAV9E6o80r8PSo1k2KAWIsDQ4+jIOn5o8cN0Hmg7WVDZOoxCb0g9cjXVq5hTaWPVPytgqmU5wLzIKic4zTZUXsMbldOy6FdRhOQWcukR4/xuOKengRkE6xmpsZWeF3ytbpvX9ADe5FDPH7DlYKCwZ5C6LnXITetuFIF7McjGDn9+gQEHUab9V1FdIEhJ8g5IwwubRStXDPaHFkHTMbbprSBsLU6UWCJxaG5i+JULFU2QPxd4+Ct+iFCsLszQkyiHXDUtFGIRqHer52KgDiAuk1awyCPhksl4ss7sDfWv3KcejnPOuA60vzH85hic20BQ3Ru0wBBaNlq4YY0UZTByjgTXUeDE/IAmrIFu0MpXjBv3evk/H02AyT5DFpcW6XcgvYLKFBFT1rtALKu0bXJokfsA7HCKsZlnVeBfZKlqsVWwRvHWUPSAyZ2KDnEMQxp1EO2s0NzmBKWSwJTuGBCSZkkNteWFlo+fodoaB8R2LSmYH/MLI2Ogea1YZ5Fe+UYa3geI4eeVATaMMFA3UV2GErXYzKUTB+3oU1f1xFosFAmxq1h/EWlArkGu1No4aRPohvlHrCzE2ewUffSiodV5q/31cCVi1+CkM6GbcAGJafM0slr2jMo/lqa67aihMhELQC8UGc2s8jTlmDj0LoE76VubyZcXZWN8C6AJrPwEplf1OS9FY9d8O95zaNjLteNbLiH4MyS1MyDQRzY+MqZYQCIilQHBU13uI0uDQkDWB5orqWW06MSB6fMe44M9Hz1KqZ+hHleYVMR8Hjbx+NtYEacSW8TvEwtDWFC4lKyUUqYOa1R1CU8PhQIqgZb5n5gNWC00o4hCTQMbCd4Rl66X9AauaNmZJAbRu1CxgF5Qa5vJQNT64AbQMHwa7xlKYUge1AnfGLHdPxU751GAFTXbU85W0DhLMfqXCIuxByGWJr6yLIuSgBmiH9bIsxuH3WMrAag9xKayCeh6yxpRyW0t9C0lCAlMenpTQH20zbKfMtZ2JI0GpQ+A2lNx9QKNr/Ivv0XLbLVAiy5+PW4CUMhoKAenMZy0QFsBS+WC0smK0tLyQsJaoZWShw4bVcyXlS0otD9fkhK/HIvdl36pWDV4L+9GUaC5bx1aSLSyfMP5HwsZh8GjgEorFbmTQjRFQeDWoMhybls+4XHuhp8l051PT83nl5zquSqynq6lS2UewB6tkWSFezgqcN+LpZGj5DXPPsWCEaCddJmPjt0Av+fVgFVQEa4dgkTuB+gpJQlolA7QkpmGDKdlUWu04vSOv5bFR66Cx6vdYt4+OZq/78jUQIWQvWZYPzCmCJyUKC+ZpFB5sTKrqA0fkrIpwLu5WUiAZ06wVsNmdWsoEHcptEKnPZOtoFgcqKyMiCNKKS9JwYXBgxNb6wFwwc3r/YXmdAXsvxeW8ctXO2OuFUFTQio2Fa2JaUbI7waT1XVOShS5cb7GUgsEzT5zrVOrVf1+e62cuIFOHs3owbqzrBsJsSEfJYOSyjLtDqVO2HdRXSBKSlcLWuX1UT3sIOa8x7GNnr6vvgSDQYiqwkTWf51O+IiqVASO0edJvdru4Vm8pe2EMhIUqioPawufIbztmVox+eyv3Z91mmfe+hgC9BCmInXo9hEtO6QvgaWcVRY8pUPaU3x9amydznrSVejMNlMguilVIsOe0dEorYDV8v4TPgYBkbc57IcyNBWuO8d2lWLyueAuQbjmD60e3oFrWjzaeTG8F/vANUhwbY4G9zbulrKBWwNQzlq0B/80IymVkh7YeMa727aKdNZqbnFAGW2I2AOtIBanAVI7vC2XJS7cVVAKWauZIh3wzQhEIAwelAl9hgJZkwcA3S/Du13z2jVlzh6gk+3rDAhECIIVUnl+sj+VHx3qVA3rLfllkElhoi6eAMDoot5vywZJaxWhYjMpz2oVc61174e2z3Oa5i92pwd0IuO1+7ldwNRA3oVlITnqL1LVAqJfjYCD4JXcaUzOHOWB6DRrFXDykpFMyrgwzhsT3oc15LyQVSizPNnhHU1Sxf77sq9tqqef2hUSPHwstJaLFzLCqSZczZJpp64Vx3G5YHPIsLMKSsUpUdS/3y2bNPOP3dVnWbDftrNHc5ISgSknhgBA9IQR5lf2QDeOGdUTINOBuYGgjswBiUe49JEDhEylseTyD4f64IMDT54Baag0YziIAJFTc2KQeUccEMSesEJAMDbhloz+p3V7vR4eSVoUQiznXDjxYPmDubxDWj4aBS9HyNTd6j5sYocoQNE0zMtpIMQdWUUkmILMIxhUb1GrPBWKJ1DGSacmpsyRyNx5QRIUhvpq7pmw8nK+6fZ/Y5ogPhEbWR1cfhmIGmSqVrmAKalpWQCjsGu/GZicxloyViMyZ/BJiXHpAB/z+mSfrgG0V9RWSTSAp9sDyx+OwsW7mFs4CY7KH4NFcIQiss9BeyxadqrFaJkqpNgksK0gPriLcNmxh0KQK1GET20GhnCKE/k4q2Qbtds2CH7/XUuggrTZH6G7T5g3yTMuggZmauW0d3zNcE/RN5yekZmoF+pA6DfO59I72e+IsJLDuaOuDHucUPkSBRNGVsUlpvyCrXyjZWios5Bnj9pKsE1ijOaHAXR5jJgSJhrWyJD5nU7xhwaui8wHUfwqU1hhk6dBCzRSxdAQOt+r3bDX1FZKElPude8QOAOKlld1hbaCr3kKD24Vm/dBSQ3HDljZEHuxFBTPqwkCKd0GgmlRJN7RIWWVHIJAsYYBbjHQLBwHwSUvfLNYn4vBPtJLgTMAq5lT7zjD1VVOAGesHsoI2FL6FiR+Q6JXjjoHFF75fG0OZJN5ncHws5UzjCcy3FqiNeAbJFYH0XBa4jyX0p61TyA8atD4jHyzUUrjdpJR59pIn1SwqBI8aCsu4UooACrAmp9hSGaBLvl6R1WeoACOey6KVNT64ditpZ43mJidLoIKxdok1aDLqBo/DRBJG2JxagBXy5Sd9DYQyhamnknuAuaVZ/vpVUqBomjzqOFhbHBYNC7wI7azNCvlmozNy/l0mqJWpL8SsS5ghpCkunVgBua89fv61QzifUy17jCk+acQuYK6PKmsMhePOktkcw1/1wro1lkiBDXeZdqnAN2i4OdifUlrti0i7jc1LJotK8vFTtoKp8b2dZae78TDPcoaMrcADMj6cy1BZZQLrsV5juwcp1zSTnjvmXUXWedAeo48HEwAqRVnTjyH5Ck77NRh7hSjjzgQIrhFBnpY150tn2tkxGsjTvDejapv9VF5QUB+rrLDwaa7IPLDaWa6dThor5w9+0AimBeaL5Wb50pnr5mGQFz8j4oOYuBsLjRauPWb+NUXveV/ATOsHWV2a4IeLwMqgodLmJZenYiGxgKRWiZRxbozeSidYIBCTpbkyY4nF1QnHp/HYq6hzxdR5EjFC9Fg3q0ZSt/u6+z0oJWFlbjExPxJidTkeBcBkGq34995rnAfttrwrNwwmtmThVlPfQpKQOmmyDfVgMmNIDKYC0++yfKIMnoPQx+xy+9avsStuJ1qxOssakSsshkDRDmA2oOxZRJYb8wsz9Lnry0mEwNHJEbPWSxjopqGw4qDTovQ7B7t94Fm8hht/3aBiZGxpuBV3+luw5HYJb63SWl9B1hZVDVlXamJTfsPf1ebiGZ91QlVZNipc5wHyCSwlxRpO+rc/fvaG2W7/uE+B9pgpVYRqxb2m/d/wMSaSxYFxbYF/JUthJxPIdm1a8wbr9kNH+cyZ3YSSAUgJVsE47vdjVeD8dlJfIUlIcCtI5locJpLvGlkgLKSwBBYU40qR2iD3/RvuP2iOQ3On4D2ST/qggSGCg1cTKM/km9EKnB3uSivV3BkPHp2goN5ZV4xUvCtvt6ZnT7FR+nBFSKnWYfaBZQXCmLTaSk+dv2HOA4T+3QfGzTZSPFEYTwE47zIhTgAHXRVdmdezXGIQMsv07EX7YEDQssaHnTk1xpgwqBWHt/Ze0N3+1i7FuITr+aYTe8Q2VqFLKyMJ8gtZJmXC8DS3qqWcQ25rFwoGZDC8gFkusZCHmTgPC8RRinnZo9Rc2g7qKyQJyQrQhPlxj/AclxwN4MspM5YfHZV1NWEFhFapD7hytJs4UxFVwjUoz9nrBTwFbMYzM0vmZrRgkCEgrZQ7pDRaN2TMHaovS8RC2zMojkwwGvALgN6oKV2adSscu/Y+ZEhpEf4Yk+aztt7FVGHGHnvrbfIhCBfVGWGfdZT1ePGIudBcKfiO2/fJ63ObV/Kl+ilWSYZeqGPpkgNA87Z+PZE+WjlGVJRV1hz7f2q4uh/swTMCABx+X7I4YJ60DDerGCPeAQuehuxr8QwukRakQFiuwSo5USjxoBS4DAkKmqTYbxf1FZKEZMVDWBHhsKzcd0i+RYYYCZI2jlvBjABoFlpwpLHCx3n/oYla5mkIZhsDQPoWWxjgew4JNYRAr3rTsaVo4EC0/PPMehVvT/p7X/bjY9K1dw/Wc2GBF7UsiTA1WBsT1ghw51UEpU0b02UfXCh9WxhsauHeaKnDUGskFFSrtgnDE1o1YsYUjy0qrQ8O6JRIm+AbrXI16LS/IGh7CQcpE88jKVaYB6mcgiVzrSyxMBDbKsWhuUCx7lYMSadsgA1ot26g9YaUX1L93tE4l3F9bhf1FZKEZMVudCLCpRugZ2oivUzbZFB4ZJdOpw9JoKC4GFOZWFKwQsa30n7lAEM75c4KUiyP10qrxuXQqh4MYSeBMpXHF97w1fcrzzD2EJROdHOp6La6Na8rNZiIR9Leh3GHcPsSSUG2S0FmkIh7Q2RE4BCbEPYHMC+0WJ46sQQMVomV3dSsCMCtS6wlLyTNIgDLgzaPVgB2nj1i1LKxkFyZasKSLMzHqMi5PEicdLdrqf3lb5cSD0KCpZNRUEM3m+aK3Q7qKyQJCSZr6/C16jIMKbffZWIDoZ9Dgn+WgVNG39oNCDdaiUJzszVWCwNAE5KYd0uQovqrdgiHc2z5VzvrxZlpNUUzbIdA0so2/gamCRKkMWpWd8uV1mpD8BqrBODbRpV+oGRIwYVa+feoisHGIYZ5C91ZlgujfGHQFDhkyWnhHx2eru4H6bYWXkgMdSoc29+Km7VmEcAYtUMarmNJsckvcT0GvUM5l+aaySzCPrBkVLsPw9VFBuAXlKmI4GrGhRdejPo4JLcwdTZXmM+uV/sNn6MWDlO3peznDa0RFppnoWR3wMBhSiMO5So8B7QaE1LYMBSJ8ZsVmS9y5lH1ARPOG3L72dLfVq4+CgJam9Uae3nNNEUjbKeauAWAsWZFZpN2C+sEC9uZAxoSr6YEhPwE4TolKIPOdI7DRxLWGA8wHzrv6RBijboOuCpcCSN12ApCriIEV2oHA9LTy5agkKfzG6+xhxn3SixZdVbcWmE9y4pquOY5JowQyxS6SwoyM2hjrVVeZ0Z4DvA5tOslLunxvBrwQLQlsSkE4DOWjKd9YHOVZaYpzKMGthfj+twu2lmjuckJfNPapCUecgoDGFeqTAkXgRbEVA58rOqnA54lCFxAvnsMiqpWT/rNEAY9ld8FofhgHlDWqDw822MZqBzrHT54b2ZxtfL5S2FasHBzw+FsKRAMUFMYUKjBj4cHvyXArRTOMiiVmg5aUjYr199/JwLtqgjKCsrYV7+LSzuEEsBgleR1hBryTdU6hPM6KxVtgK4rBUE6eiHHRGkkjyGBVRIHZJW1Af13qi1394OD3sbnSRcDgHFZMVE0yq8R1Bpa4Bz0e+X+9wBwkrsEVkypujL46gEh6DW0rkqWoXt8Zpjk6g3lBNZNWpWTPsMK6coa3e6LqFrAaMzeKY53nbpobAf1FZJE5JgV/FrFFAWznuG+eEAB2rJMuY5e8pYWidlCxUgiKApq5oRhHsd7NMbv4ClUB60eQlqwcsDksRyGPxSBeCxWgBaY2B77BhWgBvwZzUwbBitr2VGd+kJabA+h2Pixv/HYlGnxs1xSUAg14WZhwIRxTZLCiLomjFVHAwU87IH+zgs4M0zhSRsga7DnOJMwk0668VqAYL0Q4+4qp5Nr8VOoKCuWnggPUmGuofRLmWy5wiGstxXUit/X3IGYF6mURB7rMeiUKn3ukN3G1JBZXY+TQ6wLBtYtwgO65dRXSBJRyBRVt5ain1FPa2RuyM6iIBEKoUnVKaHRhzcdqY0GbNa5SVaPd8EHB2qMn9/0jLib1x3WyrlzflnEMVgC92kPLGZl47DAaBAoWkmKqltWFUEoauiqOLj19GH7hr3slVHEFljzr8UTaFDu5VugnKW2oZrfWdh/7KEHBJwZpET2klLLZNBYmBUF5VToB27KlMX1rP1cllPWAXybT7UForOKiGuUlpCy/az5hgyU5hq/f0AJYLcUfGYvgZ7zShrjWlkh680wbqeq9lYs3XZQXyFJRFagaCFuQxAiZ/0NXs1oMAJWQ5+ilC4GJEwpct0RTKXa4YgiflYaMxMQaaVxagFlmmWqqi+ryB18/NYFAmZiSxHCe1HzxRIq2nyhHpIUjxKalTXLBmIUGIwIK9uIqUGDccugZ53vlw45TNHdCo4MDiDGOmQFn1uuOO13tYMB/CJaDYk1BI+ktLhDKTRjovxBaSnsmAsphgR7H67jOmslzTd+Lq1l7gIlgqAtNFnGOgHFhwnQPnXFu6uMeQ4vlkxAMnuB2w7qKySJSAoUzZ8TtwHwkma5YEzCnTRYXeBpEfq5n17LBPACTHLr5FVulffk2BNmLQshFTR4d3eAnQAoR0rxsgIhiRBtvaowAtQ2BiiVXbtjgzKnw3qlgclBEGuWDzevUIIouHbDdae9y0oBbbXx8ximCMcqwfiMKhO9VXh1meAxrK+0h0OelsdYnZFEFIYV6Zq/YFgl7FmYdKtmj2UdZdYqBzUTlGEoOpIFxMIxcXTRu4ukd8AiaQEzhutTpeQ3S//HN1sVrC0YgzJZwJrbSTtvRDcphQKwStuHcHd7T7oNIGJei4VgysFbZkxmEyKYVbvVjxoxGx1Tp/weoMpKe8pKlS5k8ihKFGMG74y7NMfC8Md80KiEvFv2uTNVT+miZsaN0brpIqhVK23OuB+YFHKmr/zg0fogajSBJ3UYe+7WrZnxK38v4EXNWmQp2eHtma1im+KuCzeCdb5ZLryy0ispJAtemdCrROuyChYxiY/Bd9IeZep+WcaMRqnwo0ZQXsLLk3QBGfPyBYitEoFfytlnEkmAmTuB+gpJItJqOoTPGZh1Df9i2lsUtH6sGyuzCRnIcBwiUh2FjuIj94FKwtJ7bOyWznMtriIE5GJ95CzUu2YBalkQ/Ku1NYOgslOILfOxbYkLb0knlRoYMTxrvc8yezMVii2AKtaliSBjEVyN+G4rDkhS1lvpzXAxGtWGpSwpNiMrlqDwaajIrfdnnEvLCiKHyNTA8lAbSNrbUBqluk3WPAGkDhanKoJFzmUCaetlVWkvuBSZ9NwNbo07WE7d7apmFs2kGMPtpL5CkojKqXxlgjlPuzl1br9yG2jWr16T0zWfRLVXAyxIPWgMMCCmlg2UJy37An1IpnpU2rRcFGyROMb3Dch4UxAQBxeLioh2Wm0Z1wa9ieXYA6wbRkHTvhEAaxrPMvNaTnmvIny3NpdnrnnMCcI1pPHc7T6ja1aY6/UeFRLGOmW5dotKdvV8hud32rRfLssG47vfSA+GUmgpflrA8+379EKRGDMKFkrvkJRY8P/LPl5D60POjrRjqECwYjDxG6tEkdRwfKwreqULrmHnUF8hSURmnRpvUtTMelAkNO0Z79HSNXHDmRVqscAagRTSXhEnIbglgQNhKVXiZOYN6XFXhdsYzL5sJLqVex8CzNkWEuZWHwQoqq6PjUK6tRVbIN3WmBiGAr6DMiaY8BFcVz1uJjDRngP85rkb8q0NwcioRN3rrRJjFi17RJBu5e95fhhQeKyYEWG4rwgrWFILCQ1/zqWD4zukWJlcATLS4R0ds9ZK6ON5f6GR5nLd77s3Hp+q7WKLyXCR0IhjXaahYs0qp3nRw35Q661LlrUgLwCnmJLv9EA4Wu0HK+o8fJeUvw7l6C0n9/RcfC/GnfImdbMbipx/h1StEweQVbWWDcQrlrjX20KhYzI6rDWzioyVx2YdZhaoGwrhXZyVU2gx9w+fmKp1iwv9+9IhhjbarRvj0fjpy97ErylkVrmC0z57KnzOiHpGGWJSNIFVYZWY0Nr0Qqfzgn16n1eIWjwMrgnjGrOsVZbyiOrLknvCGgPjYptZtKurx0IFMFWIJReZRQDe7Ae13sJkMXZ+k2zYm08C4Im9AVpmY8uUat2CgKdg1aHQXTa6QLGe4x0aXgoT8V9u12prCA34r7WgvLA/dR6IoE7MhWMhce38WKwaJ2tEJVHGUkBZiYggTQboiznAkLI9p6BbdlI9q9+FG32sj91SrsM2jiSLEgDp4DLrNXA5lsAzVo0q0LSCKM3EbzD8ZV30TFnn98NdHm01dgyMi62MDSMRUx6hylKrnRnhN1oIuyDsMXadt5L6LptEZB3yzOHMmOjgomAsF7tEH7TfhEbuf+s9irDAjUD07wYQ9RJNe1fMLgPGW/a3cxV3McZ5A7KZMamXaUrJRAqDObV0XiaQNg8SJjI4NNj4sB1VhI7BaCAOFa0v7l32mPFEi0vI0ziN9HspLsHamxrUN77Tsr5pF5Nc1jT4on8xdEKwRubk5ccRAV+k7D6ULgGWzGTkJuLhLLlrK8K9u9hQfMO6FIVpwWFJjspxNzv/NvFeInBQQmXHQoDdDuorJIno+tKqmm3DBMoxQZI4wLWsHmuTmWBj5K0AflDZR8wHBzZ6vSERrg5HTTKFL4zTsOo8MOnTOKTCOdUEq7quxrrFxQGksUjk30eMWztAKYWEsUAQmCiwQEj4DXiPldZeprCwnESMHADPS2nH+YUiMY4E5s5S7uF22Keku4dWQ2kfAW06VFi7x5Sp8wXlz5K7sjWYky9MhpiFfBoTWL8WcTHK+YG0luVpwn2k1luXLDMYYy1gzN+I0pYyBFK4j/LiewpmStiPpJAwwheBk1KQphmnQtbfwPxbKY1W+nb12DQMg3Ybq1dk4CANUX+fptRyQa0UQiURdAj2QNqyOm4iqJey2FBKlNwGuA6SwtFr2q8VLMv2zbpcU7prmNiacjvN7RAe5JKFBFY8ZOP1IjcP+LUMXRzF39fnCoUhrfdr+FEsv+TfQhS1W4twyzH7NBbLarto543oJiVsQKmoF+OygTKj+1Tb/dzhA2Cr6EWfQWEFiprF9yzN3LixdxQbO+Cxq02zmHkkjbWDy8JZBMyg1giQIUaBXF+3g5nD996r+IFhvdHW5boHPbKE01O+mrMm7F71wZ3a+mHcWrGwZW+R0Pq56LNrtP0BvlbHk/N2ZrYBEGH3c07JjUFIxnnJXEw0hcMdvL0qTBZZsTUgZM31CpjXLLXReB5FGSVlAPKjDB4GBUWSdVgPWKGk+kjaXHfW1JbtLLBfLJ6Qo1h+OJVn2KV399WlvkKSiJA+JjEFYJk1pgEADxcEKrc54W9okkk6zyIwFBYLEt2KRXnNCy4mrU/apPf5oEtpLHAbaampMbfK3OISUTVTr4NBYjvkwGDye7GcUgp0+/fb73lpWk61dnSXV2g1y8Zhr0SdUYrZMcJ4I7NT3qe8+fiVq/K4j+cZE/Z4GKVeQjlVD6GaFga4uJispM74im0B7V4157x9r5ueAX6RcVCheKdW5LCqVEajB1fDXb5uEZS9sssvVDgaPVy+8Pw+X7G7LMooKyipELC4IoVK6QaeUBh8HY5R+xUEfp9XUuy3i/oKSSKyFAUEMWnYH6tEQGKMH11ypSBYDjfg2EJo5fdIAuyAF1zSIeP8zDhkJaUGY5H8nTjItVRQR9cW4ywprJk0RRwG+170dZvidgJ/aDg1TOGzsI2Wiku5WtZtKPY1AqwJ47lbyJgIb+/6eHTFAWB81o23TFDuLAuDlkFTABQU+KpZqkrsKMVd9zZv5dIqiYdVaF/nD/FeXQ6M1RhtrGKSktUMqLwWPpQkf5gYNbh9LNmCdG4mWH7ZKyRMnRpYczVwt1hr7HZRXyFJRGx6qgrAI1gLmlUHAJVlU22mxPMHhfLrTPR74T2Gb/XBI9WHTLjZxKAz46BmS6bjlnH2+qIajMiWEg8rsnYV9WuyfNFpeDnHBpBvvZ3Dn1GAtLcpPELwQVU/jCKlTWnHv96oxftQtjUxbo356GRbSbtuHMxWOrz2bs3FBR4463FBJIVKi1XphTC/VdlFYYxG7qokqni3LBfGYc+54HpTam7zGUNyJqD++4zbGaU+zvnsLYnmPVClpfCxhS1BHXnOIa/GIrtuJfUVkkSUm2JFREBdCLrnubUAptpGb7Dv5XeVBYKpPJFR2+e94BWFhXG7CBUSsY0xFiYzyRHkaYiEWjW/uOUiFkOiQvS9YFovFHIz/bWNrsJl5fExpuFcQAcKWtV3drkt1DbtvhpaiQFGSSJQf3PzfsWAmO/HOms4LLAsSGOGgnoiMu0X75ZA/NgUTXCWFCeWQ5knjgGosgY0NGCtmq5lJlvIlJt+siSFIa+u62tmSf1LMgxyHVmU2jsAbGm5LjWcqe70cF4hYS16DLjmdlFfIUlEiBGRYh0sxi/CQcuMgjS3ZW823QwwIcaUGt6acIvo1YLCtBGhuFmLhp9fyY0FGvDvsQPUOt+stUXWjIa+G44fcUT1s0w4lxOX1dIwUTtDvIbuftJintSNsQJJl14Lut3iCU0ZiklvnjCzgDYn7dfql0n1hhwM5ZpYd4gAi+zVQpJK1ml4Lyy2DL7Xqt4by8cMqGBIAA3sI7XewoSDTgJFsoRyqMhoPmj4UjU+7WTJGKbSmmmFiOsYNbIVmLoe8lj0Ps4IZu3usXCuHbxPu+WWYz40YYWbsFWyfTUizoKpiJsmZsXuC/FOatpvRHwM544i0oeVOcI4kHLey3t6dR9SWTbkIRsb42IR2y9knbaewCHRQiA6e1e7XHEXudoKiXEhguunl3eU2zExRmvGd1W+nwTJe8XHWW0Cpl5t6ltIEhEY6C4h4NAyPYYKiQZvDubT0kitm4flBslTcclDzcZzGOgZVh1NpLHu9wc4AOPEsUYLDb0dMh3Y7BkL1pmJD7FQKcPMB2tcL/tAY62v53zKJVOD5x6fDVFF8Jlr7zrls4K0Ns9cQBXrrOfDIUybtSHNA9cFIbwZHuOA0fQbb6/ViC3is0XavHqQUJ41OQXlWwvwtcb0vE8SkBWKjVqyjlKCSRdIDF7IuiGjw7g3RgmvqmNlgTVuB/UVkkRkCRHoG5a1wIb8tt7TYTIJZ+E5X4CsTin49rt0dwliSKzbjfbN1iGMPu5VarI4es0fwLZrpzsGo04ROzbolonmR4rziz5zQLNavSZkUGUlaHXtoEXgJQPWpn0fTNmnlKrPgN2+oAQGIqZCqmIdU6yxPebebtUSnfFzrvEY6p3oQen6AXcpT/NMq5A8nSt83B7RL042QB/2rqasw/ImjQlAh6jnEruvUA1Y+pQoIDtDtlyc5YvwzeRxTqFSXP17L3plnkdq3SgoJreEQrKyspK95z3vyT7/+c/nP/uJn/iJ7HWve13hzyc/+cn8+R/90R9l3/iN35g9/PDD2Qc/+MHs6tWr2a1CeeS5wBTI7hDRUcl6BBYSZ+izlQQGcEquLFTfTBAsp6Uoh2OWTJCovCpuduKwRkzFgJDYiBREthCelgERWhgsoQEF0izqlx/Yen8AgNLei3e+9Y598vuI9NmwLy3eATgrWkYIAw6HfrTUbOZwQhuxivVG594oWuUKCsmAaomJdYkgNkBTBieGkKK50HPqMlCNYeVKRXBLWPfmJz2onl7l2o5rYNyGeCK9C31IfGyB+53Y0+btixLCNnFRgCXRKoI3NtT+hleu2m7mXQO2Et+tzHO4IjEVh7eaehrR8vJy9kM/9EPZCy+8UPj5Sy+9lP3wD/9w9rnPfS7/8773va/17Iknnsg+8pGPZB/60Iey3/3d381u3LiRffjDH85uFcpz0YVF3uMLsEkgU2yO+vOX5vViUKHrRywYpeM54PnDBrZHXu1XeM8xr/iIm524feDglKDlAYimpSCGLrHXCynI5du8FZvCwGc7ukzeZo9MDnVhS8jvHKgNC20pk+z7IAR1t449VwwwnKV8FgqhDdoKuzRmKGAxZQTC92vKINo8fGKPqTxLB8wauT9jialR4+ikzz7SArUBgsfVHZJdU1gBKWDUUog1t1J7DBuqy3F22Qa0hKUBiQ2WDHr4hL1uq/673nJyD922ChemClL/WX9RvCWg41988cXsO7/zO7PXXnut65lTSB588MHs0KFD+Z/R0TbzOkvJu9/97uzbvu3bste//vXZT/7kT2af/exns9OnT2e3AnVqkayopuLXHa5WAhD0aDE1hBUKhEnM2SDcHNIBsYrMFeOQhxCwajxImz1PiVX8/ZZ5GBYUrbaPo/PeFWBFwmOdHjKAxVC501IgsQbWuuZzdVCOxUAfmqkcoFqWRSYH8RIO7vCmr7XBe7TvAwaENldQ6DUh+fJVXfksFi/bZVZctdw6FjBg1/sJK2dHYbTjTO4XZEXH6pb2QGGsXeE3HPZKtHb4au6+DsKorTxa7jVJscZcjgsWFMuSg+faRWGDTPu9Or9KV3peIS88MZmGoEP+DEGcyk6iaI7+whe+kL3jHe9oWTlCmpubyy5evJjdeeedlb/3+OOPZ29729vy/x87diw7fvx46+e3AuFQPjg+rDKYJCjBGlKqX+c9RVjvMs15/3pT8TkiLkMSaEveWmNtBpgoJfCezkapfg9y+6VU6TDoVRbyDaqUNt5hpd+ysTxwd2nprq3R+Sm0bp1MrR1go4SHapkwHm1c4a1JOzyhTIXrUCbwmHYThbKtzT1TrgD8tiFYLnCAuWFbxRjDsUv9xOJ8zPnv1NYQwdCqJciwTDEVjXshHLqWogO3poaR0Shd1LR5luKwtHo45TFL603PpfD72AMaXACryOESwFjelgzZWRU0rl1UqojBQ9lqsiuIlei7vuu7Kn/urCNug//Kr/xK9md/9mfZ3r17s3/0j/5R9u3f/u2t55cuXcoOHz5c+J0DBw5kFy5ciHr/ZqQqoc86fYMpp0YHW/2EXbn/h0IufE+rbaMTge3iO/LxVIyxo9gMtH9W6ivUesvfg3ehiB/+Xx5PGGdRNSf4nfCmVujH/xsH59Bgo/I92OzOvFs1Z+Gh6jZb1bzhgHECo+t7g/HD5TM6NKDOL/pzAqyKL/BvuKncAdrVLvhWHLCu/of2XiiB+bpWzHeIUSK1QeCr24vSfOAmjwOjau5bc+X7cv75qvVrzZdfo31j7e+rWiP049bT+raJkV3iu1wbF3DtYjVC3sd3lfmxPE/tfdbM0+fD9QrbIHW/zHPhu6rojI8TK48//BncMFX9oA0UN7y/+xs2lO9s1paR7mCVxhbydNgunMpwX8KdUcUXWHOXfl21noUAZC9DqsbjaJcgV7GWw2UZ5P+dfwv6Lz2HS8ddAKU5CV1PZZ4K3wkZ5ER0pVzNOj+H0hf+rGpPuL8veqC6ws+FeQqDwt14Cn2Wvi0lsf1FKyQSnTp1qiUE77777uy7v/u7sy9+8YvZv/7X/zqbmJjI3vWud2VLS0vZ0FDxFuX+74JjY+jAAQ4etxeq0/eAvy3snRzJDh6czCYmZ1r/Hxra1fr/qWttBpuaaD8fHWvPxejo7tb/Ry60A6NGR9r/b7X10dPORI2fbXi2Obh/ovWzvXM+pXJXu82FZQiC9nscDftAunH/7qN7R1vR/nv3jLb+P+atOiP+3ccPtH2RsysbeR/hjXLfvvHswIHxXGAcOTTZQkIc8reIifHh1lx+6bVr7fZTY61+pi75YL3B9pyMTbcF+OuPTrX+H9589x+YKBToOn5kqiCgDhyYyPa6OfQ3p33+W9pz6ud2bCj/WWNXe33uPNJ+V2uck9f9Gg3mP3vZr9Pk+HD+s4sr/uY90OjM6Wj7dx84vif/2Z7Z4lo4esn3t8fPvTS+J8+1/br79rTnCv20ftfP3+Bw+0Zz+/6xnFfzNv73dvnD/67Dk3k/u6FYeJ64EaBOHj08lY3s3iXwmm9zsM1rl1aaXfOwa3d7zfdMtOcLFYnbazTZUhR3+/ip+/w6l3npoItl8t9x0vHSwcls1ffr0G/xO+j50IH2eCYnfXCl32NXvNfOKZP4nXHP28Oet/ENQ54HW+swMlQYMw6x9nsmCoB5e/d11qdMJ/aPOwbKmsH7y3vvuI/bWlhvds3F/n0T2cEDY9kLPpBxj5clA0FKu/v/Wb/nx7zsaP17DHu4w1MxFNZmuuPY3myft3i5ecM85t80ONA6yO86vjc76N0Ug57Hpvw+xP4Y87/v5jWfw73tORzwa3y759XJydmcX93/l30cl/u1w4eKLtR9Tv7tHW1dapxcOHFkqmAlcb8feiQOH5zMDk6NFGSUa/OMj6fY6/fY5MS1fN1a3+F5YzzYqwMh7x4czxpeBh3w39X6Bn8eTE525FJjsP3u+wOZ0Zo7b9mYCvb+Mc8nN1Y7MjjcW/v3T+bzenzfWCu2cGS0M8a9S5BZHV4sW2pac+L5W1rrraZkComLDfn6r//6lmXEkYsTeeWVV7Lf+Z3faSkkw8PDXcqH+z9iTFi6cmW2wGgpyPGXW9g6fX/h5Sutv1eXV7Pp6dlszlckXVlZb/3/0Fh7qs9Mz7X+v+hN/ouL7fYvnG1v4MbGRuv/jm74G5eL6cDPXvaloxfmFls/m7neFl7r6+02l68g4jvLf2d5pc2A83NLrZ8t+ECtXWvtsS3MtzXspaX2WGZm2++9e/9o3kdo6r92bT67sNHZHLMzC1m2tJKteEafm19uzeXdh8Zb7qErMwutfm74ftf8e69ca39Lo9ls/R9gSo6uXpkruI6uXJkrmDpb/1/YnT3yajtTa3lxJR/roi+k5+YYP5uDiyVoN+fHs7Kylv9snweeO3tlPv/ZjPs+7yrAz676n2V+3h1dL62Fo/3enRX2VzW+O/aPtrKart9ozxX6afXrf3ZjzmcADTZyXs3bXHdtBrMbfi3Bh61/e0E2e6O9/mG14Bsz89lco1HJa4uebxbn2783MzPfNQ+PvtKe/1U/h6HQdGNcGhrMZm60+97w617mpfHmRrbglaSlheX2GH18gQtrzPnYW9zmZx3vD2Szfo+t+j02fXUuF7j4nXk/H8uet09fbO8zFxaTf+fSSmHM7lxzrLbk9kvDA3x53pu5tpBNCzE1swvtdx0aHRT33nU/5uOTQ11zcfXaXDbWXM+OTQ5nz1+cyy5ebfMMUkAduf9P7W6//5XLbVniaMG/e2mpw1MxtBgAOs5eX8jWFzvzhnl0/bqxwoIz7/jSy4E1v+6Oj1y7Z05fy+UZ5jWfQycPhgY6e9KPedbvR8ev7v8XfQyT+7XyN127OpcNrqzmlxQng0JsjjYvdpSsRScvV1YLMsq1cQHMLs5l+lp7rmf9HnPr1paFS128uxHw7kS2kcsgJ0fzPedll/sm/OyRV9pnxMZqhz9bc+fn80ZrD3uZ4/fM3ftGOrwUuLauXp3NJo7ta83rvHfZjA905qmzVzv7GQQlbtGNzfN31VpvxhlrUbKoKKflQxkBOWuJiytxdOTIkWx6errw3P3fBb7GkOOFzfhTt29EnrvgytbPSmPGYeqwFEKlB78/5m8YLiUs77fiu4HUOtgYaPdT6itMpQu/Lexj1WcrOL9sVRs8D/sotwk3h2PwQptmcSyu3HXle4yxIqvCpXBK33KXv6E5gdrVJmj3xLlOqqI2vxC2dx9or5P03ld96p5TlrraBf+H26nQn/Le/WND4jvDOihSm6d8CfnwO8vzgYDoFsps1hDHhG+U+MT9QQq5U3KkNkgfrxxT6du0tQ7B40Leb5b4ycUTaWvnyFkI8/83q2tKad9d9Qf8qv1emHIptvG8cGxqpLIN5uq+gxNR49P+FJGiZdnh3h2mVpd5GmuBNFyk3Vb31eHDqvXEmFxwsSY/QhkkPe9akxLfHZ5o80z4Ie7/LwPjoyHPyd0eA8mNt8xT4RwD+8cpQJX8mXXzQDju8rvxd6dtIIsEfnBzAiXOxQAVnmf1+Uj7w1AyheTnfu7nsve///2Fnz333HMtpcSRwx555JFH8mfnz59v/XE/vxUIm0vCbLBy7uFzfcMxXYvEBhoXAknn/Y1MC4YCfoEUhIWxaKluYZaPlZIn9XM5D67Vx6EFi2FeDxl4ILfv84pLcIPXxmwFiKHC52mfHWKNzwpOQ/yDFmT6oreOaX0B8l7LOkJKphVch7LmMLVXEfrQMgwO+cJmyKrQQLm0tUbAtphxwdTnwWEu4J2E6JUs8iXoyx5kSwuGxRrqKdA6L7BAXDHEZOeFY2MRpd+kZKthvmSMESMb0EjhDgOYrUweaS6BLaNWcPY8YwWJQsFylxOLTnm3HRPU+hSBC9MZQ0f+SaUTtpOSjci5a1zcyK//+q+3UoJ/+7d/O/v93//97AMf+EDr+T/4B/8g+4M/+IPs05/+dEtR+ZEf+ZHs677u67LbbrstuxVIApvBfUKKxMZzJmVQUxYgJtAPKnJWEbJiyum6+e3RB96JDB7cbir7wVilb/YN8NNLHsEwJiWv872y0Aq1ctyuUVreTHWtemfQH77NUiBf8cqflbGBIDZt/ZGGGrpcOmNrDw63n5P75O9EG1jbxHa+Tw08TVM6MV95GmuFEoA2yJIKA25DCt155YyIZg/8Urm+JSUtFgkVmW9Q+KoIh9aMUk36FBRPs9ZOOoXkhndluJ612kworCfxKpbJOuhDQK+u+l++EytLr5CFU9EmR11W6k3lgdClLB70DN59QMEvsniqrIxKF7hm0NjFXjm65t27GmEewz1Crd8OVEiSxZC86U1vallJfv7nf77194kTJ7Kf/umfzt7ylre0nru/f+zHfqz1/Pr169k73/nO7Md//MezW4XKB0qZ/VFrpcP4jWqsBoVJQlwOaZOiVyvNNDyQypu1WbK2lPsOD6tQyy7v+bM43IXy9Y3Shio/R/DlvJ+bhoIvYilyHaGhC3H0E8ZCVP0Gq0Dm/Rnpxohb0OpuYX5vC6wRjR4Az6BMjhsp5h1es2/M4Mcqwd9RTOUx5YGdAt+G5ncXhNv+nfJYOqbrcr8grIOkIIaB1LEHPkYYovdKPRz3rq52m2Ir7KlQ6a8aY0qFJAwY1yi0SuxS5rlT12eAxsWQ0lmlvRNaO6usOlXw9WWeOQ9kZtFK6/e5gsUD60nIU1X6D9LWy5ZJbRVv86EAGqG3MPVe6jN0zZUvkokTa7ZeIfnyl79c+L+DhXd/JHrve9/b+nMrUvnwLhNM6FLNhYveKlGFrAcKL48WUJBLM5XIgmyHQES6ptaHdgPCTVH6IowV7o8yYcOEZuIySQBxEhaGqUD4b0dNGIk6qY9cfxbIlhuXE7wabgPmXMd2sG/PTJv2+wjXXV7K3FZadFeKrjCGioI058DX0QCfUAdGYqnQZRMLHc8oqehfM5cD30PiGbwnpUKCvWiVQsC7TWWWAOvK50ucCx13xioOt+iz4zR+gLIjNbHcziFJAJEgfKeLV7Fo1b9X2+u9QMHje2NB/7aKdp7N5iYlQMNLIGGoSiuViMahHJrUdJ9pb3EbzAEB4XTApyZrbbQbkEsFdoSAXbEPySSbQ9zLPldYgsZ9eqVEwF6xKxh7XBNDEMD/bVk9V2lrhA7g1GpD3Iw7BQ+JfpQ2zvwLea9ZW57z86AJY0vZaI9J56fQQiJ9Pw5yoNX2gqgMS1ob/yHuwE+9hpJp/yUfJGwdgDHEKqlsaYKXfEkHrb6L1RfiHe4W0IvzC43geoQiogH75fJDkNtMvS0ol5bysGqUwejFAttqm1v97Lbg+1jQv62ivkKSiHBjHhcORjC2ZA0Is3Csd2i3CmsDOQsMzLOimZLw/1JVMI0A0by0uCC0YmrdWP5QxEGMGkKDvW0g5gGWlzpBrW4e8sOfKEamfSuK9GlzdsVDWKvzGvCaxgda4T0QMpwoq40RsOq6kEvFyzU9Om18PIvQBjxJJgUUKM8mUsz7DD9YygECl1GVOwWxaKNwn1rtXIaQo6uC4hcWEZT6QjkBM2jeiDHRUJzRRjrMn7pg867VRy9KxtM+UJVpy5SBAMHQeElR2reT+gpJIkItD2nz5H5fo2aCVu01FMrSBjntwYTkqHI9EIy1fsBFxRwylhlePISI2yQL881CwqPSqylcfIqnVb+ik3mhHUCBoqm0Y6LpkYYbYjKUCXOlVRINMRxUN5LnFWQjVBHmCFkyWryAXM6AsPxQh73OU/geLZBXIvjwNavA4x5viLIWGfvzxJ505eOt+QfBtWEdaFBo7yKsm9J3wqoIxUQOSNWVWIYfpDaI4cD8VF3wWCh/JnAd5MAPHc0bWYGhi1+D8i/vIysYf7uor5AkIvCibHXQbz2MVYK5vSNQFfn/WiCYpDzhlq1tsDwjR0nltIrnoQy3dThoxfdYiwZjTg8P9A3jjpwLMsMyg9uZpiAwaxJWZ2bceloMDHhRqyQK14JjAcYdw7gIUf25ivDbkmIJQarVuolJ+zUvBsI4tHXM3X2KMnOXX0PtOzq1pgxZYmR1xBCUqBe9lUcijFtKm2YyT8ptJOUP73qjUNXYil8Ks2zquq8lS2AxK0uXBVCwHMy9Rcv+QuBA8tR2AR9Zly1H0x4ocCdW+nW0M0d1k1Foch8RtNQOhkQ1M0KB0Pz1TKYINsgbBQ04DBCVNiEAfJD5o71HuwHhliQJf/h+JXwKvF8SKOFm1A7ytRLYlUY4VKygL/jINYuWIyhMUz7GSGtjCU+sv4a5YlU/LbYhXHKmome3y92ISnXdZimDpquN70PLHoMrStsfUKDFW3WNDJawponcZsMsRggekCwtVVaecqZOr2N/84kpKj3YOtAYHA3LTYTaQMOipaj9++d8QoBYyFC50Fjua8sFXkw91tcA8sqKT3P0/GVvWTUuPAUZSCgZ4KnLCizEdlJfIUlAhTLZgh8P2rEUzQ3tdk7J1rnuzYaa6dsSinlcR6sYlXQLLPqBtfeIwiKcE0kg+L+loFVk0EhxGmHxPU0hCVN4rYAy1uIy4WOFtJsuHehIupOYvpi4GyrwmQxytA54zjVnC3X0oa0fMti0uB6YuOF370XBqhdXRfCXH6PkKjvnU92ZrIqUylQYQ6JZ6cJAdgmIkLFuQsmSMhihx+MCVSbIUokfCui0ksXMUFDD77PixDDH0qW16rJmWbzC9zM8izEAWXynUV8hSUCFqrQCU+Dnkikdz13VVIu0M8IyW1O3OOIwsjInwlu/HFfTVG/FMN9LYw3foY2VtaQUSnkbmxuHK7KnJIIw1NMfOXcSSq1rtya8r67rj8EOCUlz6+TxCUZmmN7GHjP4RYt5wdxJbgLMXzgmlsIquDZuS6Pn7CbszzAVui6x6411tlBi8X2SJSiMZZPcdLAUIYOxq4+83EL1c7hQJKsjc6Hp7Cc7+0uzboaKA5Nlsyv/dj2WSQIStMZhwRpsF/UVkgQUYmFY0PDSTRKMraWHoo2WidO52eobiEk71IQOGxPT7ke3olgBuJKlhrH2hGNpl0vnTNuWZWCVwJMIA840QX/Do3ZaY4NwlARf+D5t/K969FjtdXCXWbOVY9YMyd93HiZ1oTMGrj2PaSGsOoCq19ocEA5K9F+FhssCizHKsXhhIOYCh4mUTt8LMVgxYSyPBdiFeZawjEJlQLIYYN/uHxcUEuNyhd9HVpI0Ru27z/qA2kZN6+ZSxKUoquQE6WIuZzbFYuxsFfUVkgQUpkhKh4rti2TiQ3SlJoxVkYQZzJ8aQzKmZyCk7jJ8q64H6ZMs87gVvQ7gNWvTMnEV5QPddu0wrphOf5qiiVZSJH+5DwnyPXR9jCu4LMiI0fA6MBXIHpNIgt4OCWBb0uG0HGSPSZYWJlWZyXaw9hkUi/sOyanDlgIuWfzCgFXxwkAgxWKM2ntiCUqqZfl4gYxtsNYiVEgs+AFpj531GYUWSKSdTShfVICoK/El6+rCtzhlhLkUrZJWUzbLEAQLfFhBeidRXyFJQBZAD3MDsSwbhTYKk2IDSUFLOLNg/u817Xfcm7xf84Kse6wdK4u0AZ+56AuqWZlHhmDuqoXRA6pstxlZ3xoWhkKrPxKGHN95jwAAVW43LBxExZRu222g4XVg7BooHRMUyNwioSRo6OUQuJj3+rExxiEVCRzF3LbDORAPYZ9O3m5jQAgkvOUiWBgWAYngHtEKJTJr0alTI8uHJ4PK1ZoMOi1kFNoWWHseoTiJlwAWUI5w54UEjBkx26tZbscd5fjme40sqe2ivkKSgCAgNFO6JbjzaqcqoBXq4TTs/H/BrQOGvHP/aC2zHjb7m4SUvDxOQVFqgE9x3deskQrTWTEkUlAbaNEjPrIolNo7y4XUNArN73rmBWfBsW6dhTosTIwQka3FjLvVlzL2nPclq8CaXewPB/6blVTl533KqvbtnQqzcVk21q2WrYGTu/okpTLoRwaA4w7BGIKS+uBRHZ8Ce+4hox3WQsSV8W5uTfGHq0XK9oMsk9KCgV8i8SaTmmxlAsFKa7q6iO8Nzwv0FravegN+plUjDkkrRroTqK+QJKAccZRIT5WYH3nuWtYGBBTAz3pBLmWi/LWURNZas+BdQ1rlUyhPYaGxkPaODhWyCrrGAMhkw3wMYaBZhVr9EWbkfOz++zXMCQh5DV20/V7byuB4DOe/JNTQj+Ymo+HlmcJ6rEXGUICog4FwV0JJvKzc3oEzI/FCr4d9GFMlZq8RIFqYCzWNvUYmkNwnd4NnXQRSjA4IWU6adROyUMIAseIsENx81qcPS/1LGVdh/JN4KfJ9WEBxS/5SZMkgR+6zmmS8CeTQA0c4F+NLOfzEzjz6d+aobjKyhFioZEhBkFBqpACusI0GSgRNmfGbWu+ZVG6sFpYFznb91qvfGlBOu6psPZvV0B4LZ0kJhaNVJ4RKwSULcy2sMqi3tvWDcZOFBea09yFVmoeXl+fBukUyhxyjSGOd7z44bvLtbUKgI7IQmCq1sTVqwsBXy1qkHURItd6M4nrWd18wDmgQCuJJwZZ5EVBFcXlpekGdC1iMpT2I+X7gyKT6XAsIBU9KmDDoQwqcLX+vJYO6IBPIGBJWwUAclwYdsZ3UV0gSkHVjwcHpeNs6TKo2F4ItceMeVwIW4VsMD4v8WQE9daBWwGZ+yBhZEVpczXSOSBk/J46u+DoZVd8a4wfGnFnVQ6uUlyplqFmaIyk1Lw8cXdDLrLsOGWsEfl8qJgY+wniqrHFoM+Mzf7Q2TIwMk/kDP3j1u4rfxtUUYTKuBtSq3LjRsoTKspqFswBKWMNatBkEfjbjrPyah+5NrZ1lCdKUAa3yeXvMnm+EsYDPpQB1jEGTUVDspSyyjmzZTVlpmWyYtR7A1lgXDC5axxOWHUhJfYUkASE7Ajf6MgFESLu1LniGDRWWMvoiUwALmRghFkPYuhOMJm92jFd7DyLcJZnB3Ggt86UVxwBFBXMnUa7YlG5aXfO7oeMaVN50FR+vBBNRfi+sZpqQDw9/SUGT1qI8LlSk1pA+IcRDq1GZfaH4aLf5cK6kWhtwZ4Wm84ZQB0Q7ohhXRseqNqDeIC3grzJh72vjK7huReXMNuljj0tZS72QGINQWvSx3YNcZpux/xlZBhkmYTPh+6UeOkGrujWKiSGR3aS2KzHMSmRQB1YLVd31X2BjR0CQMVZl4u2ivkKSgFDPRIpQRxnsUPPtJWAVQZ6awMUtTAJ+gjCTouTdrQSj1MYCH7FUcZTBMjns8SIk7d4CkcIhfY+SCRL6d1lsEdZMah0KGJ8VAJvD8FP4MrI7BuOy/PcWFkdrTL4vfUxEVgthFcDhda/iakGqMvZJ9bvswyHP3DBrl4xFKX2rhEk+NMVLexiKofadmxHUemTCLhdR4DFDabcUDqSc65ZaXeEFHx8XcIqsAGZYWLW04bzkhLBeF/13WJYMyInLRJXdRX/BcstrBVNDzsPanCqdeLtoZ47qJiMI1IeE+jGMvxSKgnaA4/fPK1qxFZyGsbze8KtawaJoJ2XrdHAjtCyNDfFQd2rRsz7zSAoIZRUIzO2rvpifPB47kDNsR6eYkjgpeuCrfaO0qp9G9UUEtTKZOEUUViv90g7k1JSWJ8/NmnsIaczS5aCzFmIXwvj0OW1G4qRIcWJNiV9q6iZQ5hD0KxHjUgrRZiv3R7NzQ8clK9qNbdT2agbfUqXcNQNFR6p6bVXhbgZ1aaRipuW+mODT1XU7Db7cVkvhD+mxs3bF8O2knTmqm4xgAbECnxgwMs38jUPifoX5rKJo1ljCG22dmikw+b/ogZR66QPR9YixkH6fzQywSm4jfsC6eTJomu3xceZcK0uLPfzZTImYAnxqsCriDohxM2mszEGtvQvKBqrAau8Kffnhtu01g4WZL6rWTVWb4J9O1DD8Ekuscv/k+RumbAhjugouzUb3+7Q9aZVdeM5XJcfFqex2xFxKyNbrFYVIwz7C7wituIXv2ODSoC1U2apx7yXiTaR1k4otWgr5dlNfIUlAuYAw0B85QUQAoyn9POs3qQUmRkG+M2XcDYApCackbGP1Id3aLnq/LOuKsQ4ZDOMV05LSMalq2Tjzy1ygILP2eeVkol6GlSnxvDdla+NCzAaTNaKZixlX01VvOqf2B2GxOahAx1v83yvoGLIW6uyZYpvqfsI4tc3IsrGUWWAHaUG/jAXRcvcxZReQ2WJdWKy6Xpb8UZVpMpOOnd9YlxyD8N2Ly227qK+QJKDc6tCjhSQETmNqzGjMh3iF0PVSac0x3CBam3As8kYlboy+jQkAJXwvBJXll2VdGZgzKc24PC6rHgSCGDWI9vC5dtgiy0QLBIbP/fK8jqIJKHcNAwImdU3ZAC9JODLhHGg3Migql4m4CWmPhW2YopDSXDMQ9Zqg19aa4RurTeGwTwhuBeh4C5xvnVD6imMc6MliECaKSVAJnRTu0Z74wZLLDLQ8e9lBbaSYiryDBA8iPs5at9hq5ttFO3NUNxmZh7PB+GxhLqtqqiPc2KVqjqwSYBWiQ40T+/aRoo8B9VtxwEoEE77t2uFqhLACA48RkCkR0hYtCHyroBpGY6UgYl21mCaMHcGO2jxoYwIehUZ4l3Zry91pNd065iHllazYLBt0p5nZmUtHJy2/QWTqpBPfcBVb6c5ccc7gUtPQcW7EWLdCALB16enNemErfzY/5fW0SBRozZ3IKs1V4JAaAGVISGboB7XewmQJGttMzN16cFuF0OzFGmAJZAgK6yCBmVRKdY4xfQ9I80bWorBKaSPlzoIZYYI9w8PKOmuZ6rNhUC+Dn6EpX1h7K6uHibTH2BlwPD3Lxs78wbv2C+mdYUaEBn/BQOLneBvCYT7qD+bYaqj4hoPK3M953Axpz4QBmBKvxqAJxxCsEJYyy+yRUBGQLjUAWJPWs4C7YxQitJS3Xl3pDD+hZpi2puH8Mmy1FmEhQayeVnW8ivrVfm9hOo0S1ZKPfLEtZCT2KrhJFKsEBLbkjmG061ev+dLzwu9D2bEObxwwFg6JtqnwaEwsumbEu5AbF7fWeSOlka0pA+GjgWC1x0f6l/2aIQ26ug0RQxHpz2bSdamYCAY2nsgg0voB7y8TsQtajAYUEmmbrQlKnXU0sGttud1QMA4mfml8TnllqsayBFO+hU/BVHdm+At78oaAu1Ksy6QrDJL74QVfT0eK8+JdNgPEd+iyJa+7JaQoV/FHg1hf9HuYQIANv7WPQ3ILEyplIhCwTACEklLDQtArPcum3e7YlHJLNora4dCTcEgAFnTIws4wboSv+W/VbifQqyS/KjAjRDRY0qIBc+ldBl6JNeZOf3qwbWd8nL82P/yJysGaaZj1ZzMKADO368QhTN2oiUMO/WjxKgCJ0saD/SViWxCHUK9rvUaUfoAJ/r5DEz1XBe+F2KJrebCz0i7PviLca9KeRMyU60JSKB4/d0Nd7zs8JIF0ccB+lxSSTpC44mLz8hLBvnVrABVqlhmpxLHIvo7/oIRZNXK2i3bmqG4ygqCRcsxxc5VS3MIMCk0rZpgv92kakM2SUGQZ3LoR7vcWlNPXlnqOxL/n4JgKhd7JFhlIohgAZMgSBCy40BmPZmum4eZojwOmKV/7VrimLH82soi0+UC2ltYGwcR63NOa+W2oLq0dYFACtTnHbVX7fMy1DLbF++9j15qxGnaKY9ZTwmMJ1gRrjyBeiIEE0OqlWIoVlAjNUpvLB8FiDJ6RYqVQN8za73oWmV8P44DPq7kzisN6u883n5AzFEGPn7muKjrhzITWxVgXz1bRzhzVTUZWfQzrBojNaR1wz1zQD4lCSqBREVZ6Dj+3Da6lH/K46UlKWtEXro9VChSE+VOu7tr+/Rc8FgprSZFSlTG7QKe1Dq093h+Pm5hVql3rb8Rb2U5dabetIvj/NbCpUEBrtUKAfqkhhsLsKwFLhfSyMiZY4yQLo6MvX4RAt60xY0KtJ3c7xAEn7UX2YO5lrRkAuDPe/SuNrxMXkVZ0o1yC5XLCHgHYoPadWsE5E4V53S7jAPkAC7XUh/wO/SKJb5WqDTt6EdVzjXk7OqmnKDtqlt7LrPFJPzamWB4QYB31LSS3MFn+duvWpSEOhgRXDQJPu9/TNDVgpPeJcO2+j0s+WEuiF3PBbXyTkbKnzRssR/JY28/vLVl7yqBAiD/AbT265k3p9Qj4RdVciTAHFmgSDmQJhj/s643HpszxP2iBNPl2E0J5AcakzoJCwa2j4dHgJiu5KULlU8tEstwOBSVYsEBgLZDRVqZmjbXWCjKCsCbS/oMbQVIUjdhKkbDvoVhJF548hkW5YTMuOMuixKSnWvLBUi4tsEG4r7WLImLpJH7J+/L8AauOJl+u+tRzJhNmlVCayhYS5xJKGX+UkvoWkgR0HemrwsbIC+cZBbWsOhI4ww8JAUyM1QE/B2hXmXBr1gIsw+wLyZ1ilQYPlSrJh4sy69K3sDn1sBxZmxbBZGx/VoAa69rBup5U4iMkZSm2SnO7L1vQUnEd/kDQblvMAcb41zcMvnR8a1ntwlgC8dv9WljxQb2sNZSJsIhgLK9C4ShbBeoeL50LhB2jZPIOEewMWSjd7MFbGk9Y8gFWHOnstcDZEMeiKZCQlycELJReYj2WvOKAasZqv0SBwHJbwN3vROorJAkI2rGU/XLNpyyacL2G1moJbiYlED9HjIeYgmkUaMMhJJlLYY2Q5iQGulhKC4YCZwWKMVVpQ3NqiIGg9Wfhi3QEmv5eK66BjaHIb4zGd1pxRuyhwghZJssGB7WutOiKTahsWPEh2niYEg69rjXmW0P37biddqmWKy29uBfqlLGXxx9eItSUcSLYGfMruWNxSdMyCi35gLWQrD6dys8NI9W8WVu2QAllrB67fRummjPmSYo5KrT151SPRrQtob5CkoCmvLVAOmQRLAlwJ0n4a5VVmRswhGJDEXoY46QvQS+/gwzEFNrtGR00fh8mYrkdxiBp9IhdsMyPrEsM62htWNYSAbeWJYMYSw9iEzRXNWJHtAMvjDPaXfNQycfEZNkobV6atsdt8SV4XxPkYfVYq2JyLFLrSz6WQPs1JjvLjM0iFLxYChVwjScKFiblAIbLQVvPXA4J+x9xNleUmAsoAVI8SwfZekDdLxIvwO2tuWNYeZnzB7FuK36etcrR5ZgjDS6iDOlgpShvJ/UVkgS0YpTBhhCRUsOsAK8y81kBq9qBauXu45DhU1X1sRwVUpTzA4+5XQtt8vk2NIhHT89Q+CI4jCxzPQSZtV4AMdNM9I6e8OmLWn8I7tNg8gHABotcFYUFwwCqVEWP+6qgg4T/fFqJNzqHekNKP4iNgqWkp3gAv3ZO3stlEWzF1OLrOmvNvP9ZBO+a2DvpRHeI8qmZ83H4Whka4CvEiejwBPpa3a/EFVnKrlWx+piXH9IBvUYUM833rrEeSFdnAMlWDGtgSEcmh2iLHhRKtjLwdlBfIUlA8HdWBXA6pTRHLe0R4hgE8+ZghTbs3gOBIblRrJtvM7ASaMKkadzUmgY4kvX7jtxj6BmVAqcZFIoyzNd3+5L1FpoiG5OCddCyQtr9bVAYBXf4eAFtePhWDcOCwVtBG8vCk1cFVdxXnUC9cXOupNTKkBc0jJHjCvaOmzgrQNHNbcfFZytiVtaExDvaWsOiN6SM8aSPRZACsKH8pUz7xdgbxoFpuc0cNQMe04KrtUD/UH7sroFjZMlVKxuIQZvGeqP8Q/fHNKmMoCo4g91UvIkdoA6CTN+pGTaOdu7IbiJCjrlYorri8A2f53gOJKBV6F8OzY3MDeypHGys866wdacsuJ4Dn9fVEfqpQowNvxmHQzjW8FuqalmU9TDWXAqrgOV3x+2U7c/OZuEUHKybFpMixZBUr78di2IDmjVNyPeqMTWEuSqnVoZrybjA8C5J8EoKboEn/QmmIaXCbSphW0gkzX34nZhLALhVtelkTVQfktj7cAGkoFBuaO7PBUACVKxTIzLeKcflCCwLBV42lAErS889tlK8O9DxlnvMDiQvA0k2agCjHfHKd9nyWLU0173iyigZmCcLhmA7qa+QJCCYnKWI8dN5ilv1dONWolklmFiIJZ/No91gUBkzNN3XKWcttevk5wub3QsDuKF6qWVh3Tg7fXExH3ABzRo5/Ri71R/wT0wFh1BccjeRErzGzEcoGOuC8L1Cvc++VeNGyNTykd5lFaULD7E7PYKn5mffT9xkq+KFtG+AC+D1Au4FM+/o441KGnUsQRZYijNu4xo2TYgLpPUHS5BUFA4B5tY8SG1C+SbtGculg9RrJvjbkgXnfe0exuqx7Md+r+KuKgdpMwGwWL+3374326nUV0gSEPaGFHsAnyuinMuErXWvYvoO0xol4Q7IZmjN1e/yAle4jVvQ82Efmj8ZpkkxLsB3IaU3hlVHJVcXUCOtoNZzpDDAYaRB84f4I6xrB2nfFhKkJtQAjHZDWVsET2vp43gWWkqqCLd47YBFrMAN5X3IVNDmChldotk7wGiRDgd8l4blwliQpPomVsxgZ621WjtQmuT3Q0GXeCGm8BpLGLNVbRrvtvYH5goZIFq6rFSiAu5LVATXXI9VcxEe1tJcWqUGIFekMYSAfyyQHtPuLOlaCbOeNKA6ECyDfZfNLU5WxgkEkOQygJatFTxiMBQgCOqkc+b1G4g+NOEKl5BUwRXCGTEr3c8775Ci9XFoaJk6hfZWNg6Z0w/FLxR62vgk6OoyaYcM1kPCoAnfp7lZIj0RXSBzhTH5tT+kvA/8JFnk2v2037F/1J4n6yBmLCR6vZmNnqqh5nOvVCzuBJTbfYf1rTZbIWHB1MDvUqpueS6gIPfyHZijUaPwZvg+6bn0DlysQuWmMAb/e1PKd6BrazXQbkLIbqwa+7qxWZlU95CQ5alVi99u6ltIElC+uaRbjWeACQHSmgmeCjeNZJVgggOtCpav+jonGhU2u4GJIllzLIEE4S0JpPAdFoR7nj5sCANYZSzzK/qxDhYGo8DdFNGLppBiPrSbUGfOlDZ+3q2UQrxHHZPn23FlXsHzWkCxtY7h78JS1D2Wpg1XTqTMQsbHHvjMWjNuJcTIjIuyotqCU4cYqPeYoO8cy0iJibLkEJ5Lyjx4T7Lg5fPUkC2oUJhkzBcfiKp8B9bLUr4gMhkLyajfv1ZFXlQvH6j6xoZs0WSwXbaL+gpJArJS2KoCPKuDq+wgQ00YAaZbyxCwFIHDPo1MwkwJ+9D6saLkrUJjTOlvBk00dHVZQcNILbUUHPR30ECzteagnE2kH5RMjIQd/2PxYnnsesVWgm/9mJAWq/XDxAtYpeg1frnsYwK0w9wqSS+RhXkR1izSM1k2dMtAjzgpGjH7KMwqM4tFEpYoS+Z1LLk6T8gKjQ0rgHHC9Ss913gqpnI2C4y27LViDcY/tDqy+gX6RRzhTqS+QpKAEMApKxy6yyCK8ZU6BMzBy1omblNg1rHZtX4sjAD7hrRhmltjD3wrlfM23BCNDZ7jxpgFCDEHA7VBqRgFIcX6d4+dUUjsoFYGzVUsgkbwG/NduM2inlNPl4Nm7+sD/tIKrFk4Qa/4DAlpzsP4LpYYVN7wxs5Ww66jGJtyyphvprKylWUTs+doQDnCXfe0z0Cy8F5gcWNdws/4fvsxJLc4wUUiuH1zn510M0IGBXMb1TY5ys9rm7BTxr53wY5NqJlDUeJeOhwvzAIwq0eBU7DS1D/wQ5O0hB5Z7tO6KSLrhYlrsPpjDv/HPUgT8z6tHwbjodhXPb696IOELSW53Y91I7ZvqlK6ttumADaLtZDk1jViLpBh0osifsTHoSFzD1SnVhoCj00ldc0ulBhmHGn9IdBcVji4tF+xMGdE1pXMd8alqIB/o7jbA4WE4avjCBo2mlrFBct0Yo+OcbMTqG8hSUBgXMmPaDE2AuHOCimwbE48bjCvKjcYaMcDArczPmpsBC1R44D3u0ol7seH9Nsq3mEJHPYg1/qKTQ+G6d1Cy0RwqXZnZeqrOPryxTkCpGnUrBMEvzOrJGntkK6rKQHM4QSTuRS8DItUQ+FLZNcwirSmmOJTNNjzKkJMjh4rYx9epw00ZpxtKdE2MWdW/BhbIA7ozJcUVGHISiloOp8HwwJrKSzlrKtGhGUovyiK4I3cPoGFxPE5U2V3xY9rr+GyQXA9m+GTY9woae/bTX2FJAHlFgOBsXGb2WUw9v2Hx01QIg2aGgrCg0dkwC4IzFEhYMoyY4ZtLFeJBmSFcTwgjBWpiEiJlUqDWxgXYWqcZZLuVPvV2yFVUUvxdLRh1Osop1pqwgoZWuH3dL2vWVQGNSwCaV7LNzpNOUPlXeDfVBECG7X6GeB/KYgP39wk5lr7rk6sRMOcQwa2u/h77V+cGtYCOW3LGmJHpCH2mgWkEeb/gaMTtdPT22NsFtB+tfmS1hxB1Vd8XRwpe0tab/CDhoyKlF1Jmdjjf1eqZUNVjw7aaaUaKl1eg/oaL3rZoQH9hfoxCyS5ndRXSBIQfNySoIMGuygIbiuAKxSUaqn3vGqsZGK0Mw1QB0U1fecF+uSD1rrBWJgquDlJ78DmcoJZrZ5KpA+XyVJc8FRLd22/2zYbsxHviD/SAmmZwnDoR0vdDGM2tMMTr9k/LveFYmJqUKsx7piKr9p34RCTvskdkkyAcS+XEkfT3mKgxRSBRyeELJteg26ZsUsZTOWDNcQI6jV93goGxTxItbAgHyzkXi0DDLJUYq/OGEZsxV2Ry3CThwqMRte98s4EwGqFUssERHC23+2gnTuym4jmPbKntAGh7Ut4GZZCEx7AGiiRFdQYmhgloQwQIMnV0n4P75+Vq5bqt0U8lw4yFooZQkNL+W2WvtdCPWSLX1km+magMGk3OXdM5jxSM6gV/UiAVIV+lADqdl92dkZHSR4QlQAN4pudozUiwA+xKpKrkXWf9bqHcdPWcCDY2KnNqPar8ZYbMtbQylbrpM8T7jMjhkSyoJjyIV8PeazY55IF08p4CveJdtmBmmvhFpXJWmOcCZqyH9JVf9ncwTAkfYUkBWF9pVsLhMiIlF4m+bYbcSiTuUlYQJlkAjxxA9eYlknrsxQOK9WwUwG5+vmcN1dacRy5Jaaq/kZhfgMXkBn8Wh1rUpZJuaAfkNtph0EjQoC32wh9FerdxPBaiY9K/n7mILOUJAnThp2jrvco/LDPMMEXAf96s5BoewJ7S49z0RWbzbCQaIHOVTxoYdg8m8c7yRej3BIlXZ4MxatT9dhQ3NT4Mi4ORZJRTA2mdrt2P/cZUPBla6BlsWIh/8sxbazraDto547sJqEwiE0E6TGCp57xRd00QcX4b1+cRrZOdZvQZCiax/0m00CNAIEv9bERZmkIbayASAvVEhtXi6kJ4avtDBs+G+dlnz2jHZDhzZ/C8iD98trYOoGdBHYLldHFjYnJxLHKzHOHS5rvQgG97ufBWCLM2s66hr2lWg0R/C7cyN1BbWU3bYaFBMGslquSPYBRDgLxSrXW3MQ5qh5LXrBUXQ8ry0aX252gUn3eYgrrrYZyyIghec3HwLDxTpgzpjLwTaeQrKysZO95z3uyz3/+8/nPTp8+nb3//e/P3vzmN2ff/M3fnH3uc58r/M5f/uVftn7n4Ycfzr7ne76n1f5mJy4l0TO2YNYDUI1WM2HYa8vAPKki3FzOl6qJxigkuHnsUg41mPGlbJ4ipHG1lg+/7AWf/lemCz6FWbqdMMicocIIc71EbNaOo8NG1eBWf2TAG4I9LUwTBpQKXWhtUOlTE9QQtNahlxcMU9rlVV2FMS0HcVWSYM3r2CjvyYsPUlaUhhr74KjLyCT2WrTyaDFe5q2+IEuq23wZinxChQQxQBa+CHAsTGA0/x1HPMiiWjjTAj6TFI68nlT1WIb97yH1vvIdxno8fu66OkbIFq1+UrhPGCV3JZRDhvUXQbfgfYs2A1RvRygky8vL2Q/90A9lL7zwQuGW8MEPfjA7ePBg9pnPfCb71m/91uxDH/pQdu7cudZz97d7/t73vjf7vd/7vWz//v3Z93//96uxCjcDMaBNufnRcF/cTuATvPmEjAGAfu49NK6OwwlNC1xNzaDx/bzh2KRpdrQsHNJY4Ts+dWW+1ubCe+4T3lOlRFnBr5jncaJWh6WQ4FVaqnYhTVIYm3sdE/gMt4VWghy3NC16P4zL0aYLt+XwEKrE6GnY867NEczRSBftJctlcbVzsDDpmbEKieTCA4XBojhQpcuLlk0USzj8H1CqEIf4KRqwG1NEMLRESWth4bog/uOUtwpLfPWwgpliuYXu9JY0qUAo1l0qqtoFx0AoDiuEUlrep284xlV+ZhGrt5OiR/biiy9m3/md35m99tprhZ//9V//dcvi8WM/9mPZPffck/2Tf/JPWpYSp5w4+vSnP5294Q1vyD7wgQ9k9913X/bRj340O3v2bPaFL3whu5mJ0fat9DIGKZFDvOTiNrRD/IXLc4Tp2/K9MiZZLrNCKrPOzEeMiTuq8BkRz8HAnYftLFwJzIdUyyZUApmsHindOhTmWk2ksC+mdg5uc13PCSGJ97xemSO00eYRlXQtOPJYJMuCC0L5DihLVjkFrR98p1Qluxey4rU649ug3t05+PRLj5YhhxIWN4SKwagP9pBwKbJkajGjSp9rKVCaQaQNx8IoDkuB8mMpxYwcCukCrEq3koXEKRDveMc7st/93d8t/Pzxxx/PHnzwwWxsrOOffetb35o99thj+fO3ve1t+bPR0dHsoYceyp/frASB69ZYCjTDISKllzHpoYxFwMp6YA5n3MQl1Nn2WDj/rpsPaVPR0NA1kBiLY+UUF6muRXT9CuBFGDd/vNc6BHP8iQajBNqBz4xv3R5TszbfxqG92jEkWkVnHBxzQtxRHnyuFUOsQEMp1JlS2DHJfBrf2YvBmcmaC99tKS6We9FyXYUB1EcnR3qUQbqVJlwzy3ItyXUmkDocC3PZWSNhAFg5VEWxoH9bSdHRLd/1Xd9V+fPLly9nhw8fLvzswIED2YULF6jnLG3GXKLPXvoGYJnjo/D3G8HfThA5AKypkcHOu4I+EJjpfOhVfZRvMYU2jW7t2gVDVX1LeBgVngdjgtDcM9oZa7kdbtDuFlfVTyhYpe8JhVvVWENzatWchSmUXYUug/9jrE4YSOvb0Oam1J/7N4L1hoTvL8xBxfc1yPfm69IIwOh8oFu5bdlNVjnvrp8g0FZaG/jG3U1K4rXCmIK+utr490nzv9as/v7w3wC56vouZp2DyUaMSrgPC2NR1ix8Z1c2VXDgAA+l6/c8vzjX4ITw/nxfDcR/JxT/qvFZlGdrVMiNkHfCzBVtfsI4iCq+COdLlg/ymAo8GqxV8R3Vc9mRUWGcW7DeEbIjDKSW9m44b5KsC39l3bd1SQWaHHKNwywbS2YhqNa5hCZHHGKsMICqdyUgtr9k4baLi4vZ0FDRtOX+74JfmecsHTggm5rrUi99XwkuWwcPdn5/YtIHRA0N5kL50MGJ7ODBtkl51MPFj44OZa9cafvG90yNFvqY8j8fHNyVXVxov2hkZHehzZ55X3tj10D2qjcJT06MFNoMe6vH4O7230O7dxWej4+3g2GHR3Zn2KaHD04W2oSa9W6PRjlWGsuw/7n75tbfuwYKczrl/b27BweyXT7Yde9UcaygQT/mibHh/DkOJkcH9o7ldRnKvz/q3QNujkf9t42ODHa1mwzWaHyybYYeLs2No8ur3go20Mj27e+4BA4fmiwAle2dW83XYnh8JL9ddY0Paz82lI2MtX9/fLQ4l6052OVxEqbGOjzk5xPz6t7V6muic5M8eniqcDDuBt7C5Gi2e6Zttp2c6MxrmdfGfF8jw8U5m/ZBn67v/fsncovf4UNT2X5v1l4O4iD2H3Bt2o0OHZgo9IUDdMzP0+7B4ryvD7XX0LWS1nBy0gdZ7h7MeW6q9F0T/ndHhnfnCvuJ1j7stHF7sIyZ0sX7/ha8d+9417OZjUa+P8rPsPcmJobzA+6I21tBpg/mYtzPu9sfYT+7gniRhh/Hvr1jxT3seaosHxh6xcuNcK/l4/fw5S0Z4XnK7b0uXvU8NjU52uKFS7PL2YnDUzmfur/BkwN+bV3gfGE9p7CeuzoH857iu3CwDfr1DvfNQDBPw35N3bxUyaihAFE33C+TEzPt50ODWdOf0Af3F9cc6zXnx1iWp61vGPT8ODmaDS22+W5yvDiWfO68LJzaM9rik1afpf1QDpR3++/FS6cqv9HRxZV2W/dd7pmL1UR8ypFDU9nBitRtt0da/Y1388FWUTKFZHh4OJuZaS8myCkbIyMj+fOy8uH+PzXFBeSArlyZTQ7s4vjLbZhe+r40PZtnXkz7fzuam20L+NWVtWzFC+m56wvZdKP9gkUPUrO4uNLKFHHR4PNzS4U+blxv97G2tp6Neavg6em5Qpvr1xfydN2je0eyFy9l2fTMQqHNsrfATPvMFddV+Hx+vn1ILS+t5lkPczcWsukSd+BweeF8+yDfWFsvvad9IF9Fkb9djcKc4ntcka5HXrna+vfS4kqhj3z+/JjWVtfy52GK9TXf170Hxrp+f9FbT9wcv3C2Pdbm2kZXu1lf4M+t0ZlL7cJ0TtSU283MzOcK0ZkLHR5fml3Mppc6PD0TrMX01bk8Y6VrfFj7hZXs+bPt/jYqxre27qGhr3UCe2evL2QHJobzeUWa9rRv4+TqVf9u0Ko/iGdnF7NZP6+ry6sir12d8cF3G81Cm5lrC/k8XPDz1frdmflsY3G5S2hevTKX/3/2xmI2HXjDEMx+fro91l2N4ruuzrX7c62u+Xktr6H7nvb3reXftVL6LvDR0vJq7qpZLvGc24Pt+W6KPLDhb9SOF6aHiyb6aR947awf5d/D3pubW+7MhZMDG+tdc3HF85lzxxTmPci+W/R7bHF+ubiHPU8tLRW/n6FDPsX/UklutMbvYzicjHjk1Wutfy+U3u1ozfPYjdnFbNn/e3FuKbtypX0pcfyKC4VrA/iAwnp6ueH4FUHm5XdBBGBd11c7+2smCLZ95kx7X60H8qP1PX7+rnv+cheGcL/MzrXHsLKyln/H3I2iXMZ6Nf3zV6bnu+ZjdW0t/9anz7TnbW2lOJZ87vz5cMPtQT8+54Xp6jOwPLkxH/UxXmevdL9/xu9hN+fuWfi77hxqLHcbAtwewVrH8hB7xm6ZQnLkyJFWwGtI09PTuZvGPXf/Lz9/4IEHot7jeGGzEnN66Rtap7MGhL/bDP7uBHF12oSvgYnTxW9U9RG2cUF7hTbBvyHwHHJf1Xfg0nz2+lLxeTAmMK7bqF19+P/vGelkalT1A/eSExDhnDZLEeyu+Nbs0lrlWMOibPnvh98aAMWVfz9sD1hlV7ZdWtvWj5vC3BT6a2Yr3lpS+e6KtXCVO7v6C/6e9LcSV+9Imu8w8h6xA2VeRW0fN/XS+9w/nrng00Yb1fwapmKXeaBq/jGmqjVy/0atnrBN1eAuzq6I70IwqDaeZ/13db0nmOwwmLJqLCG+jsgrFfO7bOw7KNNhTEJVOyA+l98f/vvxszfU7+xNhrXHf9veMZV37j4w3oIdcN+hzU8Y31W5dz2vuuBYiQdzADiBb5AoEM5l2A71nFzB0qp5Cot3SnsYUAHSeqEPF6wq7V339yFvpXPpv9raNJudDKYqHizM1UbHRe9qBqltm8V6UyJ/Czy3lZQs/8dhizz99NPZ0lIn7e6RRx5p/RzP3f9BzoXzzDPP5M9vVsLtRUrtbBKgTXkZaQ3DICLLRhoL3nO/kgKL+AgtBx7veUgo447ndyrFtdBGyuQ47E2KEk6JUxxa4zQQWPE9bxSi8cuHSlXqY4hQytbQYSujwsUhzWVYRMvRsICyiPdJ5QlAx/cMF0rOVxECe19RMBzCMUl8WwANHNTHfe9BmSfxTVraL/BoNCyfJ8/dUHkbwYtWCnYvwFdhsKL0fsxXOaU37PUeP0+hQliX2DL24FWteGNYs6sLybjrfQME2mtxTPgfMl8QQFt42Bprey5fL6QyY793oxp3JyOUg1ohDzp73Jo3L3cjKjS/WsGD5bc85XFSqrCeGgK2ULv9LZT2K9Hb3/727NixY9mHP/zhFj7Jr/7qr2ZPPPFE9h3f8R2t5+973/uyRx99tPVz99y1O3nyZCtj52YmbK5z3tyYKYwgpUeiTLd2eCGHXRMaz3urglw/xvs8BYYMsTg0eGGrlov1nrAPaax4LqVxQlhc9G4XidwNidmEuJloGRbtcXHCG+tuoShCsdLGlytBSsZOfiiS9XVwsGnve1jBvFkh6niElh2pHggj1DHmNymYEnjXHQIKa6j8SkXOMBZNOdSL9slzH2b2yHOhI8mGYwTuSgp6yYMtWrwDSxWDtaL1N+8TAbQ1B/hgVVZTyBOSfLAuZ1iPOW+V0vqYEFLtL83actvRS96lZ8mWkDc1vCnQEZ+BJGWNVckjd9mIwdi5aRWSXbt2ZR//+Mdb2TQO/OwP//APs1/6pV/Kjh8/3nrulI9f+IVfaOGSOCXFxZu45zt5chjCrUYqtV045AWGhAKupSxCCGgbCJVOpdQxmM+lPPRwrFr6K25wUspdnu2jbFQLWdNKaYO/35mRGdLmzdE8/MWBAllFyIiSKjeXEVjxd53xYS6kWkgFBY8syMUUctRusGijKa4FGGwJDMzPo8YrjNLCKIpwA0gVZFlLgbSvtDIGuLFrGCOrlKU0PdomMGJCDAyNtPEtETIEe0ebL+iy+3ywbixPXPIxIlplZ82i2HY9eaXbUDis8hXYR+Hl1OprxKhj4+hlr+gwmDQooRECee5EqhVD8uUvf7nw/zvuuCP75Cc/Kbb/2q/92tafW4lyU7kA/BRuHEn5av282RQrW+ZtWgW6huxy8MJYLvtNKiksGGvDKN4FsCKpn8teYQmBnjRzfhVdX9RLcOOWpZUXd4Qp12rztNr5v6XbEEj7pqr3suZRbV0ta1JY4tzCMUCchHao5OZs4mBUlZbgcJIOBosnC9YmrY6P4TplxgyXBFusrEyMO1VT9BmlKq9pldDsDtmiWV3C1ZnyMWQa7zSUNcfeqON+hlVgt/FclnXtnyM7rExhWrC0HhCREz7jRyJ8r6QIV+3jpmAZCimP9yMu9VgXqw7RdtPOHt1NQLn7QnST6Izgbm0hpLtEuL1Iik94U5PiDCDEJJN1aPbXLFdjPn1YuqXh5xLksqNxv4n3+/S8MgEyXsJ1eAUFwYybIm5s2oEfzslxBb4/bKeZ1cPD2Lq9oD/NLw/hqimJjRIPSHQOAavK+qKmiVR7KbzJaWPCuF0TiZ/AkxqvoH6TFrODWAKtDW7mkqJ10c9NLHAU1vA2pa4SY+Vxgc3W+/PA3ISWZWAp6RY429Ibrnkbq0S3xJ5UkIBza64wXyhwKfHfmJcvYjydIZcXfNqs1gbfAVRZiWAFBOikRrv9u0J3p0TgAVjGmZiUlAi/m0F9haQmveYPRmnjuCwS7eAMDxBto/+1T7mTalyEtUckxQYb6D7B7wr3gnXI4yYpBaTiPQ8qUMloIylPCGqVRrLPH+CWGfSR0zPUzQDVhy0/L2KFrBsqKqhaFpInfKClwz+RCAorbk/afEpBfKBD/kaonWdQemHmrSJcIFFVVVMGNaPNMhFojQDGsNZLmQ55/AamYrbkrgKPxNaJQQE3jccQo6RZBRBjpgUc47auWVNjCcq9JlvCOAX94rSh1i4KlQmp8Kbjlw7o3oC63pLFEjLKZT5VETJoJLm9ENQ1khS1xzykgLXHHz1znbZOPOHlgRbkDULQuVQ92hFmB59p1SHabuorJDUJByOCJ8sEk6FUETLc6NoNBYWetIMEwljyP2ITSocuDgfrlo3gWWkjonCbFF/g0mdxw7aUp2O+KnCZvuAVtLuNjXvc/76VlYB1tArK4VZyxt9mJZrwtyEHEKWOzyt1mv/+vFeCHlSCLV/1GCGWcHzOK14Q6FqFXq0gIRQzLQAUColWkfmUt35o4wYvaPFCsKJobofdxmEOHrGKzJUJN1/UCtHkgKbs4DvfqCjyuDlrsiKWMH5NyQnllKZUQcbcodzE8T7EeZQp3AtSjAcUjpP7qnnr0dPXdWux/wapwjHWQrNqQDbh0im388CTSrxVWQ5dV7LFQFgvrZYU6MuX2vvjLSf3ZDuZ+gpJTcqF2NEJVShLGi8Y38FZq+/x7SQ/pHO3QOhJm6hREhpVfTi6S0nXDU2EYW57SMgiOCNs9lA5kPyvMMEPe6h06SAPAyfruFieOj9LHUYQhG+7ba/aDmXirYJaX/K3J02AI6hOqxY67ufRKiGPar9anMJRf6t0KLiW8gs3QxXBoqPdDOHDR4l27daoWaVQeVhSgl3MEm7tUoFCkPaepsLPuiJhZ/DAcsDEh0jWjF7gI7CHtHisPObCiMVCbJiWOv3sxVm1WngoH6S5yDN+hHnABQ74PNI+lg5oBJkz2W9SxfLOu7zbmMiMWgb8gJHp5eYZ1mHr7AjbSErgTqG+QlKTmBiR1nOBseE7t2621nvCSG/ppoOtKd20rXiYcsCXdPOFYvSW26sP7dCcKx0glusEfaAcuyX8JPMw6JCPMbGyYs5eX6bWCzcdy/yP0uXaQYLsCy0NF/zx5hP6DQhR9pNKYGKOI7N/lEhPlN8HREtNaYGirSmCmCPNPQd+gGVKGi/DC7FBrUzVVwarBIf9BQFCIKTyvqkTUmIV5WSDmMND+m23yXwBK5ZkjWSqAUOptAL0JZdN58Kjy21t/3bWXZ+TjivdtmS84KutW/IltGJLPF/F/0zF4e2kvkJSkxgMEUczgl8YmrjlKkAwovSecGNqgYaanx1gV1bao1VB1yopH2IVSGPBTUuK6GcrXaLWDOqISIT50w7h8H0aAFfYn+UL7hTT0rJs7MMgr6BqzEenQmmjVlbPVc/PmuCE3vn6w5NEqXqlH/9tDvW216rCRYXEwqSJiyGBW05bH+xzpmLxvb7elUap0n6d0hgWSTSzNMiLAvOd9wiWWCb9es3YN7myIFhYMT5J7jI8BysNm7nCpJPv8xbMEHiwikLLMNMvXMw7GRTN0c4e3U1AEIRWDr+lxVrpppayASWAAd+RhNmC/4YQS6DyXUb5cBz9UvAgDk/3LVIkvvWOHP3WKP2N37Yi0S0lCzRQsqjI/XEojkwpdgarI3+fWQrdPnwYVwssSWFqb/eY2u8aG1LSdX3gLHP4uNILYhCkZzrp+zHP7X70NdHia7TxafsGMQFam85hro/PPU6F31RAkCWCci05B4uvxjvgCzFl12f9SC7hcNzSvoFbWhoHpk9yo8CSOK7I7Rx5VnEbhzG3DLZIRsA7lC8WDC+A/y0X93ZTXyGpSWMGvLAk5PAUDHJUCN4EQWCX/d/oB3xv3ZDbbarNvfhNKS0YjB+WIa/qR3yPf0MueJWDAXIyfEcjIi0wxlTeaheURNfbAVBJV3CYmz+ruFzwaLSakacD4qTPaX4bVhQXWAg0LAQJT6OqzHyVkgdewMGhfZvMb0V+qm6TdVXytciKMWFlQOv9/m8EHmoHAuP6YXgqhsLDVFOKO6n8kmwoIg83e9hruSzzv6z1AT6W+B37oWEoRVL8BWNJxLpPVSktOW8GWDyCtabw3jUADhp4SKS8AuGiOm5gpmw39RWSmgTGtqCcJcaGK8EywVo3gs57ereQQABoAZZMP/lzKaXO34Csm2r7HXoWjoUZwbgfGDdT7KHRseCQLhTlvTDjXvNVabW0ZQ2fonD4KOOHm0vFw4BJW8GS6Fid5H6gqEtKcFGxafR8qDIHDIjhy6q+taBFfIOGA9GxXuk8mBKDJEybZdwsEiJ1OSsmrFAc7dL170J2ikbSeiKwf1RQLjtyV59rZk7UMhsBXzOunRXyAsXySplHIUt2KvUVkppEKwoGBLr2++4VucWgx/iQmDYsk5v9CJsdtxsrvZh5h+XThmC0vulxn8lhva9zM9P7e+o81x9iUbR2OPy1tFcoBkxaqXUDg9KlWYEYxUyybFT1o6FYWnskFPqSBSRGgMfGZ0TNhdI30q17lSW9EHjLvVJTxJi1ZGOnkB4tXtICl65FkkJhBeF2lKLe5WlnTe2UdauvclHB3YaihJgklldzYD7CSrOd1FdIalJYUlwjKxZC2xghjLGl0TMMauW4s2ZAK8UQsOBlwqWszg0IpEFxO8JvW6Z63Pws6HVYIqxxIXXaqnOBfpjicoxw1FK2C4HPyvCZw5NRzM5et9F0mXchhVTeQx5CW/Gnwyq3GRaSfC6UvcnIibxiMYmFk4KsYGDWShWznkjRlorC5QoJ8Z2iRYyMc7O+B4UH1XESdaEcMbEexyATjaYI+reSIUCPexC3PnT8LU6MMGo/b6jZCozwb7UzlAUNnAkkgY113sHpqRZa5O0G9gdzONj+dGXemp0oCCvYN4/aN0yax2CJUBBKw/602CAnU9BOAzdKdQMPDx9NODIZTC95zAzNJYXCaECt1d6lWS4s/AZKgfKMwKCwxlpIOlW2G7XkBNpY8N5JLSSkS5OVc4zlAX0dFoKHY+LhpNISrCXTWus3KhWmmT466ehcXNKaH7cZhO/b3WFkBYJQBdsCiNxu6ltIahKq1lqbRwIjg59eel6+2VobiIn/sG6AFrpo3o+h8ZvKBBHvYrlktAMx9I+bpmYyjXidFASMVSNMRdbaATNBE64YP2V2N9YfUOLaIQXFTbuhYa5ep2CMABCNUciljIcYi44W8wKq6kdT4FDmQEPsxDxR1iKDBzW4/lhieYItK8FYUth3MhYSy/JpxXBp5RhoFziFPxMX6zFoxp5xFqvynB8aj8sg22rqKyQ1Cb54K29cEoTYc9JtIdYPyWQRWAoJI7SZfkyrEXEDsoSSdkiHpbYtnywDDhVzo4wBy7L6g9I6r7h/GH82e+CBhwBsppHmdosRxtrNLR+3lCIe4dJi0uKZ2KYqOmQUWQtTWuuknrO3bYbgypozvhkpuNr4Q8VFVYzJ+A3GdWa1sS40lqyLdd/1GlQfC7a22nO//RiSW5oAkw5kTonGhXQrbE6tim9407f8kIy7xVJq2LTHusF33O2jd5NrjIUkvwGSacTW2PO0ZEJBaLeT+0MUv1ZXA0qrHovEHXg4tLWiXZ0Mg121bsJ41z6h6jOT4tlRtGx3CMNzGhR3lY62jpRi5f0YunZhYC0H6i3X1iFNt7DGg9bsoWaX1h8bv2HFc6W4oFnPGX7R4thmfGYcYj4smifhDNgsPtB1XwaCKUuwnbSzR3cTEDaelTcuadrM5gTzWQGcrJmzrmWj085y2RigZcxYjSbajTcEoVLTg5udA92av9ySogzMWRZQYFDrLgSZYlwtmtIKQabNOKx42rKFcS0SH7g2jGIWE/syJEX/N5umGwCBw9p35X0QCjtTrKwa6M9GrT2gIPLSroyat/aqd1pxK51U3BEq7VdL42blDOOa0uaqQcACWHNZ10qD9zPf0gy0SUsOwcUvydDyj7FHUgZEbwb1FZKalEfPbwLjNyJh0lttKla0Ea2QmK+h+rGea8XiWERK5iBnKK+UbFTkfDJPDzZMwd6VUbVmnXXVLV8d4C/78Mdrqlwf5d+qivtoMAdjI04xe8EHe/aSOkmNp9RWE/qMW6f3tF97LvK+lTZIUU/hymAprBtTRbEyiAnmZr/DKlpn9cGlDdePY2H4W6vUDVoP5JUlh2DFtqqO5/15oW4lImw39RWSmmSZk0F18EPYm5OjXQlcNryFRG9nwUxbm9S6zboDgMkWkUqYhzeTzk2dSw+2vg0H9vjuQXN8VkxATBYJQM2095m3YUIBAs6K5pKCfx71njSSaxqFGDyCxcb/fc/BMdNaxqX9xolFVHamFBKlb7gPEHcg95FQIQHiJ53Oy2WrpRhjXeUxRYo3dwkkrITEWFaCdbfirtDvQ0LFZKl9v5bNLU5WwB2oV5ySwjuYDUpo9Ja/mE0rtATwASOiu+7thMZEIFMaY4JaDwlVRGMC99h6PGwQYAo+ci0gF7X33bW/fXudVxQzzJWF7qm9K+TUYePWyCjSp3y6st5P3GGKasTWvrL6xqGxd3RwyywkiC2gkYwT4JCwsWp1FYo6wGqg09712usY2EDymCD3YvkNm+cdX+a1nhKWHdgM2tmj+woARovBD2E22BkPRqWRFqzIpMLRAac1rUYo9ieRLUS5W7FWC6VnSHgi/ZGt0NtMZIJmCvkVQfjsdoeVImAxcRuMadxab+YAe/MJG1ciVmaDJ6TK1CFZiMzMIZNSISmnltflwZjg4boyMzNiRFKM4f7DE7X6AAglozis5OUwhD6Db2VdaC6erajo9GNIbmk66314FvPjJiLRHfvsW6TVB9uPNdaTezmwHYu37Swc2yXDZMbU9e2HlWDt2jOcIKDKzUeYc7XCZuycQ+BpYwqVM6aOhyZoY+rHWMGHKfiNbxOnkTAVlEFW2mzr/UY/Wp2YWEIg5QmPEmvRDV+d2aK61rwUihfz+6hOvFn8srzWLFS11mjWB54yoW9oy8iOMKV7mKk4vI3Ut5AkohDkqoosM6xljnbEBEgzKbvWRmUEqwbTzb7H2ux1C0GhTkxY66SKQgwZa8zoUzuIQ4GifSOCS62YgZj50A4CVPFlMD9Y0DntfXMRQtMK4rPQZa2xxBxSoZWIoY5iae9hoNdqZPWTEv4bSqOW6hzShJJ6HhLD03UtqFbWIZPpY+2pulZJyFImdmM9IggfSikzz6GFJGX80WZQXyGpSYD4taKXx4yyzwyjwFddO02tsbmR5aC6/krrW24zbnX4fetGGVNvAlYZrVkoADS+wPiY21MKDAGkpmu1jHC4huOrouU1u7gXk/oM2qpUVwpXIrJEex4wSyhEwC3SyJqvo0b8UgyxcUygPYRbigWgsyxRFnSHxRNU8U4ro4lgXm1fQuE7QqzZ6gbfFphEjNKFMbD4UttJfYVkm6v95s8ZTTxRpVJzLAnMrYzA0ODymXdYAg03DssPjHbMLREZOxpWRXhpYeDM7z9k+6mtvkAaOBesGvcolVjDmChNOTsHyPdddpYNBXCVID7EioNo92PvoVg/ew5Kl8gdZFmLkuKQREAKtNqR705hqbXkg/X7dxJ1Xmwk6HpyG9Y25nK2FoGmCiXDupTFjmG7aeePcIcTWyrbyn5J5f9OYiFJ9B5rY2klypl3SMKiEV3J1FAqgx/n6cFEbIj17tgUSUohUXzETJ0RNigRpm61arAXmvsJN0WdtHjQ64gAxF7LFUi/FcLrMwKfsixuYVArrFg0D9KKy0At5dlSnHvGdmpE9lFTVjJ7vIM31DTblvtleC6m3+2mvkKSKHakrqadAsQnVT8U2msCxL+6wGqpUhBjFAMmqyq0COg1PdLfTrkUxPrw5Rx0vF3wD2QGhRsBzEwfjs7O2FaUmAO/EHOzRZbFlArJAV9JGYqJRSmV57oy0ZruFK7nujIIxVcZxeG6d91ySgZvTVkg4eh3AvUVki2ykFib45pSNTW1FUUq2R3TB4I7NbIiuuvjkLAgTVY73uTOWF1Yt0e0haTmDZx5H6+QpLO2MG20In4xB/V9NdE/m2ol7oFtzz4J4ccZwvgfOMIBbKW0pGwGunXMc6bNtAGBYGWHoVTA+Ru2ItzwdjhGOURFbuYbsQ9RWXsnU18hqUms0LU26EnCF3jFZ0mobeZtRcGK0g9rwFjBvBpZmr61ma4YSpo1HwhqswvmcQBqzhjGgIaxh3o+vsg0U420cTEl5BEPYVkkUio3XAr41sRPsf1UFnAkfm/Fp4HWiluIcClZFKsUz/p09q1YC6uLFDgmduD8aK3fR1E9y/0UWtvvJdoi3o2xHOKMYuJNtpv6CklNgjyyGNOKhmYOJQZ46cC43cYaq1W5mC2BXldgjJH1HKw5t2De2XbF27BtGbDWPH+vz1hJESiojQumW0bhtM5tBrkTt8skVgHK/J4GgC1KIQmB5IjvZAKnre9IG9TKI4k6OqQA4cXAIDQI64IJnGhk0aSIp6vr0sFlZygCqXUsIiBYAyYsZxFOkhlS20l9haQGuYA21mVj5cwzm+eg9/fWxQmouwmZPph+rG8+bKS/WcKxU3COM2MDzIxJD04Rsd6IuDWnuPFBoQnTkiU6pqSYu1tfkzg8ob9Z6Zss7s1WtWFA2kDY/65b5vdSuK+swz6GYtyVMRgo40bqdIp1sNJ6mXdYM1k3jiUm+HQ9AmAPe5ipTI22jFK03dRXSGpQeM5Zm8euw7E1woxLZSNumimC1mrioZhpv359bjOKyWVEcbbyQc4AHVm3F7DP7UR6Ip9lYwesHicqsTKuGIsPMBbrxpcsNqqx+XNYpmXPE6zVIgV4GxNvxlJMnE/KdincKVaNJAtTiFlrawyW3GcqdXfaNmlMmA4Ktd32oi9u2c+yucUpRNara3LeqhsgYypNNRZLYbg8Xw9ngD4EyBuvtYZY7pS34Zjx1Z1ztu4S2491m2NSpK13gZgxM0F7qXgbBB01hNyvwxPMLdaq1hxDL1yej/pmes+lCDitKadeSVBIsW4Nsg5o3gCfgr2rkbRcwbi3orCZVNtJfQvJVikkWxARnmIcqd6zFQFjbOox6x/fRZqjU2bFtNulVEjs2Ja6FokCzoryjXTAdyKetCxcbD9xab8bBcA8iyzlK5VyxhL24KVZrqBmKksQg6Jad/8/dHRy0+UlU8na0QyRkLDXx+4xisMzF2b5QGq/Xx8k5mO7qa+QbJFCkkIopxDcWwnAZmnvddE5U6NL8u0GtvQ2ybZrJCyFrrUJjQHMEqRABj51pX2TrzuPVe4OpFv2grHTCQpNo8xWzUUsmFcMQaG6I6HbsN1uoHZw7KW5ZXUuyvPQSKB8dvVRldGkjKFOwdI1z0uM4oCMGangZsjTgKRPWQNps2jnj/AmUUgGakdj16vkyrahMiwSBbWaxfc2GYcgtt3gDu+v7nygpg8zrkuGKRr9MH56a50vex+3RgxOBqMoWla5XoHRUq01tccTgBL24saLaZfiAnbH/rFttzqnWC/eDbNB94mzh0l0uOKz3XbXrIW1FbTzR7iDac1Huzv2sWIKrOcMrgeqp9Y1hVrEbDGUv64TGGg1sQ6yeWI+YuaEbccKodTvTZEdxb6P4cdUrismFZa53aWq9htjgIhVSJiqxhaltZBw4IHl9nWto9ylx3puyVzzFWYfNlwDtxZMoOpaBC8h04rheVx2LViDnUB9haQGsSm/TBuGCZlqr6gCaUFFa8SkklmCibPm6N8DU2NdYqtcsmKeTbtky4lbKeEgK6LeGhdkF/O+/QSfpHKZHZ6wUVi3KgtNQteVzq3YtFkWkt8a43bhkDCwAowCSZWn2ALrhdXHpKEss2vBBJ+uRSiHDA5Q+XIYk86+XdRXSGoQUPgYprRaMH0AhlgjK8WSkTvMWOoGpMa4GCRiynSzxd0cnVCwN6oQTy26g8yG2E8A0VnxIUymBx7vSWT9YAWcVYo+VaAp5T5KZIIHzXpeYOfCev9VIqV3cywkad2QW5FlY015iuB8Mw6OnA+GP9YjYrxi1g04R0dJebmd1FdIalBM1kJdGPVUiJepTMIpMmCsPu6q6UPeDD8vW9Y8pj+qRg3RF6ugpbI2sIrZVmV+XSfqK9m4EkYHJSsU9vXLvoia+f5G/awNbYyxmGlPnb+xKQpJivgOKy7PykZJAb6WAq7BUTNx5eW1CGtKXoivH0Nya9OSj3BOknGSCPY6hSAY3KL3LKzoB5rVBZ32mzh7Jn27rXFHpO6LKXi3lZlfGros24+UtWAdDEyKaYrMMsmKwliHNAUoZfyUE3V1A9qZd73+8ETtd9S1gJwisE4cTRGurv3eUnq5Iruo1zpdjp69ONf6u4/UeosT/MfMTXGrbolbBsCW4D0HjdiBrc6y2anZM2yblONKiUVjHk5bPJ667rNaWTaN+tay46RrMWb8h4iYoa0EcMwSADiemVmqnSVmvYNVRGNiSO46YBfXAzFsd3zPMF14dbup77JJ4LKpyqm3sAO6n9vvqxJWsRgFdYRFI2K8lQKaGGtDnbPGtuJ8bCf+iWRtafTkIrLbXSBQT6vG1CC/LRbL4UJF+Xb2XTFxNrHxGQCJk+e0YVoOwh9tV9pvXZ7W9m0Vnb2+tOmy7N5D+sG+mckI3bzJB6ruJvtkA9RRy+tOwwW+E6ivkCTYzEyKa5KYixSulAR9MILTCkhl+khhVXK0QKa7WanM7Ljy/sgYi0VifLNLzHxy25lxSzBAWSlimhzdIHjFwqRg32VVso4Nao1L1bT7oxSSXdsY1MrEOxF8yASQm9akmvIjhUUtFXKto1VkbEWsL1PLpl9c7yuEYjazpQiMDDKptvVBzcKKtb32wbRhguvqKhysELUOIdAesjw3Wz3YwpyIGR/zSmZtWYyRMaNaK2+R2EgiVJn9wR2WaQ6YXnE8UsxXDJJs6vFbGVMsHzLYM9Y+qysfUshtFqGXWbPViOJ6+fupYO81GjZiu2nnj3AHU8xmNjcPoRWnyCOn0gqJ11gKx8l9zA2ovjWHoVHiMHO0d5TDWGCsFTFZL8z4mIJqbBVYxqInmY1jiVHemOBYZn8wI06tkJz3rgeGVRlF8DphVUuLQ5IWR4UBE2O/YbQmiJxZbZwYgxVjws6bBcdQfGdGE6PMo+xCH4fkFqcoC0kCbT0FeuY9B+2AKSZi/6IBLZ4iU8dK+2PTTplNG9Pu3kN6dD+IvXUy72WC4u4zfOZ5X4ncAlVxAL0oG6kCVhm7lXlIRSrB2JPniLlgvoFJ+02pkJz2gZ8p47GOMwom0c+IER9hHbDW/mO+2VLeWYUkpo5Mk25Jrod3jzEy5JaykPwf/8f/kb3uda8r/PnBH/zB1rNnnnkm+3t/7+9lDz/8cPa+970ve+qpp7KbnaZ9ehYFWZ1A02YYytrE1nuY27Ojuw3FJgW2hlVPhU07TR3Uunsbgl8Z5YYVOMy4GLMxUwSMUbYYXmEODya10jbBxwltoAm/zkhBTbnOsXEuGjG1UOLxktJ8g9XGvBQluARabRhFNJavDkasCeMWXvEWK9Z1fcsoJC+++GL29V//9dnnPve5/M9P/MRPZAsLC9n3fd/3ZW9729uy//Sf/lP2lre8Jfsn/+SftH5+MxP82i9cJiqR9mA+LMvO3jdQI32xKNP0bffRqOmmqJsVU57fugd62bIkRss3yP4ix8Zk4rTHZWdrXZ5fJvqpbwWTM7Iam6KwW/vw+Uv2Xq5yeaR4N9uGKZAZG7MiuRTK2VCM9TQV+KLVz73Gpejqgl608TyVSaaP4T7SWhpjIRki27p2zHp0glp3foRG0hG+9NJL2f33358dOnQo/zM1NZX98R//cTY8PJz9yI/8SHbPPfdkH/nIR7Lx8fHsv/yX/5LdCi6bt5zcY7bVzIv0Rk9w80hxa2DaMaZvq2aODSLFsS+LXjm7zGXjsEFnl32VzRTjO22gUqY2yTIpgpSrKZE7BhVLa1taDL58+MRU5c8bhoWEsgQlmgs21ilGhqUaf8q+6ma4WOUt7jlIZG5ZwGnknovZm7sT93nOK16sS/qWUkjuvPPOrp8//vjj2Vvf+tb80HV/f9VXfVX22GOPZTczsYW1LIQ8tggbbmObqpCQTJuin7r+fFYYWAUHQWyth2tEmmpMLRtmfJaLLCaodXx415a5Dpj0b2bc1uHCjqduTEBTUJSYdzPfWdet0+xZIUkTrOrommGZcHQlQc2eukGtHI+niSGJQdLdxSp+pHsd/aWMPdosSqZqN5vN7OWXX265af7n//l/ztbX17O/+3f/biuG5PLly9m9995baH/gwIHshRdeiH7PZhQsRJ+xfYebWfvdtgWEG0PhZxUBdOV2VW4HrU3VWMrATMw8uM2u9QNsjfBn5W6td1nzWjWGqhftHqxu1+WCINu5G2plu9LPXJodM5fSe8MXOwAk16aLV4M2e4Rxdc1Hy9SrNjH5yNHy+obZZnSoPW6NnG/b6sfiN0dOB7b3x0At/g/XICzp7g5riycYfqjqJ2YuGpFyDEqGtdeksVRRuObh3+HvurRfqy9TrpaeV7m4Y+UyI09j3uFoYtjeAyHtqZDzVWNzypLUb/hzp4c4Lp20xqHMZV1i+0umkJw7dy5bXFzMhoaGso997GPZmTNnWvEjS0tL+c9Dcv9fWeFM2iEdOMBB9fZCsX2PjLa/aWx0KDt4sPi7k5PXC9aC8vOxoAKtg1AvP3c0dbUYMHVg/3hXuz0LxUwT9/xg6TY5FNzAd+8e7OpjfHy4kNlSNRY864y/e8zDw52gqZWN7jmdulJ0Oxx2Y50qpQc3Ote8vVOj4lgcTUyMVD5369H1ngoLw+Rku7AY6MC+7vl1dKWUzHPsQHW7PfPFhvv2VI9/dIwb32BwAxob3V3oC/O6K7BEHa/gD0dDu4vb/NCBiexgySVT5rW9FWO/WvZoDXTzdRlH43ZhrsIb49H9Y11tNoaXzLmcPNeu0QE6eWRP13eNjxf568jhyS6MldEge21kuDjPoAE/z3v2FMd6aO9YzvuVcx/sPWYudg9178/dJWtCFZ9iDw+PVI/Dsrg6HjxQUcphJMDmOTpVvd8cDQbu16rvdPw6MKDz6tT54nru3dvdJpyrPZPF8QyWLDN7S2tVllEjQ7u6nk9MzBT+f+RwtwtPGwNodzAfw4Pd79EsHYcPTmQH941RlnTpzJr2FX7dWIFvcuTQVHZQsQI73gcvsTyUmpIpJCdOnMg+//nPZ3v27GlNwgMPPJBtbGxk/+Jf/Ivs7W9/e5fy4f4/MhJfk+HKldnoipYWOf5yCxvb98xsW2hurK5n09OzhWez/hlcD+XnC8Hm2dVodj13dON6Meh3fnYxm54uLtn1cpvrC9n0WvFgXAkQSE9fme9613wQwOhwFarG0vrOYENMzyx0tVte7gjOkz4DJpzTG9eLCsmNmYVsYKVkug3mf2FhWRyLo7m56rEuLhZ5bc7NSdZtbp6dLY5nUehvZqYY6Li2vFbZrrwWywsr1eNb4Ma3FpjIN9baPFbm1fXgUFlbqR7XymqRH2bd+zbWVV5bqpj7a9eK83B4bHdXm7JbcWNto3JMzqKaj29ptavN1VLMiJuz7j22aH7X/HxRsbl+bT5bKLkTFwO3UnO9ey+3vsN/l1vjcA/e8Htnz9BA9dwHe6+5bs/F/uFdXW1ulHBv5lpyYFflHl6umEuJFoKCeouuz6XuC+JS8O6BrFpOOVoL0IbDNQ/51Z0HoNXl7nHeuFFcz+V5tx8b4lwtLxZ5olzteXF+WZVR2Ub39ziZEpK1XkulMeTfF8yH8xxpaxLuc0dzNxaz6fV1UyFxWZ7SmTVzrb2fl9fWC3KmsSwbAZb83DheYnmIJfCBRemio1oa7d7C/10A6/Lyciu4dXp6uvDM/f/w4cPR73CTn1oh6bXvV3ylR2c50H5vl/l8oPJ5+Ue7Gt3tyv93wbNaGxfIpT13kevMHByZHFH7gcwP57TcbdVYy1gm2vOJoUFqrNL8N9l2zW4zLdPOWnezXVN+Zz6vzbh+pHbdvKbzEczZVhtproptbL6u4oVY3me+y1qzsoxY9YcJwxPMXFTJEuY7sYAtliBlWGjNqpqXMjlLL4vAXPUNxXm2+7LkQ/k5NU8Rc+3cpNYYHZCdzd+cHAgDwRn58rojk+KZ1Syl/FLjUObypgtq/fM///PsHe94R8s9A3r22WdbSooLaP3Sl76Ua5bu70cffbSFSXIzE4rqnTEyIOrWZNjKyPRUtRlSoNfOr6wlGSub7sZuQna92JLu6TI00gQks6mlVFBgono3KfqRitsV2zQ2L0sl1VwkcvBj7Ckr9KZMb7bWomwRiQ56tXiB+N4TZZezkuXC0ihRMA+xZzFIySzG1HZSshE6bBGX2vuv/tW/yk6dOpV99rOfzX7yJ38y+97v/d5WcOuNGzeyf/tv/20Lq8T97RSXd7/73dnNTNjQrz+i56JfmlvZsiqzdpZNmqjxFJvZyj5ysTUpxspuRDYbh8kQcHTYGH/M+F67RiCB0umC9vuqKlhvp7IRuhd67YepB/TKVTu9OqQXp+e3HPTMQjBmCdVlU+KLsO1SXK6OGcqAC7pmINU3m3fvJxGUNyO9OpQxMZk+N71CMjExkf36r/96dvXq1RYSq8Ma+ft//++3FBL3zGXePPLII9l73/veVhrwr/7qr2ZjYzu/HDKzoS1BYyGKTpN4FSnAhFJZSFIArNUdS2oEVvZAZ9N5UyK1vu6wLdTKcRd1LEZcGqv9vsuGMs7cdB0dINArrX3IpLY+YFwuJFRNprLzZY/sXBtSPdFNd92bBNn9MU2sJbvmTAp0Xfmwxyjg98ARPaaBsholwo/qRXHYHdnvzUBJY0juu+++7BOf+ETlsze96U3Zf/7P/zm7JWvZGILOMsExlS9ZpjI3cQLLRirFxyxclWisdF5/QpdHu790CgkjfI4SUPoNWrEdSFI8kBmTy96wx1NfGR9nKhhHgssB8PDw5FASpQrp8ltpIWH5eZKUU4x1jcH82exq4NaeSuXGiilUuTuC/xYMl3ax35tDIbk5RnmTF9eznh8iaxek8JHbsR9pDnkL7G2rChK2cQTYG0famwkbG5LKXM74nml4/ETvY9xgjKJNxRxsQe0SKTDUpXYy+BIp5jRVDMmcP9BYYEa2xgqz5kwNltrAaLWB09LwS4yS0WzGK5Rcv9sUpRpJfYVkC5BakwWSWrdWQlBZLditYw3ZqsSbQiFpEKONKblNW1JIAcN0lzJQkDEfs8oUFx+SpigeZfY23kUpNQkDN8vQ8amCURlraSrETeghbG2clBbJFHyx2QH8jOhgviNGBs2TgfCO7o6ITblKxr1tN/UVkhr03MW5RPVj0rgKmJuOFa/yGlEzpRyhX0Un945sfoxJQrjrGD9rKitSzPi2KhMlpl2dgn/pg7XTzI94CAo/xy01WXG9REoeQ8CMOeHL06fjnfo1hRjX1K66AfzGGM7MLCVZC1amMgVFezk3HL2eqEa9E6ivkNQg1Newsi7qmg7ZdlYQF8PwDxAl5QFjr1Fdq1GrjdFkOFG2SGx6sBW9H2N+Z83gqTI0HG4CQ4wbKd0hvEUKSQIluGz5fvL8jWSFL1OMsbkJLueYsaVN+603HlNhMV7AZMeEOB8SPUjK1FiXYUxcSMrCm5tJfYWkBmFDW4W/Uvi2XRPL9JckC4c0L25FhkwKV1fMBmej4Q+UoN9rVcMlBQWTicUoLVbGV0wmxxxRHTlVOmwK3mdSemOtD3d5qHqm8GUyV0ZFm17CStig/NSFN5nvdJAAllK8YcRF1JV1zFpMEZWXY5SBoQglY4jAIemM4eY46m+OUe5QYje0BYxzgQDOYeLO0hzy9nuYd1nPLWwWhlIrJKzcoOMwEo6PSTVm/M8px85kSjCpyCvEYW4FQTLjZVJ6Y4NaEUdmWQzZdFhqDye67HYsJBxPXLhhuzBY5TmFMmAFz1pK2sXZ5S1xPcXw1EVlTOVXnSNcSqBLRMr5TqC+QrIFQa2W4D5O+nC34oBOFR9hPWcEuOXySJW+GpuNwwMXMabrdEGmR4jUUz6IlrHu2G1OGtZDlhesOWesNcztk3VpdV9K7L6ZWA1GOUsFcBXC3jPExpow7ZZWNzbdmmTFhFlylwkp42KX+GP2KGnBdHRXRUFOiU7u4WNTtpP6CskW5PFbkfOuPHUKoZ1iE6cKsLX6OTBOHEIp0vLI62RMCl3K1Fk25TJFJkpqbBSmzVCi7BczoJtIa0xx6y4TKqlSgGZEqiuTGpyKZjwgnRWgDhoncFxYxc/6zhT8bq3JqLEeqS487Pw62k+6gx2NE+cGaA/hWtoJ1FdIahAbFJZCCUh1QKQINo1pV0fgWG1izesaxQQDsjdUBpviKoF2mjI4lE1B3MqsnhTZJ0x2AjMWFvyrbNFIlXFk9WMdojEEC2RKHmTnediIf0hxuaqdhZMo42mZALvrKeZtICLepB9DcuvTU2SE/VZlCDCHjdWG3Q9WPykK46VQns7McCl3hyOyccaHyOJXhAB/HZmOlyp99sx1bj62SgGm22yRtSxWyQXeTrpLRf1bO0ur3uXsKnwzlLbmVgqUVOt5o9Y4mfOeGedtEam8u2MCYCPg4GPabifdHKPcoXSP38jLa+ubni3CtDl1ZcFsM2cEPl4wAr3Y+AGrsJwkkJoRQovR+t94bMps03pXTHR7wsDQlHD1jNWJnQ8Gk4WxFKXi7RRYPqmUhqqxW4Uiw7Z13h9zK7aog6HC9UlbTxMEeqZx2QzUescrhDxNucdj5VCM8sIkTuwE6iskNQjmWsvvt1UWEuawsSwBULLqHlj2jdZ+RwpFLmVWSadP7vChIOFJobJkKL1sXynh7BlLUaq0X+uGx3z7K1ftAybGAuHguBEDlAopNpVLNTXK7FbjkGyJy8YYw0OEPE2FuQI6HQGidnWeR19linPuBOorJDUIAW3WjdnaGExK1pnracrP11GOmgmVrFQ3563MiNmu4FG2MBvTU8raG4yliIElt97XIFyEzHe94ZgNUMXi8JSDFRlFb3Z5rf5BnU4fyaa93GHXm03Vn0+AT3NWkHfhnDO1vepUHOZiYdJa3R6IqDZ9TwR0PBPPthOor5DUIEACW+Zai/Hv2NcGV9Lozv22H3KzbxUhEFHdgLGZRUI4J1BIWGF7nRhPTJ9sfRA2zZQx+44TGSLM2PnigY2eLXJhdtGoISwZoX/dZ4z0MoehDhJzm8WFhOVVJh22qj5T+LOUgdz4cJb37z5gyymtCnRodbPqUEkpueGcl/mirEtaVr4qQMvwVyQZF/Iusx5zhCKaOi6kUfr/QkRg7XZSXyFJQQbjWym7DBNOjdTHamDaaM9jNqIl2JlvTgF3vEKm87J1bGL9vFsZIMu0YeaDPfS4NRywb7rGuBkFaYSpkluj9hF+s1lR6bfdNxN0bLexsmhMRTeiqitquTCwAzGxU8x6WSmrTBkM1gqZOlC6aKWxv/XA+PC2Z84cT4R1tdnUV0gSHCYTxqFiKSTUYZMso6H3jKAYc2mvt6jUAFDhoVGnGGBIzLcfJwGO2Fo7qYLnWNwThsYJXApGqFsHWCrUWKafMVJBLCsHjO4s7eFQh7DmgrEExcbAMe7AKPydBFa4uim3Y4SC2ivfhWUCGJ5iFT5HMXcdq/hgHXyd7aK+QpLgsDMD7ioYp5EoZTG2n+o6GA1qk4eFpKpue2E/VcK3kfB2w/72Hb7WSCo3BStc2P74jB0iK4k4CBi8DqZUeUNyVZaUSMkqkMLaFr5qt7AHQ55kDrlx4iCrUnYl5ZlxB4XKmcUP9x6aSK6QpMkas91KLghYU24akbysXVg4XJje3NehkVFqU+DNCC1jnoi/6cWq28chucXJbS74M01N2zINMjDdhDB9lSgeZpF2A4sJarVumnXdMew9n0nH3AwFgvUFs++Vlr9gaWCyVYj5YHAp3PdRFYETxDUxNWCY72KCx2Pccdj/jHtBW5/QTWTti5RVW798aa7dJ8mr7NwwbjqLV+u7Y+q7hJkgXiZuj5VBsZgleyNQXS0gup1CfQtJj+RueE1y81g3s1kisJG5MbyeiNC2FIVUvsYUJtk0sRcDSdPt2P5emp6n2l0icV+kuj6h+ThVwOruGrEWdczKEt1z0LZynSUKjTFBmTEHfgeldSCq9lWdW2/dgzqk41PtvX6FzJ6JCc40g4CNeX6ZSNHWiLEWW+Ui7iX4jqEY6PjhCKsHU4oAxLrltpv6CkmPFEYtW0XgrM0hlYSPdusQ5sOtAl6yBDuLGCoRG8OiVR4NI/0fPDpRvxpn8O83n9ij9NJpea+Suheum2SZKQp5O4tEyvgKxz5E9CPxbJms+CqJmPGEdD+BeMv0ExMbteRlAHsDZvz4ltVpV8KAauCQ3KUoauEeOeoVmF4vC4uBzLQO3jcd5wD8eq3k62jf2O4tkYXWe2IrdoPGdvNxIf2031ucikF5uhC5vlQ/350xQW4FmBBL1ma23ALWKNhx3kXGkLDCh4nBaPdHBgCqadb2YRryIXMu3k3c+pjaJuxNLubGVxfMzewnMXw2UI+vEDE3qQ441pqRsjgoqG67MG4oBa6MRrcRQepbBUIXs+7HI6r9TkUUzItxG20n9S0kNTczg8hpZQDs2kHF9VLhHFjdWIeMlRaYGvCMbZfaVaQpmriBazEpoRuAQoYl5o1JMQ8DnDWyrIcMhfNQ57uYImcTEdkIjcjf2ZXgUNibsGoreCf1HpF4Pya/q25q/RiVAVY/uys56OKuzYEfYNLidwL1FZJNrvTLmMtSFf3aqoJ1SaDla7qOdrER/wnqdBRdXmmh6LV2YRaHpJCELptUitJBwt/MAr+lEITMNzJ7iKnkG3MIQQYwKeyxfUu0P2EsAOKAtpKnWapfQZ2Qlwng6xmyxlLMyGlsTiG+frXfW5tiFJIkm6dGIbMQ58Cu9tv7JgzT+upWQLbmjB1nagh3VgjwhcjkZ4w7JiZgLiV0PIvamYLuIFCKmUN1grg1xxxCcEFs5UEdA21v0TMXZtt9kuPS9lwnxH9rSj5YLJrCWlxHFoYUMx+DEW1jivb1XTa3OMHcuVUKCZOyeGm22r+8HigKluVincgEkCg8G+siufZa6yL2PUzwa8y4Yse3uCrPd6hspACJS4komhKtdsvclYkskb1cSmL7FvtIGAsAjJ5lEjxQO6DDrKuY7A+Jrhj70UIcTrHW8yv1sop6iaPaHaFkxGSwpSzKuJnUd9n0SDhIUgWbWnQ3gQ0hBSyGqaEWPsYeA1WWva1vrkvGVa/ksmIWyah1NviVqRjr6PXk+I4qQWyx1g+GQl6oY1VKnbFSd//MEAimdayMvV5KwvpPMQBZW2EhgUvQqgDOxE8V3IsJvtPKmrLGnEKJRVp0LzD/oVU6JsNlgsS0iaVUF5rNpr5CUtM9YWnyDONPEUzImNyk9yC9jxlLnYyfGPRNyyKRKvj20MRwUiCzN5LpiHUDAFPDvIMOEocPY905PWOnbTMKFcP7zFoz2U/SrTk8PKzU+dAtedlbJLXbeIh1Yd2UGbhzS2GK4ZhYHBVN0QgtFikU0RErBs0Y8ysCSGTIkiM1Y/u0S1URfTd9rMeuiDlmgft2AvUVkh4JgoZJ07KYh0PYZACtBmrXXqiTYhlmfFgCwzpALBwB9sBPWRAsph2r4GjCajMsJMxt7Q3HJs02Dx2dTALGlKL+Ul2+DV2aMYIeJnPJVepoNdgT1hjrwJ33cgN+aXohbo8oaJ+hjElxG581QNgsK94DAkhkaMnRvofhO229erUYDSl9NnoEyEuRer9VdPOMdIcRbhejRBqoZSZn/J0hqJBEEpPGxCLUQfsM/chWN9atwSo6R8NYk5DJTGppzG2HBThqJkBDjSFGkDGHOyPkmBRpRiFhfPlMP9I2DPdnjEICa4lWmLEAXJcgw806qGOITWFnLk43CLTpGDphIEZbPCrxZ7gelqJgyUJtPUN5bY01tKINsReZCCWDzYjbCdRXSHokMDaj/VrVfhlhavVRvun1iv4XIjOK7xFu7uFmtxUffd72jeoKCevOYL4nBk2RNVowGR2O9kfUo0hBTIzQVqYIclD2aeDu9wgYHqFLc6iXWjbKnBYypRJYJ1PiSeB9mttsjQyIl2RPr0q9hV9j7X9pLkNFwZK71idpcbWhC8ta9zCoeIxc3zA2yaLNcP1uFvUVkh7pos96oYorVWzkmEJ1sYBNsTUbQhodGuj5lhaaKS2yvnnIsGywdTWYGAVH46QCwQKCHRjnFBg89MYAABnvSURBVJytOPzD5R9OFITN3LoYRZgxSBwmcD6oQn8E2m1M/ANTLZe1vDliXp2yJgkOZ03RWA3WWbuVx+DhUHhJBp9aliLpHUyFZray9w0FgTtGFjJ4Q2Wai6gKfA+RELFTqK+Q1Ay6OkcEAFYJwnADM7ce5uA6INy2maJeMe+Ral9AQDNk3ais2+Id+8aS+k9ZxYDBxIiLSUkT/a7drMJDcZhIyVwgXCS377XnQfLjh3TuxnKSGzUziyL8fo/xDy9enjfXMCYO6DwxF6mK67kbNlyC2h4J93SKLCEr1Z3NJrIKLkpVy6104ZCsoFet/AV7cSm3HSD5z8IBCrvpx5B8BRBuiG84NtWTICwEV6UyW/eI5rlRqMsz0LNQZKoWg/YbLhlL0bMsKJulGPAZCWmDbuuMfz64TVnZC45OEMoGI+SYNozSwsw5YwmT5igW7RYEhNbLSn2ZmJvyg0SgcKpU6pAntFIBYRxTqndrMTcsRL7FE/cJRStjLk1W/Jm2d69HyEJGQStTjJLBYiLtBOpbSHqkVz0eBcMYVYdT8eZhb3RGsEmHYOgjr6JwLMwBKZkVY1yV1ma3DipNIIWpmakRClkFQjOphhaLsR6r4caYwZfX1qMsAIzSwgRXXiIqrjLKxlWieB1j6pYO1JhDquqwlg6/dt9pYytSWSnAE25KtPfGKFQsMd9gracdkFr9fD4iKNh8R6LslV7477VrfLX0t5zUKo/vLOorJD0S3CyXBQTV0CNRZfoLBRVjpmOCLiW+tvzYMWXBtZTcGPO0tdmt5xr+Sy9mZibYk6ncDDqh3ALD8aUKUtS+MzbKnhGQDO4Hc+NnDmHmXdL3h9Y/aa4XIoK+Q3r+8py5Z2JiSBheZfnPIvCEG7umpMYoVCzVgTAASXLXeodljQhFmCULGYWboV6y6d5E4iGxF4ydQjfPSHcYIVD0/kMTBCZH94afi0yTY4TVfkFpsYILQ5M1E3B2z4Hx2rEqdb9X86EWYax3JbV8nNzDxZBoGAbhrZMx4DCmci3ALsZ8zILJpThU2HRRJm5CijlgLJGhRS2GIOi1lPyYDDfG5XmScKcxBKuTdUBvhoVkllCqrBiS2w1kZSlV3MpOCS9VVqbPdgaLDkcoGWwCwE6gvkLSI4Hhx4VI7PCQrxLMMWlyvDm3uo31q7FCR6pVEeOLHzeyWizlRjsQe/F7szEkbJ0OLcYlvHUyLhTm8J9UsrBCK0GqGxVldifmlEIpJsYjwXgzAFWWS9MCRjs6acdEMFQnDmazanH16s7SaJzIGNRg2RkeldLpLStuDIjkdgaLrkecH1sNLVCH+gpJj3TqykItuPaYVNy6qXLWq+IrxtrZCnUFq4Ufoh2ILHhUWKGUVVxSVEaNLdrF1NvS0kFjj1tmLphpYFyRDL6O1E8okxmwPhFhuEe3xMpa04wDilH2GbesherMnlOYFyljDnSNiN+JTl0WDshQDFlKqKUQS2tiyahQFprIuttYsG49gmdTpopvNvUVkh4J7pFlL5TKtByYcasOsVj3hmU+rBMFH3tDlJBlY+S6deu3aq5o38oeAqFFh8V5YWNStPWKBSpiMAdSBTvS4GnE7ZC5XTPWH8nMXqwXYlvtJGtUCO8uUdVvYg9r3xCz1HUKFsbu/DMzS9QtP7YwKPO9YrZTsA5mQKmFY9Tj5SzGfZ2y8nIsnYhw3cWgum433Twj3WH0yOnrrb/vFHApLPdFbKoh57PvbYPERJ633tOj4hMeLHWDWjVhwAbixaZexxSq0nBBYgMFmTTJVCmZrCmaUeCQFltXWEqWgzB7SOJ9Rim6Ot9boOizF+eSulGYuUileGIPn/WKSQo8DS2OjfnO8F3Wdy4YwcK9zlPIUxZxNch43hiIYKOYQFW24vlOoL5CUrPWgmQitSwgTLn0ggmTuj31tpwsGA/oGjH2KloKAuisWAxLyGuZEayFJFQMUuOQsIW3Ur2z7qEY6kip4kOorJFFWxlmMibk2iUbteMVLEWxx5jYngJ8UxVchMvmrbftSXpxqpPGHcoHi7+OTY0kg1bv1XrJZDzFKEYbEUOeici2Yi40O4X6CkmP9PSFWTW9M4XgYCpTFmDBezTNxR6Qt/UY6R9T+dQqJqgV32MB2kJYbFYpY7NxtNtuDIAc+85pJQ2aoQJEeKIMGqYfRlhK72JgwJmUyl6DWtG3VCMnlm4j0pvZUggsWJYlMyxLRE9p3IL8CNfBCva2LIJMbFJdBcwqABgLfHgwItaDRapmK3zvFOorJDUrZYpw1IaQYzT48MYg1SApuh22Joak5/dE+GcPG6mn2oHIVEZ2NB8pbC1XEeuSYsdnwWCHdJeRBmlRiFVSB2QsVtFleElqwSj9TNptr0itQGhNJfC5Ssxp3EPYf7MRNVEYmiEsXtIeSlkLaytkIVNxnLGE93KhHOnRqrfT6db8qi0gCDop+NKK3GeEYOj/lpi/EBXeo8smOsumR/9sFHCasTlTxEz0UgVzQsmoCG/jqRBYWVwQZk30uBY7GyXUoZkgayprhBi3lFrfa3ZMavwctoyBRVzZhjQiG9gUh43g8VjSLJcxRQ4tMisn9yoLYzIFCRnExFH1opCtR8zV4cl+ls0tTSH6IiwlsXnijCbOMGgolK2ob+lxzCasE9Qalxbce9ofe+NlBUC4+dn3phgfW3WUXZO9ioJA8RqR1VIYU42skZBk/BDCHUN8Vy8WkpCXJ4hK0cyOYW71qTI7XvXQ44zSGEMaHo7FqzHyweKbwS2wkDD8PRnhYhuMuOjFuKT6xfVucQpxLqQNaNXfYDYfk40Rmv+tOAiJ4WOLL8WmAsbigzDv0L6VBXNiMwiKwa8D1Hs1eRgLNpWs+KIypwyvxboHmTYUvo7QhjGHM9/VC3R36OJiBD5A1NQ2RBxTr3uvTFNkmnss1Vlzxt1j9cE+76UkRS/viAlqPRphTZEuwptZwHMraEtHury8nP3oj/5o9ra3vS376q/+6uw3fuM3spuRLnnfsbu5SsFXlgbP+b/tDRpzuEljimFuBtY5hZnRRGpUnrNw3ax/N0wF1IL2QuVQC8qLgRO33hllaVAOzgWC18KDO1VQq8RLTWLcFnjeZkGf91L/ieF90VUWzEaquiR/8fLV1t+3RwRHMsTwhTQVMWKlShmIdSnWdQUzTWPSfocj1tZqu9YDxtJOoC0d6U/+5E9mTz31VPabv/mb2blz57J/+S//ZXb8+PHs7/7dv5vdTIS6IRpglSUIGUWCOb9j/OhytkKcydrKKpAyAWLeo8U7MM8ZYm9R4UGs/c5mHX6MAsOYqDUFjOG1Qg2eRLfDKYGXikpg7ym9sTgaLM0He5+B/2diK6R9Eyozqczvx/eMtCrGpgqSjVFCJTdRzP6pukQVXYqNnvCEYuJYGHdMjIVkIeKiYilcoczQ4t6+Yi0kCwsL2ac//ensIx/5SPbQQw9l73rXu7Lv/d7vzX7rt34ru9nowo12lcc3n5ArLlqMzRzOnF+f38TSJo0NarU2mQjRnTCCPYUvnQUoA0S4o62ujOro6JRtymVuzncomTjU4c58XzPu8JSCD1eDOZe+jeGn1LVYEHsBDAo2aJGJ8RHLUJAuQ0f/27OXosrXs8UiWcWFW/PNkQ8xQIdikkDMBY8InI1RIE9G4IVYVo/QPd5rEsItbSF57rnnsrW1tewtb3lL/rO3vvWt2a/8yq9kGxsb2QAZFd2jt0Ckawsr2e9+6Vy2PjCQLTkhQ/Dj7zx6NgdmksbzjfcfzD536morV72qzf/tq45nn3r0XPau1x0S+3jHHfuyT3z+dEtQSW3eGJShltp81ck92aNnrmff8ebjlW2+/U1Hs1/5i1dbIEnM/D50bLKy3dtv35t94bWZwnvCdn/7rn3ZJ//mjC95rr/jpDBv4Q1Pev6+h49ln3n8fPbuBw+rfXzDfQeyP31hOjs2Nay2u+9Q5yDX2j0cKKhau3x8D+jjc/FJTrB87b0Hu+YTfzvZ7uT4/+Wu/WJfDk34lauL6vsYXrv3oD0Pu4KDyylS1jo/cHSisk2YveaUz6o2X3vvgdYB7Gp1SO/5799wNPuFP3u5tTZSm29949HsD578/7d3LkBVVWsc/+yaghKBqPjA1HsdFJFQQHxkNhhqoZV3NJurjVrT6J3Maqap1Dtl6kymZmblVDOmOTVj2Yuuds2ih06CqKjkW0RALoqAHAR5iMi681/TPp3DZZ8DR2TjOf/fzIGz19qv819rfevb67ULZVpsmOk+5X8Y+OU7T8uZ4krZdaZEb9fUXXf5G42y9/fonm61GBjauBaOLSfoynS3z8qULJcVMu7ZoEvn213e19i/hcju7Evyj5jeLvdDGmD8RWIDe9aYHRhmYmeQnv85XiQhnVzfk5EXG+7j+MoDM52G3xUk+2GjTNJj/KBusjfPJncF+3ucdwHsPsbmTRxkbt8Ngv1v14tNJoT/Wc6b+pByI2XVrMyFuMkTntDU87VTnr57u5ns3LlTli1bJnv27LGHZWdnS1JSkqSlpUmXLl3ECjan5sqSfx/z6NjpcWGyalp0o3GQdV9OqQzqGdjou0HQlHwgt1Ri+gaLn4vmt4y8Uukb0lm6upj6eTi/THre6SehJqsXYmn4zP+WyYj+IY2/V+d6vezLLZWhfYKkk4sZAxcuV0tJRa1Ehd1pOg4B9xLfr4upV47f0y+ks4SY/J5zl6qk6lqdDOrReOtT3qVKvT7LwB53mN5nU7W1p1GPQLnTzWyDowWXJbhzB7fvkMDvh7PU3cVKkk29v9LKWskuviLD+5mXjeKKq3KutEpi+wa7XMfkxIVynS6uBlc2Ja81RYeckkr9tBoeap5GBWXVYquslSG9zVcKPVVYoZ3XfiaveUf67c+1SXhogASZvLAN3R3pOZckOizI9C2z6B7KyLVJbL9g0zVF1qVkydqU0/8XDk23/nOU6W9wV/aaqsWZogo96PWv3QIajUcZHvCvHdJcclYkuWz1w7Ljh87ZZHj/Li6dnLKqWjl9EXk12PR8F8trpPByjUT3CfI4PfNLq3SXeWSvxrXKulihdTbTyZ2NwpuxYQsH9wqUQL/bPU6vy1XX5GRhucTrBwXXtTHOlVXkWjuD3JJK3YXsyv4112Y5pXW+zaX9vtm0mkOSnJws69atk19++cUelp+fL4mJibJr1y7p0aNHk85z6VJFiy3VbBjr5COFUv+X26S6urbJ50ZFMiWqxy31JsXWBOUqJOSOFk8vX4e6WqMrjDVatRyXC0dr0qTIUAnzcOXilubAuTJJz7M1ef+R/YIl1sQ5aCmYX6mrYz5oM102HTt2lNpa5ylVxrafX9P7zmAsWrKCgxc8O76PdO16h5SUNL/yZGXrXh9q1PJQ19bVFQ8gM+PCTI9pC8C5aK6D0Vr3zvxKXZtCq7XLhIaGis1m0+NIDIqLi7UzEhhoPjiUEEIIId5PqzkkERER0r59ezl8+LA9LCMjQ6Kiopo8oJUQQggh3kmreQL+/v4yZcoUee211+T333+XlJQUvTDarFmzWusWCCGEENJGadWF0RYtWqQdktmzZ0tAQIAsWLBAJkyY0Jq3QAghhBBfd0jQSrJy5Ur9IYQQQggx4OANQgghhFgOHRJCCCGEWA4dEkIIIYRYDh0SQgghhFgOHRJCCCGEWA4dEkIIIYRYDh0SQgghhFgOHRJCCCGE+NbCaC31GuObdc6bcW5fhZpS11sJ5lfqeivR7hars5p6n+2UaisvzyaEEEKIr8IuG0IIIYRYDh0SQgghhFgOHRJCCCGEWA4dEkIIIYRYDh0SQgghhFgOHRJCCCGEWA4dEkIIIYRYDh0SQgghhFgOHRJCCCGEWA4dEkIIIYRYjk87JFevXpXFixdLXFycjBkzRjZu3Gj1LbUZLl68KM8++6zEx8fLvffeKytWrNB6gfz8fJkzZ44MHTpUkpKS5LfffnM6NjU1VSZPnizR0dEya9Ysvb8jH3/8sT7nsGHDtP7V1dU+lyZz586VhQsX2rePHz8ujz76qNZs6tSpcvToUaf9t2/fLomJiTp+/vz5Ulpaao/D2x/efPNNGTlypE6vVatWSX19vT3eZrPJggULtN7jxo2Tb7/9VryN2tpaWbp0qQwfPlxGjx4tb731ltYFUFvPuHDhgsybN09iYmJ0vkG5NaCmnudT2Mb09HR7mJX2NN/NtVsd5cMsW7ZMPfTQQ+ro0aPqhx9+UMOGDVM7duxQvk59fb2aPn26euqpp9Tp06fV/v371fjx49Ubb7yh46DZCy+8oM6cOaM++OADFR0drQoKCvSx+D906FD10Ucf6WOfe+45NXnyZH0c+P7771VsbKz6+eefVWZmpkpKSlJLly71qTTZvn27Cg8PVy+//LLerqysVPfcc4/WF5ouX75cjR49WocD6HT33Xerb775Rp04cUI9/vjjau7cufbzQev77rtPp1NaWpoaM2aM2rBhgz1+3rx5avbs2erUqVNq69atasiQIfqc3sQrr7yiJkyYoH9XamqqGjFihNqyZQu1vQFgA55//nmVk5OjfvzxR13OUSaZXz2jpqZGzZ8/X5f9vXv36jAr7Wm9m2tbgc86JChUUVFR9owB1q9fr429r4PMiUJTXFxsD9u2bZuu6GDsUUCMyhKgsnvnnXf097fffttJw6qqKl0IDJ1nzJhh3xegEkVli/18IU1sNpsaO3asmjp1qt0h+eKLL9S4cePsRgb/4QB+9dVXevvFF1+07wvOnz+vBg4cqM6dO6e34YwY+4Lk5GSVkJCgv+fl5em0zM/Pt8cvXrzY6XzeoOngwYNVenq6PezDDz9UCxcupLYeUlZWpvMNnFiDZ555Rld2zK/NJysrSz388MPaAXB0SKy0p6lurm0FPttlc/LkSamrq9PNXAaxsbGSmZnp1Nzti3Tr1k02bNggXbt2dQq/cuWK1mfw4MHSqVMnJ90OHz6svyMezYMG/v7+EhkZqeOvX78uR44ccYpHU+G1a9d0evhCmqxcuVIeeeQRGTBggD0Mvw+/s90f7+jGfzSTm2nas2dP6dWrlw5H1xqa1tFVYYBzFRQUSFFRkd4H+4eFhTnFHzp0SLyFjIwMCQgI0N1Vjl1i6Gaktp7h5+eny+7XX3+ty+fZs2fl4MGDEhERQU09YN++fTJixAj5/PPPncKttKeZbq5tBT7rkBQXF0twcLB06NDBHoYKGH1uZWVl4ssEBgbqPkkDZN5PP/1Uj1GAbt27d3faPyQkRAoLC/V3V/Hl5eVaX8f49u3bS1BQkI739jRJS0uTAwcOyNNPP+0U7k5TOBZm8TgWOMYbjqQR39ixcGS8BfSD9+7dW5KTk+WBBx6Q+++/X9avX6/zLbX1jI4dO8qrr76qK1CMXXjwwQdl7NixepwTNW0+M2bM0GM54FA4YqU9LXZzbStoLz4KBv44JhQwtjHwiPzJ6tWr9SC2L7/8Ug+gakw3QzMzXRFfU1PjpHPDeHQhemuawAgsWbJEG3k8fTriSjMA3ZqjqaNm7s7tDVRVVUleXp589tlnulUEhhY6w/hTW8/Jzs6WhIQEeeKJJyQrK0uWL18uo0aNoqYtiLv8WX0T7WlbtA0+65DgCaCh8MZ2wwrD152RzZs3y9q1ayU8PFzr1rC1AroZmpnpilYXxBnbDeNReaAJ0lvT5L333pMhQ4Y4tTwZmGnmTlNo5mhgGuqLeHfn9gbwVIjuxDVr1uiWEnD+/HnZsmWL9O3bl9p62JqHB5Bdu3bpvBIVFaVb1d5//33p06cPNW0hrLSnHd1c2wp8tssmNDRUT4dEH5sBnqyQGEhsIvqJaNOmTdopmThxol23kpISJ3mwbTT9mcVjXAqaElEIHOOhPwoF4r05Tb777jtJSUnR/bn4bNu2TX/w/UY0RRwwum4cvxvxZsd6C/gtyFeGMwL69++vx9ZQW8/AtHM4c46VE8YbwNGjpi2HlfY01M21rcBnHRIMzsKTleMAHgyOw5PAbbf5rCxOT/RoAsd6DpMmTbKHoz/52LFj9uZCQzeEG/HYNkCzILp7EA5doa9jPPRHOgwaNMir0+STTz7RDgjGOeCDdR3wwXdog0GmxroZ+I8BhGaaoqLFB+EwKhjg6hiP7wiDYcEgNwxwdewXRjzCvQXogC6xnJwcexgGYcJBobaegbyDbjDHJ2xoisHR1LTlsNKeRru5tiUoHwZrF0yaNEnP38Y8+5iYGLVz507l62Dab0REhFq7dq0qKipy+tTV1em57lifAPPiMb0SU8eMueuYXoqpZgg35s1jqpsxpRVrcEBn6A3doT/W3fC1NMG0W2PqbUVFhRo5cqTWAdMD8R/rkhjT8Q4ePKgiIyP1GiLGOiRYW8QAWmNKNqb34YPvGzdutMc/+eST+hgci3MgfbxtHRKsy/LYY4/p37h7926t5+bNm6mth5SXl+s8iCnnZ8+eVT/99JOKj4/Xa7swv94YjtN+rbSndW6ubQU+7ZBgrvZLL72kEwFGfNOmTVbfUpsAGROFprEPyM3NVTNnztQLbCGz79mzx+n4X3/9VS9ShfnwmNdurJfheP5Ro0bpBX0WLVqkFwzytTRxdEgADMaUKVO08Zk2bZo6duyY0/5YZwTrjUAXLK5UWlpqj4Nhef3111VcXJxeEGz16tV2gwVKSkq0A4NzY70TrCnjbaACReUJfZC33n33XbsG1NYz4BzPmTNHV2KJiYm6LFLTlnVIrLanuW6u3dq0wx/r2mcIIYQQQnx4DAkhhBBC2g50SAghhBBiOXRICCGEEGI5dEgIIYQQYjl0SAghhBBiOXRICCGEEGI5dEgIIYQQYjl0SAghhBBiOXRICCGEEGI5dEgIIYQQYjl0SAghhBAiVvM/mcD+Qcu899IAAAAASUVORK5CYII="
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 47
+   "execution_count": 57
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T03:58:57.132542Z",
-     "start_time": "2026-03-04T03:58:57.108523Z"
+     "end_time": "2026-03-04T17:40:47.473580Z",
+     "start_time": "2026-03-04T17:40:47.317130Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "speed_kph = pd.DataFrame(speed_kph).sort_index()\n",
-    "#calculated_position = pd.DataFrame(calculated_position).sort_index()\n",
-    "\n",
-    "merged_df = pd.merge_asof(\n",
-    "    speed_kph,\n",
-    "    final,\n",
-    "    left_index=True,\n",
-    "    right_index=True,\n",
-    "    direction='nearest'\n",
-    ")"
-   ],
-   "id": "a46a29e38c162456",
-   "outputs": [],
-   "execution_count": 63
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T03:59:07.624607Z",
-     "start_time": "2026-03-04T03:59:07.578876Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "merged_df.head()",
-   "id": "1d8ad291f461ae37",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "            0_x_x                                              1_x_x  \\\n",
-       "0            data  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
-       "1       file_type                                         TimeSeries   \n",
-       "2  canonical_path  production/FSGP_2024_Day_1/ingress/VehicleVelo...   \n",
-       "3        metadata                                                 {}   \n",
-       "4     description                                Speed of Brightside   \n",
-       "\n",
-       "            0_y_x                                              1_y_x  \\\n",
-       "0            data  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
-       "1       file_type                                         TimeSeries   \n",
-       "2  canonical_path  production/FSGP_2024_Day_2/ingress/VehicleVelo...   \n",
-       "3        metadata                                                 {}   \n",
-       "4     description                                Speed of Brightside   \n",
-       "\n",
-       "              0_x                                                1_x  \\\n",
-       "0            data  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
-       "1       file_type                                         TimeSeries   \n",
-       "2  canonical_path  production/FSGP_2024_Day_3/ingress/VehicleVelo...   \n",
-       "3        metadata                                                 {}   \n",
-       "4     description                                Speed of Brightside   \n",
-       "\n",
-       "            0_x_y                                              1_x_y  \\\n",
-       "0            data  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
-       "1       file_type                                         TimeSeries   \n",
-       "2  canonical_path  production/FSGP_2024_Day_1/localization/TrackI...   \n",
-       "3        metadata                                                 {}   \n",
-       "4     description  The best available TrackIndex data for the eve...   \n",
-       "\n",
-       "            0_y_y                                              1_y_y  \\\n",
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-       "1       file_type                                         TimeSeries   \n",
-       "2  canonical_path  production/FSGP_2024_Day_2/localization/TrackI...   \n",
-       "3        metadata                                                 {}   \n",
-       "4     description  The best available TrackIndex data for the eve...   \n",
-       "\n",
-       "              0_y                                                1_y  \n",
-       "0            data  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
-       "1       file_type                                         TimeSeries  \n",
-       "2  canonical_path  production/FSGP_2024_Day_3/localization/TrackI...  \n",
-       "3        metadata                                                 {}  \n",
-       "4     description  The best available TrackIndex data for the eve...  "
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0_x_x</th>\n",
-       "      <th>1_x_x</th>\n",
-       "      <th>0_y_x</th>\n",
-       "      <th>1_y_x</th>\n",
-       "      <th>0_x</th>\n",
-       "      <th>1_x</th>\n",
-       "      <th>0_x_y</th>\n",
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-       "      <th>0_y_y</th>\n",
-       "      <th>1_y_y</th>\n",
-       "      <th>0_y</th>\n",
-       "      <th>1_y</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>data</td>\n",
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-       "      <td>TimeSeries</td>\n",
-       "      <td>file_type</td>\n",
-       "      <td>TimeSeries</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>canonical_path</td>\n",
-       "      <td>production/FSGP_2024_Day_1/ingress/VehicleVelo...</td>\n",
-       "      <td>canonical_path</td>\n",
-       "      <td>production/FSGP_2024_Day_2/ingress/VehicleVelo...</td>\n",
-       "      <td>canonical_path</td>\n",
-       "      <td>production/FSGP_2024_Day_3/ingress/VehicleVelo...</td>\n",
-       "      <td>canonical_path</td>\n",
-       "      <td>production/FSGP_2024_Day_1/localization/TrackI...</td>\n",
-       "      <td>canonical_path</td>\n",
-       "      <td>production/FSGP_2024_Day_2/localization/TrackI...</td>\n",
-       "      <td>canonical_path</td>\n",
-       "      <td>production/FSGP_2024_Day_3/localization/TrackI...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>metadata</td>\n",
-       "      <td>{}</td>\n",
-       "      <td>metadata</td>\n",
-       "      <td>{}</td>\n",
-       "      <td>metadata</td>\n",
-       "      <td>{}</td>\n",
-       "      <td>metadata</td>\n",
-       "      <td>{}</td>\n",
-       "      <td>metadata</td>\n",
-       "      <td>{}</td>\n",
-       "      <td>metadata</td>\n",
-       "      <td>{}</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>description</td>\n",
-       "      <td>Speed of Brightside</td>\n",
-       "      <td>description</td>\n",
-       "      <td>Speed of Brightside</td>\n",
-       "      <td>description</td>\n",
-       "      <td>Speed of Brightside</td>\n",
-       "      <td>description</td>\n",
-       "      <td>The best available TrackIndex data for the eve...</td>\n",
-       "      <td>description</td>\n",
-       "      <td>The best available TrackIndex data for the eve...</td>\n",
-       "      <td>description</td>\n",
-       "      <td>The best available TrackIndex data for the eve...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
+    "# re query everything from sunbeam\n",
+    "# look into increasing sequence length, maybe over each lap\n",
+    "#plt.plot(pos_array)\n",
+    "plt.plot(speed_arr)"
    ],
-   "execution_count": 64
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T02:03:00.365672Z",
-     "start_time": "2026-03-04T02:03:00.091522Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "plt.plot(merged_df['0_x'],merged_df['0_y'], color = 'red')",
-   "id": "f9bda40d7ce7546c",
+   "id": "c6edb2a9f27ba9ba",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1fb848c6f00>]"
+       "[<matplotlib.lines.Line2D at 0x238c5f67290>]"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1858,13 +1489,13 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 27
+   "execution_count": 41
   },
   {
    "metadata": {},
@@ -1949,8 +1580,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T15:34:17.658043Z",
-     "start_time": "2026-02-28T15:34:17.634871Z"
+     "end_time": "2026-03-04T18:00:48.762412Z",
+     "start_time": "2026-03-04T18:00:48.758800Z"
     }
    },
    "cell_type": "code",
@@ -1966,13 +1597,13 @@
    ],
    "id": "a6707eebb9ccd8c9",
    "outputs": [],
-   "execution_count": 17
+   "execution_count": 61
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T15:34:18.313095Z",
-     "start_time": "2026-02-28T15:34:18.295440Z"
+     "end_time": "2026-03-04T18:01:22.248798Z",
+     "start_time": "2026-03-04T18:01:22.243541Z"
     }
    },
    "cell_type": "code",
@@ -1982,6 +1613,7 @@
     "# control = mbrake pressed, accelerator position\n",
     "def combine_dfs(telemetry_names, index_common, all_dfs):\n",
     "        combined_df = pd.DataFrame(index=index_common)\n",
+    "        combined_df.dropna()\n",
     "\n",
     "        for name, df in zip(telemetry_names, all_dfs):\n",
     "            #df_interp = self.resample(df, index_common)\n",
@@ -1991,13 +1623,13 @@
    ],
    "id": "3e63d74e16a13193",
    "outputs": [],
-   "execution_count": 18
+   "execution_count": 65
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T15:34:19.043390Z",
-     "start_time": "2026-02-28T15:34:18.968047Z"
+     "end_time": "2026-03-04T18:02:47.505625Z",
+     "start_time": "2026-03-04T18:02:47.458209Z"
     }
    },
    "cell_type": "code",
@@ -2008,41 +1640,119 @@
    ],
    "id": "6dea8ee6d0965889",
    "outputs": [],
-   "execution_count": 19
+   "execution_count": 70
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T15:34:19.801157Z",
-     "start_time": "2026-02-28T15:34:19.572071Z"
+     "end_time": "2026-03-04T18:04:34.382860Z",
+     "start_time": "2026-03-04T18:04:34.361208Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "pos_df.head()",
+   "id": "b48315f1d08149c4",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                               TrackIndexSpreadsheet\n",
+       "2024-07-16 07:49:53.627000093                    0.0\n",
+       "2024-07-16 07:49:53.827000618                    0.0\n",
+       "2024-07-16 07:49:54.027000904                    0.0\n",
+       "2024-07-16 07:49:54.227001429                    0.0\n",
+       "2024-07-16 07:49:54.427001953                    0.0"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>TrackIndexSpreadsheet</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:49:53.627000093</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:49:53.827000618</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:49:54.027000904</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:49:54.227001429</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:49:54.427001953</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 73
+  },
+  {
+   "metadata": {
+    "jupyter": {
+     "is_executing": true
     }
    },
    "cell_type": "code",
-   "source": "final_df = pd.concat([combined_df, merged_df], axis = 1)",
-   "id": "830ffd454d7d6e0d",
+   "source": [
+    "final_df = pd.concat([combined_df, pos_df, speed_arr], axis = 1)\n",
+    "final_df.sort_index"
+   ],
+   "id": "b4da4dfff97bc19a",
    "outputs": [],
-   "execution_count": 20
+   "execution_count": null
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T15:34:20.538903Z",
-     "start_time": "2026-02-28T15:34:20.516755Z"
+     "end_time": "2026-03-04T18:04:20.921392Z",
+     "start_time": "2026-03-04T18:04:20.910074Z"
     }
    },
    "cell_type": "code",
    "source": "final_df.head()",
-   "id": "b1208bd2410e08fc",
+   "id": "2c397a74782cbde3",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "   mech_brake_pressed  accel_position  0_x  0_y\n",
-       "0                 0.0             0.0  0.0  0.0\n",
-       "1                 0.0             0.0  0.0  0.0\n",
-       "2                 0.0             0.0  0.0  0.0\n",
-       "3                 0.0             0.0  0.0  0.0\n",
-       "4                 0.0             0.0  0.0  0.0"
+       "   mech_brake_pressed  accel_position  TrackIndexSpreadsheet   0\n",
+       "0                 0.0             0.0                    NaN NaN\n",
+       "1                 0.0             0.0                    NaN NaN\n",
+       "2                 0.0             0.0                    NaN NaN\n",
+       "3                 0.0             0.0                    NaN NaN\n",
+       "4                 0.0             0.0                    NaN NaN"
       ],
       "text/html": [
        "<div>\n",
@@ -2065,8 +1775,8 @@
        "      <th></th>\n",
        "      <th>mech_brake_pressed</th>\n",
        "      <th>accel_position</th>\n",
-       "      <th>0_x</th>\n",
-       "      <th>0_y</th>\n",
+       "      <th>TrackIndexSpreadsheet</th>\n",
+       "      <th>0</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -2074,70 +1784,164 @@
        "      <th>0</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
+       "      <th>2</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
+       "      <th>4</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 72
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T18:01:40.297473Z",
+     "start_time": "2026-03-04T18:01:40.267491Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "combined_df.head()",
+   "id": "b1208bd2410e08fc",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "   mech_brake_pressed  accel_position  position  speed\n",
+       "0                 0.0             0.0       NaN    NaN\n",
+       "1                 0.0             0.0       NaN    NaN\n",
+       "2                 0.0             0.0       NaN    NaN\n",
+       "3                 0.0             0.0       NaN    NaN\n",
+       "4                 0.0             0.0       NaN    NaN"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mech_brake_pressed</th>\n",
+       "      <th>accel_position</th>\n",
+       "      <th>position</th>\n",
+       "      <th>speed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
+       "      <th>1</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
+       "      <th>3</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 21
+   "execution_count": 68
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-02-28T15:09:24.850777Z",
-     "start_time": "2026-02-28T15:09:23.754206Z"
+     "end_time": "2026-03-04T18:02:23.548737Z",
+     "start_time": "2026-03-04T18:02:22.068957Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "plt.plot(final_df[\"0_y\"])\n",
-    "plt.plot(final_df[\"0_x\"])"
+    "plt.plot(combined_df[\"position\"])\n",
+    "plt.plot(combined_df[\"speed\"])"
    ],
    "id": "42e9fe006fc1b367",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x28580f51910>]"
+       "[<matplotlib.lines.Line2D at 0x238cd63b9b0>]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -2146,13 +1950,13 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 24
+   "execution_count": 69
   },
   {
    "metadata": {

From 0add9daefca135017fd21c1206ee06f0b1550f41 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Wed, 4 Mar 2026 14:52:46 -0800
Subject: [PATCH 24/49] clean + reorder things

---
 array_temp/Control_Model.ipynb         | 155 ++++----
 array_temp/control_model_revised.ipynb | 496 +++++++++++++++++++++++++
 2 files changed, 588 insertions(+), 63 deletions(-)
 create mode 100644 array_temp/control_model_revised.ipynb

diff --git a/array_temp/Control_Model.ipynb b/array_temp/Control_Model.ipynb
index 5c65397..76207e4 100644
--- a/array_temp/Control_Model.ipynb
+++ b/array_temp/Control_Model.ipynb
@@ -31,8 +31,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T17:24:30.228937Z",
-     "start_time": "2026-03-04T17:24:26.190575Z"
+     "end_time": "2026-03-04T21:40:55.324537Z",
+     "start_time": "2026-03-04T21:40:34.732465Z"
     }
    },
    "cell_type": "code",
@@ -161,8 +161,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T18:00:34.902333Z",
-     "start_time": "2026-03-04T18:00:34.850894Z"
+     "end_time": "2026-03-04T21:41:28.875065Z",
+     "start_time": "2026-03-04T21:41:28.804002Z"
     }
    },
    "cell_type": "code",
@@ -190,7 +190,7 @@
    ],
    "id": "31de4e6237f9a41e",
    "outputs": [],
-   "execution_count": 60
+   "execution_count": 2
   },
   {
    "metadata": {
@@ -1361,32 +1361,33 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T17:40:12.883282Z",
-     "start_time": "2026-03-04T17:40:11.071314Z"
+     "end_time": "2026-03-04T21:42:20.395132Z",
+     "start_time": "2026-03-04T21:42:17.464318Z"
     }
    },
    "cell_type": "code",
    "source": [
+    "\n",
+    "client = query.SunbeamClient()\n",
     "speed_arr = []\n",
     "for event in [\"FSGP_2024_Day_1\", \"FSGP_2024_Day_2\", \"FSGP_2024_Day_3\"]:\n",
     "    speed_arr.append(pd.DataFrame(data = client.get_file(origin = \"production\", event = event, source = \"ingress\", name = \"VehicleVelocity\" ).unwrap().data, index = client.get_file(origin = \"production\", event = event, source = \"ingress\", name = \"VehicleVelocity\" ).unwrap().data.datetime_x_axis))"
    ],
    "id": "81328e7e1792137d",
    "outputs": [],
-   "execution_count": 37
+   "execution_count": 5
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T17:58:05.475471Z",
-     "start_time": "2026-03-04T17:58:05.466062Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
    "source": [
     "def make_df(source, name):\n",
     "    dfs = []\n",
     "\n",
+    "    client = query.SunbeamClient()\n",
+    "\n",
     "    for event in [\"FSGP_2024_Day_1\", \"FSGP_2024_Day_2\", \"FSGP_2024_Day_3\"]:\n",
     "        file = client.get_file(\n",
     "            origin=\"production\",\n",
@@ -1404,15 +1405,13 @@
     "\n",
     "    return pd.concat(dfs).sort_index()"
    ],
-   "id": "878293d5e3c2267b",
-   "outputs": [],
-   "execution_count": 54
+   "id": "aed81af65ec0feda"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T17:58:22.105287Z",
-     "start_time": "2026-03-04T17:58:21.425774Z"
+     "end_time": "2026-03-04T21:42:25.922631Z",
+     "start_time": "2026-03-04T21:42:24.660509Z"
     }
    },
    "cell_type": "code",
@@ -1422,13 +1421,13 @@
    ],
    "id": "198c0fb7383fad4a",
    "outputs": [],
-   "execution_count": 55
+   "execution_count": 7
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T17:58:33.019158Z",
-     "start_time": "2026-03-04T17:58:32.863763Z"
+     "end_time": "2026-03-04T21:42:30.107536Z",
+     "start_time": "2026-03-04T21:42:29.656871Z"
     }
    },
    "cell_type": "code",
@@ -1438,10 +1437,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x238c912a150>]"
+       "[<matplotlib.lines.Line2D at 0x208db8a66f0>]"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1456,7 +1455,7 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 57
+   "execution_count": 8
   },
   {
    "metadata": {
@@ -1580,8 +1579,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T18:00:48.762412Z",
-     "start_time": "2026-03-04T18:00:48.758800Z"
+     "end_time": "2026-03-04T21:42:39.508744Z",
+     "start_time": "2026-03-04T21:42:39.482896Z"
     }
    },
    "cell_type": "code",
@@ -1597,16 +1596,13 @@
    ],
    "id": "a6707eebb9ccd8c9",
    "outputs": [],
-   "execution_count": 61
+   "execution_count": 9
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T18:01:22.248798Z",
-     "start_time": "2026-03-04T18:01:22.243541Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
+   "outputs": [],
+   "execution_count": 10,
    "source": [
     "# combine all dfs and resample, then feed to scaler.\n",
     "# states = velocity, position\n",
@@ -1621,37 +1617,18 @@
     "\n",
     "        return combined_df\n"
    ],
-   "id": "3e63d74e16a13193",
-   "outputs": [],
-   "execution_count": 65
+   "id": "3e63d74e16a13193"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T18:02:47.505625Z",
-     "start_time": "2026-03-04T18:02:47.458209Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "\n",
-    "all_dfs = [df_mech_brake_pressed, df_accel_position]\n",
-    "combined_df = combine_dfs([\"mech_brake_pressed\", \"accel_position\"], df_mech_brake_pressed.index, all_dfs)\n"
-   ],
-   "id": "6dea8ee6d0965889",
-   "outputs": [],
-   "execution_count": 70
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T18:04:34.382860Z",
-     "start_time": "2026-03-04T18:04:34.361208Z"
+     "end_time": "2026-03-04T21:45:28.386480Z",
+     "start_time": "2026-03-04T21:45:28.371915Z"
     }
    },
    "cell_type": "code",
    "source": "pos_df.head()",
-   "id": "b48315f1d08149c4",
+   "id": "67452406a84c903d",
    "outputs": [
     {
      "data": {
@@ -1711,17 +1688,54 @@
        "</div>"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 73
+   "execution_count": 19
   },
   {
    "metadata": {
-    "jupyter": {
-     "is_executing": true
+    "ExecuteTime": {
+     "end_time": "2026-03-04T21:44:56.087327Z",
+     "start_time": "2026-03-04T21:44:55.908939Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "all_dfs = [df_mech_brake_pressed, df_accel_position]\n",
+    "combined_df = combine_dfs([\"mech_brake_pressed\", \"accel_position\"], df_mech_brake_pressed.index, all_dfs)\n",
+    "dfs = [pos_df, speed_arr]\n",
+    "df = combine_dfs([\"position\", \"speed\"], pos_df.index, dfs)\n"
+   ],
+   "id": "6dea8ee6d0965889",
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Length of values (3) does not match length of index (375240)",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mValueError\u001B[39m                                Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[18]\u001B[39m\u001B[32m, line 4\u001B[39m\n\u001B[32m      2\u001B[39m combined_df = combine_dfs([\u001B[33m\"\u001B[39m\u001B[33mmech_brake_pressed\u001B[39m\u001B[33m\"\u001B[39m, \u001B[33m\"\u001B[39m\u001B[33maccel_position\u001B[39m\u001B[33m\"\u001B[39m], df_mech_brake_pressed.index, all_dfs)\n\u001B[32m      3\u001B[39m dfs = [pos_df, speed_arr]\n\u001B[32m----> \u001B[39m\u001B[32m4\u001B[39m df = \u001B[43mcombine_dfs\u001B[49m\u001B[43m(\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mposition\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mspeed\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpos_df\u001B[49m\u001B[43m.\u001B[49m\u001B[43mindex\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdfs\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[10]\u001B[39m\u001B[32m, line 10\u001B[39m, in \u001B[36mcombine_dfs\u001B[39m\u001B[34m(telemetry_names, index_common, all_dfs)\u001B[39m\n\u001B[32m      6\u001B[39m combined_df.dropna()\n\u001B[32m      8\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m name, df \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mzip\u001B[39m(telemetry_names, all_dfs):\n\u001B[32m      9\u001B[39m     \u001B[38;5;66;03m#df_interp = self.resample(df, index_common)\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m10\u001B[39m     \u001B[43mcombined_df\u001B[49m\u001B[43m[\u001B[49m\u001B[43mname\u001B[49m\u001B[43m]\u001B[49m = df\n\u001B[32m     12\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m combined_df\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:4322\u001B[39m, in \u001B[36mDataFrame.__setitem__\u001B[39m\u001B[34m(self, key, value)\u001B[39m\n\u001B[32m   4319\u001B[39m     \u001B[38;5;28mself\u001B[39m._setitem_array([key], value)\n\u001B[32m   4320\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m   4321\u001B[39m     \u001B[38;5;66;03m# set column\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m4322\u001B[39m     \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_set_item\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mvalue\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:4535\u001B[39m, in \u001B[36mDataFrame._set_item\u001B[39m\u001B[34m(self, key, value)\u001B[39m\n\u001B[32m   4525\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[34m_set_item\u001B[39m(\u001B[38;5;28mself\u001B[39m, key, value) -> \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m   4526\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m   4527\u001B[39m \u001B[33;03m    Add series to DataFrame in specified column.\u001B[39;00m\n\u001B[32m   4528\u001B[39m \n\u001B[32m   (...)\u001B[39m\u001B[32m   4533\u001B[39m \u001B[33;03m    ensure homogeneity.\u001B[39;00m\n\u001B[32m   4534\u001B[39m \u001B[33;03m    \"\"\"\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m4535\u001B[39m     value, refs = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_sanitize_column\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvalue\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   4537\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m (\n\u001B[32m   4538\u001B[39m         key \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m.columns\n\u001B[32m   4539\u001B[39m         \u001B[38;5;129;01mand\u001B[39;00m value.ndim == \u001B[32m1\u001B[39m\n\u001B[32m   4540\u001B[39m         \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(value.dtype, ExtensionDtype)\n\u001B[32m   4541\u001B[39m     ):\n\u001B[32m   4542\u001B[39m         \u001B[38;5;66;03m# broadcast across multiple columns if necessary\u001B[39;00m\n\u001B[32m   4543\u001B[39m         \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m.columns.is_unique \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(\u001B[38;5;28mself\u001B[39m.columns, MultiIndex):\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:5288\u001B[39m, in \u001B[36mDataFrame._sanitize_column\u001B[39m\u001B[34m(self, value)\u001B[39m\n\u001B[32m   5285\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m _reindex_for_setitem(value, \u001B[38;5;28mself\u001B[39m.index)\n\u001B[32m   5287\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m is_list_like(value):\n\u001B[32m-> \u001B[39m\u001B[32m5288\u001B[39m     \u001B[43mcom\u001B[49m\u001B[43m.\u001B[49m\u001B[43mrequire_length_match\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvalue\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mindex\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   5289\u001B[39m arr = sanitize_array(value, \u001B[38;5;28mself\u001B[39m.index, copy=\u001B[38;5;28;01mTrue\u001B[39;00m, allow_2d=\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[32m   5290\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m (\n\u001B[32m   5291\u001B[39m     \u001B[38;5;28misinstance\u001B[39m(value, Index)\n\u001B[32m   5292\u001B[39m     \u001B[38;5;129;01mand\u001B[39;00m value.dtype == \u001B[33m\"\u001B[39m\u001B[33mobject\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m   (...)\u001B[39m\u001B[32m   5295\u001B[39m     \u001B[38;5;66;03m# TODO: Remove kludge in sanitize_array for string mode when enforcing\u001B[39;00m\n\u001B[32m   5296\u001B[39m     \u001B[38;5;66;03m# this deprecation\u001B[39;00m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\common.py:573\u001B[39m, in \u001B[36mrequire_length_match\u001B[39m\u001B[34m(data, index)\u001B[39m\n\u001B[32m    569\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    570\u001B[39m \u001B[33;03mCheck the length of data matches the length of the index.\u001B[39;00m\n\u001B[32m    571\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    572\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(data) != \u001B[38;5;28mlen\u001B[39m(index):\n\u001B[32m--> \u001B[39m\u001B[32m573\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[32m    574\u001B[39m         \u001B[33m\"\u001B[39m\u001B[33mLength of values \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    575\u001B[39m         \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m(\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mlen\u001B[39m(data)\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m) \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    576\u001B[39m         \u001B[33m\"\u001B[39m\u001B[33mdoes not match length of index \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    577\u001B[39m         \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m(\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mlen\u001B[39m(index)\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m)\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    578\u001B[39m     )\n",
+      "\u001B[31mValueError\u001B[39m: Length of values (3) does not match length of index (375240)"
+     ]
+    }
+   ],
+   "execution_count": 18
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T21:42:48.242113Z",
+     "start_time": "2026-03-04T21:42:48.114597Z"
     }
    },
    "cell_type": "code",
@@ -1730,8 +1744,23 @@
     "final_df.sort_index"
    ],
    "id": "b4da4dfff97bc19a",
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "cannot concatenate object of type '<class 'list'>'; only Series and DataFrame objs are valid",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mTypeError\u001B[39m                                 Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[13]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m final_df = \u001B[43mpd\u001B[49m\u001B[43m.\u001B[49m\u001B[43mconcat\u001B[49m\u001B[43m(\u001B[49m\u001B[43m[\u001B[49m\u001B[43mcombined_df\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpos_df\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mspeed_arr\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[43m \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[32;43m1\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m      2\u001B[39m final_df.sort_index\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\reshape\\concat.py:382\u001B[39m, in \u001B[36mconcat\u001B[39m\u001B[34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001B[39m\n\u001B[32m    379\u001B[39m \u001B[38;5;28;01melif\u001B[39;00m copy \u001B[38;5;129;01mand\u001B[39;00m using_copy_on_write():\n\u001B[32m    380\u001B[39m     copy = \u001B[38;5;28;01mFalse\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m382\u001B[39m op = \u001B[43m_Concatenator\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    383\u001B[39m \u001B[43m    \u001B[49m\u001B[43mobjs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    384\u001B[39m \u001B[43m    \u001B[49m\u001B[43maxis\u001B[49m\u001B[43m=\u001B[49m\u001B[43maxis\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    385\u001B[39m \u001B[43m    \u001B[49m\u001B[43mignore_index\u001B[49m\u001B[43m=\u001B[49m\u001B[43mignore_index\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    386\u001B[39m \u001B[43m    \u001B[49m\u001B[43mjoin\u001B[49m\u001B[43m=\u001B[49m\u001B[43mjoin\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    387\u001B[39m \u001B[43m    \u001B[49m\u001B[43mkeys\u001B[49m\u001B[43m=\u001B[49m\u001B[43mkeys\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    388\u001B[39m \u001B[43m    \u001B[49m\u001B[43mlevels\u001B[49m\u001B[43m=\u001B[49m\u001B[43mlevels\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    389\u001B[39m \u001B[43m    \u001B[49m\u001B[43mnames\u001B[49m\u001B[43m=\u001B[49m\u001B[43mnames\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    390\u001B[39m \u001B[43m    \u001B[49m\u001B[43mverify_integrity\u001B[49m\u001B[43m=\u001B[49m\u001B[43mverify_integrity\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    391\u001B[39m \u001B[43m    \u001B[49m\u001B[43mcopy\u001B[49m\u001B[43m=\u001B[49m\u001B[43mcopy\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    392\u001B[39m \u001B[43m    \u001B[49m\u001B[43msort\u001B[49m\u001B[43m=\u001B[49m\u001B[43msort\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    393\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    395\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m op.get_result()\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\reshape\\concat.py:448\u001B[39m, in \u001B[36m_Concatenator.__init__\u001B[39m\u001B[34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001B[39m\n\u001B[32m    445\u001B[39m objs, keys = \u001B[38;5;28mself\u001B[39m._clean_keys_and_objs(objs, keys)\n\u001B[32m    447\u001B[39m \u001B[38;5;66;03m# figure out what our result ndim is going to be\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m448\u001B[39m ndims = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_get_ndims\u001B[49m\u001B[43m(\u001B[49m\u001B[43mobjs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    449\u001B[39m sample, objs = \u001B[38;5;28mself\u001B[39m._get_sample_object(objs, ndims, keys, names, levels)\n\u001B[32m    451\u001B[39m \u001B[38;5;66;03m# Standardize axis parameter to int\u001B[39;00m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\reshape\\concat.py:489\u001B[39m, in \u001B[36m_Concatenator._get_ndims\u001B[39m\u001B[34m(self, objs)\u001B[39m\n\u001B[32m    484\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(obj, (ABCSeries, ABCDataFrame)):\n\u001B[32m    485\u001B[39m         msg = (\n\u001B[32m    486\u001B[39m             \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mcannot concatenate object of type \u001B[39m\u001B[33m'\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mtype\u001B[39m(obj)\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m'\u001B[39m\u001B[33m; \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    487\u001B[39m             \u001B[33m\"\u001B[39m\u001B[33monly Series and DataFrame objs are valid\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    488\u001B[39m         )\n\u001B[32m--> \u001B[39m\u001B[32m489\u001B[39m         \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m(msg)\n\u001B[32m    491\u001B[39m     ndims.add(obj.ndim)\n\u001B[32m    492\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m ndims\n",
+      "\u001B[31mTypeError\u001B[39m: cannot concatenate object of type '<class 'list'>'; only Series and DataFrame objs are valid"
+     ]
+    }
+   ],
+   "execution_count": 13
   },
   {
    "metadata": {
diff --git a/array_temp/control_model_revised.ipynb b/array_temp/control_model_revised.ipynb
new file mode 100644
index 0000000..a265179
--- /dev/null
+++ b/array_temp/control_model_revised.ipynb
@@ -0,0 +1,496 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2026-03-04T22:40:49.533799Z",
+     "start_time": "2026-03-04T22:40:42.657722Z"
+    }
+   },
+   "source": [
+    "#necessary imports\n",
+    "import sklearn as sk\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from array_temp.DataPreprocessing import make_sequence_datasets\n",
+    "\n",
+    "\n",
+    "from data_tools import query\n",
+    "from data_tools.collections import TimeSeries\n",
+    "from datetime import datetime, date, time, timezone\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import dill\n",
+    "import os\n",
+    "import pytz\n",
+    "from datetime import datetime, time, date"
+   ],
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T22:49:33.537716Z",
+     "start_time": "2026-03-04T22:48:51.973787Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# query data from influx. looking at timestamps of 2024 FSGP: 14 - 18 overall, but for smaller data response lets try 14 -16\n",
+    "\n",
+    "#each 5 seconds\n",
+    "utc_offset_h = 7\n",
+    "start_utc = time(0+utc_offset_h, 00, 00)  #querying i svancouver time, influxdb gives utc\n",
+    "stop_utc = time(16+utc_offset_h, 45, 00)\n",
+    "date_start = date(2024, 7, 16)\n",
+    "date_stop = date(2024, 7, 18)\n",
+    "\n",
+    "vancouver = pytz.timezone(\"America/Vancouver\")\n",
+    "\n",
+    "start_local = vancouver.localize(datetime.combine(date_start, start_utc))\n",
+    "stop_local = vancouver.localize(datetime.combine(date_stop, stop_utc))\n",
+    "\n",
+    "start_time = start_local.astimezone(pytz.utc)\n",
+    "stop_time = stop_local.astimezone(pytz.utc)\n",
+    "\n",
+    "client = query.DBClient()\n",
+    "mech_brake_pressed: TimeSeries = client.query_time_series(start_time, stop_time, field=\"MechBrakePressed\")\n",
+    "accel_position: TimeSeries =   client.query_time_series(start_time, stop_time, field=\"AcceleratorPosition\")\n",
+    "speed_kph: TimeSeries = client.query_time_series(start_time, stop_time, \"VehicleVelocity\")\n",
+    "\n"
+   ],
+   "id": "bee61f82f0e2b463",
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T22:50:03.743455Z",
+     "start_time": "2026-03-04T22:50:03.690534Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "# save collected data\n",
+    "\n",
+    "out_dir = os.path.join(\"../../array_temp\", \"data\", \"control_state_fsgp_2024\")\n",
+    "os.makedirs(out_dir, exist_ok=True)\n",
+    "\n",
+    "brake_path = os.path.join(out_dir, \"brake_pressed.bin\")\n",
+    "accel_path = os.path.join(out_dir, \"acceleration.bin\")\n",
+    "speed_path = os.path.join(out_dir, \"speed_kph.bin\")\n",
+    "\n",
+    "filepaths = [brake_path, accel_path, speed_path]\n",
+    "datasets = [mech_brake_pressed, accel_position, speed_kph]\n",
+    "\n",
+    "for filepath, data in zip(filepaths, datasets):\n",
+    "    with open(filepath, \"wb\") as f:\n",
+    "        dill.dump(data, f)\n"
+   ],
+   "id": "d2c1546f1e2bbbeb",
+   "outputs": [],
+   "execution_count": 3
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T22:50:04.462882Z",
+     "start_time": "2026-03-04T22:50:04.425471Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "#loading data\n",
+    "import os\n",
+    "import dill\n",
+    "out_dir = os.path.join(\"../../array_temp\", \"data\", \"control_state_fsgp_2024\")\n",
+    "\n",
+    "brake_path = os.path.join(out_dir, \"brake_pressed.bin\")\n",
+    "accel_path = os.path.join(out_dir, \"acceleration.bin\")\n",
+    "speed_path = os.path.join(out_dir, \"speed_kph.bin\")\n",
+    "\n",
+    "filepaths = [brake_path, accel_path, speed_path]\n",
+    "\n",
+    "loaded_datasets = []\n",
+    "\n",
+    "for filepath in filepaths:\n",
+    "    with open(filepath, \"rb\") as f:\n",
+    "        data = dill.load(f)\n",
+    "        loaded_datasets.append(data)\n",
+    "\n",
+    "#unnpack\n",
+    "mech_brake_pressed, accel_position, speed_kph = loaded_datasets"
+   ],
+   "id": "d309d06cd7c5f22d",
+   "outputs": [],
+   "execution_count": 4
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T22:50:05.050786Z",
+     "start_time": "2026-03-04T22:50:05.045385Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# use sunbeam instead to save yourself a headache\n",
+    "from data_tools import *\n",
+    "import numpy as np\n",
+    "def make_df(source, name):\n",
+    "    dfs = []\n",
+    "\n",
+    "    client = query.SunbeamClient()\n",
+    "\n",
+    "    for event in [\"FSGP_2024_Day_1\", \"FSGP_2024_Day_2\", \"FSGP_2024_Day_3\"]:\n",
+    "        file = client.get_file(\n",
+    "            origin=\"production\",\n",
+    "            event=event,\n",
+    "            source=source,\n",
+    "            name=name\n",
+    "        ).unwrap()\n",
+    "\n",
+    "        dfs.append(\n",
+    "            pd.DataFrame(\n",
+    "                data=file.data,\n",
+    "                index=file.data.datetime_x_axis\n",
+    "            )\n",
+    "        )\n",
+    "\n",
+    "    return pd.concat(dfs).sort_index()"
+   ],
+   "id": "8244964eb0c13987",
+   "outputs": [],
+   "execution_count": 5
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T22:50:06.671708Z",
+     "start_time": "2026-03-04T22:50:05.811902Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "pos = []\n",
+    "pos_df = make_df(source = \"localization\", name = \"TrackIndex\")"
+   ],
+   "id": "d3a364dd0f5e0dc0",
+   "outputs": [],
+   "execution_count": 6
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T22:50:40.737471Z",
+     "start_time": "2026-03-04T22:50:39.957562Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "speed_df = make_df(source = \"ingress\", name = \"VehicleVelocity\")",
+   "id": "32163d52d6931b1c",
+   "outputs": [],
+   "execution_count": 7
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T22:50:45.830990Z",
+     "start_time": "2026-03-04T22:50:45.818243Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# combine all dfs and resample, then feed to scaler.\n",
+    "# states = velocity, position\n",
+    "# control = mbrake pressed, accelerator position\n",
+    "def combine_dfs(telemetry_names, index_common, all_dfs):\n",
+    "        combined_df = pd.DataFrame(index=index_common)\n",
+    "        combined_df.dropna()\n",
+    "\n",
+    "        for name, df in zip(telemetry_names, all_dfs):\n",
+    "            #df_interp = self.resample(df, index_common)\n",
+    "            combined_df[name] = df\n",
+    "\n",
+    "        return combined_df\n"
+   ],
+   "id": "8f9608786c013420",
+   "outputs": [],
+   "execution_count": 8
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T22:50:47.330159Z",
+     "start_time": "2026-03-04T22:50:47.255374Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "#preprocessing - convert everything to pandas dataframes.\n",
+    "\n",
+    "\n",
+    "df_mech_brake_pressed = pd.DataFrame(mech_brake_pressed)\n",
+    "df_accel_position = pd.DataFrame(accel_position)\n",
+    "#df_speed_kph = pd.DataFrame(speed_kph)\n",
+    "\n",
+    "\n",
+    "\n",
+    "all_dfs = [df_mech_brake_pressed, df_accel_position]\n",
+    "combined_df = combine_dfs([\"mech_brake_pressed\", \"accel_position\"], df_mech_brake_pressed.index, all_dfs)\n"
+   ],
+   "id": "cda1ae8e12adbc5e",
+   "outputs": [],
+   "execution_count": 9
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T22:51:04.137473Z",
+     "start_time": "2026-03-04T22:51:04.115125Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "dfs = pd.concat([pos_df, speed_df], axis = 1)\n",
+    "#df = ([\"position\", \"speed\"], pos_df.index, dfs)"
+   ],
+   "id": "6ba2f86b4ef3b7d6",
+   "outputs": [],
+   "execution_count": 11
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T22:35:33.885177Z",
+     "start_time": "2026-03-04T22:35:33.790843Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "dfs = dfs.reindex(combined_df.index)\n",
+    "#speed_df = speed_df.reindex(combined_df.index)\n",
+    "\n",
+    "final_df = pd.concat([combined_df, dfs], axis=1)"
+   ],
+   "id": "ce5abb4b9006ba",
+   "outputs": [],
+   "execution_count": 21
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T22:51:53.565655Z",
+     "start_time": "2026-03-04T22:51:53.543471Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "dfs.head()",
+   "id": "cbe9fc007a113566",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                               TrackIndexSpreadsheet    0\n",
+       "2024-07-16 07:49:53.627000093                    0.0  0.0\n",
+       "2024-07-16 07:49:53.827000618                    0.0  0.0\n",
+       "2024-07-16 07:49:54.027000904                    0.0  0.0\n",
+       "2024-07-16 07:49:54.227001429                    0.0  0.0\n",
+       "2024-07-16 07:49:54.427001953                    0.0  0.0"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>TrackIndexSpreadsheet</th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:49:53.627000093</th>\n",
+       "      <td>0.0</td>\n",
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+       "      <th>2024-07-16 07:49:53.827000618</th>\n",
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+       "      <th>2024-07-16 07:49:54.027000904</th>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:49:54.427001953</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 12
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-04T22:35:35.283409Z",
+     "start_time": "2026-03-04T22:35:35.269090Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "final_df.head()",
+   "id": "4074d4a18179d4b0",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "   mech_brake_pressed  accel_position  TrackIndexSpreadsheet   0\n",
+       "0                 0.0             0.0                    NaN NaN\n",
+       "1                 0.0             0.0                    NaN NaN\n",
+       "2                 0.0             0.0                    NaN NaN\n",
+       "3                 0.0             0.0                    NaN NaN\n",
+       "4                 0.0             0.0                    NaN NaN"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mech_brake_pressed</th>\n",
+       "      <th>accel_position</th>\n",
+       "      <th>TrackIndexSpreadsheet</th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 22
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "d57a320f8d3cb4d8"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From d463cfd60c0bf6126b426255a9ac933ad3a59bfb Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Wed, 4 Mar 2026 17:40:17 -0800
Subject: [PATCH 25/49] retrain model

---
 array_temp/control_model_revised.ipynb | 1127 ++++++++++++++++++++++--
 1 file changed, 1038 insertions(+), 89 deletions(-)

diff --git a/array_temp/control_model_revised.ipynb b/array_temp/control_model_revised.ipynb
index a265179..c46113a 100644
--- a/array_temp/control_model_revised.ipynb
+++ b/array_temp/control_model_revised.ipynb
@@ -6,8 +6,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2026-03-04T22:40:49.533799Z",
-     "start_time": "2026-03-04T22:40:42.657722Z"
+     "end_time": "2026-03-05T01:02:30.735394Z",
+     "start_time": "2026-03-05T01:02:27.514887Z"
     }
    },
    "source": [
@@ -103,8 +103,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T22:50:04.462882Z",
-     "start_time": "2026-03-04T22:50:04.425471Z"
+     "end_time": "2026-03-05T01:02:44.570426Z",
+     "start_time": "2026-03-05T01:02:44.541209Z"
     }
    },
    "cell_type": "code",
@@ -132,13 +132,13 @@
    ],
    "id": "d309d06cd7c5f22d",
    "outputs": [],
-   "execution_count": 4
+   "execution_count": 2
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T22:50:05.050786Z",
-     "start_time": "2026-03-04T22:50:05.045385Z"
+     "end_time": "2026-03-05T01:02:45.321984Z",
+     "start_time": "2026-03-05T01:02:45.315511Z"
     }
    },
    "cell_type": "code",
@@ -170,13 +170,13 @@
    ],
    "id": "8244964eb0c13987",
    "outputs": [],
-   "execution_count": 5
+   "execution_count": 3
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T22:50:06.671708Z",
-     "start_time": "2026-03-04T22:50:05.811902Z"
+     "end_time": "2026-03-05T01:02:46.516154Z",
+     "start_time": "2026-03-05T01:02:45.978503Z"
     }
    },
    "cell_type": "code",
@@ -186,26 +186,105 @@
    ],
    "id": "d3a364dd0f5e0dc0",
    "outputs": [],
-   "execution_count": 6
+   "execution_count": 4
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T22:50:40.737471Z",
-     "start_time": "2026-03-04T22:50:39.957562Z"
+     "end_time": "2026-03-05T01:02:47.628063Z",
+     "start_time": "2026-03-05T01:02:47.098667Z"
     }
    },
    "cell_type": "code",
-   "source": "speed_df = make_df(source = \"ingress\", name = \"VehicleVelocity\")",
+   "source": [
+    "\n",
+    "speed_df = make_df(source = \"ingress\", name = \"VehicleVelocity\")"
+   ],
    "id": "32163d52d6931b1c",
    "outputs": [],
-   "execution_count": 7
+   "execution_count": 5
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-05T01:02:48.300714Z",
+     "start_time": "2026-03-05T01:02:48.285784Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "speed_df.head()",
+   "id": "b2ba3bd6683e1905",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                                 0\n",
+       "2024-07-16 07:49:53.627000093  0.0\n",
+       "2024-07-16 07:49:53.827000618  0.0\n",
+       "2024-07-16 07:49:54.027000904  0.0\n",
+       "2024-07-16 07:49:54.227001429  0.0\n",
+       "2024-07-16 07:49:54.427001953  0.0"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
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+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:49:53.627000093</th>\n",
+       "      <td>0.0</td>\n",
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+       "      <th>2024-07-16 07:49:53.827000618</th>\n",
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+       "      <th>2024-07-16 07:49:54.027000904</th>\n",
+       "      <td>0.0</td>\n",
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+       "    <tr>\n",
+       "      <th>2024-07-16 07:49:54.227001429</th>\n",
+       "      <td>0.0</td>\n",
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+       "    <tr>\n",
+       "      <th>2024-07-16 07:49:54.427001953</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 6
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T22:50:45.830990Z",
-     "start_time": "2026-03-04T22:50:45.818243Z"
+     "end_time": "2026-03-05T01:02:50.447895Z",
+     "start_time": "2026-03-05T01:02:50.440488Z"
     }
    },
    "cell_type": "code",
@@ -225,13 +304,13 @@
    ],
    "id": "8f9608786c013420",
    "outputs": [],
-   "execution_count": 8
+   "execution_count": 7
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T22:50:47.330159Z",
-     "start_time": "2026-03-04T22:50:47.255374Z"
+     "end_time": "2026-03-05T01:04:33.512385Z",
+     "start_time": "2026-03-05T01:04:32.280332Z"
     }
    },
    "cell_type": "code",
@@ -239,8 +318,8 @@
     "#preprocessing - convert everything to pandas dataframes.\n",
     "\n",
     "\n",
-    "df_mech_brake_pressed = pd.DataFrame(mech_brake_pressed)\n",
-    "df_accel_position = pd.DataFrame(accel_position)\n",
+    "df_mech_brake_pressed = pd.DataFrame(mech_brake_pressed, index = mech_brake_pressed.datetime_x_axis)\n",
+    "df_accel_position = pd.DataFrame(accel_position, index = accel_position.datetime_x_axis)\n",
     "#df_speed_kph = pd.DataFrame(speed_kph)\n",
     "\n",
     "\n",
@@ -250,63 +329,125 @@
    ],
    "id": "cda1ae8e12adbc5e",
    "outputs": [],
-   "execution_count": 9
+   "execution_count": 15
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T22:51:04.137473Z",
-     "start_time": "2026-03-04T22:51:04.115125Z"
+     "end_time": "2026-03-05T01:03:50.884109Z",
+     "start_time": "2026-03-05T01:03:50.877005Z"
     }
    },
    "cell_type": "code",
-   "source": [
-    "\n",
-    "dfs = pd.concat([pos_df, speed_df], axis = 1)\n",
-    "#df = ([\"position\", \"speed\"], pos_df.index, dfs)"
+   "source": "df_accel_position.head()",
+   "id": "3a0fb159ad74ff32",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                                 0\n",
+       "2024-07-16 07:49:53.648000002  0.0\n",
+       "2024-07-16 07:49:53.747999907  0.0\n",
+       "2024-07-16 07:49:53.848000050  0.0\n",
+       "2024-07-16 07:49:53.947999954  0.0\n",
+       "2024-07-16 07:49:54.048000097  0.0"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:49:53.648000002</th>\n",
+       "      <td>0.0</td>\n",
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+       "    <tr>\n",
+       "      <th>2024-07-16 07:49:53.747999907</th>\n",
+       "      <td>0.0</td>\n",
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+       "    <tr>\n",
+       "      <th>2024-07-16 07:49:53.848000050</th>\n",
+       "      <td>0.0</td>\n",
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+       "      <th>2024-07-16 07:49:53.947999954</th>\n",
+       "      <td>0.0</td>\n",
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+       "    <tr>\n",
+       "      <th>2024-07-16 07:49:54.048000097</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
    ],
-   "id": "6ba2f86b4ef3b7d6",
-   "outputs": [],
-   "execution_count": 11
+   "execution_count": 12
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T22:35:33.885177Z",
-     "start_time": "2026-03-04T22:35:33.790843Z"
+     "end_time": "2026-03-05T01:06:22.101588Z",
+     "start_time": "2026-03-05T01:06:21.964687Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "dfs = dfs.reindex(combined_df.index)\n",
-    "#speed_df = speed_df.reindex(combined_df.index)\n",
-    "\n",
-    "final_df = pd.concat([combined_df, dfs], axis=1)"
+    "final_df = pd.merge_asof(\n",
+    "    df_mech_brake_pressed.sort_index(),\n",
+    "    df_accel_position.sort_index(),\n",
+    "    left_index=True,\n",
+    "    right_index=True,\n",
+    "    direction=\"nearest\"\n",
+    ")"
    ],
-   "id": "ce5abb4b9006ba",
+   "id": "9d28c3144f2da538",
    "outputs": [],
    "execution_count": 21
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T22:51:53.565655Z",
-     "start_time": "2026-03-04T22:51:53.543471Z"
+     "end_time": "2026-03-05T01:06:22.774549Z",
+     "start_time": "2026-03-05T01:06:22.766537Z"
     }
    },
    "cell_type": "code",
-   "source": "dfs.head()",
-   "id": "cbe9fc007a113566",
+   "source": "final_df.head()",
+   "id": "99286e4bf9908af",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "                               TrackIndexSpreadsheet    0\n",
-       "2024-07-16 07:49:53.627000093                    0.0  0.0\n",
-       "2024-07-16 07:49:53.827000618                    0.0  0.0\n",
-       "2024-07-16 07:49:54.027000904                    0.0  0.0\n",
-       "2024-07-16 07:49:54.227001429                    0.0  0.0\n",
-       "2024-07-16 07:49:54.427001953                    0.0  0.0"
+       "                               0_x  0_y\n",
+       "2024-07-16 07:05:27.380000114  0.0  0.0\n",
+       "2024-07-16 07:05:27.480000019  0.0  0.0\n",
+       "2024-07-16 07:05:27.580000162  0.0  0.0\n",
+       "2024-07-16 07:05:27.680000067  0.0  0.0\n",
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       "text/html": [
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@@ -327,33 +468,33 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>TrackIndexSpreadsheet</th>\n",
-       "      <th>0</th>\n",
+       "      <th>0_x</th>\n",
+       "      <th>0_y</th>\n",
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        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>2024-07-16 07:49:53.627000093</th>\n",
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        "    <tr>\n",
-       "      <th>2024-07-16 07:49:53.827000618</th>\n",
+       "      <th>2024-07-16 07:05:27.480000019</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2024-07-16 07:49:54.027000904</th>\n",
+       "      <th>2024-07-16 07:05:27.580000162</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2024-07-16 07:49:54.227001429</th>\n",
+       "      <th>2024-07-16 07:05:27.680000067</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2024-07-16 07:49:54.427001953</th>\n",
+       "      <th>2024-07-16 07:05:27.780000210</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
@@ -362,33 +503,33 @@
        "</div>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 12
+   "execution_count": 22
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T22:35:35.283409Z",
-     "start_time": "2026-03-04T22:35:35.269090Z"
+     "end_time": "2026-03-05T01:04:43.186875Z",
+     "start_time": "2026-03-05T01:04:43.178364Z"
     }
    },
    "cell_type": "code",
-   "source": "final_df.head()",
-   "id": "4074d4a18179d4b0",
+   "source": "all_dfs.head()",
+   "id": "e0d3ae7ad0b8ade6",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "   mech_brake_pressed  accel_position  TrackIndexSpreadsheet   0\n",
-       "0                 0.0             0.0                    NaN NaN\n",
-       "1                 0.0             0.0                    NaN NaN\n",
-       "2                 0.0             0.0                    NaN NaN\n",
-       "3                 0.0             0.0                    NaN NaN\n",
-       "4                 0.0             0.0                    NaN NaN"
+       "                                 0   0\n",
+       "2024-07-16 07:05:27.380000114  0.0 NaN\n",
+       "2024-07-16 07:05:27.480000019  0.0 NaN\n",
+       "2024-07-16 07:05:27.580000162  0.0 NaN\n",
+       "2024-07-16 07:05:27.680000067  0.0 NaN\n",
+       "2024-07-16 07:05:27.780000210  0.0 NaN"
       ],
       "text/html": [
        "<div>\n",
@@ -409,59 +550,867 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>mech_brake_pressed</th>\n",
-       "      <th>accel_position</th>\n",
-       "      <th>TrackIndexSpreadsheet</th>\n",
+       "      <th>0</th>\n",
        "      <th>0</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2024-07-16 07:05:27.380000114</th>\n",
        "      <td>0.0</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2024-07-16 07:05:27.480000019</th>\n",
        "      <td>0.0</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2024-07-16 07:05:27.580000162</th>\n",
        "      <td>0.0</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2024-07-16 07:05:27.680000067</th>\n",
        "      <td>0.0</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2024-07-16 07:05:27.780000210</th>\n",
        "      <td>0.0</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 22
+   "execution_count": 18
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-05T01:07:12.989614Z",
+     "start_time": "2026-03-05T01:07:12.981838Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "dfs = pd.concat([pos_df, speed_df], axis = 1)\n",
+    "#df = ([\"position\", \"speed\"], pos_df.index, dfs)"
+   ],
+   "id": "6ba2f86b4ef3b7d6",
+   "outputs": [],
+   "execution_count": 23
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-05T01:09:49.306532Z",
+     "start_time": "2026-03-05T01:09:49.165467Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "final_df = pd.merge_asof(\n",
+    "    final_df.sort_index(),\n",
+    "    dfs.sort_index(),\n",
+    "    left_index=True,\n",
+    "    right_index=True,\n",
+    "    direction=\"nearest\"\n",
+    ")"
+   ],
+   "id": "88421e0bc978e5e4",
+   "outputs": [],
+   "execution_count": 28
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                               TrackIndexSpreadsheet    0\n",
+       "2024-07-16 07:49:53.627000093                    0.0  0.0\n",
+       "2024-07-16 07:49:53.827000618                    0.0  0.0\n",
+       "2024-07-16 07:49:54.027000904                    0.0  0.0\n",
+       "2024-07-16 07:49:54.227001429                    0.0  0.0\n",
+       "2024-07-16 07:49:54.427001953                    0.0  0.0"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>TrackIndexSpreadsheet</th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:49:53.627000093</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:49:53.827000618</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:49:54.027000904</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:49:54.227001429</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:49:54.427001953</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 24,
+   "source": "dfs.sort_index().head()",
+   "id": "37fa610ae29876bd"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-05T01:10:21.573712Z",
+     "start_time": "2026-03-05T01:10:21.148729Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plt.plot(final_df[0])",
+   "id": "ce5abb4b9006ba",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2f5f50d4aa0>]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 30
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-05T01:13:14.231698Z",
+     "start_time": "2026-03-05T01:13:14.227201Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "final_df.columns = [\"brake_pressed\", \"accel_position\", \"position\", \"speed\"]",
+   "id": "b121a9351e0aba47",
+   "outputs": [],
+   "execution_count": 32
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                               brake_pressed  accel_position  position  speed\n",
+       "2024-07-16 07:05:27.380000114            0.0             0.0       0.0    0.0\n",
+       "2024-07-16 07:05:27.480000019            0.0             0.0       0.0    0.0\n",
+       "2024-07-16 07:05:27.580000162            0.0             0.0       0.0    0.0\n",
+       "2024-07-16 07:05:27.680000067            0.0             0.0       0.0    0.0\n",
+       "2024-07-16 07:05:27.780000210            0.0             0.0       0.0    0.0"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brake_pressed</th>\n",
+       "      <th>accel_position</th>\n",
+       "      <th>position</th>\n",
+       "      <th>speed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:05:27.380000114</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:05:27.480000019</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:05:27.580000162</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:05:27.680000067</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:05:27.780000210</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 33,
+   "source": "final_df.head()",
+   "id": "20dc87aea5bc9336"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-05T01:14:57.066594Z",
+     "start_time": "2026-03-05T01:14:57.041755Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "print(final_df.isna().sum())\n",
+    "print(final_df.shape)\n",
+    "# position has nan values"
+   ],
+   "id": "6e311b63077819a9",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "brake_pressed          0\n",
+      "accel_position         0\n",
+      "position          825917\n",
+      "speed                  0\n",
+      "dtype: int64\n",
+      "(2011945, 4)\n"
+     ]
+    }
+   ],
+   "execution_count": 35
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-05T01:15:28.076709Z",
+     "start_time": "2026-03-05T01:15:27.863023Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "final_df = final_df.sort_index()\n",
+    "final_df = final_df.ffill().dropna()"
+   ],
+   "id": "f143094a7164ac55",
+   "outputs": [],
+   "execution_count": 36
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-05T01:15:45.439304Z",
+     "start_time": "2026-03-05T01:15:45.071521Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plt.plot(final_df[\"position\"])",
+   "id": "ed4054acb3cc0dc6",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2f58e756e70>]"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 38
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-05T01:33:25.593983Z",
+     "start_time": "2026-03-05T01:33:25.133844Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "#finally cleared up all data, now move onto RNN\n",
+    "import DataPreprocessing\n",
+    "train_dataset, test_dataset, train_loader, test_loader, scaler = DataPreprocessing.make_sequence_datasets(final_df, state_cols = ['position', 'speed'], control_cols = ['brake_pressed', 'accel_position'], seq_len = 3000, train_frac=0.7, batch_size = 64)"
+   ],
+   "id": "d57a320f8d3cb4d8",
+   "outputs": [],
+   "execution_count": 53
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-05T01:33:25.628351Z",
+     "start_time": "2026-03-05T01:33:25.624316Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "state = ['position', 'speed']\n",
+    "control = ['brake_pressed', 'accel_position']"
+   ],
+   "id": "d1d762ae92b287c",
+   "outputs": [],
+   "execution_count": 54
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-05T01:33:26.315385Z",
+     "start_time": "2026-03-05T01:33:26.308623Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import pandas as pd\n",
+    "import torch\n",
+    "from torch import nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from RNN_Dataset import RNN_Dataset"
+   ],
+   "id": "1410b1caa1d1c46b",
+   "outputs": [],
+   "execution_count": 55
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-05T01:33:26.978345Z",
+     "start_time": "2026-03-05T01:33:26.969831Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "from RNN import *\n",
+    "seq_length = 3000\n",
+    "input_size = len(state)\n",
+    "output_size = len(control)\n",
+    "\n",
+    "\n",
+    "hidden_size = 128     # was 256\n",
+    "num_layers  = 2\n",
+    "model = RNN(input_size, hidden_size, num_layers, seq_length, output_size).to(device)"
+   ],
+   "id": "154939dd8005f9b7",
+   "outputs": [],
+   "execution_count": 56
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-05T01:33:28.861857Z",
+     "start_time": "2026-03-05T01:33:28.852109Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# now we define the training loop. we are pretending a trajectory of controls.\n",
+    "\n",
+    "#build the model\n",
+    "\n",
+    "\n",
+    "def train_model(model, train_loader, test_loader, epochs):\n",
+    "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "    model = model.to(device)\n",
+    "    criterion = nn.MSELoss()\n",
+    "    optimizer = torch.optim.Adam(model.parameters(), lr=1e-4, weight_decay=1e-5)\n",
+    "    train_losses = []\n",
+    "    test_losses = []\n",
+    "    print(\"NaNs in Train Loader:\", any(torch.isnan(x).any() for x, y in train_loader))\n",
+    "    print(\"NaNs in Test Loader:\", any(torch.isnan(x).any() for x, y in test_loader))\n",
+    "    for epoch in range(epochs):\n",
+    "\n",
+    "        #training loop\n",
+    "        model.train()\n",
+    "        train_loss = 0\n",
+    "        for x_batch, y_batch in train_loader:\n",
+    "            x_batch = x_batch.to(device)\n",
+    "            y_batch = y_batch.to(device)\n",
+    "            #check inputs before forward pass\n",
+    "            if torch.isnan(x_batch).any() or torch.isnan(y_batch).any():\n",
+    "                print(\"NaN in inputs, skipping batch\")\n",
+    "                continue\n",
+    "\n",
+    "\n",
+    "          #  optimizer.zero_grad() #resets the gradients to zero\n",
+    "            outputs = model(x_batch)\n",
+    "            loss = criterion(outputs, y_batch)\n",
+    "            if torch.isnan(outputs).any():\n",
+    "                print(\"NaN in model outputs\")\n",
+    "                print(\"x_batch min/max:\", x_batch.min().item(), x_batch.max().item())\n",
+    "                continue\n",
+    "\n",
+    "            loss = criterion(outputs, y_batch)\n",
+    "\n",
+    "            if torch.isnan(loss):\n",
+    "                print(\"NaN in loss\")\n",
+    "                print(\"outputs min/max:\", outputs.min().item(), outputs.max().item())\n",
+    "                print(\"y_batch min/max:\", y_batch.min().item(), y_batch.max().item())\n",
+    "                continue\n",
+    "            loss.backward()\n",
+    "            torch.nn.utils.clip_grad_norm_(model.parameters(), 1)\n",
+    "            optimizer.step()\n",
+    "\n",
+    "            train_loss += loss.item() #convert tensor to float\n",
+    "        train_loss/=len(train_loader) #average losses over batches\n",
+    "        print(f\"Epoch {epoch + 1}/{epochs}, Train Loss: {train_loss:.4f}\")\n",
+    "        train_losses.append(train_loss)\n",
+    "\n",
+    "        # testing loop\n",
+    "        model.eval()\n",
+    "        test_loss = 0\n",
+    "        with torch.no_grad():\n",
+    "            for x_batch, y_batch in test_loader:\n",
+    "                x_batch = x_batch.to(device)\n",
+    "                y_batch = y_batch.to(device)\n",
+    "                optimizer.zero_grad()\n",
+    "                predictions = model(x_batch)\n",
+    "                loss = criterion(predictions, y_batch)\n",
+    "                test_loss += loss.item()\n",
+    "        test_loss/=len(test_loader)\n",
+    "        test_losses.append(test_loss)\n",
+    "\n",
+    "    return train_losses, test_losses"
+   ],
+   "id": "902960e5d1a51324",
+   "outputs": [],
+   "execution_count": 57
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-05T01:40:06.516566Z",
+     "start_time": "2026-03-05T01:33:57.258697Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "train_losses, test_losses = train_model(model, train_loader, test_loader, epochs=50)\n",
+    "\n",
+    "# Save\n",
+    "torch.save({\n",
+    "    'model_state_dict': model.state_dict(),\n",
+    "    'input_size': input_size,\n",
+    "    'hidden_size': hidden_size,\n",
+    "    'num_layers': num_layers,\n",
+    "    'seq_length': seq_len,\n",
+    "    'output_size': output_size,\n",
+    "}, 'rnn_model.pt')"
+   ],
+   "id": "95f0eac03ab75052",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NaNs in Train Loader: False\n",
+      "NaNs in Test Loader: False\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mKeyboardInterrupt\u001B[39m                         Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[58]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m train_losses, test_losses = \u001B[43mtrain_model\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtrain_loader\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtest_loader\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mepochs\u001B[49m\u001B[43m=\u001B[49m\u001B[32;43m50\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m      3\u001B[39m \u001B[38;5;66;03m# Save\u001B[39;00m\n\u001B[32m      4\u001B[39m torch.save({\n\u001B[32m      5\u001B[39m     \u001B[33m'\u001B[39m\u001B[33mmodel_state_dict\u001B[39m\u001B[33m'\u001B[39m: model.state_dict(),\n\u001B[32m      6\u001B[39m     \u001B[33m'\u001B[39m\u001B[33minput_size\u001B[39m\u001B[33m'\u001B[39m: input_size,\n\u001B[32m   (...)\u001B[39m\u001B[32m     10\u001B[39m     \u001B[33m'\u001B[39m\u001B[33moutput_size\u001B[39m\u001B[33m'\u001B[39m: output_size,\n\u001B[32m     11\u001B[39m }, \u001B[33m'\u001B[39m\u001B[33mrnn_model.pt\u001B[39m\u001B[33m'\u001B[39m)\n",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[57]\u001B[39m\u001B[32m, line 44\u001B[39m, in \u001B[36mtrain_model\u001B[39m\u001B[34m(model, train_loader, test_loader, epochs)\u001B[39m\n\u001B[32m     42\u001B[39m     \u001B[38;5;28mprint\u001B[39m(\u001B[33m\"\u001B[39m\u001B[33my_batch min/max:\u001B[39m\u001B[33m\"\u001B[39m, y_batch.min().item(), y_batch.max().item())\n\u001B[32m     43\u001B[39m     \u001B[38;5;28;01mcontinue\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m44\u001B[39m \u001B[43mloss\u001B[49m\u001B[43m.\u001B[49m\u001B[43mbackward\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m     45\u001B[39m torch.nn.utils.clip_grad_norm_(model.parameters(), \u001B[32m1\u001B[39m)\n\u001B[32m     46\u001B[39m optimizer.step()\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\_tensor.py:630\u001B[39m, in \u001B[36mTensor.backward\u001B[39m\u001B[34m(self, gradient, retain_graph, create_graph, inputs)\u001B[39m\n\u001B[32m    620\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m has_torch_function_unary(\u001B[38;5;28mself\u001B[39m):\n\u001B[32m    621\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m handle_torch_function(\n\u001B[32m    622\u001B[39m         Tensor.backward,\n\u001B[32m    623\u001B[39m         (\u001B[38;5;28mself\u001B[39m,),\n\u001B[32m   (...)\u001B[39m\u001B[32m    628\u001B[39m         inputs=inputs,\n\u001B[32m    629\u001B[39m     )\n\u001B[32m--> \u001B[39m\u001B[32m630\u001B[39m \u001B[43mtorch\u001B[49m\u001B[43m.\u001B[49m\u001B[43mautograd\u001B[49m\u001B[43m.\u001B[49m\u001B[43mbackward\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    631\u001B[39m \u001B[43m    \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mgradient\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mretain_graph\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcreate_graph\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minputs\u001B[49m\u001B[43m=\u001B[49m\u001B[43minputs\u001B[49m\n\u001B[32m    632\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\autograd\\__init__.py:364\u001B[39m, in \u001B[36mbackward\u001B[39m\u001B[34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001B[39m\n\u001B[32m    359\u001B[39m     retain_graph = create_graph\n\u001B[32m    361\u001B[39m \u001B[38;5;66;03m# The reason we repeat the same comment below is that\u001B[39;00m\n\u001B[32m    362\u001B[39m \u001B[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001B[39;00m\n\u001B[32m    363\u001B[39m \u001B[38;5;66;03m# calls in the traceback and some print out the last line\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m364\u001B[39m \u001B[43m_engine_run_backward\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    365\u001B[39m \u001B[43m    \u001B[49m\u001B[43mtensors\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    366\u001B[39m \u001B[43m    \u001B[49m\u001B[43mgrad_tensors_\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    367\u001B[39m \u001B[43m    \u001B[49m\u001B[43mretain_graph\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    368\u001B[39m \u001B[43m    \u001B[49m\u001B[43mcreate_graph\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    369\u001B[39m \u001B[43m    \u001B[49m\u001B[43minputs_tuple\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    370\u001B[39m \u001B[43m    \u001B[49m\u001B[43mallow_unreachable\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[32m    371\u001B[39m \u001B[43m    \u001B[49m\u001B[43maccumulate_grad\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[32m    372\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\autograd\\graph.py:865\u001B[39m, in \u001B[36m_engine_run_backward\u001B[39m\u001B[34m(t_outputs, *args, **kwargs)\u001B[39m\n\u001B[32m    863\u001B[39m     unregister_hooks = _register_logging_hooks_on_whole_graph(t_outputs)\n\u001B[32m    864\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m865\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mVariable\u001B[49m\u001B[43m.\u001B[49m\u001B[43m_execution_engine\u001B[49m\u001B[43m.\u001B[49m\u001B[43mrun_backward\u001B[49m\u001B[43m(\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001B[39;49;00m\n\u001B[32m    866\u001B[39m \u001B[43m        \u001B[49m\u001B[43mt_outputs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\n\u001B[32m    867\u001B[39m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m  \u001B[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001B[39;00m\n\u001B[32m    868\u001B[39m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[32m    869\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m attach_logging_hooks:\n",
+      "\u001B[31mKeyboardInterrupt\u001B[39m: "
+     ]
+    }
+   ],
+   "execution_count": 58
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "#remember we also need to save the scaler to apply at inference\n",
+    "#using job lib for this purpose\n",
+    "import joblib\n",
+    "joblib.dump(scaler, 'scaler.pkl')\n",
+    "\n",
+    "# Later at inference\n",
+    "scaler = joblib.load('scaler.pkl')\n",
+    "x_new_scaled = scaler.transform(x_new_raw)"
+   ],
+   "id": "be2c544830173066"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "#now we evaluate model for inference\n",
+    "checkpoint = torch.load('rnn_model.pt', map_location=device)\n",
+    "\n",
+    "model = RNN(\n",
+    "    input_size  = checkpoint['input_size'],\n",
+    "    hidden_size = checkpoint['hidden_size'],\n",
+    "    num_layers  = checkpoint['num_layers'],\n",
+    "    seq_length  = checkpoint['seq_length'],\n",
+    "    output_size = checkpoint['output_size'],\n",
+    ")\n",
+    "model.load_state_dict(checkpoint['model_state_dict'])\n",
+    "model.eval()  # important — disables dropout/batchnorm for inference"
+   ],
+   "id": "23676ff6fae304c2"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-05T01:16:30.451499Z",
+     "start_time": "2026-03-05T01:16:30.421585Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "print(\"Any inf in final_df:\", np.isinf(final_df.values).any())\n",
+    "print(\"Any NaN in final_df:\", np.isnan(final_df.values).any())"
+   ],
+   "id": "11d2951061944ddb",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Any inf in final_df: False\n",
+      "Any NaN in final_df: False\n"
+     ]
+    }
+   ],
+   "execution_count": 40
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-05T01:27:55.907627Z",
+     "start_time": "2026-03-05T01:27:55.891814Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "",
+   "id": "9dd6e95ff8fee55f",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "koko\n"
+     ]
+    }
+   ],
+   "execution_count": 48
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-05T01:27:45.761363Z",
+     "start_time": "2026-03-05T01:22:16.098562Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import numpy as np\n",
+    "from RNN import RNN\n",
+    "\n",
+    "device = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else \"cpu\"\n",
+    "\n",
+    "\n",
+    "def train_rnn(\n",
+    "    train_loader,\n",
+    "    test_loader,\n",
+    "    input_size,\n",
+    "    output_size,\n",
+    "    hidden_size=128,\n",
+    "    num_layers=2,\n",
+    "    seq_length=50,\n",
+    "    num_epochs=50,\n",
+    "    learning_rate=1e-3,\n",
+    "    patience=10,\n",
+    "    save_path=\"best_rnn.pt\",\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Train an RNN (LSTM) model on sequence data.\n",
+    "\n",
+    "    Args:\n",
+    "        train_loader:   DataLoader for training set\n",
+    "        test_loader:    DataLoader for validation/test set\n",
+    "        input_size:     Number of input features (len(state_cols))\n",
+    "        output_size:    Number of output features (len(control_cols))\n",
+    "        hidden_size:    LSTM hidden dimension\n",
+    "        num_layers:     Number of stacked LSTM layers\n",
+    "        seq_length:     Sequence length (must match dataset seq_len)\n",
+    "        num_epochs:     Maximum training epochs\n",
+    "        learning_rate:  Adam learning rate\n",
+    "        patience:       Early stopping patience (epochs without val improvement)\n",
+    "        save_path:      Where to save the best model weights\n",
+    "\n",
+    "    Returns:\n",
+    "        model:          Trained RNN model (best weights loaded)\n",
+    "        history:        Dict with 'train_loss' and 'val_loss' lists\n",
+    "    \"\"\"\n",
+    "\n",
+    "    model = RNN(\n",
+    "        input_size=input_size,\n",
+    "        hidden_size=hidden_size,\n",
+    "        num_layers=num_layers,\n",
+    "        seq_length=seq_length,\n",
+    "        output_size=output_size,\n",
+    "    ).to(device)\n",
+    "\n",
+    "    criterion = nn.MSELoss()\n",
+    "    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
+    "\n",
+    "    # Reduce LR if val loss plateaus\n",
+    "    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n",
+    "        optimizer, mode=\"min\", factor=0.5, patience=patience // 2\n",
+    "    )\n",
+    "\n",
+    "    history = {\"train_loss\": [], \"val_loss\": []}\n",
+    "    best_val_loss = float(\"inf\")\n",
+    "    epochs_no_improve = 0\n",
+    "\n",
+    "    for epoch in range(1, num_epochs + 1):\n",
+    "\n",
+    "        # ── Training ──────────────────────────────────────────────\n",
+    "        model.train()\n",
+    "        train_losses = []\n",
+    "\n",
+    "        for x_batch, y_batch in train_loader:\n",
+    "            x_batch = x_batch.to(device)   # [B, seq_len, input_size]\n",
+    "            y_batch = y_batch.to(device)   # [B, seq_len, output_size]\n",
+    "\n",
+    "            optimizer.zero_grad()\n",
+    "            preds = model(x_batch)         # [B, seq_len, output_size]\n",
+    "            loss = criterion(preds, y_batch)\n",
+    "            loss.backward()\n",
+    "\n",
+    "            # Gradient clipping to prevent exploding gradients in LSTMs\n",
+    "            nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
+    "\n",
+    "            optimizer.step()\n",
+    "            train_losses.append(loss.item())\n",
+    "\n",
+    "        # ── Validation ────────────────────────────────────────────\n",
+    "        model.eval()\n",
+    "        val_losses = []\n",
+    "\n",
+    "        with torch.no_grad():\n",
+    "            for x_batch, y_batch in test_loader:\n",
+    "                x_batch = x_batch.to(device)\n",
+    "                y_batch = y_batch.to(device)\n",
+    "\n",
+    "                preds = model(x_batch)\n",
+    "                loss = criterion(preds, y_batch)\n",
+    "                val_losses.append(loss.item())\n",
+    "\n",
+    "        train_loss = np.mean(train_losses)\n",
+    "        val_loss   = np.mean(val_losses)\n",
+    "\n",
+    "        history[\"train_loss\"].append(train_loss)\n",
+    "        history[\"val_loss\"].append(val_loss)\n",
+    "\n",
+    "        scheduler.step(val_loss)\n",
+    "\n",
+    "        # ── Logging ───────────────────────────────────────────────\n",
+    "        print(\n",
+    "            f\"Epoch [{epoch:>3}/{num_epochs}]  \"\n",
+    "            f\"Train Loss: {train_loss:.6f}  |  \"\n",
+    "            f\"Val Loss: {val_loss:.6f}\"\n",
+    "        )\n",
+    "\n",
+    "        # ── Early stopping + checkpoint ───────────────────────────\n",
+    "        if val_loss < best_val_loss:\n",
+    "            best_val_loss = val_loss\n",
+    "            epochs_no_improve = 0\n",
+    "            torch.save(model.state_dict(), save_path)\n",
+    "            print(f\"  ✓ New best model saved  (val_loss={best_val_loss:.6f})\")\n",
+    "        else:\n",
+    "            epochs_no_improve += 1\n",
+    "            if epochs_no_improve >= patience:\n",
+    "                print(f\"\\nEarly stopping triggered after {epoch} epochs.\")\n",
+    "                break\n",
+    "\n",
+    "    # Load best weights before returning\n",
+    "    model.load_state_dict(torch.load(save_path, map_location=device))\n",
+    "    print(f\"\\nTraining complete. Best val loss: {best_val_loss:.6f}\")\n",
+    "    return model, history\n",
+    "\n",
+    "\n",
+    "# ── Example usage ─────────────────────────────────────────────────────────────\n",
+    "if __name__ == \"__main__\":\n",
+    "    import pandas as pd\n",
+    "    from DataPreprocessing import make_sequence_datasets\n",
+    "\n",
+    "    df = final_df\n",
+    "\n",
+    "    state_cols   = [\"position\", \"speed\"]\n",
+    "    control_cols = [\"brake_pressed\", \"accel_position\"]\n",
+    "\n",
+    "    seq_len    = 3000\n",
+    "    batch_size = 64\n",
+    "\n",
+    "    train_dataset, test_dataset, train_loader, test_loader, scaler = make_sequence_datasets(\n",
+    "        df_xy        = df,\n",
+    "        state_cols   = state_cols,\n",
+    "        control_cols = control_cols,\n",
+    "        seq_len      = seq_len,\n",
+    "        stride       = 10,\n",
+    "        train_frac   = 0.8,\n",
+    "        batch_size   = batch_size,\n",
+    "    )\n",
+    "\n",
+    "    model, history = train_rnn(\n",
+    "        train_loader = train_loader,\n",
+    "        test_loader  = test_loader,\n",
+    "        input_size   = len(state_cols),\n",
+    "        output_size  = len(control_cols),\n",
+    "        hidden_size  = 128,\n",
+    "        num_layers   = 2,\n",
+    "        seq_length   = seq_len,\n",
+    "        num_epochs   = 50,\n",
+    "        learning_rate= 1e-3,\n",
+    "        patience     = 10,\n",
+    "        save_path    = \"rnn_first.pt\",\n",
+    "    )"
+   ],
+   "id": "1a642655c3c62057",
+   "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mKeyboardInterrupt\u001B[39m                         Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[47]\u001B[39m\u001B[32m, line 153\u001B[39m\n\u001B[32m    141\u001B[39m batch_size = \u001B[32m64\u001B[39m\n\u001B[32m    143\u001B[39m train_dataset, test_dataset, train_loader, test_loader, scaler = make_sequence_datasets(\n\u001B[32m    144\u001B[39m     df_xy        = df,\n\u001B[32m    145\u001B[39m     state_cols   = state_cols,\n\u001B[32m   (...)\u001B[39m\u001B[32m    150\u001B[39m     batch_size   = batch_size,\n\u001B[32m    151\u001B[39m )\n\u001B[32m--> \u001B[39m\u001B[32m153\u001B[39m model, history = \u001B[43mtrain_rnn\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    154\u001B[39m \u001B[43m    \u001B[49m\u001B[43mtrain_loader\u001B[49m\u001B[43m \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[43mtrain_loader\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    155\u001B[39m \u001B[43m    \u001B[49m\u001B[43mtest_loader\u001B[49m\u001B[43m  \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[43mtest_loader\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    156\u001B[39m \u001B[43m    \u001B[49m\u001B[43minput_size\u001B[49m\u001B[43m   \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mlen\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mstate_cols\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    157\u001B[39m \u001B[43m    \u001B[49m\u001B[43moutput_size\u001B[49m\u001B[43m  \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mlen\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mcontrol_cols\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    158\u001B[39m \u001B[43m    \u001B[49m\u001B[43mhidden_size\u001B[49m\u001B[43m  \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[32;43m128\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m    159\u001B[39m \u001B[43m    \u001B[49m\u001B[43mnum_layers\u001B[49m\u001B[43m   \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[32;43m2\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m    160\u001B[39m \u001B[43m    \u001B[49m\u001B[43mseq_length\u001B[49m\u001B[43m   \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[43mseq_len\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    161\u001B[39m \u001B[43m    \u001B[49m\u001B[43mnum_epochs\u001B[49m\u001B[43m   \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[32;43m50\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m    162\u001B[39m \u001B[43m    \u001B[49m\u001B[43mlearning_rate\u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[32;43m1e-3\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m    163\u001B[39m \u001B[43m    \u001B[49m\u001B[43mpatience\u001B[49m\u001B[43m     \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[32;43m10\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m    164\u001B[39m \u001B[43m    \u001B[49m\u001B[43msave_path\u001B[49m\u001B[43m    \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mrnn_first.pt\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m    165\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[47]\u001B[39m\u001B[32m, line 74\u001B[39m, in \u001B[36mtrain_rnn\u001B[39m\u001B[34m(train_loader, test_loader, input_size, output_size, hidden_size, num_layers, seq_length, num_epochs, learning_rate, patience, save_path)\u001B[39m\n\u001B[32m     71\u001B[39m y_batch = y_batch.to(device)   \u001B[38;5;66;03m# [B, seq_len, output_size]\u001B[39;00m\n\u001B[32m     73\u001B[39m optimizer.zero_grad()\n\u001B[32m---> \u001B[39m\u001B[32m74\u001B[39m preds = \u001B[43mmodel\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx_batch\u001B[49m\u001B[43m)\u001B[49m         \u001B[38;5;66;03m# [B, seq_len, output_size]\u001B[39;00m\n\u001B[32m     75\u001B[39m loss = criterion(preds, y_batch)\n\u001B[32m     76\u001B[39m loss.backward()\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1776\u001B[39m, in \u001B[36mModule._wrapped_call_impl\u001B[39m\u001B[34m(self, *args, **kwargs)\u001B[39m\n\u001B[32m   1774\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._compiled_call_impl(*args, **kwargs)  \u001B[38;5;66;03m# type: ignore[misc]\u001B[39;00m\n\u001B[32m   1775\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1776\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_call_impl\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1787\u001B[39m, in \u001B[36mModule._call_impl\u001B[39m\u001B[34m(self, *args, **kwargs)\u001B[39m\n\u001B[32m   1782\u001B[39m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[32m   1783\u001B[39m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[32m   1784\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m._backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._forward_pre_hooks\n\u001B[32m   1785\u001B[39m         \u001B[38;5;129;01mor\u001B[39;00m _global_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[32m   1786\u001B[39m         \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[32m-> \u001B[39m\u001B[32m1787\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mforward_call\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   1789\u001B[39m result = \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m   1790\u001B[39m called_always_called_hooks = \u001B[38;5;28mset\u001B[39m()\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\array_temp\\RNN.py:41\u001B[39m, in \u001B[36mRNN.forward\u001B[39m\u001B[34m(self, x)\u001B[39m\n\u001B[32m     38\u001B[39m cell_states = torch.zeros(\u001B[38;5;28mself\u001B[39m.num_layers, x.size(\u001B[32m0\u001B[39m), \u001B[38;5;28mself\u001B[39m.hidden_size).to(x.device)\n\u001B[32m     40\u001B[39m \u001B[38;5;66;03m#forward propagate lstm\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m41\u001B[39m out, _ = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mlstm\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m(\u001B[49m\u001B[43mhidden_state\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcell_states\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;66;03m#out; tensor of shape(batch_size, seq_length, hidden_size) - at the final time step\u001B[39;00m\n\u001B[32m     42\u001B[39m \u001B[38;5;66;03m#decode the hidden state of t\u001B[39;00m\n\u001B[32m     43\u001B[39m \u001B[38;5;66;03m# predicted is a series of controls\u001B[39;00m\n\u001B[32m     44\u001B[39m out = \u001B[38;5;28mself\u001B[39m.fc(out)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1776\u001B[39m, in \u001B[36mModule._wrapped_call_impl\u001B[39m\u001B[34m(self, *args, **kwargs)\u001B[39m\n\u001B[32m   1774\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._compiled_call_impl(*args, **kwargs)  \u001B[38;5;66;03m# type: ignore[misc]\u001B[39;00m\n\u001B[32m   1775\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1776\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_call_impl\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1787\u001B[39m, in \u001B[36mModule._call_impl\u001B[39m\u001B[34m(self, *args, **kwargs)\u001B[39m\n\u001B[32m   1782\u001B[39m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[32m   1783\u001B[39m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[32m   1784\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m._backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._forward_pre_hooks\n\u001B[32m   1785\u001B[39m         \u001B[38;5;129;01mor\u001B[39;00m _global_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[32m   1786\u001B[39m         \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[32m-> \u001B[39m\u001B[32m1787\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mforward_call\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   1789\u001B[39m result = \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m   1790\u001B[39m called_always_called_hooks = \u001B[38;5;28mset\u001B[39m()\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\rnn.py:1141\u001B[39m, in \u001B[36mLSTM.forward\u001B[39m\u001B[34m(self, input, hx)\u001B[39m\n\u001B[32m   1138\u001B[39m         hx = \u001B[38;5;28mself\u001B[39m.permute_hidden(hx, sorted_indices)\n\u001B[32m   1140\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m batch_sizes \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1141\u001B[39m     result = \u001B[43m_VF\u001B[49m\u001B[43m.\u001B[49m\u001B[43mlstm\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m   1142\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43minput\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m   1143\u001B[39m \u001B[43m        \u001B[49m\u001B[43mhx\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1144\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_flat_weights\u001B[49m\u001B[43m,\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# type: ignore[arg-type]\u001B[39;49;00m\n\u001B[32m   1145\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mbias\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1146\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mnum_layers\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1147\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mdropout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1148\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mtraining\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1149\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mbidirectional\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1150\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mbatch_first\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1151\u001B[39m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   1152\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m   1153\u001B[39m     result = _VF.lstm(\n\u001B[32m   1154\u001B[39m         \u001B[38;5;28minput\u001B[39m,\n\u001B[32m   1155\u001B[39m         batch_sizes,\n\u001B[32m   (...)\u001B[39m\u001B[32m   1162\u001B[39m         \u001B[38;5;28mself\u001B[39m.bidirectional,\n\u001B[32m   1163\u001B[39m     )\n",
+      "\u001B[31mKeyboardInterrupt\u001B[39m: "
+     ]
+    }
+   ],
+   "execution_count": 47
   },
   {
    "metadata": {},
@@ -469,7 +1418,7 @@
    "outputs": [],
    "execution_count": null,
    "source": "",
-   "id": "d57a320f8d3cb4d8"
+   "id": "2bb7b192d6146b75"
   }
  ],
  "metadata": {

From 0ca6b201d092e195835827ced77d72e442d5a08a Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Wed, 4 Mar 2026 19:13:12 -0800
Subject: [PATCH 26/49] retrain model

---
 array_temp/control_model_revised.ipynb | 453 ++++++++-----------------
 1 file changed, 136 insertions(+), 317 deletions(-)

diff --git a/array_temp/control_model_revised.ipynb b/array_temp/control_model_revised.ipynb
index c46113a..9ebc5f2 100644
--- a/array_temp/control_model_revised.ipynb
+++ b/array_temp/control_model_revised.ipynb
@@ -6,8 +6,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2026-03-05T01:02:30.735394Z",
-     "start_time": "2026-03-05T01:02:27.514887Z"
+     "end_time": "2026-03-05T02:13:33.495091Z",
+     "start_time": "2026-03-05T02:13:33.488092Z"
     }
    },
    "source": [
@@ -32,7 +32,7 @@
     "from datetime import datetime, time, date"
    ],
    "outputs": [],
-   "execution_count": 1
+   "execution_count": 2
   },
   {
    "metadata": {
@@ -103,8 +103,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T01:02:44.570426Z",
-     "start_time": "2026-03-05T01:02:44.541209Z"
+     "end_time": "2026-03-05T02:13:37.451775Z",
+     "start_time": "2026-03-05T02:13:37.412521Z"
     }
    },
    "cell_type": "code",
@@ -132,13 +132,13 @@
    ],
    "id": "d309d06cd7c5f22d",
    "outputs": [],
-   "execution_count": 2
+   "execution_count": 3
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T01:02:45.321984Z",
-     "start_time": "2026-03-05T01:02:45.315511Z"
+     "end_time": "2026-03-05T02:13:40.153086Z",
+     "start_time": "2026-03-05T02:13:40.146328Z"
     }
    },
    "cell_type": "code",
@@ -170,13 +170,13 @@
    ],
    "id": "8244964eb0c13987",
    "outputs": [],
-   "execution_count": 3
+   "execution_count": 4
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T01:02:46.516154Z",
-     "start_time": "2026-03-05T01:02:45.978503Z"
+     "end_time": "2026-03-05T02:13:41.765544Z",
+     "start_time": "2026-03-05T02:13:40.921208Z"
     }
    },
    "cell_type": "code",
@@ -186,13 +186,13 @@
    ],
    "id": "d3a364dd0f5e0dc0",
    "outputs": [],
-   "execution_count": 4
+   "execution_count": 5
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T01:02:47.628063Z",
-     "start_time": "2026-03-05T01:02:47.098667Z"
+     "end_time": "2026-03-05T02:13:43.337567Z",
+     "start_time": "2026-03-05T02:13:42.311387Z"
     }
    },
    "cell_type": "code",
@@ -202,13 +202,13 @@
    ],
    "id": "32163d52d6931b1c",
    "outputs": [],
-   "execution_count": 5
+   "execution_count": 6
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T01:02:48.300714Z",
-     "start_time": "2026-03-05T01:02:48.285784Z"
+     "end_time": "2026-03-05T02:13:44.001530Z",
+     "start_time": "2026-03-05T02:13:43.984355Z"
     }
    },
    "cell_type": "code",
@@ -273,18 +273,18 @@
        "</div>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 6
+   "execution_count": 7
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T01:02:50.447895Z",
-     "start_time": "2026-03-05T01:02:50.440488Z"
+     "end_time": "2026-03-05T02:13:44.936691Z",
+     "start_time": "2026-03-05T02:13:44.931415Z"
     }
    },
    "cell_type": "code",
@@ -304,13 +304,13 @@
    ],
    "id": "8f9608786c013420",
    "outputs": [],
-   "execution_count": 7
+   "execution_count": 8
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T01:04:33.512385Z",
-     "start_time": "2026-03-05T01:04:32.280332Z"
+     "end_time": "2026-03-05T02:13:47.017463Z",
+     "start_time": "2026-03-05T02:13:45.816087Z"
     }
    },
    "cell_type": "code",
@@ -329,13 +329,13 @@
    ],
    "id": "cda1ae8e12adbc5e",
    "outputs": [],
-   "execution_count": 15
+   "execution_count": 9
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T01:03:50.884109Z",
-     "start_time": "2026-03-05T01:03:50.877005Z"
+     "end_time": "2026-03-05T01:52:36.174521Z",
+     "start_time": "2026-03-05T01:52:36.162809Z"
     }
    },
    "cell_type": "code",
@@ -400,18 +400,18 @@
        "</div>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 12
+   "execution_count": 9
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T01:06:22.101588Z",
-     "start_time": "2026-03-05T01:06:21.964687Z"
+     "end_time": "2026-03-05T02:13:49.537318Z",
+     "start_time": "2026-03-05T02:13:49.396981Z"
     }
    },
    "cell_type": "code",
@@ -426,13 +426,13 @@
    ],
    "id": "9d28c3144f2da538",
    "outputs": [],
-   "execution_count": 21
+   "execution_count": 10
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T01:06:22.774549Z",
-     "start_time": "2026-03-05T01:06:22.766537Z"
+     "end_time": "2026-03-05T01:52:38.226278Z",
+     "start_time": "2026-03-05T01:52:38.215218Z"
     }
    },
    "cell_type": "code",
@@ -503,12 +503,12 @@
        "</div>"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 22
+   "execution_count": 11
   },
   {
    "metadata": {
@@ -595,8 +595,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T01:07:12.989614Z",
-     "start_time": "2026-03-05T01:07:12.981838Z"
+     "end_time": "2026-03-05T02:13:52.443451Z",
+     "start_time": "2026-03-05T02:13:52.436253Z"
     }
    },
    "cell_type": "code",
@@ -607,13 +607,13 @@
    ],
    "id": "6ba2f86b4ef3b7d6",
    "outputs": [],
-   "execution_count": 23
+   "execution_count": 11
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T01:09:49.306532Z",
-     "start_time": "2026-03-05T01:09:49.165467Z"
+     "end_time": "2026-03-05T02:13:53.901920Z",
+     "start_time": "2026-03-05T02:13:53.735030Z"
     }
    },
    "cell_type": "code",
@@ -628,11 +628,18 @@
    ],
    "id": "88421e0bc978e5e4",
    "outputs": [],
-   "execution_count": 28
+   "execution_count": 12
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-05T02:13:54.628676Z",
+     "start_time": "2026-03-05T02:13:54.613272Z"
+    }
+   },
    "cell_type": "code",
+   "source": "dfs.sort_index().head()",
+   "id": "37fa610ae29876bd",
    "outputs": [
     {
      "data": {
@@ -698,20 +705,18 @@
        "</div>"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 24,
-   "source": "dfs.sort_index().head()",
-   "id": "37fa610ae29876bd"
+   "execution_count": 13
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T01:10:21.573712Z",
-     "start_time": "2026-03-05T01:10:21.148729Z"
+     "end_time": "2026-03-05T01:52:49.195311Z",
+     "start_time": "2026-03-05T01:52:48.611926Z"
     }
    },
    "cell_type": "code",
@@ -721,10 +726,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x2f5f50d4aa0>]"
+       "[<matplotlib.lines.Line2D at 0x211272deb10>]"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -739,24 +744,31 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 30
+   "execution_count": 15
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T01:13:14.231698Z",
-     "start_time": "2026-03-05T01:13:14.227201Z"
+     "end_time": "2026-03-05T02:13:59.285114Z",
+     "start_time": "2026-03-05T02:13:59.280601Z"
     }
    },
    "cell_type": "code",
    "source": "final_df.columns = [\"brake_pressed\", \"accel_position\", \"position\", \"speed\"]",
    "id": "b121a9351e0aba47",
    "outputs": [],
-   "execution_count": 32
+   "execution_count": 14
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-05T01:52:51.811427Z",
+     "start_time": "2026-03-05T01:52:51.800907Z"
+    }
+   },
    "cell_type": "code",
+   "source": "final_df.head()",
+   "id": "20dc87aea5bc9336",
    "outputs": [
     {
      "data": {
@@ -834,20 +846,18 @@
        "</div>"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 33,
-   "source": "final_df.head()",
-   "id": "20dc87aea5bc9336"
+   "execution_count": 17
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T01:14:57.066594Z",
-     "start_time": "2026-03-05T01:14:57.041755Z"
+     "end_time": "2026-03-05T01:52:54.223011Z",
+     "start_time": "2026-03-05T01:52:54.206405Z"
     }
    },
    "cell_type": "code",
@@ -871,13 +881,13 @@
      ]
     }
    ],
-   "execution_count": 35
+   "execution_count": 18
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T01:15:28.076709Z",
-     "start_time": "2026-03-05T01:15:27.863023Z"
+     "end_time": "2026-03-05T02:14:03.452596Z",
+     "start_time": "2026-03-05T02:14:03.284853Z"
     }
    },
    "cell_type": "code",
@@ -887,13 +897,13 @@
    ],
    "id": "f143094a7164ac55",
    "outputs": [],
-   "execution_count": 36
+   "execution_count": 15
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T01:15:45.439304Z",
-     "start_time": "2026-03-05T01:15:45.071521Z"
+     "end_time": "2026-03-05T01:52:57.454058Z",
+     "start_time": "2026-03-05T01:52:57.062763Z"
     }
    },
    "cell_type": "code",
@@ -903,10 +913,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x2f58e756e70>]"
+       "[<matplotlib.lines.Line2D at 0x2112737ad50>]"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -921,30 +931,30 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 38
+   "execution_count": 20
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T01:33:25.593983Z",
-     "start_time": "2026-03-05T01:33:25.133844Z"
+     "end_time": "2026-03-05T02:14:14.587552Z",
+     "start_time": "2026-03-05T02:14:13.729686Z"
     }
    },
    "cell_type": "code",
    "source": [
     "#finally cleared up all data, now move onto RNN\n",
     "import DataPreprocessing\n",
-    "train_dataset, test_dataset, train_loader, test_loader, scaler = DataPreprocessing.make_sequence_datasets(final_df, state_cols = ['position', 'speed'], control_cols = ['brake_pressed', 'accel_position'], seq_len = 3000, train_frac=0.7, batch_size = 64)"
+    "train_dataset, test_dataset, train_loader, test_loader, scaler = DataPreprocessing.make_sequence_datasets(final_df, state_cols = ['position', 'speed'], control_cols = ['brake_pressed', 'accel_position'], seq_len = 600, train_frac=0.7, batch_size = 64)"
    ],
    "id": "d57a320f8d3cb4d8",
    "outputs": [],
-   "execution_count": 53
+   "execution_count": 16
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T01:33:25.628351Z",
-     "start_time": "2026-03-05T01:33:25.624316Z"
+     "end_time": "2026-03-05T02:14:15.357379Z",
+     "start_time": "2026-03-05T02:14:15.353862Z"
     }
    },
    "cell_type": "code",
@@ -954,13 +964,13 @@
    ],
    "id": "d1d762ae92b287c",
    "outputs": [],
-   "execution_count": 54
+   "execution_count": 17
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T01:33:26.315385Z",
-     "start_time": "2026-03-05T01:33:26.308623Z"
+     "end_time": "2026-03-05T02:14:18.363956Z",
+     "start_time": "2026-03-05T02:14:18.359779Z"
     }
    },
    "cell_type": "code",
@@ -974,20 +984,20 @@
    ],
    "id": "1410b1caa1d1c46b",
    "outputs": [],
-   "execution_count": 55
+   "execution_count": 18
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T01:33:26.978345Z",
-     "start_time": "2026-03-05T01:33:26.969831Z"
+     "end_time": "2026-03-05T02:14:21.847976Z",
+     "start_time": "2026-03-05T02:14:21.825054Z"
     }
    },
    "cell_type": "code",
    "source": [
     "\n",
     "from RNN import *\n",
-    "seq_length = 3000\n",
+    "seq_length = 600\n",
     "input_size = len(state)\n",
     "output_size = len(control)\n",
     "\n",
@@ -998,13 +1008,13 @@
    ],
    "id": "154939dd8005f9b7",
    "outputs": [],
-   "execution_count": 56
+   "execution_count": 19
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T01:33:28.861857Z",
-     "start_time": "2026-03-05T01:33:28.852109Z"
+     "end_time": "2026-03-05T02:14:22.900906Z",
+     "start_time": "2026-03-05T02:14:22.890230Z"
     }
    },
    "cell_type": "code",
@@ -1079,13 +1089,13 @@
    ],
    "id": "902960e5d1a51324",
    "outputs": [],
-   "execution_count": 57
+   "execution_count": 20
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T01:40:06.516566Z",
-     "start_time": "2026-03-05T01:33:57.258697Z"
+     "end_time": "2026-03-05T03:13:04.474923Z",
+     "start_time": "2026-03-05T02:14:23.732395Z"
     }
    },
    "cell_type": "code",
@@ -1098,7 +1108,7 @@
     "    'input_size': input_size,\n",
     "    'hidden_size': hidden_size,\n",
     "    'num_layers': num_layers,\n",
-    "    'seq_length': seq_len,\n",
+    "    'seq_length': seq_length,\n",
     "    'output_size': output_size,\n",
     "}, 'rnn_model.pt')"
    ],
@@ -1109,7 +1119,32 @@
      "output_type": "stream",
      "text": [
       "NaNs in Train Loader: False\n",
-      "NaNs in Test Loader: False\n"
+      "NaNs in Test Loader: False\n",
+      "Epoch 1/50, Train Loss: 0.7853\n",
+      "Epoch 2/50, Train Loss: 0.7139\n",
+      "Epoch 3/50, Train Loss: 0.7118\n",
+      "Epoch 4/50, Train Loss: 0.7103\n",
+      "Epoch 5/50, Train Loss: 0.7095\n",
+      "Epoch 6/50, Train Loss: 0.7082\n",
+      "Epoch 7/50, Train Loss: 0.7049\n",
+      "Epoch 8/50, Train Loss: 0.6993\n",
+      "Epoch 9/50, Train Loss: 0.6996\n",
+      "Epoch 10/50, Train Loss: 0.6978\n",
+      "Epoch 11/50, Train Loss: 0.6988\n",
+      "Epoch 12/50, Train Loss: 0.6961\n",
+      "Epoch 13/50, Train Loss: 0.6969\n",
+      "Epoch 14/50, Train Loss: 0.6996\n",
+      "Epoch 15/50, Train Loss: 0.6933\n",
+      "Epoch 16/50, Train Loss: 0.6890\n",
+      "Epoch 17/50, Train Loss: 0.6842\n",
+      "Epoch 18/50, Train Loss: 0.6750\n",
+      "Epoch 19/50, Train Loss: 0.6621\n",
+      "Epoch 20/50, Train Loss: 0.6513\n",
+      "Epoch 21/50, Train Loss: 0.6431\n",
+      "Epoch 22/50, Train Loss: 0.6389\n",
+      "Epoch 23/50, Train Loss: 0.6333\n",
+      "Epoch 24/50, Train Loss: 0.6302\n",
+      "Epoch 25/50, Train Loss: 0.6273\n"
      ]
     },
     {
@@ -1119,16 +1154,19 @@
      "traceback": [
       "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
       "\u001B[31mKeyboardInterrupt\u001B[39m                         Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[58]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m train_losses, test_losses = \u001B[43mtrain_model\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtrain_loader\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtest_loader\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mepochs\u001B[49m\u001B[43m=\u001B[49m\u001B[32;43m50\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m      3\u001B[39m \u001B[38;5;66;03m# Save\u001B[39;00m\n\u001B[32m      4\u001B[39m torch.save({\n\u001B[32m      5\u001B[39m     \u001B[33m'\u001B[39m\u001B[33mmodel_state_dict\u001B[39m\u001B[33m'\u001B[39m: model.state_dict(),\n\u001B[32m      6\u001B[39m     \u001B[33m'\u001B[39m\u001B[33minput_size\u001B[39m\u001B[33m'\u001B[39m: input_size,\n\u001B[32m   (...)\u001B[39m\u001B[32m     10\u001B[39m     \u001B[33m'\u001B[39m\u001B[33moutput_size\u001B[39m\u001B[33m'\u001B[39m: output_size,\n\u001B[32m     11\u001B[39m }, \u001B[33m'\u001B[39m\u001B[33mrnn_model.pt\u001B[39m\u001B[33m'\u001B[39m)\n",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[57]\u001B[39m\u001B[32m, line 44\u001B[39m, in \u001B[36mtrain_model\u001B[39m\u001B[34m(model, train_loader, test_loader, epochs)\u001B[39m\n\u001B[32m     42\u001B[39m     \u001B[38;5;28mprint\u001B[39m(\u001B[33m\"\u001B[39m\u001B[33my_batch min/max:\u001B[39m\u001B[33m\"\u001B[39m, y_batch.min().item(), y_batch.max().item())\n\u001B[32m     43\u001B[39m     \u001B[38;5;28;01mcontinue\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m44\u001B[39m \u001B[43mloss\u001B[49m\u001B[43m.\u001B[49m\u001B[43mbackward\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m     45\u001B[39m torch.nn.utils.clip_grad_norm_(model.parameters(), \u001B[32m1\u001B[39m)\n\u001B[32m     46\u001B[39m optimizer.step()\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\_tensor.py:630\u001B[39m, in \u001B[36mTensor.backward\u001B[39m\u001B[34m(self, gradient, retain_graph, create_graph, inputs)\u001B[39m\n\u001B[32m    620\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m has_torch_function_unary(\u001B[38;5;28mself\u001B[39m):\n\u001B[32m    621\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m handle_torch_function(\n\u001B[32m    622\u001B[39m         Tensor.backward,\n\u001B[32m    623\u001B[39m         (\u001B[38;5;28mself\u001B[39m,),\n\u001B[32m   (...)\u001B[39m\u001B[32m    628\u001B[39m         inputs=inputs,\n\u001B[32m    629\u001B[39m     )\n\u001B[32m--> \u001B[39m\u001B[32m630\u001B[39m \u001B[43mtorch\u001B[49m\u001B[43m.\u001B[49m\u001B[43mautograd\u001B[49m\u001B[43m.\u001B[49m\u001B[43mbackward\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    631\u001B[39m \u001B[43m    \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mgradient\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mretain_graph\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcreate_graph\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minputs\u001B[49m\u001B[43m=\u001B[49m\u001B[43minputs\u001B[49m\n\u001B[32m    632\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\autograd\\__init__.py:364\u001B[39m, in \u001B[36mbackward\u001B[39m\u001B[34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001B[39m\n\u001B[32m    359\u001B[39m     retain_graph = create_graph\n\u001B[32m    361\u001B[39m \u001B[38;5;66;03m# The reason we repeat the same comment below is that\u001B[39;00m\n\u001B[32m    362\u001B[39m \u001B[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001B[39;00m\n\u001B[32m    363\u001B[39m \u001B[38;5;66;03m# calls in the traceback and some print out the last line\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m364\u001B[39m \u001B[43m_engine_run_backward\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    365\u001B[39m \u001B[43m    \u001B[49m\u001B[43mtensors\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    366\u001B[39m \u001B[43m    \u001B[49m\u001B[43mgrad_tensors_\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    367\u001B[39m \u001B[43m    \u001B[49m\u001B[43mretain_graph\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    368\u001B[39m \u001B[43m    \u001B[49m\u001B[43mcreate_graph\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    369\u001B[39m \u001B[43m    \u001B[49m\u001B[43minputs_tuple\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    370\u001B[39m \u001B[43m    \u001B[49m\u001B[43mallow_unreachable\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[32m    371\u001B[39m \u001B[43m    \u001B[49m\u001B[43maccumulate_grad\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[32m    372\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\autograd\\graph.py:865\u001B[39m, in \u001B[36m_engine_run_backward\u001B[39m\u001B[34m(t_outputs, *args, **kwargs)\u001B[39m\n\u001B[32m    863\u001B[39m     unregister_hooks = _register_logging_hooks_on_whole_graph(t_outputs)\n\u001B[32m    864\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m865\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mVariable\u001B[49m\u001B[43m.\u001B[49m\u001B[43m_execution_engine\u001B[49m\u001B[43m.\u001B[49m\u001B[43mrun_backward\u001B[49m\u001B[43m(\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001B[39;49;00m\n\u001B[32m    866\u001B[39m \u001B[43m        \u001B[49m\u001B[43mt_outputs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\n\u001B[32m    867\u001B[39m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m  \u001B[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001B[39;00m\n\u001B[32m    868\u001B[39m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[32m    869\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m attach_logging_hooks:\n",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[21]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m train_losses, test_losses = \u001B[43mtrain_model\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtrain_loader\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtest_loader\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mepochs\u001B[49m\u001B[43m=\u001B[49m\u001B[32;43m50\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m      3\u001B[39m \u001B[38;5;66;03m# Save\u001B[39;00m\n\u001B[32m      4\u001B[39m torch.save({\n\u001B[32m      5\u001B[39m     \u001B[33m'\u001B[39m\u001B[33mmodel_state_dict\u001B[39m\u001B[33m'\u001B[39m: model.state_dict(),\n\u001B[32m      6\u001B[39m     \u001B[33m'\u001B[39m\u001B[33minput_size\u001B[39m\u001B[33m'\u001B[39m: input_size,\n\u001B[32m   (...)\u001B[39m\u001B[32m     10\u001B[39m     \u001B[33m'\u001B[39m\u001B[33moutput_size\u001B[39m\u001B[33m'\u001B[39m: output_size,\n\u001B[32m     11\u001B[39m }, \u001B[33m'\u001B[39m\u001B[33mrnn_model.pt\u001B[39m\u001B[33m'\u001B[39m)\n",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[20]\u001B[39m\u001B[32m, line 61\u001B[39m, in \u001B[36mtrain_model\u001B[39m\u001B[34m(model, train_loader, test_loader, epochs)\u001B[39m\n\u001B[32m     59\u001B[39m y_batch = y_batch.to(device)\n\u001B[32m     60\u001B[39m optimizer.zero_grad()\n\u001B[32m---> \u001B[39m\u001B[32m61\u001B[39m predictions = \u001B[43mmodel\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx_batch\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m     62\u001B[39m loss = criterion(predictions, y_batch)\n\u001B[32m     63\u001B[39m test_loss += loss.item()\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1776\u001B[39m, in \u001B[36mModule._wrapped_call_impl\u001B[39m\u001B[34m(self, *args, **kwargs)\u001B[39m\n\u001B[32m   1774\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._compiled_call_impl(*args, **kwargs)  \u001B[38;5;66;03m# type: ignore[misc]\u001B[39;00m\n\u001B[32m   1775\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1776\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_call_impl\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1787\u001B[39m, in \u001B[36mModule._call_impl\u001B[39m\u001B[34m(self, *args, **kwargs)\u001B[39m\n\u001B[32m   1782\u001B[39m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[32m   1783\u001B[39m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[32m   1784\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m._backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._forward_pre_hooks\n\u001B[32m   1785\u001B[39m         \u001B[38;5;129;01mor\u001B[39;00m _global_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[32m   1786\u001B[39m         \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[32m-> \u001B[39m\u001B[32m1787\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mforward_call\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   1789\u001B[39m result = \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m   1790\u001B[39m called_always_called_hooks = \u001B[38;5;28mset\u001B[39m()\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\array_temp\\RNN.py:41\u001B[39m, in \u001B[36mRNN.forward\u001B[39m\u001B[34m(self, x)\u001B[39m\n\u001B[32m     38\u001B[39m cell_states = torch.zeros(\u001B[38;5;28mself\u001B[39m.num_layers, x.size(\u001B[32m0\u001B[39m), \u001B[38;5;28mself\u001B[39m.hidden_size).to(x.device)\n\u001B[32m     40\u001B[39m \u001B[38;5;66;03m#forward propagate lstm\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m41\u001B[39m out, _ = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mlstm\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m(\u001B[49m\u001B[43mhidden_state\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcell_states\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;66;03m#out; tensor of shape(batch_size, seq_length, hidden_size) - at the final time step\u001B[39;00m\n\u001B[32m     42\u001B[39m \u001B[38;5;66;03m#decode the hidden state of t\u001B[39;00m\n\u001B[32m     43\u001B[39m \u001B[38;5;66;03m# predicted is a series of controls\u001B[39;00m\n\u001B[32m     44\u001B[39m out = \u001B[38;5;28mself\u001B[39m.fc(out)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1776\u001B[39m, in \u001B[36mModule._wrapped_call_impl\u001B[39m\u001B[34m(self, *args, **kwargs)\u001B[39m\n\u001B[32m   1774\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._compiled_call_impl(*args, **kwargs)  \u001B[38;5;66;03m# type: ignore[misc]\u001B[39;00m\n\u001B[32m   1775\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1776\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_call_impl\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1787\u001B[39m, in \u001B[36mModule._call_impl\u001B[39m\u001B[34m(self, *args, **kwargs)\u001B[39m\n\u001B[32m   1782\u001B[39m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[32m   1783\u001B[39m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[32m   1784\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m._backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._forward_pre_hooks\n\u001B[32m   1785\u001B[39m         \u001B[38;5;129;01mor\u001B[39;00m _global_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[32m   1786\u001B[39m         \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[32m-> \u001B[39m\u001B[32m1787\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mforward_call\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   1789\u001B[39m result = \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m   1790\u001B[39m called_always_called_hooks = \u001B[38;5;28mset\u001B[39m()\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\rnn.py:1141\u001B[39m, in \u001B[36mLSTM.forward\u001B[39m\u001B[34m(self, input, hx)\u001B[39m\n\u001B[32m   1138\u001B[39m         hx = \u001B[38;5;28mself\u001B[39m.permute_hidden(hx, sorted_indices)\n\u001B[32m   1140\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m batch_sizes \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1141\u001B[39m     result = \u001B[43m_VF\u001B[49m\u001B[43m.\u001B[49m\u001B[43mlstm\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m   1142\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43minput\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m   1143\u001B[39m \u001B[43m        \u001B[49m\u001B[43mhx\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1144\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_flat_weights\u001B[49m\u001B[43m,\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# type: ignore[arg-type]\u001B[39;49;00m\n\u001B[32m   1145\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mbias\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1146\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mnum_layers\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1147\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mdropout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1148\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mtraining\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1149\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mbidirectional\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1150\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mbatch_first\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1151\u001B[39m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   1152\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m   1153\u001B[39m     result = _VF.lstm(\n\u001B[32m   1154\u001B[39m         \u001B[38;5;28minput\u001B[39m,\n\u001B[32m   1155\u001B[39m         batch_sizes,\n\u001B[32m   (...)\u001B[39m\u001B[32m   1162\u001B[39m         \u001B[38;5;28mself\u001B[39m.bidirectional,\n\u001B[32m   1163\u001B[39m     )\n",
       "\u001B[31mKeyboardInterrupt\u001B[39m: "
      ]
     }
    ],
-   "execution_count": 58
+   "execution_count": 21
   },
   {
    "metadata": {},
@@ -1193,225 +1231,6 @@
    ],
    "execution_count": 40
   },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-05T01:27:55.907627Z",
-     "start_time": "2026-03-05T01:27:55.891814Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "",
-   "id": "9dd6e95ff8fee55f",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "koko\n"
-     ]
-    }
-   ],
-   "execution_count": 48
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-05T01:27:45.761363Z",
-     "start_time": "2026-03-05T01:22:16.098562Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import numpy as np\n",
-    "from RNN import RNN\n",
-    "\n",
-    "device = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else \"cpu\"\n",
-    "\n",
-    "\n",
-    "def train_rnn(\n",
-    "    train_loader,\n",
-    "    test_loader,\n",
-    "    input_size,\n",
-    "    output_size,\n",
-    "    hidden_size=128,\n",
-    "    num_layers=2,\n",
-    "    seq_length=50,\n",
-    "    num_epochs=50,\n",
-    "    learning_rate=1e-3,\n",
-    "    patience=10,\n",
-    "    save_path=\"best_rnn.pt\",\n",
-    "):\n",
-    "    \"\"\"\n",
-    "    Train an RNN (LSTM) model on sequence data.\n",
-    "\n",
-    "    Args:\n",
-    "        train_loader:   DataLoader for training set\n",
-    "        test_loader:    DataLoader for validation/test set\n",
-    "        input_size:     Number of input features (len(state_cols))\n",
-    "        output_size:    Number of output features (len(control_cols))\n",
-    "        hidden_size:    LSTM hidden dimension\n",
-    "        num_layers:     Number of stacked LSTM layers\n",
-    "        seq_length:     Sequence length (must match dataset seq_len)\n",
-    "        num_epochs:     Maximum training epochs\n",
-    "        learning_rate:  Adam learning rate\n",
-    "        patience:       Early stopping patience (epochs without val improvement)\n",
-    "        save_path:      Where to save the best model weights\n",
-    "\n",
-    "    Returns:\n",
-    "        model:          Trained RNN model (best weights loaded)\n",
-    "        history:        Dict with 'train_loss' and 'val_loss' lists\n",
-    "    \"\"\"\n",
-    "\n",
-    "    model = RNN(\n",
-    "        input_size=input_size,\n",
-    "        hidden_size=hidden_size,\n",
-    "        num_layers=num_layers,\n",
-    "        seq_length=seq_length,\n",
-    "        output_size=output_size,\n",
-    "    ).to(device)\n",
-    "\n",
-    "    criterion = nn.MSELoss()\n",
-    "    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
-    "\n",
-    "    # Reduce LR if val loss plateaus\n",
-    "    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n",
-    "        optimizer, mode=\"min\", factor=0.5, patience=patience // 2\n",
-    "    )\n",
-    "\n",
-    "    history = {\"train_loss\": [], \"val_loss\": []}\n",
-    "    best_val_loss = float(\"inf\")\n",
-    "    epochs_no_improve = 0\n",
-    "\n",
-    "    for epoch in range(1, num_epochs + 1):\n",
-    "\n",
-    "        # ── Training ──────────────────────────────────────────────\n",
-    "        model.train()\n",
-    "        train_losses = []\n",
-    "\n",
-    "        for x_batch, y_batch in train_loader:\n",
-    "            x_batch = x_batch.to(device)   # [B, seq_len, input_size]\n",
-    "            y_batch = y_batch.to(device)   # [B, seq_len, output_size]\n",
-    "\n",
-    "            optimizer.zero_grad()\n",
-    "            preds = model(x_batch)         # [B, seq_len, output_size]\n",
-    "            loss = criterion(preds, y_batch)\n",
-    "            loss.backward()\n",
-    "\n",
-    "            # Gradient clipping to prevent exploding gradients in LSTMs\n",
-    "            nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
-    "\n",
-    "            optimizer.step()\n",
-    "            train_losses.append(loss.item())\n",
-    "\n",
-    "        # ── Validation ────────────────────────────────────────────\n",
-    "        model.eval()\n",
-    "        val_losses = []\n",
-    "\n",
-    "        with torch.no_grad():\n",
-    "            for x_batch, y_batch in test_loader:\n",
-    "                x_batch = x_batch.to(device)\n",
-    "                y_batch = y_batch.to(device)\n",
-    "\n",
-    "                preds = model(x_batch)\n",
-    "                loss = criterion(preds, y_batch)\n",
-    "                val_losses.append(loss.item())\n",
-    "\n",
-    "        train_loss = np.mean(train_losses)\n",
-    "        val_loss   = np.mean(val_losses)\n",
-    "\n",
-    "        history[\"train_loss\"].append(train_loss)\n",
-    "        history[\"val_loss\"].append(val_loss)\n",
-    "\n",
-    "        scheduler.step(val_loss)\n",
-    "\n",
-    "        # ── Logging ───────────────────────────────────────────────\n",
-    "        print(\n",
-    "            f\"Epoch [{epoch:>3}/{num_epochs}]  \"\n",
-    "            f\"Train Loss: {train_loss:.6f}  |  \"\n",
-    "            f\"Val Loss: {val_loss:.6f}\"\n",
-    "        )\n",
-    "\n",
-    "        # ── Early stopping + checkpoint ───────────────────────────\n",
-    "        if val_loss < best_val_loss:\n",
-    "            best_val_loss = val_loss\n",
-    "            epochs_no_improve = 0\n",
-    "            torch.save(model.state_dict(), save_path)\n",
-    "            print(f\"  ✓ New best model saved  (val_loss={best_val_loss:.6f})\")\n",
-    "        else:\n",
-    "            epochs_no_improve += 1\n",
-    "            if epochs_no_improve >= patience:\n",
-    "                print(f\"\\nEarly stopping triggered after {epoch} epochs.\")\n",
-    "                break\n",
-    "\n",
-    "    # Load best weights before returning\n",
-    "    model.load_state_dict(torch.load(save_path, map_location=device))\n",
-    "    print(f\"\\nTraining complete. Best val loss: {best_val_loss:.6f}\")\n",
-    "    return model, history\n",
-    "\n",
-    "\n",
-    "# ── Example usage ─────────────────────────────────────────────────────────────\n",
-    "if __name__ == \"__main__\":\n",
-    "    import pandas as pd\n",
-    "    from DataPreprocessing import make_sequence_datasets\n",
-    "\n",
-    "    df = final_df\n",
-    "\n",
-    "    state_cols   = [\"position\", \"speed\"]\n",
-    "    control_cols = [\"brake_pressed\", \"accel_position\"]\n",
-    "\n",
-    "    seq_len    = 3000\n",
-    "    batch_size = 64\n",
-    "\n",
-    "    train_dataset, test_dataset, train_loader, test_loader, scaler = make_sequence_datasets(\n",
-    "        df_xy        = df,\n",
-    "        state_cols   = state_cols,\n",
-    "        control_cols = control_cols,\n",
-    "        seq_len      = seq_len,\n",
-    "        stride       = 10,\n",
-    "        train_frac   = 0.8,\n",
-    "        batch_size   = batch_size,\n",
-    "    )\n",
-    "\n",
-    "    model, history = train_rnn(\n",
-    "        train_loader = train_loader,\n",
-    "        test_loader  = test_loader,\n",
-    "        input_size   = len(state_cols),\n",
-    "        output_size  = len(control_cols),\n",
-    "        hidden_size  = 128,\n",
-    "        num_layers   = 2,\n",
-    "        seq_length   = seq_len,\n",
-    "        num_epochs   = 50,\n",
-    "        learning_rate= 1e-3,\n",
-    "        patience     = 10,\n",
-    "        save_path    = \"rnn_first.pt\",\n",
-    "    )"
-   ],
-   "id": "1a642655c3c62057",
-   "outputs": [
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mKeyboardInterrupt\u001B[39m                         Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[47]\u001B[39m\u001B[32m, line 153\u001B[39m\n\u001B[32m    141\u001B[39m batch_size = \u001B[32m64\u001B[39m\n\u001B[32m    143\u001B[39m train_dataset, test_dataset, train_loader, test_loader, scaler = make_sequence_datasets(\n\u001B[32m    144\u001B[39m     df_xy        = df,\n\u001B[32m    145\u001B[39m     state_cols   = state_cols,\n\u001B[32m   (...)\u001B[39m\u001B[32m    150\u001B[39m     batch_size   = batch_size,\n\u001B[32m    151\u001B[39m )\n\u001B[32m--> \u001B[39m\u001B[32m153\u001B[39m model, history = \u001B[43mtrain_rnn\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    154\u001B[39m \u001B[43m    \u001B[49m\u001B[43mtrain_loader\u001B[49m\u001B[43m \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[43mtrain_loader\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    155\u001B[39m \u001B[43m    \u001B[49m\u001B[43mtest_loader\u001B[49m\u001B[43m  \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[43mtest_loader\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    156\u001B[39m \u001B[43m    \u001B[49m\u001B[43minput_size\u001B[49m\u001B[43m   \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mlen\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mstate_cols\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    157\u001B[39m \u001B[43m    \u001B[49m\u001B[43moutput_size\u001B[49m\u001B[43m  \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mlen\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mcontrol_cols\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    158\u001B[39m \u001B[43m    \u001B[49m\u001B[43mhidden_size\u001B[49m\u001B[43m  \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[32;43m128\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m    159\u001B[39m \u001B[43m    \u001B[49m\u001B[43mnum_layers\u001B[49m\u001B[43m   \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[32;43m2\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m    160\u001B[39m \u001B[43m    \u001B[49m\u001B[43mseq_length\u001B[49m\u001B[43m   \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[43mseq_len\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    161\u001B[39m \u001B[43m    \u001B[49m\u001B[43mnum_epochs\u001B[49m\u001B[43m   \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[32;43m50\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m    162\u001B[39m \u001B[43m    \u001B[49m\u001B[43mlearning_rate\u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[32;43m1e-3\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m    163\u001B[39m \u001B[43m    \u001B[49m\u001B[43mpatience\u001B[49m\u001B[43m     \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[32;43m10\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m    164\u001B[39m \u001B[43m    \u001B[49m\u001B[43msave_path\u001B[49m\u001B[43m    \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mrnn_first.pt\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m    165\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[47]\u001B[39m\u001B[32m, line 74\u001B[39m, in \u001B[36mtrain_rnn\u001B[39m\u001B[34m(train_loader, test_loader, input_size, output_size, hidden_size, num_layers, seq_length, num_epochs, learning_rate, patience, save_path)\u001B[39m\n\u001B[32m     71\u001B[39m y_batch = y_batch.to(device)   \u001B[38;5;66;03m# [B, seq_len, output_size]\u001B[39;00m\n\u001B[32m     73\u001B[39m optimizer.zero_grad()\n\u001B[32m---> \u001B[39m\u001B[32m74\u001B[39m preds = \u001B[43mmodel\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx_batch\u001B[49m\u001B[43m)\u001B[49m         \u001B[38;5;66;03m# [B, seq_len, output_size]\u001B[39;00m\n\u001B[32m     75\u001B[39m loss = criterion(preds, y_batch)\n\u001B[32m     76\u001B[39m loss.backward()\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1776\u001B[39m, in \u001B[36mModule._wrapped_call_impl\u001B[39m\u001B[34m(self, *args, **kwargs)\u001B[39m\n\u001B[32m   1774\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._compiled_call_impl(*args, **kwargs)  \u001B[38;5;66;03m# type: ignore[misc]\u001B[39;00m\n\u001B[32m   1775\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1776\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_call_impl\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1787\u001B[39m, in \u001B[36mModule._call_impl\u001B[39m\u001B[34m(self, *args, **kwargs)\u001B[39m\n\u001B[32m   1782\u001B[39m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[32m   1783\u001B[39m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[32m   1784\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m._backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._forward_pre_hooks\n\u001B[32m   1785\u001B[39m         \u001B[38;5;129;01mor\u001B[39;00m _global_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[32m   1786\u001B[39m         \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[32m-> \u001B[39m\u001B[32m1787\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mforward_call\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   1789\u001B[39m result = \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m   1790\u001B[39m called_always_called_hooks = \u001B[38;5;28mset\u001B[39m()\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\array_temp\\RNN.py:41\u001B[39m, in \u001B[36mRNN.forward\u001B[39m\u001B[34m(self, x)\u001B[39m\n\u001B[32m     38\u001B[39m cell_states = torch.zeros(\u001B[38;5;28mself\u001B[39m.num_layers, x.size(\u001B[32m0\u001B[39m), \u001B[38;5;28mself\u001B[39m.hidden_size).to(x.device)\n\u001B[32m     40\u001B[39m \u001B[38;5;66;03m#forward propagate lstm\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m41\u001B[39m out, _ = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mlstm\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m(\u001B[49m\u001B[43mhidden_state\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcell_states\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;66;03m#out; tensor of shape(batch_size, seq_length, hidden_size) - at the final time step\u001B[39;00m\n\u001B[32m     42\u001B[39m \u001B[38;5;66;03m#decode the hidden state of t\u001B[39;00m\n\u001B[32m     43\u001B[39m \u001B[38;5;66;03m# predicted is a series of controls\u001B[39;00m\n\u001B[32m     44\u001B[39m out = \u001B[38;5;28mself\u001B[39m.fc(out)\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1776\u001B[39m, in \u001B[36mModule._wrapped_call_impl\u001B[39m\u001B[34m(self, *args, **kwargs)\u001B[39m\n\u001B[32m   1774\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._compiled_call_impl(*args, **kwargs)  \u001B[38;5;66;03m# type: ignore[misc]\u001B[39;00m\n\u001B[32m   1775\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1776\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_call_impl\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1787\u001B[39m, in \u001B[36mModule._call_impl\u001B[39m\u001B[34m(self, *args, **kwargs)\u001B[39m\n\u001B[32m   1782\u001B[39m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[32m   1783\u001B[39m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[32m   1784\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m._backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._forward_pre_hooks\n\u001B[32m   1785\u001B[39m         \u001B[38;5;129;01mor\u001B[39;00m _global_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[32m   1786\u001B[39m         \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[32m-> \u001B[39m\u001B[32m1787\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mforward_call\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   1789\u001B[39m result = \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m   1790\u001B[39m called_always_called_hooks = \u001B[38;5;28mset\u001B[39m()\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\rnn.py:1141\u001B[39m, in \u001B[36mLSTM.forward\u001B[39m\u001B[34m(self, input, hx)\u001B[39m\n\u001B[32m   1138\u001B[39m         hx = \u001B[38;5;28mself\u001B[39m.permute_hidden(hx, sorted_indices)\n\u001B[32m   1140\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m batch_sizes \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1141\u001B[39m     result = \u001B[43m_VF\u001B[49m\u001B[43m.\u001B[49m\u001B[43mlstm\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m   1142\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43minput\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m   1143\u001B[39m \u001B[43m        \u001B[49m\u001B[43mhx\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1144\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_flat_weights\u001B[49m\u001B[43m,\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# type: ignore[arg-type]\u001B[39;49;00m\n\u001B[32m   1145\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mbias\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1146\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mnum_layers\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1147\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mdropout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1148\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mtraining\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1149\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mbidirectional\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1150\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mbatch_first\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1151\u001B[39m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   1152\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m   1153\u001B[39m     result = _VF.lstm(\n\u001B[32m   1154\u001B[39m         \u001B[38;5;28minput\u001B[39m,\n\u001B[32m   1155\u001B[39m         batch_sizes,\n\u001B[32m   (...)\u001B[39m\u001B[32m   1162\u001B[39m         \u001B[38;5;28mself\u001B[39m.bidirectional,\n\u001B[32m   1163\u001B[39m     )\n",
-      "\u001B[31mKeyboardInterrupt\u001B[39m: "
-     ]
-    }
-   ],
-   "execution_count": 47
-  },
   {
    "metadata": {},
    "cell_type": "code",

From 01dbddba1461c8d0e7e11009e90d113dc250e79f Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Thu, 5 Mar 2026 12:06:54 -0800
Subject: [PATCH 27/49] retrain model

---
 array_temp/Control_Model.ipynb         |  26 +-
 array_temp/control_model_revised.ipynb | 441 ++++++++++++++++++-------
 2 files changed, 340 insertions(+), 127 deletions(-)

diff --git a/array_temp/Control_Model.ipynb b/array_temp/Control_Model.ipynb
index 76207e4..8a2bfbd 100644
--- a/array_temp/Control_Model.ipynb
+++ b/array_temp/Control_Model.ipynb
@@ -31,8 +31,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T21:40:55.324537Z",
-     "start_time": "2026-03-04T21:40:34.732465Z"
+     "end_time": "2026-03-05T16:57:17.169521Z",
+     "start_time": "2026-03-05T16:57:14.222827Z"
     }
    },
    "cell_type": "code",
@@ -161,8 +161,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T21:41:28.875065Z",
-     "start_time": "2026-03-04T21:41:28.804002Z"
+     "end_time": "2026-03-05T16:57:21.492692Z",
+     "start_time": "2026-03-05T16:57:21.434274Z"
     }
    },
    "cell_type": "code",
@@ -1045,8 +1045,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T17:24:38.250033Z",
-     "start_time": "2026-03-04T17:24:38.240610Z"
+     "end_time": "2026-03-05T16:57:40.614624Z",
+     "start_time": "2026-03-05T16:57:40.605542Z"
     }
    },
    "cell_type": "code",
@@ -1060,26 +1060,26 @@
    ],
    "id": "84e31e6326fbd121",
    "outputs": [],
-   "execution_count": 2
+   "execution_count": 3
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T17:24:40.966989Z",
-     "start_time": "2026-03-04T17:24:40.960108Z"
+     "end_time": "2026-03-05T16:57:41.310117Z",
+     "start_time": "2026-03-05T16:57:41.304532Z"
     }
    },
    "cell_type": "code",
    "source": "from datetime import *",
    "id": "8bfb296ce8b6d5f6",
    "outputs": [],
-   "execution_count": 3
+   "execution_count": 4
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T01:59:01.011369Z",
-     "start_time": "2026-03-04T01:59:00.995009Z"
+     "end_time": "2026-03-05T16:57:42.131100Z",
+     "start_time": "2026-03-05T16:57:42.085969Z"
     }
    },
    "cell_type": "code",
@@ -1121,7 +1121,7 @@
    ],
    "id": "59db5061a42a09db",
    "outputs": [],
-   "execution_count": 16
+   "execution_count": 5
   },
   {
    "metadata": {},
diff --git a/array_temp/control_model_revised.ipynb b/array_temp/control_model_revised.ipynb
index 9ebc5f2..0d74fac 100644
--- a/array_temp/control_model_revised.ipynb
+++ b/array_temp/control_model_revised.ipynb
@@ -6,8 +6,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2026-03-05T02:13:33.495091Z",
-     "start_time": "2026-03-05T02:13:33.488092Z"
+     "end_time": "2026-03-05T19:31:59.447829Z",
+     "start_time": "2026-03-05T19:31:41.951092Z"
     }
    },
    "source": [
@@ -29,10 +29,10 @@
     "import dill\n",
     "import os\n",
     "import pytz\n",
-    "from datetime import datetime, time, date"
+    "from datetime import datetime, time, date\n"
    ],
    "outputs": [],
-   "execution_count": 2
+   "execution_count": 1
   },
   {
    "metadata": {
@@ -103,8 +103,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T02:13:37.451775Z",
-     "start_time": "2026-03-05T02:13:37.412521Z"
+     "end_time": "2026-03-05T19:33:01.140035Z",
+     "start_time": "2026-03-05T19:33:01.002991Z"
     }
    },
    "cell_type": "code",
@@ -132,13 +132,13 @@
    ],
    "id": "d309d06cd7c5f22d",
    "outputs": [],
-   "execution_count": 3
+   "execution_count": 2
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T02:13:40.153086Z",
-     "start_time": "2026-03-05T02:13:40.146328Z"
+     "end_time": "2026-03-05T19:33:02.257426Z",
+     "start_time": "2026-03-05T19:33:02.235778Z"
     }
    },
    "cell_type": "code",
@@ -170,13 +170,13 @@
    ],
    "id": "8244964eb0c13987",
    "outputs": [],
-   "execution_count": 4
+   "execution_count": 3
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T02:13:41.765544Z",
-     "start_time": "2026-03-05T02:13:40.921208Z"
+     "end_time": "2026-03-05T19:33:04.703377Z",
+     "start_time": "2026-03-05T19:33:02.964209Z"
     }
    },
    "cell_type": "code",
@@ -186,29 +186,26 @@
    ],
    "id": "d3a364dd0f5e0dc0",
    "outputs": [],
-   "execution_count": 5
+   "execution_count": 4
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T02:13:43.337567Z",
-     "start_time": "2026-03-05T02:13:42.311387Z"
+     "end_time": "2026-03-05T19:33:21.758559Z",
+     "start_time": "2026-03-05T19:33:19.845165Z"
     }
    },
    "cell_type": "code",
-   "source": [
-    "\n",
-    "speed_df = make_df(source = \"ingress\", name = \"VehicleVelocity\")"
-   ],
+   "source": "speed_df = make_df(source = \"ingress\", name = \"VehicleVelocity\")",
    "id": "32163d52d6931b1c",
    "outputs": [],
-   "execution_count": 6
+   "execution_count": 5
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T02:13:44.001530Z",
-     "start_time": "2026-03-05T02:13:43.984355Z"
+     "end_time": "2026-03-05T16:54:18.017683Z",
+     "start_time": "2026-03-05T16:54:17.993615Z"
     }
    },
    "cell_type": "code",
@@ -273,18 +270,18 @@
        "</div>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 7
+   "execution_count": 6
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T02:13:44.936691Z",
-     "start_time": "2026-03-05T02:13:44.931415Z"
+     "end_time": "2026-03-05T19:33:25.446763Z",
+     "start_time": "2026-03-05T19:33:25.439698Z"
     }
    },
    "cell_type": "code",
@@ -304,13 +301,13 @@
    ],
    "id": "8f9608786c013420",
    "outputs": [],
-   "execution_count": 8
+   "execution_count": 6
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T02:13:47.017463Z",
-     "start_time": "2026-03-05T02:13:45.816087Z"
+     "end_time": "2026-03-05T19:33:29.392736Z",
+     "start_time": "2026-03-05T19:33:26.152247Z"
     }
    },
    "cell_type": "code",
@@ -329,7 +326,7 @@
    ],
    "id": "cda1ae8e12adbc5e",
    "outputs": [],
-   "execution_count": 9
+   "execution_count": 7
   },
   {
    "metadata": {
@@ -410,8 +407,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T02:13:49.537318Z",
-     "start_time": "2026-03-05T02:13:49.396981Z"
+     "end_time": "2026-03-05T19:33:33.543987Z",
+     "start_time": "2026-03-05T19:33:33.136197Z"
     }
    },
    "cell_type": "code",
@@ -426,7 +423,7 @@
    ],
    "id": "9d28c3144f2da538",
    "outputs": [],
-   "execution_count": 10
+   "execution_count": 8
   },
   {
    "metadata": {
@@ -595,8 +592,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T02:13:52.443451Z",
-     "start_time": "2026-03-05T02:13:52.436253Z"
+     "end_time": "2026-03-05T19:33:38.996012Z",
+     "start_time": "2026-03-05T19:33:38.967266Z"
     }
    },
    "cell_type": "code",
@@ -607,13 +604,13 @@
    ],
    "id": "6ba2f86b4ef3b7d6",
    "outputs": [],
-   "execution_count": 11
+   "execution_count": 9
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T02:13:53.901920Z",
-     "start_time": "2026-03-05T02:13:53.735030Z"
+     "end_time": "2026-03-05T19:33:40.573663Z",
+     "start_time": "2026-03-05T19:33:40.110481Z"
     }
    },
    "cell_type": "code",
@@ -628,7 +625,7 @@
    ],
    "id": "88421e0bc978e5e4",
    "outputs": [],
-   "execution_count": 12
+   "execution_count": 10
   },
   {
    "metadata": {
@@ -749,21 +746,21 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T02:13:59.285114Z",
-     "start_time": "2026-03-05T02:13:59.280601Z"
+     "end_time": "2026-03-05T19:33:45.765Z",
+     "start_time": "2026-03-05T19:33:45.755926Z"
     }
    },
    "cell_type": "code",
    "source": "final_df.columns = [\"brake_pressed\", \"accel_position\", \"position\", \"speed\"]",
    "id": "b121a9351e0aba47",
    "outputs": [],
-   "execution_count": 14
+   "execution_count": 11
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T01:52:51.811427Z",
-     "start_time": "2026-03-05T01:52:51.800907Z"
+     "end_time": "2026-03-05T19:33:46.667704Z",
+     "start_time": "2026-03-05T19:33:46.632466Z"
     }
    },
    "cell_type": "code",
@@ -846,12 +843,12 @@
        "</div>"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 17
+   "execution_count": 12
   },
   {
    "metadata": {
@@ -886,8 +883,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T02:14:03.452596Z",
-     "start_time": "2026-03-05T02:14:03.284853Z"
+     "end_time": "2026-03-05T19:33:51.345807Z",
+     "start_time": "2026-03-05T19:33:50.937643Z"
     }
    },
    "cell_type": "code",
@@ -897,7 +894,7 @@
    ],
    "id": "f143094a7164ac55",
    "outputs": [],
-   "execution_count": 15
+   "execution_count": 13
   },
   {
    "metadata": {
@@ -936,25 +933,25 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T02:14:14.587552Z",
-     "start_time": "2026-03-05T02:14:13.729686Z"
+     "end_time": "2026-03-05T19:33:58.095873Z",
+     "start_time": "2026-03-05T19:33:57.162966Z"
     }
    },
    "cell_type": "code",
    "source": [
     "#finally cleared up all data, now move onto RNN\n",
     "import DataPreprocessing\n",
-    "train_dataset, test_dataset, train_loader, test_loader, scaler = DataPreprocessing.make_sequence_datasets(final_df, state_cols = ['position', 'speed'], control_cols = ['brake_pressed', 'accel_position'], seq_len = 600, train_frac=0.7, batch_size = 64)"
+    "train_dataset, test_dataset, train_loader, test_loader, scaler = DataPreprocessing.make_sequence_datasets(final_df, state_cols = ['position', 'speed'], control_cols = ['brake_pressed', 'accel_position'], seq_len = 600, train_frac=0.7, batch_size = 128)"
    ],
    "id": "d57a320f8d3cb4d8",
    "outputs": [],
-   "execution_count": 16
+   "execution_count": 14
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T02:14:15.357379Z",
-     "start_time": "2026-03-05T02:14:15.353862Z"
+     "end_time": "2026-03-05T19:33:59.630325Z",
+     "start_time": "2026-03-05T19:33:59.608997Z"
     }
    },
    "cell_type": "code",
@@ -964,13 +961,13 @@
    ],
    "id": "d1d762ae92b287c",
    "outputs": [],
-   "execution_count": 17
+   "execution_count": 15
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T02:14:18.363956Z",
-     "start_time": "2026-03-05T02:14:18.359779Z"
+     "end_time": "2026-03-05T19:34:00.292983Z",
+     "start_time": "2026-03-05T19:34:00.275143Z"
     }
    },
    "cell_type": "code",
@@ -984,13 +981,13 @@
    ],
    "id": "1410b1caa1d1c46b",
    "outputs": [],
-   "execution_count": 18
+   "execution_count": 16
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T02:14:21.847976Z",
-     "start_time": "2026-03-05T02:14:21.825054Z"
+     "end_time": "2026-03-05T19:34:01.711645Z",
+     "start_time": "2026-03-05T19:34:01.629599Z"
     }
    },
    "cell_type": "code",
@@ -1008,13 +1005,13 @@
    ],
    "id": "154939dd8005f9b7",
    "outputs": [],
-   "execution_count": 19
+   "execution_count": 17
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T02:14:22.900906Z",
-     "start_time": "2026-03-05T02:14:22.890230Z"
+     "end_time": "2026-03-05T19:34:18.008584Z",
+     "start_time": "2026-03-05T19:34:17.984795Z"
     }
    },
    "cell_type": "code",
@@ -1089,18 +1086,18 @@
    ],
    "id": "902960e5d1a51324",
    "outputs": [],
-   "execution_count": 20
+   "execution_count": 18
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T03:13:04.474923Z",
-     "start_time": "2026-03-05T02:14:23.732395Z"
+     "end_time": "2026-03-05T19:57:27.552267Z",
+     "start_time": "2026-03-05T19:34:22.313235Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "train_losses, test_losses = train_model(model, train_loader, test_loader, epochs=50)\n",
+    "train_losses, test_losses = train_model(model, train_loader, test_loader, epochs=10)\n",
     "\n",
     "# Save\n",
     "torch.save({\n",
@@ -1120,49 +1117,37 @@
      "text": [
       "NaNs in Train Loader: False\n",
       "NaNs in Test Loader: False\n",
-      "Epoch 1/50, Train Loss: 0.7853\n",
-      "Epoch 2/50, Train Loss: 0.7139\n",
-      "Epoch 3/50, Train Loss: 0.7118\n",
-      "Epoch 4/50, Train Loss: 0.7103\n",
-      "Epoch 5/50, Train Loss: 0.7095\n",
-      "Epoch 6/50, Train Loss: 0.7082\n",
-      "Epoch 7/50, Train Loss: 0.7049\n",
-      "Epoch 8/50, Train Loss: 0.6993\n",
-      "Epoch 9/50, Train Loss: 0.6996\n",
-      "Epoch 10/50, Train Loss: 0.6978\n",
-      "Epoch 11/50, Train Loss: 0.6988\n",
-      "Epoch 12/50, Train Loss: 0.6961\n",
-      "Epoch 13/50, Train Loss: 0.6969\n",
-      "Epoch 14/50, Train Loss: 0.6996\n",
-      "Epoch 15/50, Train Loss: 0.6933\n",
-      "Epoch 16/50, Train Loss: 0.6890\n",
-      "Epoch 17/50, Train Loss: 0.6842\n",
-      "Epoch 18/50, Train Loss: 0.6750\n",
-      "Epoch 19/50, Train Loss: 0.6621\n",
-      "Epoch 20/50, Train Loss: 0.6513\n",
-      "Epoch 21/50, Train Loss: 0.6431\n",
-      "Epoch 22/50, Train Loss: 0.6389\n",
-      "Epoch 23/50, Train Loss: 0.6333\n",
-      "Epoch 24/50, Train Loss: 0.6302\n",
-      "Epoch 25/50, Train Loss: 0.6273\n"
+      "Epoch 1/10, Train Loss: 0.8640\n",
+      "Epoch 2/10, Train Loss: 0.7180\n",
+      "Epoch 3/10, Train Loss: 0.7143\n",
+      "Epoch 4/10, Train Loss: 0.7126\n",
+      "Epoch 5/10, Train Loss: 0.7116\n",
+      "Epoch 6/10, Train Loss: 0.7109\n",
+      "Epoch 7/10, Train Loss: 0.7087\n",
+      "Epoch 8/10, Train Loss: 0.7094\n",
+      "Epoch 9/10, Train Loss: 0.7076\n",
+      "Epoch 10/10, Train Loss: 0.7031\n"
      ]
-    },
+    }
+   ],
+   "execution_count": 19
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-05T19:58:07.816380Z",
+     "start_time": "2026-03-05T19:58:07.798335Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "print(test_losses)",
+   "id": "422439d81b1a514a",
+   "outputs": [
     {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mKeyboardInterrupt\u001B[39m                         Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[21]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m train_losses, test_losses = \u001B[43mtrain_model\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtrain_loader\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtest_loader\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mepochs\u001B[49m\u001B[43m=\u001B[49m\u001B[32;43m50\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m      3\u001B[39m \u001B[38;5;66;03m# Save\u001B[39;00m\n\u001B[32m      4\u001B[39m torch.save({\n\u001B[32m      5\u001B[39m     \u001B[33m'\u001B[39m\u001B[33mmodel_state_dict\u001B[39m\u001B[33m'\u001B[39m: model.state_dict(),\n\u001B[32m      6\u001B[39m     \u001B[33m'\u001B[39m\u001B[33minput_size\u001B[39m\u001B[33m'\u001B[39m: input_size,\n\u001B[32m   (...)\u001B[39m\u001B[32m     10\u001B[39m     \u001B[33m'\u001B[39m\u001B[33moutput_size\u001B[39m\u001B[33m'\u001B[39m: output_size,\n\u001B[32m     11\u001B[39m }, \u001B[33m'\u001B[39m\u001B[33mrnn_model.pt\u001B[39m\u001B[33m'\u001B[39m)\n",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[20]\u001B[39m\u001B[32m, line 61\u001B[39m, in \u001B[36mtrain_model\u001B[39m\u001B[34m(model, train_loader, test_loader, epochs)\u001B[39m\n\u001B[32m     59\u001B[39m y_batch = y_batch.to(device)\n\u001B[32m     60\u001B[39m optimizer.zero_grad()\n\u001B[32m---> \u001B[39m\u001B[32m61\u001B[39m predictions = \u001B[43mmodel\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx_batch\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m     62\u001B[39m loss = criterion(predictions, y_batch)\n\u001B[32m     63\u001B[39m test_loss += loss.item()\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1776\u001B[39m, in \u001B[36mModule._wrapped_call_impl\u001B[39m\u001B[34m(self, *args, **kwargs)\u001B[39m\n\u001B[32m   1774\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._compiled_call_impl(*args, **kwargs)  \u001B[38;5;66;03m# type: ignore[misc]\u001B[39;00m\n\u001B[32m   1775\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1776\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_call_impl\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1787\u001B[39m, in \u001B[36mModule._call_impl\u001B[39m\u001B[34m(self, *args, **kwargs)\u001B[39m\n\u001B[32m   1782\u001B[39m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[32m   1783\u001B[39m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[32m   1784\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m._backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._forward_pre_hooks\n\u001B[32m   1785\u001B[39m         \u001B[38;5;129;01mor\u001B[39;00m _global_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[32m   1786\u001B[39m         \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[32m-> \u001B[39m\u001B[32m1787\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mforward_call\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   1789\u001B[39m result = \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m   1790\u001B[39m called_always_called_hooks = \u001B[38;5;28mset\u001B[39m()\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\array_temp\\RNN.py:41\u001B[39m, in \u001B[36mRNN.forward\u001B[39m\u001B[34m(self, x)\u001B[39m\n\u001B[32m     38\u001B[39m cell_states = torch.zeros(\u001B[38;5;28mself\u001B[39m.num_layers, x.size(\u001B[32m0\u001B[39m), \u001B[38;5;28mself\u001B[39m.hidden_size).to(x.device)\n\u001B[32m     40\u001B[39m \u001B[38;5;66;03m#forward propagate lstm\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m41\u001B[39m out, _ = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mlstm\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m(\u001B[49m\u001B[43mhidden_state\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcell_states\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;66;03m#out; tensor of shape(batch_size, seq_length, hidden_size) - at the final time step\u001B[39;00m\n\u001B[32m     42\u001B[39m \u001B[38;5;66;03m#decode the hidden state of t\u001B[39;00m\n\u001B[32m     43\u001B[39m \u001B[38;5;66;03m# predicted is a series of controls\u001B[39;00m\n\u001B[32m     44\u001B[39m out = \u001B[38;5;28mself\u001B[39m.fc(out)\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1776\u001B[39m, in \u001B[36mModule._wrapped_call_impl\u001B[39m\u001B[34m(self, *args, **kwargs)\u001B[39m\n\u001B[32m   1774\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._compiled_call_impl(*args, **kwargs)  \u001B[38;5;66;03m# type: ignore[misc]\u001B[39;00m\n\u001B[32m   1775\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1776\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_call_impl\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1787\u001B[39m, in \u001B[36mModule._call_impl\u001B[39m\u001B[34m(self, *args, **kwargs)\u001B[39m\n\u001B[32m   1782\u001B[39m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[32m   1783\u001B[39m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[32m   1784\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m._backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._forward_pre_hooks\n\u001B[32m   1785\u001B[39m         \u001B[38;5;129;01mor\u001B[39;00m _global_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[32m   1786\u001B[39m         \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[32m-> \u001B[39m\u001B[32m1787\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mforward_call\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   1789\u001B[39m result = \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m   1790\u001B[39m called_always_called_hooks = \u001B[38;5;28mset\u001B[39m()\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\rnn.py:1141\u001B[39m, in \u001B[36mLSTM.forward\u001B[39m\u001B[34m(self, input, hx)\u001B[39m\n\u001B[32m   1138\u001B[39m         hx = \u001B[38;5;28mself\u001B[39m.permute_hidden(hx, sorted_indices)\n\u001B[32m   1140\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m batch_sizes \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1141\u001B[39m     result = \u001B[43m_VF\u001B[49m\u001B[43m.\u001B[49m\u001B[43mlstm\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m   1142\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43minput\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m   1143\u001B[39m \u001B[43m        \u001B[49m\u001B[43mhx\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1144\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_flat_weights\u001B[49m\u001B[43m,\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# type: ignore[arg-type]\u001B[39;49;00m\n\u001B[32m   1145\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mbias\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1146\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mnum_layers\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1147\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mdropout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1148\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mtraining\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1149\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mbidirectional\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1150\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mbatch_first\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1151\u001B[39m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   1152\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m   1153\u001B[39m     result = _VF.lstm(\n\u001B[32m   1154\u001B[39m         \u001B[38;5;28minput\u001B[39m,\n\u001B[32m   1155\u001B[39m         batch_sizes,\n\u001B[32m   (...)\u001B[39m\u001B[32m   1162\u001B[39m         \u001B[38;5;28mself\u001B[39m.bidirectional,\n\u001B[32m   1163\u001B[39m     )\n",
-      "\u001B[31mKeyboardInterrupt\u001B[39m: "
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.8061733793001622, 0.8193665951257572, 0.7946931755868718, 0.814770182284216, 0.8071958554210141, 0.7809592337580398, 0.7720132017663369, 0.7666141268952439, 0.7487051921198145, 0.7230261662819734]\n"
      ]
     }
    ],
@@ -1181,15 +1166,18 @@
     "\n",
     "# Later at inference\n",
     "scaler = joblib.load('scaler.pkl')\n",
-    "x_new_scaled = scaler.transform(x_new_raw)"
+    "x_new_scaled = scaler.transform(x)"
    ],
    "id": "be2c544830173066"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-05T19:57:52.530545Z",
+     "start_time": "2026-03-05T19:57:52.486087Z"
+    }
+   },
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
     "#now we evaluate model for inference\n",
     "checkpoint = torch.load('rnn_model.pt', map_location=device)\n",
@@ -1204,7 +1192,232 @@
     "model.load_state_dict(checkpoint['model_state_dict'])\n",
     "model.eval()  # important — disables dropout/batchnorm for inference"
    ],
-   "id": "23676ff6fae304c2"
+   "id": "23676ff6fae304c2",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RNN(\n",
+       "  (lstm): LSTM(2, 128, num_layers=2, batch_first=True)\n",
+       "  (fc): Linear(in_features=128, out_features=2, bias=True)\n",
+       ")"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 20
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-05T20:00:48.979478Z",
+     "start_time": "2026-03-05T20:00:48.954744Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "def plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx):\n",
+    "    model.eval()\n",
+    "    x_input, y_target = test_dataset[sample_idx]\n",
+    "\n",
+    "# x_input should be [seq_len, n_states]\n",
+    "    print(x_input.shape)\n",
+    "\n",
+    "    x_np = x_input.numpy()  # [seq_len, n_states]\n",
+    "\n",
+    "    # derive time axes from actual array shapes, not seq_len variable\n",
+    "    time_controls = np.arange(y_target.shape[0]) * 0.1\n",
+    "    time_states   = np.arange(x_np.shape[0]) * 0.1\n",
+    "    x_input, y_target = test_dataset[sample_idx]\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        device = next(model.parameters()).device\n",
+    "        x_tensor = x_input.to(device).unsqueeze(0)\n",
+    "        y_pred = model(x_tensor).squeeze(0).cpu().numpy()\n",
+    "\n",
+    "    y_target = y_target.numpy()\n",
+    "    x_np = x_input.numpy()  # [seq_len, n_states]\n",
+    "\n",
+    "    # inverse transform controls\n",
+    "    # scaler was fit on state_cols + control_cols so controls start at index n_states\n",
+    "    n_states = len(state_cols)\n",
+    "    n_controls = len(control_cols)\n",
+    "    seq_len = 600\n",
+    "    def unscale_states(arr):\n",
+    "        state_mean = scaler.mean_[:n_states]\n",
+    "        state_std  = scaler.scale_[:n_states]\n",
+    "        return arr * state_std + state_mean\n",
+    "    def unscale_controls(arr):\n",
+    "        dummy = np.zeros((seq_len, n_states + n_controls))\n",
+    "        dummy[:, n_states:] = arr\n",
+    "        return scaler.inverse_transform(dummy)[:, n_states:]\n",
+    "\n",
+    "\n",
+    "\n",
+    "    y_target_unscaled = unscale_controls(y_target)\n",
+    "    y_pred_unscaled   = unscale_controls(y_pred)\n",
+    "    x_unscaled        = unscale_states(x_np)\n",
+    "\n",
+    "    time_controls = np.arange(seq_len) * 0.1\n",
+    "    time_states   = np.arange(x_np.shape[0]) * 0.1\n",
+    "\n",
+    "    # plot controls\n",
+    "    fig, axes = plt.subplots(n_controls, 1, figsize=(10, 4 * n_controls), sharex=True)\n",
+    "    if n_controls == 1:\n",
+    "        axes = [axes]\n",
+    "\n",
+    "    for i, col in enumerate(control_cols):\n",
+    "        axes[i].plot(time_controls, y_target_unscaled[:, i], 'g-', label='Actual (Driver)')\n",
+    "        axes[i].plot(time_controls, y_pred_unscaled[:, i], 'r--', label='Predicted (RNN)')\n",
+    "        axes[i].set_ylabel(col)\n",
+    "        axes[i].legend()\n",
+    "        axes[i].grid(True)\n",
+    "\n",
+    "    axes[-1].set_xlabel(\"Time (seconds)\")\n",
+    "    plt.suptitle(f\"Control Trajectory — Sample {sample_idx}\")\n",
+    "    plt.tight_layout()\n",
+    "\n",
+    "    # plot states\n",
+    "    fig2, axes2 = plt.subplots(n_states, 1, figsize=(10, 4 * n_states), sharex=True)\n",
+    "    if n_states == 1:\n",
+    "        axes2 = [axes2]\n",
+    "    print(x_np.shape)        # should be [seq_len, n_states]\n",
+    "    print(x_unscaled.shape)  # should match\n",
+    "    print(time_states.shape)\n",
+    "    for i, col in enumerate(state_cols):\n",
+    "        axes2[i].plot(time_states, x_unscaled[:, i], 'b-', label=col)\n",
+    "        axes2[i].set_ylabel(col)\n",
+    "        axes2[i].legend()\n",
+    "        axes2[i].grid(True)\n",
+    "\n",
+    "    axes2[-1].set_xlabel(\"Time (seconds)\")\n",
+    "    plt.suptitle(f\"Input State Sequence — Sample {sample_idx}\")\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ],
+   "id": "193451d65b640afb",
+   "outputs": [],
+   "execution_count": 25
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-05T20:01:20.889474Z",
+     "start_time": "2026-03-05T20:01:20.677195Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "for i in range(len(test_dataset)):\n",
+    "    x, y = test_dataset[i]\n",
+    "    if y.abs().mean() >= 0.88:\n",
+    "        print(f\"use sample_idx={i}\")\n",
+    "        break"
+   ],
+   "id": "e02909466d882593",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "use sample_idx=3140\n"
+     ]
+    }
+   ],
+   "execution_count": 27
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-05T20:01:28.777078Z",
+     "start_time": "2026-03-05T20:01:27.371664Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "state_cols = ['position', 'speed']\n",
+    "control_cols = ['brake_pressed', 'accel_position']\n",
+    "plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx=3140)\n",
+    "plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx=150)"
+   ],
+   "id": "7ca721093c670d9",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([600, 2])\n",
+      "(600, 2)\n",
+      "(600, 2)\n",
+      "(600,)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([600, 2])\n",
+      "(600, 2)\n",
+      "(600, 2)\n",
+      "(600,)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ],
+      "image/png": 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yKGcX8wzy2cqbE1DlBFPZdbjWNTcDZ87CnWXKFtcMuhlQcvKsfI58rJydPANijtvNScoy7OSs3Tn2utaNOA8CZPfw7Jp9wQUXlMfLgwk5RjkD2vgODtTkwYarr766lDnDSrYG5/Nm9/KczTtfc83EPEe21Ga5sgt0TiKXrbIZlJrPdJ2BLANtvqY8b3m2uOfpu7JlvflpnWr3z5b4fM4Me9nCmveZlPG5+XnnY2Q4rb2vGeLz9dUOKOSM8xngMvxnK3i2zuZnkwcRprQ8fVse+Mk6lAcs8sBFvu/ZPTpn3K+16uY2GSgzCGe9yteSE8ZdeumlpZ5kfamdZ/77mnXWWUugz88qD8astNJKpT5OaLbx71sPJ0Ye0MrPLh87P5c8SJPnf8/6UOv9ketzlvr8H8pTy+UBlmzhzv/vKTkDPQDfraFxcvTJAqDVZVfX1VdfvYTAPO0WrSPDX56zOVsraRvyAEUeqMqeCwAwuRnTDQAAABURugEAAKAiupcDAABARbR0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAiQjcAAABUpEtVD9weDR36eTQ2Rt1qaIjo06d33ZeT1qOOoI7guwS/N9QL+yW09TpSK993EbonQX7Q9fhht9Vy0nrUEdQRfJfg94Z6Yb+E9l5HdC8HAACAigjdAAAAUBGhGwAAACpiTDcAAEAdGzt2bIwZMzo6moaGiBEjRsTXX49qlTHdnTt3iU6d/vt2aqEbAACgDjU2NsZnnw2L4cO/iI5q2LBO5aBDa+nRo1dMM80M0ZBHAL4noRsAAKAO1QJ3r17TR9eu3f6r4NdWde7cEGPGNLbKAY9Ro0bGF198XG5PO22f7/1YQjcAAECdGTt2TFPg7tVrmuiounTpFKNHt05Ldx7oSBm8e/ee/nt3NTeRGgAAQJ0ZM2ZMi+BH66i9///NmHqhGwAAoE51xC7l7e39F7oBAACgIkI3AAAAVEToBgAAoK5tssl6cdttt5TrX331Zdx++63jXVePzF4OAABAXTv//Eti6ql7lOtXXnl5PPXUE7HWWut+Y109EroBAACoa9NPP32Lc2hPaF090r0cAACgjci8+eWXU/YyTsb9Vh988H70779s3HXXHbHhhmvFz38+IE477eQYPfo/p9x6+OG/xY47bhWrrfbj2HrrTeOBB+5ruu+rr74Su+22Y6y++o/Lff/4x/O/0YU8L7n8mWeeKs/TfF0aO3Zs/PnPl8Smm25QnmOvvXaN119/relx8j533nlbbLPNZrHqqivG7rv/Mt5//72okpZuAACANiDD77rrTh2PP955ij7v8suPjltuGR6TcvasP/7xvDjqqOPL+a2POebw6NGjRyy77PJxyCEHxO677x0rrPDj+Pvf/xaHH35QnHvun2LBBReKY489IhZffMk4/PBj4p133o5DDx0YiyyySCy//EpNj7v66mvGG2+8Hs8//1wcd9yg8Tzv+XHjjdfFb35zSMw++w/j8ssvjv333yuuuOL6UoZ04YXnxm9+c2hpIT/ssAPj/PP/EEcccWxURUs3AABAG9HQMAnNzq0og/USSywZSy+9bPzyl7vFLbfcGNdee1UMGLB6bLbZlvHDH84Zm2++dQwYsFpcccWl5T4ffvh+TDvttPGDH8wSK6ywUpx22tmxwAILtnjcbt26l/DcpUuX6NOnb4t12e38uuuuLs/Xv/9PYq655i7hulOnTqV1u+Z//merWGaZ5WKeeeaLDTfcJF588YVK3wst3QAAAG1AtjRni/NXX03Z55166v8896RYbLElm64vuODC8cknH8c777wVG2zwixbbLbroEvGXv9xcrm+zzQ5x7rlnxU03XR8rrdQ/fvaztUuwHj167EQ958cfD4vPPvs0Fl540aZlGc7z+d9++62mZXPM8cOm6z179iyt8VUSugEAANqIDL89e0bdy7BbM3bsmPJ31KhR39gu19XWb7319rHaamvGgw/eX8Z+77PPr+LAAw+NddbZYKKes2vXbuNd3vw5xi3b+CZmm9x0LwcAAGCyevXVl5uuv/TSi9G374yx8MKLxD//+Y8W2z3//D9KV/ORI0eWCdemmmqq0u38978/N9Zff6O4//57v/HYDRNodu/Vq1fMMEOfFs+RE7i9/PJL5Tlai9ANAADAZHX66b+Ll156IR5//NG44IJzYuONN43NNtsq/vrXe+Pqq6+Id999J6666vLSqr3RRptGt27d4rnnnolTTz2pdEPP+z777NOxwAILfOOxu3fvEUOGDCkzpY/rf/5nyzJR2kMPPRhvvfVmnHjisTFq1MhYbbWfttonrHs5AAAAk1XOMn7AAf8bjY1jy2Rl2XU8JzQ77LCj46KLzos//OGM0vp89NHHl0nNUl4/5ZQT45e/3C46d+4cq622Ruyww87feOyf/GTVuOmm68opx6699j+nCqvJVvIvv/wyBg06Lr788osyZjxbzVvzXN4NjVV3YG9Hhgz5fJLOUTelZS+Lvn171305aT3qCOoIvkvwe0O9sF/y7b7+elQMHfpB9OkzS0w1VddoK7L1edNN149rrrk5Zpll1v/68bp06TTRE6lN6c+hVoe/i+7lAAAAUBGhGwAAACpiTDcAAACTRXYpf+ihJ7ybzWjpBgAAqFOm4Gr777/QDQAAUGdy9u6Up7ui9dTe/86dv38ncd3LAQAA6kynTp2jR49e8cUXH5fbXbt2i4acLruDGTu2IcaMaWyVFu4M3Pn+5+eQpzv7voRuAACAOjTNNDOUv7Xg3RF16tQpxo5tvVOGZeCufQ7fl9ANAABQh7Jle9pp+0Tv3tPHmDGjo6NpaIiYfvqe8fHHX8ZkGFo9ybJL+X/Twl0jdAMAANSxDH6dOnWNjhi6u3fvHlNN9XWrhO7JxURqAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCOH7lGjRsW6664bjz766AS3eeGFF2LTTTeNJZZYIn7xi1/E888/P97tbr/99lhggQUqLC0AAAC0kdA9cuTI2G+//eLVV1+d4DZfffVV7LLLLrHsssvG9ddfH0sttVTsuuuuZXlzn332WRx33HFToNQAAABQ56H7tddei8022yzeeeedb93utttui27dusXAgQNj3nnnjUMOOSR69uwZd9xxR4vtBg0aFHPMMUfFpQYAAIA2ELofe+yx6NevX1x11VXfut2zzz4byyyzTDQ0NJTb+XfppZeOZ555psVj5WW33XarvNwAAACQutTz27DllltO1HaDBw+O+eabr8WyPn36NHVJzzHhhx12WBx++OEx1VRTfe/y/L9MX7dq5av3ctJ61BHUEXyX4PeGemG/hLZeRya2XHUduifW8OHDo2vXri2W5e0M2+mss86KRRZZJPr37/+tk7F9lz59ekdb0FbKSetRR1BH8F2C3xvqhf0S2nsdaRehO8dz1wJ2Td7u3r17vPLKK3H11VfHLbfc8l8/z9Chn0djY9StPNKSFbLey0nrUUdQR/Bdgt8b6oX9Etp6HamVr0OE7plnnjmGDBnSYlnenmmmmeKuu+6KTz/9NNZcc82yfMyYMeVvznB+1FFHxfrrrz/Rz5MfdD1+2G21nLQedQR1BN8l+L2hXtgvob3XkXYRuvPc3Oeff340NjaWSdTy71NPPVUmTVt99dVjvfXWazHp2gEHHBA33nhjGfcNAAAAHXL28u+aPG3EiBHl+s9//vOmc3Dnacbyb47zXmuttWK66aaLOeecs+mSreIpr/fq1auVXwUAAADtWZsN3TkpWp6fO2V4Pvfcc+PJJ5+MjTfeuLRmn3feeTH11FO3djEBAADowBoasy82E2XIkPocwN98IH/fvr3rvpy0HnUEdQTfJfi9oV7YL6Gt15Fa+dptSzcAAADUO6EbAAAAKiJ0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAiQjcAAABUROgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAiQjcAAABUROgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAADQkUP3qFGjYt11141HH310gtu88MILsemmm8YSSywRv/jFL+L5559vWtfY2BjnnXderLbaarH00kvHdtttF6+99toUKj0AAAAdVd2H7pEjR8Z+++0Xr7766gS3+eqrr2KXXXaJZZddNq6//vpYaqmlYtdddy3L05VXXhkXXXRRHHbYYXHdddfF7LPPHjvvvHMMHz58Cr4SAAAAOpq6Dt3ZGr3ZZpvFO++8863b3XbbbdGtW7cYOHBgzDvvvHHIIYdEz54944477ijrb7jhhthxxx1j1VVXjbnnnjuOPPLI+OSTT+Kpp56aQq8EAACAjqhL1LHHHnss+vXrF/vuu28sueSSE9zu2WefjWWWWSYaGhrK7fyb3cifeeaZ2HjjjUsYz9btmlyfXc4///zzSSrP/3v4ulUrX72Xk9ajjqCO4LsEvzfUC/sltPU6MrHlquvQveWWW07UdoMHD4755puvxbI+ffo0dUnPbufNXXPNNTF69OgS1CdFnz69oy1oK+Wk9agjqCP4LsHvDfXCfgntvY7UdeieWDk2u2vXri2W5e2cgG18reInnnhi7LTTTjHjjDNO0vMMHfp5NDZG3cojLVkh672ctB51BHUE3yX4vaFe2C+hrdeRWvk6ROjO8dzjBuy83b179xbLnn766TKB2iqrrBL77LPPJD9PftD1+GG31XLSetQR1BF8l+D3hnphv4T2XkfqeiK1iTXzzDPHkCFDWizL2zPNNFPT7TzdWE6mtsIKK8Tvfve76NSpXbx0AAAA6li7SJ55bu5sxc7J0VL+zZnJc3l65ZVX4le/+lWsvPLKcdppp8VUU03VyiUGAACgI2izoTsnTxsxYkS5/vOf/zw+++yzOO6448ppxvJvjvNea621yvrDDz88ZpllljjooIPi448/Lvdtfn8AAACoQpsN3f379y/n5069evWKc889N5588slyirCcLO28886LqaeeuoTrbAXPMD5gwIByv9qldn8AAACoQpuZSO3ll1/+1tuLL7543HDDDd+4X85QPu62AAAAMCW02ZZuAAAAqHdCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAiQjcAAABUROgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIl2qeND33nsvTjvttPjHP/4Ro0ePjsbGxhbr77333iqeFgAAANp/6B44cGB8/PHHsdVWW0WvXr2qeAoAAADomKH7ueeeixtuuCHmm2++Kh4eAAAAOu6Y7rnmmiuGDRtWxUMDAABAx27p3nnnnePQQw+NHXbYIeacc86YaqqpWqxfbrnlqnhaAAAA6BhjutNRRx31jXUNDQ3x4osvVvG0AAAA0P5D90svvVTFwwIAAECbUknoTiNGjIibb745Xn/99RgzZkzMM888sfbaa8d0001X1VMCAABA+59I7ZVXXomf/vSn8Yc//CHef//9cjn33HNjrbXWitdee62KpwQAAICO0dJ93HHHxY9//OM45phjokuX/zzF6NGjy+Rqv/3tb+Oiiy6q4mkBAACg/bd0P/PMM2UG81rgTnk9lz399NNVPCUAAAB0jNA944wzxjvvvPON5bmsZ8+eVTwlAAAAdIzu5ZtvvnnpSr7PPvvE4osvXpY9++yzccYZZ8Smm25axVMCAABAxwjdO+20UwwfPjxOPvnk+PTTT8uyvn37xvbbbx877rhjFU8JAAAAHSN0NzQ0xF577VUuQ4cOjW7dukWvXr2qeCoAAABo/6H7xhtvLOfh7tq1a7n+bTbccMNJeuxRo0bFxhtvHIcddlj069dvvNu88MILccQRR5TTlc0333xx1FFHxaKLLtq0/tZbb43TTjstBg8eHP379y8zq88wwwyTVA4AAABoldCd47V/8pOflNCd17+tFXxSQvfIkSNj//33j1dffXWC23z11Vexyy67xHrrrRcnnHBCXHHFFbHrrrvG3XffHVNPPXU899xzccghh5QgvuCCC5ZTmh100EHl3OEAAABQ96H7vvvuG+/1cQ0bNmyiH/O1114rgbuxsfFbt7vttttKF/aBAweWUJ8B+8EHH4w77rijtJBfdtllsdZaazWF/UGDBsWqq64a7777bswxxxwTXR4AAABo9THdCy20UDz88MPf6L793nvvxbrrrjvR5+p+7LHHSnfyfffdN5ZccskJbpczoy+zzDIlcKf8u/TSS5fzhWfozvV5jvCaWWaZJWadddayvD2F7jw28eWX/7l8x3EKOqj8F+nRQx1BHcF3CX5vaH32S/gu7eVs05N1TPf1119frmfL9B577BFTTTVVi23+/e9/l3N4T6wtt9xyorbLcdo5jru5Pn36NHVJz+edaaaZvrH+ww8/jEnx/zJ9XcqQve66U8djj+Wt3q1dHOqeOoI6gu8S/N5QL+yXMH79+o2O//u/+s1hE1uuyRa611xzzfjXv/7V1EKdLdM9xzk0keOrc7vJLU9PlmPJm8vbOQFbGjFixLeun1h9+vSu69A9zjEOAACANqtLly51n8OmaOjOgL3nnnuW67PNNluss8463wi6Vcnx3OMG6LzdvXv3b13fI/vZToKhQz+v627bOWl8jx69676ctO7RuPzSUkdQR/Bdgt8bWpv9Er5LtuE2NNTvvmutDrfKKcNyTHVObja5Thn2XWaeeeYYMmRIi2V5u9alfELrJ6Wre8oPuh4/7OYfelbM4cPru5y0HnUEdQTfJfi9oV7YL6G95LA2f8qwibHEEkvE+eefX8aS5+Pn36eeeip22223pvVPPvlkmVQtffDBB+WSywEAAKDdnDJscsnJ03r37l26kP/85z+P3/3ud+X825tvvnlceeWVZZx3niYsbbHFFrHNNtuUceaLLbZY2W7AgAHtauZyAAAA6k+nqh44z5M9dOjQcv3aa6+NXXbZJU477bRJnrxsQvr379/Uhb1Xr15x7rnnNrVm56nAzjvvvDJxW1pqqaXi6KOPjrPOOqsE8GmnnTaOP/74yVIOAAAAmJCGxuyLPZlluL3gggviT3/6U4wcOTK233772HTTTeOJJ56I5ZdfPo444ohoi4YMqc8B/M3HxfTt27vuy0nrUUdQR/Bdgt8b6oX9Etp6HamVr1Vauq+++ur4/e9/X8ZM33TTTbHccsvFUUcdFSeccMK3TrAGAAAA7UklofvTTz+NeeaZp0xo9te//jVWXXXVpm7gY8aMqeIpAQAAoP1OpNbcggsuGBdeeGFMN910MWzYsFhzzTXjo48+ilNOOaVMZgYAAAAdQSUt3UceeWQZv33xxRfHfvvtF7PNNlsZ4/3ee++12fHcAAAAUDct3TmWu7kDDjignMMbAAAAOopKQnd64YUXShfzN954o4zjnnvuuWOrrbYqs5cDAABAR1BJ9/K77747NttsszKRWp43Oy8NDQ2x4447xj333FPFUwIAAEDHaOk+/fTT49e//nU5P3dzed7uPJXYGmusUcXTAgAAQPtv6X733XebThPWXC578803q3hKAAAA6Bihe955540HH3zwG8sfeOCBMpM5AAAAdASVdC/fa6+9yuXZZ5+NJZZYoix75pln4s4774xBgwZV8ZQAAADQMVq6sxt5npd7xIgRceWVV5bzdac///nPsfbaa1fxlAAAANAxWrrHjh0bjzzySDz11FMxbNiwsuzrr7+OBRZYIBZffPEqnhIAAAA6Rug+/vjj46677iozmC+66KIlhP/jH/+IM844I0aNGhV77rlnFU8LAAAA7T9033TTTXHmmWfG8ssv37RswQUXLJOoZRAXugEAAOgIKhnT3b1795hqqqm+sXyaaaaJhoaGKp4SAAAAOkboHjhwYBx88MFx//33xyeffBJffPFFPPHEE3HYYYfFdtttF++//37TBQAAANqrhsbGxsbJ/aDZlbzpCf5fy3bzp8lleTv/vvjii9FWDBnyeUz+d2vyybe6b9/edV9OWo86gjqC7xL83lAv7JfQ1utIrXytMqb73nvvreJhAQAAoE2pJHTnhGkAAADQ0VUyphsAAAAQugEAAKAyWroBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAiQjcAAABUROgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgI4YukeOHBkHH3xwLLvsstG/f/+46KKLJrjtQw89FOuvv34stdRSsf3228cbb7zRtK6xsTF+//vfxyqrrBLLLbdc/O///m8MGzZsCr0KAAAAOqq6Dt2DBg2K559/Pi6++OI44ogj4swzz4w77rjjG9u9+uqrseuuu8bqq68e1113XSy88MKx3XbbxZdfflnWX3XVVXHttdfGySefHJdffnn8+9//jkMOOaQVXhEAAAAdSd2G7q+++iquueaaEo4XWWSRWHPNNeOXv/xlCc3juuKKK0oL9z777BPzzDNPHHDAAdG7d++45ZZbyvoHHngg1l577Vh++eVj/vnnL4/zyCOPtMKrAgAAoCPpEnXqpZdeitGjR5cwXbPMMsvEOeecE2PHjo1Onf7/4wXvvvtuLL744k23GxoaSrh+5plnYvPNN4/pppsu/vrXv5Zu59NOO2385S9/iYUWWmiSy9TQEHWtVr56LyetRx1BHcF3CX5vqBf2S2jrdWRiy1W3oXvw4MEx/fTTR9euXZuW9e3bt4zz/uSTT2KGGWZosfyjjz5qcf8PP/ywBOy0xx57xK9+9asyprtz584x44wzli7nk6pPn97RFrSVctJ61BHUEXyX4PeGemG/hPZeR+o2dA8fPrxF4E6126NGjWqxfK211ordd9891l133Vh55ZVLt/J//OMf0a9fv7L+vffei+7du5dW8mmmmaaMFc8J2r5tYrbxGTr082hsjLqVR1qyQtZ7OWk96gjqCL5L8HtDvbBfQluvI7XytdnQ3a1bt2+E69rtDNDNZQt2tmbvtddeMWbMmBK2N9hgg/jiiy/KzOW/+c1vYuDAgbHqqquW7U877bRy/dlnn40lllhiosuUH3Q9fthttZy0HnUEdQTfJfi9oV7YL6G915G6nUht5plnjo8//riM627e5TwDd7ZWjyu7jz/11FPl1GF/+tOfyszls802Wzk12AcffBALLLBA07azzDJL6bqeLeAAAADQ4UJ3TnTWpUuXMhlazZNPPhmLLbZYi0nU0q233hrHHXdc6X7ep0+fGDFiRDz66KOlxTvHdefy119/vWn7DOI5Lnz22Wefoq8JAACAjqVuQ3ePHj1iww03jCOPPDKee+65uOeee8oY7G233bap1TvDdZprrrniyiuvjLvuuiveeuut2H///UtrdnY7z+C+8cYbx4knnhiPP/54vPLKK+WUYtmtPAM8AAAAdLjQnQ466KByju7tttsujjrqqDJm+6c//WlZ179//7jtttvK9UUXXbSE8xNOOKEE7HTuuec2tYjnpGl5vwzj22yzTemefvbZZ5dTiwEAAEBVGhpzpjEmypAh9TlrXk0eQ+jbt3fdl5PWo46gjuC7BL831Av7JbT1OlIrX5tu6QYAAIC2TOgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAiQjcAAABUROgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAjhu6RI0fGwQcfHMsuu2z0798/Lrrooglu+9BDD8X6668fSy21VGy//fbxxhtvtFh/xx13xM9+9rNYcsklY8cdd4z33ntvCrwCAAAAOrK6Dt2DBg2K559/Pi6++OI44ogj4swzzyzheVyvvvpq7LrrrrH66qvHddddFwsvvHBst9128eWXX5b1Tz31VOy///6xww47xPXXXx9du3aN/fbbrxVeEQAAAB1J3Ybur776Kq655po45JBDYpFFFok111wzfvnLX8bll1/+jW2vuOKK0sK9zz77xDzzzBMHHHBA9O7dO2655ZayPlvIsxV88803L+vzMQcPHhzDhg1rhVcGAABAR1G3ofull16K0aNHlzBds8wyy8Szzz4bY8eObbHtu+++G4svvnjT7YaGhph//vnjmWeeKbcfe+yxEtpr5phjjrjvvvtihhlmmCKvBQAAgI6pS9SpbImefvrpS1fwmr59+5Zx3p988kmLwJzLP/rooxb3//DDD2PaaaeNzz77LD799NMYM2ZM7LTTTiXMZ0A/8sgjY+aZZ56kMjU0RF2rla/ey0nrUUdQR/Bdgt8b6oX9Etp6HZnYctVt6B4+fHiLwJ1qt0eNGtVi+VprrRW77757rLvuurHyyiuXbuX/+Mc/ol+/fqWbejr22GNj3333LV3QTz/99DIGPMd3d+o08Y39ffr0jragrZST1qOOoI7guwS/N9QL+yW09zpSt6G7W7du3wjXtdvdu3dvsXyVVVaJPfbYI/baa6/Sop1he4MNNogvvvgiOnfuXLbZdNNNY8MNNyzXTz755Pjxj39cup8vvfTSE12moUM/j8bGqFt5pCUrZL2Xk9ajjqCO4LsEvzfUC/sltPU6Uitfmw3d2fX7448/LuO6u3Tp0tTlPAP3NNNM843tf/WrX5Xu459//nn06dOntGjPNttspYv6VFNNVSZQq8ll0003XemCPinyg67HD7utlpPWo46gjuC7BL831Av7JbT3OlK3E6kttNBCJWzXJkNLTz75ZCy22GLf6BJ+6623xnHHHVe6n2fgHjFiRDz66KOlxTsfI2c/z7HcNTlreQb6DOUAAADQ4UJ3jx49SnfwnPDsueeei3vuuaec+mvbbbdtavXOcJ3mmmuuuPLKK+Ouu+6Kt956q5yTe5ZZZindzlOen/vSSy+N22+/PV5//fU4+OCDS6hvPuM5AAAATG512708HXTQQSV0b7fddtGrV68yZvunP/1pWde/f/84/vjjY+ONN45FF120bHfCCSeUmc1XXHHFOPfcc5taxH/+85+XWcxPOumkGDp0aCy//PJx9tlnl1OLAQAAQFUaGhvbcu/4KWvIkPocwF+TxxD69u1d9+Wk9agjqCP4LsHvDfXCfgltvY7Uytdmu5cDAABAWyd0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAiQjcAAABUROgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAiXap64PaooSHaRPnqvZy0HnUEdQTfJfi9oV7YL6Gt15GJLVdDY2NjY9WFAQAAgI5I93IAAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInR/T6NGjYp11103Hn300cn2Ydx9992x1lprxVJLLRVbbLFF/POf/5xsjw0AAMCUJ3R/DyNHjoz99tsvXn311cn2QeRj7b///rHrrrvGTTfdFAsttFC5Pnz48Mn2HAAAAExZQvckeu2112KzzTaLd955Z7J+EA8//HDMN998seGGG8YPf/jDEuoHDx5cng8AAIC2SeieRI899lj069cvrrrqqm+se+KJJ2LjjTeOxRdfPNZbb7248847J/pxp5tuuhKwn3zyyRg7dmxcf/310atXrxLAAQAAaJu6tHYB2pott9xyvMuzVTq7g++7776x8sorxzPPPBMHHnhg9OnTJ5ZddtnvfNy111477rvvvvL4nTt3jk6dOsW5554b0047bQWvAgAAgClB6J5MLr/88lhppZVi6623LrfnnHPOePHFF+Piiy8uofujjz6KESNGfON+Xbt2jVlmmSU+/vjjEtwPP/zwWGKJJeKKK66Igw46KG644YYS3AEAAGh7hO7J5I033oj777+/zDxe8/XXX8fcc89drv/6178uXdPHteCCC5aJ004++eSYf/75Y6uttirLjznmmDKT+XXXXRe77LLL5ComAAAAU5DQPZmMHj26jOPebbfdWr7BXf7zFl966aXfev88Pdg222zTdDu7l2cgf//99ydXEQEAAJjCTKQ2mWSL9ttvv126ldcu9957b9xyyy0Tdf+ZZpopXn/99RbL3nzzzZh99tknVxEBAACYwoTuySQnQHv++efj1FNPjbfeequE7VNOOSVmnXXWibp/nobs6quvjhtvvLGE9+xunq3cG2200eQqIgAAAFOY7uWTyWyzzRbnnHNOCcsXXnhhzDzzzGX28vXXX3+i7p+zl3/55ZdlxvIPP/wwFlpooTIJm0nUAAAA2q6GxsbGxtYuBAAAALRHupcDAABARYRuAAAAqIjQDQAAABUxkdokGDr086jnEfANDRF9+vSu+3LSetQR1BF8l+D3hnphv4S2Xkdq5fsuQvckyA+6Hj/stlpOWo86gjqC7xL83lAv7JfQ3uuI7uUAAABQEaEbAAAAKiJ0AwAAQEWM6QYAAGgHxo4dG2PGjI72oqEhYsSIEfH116NaZUx3585dolOn/76dWugGAABowxobG+Ozz4bF8OFfRHszbFincjChtfTo0SummWaGaMgjAN+T0A0AANCG1QJ3r17TR9eu3f6rgFhvOnduiDFjGlvlQMaoUSPjiy8+LrennbbP934soRsAAKCNGjt2TFPg7tVrmmhvunTpFKNHt05Ldx7ASBm8e/ee/nt3NTeRGgAAQBs1ZsyYFgGRyav2vv43Y+WFbgAAgDauPXUpb2/vq9ANAAAAFRG6AQAAoCJCNwAAAB3ShReeG3vuuUulzyF0AwAAQEWEbgAAAKiI83QDAAC0M42NEV99NWWfc+qpc7bvid/+mmuujCuvvCw+/nhYzD33vLH33vuXU3Mdc8zhseWW28Yf/3h+dO7cOTbZ5H9iu+12arrfjTdeF5dffnF88snHscACC8W++w6Meeedr6wbNWpUnH32GXH33beX2/36rRT/+7+/jmmmmbbcfvPNN2LQoOPilVdeikUWWSzmmmvuqJrQDQAA0M4C97rrTh2PP955ij7v8suPjltuGT5RwfuVV16Ks88+PY477qSYe+554pprrojDD/9NHH74sTFs2NC4446/xKmnnhVDhnwURx99eEw//Qyx/vobxUMPPRh//ON5MXDgofHDH85Zttt7713jiituiGmmmSbOPfeseOmlF+Kkk06Pbt26l9uHHXZgnH76H0ogHzjwf2PxxZeMAw88LJ588vE4/fSTY7HFlqj0fRG6AQAA2pmGhsaoZx988EE5B/YPfvCDmGWWWWPnnXePlVZaOcaOHRtjxowpofhHP5o/Fllk4dhssy3jppuuL6H7z3++JLbZZof48Y9XLo+z886/iv/7v4fjrrtui3XX3TCuv/7quOCCS5tavg877OhYZ53V4/XXX4uPPvowPv300/j1rw+KHj16xJxzzhVPP/1kaWmvktANAADQjmRLc7Y413P38n79Vox55pkvtt1285h//gWif/+flFD9zjtvR48eU5fAXZNdyK+44tJy/e2334yzz/59acGuyRbsd999J95//1/x9ddfx2677dDiuTLIv/vu2/H+++/F7LPPUQJ3zUILLRx///tDUSWhGwAAoJ3J8NuzZ9St7t27x3nn/SmeeeapePjhB+O2224pY7X33PN/yzjucUNzQ8N/5gDPVvC9994vll12+Rbb9OzZM4YMGVyun332BSW4NzfDDDOUx49o2QOgS5epompmLwcAAGCKev755+LSS/8YSy+9bOy1137x5z9fF6NGjSyB+4svPo8PPni/adscoz3ffP/pLj7HHHPG4MH/Li3Wtcsll1wU//znP2K22WYv988u5LV1GcbPOOOUGDbsP5O1ZYv4F1980fTYr776cuWvVegGAABgiurWrVuZnfyWW24sAfvee++K4cOHx2effVbWn3jisfHGG6/FfffdG9dee1VstNFmZfnmm28VV199RZlA7b33/lVmKr/vvrtjzjnnjqmn7hnrrbdhnHzyCfHUU0+UmcqPOeaIeO+9d8u48eWW6xczz/yDOOGEo+Ott94srev33nt35a9V93IAAACmqB/9aIE46KDD409/uiBOPXVQCcM56dkMM/Qp61dYYaXYffdflm7iu+66e/z0pz8vy1df/ael1fqCC875f63X88SJJ54ac8zxw7J+zz33jTPPPC0OPfQ3MXr06FhyyaXKTOa1LuuDBp1WAv2OO25dJlvbeONNS0t6lRoaG3NCeSbGkCGfl+n363ncRt++veu+nLQedQR1BN8l+L2hXtgvmTy+/npUDB36QfTpM0tMNVXXaOueeuqJ2Hvv3eKhh54ot7t06RSjR4+ty/e3Voe/i+7lAAAAUBGhGwAAACoidAMAAFAXll562aau5e2F0A0AAAAVEboBAADaOPNj1+/7KnQDAAC0UbVTYY0aNbK1i9Iujfp/72vnzt//bNvO0w0AANBGderUOXr06BVffPFxud21a7doyHNZtRNjxzbEmDGNrdLCnYE739d8fzt1+v7t1UI3AABAGzbNNDOUv7Xg3Z506tQpxo5tvfN0Z+Cuvb/fl9ANAADQhmXL9rTT9onevaePMWNGR3vR0BAx/fQ94+OPv4zJMLR6kmWX8v+mhbtG6AYAAGgHMiB26tQ12lPo7t69e0w11detEronFxOpAQAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAAB0tdH/22WdxyCGHxEorrRQrrLBCHHjggWXZd/n8889j5ZVXjuuvv77F8ltvvTXWWGONWGKJJWKPPfaIYcOGVVh6AAAAqOPQfcQRR8RLL70U5513Xlx44YXx+uuvx6GHHvqd9zvppJPi3//+d4tlzz33XAnwe+65Z1x11VUlvB900EEVlh4AAAAiutTjm/DVV1/FnXfeGVdccUUsuuiiZdnBBx8cW221VYwcOTK6des23vs98cQT8cgjj8SMM87YYvlll10Wa621Vmy44Ybl9qBBg2LVVVeNd999N+aYY44p8IoAAADoiOoydHfq1CnOOeecWGihhVosHzNmTHz55ZfjDd2jRo2Kww47LA4//PByae7ZZ5+NnXfeuen2LLPMErPOOmtZPimhu6Eh6lqtfPVeTlqPOoI6gu8S/N5QL+yX0NbryMSWqy5Dd/fu3WOVVVZpseySSy6JBRZYIGaYYYbx3idD+sILLxz9+/f/xrrsbj7TTDO1WNanT5/48MMPJ6lcffr0jragrZST1qOOoI7guwS/N9QL+yW09zrSaqF7xIgR8dFHH413XXYPn3rqqVt0D7/99tvjggsuGO/2r732Wlx55ZVx8803T/C5unbt2mJZ3s7W8UkxdOjn0dgYdSuPtGSFrPdy0nrUEdQRfJfg94Z6Yb+Etl5HauWr29CdXbu33Xbb8a4766yzykzj6fLLL49jjz22THw2vlbsxsbGMsHa3nvvHX379h3v42V39HEDdt7u0aPHJJU5P+h6/LDbajlpPeoI6gi+S/B7Q72wX0J7ryOtFrr79esXL7/88rduk7OW56RnAwcOjO22226827z//vvx9NNPl8c68cQTy7Lhw4eX2c9vu+220jo+88wzx5AhQ1rcL2+PO+EaAAAATE51OaY73XDDDSVwZwv39ttvP8HtMlDfddddLZZts8025bL++uuX23lu7ieffDI23njjcvuDDz4ol1wOAAAAHSp0f/LJJ3H00UfHRhttFOuss04MHjy4aV1OpNa5c+cYNmxY6Tbes2fPmHPOOVvcv0uXLmWitAzkaYsttighfMkll4zFFlssjjvuuBgwYIDThQEAANDxQvfDDz9cztWdrd15ae7ee++N2WefPTbZZJMSyvfaa6/vfLylllqqhPgzzjgjPv300/jxj38cxxxzTIWvAAAAACIaGnMmMibKkCH1OWte89nz+vbtXfflpPWoI6gj+C7B7w31wn4Jbb2O1Mr3XTpNkdIAAABAByR0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAAEI3AAAAtC1augEAAKAiQjcAAABUROgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAiQjcAAABUROgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICOFro/++yzOOSQQ2KllVaKFVZYIQ488MCy7Lt8/vnnsfLKK8f111/fYvmyyy4bCyywQIvLl19+WeErAAAAoKPrEnXqiCOOiHfeeSfOO++8aGhoiCOPPDIOPfTQOOOMM771fieddFL8+9//brHso48+KmH8nnvuie7duzctn3rqqSsrPwAAANRl6P7qq6/izjvvjCuuuCIWXXTRsuzggw+OrbbaKkaOHBndunUb7/2eeOKJeOSRR2LGGWdssfz1118vy+aYY44pUn4AAACo2+7lnTp1inPOOScWWmihFsvHjBkzwS7ho0aNisMOOywOP/zw6Nq1a4t1r732Wsw999yVlhkAAADaREt3dgFfZZVVWiy75JJLyjjsGWaYYbz3yZC+8MILR//+/b+xLlu6hw8fHttss028+eabJcxny/mkBvGGhqhrtfLVezlpPeoI6gi+S/B7Q72wX0JbryMTW65WC90jRowoY63HJ7uCNx9vfdlll8Xtt98eF1xwwXi3z5bsK6+8Mm6++ebxrn/jjTfi008/jf322y969eoV559/fmy//fbxl7/8pdyeWH369I62oK2Uk9ajjqCO4LsEvzfUC/sltPc60mqh+9lnn41tt912vOvOOuusWGONNcr1yy+/PI499tg46KCDxtuK3djYWCZY23vvvaNv377jfbwLL7wwvv766+jZs2e5ffLJJ8dPfvKTuP/++2O99dab6DIPHfp5NDZG3cojLVkh672ctB51BHUE3yX4vaFe2C+hrdeRWvnqNnT369cvXn755W/dJsPyoEGDYuDAgbHddtuNd5v3338/nn766fJYJ554YlmWXclz9vPbbruttI7nGO/m47xzIrbZZ599gi3tE5IfdD1+2G21nLQedQR1BN8l+L2hXtgvob3Xkboc051uuOGGErizhTu7gk/IzDPPHHfddVeLZTl2Oy/rr79+aQlfc801Y/fdd4+NN964aXb0t99+O+aZZ57KXwcAAAAdV12G7k8++SSOPvro2GijjWKdddaJwYMHN63LidQ6d+4cw4YNKy3W2WV8zjnnbHH/Ll26RJ8+fUogTwMGDIjf//73Mdtss5X7n3766fGDH/ygdDEHAACADhW6H3744dIana3deWnu3nvvLV3DN9lkkxLK99prr+98vAMOOKAE8f333z+++OKLWGGFFeK8884r4R0AAACq0tCY/a+ZKEOG1OcA/uYD+fv27V335aT1qCOoI/guwe8N9cJ+CW29jtTK9106TZHSAAAAQAckdAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAgNANAAAAbYuWbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAiQjcAAABUROgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQkS4Ts9E222wTDQ0NE/WAl1xyyX9bJgAAAOg4obtfv35N1z/++OO46qqrYo011ojFFlsspppqqnjxxRfjtttui6222qrKsgIAAED7C9177rln0/Xtt98+Dj744Nhyyy1bbLPccsuVMA4AAAB8zzHdzzzzTKy44orfWL7EEkvEyy+/PKkPBwAAAO3WJIfuhRdeOM4777wYOXJk07IvvvgizjjjjFhyySUnd/kAAACgfXcvb+6YY46JXXbZJX784x/HnHPOGY2NjfHWW2/FrLPOGueee241pQQAAICOELrnnXfeuP322+Pvf/97vP7662XZj370o1hppZWiS5dJfjgAAABot75XSu7atWvMNtts8fXXX5ewPWzYsOjcufPkLx0AAAB0pND96aefxj777BOPPfZYuX3nnXfGcccdF++++24Z651hHAAAAPgeE6kde+yx0aNHj3jkkUeiW7duZVmG7h/84AdlHQAAAPA9Q/ff/va32G+//WKaaaZpWtanT5846KCD4vHHH5/UhwMAAIB2a5JDd2p+urCaHNdtIjUAAAD4L0L3uuuuW7qTv/rqq9HQ0BBfffVV6Wp+2GGHxdprrz2pDwcAAADt1iRPpDZw4MA45ZRTYuONNy6zl2+wwQZl5vJNN920rKP1NDZGfPnlfy55HcbV0BDRo4c6woSpI0wM9QR1hMnBdwnfpWfPaBcaGhu/XzwbMWJEmbF8zJgxMcccc0TP9vKOfIshQz6v2zCb5Vp33anj8cedug0AAGj7ll9+dDzySJcYOrQ+c1geOOrbt3c1Y7o///zzuO666+Kqq66KmWaaqZw+7J133vk+D8Vk1NBQhzURAACgA5vk7uWvvPJKbLfddjHLLLM0Xb/rrrvijjvuiHPPPTeWX375yVKwzz77LE488cS4//77Y+zYsTFgwIA4+OCDW8ya3lyeruzSSy9tsSzHmW+99dbl+q233hqnnXZaDB48OPr37x/HHHNMzDDDDNFe5FGWW28dHlNP3buuW+Spj6Nx6gjqCL5L8HtDa7NfwnfJztQNDd/dktzuupdvu+22seyyy8bee+8dSy21VNx8882le/lJJ50Ujz76aFx77bWTpWD77rtvaT0/6qijyoRtRx55ZAn6Z5xxxni332GHHWLFFVeMjTbaqGlZr169yjnFn3vuudhmm23KYy244IJlIripp566HCSYFPUeVHxxoY7gewS/N9QD+ySoJ3SE75KGqrqX/+Mf/4gNN9zwG8s333zzeO2112JyyBnR77zzzjj88MNj0UUXjUUWWaS0ct9zzz3jPV1Zev3112PhhReOGWecsemSgTtddtllsdZaa5VyZ+geNGhQPPDAA2VMOgAAAFRlkkN3dsl+8803v7H8qaeeij59+kyeQnXqFOecc04stNBCLZbnpG1f5tTc4/jiiy/io48+irnmmmu8j/fss8+W1vmabDGfddZZy3IAAAComzHdO++8cxx66KGx2267RfZMz3N033DDDXHxxReXLuGTQ/fu3WOVVVZpseySSy6JBRZYYLzjsLOVO7ugZ1B/8MEHY7rppivdzWtdzf/973+XCd+aywMEH3744SR3H6hntfLVezlpPeoI6gi+S/B7Q72wX0JbryMTW65JDt3ZjTwD7IUXXljCcXbVnnvuucvEZGuvvfYknXIsW6fHJ7uG55jrmuwefvvtt8cFF1ww3u3feOONErrnmWeeMnHa448/XiZRyzHda665Znmurl27trhP3h41alRMij592sYg/rZSTlqPOoI6gu8S/N5QL+yX0N7ryCSH7rTaaquVy38ju3bnpGzjc9ZZZ8Uaa6xRrl9++eVlZvKDDjqozDo+PjlWe9VVVy0t3CnHbb/11ltxxRVXlNDdrVu3bwTsvF0b8z2x6vX8cM2PtGSFrPdy0nrUEdQRfJfg94Z6Yb+Etl5HauWrJHRfc8015Rzd2a07x19nt+9sYZ6Ulu5+/frFyy+//K3bZGt6tqQPHDiwnJpsQrKVuxa4a7LVO7u+p5lnnjmGDBnSYn3ezhb1SZEfdD1+2G21nLQedQR1BN8l+L2hXtgvob3XkUmeSC3HTef5s3/yk5/EKaecUq4vt9xypTv3n//858lWsBwnnoE7W7h32mmnb9329NNPj+23377FspdeeqkE77TEEkvEk08+2bTugw8+KJdcDgAAAFWZ5JbuHF+dQXv11VdvWpZdwfN0Xccff3xsueWW/3WhPvnkkzj66KPLRGjrrLNODB48uGldTqTWuXPnGDZsWOk23rNnz9K1/Lzzzist49md/KGHHoobb7yxTL6Wtthii3Ke7iWXXDIWW2yxcp7uAQMGlPOLAwAAQN2E7q+//jpmm222byzPVuXxnc7r+3j44YfLubqztTsvzd17770x++yzxyabbFJC+V577RWLL754ae0+44wzyt8s3+9+97tYaqmlyn3yb4b4XP/pp5/Gj3/84zLxGwAAAFSpoTHP+zUJLr300rjpppvit7/9bcw///xl2fvvv1+6l6+88srf6ObdngwZUp8D+JsP5O/bt3fdl5PWo46gjuC7BL831Av7JbT1OlIr32Rv6c7Tdg0dOjQ22GCDclqvLl26xGeffVbO2Z0t1Nn1vObFF1+c9JIDAABAOzHJofukk06KsWPHlpCdY6vzXNv5N4N3beIyAAAA4HuE7gzY+++/fwnfc801V+y3334xcuTIGD58eFm21lpreV8BAADg+5wyLGcozxnF83RbV199dZlBPLuV58RkOVEZAAAA8D1D9yuvvBLbbrtt9OjRI+6777746U9/Gl27do3ll1++TKgGAAAAfM/Q3bdv33jttdfK5YUXXijnyE5///vfY5ZZZpnUhwMAAIB2a5LHdOcpwfbYY4/o1KlTLLbYYqWF+5xzzokzzzyzdD0HAAAAvmfozq7lyy23XLz33nvRv3//smyFFVaIAQMGxIILLjipDwcAAADt1iSH7rTQQguVS82SSy45OcsEAAAAHXNMNwAAADBxhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAiQjcAAABUROgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAq0iXq1GeffRYnnnhi3H///TF27NgYMGBAHHzwwTHNNNOMd/tjjz02Lr300hbLDjvssNh6663L9WWXXTY+//zzFuufeuqp6NmzZ4WvAgAAgI6sbkP3EUccEe+8806cd9550dDQEEceeWQceuihccYZZ4x3+9dffz3233//2GijjZqW9erVq/z96KOPSuC+5557onv37k3rp5566inwSgAAAOio6jJ0f/XVV3HnnXfGFVdcEYsuumhZlq3cW221VYwcOTK6des23tC90047xYwzzjjedbl8jjnmmCLlBwAAgLoN3Z06dYpzzjknFlpooRbLx4wZE19++eU3QvcXX3xRWrPnmmuu8T7ea6+9FnPPPfd/Xa6GhqhrtfLVezlpPeoI6gi+S/B7Q72wX0JbryMTW66GxsbGxmgD9t1339JiffPNN39j3bPPPhv/8z//E5tsskk8+OCDMd1008UOO+zQ1NU8u6r/85//jB49esSbb75Zwny2nE+OIA4AAAB119I9YsSI0jo9PtkVvPl468suuyxuv/32uOCCC8a7/RtvvFHGfc8zzzxl4rTHH3+8TKKWY7rXXHPNsv7TTz+N/fbbryw7//zzY/vtt4+//OUvTeO+J8bQoZ9HPR+iyCMtffr0rvty0nrUEdQRfJfg94Z6Yb+Etl5HauWr29CdrdPbbrvteNedddZZscYaa5Trl19+eZmZ/KCDDor+/fuPd/sNN9wwVl111dLCnRZccMF46623ypjwDN0XXnhhfP31100zlZ988snxk5/8pMyMvt566010mfODrscPu62Wk9ajjqCO4LsEvzfUC/sltPc60mqhu1+/fvHyyy9/6zYZlgcNGhQDBw6M7bbbboLbZSt3LXDXZKv3I488Uq537dq1XGpyTPjss88+wZZ2AAAAmBw6RZ264YYbSuDOFu6clfzbnH766aW7eHMvvfRSCd45ZD1bza+//voWs6O//fbbZT0AAAB0qNnLP/nkkzj66KPLRGjrrLNODB48uGndDDPMEJ07d45hw4aVFuvsMp5dy/N83tkynt3JH3roobjxxhvjkksuKa3gAwYMiN///vcx22yzlftnSP/BD35QupgDAABAhwrdDz/8cGmNztbuvDR37733lq7hOVN5hvK99torFl988RKkzzjjjPI3w/Xvfve7WGqppcp9DjjggOjSpUvsv//+5fRiK6ywQgnpGd4BAACgKm3mlGH1YMiQ+pw1r/nseX379q77ctJ61BHUEXyX4PeGemG/hLZeR2rla7NjugEAAKCtE7oBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAiQjcAAABUROgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAiQjcAAABUROgGAACAigjdAAAAIHQDAABA26KlGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAQEWEbgAAAOhoofuzzz6LQw45JFZaaaVYYYUV4sADDyzLJuT999+PnXfeOZZYYolYc80147bbbmux/tZbb4011lijrN9jjz1i2LBhU+BVAAAA0JHVbeg+4ogj4qWXXorzzjsvLrzwwnj99dfj0EMPHe+2o0ePjl133TW6dOkSN9xwQ+y0004xcODAeOWVV8r65557rgT4PffcM6666qoS3g866KAp/IoAAADoaLpEHfrqq6/izjvvjCuuuCIWXXTRsuzggw+OrbbaKkaOHBndunVrsf0DDzwQH3zwQdm+V69eMc8888SDDz4YTz/9dMw///xx2WWXxVprrRUbbrhh2X7QoEGx6qqrxrvvvhtzzDFHq7xGAAAA2r+6DN2dOnWKc845JxZaaKEWy8eMGRNffvnlN0L3Y489FiuuuGIJ3DVnn3120/Vnn322dD2vmWWWWWLWWWctyycldDc0RF2rla/ey0nrUUdQR/Bdgt8b6oX9Etp6HZnYctVl6O7evXusssoqLZZdcsklscACC8QMM8zwje2zxXq22WaLk08+OW666aaYfvrpY++99y5juNO///3vmGmmmVrcp0+fPvHhhx9OUrn69OkdbUFbKSetRx1BHcF3CX5vqBf2S2jvdaTVQveIESPio48+Gu+6GWecMaaeeuqm29k9/Pbbb48LLrhggt3Rcyz32muvXVrIH3300RK6c/z2YostVp6ra9euLe6Tt0eNGjVJZR469PNobIy6lUdaskLWezlpPeoI6gi+S/B7Q72wX0JbryO18tVt6M6u3dtuu+1415111llNrdSXX355HHvssWXis/79+493+86dO8d0000XRx55ZOmavsgii8QTTzwRV199dQnd2R193ICdt3v06DFJZc4Puh4/7LZaTlqPOoI6gu8S/N5QL+yX0N7rSKuF7n79+sXLL7/8rdvkrOU56VnORL7ddttNcLvsOt7Q0FACd83cc8/d9PgzzzxzDBkypMV98na2qAMAAECHO2VYdhfPwJ0t3HkKsG+T595+9dVXy0RrNXmKsRznXVv/5JNPNq3Lmc7zkssBAACgQ4XuTz75JI4++ujYaKONYp111onBgwc3XWrBetiwYWUm87TuuuvG2LFj46ijjoq33367dEn/29/+FptttllZv8UWW5QJ1q655ppy7u9sOR8wYIDThQEAANDxQvfDDz/cNDlajuNufskW6rTJJpvERRddVK7nqcL++Mc/xhtvvFECeM50fuqpp5ax3WmppZYqIT7HimcAn3baaeP4449v1dcIAABA+9fQ2NiWh6RPWUOG1Oesec1nz+vbt3fdl5PWo46gjuC7BL831Av7JbT1OlIrX5ts6QYAAID2QOgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAiQjcAAABUROgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFelS1QO3Rw0N0SbKV+/lpPWoI6gj+C7B7w31wn4Jbb2OTGy5GhobGxurLgwAAAB0RLqXAwAAgNANAAAAbYuWbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInS3AyNHjoyDDz44ll122ejfv39cdNFFrV0k6sioUaNi3XXXjUcffbRp2bvvvhvbb799LLnkkrH22mvHQw891KplpHV89NFHsffee8fyyy8fK6+8chx//PHl+ySpI9S8/fbbsdNOO8VSSy0VAwYMiAsuuKBpnXrCuHbZZZc48MADm26/8MILsemmm8YSSywRv/jFL+L555/3pnVQd999dyywwAItLvkblNQTavusRx11VCy33HKx0korxSmnnBKNjY3too4I3e3AoEGDSsW7+OKL44gjjogzzzwz7rjjjtYuFnUgA9R+++0Xr776atOy/PLaY489om/fvnHdddfFBhtsEHvuuWe8//77rVpWpqysB7mzM3z48Lj88svj1FNPjfvvvz9OO+00dYQmY8eOLSFq+umnjxtuuKHsDP3hD3+IW265RT3hG/7yl7/EAw880HT7q6++KvUnGwWuv/76cuBm1113LcvpeF577bVYddVVy4H+2uXYY49VT2iS9eHvf/97XHjhhfG73/0urr766rjqqqvaRR3p0toF4L+Tle2aa66J888/PxZZZJFyyYCVO9E///nPvb0d/Mdt//33bzpCWPPII4+U1qkrr7wypp566ph33nnj//7v/0oA32uvvVqtvExZb7zxRjzzzDPx8MMPlwMwKUP4iSeeGKussoo6QjFkyJBYaKGF4sgjj4xevXrFXHPNFSuuuGI8+eSTpd74LqHmk08+KY0Aiy22WNOy2267Lbp16xYDBw6MhoaGOOSQQ+LBBx8sDQMbb7yxN6+Def3112P++eePGWecscXya6+9Vj0h8jsk90X/+Mc/xuKLL17ekR133DGeffbZ6NKlS5uvI1q627iXXnopRo8eXY741CyzzDKlgmYLBR3XY489Fv369StHCJvLurHwwguXwN28zmQAo+PInZ7sJlwL3DVffPGFOkKTmWaaqfR+yMCdB/AybD/++ONlSILvEprLA3bZc2q++eZrWpZ1JH9fcic55d+ll17a700HDt154G5c6gkpf1/ytyZ/X2qydTuHvrWHOiJ0t3GDBw8u3f66du3atCx3orNbcR4xouPacssty1j/Hj16fKPO5I50c3369IkPP/xwCpeQ1jTNNNOUcdw1eZDusssuixVWWEEdYbxWW2218r2SB3l/9rOfqSc0yd5STzzxROy+++4t3hW/N9TkQbs333yzdCnP74811lgjTj755DKGVz0hZc+p2WabLW688cbSW3f11VePs846q+yftIc6ont5G5fjMZsH7lS7nV9kMLF1Rn3p2E466aQySUl28/vTn/6kjvANZ5xxRulunl3Ns+XBdwkpD/LnfDKHH354dO/evcWboo5Qk/PG1OpD9p7517/+VcbvjhgxQj2hachsTtyZwx/zNyaDdn6vZONRe/guEbrbuBzfMG6Fq90e98cPanVm3F4QWWfUl44duHMixpxMLcfbqSOMT22sboasX//612X22NwRas53SceTk7cuuuiiLXrOfNc+it+bjidbMPMsKtNOO23pGpxzRWQL5gEHHFC6E6sndOnSpQxxywnUsr7UDtZcccUVMeecc7b5OiJ0t3EzzzxzfPzxx2Vcd1bWlEeGshJm91EYX53JSdaay9arcbvt0DEcc8wx5Qctg3d2+UvqCM2/G3LMXHYFrckxu19//XWZFyAn5GvOd0nHnLE8P/fa3DK1HeM777yznK4y1zWnjnRc0003XYvbOZFrHsTL7xL1hBlnnLEcqKsF7jT33HPHBx98UA7MtPU6Ykx3G5dHCjNsN59IICciyBaJTp18vHxTnt/wn//8Z+nS1bzO5HI6XgtVduPK82Cus846TcvVEWqyC2ieUjDP6V6Tp6icYYYZyqQ2vku49NJLyynkchxmXnLsf17yen6XPP30001n0ci/Tz31lN+bDuhvf/tbmdy1ee+YF198sQTx/C5RT1hiiSXKQZgc+1+TB3YzhLeH7xKprI3LcQ4bbrhhGWP33HPPxT333BMXXXRRbLvttq1dNOpUHi2cZZZZ4qCDDiqnlzvvvPNK3dlkk01au2hM4Vlkzz777Nh5553LDk/2kKld1BFq8gBunooyJ2XMHjJ5DubsFbHbbrupJxS5Q5xdP2uXnj17lktez8mQPvvsszjuuONK/cm/GbrWWmst714Hkz0hshXz0EMPLUEqv0vyFHO//OUv1ROKeeaZJwYMGFD2T/PsTHmgJvdRt9hii3ZRRxoaxz2JL21OVroM3XfddVeZan+nnXaK7bffvrWLRR1ZYIEF4pJLLilHmVNOVJHnOMxTMOSOUe5Qr7TSSq1dTKag/CHLcVPj8/LLL6sjNMlW7hyGkDNU54HerbfeOnbdddcyLtN3CeM68MADy98TTjih/M2DujnRWh7oy9+io446qpy2ko4nD/T/9re/Lb0z88DM5ptvHnvssUf5LlFPSJ9//nn5vbn77rvL702eMaO91BGhGwAAACqiezkAAABUROgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwC0ggMPPDAWWGCBCV6uv/768vdf//rXFClPY2NjbLPNNvH6669HW3oP8/Jdrr766jj11FOnSJkAYFxdvrEEAKjcIYccEvvvv3+5ftttt8VFF10U1157bdP6aaedNlZeeeWYYYYZpsinccMNN8Sss84a8847b7Q3G2+8cay33nqx4YYbxtxzz93axQGgg9HSDQCtoHfv3jHjjDOWS17v3Llz0+28dO3atfzN5VOilfsPf/hDbLHFFtEedenSJTbaaKM4//zzW7soAHRAQjcA1KHsVt68e3lev/3222OttdaKJZZYIvbbb7949913Y9ttty23t9xyy/joo4+a7n/33XfH2muvXdZtsskm8dhjj03wuR566KEYPnx42bbmlFNOif79+8fiiy9eup2/+uqrTeueeOKJ0nqc67IF+c4772zxeH/84x9jtdVWi6WWWip22mmnUs40duzYuOCCC2L11VdvetyXX3656X75Gm+66aZYd911Y9FFFy2vqXbf2vNma3Xed5999illrvnss89ir732imWXXTaWW265+PWvfx1ffPFF0/p8zr/85S9lOwCYkoRuAGgjzjjjjDjhhBPi3HPPjbvuuqu0TOflyiuvjMGDBze15L700kvxm9/8Jn71q1/FzTffHOuvv37svPPO8fbbb4/3cf/2t7/FiiuuGA0NDU2B/aqrrorTTjstbr311ujbt28cdNBBZV0+z6677lpC9y233BK//OUvy7jqDMQpy3LmmWeW0Jtd1nv27FkCcjrrrLNKN/qDDz64rJttttnK/b/66qumsvz+978vXe9zTPvHH39cypCGDRtWnnellVaKG2+8Meabb7644447Wrw3WbYrrrgiLrnkkvIenH322U3rs9t8dtl//PHHK/hkAGDCjOkGgDZi++23b2qNXmihhcr45Gz5Tj/96U9L0EwXXnhhbLbZZqUVOmVreIbNDKTjm3jshRdeKK3aNe+9915MNdVUZYx3Xg477LB44403yrrLL7+8BN+tt9663J5zzjnjxRdfjIsvvri0MmdYz3JmK3s6/PDDS3lGjBgRl112WWmhz1bndMwxx8Saa65ZDgxsvvnmZdkOO+xQDgCkPKCQz5eylT/Htx9wwAHl4EC2aj/wwAMtypwBf/bZZ48ePXrE6aef/o3XmUE9X2vt+QFgShC6AaCNmGOOOZqud+/evbQUN789atSocj1nIM+QmgG45uuvv24RrJvLVuTpp5++6fY666xTAnKG0yWXXDLWWGON0kU9Zfi+//77S9fx5o9dm6DszTffjEUWWaRpXbaSZ6v7kCFD4pNPPmnRhT2DfXYjbz5jeob4ml69epXHTq+99losuOCCTa3xabHFFmvqYp4HFnbfffcS2PPys5/9rOmgQ810000XQ4cO/c73GQAmJ6EbANqIcSdV69Rp/KPExowZU7qT5/jn5jKYj08G2bxPTU7glqH94YcfLgE7W6rztFvZrXv06NElzO62227fmKys+d9xdevWbYJlzbHezYP4t0341lxuWwvdGbSz5fvee++Nv/71r6WFPceqn3zyyU3b5/NM6D0DgKr45QGAdiZbnXMCtmw1rl2y1fvBBx8c7/Z9+vQprdA1GVqvueaaGDBgQBx11FFlcrO33norXnnllfLYOTa8+WNn0M3x3Slv17q5pxyXvcIKK8Snn35aWr2feeaZpnXZiv3Pf/5zok7j9aMf/ah0DW9+cCC7tdf86U9/Ko+Vs5Rn1/Ljjz++jHtvLsuSZQCAKUnoBoB2JsdU57m/c0Kxd955pwTSvMw111zj3X7hhRduMYt4tggPGjSoTKiW4T0nNctx0nn/nFH8+eefj1NPPbUE8QzbOdN5jv1OOSN5ju++5557SlfzI444ooyzzkuWKyc8u++++0qX8hwrPnLkyKbx398mu7xnq/Zxxx1XurjnLOhPPvlk0/oPP/wwjj766BLqs1w5o3q+rubyoEHzru8AMCXoXg4A7UyOw87QnDOB598f/vCH8bvf/a6cSmt8Vl555TLBWnbfzq7mebqvvffeu7QW54zg88wzT5kJPGf/zss555xTum1nt/OZZ5653DdnSE8bbLBBOXVZtpDnKbuWX375ErTTjjvuWJZl2M6/OS780ksvLROkfZd83gzaRx55ZHmOfC35t9blPGdI//zzz8uM7Tkbeq4/6aSTmu6fQf3LL78s5QGAKamhcdwBUgBAh5JdtnPisQzZEwrmbV2exuyDDz4oLeUAMCXpXg4AHVxO0LbLLruUc2y3Rzl2PMelZ0s7AExpQjcAUE4J9v7777c4fVd7cd1115WW/Hnnnbe1iwJAB6R7OQAAAFRESzcAAABUROgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAIKrx/wHfg1zNLVdPogAAAABJRU5ErkJggg=="
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 28
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "# so how much does scaling the acceleraotr position before hand matter? why has the training time increased so much",
+   "id": "fbd9ce1ac3a4153d"
   },
   {
    "metadata": {

From 9d8ae03ca7572d9ad2dbe8cb36ffe0c11f1c25fd Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Thu, 5 Mar 2026 18:07:11 -0800
Subject: [PATCH 28/49] retrain model

---
 array_temp/Control_Model.ipynb                |  210 +--
 array_temp/control_model_revised.ipynb        | 1477 -----------------
 .../DataPreprocessing.py                      |    8 +-
 {array_temp => control_model}/RNN.py          |    9 +-
 {array_temp => control_model}/RNN_Dataset.py  |    0
 control_model/control_model_revised.ipynb     |  826 +++++++++
 6 files changed, 924 insertions(+), 1606 deletions(-)
 delete mode 100644 array_temp/control_model_revised.ipynb
 rename {array_temp => control_model}/DataPreprocessing.py (90%)
 rename {array_temp => control_model}/RNN.py (94%)
 rename {array_temp => control_model}/RNN_Dataset.py (100%)
 create mode 100644 control_model/control_model_revised.ipynb

diff --git a/array_temp/Control_Model.ipynb b/array_temp/Control_Model.ipynb
index 8a2bfbd..f007371 100644
--- a/array_temp/Control_Model.ipynb
+++ b/array_temp/Control_Model.ipynb
@@ -31,27 +31,19 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T16:57:17.169521Z",
-     "start_time": "2026-03-05T16:57:14.222827Z"
+     "end_time": "2026-03-06T01:15:12.805666Z",
+     "start_time": "2026-03-06T01:15:11.379578Z"
     }
    },
    "cell_type": "code",
    "source": [
     "#necessary imports\n",
-    "import sklearn as sk\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from array_temp.DataPreprocessing import make_sequence_datasets\n",
     "\n",
     "\n",
     "from data_tools import query\n",
     "from data_tools.collections import TimeSeries\n",
-    "from datetime import datetime, date, time, timezone\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
     "import dill\n",
     "import os\n",
     "import pytz\n",
@@ -161,8 +153,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-05T16:57:21.492692Z",
-     "start_time": "2026-03-05T16:57:21.434274Z"
+     "end_time": "2026-03-06T01:15:19.477772Z",
+     "start_time": "2026-03-06T01:15:19.402215Z"
     }
    },
    "cell_type": "code",
@@ -599,8 +591,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T06:04:38.193513Z",
-     "start_time": "2026-03-04T06:04:38.171982Z"
+     "end_time": "2026-03-06T01:15:30.319687Z",
+     "start_time": "2026-03-06T01:15:30.287590Z"
     }
    },
    "cell_type": "code",
@@ -902,18 +894,18 @@
    ],
    "id": "2741c957d09edb03",
    "outputs": [],
-   "execution_count": 5
+   "execution_count": 3
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T02:34:08.877159Z",
-     "start_time": "2026-03-04T02:34:08.863443Z"
+     "end_time": "2026-03-06T01:15:32.952293Z",
+     "start_time": "2026-03-06T01:15:31.132927Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "from data_tools import lap_tools\n",
+    "\n",
     "from physics.environment.gis.gis import GIS\n",
     "from physics.environment.gis.gis import calculate_path_distances\n",
     "\n",
@@ -935,7 +927,7 @@
    ],
    "id": "88a4ae7fb0d75eed",
    "outputs": [],
-   "execution_count": 28
+   "execution_count": 4
   },
   {
    "metadata": {
@@ -1053,9 +1045,7 @@
    "source": [
     "from data_tools import *\n",
     "from data_tools.collections.time_series import TimeSeries\n",
-    "from data_tools.query.influxdb_query import DBClient\n",
     "# from data_tools.query.postgresql_query import PostgresClient\n",
-    "from datetime import datetime, timezone\n",
     "#from data_tools.fsgp_2024_laps import FSGPDayLaps"
    ],
    "id": "84e31e6326fbd121",
@@ -1085,9 +1075,7 @@
    "cell_type": "code",
    "source": [
     "from scipy import integrate as intg\n",
-    "import datetime\n",
     "from datetime import datetime\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "day_num = 1\n",
     "day = FSGPDayLaps(day_num)\n",
@@ -1224,6 +1212,39 @@
    ],
    "execution_count": 76
   },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-06T01:16:09.423186Z",
+     "start_time": "2026-03-06T01:16:02.705176Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "\n",
+    "# General best practice for device setup\n",
+    "if torch.cuda.is_available():\n",
+    "    device = torch.device(\"cuda\")\n",
+    "elif torch.backends.mps.is_available(): # For Apple Silicon GPUs\n",
+    "    device = torch.device(\"mps\")\n",
+    "else:\n",
+    "    device = torch.device(\"cpu\")\n",
+    "\n",
+    "print(f\"Using device: {device}\")\n"
+   ],
+   "id": "e154d3edb6dbe5c4",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using device: cpu\n"
+     ]
+    }
+   ],
+   "execution_count": 5
+  },
   {
    "metadata": {
     "ExecuteTime": {
@@ -1579,14 +1600,14 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T21:42:39.508744Z",
-     "start_time": "2026-03-04T21:42:39.482896Z"
+     "end_time": "2026-03-06T01:16:33.816917Z",
+     "start_time": "2026-03-06T01:16:33.810092Z"
     }
    },
    "cell_type": "code",
    "source": [
     "#preprocessing - convert everything to pandas dataframes.\n",
-    "\n",
+    "import pandas as pd\n",
     "\n",
     "df_mech_brake_pressed = pd.DataFrame(mech_brake_pressed)\n",
     "df_accel_position = pd.DataFrame(accel_position)\n",
@@ -1596,13 +1617,16 @@
    ],
    "id": "a6707eebb9ccd8c9",
    "outputs": [],
-   "execution_count": 9
+   "execution_count": 7
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-06T01:16:34.831835Z",
+     "start_time": "2026-03-06T01:16:34.821775Z"
+    }
+   },
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": 10,
    "source": [
     "# combine all dfs and resample, then feed to scaler.\n",
     "# states = velocity, position\n",
@@ -1617,13 +1641,15 @@
     "\n",
     "        return combined_df\n"
    ],
-   "id": "3e63d74e16a13193"
+   "id": "3e63d74e16a13193",
+   "outputs": [],
+   "execution_count": 8
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T21:45:28.386480Z",
-     "start_time": "2026-03-04T21:45:28.371915Z"
+     "end_time": "2026-03-06T01:16:35.564994Z",
+     "start_time": "2026-03-06T01:16:35.519935Z"
     }
    },
    "cell_type": "code",
@@ -1631,75 +1657,24 @@
    "id": "67452406a84c903d",
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "                               TrackIndexSpreadsheet\n",
-       "2024-07-16 07:49:53.627000093                    0.0\n",
-       "2024-07-16 07:49:53.827000618                    0.0\n",
-       "2024-07-16 07:49:54.027000904                    0.0\n",
-       "2024-07-16 07:49:54.227001429                    0.0\n",
-       "2024-07-16 07:49:54.427001953                    0.0"
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>TrackIndexSpreadsheet</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2024-07-16 07:49:53.627000093</th>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2024-07-16 07:49:53.827000618</th>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2024-07-16 07:49:54.027000904</th>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2024-07-16 07:49:54.227001429</th>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2024-07-16 07:49:54.427001953</th>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "NameError",
+     "evalue": "name 'pos_df' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mNameError\u001B[39m                                 Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[9]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[43mpos_df\u001B[49m.head()\n",
+      "\u001B[31mNameError\u001B[39m: name 'pos_df' is not defined"
+     ]
     }
    ],
-   "execution_count": 19
+   "execution_count": 9
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T21:44:56.087327Z",
-     "start_time": "2026-03-04T21:44:55.908939Z"
+     "end_time": "2026-03-06T01:16:38.119880Z",
+     "start_time": "2026-03-06T01:16:38.018786Z"
     }
    },
    "cell_type": "code",
@@ -1713,23 +1688,18 @@
    "id": "6dea8ee6d0965889",
    "outputs": [
     {
-     "ename": "ValueError",
-     "evalue": "Length of values (3) does not match length of index (375240)",
+     "ename": "NameError",
+     "evalue": "name 'pos_df' is not defined",
      "output_type": "error",
      "traceback": [
       "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mValueError\u001B[39m                                Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[18]\u001B[39m\u001B[32m, line 4\u001B[39m\n\u001B[32m      2\u001B[39m combined_df = combine_dfs([\u001B[33m\"\u001B[39m\u001B[33mmech_brake_pressed\u001B[39m\u001B[33m\"\u001B[39m, \u001B[33m\"\u001B[39m\u001B[33maccel_position\u001B[39m\u001B[33m\"\u001B[39m], df_mech_brake_pressed.index, all_dfs)\n\u001B[32m      3\u001B[39m dfs = [pos_df, speed_arr]\n\u001B[32m----> \u001B[39m\u001B[32m4\u001B[39m df = \u001B[43mcombine_dfs\u001B[49m\u001B[43m(\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mposition\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mspeed\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpos_df\u001B[49m\u001B[43m.\u001B[49m\u001B[43mindex\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdfs\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[10]\u001B[39m\u001B[32m, line 10\u001B[39m, in \u001B[36mcombine_dfs\u001B[39m\u001B[34m(telemetry_names, index_common, all_dfs)\u001B[39m\n\u001B[32m      6\u001B[39m combined_df.dropna()\n\u001B[32m      8\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m name, df \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mzip\u001B[39m(telemetry_names, all_dfs):\n\u001B[32m      9\u001B[39m     \u001B[38;5;66;03m#df_interp = self.resample(df, index_common)\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m10\u001B[39m     \u001B[43mcombined_df\u001B[49m\u001B[43m[\u001B[49m\u001B[43mname\u001B[49m\u001B[43m]\u001B[49m = df\n\u001B[32m     12\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m combined_df\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:4322\u001B[39m, in \u001B[36mDataFrame.__setitem__\u001B[39m\u001B[34m(self, key, value)\u001B[39m\n\u001B[32m   4319\u001B[39m     \u001B[38;5;28mself\u001B[39m._setitem_array([key], value)\n\u001B[32m   4320\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m   4321\u001B[39m     \u001B[38;5;66;03m# set column\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m4322\u001B[39m     \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_set_item\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mvalue\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:4535\u001B[39m, in \u001B[36mDataFrame._set_item\u001B[39m\u001B[34m(self, key, value)\u001B[39m\n\u001B[32m   4525\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[34m_set_item\u001B[39m(\u001B[38;5;28mself\u001B[39m, key, value) -> \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m   4526\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m   4527\u001B[39m \u001B[33;03m    Add series to DataFrame in specified column.\u001B[39;00m\n\u001B[32m   4528\u001B[39m \n\u001B[32m   (...)\u001B[39m\u001B[32m   4533\u001B[39m \u001B[33;03m    ensure homogeneity.\u001B[39;00m\n\u001B[32m   4534\u001B[39m \u001B[33;03m    \"\"\"\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m4535\u001B[39m     value, refs = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_sanitize_column\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvalue\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   4537\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m (\n\u001B[32m   4538\u001B[39m         key \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m.columns\n\u001B[32m   4539\u001B[39m         \u001B[38;5;129;01mand\u001B[39;00m value.ndim == \u001B[32m1\u001B[39m\n\u001B[32m   4540\u001B[39m         \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(value.dtype, ExtensionDtype)\n\u001B[32m   4541\u001B[39m     ):\n\u001B[32m   4542\u001B[39m         \u001B[38;5;66;03m# broadcast across multiple columns if necessary\u001B[39;00m\n\u001B[32m   4543\u001B[39m         \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m.columns.is_unique \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(\u001B[38;5;28mself\u001B[39m.columns, MultiIndex):\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:5288\u001B[39m, in \u001B[36mDataFrame._sanitize_column\u001B[39m\u001B[34m(self, value)\u001B[39m\n\u001B[32m   5285\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m _reindex_for_setitem(value, \u001B[38;5;28mself\u001B[39m.index)\n\u001B[32m   5287\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m is_list_like(value):\n\u001B[32m-> \u001B[39m\u001B[32m5288\u001B[39m     \u001B[43mcom\u001B[49m\u001B[43m.\u001B[49m\u001B[43mrequire_length_match\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvalue\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mindex\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   5289\u001B[39m arr = sanitize_array(value, \u001B[38;5;28mself\u001B[39m.index, copy=\u001B[38;5;28;01mTrue\u001B[39;00m, allow_2d=\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[32m   5290\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m (\n\u001B[32m   5291\u001B[39m     \u001B[38;5;28misinstance\u001B[39m(value, Index)\n\u001B[32m   5292\u001B[39m     \u001B[38;5;129;01mand\u001B[39;00m value.dtype == \u001B[33m\"\u001B[39m\u001B[33mobject\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m   (...)\u001B[39m\u001B[32m   5295\u001B[39m     \u001B[38;5;66;03m# TODO: Remove kludge in sanitize_array for string mode when enforcing\u001B[39;00m\n\u001B[32m   5296\u001B[39m     \u001B[38;5;66;03m# this deprecation\u001B[39;00m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\common.py:573\u001B[39m, in \u001B[36mrequire_length_match\u001B[39m\u001B[34m(data, index)\u001B[39m\n\u001B[32m    569\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    570\u001B[39m \u001B[33;03mCheck the length of data matches the length of the index.\u001B[39;00m\n\u001B[32m    571\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    572\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(data) != \u001B[38;5;28mlen\u001B[39m(index):\n\u001B[32m--> \u001B[39m\u001B[32m573\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[32m    574\u001B[39m         \u001B[33m\"\u001B[39m\u001B[33mLength of values \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    575\u001B[39m         \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m(\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mlen\u001B[39m(data)\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m) \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    576\u001B[39m         \u001B[33m\"\u001B[39m\u001B[33mdoes not match length of index \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    577\u001B[39m         \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m(\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mlen\u001B[39m(index)\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m)\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    578\u001B[39m     )\n",
-      "\u001B[31mValueError\u001B[39m: Length of values (3) does not match length of index (375240)"
+      "\u001B[31mNameError\u001B[39m                                 Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[10]\u001B[39m\u001B[32m, line 3\u001B[39m\n\u001B[32m      1\u001B[39m all_dfs = [df_mech_brake_pressed, df_accel_position]\n\u001B[32m      2\u001B[39m combined_df = combine_dfs([\u001B[33m\"\u001B[39m\u001B[33mmech_brake_pressed\u001B[39m\u001B[33m\"\u001B[39m, \u001B[33m\"\u001B[39m\u001B[33maccel_position\u001B[39m\u001B[33m\"\u001B[39m], df_mech_brake_pressed.index, all_dfs)\n\u001B[32m----> \u001B[39m\u001B[32m3\u001B[39m dfs = [\u001B[43mpos_df\u001B[49m, speed_arr]\n\u001B[32m      4\u001B[39m df = combine_dfs([\u001B[33m\"\u001B[39m\u001B[33mposition\u001B[39m\u001B[33m\"\u001B[39m, \u001B[33m\"\u001B[39m\u001B[33mspeed\u001B[39m\u001B[33m\"\u001B[39m], pos_df.index, dfs)\n",
+      "\u001B[31mNameError\u001B[39m: name 'pos_df' is not defined"
      ]
     }
    ],
-   "execution_count": 18
+   "execution_count": 10
   },
   {
    "metadata": {
@@ -2006,30 +1976,34 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-04T03:28:54.818190Z",
-     "start_time": "2026-03-04T03:28:54.773514Z"
+     "end_time": "2026-03-06T01:17:46.392744Z",
+     "start_time": "2026-03-06T01:17:46.282163Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "import DataPreprocessing\n",
+    "\n",
+    "from control_model import DataPreprocessing\n",
+    "from control_model.RNN import *\n",
+    "from control_model.RNN_Dataset import *\n",
     "train_dataset, test_dataset, train_loader, test_loader, scaler = DataPreprocessing.make_sequence_datasets(final_df, state_cols = ['0_x', '0_y'], control_cols = ['mech_brake_pressed', 'accel_position'], seq_len = 100, train_frac=0.7, batch_size = 64)"
    ],
-   "id": "caa83b2458af2b80",
+   "id": "c00f5becff80fe17",
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'final_df' is not defined",
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'RNN_Dataset'",
      "output_type": "error",
      "traceback": [
       "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mNameError\u001B[39m                                 Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[56]\u001B[39m\u001B[32m, line 2\u001B[39m\n\u001B[32m      1\u001B[39m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[34;01mDataPreprocessing\u001B[39;00m\n\u001B[32m----> \u001B[39m\u001B[32m2\u001B[39m train_dataset, test_dataset, train_loader, test_loader, scaler = DataPreprocessing.make_sequence_datasets(\u001B[43mfinal_df\u001B[49m, state_cols = [\u001B[33m'\u001B[39m\u001B[33m0_x\u001B[39m\u001B[33m'\u001B[39m, \u001B[33m'\u001B[39m\u001B[33m0_y\u001B[39m\u001B[33m'\u001B[39m], control_cols = [\u001B[33m'\u001B[39m\u001B[33mmech_brake_pressed\u001B[39m\u001B[33m'\u001B[39m, \u001B[33m'\u001B[39m\u001B[33maccel_position\u001B[39m\u001B[33m'\u001B[39m], seq_len = \u001B[32m100\u001B[39m, train_frac=\u001B[32m0.7\u001B[39m, batch_size = \u001B[32m64\u001B[39m)\n",
-      "\u001B[31mNameError\u001B[39m: name 'final_df' is not defined"
+      "\u001B[31mModuleNotFoundError\u001B[39m                       Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[13]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[34;01mcontrol_model\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m DataPreprocessing\n\u001B[32m      2\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[34;01mcontrol_model\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01mRNN\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m *\n\u001B[32m      3\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[34;01mcontrol_model\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01mRNN_Dataset\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m *\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\control_model\\DataPreprocessing.py:6\u001B[39m\n\u001B[32m      4\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[34;01mtorch\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01mutils\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01mdata\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m DataLoader\n\u001B[32m      5\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[34;01msklearn\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01mpreprocessing\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m StandardScaler\n\u001B[32m----> \u001B[39m\u001B[32m6\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[34;01mRNN_Dataset\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m RNN_Dataset\n\u001B[32m      9\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[34mmake_sequence_datasets\u001B[39m(\n\u001B[32m     10\u001B[39m     df_xy,\n\u001B[32m     11\u001B[39m     state_cols,\n\u001B[32m   (...)\u001B[39m\u001B[32m     16\u001B[39m     batch_size=\u001B[32m64\u001B[39m,\n\u001B[32m     17\u001B[39m ):\n\u001B[32m     20\u001B[39m     cols_to_scale = state_cols + control_cols\n",
+      "\u001B[31mModuleNotFoundError\u001B[39m: No module named 'RNN_Dataset'"
      ]
     }
    ],
-   "execution_count": 56
+   "execution_count": 13
   },
   {
    "metadata": {},
@@ -2072,7 +2046,7 @@
    "cell_type": "code",
    "source": [
     "\n",
-    "from RNN import *\n",
+    "from control_model.RNN import *\n",
     "state   = [\"0_x\", \"0_y\"]\n",
     "control = [\"mech_brake_pressed\", \"accel_position\"]\n",
     "seq_length = 100\n",
diff --git a/array_temp/control_model_revised.ipynb b/array_temp/control_model_revised.ipynb
deleted file mode 100644
index 0d74fac..0000000
--- a/array_temp/control_model_revised.ipynb
+++ /dev/null
@@ -1,1477 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "id": "initial_id",
-   "metadata": {
-    "collapsed": true,
-    "ExecuteTime": {
-     "end_time": "2026-03-05T19:31:59.447829Z",
-     "start_time": "2026-03-05T19:31:41.951092Z"
-    }
-   },
-   "source": [
-    "#necessary imports\n",
-    "import sklearn as sk\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from array_temp.DataPreprocessing import make_sequence_datasets\n",
-    "\n",
-    "\n",
-    "from data_tools import query\n",
-    "from data_tools.collections import TimeSeries\n",
-    "from datetime import datetime, date, time, timezone\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "import dill\n",
-    "import os\n",
-    "import pytz\n",
-    "from datetime import datetime, time, date\n"
-   ],
-   "outputs": [],
-   "execution_count": 1
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T22:49:33.537716Z",
-     "start_time": "2026-03-04T22:48:51.973787Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "# query data from influx. looking at timestamps of 2024 FSGP: 14 - 18 overall, but for smaller data response lets try 14 -16\n",
-    "\n",
-    "#each 5 seconds\n",
-    "utc_offset_h = 7\n",
-    "start_utc = time(0+utc_offset_h, 00, 00)  #querying i svancouver time, influxdb gives utc\n",
-    "stop_utc = time(16+utc_offset_h, 45, 00)\n",
-    "date_start = date(2024, 7, 16)\n",
-    "date_stop = date(2024, 7, 18)\n",
-    "\n",
-    "vancouver = pytz.timezone(\"America/Vancouver\")\n",
-    "\n",
-    "start_local = vancouver.localize(datetime.combine(date_start, start_utc))\n",
-    "stop_local = vancouver.localize(datetime.combine(date_stop, stop_utc))\n",
-    "\n",
-    "start_time = start_local.astimezone(pytz.utc)\n",
-    "stop_time = stop_local.astimezone(pytz.utc)\n",
-    "\n",
-    "client = query.DBClient()\n",
-    "mech_brake_pressed: TimeSeries = client.query_time_series(start_time, stop_time, field=\"MechBrakePressed\")\n",
-    "accel_position: TimeSeries =   client.query_time_series(start_time, stop_time, field=\"AcceleratorPosition\")\n",
-    "speed_kph: TimeSeries = client.query_time_series(start_time, stop_time, \"VehicleVelocity\")\n",
-    "\n"
-   ],
-   "id": "bee61f82f0e2b463",
-   "outputs": [],
-   "execution_count": 2
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T22:50:03.743455Z",
-     "start_time": "2026-03-04T22:50:03.690534Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "\n",
-    "# save collected data\n",
-    "\n",
-    "out_dir = os.path.join(\"../../array_temp\", \"data\", \"control_state_fsgp_2024\")\n",
-    "os.makedirs(out_dir, exist_ok=True)\n",
-    "\n",
-    "brake_path = os.path.join(out_dir, \"brake_pressed.bin\")\n",
-    "accel_path = os.path.join(out_dir, \"acceleration.bin\")\n",
-    "speed_path = os.path.join(out_dir, \"speed_kph.bin\")\n",
-    "\n",
-    "filepaths = [brake_path, accel_path, speed_path]\n",
-    "datasets = [mech_brake_pressed, accel_position, speed_kph]\n",
-    "\n",
-    "for filepath, data in zip(filepaths, datasets):\n",
-    "    with open(filepath, \"wb\") as f:\n",
-    "        dill.dump(data, f)\n"
-   ],
-   "id": "d2c1546f1e2bbbeb",
-   "outputs": [],
-   "execution_count": 3
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-05T19:33:01.140035Z",
-     "start_time": "2026-03-05T19:33:01.002991Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "#loading data\n",
-    "import os\n",
-    "import dill\n",
-    "out_dir = os.path.join(\"../../array_temp\", \"data\", \"control_state_fsgp_2024\")\n",
-    "\n",
-    "brake_path = os.path.join(out_dir, \"brake_pressed.bin\")\n",
-    "accel_path = os.path.join(out_dir, \"acceleration.bin\")\n",
-    "speed_path = os.path.join(out_dir, \"speed_kph.bin\")\n",
-    "\n",
-    "filepaths = [brake_path, accel_path, speed_path]\n",
-    "\n",
-    "loaded_datasets = []\n",
-    "\n",
-    "for filepath in filepaths:\n",
-    "    with open(filepath, \"rb\") as f:\n",
-    "        data = dill.load(f)\n",
-    "        loaded_datasets.append(data)\n",
-    "\n",
-    "#unnpack\n",
-    "mech_brake_pressed, accel_position, speed_kph = loaded_datasets"
-   ],
-   "id": "d309d06cd7c5f22d",
-   "outputs": [],
-   "execution_count": 2
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-05T19:33:02.257426Z",
-     "start_time": "2026-03-05T19:33:02.235778Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "# use sunbeam instead to save yourself a headache\n",
-    "from data_tools import *\n",
-    "import numpy as np\n",
-    "def make_df(source, name):\n",
-    "    dfs = []\n",
-    "\n",
-    "    client = query.SunbeamClient()\n",
-    "\n",
-    "    for event in [\"FSGP_2024_Day_1\", \"FSGP_2024_Day_2\", \"FSGP_2024_Day_3\"]:\n",
-    "        file = client.get_file(\n",
-    "            origin=\"production\",\n",
-    "            event=event,\n",
-    "            source=source,\n",
-    "            name=name\n",
-    "        ).unwrap()\n",
-    "\n",
-    "        dfs.append(\n",
-    "            pd.DataFrame(\n",
-    "                data=file.data,\n",
-    "                index=file.data.datetime_x_axis\n",
-    "            )\n",
-    "        )\n",
-    "\n",
-    "    return pd.concat(dfs).sort_index()"
-   ],
-   "id": "8244964eb0c13987",
-   "outputs": [],
-   "execution_count": 3
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-05T19:33:04.703377Z",
-     "start_time": "2026-03-05T19:33:02.964209Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "pos = []\n",
-    "pos_df = make_df(source = \"localization\", name = \"TrackIndex\")"
-   ],
-   "id": "d3a364dd0f5e0dc0",
-   "outputs": [],
-   "execution_count": 4
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-05T19:33:21.758559Z",
-     "start_time": "2026-03-05T19:33:19.845165Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "speed_df = make_df(source = \"ingress\", name = \"VehicleVelocity\")",
-   "id": "32163d52d6931b1c",
-   "outputs": [],
-   "execution_count": 5
-  },
-  {
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-     "start_time": "2026-03-05T19:33:25.439698Z"
-    }
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-   "cell_type": "code",
-   "source": [
-    "# combine all dfs and resample, then feed to scaler.\n",
-    "# states = velocity, position\n",
-    "# control = mbrake pressed, accelerator position\n",
-    "def combine_dfs(telemetry_names, index_common, all_dfs):\n",
-    "        combined_df = pd.DataFrame(index=index_common)\n",
-    "        combined_df.dropna()\n",
-    "\n",
-    "        for name, df in zip(telemetry_names, all_dfs):\n",
-    "            #df_interp = self.resample(df, index_common)\n",
-    "            combined_df[name] = df\n",
-    "\n",
-    "        return combined_df\n"
-   ],
-   "id": "8f9608786c013420",
-   "outputs": [],
-   "execution_count": 6
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-05T19:33:29.392736Z",
-     "start_time": "2026-03-05T19:33:26.152247Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "#preprocessing - convert everything to pandas dataframes.\n",
-    "\n",
-    "\n",
-    "df_mech_brake_pressed = pd.DataFrame(mech_brake_pressed, index = mech_brake_pressed.datetime_x_axis)\n",
-    "df_accel_position = pd.DataFrame(accel_position, index = accel_position.datetime_x_axis)\n",
-    "#df_speed_kph = pd.DataFrame(speed_kph)\n",
-    "\n",
-    "\n",
-    "\n",
-    "all_dfs = [df_mech_brake_pressed, df_accel_position]\n",
-    "combined_df = combine_dfs([\"mech_brake_pressed\", \"accel_position\"], df_mech_brake_pressed.index, all_dfs)\n"
-   ],
-   "id": "cda1ae8e12adbc5e",
-   "outputs": [],
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-    "final_df = pd.merge_asof(\n",
-    "    df_mech_brake_pressed.sort_index(),\n",
-    "    df_accel_position.sort_index(),\n",
-    "    left_index=True,\n",
-    "    right_index=True,\n",
-    "    direction=\"nearest\"\n",
-    ")"
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-   "execution_count": 18
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-   "cell_type": "code",
-   "source": [
-    "\n",
-    "dfs = pd.concat([pos_df, speed_df], axis = 1)\n",
-    "#df = ([\"position\", \"speed\"], pos_df.index, dfs)"
-   ],
-   "id": "6ba2f86b4ef3b7d6",
-   "outputs": [],
-   "execution_count": 9
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-   "cell_type": "code",
-   "source": [
-    "final_df = pd.merge_asof(\n",
-    "    final_df.sort_index(),\n",
-    "    dfs.sort_index(),\n",
-    "    left_index=True,\n",
-    "    right_index=True,\n",
-    "    direction=\"nearest\"\n",
-    ")"
-   ],
-   "id": "88421e0bc978e5e4",
-   "outputs": [],
-   "execution_count": 10
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-   "cell_type": "code",
-   "source": "dfs.sort_index().head()",
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-       "<Figure size 640x480 with 1 Axes>"
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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 15
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-05T19:33:45.765Z",
-     "start_time": "2026-03-05T19:33:45.755926Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "final_df.columns = [\"brake_pressed\", \"accel_position\", \"position\", \"speed\"]",
-   "id": "b121a9351e0aba47",
-   "outputs": [],
-   "execution_count": 11
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-05T19:33:46.667704Z",
-     "start_time": "2026-03-05T19:33:46.632466Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "final_df.head()",
-   "id": "20dc87aea5bc9336",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "                               brake_pressed  accel_position  position  speed\n",
-       "2024-07-16 07:05:27.380000114            0.0             0.0       0.0    0.0\n",
-       "2024-07-16 07:05:27.480000019            0.0             0.0       0.0    0.0\n",
-       "2024-07-16 07:05:27.580000162            0.0             0.0       0.0    0.0\n",
-       "2024-07-16 07:05:27.680000067            0.0             0.0       0.0    0.0\n",
-       "2024-07-16 07:05:27.780000210            0.0             0.0       0.0    0.0"
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>brake_pressed</th>\n",
-       "      <th>accel_position</th>\n",
-       "      <th>position</th>\n",
-       "      <th>speed</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2024-07-16 07:05:27.380000114</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2024-07-16 07:05:27.480000019</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2024-07-16 07:05:27.580000162</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2024-07-16 07:05:27.680000067</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2024-07-16 07:05:27.780000210</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 12
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-05T01:52:54.223011Z",
-     "start_time": "2026-03-05T01:52:54.206405Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "print(final_df.isna().sum())\n",
-    "print(final_df.shape)\n",
-    "# position has nan values"
-   ],
-   "id": "6e311b63077819a9",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "brake_pressed          0\n",
-      "accel_position         0\n",
-      "position          825917\n",
-      "speed                  0\n",
-      "dtype: int64\n",
-      "(2011945, 4)\n"
-     ]
-    }
-   ],
-   "execution_count": 18
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-05T19:33:51.345807Z",
-     "start_time": "2026-03-05T19:33:50.937643Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "final_df = final_df.sort_index()\n",
-    "final_df = final_df.ffill().dropna()"
-   ],
-   "id": "f143094a7164ac55",
-   "outputs": [],
-   "execution_count": 13
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-05T01:52:57.454058Z",
-     "start_time": "2026-03-05T01:52:57.062763Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "plt.plot(final_df[\"position\"])",
-   "id": "ed4054acb3cc0dc6",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x2112737ad50>]"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 20
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-05T19:33:58.095873Z",
-     "start_time": "2026-03-05T19:33:57.162966Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "#finally cleared up all data, now move onto RNN\n",
-    "import DataPreprocessing\n",
-    "train_dataset, test_dataset, train_loader, test_loader, scaler = DataPreprocessing.make_sequence_datasets(final_df, state_cols = ['position', 'speed'], control_cols = ['brake_pressed', 'accel_position'], seq_len = 600, train_frac=0.7, batch_size = 128)"
-   ],
-   "id": "d57a320f8d3cb4d8",
-   "outputs": [],
-   "execution_count": 14
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-05T19:33:59.630325Z",
-     "start_time": "2026-03-05T19:33:59.608997Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "state = ['position', 'speed']\n",
-    "control = ['brake_pressed', 'accel_position']"
-   ],
-   "id": "d1d762ae92b287c",
-   "outputs": [],
-   "execution_count": 15
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-05T19:34:00.292983Z",
-     "start_time": "2026-03-05T19:34:00.275143Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "import pandas as pd\n",
-    "import torch\n",
-    "from torch import nn\n",
-    "from torch.utils.data import DataLoader\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from RNN_Dataset import RNN_Dataset"
-   ],
-   "id": "1410b1caa1d1c46b",
-   "outputs": [],
-   "execution_count": 16
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-05T19:34:01.711645Z",
-     "start_time": "2026-03-05T19:34:01.629599Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "\n",
-    "from RNN import *\n",
-    "seq_length = 600\n",
-    "input_size = len(state)\n",
-    "output_size = len(control)\n",
-    "\n",
-    "\n",
-    "hidden_size = 128     # was 256\n",
-    "num_layers  = 2\n",
-    "model = RNN(input_size, hidden_size, num_layers, seq_length, output_size).to(device)"
-   ],
-   "id": "154939dd8005f9b7",
-   "outputs": [],
-   "execution_count": 17
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-05T19:34:18.008584Z",
-     "start_time": "2026-03-05T19:34:17.984795Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "# now we define the training loop. we are pretending a trajectory of controls.\n",
-    "\n",
-    "#build the model\n",
-    "\n",
-    "\n",
-    "def train_model(model, train_loader, test_loader, epochs):\n",
-    "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "    model = model.to(device)\n",
-    "    criterion = nn.MSELoss()\n",
-    "    optimizer = torch.optim.Adam(model.parameters(), lr=1e-4, weight_decay=1e-5)\n",
-    "    train_losses = []\n",
-    "    test_losses = []\n",
-    "    print(\"NaNs in Train Loader:\", any(torch.isnan(x).any() for x, y in train_loader))\n",
-    "    print(\"NaNs in Test Loader:\", any(torch.isnan(x).any() for x, y in test_loader))\n",
-    "    for epoch in range(epochs):\n",
-    "\n",
-    "        #training loop\n",
-    "        model.train()\n",
-    "        train_loss = 0\n",
-    "        for x_batch, y_batch in train_loader:\n",
-    "            x_batch = x_batch.to(device)\n",
-    "            y_batch = y_batch.to(device)\n",
-    "            #check inputs before forward pass\n",
-    "            if torch.isnan(x_batch).any() or torch.isnan(y_batch).any():\n",
-    "                print(\"NaN in inputs, skipping batch\")\n",
-    "                continue\n",
-    "\n",
-    "\n",
-    "          #  optimizer.zero_grad() #resets the gradients to zero\n",
-    "            outputs = model(x_batch)\n",
-    "            loss = criterion(outputs, y_batch)\n",
-    "            if torch.isnan(outputs).any():\n",
-    "                print(\"NaN in model outputs\")\n",
-    "                print(\"x_batch min/max:\", x_batch.min().item(), x_batch.max().item())\n",
-    "                continue\n",
-    "\n",
-    "            loss = criterion(outputs, y_batch)\n",
-    "\n",
-    "            if torch.isnan(loss):\n",
-    "                print(\"NaN in loss\")\n",
-    "                print(\"outputs min/max:\", outputs.min().item(), outputs.max().item())\n",
-    "                print(\"y_batch min/max:\", y_batch.min().item(), y_batch.max().item())\n",
-    "                continue\n",
-    "            loss.backward()\n",
-    "            torch.nn.utils.clip_grad_norm_(model.parameters(), 1)\n",
-    "            optimizer.step()\n",
-    "\n",
-    "            train_loss += loss.item() #convert tensor to float\n",
-    "        train_loss/=len(train_loader) #average losses over batches\n",
-    "        print(f\"Epoch {epoch + 1}/{epochs}, Train Loss: {train_loss:.4f}\")\n",
-    "        train_losses.append(train_loss)\n",
-    "\n",
-    "        # testing loop\n",
-    "        model.eval()\n",
-    "        test_loss = 0\n",
-    "        with torch.no_grad():\n",
-    "            for x_batch, y_batch in test_loader:\n",
-    "                x_batch = x_batch.to(device)\n",
-    "                y_batch = y_batch.to(device)\n",
-    "                optimizer.zero_grad()\n",
-    "                predictions = model(x_batch)\n",
-    "                loss = criterion(predictions, y_batch)\n",
-    "                test_loss += loss.item()\n",
-    "        test_loss/=len(test_loader)\n",
-    "        test_losses.append(test_loss)\n",
-    "\n",
-    "    return train_losses, test_losses"
-   ],
-   "id": "902960e5d1a51324",
-   "outputs": [],
-   "execution_count": 18
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-05T19:57:27.552267Z",
-     "start_time": "2026-03-05T19:34:22.313235Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "train_losses, test_losses = train_model(model, train_loader, test_loader, epochs=10)\n",
-    "\n",
-    "# Save\n",
-    "torch.save({\n",
-    "    'model_state_dict': model.state_dict(),\n",
-    "    'input_size': input_size,\n",
-    "    'hidden_size': hidden_size,\n",
-    "    'num_layers': num_layers,\n",
-    "    'seq_length': seq_length,\n",
-    "    'output_size': output_size,\n",
-    "}, 'rnn_model.pt')"
-   ],
-   "id": "95f0eac03ab75052",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "NaNs in Train Loader: False\n",
-      "NaNs in Test Loader: False\n",
-      "Epoch 1/10, Train Loss: 0.8640\n",
-      "Epoch 2/10, Train Loss: 0.7180\n",
-      "Epoch 3/10, Train Loss: 0.7143\n",
-      "Epoch 4/10, Train Loss: 0.7126\n",
-      "Epoch 5/10, Train Loss: 0.7116\n",
-      "Epoch 6/10, Train Loss: 0.7109\n",
-      "Epoch 7/10, Train Loss: 0.7087\n",
-      "Epoch 8/10, Train Loss: 0.7094\n",
-      "Epoch 9/10, Train Loss: 0.7076\n",
-      "Epoch 10/10, Train Loss: 0.7031\n"
-     ]
-    }
-   ],
-   "execution_count": 19
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-05T19:58:07.816380Z",
-     "start_time": "2026-03-05T19:58:07.798335Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "print(test_losses)",
-   "id": "422439d81b1a514a",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.8061733793001622, 0.8193665951257572, 0.7946931755868718, 0.814770182284216, 0.8071958554210141, 0.7809592337580398, 0.7720132017663369, 0.7666141268952439, 0.7487051921198145, 0.7230261662819734]\n"
-     ]
-    }
-   ],
-   "execution_count": 21
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
-   "source": [
-    "#remember we also need to save the scaler to apply at inference\n",
-    "#using job lib for this purpose\n",
-    "import joblib\n",
-    "joblib.dump(scaler, 'scaler.pkl')\n",
-    "\n",
-    "# Later at inference\n",
-    "scaler = joblib.load('scaler.pkl')\n",
-    "x_new_scaled = scaler.transform(x)"
-   ],
-   "id": "be2c544830173066"
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-05T19:57:52.530545Z",
-     "start_time": "2026-03-05T19:57:52.486087Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "#now we evaluate model for inference\n",
-    "checkpoint = torch.load('rnn_model.pt', map_location=device)\n",
-    "\n",
-    "model = RNN(\n",
-    "    input_size  = checkpoint['input_size'],\n",
-    "    hidden_size = checkpoint['hidden_size'],\n",
-    "    num_layers  = checkpoint['num_layers'],\n",
-    "    seq_length  = checkpoint['seq_length'],\n",
-    "    output_size = checkpoint['output_size'],\n",
-    ")\n",
-    "model.load_state_dict(checkpoint['model_state_dict'])\n",
-    "model.eval()  # important — disables dropout/batchnorm for inference"
-   ],
-   "id": "23676ff6fae304c2",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RNN(\n",
-       "  (lstm): LSTM(2, 128, num_layers=2, batch_first=True)\n",
-       "  (fc): Linear(in_features=128, out_features=2, bias=True)\n",
-       ")"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 20
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-05T20:00:48.979478Z",
-     "start_time": "2026-03-05T20:00:48.954744Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "\n",
-    "def plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx):\n",
-    "    model.eval()\n",
-    "    x_input, y_target = test_dataset[sample_idx]\n",
-    "\n",
-    "# x_input should be [seq_len, n_states]\n",
-    "    print(x_input.shape)\n",
-    "\n",
-    "    x_np = x_input.numpy()  # [seq_len, n_states]\n",
-    "\n",
-    "    # derive time axes from actual array shapes, not seq_len variable\n",
-    "    time_controls = np.arange(y_target.shape[0]) * 0.1\n",
-    "    time_states   = np.arange(x_np.shape[0]) * 0.1\n",
-    "    x_input, y_target = test_dataset[sample_idx]\n",
-    "\n",
-    "    with torch.no_grad():\n",
-    "        device = next(model.parameters()).device\n",
-    "        x_tensor = x_input.to(device).unsqueeze(0)\n",
-    "        y_pred = model(x_tensor).squeeze(0).cpu().numpy()\n",
-    "\n",
-    "    y_target = y_target.numpy()\n",
-    "    x_np = x_input.numpy()  # [seq_len, n_states]\n",
-    "\n",
-    "    # inverse transform controls\n",
-    "    # scaler was fit on state_cols + control_cols so controls start at index n_states\n",
-    "    n_states = len(state_cols)\n",
-    "    n_controls = len(control_cols)\n",
-    "    seq_len = 600\n",
-    "    def unscale_states(arr):\n",
-    "        state_mean = scaler.mean_[:n_states]\n",
-    "        state_std  = scaler.scale_[:n_states]\n",
-    "        return arr * state_std + state_mean\n",
-    "    def unscale_controls(arr):\n",
-    "        dummy = np.zeros((seq_len, n_states + n_controls))\n",
-    "        dummy[:, n_states:] = arr\n",
-    "        return scaler.inverse_transform(dummy)[:, n_states:]\n",
-    "\n",
-    "\n",
-    "\n",
-    "    y_target_unscaled = unscale_controls(y_target)\n",
-    "    y_pred_unscaled   = unscale_controls(y_pred)\n",
-    "    x_unscaled        = unscale_states(x_np)\n",
-    "\n",
-    "    time_controls = np.arange(seq_len) * 0.1\n",
-    "    time_states   = np.arange(x_np.shape[0]) * 0.1\n",
-    "\n",
-    "    # plot controls\n",
-    "    fig, axes = plt.subplots(n_controls, 1, figsize=(10, 4 * n_controls), sharex=True)\n",
-    "    if n_controls == 1:\n",
-    "        axes = [axes]\n",
-    "\n",
-    "    for i, col in enumerate(control_cols):\n",
-    "        axes[i].plot(time_controls, y_target_unscaled[:, i], 'g-', label='Actual (Driver)')\n",
-    "        axes[i].plot(time_controls, y_pred_unscaled[:, i], 'r--', label='Predicted (RNN)')\n",
-    "        axes[i].set_ylabel(col)\n",
-    "        axes[i].legend()\n",
-    "        axes[i].grid(True)\n",
-    "\n",
-    "    axes[-1].set_xlabel(\"Time (seconds)\")\n",
-    "    plt.suptitle(f\"Control Trajectory — Sample {sample_idx}\")\n",
-    "    plt.tight_layout()\n",
-    "\n",
-    "    # plot states\n",
-    "    fig2, axes2 = plt.subplots(n_states, 1, figsize=(10, 4 * n_states), sharex=True)\n",
-    "    if n_states == 1:\n",
-    "        axes2 = [axes2]\n",
-    "    print(x_np.shape)        # should be [seq_len, n_states]\n",
-    "    print(x_unscaled.shape)  # should match\n",
-    "    print(time_states.shape)\n",
-    "    for i, col in enumerate(state_cols):\n",
-    "        axes2[i].plot(time_states, x_unscaled[:, i], 'b-', label=col)\n",
-    "        axes2[i].set_ylabel(col)\n",
-    "        axes2[i].legend()\n",
-    "        axes2[i].grid(True)\n",
-    "\n",
-    "    axes2[-1].set_xlabel(\"Time (seconds)\")\n",
-    "    plt.suptitle(f\"Input State Sequence — Sample {sample_idx}\")\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()"
-   ],
-   "id": "193451d65b640afb",
-   "outputs": [],
-   "execution_count": 25
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-05T20:01:20.889474Z",
-     "start_time": "2026-03-05T20:01:20.677195Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "for i in range(len(test_dataset)):\n",
-    "    x, y = test_dataset[i]\n",
-    "    if y.abs().mean() >= 0.88:\n",
-    "        print(f\"use sample_idx={i}\")\n",
-    "        break"
-   ],
-   "id": "e02909466d882593",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "use sample_idx=3140\n"
-     ]
-    }
-   ],
-   "execution_count": 27
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-05T20:01:28.777078Z",
-     "start_time": "2026-03-05T20:01:27.371664Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "state_cols = ['position', 'speed']\n",
-    "control_cols = ['brake_pressed', 'accel_position']\n",
-    "plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx=3140)\n",
-    "plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx=150)"
-   ],
-   "id": "7ca721093c670d9",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([600, 2])\n",
-      "(600, 2)\n",
-      "(600, 2)\n",
-      "(600,)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1000x800 with 2 Axes>"
-      ],
-      "image/png": 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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1000x800 with 2 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([600, 2])\n",
-      "(600, 2)\n",
-      "(600, 2)\n",
-      "(600,)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1000x800 with 2 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1000x800 with 2 Axes>"
-      ],
-      "image/png": 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yKGcX8wzy2cqbE1DlBFPZdbjWNTcDZ87CnWXKFtcMuhlQcvKsfI58rJydPANijtvNScoy7OSs3Tn2utaNOA8CZPfw7Jp9wQUXlMfLgwk5RjkD2vgODtTkwYarr766lDnDSrYG5/Nm9/KczTtfc83EPEe21Ga5sgt0TiKXrbIZlJrPdJ2BLANtvqY8b3m2uOfpu7JlvflpnWr3z5b4fM4Me9nCmveZlPG5+XnnY2Q4rb2vGeLz9dUOKOSM8xngMvxnK3i2zuZnkwcRprQ8fVse+Mk6lAcs8sBFvu/ZPTpn3K+16uY2GSgzCGe9yteSE8ZdeumlpZ5kfamdZ/77mnXWWUugz88qD8astNJKpT5OaLbx71sPJ0Ye0MrPLh87P5c8SJPnf8/6UOv9ketzlvr8H8pTy+UBlmzhzv/vKTkDPQDfraFxcvTJAqDVZVfX1VdfvYTAPO0WrSPDX56zOVsraRvyAEUeqMqeCwAwuRnTDQAAABURugEAAKAiupcDAABARbR0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAiQjcAAABUpEtVD9weDR36eTQ2Rt1qaIjo06d33ZeT1qOOoI7guwS/N9QL+yW09TpSK993EbonQX7Q9fhht9Vy0nrUEdQRfJfg94Z6Yb+E9l5HdC8HAACAigjdAAAAUBGhGwAAACpiTDcAAEAdGzt2bIwZMzo6moaGiBEjRsTXX49qlTHdnTt3iU6d/vt2aqEbAACgDjU2NsZnnw2L4cO/iI5q2LBO5aBDa+nRo1dMM80M0ZBHAL4noRsAAKAO1QJ3r17TR9eu3f6r4NdWde7cEGPGNLbKAY9Ro0bGF198XG5PO22f7/1YQjcAAECdGTt2TFPg7tVrmuiounTpFKNHt05Ldx7oSBm8e/ee/nt3NTeRGgAAQJ0ZM2ZMi+BH66i9///NmHqhGwAAoE51xC7l7e39F7oBAACgIkI3AAAAVEToBgAAoK5tssl6cdttt5TrX331Zdx++63jXVePzF4OAABAXTv//Eti6ql7lOtXXnl5PPXUE7HWWut+Y109EroBAACoa9NPP32Lc2hPaF090r0cAACgjci8+eWXU/YyTsb9Vh988H70779s3HXXHbHhhmvFz38+IE477eQYPfo/p9x6+OG/xY47bhWrrfbj2HrrTeOBB+5ruu+rr74Su+22Y6y++o/Lff/4x/O/0YU8L7n8mWeeKs/TfF0aO3Zs/PnPl8Smm25QnmOvvXaN119/relx8j533nlbbLPNZrHqqivG7rv/Mt5//72okpZuAACANiDD77rrTh2PP955ij7v8suPjltuGR6TcvasP/7xvDjqqOPL+a2POebw6NGjRyy77PJxyCEHxO677x0rrPDj+Pvf/xaHH35QnHvun2LBBReKY489IhZffMk4/PBj4p133o5DDx0YiyyySCy//EpNj7v66mvGG2+8Hs8//1wcd9yg8Tzv+XHjjdfFb35zSMw++w/j8ssvjv333yuuuOL6UoZ04YXnxm9+c2hpIT/ssAPj/PP/EEcccWxURUs3AABAG9HQMAnNzq0og/USSywZSy+9bPzyl7vFLbfcGNdee1UMGLB6bLbZlvHDH84Zm2++dQwYsFpcccWl5T4ffvh+TDvttPGDH8wSK6ywUpx22tmxwAILtnjcbt26l/DcpUuX6NOnb4t12e38uuuuLs/Xv/9PYq655i7hulOnTqV1u+Z//merWGaZ5WKeeeaLDTfcJF588YVK3wst3QAAAG1AtjRni/NXX03Z55166v8896RYbLElm64vuODC8cknH8c777wVG2zwixbbLbroEvGXv9xcrm+zzQ5x7rlnxU03XR8rrdQ/fvaztUuwHj167EQ958cfD4vPPvs0Fl540aZlGc7z+d9++62mZXPM8cOm6z179iyt8VUSugEAANqIDL89e0bdy7BbM3bsmPJ31KhR39gu19XWb7319rHaamvGgw/eX8Z+77PPr+LAAw+NddbZYKKes2vXbuNd3vw5xi3b+CZmm9x0LwcAAGCyevXVl5uuv/TSi9G374yx8MKLxD//+Y8W2z3//D9KV/ORI0eWCdemmmqq0u38978/N9Zff6O4//57v/HYDRNodu/Vq1fMMEOfFs+RE7i9/PJL5Tlai9ANAADAZHX66b+Ll156IR5//NG44IJzYuONN43NNtsq/vrXe+Pqq6+Id999J6666vLSqr3RRptGt27d4rnnnolTTz2pdEPP+z777NOxwAILfOOxu3fvEUOGDCkzpY/rf/5nyzJR2kMPPRhvvfVmnHjisTFq1MhYbbWfttonrHs5AAAAk1XOMn7AAf8bjY1jy2Rl2XU8JzQ77LCj46KLzos//OGM0vp89NHHl0nNUl4/5ZQT45e/3C46d+4cq622Ruyww87feOyf/GTVuOmm68opx6699j+nCqvJVvIvv/wyBg06Lr788osyZjxbzVvzXN4NjVV3YG9Hhgz5fJLOUTelZS+Lvn171305aT3qCOoIvkvwe0O9sF/y7b7+elQMHfpB9OkzS0w1VddoK7L1edNN149rrrk5Zpll1v/68bp06TTRE6lN6c+hVoe/i+7lAAAAUBGhGwAAACpiTDcAAACTRXYpf+ihJ7ybzWjpBgAAqFOm4Gr777/QDQAAUGdy9u6Up7ui9dTe/86dv38ncd3LAQAA6kynTp2jR49e8cUXH5fbXbt2i4acLruDGTu2IcaMaWyVFu4M3Pn+5+eQpzv7voRuAACAOjTNNDOUv7Xg3RF16tQpxo5tvVOGZeCufQ7fl9ANAABQh7Jle9pp+0Tv3tPHmDGjo6NpaIiYfvqe8fHHX8ZkGFo9ybJL+X/Twl0jdAMAANSxDH6dOnWNjhi6u3fvHlNN9XWrhO7JxURqAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCOH7lGjRsW6664bjz766AS3eeGFF2LTTTeNJZZYIn7xi1/E888/P97tbr/99lhggQUqLC0AAAC0kdA9cuTI2G+//eLVV1+d4DZfffVV7LLLLrHsssvG9ddfH0sttVTsuuuuZXlzn332WRx33HFToNQAAABQ56H7tddei8022yzeeeedb93utttui27dusXAgQNj3nnnjUMOOSR69uwZd9xxR4vtBg0aFHPMMUfFpQYAAIA2ELofe+yx6NevX1x11VXfut2zzz4byyyzTDQ0NJTb+XfppZeOZ555psVj5WW33XarvNwAAACQutTz27DllltO1HaDBw+O+eabr8WyPn36NHVJzzHhhx12WBx++OEx1VRTfe/y/L9MX7dq5av3ctJ61BHUEXyX4PeGemG/hLZeRya2XHUduifW8OHDo2vXri2W5e0M2+mss86KRRZZJPr37/+tk7F9lz59ekdb0FbKSetRR1BH8F2C3xvqhf0S2nsdaRehO8dz1wJ2Td7u3r17vPLKK3H11VfHLbfc8l8/z9Chn0djY9StPNKSFbLey0nrUUdQR/Bdgt8b6oX9Etp6HamVr0OE7plnnjmGDBnSYlnenmmmmeKuu+6KTz/9NNZcc82yfMyYMeVvznB+1FFHxfrrrz/Rz5MfdD1+2G21nLQedQR1BN8l+L2hXtgvob3XkXYRuvPc3Oeff340NjaWSdTy71NPPVUmTVt99dVjvfXWazHp2gEHHBA33nhjGfcNAAAAHXL28u+aPG3EiBHl+s9//vOmc3Dnacbyb47zXmuttWK66aaLOeecs+mSreIpr/fq1auVXwUAAADtWZsN3TkpWp6fO2V4Pvfcc+PJJ5+MjTfeuLRmn3feeTH11FO3djEBAADowBoasy82E2XIkPocwN98IH/fvr3rvpy0HnUEdQTfJfi9oV7YL6Gt15Fa+dptSzcAAADUO6EbAAAAKiJ0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAiQjcAAABUROgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAiQjcAAABUROgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAADQkUP3qFGjYt11141HH310gtu88MILsemmm8YSSywRv/jFL+L5559vWtfY2BjnnXderLbaarH00kvHdtttF6+99toUKj0AAAAdVd2H7pEjR8Z+++0Xr7766gS3+eqrr2KXXXaJZZddNq6//vpYaqmlYtdddy3L05VXXhkXXXRRHHbYYXHdddfF7LPPHjvvvHMMHz58Cr4SAAAAOpq6Dt3ZGr3ZZpvFO++8863b3XbbbdGtW7cYOHBgzDvvvHHIIYdEz54944477ijrb7jhhthxxx1j1VVXjbnnnjuOPPLI+OSTT+Kpp56aQq8EAACAjqhL1LHHHnss+vXrF/vuu28sueSSE9zu2WefjWWWWSYaGhrK7fyb3cifeeaZ2HjjjUsYz9btmlyfXc4///zzSSrP/3v4ulUrX72Xk9ajjqCO4LsEvzfUC/sltPU6MrHlquvQveWWW07UdoMHD4755puvxbI+ffo0dUnPbufNXXPNNTF69OgS1CdFnz69oy1oK+Wk9agjqCP4LsHvDfXCfgntvY7UdeieWDk2u2vXri2W5e2cgG18reInnnhi7LTTTjHjjDNO0vMMHfp5NDZG3cojLVkh672ctB51BHUE3yX4vaFe2C+hrdeRWvk6ROjO8dzjBuy83b179xbLnn766TKB2iqrrBL77LPPJD9PftD1+GG31XLSetQR1BF8l+D3hnphv4T2XkfqeiK1iTXzzDPHkCFDWizL2zPNNFPT7TzdWE6mtsIKK8Tvfve76NSpXbx0AAAA6li7SJ55bu5sxc7J0VL+zZnJc3l65ZVX4le/+lWsvPLKcdppp8VUU03VyiUGAACgI2izoTsnTxsxYkS5/vOf/zw+++yzOO6448ppxvJvjvNea621yvrDDz88ZpllljjooIPi448/Lvdtfn8AAACoQpsN3f379y/n5069evWKc889N5588slyirCcLO28886LqaeeuoTrbAXPMD5gwIByv9qldn8AAACoQpuZSO3ll1/+1tuLL7543HDDDd+4X85QPu62AAAAMCW02ZZuAAAAqHdCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAiQjcAAABUROgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIl2qeND33nsvTjvttPjHP/4Ro0ePjsbGxhbr77333iqeFgAAANp/6B44cGB8/PHHsdVWW0WvXr2qeAoAAADomKH7ueeeixtuuCHmm2++Kh4eAAAAOu6Y7rnmmiuGDRtWxUMDAABAx27p3nnnnePQQw+NHXbYIeacc86YaqqpWqxfbrnlqnhaAAAA6BhjutNRRx31jXUNDQ3x4osvVvG0AAAA0P5D90svvVTFwwIAAECbUknoTiNGjIibb745Xn/99RgzZkzMM888sfbaa8d0001X1VMCAABA+59I7ZVXXomf/vSn8Yc//CHef//9cjn33HNjrbXWitdee62KpwQAAICO0dJ93HHHxY9//OM45phjokuX/zzF6NGjy+Rqv/3tb+Oiiy6q4mkBAACg/bd0P/PMM2UG81rgTnk9lz399NNVPCUAAAB0jNA944wzxjvvvPON5bmsZ8+eVTwlAAAAdIzu5ZtvvnnpSr7PPvvE4osvXpY9++yzccYZZ8Smm25axVMCAABAxwjdO+20UwwfPjxOPvnk+PTTT8uyvn37xvbbbx877rhjFU8JAAAAHSN0NzQ0xF577VUuQ4cOjW7dukWvXr2qeCoAAABo/6H7xhtvLOfh7tq1a7n+bTbccMNJeuxRo0bFxhtvHIcddlj069dvvNu88MILccQRR5TTlc0333xx1FFHxaKLLtq0/tZbb43TTjstBg8eHP379y8zq88wwwyTVA4AAABoldCd47V/8pOflNCd17+tFXxSQvfIkSNj//33j1dffXWC23z11Vexyy67xHrrrRcnnHBCXHHFFbHrrrvG3XffHVNPPXU899xzccghh5QgvuCCC5ZTmh100EHl3OEAAABQ96H7vvvuG+/1cQ0bNmyiH/O1114rgbuxsfFbt7vttttKF/aBAweWUJ8B+8EHH4w77rijtJBfdtllsdZaazWF/UGDBsWqq64a7777bswxxxwTXR4AAABo9THdCy20UDz88MPf6L793nvvxbrrrjvR5+p+7LHHSnfyfffdN5ZccskJbpczoy+zzDIlcKf8u/TSS5fzhWfozvV5jvCaWWaZJWadddayvD2F7jw28eWX/7l8x3EKOqj8F+nRQx1BHcF3CX5vaH32S/gu7eVs05N1TPf1119frmfL9B577BFTTTVVi23+/e9/l3N4T6wtt9xyorbLcdo5jru5Pn36NHVJz+edaaaZvrH+ww8/jEnx/zJ9XcqQve66U8djj+Wt3q1dHOqeOoI6gu8S/N5QL+yXMH79+o2O//u/+s1hE1uuyRa611xzzfjXv/7V1EKdLdM9xzk0keOrc7vJLU9PlmPJm8vbOQFbGjFixLeun1h9+vSu69A9zjEOAACANqtLly51n8OmaOjOgL3nnnuW67PNNluss8463wi6Vcnx3OMG6LzdvXv3b13fI/vZToKhQz+v627bOWl8jx69676ctO7RuPzSUkdQR/Bdgt8bWpv9Er5LtuE2NNTvvmutDrfKKcNyTHVObja5Thn2XWaeeeYYMmRIi2V5u9alfELrJ6Wre8oPuh4/7OYfelbM4cPru5y0HnUEdQTfJfi9oV7YL6G95LA2f8qwibHEEkvE+eefX8aS5+Pn36eeeip22223pvVPPvlkmVQtffDBB+WSywEAAKDdnDJscsnJ03r37l26kP/85z+P3/3ud+X825tvvnlceeWVZZx3niYsbbHFFrHNNtuUceaLLbZY2W7AgAHtauZyAAAA6k+nqh44z5M9dOjQcv3aa6+NXXbZJU477bRJnrxsQvr379/Uhb1Xr15x7rnnNrVm56nAzjvvvDJxW1pqqaXi6KOPjrPOOqsE8GmnnTaOP/74yVIOAAAAmJCGxuyLPZlluL3gggviT3/6U4wcOTK233772HTTTeOJJ56I5ZdfPo444ohoi4YMqc8B/M3HxfTt27vuy0nrUUdQR/Bdgt8b6oX9Etp6HamVr1Vauq+++ur4/e9/X8ZM33TTTbHccsvFUUcdFSeccMK3TrAGAAAA7UklofvTTz+NeeaZp0xo9te//jVWXXXVpm7gY8aMqeIpAQAAoP1OpNbcggsuGBdeeGFMN910MWzYsFhzzTXjo48+ilNOOaVMZgYAAAAdQSUt3UceeWQZv33xxRfHfvvtF7PNNlsZ4/3ee++12fHcAAAAUDct3TmWu7kDDjignMMbAAAAOopKQnd64YUXShfzN954o4zjnnvuuWOrrbYqs5cDAABAR1BJ9/K77747NttsszKRWp43Oy8NDQ2x4447xj333FPFUwIAAEDHaOk+/fTT49e//nU5P3dzed7uPJXYGmusUcXTAgAAQPtv6X733XebThPWXC578803q3hKAAAA6Bihe955540HH3zwG8sfeOCBMpM5AAAAdASVdC/fa6+9yuXZZ5+NJZZYoix75pln4s4774xBgwZV8ZQAAADQMVq6sxt5npd7xIgRceWVV5bzdac///nPsfbaa1fxlAAAANAxWrrHjh0bjzzySDz11FMxbNiwsuzrr7+OBRZYIBZffPEqnhIAAAA6Rug+/vjj46677iozmC+66KIlhP/jH/+IM844I0aNGhV77rlnFU8LAAAA7T9033TTTXHmmWfG8ssv37RswQUXLJOoZRAXugEAAOgIKhnT3b1795hqqqm+sXyaaaaJhoaGKp4SAAAAOkboHjhwYBx88MFx//33xyeffBJffPFFPPHEE3HYYYfFdtttF++//37TBQAAANqrhsbGxsbJ/aDZlbzpCf5fy3bzp8lleTv/vvjii9FWDBnyeUz+d2vyybe6b9/edV9OWo86gjqC7xL83lAv7JfQ1utIrXytMqb73nvvreJhAQAAoE2pJHTnhGkAAADQ0VUyphsAAAAQugEAAKAyWroBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAiQjcAAABUROgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgI4YukeOHBkHH3xwLLvsstG/f/+46KKLJrjtQw89FOuvv34stdRSsf3228cbb7zRtK6xsTF+//vfxyqrrBLLLbdc/O///m8MGzZsCr0KAAAAOqq6Dt2DBg2K559/Pi6++OI44ogj4swzz4w77rjjG9u9+uqrseuuu8bqq68e1113XSy88MKx3XbbxZdfflnWX3XVVXHttdfGySefHJdffnn8+9//jkMOOaQVXhEAAAAdSd2G7q+++iquueaaEo4XWWSRWHPNNeOXv/xlCc3juuKKK0oL9z777BPzzDNPHHDAAdG7d++45ZZbyvoHHngg1l577Vh++eVj/vnnL4/zyCOPtMKrAgAAoCPpEnXqpZdeitGjR5cwXbPMMsvEOeecE2PHjo1Onf7/4wXvvvtuLL744k23GxoaSrh+5plnYvPNN4/pppsu/vrXv5Zu59NOO2385S9/iYUWWmiSy9TQEHWtVr56LyetRx1BHcF3CX5vqBf2S2jrdWRiy1W3oXvw4MEx/fTTR9euXZuW9e3bt4zz/uSTT2KGGWZosfyjjz5qcf8PP/ywBOy0xx57xK9+9asyprtz584x44wzli7nk6pPn97RFrSVctJ61BHUEXyX4PeGemG/hPZeR+o2dA8fPrxF4E6126NGjWqxfK211ordd9891l133Vh55ZVLt/J//OMf0a9fv7L+vffei+7du5dW8mmmmaaMFc8J2r5tYrbxGTr082hsjLqVR1qyQtZ7OWk96gjqCL5L8HtDvbBfQluvI7XytdnQ3a1bt2+E69rtDNDNZQt2tmbvtddeMWbMmBK2N9hgg/jiiy/KzOW/+c1vYuDAgbHqqquW7U877bRy/dlnn40lllhiosuUH3Q9fthttZy0HnUEdQTfJfi9oV7YL6G915G6nUht5plnjo8//riM627e5TwDd7ZWjyu7jz/11FPl1GF/+tOfyszls802Wzk12AcffBALLLBA07azzDJL6bqeLeAAAADQ4UJ3TnTWpUuXMhlazZNPPhmLLbZYi0nU0q233hrHHXdc6X7ep0+fGDFiRDz66KOlxTvHdefy119/vWn7DOI5Lnz22Wefoq8JAACAjqVuQ3ePHj1iww03jCOPPDKee+65uOeee8oY7G233bap1TvDdZprrrniyiuvjLvuuiveeuut2H///UtrdnY7z+C+8cYbx4knnhiPP/54vPLKK+WUYtmtPAM8AAAAdLjQnQ466KByju7tttsujjrqqDJm+6c//WlZ179//7jtttvK9UUXXbSE8xNOOKEE7HTuuec2tYjnpGl5vwzj22yzTemefvbZZ5dTiwEAAEBVGhpzpjEmypAh9TlrXk0eQ+jbt3fdl5PWo46gjuC7BL831Av7JbT1OlIrX5tu6QYAAIC2TOgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAiQjcAAABUROgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAjhu6RI0fGwQcfHMsuu2z0798/Lrrooglu+9BDD8X6668fSy21VGy//fbxxhtvtFh/xx13xM9+9rNYcsklY8cdd4z33ntvCrwCAAAAOrK6Dt2DBg2K559/Pi6++OI44ogj4swzzyzheVyvvvpq7LrrrrH66qvHddddFwsvvHBst9128eWXX5b1Tz31VOy///6xww47xPXXXx9du3aN/fbbrxVeEQAAAB1J3Ybur776Kq655po45JBDYpFFFok111wzfvnLX8bll1/+jW2vuOKK0sK9zz77xDzzzBMHHHBA9O7dO2655ZayPlvIsxV88803L+vzMQcPHhzDhg1rhVcGAABAR1G3ofull16K0aNHlzBds8wyy8Szzz4bY8eObbHtu+++G4svvnjT7YaGhph//vnjmWeeKbcfe+yxEtpr5phjjrjvvvtihhlmmCKvBQAAgI6pS9SpbImefvrpS1fwmr59+5Zx3p988kmLwJzLP/rooxb3//DDD2PaaaeNzz77LD799NMYM2ZM7LTTTiXMZ0A/8sgjY+aZZ56kMjU0RF2rla/ey0nrUUdQR/Bdgt8b6oX9Etp6HZnYctVt6B4+fHiLwJ1qt0eNGtVi+VprrRW77757rLvuurHyyiuXbuX/+Mc/ol+/fqWbejr22GNj3333LV3QTz/99DIGPMd3d+o08Y39ffr0jragrZST1qOOoI7guwS/N9QL+yW09zpSt6G7W7du3wjXtdvdu3dvsXyVVVaJPfbYI/baa6/Sop1he4MNNogvvvgiOnfuXLbZdNNNY8MNNyzXTz755Pjxj39cup8vvfTSE12moUM/j8bGqFt5pCUrZL2Xk9ajjqCO4LsEvzfUC/sltPU6Uitfmw3d2fX7448/LuO6u3Tp0tTlPAP3NNNM843tf/WrX5Xu459//nn06dOntGjPNttspYv6VFNNVSZQq8ll0003XemCPinyg67HD7utlpPWo46gjuC7BL831Av7JbT3OlK3E6kttNBCJWzXJkNLTz75ZCy22GLf6BJ+6623xnHHHVe6n2fgHjFiRDz66KOlxTsfI2c/z7HcNTlreQb6DOUAAADQ4UJ3jx49SnfwnPDsueeei3vuuaec+mvbbbdtavXOcJ3mmmuuuPLKK+Ouu+6Kt956q5yTe5ZZZindzlOen/vSSy+N22+/PV5//fU4+OCDS6hvPuM5AAAATG512708HXTQQSV0b7fddtGrV68yZvunP/1pWde/f/84/vjjY+ONN45FF120bHfCCSeUmc1XXHHFOPfcc5taxH/+85+XWcxPOumkGDp0aCy//PJx9tlnl1OLAQAAQFUaGhvbcu/4KWvIkPocwF+TxxD69u1d9+Wk9agjqCP4LsHvDfXCfgltvY7Uytdmu5cDAABAWyd0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAiQjcAAABUROgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAiXap64PaooSHaRPnqvZy0HnUEdQTfJfi9oV7YL6Gt15GJLVdDY2NjY9WFAQAAgI5I93IAAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInR/T6NGjYp11103Hn300cn2Ydx9992x1lprxVJLLRVbbLFF/POf/5xsjw0AAMCUJ3R/DyNHjoz99tsvXn311cn2QeRj7b///rHrrrvGTTfdFAsttFC5Pnz48Mn2HAAAAExZQvckeu2112KzzTaLd955Z7J+EA8//HDMN998seGGG8YPf/jDEuoHDx5cng8AAIC2SeieRI899lj069cvrrrqqm+se+KJJ2LjjTeOxRdfPNZbb7248847J/pxp5tuuhKwn3zyyRg7dmxcf/310atXrxLAAQAAaJu6tHYB2pott9xyvMuzVTq7g++7776x8sorxzPPPBMHHnhg9OnTJ5ZddtnvfNy111477rvvvvL4nTt3jk6dOsW5554b0047bQWvAgAAgClB6J5MLr/88lhppZVi6623LrfnnHPOePHFF+Piiy8uofujjz6KESNGfON+Xbt2jVlmmSU+/vjjEtwPP/zwWGKJJeKKK66Igw46KG644YYS3AEAAGh7hO7J5I033oj777+/zDxe8/XXX8fcc89drv/6178uXdPHteCCC5aJ004++eSYf/75Y6uttirLjznmmDKT+XXXXRe77LLL5ComAAAAU5DQPZmMHj26jOPebbfdWr7BXf7zFl966aXfev88Pdg222zTdDu7l2cgf//99ydXEQEAAJjCTKQ2mWSL9ttvv126ldcu9957b9xyyy0Tdf+ZZpopXn/99RbL3nzzzZh99tknVxEBAACYwoTuySQnQHv++efj1FNPjbfeequE7VNOOSVmnXXWibp/nobs6quvjhtvvLGE9+xunq3cG2200eQqIgAAAFOY7uWTyWyzzRbnnHNOCcsXXnhhzDzzzGX28vXXX3+i7p+zl3/55ZdlxvIPP/wwFlpooTIJm0nUAAAA2q6GxsbGxtYuBAAAALRHupcDAABARYRuAAAAqIjQDQAAABUxkdokGDr086jnEfANDRF9+vSu+3LSetQR1BF8l+D3hnphv4S2Xkdq5fsuQvckyA+6Hj/stlpOWo86gjqC7xL83lAv7JfQ3uuI7uUAAABQEaEbAAAAKiJ0AwAAQEWM6QYAAGgHxo4dG2PGjI72oqEhYsSIEfH116NaZUx3585dolOn/76dWugGAABowxobG+Ozz4bF8OFfRHszbFincjChtfTo0SummWaGaMgjAN+T0A0AANCG1QJ3r17TR9eu3f6rgFhvOnduiDFjGlvlQMaoUSPjiy8+LrennbbP934soRsAAKCNGjt2TFPg7tVrmmhvunTpFKNHt05Ldx7ASBm8e/ee/nt3NTeRGgAAQBs1ZsyYFgGRyav2vv43Y+WFbgAAgDauPXUpb2/vq9ANAAAAFRG6AQAAoCJCNwAAAB3ShReeG3vuuUulzyF0AwAAQEWEbgAAAKiI83QDAAC0M42NEV99NWWfc+qpc7bvid/+mmuujCuvvCw+/nhYzD33vLH33vuXU3Mdc8zhseWW28Yf/3h+dO7cOTbZ5H9iu+12arrfjTdeF5dffnF88snHscACC8W++w6Meeedr6wbNWpUnH32GXH33beX2/36rRT/+7+/jmmmmbbcfvPNN2LQoOPilVdeikUWWSzmmmvuqJrQDQAA0M4C97rrTh2PP955ij7v8suPjltuGT5RwfuVV16Ks88+PY477qSYe+554pprrojDD/9NHH74sTFs2NC4446/xKmnnhVDhnwURx99eEw//Qyx/vobxUMPPRh//ON5MXDgofHDH85Zttt7713jiituiGmmmSbOPfeseOmlF+Kkk06Pbt26l9uHHXZgnH76H0ogHzjwf2PxxZeMAw88LJ588vE4/fSTY7HFlqj0fRG6AQAA2pmGhsaoZx988EE5B/YPfvCDmGWWWWPnnXePlVZaOcaOHRtjxowpofhHP5o/Fllk4dhssy3jppuuL6H7z3++JLbZZof48Y9XLo+z886/iv/7v4fjrrtui3XX3TCuv/7quOCCS5tavg877OhYZ53V4/XXX4uPPvowPv300/j1rw+KHj16xJxzzhVPP/1kaWmvktANAADQjmRLc7Y413P38n79Vox55pkvtt1285h//gWif/+flFD9zjtvR48eU5fAXZNdyK+44tJy/e2334yzz/59acGuyRbsd999J95//1/x9ddfx2677dDiuTLIv/vu2/H+++/F7LPPUQJ3zUILLRx///tDUSWhGwAAoJ3J8NuzZ9St7t27x3nn/SmeeeapePjhB+O2224pY7X33PN/yzjucUNzQ8N/5gDPVvC9994vll12+Rbb9OzZM4YMGVyun332BSW4NzfDDDOUx49o2QOgS5epompmLwcAAGCKev755+LSS/8YSy+9bOy1137x5z9fF6NGjSyB+4svPo8PPni/adscoz3ffP/pLj7HHHPG4MH/Li3Wtcsll1wU//znP2K22WYv988u5LV1GcbPOOOUGDbsP5O1ZYv4F1980fTYr776cuWvVegGAABgiurWrVuZnfyWW24sAfvee++K4cOHx2effVbWn3jisfHGG6/FfffdG9dee1VstNFmZfnmm28VV199RZlA7b33/lVmKr/vvrtjzjnnjqmn7hnrrbdhnHzyCfHUU0+UmcqPOeaIeO+9d8u48eWW6xczz/yDOOGEo+Ott94srev33nt35a9V93IAAACmqB/9aIE46KDD409/uiBOPXVQCcM56dkMM/Qp61dYYaXYffdflm7iu+66e/z0pz8vy1df/ael1fqCC875f63X88SJJ54ac8zxw7J+zz33jTPPPC0OPfQ3MXr06FhyyaXKTOa1LuuDBp1WAv2OO25dJlvbeONNS0t6lRoaG3NCeSbGkCGfl+n363ncRt++veu+nLQedQR1BN8l+L2hXtgvmTy+/npUDB36QfTpM0tMNVXXaOueeuqJ2Hvv3eKhh54ot7t06RSjR4+ty/e3Voe/i+7lAAAAUBGhGwAAACoidAMAAFAXll562aau5e2F0A0AAAAVEboBAADaOPNj1+/7KnQDAAC0UbVTYY0aNbK1i9Iujfp/72vnzt//bNvO0w0AANBGderUOXr06BVffPFxud21a7doyHNZtRNjxzbEmDGNrdLCnYE739d8fzt1+v7t1UI3AABAGzbNNDOUv7Xg3Z506tQpxo5tvfN0Z+Cuvb/fl9ANAADQhmXL9rTT9onevaePMWNGR3vR0BAx/fQ94+OPv4zJMLR6kmWX8v+mhbtG6AYAAGgHMiB26tQ12lPo7t69e0w11detEronFxOpAQAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAAB0tdH/22WdxyCGHxEorrRQrrLBCHHjggWXZd/n8889j5ZVXjuuvv77F8ltvvTXWWGONWGKJJWKPPfaIYcOGVVh6AAAAqOPQfcQRR8RLL70U5513Xlx44YXx+uuvx6GHHvqd9zvppJPi3//+d4tlzz33XAnwe+65Z1x11VUlvB900EEVlh4AAAAiutTjm/DVV1/FnXfeGVdccUUsuuiiZdnBBx8cW221VYwcOTK6des23vs98cQT8cgjj8SMM87YYvlll10Wa621Vmy44Ybl9qBBg2LVVVeNd999N+aYY44p8IoAAADoiOoydHfq1CnOOeecWGihhVosHzNmTHz55ZfjDd2jRo2Kww47LA4//PByae7ZZ5+NnXfeuen2LLPMErPOOmtZPimhu6Eh6lqtfPVeTlqPOoI6gu8S/N5QL+yX0NbryMSWqy5Dd/fu3WOVVVZpseySSy6JBRZYIGaYYYbx3idD+sILLxz9+/f/xrrsbj7TTDO1WNanT5/48MMPJ6lcffr0jragrZST1qOOoI7guwS/N9QL+yW09zrSaqF7xIgR8dFHH413XXYPn3rqqVt0D7/99tvjggsuGO/2r732Wlx55ZVx8803T/C5unbt2mJZ3s7W8UkxdOjn0dgYdSuPtGSFrPdy0nrUEdQRfJfg94Z6Yb+Etl5HauWr29CdXbu33Xbb8a4766yzykzj6fLLL49jjz22THw2vlbsxsbGMsHa3nvvHX379h3v42V39HEDdt7u0aPHJJU5P+h6/LDbajlpPeoI6gi+S/B7Q72wX0J7ryOtFrr79esXL7/88rduk7OW56RnAwcOjO22226827z//vvx9NNPl8c68cQTy7Lhw4eX2c9vu+220jo+88wzx5AhQ1rcL2+PO+EaAAAATE51OaY73XDDDSVwZwv39ttvP8HtMlDfddddLZZts8025bL++uuX23lu7ieffDI23njjcvuDDz4ol1wOAAAAHSp0f/LJJ3H00UfHRhttFOuss04MHjy4aV1OpNa5c+cYNmxY6Tbes2fPmHPOOVvcv0uXLmWitAzkaYsttighfMkll4zFFlssjjvuuBgwYIDThQEAANDxQvfDDz9cztWdrd15ae7ee++N2WefPTbZZJMSyvfaa6/vfLylllqqhPgzzjgjPv300/jxj38cxxxzTIWvAAAAACIaGnMmMibKkCH1OWte89nz+vbtXfflpPWoI6gj+C7B7w31wn4Jbb2O1Mr3XTpNkdIAAABAByR0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAAEI3AAAAtC1augEAAKAiQjcAAABUROgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAiQjcAAABUROgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICOFro/++yzOOSQQ2KllVaKFVZYIQ488MCy7Lt8/vnnsfLKK8f111/fYvmyyy4bCyywQIvLl19+WeErAAAAoKPrEnXqiCOOiHfeeSfOO++8aGhoiCOPPDIOPfTQOOOMM771fieddFL8+9//brHso48+KmH8nnvuie7duzctn3rqqSsrPwAAANRl6P7qq6/izjvvjCuuuCIWXXTRsuzggw+OrbbaKkaOHBndunUb7/2eeOKJeOSRR2LGGWdssfz1118vy+aYY44pUn4AAACo2+7lnTp1inPOOScWWmihFsvHjBkzwS7ho0aNisMOOywOP/zw6Nq1a4t1r732Wsw999yVlhkAAADaREt3dgFfZZVVWiy75JJLyjjsGWaYYbz3yZC+8MILR//+/b+xLlu6hw8fHttss028+eabJcxny/mkBvGGhqhrtfLVezlpPeoI6gi+S/B7Q72wX0JbryMTW65WC90jRowoY63HJ7uCNx9vfdlll8Xtt98eF1xwwXi3z5bsK6+8Mm6++ebxrn/jjTfi008/jf322y969eoV559/fmy//fbxl7/8pdyeWH369I62oK2Uk9ajjqCO4LsEvzfUC/sltPc60mqh+9lnn41tt912vOvOOuusWGONNcr1yy+/PI499tg46KCDxtuK3djYWCZY23vvvaNv377jfbwLL7wwvv766+jZs2e5ffLJJ8dPfvKTuP/++2O99dab6DIPHfp5NDZG3cojLVkh672ctB51BHUE3yX4vaFe2C+hrdeRWvnqNnT369cvXn755W/dJsPyoEGDYuDAgbHddtuNd5v3338/nn766fJYJ554YlmWXclz9vPbbruttI7nGO/m47xzIrbZZ599gi3tE5IfdD1+2G21nLQedQR1BN8l+L2hXtgvob3Xkboc051uuOGGErizhTu7gk/IzDPPHHfddVeLZTl2Oy/rr79+aQlfc801Y/fdd4+NN964aXb0t99+O+aZZ57KXwcAAAAdV12G7k8++SSOPvro2GijjWKdddaJwYMHN63LidQ6d+4cw4YNKy3W2WV8zjnnbHH/Ll26RJ8+fUogTwMGDIjf//73Mdtss5X7n3766fGDH/ygdDEHAACADhW6H3744dIana3deWnu3nvvLV3DN9lkkxLK99prr+98vAMOOKAE8f333z+++OKLWGGFFeK8884r4R0AAACq0tCY/a+ZKEOG1OcA/uYD+fv27V335aT1qCOoI/guwe8N9cJ+CW29jtTK9106TZHSAAAAQAckdAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAgNANAAAAbYuWbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAiQjcAAABUROgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQkS4Ts9E222wTDQ0NE/WAl1xyyX9bJgAAAOg4obtfv35N1z/++OO46qqrYo011ojFFlsspppqqnjxxRfjtttui6222qrKsgIAAED7C9177rln0/Xtt98+Dj744Nhyyy1bbLPccsuVMA4AAAB8zzHdzzzzTKy44orfWL7EEkvEyy+/PKkPBwAAAO3WJIfuhRdeOM4777wYOXJk07IvvvgizjjjjFhyySUnd/kAAACgfXcvb+6YY46JXXbZJX784x/HnHPOGY2NjfHWW2/FrLPOGueee241pQQAAICOELrnnXfeuP322+Pvf/97vP7662XZj370o1hppZWiS5dJfjgAAABot75XSu7atWvMNtts8fXXX5ewPWzYsOjcufPkLx0AAAB0pND96aefxj777BOPPfZYuX3nnXfGcccdF++++24Z651hHAAAAPgeE6kde+yx0aNHj3jkkUeiW7duZVmG7h/84AdlHQAAAPA9Q/ff/va32G+//WKaaaZpWtanT5846KCD4vHHH5/UhwMAAIB2a5JDd2p+urCaHNdtIjUAAAD4L0L3uuuuW7qTv/rqq9HQ0BBfffVV6Wp+2GGHxdprrz2pDwcAAADt1iRPpDZw4MA45ZRTYuONNy6zl2+wwQZl5vJNN920rKP1NDZGfPnlfy55HcbV0BDRo4c6woSpI0wM9QR1hMnBdwnfpWfPaBcaGhu/XzwbMWJEmbF8zJgxMcccc0TP9vKOfIshQz6v2zCb5Vp33anj8cedug0AAGj7ll9+dDzySJcYOrQ+c1geOOrbt3c1Y7o///zzuO666+Kqq66KmWaaqZw+7J133vk+D8Vk1NBQhzURAACgA5vk7uWvvPJKbLfddjHLLLM0Xb/rrrvijjvuiHPPPTeWX375yVKwzz77LE488cS4//77Y+zYsTFgwIA4+OCDW8ya3lyeruzSSy9tsSzHmW+99dbl+q233hqnnXZaDB48OPr37x/HHHNMzDDDDNFe5FGWW28dHlNP3buuW+Spj6Nx6gjqCL5L8HtDa7NfwnfJztQNDd/dktzuupdvu+22seyyy8bee+8dSy21VNx8882le/lJJ50Ujz76aFx77bWTpWD77rtvaT0/6qijyoRtRx55ZAn6Z5xxxni332GHHWLFFVeMjTbaqGlZr169yjnFn3vuudhmm23KYy244IJlIripp566HCSYFPUeVHxxoY7gewS/N9QD+ySoJ3SE75KGqrqX/+Mf/4gNN9zwG8s333zzeO2112JyyBnR77zzzjj88MNj0UUXjUUWWaS0ct9zzz3jPV1Zev3112PhhReOGWecsemSgTtddtllsdZaa5VyZ+geNGhQPPDAA2VMOgAAAFRlkkN3dsl+8803v7H8qaeeij59+kyeQnXqFOecc04stNBCLZbnpG1f5tTc4/jiiy/io48+irnmmmu8j/fss8+W1vmabDGfddZZy3IAAAComzHdO++8cxx66KGx2267RfZMz3N033DDDXHxxReXLuGTQ/fu3WOVVVZpseySSy6JBRZYYLzjsLOVO7ugZ1B/8MEHY7rppivdzWtdzf/973+XCd+aywMEH3744SR3H6hntfLVezlpPeoI6gi+S/B7Q72wX0JbryMTW65JDt3ZjTwD7IUXXljCcXbVnnvuucvEZGuvvfYknXIsW6fHJ7uG55jrmuwefvvtt8cFF1ww3u3feOONErrnmWeeMnHa448/XiZRyzHda665Znmurl27trhP3h41alRMij592sYg/rZSTlqPOoI6gu8S/N5QL+yX0N7ryCSH7rTaaquVy38ju3bnpGzjc9ZZZ8Uaa6xRrl9++eVlZvKDDjqozDo+PjlWe9VVVy0t3CnHbb/11ltxxRVXlNDdrVu3bwTsvF0b8z2x6vX8cM2PtGSFrPdy0nrUEdQRfJfg94Z6Yb+Etl5HauWrJHRfc8015Rzd2a07x19nt+9sYZ6Ulu5+/frFyy+//K3bZGt6tqQPHDiwnJpsQrKVuxa4a7LVO7u+p5lnnjmGDBnSYn3ezhb1SZEfdD1+2G21nLQedQR1BN8l+L2hXtgvob3XkUmeSC3HTef5s3/yk5/EKaecUq4vt9xypTv3n//858lWsBwnnoE7W7h32mmnb9329NNPj+23377FspdeeqkE77TEEkvEk08+2bTugw8+KJdcDgAAAFWZ5JbuHF+dQXv11VdvWpZdwfN0Xccff3xsueWW/3WhPvnkkzj66KPLRGjrrLNODB48uGldTqTWuXPnGDZsWOk23rNnz9K1/Lzzzist49md/KGHHoobb7yxTL6Wtthii3Ke7iWXXDIWW2yxcp7uAQMGlPOLAwAAQN2E7q+//jpmm222byzPVuXxnc7r+3j44YfLubqztTsvzd17770x++yzxyabbFJC+V577RWLL754ae0+44wzyt8s3+9+97tYaqmlyn3yb4b4XP/pp5/Gj3/84zLxGwAAAFSpoTHP+zUJLr300rjpppvit7/9bcw///xl2fvvv1+6l6+88srf6ObdngwZUp8D+JsP5O/bt3fdl5PWo46gjuC7BL831Av7JbT1OlIr32Rv6c7Tdg0dOjQ22GCDclqvLl26xGeffVbO2Z0t1Nn1vObFF1+c9JIDAABAOzHJofukk06KsWPHlpCdY6vzXNv5N4N3beIyAAAA4HuE7gzY+++/fwnfc801V+y3334xcuTIGD58eFm21lpreV8BAADg+5wyLGcozxnF83RbV199dZlBPLuV58RkOVEZAAAA8D1D9yuvvBLbbrtt9OjRI+6777746U9/Gl27do3ll1++TKgGAAAAfM/Q3bdv33jttdfK5YUXXijnyE5///vfY5ZZZpnUhwMAAIB2a5LHdOcpwfbYY4/o1KlTLLbYYqWF+5xzzokzzzyzdD0HAAAAvmfozq7lyy23XLz33nvRv3//smyFFVaIAQMGxIILLjipDwcAAADt1iSH7rTQQguVS82SSy45OcsEAAAAHXNMNwAAADBxhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAiQjcAAABUROgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAq0iXq1GeffRYnnnhi3H///TF27NgYMGBAHHzwwTHNNNOMd/tjjz02Lr300hbLDjvssNh6663L9WWXXTY+//zzFuufeuqp6NmzZ4WvAgAAgI6sbkP3EUccEe+8806cd9550dDQEEceeWQceuihccYZZ4x3+9dffz3233//2GijjZqW9erVq/z96KOPSuC+5557onv37k3rp5566inwSgAAAOio6jJ0f/XVV3HnnXfGFVdcEYsuumhZlq3cW221VYwcOTK6des23tC90047xYwzzjjedbl8jjnmmCLlBwAAgLoN3Z06dYpzzjknFlpooRbLx4wZE19++eU3QvcXX3xRWrPnmmuu8T7ea6+9FnPPPfd/Xa6GhqhrtfLVezlpPeoI6gi+S/B7Q72wX0JbryMTW66GxsbGxmgD9t1339JiffPNN39j3bPPPhv/8z//E5tsskk8+OCDMd1008UOO+zQ1NU8u6r/85//jB49esSbb75Zwny2nE+OIA4AAAB119I9YsSI0jo9PtkVvPl468suuyxuv/32uOCCC8a7/RtvvFHGfc8zzzxl4rTHH3+8TKKWY7rXXHPNsv7TTz+N/fbbryw7//zzY/vtt4+//OUvTeO+J8bQoZ9HPR+iyCMtffr0rvty0nrUEdQRfJfg94Z6Yb+Etl5HauWr29CdrdPbbrvteNedddZZscYaa5Trl19+eZmZ/KCDDor+/fuPd/sNN9wwVl111dLCnRZccMF46623ypjwDN0XXnhhfP31100zlZ988snxk5/8pMyMvt566010mfODrscPu62Wk9ajjqCO4LsEvzfUC/sltPc60mqhu1+/fvHyyy9/6zYZlgcNGhQDBw6M7bbbboLbZSt3LXDXZKv3I488Uq537dq1XGpyTPjss88+wZZ2AAAAmBw6RZ264YYbSuDOFu6clfzbnH766aW7eHMvvfRSCd45ZD1bza+//voWs6O//fbbZT0AAAB0qNnLP/nkkzj66KPLRGjrrLNODB48uGndDDPMEJ07d45hw4aVFuvsMp5dy/N83tkynt3JH3roobjxxhvjkksuKa3gAwYMiN///vcx22yzlftnSP/BD35QupgDAABAhwrdDz/8cGmNztbuvDR37733lq7hOVN5hvK99torFl988RKkzzjjjPI3w/Xvfve7WGqppcp9DjjggOjSpUvsv//+5fRiK6ywQgnpGd4BAACgKm3mlGH1YMiQ+pw1r/nseX379q77ctJ61BHUEXyX4PeGemG/hLZeR2rla7NjugEAAKCtE7oBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAiQjcAAABUROgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAiQjcAAABUROgGAACAigjdAAAAIHQDAABA26KlGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAQEWEbgAAAOhoofuzzz6LQw45JFZaaaVYYYUV4sADDyzLJuT999+PnXfeOZZYYolYc80147bbbmux/tZbb4011lijrN9jjz1i2LBhU+BVAAAA0JHVbeg+4ogj4qWXXorzzjsvLrzwwnj99dfj0EMPHe+2o0ePjl133TW6dOkSN9xwQ+y0004xcODAeOWVV8r65557rgT4PffcM6666qoS3g866KAp/IoAAADoaLpEHfrqq6/izjvvjCuuuCIWXXTRsuzggw+OrbbaKkaOHBndunVrsf0DDzwQH3zwQdm+V69eMc8888SDDz4YTz/9dMw///xx2WWXxVprrRUbbrhh2X7QoEGx6qqrxrvvvhtzzDFHq7xGAAAA2r+6DN2dOnWKc845JxZaaKEWy8eMGRNffvnlN0L3Y489FiuuuGIJ3DVnn3120/Vnn322dD2vmWWWWWLWWWctyycldDc0RF2rla/ey0nrUUdQR/Bdgt8b6oX9Etp6HZnYctVl6O7evXusssoqLZZdcsklscACC8QMM8zwje2zxXq22WaLk08+OW666aaYfvrpY++99y5juNO///3vmGmmmVrcp0+fPvHhhx9OUrn69OkdbUFbKSetRx1BHcF3CX5vqBf2S2jvdaTVQveIESPio48+Gu+6GWecMaaeeuqm29k9/Pbbb48LLrhggt3Rcyz32muvXVrIH3300RK6c/z2YostVp6ra9euLe6Tt0eNGjVJZR469PNobIy6lUdaskLWezlpPeoI6gi+S/B7Q72wX0JbryO18tVt6M6u3dtuu+1415111llNrdSXX355HHvssWXis/79+493+86dO8d0000XRx55ZOmavsgii8QTTzwRV199dQnd2R193ICdt3v06DFJZc4Puh4/7LZaTlqPOoI6gu8S/N5QL+yX0N7rSKuF7n79+sXLL7/8rdvkrOU56VnORL7ddttNcLvsOt7Q0FACd83cc8/d9PgzzzxzDBkypMV98na2qAMAAECHO2VYdhfPwJ0t3HkKsG+T595+9dVXy0RrNXmKsRznXVv/5JNPNq3Lmc7zkssBAACgQ4XuTz75JI4++ujYaKONYp111onBgwc3XWrBetiwYWUm87TuuuvG2LFj46ijjoq33367dEn/29/+FptttllZv8UWW5QJ1q655ppy7u9sOR8wYIDThQEAANDxQvfDDz/cNDlajuNufskW6rTJJpvERRddVK7nqcL++Mc/xhtvvFECeM50fuqpp5ax3WmppZYqIT7HimcAn3baaeP4449v1dcIAABA+9fQ2NiWh6RPWUOG1Oesec1nz+vbt3fdl5PWo46gjuC7BL831Av7JbT1OlIrX5ts6QYAAID2QOgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwAAAFRE6AYAAICKCN0AAABQEaEbAAAAKiJ0AwAAQEWEbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInQDAABARYRuAAAAqIjQDQAAABURugEAAKAiQjcAAABUROgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFelS1QO3Rw0N0SbKV+/lpPWoI6gj+C7B7w31wn4Jbb2OTGy5GhobGxurLgwAAAB0RLqXAwAAgNANAAAAbYuWbgAAAKiI0A0AAAAVEboBAACgIkI3AAAAVEToBgAAgIoI3QAAAFARoRsAAAAqInS3AyNHjoyDDz44ll122ejfv39cdNFFrV0k6sioUaNi3XXXjUcffbRp2bvvvhvbb799LLnkkrH22mvHQw891KplpHV89NFHsffee8fyyy8fK6+8chx//PHl+ySpI9S8/fbbsdNOO8VSSy0VAwYMiAsuuKBpnXrCuHbZZZc48MADm26/8MILsemmm8YSSywRv/jFL+L555/3pnVQd999dyywwAItLvkblNQTavusRx11VCy33HKx0korxSmnnBKNjY3too4I3e3AoEGDSsW7+OKL44gjjogzzzwz7rjjjtYuFnUgA9R+++0Xr776atOy/PLaY489om/fvnHdddfFBhtsEHvuuWe8//77rVpWpqysB7mzM3z48Lj88svj1FNPjfvvvz9OO+00dYQmY8eOLSFq+umnjxtuuKHsDP3hD3+IW265RT3hG/7yl7/EAw880HT7q6++KvUnGwWuv/76cuBm1113LcvpeF577bVYddVVy4H+2uXYY49VT2iS9eHvf/97XHjhhfG73/0urr766rjqqqvaRR3p0toF4L+Tle2aa66J888/PxZZZJFyyYCVO9E///nPvb0d/Mdt//33bzpCWPPII4+U1qkrr7wypp566ph33nnj//7v/0oA32uvvVqtvExZb7zxRjzzzDPx8MMPlwMwKUP4iSeeGKussoo6QjFkyJBYaKGF4sgjj4xevXrFXHPNFSuuuGI8+eSTpd74LqHmk08+KY0Aiy22WNOy2267Lbp16xYDBw6MhoaGOOSQQ+LBBx8sDQMbb7yxN6+Def3112P++eePGWecscXya6+9Vj0h8jsk90X/+Mc/xuKLL17ekR133DGeffbZ6NKlS5uvI1q627iXXnopRo8eXY741CyzzDKlgmYLBR3XY489Fv369StHCJvLurHwwguXwN28zmQAo+PInZ7sJlwL3DVffPGFOkKTmWaaqfR+yMCdB/AybD/++ONlSILvEprLA3bZc2q++eZrWpZ1JH9fcic55d+ll17a700HDt154G5c6gkpf1/ytyZ/X2qydTuHvrWHOiJ0t3GDBw8u3f66du3atCx3orNbcR4xouPacssty1j/Hj16fKPO5I50c3369IkPP/xwCpeQ1jTNNNOUcdw1eZDusssuixVWWEEdYbxWW2218r2SB3l/9rOfqSc0yd5STzzxROy+++4t3hW/N9TkQbs333yzdCnP74811lgjTj755DKGVz0hZc+p2WabLW688cbSW3f11VePs846q+yftIc6ont5G5fjMZsH7lS7nV9kMLF1Rn3p2E466aQySUl28/vTn/6kjvANZ5xxRulunl3Ns+XBdwkpD/LnfDKHH354dO/evcWboo5Qk/PG1OpD9p7517/+VcbvjhgxQj2hachsTtyZwx/zNyaDdn6vZONRe/guEbrbuBzfMG6Fq90e98cPanVm3F4QWWfUl44duHMixpxMLcfbqSOMT22sboasX//612X22NwRas53SceTk7cuuuiiLXrOfNc+it+bjidbMPMsKtNOO23pGpxzRWQL5gEHHFC6E6sndOnSpQxxywnUsr7UDtZcccUVMeecc7b5OiJ0t3EzzzxzfPzxx2Vcd1bWlEeGshJm91EYX53JSdaay9arcbvt0DEcc8wx5Qctg3d2+UvqCM2/G3LMXHYFrckxu19//XWZFyAn5GvOd0nHnLE8P/fa3DK1HeM777yznK4y1zWnjnRc0003XYvbOZFrHsTL7xL1hBlnnLEcqKsF7jT33HPHBx98UA7MtPU6Ykx3G5dHCjNsN59IICciyBaJTp18vHxTnt/wn//8Z+nS1bzO5HI6XgtVduPK82Cus846TcvVEWqyC2ieUjDP6V6Tp6icYYYZyqQ2vku49NJLyynkchxmXnLsf17yen6XPP30001n0ci/Tz31lN+bDuhvf/tbmdy1ee+YF198sQTx/C5RT1hiiSXKQZgc+1+TB3YzhLeH7xKprI3LcQ4bbrhhGWP33HPPxT333BMXXXRRbLvttq1dNOpUHi2cZZZZ4qCDDiqnlzvvvPNK3dlkk01au2hM4Vlkzz777Nh5553LDk/2kKld1BFq8gBunooyJ2XMHjJ5DubsFbHbbrupJxS5Q5xdP2uXnj17lktez8mQPvvsszjuuONK/cm/GbrWWmst714Hkz0hshXz0EMPLUEqv0vyFHO//OUv1ROKeeaZJwYMGFD2T/PsTHmgJvdRt9hii3ZRRxoaxz2JL21OVroM3XfddVeZan+nnXaK7bffvrWLRR1ZYIEF4pJLLilHmVNOVJHnOMxTMOSOUe5Qr7TSSq1dTKag/CHLcVPj8/LLL6sjNMlW7hyGkDNU54HerbfeOnbdddcyLtN3CeM68MADy98TTjih/M2DujnRWh7oy9+io446qpy2ko4nD/T/9re/Lb0z88DM5ptvHnvssUf5LlFPSJ9//nn5vbn77rvL702eMaO91BGhGwAAACqiezkAAABUROgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAoCJCNwC0ggMPPDAWWGCBCV6uv/768vdf//rXFClPY2NjbLPNNvH6669HW3oP8/Jdrr766jj11FOnSJkAYFxdvrEEAKjcIYccEvvvv3+5ftttt8VFF10U1157bdP6aaedNlZeeeWYYYYZpsinccMNN8Sss84a8847b7Q3G2+8cay33nqx4YYbxtxzz93axQGgg9HSDQCtoHfv3jHjjDOWS17v3Llz0+28dO3atfzN5VOilfsPf/hDbLHFFtEedenSJTbaaKM4//zzW7soAHRAQjcA1KHsVt68e3lev/3222OttdaKJZZYIvbbb7949913Y9ttty23t9xyy/joo4+a7n/33XfH2muvXdZtsskm8dhjj03wuR566KEYPnx42bbmlFNOif79+8fiiy9eup2/+uqrTeueeOKJ0nqc67IF+c4772zxeH/84x9jtdVWi6WWWip22mmnUs40duzYuOCCC2L11VdvetyXX3656X75Gm+66aZYd911Y9FFFy2vqXbf2vNma3Xed5999illrvnss89ir732imWXXTaWW265+PWvfx1ffPFF0/p8zr/85S9lOwCYkoRuAGgjzjjjjDjhhBPi3HPPjbvuuqu0TOflyiuvjMGDBze15L700kvxm9/8Jn71q1/FzTffHOuvv37svPPO8fbbb4/3cf/2t7/FiiuuGA0NDU2B/aqrrorTTjstbr311ujbt28cdNBBZV0+z6677lpC9y233BK//OUvy7jqDMQpy3LmmWeW0Jtd1nv27FkCcjrrrLNKN/qDDz64rJttttnK/b/66qumsvz+978vXe9zTPvHH39cypCGDRtWnnellVaKG2+8Meabb7644447Wrw3WbYrrrgiLrnkkvIenH322U3rs9t8dtl//PHHK/hkAGDCjOkGgDZi++23b2qNXmihhcr45Gz5Tj/96U9L0EwXXnhhbLbZZqUVOmVreIbNDKTjm3jshRdeKK3aNe+9915MNdVUZYx3Xg477LB44403yrrLL7+8BN+tt9663J5zzjnjxRdfjIsvvri0MmdYz3JmK3s6/PDDS3lGjBgRl112WWmhz1bndMwxx8Saa65ZDgxsvvnmZdkOO+xQDgCkPKCQz5eylT/Htx9wwAHl4EC2aj/wwAMtypwBf/bZZ48ePXrE6aef/o3XmUE9X2vt+QFgShC6AaCNmGOOOZqud+/evbQUN789atSocj1nIM+QmgG45uuvv24RrJvLVuTpp5++6fY666xTAnKG0yWXXDLWWGON0kU9Zfi+//77S9fx5o9dm6DszTffjEUWWaRpXbaSZ6v7kCFD4pNPPmnRhT2DfXYjbz5jeob4ml69epXHTq+99losuOCCTa3xabHFFmvqYp4HFnbfffcS2PPys5/9rOmgQ810000XQ4cO/c73GQAmJ6EbANqIcSdV69Rp/KPExowZU7qT5/jn5jKYj08G2bxPTU7glqH94YcfLgE7W6rztFvZrXv06NElzO62227fmKys+d9xdevWbYJlzbHezYP4t0341lxuWwvdGbSz5fvee++Nv/71r6WFPceqn3zyyU3b5/NM6D0DgKr45QGAdiZbnXMCtmw1rl2y1fvBBx8c7/Z9+vQprdA1GVqvueaaGDBgQBx11FFlcrO33norXnnllfLYOTa8+WNn0M3x3Slv17q5pxyXvcIKK8Snn35aWr2feeaZpnXZiv3Pf/5zok7j9aMf/ah0DW9+cCC7tdf86U9/Ko+Vs5Rn1/Ljjz++jHtvLsuSZQCAKUnoBoB2JsdU57m/c0Kxd955pwTSvMw111zj3X7hhRduMYt4tggPGjSoTKiW4T0nNctx0nn/nFH8+eefj1NPPbUE8QzbOdN5jv1OOSN5ju++5557SlfzI444ooyzzkuWKyc8u++++0qX8hwrPnLkyKbx398mu7xnq/Zxxx1XurjnLOhPPvlk0/oPP/wwjj766BLqs1w5o3q+rubyoEHzru8AMCXoXg4A7UyOw87QnDOB598f/vCH8bvf/a6cSmt8Vl555TLBWnbfzq7mebqvvffeu7QW54zg88wzT5kJPGf/zss555xTum1nt/OZZ5653DdnSE8bbLBBOXVZtpDnKbuWX375ErTTjjvuWJZl2M6/OS780ksvLROkfZd83gzaRx55ZHmOfC35t9blPGdI//zzz8uM7Tkbeq4/6aSTmu6fQf3LL78s5QGAKamhcdwBUgBAh5JdtnPisQzZEwrmbV2exuyDDz4oLeUAMCXpXg4AHVxO0LbLLruUc2y3Rzl2PMelZ0s7AExpQjcAUE4J9v7777c4fVd7cd1115WW/Hnnnbe1iwJAB6R7OQAAAFRESzcAAABUROgGAACAigjdAAAAUBGhGwAAACoidAMAAEBFhG4AAACoiNANAAAAFRG6AQAAIKrx/wHfg1zNLVdPogAAAABJRU5ErkJggg=="
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 28
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
-   "source": "# so how much does scaling the acceleraotr position before hand matter? why has the training time increased so much",
-   "id": "fbd9ce1ac3a4153d"
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-05T01:16:30.451499Z",
-     "start_time": "2026-03-05T01:16:30.421585Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "print(\"Any inf in final_df:\", np.isinf(final_df.values).any())\n",
-    "print(\"Any NaN in final_df:\", np.isnan(final_df.values).any())"
-   ],
-   "id": "11d2951061944ddb",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Any inf in final_df: False\n",
-      "Any NaN in final_df: False\n"
-     ]
-    }
-   ],
-   "execution_count": 40
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
-   "source": "",
-   "id": "2bb7b192d6146b75"
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/array_temp/DataPreprocessing.py b/control_model/DataPreprocessing.py
similarity index 90%
rename from array_temp/DataPreprocessing.py
rename to control_model/DataPreprocessing.py
index 4238920..25cf56e 100644
--- a/array_temp/DataPreprocessing.py
+++ b/control_model/DataPreprocessing.py
@@ -58,14 +58,14 @@ def make_sequence_datasets(
     # DataLoaders
     train_loader = DataLoader(
         train_dataset,
-        batch_size=batch_size,
-        shuffle=True
+        batch_size=batch_size, num_workers=0,
+        shuffle=True, pin_memory = True
     )
 
     test_loader = DataLoader(
         test_dataset,
-        batch_size=batch_size,
-        shuffle=False
+        batch_size=batch_size,num_workers=0,
+        shuffle=False, pin_memory = True
     )
 
     return train_dataset, test_dataset, train_loader, test_loader, scaler
diff --git a/array_temp/RNN.py b/control_model/RNN.py
similarity index 94%
rename from array_temp/RNN.py
rename to control_model/RNN.py
index 536852d..873bcd1 100644
--- a/array_temp/RNN.py
+++ b/control_model/RNN.py
@@ -1,8 +1,5 @@
 import torch
 from torch import nn
-
-
-
 #model class to declare RNN and defining a forward pass of the model
 
 device = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else "cpu"
@@ -16,6 +13,7 @@ def __init__(self, input_size, hidden_size, num_layers, seq_length, output_size)
         self.num_layers = num_layers  #stacked lstm layers
 
         #lstm: long short term memory - looks at long term dependencies in sequential data
+        #default activation is tanh
         self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)  #correspond to input data shape
         self.seq_length = seq_length  #no of timestamps to look at to predict the next control output
 
@@ -27,16 +25,13 @@ def __init__(self, input_size, hidden_size, num_layers, seq_length, output_size)
     def forward(self, x):
 
         #x shape : [batch size, input size]
-        # associate the state array (start + goal state) with timeseries dependency
-
+        # associate the state array sequence with timeseries dependency
         #inital hidden, cell states - these are internal memory vectors
         #hidden = short term memory, current output of LSTM at a given time
         hidden_state = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
 
         #cell state = long term memory, stores trends (remmebers info over many time steps)
-
         cell_states = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
-
         #forward propagate lstm
         out, _ = self.lstm(x, (hidden_state, cell_states)) #out; tensor of shape(batch_size, seq_length, hidden_size) - at the final time step
         #decode the hidden state of t
diff --git a/array_temp/RNN_Dataset.py b/control_model/RNN_Dataset.py
similarity index 100%
rename from array_temp/RNN_Dataset.py
rename to control_model/RNN_Dataset.py
diff --git a/control_model/control_model_revised.ipynb b/control_model/control_model_revised.ipynb
new file mode 100644
index 0000000..9325c54
--- /dev/null
+++ b/control_model/control_model_revised.ipynb
@@ -0,0 +1,826 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2026-03-06T02:01:15.867972Z",
+     "start_time": "2026-03-06T02:01:13.560914Z"
+    }
+   },
+   "source": [
+    "#necessary imports\n",
+    "\n",
+    "\n",
+    "from data_tools import query\n",
+    "from data_tools.collections import TimeSeries\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import dill\n",
+    "import os\n",
+    "import pytz\n",
+    "from datetime import datetime, time, date\n"
+   ],
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-06T02:01:20.645793Z",
+     "start_time": "2026-03-06T02:01:16.847469Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "\n",
+    "# General best practice for device setup\n",
+    "if torch.cuda.is_available():\n",
+    "    device = torch.device(\"cuda\")\n",
+    "elif torch.backends.mps.is_available(): # For Apple Silicon GPUs\n",
+    "    device = torch.device(\"mps\")\n",
+    "else:\n",
+    "    device = torch.device(\"cpu\")\n",
+    "\n",
+    "print(f\"Using device: {device}\")\n"
+   ],
+   "id": "4c202bb9fba2781",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using device: cpu\n"
+     ]
+    }
+   ],
+   "execution_count": 2
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "",
+   "id": "2706b06256095c75",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "# query data from influx. looking at timestamps of 2024 FSGP: 14 - 18 overall, but for smaller data response lets try 14 -16\n",
+    "\n",
+    "#each 5 seconds\n",
+    "utc_offset_h = 7\n",
+    "start_utc = time(0+utc_offset_h, 00, 00)  #querying i svancouver time, influxdb gives utc\n",
+    "stop_utc = time(16+utc_offset_h, 45, 00)\n",
+    "date_start = date(2024, 7, 16)\n",
+    "date_stop = date(2024, 7, 18)\n",
+    "\n",
+    "vancouver = pytz.timezone(\"America/Vancouver\")\n",
+    "\n",
+    "start_local = vancouver.localize(datetime.combine(date_start, start_utc))\n",
+    "stop_local = vancouver.localize(datetime.combine(date_stop, stop_utc))\n",
+    "\n",
+    "start_time = start_local.astimezone(pytz.utc)\n",
+    "stop_time = stop_local.astimezone(pytz.utc)\n",
+    "\n",
+    "client = query.DBClient()\n",
+    "mech_brake_pressed: TimeSeries = client.query_time_series(start_time, stop_time, field=\"MechBrakePressed\")\n",
+    "accel_position: TimeSeries =   client.query_time_series(start_time, stop_time, field=\"AcceleratorPosition\")\n",
+    "speed_kph: TimeSeries = client.query_time_series(start_time, stop_time, \"VehicleVelocity\")\n",
+    "\n"
+   ],
+   "id": "bee61f82f0e2b463",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "# save collected data\n",
+    "\n",
+    "out_dir = os.path.join(\"../../array_temp\", \"data\", \"control_state_fsgp_2024\")\n",
+    "os.makedirs(out_dir, exist_ok=True)\n",
+    "\n",
+    "brake_path = os.path.join(out_dir, \"brake_pressed.bin\")\n",
+    "accel_path = os.path.join(out_dir, \"acceleration.bin\")\n",
+    "speed_path = os.path.join(out_dir, \"speed_kph.bin\")\n",
+    "\n",
+    "filepaths = [brake_path, accel_path, speed_path]\n",
+    "datasets = [mech_brake_pressed, accel_position, speed_kph]\n",
+    "\n",
+    "for filepath, data in zip(filepaths, datasets):\n",
+    "    with open(filepath, \"wb\") as f:\n",
+    "        dill.dump(data, f)\n"
+   ],
+   "id": "d2c1546f1e2bbbeb",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-06T02:01:24.704970Z",
+     "start_time": "2026-03-06T02:01:24.653243Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "#loading data\n",
+    "import os\n",
+    "import dill\n",
+    "out_dir = os.path.join(\"../../array_temp\", \"data\", \"control_state_fsgp_2024\")\n",
+    "\n",
+    "brake_path = os.path.join(out_dir, \"brake_pressed.bin\")\n",
+    "accel_path = os.path.join(out_dir, \"acceleration.bin\")\n",
+    "speed_path = os.path.join(out_dir, \"speed_kph.bin\")\n",
+    "\n",
+    "filepaths = [brake_path, accel_path, speed_path]\n",
+    "\n",
+    "loaded_datasets = []\n",
+    "\n",
+    "for filepath in filepaths:\n",
+    "    with open(filepath, \"rb\") as f:\n",
+    "        data = dill.load(f)\n",
+    "        loaded_datasets.append(data)\n",
+    "\n",
+    "#unnpack\n",
+    "mech_brake_pressed, accel_position, speed_kph = loaded_datasets"
+   ],
+   "id": "d309d06cd7c5f22d",
+   "outputs": [],
+   "execution_count": 3
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-06T02:01:25.407594Z",
+     "start_time": "2026-03-06T02:01:25.392802Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# use sunbeam instead to save yourself a headache\n",
+    "def make_df(source, name):\n",
+    "    dfs = []\n",
+    "\n",
+    "    client = query.SunbeamClient()\n",
+    "\n",
+    "    for event in [\"FSGP_2024_Day_1\", \"FSGP_2024_Day_2\", \"FSGP_2024_Day_3\"]:\n",
+    "        file = client.get_file(\n",
+    "            origin=\"production\",\n",
+    "            event=event,\n",
+    "            source=source,\n",
+    "            name=name\n",
+    "        ).unwrap()\n",
+    "\n",
+    "        dfs.append(\n",
+    "            pd.DataFrame(\n",
+    "                data=file.data,\n",
+    "                index=file.data.datetime_x_axis\n",
+    "            )\n",
+    "        )\n",
+    "\n",
+    "    return pd.concat(dfs).sort_index()"
+   ],
+   "id": "8244964eb0c13987",
+   "outputs": [],
+   "execution_count": 4
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-06T02:01:25.918480Z",
+     "start_time": "2026-03-06T02:01:25.908039Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# combine all dfs and resample, then feed to scaler.\n",
+    "# states = velocity, position\n",
+    "# control = mbrake pressed, accelerator position\n",
+    "def combine_dfs(telemetry_names, index_common, all_dfs):\n",
+    "        combined_df = pd.DataFrame(index=index_common)\n",
+    "        combined_df.dropna()\n",
+    "\n",
+    "        for name, df in zip(telemetry_names, all_dfs):\n",
+    "            #df_interp = self.resample(df, index_common)\n",
+    "            combined_df[name] = df\n",
+    "\n",
+    "        return combined_df\n"
+   ],
+   "id": "8f9608786c013420",
+   "outputs": [],
+   "execution_count": 5
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-06T02:01:30.523800Z",
+     "start_time": "2026-03-06T02:01:26.434640Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "pos_df = make_df(source = \"localization\", name = \"TrackIndex\")\n",
+    "speed_df = make_df(source = \"ingress\", name = \"VehicleVelocity\")\n",
+    "#preprocessing - convert everything to pandas dataframes.\n",
+    "\n",
+    "\n",
+    "df_mech_brake_pressed = pd.DataFrame(mech_brake_pressed, index = mech_brake_pressed.datetime_x_axis)\n",
+    "df_accel_position = pd.DataFrame(accel_position, index = accel_position.datetime_x_axis)\n",
+    "#df_speed_kph = pd.DataFrame(speed_kph)\n",
+    "\n",
+    "\n",
+    "\n",
+    "all_dfs = [df_mech_brake_pressed, df_accel_position]\n",
+    "combined_df = combine_dfs([\"mech_brake_pressed\", \"accel_position\"], df_mech_brake_pressed.index, all_dfs)\n",
+    "final_df = pd.merge_asof(\n",
+    "    df_mech_brake_pressed.sort_index(),\n",
+    "    df_accel_position.sort_index(),\n",
+    "    left_index=True,\n",
+    "    right_index=True,\n",
+    "    direction=\"nearest\"\n",
+    ")"
+   ],
+   "id": "d3a364dd0f5e0dc0",
+   "outputs": [],
+   "execution_count": 6
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-06T02:01:32.336432Z",
+     "start_time": "2026-03-06T02:01:32.113292Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "dfs = pd.concat([pos_df, speed_df], axis = 1)\n",
+    "final_df = pd.merge_asof(\n",
+    "    final_df.sort_index(),\n",
+    "    dfs.sort_index(),\n",
+    "    left_index=True,\n",
+    "    right_index=True,\n",
+    "    direction=\"nearest\"\n",
+    ")\n",
+    "final_df.columns = [\"brake_pressed\", \"accel_position\", \"position\", \"speed\"]"
+   ],
+   "id": "6ba2f86b4ef3b7d6",
+   "outputs": [],
+   "execution_count": 7
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "final_df.head()",
+   "id": "20dc87aea5bc9336",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "print(final_df.isna().sum())\n",
+    "print(final_df.shape)\n",
+    "# position has nan values"
+   ],
+   "id": "6e311b63077819a9",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-06T02:01:46.534209Z",
+     "start_time": "2026-03-06T02:01:46.273017Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "final_df = final_df.sort_index()\n",
+    "final_df = final_df.ffill().dropna()"
+   ],
+   "id": "f143094a7164ac55",
+   "outputs": [],
+   "execution_count": 9
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-06T02:01:48.118926Z",
+     "start_time": "2026-03-06T02:01:47.202769Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plt.plot(final_df[\"position\"])",
+   "id": "ed4054acb3cc0dc6",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x26cc8c7db80>]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 10
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-06T02:01:52.497410Z",
+     "start_time": "2026-03-06T02:01:49.648979Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "#finally cleared up all data, now move onto RNN\n",
+    "from control_model import DataPreprocessing\n",
+    "\n",
+    "train_dataset, test_dataset, train_loader, test_loader, scaler = DataPreprocessing.make_sequence_datasets(final_df, state_cols = ['position', 'speed'], control_cols = ['brake_pressed', 'accel_position'], seq_len = 600, train_frac=0.7, batch_size = 128)"
+   ],
+   "id": "d57a320f8d3cb4d8",
+   "outputs": [],
+   "execution_count": 11
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-06T02:01:53.240783Z",
+     "start_time": "2026-03-06T02:01:53.232733Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "state = ['position', 'speed']\n",
+    "control = ['brake_pressed', 'accel_position']"
+   ],
+   "id": "d1d762ae92b287c",
+   "outputs": [],
+   "execution_count": 12
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-06T02:01:53.977910Z",
+     "start_time": "2026-03-06T02:01:53.906047Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import time\n",
+    "\n",
+    "# Test 1: raw tensor indexing speed (no dataloader at all)\n",
+    "t = time.time()\n",
+    "for i in range(5):\n",
+    "    idx = train_dataset.indices[i]\n",
+    "    x = train_dataset.states[idx:idx+train_dataset.seq_len]\n",
+    "    y = train_dataset.controls[idx:idx+train_dataset.seq_len]\n",
+    "print(f\"Raw tensor access (5 samples): {time.time()-t:.4f}s\")\n",
+    "\n",
+    "# Test 2: dataset __getitem__ speed\n",
+    "t = time.time()\n",
+    "for i in range(5):\n",
+    "    x, y = train_dataset[i]\n",
+    "print(f\"Dataset __getitem__ (5 samples): {time.time()-t:.4f}s\")\n",
+    "\n",
+    "# Test 3: dataloader speed\n",
+    "t = time.time()\n",
+    "for i, (x, y) in enumerate(train_loader):\n",
+    "    if i == 5: break\n",
+    "print(f\"DataLoader (5 batches): {time.time()-t:.4f}s\")\n",
+    "\n",
+    "# Test 4: how big is your data?\n",
+    "print(f\"\\nTotal sequences: {len(train_dataset)}\")\n",
+    "print(f\"Batches per epoch: {len(train_loader)}\")\n",
+    "print(f\"states tensor size: {train_dataset.states.shape}\")\n",
+    "print(f\"controls tensor size: {train_dataset.controls.shape}\")"
+   ],
+   "id": "1410b1caa1d1c46b",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Raw tensor access (5 samples): 0.0088s\n",
+      "Dataset __getitem__ (5 samples): 0.0096s\n",
+      "DataLoader (5 batches): 0.0418s\n",
+      "\n",
+      "Total sequences: 14078\n",
+      "Batches per epoch: 110\n",
+      "states tensor size: torch.Size([1408361, 2])\n",
+      "controls tensor size: torch.Size([1408361, 2])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sanar\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:775: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+      "  super().__init__(loader)\n"
+     ]
+    }
+   ],
+   "execution_count": 13
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-06T02:01:56.835597Z",
+     "start_time": "2026-03-06T02:01:56.814642Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "from control_model.RNN import *\n",
+    "seq_length = 600\n",
+    "input_size = len(state)\n",
+    "output_size = len(control)\n",
+    "\n",
+    "\n",
+    "hidden_size = 128     # was 256\n",
+    "num_layers  = 2\n",
+    "model = RNN(input_size, hidden_size, num_layers, seq_length, output_size).to(device)"
+   ],
+   "id": "154939dd8005f9b7",
+   "outputs": [],
+   "execution_count": 14
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-06T02:01:57.493893Z",
+     "start_time": "2026-03-06T02:01:57.482953Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# now we define the training loop. we are pretending a trajectory of controls.\n",
+    "#build the model\n",
+    "def train_model(model, train_loader, test_loader, epochs):\n",
+    "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "    model = model.to(device)\n",
+    "    criterion = nn.MSELoss()\n",
+    "    optimizer = torch.optim.Adam(model.parameters(), lr=1e-4, weight_decay=1e-5)\n",
+    "    train_losses = []\n",
+    "    test_losses = []\n",
+    "    print(\"NaNs in Train Loader:\", any(torch.isnan(x).any() for x, y in train_loader))\n",
+    "    print(\"NaNs in Test Loader:\", any(torch.isnan(x).any() for x, y in test_loader))\n",
+    "    for epoch in range(epochs):\n",
+    "\n",
+    "        #training loop\n",
+    "        model.train()\n",
+    "        train_loss = 0\n",
+    "        for x_batch, y_batch in train_loader:\n",
+    "            x_batch = x_batch.to(device)\n",
+    "            y_batch = y_batch.to(device)\n",
+    "            #check inputs before forward pass\n",
+    "            if torch.isnan(x_batch).any() or torch.isnan(y_batch).any():\n",
+    "                print(\"NaN in inputs, skipping batch\")\n",
+    "                continue\n",
+    "\n",
+    "\n",
+    "            optimizer.zero_grad() #resets the gradients to zero\n",
+    "            outputs = model(x_batch)\n",
+    "            loss = criterion(outputs, y_batch)\n",
+    "            if torch.isnan(outputs).any():\n",
+    "                print(\"NaN in model outputs\")\n",
+    "                print(\"x_batch min/max:\", x_batch.min().item(), x_batch.max().item())\n",
+    "                continue\n",
+    "\n",
+    "            loss = criterion(outputs, y_batch)\n",
+    "\n",
+    "            if torch.isnan(loss):\n",
+    "                print(\"NaN in loss\")\n",
+    "                print(\"outputs min/max:\", outputs.min().item(), outputs.max().item())\n",
+    "                print(\"y_batch min/max:\", y_batch.min().item(), y_batch.max().item())\n",
+    "                continue\n",
+    "            loss.backward()\n",
+    "            torch.nn.utils.clip_grad_norm_(model.parameters(), 1)\n",
+    "            optimizer.step()\n",
+    "\n",
+    "            train_loss += loss.item() #convert tensor to float\n",
+    "        train_loss/=len(train_loader) #average losses over batches\n",
+    "        print(f\"Epoch {epoch + 1}/{epochs}, Train Loss: {train_loss:.4f}\")\n",
+    "        train_losses.append(train_loss)\n",
+    "\n",
+    "        # testing loop\n",
+    "        model.eval()\n",
+    "        test_loss = 0\n",
+    "        with torch.no_grad():\n",
+    "            for x_batch, y_batch in test_loader:\n",
+    "                x_batch = x_batch.to(device)\n",
+    "                y_batch = y_batch.to(device)\n",
+    "                optimizer.zero_grad()\n",
+    "                predictions = model(x_batch)\n",
+    "                loss = criterion(predictions, y_batch)\n",
+    "                test_loss += loss.item()\n",
+    "        test_loss/=len(test_loader)\n",
+    "        test_losses.append(test_loss)\n",
+    "\n",
+    "    return train_losses, test_losses"
+   ],
+   "id": "902960e5d1a51324",
+   "outputs": [],
+   "execution_count": 15
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-06T02:05:45.783936Z",
+     "start_time": "2026-03-06T02:01:58.163045Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "train_losses, test_losses = train_model(model, train_loader, test_loader, epochs=10)\n",
+    "\n",
+    "# Save\n",
+    "torch.save({\n",
+    "    'model_state_dict': model.state_dict(),\n",
+    "    'input_size': input_size,\n",
+    "    'hidden_size': hidden_size,\n",
+    "    'num_layers': num_layers,\n",
+    "    'seq_length': seq_length,\n",
+    "    'output_size': output_size,\n",
+    "}, '../array_temp/rnn_model.pt')"
+   ],
+   "id": "95f0eac03ab75052",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sanar\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:775: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+      "  super().__init__(loader)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NaNs in Train Loader: False\n",
+      "NaNs in Test Loader: False\n",
+      "Epoch 1/10, Train Loss: 0.8423\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mKeyboardInterrupt\u001B[39m                         Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[16]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m train_losses, test_losses = \u001B[43mtrain_model\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtrain_loader\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtest_loader\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mepochs\u001B[49m\u001B[43m=\u001B[49m\u001B[32;43m10\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m      3\u001B[39m \u001B[38;5;66;03m# Save\u001B[39;00m\n\u001B[32m      4\u001B[39m torch.save({\n\u001B[32m      5\u001B[39m     \u001B[33m'\u001B[39m\u001B[33mmodel_state_dict\u001B[39m\u001B[33m'\u001B[39m: model.state_dict(),\n\u001B[32m      6\u001B[39m     \u001B[33m'\u001B[39m\u001B[33minput_size\u001B[39m\u001B[33m'\u001B[39m: input_size,\n\u001B[32m   (...)\u001B[39m\u001B[32m     10\u001B[39m     \u001B[33m'\u001B[39m\u001B[33moutput_size\u001B[39m\u001B[33m'\u001B[39m: output_size,\n\u001B[32m     11\u001B[39m }, \u001B[33m'\u001B[39m\u001B[33m../array_temp/rnn_model.pt\u001B[39m\u001B[33m'\u001B[39m)\n",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[15]\u001B[39m\u001B[32m, line 41\u001B[39m, in \u001B[36mtrain_model\u001B[39m\u001B[34m(model, train_loader, test_loader, epochs)\u001B[39m\n\u001B[32m     39\u001B[39m     \u001B[38;5;28mprint\u001B[39m(\u001B[33m\"\u001B[39m\u001B[33my_batch min/max:\u001B[39m\u001B[33m\"\u001B[39m, y_batch.min().item(), y_batch.max().item())\n\u001B[32m     40\u001B[39m     \u001B[38;5;28;01mcontinue\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m41\u001B[39m \u001B[43mloss\u001B[49m\u001B[43m.\u001B[49m\u001B[43mbackward\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m     42\u001B[39m torch.nn.utils.clip_grad_norm_(model.parameters(), \u001B[32m1\u001B[39m)\n\u001B[32m     43\u001B[39m optimizer.step()\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\_tensor.py:630\u001B[39m, in \u001B[36mTensor.backward\u001B[39m\u001B[34m(self, gradient, retain_graph, create_graph, inputs)\u001B[39m\n\u001B[32m    620\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m has_torch_function_unary(\u001B[38;5;28mself\u001B[39m):\n\u001B[32m    621\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m handle_torch_function(\n\u001B[32m    622\u001B[39m         Tensor.backward,\n\u001B[32m    623\u001B[39m         (\u001B[38;5;28mself\u001B[39m,),\n\u001B[32m   (...)\u001B[39m\u001B[32m    628\u001B[39m         inputs=inputs,\n\u001B[32m    629\u001B[39m     )\n\u001B[32m--> \u001B[39m\u001B[32m630\u001B[39m \u001B[43mtorch\u001B[49m\u001B[43m.\u001B[49m\u001B[43mautograd\u001B[49m\u001B[43m.\u001B[49m\u001B[43mbackward\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    631\u001B[39m \u001B[43m    \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mgradient\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mretain_graph\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcreate_graph\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minputs\u001B[49m\u001B[43m=\u001B[49m\u001B[43minputs\u001B[49m\n\u001B[32m    632\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\autograd\\__init__.py:364\u001B[39m, in \u001B[36mbackward\u001B[39m\u001B[34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001B[39m\n\u001B[32m    359\u001B[39m     retain_graph = create_graph\n\u001B[32m    361\u001B[39m \u001B[38;5;66;03m# The reason we repeat the same comment below is that\u001B[39;00m\n\u001B[32m    362\u001B[39m \u001B[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001B[39;00m\n\u001B[32m    363\u001B[39m \u001B[38;5;66;03m# calls in the traceback and some print out the last line\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m364\u001B[39m \u001B[43m_engine_run_backward\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    365\u001B[39m \u001B[43m    \u001B[49m\u001B[43mtensors\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    366\u001B[39m \u001B[43m    \u001B[49m\u001B[43mgrad_tensors_\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    367\u001B[39m \u001B[43m    \u001B[49m\u001B[43mretain_graph\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    368\u001B[39m \u001B[43m    \u001B[49m\u001B[43mcreate_graph\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    369\u001B[39m \u001B[43m    \u001B[49m\u001B[43minputs_tuple\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    370\u001B[39m \u001B[43m    \u001B[49m\u001B[43mallow_unreachable\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[32m    371\u001B[39m \u001B[43m    \u001B[49m\u001B[43maccumulate_grad\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[32m    372\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\autograd\\graph.py:865\u001B[39m, in \u001B[36m_engine_run_backward\u001B[39m\u001B[34m(t_outputs, *args, **kwargs)\u001B[39m\n\u001B[32m    863\u001B[39m     unregister_hooks = _register_logging_hooks_on_whole_graph(t_outputs)\n\u001B[32m    864\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m865\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mVariable\u001B[49m\u001B[43m.\u001B[49m\u001B[43m_execution_engine\u001B[49m\u001B[43m.\u001B[49m\u001B[43mrun_backward\u001B[49m\u001B[43m(\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001B[39;49;00m\n\u001B[32m    866\u001B[39m \u001B[43m        \u001B[49m\u001B[43mt_outputs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\n\u001B[32m    867\u001B[39m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m  \u001B[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001B[39;00m\n\u001B[32m    868\u001B[39m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[32m    869\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m attach_logging_hooks:\n",
+      "\u001B[31mKeyboardInterrupt\u001B[39m: "
+     ]
+    }
+   ],
+   "execution_count": 16
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "",
+   "id": "c05e3a75199b04c2",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "print(test_losses)",
+   "id": "422439d81b1a514a",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "#remember we also need to save the scaler to apply at inference\n",
+    "#using job lib for this purpose\n",
+    "import joblib\n",
+    "joblib.dump(scaler, 'scaler.pkl')\n",
+    "\n",
+    "# Later at inference\n",
+    "scaler = joblib.load('scaler.pkl')\n",
+    "x_new_scaled = scaler.transform(x)"
+   ],
+   "id": "be2c544830173066",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "#now we evaluate model for inference\n",
+    "checkpoint = torch.load('../array_temp/rnn_model.pt', map_location=device)\n",
+    "\n",
+    "model = RNN(\n",
+    "    input_size  = checkpoint['input_size'],\n",
+    "    hidden_size = checkpoint['hidden_size'],\n",
+    "    num_layers  = checkpoint['num_layers'],\n",
+    "    seq_length  = checkpoint['seq_length'],\n",
+    "    output_size = checkpoint['output_size'],\n",
+    ")\n",
+    "model.load_state_dict(checkpoint['model_state_dict'])\n",
+    "model.eval()  # important — disables dropout/batchnorm for inference"
+   ],
+   "id": "23676ff6fae304c2",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "def plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx):\n",
+    "    model.eval()\n",
+    "    x_input, y_target = test_dataset[sample_idx]\n",
+    "\n",
+    "# x_input should be [seq_len, n_states]\n",
+    "    print(x_input.shape)\n",
+    "\n",
+    "    x_np = x_input.numpy()  # [seq_len, n_states]\n",
+    "\n",
+    "    # derive time axes from actual array shapes, not seq_len variable\n",
+    "    time_controls = np.arange(y_target.shape[0]) * 0.1\n",
+    "    time_states   = np.arange(x_np.shape[0]) * 0.1\n",
+    "    x_input, y_target = test_dataset[sample_idx]\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        device = next(model.parameters()).device\n",
+    "        x_tensor = x_input.to(device).unsqueeze(0)\n",
+    "        y_pred = model(x_tensor).squeeze(0).cpu().numpy()\n",
+    "\n",
+    "    y_target = y_target.numpy()\n",
+    "    x_np = x_input.numpy()  # [seq_len, n_states]\n",
+    "\n",
+    "    # inverse transform controls\n",
+    "    # scaler was fit on state_cols + control_cols so controls start at index n_states\n",
+    "    n_states = len(state_cols)\n",
+    "    n_controls = len(control_cols)\n",
+    "    seq_len = 600\n",
+    "    def unscale_states(arr):\n",
+    "        state_mean = scaler.mean_[:n_states]\n",
+    "        state_std  = scaler.scale_[:n_states]\n",
+    "        return arr * state_std + state_mean\n",
+    "    def unscale_controls(arr):\n",
+    "        dummy = np.zeros((seq_len, n_states + n_controls))\n",
+    "        dummy[:, n_states:] = arr\n",
+    "        return scaler.inverse_transform(dummy)[:, n_states:]\n",
+    "\n",
+    "    y_target_unscaled = unscale_controls(y_target)\n",
+    "    y_pred_unscaled   = unscale_controls(y_pred)\n",
+    "    x_unscaled        = unscale_states(x_np)\n",
+    "\n",
+    "    time_controls = np.arange(seq_len) * 0.1\n",
+    "    time_states   = np.arange(x_np.shape[0]) * 0.1\n",
+    "\n",
+    "    # plot controls\n",
+    "    fig, axes = plt.subplots(n_controls, 1, figsize=(10, 4 * n_controls), sharex=True)\n",
+    "    if n_controls == 1:\n",
+    "        axes = [axes]\n",
+    "\n",
+    "    for i, col in enumerate(control_cols):\n",
+    "        axes[i].plot(time_controls, y_target_unscaled[:, i], 'g-', label='Actual (Driver)')\n",
+    "        axes[i].plot(time_controls, y_pred_unscaled[:, i], 'r--', label='Predicted (RNN)')\n",
+    "        axes[i].set_ylabel(col)\n",
+    "        axes[i].legend()\n",
+    "        axes[i].grid(True)\n",
+    "\n",
+    "    axes[-1].set_xlabel(\"Time (seconds)\")\n",
+    "    plt.suptitle(f\"Control Trajectory — Sample {sample_idx}\")\n",
+    "    plt.tight_layout()\n",
+    "\n",
+    "    # plot states\n",
+    "    fig2, axes2 = plt.subplots(n_states, 1, figsize=(10, 4 * n_states), sharex=True)\n",
+    "    if n_states == 1:\n",
+    "        axes2 = [axes2]\n",
+    "    print(x_np.shape)        # should be [seq_len, n_states]\n",
+    "    print(x_unscaled.shape)  # should match\n",
+    "    print(time_states.shape)\n",
+    "    for i, col in enumerate(state_cols):\n",
+    "        axes2[i].plot(time_states, x_unscaled[:, i], 'b-', label=col)\n",
+    "        axes2[i].set_ylabel(col)\n",
+    "        axes2[i].legend()\n",
+    "        axes2[i].grid(True)\n",
+    "\n",
+    "    axes2[-1].set_xlabel(\"Time (seconds)\")\n",
+    "    plt.suptitle(f\"Input State Sequence — Sample {sample_idx}\")\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ],
+   "id": "193451d65b640afb",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "for i in range(len(test_dataset)):\n",
+    "    x, y = test_dataset[i]\n",
+    "    if y.abs().mean() >= 0.88:\n",
+    "        print(f\"use sample_idx={i}\")\n",
+    "        break"
+   ],
+   "id": "e02909466d882593",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "state_cols = ['position', 'speed']\n",
+    "control_cols = ['brake_pressed', 'accel_position']\n",
+    "plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx=3140)\n",
+    "plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx=150)"
+   ],
+   "id": "7ca721093c670d9",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "# so how much does scaling the acceleraotr position before hand matter? why has the training time increased so much",
+   "id": "fbd9ce1ac3a4153d",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "print(\"Any inf in final_df:\", np.isinf(final_df.values).any())\n",
+    "print(\"Any NaN in final_df:\", np.isnan(final_df.values).any())"
+   ],
+   "id": "11d2951061944ddb",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "",
+   "id": "2bb7b192d6146b75",
+   "outputs": [],
+   "execution_count": null
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 85c16a8d9f72887ddabda6cd3d57f55c5f451ffa Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Fri, 6 Mar 2026 12:50:33 -0800
Subject: [PATCH 29/49] retrain model, created python script

---
 control_model/DataPreprocessing.py        |  93 +++++
 control_model/RNN.py                      |   3 +-
 control_model/RNN_Training.py             | 128 +++++++
 control_model/control_model_revised.ipynb | 441 +++++++++++++++-------
 4 files changed, 537 insertions(+), 128 deletions(-)
 create mode 100644 control_model/RNN_Training.py

diff --git a/control_model/DataPreprocessing.py b/control_model/DataPreprocessing.py
index 25cf56e..6422b1c 100644
--- a/control_model/DataPreprocessing.py
+++ b/control_model/DataPreprocessing.py
@@ -4,8 +4,101 @@
 from torch.utils.data import DataLoader
 from sklearn.preprocessing import StandardScaler
 from RNN_Dataset import RNN_Dataset
+#necessary imports
 
 
+from data_tools import query
+from data_tools.collections import TimeSeries
+import matplotlib.pyplot as plt
+import pandas as pd
+import dill
+import os
+import pytz
+from datetime import datetime, time, date
+
+
+import os
+import dill
+
+
+def combine_dfs(telemetry_names, index_common, all_dfs):
+    combined_df = pd.DataFrame(index=index_common)
+    combined_df.dropna()
+
+    for name, df in zip(telemetry_names, all_dfs):
+        # df_interp = self.resample(df, index_common)
+        combined_df[name] = df
+
+    return combined_df
+# get data from sunbeam and influx.
+# use sunbeam instead to save yourself a headache
+def make_df(source, name):
+    dfs = []
+
+    client = query.SunbeamClient()
+
+    for event in ["FSGP_2024_Day_1", "FSGP_2024_Day_2", "FSGP_2024_Day_3"]:
+        file = client.get_file(
+            origin="production",
+            event=event,
+            source=source,
+            name=name
+        ).unwrap()
+
+        dfs.append(
+            pd.DataFrame(
+                data=file.data,
+                index=file.data.datetime_x_axis
+            )
+        )
+
+    return pd.concat(dfs).sort_index()
+
+def make_single_df():
+    #mech_brake_pressed, accel_position, speed_kph, position = get_data()
+    out_dir = os.path.join("../../array_temp", "data", "control_state_fsgp_2024")
+
+    brake_path = os.path.join(out_dir, "brake_pressed.bin")
+    accel_path = os.path.join(out_dir, "acceleration.bin")
+    speed_path = os.path.join(out_dir, "speed_kph.bin")
+
+    filepaths = [brake_path, accel_path, speed_path]
+
+    loaded_datasets = []
+
+    for filepath in filepaths:
+        with open(filepath, "rb") as f:
+            data = dill.load(f)
+            loaded_datasets.append(data)
+
+    # unnpack
+    mech_brake_pressed, accel_position, speed_kph = loaded_datasets
+    df_mech_brake_pressed = pd.DataFrame(mech_brake_pressed, index=mech_brake_pressed.datetime_x_axis)
+    df_accel_position = pd.DataFrame(accel_position, index=accel_position.datetime_x_axis)
+    pos_df = make_df(source="localization", name="TrackIndex")
+    speed_df = make_df(source="ingress", name="VehicleVelocity")
+    all_dfs = [df_mech_brake_pressed, df_accel_position]
+    combined_df = combine_dfs(["mech_brake_pressed", "accel_position"], df_mech_brake_pressed.index, all_dfs)
+    final_df = pd.merge_asof(
+        df_mech_brake_pressed.sort_index(),
+        df_accel_position.sort_index(),
+        left_index=True,
+        right_index=True,
+        direction="nearest"
+    )
+    dfs = pd.concat([pos_df, speed_df], axis=1)
+    final_df = pd.merge_asof(
+        final_df.sort_index(),
+        dfs.sort_index(),
+        left_index=True,
+        right_index=True,
+        direction="nearest"
+    )
+    final_df.columns = ["brake_pressed", "accel_position", "position", "speed"]
+    final_df = final_df.sort_index()
+    final_df = final_df.ffill().dropna()
+    return final_df
+
 def make_sequence_datasets(
     df_xy,
     state_cols,
diff --git a/control_model/RNN.py b/control_model/RNN.py
index 873bcd1..3a27ef6 100644
--- a/control_model/RNN.py
+++ b/control_model/RNN.py
@@ -9,7 +9,8 @@ class RNN(nn.Module):
     def __init__(self, input_size, hidden_size, num_layers, seq_length, output_size):
         #inherits from nn.Module
         super(RNN, self).__init__()
-        self.hidden_size = hidden_size  #dim of memory inside lstm
+        self.hidden_size = hidden_size  #dim of memory inside lstm ie no of features in the hidden state that persists between timesteps
+        # a higher hidden size usually corresponds to complex dependencies
         self.num_layers = num_layers  #stacked lstm layers
 
         #lstm: long short term memory - looks at long term dependencies in sequential data
diff --git a/control_model/RNN_Training.py b/control_model/RNN_Training.py
new file mode 100644
index 0000000..1b2bfce
--- /dev/null
+++ b/control_model/RNN_Training.py
@@ -0,0 +1,128 @@
+#necessary imports
+import os
+import gc
+import torch
+import torch.nn as nn
+from torch.utils.data import Dataset, DataLoader
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+from RNN_Dataset import RNN_Dataset
+from RNN import RNN
+from DataPreprocessing import *
+from data_tools import query
+from data_tools.collections import TimeSeries
+import matplotlib.pyplot as plt
+import pandas as pd
+import dill
+import os
+import pytz
+from datetime import datetime, time, date
+
+
+#create training loop
+def train_model(model, train_loader, test_loader, epochs):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model = model.to(device)
+    criterion = nn.MSELoss() #mean squared error loss
+    optimizer = torch.optim.Adam(model.parameters(), lr=1e-4, weight_decay=1e-5)
+    train_losses, test_losses = [], []
+    for epoch in range(epochs):
+
+        model.train()
+        train_loss = 0.0
+        for x_batch, y_batch in train_loader:
+            x_batch = x_batch.to(device)
+            y_batch = y_batch.to(device)
+    #check inputs before forward pass
+            if torch.isnan(x_batch).any():
+                print("nan in inputs, skip batch")
+                continue
+            optimizer.zero_grad() #reset gradients of all parameters to zero
+            outputs = model(x_batch)
+            loss    = criterion(outputs, y_batch)
+
+            if torch.isnan(loss):
+                print(f"  [warn] NaN loss at epoch {epoch+1}, skipping batch")
+                continue
+
+            loss.backward()
+            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
+            optimizer.step()
+            train_loss += loss.item()
+
+        train_loss /= len(train_loader)
+        train_losses.append(train_loss)
+
+        # evaluate
+        model.eval()
+        test_loss = 0.0
+
+        with torch.no_grad():
+            for x_batch, y_batch in test_loader:
+                x_batch = x_batch.to(device)
+                y_batch = y_batch.to(device)
+                predictions = model(x_batch)
+                test_loss  += criterion(predictions, y_batch).item()
+
+        test_loss /= len(test_loader)
+        test_losses.append(test_loss)
+
+        print(f"Epoch {epoch+1}/{epochs} | "
+              f"Train Loss: {train_loss:.4f} | Test Loss: {test_loss:.4f}")
+
+        # Free memory each epoch
+        gc.collect()
+
+    return train_losses, test_losses
+
+
+#training script.
+if __name__ == "__main__":
+
+    # requirements
+    STATE_COLS   = ["position", "speed"] #states
+    CONTROL_COLS = ["brake_pressed", "accel_position"]  #controls
+    SEQ_LEN      = 600 #one minute
+    STRIDE       = 100 #sliding window change between consecutive sequences, reduces overlapping
+    BATCH_SIZE   = 128
+    EPOCHS       = 30
+    HIDDEN_SIZE  = 64
+    NUM_LAYERS   = 2
+
+    if torch.cuda.is_available():
+        device = torch.device("cuda")
+    else:
+        device = torch.device("cpu")
+    #load required data
+
+    df = make_single_df()
+    gc.collect()
+
+    print("Building datasets...")
+    train_dataset, test_dataset, train_loader, test_loader, scaler = make_sequence_datasets(
+        df, STATE_COLS, CONTROL_COLS,
+        seq_len=SEQ_LEN, stride=STRIDE, batch_size=BATCH_SIZE
+    )
+
+    # Free original dataframe — no longer needed
+    del df
+    gc.collect()
+
+    print(f"Train sequences : {len(train_dataset)}")
+    print(f"Test  sequences : {len(test_dataset)}")
+    print(f"Batches / epoch : {len(train_loader)}")
+
+    model = RNN(
+        input_size=len(STATE_COLS),
+        hidden_size=HIDDEN_SIZE,
+        num_layers=NUM_LAYERS, seq_length=SEQ_LEN,output_size=len(CONTROL_COLS)
+    ).to(device)
+
+    train_losses, test_losses = train_model(
+        model, train_loader, test_loader, epochs=EPOCHS
+    )
+
+    # Save model
+    torch.save(model.state_dict(), "rnn_model.pth")
+    print("Model saved to rnn_model.pth")
diff --git a/control_model/control_model_revised.ipynb b/control_model/control_model_revised.ipynb
index 9325c54..c0085d0 100644
--- a/control_model/control_model_revised.ipynb
+++ b/control_model/control_model_revised.ipynb
@@ -6,8 +6,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2026-03-06T02:01:15.867972Z",
-     "start_time": "2026-03-06T02:01:13.560914Z"
+     "end_time": "2026-03-06T19:17:10.713610Z",
+     "start_time": "2026-03-06T19:17:08.135522Z"
     }
    },
    "source": [
@@ -27,12 +27,7 @@
    "execution_count": 1
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-06T02:01:20.645793Z",
-     "start_time": "2026-03-06T02:01:16.847469Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "import torch\n",
@@ -48,16 +43,8 @@
     "print(f\"Using device: {device}\")\n"
    ],
    "id": "4c202bb9fba2781",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using device: cpu\n"
-     ]
-    }
-   ],
-   "execution_count": 2
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -98,36 +85,11 @@
    "outputs": [],
    "execution_count": null
   },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "\n",
-    "# save collected data\n",
-    "\n",
-    "out_dir = os.path.join(\"../../array_temp\", \"data\", \"control_state_fsgp_2024\")\n",
-    "os.makedirs(out_dir, exist_ok=True)\n",
-    "\n",
-    "brake_path = os.path.join(out_dir, \"brake_pressed.bin\")\n",
-    "accel_path = os.path.join(out_dir, \"acceleration.bin\")\n",
-    "speed_path = os.path.join(out_dir, \"speed_kph.bin\")\n",
-    "\n",
-    "filepaths = [brake_path, accel_path, speed_path]\n",
-    "datasets = [mech_brake_pressed, accel_position, speed_kph]\n",
-    "\n",
-    "for filepath, data in zip(filepaths, datasets):\n",
-    "    with open(filepath, \"wb\") as f:\n",
-    "        dill.dump(data, f)\n"
-   ],
-   "id": "d2c1546f1e2bbbeb",
-   "outputs": [],
-   "execution_count": null
-  },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-06T02:01:24.704970Z",
-     "start_time": "2026-03-06T02:01:24.653243Z"
+     "end_time": "2026-03-06T19:17:41.515423Z",
+     "start_time": "2026-03-06T19:17:41.412223Z"
     }
    },
    "cell_type": "code",
@@ -155,13 +117,13 @@
    ],
    "id": "d309d06cd7c5f22d",
    "outputs": [],
-   "execution_count": 3
+   "execution_count": 2
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-06T02:01:25.407594Z",
-     "start_time": "2026-03-06T02:01:25.392802Z"
+     "end_time": "2026-03-06T19:17:42.112620Z",
+     "start_time": "2026-03-06T19:17:42.099738Z"
     }
    },
    "cell_type": "code",
@@ -191,13 +153,13 @@
    ],
    "id": "8244964eb0c13987",
    "outputs": [],
-   "execution_count": 4
+   "execution_count": 3
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-06T02:01:25.918480Z",
-     "start_time": "2026-03-06T02:01:25.908039Z"
+     "end_time": "2026-03-06T19:17:42.716847Z",
+     "start_time": "2026-03-06T19:17:42.704737Z"
     }
    },
    "cell_type": "code",
@@ -217,13 +179,13 @@
    ],
    "id": "8f9608786c013420",
    "outputs": [],
-   "execution_count": 5
+   "execution_count": 4
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-06T02:01:30.523800Z",
-     "start_time": "2026-03-06T02:01:26.434640Z"
+     "end_time": "2026-03-06T19:17:46.712516Z",
+     "start_time": "2026-03-06T19:17:43.248266Z"
     }
    },
    "cell_type": "code",
@@ -252,13 +214,13 @@
    ],
    "id": "d3a364dd0f5e0dc0",
    "outputs": [],
-   "execution_count": 6
+   "execution_count": 5
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-06T02:01:32.336432Z",
-     "start_time": "2026-03-06T02:01:32.113292Z"
+     "end_time": "2026-03-06T19:17:47.572003Z",
+     "start_time": "2026-03-06T19:17:47.391153Z"
     }
    },
    "cell_type": "code",
@@ -275,15 +237,101 @@
    ],
    "id": "6ba2f86b4ef3b7d6",
    "outputs": [],
-   "execution_count": 7
+   "execution_count": 6
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-06T19:17:48.233835Z",
+     "start_time": "2026-03-06T19:17:48.203267Z"
+    }
+   },
    "cell_type": "code",
    "source": "final_df.head()",
    "id": "20dc87aea5bc9336",
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                               brake_pressed  accel_position  position  speed\n",
+       "2024-07-16 07:05:27.380000114            0.0             0.0       0.0    0.0\n",
+       "2024-07-16 07:05:27.480000019            0.0             0.0       0.0    0.0\n",
+       "2024-07-16 07:05:27.580000162            0.0             0.0       0.0    0.0\n",
+       "2024-07-16 07:05:27.680000067            0.0             0.0       0.0    0.0\n",
+       "2024-07-16 07:05:27.780000210            0.0             0.0       0.0    0.0"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brake_pressed</th>\n",
+       "      <th>accel_position</th>\n",
+       "      <th>position</th>\n",
+       "      <th>speed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:05:27.380000114</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:05:27.480000019</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:05:27.580000162</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:05:27.680000067</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:05:27.780000210</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 7
   },
   {
    "metadata": {},
@@ -300,8 +348,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-06T02:01:46.534209Z",
-     "start_time": "2026-03-06T02:01:46.273017Z"
+     "end_time": "2026-03-06T19:17:52.419467Z",
+     "start_time": "2026-03-06T19:17:52.223051Z"
     }
    },
    "cell_type": "code",
@@ -311,13 +359,13 @@
    ],
    "id": "f143094a7164ac55",
    "outputs": [],
-   "execution_count": 9
+   "execution_count": 8
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-06T02:01:48.118926Z",
-     "start_time": "2026-03-06T02:01:47.202769Z"
+     "end_time": "2026-03-06T05:44:34.037179Z",
+     "start_time": "2026-03-06T05:44:33.445541Z"
     }
    },
    "cell_type": "code",
@@ -327,7 +375,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x26cc8c7db80>]"
+       "[<matplotlib.lines.Line2D at 0x1f0a8d0ed50>]"
       ]
      },
      "execution_count": 10,
@@ -350,8 +398,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-06T02:01:52.497410Z",
-     "start_time": "2026-03-06T02:01:49.648979Z"
+     "end_time": "2026-03-06T19:18:01.176783Z",
+     "start_time": "2026-03-06T19:17:55.630297Z"
     }
    },
    "cell_type": "code",
@@ -359,17 +407,17 @@
     "#finally cleared up all data, now move onto RNN\n",
     "from control_model import DataPreprocessing\n",
     "\n",
-    "train_dataset, test_dataset, train_loader, test_loader, scaler = DataPreprocessing.make_sequence_datasets(final_df, state_cols = ['position', 'speed'], control_cols = ['brake_pressed', 'accel_position'], seq_len = 600, train_frac=0.7, batch_size = 128)"
+    "train_dataset, test_dataset, train_loader, test_loader, scaler = DataPreprocessing.make_sequence_datasets(final_df, state_cols = ['position', 'speed'], control_cols = ['brake_pressed', 'accel_position'], seq_len = 1000, train_frac=0.7, batch_size = 128)"
    ],
    "id": "d57a320f8d3cb4d8",
    "outputs": [],
-   "execution_count": 11
+   "execution_count": 9
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-06T02:01:53.240783Z",
-     "start_time": "2026-03-06T02:01:53.232733Z"
+     "end_time": "2026-03-06T19:18:02.191104Z",
+     "start_time": "2026-03-06T19:18:02.185753Z"
     }
    },
    "cell_type": "code",
@@ -379,13 +427,13 @@
    ],
    "id": "d1d762ae92b287c",
    "outputs": [],
-   "execution_count": 12
+   "execution_count": 10
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-06T02:01:53.977910Z",
-     "start_time": "2026-03-06T02:01:53.906047Z"
+     "end_time": "2026-03-06T19:18:03.389242Z",
+     "start_time": "2026-03-06T19:18:03.315530Z"
     }
    },
    "cell_type": "code",
@@ -424,11 +472,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Raw tensor access (5 samples): 0.0088s\n",
-      "Dataset __getitem__ (5 samples): 0.0096s\n",
-      "DataLoader (5 batches): 0.0418s\n",
+      "Raw tensor access (5 samples): 0.0121s\n",
+      "Dataset __getitem__ (5 samples): 0.0138s\n",
+      "DataLoader (5 batches): 0.0408s\n",
       "\n",
-      "Total sequences: 14078\n",
+      "Total sequences: 14074\n",
       "Batches per epoch: 110\n",
       "states tensor size: torch.Size([1408361, 2])\n",
       "controls tensor size: torch.Size([1408361, 2])\n"
@@ -443,20 +491,20 @@
      ]
     }
    ],
-   "execution_count": 13
+   "execution_count": 11
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-06T02:01:56.835597Z",
-     "start_time": "2026-03-06T02:01:56.814642Z"
+     "end_time": "2026-03-06T19:18:05.468263Z",
+     "start_time": "2026-03-06T19:18:05.375221Z"
     }
    },
    "cell_type": "code",
    "source": [
     "\n",
     "from control_model.RNN import *\n",
-    "seq_length = 600\n",
+    "seq_length = 1000\n",
     "input_size = len(state)\n",
     "output_size = len(control)\n",
     "\n",
@@ -467,13 +515,13 @@
    ],
    "id": "154939dd8005f9b7",
    "outputs": [],
-   "execution_count": 14
+   "execution_count": 12
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-06T02:01:57.493893Z",
-     "start_time": "2026-03-06T02:01:57.482953Z"
+     "end_time": "2026-03-06T19:18:06.115466Z",
+     "start_time": "2026-03-06T19:18:06.105749Z"
     }
    },
    "cell_type": "code",
@@ -545,13 +593,13 @@
    ],
    "id": "902960e5d1a51324",
    "outputs": [],
-   "execution_count": 15
+   "execution_count": 13
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-06T02:05:45.783936Z",
-     "start_time": "2026-03-06T02:01:58.163045Z"
+     "end_time": "2026-03-06T19:59:44.493871Z",
+     "start_time": "2026-03-06T19:18:06.847839Z"
     }
    },
    "cell_type": "code",
@@ -584,26 +632,20 @@
      "text": [
       "NaNs in Train Loader: False\n",
       "NaNs in Test Loader: False\n",
-      "Epoch 1/10, Train Loss: 0.8423\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mKeyboardInterrupt\u001B[39m                         Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[16]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m train_losses, test_losses = \u001B[43mtrain_model\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtrain_loader\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtest_loader\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mepochs\u001B[49m\u001B[43m=\u001B[49m\u001B[32;43m10\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m      3\u001B[39m \u001B[38;5;66;03m# Save\u001B[39;00m\n\u001B[32m      4\u001B[39m torch.save({\n\u001B[32m      5\u001B[39m     \u001B[33m'\u001B[39m\u001B[33mmodel_state_dict\u001B[39m\u001B[33m'\u001B[39m: model.state_dict(),\n\u001B[32m      6\u001B[39m     \u001B[33m'\u001B[39m\u001B[33minput_size\u001B[39m\u001B[33m'\u001B[39m: input_size,\n\u001B[32m   (...)\u001B[39m\u001B[32m     10\u001B[39m     \u001B[33m'\u001B[39m\u001B[33moutput_size\u001B[39m\u001B[33m'\u001B[39m: output_size,\n\u001B[32m     11\u001B[39m }, \u001B[33m'\u001B[39m\u001B[33m../array_temp/rnn_model.pt\u001B[39m\u001B[33m'\u001B[39m)\n",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[15]\u001B[39m\u001B[32m, line 41\u001B[39m, in \u001B[36mtrain_model\u001B[39m\u001B[34m(model, train_loader, test_loader, epochs)\u001B[39m\n\u001B[32m     39\u001B[39m     \u001B[38;5;28mprint\u001B[39m(\u001B[33m\"\u001B[39m\u001B[33my_batch min/max:\u001B[39m\u001B[33m\"\u001B[39m, y_batch.min().item(), y_batch.max().item())\n\u001B[32m     40\u001B[39m     \u001B[38;5;28;01mcontinue\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m41\u001B[39m \u001B[43mloss\u001B[49m\u001B[43m.\u001B[49m\u001B[43mbackward\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m     42\u001B[39m torch.nn.utils.clip_grad_norm_(model.parameters(), \u001B[32m1\u001B[39m)\n\u001B[32m     43\u001B[39m optimizer.step()\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\_tensor.py:630\u001B[39m, in \u001B[36mTensor.backward\u001B[39m\u001B[34m(self, gradient, retain_graph, create_graph, inputs)\u001B[39m\n\u001B[32m    620\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m has_torch_function_unary(\u001B[38;5;28mself\u001B[39m):\n\u001B[32m    621\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m handle_torch_function(\n\u001B[32m    622\u001B[39m         Tensor.backward,\n\u001B[32m    623\u001B[39m         (\u001B[38;5;28mself\u001B[39m,),\n\u001B[32m   (...)\u001B[39m\u001B[32m    628\u001B[39m         inputs=inputs,\n\u001B[32m    629\u001B[39m     )\n\u001B[32m--> \u001B[39m\u001B[32m630\u001B[39m \u001B[43mtorch\u001B[49m\u001B[43m.\u001B[49m\u001B[43mautograd\u001B[49m\u001B[43m.\u001B[49m\u001B[43mbackward\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    631\u001B[39m \u001B[43m    \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mgradient\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mretain_graph\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcreate_graph\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minputs\u001B[49m\u001B[43m=\u001B[49m\u001B[43minputs\u001B[49m\n\u001B[32m    632\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\autograd\\__init__.py:364\u001B[39m, in \u001B[36mbackward\u001B[39m\u001B[34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001B[39m\n\u001B[32m    359\u001B[39m     retain_graph = create_graph\n\u001B[32m    361\u001B[39m \u001B[38;5;66;03m# The reason we repeat the same comment below is that\u001B[39;00m\n\u001B[32m    362\u001B[39m \u001B[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001B[39;00m\n\u001B[32m    363\u001B[39m \u001B[38;5;66;03m# calls in the traceback and some print out the last line\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m364\u001B[39m \u001B[43m_engine_run_backward\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    365\u001B[39m \u001B[43m    \u001B[49m\u001B[43mtensors\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    366\u001B[39m \u001B[43m    \u001B[49m\u001B[43mgrad_tensors_\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    367\u001B[39m \u001B[43m    \u001B[49m\u001B[43mretain_graph\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    368\u001B[39m \u001B[43m    \u001B[49m\u001B[43mcreate_graph\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    369\u001B[39m \u001B[43m    \u001B[49m\u001B[43minputs_tuple\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    370\u001B[39m \u001B[43m    \u001B[49m\u001B[43mallow_unreachable\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[32m    371\u001B[39m \u001B[43m    \u001B[49m\u001B[43maccumulate_grad\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[32m    372\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\autograd\\graph.py:865\u001B[39m, in \u001B[36m_engine_run_backward\u001B[39m\u001B[34m(t_outputs, *args, **kwargs)\u001B[39m\n\u001B[32m    863\u001B[39m     unregister_hooks = _register_logging_hooks_on_whole_graph(t_outputs)\n\u001B[32m    864\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m865\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mVariable\u001B[49m\u001B[43m.\u001B[49m\u001B[43m_execution_engine\u001B[49m\u001B[43m.\u001B[49m\u001B[43mrun_backward\u001B[49m\u001B[43m(\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001B[39;49;00m\n\u001B[32m    866\u001B[39m \u001B[43m        \u001B[49m\u001B[43mt_outputs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\n\u001B[32m    867\u001B[39m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m  \u001B[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001B[39;00m\n\u001B[32m    868\u001B[39m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[32m    869\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m attach_logging_hooks:\n",
-      "\u001B[31mKeyboardInterrupt\u001B[39m: "
+      "Epoch 1/10, Train Loss: 0.8426\n",
+      "Epoch 2/10, Train Loss: 0.7102\n",
+      "Epoch 3/10, Train Loss: 0.7090\n",
+      "Epoch 4/10, Train Loss: 0.7085\n",
+      "Epoch 5/10, Train Loss: 0.7083\n",
+      "Epoch 6/10, Train Loss: 0.7076\n",
+      "Epoch 7/10, Train Loss: 0.7072\n",
+      "Epoch 8/10, Train Loss: 0.7068\n",
+      "Epoch 9/10, Train Loss: 0.7065\n",
+      "Epoch 10/10, Train Loss: 0.7053\n"
      ]
     }
    ],
-   "execution_count": 16
+   "execution_count": 14
   },
   {
    "metadata": {},
@@ -614,15 +656,33 @@
    "execution_count": null
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-06T20:00:00.490405Z",
+     "start_time": "2026-03-06T20:00:00.476820Z"
+    }
+   },
    "cell_type": "code",
    "source": "print(test_losses)",
    "id": "422439d81b1a514a",
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.7880346009042114, 0.7750665485315645, 0.7742923867966359, 0.7722918457972506, 0.7733135666542997, 0.7732195612043142, 0.7729957114206627, 0.7712206876603886, 0.7692550068022683, 0.7723857162830731]\n"
+     ]
+    }
+   ],
+   "execution_count": 15
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-06T06:14:35.503770Z",
+     "start_time": "2026-03-06T06:14:35.213107Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "#remember we also need to save the scaler to apply at inference\n",
@@ -635,11 +695,40 @@
     "x_new_scaled = scaler.transform(x)"
    ],
    "id": "be2c544830173066",
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sanar\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py:2691: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "Found array with dim 3, while dim <= 2 is required by StandardScaler.",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mValueError\u001B[39m                                Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[18]\u001B[39m\u001B[32m, line 8\u001B[39m\n\u001B[32m      6\u001B[39m \u001B[38;5;66;03m# Later at inference\u001B[39;00m\n\u001B[32m      7\u001B[39m scaler = joblib.load(\u001B[33m'\u001B[39m\u001B[33mscaler.pkl\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m----> \u001B[39m\u001B[32m8\u001B[39m x_new_scaled = \u001B[43mscaler\u001B[49m\u001B[43m.\u001B[49m\u001B[43mtransform\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\sklearn\\utils\\_set_output.py:316\u001B[39m, in \u001B[36m_wrap_method_output.<locals>.wrapped\u001B[39m\u001B[34m(self, X, *args, **kwargs)\u001B[39m\n\u001B[32m    314\u001B[39m \u001B[38;5;129m@wraps\u001B[39m(f)\n\u001B[32m    315\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[34mwrapped\u001B[39m(\u001B[38;5;28mself\u001B[39m, X, *args, **kwargs):\n\u001B[32m--> \u001B[39m\u001B[32m316\u001B[39m     data_to_wrap = \u001B[43mf\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mX\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    317\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(data_to_wrap, \u001B[38;5;28mtuple\u001B[39m):\n\u001B[32m    318\u001B[39m         \u001B[38;5;66;03m# only wrap the first output for cross decomposition\u001B[39;00m\n\u001B[32m    319\u001B[39m         return_tuple = (\n\u001B[32m    320\u001B[39m             _wrap_data_with_container(method, data_to_wrap[\u001B[32m0\u001B[39m], X, \u001B[38;5;28mself\u001B[39m),\n\u001B[32m    321\u001B[39m             *data_to_wrap[\u001B[32m1\u001B[39m:],\n\u001B[32m    322\u001B[39m         )\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\sklearn\\preprocessing\\_data.py:1094\u001B[39m, in \u001B[36mStandardScaler.transform\u001B[39m\u001B[34m(self, X, copy)\u001B[39m\n\u001B[32m   1091\u001B[39m check_is_fitted(\u001B[38;5;28mself\u001B[39m)\n\u001B[32m   1093\u001B[39m copy = copy \u001B[38;5;28;01mif\u001B[39;00m copy \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28mself\u001B[39m.copy\n\u001B[32m-> \u001B[39m\u001B[32m1094\u001B[39m X = \u001B[43mvalidate_data\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m   1095\u001B[39m \u001B[43m    \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m   1096\u001B[39m \u001B[43m    \u001B[49m\u001B[43mX\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1097\u001B[39m \u001B[43m    \u001B[49m\u001B[43mreset\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[32m   1098\u001B[39m \u001B[43m    \u001B[49m\u001B[43maccept_sparse\u001B[49m\u001B[43m=\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mcsr\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m   1099\u001B[39m \u001B[43m    \u001B[49m\u001B[43mcopy\u001B[49m\u001B[43m=\u001B[49m\u001B[43mcopy\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1100\u001B[39m \u001B[43m    \u001B[49m\u001B[43mdtype\u001B[49m\u001B[43m=\u001B[49m\u001B[43msupported_float_dtypes\u001B[49m\u001B[43m(\u001B[49m\u001B[43mxp\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mX_device\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1101\u001B[39m \u001B[43m    \u001B[49m\u001B[43mforce_writeable\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[32m   1102\u001B[39m \u001B[43m    \u001B[49m\u001B[43mensure_all_finite\u001B[49m\u001B[43m=\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mallow-nan\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m   1103\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   1105\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m sparse.issparse(X):\n\u001B[32m   1106\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m.with_mean:\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py:2902\u001B[39m, in \u001B[36mvalidate_data\u001B[39m\u001B[34m(_estimator, X, y, reset, validate_separately, skip_check_array, **check_params)\u001B[39m\n\u001B[32m   2900\u001B[39m         out = X, y\n\u001B[32m   2901\u001B[39m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m no_val_X \u001B[38;5;129;01mand\u001B[39;00m no_val_y:\n\u001B[32m-> \u001B[39m\u001B[32m2902\u001B[39m     out = \u001B[43mcheck_array\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minput_name\u001B[49m\u001B[43m=\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mX\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mcheck_params\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   2903\u001B[39m \u001B[38;5;28;01melif\u001B[39;00m no_val_X \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m no_val_y:\n\u001B[32m   2904\u001B[39m     out = _check_y(y, **check_params)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py:1068\u001B[39m, in \u001B[36mcheck_array\u001B[39m\u001B[34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_writeable, ensure_all_finite, ensure_non_negative, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001B[39m\n\u001B[32m   1063\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[32m   1064\u001B[39m         \u001B[33m\"\u001B[39m\u001B[33mdtype=\u001B[39m\u001B[33m'\u001B[39m\u001B[33mnumeric\u001B[39m\u001B[33m'\u001B[39m\u001B[33m is not compatible with arrays of bytes/strings.\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m   1065\u001B[39m         \u001B[33m\"\u001B[39m\u001B[33mConvert your data to numeric values explicitly instead.\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m   1066\u001B[39m     )\n\u001B[32m   1067\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m allow_nd \u001B[38;5;129;01mand\u001B[39;00m array.ndim >= \u001B[32m3\u001B[39m:\n\u001B[32m-> \u001B[39m\u001B[32m1068\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[32m   1069\u001B[39m         \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mFound array with dim \u001B[39m\u001B[38;5;132;01m{\u001B[39;00marray.ndim\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m,\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m   1070\u001B[39m         \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m while dim <= 2 is required\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m.\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m   1071\u001B[39m     )\n\u001B[32m   1073\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m ensure_all_finite:\n\u001B[32m   1074\u001B[39m     _assert_all_finite(\n\u001B[32m   1075\u001B[39m         array,\n\u001B[32m   1076\u001B[39m         input_name=input_name,\n\u001B[32m   1077\u001B[39m         estimator_name=estimator_name,\n\u001B[32m   1078\u001B[39m         allow_nan=ensure_all_finite == \u001B[33m\"\u001B[39m\u001B[33mallow-nan\u001B[39m\u001B[33m\"\u001B[39m,\n\u001B[32m   1079\u001B[39m     )\n",
+      "\u001B[31mValueError\u001B[39m: Found array with dim 3, while dim <= 2 is required by StandardScaler."
+     ]
+    }
+   ],
+   "execution_count": 18
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-06T20:00:16.801517Z",
+     "start_time": "2026-03-06T20:00:16.640558Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "#now we evaluate model for inference\n",
@@ -656,11 +745,30 @@
     "model.eval()  # important — disables dropout/batchnorm for inference"
    ],
    "id": "23676ff6fae304c2",
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RNN(\n",
+       "  (lstm): LSTM(2, 128, num_layers=2, batch_first=True)\n",
+       "  (fc): Linear(in_features=128, out_features=2, bias=True)\n",
+       ")"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 16
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-06T20:00:18.407945Z",
+     "start_time": "2026-03-06T20:00:18.334979Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -692,7 +800,7 @@
     "    # scaler was fit on state_cols + control_cols so controls start at index n_states\n",
     "    n_states = len(state_cols)\n",
     "    n_controls = len(control_cols)\n",
-    "    seq_len = 600\n",
+    "    seq_len = 1000\n",
     "    def unscale_states(arr):\n",
     "        state_mean = scaler.mean_[:n_states]\n",
     "        state_std  = scaler.scale_[:n_states]\n",
@@ -745,34 +853,113 @@
    ],
    "id": "193451d65b640afb",
    "outputs": [],
-   "execution_count": null
+   "execution_count": 17
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-06T20:00:19.181904Z",
+     "start_time": "2026-03-06T20:00:19.009003Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "for i in range(len(test_dataset)):\n",
     "    x, y = test_dataset[i]\n",
-    "    if y.abs().mean() >= 0.88:\n",
+    "    if y.abs().mean() >= .960:\n",
     "        print(f\"use sample_idx={i}\")\n",
     "        break"
    ],
    "id": "e02909466d882593",
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "use sample_idx=3137\n"
+     ]
+    }
+   ],
+   "execution_count": 18
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-06T20:00:28.828883Z",
+     "start_time": "2026-03-06T20:00:25.979260Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "state_cols = ['position', 'speed']\n",
     "control_cols = ['brake_pressed', 'accel_position']\n",
-    "plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx=3140)\n",
+    "plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx=3137)\n",
     "plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx=150)"
    ],
    "id": "7ca721093c670d9",
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1000, 2])\n",
+      "(1000, 2)\n",
+      "(1000, 2)\n",
+      "(1000,)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1000, 2])\n",
+      "(1000, 2)\n",
+      "(1000, 2)\n",
+      "(1000,)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ],
+      "image/png": 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v9LzyyiueU6dOpe6XmJjoGT16tFnGKjIy0hMbG+sZNmxYmn2ULofUvn37dO/TvHlzc/E2Y8YMT5UqVcwSWt5LHWX0Gmr79u1m2asiRYp48ubN62ncuLFn7ty5afa5kEuG5c+f3+/+mzdv9rRs2dJToEABT/Hixc0x/PHHH9O9r++SYRZdtumqq64yr6+XGjVqmOW/dJkub999952nVatW5nvR/S677DLz3XgvbdW3b19PiRIlzPJW3u+ly6kNGDDAU7ZsWfOdVatWzXxO36W+9Dn63pl58MEHzX4zZ870OOn333/3jBgxwnPFFVd4SpYsaZZZ08+u9cV76Tr1559/ejp27GjqSuHChT2dO3f27N2713wO/V58v6ODBw+meX5G37/WY11Wy3fJMF2OTP9NaLl0WTEtk/fycf6WDLNMnz7d07BhQ/M8/a7r1Knjeeyxx0x5z0ffO6OL93HTz6//zvXfTUxMjHk/Xf7L39JvJ0+eNEsCli5d2iz3d/nll6dbwg8A4B5h+h+ngz8AAE548sknTSumdhUOZDqZmrZy7t+/X2JiYpwujqssWbLE9CrQlunztUoDAGAHxnQDAEKWjun1XXIq0OhSUTpr+a233krgBgDAhRjTDQAIOTrOWMfMauunzhQfiHQ8r86srROC6QRv/fv3d7pIAADAD0I3ACDkLFu2zMxQrutNW7O8BxqdDVyXCdOJ015++WWWigIAwKUY0w0AAAAAgE0Y0w0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANgkwq4XDmTJycmyd+9eKViwoISFhTldHAAAAACAy3g8Hjl27JiULVtW8uTJuD2b0O2HBu7Y2Fg7vx8AAAAAQBDYvXu3lC9fPsPHCd1+aAu3dfAKFSokbpWYmCgLFy6U1q1bS2RkpNPFAaiTcCV+K+E21Em4EfUSbpMYAFknPj7eNNZa+TEjhG4/rC7lGrjdHrpjYmJMGd1aERFaqJNwI+ol3IY6CTeiXsJtEgMo65xvSDITqQEAAAAAYBNCNwAAAAAANiF0AwAAAABgE8Z0AwAAAHC9pKQkM84XoSExMVEiIiLk1KlT5rt3go4lDw8P/9evQ+gGAAAA4Oq1kPfv3y9HjhxxuijI5e+9dOnSZkWp801UZqciRYqYcvybMhC6AQAAALiWFbhLlixpZrN2MoAh9yQnJ8vx48elQIECkidPHkdC/4kTJyQuLs7cL1OmTI5fi9ANAAAAwJW0W7EVuIsVK+Z0cZDLofvMmTOSN29eR0K3ypcvn7nW4K11MKddzZlIDQAAAIArWWO4tYUbcIJV9/7NfAKEbgAAAACuRpdyBHLdI3QDAAAAAGATQjcAAAAAhFjr7Zw5czLd56+//jLjmHfu3HnB3vfaa6+VRx55ROy2YMECqVevnhkX7gaEbgAAAACwwYoVK8zkW+3bt8/2cytVqiQTJ0507Ht5+umn5ZZbbjHlUBq+Naxbl4IFC8qll14qDz30kGzdujVLr/nxxx/LmDFjbC65SNu2bc0a2++++664AaE7GOhi8UePOl0KAAAAAF5ef/116du3ryxbtkz27t0bMMdGl8rSsvfu3TvdY1999ZXs27dPfvzxR3nmmWfkl19+kbp168rixYszfL0zZ86Y64suusiEdTtZE57dfffd8vLLL4sbELoDXOzXX0tEuXIijz3mdFEAAAAA/EPXmJ49e7Y88MADpqX7rbfeSndsPv/8c7n88svNsljFixeXjh07pnbD/uOPP2TAgAGpLctq1KhRptu0N20Nt1qj1Q8//CCtWrUyr1e4cGFp3ry5rFu3Llvfy7x58yQ6OlquuOKKdI/p0m2lS5eWKlWqmJZwDeFNmjQxAV2XePMu52uvvSaVK1c2n8+3e/nw4cPN83xpgH/qqadS7+tr1KxZ07xGjRo15D//+U/qY1brux5n/Zy6j9W6fdNNN8maNWtk+/bt4jRCdwALe/ddKblunYQdPiwyf76u4O50kQAAAABbeTweSTiTkOsXfd/seP/9901IrF69utx5553yxhtvpHmNL774woTsdu3ayfr1601LcePGjVO7YZcvX96ET21V1ktWHTt2THr27CnfffedrFy5UqpVq2beQ7dn1bfffisNGzbM0r66hnb//v3NSYK1a9embt+2bZt89NFH5rNs2LAh3fO6d+8uq1evThOKN23aJD/99JN069Yt9RhqgNeu7tqiri3rTz75pLz99ttpXmvo0KGmDLpPmzZtzLYKFSpIqVKlzGdxWoTTBUDOhT/9tJTfti3lzu7dIvqPsWxZDikAAACC1onEE1JgXIFcf9/jw45L/qj8Wd5fu2dr2LbGGB89elSWLl1qWnuVBsk77rhDRo8enaaV1+qGrWPBtSu2tipnR4sWLdLcnz59uhQpUsS894033pil19AAXTYbuUJPLlgtz9aJA+1S/s4770iJEiX8PkfHg+vnnTlzpgnSSluptfX74osvNpOgPfvss/LCCy9Ip06dzOPaar5582Z59dVXzYkFi7aeW/t408+gn8VptHQHKj1L5nvGK4sTGAAAAACwz5YtW0wrbteuXc39iIgI6dKliwniFm39vf766y/4ex84cED69OljWri1e3mhQoVMV/ddu3Zl+TVOnjyZ2iU8K6wWfO81rStWrJhh4PZu7dbQbb3Ge++9Z7aphIQE2bFjh/ksBQoUSL2MHTs2XZfxRo0aiT/58uUz49OdRkt3oDp2TMISEszN5CuvlDzLl6eE7ubNnS4ZAAAAYJuYyBjT6uzE+2aVhuuzZ8+maS3WUKnjpCdPnmzCsAbC7NKu3L7d3K2JwyzaAqzLfU2aNMkEX33Ppk2bpk5mlhU6Hvzvv//O8v7ardtqibbkz3/+XgFdu3aVIUOGmDHnGvR3795tTk4oPVGgtFVby+9NewF4y+i9Dh8+fN7gnxsI3YHqn1buxHz5JI9OpmCFbgAAACCIaWtqdrp55zYN29qtevz48dK6des0j3Xo0MG05t5///1y2WWXmXHcvXr18vs6UVFRqROTWTRA7t+/3wRvq1XZd7z08uXLzWRjOo5baZA9dOhQtj5D/fr15X//+1+W9tVu4DpLuAZufV52lC9f3kyApt3KNXTrBHC6NrjS8dhlypQxrd133XWXZNepU6dMi3h2y2QHQneAh+5TF10kMRdfnLLtt9+cLRMAAAAQ4ubOnWtaiXU2b23R9nbrrbeaVnAN3SNHjjTdy6tWrWrGdmtY11nDteVX6YzkutSYPqat1dr6rOPBDx48KM8//7zcdtttsmDBApk/f77pQm7RbuX//e9/TZfr+Ph4GTx4cLZb1XUysmHDhpnPUbRo0TSPaSu6Bn/ttr1x40Yze7p2pdeJ4XxboLOie/fu5lhoS/xLL72UboI0veiYdB0Xf/r0aTMjuZZr4MCBmb6uTiJntfI7zRVjuqdMmWIqlY4b0IHz+qVl5oMPPjCD9XX/OnXqmMrpTddk8164XS/6JQVl6C5aVDza0n3DDSIuqFAAAABAKNNQ3bJly3SB2wrdGhp1hm4N0JprPvvsM7O8lk6A5p2DdOZynZhMQ7nVRVqXztJWbM1POgmZ7j9o0KB076+htEGDBqaFuF+/fqmtx1mlGUufr7OH+9LPpi3Quo8GYi2Tfp7rrrtOcuK2224zQV5DvPYE8NajRw8zEdybb75p3k9bxXXpNe9u7BmxxofHxGR9WIBdwjzZnfv+AtM11fRgTps2zQRuPVOilU8nH/BXOb7//nu55pprZNy4cWb2PR14/9xzz5lxALVr104N3TqBgH45Fj3L4XuWJiN6Rkj/kegMg95njVxl/HiRQYPkz6uvllKLF0tkZKTTJQLMmCI9CabdmaiTcAvqJdyGOgk3cmu91C7C2r3Ye61n5A5tudZWcm3N1rHkuS05OdnkMs1j2X1/7U6vS7XpCY6sBPSc1sGs5kbHW7onTJhgZqTTsQy1atUy4VvPRug6dv7ohADaaq0VQM+qjBkzxpyF0QkJvGnI1un1rUtWA3fA0C4oc+fK9ptvdrokAAAAAIJM+/bt5b777pM9e/ZIoNm5c6fpEfBvA/eF4mjo1n77uoC6dlFILVCePOb+ihUr/D5Ht3vvb4058N1/yZIlpqVcz3A88MADpstCUClXTjytW8uRatVS7muHhSNH9FSM0yUDAAAAEAR0/evY2FgJNI0aNUqdBd0NHJ1ITZv9dUY+nZnOm97/9ddf/T5HB+3721+3W7QlXBdH1zMbOmPd8OHD5YYbbjDB3N/gfh2QrxfvbgJWNxvfKfjdxCqbXodfc43kWblSzn72mXiCbfw6AoZ3nQTcgnoJt6FOwo3cWi+1PDoaVrsa6wWhw/PPKGjr+3eKvreWwWQunyyZ1X8vQTl7uc7wZ9EB9zodv05AoK3f/hag1/Hho0ePTrd94cKFrhh4fz6LFi2SJmfPSmkR2Th/vvzBDxJcUCcBt6Fewm2ok3Ajt9XLiIgIM1RU12zOzjrTCB7Hjh1z9P213ulyZjqTvM4w700nf3N96NZp7/VsgU565k3v6z8uf3R7dvZXVapUMe+1bds2v6Fbp8P3nnJeW7q1G4Wuq+faidT+ObOiP4y6nl20zuC+Zo3UKVJELv1nTT7AyTrppklYENqol3Ab6iTcyK31Uiex0nWmCxQowERqIcbj8ZjAXbBgwdQ1yZ2qg7rkmk7m7W8iNdeHbl3wvWHDhmZReGt6eG2+1/sPP/yw3+foOmv6uI4vsOgPRGbrr/35559mTLdObe+PTrqmF1/6g+OmH52MaBnDK1Y0t8P37ZPwACgzglug/NtBaKFewm2ok3Ajt9VLHYqqgUvnfXJiBm04J/mf3rvW9+8UfW8tg79/G1n9t+J4zdUW5hkzZsjbb78tv/zyi5n0LCEhwcxmrnQ5MW2JtvTv398sAj9+/Hgz7nvUqFFmKngrpGvXE53ZXBdD11nrNKDfcsstcvHFF5sJ14KWNcHB7t1OlwQAAAAA4JYx3Tqr3MGDB2XEiBFmMjRdGF5DtTVZ2q5du9Kc2WjWrJlZm/uJJ54wE6RVq1ZN5syZk7pGt3ZX18XZNcQfOXJEypYta7qJ69Ji/lqzg0b58inXhG4AAAAAcA3HQ7fSVuqMupPr5Ge+OnfubC7+aH/7L7/8UkKOd0u3zvTn4LgHAAAAAIBLupfjArZ033ijyN136xR7HFYAAAAgBNx9992p82Opa6+9Ns38V7lFG0t17LP2Ns7M4sWLpWbNmma8vtPLV5csWdLM/2U3Qnew0Jn0Pv9c5D//0ZnhnC4NAAAAENJBWAOoXnTyaJ1f6qmnnkq35JQdPv74YzO09kIG5QvpscceM0OFrTWv33rrrdRjpcOKdfJrHYKsw4y96ckE3WfWrFlptk+cOFEqVaqUet96vbZt26bZTz+jbrd6UuvqVjp/2MiRI8VuhG4AAAAAuMA09O3bt0+2bt0qjz76qJkA+oUXXvC774Vcg/yiiy4yy2y50XfffSfbt2+XW2+9Nc12XaZZj9WePXvko48+ki1btpjg7UuX7NLArkvcnW9996+++kq++eabTPfTybvfffddOXz4sNiJ0B1MdCz333+nXAAAAAA4RidxLl26tFSsWNGs0NSyZUv57LPP0nQJf/rpp83Ez9WrVzfbdU3y22+/XYoUKWLCs67CpCsyWbRLtq7+pI8XK1bMtBrretbefLuXnz59WoYMGSKxsbGmTNrq/vrrr5vXve6668w+RYsWNa3AWi5rua5x48ZJ5cqVzZxZdevWlQ8//DDN+8ybN08uueQS87i+jnc5MzJr1iyzFrzvetf63nqstJVbJ87u3bu3rF69Ot062F27djUt1rr6VWby588v99xzjwwdOjTT/S699FJz/D/55BOxE6E7mAwcqKe2RF580emSAAAAAPZKSMj4cupU1vc9efL8+14AGk69W7R1bLO26C5atEjmzp1rWm91iWNtpf72229l+fLlUqBAAdNibj1Pl03W7tNvvPGGaTXWFtrzBUbtQv3ee+/Jyy+/bJZofvXVV83ragjXVmWl5dCW5kmTJpn7GrjfeecdmTZtmmzatEkGDBggd955pyxdujT15ECnTp3kpptukg0bNsi999573oCr9HM1atRIMhMXF2c+k3Y/t7qge7eIP/7446arvi4znRntWfDzzz+nO1ngq3HjxqZcQT97OS6Q0qVTrlk2DAAAAMGuQIGMH2vXTuSLL87dL1lS5MQJ//s2b66Dm8/d1/HBhw6l3cenNTk7tCVaA7ausNS3b980rbGvvfaaGfOt/ve//5kWZt2mLb/qzTffNK3aOg5Zl0HW8cvDhg0zgVdpKM5s5abffvtN3n//fRPstaVdValSJfVxbU1XOqGYvo/VMv7MM8+Y7tlNmzZNfY6GfA3szZs3l6lTp0rVqlXNSQClLfUacJ977rlMj8Uff/xhWpZ9HT161JwI0GN14p/vSY+VHiNfDz74oDk5MGHCBHnyySczfC99n/79+5uQ7j3RnL/91q9fL3YidAcT1uoGAAAAXEFbrzVIagu2hulu3bqZ1ldLnTp1UgO3+vHHH2Xbtm3pxmOfOnXKjIPWYKqt0U2aNEkzdllbjn27mFu0FVpbizUoZ5WWQYOvdgP3pq3t9evXN7e1xdy7HMoK6Jk5efJkuq7lSj/zunXrzLGaP3++GWc9duxYc9x8aRd5benWUK7d9jOj3er1RIH2DNBu+xn1QLCCvl0I3cG4VncuTHsPAAAAOOr48Ywf8+mWLHFxGe+bx2fEbRbGJmeFjnPWFmEN1tqaqgHZm28r7vHjx6Vhw4YmcPoqUaJEjsqggTK7tBzqiy++kHLlyqULvP9G8eLF5W8/80/prOU61lzpcmJ6kkFbtCdPnuz3dbSr+4svvmiCuffM5b609V57BowePVpu1OWV/dAu+jk9vlnFmO5gDd3/ogsMAAAA4HoaWjO6+LamZravbzD1t0+OipffBMkKFSqkC9z+NGjQwMx0rl299Xnel8KFC5uLTjS2atWq1OfoEmRr167N8DW1NV1bi62x2L6slnbvNbNr1aplwrUu2eVbDh0HbgVjnejM28qVK8/7GevXry+bN28+7346Ply7xWvrvz8a0nXcuZ7UON8Ebtoirvtb49V9bdy4MbUF3y6E7mBijY/QiSN8x6EAAAAAcK3u3bublmCdsVwn9tqxY4cZy92vXz/585+erDpG+dlnn5U5c+bIr7/+alqDM1tjW1uBe/bsaWby1udYr6mBVunM6jp+XLvCHzx40LRya1fvQYMGmcnT3n77bdPqrF2/X3nlFXNf3X///eYEweDBg80kbDNnzjQTvJ1PmzZtzNjw89Fwr+OwdWx5Rtq3b2+6uGv38cxod3Zt6daJ5Hxpt3I9aaHj5e1E6A4m2t2jVKmU23QxBwAAAAJGTEyMLFu2zLSM60Rp2pqsS2fpmG6dtVvpet933XWXCdI6hloDcseOHTN9XW0Nvu2220xAr1GjhvTp0yd15m/tPq6BVFuWS5UqJQ8//LDZPmbMGDNJmbYmazl0BnXtbq5LiCkto858rkFelxPTCd0yC8jeJxZ0NnQN6uejy54tXLgwXYu6N524TY/P+ejx8p5AzvLpp5+az3L11VeLncI8GY26D2G6Hpx239DJCqwK7kY60YCuj9euXTuJjIw8t2zY6dMiAwaI/DMuAnC0TgIOo17CbaiTcCO31ksNVNo6q2HP3wRcCDyDBw82eet8LdTaLV730zym3cPtcMUVV5ieBDrJXU7qYFZzIxOpBZsJE5wuAQAAAAD49fjjj8t//vMfE6rtCtNZcejQIdOjoGvXrra/F6EbAAAAAJArihQpIsOHD3f8aOv4+cceeyxX3osx3cFGRwvoNPy7dztdEgAAAAAIeYTuYPPBByIXXaSzFDhdEgAAAAAIeYTuYGMtYE9LNwAAAIIEcz8jkOseoTvY/LNgvezZo1P+OV0aAAAAIMesmdR1PWXACVbd+zez+jORWrApU0YkLEzXfRCJixMpXdrpEgEAAAA5Eh4ebibeitO/a/9ZyzpM/9ZF0EtOTpYzZ86YJbucmOVcW7g1cGvd0zqodTGnCN3BRs/AlC2b0tL9xx+EbgAAAAS00v80IlnBG6HB4/HIyZMnJV++fI6eaNHAbdXBnCJ0B6PKlVNC944dIk2aOF0aAAAAIMc0cJUpU0ZKliwpidqbEyEhMTFRli1bJtdcc82/6tr9b+j7/psWbguhOxhVrSry3Xci27c7XRIAAADggtDwcyECEAJDeHi4nD17VvLmzetY6L5QCN3BqHVrkfz5RRo1crokAAAAABDSCN3BqFu3lAsAAAAAwFEsGQYAAAAAgE0I3cHq6FGR9etTlg4DAAAAADiC0B2MPJ6UZcMaNEiZwRwAAAAA4AhCdzDSdeyqVEm5/fvvTpcGAAAAAEIWoTuYlw1TLBsGAAAAAI4hdAcrWroBAAAAwHGE7mBv6aZ7OQAAAAA4htAd7C3d27Y5XRIAAAAACFmE7mB1ySUp11u3iiQlOV0aAAAAAAhJEU4XADapVEnkvvtEatRIWas7PJxDDQAAAAC5jNAdrDRkv/qq06UAAAAAgJBG93IAAAAAAGxC6A5mp0+L/PSTyMqVTpcEAAAAAEISoTuYLVggUreuyP33O10SAAAAAAhJhO5gVr9+yvWmTSmt3gAAAACAXEXoDmaxsSJFi4qcPSuyebPTpQEAAACAkEPoDmZhYSL16qXcXr/e6dIAAAAAQMghdAe7Bg1Srn/4wemSAAAAAEDIIXQHuyuuSLlmBnMAAAAAyHWE7mDXtGnKtS4ddvy406UBAAAAgJAS4XQBYLNy5USef17ksstEoqI43AAAAACQiwjdoWDwYKdLAAAAAAAhie7lAAAAAADYhNAdCjwekS++EBkwQCQ+3unSAAAAAEDIcEXonjJlilSqVEny5s0rTZo0kdWrV2e6/wcffCA1atQw+9epU0fmzZuX4b7333+/hIWFycSJEyWk1+vWwK3H4MsvnS4NAAAAAIQMx0P37NmzZeDAgTJy5EhZt26d1K1bV9q0aSNxcXF+9//++++la9eu0rt3b1m/fr106NDBXDZu3Jhu308++URWrlwpZcuWzYVP4nIdOqRcf/KJ0yUBAAAAgJDheOieMGGC9OnTR3r16iW1atWSadOmSUxMjLzxxht+9580aZK0bdtWBg8eLDVr1pQxY8ZIgwYNZPLkyWn227Nnj/Tt21feffddiYyMzKVPEwChe+5ckZMnnS4NAAAAAIQER0P3mTNnZO3atdKyZctzBcqTx9xfsWKF3+fodu/9lbaMe++fnJwsd911lwnml156qY2fIIBccYVIhQoix46JzJnjdGkAAAAAICQ4umTYoUOHJCkpSUqVKpVmu97/9ddf/T5n//79fvfX7ZbnnntOIiIipF+/flkqx+nTp83FEv/PZGOJiYnm4lZW2bJaxjx33inhzzwjya+9Jkm33WZz6RCKslsngdxAvYTbUCfhRtRLuE1iAPxdmdWyBd063dpyrl3QdXy4TqCWFePGjZPRo0en275w4ULT1d3tFi1alKX98lWsKK3y5JE8X38tS6dMkfjKlW0vG0JTVuskkJuol3Ab6iTciHoJt1nk4r8rT5w44f7QXbx4cQkPD5cDBw6k2a73S5cu7fc5uj2z/b/99lszCVsF7Ur9D21Nf/TRR80M5jt37kz3msOGDTOTuXm3dMfGxkrr1q2lUKFC4uYzK1oJW7VqleVx6x6ttOvXy9UNGoinaVPby4jQkpM6CdiNegm3oU7CjaiXcJvEAPi70uoh7erQHRUVJQ0bNpTFixebGcit8dh6/+GHH/b7nKZNm5rHH3nkkdRt+mXodqVjuf2N+dbtOlmbP9HR0ebiS79ct37BOS6nTjhXtKhEREXZXSyEsED5t4PQQr2E21An4UbUS7hNpIv/rsxquRzvXq4tzD179pRGjRpJ48aNTWt0QkJCakDu0aOHlCtXznQBV/3795fmzZvL+PHjpX379jJr1ixZs2aNTJ8+3TxerFgxc/E9GNoSXr16dQc+ocv4jIcXjydlHW8AAAAAwAXneOju0qWLHDx4UEaMGGEmQ6tXr54sWLAgdbK0Xbt2mRnNLc2aNZOZM2fKE088IcOHD5dq1arJnDlzpHbt2g5+igCkYfuFF0Q2bRJ5802dNt7pEgEAAABA0HE8dCvtSp5Rd/IlS5ak29a5c2dzySp/47hDnobt4cN1wLtO3y7y9tvazz7kDwsAAAAAXEg0b4Yq7Rnw3/9q33uR2bN1sLwugu50qQAAAAAgqBC6Q1nXriLz5okUKWJmNJdmzUSuuUbkjTecLhkAAAAABAVXdC+Hg3Sm9y1bdN00kbfe0jXXRE6dErnnnnP7PPCATisvctFFKQE9b16dej6llbxiRZFWrc7tO2dOSpd1a3I27+vixUWuuurcvl9+KXLmTPr9VOHCaff9+muRkyf9f4YCBUSaNz93f+lSkePH/e+bL59Iixbn7n/3ncjRo/731c/o/dm0J8Dhw/73DQ8Xadv23P3Vq0UOHpQMtW9/7vbatSL792e8r76uvr7asEFkz57Mv09rmMDGjSJ//JHxvtddJ2KtQ//LLyK//57xvnoypmDBlNu//Saydavf3cLOnpUI72O/fbvIr79m/LpXXKGzH6bc1mEgOuwhI5dfLlKyZMrt3btFfvop430bNBApUybl9t69KSeVMlK3rkj58im34+JEfvgh430vvVSkUqWU24cOiaxalfG+NWqIVK2acvvIEZHlyzPet1o1kUsuSbl97JjIsmUZ71ulikjNmim3dW3Ib77JeF/992nNd6HDSL76KuN99RjosVBnz6b8+8yIHls9xtb8EHryLiMlSog0bnzu/vz5Kb8n/mhd0Dph0SUOrd8IX9n4jQjT3yxv/EY49hthXHllyv9LQvg3Qn8rY/btO7eB3whHfyP4O+JcvcyTmHjuuPAbkXIc+DvCub8jSpSQoOFBOkePHvXoodFrNztz5oxnzpw55vqC+PNPj+eZZzyeqVPPbTt50uMJC9N/Dv4v7dqlfY28eTPe99pr0+5brFjG+15+edp9K1TIeN9atdLuW7NmxvtWqpR230aNMt63RIm0+15zTcb7xsSk3feGGzLe1/ef3W23Zb5vQsK5fXv0yHzfuLhz+z7wQOb77tx5bt9BgzLfd9Omc/uOGJHpvkteeOFcnXzuucxfd8mSc687eXLm+37xxbl933gj833ff//cvrNnZ77vW2+d23fu3Mz3nTLl3L5ff535vs8/f27fVasy33fUqHP7btyY+b6DB5/b9/ffM9/3wQfP7XvgQOb73n33uX2PH898386dz+2bnJz5vr6/EfnyZf03onjxC/IbkVyzZtrfSn4jHP2N8KxefW7fEP6N2Niz57k6yW+Eo78R/B1x7ljMe/vtc/WS3whHfyP4O0I8SXfccWGzjoO5kZZunFOuXEqLtzc9UzVpksjff6e08urZeD3TpWdC9exyw4Zp99cu6rpdfy7Ondk51wLgTZ+rrczW497PqVUr7b565sx3uTNL5cpp79epk3LW2p+yZdPe1/fJaMm0okXTt1xmdJbctyVNWy21lSMrLr44pYUmI94zy+tnzWzfiIi0rZyZ7eu9VntsbOb7en8+rScZ7KvxK8l739KlM39dq2VMaQtVZvtqq4VFe01ktq/3d6c9NDLbV1/L+z0y29dqRbPKntm++tkt+fNnvq93vdTeGJntq9+V93eY2b5aB7zrRmb7ev870jqX2b5WC74ls32tFnzvf/f6G+KP/hvzVr9+ym+OP9n4jfB4HwfFb4RjvxGp/x5C/DdCfytPWa39it8IR38j+DviXL30WD3rFL8RKfg7wrG/Izy+f+MHsDBN3k4Xwm3i4+OlcOHCcvToUSlUqJC4VWJiosybN0/atWvn2gXjEVqok3Aj6iXchjoJN6Jewm0SAyDrZDU3MpEaAAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANonI6o4DBw7M8otOmDAhp+UBAAAAACD0Qvf69evT3F+3bp2cPXtWqlevbu7/9ttvEh4eLg0bNrzwpQQAAAAAIJhD9zfffJOmJbtgwYLy9ttvS9GiRc22v//+W3r16iVXX321PSUFAAAAACAUxnSPHz9exo0blxq4ld4eO3aseQwAAAAAAOQwdMfHx8vBgwfTbddtx44d47gCAAAAAJDT0N2xY0fTlfzjjz+WP//801w++ugj6d27t3Tq1IkDCwAAAABAdsZ0e5s2bZoMGjRIunXrJomJiWZbRESECd0vvPACBxYAAAAAgJyG7piYGPnPf/5jAvb27dvNtqpVq0r+/Pk5qAAAAAAA/Jvu5ZZ9+/aZS7Vq1Uzg9ng8/+blAAAAAAAIKjkK3X/99Zdcf/31cskll0i7du1M8FbavfzRRx+90GUEAAAAACB0QveAAQMkMjJSdu3aZbqaW7p06SILFiy4kOUDAAAAACC0xnQvXLhQvvzySylfvnya7drN/I8//rhQZQMAAAAAIPRauhMSEtK0cFsOHz4s0dHRF6JcAAAAAACEZui++uqr5Z133km9HxYWJsnJyfL888/LdddddyHLBwAAAABAaHUv13CtE6mtWbNGzpw5I4899phs2rTJtHQvX778wpcSAAAAAIBQaemuXbu2/Pbbb3LVVVfJLbfcYrqbd+rUSdavX2/W6wYAAAAAADls6VaFCxeWxx9/nGMIAAAAAMCFbOnWZcG+++671PtTpkyRevXqSbdu3eTvv//OyUsCAAAAABB0chS6Bw8eLPHx8eb2zz//LAMHDpR27drJjh07zG0AAAAAAJDD0K3hulatWub2Rx99JDfddJM888wzpsV7/vz52X49fV6lSpUkb9680qRJE1m9enWm+3/wwQdSo0YNs3+dOnVk3rx5aR4fNWqUeTx//vxStGhRadmypaxatSrb5QIAAAAAINdDd1RUlJw4ccLc/uqrr6R169bm9kUXXZTaAp5Vs2fPNq3jI0eOlHXr1kndunWlTZs2EhcX53f/77//Xrp27Sq9e/c2E7d16NDBXDZu3Ji6zyWXXCKTJ082rfDaDV4DvZbx4MGDOfm4AAAAAADkXujWWcs1KI8ZM8a0Srdv395s1xnNy5cvn63XmjBhgvTp00d69eplWs+nTZsmMTEx8sYbb/jdf9KkSdK2bVvTxb1mzZqmDA0aNDAh26Jjy7V1u0qVKnLppZea99CTAT/99FNOPi4AAAAAALk3e7kG3AcffFA+/PBDmTp1qpQrV85s167lGoizStf4Xrt2rQwbNix1W548eUxgXrFihd/n6HbfcePaMj5nzpwM32P69OlmtnVtRffn9OnT5mKxWusTExPNxa2ssrm5jAgt1Em4EfUSbkOdhBtRL+E2iQGQdbJathyF7goVKsjcuXPTbX/ppZey9TqHDh2SpKQkKVWqVJrtev/XX3/1+5z9+/f73V+3e9Py3XHHHaYbfJkyZWTRokVSvHhxv685btw4GT16dLrtCxcuNK3ubqefDXAT6iTciHoJt6FOwo2ol3CbRS7OOtaQa1tCt469joyMNJOYqU8//VTefPNN0z1cJzHTMd9Ou+6662TDhg0m2M+YMUNuv/12M5layZIl0+2rLe3erefa0h0bG2vGgRcqVEjcfGZFK2GrVq3M9wE4jToJN6Jewm2ok3Aj6iXcJjEAsk5W5zPLUej+v//7Pxk6dKgJ3b///rtpUe7YsaOZVVzT/sSJE7P0OtryHB4eLgcOHEizXe+XLl3a73N0e1b215nLL774YnO54oorpFq1avL666+n6cpuiY6ONhdf+uW69QsOxHIidFAn4UbUS7gNdRJuRL2E20S6OOtktVw5mkhNJ0yrV6+eua1B+5prrpGZM2fKW2+9ZZYQyyptEW/YsKEsXrw4dVtycrK537RpU7/P0e3e+ys9A5LR/t6v6z1uGwAAAAAAu+Wopdvj8ZgQay0ZduONN5rb2iVbu3Nnh3br7tmzpzRq1EgaN25sWskTEhLMbOaqR48eZqI2HXet+vfvL82bN5fx48ebWdNnzZola9asMZOlKX3u008/LTfffLMZy63l0XXA9+zZI507d87JxwUAAAAAIPdCtwbksWPHmlnGly5damYwVzt27Eg3ydn5dOnSxayfPWLECDMZmragL1iwIPV1du3aZWY0tzRr1sy0qj/xxBMyfPhw021cZy6vXbu2eVy7q+skbG+//bYJ3MWKFZPLL79cvv32W7N8GAAAAAAArg7d2hrdvXt3E3Yff/xxM25a6RJiGoqz6+GHHzYXf5YsWZJum7ZYZ9RqnTdvXvn444+zXQYAAAAAAFwRui+77DL5+eef021/4YUXTEszAAAAAADI4URq6siRI/Laa6+Z2cAPHz5stm3evFni4uI4rgAAAAAA5LSl+6effpLrr79eihQpIjt37pQ+ffrIRRddZLp16xjsd955h4MLAAAAAAh5OWrp1hnHdXbxrVu3mjHUlnbt2smyZctC/qACAAAAAJDj0P3DDz/I//3f/6Xbrkt76QzkAAAAAAAgh6E7Ojpa4uPj023/7bffpESJEhxXAAAAAAByGrpvvvlmeeqppyQxMdHcDwsLM2O5hwwZIrfeeisHFgAAAACAnIbu8ePHy/Hjx6VkyZJy8uRJad68uVmru2DBgvL0009zYAEAAAAAyOns5YULF5ZFixbJ8uXL5ccffzQBvEGDBtKyZUsOKgAAAAAAOQ3d2qU8X758smHDBrnyyivNBQAAAAAAXIDu5ZGRkVKhQgVJSkrK7lMBAAAAAAgpORrT/fjjj8vw4cPl8OHDF75EAAAAAACE8pjuyZMny7Zt26Rs2bJSsWJFyZ8/f5rH161bd6HKBwAAAABAwMpR6O7QocOFLwkAAAAAAEEmR6F75MiRF74kAAAAAAAEmRyFbsuaNWvkl19+Mbdr1aolDRs2vFDlAgAAAAAgNEP3n3/+KV27djXrdBcpUsRsO3LkiDRr1kxmzZol5cuXv9DlBAAAAAAgNGYvv/fee8163drKrTOY60VvJycnm8cAAAAAAEAOW7qXLl0q33//vVSvXj11m95+5ZVX5Oqrr+a4AgAAAACQ05bu2NhY09LtKykpySwjBgAAAAAAchi6X3jhBenbt6+ZSM2it/v37y8vvvgixxUAAAAAgJx2L7/77rvlxIkT0qRJE4mISHmJs2fPmtv33HOPuVh0vDcAAAAAAKEoR6F74sSJF74kAAAAAAAEmRyF7p49e2Zpv2effdYsJWYtKwYAAAAAQCjJ0ZjurHrmmWfoXg4AAAAACFm2hm6Px2PnywMAAAAAELqhGwAAAACAUEboBgAAAADAJoRuAAAAAABsQugGAAAAACAQQ/fVV18t+fLls/MtAAAAAAAIvtC9fft2eeKJJ6Rr164SFxdnts2fP182bdqUus+8efOkTJkyF6akAAAAAACEQuheunSp1KlTR1atWiUff/yxHD9+3Gz/8ccfZeTIkRe6jAAAAAAAhE7oHjp0qIwdO1YWLVokUVFRqdtbtGghK1euvJDlAwAAAAAgtEL3zz//LB07dky3vWTJknLo0KELUS4AAAAAAEIzdBcpUkT27duXbvv69eulXLlyF6JcAAAAAACEZui+4447ZMiQIbJ//34JCwuT5ORkWb58uQwaNEh69Ohx4UsJAAAAAECohO5nnnlGatSoIbGxsWYStVq1ask111wjzZo1MzOaAwAAAAAAkYicHASdPG3GjBkyYsQIM75bg3f9+vWlWrVqcvLkSdbmBgAAAAAgpy3d/fr1M9fa0t2uXTu5/fbbTeBOSEgw9wEAAAAAQA5D9xdffJFuPW4N3G3btpWzZ89yXAEAAAAAyGn38oULF8rVV18tRYsWlUceeUSOHTsmbdq0kYiICJk/fz4HFgAAAACAnIbuqlWryoIFC+S6666TPHnyyHvvvSfR0dGmBTx//vwcWAAAAAAAchq61WWXXSZz586VVq1aSZMmTcztfPnycVABAAAAAMhu6NbZyXVNbl/awr1371658sorU7etW7cuqy8LAAAAAEDQynLo7tChg70lAQAAAAAgVEO372zlAAAAAADAhiXDAAAAAACATaE7KSlJXnzxRWncuLGULl1aLrroojSX7JoyZYpUqlRJ8ubNayZlW716dab7f/DBB1KjRg2zf506dWTevHmpjyUmJsqQIUPMdp1JvWzZstKjRw8z7hwAAAAAANeH7tGjR8uECROkS5cucvToURk4cKB06tTJLB82atSobL3W7NmzzfO1+7pOwFa3bl2z5ndcXJzf/b///nvp2rWr9O7dW9avX2/Gmutl48aN5vETJ06Y13nyySfN9ccffyxbtmyRm2++OScfFQAAAACA3A3d7777rsyYMUMeffRRiYiIMCH4tddekxEjRsjKlSuz9Voa3vv06SO9evWSWrVqybRp0yQmJkbeeOMNv/tPmjRJ2rZtK4MHD5aaNWvKmDFjpEGDBjJ58mTzeOHChWXRokVy++23S/Xq1eWKK64wj61du1Z27dqVk48LAAAAAEDurdO9f/9+031bFShQwLR2qxtvvNG0MGfVmTNnTBgeNmxY6jZtLW/ZsqWsWLHC73N0u7aMe9OW8Tlz5mT4Plo+Xe6sSJEifh8/ffq0uVji4+NTu6rrxa2ssrm5jAgt1Em4EfUSbkOdhBtRL+E2iQGQdbJathyF7vLly8u+ffukQoUKUrVqVVm4cKFpbf7hhx/Mut1ZdejQITM+vFSpUmm26/1ff/01w8Dvb3/d7s+pU6fMGG9tjS9UqJDffcaNG2e6zPvSz6Wt7m6nLfuAm1An4UbUS7gNdRJuRL2E2yxycdbRoc22he6OHTvK4sWLzaRnffv2lTvvvFNef/110317wIAB4qYzD9rN3OPxyNSpUzPcT1vavVvPtaU7NjZWWrdunWFQd8vn00rYqlUriYyMdLo4AHUSrsRvJdyGOgk3ol7CbRIDIOtYPaRtCd3PPvts6m2dTK1ixYpmgrNq1arJTTfdlOXXKV68uISHh8uBAwfSbNf7Oiu6P7o9K/tbgfuPP/6Qr7/+OtPwrK3z/lro9ct16xcciOVE6KBOwo2ol3Ab6iTciHoJt4l0cdbJarmyPZGahtl77rlHduzYkbpNJyvTluLsBG4VFRUlDRs2NK3mluTkZHO/adOmfp+j2733V3oGxHt/K3Bv3bpVvvrqKylWrFi2ygUAAAAAwIWQJydp/qOPPpILRcO6zoT+9ttvyy+//CIPPPCAJCQkmNnMla6x7T3RWv/+/WXBggUyfvx4M+5blyhbs2aNPPzww6mB+7bbbjPbdJZ1HTOu4731ohO3AQAAAACQW3LUvVzXxdbZwi/E+G3tnn7w4EGz3JgG43r16plQbU2WpuPEdUZzS7NmzWTmzJnyxBNPyPDhw02Xdi1L7dq1zeN79uyRzz77zNzW1/L2zTffyLXXXvuvywwAAAAAgG2hW4PuU089JcuXLzfdw/Pnz5/m8X79+mXr9bSV2mqp9rVkyZJ02zp37mwu/lSqVMlMnAYAAAAAQECGbp2pXNe81jW29eJN18PObugGAAAAACAY5Sh0e0+iZrUqa9gGAAAAAAD/YiI179ZuHUedN29ec9Hbr732Wk5fDgAAAACAoJOjlm6d9GzChAnSt2/f1KW6VqxYYSZW04nPdLw3AAAAAAChLkehe+rUqWaZr65du6Zuu/nmm+Wyyy4zQZzQDQAAAABADruX61rYjRo1SrddZzI/e/YsxxUAAAAAgJyG7rvuusu0dvuaPn26dO/enQMLAAAAAEB2upcPHDgw9bbOVK6Tpi1cuFCuuOIKs23VqlVmPHePHj04sAAAAAAAZCd0r1+/Pl1XcrV9+3ZzXbx4cXPZtGkTBxYAAAAAgOyE7m+++YYDBgAAAABAbqzTDQAAAAAAMkfoBgAAAADAJoRuAAAAAABsQugGAAAAAMAmhG4AAAAAAGxC6AYAAAAAwCaEbgAAAAAAbELoBgAAAADAJoRuAAAAAABsQugGAAAAAMAmhG4AAAAAAGxC6AYAAAAAwCaEbgAAAAAAbELoBgAAAADAJoRuAAAAAABsQugGAAAAAMAmhG4AAAAAAGxC6AYAAAAAwCaEbgAAAAAAbELoBgAAAADAJoRuAAAAAABsQugGAAAAAMAmhG4AAAAAAGxC6AYAAAAAwCaEbgAAAAAAbELoBgAAAADAJoRuAAAAAABsQugGAAAAAMAmhG4AAAAAAGxC6AYAAAAAwCaEbgAAAAAAbELoBgAAAADAJoRuAAAAAABsQugGAAAAAMAmhG4AAAAAAGxC6AYAAAAAwCaEbgAAAAAAgjl0T5kyRSpVqiR58+aVJk2ayOrVqzPd/4MPPpAaNWqY/evUqSPz5s1L8/jHH38srVu3lmLFiklYWJhs2LDB5k8AAAAAAIALQ/fs2bNl4MCBMnLkSFm3bp3UrVtX2rRpI3FxcX73//7776Vr167Su3dvWb9+vXTo0MFcNm7cmLpPQkKCXHXVVfLcc8/l4icBAAAAAMBloXvChAnSp08f6dWrl9SqVUumTZsmMTEx8sYbb/jdf9KkSdK2bVsZPHiw1KxZU8aMGSMNGjSQyZMnp+5z1113yYgRI6Rly5a5+EkAAAAAAHBR6D5z5oysXbs2TTjOkyePub9ixQq/z9HtvmFaW8Yz2h8AAAAAAKdEOPbOInLo0CFJSkqSUqVKpdmu93/99Ve/z9m/f7/f/XV7Tp0+fdpcLPHx8eY6MTHRXNzKKpuby4jQQp2EG1Ev4TbUSbgR9RJukxgAWSerZXM0dLvFuHHjZPTo0em2L1y40HR1d7tFixY5XQQgDeok3Ih6CbehTsKNqJdwm0UuzjonTpxwf+guXry4hIeHy4EDB9Js1/ulS5f2+xzdnp39s2LYsGFmMjfvlu7Y2FgzA3qhQoXEzWdWtBK2atVKIiMjnS4OQJ2EK/FbCbehTsKNqJdwm8QAyDpWD2lXh+6oqChp2LChLF682MxArpKTk839hx9+2O9zmjZtah5/5JFHUrfpl6Hbcyo6OtpcfOmX69YvOBDLidBBnYQbUS/hNtRJuBH1Em4T6eKsk9VyOd69XFuYe/bsKY0aNZLGjRvLxIkTzZJfOpu56tGjh5QrV850AVf9+/eX5s2by/jx46V9+/Yya9YsWbNmjUyfPj31NQ8fPiy7du2SvXv3mvtbtmwx19oa/m9axAEAAAAAyA7HQ3eXLl3k4MGDZokvnQytXr16smDBgtTJ0jQ864zmlmbNmsnMmTPliSeekOHDh0u1atVkzpw5Urt27dR9Pvvss9TQru644w5zrWuBjxo1Klc/HwAAAAAgdDkeupV2Jc+oO/mSJUvSbevcubO5ZOTuu+82FwAAAAAAQnadbgAAAAAAghmhGwAAAAAAmxC6AQAAAACwCaEbAAAAAACbELoBAAAAALAJoRsAAAAAAJsQugEAAAAAsAmhGwAAAAAAmxC6AQAAAACwCaEbAAAAAACbELoBAAAAALAJoRsAAAAAAJsQugEAAAAAsAmhGwAAAAAAmxC6AQAAAACwCaEbAAAAAACbELoBAAAAALAJoRsAAAAAAJsQugEAAAAAsAmhGwAAAAAAmxC6AQAAAACwCaEbAAAAAACbELoBAAAAALAJoRsAAAAAAJsQugEAAAAAsAmhGwAAAAAAmxC6AQAAAACwCaEbAAAAAACbELoBAAAAALAJoRsAAAAAAJsQugEAAAAAsAmhGwAAAAAAmxC6AQAAAACwCaEbAAAAAACbELoBAAAAALAJoRsAAAAAAJsQugEAAAAAsAmhGwAAAAAAmxC6AQAAAACwCaEbAAAAAACbELoBAAAAALAJoRsAAAAAAJsQugEAAAAAsAmhGwAAAAAAmxC6AQAAAACwCaEbAAAAAACbELoBAAAAALAJoRsAAAAAgGAO3VOmTJFKlSpJ3rx5pUmTJrJ69epM9//ggw+kRo0aZv86derIvHnz0jzu8XhkxIgRUqZMGcmXL5+0bNlStm7davOnAAAAAADAZaF79uzZMnDgQBk5cqSsW7dO6tatK23atJG4uDi/+3///ffStWtX6d27t6xfv146dOhgLhs3bkzd5/nnn5eXX35Zpk2bJqtWrZL8+fOb1zx16lQufjIAAAAAQKiLcLoAEyZMkD59+kivXr3MfQ3KX3zxhbzxxhsydOjQdPtPmjRJ2rZtK4MHDzb3x4wZI4sWLZLJkyeb52or98SJE+WJJ56QW265xezzzjvvSKlSpWTOnDlyxx13SDDQz5lwJkFOJZ0y15GeSKeLBEhiYiJ1Eq5DvYTbUCfhRtRLuLVOejweCXSOhu4zZ87I2rVrZdiwYanb8uTJY7qDr1ixwu9zdLu2jHvTVmwN1GrHjh2yf/9+8xqWwoULm27r+lx/ofv06dPmYomPj0/9ovXiRhq0i75YNOXOz06XBvBBnYQbUS/hNtRJuBH1Ei4T1yJOioQVETfKalZ0NHQfOnRIkpKSTCu0N73/66+/+n2OBmp/++t263FrW0b7+Bo3bpyMHj063faFCxdKTEyMuJGe9QEAAACAYPb1119L3vC84kYnTpwIjO7lbqAt7d6t59rSHRsbK61bt5ZChQqJG2k3Cz3ro5WwRYsWEhlJ93K442wfdRJuQ72E21An4UbUS7i1Tt7Y5kaJiooSN7J6SLs6dBcvXlzCw8PlwIEDabbr/dKlS/t9jm7PbH/rWrfp7OXe+9SrV8/va0ZHR5uLLw2ybg6z2s1Cz/oUyV/E1eVEaP04UifhNtRLuA11Em5EvYRb62RUVJRrs05Wy+Xo7OV6ABs2bCiLFy9O3ZacnGzuN23a1O9zdLv3/konUrP2r1y5sgne3vvoGQidxTyj1wQAAAAAwA6Ody/Xbt09e/aURo0aSePGjc3M4wkJCamzmffo0UPKlStnxl2r/v37S/PmzWX8+PHSvn17mTVrlqxZs0amT59uHg8LC5NHHnlExo4dK9WqVTMh/Mknn5SyZcuapcUAAAAAAAiZ0N2lSxc5ePCgjBgxwkx0pl3AFyxYkDoR2q5du8yM5pZmzZrJzJkzzZJgw4cPN8FaZy6vXbt26j6PPfaYCe733XefHDlyRK666irzmnnzunMAPgAAAAAgODkeutXDDz9sLv4sWbIk3bbOnTubS0a0tfupp54yFwAAAAAAnOLomG4AAAAAAIIZoRsAAAAAAJsQugEAAAAAsAmhGwAAAAAAmxC6AQAAAACwCaEbAAAAAACbELoBAAAAALAJoRsAAAAAAJtE2PXCgczj8Zjr+Ph4cbPExEQ5ceKEKWdkZKTTxQGok3AlfivhNtRJuBH1Em6TGABZx8qLVn7MCKHbj2PHjpnr2NhYO74bAAAAAEAQ5cfChQtn+HiY53yxPAQlJyfL3r17pWDBghIWFiZupWdW9MTA7t27pVChQk4XB6BOwpX4rYTbUCfhRtRLuE18AGQdjdIauMuWLSt58mQ8cpuWbj/0gJUvX14ChVZCt1ZEhCbqJNyIegm3oU7CjaiXcJtCLs86mbVwW5hIDQAAAAAAmxC6AQAAAACwCaE7gEVHR8vIkSPNNeAG1Em4EfUSbkOdhBtRL+E20UGUdZhIDQAAAAAAm9DSDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2CTCrhcOZMnJybJ3714pWLCghIWFOV0cAAAAAIDLeDweOXbsmJQtW1by5Mm4PZvQ7YcG7tjYWDu/HwAAAABAENi9e7eUL18+w8cJ3X5oC7d18AoVKiRulZiYKAsXLpTWrVtLZGSk08UBqJNwJX4r4TbUSbgR9RJukxgAWSc+Pt401lr5MeRC9549e2TIkCEyf/58OXHihFx88cXy5ptvSqNGjc77XKtLuQZut4fumJgYU0a3VkSEFuok3Ih6CbehTsKNqJdwm8QAyjrnG5IclKH777//liuvvFKuu+46E7pLlCghW7dulaJFizpdNAAAAABACAnK0P3cc8+ZZn5t2bZUrlzZ0TIBAAAAAEJPUIbuzz77TNq0aSOdO3eWpUuXSrly5eTBBx+UPn36+N3/9OnT5uLdN9/q0qAXt7LK5uYyIrRQJ+FG1Eu4DXUSbkS9hNskBkDWyWrZwjw6z3mQyZs3r7keOHCgCd4//PCD9O/fX6ZNmyY9e/ZMt/+oUaNk9OjR6bbPnDnTjCMAAAAA4Py42fDwcL4G5JqkpCSzLFhGdO6wbt26ydGjRzOdCywoQ3dUVJSZMO37779P3davXz8TvlesWJGllm7tnn7o0CHXT6S2aNEiadWqlesnF0BooE7CjaiXcBvqJNzIzfVS40pcXFxqb1SEBo/HI6dOnTINquebqMxOmgdLlizptwxaJ4sXL37e0B2U3cvLlCkjtWrVSrOtZs2a8tFHH/ndPzo62lx86Q+O2350/AmUciJ0UCfhRtRLuA11Em7kxnq5b98+OXbsmJQqVcr0QnUygCH3JCcny/Hjx6VAgQKSJ08eR0K/tmTrCR/tYaEZ01dW/60EZejWmcu3bNmSZttvv/0mFStWdKxMAAAAALLfvffIkSOmpbFYsWIcvhAL3WfOnDEt3U6EbpUvXz5zrcFb62BOhzc4U3qbDRgwQFauXCnPPPOMbNu2zYzNnj59ujz00ENOFw0AAABANieqYp4lOMWqe/9mQregDN2XX365fPLJJ/Lee+9J7dq1ZcyYMTJx4kTp3r2700UDAAAAkE10KUcg172g7F6ubrzxRnMBAAAAAMApQdnSDQAAAADIuPV2zpw5mR6ev/76y4xj3rlz5wU7jNdee6088sgjtn8tCxYskHr16plx4W5A6AYAAAAAG+hyxTr5Vvv27bP93EqVKpkhsk55+umn5ZZbbjHlUBq+Naxbl4IFC8qll15q5s3aunVrll7z448/NkN/7da2bVszs/i7775r+3tlBaE7UL3/voR37iwVv/zS6ZIAAAAA8OP111+Xvn37yrJly2Tv3r0Bc4x0qSwte+/evdM99tVXX5ll3H788UczcfUvv/widevWlcWLF2f4emfOnDHXF110kQnrdrImPLv77rvl5ZdfFjcgdAeqLVskz6efSpHt250uCQAAAAAfusb07Nmz5YEHHjAt3W+99Va6Y/T555+bSaB1WazixYtLx44dU7th//HHH2ZVJqtlWY0aNcp0m/amreFWa7T64YcfpFWrVub1ChcuLM2bN5d169Zl6/uZN2+eREdHyxVXXJHuMV26rXTp0lKlShXTEq4hvEmTJiag6xJv3uV87bXXpHLlyubz+XYvHz58uHmeLw3wTz31VOp9fY2aNWua16hRo4b85z//SX3Man3X46yfU/exWrdvuukmWbNmjWx3QV4idAeq6GhzledfTF0PAAAABBqPxyMJZxJy/aLvmx3vv/++CYnVq1eXO++8U9544400r/HFF1+YkN2uXTtZv369aSlu3Lhxajfs8uXLm/Cprcp6yapjx45Jz5495bvvvjPLKFerVs28h27Pqm+//VYaNmyYpX11De3+/fubkwRr165N3a5LN3/00Ufms2zYsCHd83RlqdWrV6cJxZs2bZKffvpJunXrlnoMNcBrV3dtUdeW9SeffFLefvvtNK81dOhQUwbdp02bNmZbhQoVpFSpUuazOC1oZy8PmdB99qzTJQEAAAByzYnEE1JgXIFcP+LHhx2X/FH5s7y/ds/WsG2NMT569KgsXbrUtPYqDZJ33HGHjB49Ok0rr9UNW8eCa1dsbVXOjhYtWqS5P336dClSpIh576yu7qQBumzZsll+Tz25YLU8WycOtEv5O++8IyVKlPD7HB0Prp935syZJkgrbaXW1u+LL77YTIL27LPPygsvvCCdOnUyj2ur+ebNm+XVV181JxYs2npu7eNNP4N+FqfR0h2oaOkGAAAAXGnLli2mFbdr167mfkREhHTp0sUEcYu2/l5//fUX/L0PHDggffr0MS3c2r28UKFCpqv7rl27svwaJ0+eTO0SnhVWC773mtYVK1bMMHB7t3Zr6LZe47333jPbVEJCguzYscN8lgIFCqRexo4dm67LeKNGjcSffPnymfHpTqOlO1ARugEAABCCYiJjTKuzE++bVRquz549m6a1WEOljpOePHmyCcMaCLNLu3L7dnO3Jg6zaAuwLvc1adIkE3z1PZs2bZo6mVlW6Hjwv//+O8v7a7duqyXakj//+XsFdO3aVYYMGWLGnGvQ3717tzk5ofREgdJWbS2/N+0F4C2j9zp8+PB5g39uIHQHeOgOz8Y/HgAAACDQaWtqdrp55zYN29qtevz48dK6des0j3Xo0MG05t5///1y2WWXmXHcvXr18vs6UVFRqROTWTRA7t+/3wRvq1XZd7z08uXLzWRjOo5baZA9dOhQtj5D/fr15X//+1+W9tVu4DpLuAZufV52lC9f3kyApt3KNXTrBHC6NrjS8dhlypQxrd133XWXZNepU6dMi3h2y2QHQnegYkw3AAAA4Dpz5841rcQ6m7e2aHu79dZbTSu4hu6RI0ea7uVVq1Y1Y7s1rOus4dryq3RGcl1qTB/T1mptfdbx4AcPHpTnn39ebrvtNlmwYIHMnz/fdCG3aLfy//73v6bLdXx8vAwePDjbreo6GdmwYcPM5yhatGiax7QVXYO/dtveuHGjmT1du9LrxHC+LdBZ0b17d3MstCX+pZdeSjdBml50TLqOiz99+rSZkVzLNXDgwExfVyeRs1r5ncaY7kB1yy2SGB8v3z39tNMlAQAAAPAPDdUtW7ZMF7it0K2hUWfo1gD9wQcfyGeffWaW19IJ0DS8WnTmcp2YTEO51UVal87SVuwpU6aYSch0/0GDBqV7fw2lDRo0MC3E/fr1S209zqo6deqY5+vs4b70s2kLtO6jgVjLpJ/nuuuuy1EduO2220yQ1xCvPQG89ejRw0wE9+abb5r301ZxXXrNuxt7Rqzx4TExWR8WYJcwT3bnvg8BekZI/5HoDIPeZ43cRsdv6Nkw7ToSGRnpdHEA6iRcid9KuA11Em7k1nqpXYS1e7H3Ws/IHdpyra3k2pqtY8lzW3Jyssllmsey+/7anV6XatMTHFkJ6Dmtg1nNjXQvBwAAAACk0b59e9m6davs2bNHYmNjA+ro7Ny50/QI+LeB+0IhdAeqHTsk/Ikn5DKdVfCfSRIAAAAA4ELR9a8DUaNGjTJcRswJhO5AFR8veWbOlDI+ExsAAAAAANyDidQCFet0AwAAAIDrEboDFaEbAAAAAFyP0B2oCN0AAAAA4HqE7kAP3cnJIklJTpcGAAAAAOAHoTvAQ7dx+rSTJQEAAAAAZIDQHagI3QAAAADgeoTuQBURIYl798oX770nUqSI06UBAAAA4IC7775bOnTokHr/2muvdWR97SVLlkhYWJgcOXIk0/0WL14sNWvWlCSHh8geOnRISpYsKX/++aft70XoDlRhYSLFi8vZfPlSbgMAAABwTRDWAKqXqKgoufjii+Wpp56Ss2fP2v7eH3/8sYwZM+aCBuUL6bHHHpMnnnhCwsPDzf233nor9VjlyZNHypQpI126dJFdu3aleZ6eTNB9Zs2alWb7xIkTpVKlSqn3rddr27Ztmv30M+p2/cyqePHi0qNHDxk5cqTYjdANAAAAABeYhr59+/bJ1q1b5dFHH5VRo0bJCy+84HffM2fOXLD3veiii6RgwYLiRt99951s375dbr311jTbCxUqZI7Vnj175KOPPpItW7aY4O0rb968JrAnJiZm+j4RERHy1VdfyTfffJPpfr169ZJ3331XDh8+LHYidAewPMOHS71XXhHxOQsEAAAAwFnR0dFSunRpqVixojzwwAPSsmVL+eyzz9J0CX/66aelbNmyUr16dbN99+7dcvvtt0uRIkVMeL7llltk586dqa+pXbIHDhxoHi9WrJhpNfZ4PGne17d7+enTp2XIkCESGxtryqSt7q+//rp53euuu87sU7RoUdMKrOVSycnJMm7cOKlcubLky5dP6tatKx9++GGa95k3b55ccskl5nF9He9yZmTWrFnSqlUrE5696XvrsdJW7mbNmknv3r1l9erVEh8fn2a/rl27mhbrGTNmZPo++fPnl3vuuUeGDh2a6X6XXnqpOf6ffPKJ2InQHcDyvP++VFy8WMLi4pwuCgAAAJC7EhIyvpw6lfV9T548/74XgIZT7xZtHdusLbqLFi2SuXPnmtbbNm3amFbqb7/9VpYvXy4FChQwLebW88aPH2+6T7/xxhum1VhbaM8XGLUL9XvvvScvv/yy/PLLL/Lqq6+a19UQrq3KSsuhLc2TJk0y9zVwv/POOzJt2jTZtGmTDBgwQO68805ZunRp6smBTp06yU033SQbNmyQe++997wBV+nnatSokWQmLi7OfCbtfm51QfduEX/88cdNV/2E83wv2rPg559/TneywFfjxo1NuewUYeurw15RUSnXLBkGAACAUFOgQMaPtWsn8sUX5+6XLCly4oT/fZs318HN5+7r+OBDh9Lu49OanB3aEq0B+8svv5S+ffumaY197bXXzJhv9b///c+0MOs2bflVb775pmnV1nHIrVu3NuOXhw0bZgKv0lCsr5uR3377Td5//30T7LWlXVWpUiX1cW1NVzqhmL6P1TL+zDPPmO7ZTZs2TX2OhnwN7M2bN5epU6dK1apVzUkApS31GnCfe+65TI/FH3/8YVqWfR09etScCNBjdeKf70mPlR4jXw8++KA5OTBhwgR58sknM3wvfZ/+/fubkO490Zy//davXy92InQHw7JhhG4AAADAVbT1WoOktmBrmO7WrZtpfbXUqVMnNXCrH3/8UbZt25ZuPPapU6fMOGgNptoa3aRJkzRjl7Xl2LeLuUVbobW1WINyVmkZNPhqN3Bv2tpev359c1tbzL3LoayAnpmTJ0+m61qu9DOvW7fOHKv58+ebcdZjx441x82XdpHXlm4N5dptPzParV5PFGjPAO22n1EPBCvo24XQHcA80dFizoERugEAABBqjh/P+DGfbsmS2XDMPD4jbrMwNjkrdJyztghrsNbWVA3I3nxbcY8fPy4NGzY0gdNXiRIlclQGDZTZpeVQX3zxhZQrVy5d4P03ihcvLn///Xe67TpruY41V7qcmJ5k0BbtyZMn+30d7er+4osvmmDuPXO5L229154Bo0ePlhtvvNHvPtpFP6fHN6sY0x3IaOkGAABAqNLQmtHFtzU1s319g6m/fXJUvPwmSFaoUCFd4PanQYMGZqZz7eqtz/O+FC5c2Fx0orFVq1alPkeXIFu7dm2Gr6mt6dpabI3F9mW1tHuvmV2rVi0TrnXJLt9y6DhwKxjrRGfeVq5ced7PWL9+fdm8efN599Px4dotXlv//dGQruPO9aTG+SZw0xZx3d8ar+5r48aNqS34diF0BzJCNwAAABAUunfvblqCdcZyndhrx44dZix3v3795M8//zT76BjlZ599VubMmSO//vqraQ3ObI1tbQXu2bOnmclbn2O9pgZapTOr6/hx7Qp/8OBB08qtXb0HDRpkJk97++23Tauzdv1+5ZVXzH11//33mxMEgwcPNpOwzZw500zwdj5t2rQxY8PPR8O9jsPWseUZad++venirt3HM6Pd2bWlWyeS86XdyvWkhY6XtxOhO5ARugEAAICgEBMTI8uWLTMt4zpRmrYm69JZOqZbZ+1Wut73XXfdZYK0jqHWgNyxY8dMX1dbg2+77TYT0GvUqCF9+vRJnflbu49rINWW5VKlSsnDDz9sto8ZM8ZMUqatyVoOnUFdu5vrEmJKy6gzn2uQ1+XEdEK3zAKy94kFnQ1dg/r56LJnCxcuTNei7k0nbtPjcz56vLwnkLN8+umn5rNcffXVYqcwT0aj7kOYrgen3Td0sgKrgrtR4p49snjhQrn+ttsk0mfCBcAJOvmFrtnYrl07iYyM5EuAK1Av4TbUSbiRW+ulBiptndWw528CLgSewYMHm7x1vhZq7Rav+2ke0+7hdrjiiitMTwKd5C4ndTCruZGW7kBWsqSc1mn++QECAAAAEAAef/xx063d38zkuenQoUOmR0HXrl1tfy9mLwcAAAAA5IoiRYrI8OHDHT/aOn7+sccey5X3InQHsLCPPpI6//ufmfxAbr7Z6eIAAAAAAHzQvTyAhS1bJlW++ELCsjA9PwAAAAAg9xG6AxmzlwMAACAEMPczArnuEboD2T+L2cuZM06XBAAAALjgrJnUdT1lwAlW3fs3s/ozpjuQ0dINAACAIBYeHm4m3oqLi0tdy9rMZ4Sgl5ycLGfOnDFLdtm1ZNj5Wrg1cGvd0zqodTGnCN1BELrDTp92uiQAAACALUqXLm2ureCN0ODxeOTkyZOSL18+R0+0aOC26mBOBWXoHjVqlIwePTrNturVq8uvv/4qQYWWbgAAAAQ5DVxlypSRkiVLSmJiotPFQS5JTEyUZcuWyTXXXPOvunb/G/q+/6aFO6hDt7r00kvlq6++Sr0fERGEH5XQDQAAgBCh4edCBCAEhvDwcDl79qzkzZvXsdB9oQRhEj0Xsv9tNwC3S+7SRb6JjJTmN9/MjHgAAAAA4EJBO3v51q1bpWzZslKlShXp3r277Nq1S4JO0aKSUKaMSIkSTpcEAAAAABAqLd1NmjSRt956y4zj3rdvnxnfffXVV8vGjRulYMGC6fY/ffq0uVji4+NTxxG4edyIVTY3lxGhhToJN6Jewm2ok3Aj6iXcJjEAsk5WyxbmCYGV5o8cOSIVK1aUCRMmSO/evbM08ZqaOXOmWZbArfLv2SMVvv5aThcuLL/ffLPTxQEAAACAkHHixAnp1q2bHD16VAoVKhTaoVtdfvnl0rJlSxk3blyWWrpjY2Pl0KFDmR48pyXNny95b7lFkuvUkaS1a50uDmDO9i1atEhatWoV8BNeIHhQL+E21Em4EfUSbpMYAH9Xam4sXrz4eUN3UHYv93X8+HHZvn273HXXXX4fj46ONhdf+uW69QtWYfnzp1yfOePqciL0uP3fDkIT9RJuQ52EG1Ev4TaRLv67MqvlCsqJ1AYNGiRLly6VnTt3yvfffy8dO3Y0U8537dpVgop1ouDMGadLAgAAAAAIlZbuP//80wTsv/76S0qUKCFXXXWVrFy50twOJp6oqJQbXl3jAQAAAADuEZShe9asWRISrJZuQjcAAAAAuFJQdi8PGYRuAAAAAHA1QncgI3QDAAAAgKsRugNZqVKyZMIEOfvDD06XBAAAAADgB6E7kEVGytEqVURq1XK6JAAAAAAAPwjdAAAAAADYhNAd4Kp9+KHkGTVK5Ngxp4sCAAAAAAiFJcNCSfXZsyU8MVHk//5PpGBBp4sDAAAAAPBCS3eAS46MTLnBWt0AAAAA4DqE7gBH6AYAAAAA9yJ0B7jkiH9GCNDSDQAAAACuQ+gOcEl0LwcAAAAA1yJ0Bzi6lwMAAACAexG6AxyhGwAAAADci9Ad4Nb37Stnv/9epGlTp4sCAAAAAPDBOt0BLr5yZfE0aiRije0GAAAAALgGLd0AAAAAANiElu4AV2r1asmzebNImzYiDRo4XRwAAAAAgBdCd4CLXbJEwnVMd8GChG4AAAAAcBm6lwc4Zi8HAAAAAPcidAc4QjcAAAAAuBehO8ARugEAAADAvQjdAS7JWirs9GmniwIAAAAA8EHoDnC0dAMAAACAexG6A1xyxD8T0NPSDQAAAACuw5JhAW73ddfJxb17S0Tlyk4XBQAAAADgg9Ad4E6ULi2ea68VscZ2AwAAAABcg+7lAAAAAADYhNAd4Ars2SN5pk0T+fRTp4sCAAAAAPBB6A5wRX77TcL79ROZMsXpogAAAAAAfBC6AxxLhgEAAACAexG6AxyhGwAAAADci9Ad4AjdAAAAAOBehO4AR+gGAAAAAPcidAe4JGt97tOnnS4KAAAAAMAHoTvAJUdEpNwgdAMAAACA6/yT2BCoEsqUkbOffCIRRYo4XRQAAAAAgA9Cd4A7mz+/eNq1E7G6mQMAAAAAXIPu5QAAAAAA2ITQHeDyJCZK2DvviMyYIeLxOF0cAAAAAIAXupcHuDxnzkjEvfem3OnRQyQ62ukiAQAAAAD+QUt3sKzTrZjBHAAAAABchdAd4FKXDFOEbgAAAABwlaAP3c8++6yEhYXJI488IkEpTx7xWK3dhG4AAAAAcJWgDt0//PCDvPrqq3LZZZdJULPGcRO6AQAAAMBVgjZ0Hz9+XLp37y4zZsyQokWLSlAjdAMAAACAKwXt7OUPPfSQtG/fXlq2bCljx47NdN/Tp0+biyU+Pt5cJyYmmotbWWXzREVJmN4/flw3Ol0shDCrTrr53w1CD/USbkOdhBtRL+E2iQHwd2VWyxaUoXvWrFmybt060708K8aNGyejR49Ot33hwoUSExMjbre6Vy/Jk5wsB7dulbP79jldHEAWLVrEUYDrUC/hNtRJuBH1Em6zyMV/V544cSJL+4V5PB6PBJHdu3dLo0aNzJdjjeW+9tprpV69ejJx4sQst3THxsbKoUOHpFChQuLmMyv6OVu1aiWR3kuHAQ6hTsKNqJdwG+ok3Ih6CbdJDICso7mxePHicvTo0UxzY9C1dK9du1bi4uKkQYMGqduSkpJk2bJlMnnyZBOuw8PD0zwnOjraXHzpl+vWLzgQy4nQQZ2EG1Ev4TbUSbgR9RJuE+nirJPVcgVd6L7++uvl559/TrOtV69eUqNGDRkyZEi6wB0MwpYuFYmLE7nqKpEKFZwuDgAAAAAgWEN3wYIFpXbt2mm25c+fX4oVK5Zue7DI89RTIt9+K/L++4RuAAAAAHCRoF0yLKSwZBgAAAAAuFLQtXT7s2TJEglqUVEp116TwQEAAAAAnEdLdzC1dJ865XRJAAAAAABeCN3BgO7lAAAAAOBKhO5gQOgGAAAAAFdy5ZjuM2fOmLW2k5OT02yvwHJYfnkI3QAAAADgSq4K3Vu3bpV77rlHvv/++zTbPR6PhIWFSVJSkmNlczPPXXelrNFdt67TRQEAAAAAuDV033333RIRESFz586VMmXKmKCN8/M0aZISugEAAAAAruKq0L1hwwZZu3at1KhRw+miAAAAAAAQXKG7Vq1acujQIaeLEXh+/11kyxaRsmVFGjVyujQAAAAAADfOXv7cc8/JY489JkuWLJG//vpL4uPj01zgX545c0RuuUVk0iQOEQAAAAC4iKtaulu2bGmur7/++jTbmUjtPJi9HAAAAABcyVWh+5tvvnG6CAGJJcMAAAAAwJ1cFbqbN2/udBECU1RUyvXp006XBAAAAADg1tCtjhw5Iq+//rr88ssv5v6ll15q1u4uXLiw00VzL7qXAwAAAIAruWoitTVr1kjVqlXlpZdeksOHD5vLhAkTzLZ169Y5XTz3h+5Tp5wuCQAAAADArS3dAwYMkJtvvllmzJghEREpRTt79qzce++98sgjj8iyZcucLqI70dINAAAAAK4U4baWbu/ArfS2LiPWiPWnM+SpXVtk6lSRUqVy54sCAAAAAARe6C5UqJDs2rVLatSokWb77t27pWDBgo6Vy/ViY0Xuv9/pUgAAAAAA3Dymu0uXLtK7d2+ZPXu2Cdp6mTVrlule3rVrV6eLBwAAAABA4LZ0v/jiixIWFiY9evQwY7lVZGSkPPDAA/Lss886XTz3OnlS5NtvdQC8SOvWTpcGAAAAAODG0B0VFSWTJk2ScePGyfbt2802nbk8JibG6aK52/79ItdfL6LHKSHB6dIAAAAAANwYui0asuvUqeN0MQJHvnwp1ydOiHg8ImFhTpcIAAAAAOCG0N2pUyd56623zCRqejszH3/8ca6VKyBDtzp9WiRvXidLAwAAAABwS+guXLiwGcetNHhbt5HD0K3juwndAAAAAOAKjofuN998M/W2tngjByIjRcLDRZKSUkJ30aIcRgAAAABwAVctGdaiRQs5cuRIuu3x8fHmMWRAewdYrd0augEAAAAAruCq0L1kyRI5c+ZMuu2nTp2Sb3VJLGRtMjUAAAAAgCs43r1c/fTTT6m3N2/eLPt1Cax/JCUlyYIFC6RcuXIOlS5APPVUyjrdpUs7XRIAAAAAgJtCd7169cwEanrx1408X7588sorrzhStoBx//1OlwAAAAAA4MbQvWPHDvF4PFKlShVZvXq1lChRIvWxqKgoKVmypITrRGEAAAAAAAQQV4TuihUrmuvk5GSnixK4fvlF5OBBkerVRUqVcro0AAAAAAA3hO7PPvtMbrjhBomMjDS3M3PzzTfnWrkCTr9+Il99JfK//4l07+50aQAAAAAAbgjdHTp0MBOnaRdyvZ0RHe+tk6ohAywZBgAAAACu43jo9u5STvfyf4HQDQAAAACu46p1uv05cuSI00UIDKzTDQAAAACu46rQ/dxzz8ns2bNT73fu3Fkuuugis0b3jz/+6GjZXI+WbgAAAABwHVeF7mnTpklsbKy5vWjRIvnqq69kwYIFZqK1wYMHO108d4uJSbk+edLpkgAAAAAA3DKm25tOqGaF7rlz58rtt98urVu3lkqVKkmTJk2cLp670dINAAAAAK7jqtBdtGhR2b17twne2sI9duxYs93j8TBz+fm0aiWSP7/I5ZfnwjcFAAAAAAi40N2pUyfp1q2bVKtWTf766y/TrVytX79eLr74YqeL527Nm6dcAAAAAACu4arQ/dJLL5mu5Nra/fzzz0uBAgXM9n379smDDz7odPEAAAAAAAjc0B0ZGSmDBg1Kt33AgAGOlCeg6NJqv/8ukjevSK1aTpcGAAAAAOC22cvV9u3bpW/fvtKyZUtz6devn/yuYTIbpk6dKpdddpkUKlTIXJo2bSrz58+XoPbllyING4rQIwAAAAAAXMNVofvLL7+UWrVqyerVq01o1suqVavMNl1CLKvKly8vzz77rKxdu1bWrFkjLVq0kFtuuUU2bdokQYvZywEAAADAdVzVvXzo0KGmK7kGZt/tQ4YMkVY6Q3cW3HTTTWnuP/3006b1e+XKlXLppZdKUGKdbgAAAABwHVe1dP/yyy/Su3fvdNvvuece2bx5c45eMykpSWbNmiUJCQmmm3nQoqUbAAAAAFzHVS3dJUqUkA0bNpglw7zptpIlS2brtX7++WcTsk+dOmVmQf/kk09MN3V/Tp8+bS6W+Ph4c52YmGgubmWVzVxHRkqkrml+4oScdXGZEdzS1EnAJaiXcBvqJNyIegm3SQyAvyuzWjZXhe4+ffrIfffdZyZOa9asmdm2fPlyee6552TgwIHZeq3q1aubsH706FH58MMPpWfPnrJ06VK/wXvcuHEyevTodNsXLlwoMVa3bRfT8e4Fdu+W6/WLj4+X+fPmOV0khLjszMEA5BbqJdyGOgk3ol7CbRa5+O/KEydOZGm/MI/H4xGX0KJMnDhRxo8fL3v37jXbypYtK4MHDzazmIeFheX4tXUm9KpVq8qrr76apZbu2NhYOXTokJn93M1nVrQS6lj3yD17JPKSS8STN6+c/aelHnC0TkZq3wvAedRLuA11Em5EvYTbJAbA35WaG4sXL24aejPLja5q6dZQrROp6eXYsWNmW8GCBS/IaycnJ6cJ1t6io6PNxZd+uW79gtOVs3RpkWHDJCwmJiDKjOAWKP92EFqol3Ab6iTciHoJt4l08d+VWS2Xq0K3JS4uTrZs2WJu16hRw4z1zo5hw4bJDTfcIBUqVDDhfebMmbJkyRKzJFnQ0pMTzzzjdCkAAAAAAG4N3RqQH3zwQXnvvfdMy7QKDw+XLl26yJQpU6Rw4cJZDu09evSQffv2mefoet8auLO65BgAAAAAAEEXuu+9915Zv369fPHFF6nLe61YsUL69+8v//d//2eW/sqK119/XULStm06ml9nkdM+806XBgAAAABCnqtC99y5c02L9FVXXZW6rU2bNjJjxgxp27ato2ULCA0b6mh+kd9+E/FZdg0AAAAAkPvyiIsUK1bMbxdy3Va0aFFHyhRQ8uVLuc7i1PUAAAAAgBAK3U888YRZj3v//v2p2/S2Lhn25JNPOlq2gArdJ086XRIAAAAAgNvGdE+dOlW2bdtmZh3Xi9q1a5dZzuvgwYNp1thet26dgyV1qZiYlGtCNwAAAAC4gqtCd4cOHZwuQmCjpRsAAAAAXMVVoXvkyJFZ2k+XFEtISJD8+fPbXqaAQugGAAAAAFdx1ZjurNLlww4cOOB0MdyHidQAAAAAwFVc1dKdVR6Px+kiuFPnziL164vUquV0SQAAAAAAgRq6kYE+fTg0AAAAAOAiAdm9HAAAAACAQEBLdzA5elTkr79EChQQKVnS6dIAAAAAQMijpTuYjB0rUrWqyAsvOF0SAAAAAECgdi+vWLGiREZGOl0M97GWUEtIcLokAAAAAIBA7V6+ceNGp4vgTtqtXBG6AQAAAMAVHA/dRYsWlbCwsCzte/jwYdvLExQt3cePO10SAAAAAIAbQvfEiROdLkLwIHQDAAAAgKs4Hrp79uzpdBGCB93LAQAAAMBVXDeR2vbt2+WJJ56Qrl27SlxcnNk2f/582bRpk9NFcz9augEAAADAVVwVupcuXSp16tSRVatWyccffyzH/xmb/OOPP8rIkSOdLp77Va4s8uCDIt26OV0SAAAAAIDbQvfQoUNl7NixsmjRIomKikrd3qJFC1m5cqWjZQsIl1wiMmWKyGOPOV0SAAAAAIDbQvfPP/8sHTt2TLe9ZMmScujQIUfKBAAAAABAUITuIkWKyL59+9JtX79+vZQrV86RMgUUj0dET07s3JlyGwAAAADgKFeF7jvuuEOGDBki+/fvN2t3Jycny/Lly2XQoEHSo0cPp4vnfgkJIiVKpIztPnXK6dIAAAAAQMhzVeh+5plnpEaNGhIbG2smUatVq5Zcc8010qxZMzOjOc4jJubc7X8moQMAAAAAhPA63d508rQZM2bIiBEjzPhuDd7169eXatWqOV20wJAnj0i+fCInT55r9QYAAAAAOMZVoduiLd16QQ4UKJASumnpBgAAAADHuap7+a233irPPfdcuu3PP/+8dO7c2ZEyBZz8+VOutaUbAAAAAOAoV4XuZcuWSbt27dJtv+GGG8xjyGJLt6KlGwAAAAAc56rQrWO4dVy3r8jISImPj3ekTAHb0k3oBgAAAADHuSp016lTR2bPnp1u+6xZs8xM5siCDh1E/u//dGA8hwsAAAAAHOaqidSefPJJ6dSpk2zfvl1atGhhti1evFjee+89+eCDD5wuXmAYOtTpEgAAAAAA3Bi6b7rpJpkzZ45Zr/vDDz+UfPnyyWWXXSZfffWVNG/e3OniAQAAAAAQuKFbtW/f3lyQQ2fPiuj4d12zu0gRDiMAAAAAOMhVY7p/+OEHWbVqVbrtum3NmjWOlCngjBghUqyYyKhRTpcEAAAAAEKeq0L3Qw89JLt37063fc+ePeYxZEGhQinXzPYOAAAAAI5zVejevHmzNGjQIN32+vXrm8eQjdB97BiHCwAAAAAc5qrQHR0dLQcOHEi3fd++fRIR4brh5+5UsGDKNS3dAAAAAOA4V4Xu1q1by7Bhw+To0aOp244cOSLDhw+XVq1aOVq2gEH3cgAAAABwDVc1H7/44otyzTXXSMWKFU2XcrVhwwYpVaqU/Pe//3W6eIGB7uUAAAAA4BquCt3lypWTn376Sd5991358ccfzTrdvXr1kq5du0pkZKTTxQsMdC8HAAAAANdwVehW+fPnl6uuukoqVKggZ86cMdvmz59vrm+++WaHSxcASpcW6do15RoAAAAA4ChXhe7ff/9dOnbsKD///LOEhYWJx+Mx15akpCRHyxcQypcXmTnT6VIAAAAAANw2kVr//v2lcuXKEhcXJzExMbJx40ZZunSpNGrUSJYsWZLl1xk3bpxcfvnlUrBgQSlZsqR06NBBtmzZYmvZAQAAAABwdehesWKFPPXUU1K8eHHJkyePhIeHm67mGqL79euX5dfRoP7QQw/JypUrZdGiRZKYmGhmRk9ISJCQcPasyN9/a9cAp0sCAAAAACHNVd3Ltfu4tk4rDd579+6V6tWrm9nMs9NSvWDBgjT333rrLdPivXbtWjM7etArXlxEl13TY3bJJU6XBgAAAABClqtCd+3atc2s5drFvEmTJvL8889LVFSUTJ8+XapUqZLj17XW/b7ooov8Pn769GlzscTHx5trbSHXi1tZZfMtY0ShQhJ29Kic/esv8bi4/Ag+GdVJwEnUS7gNdRJuRL2E2yQGwN+VWS1bmEdnK3OJL7/80nQB79Spk2zbtk1uvPFG+e2336RYsWIye/ZsadGiRbZfMzk52cx6fuTIEfnuu+/87jNq1CgZPXp0uu0zZ840Y8sDzbX9+0vhP/6Q70eNkoP16jldHAAAAAAIOidOnJBu3bqZRt5ChQoFRuj25/Dhw1K0aNE0s5hnxwMPPGCWHNPAXV5n9s5iS3dsbKwcOnQo04PnhjMrOma9VatWadYxD7/+esnz7bdyduZM8dx2m6NlRGjJqE4CTqJewm2ok3Aj6iXcJjEA/q7U3KjDos8Xul3VvdyfjLqEZ8XDDz8sc+fOlWXLlmUYuFV0dLS5+NIv161fcKblLFrUXEUcP64POlcwhKxA+beD0EK9hNtQJ+FG1Eu4TaSL/67MarlcH7pzQhvv+/btK5988olZakzHiIeUIkVSro8ccbokAAAAABDSgjJ063JhOh77008/NbOh79+/32wvXLiw5MuXT4IeoRsAAAAAXMFV63RfKFOnTjX96q+99lopU6ZM6kUnYwsJl18ucscdOh280yUBAAAAgJAWlC3dLp8bzn533plyAQAAAAA4KihbugEAAAAAcANCd7A6e1bnsHe6FAAAAAAQ0gjdwWjZspSlwq64wumSAAAAAEBII3QHI2th9r//drokAAAAABDSCN3BiCXDAAAAAMAVCN3BqGjRlOtTp1IuAAAAAABHELqDtXt5eHjK7b/+cro0AAAAABCyCN3BKCxMpFixlNuHDjldGgAAAAAIWYTuYGWFblq6AQAAAMAxEc69NWzVtq1I3brnxncDAAAAAHIdoTtYTZjgdAkAAAAAIOTRvRwAAAAAAJsQuoPZ2bMiJ044XQoAAAAACFmE7mD18ssikZEi99/vdEkAAAAAIGQRuoNVwYIp1ywZBgAAAACOIXQHK5YMAwAAAADHEbqDVfHiKddxcU6XBAAAAABCFqE7WJUqlXJ94ICIx+N0aQAAAAAgJBG6gz10nzwpcvy406UBAAAAgJBE6A5WBQqI5M9/rrUbAAAAAJDrInL/LZFrOnYUSU4WieBrBgAAAAAnkMaC2X//63QJAAAAACCk0b0cAAAAAACbELqDXWKiSEKC06UAAAAAgJBE6A5mzz4rEhUl8uijTpcEAAAAAEISoTuYFSmScr1vn9MlAQAAAICQROgOZuXKpVzv2eN0SQAAAAAgJBG6g1n58inXf/7pdEkAAAAAICQRukMhdB84IHLmjNOlAQAAAICQQ+gOZsWLi0RHp9zeu9fp0gAAAABAyCF0B7OwMLqYAwAAAICDIpx8c+SCm24S+esvkYIFOdwAAAAAkMsI3cHupZecLgEAAAAAhCy6lwMAAAAAYBNCdyjQmcv37XO6FAAAAAAQcgjdwW75cpF8+USuvdbpkgAAAABAyCF0h8Ja3cnJIjt3iiQlOV0aAAAAAAgphO5QCN2RkSldzFmrGwAAAAByFaE72IWHi1SqlHJ7+3anSwMAAAAAIYXQHQouvjjl+rffnC4JAAAAAIQUQncoqFkz5fqXX5wuCQAAAACElKAM3cuWLZObbrpJypYtK2FhYTJnzhwJaVbo3rzZ6ZIAAAAAQEgJytCdkJAgdevWlSlTpjhdFHdo1EikWzeRm292uiQAAAAAEFIiJAjdcMMN5oJ/1Ksn8u67HA4AAAAAyGVB2dINAAAAAIAbBGVLd3adPn3aXCzx8fHmOjEx0VzcyipblsqYnHxuybBq1WwuGUJVtuokkEuol3Ab6iTciHoJt0kMgL8rs1q2MI/H45EgphOpffLJJ9KhQ4cM9xk1apSMHj063faZM2dKTEyMBINLZs+Wmu+9J7tatJD1/fo5XRwAAAAACGgnTpyQbt26ydGjR6VQoUIZ7kfozqClOzY2Vg4dOpTpwXPDmZVFixZJq1atJDIyMtN9wz7/XCJuvVU8tWvL2XXrcq2MCC3ZqZNAbqFewm2ok3Aj6iXcJjEA/q7U3Fi8ePHzhm66l4tIdHS0ufjSL9etX3C2y9mkibkK27xZIk+dEilYMHcKh5AUKP92EFqol3Ab6iTciHoJt4l08d+VWS1XUE6kdvz4cdmwYYO5qB07dpjbu3btkpBVrpxIpUopY7u//97p0gAAAABASAjK0L1mzRqpX7++uaiBAwea2yNGjJCQ1rx5yvWyZU6XBAAAAABCQlB2L7/22mslyOeHy5lrrhF5+22RJUucLgkAAAAAhISgbOlGBq6/PuV65UqRw4c5TAAAAABgs6Bs6UYGKlYUeeopkcsvFylQgMMEAAAAADYjdIeaJ590ugQAAAAAEDLoXg4AAAAAgE0I3aFo0yaRAQNE/vtfp0sCAAAAAEGN0B2KFi8WmThRZMwYkaQkp0sDAAAAAEGL0B2K7rlHpEgRka1bRT74wOnSAAAAAEDQInSHIp25fODAlNvDhomcOOF0iQAAAAAgKBG6Q5WG7vLlRXbuFBk82OnSAAAAAEBQInSHqvz5RV5/PeX2f/4jMmWK0yUCAAAAgKBD6A5lrVuLjByZcnvWLCZVAwAAAIALLOJCvyACjIbuggVFevYUCQ9P2bZ/v0jx4iIRVA8AAAAA+DdIVaEuLEzk0UfTbuvVS2TdOpE2bUSuuEKkWjWRKlVESpcWiYlJeQ4AAAAA4LwI3Ujr1CmRH38UiYsT+e9/Uy7emjQRWbny3P3mzUX+/lskMjKlZVyv8/wzaqF6dZEZM87t27WryL595+57h/eKFUXeeivtsmY6yZvvvnpdsqTIzJnnHnv4YZFff/X/TRYqJPLxx+fu6wkG/Xz+REWJzJt37v7jj4usWiUZWrToXLl0zfOlSzPe9/PPRfLlS7n9wgsiX36Z8b66jFvRoim3X3lF5NNPM95Xv58yZVJu67GePTvjffXxypVTbr/zTsolI/q+NWum3H7/fZHp0zPe98UXRerVMzfDPv9cmo0eLeEvv+z/5MzYsSknctTChSLPP5/x6z7xhMi116bcXrZM5KmnMt530CCRtm1Tbq9eLTJ8eMb7an3p0CHlttYF35NO3u69V+SOO1Jub9ki8tBDGe97110pPUbUH3+I9O6d8b6dO4v83/+l3D5wQKR794z3vekmkf79U24fPSpy660Z79uqlciQISm3T58Wad8+432vvvrc8BLVsmXG+15+uci4cefu33hjym+FP5ddJjJhwrn7t90mcuSI/30vuSRlTgnLnXem9LTxp0IFkTfeSPvdeP9GePP5jcjTv780W77cf73kNyJXfyPks89E9HvISAj9RjQbMSLj30p+I3L1N4K/I1L+jgh7/XVpNnVqxvWS34hz9YW/I3Lv74ggQehGWnnzpgQGDZDffiuydq3I77+L7NiR8o9Du6J727xZ5NAh/0fRdykyDesZ/Q+wRo209/WPok2bMv4fq7cffkjZ359ixdLe1xb8JUsy/uze9I+txYslSzZuzHzfpKS0xyyzfc+cOXdb/4jLbF/vH6xt2zLfNyHh3G39PjPbNz7+3O1duzLfV0+6/CNs714p8dNPGe/7yCPnbusJmMxet0+fc7f1JFBm+3qH1r/+ynxf63+USsNgZvtqiLUcO5b5vldembbuZ7avFUCs7zCzfbWniSUxMfN9y5U7dzs5OfN9ixRJez+zffVkmjf9N+Rdn7xpGb3p74h+f/74hvHly7P+G6G/J1n8jQhbsybjeslvRK7+RsiePZnvG0K/EZn+VvIbce7458JvBH9HpAjbti3zeslvRAr+jsjdvyOCBKEb/v9h6Nkq7zNWHk/KPw7vQKg++SQlNOg/kLNnU651X3//GCdPPhfErX0svmFeW4O1Vc+b9Rzt4u5NW5n1j6iMWq99W0buu8//vtaYdosupZZZC6S3fv3S/gj7io4+d1tbOHUSu4wULnzu9t13p/1DzVeJEml7Enj/oeZLl4izdOqU0oKQkapVz93W1lLvMOerVq3Um8ktWsj6gQOlXr16EuFvToD69dOeIfVuafBltXapxo0z31d7YFjq1s1834YNz93WlrrM9tXXsmgLYGb71q597nbZspnv6/3Hoc6fkNm+F1+c9t9JZvtarZTWv+PM9o2NTXs/s32t3hSWN99M+fd+vjqppk5NaXX356KL0t6fNCnj/wlri7Q3bQH1/Y2w+PxGJI8aJesWL/ZfL/mNyNXfCPP/lczqWgj9RqzJ7LeS34hc/Y3g74gUyV26yHqPJ+N6yW9ECv6OyN2/I4JEmMfjm34QHx8vhQsXlqNHj0oh3x9xF0lMTJR58+ZJu3btJNL3DBLgAOok3Ih6CbehTsKNqJdwm8QAyDpZzY0sGQYAAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE2COnRPmTJFKlWqJHnz5pUmTZrI6tWrnS4SAAAAACCEBG3onj17tgwcOFBGjhwp69atk7p160qbNm0kLi7O6aIBAAAAAEJEhASpCRMmSJ8+faRXr17m/rRp0+SLL76QN954Q4YOHSqBzuPxSMKZBDmVdMpcR3oinS4SIImJidRJuA71Em5DnYQbUS/h1jrp8Xgk0AVl6D5z5oysXbtWhg0blrotT5480rJlS1mxYkW6/U+fPm0ulvj4+NQvWi9upEG76ItFU+787HRpAB/USbgR9RJuQ52EG1Ev4TJxLeKkSFgRcaOsZsWgDN2HDh2SpKQkKVWqVJrtev/XX39Nt/+4ceNk9OjR6bYvXLhQYmJixI30rA8AAAAABLOvv/5a8obnFTc6ceJE6Ibu7NIWcR3/7d3SHRsbK61bt5ZChQqJG2k3Cz3ro5WwRYsWEhlJ93K442wfdRJuQ72E21An4UbUS7i1Tt7Y5kaJiooSN7J6SIdk6C5evLiEh4fLgQMH0mzX+6VLl063f3R0tLn40iDr5jCr3Sz0rE+R/EVcXU6E1o8jdRJuQ72E21An4UbUS7i1TkZFRbk262S1XEE5e7l+MQ0bNpTFixenbktOTjb3mzZt6mjZAAAAAAChIyhbupV2F+/Zs6c0atRIGjduLBMnTpSEhITU2cwBAAAAALBb0IbuLl26yMGDB2XEiBGyf/9+qVevnixYsCDd5GoAAAAAANglaEO3evjhh80FAAAAAAAnBOWYbgAAAAAA3IDQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATSLseuFA5vF4zHV8fLy4WWJiopw4ccKUMzIy0uniANRJuBK/lXAb6iTciHoJt0kMgKxj5UUrP2aE0O3HsWPHzHVsbKwd3w0AAAAAIEhofixcuHCGj4d5zhfLQ1BycrLs3btXChYsKGFhYeJWemZFTwzs3r1bChUq5HRxAOokXInfSrgNdRJuRL2E28QHQNbRKK2Bu2zZspInT8Yjt2np9kMPWPny5SVQaCV0a0VEaKJOwo2ol3Ab6iTciHoJtynk8qyTWQu3hYnUAAAAAACwCaEbAAAAAACbELoDWHR0tIwcOdJcA25AnYQbUS/hNtRJuBH1Em4THURZh4nUAAAAAACwCS3dAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0B2gpkyZIpUqVZK8efNKkyZNZPXq1U4XCSFi3Lhxcvnll0vBggWlZMmS0qFDB9myZUuafU6dOiUPPfSQFCtWTAoUKCC33nqrHDhwwLEyI/Q8++yzEhYWJo888kjqNuolctuePXvkzjvvNL+F+fLlkzp16siaNWtSH/d4PDJixAgpU6aMebxly5aydetWvijYJikpSZ588kmpXLmyqXNVq1aVMWPGmLpIvURuWbZsmdx0001StmxZ8//qOXPmpHk8K7+Nhw8flu7du5v1u4sUKSK9e/eW48ePu/ZLJHQHoNmzZ8vAgQPNbH7r1q2TunXrSps2bSQuLs7poiEELF261ATqlStXyqJFiyQxMVFat24tCQkJqfsMGDBAPv/8c/nggw/M/nv37pVOnTo5Wm6Ejh9++EFeffVVueyyy9Jsp14iN/39999y5ZVXSmRkpMyfP182b94s48ePl6JFi6bu8/zzz8vLL78s06ZNk1WrVkn+/PnN/8/1BBFgh+eee06mTp0qkydPll9++cXc13r4yiuvUC+RaxISEkx+0UZEf7Ly26iBe9OmTeZv0blz55ogf99997n3W/Qg4DRu3Njz0EMPpd5PSkrylC1b1jNu3DhHy4XQFBcXp6fHPUuXLjX3jxw54omMjPR88MEHqfv88ssvZp8VK1Y4WFKEgmPHjnmqVavmWbRokad58+ae/v37m+3US+S2IUOGeK666qoMH09OTvaULl3a88ILL6Ru03oaHR3tee+993KplAg17du399xzzz1ptnXq1MnTvXt3c5t6idwmIp5PPvkk9X5W6uDmzZvN83744YfUfebPn+8JCwvz7Nmzx+NGtHQHmDNnzsjatWtNNwtLnjx5zP0VK1Y4WjaEpqNHj5rriy66yFxr/dTWb+86WqNGDalQoQJ1FLbTXhjt27dPU/+ol3DCZ599Jo0aNZLOnTuboTj169eXGTNmpD6+Y8cO2b9/f5q6WrhwYTNkjP+fwy7NmjWTxYsXy2+//Wbu//jjj/Ldd9/JDTfcQL2EK+zIwm+jXmuXcv2Ntej+mom0ZdyNIpwuALLn0KFDZjxOqVKl0mzX+7/++iuHE7kqOTnZjJnVLpS1a9c22/SHMioqyvwY+tZRfQywy6xZs8yQG+1e7ot6idz2+++/m268Ohxs+PDhpl7269fP/D727Nkz9ffQ3//P+a2EXYYOHSrx8fHmZHh4eLj5m/Lpp582XXUV9RJO25+F30a91pOZ3iIiIkwDkFt/PwndAP5Vq+LGjRvNWXLASbt375b+/fubsV06wSTghpOS2grzzDPPmPva0q2/lzpGUUM34IT3339f3n33XZk5c6ZceumlsmHDBnPyXCe0ol4C9qF7eYApXry4OTPpOxO03i9durRj5ULoefjhh83EFd98842UL18+dbvWQx0GceTIkTT7U0dhJx3WoJNJNmjQwJzt1otO4qcTsehtPUNOvURu0ll3a9WqlWZbzZo1ZdeuXea29f9s/n+O3DR48GDT2n3HHXeY2fTvuusuM8mkrkxCvYQblM7Cb6Ne+04gffbsWTOjuVvzEKE7wGi3tIYNG5rxON5n0/V+06ZNHS0bQoPOeaGB+5NPPpGvv/7aLDviTeunztbrXUd1STH9Q5M6Crtcf/318vPPP5tWG+uirYzaZdK6Tb1EbtJhN77LKeo42ooVK5rb+tupfxx6/1Zqt18dj8hvJexy4sQJM+7Vmzbm6N+S1Eu4QeUs/DbqtTbu6Al3i/5NqvVYx367Ed3LA5COD9MuQPpHZOPGjWXixIlm6v1evXo5XTSESJdy7Zb26aefmrW6rbEzOsmFrqWo17pWotZTHVuj6yf27dvX/EBeccUVThcfQUrrojWvgEWXGNH1ka3t1EvkJm091EmrtHv57bffLqtXr5bp06ebi7LWkR87dqxUq1bN/KGp6ydrN98OHTrwZcEWujayjuHWyU21e/n69etlwoQJcs8991AvkWuOHz8u27ZtSzN5mp4g178btW6e77dRew21bdtW+vTpY4bs6AS+2iCkPTh0P1dyevp05Mwrr7ziqVChgicqKsosIbZy5UoOJXKF/mz4u7z55pup+5w8edLz4IMPeooWLeqJiYnxdOzY0bNv3z6+IeQq7yXDqJdwwueff+6pXbu2WeqmRo0anunTp6d5XJfGefLJJz2lSpUy+1x//fWeLVu28GXBNvHx8eZ3Uf+GzJs3r6dKlSqexx9/3HP69GnqJXLNN9984/dvyZ49e2b5t/Gvv/7ydO3a1VOgQAFPoUKFPL169TLLhrpVmP7H6eAPAAAAAEAwYkw3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMA4JC7775bOnTo4Njxv+uuu+SZZ56RQPbWW29JkSJFsrTvggULpF69epKcnGx7uQAAsBC6AQCwQVhYWKaXUaNGyaRJk0xodMKPP/4o8+bNk379+kmoaNu2rURGRsq7777rdFEAACEkwukCAAAQjPbt25d6e/bs2TJixAjZsmVL6rYCBQqYi1NeeeUV6dy5s6NlcKp3wcsvv2xa+QEAyA20dAMAYIPSpUunXgoXLmxat723adj17V5+7bXXSt++feWRRx6RokWLSqlSpWTGjBmSkJAgvXr1koIFC8rFF18s8+fPT/NeGzdulBtuuMG8pj5HA+WhQ4cyLFtSUpJ8+OGHctNNN6XZ/p///EeqVasmefPmNa9z2223pT6mXbLHjRsnlStXlnz58kndunXNa3jbtGmT3HjjjVKoUCFT1quvvlq2b9+e+vynnnpKypcvL9HR0aabt3b3tuzcudMco48//liuu+46iYmJMe+xYsWKNO+hPQMqVKhgHu/YsaP89ddf6Vrw9fn6/lqOhg0bypo1a1If18+s961yAQBgN0I3AAAu8vbbb0vx4sVl9erVJoA/8MADpkW6WbNmsm7dOmndurUJ1SdOnDD7HzlyRFq0aCH169c3YVKD7IEDB+T222/P8D1++uknOXr0qDRq1Ch1mz5Xu5prMNYWeX2da665JvVxDdzvvPOOTJs2zYTrAQMGyJ133ilLly41j+/Zs8fsr4H666+/lrVr18o999wjZ8+eNY9rV/rx48fLiy++aN6/TZs2cvPNN8vWrVvTlO3xxx+XQYMGyYYNG+SSSy6Rrl27pr7GqlWrpHfv3vLwww+bxzVcjx07Ns3zu3fvboL9Dz/8YMowdOhQ06XcooFdTyh8++23//KbAgAgizwAAMBWb775pqdw4cLptvfs2dNzyy23pN5v3ry556qrrkq9f/bsWU/+/Pk9d911V+q2ffv2efR/3ytWrDD3x4wZ42ndunWa1929e7fZZ8uWLX7L88knn3jCw8M9ycnJqds++ugjT6FChTzx8fHp9j916pQnJibG8//t3b8rfXEcx/HP90sWKSWDK/mx+BVFCRkslIwWPwYLE4MigyIkhVkmRRmYxcDCgPwD3EWJwWAwSNE1nG+vd53TPe7lewdH5Pkodc/nnO/5nHOXb+/7/rzfn7Ozs9D40NCQ19/fb5+npqa88vJyL5FIpJ0zFot5i4uLobGmpiZvZGTEPl9fX9szr6+vB+cvLi5sLB6P27Hm6u7uDt2jt7c39N3m5eV5m5ub3kcaGhq8ubm5D68BAOCzkOkGAOAbqa+vDz5nZWW5goICV1dXF4wpSyv39/fBcuqjo6OgRlx/VVVVdu69JdTPz8+WkdZybl9nZ6crLS11FRUVlklXszE/m351dWWfdU3yPMp8+3Mo86zl5MlZZd/j46O7u7tzbW1toXEdx+Pxd9+/qKgo9K66trm5OXR9a2tr6Hh8fNwNDw+7jo4Ot7S0lPY70PJ4/90AAIgajdQAAPhG3gatCoyTx/xA2d/26unpyeqUl5eXU+7lB61vafm6gs5EIuFycnJsTDXQWr5+fHzsDg8PrfGbOqxrmbbmkP39fVdcXBy6l4J3P5D9DB+9ayb0zAMDA/asqn2fnZ11Ozs7Vv/te3h4cIWFhZ/yvAAA/A+ZbgAAfrDGxkarsS4rK7Mma8l/ubm5af+NmpjJ5eVlaDw7O9syxCsrK1Z3reZmqs+uqamx4Pr29jZljpKSkiBDrTrp19fXlPnU0CwWi7nT09PQuI5170xVV1dbXXey8/PzlOtUC66ac/140NPT4zY2NoJzLy8vlv1WDTwAAF+BoBsAgB9sdHTUMrdqOKastALKg4MD63auLuXpKMurYP3k5CQY29vbs620tEz85ubGlo4rw1xZWWlZcDU3UyCrRm+aQ1lxbTumY1FzMy0j7+vrs6ZsapC2tbUVbJM2OTlp2Xhtn6YxNTjTXGNjYxm/qxq9qcGbmrHp/qurq6EO6Fo2r+dQtl7voKBe34mC9eQgXT8gvF2WDgBAVAi6AQD4wfwMsgJsdTZX/be2HMvPz3d//77/37zqnlW37dP12q5LndAVpKpL+fb2tqutrbXzCwsLbmZmxrqY63xXV5ct4dYWYqLac2XFtRS9vb3dturSdmf+cnEFzKq3npiYsGdUsLy7u2tblGWqpaXF7qlO6NpOTJns6enpUA28thAbHBy0bLc6uGsrtfn5+eAavZM6nGvLMQAAvsIfdVP7kpkAAMC3oaywstjKPP+WrK/2Ltc7KxPv/1gAAEDUyHQDAPALqfGZlpArEP0tVKO+trZGwA0A+FJkugEAAAAAiAiZbgAAAAAAIkLQDQAAAABARAi6AQAAAACICEE3AAAAAAARIegGAAAAACAiBN0AAAAAAESEoBsAAAAAgIgQdAMAAAAAEBGCbgAAAAAAXDT+AQ2KAGwqu0RAAAAAAElFTkSuQmCC"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 19
   },
   {
    "metadata": {},

From 50b13da9b3a6e01bcca39def3724c2de5a978a1c Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Fri, 6 Mar 2026 12:50:46 -0800
Subject: [PATCH 30/49] retrain model, created python script

---
 array_temp/rnn_model.pt  | Bin 0 -> 803941 bytes
 control_model/scaler.pkl |   3 +++
 2 files changed, 3 insertions(+)
 create mode 100644 array_temp/rnn_model.pt
 create mode 100644 control_model/scaler.pkl

diff --git a/array_temp/rnn_model.pt b/array_temp/rnn_model.pt
new file mode 100644
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HcmV?d00001

diff --git a/control_model/scaler.pkl b/control_model/scaler.pkl
new file mode 100644
index 0000000..ba60be9
--- /dev/null
+++ b/control_model/scaler.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b959b120cda78965af757c00f2e1ef866e323665862f66fdaa95115cd85503b1
+size 983

From 0ab5faa2d5282992413e3567baf904e8ab33a429 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Fri, 6 Mar 2026 16:26:16 -0800
Subject: [PATCH 31/49] retrain model, created python script

---
 control_model/RNN_Training.py             |   6 +-
 control_model/control_model_revised.ipynb | 146 ++++++--
 control_model/inference.py                | 432 ++++++++++++++++++++++
 3 files changed, 558 insertions(+), 26 deletions(-)
 create mode 100644 control_model/inference.py

diff --git a/control_model/RNN_Training.py b/control_model/RNN_Training.py
index 1b2bfce..802e034 100644
--- a/control_model/RNN_Training.py
+++ b/control_model/RNN_Training.py
@@ -83,11 +83,11 @@ def train_model(model, train_loader, test_loader, epochs):
     # requirements
     STATE_COLS   = ["position", "speed"] #states
     CONTROL_COLS = ["brake_pressed", "accel_position"]  #controls
-    SEQ_LEN      = 600 #one minute
+    SEQ_LEN      = 100 #one minute
     STRIDE       = 100 #sliding window change between consecutive sequences, reduces overlapping
     BATCH_SIZE   = 128
-    EPOCHS       = 30
-    HIDDEN_SIZE  = 64
+    EPOCHS       = 50
+    HIDDEN_SIZE  = 128
     NUM_LAYERS   = 2
 
     if torch.cuda.is_available():
diff --git a/control_model/control_model_revised.ipynb b/control_model/control_model_revised.ipynb
index c0085d0..728bf32 100644
--- a/control_model/control_model_revised.ipynb
+++ b/control_model/control_model_revised.ipynb
@@ -725,14 +725,51 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-06T20:00:16.801517Z",
-     "start_time": "2026-03-06T20:00:16.640558Z"
+     "end_time": "2026-03-07T00:01:52.509885Z",
+     "start_time": "2026-03-07T00:01:39.896894Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "#necessary imports\n",
+    "import os\n",
+    "import gc\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from RNN_Dataset import RNN_Dataset\n",
+    "from RNN import RNN\n",
+    "from DataPreprocessing import *\n",
+    "from data_tools import query\n",
+    "from data_tools.collections import TimeSeries\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import dill\n",
+    "import os\n",
+    "import pytz\n",
+    "from datetime import datetime, time, date\n",
+    "\n"
+   ],
+   "id": "466f94729ef1fb60",
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-07T00:02:18.237680Z",
+     "start_time": "2026-03-07T00:02:17.983620Z"
     }
    },
    "cell_type": "code",
    "source": [
     "#now we evaluate model for inference\n",
-    "checkpoint = torch.load('../array_temp/rnn_model.pt', map_location=device)\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "checkpoint = torch.load('rnn_model.pth', map_location=device)\n",
     "\n",
     "model = RNN(\n",
     "    input_size  = checkpoint['input_size'],\n",
@@ -747,26 +784,24 @@
    "id": "23676ff6fae304c2",
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "RNN(\n",
-       "  (lstm): LSTM(2, 128, num_layers=2, batch_first=True)\n",
-       "  (fc): Linear(in_features=128, out_features=2, bias=True)\n",
-       ")"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "KeyError",
+     "evalue": "'input_size'",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mKeyError\u001B[39m                                  Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[4]\u001B[39m\u001B[32m, line 7\u001B[39m\n\u001B[32m      2\u001B[39m device = torch.device(\u001B[33m\"\u001B[39m\u001B[33mcuda\u001B[39m\u001B[33m\"\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m torch.cuda.is_available() \u001B[38;5;28;01melse\u001B[39;00m \u001B[33m\"\u001B[39m\u001B[33mcpu\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m      4\u001B[39m checkpoint = torch.load(\u001B[33m'\u001B[39m\u001B[33mrnn_model.pth\u001B[39m\u001B[33m'\u001B[39m, map_location=device)\n\u001B[32m      6\u001B[39m model = RNN(\n\u001B[32m----> \u001B[39m\u001B[32m7\u001B[39m     input_size  = \u001B[43mcheckpoint\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43minput_size\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m]\u001B[49m,\n\u001B[32m      8\u001B[39m     hidden_size = checkpoint[\u001B[33m'\u001B[39m\u001B[33mhidden_size\u001B[39m\u001B[33m'\u001B[39m],\n\u001B[32m      9\u001B[39m     num_layers  = checkpoint[\u001B[33m'\u001B[39m\u001B[33mnum_layers\u001B[39m\u001B[33m'\u001B[39m],\n\u001B[32m     10\u001B[39m     seq_length  = checkpoint[\u001B[33m'\u001B[39m\u001B[33mseq_length\u001B[39m\u001B[33m'\u001B[39m],\n\u001B[32m     11\u001B[39m     output_size = checkpoint[\u001B[33m'\u001B[39m\u001B[33moutput_size\u001B[39m\u001B[33m'\u001B[39m],\n\u001B[32m     12\u001B[39m )\n\u001B[32m     13\u001B[39m model.load_state_dict(checkpoint[\u001B[33m'\u001B[39m\u001B[33mmodel_state_dict\u001B[39m\u001B[33m'\u001B[39m])\n\u001B[32m     14\u001B[39m model.eval()  \u001B[38;5;66;03m# important — disables dropout/batchnorm for inference\u001B[39;00m\n",
+      "\u001B[31mKeyError\u001B[39m: 'input_size'"
+     ]
     }
    ],
-   "execution_count": 16
+   "execution_count": 4
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-06T20:00:18.407945Z",
-     "start_time": "2026-03-06T20:00:18.334979Z"
+     "end_time": "2026-03-07T00:14:11.977176Z",
+     "start_time": "2026-03-07T00:14:11.958258Z"
     }
    },
    "cell_type": "code",
@@ -800,7 +835,7 @@
     "    # scaler was fit on state_cols + control_cols so controls start at index n_states\n",
     "    n_states = len(state_cols)\n",
     "    n_controls = len(control_cols)\n",
-    "    seq_len = 1000\n",
+    "    seq_len = 600\n",
     "    def unscale_states(arr):\n",
     "        state_mean = scaler.mean_[:n_states]\n",
     "        state_std  = scaler.scale_[:n_states]\n",
@@ -853,13 +888,78 @@
    ],
    "id": "193451d65b640afb",
    "outputs": [],
-   "execution_count": 17
+   "execution_count": 8
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-07T00:21:51.074226Z",
+     "start_time": "2026-03-07T00:21:47.518973Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from inference import load_model\n",
+    "STATE_COLS   = [\"position\", \"speed\"]\n",
+    "CONTROL_COLS = [\"brake_pressed\", \"accel_position\"]\n",
+    "SEQ_LEN      = 600\n",
+    "STRIDE       = 100\n",
+    "BATCH_SIZE   = 128\n",
+    "HIDDEN_SIZE  = 64\n",
+    "NUM_LAYERS   = 2\n",
+    "MODEL_PATH   = \"rnn_model.pth\"\n",
+    "from DataPreprocessing import make_single_df, make_sequence_datasets\n",
+    "\n",
+    "df = make_single_df()\n",
+    "_, test_dataset, _, _, scaler = make_sequence_datasets(\n",
+    "    df, STATE_COLS, CONTROL_COLS, seq_len=SEQ_LEN, stride=STRIDE, batch_size=BATCH_SIZE\n",
+    ")\n",
+    "model = load_model(\"rnn_model.pth\")\n",
+    "\n",
+    "# ← change this number to any sequence you want to inspect\n",
+    "plot_control_trajectory(model, test_dataset, scaler, state_cols=STATE_COLS, control_cols=CONTROL_COLS, sample_idx=1128)"
+   ],
+   "id": "dae568af2bf31f42",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[load_model] Loaded 'rnn_model.pth' on cpu\n",
+      "torch.Size([600, 2])\n",
+      "(600, 2)\n",
+      "(600, 2)\n",
+      "(600,)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ],
+      "image/png": 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BxcBfMcBV1E5GDWmplrUkAm8MXhXHHbW+EcJjwLgIjr///e9zqInnE48TfYwjxMQAVBFsq/c1rS36o8f+YuqrOMYYfCwCcgwGFcHvJz/5Sd4ugkVj9xPznEeT2xi87Mc//nEOXTGFUtzv5ZdfrrH/aAYd03vF32ghEAE8ahZri21iP/GcjzrqqPw6RbPpON9RCxmXv4l4PvF6RE1mNJeP5xN9j+PHmZgmrPQDQcxTHeUrmj/HfuN1idrNCGMRrKrPp76wNPY1i+bkUW6ieXy8TvEaxQ8XjZk3e359/fXXucY6gnS0bIgfUOL8xZRg9TnkkEPyGAwxMFy8xvFjRYTeeC1ifbwWpdYj9TUBj/IVSj+kXXXVVfn/SywxjVlJlK/Sjzzxo0kE+vg/GWKqtFjCaaedlvvBR/mIsnXTTTfV2Gd0dSi1woj3l/i/Ec873gti0L143vGDwVlnnfUtziYApgwDKGjKsCWWWGKubWN6orqmm4opgi699NLKXr165Wm2ttxyy8qXXnpprvvfdNNNlX369KlcbLHF8vReDzzwwFxThoWY5iemPYrt5jV9WExldNxxx1V+5zvfydNWtWvXrnKllVaqPOywwyonTpw41zRPu+22W+VSSy2VH7c0VVFMRXXaaafl6bBieqbNN9+88umnn65zmrOYdmmttdaqbNu27VxTbb3wwguV++yzT+Wyyy6bz0M8r/3337/y4YcfbvA1ePnllyvPOOOMyg022KBymWWWyY8dx/K9732vcty4cXNt39j9PPbYY1XnMc57TP9U+zUsTWkV04HF+YtzE481ZcqUOs99TM0U5zte6zjX3bt3z1M1/fa3v63apjRl2F//+tca961verInn3wyT3cW+45y179//8orr7yyxjbxWg4ePDjvL/a7wgorVO6+++6Vt912W2VT+CavWUzVtsMOO1QuueSSeRqro446Kv//qH0u6vt/F2UwptaqLV73KM+1/y/H63700UdXLr300nmfBx10UOX//ve/uR6zdtmOacZGjBiR9xXlKu4f5Wf48OGVU6dObfB8lF7bupba/79LZbCupXp5i+Orb7vaZbiioiKXmXhfieccy7bbbls5ZsyYBo8bgHlrFf/47QGgacRoyFHLGwN7RU0e5W/YsGG5FtzHZ8tzww035NYCzz33XIO10gDwTejTDQAAAAURugEAAKAgQjcAAAAURJ9uAAAAKIiabgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChewF6/PHH0x577JF69uyZWrVqle66665UpN69e+f91F6OO+64QvcLAABA4wjdC9AXX3yR1l133XT11VenheG5555LH3zwQdUyevTovP573/veQtk/AAAADRO6F6Bddtkl/exnP0t77713nbfPnDkznX766WmFFVZISyyxRNpkk03So48+Ot/769atW+revXvVMmrUqLTqqqumrbfe+ls8CwAAABYUoXshOv7449PTTz+dbr311vTyyy/nGumdd945vfnmm9/6sb/++ut00003pR/+8Ie5iTkAAABNr1VlZWVlUx9ESxTB984770yDBg3K1997773Up0+f/Df6fJfssMMOaeONN04XXnjht9rfX/7yl3TggQfO9fgAAAA0HTXdC8m//vWvNHv27LTGGmukJZdcsmp57LHH0sSJE/M2r7/+ep0Do1VfhgwZUufjX3fddbl5u8ANAABQPto29QEsKqZPn57atGmTnn/++fy3ugjfIWrCx48f3+DjLLvssnOte/fdd9NDDz2U7rjjjgV81AAAAHwbQvdCsv766+ea7ilTpqQtt9yyzm0WW2yx1Ldv32/82Ndff31abrnl0m677bYAjhQAAIAFRehewLXZb731VtX1t99+O7344otpmWWWyc3KDzrooDR48OB06aWX5hD+3//+Nz388MOpf//+8x2Y58yZk0P3oYcemtq29XICAACUEwOpLUAx/de222471/oIxDfccEOqqKjIU4qNHDkyTZo0KXXt2jVtuummafjw4WmdddaZr30++OCDaaeddkoTJkzIwR4AAIDyIXQDAABAQYxeDgAAAAXRCXgBiH7VkydPTksttVSe1gsAAICWrbKyMn3++ed52ubWreuvzxa6F4AI3L169VoQDwUAAEAz8v7776cVV1yx3tuF7gUgarhLJ7tTp06p3MQAbjHg2sCBA1O7du2a+nAoQ8oIygfeQ/AZg+8glKOKMs4y06ZNy5WvpTxYH6F7ASg1KY/AXa6hu2PHjvnYyq2gUh6UEZQPvIfgMwbfQShHFc0gy8yri7GB1AAAAKAgQjcAAAAUROgGAACAgujTDQAA0MRmz56d+y9TU5yTtm3bpq+++iqfo4Up+pC3adPmWz+O0A0AANCEcz1/+OGH6bPPPvMa1HN+unfvnmeKmteAZUXo0qVL3v+32bfQDQAA0ERKgXu55ZbLo3Q3RbAsZ3PmzEnTp09PSy65ZGrduvVCDfszZsxIU6ZMydd79Ogx348ldAMAADSBaC5dCtzLLrus16Ce0P3111+nDh06LNTQHRZffPH8N4J3vEbz29TcQGoAAABNoNSHO2q4KU+l1+bb9LcXugEAAJqQJuUt+7URugEAAKAgQjcAAAAUpEWF7jfeeCPttddeqWvXrqlTp05piy22SI888ki920e7/DPPPDOts846aYkllkg9e/ZMgwcPTpMnT16oxw0AAMD/59FHH83Nuuc1jVrv3r3T5Zdfnspdiwrdu+++e5o1a1YaM2ZMev7559O6666b18Uw/HWJIeDHjRuXhg4dmv/ecccdacKECWnPPfdc6McOAABASptttln64IMPUufOnfPpuOWWW9Iyyywz16l57rnn0tFHH132p6zFTBn28ccfpzfffDNdd911qX///nndxRdfnK655pr0yiuv5AnNa4sXcfTo0TXWXXXVVWnjjTdO7733XlpppZUW2vEDAACQ0mKLLVaV32K+7Pp069atWZyuFhO6Y167NddcM40cOTJtsMEGqX379uk3v/lNnk9tww03bPTjTJ06NTdl6NKlS73bzJw5My8l06ZNq2qu/m2Gki9K6ZjK8dgoD8oIygfeQ/AZg+8gTfMdLEJlzEUdS4iMOWPGwj+WmBnrmwzUvd1226W11147X77ppptSu3bt0jHHHJOGDx+e89Snn36aTj755DRq1Kicnbbaaqv0q1/9Kq2++ur5Pu+++2464YQT0j/+8Y88D3c0FR8xYkTaddddc/Py7bffPv3vf/9LL7zwQjruuONqjCR+zjnnpHPPPTf16dMnnXTSSXkJUXF64okn5pbPMaf3TjvtlK644oq0/PLL59vj2P72t7+lU045Jd8/jnHnnXdOv/3tb9NSSy1V5/OM1yVeo3itas/T3dh81WJCd7wADz30UBo0aFA+YXGSI3Dff//9aemll27UY3z11Ve5j/cBBxyQ+4TX56KLLsovWG0PPvhgWc+xV7tWH5QRvIfgc4aFxfcQlI+5tW3bNtfoTp8+PQfP8MUXKa24Yv0VgEX5z38+S0ss0fjto1tvVHgefPDBOYdFOI4wG7XPhx56aDrkkEPSv//973TzzTfnfBb5KQL1M888UxXQI7RGKI/xtV5//fWc6aJCM7oBh88//zyPvxX568ILL8zNyUNsH9tFII4MV7oc3YTjtnjMOL4zzjgjfe9738vXQ4T/iRMnpttvvz03WY8+4z/84Q/Teeedl7sc1yVely+//DI9/vjj+TGrKx1nsw/dQ4YMyb94NGT8+PG5ljt+AYmg/cQTT6TFF188/f73v0977LFHfnF69OjR4GPEC77//vvnXzF+/etfN7jtWWedlU499dSq6/Ei9+rVKw0cOLDBsN5U4rnFB92OO+6YCzgoI3gPwecMvodQDhb176kRGN9///205JJLpg4dOuR1tSpTF5rIMd8kdMcPBpGBontuhOVoXRyBNlobR+3x3//+95zLNttss7z9n/70p7TyyivnWugIwtFne5999kkDBgzIt5e6CIdSRWaE9egSHMcWlaqlWvKSWBfnLW6PcvTaa6/lY4jjCn/84x9zaI9xu7773e/m1tARzmN9qWY7fhyI46wvx8VrFNkyaupLr1HtFs/zPFepzJ122mnpsMMOa3CbaFYQL178ghFNBEonLPpzx8m/8cYbc3ifV+COJg7xOPMKzvFixVJbvFGU85tFuR8fTU8ZQfnAewg+Y/AdZOGZPXt2DqwRHmMJSy6Z0vTpC/9V6Nix9TdqXh423XTTGk2uI2BfdtlludY6QnkE6tb//+cVNeBRURoBONZFM/Bjjz0257Uddtgh7bvvvlXBu3Sf+FtqUl59fXWl8xePG2E7gn3Jd77zndxtOG7bZJNN8rbRjL00QFuIGaymTJlS52NXP4a6vic3NluVfeiOF6cxHeRLVfu1T1ZcL/WPaChwxyBsMb1Y9A0HAABoCpExv0mNc3N15JFH5j7X9957b+6mG03IL7300tzPu0i1g3IE6oby4oLQYqYMi19Rou929B946aWX8pzd0Yb/7bffTrvttlvVdn379k133nlnVeDeb7/90j//+c/c1yB+aYrpxWIp9akAAABgbmPHjq1xPfprRxPwtdZaK/d/rn77//73v1zjHLeVRM109O2OqZujhfPvfve7eoNyZLWG9OvXLzfVj6UkmptHv+3q+2wKLSZ0d+3aNQ+aFoMQxEh6G220UXryySfz6HQxX3dJvNAxQnmYNGlSuvvuu9N//vOftN566+V+36XlqaeeasJnAwAAUN5itPAY6yoyVvTZvvLKK/NI4hG899prr3TUUUflTBaVojHg2gorrJDXhxjZ/IEHHsiVpOPGjcutjiM41yWmco6c9/DDD+epousawCyaqEf/7YMOOig/3rPPPpsGDx6ctt5665wNm1LZNy//JuJkxgvXkOrzvEV7/obmfQMAAKBuEWpjZO+NN9449+2OwH300Ufn266//vp8fffdd8+tiGMgsvvuu6+qeXfUXMdA2FEBGmNqxeBrv/zlL+vcT/TH/tGPfpS+//3v5xrzmO5r2LBhczUTjwrXaJ4e+4puxvGY8UNAU2tRoRsAAICFIwL05ZdfXufsT9H1N6YUq09DYXibbbapqhwt9beOQbKvvfbaGtu98847c9WIR/CuTwT12mE9atxjKVKLaV4OAAAA5UboBgAAgIJoXg4AAMA38uijjzpjjaSmGwAAoAkZ3LllvzZCNwAAQBMojeRd1xRYlIfSa1N6reaH5uUAAABNIKbZ6tKlS5oyZUq+3rFjxzz1Ff9PjF4eU4599dVXeRqwhVnDHYE7Xpt4jeK1ml9CNwAAQBPp3r17/lsK3swdfmMu8MUXX7xJfpCIwF16jeaX0A0AANBEIkj26NEjLbfccqmiosLrUEuck8cffzxttdVW36qJ9/yI/X2bGu4SoRsAAKCJRbhbEAGvpWnTpk2aNWtW6tChw0IP3QuKgdQAAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAAChIiwrdb7zxRtprr71S165dU6dOndIWW2yRHnnkkUbf/5hjjkmtWrVKl19+eaHHCQAAwKKhRYXu3XffPc2aNSuNGTMmPf/882ndddfN6z788MN53vfOO+9MzzzzTOrZs+dCOVYAAABavhYTuj/++OP05ptvpiFDhqT+/fun1VdfPV188cVpxowZ6ZVXXmnwvpMmTUonnHBCuvnmm1O7du0W2jEDAADQsrVNLcSyyy6b1lxzzTRy5Mi0wQYbpPbt26ff/OY3abnllksbbrhhvfebM2dOOuSQQ9IZZ5yR1l577Ubta+bMmXkpmTZtWv5bUVGRl3JTOqZyPDbKgzKC8oH3EHzG4DsI5aiijLNMY4+pVWVlZWVqIf7zn/+kQYMGpXHjxqXWrVvnwH3vvfem9ddfv977XHTRRbnf9wMPPJD7c/fu3TudfPLJeanPsGHD0vDhw+daf8stt6SOHTsusOcDAABAeYpW1QceeGCaOnVqHlOs2dZ0R3PxESNGNLjN+PHjcy33cccdl4P2E088kRZffPH0+9//Pu2xxx7pueeeSz169JjrftHv+1e/+lUO6RG4G+uss85Kp556ao2a7l69eqWBAwc2eLKb8heY0aNHpx133FHzeZQRvIfgcwbfQygbvqfSnMtIqcXzvJR96D7ttNPSYYcd1uA2ffr0yYOnjRo1Kn366adVwfeaa67JL9CNN96Yw3ttEc6nTJmSVlpppap1s2fPzvuMEczfeeedOvcXTddjqS0KQbkVhOZ0fDQ9ZQTlA+8h+IzBdxDKUbsyzDKNPZ6yD93dunXLS2Oq9kM0K68urke/7bpEX+4ddtihxrqddtoprz/88MO/1XEDAABA2YfuxhowYEBaeuml06GHHprOOeec3Lz8d7/7XXr77bfTbrvtVrVd3759cz/uvffeOw++FkvtXyu6d++em6sDAADAt9Fipgzr2rVruv/++9P06dPTdtttlzbaaKP05JNPpr/97W95vu6SCRMm5I7uAAAAULQWU9MdImjHKOQNmddg7fX14wYAAIBvqsXUdAMAAEC5EboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACtK2qAf+4osv0sUXX5wefvjhNGXKlDRnzpwat//73/8uatcAAADQskP3kUcemR577LF0yCGHpB49eqRWrVoVtSsAAABYtEL33//+93TvvfemzTffvKhdAAAAwKLZp3vppZdOyyyzTFEPDwAAAItu6D7//PPTOeeck2bMmFHULgAAAGDRbF5+6aWXpokTJ6bll18+9e7dO7Vr167G7ePGjStq1wAAANCyQ/egQYOKemgAAABYtEP3ueeeW9RDAwAAwKIdukuef/75NH78+Hx57bXXTuuvv37RuwQAAICWHbqnTJmSfvCDH6RHH300denSJa/77LPP0rbbbptuvfXW1K1bt6J2DQAAAC179PITTjghff755+nVV19Nn3zySV5eeeWVNG3atHTiiScWtVsAAABo+aH7/vvvT9dcc03q169f1bq11lorXX311envf/97Ift844030l577ZW6du2aOnXqlLbYYov0yCOPzPN+0fx9zz33TJ07d05LLLFE+u53v5vee++9Qo4RAACARUdhoXvOnDlzTRMWYl3cVoTdd989zZo1K40ZMyb3JV933XXzug8//LDe+8S0ZhHO+/btm5vCv/zyy2no0KGpQ4cOhRwjAAAAi47CQvd2222XTjrppDR58uSqdZMmTUqnnHJK2n777Rf4/j7++OP05ptvpiFDhqT+/fun1VdfPV188cVpxowZuVl7fX7605+mXXfdNV1yySV5kLdVV10113ovt9xyC/wYAQAAWLQUNpDaVVddlcNr7969U69evfK6999/P33nO99JN9100wLf37LLLpvWXHPNNHLkyLTBBhuk9u3bp9/85jc5PG+44YZ13idq3O+99970f//3f2mnnXZKL7zwQlpllVXSWWed1eA84zNnzsxLSfRTDxUVFXkpN6VjKsdjozwoIygfeA/BZwy+g1COKso4yzT2mFpVVlZWFnUQ8dAPPfRQev311/P16N+9ww47FLW79J///CeH5XHjxqXWrVvnwB2hur5pyqLZeY8ePVLHjh3Tz372szyyevRF/8lPfpL7gm+99dZ13m/YsGFp+PDhc62/5ZZb8mMBAADQskWr6gMPPDBNnTo1jynWJKF7QYjm4iNGjJjnQGhRyx2BO35tiCbjiy++ePr973+f7r777vTcc8/lcF1bNH1fYYUV0gEHHJADc0nU0MeAan/6058aXdMdtfnRxL2hk91U4pyMHj067bjjjnX2swdlBO8h+JzB9xCagu8gNOcyEjkwBvGeV+heoM3Lr7jiinT00UfnQcjickMaO23Yaaedlg477LAGt+nTp08ePG3UqFHp008/rXrCMXp6vEA33nhjDu+1xQlq27ZtHlW9uqiRf/LJJ+vdXzRdj6W2KATlVhCa0/HR9JQRlA+8h+AzBt9BKEftyjDLNPZ4Fmjo/uUvf5kOOuigHLrjcn1atWrV6NDdrVu3vDSmaj9Es/Lq4np9o6UvtthieXqwCRMmzDX12Morr9yo4wMAAICFErrffvvtOi8vDAMGDEhLL710OvTQQ9M555yTm5f/7ne/y8ex2267VW0XU4NddNFFae+9987XzzjjjPT9738/bbXVVlV9uu+55548fRgAAACU5ejl5513Xjr99NPnGljsyy+/TD//+c9zMF6Qoql4BObozx3TlUXb/7XXXjv97W9/y/N1l0StdrS5L4nwfe211+YgHrXv0Tf89ttvz3N3twTRY/+LL1L66qs2+W+ZtcigTMTAi8oIygfeQ/AZg+8glJt2LSC/FDaQWps2bdIHH3ww13zX//vf//K62bNnp5YiOtB37tx5nh3om0IE7SWXbOqjAAAA+OY+/bQiPfbYfWnXXXctuz7djc2BNTtAL0CR5aPvdm0vvfRSWmaZZYraLQAAALTc5uXRrzrCdixrrLFGjeAdtdvTp09PxxxzzILeLfWI1v3x69ADDzyQdtppp7L7dYjyEN0xlBGUD7yH4DMG30EoN+1aQHxZ4KH78ssvz7XcP/zhD9Pw4cNzdXv10cJ79+6dBz1j4YjfPJZYIqUOHWbnvy2h0FJMn25lBOUD7yEUwWcMygff9j2kuVvgoTtGDw+rrLJK2myzzdSsAgAAsMhqu6A7kpc6kK+//vp5pPJY6lJuA44BAABAWYfu6M9dGrG8S5cudQ6kVhpgrSWNXg4AAACFh+4xY8ZUjUz+yCOPLMiHBgAAgEU7dG+99dZ1XgYAAIBFUWHzdN9///3pySefrLp+9dVXp/XWWy8deOCB6dNPPy1qtwAAANDyQ/cZZ5yRB1YL//rXv9Kpp56adt111/T222/nywAAANDSLfApw0oiXK+11lr58u2335722GOPdOGFF6Zx48bl8A0AAAAtXWE13YsttliaMWNGvvzQQw+lgQMH5ssx0FqpBhwAAABassJqurfYYovcjHzzzTdPzz77bPrzn/+c17/xxhtpxRVXLGq3AAAA0PJruq+66qrUtm3bdNttt6Vf//rXaYUVVsjr//73v6edd965qN0CAABAy6/pXmmlldKoUaPmWv/LX/6yqF0CAADAohG6w+zZs9Ndd92Vxo8fn6+vvfbaac8990xt2rQpcrcAAADQskP3W2+9lUcpnzRpUlpzzTXzuosuuij16tUr3XvvvWnVVVctatcAAADQsvt0n3jiiTlYv//++3masFjee++9tMoqq+TbAAAAoKUrrKb7scceS88880yeIqxk2WWXTRdffHEe0RwAAABausJqutu3b58+//zzudZPnz49z+ENAAAALV1hoXv33XdPRx99dBo7dmyqrKzMS9R8H3PMMXkwNQAAAGjpCgvdV1xxRe7TPWDAgNShQ4e8bLbZZmm11VZLv/rVr4raLQAAALT8Pt1dunRJf/vb3/Io5q+99lpet9Zaa+XQDQAAAIuCQufpvu6669Ivf/nL9Oabb+brq6++ejr55JPTkUceWeRuAQAAoGWH7nPOOSdddtll6YQTTshNzMPTTz+dTjnllDx12HnnnVfUrgEAAKBlh+5f//rX6Xe/+1064IADqtbFAGr9+/fPQVzoBgAAoKUrbCC1ioqKtNFGG821fsMNN0yzZs0qarcAAADQ8kP3IYcckmu7a/vtb3+bDjrooKJ2CwAAAIvOQGoPPvhg2nTTTfP1mLM7+nMPHjw4nXrqqVXbRd9vAAAAaGkKC92vvPJK2mCDDfLliRMn5r9du3bNS9xW0qpVq6IOAQAAAFpm6H7kkUeKemgAAABYtPt0AwAAwKJO6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAI3QAAANC8qOkGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABSkRYXuN954I+21116pa9euqVOnTmmLLbZIjzzySIP3mT59ejr++OPTiiuumBZffPG01lprpWuvvXahHTMAAAAtV4sK3bvvvnuaNWtWGjNmTHr++efTuuuum9d9+OGH9d7n1FNPTffff3+66aab0vjx49PJJ5+cQ/jdd9+9UI8dAACAlqdtaiE+/vjj9Oabb6brrrsu9e/fP6+7+OKL0zXXXJNeeeWV1L179zrv99RTT6VDDz00bbPNNvn60UcfnX7zm9+kZ599Nu2555513mfmzJl5KZk2bVr+W1FRkZdyUzqmcjw2yoMygvKB9xB8xuA7COWoooyzTGOPqVVlZWVlagHiafTr1y9tueWW6fLLL0/t27fPf3/+85+n119/PS299NJ13i9C9gsvvJDuuuuu1LNnz/Too4/msH3vvfemrbbaqs77DBs2LA0fPnyu9bfcckvq2LHjAn9uAAAAlJcZM2akAw88ME2dOjV3b27xoTv85z//SYMGDUrjxo1LrVu3Tsstt1wOz+uvv36994ka6wjeI0eOTG3bts33+93vfpcGDx7c4H1q13T36tUr17Y3dLKb8heY0aNHpx133DG1a9euqQ+HMqSMoHzgPQSfMfgOQjmqKOMsEzkwxhObV+gu++blQ4YMSSNGjGhwm+iLveaaa6bjjjsuB+0nnngiD4r2+9//Pu2xxx7pueeeSz169KjzvldeeWV65plnch/ulVdeOT3++OP5caLWe4cddqjzPlGLHkttUQjKrSA0p+Oj6SkjKB94D8FnDL6DUI7alWGWaezxlH3oPu2009Jhhx3W4DZ9+vTJg6eNGjUqffrpp1W/MkR/7vhV5MYbb8zhvbYvv/wy/eQnP0l33nln2m233fK66A/+4osvpl/84hf1hm4AAABoEaG7W7dueWlMe/oQzcOri+tz5syp8z6lgc9q36dNmzb13gcAAAAWuSnDBgwYkAdLi5HIX3rppTxn9xlnnJHefvvtqlrs0Ldv31yzHaJGfOutt87bxQBqse0NN9yQ+3fvvffeTfhsAAAAaAlaTOiODuwx3/b06dPTdtttlzbaaKP05JNPpr/97W95vu6SCRMm5I7uJbfeemv67ne/mw466KC01lpr5WnGLrjggnTMMcc00TMBAACgpSj75uXfRATtBx54oMFtag/WHvN3X3/99QUfGQAAAIuiFlPTDQAAAOVG6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFKRFhe5x48alHXfcMXXp0iUtu+yy6eijj07Tp09v8D6VlZXpnHPOST169EiLL7542mGHHdKbb7650I4ZAACAlqvFhO7JkyfnwLzaaqulsWPHpvvvvz+9+uqr6bDDDmvwfpdcckm64oor0rXXXpvvt8QSS6SddtopffXVVwvt2AEAAGiZ2qYWYtSoUaldu3bp6quvTq1b/3+/JUSQ7t+/f3rrrbdyGK+rlvvyyy9PZ599dtprr73yupEjR6bll18+3XXXXekHP/hBnfuaOXNmXkqmTZuW/1ZUVOSl3JSOqRyPjfKgjKB84D0EnzH4DkI5qijjLNPYY2oxoTtC8GKLLVYVuEM0Fw9PPvlknaH77bffTh9++GGuIS/p3Llz2mSTTdLTTz9db+i+6KKL0vDhw+da/+CDD6aOHTumcjV69OimPgTKnDKC8oH3EHzG4DsI5Wh0GWaZGTNmLFqhe7vttkunnnpq+vnPf55OOumk9MUXX6QhQ4bk2z744IM67xOBO0TNdnVxvXRbXc4666y8r+o13b169UoDBw5MnTp1SuX4C0wU0ujvHq0BQBnBewg+Z/A9hHLgeyrNuYyUWjw3+9AdwXnEiBENbjN+/Pi09tprpxtvvDGH4QjFbdq0SSeeeGIO0NVrvxeE9u3b56W2KATlVhCa0/HR9JQRlA+8h+AzBt9BKEftyjDLNPZ4yj50n3baafMcDK1Pnz7574EHHpiXjz76KA+I1qpVq3TZZZdV3V5b9+7d89/YPkYvL4nr66233gJ9HgAAACx6yj50d+vWLS/fRKm5+B/+8IfUoUOH3BShLqusskoO3g8//HBVyI4mAjGK+bHHHrsAjh4AAIBFWYuZMixcddVVea7uN954I49ifvzxx+dBz2Le7pK+ffumO++8M1+OmvCTTz45/exnP0t33313+te//pUGDx6cevbsmQYNGtSEzwQAAICWoOxrur+JZ599Np177rlp+vTpOVz/5je/SYccckiNbSZMmJCmTp1adf3//u//8qBrRx99dPrss8/SFltskef4jhpyAAAA+DZaVOiOObbnJebmri5qu88777y8AAAAwILUopqXAwAAQDkRugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKEjboh54UVJZWZn/Tps2LZWjioqKNGPGjHx87dq1a+rDoQwpIygfeA/BZwy+g1COKso4y5TyXykP1kfoXgA+//zz/LdXr14L4uEAAABoRnmwc+fO9d7eqnJesZx5mjNnTpo8eXJaaqmlUqtWrcrujMUvMPGDwPvvv586derU1IdDGVJGUD7wHoLPGHwHoRxNK+MsE1E6AnfPnj1T69b199xW070AxAleccUVU7mLQlpuBZXyooygfOA9BJ8x+A5COepUplmmoRruEgOpAQAAQEGEbgAAACiI0L0IaN++fTr33HPzX1BG8B6Czxl8D6Fc+J7KolBGDKQGAAAABVHTDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGE7gXo8ccfT3vssUfq2bNnatWqVbrrrrtSkWbPnp2GDh2aVllllbT44ounVVddNZ1//vmpsrKy0P0CAADQOG0buR2N8MUXX6R11103/fCHP0z77LNP4edsxIgR6de//nW68cYb09prr53++c9/psMPPzx17tw5nXjiiV4zAACAJiZ0L0C77LJLXuozc+bM9NOf/jT96U9/Sp999ln6zne+k4PzNttsM1/7e+qpp9Jee+2Vdtttt3y9d+/e+bGfffbZ+X4OAAAALDialy9Exx9/fHr66afTrbfeml5++eX0ve99L+28887pzTffnK/H22yzzdLDDz+c3njjjXz9pZdeSk8++WSDwR8AAICFR033QvLee++l66+/Pv+NPt/h9NNPT/fff39ef+GFF37jxxwyZEiaNm1a6tu3b2rTpk3u433BBRekgw46qIBnAAAAwDelpnsh+de//pVD8RprrJGWXHLJquWxxx5LEydOzNu8/vrreQC2hpYI2iV/+ctf0s0335xuueWWNG7cuNy3+xe/+EX+CwAAQNNT072QTJ8+PddGP//88/lvdRG+Q58+fdL48eMbfJxll1226vIZZ5yRQ/gPfvCDfH2dddZJ7777brrooovSoYceWsjzAAAAoPGE7oVk/fXXzzXdU6ZMSVtuuWWd2yy22GK5qXhjzZgxI7VuXbOxQgT6OXPmfOvjBQAA4NsTuhdwbfZbb71Vdf3tt99OL774YlpmmWVys/Loaz148OB06aWX5hD+3//+Nw+E1r9//6oRyL+JmBM8+nCvtNJKecqwF154IV122WV5yjIAAACaXqvKysrKpj6IluLRRx9N22677Vzro6n3DTfckCoqKtLPfvazNHLkyDRp0qTUtWvXtOmmm6bhw4fnpuHf1Oeff56GDh2a7rzzzlyDHgO0HXDAAemcc87JteYAAAA0LaEbAAAACmL0cgAAACiIPt0LQAxcNnny5LTUUkvlab0AAABo2aKndnT5jW6+tQe4rk7oXgAicPfq1WtBPBQAAADNyPvvv59WXHHFem8XuheAqOEunexOnTqlchMDuD344INp4MCBqV27dk19OJQhZQTlA+8h+IzBdxDKUUUZZ5lp06blytdSHqyP0L0AlJqUR+Au19DdsWPHfGzlVlApD8oIygfeQ/AZg+8glKOKZpBl5tXF2EBqAAAAUBChGwAAAAoidAMAAEBB9OkGAABohmbPnp37PLdkFRUVqW3btumrr77Kz3dhij7kbdq0+daPI3QDAAA0s/mhP/zww/TZZ5+lReG5du/ePc8UNa8By4rQpUuXvP9vs2+hGwAAoBkpBe7lllsuj+zdFGF0YZkzZ06aPn16WnLJJVPr1q0XatifMWNGmjJlSr7eo0eP+X4soRsAAKCZiCbWpcC97LLLppZuzpw56euvv04dOnRYqKE7LL744vlvBO843/Pb1NxAagAAAM1EqQ931HBTvNJ5/jZ954VuAACAZqYlNylvaedZ6AYAAICCCN0AAAAskrbZZpt08sknF7oPoRsAAAAKInQDAABAQYRuAAAACnfbbbelddZZJ0/FFdOd7bDDDumLL75Ihx12WBo0aFAaPnx46tatW+rUqVM65phj8lRh1acOu+iii9Iqq6yS77/uuuvmx6vulVdeSbvsskue03v55ZdPhxxySPr444+rbo99DR48ON8e825feumlC+VVN083AABAM1ZZmdKMGU2z75hRqzEDfH/wwQfpgAMOSJdccknae++90+eff56eeOKJVBkHn1J6+OGH81zcjz76aHrnnXfS4YcfnoP5+eefn2+/+OKL080335yuvfbatPrqq6fHH388HXzwwTmkb7311nnu8u222y4deeSR6Ze//GX68ssv05lnnpn233//NGbMmPwYZ5xxRnrsscfS3/72tzzv9k9+8pM0bty4tN566xV6joRuAACAZiwC95JLNs2+p09PaYklGhe6Z82alfbZZ5+08sor53VR612y2GKLpT/84Q95Xuy11147nXfeeTkkR+33zJkzcy33Qw89lAYMGJC379OnT3ryySfTb37zmxy6r7rqqrT++uunCy+8sOox4/F69eqV3njjjdSzZ8903XXXpZtuuiltv/32+fYbb7wxrbjiiqloQjcAAACFWnfddXPYjaC90047pYEDB6b99tsvLb300lW3R+AuiXA9ffr09P7776cPP/wwzZgxI+244441HjOan0fQDi+99FJ65JFHctPx2iZOnJhrvmP7TTbZpGr9Msssk9Zcc81UNKEbAACgGYusGjXOTbXvxmjTpk0aPXp0euqpp9KDDz6YrrzyyvTTn/40jR07dp73jb7Y4d57700rrLBCjdvat2+f/0ZA32OPPdKIESPmun/0337rrbdSUxG6AQAAmrHoU92YJt5NrVWrVmnzzTfPyznnnJObmd95551VNdVRGx2DpIVnnnkm11pH8/C2bdvmcP3ee+/lpuR12WCDDdLtt9+eevfunbevbdVVV03t2rXLIX+llVbK6z799NPc9Ly+x1zkRi/fc88988mJzvXxS0WMRDd58uRG3Tc658codvEi33XXXTVui3W1l1tvvbWgZwEAALDoGTt2bO5v/c9//jOH5zvuuCP997//Tf369cu3R9PvI444Ir322mvpvvvuS+eee246/vjjU+vWrdNSSy2VTjvttHTKKafkftjRXDwGQIva8rgejjvuuPTJJ5/kwdqee+65vM0DDzyQB2SbPXt2DvDx+NFPPAZWi5HOY9T0ePyiNZua7m233TaPLheBe9KkSen000/PfQCiecK8XH755TlM1+f6669PO++8c9X1Ll26LLDjBgAAWNR16tQpjzge2WzatGm5ljum7IrK0T//+c+5v3eMSr7VVlvlgdMiPA8bNqzq/jGwWow4HgOq/fvf/86ZLWq3IyOGGCjtH//4Rx6xPPqLx2PEPiLnlYL1z3/+86pm6KUgP3Xq1MKfe7MJ3fGrRkmcvCFDhuS53CoqKnIzgfq8+OKL+cWMX1QisNclXrDu3bsXctwAAACLun79+qX777+/wW1ipPJYqov5uUNUop500kl5qU+E9qhBr0/Udv/xj3/MS0nUfBet2YTu6qLZQMzRttlmmzUYuGOEuwMPPDBdffXVDYbqaIoQ87nFsPMxCXs0QWioZjx+NYmlJH6pCfEDQCzlpnRM5XhslAdlBOUD7yH4jMF3kObzvS26z0YYLQXS5q6ysrLqOdV1W+lvUzzf2GfsO857DAZXXWPzVbMK3dFUIOZfizC96aabplGjRs2zdjyC+V577VXvNtFMISZRj+HpYxS9H//4x7nJwYknnljvfaJJQ+1fYELcv/ow9+UmRgsEZQTvIficoSn4HoLysWDEIGFRoRiZJfpBtwQVFRV5Du9SZWZdPv/889QU4hzHAG/RND6OsbrIpY3RqrL000ETiCbidQ3pXt348eNT37598+WPP/4413K/++67OfR27tw5B++6aqXvvvvu3Eb/hRdeqJqrLbaL0fGiWXp9YhS96OMd88F9k5ruGFUvji/6KpRjIY4PupjXrqGWASy6lBGUD7yH4DMG30Gah6+++ipnlRilOwaZbukqKytz4I4+2A21Ri7yfL/zzjs579U+35EDu3btmvuFN5QDm7SmO0JxjBjXkGjyXRJPKJY11lgj9wmIJx5DycfE6bXFiHQxYl3tQdH23XfftOWWW6ZHH320zv3FZOnnn39+DtWlOd9qi/V13RaBtpxDbbkfH01PGUH5wHsIPmPwHaS8xUjcET5jcLCFMfJ2U5tTrU93Uzzf2Gfsu67vyY3NVk0aurt165aXb3Pyq9c4165Fj37a1a2zzjrpl7/8ZR6trqGB15Zeeul6AzcAAEBTa8IGy4uUygVwnts2lzndYq61LbbYIgfiqMEeOnRonuC8VMsd04jFMPMjR45MG2+8ce7nUNfgaTHX9yqrrJIv33PPPemjjz7K/cOjqUA0wY6542I6MgAAgHJTql2N/sSLL754Ux9Oizfj/99v+9u0GG4WoTsGJ4uh32OC9C+++CJP/RXzrZ199tlVNdLRJ3XChAmN7sxeOnExsnkMuBa/YKy22mrpsssuS0cddVSBzwYAAGD+xAja0YV2ypQpVVmpKfo6Lyxz5szJg5lF3+qF2bw88mFkyzjPcb5rj1ze4kJ3NAuPPtoNiYEE5lX1X/v2CO6xAAAANBelFr2l4N2SVVZW5tHDo1a/KX5ciMDd0PTTLSZ0AwAA8P+J8Bmtf5dbbrlGzxXdXFVUVOTpurbaaquFPih07O/b1HCXCN0AAADNUATCBREKy1mbNm3y/NgxBldznYmp5Y8xDwAAAE1E6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAQOgGAACA5kVNNwAAABRE6AYAAICCCN0AAACwqIfuPffcM6200kqpQ4cOqUePHumQQw5JkydPbvA+22yzTWrVqlWN5ZhjjqmxzXvvvZd222231LFjx7TccsulM844I82aNavgZwMAAMCioG1qJrbddtv0k5/8JAfuSZMmpdNPPz3tt99+6amnnmrwfkcddVQ677zzqq5HuC6ZPXt2Dtzdu3fPj/PBBx+kwYMHp3bt2qULL7yw0OcDAABAy9dsQvcpp5xSdXnllVdOQ4YMSYMGDUoVFRU5JNcnQnaE6ro8+OCD6bXXXksPPfRQWn755dN6662Xzj///HTmmWemYcOGpcUWW6zO+82cOTMvJdOmTct/41hiKTelYyrHY6M8KCMoH3gPwWcMvoNQjirKOMs09phaVVZWVqZm5pNPPknHHntsrvF+8sknG2xe/uqrr6Z4ihG899hjjzR06NCq2u5zzjkn3X333enFF1+sus/bb7+d+vTpk8aNG5fWX3/9Oh83Avnw4cPnWn/LLbfUqEkHAACgZZoxY0Y68MAD09SpU1OnTp2af013iBroq666Kj+5TTfdNI0aNarB7eMERK14z54908svv5zvP2HChHTHHXfk2z/88MNcw11d6XrcVp+zzjornXrqqTVqunv16pUGDhzY4Mluyl9gRo8enXbccccGWwWw6FJGUD7wHoLPGHwHoRxVlHGWKbV4npcmDd3RRHzEiBENbjN+/PjUt2/ffDkGOTviiCPSu+++m2uao/91BO8YIK0uRx99dNXlddZZJ/cH33777dPEiRPTqquuOt/H3b59+7zUFoWg3ApCczo+mp4ygvKB9xB8xuA7COWoXRlmmcYeT5OG7tNOOy0ddthhDW4TTb1Lunbtmpc11lgj9evXL9cuP/PMM2nAgAGN2t8mm2yS/7711ls5dEeT82effbbGNh999FH+W18/cAAAAGisJg3d3bp1y8v8mDNnTv5bfUCzeSn13Y4a7xBh/YILLkhTpkzJ04WFaLoQTcTXWmut+TouAAAAaFbzdI8dOzb35Y7QHE3Lx4wZkw444IBcW12q5Y5B1aIZeqnmOpqQx0jkzz//fHrnnXfygGnRHH2rrbZK/fv3z9tEH+wI1zHn90svvZQeeOCBdPbZZ6fjjjuuzubjAAAA0OJCd4wIHoOfRX/sNddcM/frjuD82GOPVYXj6GAfg6TFIGshpvuKqcAiWEcYj6bs++67b7rnnnuqHrdNmza5T3j8jfB+8MEH52BefV5vAAAAmF/NYvTyGAQtarcb0rt37zw1WEn0945QPi8xuvl99923QI4TAAAAml1NNwAAADRHQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAABb10L3nnnumlVZaKXXo0CH16NEjHXLIIWny5MkN3mebbbZJrVq1qrEcc8wxNbapfXsst956a8HPBgAAgEVB29RMbLvttuknP/lJDtyTJk1Kp59+etpvv/3SU0891eD9jjrqqHTeeedVXe/YseNc21x//fVp5513rrrepUuXBXz0AAAALIqaTeg+5ZRTqi6vvPLKaciQIWnQoEGpoqIitWvXrt77Rcju3r17g48dIXte2wAAAECLDd3VffLJJ+nmm29Om222WYOBO8R2N910Uw7Ve+yxRxo6dOhctd3HHXdcOvLII1OfPn1y8/PDDz88NzOvz8yZM/NSMm3atPw3fgCIpdyUjqkcj43yoIygfOA9BJ8x+A5COaoo4yzT2GNqVVlZWZmaiTPPPDNdddVVacaMGWnTTTdNo0aNSssuu2y92//2t7/NteI9e/ZML7/8cr7/xhtvnO64446qbc4///y03Xbb5SD+4IMPpnPPPTddcskl6cQTT6z3cYcNG5aGDx8+1/pbbrmlzubrAAAAtCyRSw888MA0derU1KlTp/IM3dFEfMSIEQ1uM378+NS3b998+eOPP8613O+++24OvZ07d87Bu6Fa6erGjBmTtt9++/TWW2+lVVddtc5tzjnnnNzH+/333/9GNd29evXKx9fQyW7KX2BGjx6ddtxxx3m2DGDRpIygfOA9BJ8x+A5COaoo4ywTObBr167zDN1N2rz8tNNOS4cddliD20ST75J4QrGsscYaqV+/fjnoPvPMM2nAgAGN2t8mm2yS/zYUumObqP2OUN2+ffs6t4n1dd0WhaDcCkJzOj6anjKC8oH3EHzG4DsI5ahdGWaZxh5Pk4bubt265WV+zJkzJ/+tXuM8Ly+++GL+GyOgN7TN0ksvXW/gBgAAgBY1kNrYsWPTc889l7bYYosciCdOnJgHRIva6lItd0wjFk3HR44cmfttxzbRx3rXXXfN/b6jT3eMgL7VVlul/v375/vcc8896aOPPsr9w2P+72i2cOGFF+bpyAAAAGCRCN0xOFkMfhaDnH3xxRe5pjrm1T777LOraqSjrf+ECRNyZ/aw2GKLpYceeihdfvnl+T7RFH3ffffN96neHODqq6/OYTy6tq+22mrpsssuy3N7AwAAwCIRutdZZ508CFpDevfunYNzSYTsxx57rMH7RHCPBQAAAIrQupBHBQAAAIRuAAAAKIqabgAAABC6AQAAoHlR0w0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIG0bu+Gpp57a6Ae97LLL0oK25557phdffDFNmTIlLb300mmHHXZII0aMSD179mzwfk8//XT66U9/msaOHZvatGmT1ltvvfTAAw+kxRdfPN/+ySefpBNOOCHdc889qXXr1mnfffdNv/rVr9KSSy65wJ8DAAAAi5ZGh+4XXnihxvVx48alWbNmpTXXXDNff+ONN3Ko3XDDDRf8UaaUtt122/STn/wk9ejRI02aNCmdfvrpab/99ktPPfVUg4F75513TmeddVa68sorU9u2bdNLL72Uw3XJQQcdlD744IM0evToVFFRkQ4//PB09NFHp1tuuaWQ5wEAAMCio9Gh+5FHHqlRk73UUkulG2+8Mdc6h08//TQH1i233LKQAz3llFOqLq+88sppyJAhadCgQTkot2vXrt77nHjiiXnbktKPBGH8+PHp/vvvT88991zaaKON8roI57vuumv6xS9+UW8t+syZM/NSMm3atPw3jiWWclM6pnI8NsqDMoLygfcQfMbgOwjlqKKMs0xjj6lVZWVl5Td98BVWWCE9+OCDae21166x/pVXXkkDBw5MkydPTkWKJuHHHntsrvF+8skn69wmmqEvv/zy6Yorrkh/+tOf0sSJE1Pfvn3TBRdckLbYYou8zR/+8Id02mmn5R8MSqL2vkOHDumvf/1r2nvvvet87GHDhqXhw4fPtT5qxzt27LjAnicAAADlacaMGenAAw9MU6dOTZ06dfr2Nd3VRc3uf//737nWx7rPP/88FeXMM89MV111VX5ym266aRo1alS92/773/+uCshRax19uUeOHJm23377/OPA6quvnj788MO03HLL1bhfNEFfZpll8m31iebq1fu4x/no1atX/sGhoZPdlL/ARPP5HXfcsd5WASzalBGUD7yH4DMG30EoRxVlnGVKLZ7nZb5Cd9QAR1PySy+9NG288cZ5XQxUdsYZZ6R99tmn0Y8Tzb5jMLSGRBPwqKEO8fhHHHFEevfdd3NN8+DBg3PwbtWq1Vz3mzNnTv77ox/9KB9rWH/99dPDDz+ca7gvuuiiNL/at2+fl9qiEJRbQWhOx0fTU0ZQPvAegs8YfAehHLUrwyzT2OOZr9B97bXX5oHMoiq91I49aogjEP/85z9v9ONE0+7DDjuswW369OlTdblr1655WWONNVK/fv1y7fIzzzyTBgwYMNf9YsC1sNZaa9VYH/d777338uXu3bvnZujVRfPyaL4etwEAAMC3MV+hO/otX3PNNTlgR1/psOqqq6YllljiGz1Ot27d8jI/SjXZ1Qc0q6537955ILQJEybUWB+jrO+yyy75coT1zz77LD3//PNVo66PGTMmP/Ymm2wyX8cFAAAAJf9v7qz5EFNtxRL9oyNwz8eYbI0STdejL3fM0x1NyyMYH3DAATnol2q5Y1C1aIb+7LPP5uvR5Dyao8dAarfddlt666230tChQ9Prr7+ea+RLtd4xpdhRRx2V7/ePf/wjHX/88ekHP/jBPOf/BgAAgEJquv/3v/+l/fffP08jFuH2zTffzM3AI8zGFGLR13tBipr1O+64I5177rnpiy++yE3HIyyfffbZVX2ro5l71GrHIGslJ598cvrqq6/y1GHRZHzdddfNnfAjrJfcfPPNOWjHAGsxf/e+++6bgzoAAAA0SeiOEBudxqNvdNQWl3z/+9/Po3ov6NC9zjrr5NrthkRz8rpq2mOwturzdNcWI5XHVF8AAABQFqE75uh+4IEH0oorrlhjfTQzj+bfAAAAwHz26Y4m3tHku7Zowl3XVFoAAACwKJqv0L3lllumkSNHVl2Pft0x4vcll1yStt122wV5fAAAALBoNS+PcB0Dj/3zn/9MX3/9dfq///u/9Oqrr+aa7hgBHAAAAJjP0P2d73wnz3d95ZVXpqWWWipNnz497bPPPum4447LI4tTPmJsuS++SOmrr9rkv+3aNfURUY4qKpQRlA+8h+AzBt9BKD/tWkB+aVVZ1OTai5Bp06alzp07p6lTp6ZOnTqlchJBe8klm/ooAAAAvrlPP61Ijz12X9p1113zDFrNMQfOV5/u8MQTT6SDDz44bbbZZmnSpEl53R//+Mf05JNPzu9DAgAAQIsyX83Lb7/99nTIIYekgw46KI0bNy7NnDkzr4+Ef+GFF6b77rtvQR8n8ykGmY9fh2KKt5122qnsfh2iPFRUKCMoH3gPwWcMvoNQftq1W0RD989+9rN07bXXpsGDB6dbb721av3mm2+eb6N8tGqV0hJLpNShw+z8tyUUWorp062MoHzgPYQi+IxB+eDbvoc0d/PVvHzChAlpq622mmt9tGf/7LPPFsRxAQAAwKIZurt3757eeuutudZHf+4+ffosiOMCAACARTN0H3XUUemkk05KY8eOTa1atUqTJ09ON998czr99NPTscceu+CPEgAAABaVPt1DhgxJc+bMSdtvv32aMWNGbmrevn37HLpPOOGEBX+UAAAAsKiE7qjd/ulPf5rOOOOM3Mx8+vTpaa211kpLmhAaAAAAvl3oLllsscXSUkstlReBGwAAABZAn+5Zs2aloUOH5tHKe/funZe4fPbZZ+f5fgEAAID5rOmOftt33HFHuuSSS9KAAQPyuqeffjoNGzYs/e9//0u//vWvnVsAAAAWefMVum+55ZZ06623pl122aVqXf/+/VOvXr3SAQccIHQDAADA/DYvj5HKo0l5bausskru5w0AAADMZ+g+/vjj0/nnn59mzpxZtS4uX3DBBfk2AAAAYD77dL/wwgvp4YcfTiuuuGJad91187qXXnopff3113nu7n322adq2+j7DQAAAIui+QrdXbp0Sfvuu2+NddGfGwAAAPiWofuaa65Jc+bMSUsssUS+/s4776S77ror9evXL+20007z85AAAADQ4sxXn+699tor/fGPf8yXP/vss7TpppumSy+9NA0aNMjI5QAAAPBtQve4cePSlltumS/fdtttafnll0/vvvtuGjlyZLriiivm5yEBAACgxZmv0D1jxoy01FJL5csPPvhgHjitdevWucY7wjcAAAAwn6F7tdVWy32433///fTAAw+kgQMH5vVTpkxJnTp1cl4BAABgfkP3Oeeck04//fTUu3fvtMkmm6QBAwZU1Xqvv/76TiwAAADM7+jl++23X9piiy3SBx98UDVPd4g5uvfee28nFgAAAOa3pjt0794912pHX+6SjTfeOPXt27eQE7vnnnumlVZaKXXo0CH16NEjHXLIIWny5MnzvN/TTz+dtttuuzy9WTR932qrrdKXX35ZdXvU1rdq1arGcvHFFxfyHAAAAFi0zHfoXti23Xbb9Je//CVNmDAh3X777WnixIm5xn1egXvnnXfOfc6fffbZ9Nxzz6Xjjz++xg8F4bzzzsu19qXlhBNOKPjZAAAAsCiYr+blTeGUU06purzyyiunIUOG5HnBKyoqUrt27eq9z4knnpi3LVlzzTXn2i5GYo+aewAAAFgkQ3d1n3zySbr55pvTZpttVm/gjpHUx44dmw466KC8XdSMR9P3Cy64IPdHry6ak59//vm5+fqBBx6Yw3rbtvWfmpkzZ+alZNq0aflv/AAQS7kpHVM5HhvlQRlB+cB7CD5j8B2EclRRxlmmscfUqrKysjI1E2eeeWa66qqr8jzhMSf4qFGj0rLLLlvnts8880weVX2ZZZZJv/jFL9J6662XRo4cma655pr0yiuvpNVXXz1vd9lll6UNNtggb/fUU0+ls846Kx1++OF5fX2GDRuWhg8fPtf6W265JXXs2HEBPmMAAADKUeTSqLSdOnVqg1NnN2nojmbfI0aMaHCb8ePHVw3O9vHHH+da7nfffTeH3s6dO+fgHYOf1RYBevPNN88h+sILL6xa379//7Tbbruliy66qM79/eEPf0g/+tGP0vTp01P79u0bXdPdq1evfHzlOE95/AIzevTotOOOO9bbMoBFmzKC8oH3EHzG4DsI5aiijLNM5MCuXbvOM3Q3afPy0047LR122GENbtOnT5+qy/GEYlljjTVSv379ctAt1WjXFiOch7XWWqvG+rjfe++9V+/+Yt7xWbNmpXfeeafO/t8hwnhdgTwKQbkVhOZ0fDQ9ZQTlA+8h+IzBdxDKUbsyzDKNPZ4mDd3dunXLy/yYM2dO/lu9xrm6mAqsZ8+eebTz6t544420yy671Pu4L774Yh7dfLnllpuv4wIAAIBmNZBaDIgW033FAGhLL710HhRt6NChadVVV62q5Z40aVLafvvtc7/tmC88mpyfccYZ6dxzz03rrrtu7tN94403ptdffz3ddtttVVOKxWPHdGQxgnlcj0HUDj744LwfAAAAaPGhOwYnu+OOO3KA/uKLL3LT8Zh/++yzz65q5h1t/aNWOzqzl5x88snpq6++ykE6+oJH+I7+ABHWQ9z31ltvzQOjRY35Kquskrc99dRTm+y5AgAA0HI0i9C9zjrrpDFjxjS4TTQnr2tMuBisrfo83dXFqOXRJxwAAACK0LqQRwUAAACEbgAAACiKmm4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAACzqoXvPPfdMK620UurQoUPq0aNHOuSQQ9LkyZPr3f6dd95JrVq1qnP561//WrXde++9l3bbbbfUsWPHtNxyy6UzzjgjzZo1ayE9KwAAAFqyZhO6t9122/SXv/wlTZgwId1+++1p4sSJab/99qt3+169eqUPPvigxjJ8+PC05JJLpl122SVvM3v27By4v/766/TUU0+lG2+8Md1www3pnHPOWYjPDAAAgJaqbWomTjnllKrLK6+8choyZEgaNGhQqqioSO3atZtr+zZt2qTu3bvXWHfnnXem/fffPwfv8OCDD6bXXnstPfTQQ2n55ZdP6623Xjr//PPTmWeemYYNG5YWW2yxOo9l5syZeSmZNm1a/hvHEku5KR1TOR4b5UEZQfnAewg+Y/AdhHJUUcZZprHH1KqysrIyNTOffPJJOvbYY9OkSZPSk08+2aj7PP/882mjjTZK//jHP9Jmm22W10WN9t13351efPHFqu3efvvt1KdPnzRu3Li0/vrr1/lYEcij1ry2W265JTdTBwAAoGWbMWNGOvDAA9PUqVNTp06dmn9Nd4ga6Kuuuio/uU033TSNGjWq0fe97rrrUr9+/aoCd/jwww9zDXd1petxW33OOuusdOqpp9ao6Y7m7AMHDmzwZDflLzCjR49OO+64Y52tAkAZwXsIPmfwPYSm4DsIzbmMlFo8z0uThu5oIj5ixIgGtxk/fnzq27dvvhyDnB1xxBHp3XffzTXNgwcPzsE7BkdryJdffplroYcOHbpAjrt9+/Z5qS0KQbkVhOZ0fDQ9ZQTlA+8h+IzBdxDKUbsyzDKNPZ4mDd2nnXZaOuywwxrcJpp6l3Tt2jUva6yxRq61jtrlZ555Jg0YMKDBx7jtttty7XiE9Oqiz/ezzz5bY91HH31UdRsAAAB8G00aurt165aX+TFnzpz8t/qAZg01LY8px2rvK8L6BRdckKZMmZKnCwvRdCGaiK+11lrzdVwAAADQrKYMGzt2bO7LHQOeRdPyMWPGpAMOOCCtuuqqVbXcMahaNEOvXXP91ltvpccffzwdeeSRcz1u9MGOcB1zfr/00kvpgQceSGeffXY67rjj6mw+DgAAAC0udMeI4HfccUfafvvt05prrpn7dffv3z899thjVeE4OtjHHN7RjLy6P/zhD2nFFVfMAbuuacWiT3j8jfB+8MEH5ybo55133kJ7bgAAALRczWL08nXWWSfXbjekd+/eqa7Zzy688MK81Cfm/L7vvvsWyHECAABAs6vpBgAAgOZI6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAwKIeuvfcc8+00korpQ4dOqQePXqkQw45JE2ePLne7d95553UqlWrOpe//vWvVdvVdfutt966kJ4VAAAALVmzCd3bbrtt+stf/pImTJiQbr/99jRx4sS033771bt9r1690gcffFBjGT58eFpyySXTLrvsUmPb66+/vsZ2gwYNWgjPCAAAgJaubWomTjnllKrLK6+8choyZEgOxxUVFaldu3Zzbd+mTZvUvXv3GuvuvPPOtP/+++fgXV2XLl3m2hYAAAAWmdBd3SeffJJuvvnmtNlmm9UZuOvy/PPPpxdffDFdffXVc9123HHHpSOPPDL16dMnHXPMMenwww/PzczrM3PmzLyUTJs2Lf+NHwBiKTelYyrHY6M8KCMoH3gPwWcMvoNQjirKOMs09phaVVZWVqZm4swzz0xXXXVVmjFjRtp0003TqFGj0rLLLtuo+/74xz9Ojz76aHrttddqrD///PPTdtttlzp27JgefPDBdO6556ZLLrkknXjiifU+1rBhw3JT9dpuueWW/DgAAAC0bJFLDzzwwDR16tTUqVOn8gzd0UR8xIgRDW4zfvz41Ldv33z5448/zrXc7777bg69nTt3zsG7oVrp8OWXX+bB14YOHZpOO+20Brc955xzch/v999//xvVdEcf8ji+hk52U/4CM3r06LTjjjs2umUAixZlBOUD7yH4jMF3EMpRRRlnmciBXbt2nWfobtLm5RGADzvssAa3iSbfJfGEYlljjTVSv379ctB95pln0oABAxp8jNtuuy3/CjF48OB5HtMmm2ySa78jVLdv377ObWJ9XbdFISi3gtCcjo+mp4ygfOA9BJ8x+A5COWpXhlmmscfTpKG7W7dueZkfc+bMyX+r1zjX57rrrstTjjVmX9Hve+mll643cAMAAECLGkht7Nix6bnnnktbbLFFDsQxXVg0FV911VWrarknTZqUtt9++zRy5Mi08cYbV933rbfeSo8//ni677775nrce+65J3300Ue5f3jM/x3NFi688MJ0+umnL9TnBwAAQMvULEJ3DE52xx135EHOvvjii9w/e+edd05nn312VY10tPWPObyjGXl1f/jDH9KKK66YBg4cWGdzgBjNPKYji67tq622WrrsssvSUUcdtdCeGwAAAC1Xswjd66yzThozZkyD2/Tu3TsH59qi5jqWukRwjwUAAACK0LqQRwUAAACEbgAAACiKmm4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAYFEP3XvuuWdaaaWVUocOHVKPHj3SIYcckiZPntzgfT788MO8Xffu3dMSSyyRNthgg3T77bfX2OaTTz5JBx10UOrUqVPq0qVLOuKII9L06dMLfjYAAAAsCppN6N52223TX/7ylzRhwoQcnCdOnJj222+/Bu8zePDgvP3dd9+d/vWvf6V99tkn7b///umFF16o2iYC96uvvppGjx6dRo0alR5//PF09NFHL4RnBAAAQEvXbEL3KaeckjbddNO08sorp8022ywNGTIkPfPMM6mioqLe+zz11FPphBNOSBtvvHHq06dPOvvss3Nt9vPPP59vHz9+fLr//vvT73//+7TJJpukLbbYIl155ZXp1ltvnWctOgAAAMxL29QMRZPwm2++OYfvdu3a1btd3P7nP/857bbbbjlsR035V199lbbZZpt8+9NPP53Xb7TRRlX32WGHHVLr1q3T2LFj0957713n486cOTMvJdOmTct/4weAhn4EaCqlYyrHY6M8KCMoH3gPwWcMvoNQjirKOMs09piaVeg+88wz01VXXZVmzJiRa72jOXhDImR///vfT8suu2xq27Zt6tixY7rzzjvTaqutVtXne7nllqtxn9humWWWybfV56KLLkrDhw+fa/2DDz6Y91Guogk9KCN4D8HnDE3B9xCUD1rae0jk0rIP3dFEfMSIEQ1uE03A+/btmy+fccYZeaCzd999N4fe6LMdwbtVq1Z13nfo0KHps88+Sw899FDq2rVruuuuu3Kf7ieeeCKts846833cZ511Vjr11FNr1HT36tUrDRw4MA/IVo6/wEQh3XHHHRtsGcCiSxlB+cB7CD5j8B2EclRRxlmm1OK5rEP3aaedlg477LAGt4m+2CURnGNZY401Ur9+/XLQjX7dAwYMmOt+MdBa1Iq/8sorae21187r1l133Ry4r7766nTttdfmUc2nTJlS436zZs3Kzdfjtvq0b98+L7VFISi3gtCcjo+mp4ygfOA9BJ8x+A5COWpXhlmmscfTpKG7W7dueZkfc+bMyX+r962uq6o/+mdX16ZNm6r7RliPmvAYWG3DDTfM68aMGZNvj4HVAAAAoMWPXh6DmkWt9YsvvpiblkcwPuCAA9Kqq65aVcs9adKk3Az92WefzdfjcvTd/tGPfpTXRc33pZdempsmDBo0KG8TteU777xzOuqoo/I2//jHP9Lxxx+ffvCDH6SePXs26XMGAACg+WsWoTsGJ7vjjjvS9ttvn9Zcc83cr7t///7pscceq2rmHW39Y07uUg13VPXfd999uSZ9jz32yNuPHDky3XjjjWnXXXeteuwYBT0Cejx2rI9pw37729822XMFAACg5WgWo5fHoGdRu92Q3r17p8rKyhrrVl999XT77bc3eL8YqfyWW25ZIMcJAAAAza6mGwAAAJojoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAIvy6OXlrjRq+rRp01I5iunUYiq1OL6YSg2UEbyH4HMG30MoB76n0pzLSCn/1Z5FqzahewH4/PPP899evXotiIcDAACgGeXBzp0713t7q8p5xXLmac6cOWny5MlpqaWWSq1atSq7Mxa/wMQPAu+//37q1KlTUx8OZUgZQfnAewg+Y/AdhHJUzt9TI0pH4O7Zs2dq3br+nttquheAOMErrrhiKndRSMutoFJelBGUD7yH4DMG30EoR53KNMs0VMNdYiA1AAAAKIjQDQAAAAURuhcB7du3T+eee27+C8oI3kPwOYPvIZQL31NZFMqIgdQAAACgIGq6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKF7EXD11Ven3r17pw4dOqRNNtkkPfvss019SDSRxx9/PO2xxx6pZ8+eqVWrVumuu+6qcXtlZWU655xzUo8ePdLiiy+edthhh/Tmm296vRYBF110Ufrud7+bllpqqbTccsulQYMGpQkTJtTY5quvvkrHHXdcWnbZZdOSSy6Z9t133/TRRx812TGzcP36179O/fv3T506dcrLgAED0t///veq25UPqrv44ovz58zJJ5+sjJANGzYsl4nqS9++fZUPqkyaNCkdfPDB+XtGfA9dZ5110j//+c8W8T1V6G7h/vznP6dTTz01D7M/bty4tO6666addtopTZkypakPjSbwxRdf5DIQP8TU5ZJLLklXXHFFuvbaa9PYsWPTEksskctLfJmmZXvsscdyoH7mmWfS6NGjU0VFRRo4cGAuMyWnnHJKuueee9Jf//rXvP3kyZPTPvvs06THzcKz4oor5iD1/PPP5y9B2223Xdprr73Sq6++mm9XPih57rnn0m9+85v8I011yghrr712+uCDD6qWJ598Uvkg+/TTT9Pmm2+e2rVrl3/Qfe2119Kll16all566ZbxPbWSFm3jjTeuPO6446quz549u7Jnz56VF110UZMeF00v/vvfeeedVdfnzJlT2b1798qf//znVes+++yzyvbt21f+6U9/aqKjpKlMmTIll5HHHnusqiy0a9eu8q9//WvVNuPHj8/bPP30016oRdTSSy9d+fvf/175oMrnn39eufrqq1eOHj26cuutt6486aST8nrvIZx77rmV6667bp0nQvngzDPPrNxiiy3qPRHN/Xuqmu4W7Ouvv841EtH0oqR169b5+tNPP92kx0b5efvtt9OHH35Yo7x07tw5d0lQXhY9U6dOzX+XWWaZ/DfeS6L2u3r5iGaBK620kvKxCJo9e3a69dZbc0uIaGaufFASLWZ22223Gu8VQRkhRFPg6OLWp0+fdNBBB6X33ntP+SC7++6700YbbZS+973v5W5u66+/fvrd737XYr6nCt0t2Mcff5y/GC2//PI11sf1KLRQXalMKC/MmTMn98OMZl7f+c53qsrHYostlrp06eL9ZBH2r3/9K/fnb9++fTrmmGPSnXfemdZaay3lgyx+iImubDFGRG3eQ4hwdMMNN6T7778/jxERIWrLLbdMn3/+ufJB+ve//53Lxeqrr54eeOCBdOyxx6YTTzwx3XjjjVXvIc35e2rbpj4AAMqvpuqVV16p0dcOwpprrplefPHF3BLitttuS4ceemju3w/vv/9+Oumkk/KYEDFwK9S2yy67VF2O/v4RwldeeeX0l7/8JQ+KxaJtzpw5uab7wgsvzNejpju+i0T/7fisae7UdLdgXbt2TW3atJlrdOG43r179yY7LspTqUwoL4u2448/Po0aNSo98sgjeeCs6uUjuqx89tlnNbb3frJoidYOq622Wtpwww1zbWYMzPirX/1K+SA3H49BWjfYYIPUtm3bvMQPMjHoUVyO2ijvIVQXLafWWGON9NZbb3kPIcWI5NFyqrp+/fpVdUFo7t9The4W/uUovhg9/PDDNX5FiuvRBw+qW2WVVfKbVvXyMm3atDw6pPLS8sXYehG4o7nwmDFjcnmoLt5LYkTR6uUjphSLD0PlY9EVnykzZ85UPkjbb7997n4QLSFKS9RaRb/d0mXvIVQ3ffr0NHHixBy2fMaw+eabzzVV6RtvvJFbQ7SE76mal7dwMV1YNMmID7uNN944XX755Xngm8MPP7ypD40m+oCLX5RLoj9VfBmKwbJiQKzox/uzn/0s96eJN7ehQ4fmAU9izmZafpPyW265Jf3tb3/Lc3WX+kfFICXR7C/+HnHEEfk9JcpLzNN8wgkn5A+6TTfdtKkPn4XgrLPOys1D470i+mBGeXn00Udz3zvlg3jfKI0BURLT+cR8u6X13kMWbaeffnraY489coiKKSdjOttokXnAAQd4DyHFlIKbbbZZbl6+//77p2effTb99re/zUuIed2b9ffUph4+neJdeeWVlSuttFLlYostlqcQe+aZZ5z2RdQjjzySp3iqvRx66KFV0zEMHTq0cvnll89TMGy//faVEyZMaOrDZiGoq1zEcv3111dt8+WXX1b++Mc/ztNEdezYsXLvvfeu/OCDD7w+i4gf/vCHlSuvvHL+LOnWrVt+f3jwwQerblc+qK36lGHKCN///vcre/Tokd9DVlhhhXz9rbfeUj6ocs8991R+5zvfyd9B+/btW/nb3/72/93YzL+ntop/mjr4AwAAQEukTzcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwCUmcMOOywNGjSoyfZ/yCGHpAsvvDA1ZzfccEPq0qVLo7a9//7703rrrZfmzJlT+HEBsOgRugFgIWrVqlWDy7Bhw9KvfvWrHBqbwksvvZTuu+++dOKJJ6ZFxc4775zatWuXbr755qY+FABaoLZNfQAAsCj54IMPqi7/+c9/Tuecc06aMGFC1boll1wyL03lyiuvTN/73vea9BiaqnXBFVdckWv5AWBBUtMNAAtR9+7dq5bOnTvn2u3q6yLs1m5evs0226QTTjghnXzyyWnppZdOyy+/fPrd736Xvvjii3T44YenpZZaKq222mrp73//e419vfLKK2mXXXbJjxn3iUD58ccf13tss2fPTrfddlvaY489aqy/5ppr0uqrr546dOiQH2e//farui2aZF900UVplVVWSYsvvnhad91182NU9+qrr6bdd989derUKR/rlltumSZOnFh1//POOy+tuOKKqX379rmZdzT3LnnnnXfyObrjjjvStttumzp27Jj38fTTT9fYR7QMWGmllfLte++9d/rf//43Vw1+3D/2H8ex4YYbpn/+859Vt8dzjuul4wKABUXoBoBm4MYbb0xdu3ZNzz77bA7gxx57bK6R3myzzdK4cePSwIEDc6ieMWNG3v6zzz5L2223XVp//fVzmIwg+9FHH6X999+/3n28/PLLaerUqWmjjTaqWhf3jabmEYyjRj4eZ6uttqq6PQL3yJEj07XXXpvD9SmnnJIOPvjg9Nhjj+XbJ02alLePQD1mzJj0/PPPpx/+8Idp1qxZ+fZoSn/ppZemX/ziF3n/O+20U9pzzz3Tm2++WePYfvrTn6bTTz89vfjii2mNNdZIBxxwQNVjjB07Nh1xxBHp+OOPz7dHuP7Zz35W4/4HHXRQDvbPPfdcPoYhQ4bkJuUlEdjjB4UnnnjiW75SAFBLJQDQJK6//vrKzp07z7X+0EMPrdxrr72qrm+99daVW2yxRdX1WbNmVS6xxBKVhxxySNW6Dz74oDI+1p9++ul8/fzzz68cOHBgjcd9//338zYTJkyo83juvPPOyjZt2lTOmTOnat3tt99e2alTp8pp06bNtf1XX31V2bFjx8qnnnqqxvojjjii8oADDsiXzzrrrMpVVlml8uuvv65znz179qy84IILaqz77ne/W/njH/84X3777bfzMf/+97+vuv3VV1/N68aPH5+vx7523XXXGo/x/e9/v8a5XWqppSpvuOGGyoasv/76lcOGDWtwGwD4ptR0A0Az0L9//6rLbdq0Scsuu2xaZ511qtZFLW2YMmVKVXPqRx55pKqPeCx9+/bNt9XXhPrLL7/MNdLRnLtkxx13TCuvvHLq06dPrkmPwcZKtelvvfVWvhzbVN9P1HyX9hE1z9GcvHqtcsm0adPS5MmT0+abb15jfVwfP358vc+/R48eNZ5rbLvJJpvU2H7AgAE1rp966qnpyCOPTDvssEO6+OKL6zwH0Ty+9NwAYEExkBoANAO1Q2sE4+rrSkG5NO3V9OnTcz/lESNGzPVYpdBaWzRfj9D59ddfp8UWWyyviz7Q0Xz90UcfTQ8++GAe+C1GWI9m2rGPcO+996YVVlihxmNFeC8F2QWhoefaGHHMBx54YD7W6Pt+7rnnpltvvTX3/y755JNPUrdu3RbI8QJAiZpuAGiBNthgg9zHunfv3nmQterLEkssUed9YhCz8Nprr9VY37Zt21xDfMkll+R+1zG4WfTPXmuttXK4fu+99+baR69evapqqKOfdEVFxVz7iwHNevbsmf7xj3/UWB/X47Ebq1+/frlfd3XPPPPMXNtFX/Docx4/Huyzzz7p+uuvr7rtq6++yrXf0QceABYkoRsAWqDjjjsu19zGgGNRKx2B8oEHHsijncco5XWJWt4I608++WTVulGjRuWptKKZ+LvvvpubjkcN85prrplrwWNwswiyMdBb7CNqxWPasbgeYnCzaEb+gx/8IA/KFgOk/fGPf6yaJu2MM87ItfExfVqsiwHOYl8nnXRSo59rDPQWA7zFYGzx+FdddVWNEdCj2XwcR9TWx3OIUB/nJMJ69ZAePyDUbpYOAN+W0A0ALVCpBjkCdoxsHv2/Y8qxLl26pNat6//4j37P0W+7JLaP6bpiJPQIqTFK+Z/+9Ke09tpr59vPP//8NHTo0DyKedy+88475ybcMYVYiL7nUSseTdG33nrrPFVXTHdWai4egTn6W5922mn5GCMs33333XmKssbadNNN82PGSOgxnVjUZJ999tk1+sDHFGKDBw/Otd0xgntMpTZ8+PCqbeI5xQjnMeUYACxIrWI0tQX6iABAsxW1wlGLHTXPi0qtb8xdHs85auJLPxYAwIKiphsAqBIDn0UT8giii4roo37NNdcI3AAUQk03AAAAFERNNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAApGL8/wBBySfqIDialQAAAABJRU5ErkJggg=="
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 13
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-06T20:00:19.181904Z",
-     "start_time": "2026-03-06T20:00:19.009003Z"
+     "end_time": "2026-03-07T00:21:37.667976Z",
+     "start_time": "2026-03-07T00:21:37.620002Z"
     }
    },
    "cell_type": "code",
@@ -876,11 +976,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "use sample_idx=3137\n"
+      "use sample_idx=1128\n"
      ]
     }
    ],
-   "execution_count": 18
+   "execution_count": 12
   },
   {
    "metadata": {
diff --git a/control_model/inference.py b/control_model/inference.py
new file mode 100644
index 0000000..0ad738f
--- /dev/null
+++ b/control_model/inference.py
@@ -0,0 +1,432 @@
+"""
+rnn_inference.py
+================
+Inference and visualisation utilities for the trained RNN model.
+
+Usage examples
+--------------
+# 1. Quick plot – compare real vs predicted on the test split
+python rnn_inference.py --mode plot
+
+# 2. Run inference on a raw dataframe and get unscaled predictions back
+python rnn_inference.py --mode predict
+
+# 3. Step-by-step single-sequence prediction (e.g. live / streaming use)
+python rnn_inference.py --mode single
+"""
+
+import os
+import gc
+import argparse
+import numpy as np
+import pandas as pd
+import torch
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import StandardScaler
+
+# ── project imports (adjust paths if needed) ──────────────────────────────────
+from RNN import RNN
+from RNN_Dataset import RNN_Dataset
+from DataPreprocessing import make_single_df, make_sequence_datasets
+
+# ── constants – must match training ──────────────────────────────────────────
+STATE_COLS   = ["position", "speed"]
+CONTROL_COLS = ["brake_pressed", "accel_position"]
+SEQ_LEN      = 600
+STRIDE       = 100
+BATCH_SIZE   = 128
+HIDDEN_SIZE  = 64
+NUM_LAYERS   = 2
+MODEL_PATH   = "rnn_model.pth"
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# 1.  Model loader
+# ─────────────────────────────────────────────────────────────────────────────
+
+def load_model(model_path: str = MODEL_PATH, device: torch.device = None) -> RNN:
+    """Load a saved RNN from its state-dict file."""
+    if device is None:
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    model = RNN(
+        input_size=len(STATE_COLS),
+        hidden_size=HIDDEN_SIZE,
+        num_layers=NUM_LAYERS,
+        seq_length=SEQ_LEN,
+        output_size=len(CONTROL_COLS),
+    ).to(device)
+
+    model.load_state_dict(torch.load(model_path, map_location=device))
+    model.eval()
+    print(f"[load_model] Loaded '{model_path}' on {device}")
+    return model
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# 2.  Core prediction helpers
+# ─────────────────────────────────────────────────────────────────────────────
+
+def predict_loader(
+    model: RNN,
+    loader: torch.utils.data.DataLoader,
+    scaler: StandardScaler,
+    device: torch.device = None,
+) -> tuple[np.ndarray, np.ndarray]:
+    """
+    Run inference over an entire DataLoader.
+
+    Returns
+    -------
+    y_real : np.ndarray  shape (N, n_controls)  – unscaled ground-truth
+    y_pred : np.ndarray  shape (N, n_controls)  – unscaled predictions
+    """
+    if device is None:
+        device = next(model.parameters()).device
+
+    all_real, all_pred = [], []
+
+    model.eval()
+    with torch.no_grad():
+        for x_batch, y_batch in loader:
+            x_batch = x_batch.to(device)
+            preds   = model(x_batch).cpu().numpy()   # (B, n_controls) scaled
+            real    = y_batch.numpy()                # (B, n_controls) scaled
+            all_pred.append(preds)
+            all_real.append(real)
+
+    y_pred_scaled = np.concatenate(all_pred, axis=0)
+    y_real_scaled = np.concatenate(all_real, axis=0)
+
+    # ── inverse-transform ────────────────────────────────────────────────────
+    # The scaler was fitted on [STATE_COLS + CONTROL_COLS].
+    # Controls occupy the last len(CONTROL_COLS) columns.
+    n_state   = len(STATE_COLS)
+    n_control = len(CONTROL_COLS)
+    n_total   = n_state + n_control
+
+    def _unscale(arr: np.ndarray) -> np.ndarray:
+        """
+        Inverse-transform control outputs regardless of shape:
+          - 2D (N, n_controls)          → return (N, n_controls)
+          - 3D (N, seq_len, n_controls) → flatten, unscale, reshape back
+        """
+        original_shape = arr.shape
+        if arr.ndim == 3:
+            # (N, T, C) → (N*T, C)
+            N, T, C = arr.shape
+            arr_2d = arr.reshape(-1, C)
+        else:
+            arr_2d = arr
+
+        dummy = np.zeros((len(arr_2d), n_total), dtype=np.float32)
+        dummy[:, n_state:] = arr_2d
+        unscaled = scaler.inverse_transform(dummy)[:, n_state:]
+
+        if original_shape.ndim if hasattr(original_shape, 'ndim') else len(original_shape) == 3:
+            unscaled = unscaled.reshape(N, T, C)
+
+        return unscaled
+
+    # Handle both 2D and 3D outputs cleanly
+    def _safe_unscale(arr: np.ndarray) -> np.ndarray:
+        if arr.ndim == 3:
+            N, T, C = arr.shape
+            flat = arr.reshape(-1, C)
+            dummy = np.zeros((len(flat), n_total), dtype=np.float32)
+            dummy[:, n_state:] = flat
+            unscaled_flat = scaler.inverse_transform(dummy)[:, n_state:]
+            return unscaled_flat.reshape(N, T, C)
+        else:
+            dummy = np.zeros((len(arr), n_total), dtype=np.float32)
+            dummy[:, n_state:] = arr
+            return scaler.inverse_transform(dummy)[:, n_state:]
+
+    y_real = _safe_unscale(y_real_scaled)
+    y_pred = _safe_unscale(y_pred_scaled)
+
+    # If 3D (N, seq_len, n_controls), take the last timestep for plotting/metrics
+    # This gives one prediction per sequence — change to [:, 0, :] for first step
+    # or reshape to (N*T, C) if you want every timestep unrolled.
+    if y_pred.ndim == 3:
+        print(f"[predict_loader] Model output is 3D {y_pred.shape} — "
+              f"using last timestep per sequence for y_pred/y_real.")
+        y_real = y_real[:, -1, :]   # (N, n_controls)
+        y_pred = y_pred[:, -1, :]   # (N, n_controls)
+
+    return y_real, y_pred
+
+
+def predict_dataframe(
+    model: RNN,
+    df: pd.DataFrame,
+    scaler: StandardScaler,
+    device: torch.device = None,
+    stride: int = STRIDE,
+    seq_len: int = SEQ_LEN,
+    batch_size: int = BATCH_SIZE,
+) -> tuple[np.ndarray, np.ndarray]:
+    """
+    Run inference directly from a raw (unscaled) DataFrame.
+
+    The function scales the data using the provided scaler, builds sequences,
+    and returns unscaled real + predicted arrays.
+
+    Parameters
+    ----------
+    df       : DataFrame with at least STATE_COLS + CONTROL_COLS columns.
+    scaler   : The fitted StandardScaler from training.
+
+    Returns
+    -------
+    y_real, y_pred : unscaled numpy arrays of shape (N, n_controls)
+    """
+    if device is None:
+        device = next(model.parameters()).device
+
+    features = STATE_COLS + CONTROL_COLS
+    scaled   = scaler.transform(df[features].values.astype(np.float32))
+    scaled_df = pd.DataFrame(scaled, columns=features)
+
+    dataset = RNN_Dataset(scaled_df, STATE_COLS, CONTROL_COLS,
+                          seq_len=seq_len, stride=stride)
+    loader  = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
+                                          shuffle=False)
+
+    return predict_loader(model, loader, scaler, device)
+
+
+def predict_single_sequence(
+    model: RNN,
+    sequence: np.ndarray,          # shape (seq_len, n_states)  – UNSCALED
+    scaler: StandardScaler,
+    device: torch.device = None,
+) -> np.ndarray:
+    """
+    Predict control outputs for ONE sequence (e.g. a live window).
+
+    Parameters
+    ----------
+    sequence : raw (unscaled) state values, shape (seq_len, len(STATE_COLS))
+
+    Returns
+    -------
+    prediction : unscaled control outputs, shape (len(CONTROL_COLS),)
+    """
+    if device is None:
+        device = next(model.parameters()).device
+
+    n_state   = len(STATE_COLS)
+    n_control = len(CONTROL_COLS)
+    n_total   = n_state + n_control
+
+    # Scale the state part only (pad controls with 0 for the scaler)
+    dummy = np.zeros((len(sequence), n_total), dtype=np.float32)
+    dummy[:, :n_state] = sequence
+    scaled_states = scaler.transform(dummy)[:, :n_state]
+
+    x = torch.tensor(scaled_states, dtype=torch.float32).unsqueeze(0).to(device)  # (1, T, n_state)
+
+    model.eval()
+    with torch.no_grad():
+        pred_scaled = model(x).cpu().numpy()[0]  # (n_control,)
+
+    # Unscale prediction
+    dummy_out = np.zeros((1, n_total), dtype=np.float32)
+    dummy_out[0, n_state:] = pred_scaled
+    unscaled = scaler.inverse_transform(dummy_out)[0, n_state:]
+
+    return unscaled
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# 3.  Visualisation
+# ─────────────────────────────────────────────────────────────────────────────
+
+def plot_predictions(
+    y_real: np.ndarray,
+    y_pred: np.ndarray,
+    control_cols: list[str] = CONTROL_COLS,
+    n_samples: int = 500,
+    title_prefix: str = "RNN",
+    save_path: str = None,
+) -> None:
+    """
+    Plot unscaled real vs predicted control outputs side-by-side.
+
+    Parameters
+    ----------
+    y_real, y_pred : outputs from predict_loader / predict_dataframe
+    n_samples      : how many time-steps to display (keeps plots readable)
+    save_path      : if given, save figure to this file instead of showing
+    """
+    n_controls = len(control_cols)
+    xs = np.arange(min(n_samples, len(y_real)))
+
+    fig, axes = plt.subplots(n_controls, 1,
+                             figsize=(14, 4 * n_controls),
+                             sharex=True)
+
+    if n_controls == 1:
+        axes = [axes]
+
+    for i, (ax, col) in enumerate(zip(axes, control_cols)):
+        ax.plot(xs, y_real[:len(xs), i], label="Real",      linewidth=1.2,
+                color="steelblue")
+        ax.plot(xs, y_pred[:len(xs), i], label="Predicted", linewidth=1.2,
+                color="tomato", linestyle="--")
+        ax.set_ylabel(col, fontsize=11)
+        ax.legend(loc="upper right", fontsize=9)
+        ax.grid(True, alpha=0.3)
+
+        # residual shading
+        ax.fill_between(xs,
+                        y_real[:len(xs), i],
+                        y_pred[:len(xs), i],
+                        alpha=0.15, color="orange", label="Error")
+
+    axes[-1].set_xlabel("Sequence index", fontsize=11)
+    fig.suptitle(f"{title_prefix} – Real vs Predicted (unscaled)", fontsize=13)
+    plt.tight_layout()
+
+    if save_path:
+        plt.savefig(save_path, dpi=150)
+        print(f"[plot] Figure saved to '{save_path}'")
+    else:
+        plt.show()
+
+
+def plot_error_distribution(
+    y_real: np.ndarray,
+    y_pred: np.ndarray,
+    control_cols: list[str] = CONTROL_COLS,
+    save_path: str = None,
+) -> None:
+    """Histogram of residuals (real – predicted) for each control output."""
+    n_controls = len(control_cols)
+    fig, axes = plt.subplots(1, n_controls,
+                             figsize=(6 * n_controls, 4))
+    if n_controls == 1:
+        axes = [axes]
+
+    for ax, col, i in zip(axes, control_cols, range(n_controls)):
+        residuals = y_real[:, i] - y_pred[:, i]
+        ax.hist(residuals, bins=60, color="steelblue", edgecolor="white",
+                alpha=0.8)
+        ax.axvline(0, color="red", linestyle="--", linewidth=1.5)
+        ax.set_title(f"{col} – residual distribution", fontsize=11)
+        ax.set_xlabel("Error (real – predicted)")
+        ax.set_ylabel("Count")
+        ax.grid(True, alpha=0.3)
+
+    plt.tight_layout()
+    if save_path:
+        plt.savefig(save_path, dpi=150)
+        print(f"[plot] Figure saved to '{save_path}'")
+    else:
+        plt.show()
+
+
+def print_metrics(y_real: np.ndarray, y_pred: np.ndarray,
+                  control_cols: list[str] = CONTROL_COLS) -> None:
+    """Print MAE, RMSE and max-error per control output."""
+    print("\n── Prediction Metrics (unscaled) ────────────────────────")
+    for i, col in enumerate(control_cols):
+        err  = y_real[:, i] - y_pred[:, i]
+        mae  = np.mean(np.abs(err))
+        rmse = np.sqrt(np.mean(err ** 2))
+        maxe = np.max(np.abs(err))
+        print(f"  {col:25s}  MAE={mae:.4f}  RMSE={rmse:.4f}  MaxErr={maxe:.4f}")
+    print("─" * 55)
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# 4.  Convenience "run everything" function
+# ─────────────────────────────────────────────────────────────────────────────
+
+def evaluate_and_plot(
+    model_path: str = MODEL_PATH,
+    save_fig: str = None,
+) -> tuple[np.ndarray, np.ndarray]:
+    """
+    End-to-end helper:
+      1. Loads data & rebuilds the scaler (same pipeline as training).
+      2. Loads the saved model.
+      3. Runs inference on the test split.
+      4. Prints metrics and shows the comparison plot.
+
+    Returns
+    -------
+    y_real, y_pred  – unscaled arrays you can use for further analysis.
+    """
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    print("[evaluate_and_plot] Loading data …")
+    df = make_single_df()
+    gc.collect()
+
+    _, test_dataset, _, test_loader, scaler = make_sequence_datasets(
+        df, STATE_COLS, CONTROL_COLS,
+        seq_len=SEQ_LEN, stride=STRIDE, batch_size=BATCH_SIZE
+    )
+    del df
+    gc.collect()
+
+    model  = load_model(model_path, device)
+    y_real, y_pred = predict_loader(model, test_loader, scaler, device)
+
+    print_metrics(y_real, y_pred)
+    plot_predictions(y_real, y_pred, save_path=save_fig)
+    plot_error_distribution(y_real, y_pred)
+
+    return y_real, y_pred
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# 5.  CLI entry-point
+# ─────────────────────────────────────────────────────────────────────────────
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="RNN inference & visualisation")
+    parser.add_argument("--mode", choices=["plot", "predict", "single"],
+                        default="plot",
+                        help="plot  – evaluate on test split and show plots\n"
+                             "predict – return predictions from a raw df\n"
+                             "single  – demo of single-sequence inference")
+    parser.add_argument("--model", default=MODEL_PATH,
+                        help="Path to saved model state-dict (.pth)")
+    parser.add_argument("--save_fig", default=None,
+                        help="Optional path to save the comparison figure")
+    args = parser.parse_args()
+
+    if args.mode == "plot":
+        evaluate_and_plot(model_path=args.model, save_fig=args.save_fig)
+
+    elif args.mode == "predict":
+        # Example: load your own df here and call predict_dataframe
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        df     = make_single_df()
+        _, _, _, _, scaler = make_sequence_datasets(
+            df, STATE_COLS, CONTROL_COLS,
+            seq_len=SEQ_LEN, stride=STRIDE, batch_size=BATCH_SIZE
+        )
+        model  = load_model(args.model, device)
+        y_real, y_pred = predict_dataframe(model, df, scaler, device)
+        print_metrics(y_real, y_pred)
+        plot_predictions(y_real, y_pred, save_path=args.save_fig)
+
+    elif args.mode == "single":
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        df     = make_single_df()
+        _, _, _, _, scaler = make_sequence_datasets(
+            df, STATE_COLS, CONTROL_COLS,
+            seq_len=SEQ_LEN, stride=STRIDE, batch_size=BATCH_SIZE
+        )
+        model = load_model(args.model, device)
+
+        # Grab one raw window as a demo
+        raw_window = df[STATE_COLS].values[:SEQ_LEN].astype(np.float32)
+        pred = predict_single_sequence(model, raw_window, scaler, device)
+        print(f"\n[single] Predicted controls for first {SEQ_LEN} steps:")
+        for col, val in zip(CONTROL_COLS, pred):
+            print(f"  {col}: {val:.4f}")
\ No newline at end of file

From 6e42158ea90c5f4a3b5b3ea1ffbc6bdedd7bb2ed Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Sat, 7 Mar 2026 11:16:33 -0800
Subject: [PATCH 32/49] retrain model, created python script

---
 control_model/DataPreprocessing.py        |  31 +-
 control_model/RNN_Training.py             |  33 +--
 control_model/control_model_revised.ipynb | 137 ++++++---
 control_model/inference.py                | 341 ++++++++++++++++------
 4 files changed, 358 insertions(+), 184 deletions(-)

diff --git a/control_model/DataPreprocessing.py b/control_model/DataPreprocessing.py
index 6422b1c..89d1657 100644
--- a/control_model/DataPreprocessing.py
+++ b/control_model/DataPreprocessing.py
@@ -1,22 +1,10 @@
-import pandas as pd
-import torch
-from torch import nn
 from torch.utils.data import DataLoader
 from sklearn.preprocessing import StandardScaler
 from RNN_Dataset import RNN_Dataset
 #necessary imports
-
-
+from sklearn.preprocessing import MinMaxScaler
 from data_tools import query
-from data_tools.collections import TimeSeries
-import matplotlib.pyplot as plt
 import pandas as pd
-import dill
-import os
-import pytz
-from datetime import datetime, time, date
-
-
 import os
 import dill
 
@@ -26,7 +14,6 @@ def combine_dfs(telemetry_names, index_common, all_dfs):
     combined_df.dropna()
 
     for name, df in zip(telemetry_names, all_dfs):
-        # df_interp = self.resample(df, index_common)
         combined_df[name] = df
 
     return combined_df
@@ -74,27 +61,29 @@ def make_single_df():
     # unnpack
     mech_brake_pressed, accel_position, speed_kph = loaded_datasets
     df_mech_brake_pressed = pd.DataFrame(mech_brake_pressed, index=mech_brake_pressed.datetime_x_axis)
-    df_accel_position = pd.DataFrame(accel_position, index=accel_position.datetime_x_axis)
-    pos_df = make_df(source="localization", name="TrackIndex")
+   # df_accel_position = pd.DataFrame(accel_position, index=accel_position.datetime_x_axis)
+
+    scaler = MinMaxScaler(feature_range=(0, 1))
+    df_accel_position = scaler.fit_transform(accel_position.reshape(-1, 1))
+   # pos_df = make_df(source="localization", name="TrackIndex")
     speed_df = make_df(source="ingress", name="VehicleVelocity")
     all_dfs = [df_mech_brake_pressed, df_accel_position]
-    combined_df = combine_dfs(["mech_brake_pressed", "accel_position"], df_mech_brake_pressed.index, all_dfs)
     final_df = pd.merge_asof(
         df_mech_brake_pressed.sort_index(),
-        df_accel_position.sort_index(),
+        pd.DataFrame(df_accel_position, index = accel_position.datetime_x_axis),
         left_index=True,
         right_index=True,
         direction="nearest"
     )
-    dfs = pd.concat([pos_df, speed_df], axis=1)
+    # dfs = pd.concat([pos_df, speed_df], axis=1)
     final_df = pd.merge_asof(
         final_df.sort_index(),
-        dfs.sort_index(),
+        speed_df.sort_index(),
         left_index=True,
         right_index=True,
         direction="nearest"
     )
-    final_df.columns = ["brake_pressed", "accel_position", "position", "speed"]
+    final_df.columns = ["brake_pressed", "accel_position", "speed"]
     final_df = final_df.sort_index()
     final_df = final_df.ffill().dropna()
     return final_df
diff --git a/control_model/RNN_Training.py b/control_model/RNN_Training.py
index 802e034..169dae4 100644
--- a/control_model/RNN_Training.py
+++ b/control_model/RNN_Training.py
@@ -1,23 +1,13 @@
 #necessary imports
-import os
-import gc
 import torch
-import torch.nn as nn
-from torch.utils.data import Dataset, DataLoader
-import pandas as pd
-import numpy as np
-from sklearn.preprocessing import StandardScaler
-from RNN_Dataset import RNN_Dataset
+from torch import *
+from torch import nn
+import gc
 from RNN import RNN
 from DataPreprocessing import *
-from data_tools import query
-from data_tools.collections import TimeSeries
+import numpy as np
 import matplotlib.pyplot as plt
 import pandas as pd
-import dill
-import os
-import pytz
-from datetime import datetime, time, date
 
 
 #create training loop
@@ -27,7 +17,7 @@ def train_model(model, train_loader, test_loader, epochs):
     criterion = nn.MSELoss() #mean squared error loss
     optimizer = torch.optim.Adam(model.parameters(), lr=1e-4, weight_decay=1e-5)
     train_losses, test_losses = [], []
-    for epoch in range(epochs):
+    for epoch in range(1,epochs):
 
         model.train()
         train_loss = 0.0
@@ -47,7 +37,7 @@ def train_model(model, train_loader, test_loader, epochs):
                 continue
 
             loss.backward()
-            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
+            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) # clips gradients so they don't become too large
             optimizer.step()
             train_loss += loss.item()
 
@@ -71,7 +61,7 @@ def train_model(model, train_loader, test_loader, epochs):
         print(f"Epoch {epoch+1}/{epochs} | "
               f"Train Loss: {train_loss:.4f} | Test Loss: {test_loss:.4f}")
 
-        # Free memory each epoch
+        #free memory after each epoch
         gc.collect()
 
     return train_losses, test_losses
@@ -81,12 +71,12 @@ def train_model(model, train_loader, test_loader, epochs):
 if __name__ == "__main__":
 
     # requirements
-    STATE_COLS   = ["position", "speed"] #states
+    STATE_COLS   = ["speed"] #states
     CONTROL_COLS = ["brake_pressed", "accel_position"]  #controls
-    SEQ_LEN      = 100 #one minute
+    SEQ_LEN      = 300 #30 seconds
     STRIDE       = 100 #sliding window change between consecutive sequences, reduces overlapping
     BATCH_SIZE   = 128
-    EPOCHS       = 50
+    EPOCHS       = 40
     HIDDEN_SIZE  = 128
     NUM_LAYERS   = 2
 
@@ -104,8 +94,7 @@ def train_model(model, train_loader, test_loader, epochs):
         df, STATE_COLS, CONTROL_COLS,
         seq_len=SEQ_LEN, stride=STRIDE, batch_size=BATCH_SIZE
     )
-
-    # Free original dataframe — no longer needed
+    #delete stuff not required
     del df
     gc.collect()
 
diff --git a/control_model/control_model_revised.ipynb b/control_model/control_model_revised.ipynb
index 728bf32..ad39dbe 100644
--- a/control_model/control_model_revised.ipynb
+++ b/control_model/control_model_revised.ipynb
@@ -725,8 +725,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-07T00:01:52.509885Z",
-     "start_time": "2026-03-07T00:01:39.896894Z"
+     "end_time": "2026-03-07T01:09:51.506069Z",
+     "start_time": "2026-03-07T01:09:48.356974Z"
     }
    },
    "cell_type": "code",
@@ -755,13 +755,13 @@
    ],
    "id": "466f94729ef1fb60",
    "outputs": [],
-   "execution_count": 2
+   "execution_count": 1
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-07T00:02:18.237680Z",
-     "start_time": "2026-03-07T00:02:17.983620Z"
+     "end_time": "2026-03-07T01:09:53.990211Z",
+     "start_time": "2026-03-07T01:09:53.509856Z"
     }
    },
    "cell_type": "code",
@@ -790,18 +790,18 @@
      "traceback": [
       "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
       "\u001B[31mKeyError\u001B[39m                                  Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[4]\u001B[39m\u001B[32m, line 7\u001B[39m\n\u001B[32m      2\u001B[39m device = torch.device(\u001B[33m\"\u001B[39m\u001B[33mcuda\u001B[39m\u001B[33m\"\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m torch.cuda.is_available() \u001B[38;5;28;01melse\u001B[39;00m \u001B[33m\"\u001B[39m\u001B[33mcpu\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m      4\u001B[39m checkpoint = torch.load(\u001B[33m'\u001B[39m\u001B[33mrnn_model.pth\u001B[39m\u001B[33m'\u001B[39m, map_location=device)\n\u001B[32m      6\u001B[39m model = RNN(\n\u001B[32m----> \u001B[39m\u001B[32m7\u001B[39m     input_size  = \u001B[43mcheckpoint\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43minput_size\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m]\u001B[49m,\n\u001B[32m      8\u001B[39m     hidden_size = checkpoint[\u001B[33m'\u001B[39m\u001B[33mhidden_size\u001B[39m\u001B[33m'\u001B[39m],\n\u001B[32m      9\u001B[39m     num_layers  = checkpoint[\u001B[33m'\u001B[39m\u001B[33mnum_layers\u001B[39m\u001B[33m'\u001B[39m],\n\u001B[32m     10\u001B[39m     seq_length  = checkpoint[\u001B[33m'\u001B[39m\u001B[33mseq_length\u001B[39m\u001B[33m'\u001B[39m],\n\u001B[32m     11\u001B[39m     output_size = checkpoint[\u001B[33m'\u001B[39m\u001B[33moutput_size\u001B[39m\u001B[33m'\u001B[39m],\n\u001B[32m     12\u001B[39m )\n\u001B[32m     13\u001B[39m model.load_state_dict(checkpoint[\u001B[33m'\u001B[39m\u001B[33mmodel_state_dict\u001B[39m\u001B[33m'\u001B[39m])\n\u001B[32m     14\u001B[39m model.eval()  \u001B[38;5;66;03m# important — disables dropout/batchnorm for inference\u001B[39;00m\n",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[2]\u001B[39m\u001B[32m, line 7\u001B[39m\n\u001B[32m      2\u001B[39m device = torch.device(\u001B[33m\"\u001B[39m\u001B[33mcuda\u001B[39m\u001B[33m\"\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m torch.cuda.is_available() \u001B[38;5;28;01melse\u001B[39;00m \u001B[33m\"\u001B[39m\u001B[33mcpu\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m      4\u001B[39m checkpoint = torch.load(\u001B[33m'\u001B[39m\u001B[33mrnn_model.pth\u001B[39m\u001B[33m'\u001B[39m, map_location=device)\n\u001B[32m      6\u001B[39m model = RNN(\n\u001B[32m----> \u001B[39m\u001B[32m7\u001B[39m     input_size  = \u001B[43mcheckpoint\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43minput_size\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m]\u001B[49m,\n\u001B[32m      8\u001B[39m     hidden_size = checkpoint[\u001B[33m'\u001B[39m\u001B[33mhidden_size\u001B[39m\u001B[33m'\u001B[39m],\n\u001B[32m      9\u001B[39m     num_layers  = checkpoint[\u001B[33m'\u001B[39m\u001B[33mnum_layers\u001B[39m\u001B[33m'\u001B[39m],\n\u001B[32m     10\u001B[39m     seq_length  = checkpoint[\u001B[33m'\u001B[39m\u001B[33mseq_length\u001B[39m\u001B[33m'\u001B[39m],\n\u001B[32m     11\u001B[39m     output_size = checkpoint[\u001B[33m'\u001B[39m\u001B[33moutput_size\u001B[39m\u001B[33m'\u001B[39m],\n\u001B[32m     12\u001B[39m )\n\u001B[32m     13\u001B[39m model.load_state_dict(checkpoint[\u001B[33m'\u001B[39m\u001B[33mmodel_state_dict\u001B[39m\u001B[33m'\u001B[39m])\n\u001B[32m     14\u001B[39m model.eval()  \u001B[38;5;66;03m# important — disables dropout/batchnorm for inference\u001B[39;00m\n",
       "\u001B[31mKeyError\u001B[39m: 'input_size'"
      ]
     }
    ],
-   "execution_count": 4
+   "execution_count": 2
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-07T00:14:11.977176Z",
-     "start_time": "2026-03-07T00:14:11.958258Z"
+     "end_time": "2026-03-07T01:31:21.448982Z",
+     "start_time": "2026-03-07T01:31:21.436345Z"
     }
    },
    "cell_type": "code",
@@ -835,7 +835,7 @@
     "    # scaler was fit on state_cols + control_cols so controls start at index n_states\n",
     "    n_states = len(state_cols)\n",
     "    n_controls = len(control_cols)\n",
-    "    seq_len = 600\n",
+    "    seq_len = 100\n",
     "    def unscale_states(arr):\n",
     "        state_mean = scaler.mean_[:n_states]\n",
     "        state_std  = scaler.scale_[:n_states]\n",
@@ -893,8 +893,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-07T00:21:51.074226Z",
-     "start_time": "2026-03-07T00:21:47.518973Z"
+     "end_time": "2026-03-07T01:33:31.616070Z",
+     "start_time": "2026-03-07T01:33:28.387342Z"
     }
    },
    "cell_type": "code",
@@ -902,10 +902,10 @@
     "from inference import load_model\n",
     "STATE_COLS   = [\"position\", \"speed\"]\n",
     "CONTROL_COLS = [\"brake_pressed\", \"accel_position\"]\n",
-    "SEQ_LEN      = 600\n",
+    "SEQ_LEN      = 100\n",
     "STRIDE       = 100\n",
     "BATCH_SIZE   = 128\n",
-    "HIDDEN_SIZE  = 64\n",
+    "HIDDEN_SIZE  = 128\n",
     "NUM_LAYERS   = 2\n",
     "MODEL_PATH   = \"rnn_model.pth\"\n",
     "from DataPreprocessing import make_single_df, make_sequence_datasets\n",
@@ -915,8 +915,6 @@
     "    df, STATE_COLS, CONTROL_COLS, seq_len=SEQ_LEN, stride=STRIDE, batch_size=BATCH_SIZE\n",
     ")\n",
     "model = load_model(\"rnn_model.pth\")\n",
-    "\n",
-    "# ← change this number to any sequence you want to inspect\n",
     "plot_control_trajectory(model, test_dataset, scaler, state_cols=STATE_COLS, control_cols=CONTROL_COLS, sample_idx=1128)"
    ],
    "id": "dae568af2bf31f42",
@@ -926,10 +924,10 @@
      "output_type": "stream",
      "text": [
       "[load_model] Loaded 'rnn_model.pth' on cpu\n",
-      "torch.Size([600, 2])\n",
-      "(600, 2)\n",
-      "(600, 2)\n",
-      "(600,)\n"
+      "torch.Size([100, 2])\n",
+      "(100, 2)\n",
+      "(100, 2)\n",
+      "(100,)\n"
      ]
     },
     {
@@ -937,7 +935,7 @@
       "text/plain": [
        "<Figure size 1000x800 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -947,19 +945,19 @@
       "text/plain": [
        "<Figure size 1000x800 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 13
+   "execution_count": 14
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-07T00:21:37.667976Z",
-     "start_time": "2026-03-07T00:21:37.620002Z"
+     "end_time": "2026-03-07T01:35:29.221747Z",
+     "start_time": "2026-03-07T01:35:29.178057Z"
     }
    },
    "cell_type": "code",
@@ -976,24 +974,24 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "use sample_idx=1128\n"
+      "use sample_idx=1060\n"
      ]
     }
    ],
-   "execution_count": 12
+   "execution_count": 15
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-06T20:00:28.828883Z",
-     "start_time": "2026-03-06T20:00:25.979260Z"
+     "end_time": "2026-03-07T01:33:20.579626Z",
+     "start_time": "2026-03-07T01:33:19.725323Z"
     }
    },
    "cell_type": "code",
    "source": [
     "state_cols = ['position', 'speed']\n",
     "control_cols = ['brake_pressed', 'accel_position']\n",
-    "plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx=3137)\n",
+    "plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx=1060)\n",
     "plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx=150)"
    ],
    "id": "7ca721093c670d9",
@@ -1002,10 +1000,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "torch.Size([1000, 2])\n",
-      "(1000, 2)\n",
-      "(1000, 2)\n",
-      "(1000,)\n"
+      "torch.Size([100, 2])\n",
+      "(100, 2)\n",
+      "(100, 2)\n",
+      "(100,)\n"
      ]
     },
     {
@@ -1013,7 +1011,7 @@
       "text/plain": [
        "<Figure size 1000x800 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -1023,7 +1021,7 @@
       "text/plain": [
        "<Figure size 1000x800 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -1032,10 +1030,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "torch.Size([1000, 2])\n",
-      "(1000, 2)\n",
-      "(1000, 2)\n",
-      "(1000,)\n"
+      "torch.Size([100, 2])\n",
+      "(100, 2)\n",
+      "(100, 2)\n",
+      "(100,)\n"
      ]
     },
     {
@@ -1043,7 +1041,7 @@
       "text/plain": [
        "<Figure size 1000x800 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -1053,13 +1051,13 @@
       "text/plain": [
        "<Figure size 1000x800 with 2 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 19
+   "execution_count": 13
   },
   {
    "metadata": {},
@@ -1081,12 +1079,59 @@
    "execution_count": null
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-07T01:59:15.751207Z",
+     "start_time": "2026-03-07T01:59:11.703937Z"
+    }
+   },
    "cell_type": "code",
-   "source": "",
+   "source": "df = make_single_df()",
    "id": "2bb7b192d6146b75",
    "outputs": [],
-   "execution_count": null
+   "execution_count": 17
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-07T01:59:32.418943Z",
+     "start_time": "2026-03-07T01:59:31.324325Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plt.plot(df[\"accel_position\"])",
+   "id": "202c073915027e7f",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1db83471fa0>]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 18
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "a132cf26072c3309"
   }
  ],
  "metadata": {
diff --git a/control_model/inference.py b/control_model/inference.py
index 0ad738f..73c6455 100644
--- a/control_model/inference.py
+++ b/control_model/inference.py
@@ -1,20 +1,3 @@
-"""
-rnn_inference.py
-================
-Inference and visualisation utilities for the trained RNN model.
-
-Usage examples
---------------
-# 1. Quick plot – compare real vs predicted on the test split
-python rnn_inference.py --mode plot
-
-# 2. Run inference on a raw dataframe and get unscaled predictions back
-python rnn_inference.py --mode predict
-
-# 3. Step-by-step single-sequence prediction (e.g. live / streaming use)
-python rnn_inference.py --mode single
-"""
-
 import os
 import gc
 import argparse
@@ -23,29 +6,22 @@
 import torch
 import matplotlib.pyplot as plt
 from sklearn.preprocessing import StandardScaler
-
-# ── project imports (adjust paths if needed) ──────────────────────────────────
 from RNN import RNN
 from RNN_Dataset import RNN_Dataset
 from DataPreprocessing import make_single_df, make_sequence_datasets
 
-# ── constants – must match training ──────────────────────────────────────────
+#constants
 STATE_COLS   = ["position", "speed"]
 CONTROL_COLS = ["brake_pressed", "accel_position"]
-SEQ_LEN      = 600
+SEQ_LEN      = 300
 STRIDE       = 100
 BATCH_SIZE   = 128
-HIDDEN_SIZE  = 64
+HIDDEN_SIZE  = 128
 NUM_LAYERS   = 2
 MODEL_PATH   = "rnn_model.pth"
 
 
-# ─────────────────────────────────────────────────────────────────────────────
-# 1.  Model loader
-# ─────────────────────────────────────────────────────────────────────────────
-
-def load_model(model_path: str = MODEL_PATH, device: torch.device = None) -> RNN:
-    """Load a saved RNN from its state-dict file."""
+def load_model(model_path: str = MODEL_PATH, device: torch.device = None):
     if device is None:
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
@@ -63,24 +39,14 @@ def load_model(model_path: str = MODEL_PATH, device: torch.device = None) -> RNN
     return model
 
 
-# ─────────────────────────────────────────────────────────────────────────────
-# 2.  Core prediction helpers
-# ─────────────────────────────────────────────────────────────────────────────
 
 def predict_loader(
     model: RNN,
     loader: torch.utils.data.DataLoader,
     scaler: StandardScaler,
     device: torch.device = None,
-) -> tuple[np.ndarray, np.ndarray]:
-    """
-    Run inference over an entire DataLoader.
-
-    Returns
-    -------
-    y_real : np.ndarray  shape (N, n_controls)  – unscaled ground-truth
-    y_pred : np.ndarray  shape (N, n_controls)  – unscaled predictions
-    """
+):
+    #returns rescaled real y values and predicted, given the input RNN and scaler.
     if device is None:
         device = next(model.parameters()).device
 
@@ -90,30 +56,22 @@ def predict_loader(
     with torch.no_grad():
         for x_batch, y_batch in loader:
             x_batch = x_batch.to(device)
-            preds   = model(x_batch).cpu().numpy()   # (B, n_controls) scaled
-            real    = y_batch.numpy()                # (B, n_controls) scaled
+            preds   = model(x_batch).cpu().numpy()
+            real    = y_batch.numpy()
             all_pred.append(preds)
             all_real.append(real)
 
     y_pred_scaled = np.concatenate(all_pred, axis=0)
     y_real_scaled = np.concatenate(all_real, axis=0)
 
-    # ── inverse-transform ────────────────────────────────────────────────────
-    # The scaler was fitted on [STATE_COLS + CONTROL_COLS].
-    # Controls occupy the last len(CONTROL_COLS) columns.
+    # perform inverse transform
     n_state   = len(STATE_COLS)
     n_control = len(CONTROL_COLS)
     n_total   = n_state + n_control
 
     def _unscale(arr: np.ndarray) -> np.ndarray:
-        """
-        Inverse-transform control outputs regardless of shape:
-          - 2D (N, n_controls)          → return (N, n_controls)
-          - 3D (N, seq_len, n_controls) → flatten, unscale, reshape back
-        """
         original_shape = arr.shape
         if arr.ndim == 3:
-            # (N, T, C) → (N*T, C)
             N, T, C = arr.shape
             arr_2d = arr.reshape(-1, C)
         else:
@@ -165,7 +123,7 @@ def predict_dataframe(
     stride: int = STRIDE,
     seq_len: int = SEQ_LEN,
     batch_size: int = BATCH_SIZE,
-) -> tuple[np.ndarray, np.ndarray]:
+):
     """
     Run inference directly from a raw (unscaled) DataFrame.
 
@@ -247,7 +205,7 @@ def plot_predictions(
     y_real: np.ndarray,
     y_pred: np.ndarray,
     control_cols: list[str] = CONTROL_COLS,
-    n_samples: int = 500,
+    n_samples: int = 5000,
     title_prefix: str = "RNN",
     save_path: str = None,
 ) -> None:
@@ -283,7 +241,7 @@ def plot_predictions(
         ax.fill_between(xs,
                         y_real[:len(xs), i],
                         y_pred[:len(xs), i],
-                        alpha=0.15, color="orange", label="Error")
+                        alpha=0.15, color="red", label="Error")
 
     axes[-1].set_xlabel("Sequence index", fontsize=11)
     fig.suptitle(f"{title_prefix} – Real vs Predicted (unscaled)", fontsize=13)
@@ -382,51 +340,244 @@ def evaluate_and_plot(
     return y_real, y_pred
 
 
-# ─────────────────────────────────────────────────────────────────────────────
-# 5.  CLI entry-point
-# ─────────────────────────────────────────────────────────────────────────────
+def get_sequence_timestamps(test_dataset, sample_idx, df_raw,
+                            seq_len=SEQ_LEN, stride=STRIDE,
+                            timestamp_col="timestamp"):
+    """
+    Get the timestamps corresponding to a sequence from test_dataset.
 
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="RNN inference & visualisation")
-    parser.add_argument("--mode", choices=["plot", "predict", "single"],
-                        default="plot",
-                        help="plot  – evaluate on test split and show plots\n"
-                             "predict – return predictions from a raw df\n"
-                             "single  – demo of single-sequence inference")
-    parser.add_argument("--model", default=MODEL_PATH,
-                        help="Path to saved model state-dict (.pth)")
-    parser.add_argument("--save_fig", default=None,
-                        help="Optional path to save the comparison figure")
-    args = parser.parse_args()
-
-    if args.mode == "plot":
-        evaluate_and_plot(model_path=args.model, save_fig=args.save_fig)
-
-    elif args.mode == "predict":
-        # Example: load your own df here and call predict_dataframe
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        df     = make_single_df()
-        _, _, _, _, scaler = make_sequence_datasets(
-            df, STATE_COLS, CONTROL_COLS,
-            seq_len=SEQ_LEN, stride=STRIDE, batch_size=BATCH_SIZE
+    Parameters
+    ----------
+    test_dataset : RNN_Dataset (test split)
+    sample_idx   : which sequence you're inspecting
+    df_raw       : the original unprocessed dataframe (with timestamp column)
+    timestamp_col: name of the datetime column in df_raw
+
+    Returns
+    -------
+    timestamps : pd.Series of timestamps for that sequence window
+    start_row  : integer row index in df_raw where the sequence starts
+    end_row    : integer row index in df_raw where the sequence ends
+    """
+    # ── how many sequences are in the train split? ─────────────────────────
+    # test_dataset starts after the train split in df_raw.
+    # make_sequence_datasets does an 80/20 split on df_raw rows first,
+    # so the test portion starts at this row:
+    n_total = len(df_raw)
+    train_rows = int(n_total * 0.8)  # must match your split ratio
+
+    # within the test portion, sequence i starts at row: i * stride
+    seq_start_in_test = sample_idx * stride
+    start_row = train_rows + seq_start_in_test
+    end_row = start_row + seq_len
+
+    if end_row > n_total:
+        raise IndexError(
+            f"sample_idx {sample_idx} goes out of bounds "
+            f"(start_row={start_row}, df length={n_total})"
         )
-        model  = load_model(args.model, device)
-        y_real, y_pred = predict_dataframe(model, df, scaler, device)
-        print_metrics(y_real, y_pred)
-        plot_predictions(y_real, y_pred, save_path=args.save_fig)
 
-    elif args.mode == "single":
+    timestamps = df_raw[timestamp_col].iloc[start_row:end_row].reset_index(drop=True)
+
+    print(f"Sequence {sample_idx}")
+    print(f"  df rows  : {start_row} → {end_row}")
+    print(f"  start    : {timestamps.iloc[0]}")
+    print(f"  end      : {timestamps.iloc[-1]}")
+    print(f"  duration : {timestamps.iloc[-1] - timestamps.iloc[0]}")
+
+    return timestamps, start_row, end_row
+
+
+
+import matplotlib.pyplot as plt
+import numpy as np
+def plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx):
+    """
+    sample_idx: int or list of ints
+    """
+    # Normalize sample_idx to always be a list
+    if isinstance(sample_idx, int):
+        sample_idx = [sample_idx]
+
+    model.eval()
+    device = next(model.parameters()).device
+    n_states   = len(state_cols)
+    n_controls = len(control_cols)
+    seq_len    = 300
+
+    def unscale_states(arr):
+        state_mean = scaler.mean_[:n_states]
+        state_std  = scaler.scale_[:n_states]
+        return arr * state_std + state_mean
+
+    def unscale_controls(arr):
+        dummy = np.zeros((seq_len, n_states + n_controls))
+        dummy[:, n_states:] = arr
+        return scaler.inverse_transform(dummy)[:, n_states:]
+
+    time_controls = np.arange(seq_len) * 0.1
+
+    # ── Collect predictions for all samples ──────────────────────────────────
+    results = []
+    for idx in sample_idx:
+        x_input, y_target = test_dataset[idx]
+
+        with torch.no_grad():
+            x_tensor = x_input.to(device).unsqueeze(0)
+            y_pred   = model(x_tensor).squeeze(0).cpu().numpy()
+
+        x_np     = x_input.numpy()
+        y_target = y_target.numpy()
+
+        results.append({
+            "idx":       idx,
+            "x_np":      x_np,
+            "y_target":  unscale_controls(y_target),
+            "y_pred":    unscale_controls(y_pred),
+            "x_unscaled": unscale_states(x_np),
+        })
+
+    n_samples      = len(results)
+    time_states    = np.arange(results[0]["x_np"].shape[0]) * 0.1
+
+    # ── Plot controls ─────────────────────────────────────────────────────────
+    fig, axes = plt.subplots(
+        n_controls, n_samples,
+        figsize=(8 * n_samples, 4 * n_controls),
+        sharex=True, sharey="row",
+        squeeze=False,          # always 2-D array of axes
+    )
+
+    for col_j, r in enumerate(results):
+        for row_i, col_name in enumerate(control_cols):
+            ax = axes[row_i, col_j]
+            ax.plot(time_controls, r["y_target"][:, row_i], "g-",  label="Actual (Driver)")
+            ax.plot(time_controls, r["y_pred"][:,   row_i], "r--", label="Predicted (RNN)")
+            ax.set_ylabel(col_name)
+            ax.grid(True)
+            if row_i == 0:
+                ax.set_title(f"Sample {r['idx']}")
+            if row_i == n_controls - 1:
+                ax.set_xlabel("Time (seconds)")
+            if col_j == 0:
+                ax.legend()
+
+    fig.suptitle("Control Trajectories", y=1.01)
+    plt.tight_layout()
+    plt.show()
+
+    # ── Plot states ───────────────────────────────────────────────────────────
+    fig2, axes2 = plt.subplots(
+        n_states, n_samples,
+        figsize=(8 * n_samples, 4 * n_states),
+        sharex=True, sharey="row",
+        squeeze=False,
+    )
+
+    for col_j, r in enumerate(results):
+        for row_i, col_name in enumerate(state_cols):
+            ax = axes2[row_i, col_j]
+            ax.plot(time_states, r["x_unscaled"][:, row_i], "b-", label=col_name)
+            ax.set_ylabel(col_name)
+            ax.grid(True)
+            if row_i == 0:
+                ax.set_title(f"Sample {r['idx']}")
+            if row_i == n_states - 1:
+                ax.set_xlabel("Time (seconds)")
+            if col_j == 0:
+                ax.legend()
+
+    fig2.suptitle("Input State Sequences", y=1.01)
+    plt.tight_layout()
+    plt.show()
+#
+# def plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx):
+#     model.eval()
+#     x_input, y_target = test_dataset[sample_idx]
+#
+# # x_input should be [seq_len, n_states]
+#     print(x_input.shape)
+#
+#     x_np = x_input.numpy()  # [seq_len, n_states]
+#
+#     # derive time axes from actual array shapes, not seq_len variable
+#     time_controls = np.arange(y_target.shape[0]) * 0.1
+#     time_states   = np.arange(x_np.shape[0]) * 0.1
+#     x_input, y_target = test_dataset[sample_idx]
+#
+#     with torch.no_grad():
+#         device = next(model.parameters()).device
+#         x_tensor = x_input.to(device).unsqueeze(0)
+#         y_pred = model(x_tensor).squeeze(0).cpu().numpy()
+#
+#     y_target = y_target.numpy()
+#     x_np = x_input.numpy()  # [seq_len, n_states]
+#
+#     # inverse transform controls
+#     # scaler was fit on state_cols + control_cols so controls start at index n_states
+#     n_states = len(state_cols)
+#     n_controls = len(control_cols)
+#     seq_len = 300
+#     def unscale_states(arr):
+#         state_mean = scaler.mean_[:n_states]
+#         state_std  = scaler.scale_[:n_states]
+#         return arr * state_std + state_mean
+#     def unscale_controls(arr):
+#         dummy = np.zeros((seq_len, n_states + n_controls))
+#         dummy[:, n_states:] = arr
+#         return scaler.inverse_transform(dummy)[:, n_states:]
+#
+#     y_target_unscaled = unscale_controls(y_target)
+#     y_pred_unscaled   = unscale_controls(y_pred)
+#     x_unscaled        = unscale_states(x_np)
+#
+#     time_controls = np.arange(seq_len) * 0.1
+#     time_states   = np.arange(x_np.shape[0]) * 0.1
+#
+#     # plot controls
+#     fig, axes = plt.subplots(n_controls, 1, figsize=(10, 4 * n_controls), sharex=True)
+#     if n_controls == 1:
+#         axes = [axes]
+#
+#     for i, col in enumerate(control_cols):
+#         axes[i].plot(time_controls, y_target_unscaled[:, i], 'g-', label='Actual (Driver)')
+#         axes[i].plot(time_controls, y_pred_unscaled[:, i], 'r--', label='Predicted (RNN)')
+#         axes[i].set_ylabel(col)
+#         axes[i].legend()
+#         axes[i].grid(True)
+#
+#     axes[-1].set_xlabel("Time (seconds)")
+#     plt.suptitle(f"Control Trajectory — Sample {sample_idx}")
+#     plt.tight_layout()
+#
+#     # plot states
+#     fig2, axes2 = plt.subplots(n_states, 1, figsize=(10, 4 * n_states), sharex=True)
+#     if n_states == 1:
+#         axes2 = [axes2]
+#     print(x_np.shape)        # should be [seq_len, n_states]
+#     print(x_unscaled.shape)  # should match
+#     print(time_states.shape)
+#     for i, col in enumerate(state_cols):
+#         axes2[i].plot(time_states, x_unscaled[:, i], 'b-', label=col)
+#         axes2[i].set_ylabel(col)
+#         axes2[i].legend()
+#         axes2[i].grid(True)
+#
+#     axes2[-1].set_xlabel("Time (seconds)")
+#     plt.suptitle(f"Input State Sequence — Sample {sample_idx}")
+#     plt.tight_layout()
+#     plt.show()
+
+
+
+if __name__ == "__main__":
+
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        df     = make_single_df()
-        _, _, _, _, scaler = make_sequence_datasets(
+        df = make_single_df()
+        train_dataset, test_dataset, train_loader, test_loader, scaler = make_sequence_datasets(
             df, STATE_COLS, CONTROL_COLS,
             seq_len=SEQ_LEN, stride=STRIDE, batch_size=BATCH_SIZE
         )
-        model = load_model(args.model, device)
-
-        # Grab one raw window as a demo
-        raw_window = df[STATE_COLS].values[:SEQ_LEN].astype(np.float32)
-        pred = predict_single_sequence(model, raw_window, scaler, device)
-        print(f"\n[single] Predicted controls for first {SEQ_LEN} steps:")
-        for col, val in zip(CONTROL_COLS, pred):
-            print(f"  {col}: {val:.4f}")
\ No newline at end of file
+        model = load_model("rnn_model.pth", device)
+        #evaluate_and_plot(model_path=args.model, save_fig=args.save_fig)
+        plot_control_trajectory(model, test_dataset, scaler, STATE_COLS, CONTROL_COLS, [3820, 3821, 3822])
\ No newline at end of file

From 0eb146532ab1392526791b4140eadc11760fe8b7 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Sat, 7 Mar 2026 11:16:45 -0800
Subject: [PATCH 33/49] retrain model, created python script

---
 control_model/rnn_model.pth | Bin 0 -> 803813 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 control_model/rnn_model.pth

diff --git a/control_model/rnn_model.pth b/control_model/rnn_model.pth
new file mode 100644
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From 89c39cdb51ee1bba885d82b7abfd58b59ecfc9db Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Sat, 7 Mar 2026 21:45:10 -0800
Subject: [PATCH 34/49] retrain model, created python script

---
 control_model/DataPreprocessing.py        |   7 +-
 control_model/RNN_Training.py             |   1 -
 control_model/control_model_revised.ipynb | 168 +++++++++++++++-
 control_model/rnn_model.pth               | Bin 803813 -> 801765 bytes
 control_model/visualization.py            | 233 ++++++++++++++++++++++
 5 files changed, 399 insertions(+), 10 deletions(-)
 create mode 100644 control_model/visualization.py

diff --git a/control_model/DataPreprocessing.py b/control_model/DataPreprocessing.py
index 89d1657..c33ceb3 100644
--- a/control_model/DataPreprocessing.py
+++ b/control_model/DataPreprocessing.py
@@ -9,6 +9,8 @@
 import dill
 
 
+# this file will create a single dataframe to use for the RNN. Further scales the data, creates a testing/training split and makes individual sequences to furhter feed into the RNN.
+
 def combine_dfs(telemetry_names, index_common, all_dfs):
     combined_df = pd.DataFrame(index=index_common)
     combined_df.dropna()
@@ -23,7 +25,6 @@ def make_df(source, name):
     dfs = []
 
     client = query.SunbeamClient()
-
     for event in ["FSGP_2024_Day_1", "FSGP_2024_Day_2", "FSGP_2024_Day_3"]:
         file = client.get_file(
             origin="production",
@@ -88,6 +89,10 @@ def make_single_df():
     final_df = final_df.ffill().dropna()
     return final_df
 
+
+#given the raw dataframe, creates a testing / training split. Only training data is scaled.
+#create sequences of given length and feed to dataloaders (tensor conversions are done via class RNN_Dataset).
+# returns scaled training dataset, unscaled testing dataset, train_loader and test_loader (Dataloaders for iterating over the dataset and can return batches of samples).
 def make_sequence_datasets(
     df_xy,
     state_cols,
diff --git a/control_model/RNN_Training.py b/control_model/RNN_Training.py
index 169dae4..cae4c71 100644
--- a/control_model/RNN_Training.py
+++ b/control_model/RNN_Training.py
@@ -18,7 +18,6 @@ def train_model(model, train_loader, test_loader, epochs):
     optimizer = torch.optim.Adam(model.parameters(), lr=1e-4, weight_decay=1e-5)
     train_losses, test_losses = [], []
     for epoch in range(1,epochs):
-
         model.train()
         train_loss = 0.0
         for x_batch, y_batch in train_loader:
diff --git a/control_model/control_model_revised.ipynb b/control_model/control_model_revised.ipynb
index ad39dbe..c53a055 100644
--- a/control_model/control_model_revised.ipynb
+++ b/control_model/control_model_revised.ipynb
@@ -6,8 +6,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2026-03-06T19:17:10.713610Z",
-     "start_time": "2026-03-06T19:17:08.135522Z"
+     "end_time": "2026-03-07T19:52:43.240295Z",
+     "start_time": "2026-03-07T19:52:37.861819Z"
     }
    },
    "source": [
@@ -47,12 +47,164 @@
    "execution_count": null
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-07T21:53:13.331920Z",
+     "start_time": "2026-03-07T21:52:59.660848Z"
+    }
+   },
    "cell_type": "code",
-   "source": "",
+   "source": [
+    "#necessary imports\n",
+    "import torch\n",
+    "from torch import *\n",
+    "from torch import nn\n",
+    "import gc\n",
+    "from RNN import RNN\n",
+    "from DataPreprocessing import *\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "STATE_COLS   = [\"speed\"] #states\n",
+    "CONTROL_COLS = [\"brake_pressed\", \"accel_position\"]  #controls\n",
+    "SEQ_LEN      = 300 #30 seconds\n",
+    "STRIDE       = 100 #sliding window change between consecutive sequences, reduces overlapping\n",
+    "BATCH_SIZE   = 128\n",
+    "EPOCHS       = 40\n",
+    "HIDDEN_SIZE  = 128\n",
+    "NUM_LAYERS   = 2\n",
+    "\n",
+    "if torch.cuda.is_available():\n",
+    "    device = torch.device(\"cuda\")\n",
+    "else:\n",
+    "    device = torch.device(\"cpu\")\n",
+    "#load required data\n",
+    "\n",
+    "df = make_single_df()\n",
+    "gc.collect()\n",
+    "\n",
+    "print(\"Building datasets...\")\n",
+    "train_dataset, test_dataset, train_loader, test_loader, scaler = make_sequence_datasets(\n",
+    "    df, STATE_COLS, CONTROL_COLS,\n",
+    "    seq_len=SEQ_LEN, stride=STRIDE, batch_size=BATCH_SIZE\n",
+    ")"
+   ],
    "id": "2706b06256095c75",
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Building datasets...\n"
+     ]
+    }
+   ],
+   "execution_count": 1
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-07T22:24:57.295045Z",
+     "start_time": "2026-03-07T21:58:54.268480Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "`from RNN_Training import *\n",
+    "import torch\n",
+    "print(f\"Train sequences : {len(train_dataset)}\")\n",
+    "print(f\"Test  sequences : {len(test_dataset)}\")\n",
+    "print(f\"Batches / epoch : {len(train_loader)}\")\n",
+    "if torch.cuda.is_available():\n",
+    "    device = torch.device(\"cuda\")\n",
+    "else:\n",
+    "    device = torch.device(\"cpu\")\n",
+    "model = RNN(\n",
+    "    input_size=len(STATE_COLS),\n",
+    "    hidden_size=HIDDEN_SIZE,\n",
+    "    num_layers=NUM_LAYERS, seq_length=SEQ_LEN,output_size=len(CONTROL_COLS)).to(device)\n",
+    "\n",
+    "train_losses, test_losses = train_model(model, train_loader, test_loader, epochs=EPOCHS)\n",
+    "\n",
+    "# Save model\n",
+    "torch.save(model.state_dict(), \"rnn_model.pth\")\n",
+    "print(\"Model saved to rnn_model.pth\")"
+   ],
+   "id": "ee02dc1ff35f73de",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train sequences : 16093\n",
+      "Test  sequences : 4021\n",
+      "Batches / epoch : 126\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sanar\\PycharmProjects\\data_analysis\\control_model\\RNN_Training.py:20: UserWarning: torch.range is deprecated and will be removed in a future release because its behavior is inconsistent with Python's range builtin. Instead, use torch.arange, which produces values in [start, end).\n",
+      "  for epoch in range(1,epochs):\n",
+      "C:\\Users\\sanar\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:775: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
+      "  super().__init__(loader)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2.0/40 | Train Loss: 0.8428 | Test Loss: 1.2438\n",
+      "Epoch 3.0/40 | Train Loss: 0.7096 | Test Loss: 1.2157\n",
+      "Epoch 4.0/40 | Train Loss: 0.7103 | Test Loss: 1.2165\n",
+      "Epoch 5.0/40 | Train Loss: 0.7090 | Test Loss: 1.2156\n",
+      "Epoch 6.0/40 | Train Loss: 0.7089 | Test Loss: 1.2126\n",
+      "Epoch 7.0/40 | Train Loss: 0.7087 | Test Loss: 1.2189\n",
+      "Epoch 8.0/40 | Train Loss: 0.7067 | Test Loss: 1.2203\n",
+      "Epoch 9.0/40 | Train Loss: 0.7062 | Test Loss: 1.2223\n",
+      "Epoch 10.0/40 | Train Loss: 0.7058 | Test Loss: 1.2255\n",
+      "Epoch 11.0/40 | Train Loss: 0.7041 | Test Loss: 1.2257\n",
+      "Epoch 12.0/40 | Train Loss: 0.7018 | Test Loss: 1.2308\n",
+      "Epoch 13.0/40 | Train Loss: 0.7011 | Test Loss: 1.2285\n",
+      "Epoch 14.0/40 | Train Loss: 0.6983 | Test Loss: 1.2463\n",
+      "Epoch 15.0/40 | Train Loss: 0.6972 | Test Loss: 1.2400\n",
+      "Epoch 16.0/40 | Train Loss: 0.6966 | Test Loss: 1.2434\n",
+      "Epoch 17.0/40 | Train Loss: 0.6929 | Test Loss: 1.2297\n",
+      "Epoch 18.0/40 | Train Loss: 0.6894 | Test Loss: 1.2425\n",
+      "Epoch 19.0/40 | Train Loss: 0.6851 | Test Loss: 1.2429\n",
+      "Epoch 20.0/40 | Train Loss: 0.6822 | Test Loss: 1.2359\n",
+      "Epoch 21.0/40 | Train Loss: 0.6770 | Test Loss: 1.2663\n",
+      "Epoch 22.0/40 | Train Loss: 0.6741 | Test Loss: 1.2437\n",
+      "Epoch 23.0/40 | Train Loss: 0.6705 | Test Loss: 1.2155\n",
+      "Epoch 24.0/40 | Train Loss: 0.6673 | Test Loss: 1.2517\n",
+      "Epoch 25.0/40 | Train Loss: 0.6656 | Test Loss: 1.2294\n",
+      "Epoch 26.0/40 | Train Loss: 0.6620 | Test Loss: 1.2365\n",
+      "Epoch 27.0/40 | Train Loss: 0.6584 | Test Loss: 1.2237\n",
+      "Epoch 28.0/40 | Train Loss: 0.6567 | Test Loss: 1.2614\n",
+      "Epoch 29.0/40 | Train Loss: 0.6540 | Test Loss: 1.2673\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mKeyboardInterrupt\u001B[39m                         Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[8]\u001B[39m\u001B[32m, line 15\u001B[39m\n\u001B[32m      9\u001B[39m     device = torch.device(\u001B[33m\"\u001B[39m\u001B[33mcpu\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m     10\u001B[39m model = RNN(\n\u001B[32m     11\u001B[39m     input_size=\u001B[38;5;28mlen\u001B[39m(STATE_COLS),\n\u001B[32m     12\u001B[39m     hidden_size=HIDDEN_SIZE,\n\u001B[32m     13\u001B[39m     num_layers=NUM_LAYERS, seq_length=SEQ_LEN,output_size=\u001B[38;5;28mlen\u001B[39m(CONTROL_COLS)).to(device)\n\u001B[32m---> \u001B[39m\u001B[32m15\u001B[39m train_losses, test_losses = \u001B[43mtrain_model\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtrain_loader\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtest_loader\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mepochs\u001B[49m\u001B[43m=\u001B[49m\u001B[43mEPOCHS\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m     17\u001B[39m \u001B[38;5;66;03m# Save model\u001B[39;00m\n\u001B[32m     18\u001B[39m torch.save(model.state_dict(), \u001B[33m\"\u001B[39m\u001B[33mrnn_model.pth\u001B[39m\u001B[33m\"\u001B[39m)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\control_model\\RNN_Training.py:32\u001B[39m, in \u001B[36mtrain_model\u001B[39m\u001B[34m(model, train_loader, test_loader, epochs)\u001B[39m\n\u001B[32m     30\u001B[39m optimizer.zero_grad() \u001B[38;5;66;03m#reset gradients of all parameters to zero\u001B[39;00m\n\u001B[32m     31\u001B[39m outputs = model(x_batch)\n\u001B[32m---> \u001B[39m\u001B[32m32\u001B[39m loss    = criterion(outputs, y_batch)\n\u001B[32m     34\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m torch.isnan(loss):\n\u001B[32m     35\u001B[39m     \u001B[38;5;28mprint\u001B[39m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m  [warn] NaN loss at epoch \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mepoch+\u001B[32m1\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m, skipping batch\u001B[39m\u001B[33m\"\u001B[39m)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1776\u001B[39m, in \u001B[36mModule._wrapped_call_impl\u001B[39m\u001B[34m(self, *args, **kwargs)\u001B[39m\n\u001B[32m   1774\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._compiled_call_impl(*args, **kwargs)  \u001B[38;5;66;03m# type: ignore[misc]\u001B[39;00m\n\u001B[32m   1775\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1776\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_call_impl\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1787\u001B[39m, in \u001B[36mModule._call_impl\u001B[39m\u001B[34m(self, *args, **kwargs)\u001B[39m\n\u001B[32m   1782\u001B[39m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[32m   1783\u001B[39m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[32m   1784\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m._backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._forward_pre_hooks\n\u001B[32m   1785\u001B[39m         \u001B[38;5;129;01mor\u001B[39;00m _global_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[32m   1786\u001B[39m         \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[32m-> \u001B[39m\u001B[32m1787\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mforward_call\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   1789\u001B[39m result = \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m   1790\u001B[39m called_always_called_hooks = \u001B[38;5;28mset\u001B[39m()\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\control_model\\RNN.py:37\u001B[39m, in \u001B[36mRNN.forward\u001B[39m\u001B[34m(self, x)\u001B[39m\n\u001B[32m     35\u001B[39m cell_states = torch.zeros(\u001B[38;5;28mself\u001B[39m.num_layers, x.size(\u001B[32m0\u001B[39m), \u001B[38;5;28mself\u001B[39m.hidden_size).to(x.device)\n\u001B[32m     36\u001B[39m \u001B[38;5;66;03m#forward propagate lstm\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m37\u001B[39m out, _ = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mlstm\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m(\u001B[49m\u001B[43mhidden_state\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcell_states\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;66;03m#out; tensor of shape(batch_size, seq_length, hidden_size) - at the final time step\u001B[39;00m\n\u001B[32m     38\u001B[39m \u001B[38;5;66;03m#decode the hidden state of t\u001B[39;00m\n\u001B[32m     39\u001B[39m \u001B[38;5;66;03m# predicted is a series of controls\u001B[39;00m\n\u001B[32m     40\u001B[39m out = \u001B[38;5;28mself\u001B[39m.fc(out)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1776\u001B[39m, in \u001B[36mModule._wrapped_call_impl\u001B[39m\u001B[34m(self, *args, **kwargs)\u001B[39m\n\u001B[32m   1774\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._compiled_call_impl(*args, **kwargs)  \u001B[38;5;66;03m# type: ignore[misc]\u001B[39;00m\n\u001B[32m   1775\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1776\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_call_impl\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1787\u001B[39m, in \u001B[36mModule._call_impl\u001B[39m\u001B[34m(self, *args, **kwargs)\u001B[39m\n\u001B[32m   1782\u001B[39m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[32m   1783\u001B[39m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[32m   1784\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m._backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m._forward_pre_hooks\n\u001B[32m   1785\u001B[39m         \u001B[38;5;129;01mor\u001B[39;00m _global_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[32m   1786\u001B[39m         \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[32m-> \u001B[39m\u001B[32m1787\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mforward_call\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   1789\u001B[39m result = \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m   1790\u001B[39m called_always_called_hooks = \u001B[38;5;28mset\u001B[39m()\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\torch\\nn\\modules\\rnn.py:1141\u001B[39m, in \u001B[36mLSTM.forward\u001B[39m\u001B[34m(self, input, hx)\u001B[39m\n\u001B[32m   1138\u001B[39m         hx = \u001B[38;5;28mself\u001B[39m.permute_hidden(hx, sorted_indices)\n\u001B[32m   1140\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m batch_sizes \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1141\u001B[39m     result = \u001B[43m_VF\u001B[49m\u001B[43m.\u001B[49m\u001B[43mlstm\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m   1142\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43minput\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m   1143\u001B[39m \u001B[43m        \u001B[49m\u001B[43mhx\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1144\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_flat_weights\u001B[49m\u001B[43m,\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# type: ignore[arg-type]\u001B[39;49;00m\n\u001B[32m   1145\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mbias\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1146\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mnum_layers\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1147\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mdropout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1148\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mtraining\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1149\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mbidirectional\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1150\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mbatch_first\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1151\u001B[39m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   1152\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m   1153\u001B[39m     result = _VF.lstm(\n\u001B[32m   1154\u001B[39m         \u001B[38;5;28minput\u001B[39m,\n\u001B[32m   1155\u001B[39m         batch_sizes,\n\u001B[32m   (...)\u001B[39m\u001B[32m   1162\u001B[39m         \u001B[38;5;28mself\u001B[39m.bidirectional,\n\u001B[32m   1163\u001B[39m     )\n",
+      "\u001B[31mKeyboardInterrupt\u001B[39m: "
+     ]
+    }
+   ],
+   "execution_count": 8
   },
   {
    "metadata": {},
@@ -88,8 +240,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-06T19:17:41.515423Z",
-     "start_time": "2026-03-06T19:17:41.412223Z"
+     "end_time": "2026-03-07T19:53:03.511120Z",
+     "start_time": "2026-03-07T19:53:03.397577Z"
     }
    },
    "cell_type": "code",
diff --git a/control_model/rnn_model.pth b/control_model/rnn_model.pth
index 0f5f39419266c088f2aba5821e5ee5a0d0539c05..d604a8fc70b146c50de127ac90a4e21de7f26b19 100644
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diff --git a/control_model/visualization.py b/control_model/visualization.py
new file mode 100644
index 0000000..8cc5520
--- /dev/null
+++ b/control_model/visualization.py
@@ -0,0 +1,233 @@
+import os
+import gc
+import argparse
+import numpy as np
+import pandas as pd
+import torch
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import StandardScaler
+from RNN import RNN
+from RNN_Dataset import RNN_Dataset
+from DataPreprocessing import make_single_df, make_sequence_datasets
+
+#constants
+STATE_COLS   = ["speed"]
+CONTROL_COLS = ["brake_pressed", "accel_position"]
+SEQ_LEN      = 300
+STRIDE       = 100
+BATCH_SIZE   = 128
+HIDDEN_SIZE  = 128
+NUM_LAYERS   = 2
+MODEL_PATH   = "rnn_model.pth"
+
+
+# the purpose of this class is majorly to evaluate and visualize
+
+
+def load_model(model_path: str = MODEL_PATH, device: torch.device = None):
+    if device is None:
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    model = RNN(
+        input_size=len(STATE_COLS),
+        hidden_size=HIDDEN_SIZE,
+        num_layers=NUM_LAYERS,
+        seq_length=SEQ_LEN,
+        output_size=len(CONTROL_COLS),
+    ).to(device)
+
+    model.load_state_dict(torch.load(model_path, map_location=device))
+    model.eval()
+    print(f"[load_model] Loaded '{model_path}' on {device}")
+    return model
+
+
+import matplotlib.pyplot as plt
+import numpy as np
+def plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx):
+    # Normalize sample_idx to always be a list
+    if isinstance(sample_idx, int):
+        sample_idx = [sample_idx]
+
+    model.eval()
+    device = next(model.parameters()).device
+    n_states   = len(state_cols)
+    n_controls = len(control_cols)
+    seq_len    = 300
+
+    def unscale_states(arr):
+        state_mean = scaler.mean_[:n_states]
+        state_std  = scaler.scale_[:n_states]
+        return arr * state_std + state_mean
+
+    def unscale_controls(arr):
+        dummy = np.zeros((seq_len, n_states + n_controls))
+        dummy[:, n_states:] = arr
+        return scaler.inverse_transform(dummy)[:, n_states:]
+
+    time_controls = np.arange(seq_len) * 0.1
+
+    # ── Collect predictions for all samples ──────────────────────────────────
+    results = []
+    for idx in sample_idx:
+        x_input, y_target = test_dataset[idx]
+
+        with torch.no_grad():
+            x_tensor = x_input.to(device).unsqueeze(0)
+            y_pred   = model(x_tensor).squeeze(0).cpu().numpy()
+
+        x_np     = x_input.numpy()
+        y_target = y_target.numpy()
+
+        results.append({
+            "idx":       idx,
+            "x_np":      x_np,
+            "y_target":  unscale_controls(y_target),
+            "y_pred":    unscale_controls(y_pred),
+            "x_unscaled": unscale_states(x_np),
+        })
+
+    n_samples      = len(results)
+    time_states    = np.arange(results[0]["x_np"].shape[0]) * 0.1
+
+    # ── Plot controls ─────────────────────────────────────────────────────────
+    fig, axes = plt.subplots(
+        n_controls, n_samples,
+        figsize=(8 * n_samples, 4 * n_controls),
+        sharex=True, sharey="row",
+        squeeze=False,          # always 2-D array of axes
+    )
+
+    for col_j, r in enumerate(results):
+        for row_i, col_name in enumerate(control_cols):
+            ax = axes[row_i, col_j]
+            ax.plot(time_controls, r["y_target"][:, row_i], "g-",  label="Actual (Driver)")
+            ax.plot(time_controls, r["y_pred"][:,   row_i], "r--", label="Predicted (RNN)")
+            ax.set_ylabel(col_name)
+            ax.grid(True)
+            if row_i == 0:
+                ax.set_title(f"Sample {r['idx']}")
+            if row_i == n_controls - 1:
+                ax.set_xlabel("Time (seconds)")
+            if col_j == 0:
+                ax.legend()
+
+    fig.suptitle("Control Trajectories", y=1.01)
+    plt.tight_layout()
+    plt.show()
+
+
+    fig2, axes2 = plt.subplots(
+        n_states, n_samples,
+        figsize=(8 * n_samples, 4 * n_states),
+        sharex=True, sharey="row",
+        squeeze=False,
+    )
+
+    for col_j, r in enumerate(results):
+        for row_i, col_name in enumerate(state_cols):
+            ax = axes2[row_i, col_j]
+            ax.plot(time_states, r["x_unscaled"][:, row_i], "b-", label=col_name)
+            ax.set_ylabel(col_name)
+            ax.grid(True)
+            if row_i == 0:
+                ax.set_title(f"Sample {r['idx']}")
+            if row_i == n_states - 1:
+                ax.set_xlabel("Time (seconds)")
+            if col_j == 0:
+                ax.legend()
+
+    fig2.suptitle("Input State Sequences", y=1.01)
+    plt.tight_layout()
+    plt.show()
+#
+# def plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx):
+#     model.eval()
+#     x_input, y_target = test_dataset[sample_idx]
+#
+# # x_input should be [seq_len, n_states]
+#     print(x_input.shape)
+#
+#     x_np = x_input.numpy()  # [seq_len, n_states]
+#
+#     # derive time axes from actual array shapes, not seq_len variable
+#     time_controls = np.arange(y_target.shape[0]) * 0.1
+#     time_states   = np.arange(x_np.shape[0]) * 0.1
+#     x_input, y_target = test_dataset[sample_idx]
+#
+#     with torch.no_grad():
+#         device = next(model.parameters()).device
+#         x_tensor = x_input.to(device).unsqueeze(0)
+#         y_pred = model(x_tensor).squeeze(0).cpu().numpy()
+#
+#     y_target = y_target.numpy()
+#     x_np = x_input.numpy()  # [seq_len, n_states]
+#
+#     # inverse transform controls
+#     # scaler was fit on state_cols + control_cols so controls start at index n_states
+#     n_states = len(state_cols)
+#     n_controls = len(control_cols)
+#     seq_len = 300
+#     def unscale_states(arr):
+#         state_mean = scaler.mean_[:n_states]
+#         state_std  = scaler.scale_[:n_states]
+#         return arr * state_std + state_mean
+#     def unscale_controls(arr):
+#         dummy = np.zeros((seq_len, n_states + n_controls))
+#         dummy[:, n_states:] = arr
+#         return scaler.inverse_transform(dummy)[:, n_states:]
+#
+#     y_target_unscaled = unscale_controls(y_target)
+#     y_pred_unscaled   = unscale_controls(y_pred)
+#     x_unscaled        = unscale_states(x_np)
+#
+#     time_controls = np.arange(seq_len) * 0.1
+#     time_states   = np.arange(x_np.shape[0]) * 0.1
+#
+#     # plot controls
+#     fig, axes = plt.subplots(n_controls, 1, figsize=(10, 4 * n_controls), sharex=True)
+#     if n_controls == 1:
+#         axes = [axes]
+#
+#     for i, col in enumerate(control_cols):
+#         axes[i].plot(time_controls, y_target_unscaled[:, i], 'g-', label='Actual (Driver)')
+#         axes[i].plot(time_controls, y_pred_unscaled[:, i], 'r--', label='Predicted (RNN)')
+#         axes[i].set_ylabel(col)
+#         axes[i].legend()
+#         axes[i].grid(True)
+#
+#     axes[-1].set_xlabel("Time (seconds)")
+#     plt.suptitle(f"Control Trajectory — Sample {sample_idx}")
+#     plt.tight_layout()
+#
+#     # plot states
+#     fig2, axes2 = plt.subplots(n_states, 1, figsize=(10, 4 * n_states), sharex=True)
+#     if n_states == 1:
+#         axes2 = [axes2]
+#     print(x_np.shape)        # should be [seq_len, n_states]
+#     print(x_unscaled.shape)  # should match
+#     print(time_states.shape)
+#     for i, col in enumerate(state_cols):
+#         axes2[i].plot(time_states, x_unscaled[:, i], 'b-', label=col)
+#         axes2[i].set_ylabel(col)
+#         axes2[i].legend()
+#         axes2[i].grid(True)
+#
+#     axes2[-1].set_xlabel("Time (seconds)")
+#     plt.suptitle(f"Input State Sequence — Sample {sample_idx}")
+#     plt.tight_layout()
+#     plt.show()
+
+
+
+if __name__ == "__main__":
+
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        df = make_single_df()
+        train_dataset, test_dataset, train_loader, test_loader, scaler = make_sequence_datasets(
+            df, STATE_COLS, CONTROL_COLS,
+            seq_len=SEQ_LEN, stride=STRIDE, batch_size=BATCH_SIZE
+        )
+        model = load_model("rnn_model.pth", device)
+        #evaluate_and_plot(model_path=args.model, save_fig=args.save_fig)
+        plot_control_trajectory(model, test_dataset, scaler, STATE_COLS, CONTROL_COLS, [3820, 3821, 3822])
\ No newline at end of file

From 4b99e5145691306063c982396c374e56c5b8a7b6 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Sat, 7 Mar 2026 21:46:42 -0800
Subject: [PATCH 35/49] retrain model, created python script

---
 control_model/inference.py | 45 +++++++-------------------------------
 1 file changed, 8 insertions(+), 37 deletions(-)

diff --git a/control_model/inference.py b/control_model/inference.py
index 73c6455..adebc5a 100644
--- a/control_model/inference.py
+++ b/control_model/inference.py
@@ -11,7 +11,7 @@
 from DataPreprocessing import make_single_df, make_sequence_datasets
 
 #constants
-STATE_COLS   = ["position", "speed"]
+STATE_COLS   = ["speed"]
 CONTROL_COLS = ["brake_pressed", "accel_position"]
 SEQ_LEN      = 300
 STRIDE       = 100
@@ -21,6 +21,9 @@
 MODEL_PATH   = "rnn_model.pth"
 
 
+# the purpose of this class is majorly to evaluate and visualize
+
+
 def load_model(model_path: str = MODEL_PATH, device: torch.device = None):
     if device is None:
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -260,7 +263,7 @@ def plot_error_distribution(
     control_cols: list[str] = CONTROL_COLS,
     save_path: str = None,
 ) -> None:
-    """Histogram of residuals (real – predicted) for each control output."""
+    
     n_controls = len(control_cols)
     fig, axes = plt.subplots(1, n_controls,
                              figsize=(6 * n_controls, 4))
@@ -287,8 +290,8 @@ def plot_error_distribution(
 
 def print_metrics(y_real: np.ndarray, y_pred: np.ndarray,
                   control_cols: list[str] = CONTROL_COLS) -> None:
-    """Print MAE, RMSE and max-error per control output."""
-    print("\n── Prediction Metrics (unscaled) ────────────────────────")
+
+    print("\n── Prediction Metrics (unscaled)")
     for i, col in enumerate(control_cols):
         err  = y_real[:, i] - y_pred[:, i]
         mae  = np.mean(np.abs(err))
@@ -298,25 +301,12 @@ def print_metrics(y_real: np.ndarray, y_pred: np.ndarray,
     print("─" * 55)
 
 
-# ─────────────────────────────────────────────────────────────────────────────
-# 4.  Convenience "run everything" function
-# ─────────────────────────────────────────────────────────────────────────────
 
 def evaluate_and_plot(
     model_path: str = MODEL_PATH,
     save_fig: str = None,
 ) -> tuple[np.ndarray, np.ndarray]:
-    """
-    End-to-end helper:
-      1. Loads data & rebuilds the scaler (same pipeline as training).
-      2. Loads the saved model.
-      3. Runs inference on the test split.
-      4. Prints metrics and shows the comparison plot.
 
-    Returns
-    -------
-    y_real, y_pred  – unscaled arrays you can use for further analysis.
-    """
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
     print("[evaluate_and_plot] Loading data …")
@@ -343,23 +333,7 @@ def evaluate_and_plot(
 def get_sequence_timestamps(test_dataset, sample_idx, df_raw,
                             seq_len=SEQ_LEN, stride=STRIDE,
                             timestamp_col="timestamp"):
-    """
-    Get the timestamps corresponding to a sequence from test_dataset.
 
-    Parameters
-    ----------
-    test_dataset : RNN_Dataset (test split)
-    sample_idx   : which sequence you're inspecting
-    df_raw       : the original unprocessed dataframe (with timestamp column)
-    timestamp_col: name of the datetime column in df_raw
-
-    Returns
-    -------
-    timestamps : pd.Series of timestamps for that sequence window
-    start_row  : integer row index in df_raw where the sequence starts
-    end_row    : integer row index in df_raw where the sequence ends
-    """
-    # ── how many sequences are in the train split? ─────────────────────────
     # test_dataset starts after the train split in df_raw.
     # make_sequence_datasets does an 80/20 split on df_raw rows first,
     # so the test portion starts at this row:
@@ -392,9 +366,6 @@ def get_sequence_timestamps(test_dataset, sample_idx, df_raw,
 import matplotlib.pyplot as plt
 import numpy as np
 def plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx):
-    """
-    sample_idx: int or list of ints
-    """
     # Normalize sample_idx to always be a list
     if isinstance(sample_idx, int):
         sample_idx = [sample_idx]
@@ -466,7 +437,7 @@ def unscale_controls(arr):
     plt.tight_layout()
     plt.show()
 
-    # ── Plot states ───────────────────────────────────────────────────────────
+
     fig2, axes2 = plt.subplots(
         n_states, n_samples,
         figsize=(8 * n_samples, 4 * n_states),

From 31ab94a38affd23dd344eb3d23c0946e99066065 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Sat, 14 Mar 2026 14:36:04 -0700
Subject: [PATCH 36/49] retrain model, created python script

---
 array_temp/Control_Model.ipynb     | 581 ++++++++++++++++++++++++++++-
 control_model/DataPreprocessing.py | 131 ++++---
 control_model/localization_roc.py  | 363 ++++++++++++++++++
 3 files changed, 1028 insertions(+), 47 deletions(-)
 create mode 100644 control_model/localization_roc.py

diff --git a/array_temp/Control_Model.ipynb b/array_temp/Control_Model.ipynb
index f007371..84e6583 100644
--- a/array_temp/Control_Model.ipynb
+++ b/array_temp/Control_Model.ipynb
@@ -31,8 +31,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-06T01:15:12.805666Z",
-     "start_time": "2026-03-06T01:15:11.379578Z"
+     "end_time": "2026-03-14T21:31:07.379449Z",
+     "start_time": "2026-03-14T21:31:06.558101Z"
     }
    },
    "cell_type": "code",
@@ -53,6 +53,583 @@
    "outputs": [],
    "execution_count": 1
   },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-14T21:31:16.934611Z",
+     "start_time": "2026-03-14T21:31:14.765114Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from torch.utils.data import DataLoader\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "#from RNN_Dataset import RNN_Dataset\n",
+    "#necessary imports\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from data_tools import query\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import dill\n",
+    "from data_tools import *\n",
+    "import control_model.localization_roc\n",
+    "from control_model.localization_roc import *\n",
+    "\n",
+    "\n",
+    "#this file will create a single dataframe to use for the RNN. Further scales the data, creates a testing/training split and makes individual sequences to furhter feed into the RNN.\n",
+    "\n",
+    "\n",
+    "def combine_dfs(telemetry_names, index_common, all_dfs):\n",
+    "    combined_df = pd.DataFrame(index=index_common)\n",
+    "    combined_df.dropna()\n",
+    "\n",
+    "    for name, df in zip(telemetry_names, all_dfs):\n",
+    "        combined_df[name] = df\n",
+    "\n",
+    "    return combined_df\n",
+    "# get data from sunbeam and influx.\n",
+    "# use sunbeam instead to save yourself a headache\n",
+    "def make_df(source, event):\n",
+    "    dfs = []\n",
+    "    files = []\n",
+    "    client = query.SunbeamClient()\n",
+    "\n",
+    "    for name in [\"VehicleVelocity\", \"MechBrakePressed\", \"AcceleratorPosition\"]:\n",
+    "        file = client.get_file(\n",
+    "            origin=\"production\",\n",
+    "            event=event,\n",
+    "            source=source,\n",
+    "            name=name\n",
+    "        ).unwrap().data\n",
+    "        files.append(file)\n",
+    "\n",
+    "\n",
+    "    file_pos = client.get_file(\n",
+    "            origin=\"production\",\n",
+    "            event=event,\n",
+    "            source=\"localization\",\n",
+    "            name=\"TrackIndex\"\n",
+    "        ).unwrap().data\n",
+    "\n",
+    "    files = TimeSeries.align(files[0], files[1], files[2], file_pos);\n",
+    "    last_idx = np.where(np.isnan(file_pos))[0][0]\n",
+    "    file_pos = file_pos[0:last_idx]\n",
+    "    files.append(file_pos)\n",
+    "    files = TimeSeries.align(files[0], files[1], files[2], files[3]);\n",
+    "    for file2 in files:\n",
+    "            dfs.append(\n",
+    "                pd.DataFrame(\n",
+    "                    data=file2,\n",
+    "                    index=file2.datetime_x_axis\n",
+    "                )\n",
+    "            )\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "    return dfs\n"
+   ],
+   "id": "5b3055d0bd192641",
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-14T21:31:23.352755Z",
+     "start_time": "2026-03-14T21:31:18.279901Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "speed_kph, mech_brake_pressed, accel_position, position = make_df(source = \"ingress\", event = \"FSGP_2024_Day_1\")",
+   "id": "9d99a3359b312d65",
+   "outputs": [],
+   "execution_count": 3
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-14T21:29:36.577696Z",
+     "start_time": "2026-03-14T21:29:36.558210Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "position.index",
+   "id": "5686704ed95bfd2a",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatetimeIndex(['2024-07-16 07:49:53.648000002',\n",
+       "               '2024-07-16 07:49:53.747999668',\n",
+       "               '2024-07-16 07:49:53.847999573',\n",
+       "               '2024-07-16 07:49:53.947999239',\n",
+       "               '2024-07-16 07:49:54.047998905',\n",
+       "               '2024-07-16 07:49:54.147998810',\n",
+       "               '2024-07-16 07:49:54.247998476',\n",
+       "               '2024-07-16 07:49:54.347998381',\n",
+       "               '2024-07-16 07:49:54.447998047',\n",
+       "               '2024-07-16 07:49:54.547997713',\n",
+       "               ...\n",
+       "               '2024-07-16 14:53:48.585002184',\n",
+       "               '2024-07-16 14:53:48.685001850',\n",
+       "               '2024-07-16 14:53:48.785001516',\n",
+       "               '2024-07-16 14:53:48.885001421',\n",
+       "               '2024-07-16 14:53:48.985001087',\n",
+       "               '2024-07-16 14:53:49.085000992',\n",
+       "               '2024-07-16 14:53:49.185000658',\n",
+       "               '2024-07-16 14:53:49.285000324',\n",
+       "               '2024-07-16 14:53:49.385000229',\n",
+       "               '2024-07-16 14:53:49.484999895'],\n",
+       "              dtype='datetime64[ns]', length=254360, freq=None)"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 56
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-14T21:33:09.955841Z",
+     "start_time": "2026-03-14T21:33:09.948656Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "radius_of_curvature = calculate_circular_track_curvature(coords(), step=2)\n",
+    "calculated_roc = [radius_of_curvature[int(i) - 1] for i in position.sort_index()]\n",
+    "#calculated_roc_df = pd.DataFrame(calculated_roc, index = position.index).sort_index().dropna()\n"
+   ],
+   "id": "e1c5ce22d0acb6ed",
+   "outputs": [],
+   "execution_count": 6
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-14T21:33:58.330729Z",
+     "start_time": "2026-03-14T21:33:57.367685Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plt.plot(position)",
+   "id": "9387f1f94cc5d55c",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x21660a11a90>]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 8
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-14T21:09:35.255730Z",
+     "start_time": "2026-03-14T21:09:35.247563Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "def make_single_df():\n",
+    "   for event in [\"FSGP_2024_Day_1\", \"FSGP_2024_Day_2\", \"FSGP_2024_Day_3\"]:\n",
+    "       speed_kph, mech_brake_pressed, accel_position, position = make_df(source=\"ingress\", event=event)\n",
+    "\n",
+    "   scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "  #scale acceleration position before standard scaling.\n",
+    "   df_accel_position = scaler.fit_transform(accel_position)\n",
+    "   radius_of_curvature = calculate_circular_track_curvature(coords(), step=2)\n",
+    "   calculated_roc = [radius_of_curvature[int(i) - 1] for i in position]\n",
+    "   calculated_roc_df = pd.DataFrame(calculated_roc, index = position.index).sort_index().dropna()\n",
+    "\n",
+    "   final_df = pd.merge_asof(\n",
+    "        mech_brake_pressed.sort_index(),\n",
+    "       #df_accel_position.sort_index(),\n",
+    "        pd.DataFrame(df_accel_position, index = accel_position.index),\n",
+    "        left_index=True,\n",
+    "        right_index=True,\n",
+    "        direction=\"nearest\"\n",
+    "    )\n",
+    "   dfs = pd.merge_asof(speed_kph.sort_index().dropna(), calculated_roc_df.sort_index().dropna(), left_index=True, right_index=True, direction=\"nearest\")\n",
+    "   final_df = pd.merge_asof(\n",
+    "        final_df.sort_index().dropna(),\n",
+    "        dfs.sort_index().dropna(),\n",
+    "        left_index=True,\n",
+    "        right_index=True,\n",
+    "        direction=\"nearest\"\n",
+    "    )\n",
+    "   final_df.columns = [\"brake_pressed\", \"accel_position\", \"speed\", \"ROC\"]\n",
+    "   final_df = final_df.sort_index()\n",
+    "   final_df = final_df.ffill().dropna()\n",
+    "   return final_df"
+   ],
+   "id": "e0b515f4c0c30bed",
+   "outputs": [],
+   "execution_count": 34
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-14T21:09:41.770116Z",
+     "start_time": "2026-03-14T21:09:36.423881Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df = make_single_df()",
+   "id": "361b88fadb02073b",
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Shape of passed values is (1, 1), indices imply (287218, 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mValueError\u001B[39m                                Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[35]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m df = \u001B[43mmake_single_df\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[34]\u001B[39m\u001B[32m, line 13\u001B[39m, in \u001B[36mmake_single_df\u001B[39m\u001B[34m()\u001B[39m\n\u001B[32m     11\u001B[39m radius_of_curvature = calculate_circular_track_curvature(coords(), step=\u001B[32m2\u001B[39m)\n\u001B[32m     12\u001B[39m calculated_roc = [radius_of_curvature[\u001B[38;5;28mint\u001B[39m(i) - \u001B[32m1\u001B[39m] \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m position]\n\u001B[32m---> \u001B[39m\u001B[32m13\u001B[39m calculated_roc_df = \u001B[43mpd\u001B[49m\u001B[43m.\u001B[49m\u001B[43mDataFrame\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcalculated_roc\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mindex\u001B[49m\u001B[43m \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[43mposition\u001B[49m\u001B[43m.\u001B[49m\u001B[43mindex\u001B[49m\u001B[43m)\u001B[49m.sort_index().dropna()\n\u001B[32m     15\u001B[39m final_df = pd.merge_asof(\n\u001B[32m     16\u001B[39m      mech_brake_pressed.sort_index(),\n\u001B[32m     17\u001B[39m     \u001B[38;5;66;03m#df_accel_position.sort_index(),\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m     21\u001B[39m      direction=\u001B[33m\"\u001B[39m\u001B[33mnearest\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m     22\u001B[39m  )\n\u001B[32m     23\u001B[39m dfs = pd.merge_asof(speed_kph.sort_index().dropna(), calculated_roc_df.sort_index().dropna(), left_index=\u001B[38;5;28;01mTrue\u001B[39;00m, right_index=\u001B[38;5;28;01mTrue\u001B[39;00m, direction=\u001B[33m\"\u001B[39m\u001B[33mnearest\u001B[39m\u001B[33m\"\u001B[39m)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:871\u001B[39m, in \u001B[36mDataFrame.__init__\u001B[39m\u001B[34m(self, data, index, columns, dtype, copy)\u001B[39m\n\u001B[32m    863\u001B[39m         mgr = arrays_to_mgr(\n\u001B[32m    864\u001B[39m             arrays,\n\u001B[32m    865\u001B[39m             columns,\n\u001B[32m   (...)\u001B[39m\u001B[32m    868\u001B[39m             typ=manager,\n\u001B[32m    869\u001B[39m         )\n\u001B[32m    870\u001B[39m     \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m871\u001B[39m         mgr = \u001B[43mndarray_to_mgr\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    872\u001B[39m \u001B[43m            \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    873\u001B[39m \u001B[43m            \u001B[49m\u001B[43mindex\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    874\u001B[39m \u001B[43m            \u001B[49m\u001B[43mcolumns\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    875\u001B[39m \u001B[43m            \u001B[49m\u001B[43mdtype\u001B[49m\u001B[43m=\u001B[49m\u001B[43mdtype\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    876\u001B[39m \u001B[43m            \u001B[49m\u001B[43mcopy\u001B[49m\u001B[43m=\u001B[49m\u001B[43mcopy\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    877\u001B[39m \u001B[43m            \u001B[49m\u001B[43mtyp\u001B[49m\u001B[43m=\u001B[49m\u001B[43mmanager\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    878\u001B[39m \u001B[43m        \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    879\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m    880\u001B[39m     mgr = dict_to_mgr(\n\u001B[32m    881\u001B[39m         {},\n\u001B[32m    882\u001B[39m         index,\n\u001B[32m   (...)\u001B[39m\u001B[32m    885\u001B[39m         typ=manager,\n\u001B[32m    886\u001B[39m     )\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\construction.py:336\u001B[39m, in \u001B[36mndarray_to_mgr\u001B[39m\u001B[34m(values, index, columns, dtype, copy, typ)\u001B[39m\n\u001B[32m    331\u001B[39m \u001B[38;5;66;03m# _prep_ndarraylike ensures that values.ndim == 2 at this point\u001B[39;00m\n\u001B[32m    332\u001B[39m index, columns = _get_axes(\n\u001B[32m    333\u001B[39m     values.shape[\u001B[32m0\u001B[39m], values.shape[\u001B[32m1\u001B[39m], index=index, columns=columns\n\u001B[32m    334\u001B[39m )\n\u001B[32m--> \u001B[39m\u001B[32m336\u001B[39m \u001B[43m_check_values_indices_shape_match\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvalues\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mindex\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcolumns\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    338\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m typ == \u001B[33m\"\u001B[39m\u001B[33marray\u001B[39m\u001B[33m\"\u001B[39m:\n\u001B[32m    339\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28missubclass\u001B[39m(values.dtype.type, \u001B[38;5;28mstr\u001B[39m):\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\construction.py:420\u001B[39m, in \u001B[36m_check_values_indices_shape_match\u001B[39m\u001B[34m(values, index, columns)\u001B[39m\n\u001B[32m    418\u001B[39m passed = values.shape\n\u001B[32m    419\u001B[39m implied = (\u001B[38;5;28mlen\u001B[39m(index), \u001B[38;5;28mlen\u001B[39m(columns))\n\u001B[32m--> \u001B[39m\u001B[32m420\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mShape of passed values is \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mpassed\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m, indices imply \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mimplied\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m\"\u001B[39m)\n",
+      "\u001B[31mValueError\u001B[39m: Shape of passed values is (1, 1), indices imply (287218, 1)"
+     ]
+    }
+   ],
+   "execution_count": 35
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-14T21:18:03.261897Z",
+     "start_time": "2026-03-14T21:18:00.185080Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "def make_single_df():\n",
+    "    for event in [\"FSGP_2024_Day_1\", \"FSGP_2024_Day_2\", \"FSGP_2024_Day_3\"]:\n",
+    "        speed_kph, mech_brake_pressed, accel_position, position = make_df(source=\"ingress\", event=event)\n",
+    "    radius_of_curvature = calculate_circular_track_curvature(coords(), step=2)\n",
+    "    calculated_roc = [radius_of_curvature[int(i) - 1] for i in position]\n",
+    "    calculated_roc_df = pd.DataFrame(data = calculated_roc, index = position.index).sort_index()\n",
+    "    final_df = pd.concat([speed_kph, mech_brake_pressed, accel_position, calculated_roc_df], axis = 1).sort_index().ffill().dropna()\n",
+    "    final_df.columns = [\"brake_pressed\", \"accel_position\", \"speed\", \"ROC\"]\n",
+    "    return final_df\n",
+    "df = make_single_df()\n"
+   ],
+   "id": "14301a27051c6dd2",
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Shape of passed values is (1, 1), indices imply (287218, 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mValueError\u001B[39m                                Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[50]\u001B[39m\u001B[32m, line 10\u001B[39m\n\u001B[32m      8\u001B[39m     final_df.columns = [\u001B[33m\"\u001B[39m\u001B[33mbrake_pressed\u001B[39m\u001B[33m\"\u001B[39m, \u001B[33m\"\u001B[39m\u001B[33maccel_position\u001B[39m\u001B[33m\"\u001B[39m, \u001B[33m\"\u001B[39m\u001B[33mspeed\u001B[39m\u001B[33m\"\u001B[39m, \u001B[33m\"\u001B[39m\u001B[33mROC\u001B[39m\u001B[33m\"\u001B[39m]\n\u001B[32m      9\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m final_df\n\u001B[32m---> \u001B[39m\u001B[32m10\u001B[39m df = \u001B[43mmake_single_df\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[50]\u001B[39m\u001B[32m, line 6\u001B[39m, in \u001B[36mmake_single_df\u001B[39m\u001B[34m()\u001B[39m\n\u001B[32m      4\u001B[39m radius_of_curvature = calculate_circular_track_curvature(coords(), step=\u001B[32m2\u001B[39m)\n\u001B[32m      5\u001B[39m calculated_roc = [radius_of_curvature[\u001B[38;5;28mint\u001B[39m(i) - \u001B[32m1\u001B[39m] \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m position]\n\u001B[32m----> \u001B[39m\u001B[32m6\u001B[39m calculated_roc_df = \u001B[43mpd\u001B[49m\u001B[43m.\u001B[49m\u001B[43mDataFrame\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdata\u001B[49m\u001B[43m \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[43mcalculated_roc\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mindex\u001B[49m\u001B[43m \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[43mposition\u001B[49m\u001B[43m.\u001B[49m\u001B[43mindex\u001B[49m\u001B[43m)\u001B[49m.sort_index()\n\u001B[32m      7\u001B[39m final_df = pd.concat([speed_kph, mech_brake_pressed, accel_position, calculated_roc_df], axis = \u001B[32m1\u001B[39m).sort_index().ffill().dropna()\n\u001B[32m      8\u001B[39m final_df.columns = [\u001B[33m\"\u001B[39m\u001B[33mbrake_pressed\u001B[39m\u001B[33m\"\u001B[39m, \u001B[33m\"\u001B[39m\u001B[33maccel_position\u001B[39m\u001B[33m\"\u001B[39m, \u001B[33m\"\u001B[39m\u001B[33mspeed\u001B[39m\u001B[33m\"\u001B[39m, \u001B[33m\"\u001B[39m\u001B[33mROC\u001B[39m\u001B[33m\"\u001B[39m]\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:871\u001B[39m, in \u001B[36mDataFrame.__init__\u001B[39m\u001B[34m(self, data, index, columns, dtype, copy)\u001B[39m\n\u001B[32m    863\u001B[39m         mgr = arrays_to_mgr(\n\u001B[32m    864\u001B[39m             arrays,\n\u001B[32m    865\u001B[39m             columns,\n\u001B[32m   (...)\u001B[39m\u001B[32m    868\u001B[39m             typ=manager,\n\u001B[32m    869\u001B[39m         )\n\u001B[32m    870\u001B[39m     \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m871\u001B[39m         mgr = \u001B[43mndarray_to_mgr\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    872\u001B[39m \u001B[43m            \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    873\u001B[39m \u001B[43m            \u001B[49m\u001B[43mindex\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    874\u001B[39m \u001B[43m            \u001B[49m\u001B[43mcolumns\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    875\u001B[39m \u001B[43m            \u001B[49m\u001B[43mdtype\u001B[49m\u001B[43m=\u001B[49m\u001B[43mdtype\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    876\u001B[39m \u001B[43m            \u001B[49m\u001B[43mcopy\u001B[49m\u001B[43m=\u001B[49m\u001B[43mcopy\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    877\u001B[39m \u001B[43m            \u001B[49m\u001B[43mtyp\u001B[49m\u001B[43m=\u001B[49m\u001B[43mmanager\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    878\u001B[39m \u001B[43m        \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    879\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m    880\u001B[39m     mgr = dict_to_mgr(\n\u001B[32m    881\u001B[39m         {},\n\u001B[32m    882\u001B[39m         index,\n\u001B[32m   (...)\u001B[39m\u001B[32m    885\u001B[39m         typ=manager,\n\u001B[32m    886\u001B[39m     )\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\construction.py:336\u001B[39m, in \u001B[36mndarray_to_mgr\u001B[39m\u001B[34m(values, index, columns, dtype, copy, typ)\u001B[39m\n\u001B[32m    331\u001B[39m \u001B[38;5;66;03m# _prep_ndarraylike ensures that values.ndim == 2 at this point\u001B[39;00m\n\u001B[32m    332\u001B[39m index, columns = _get_axes(\n\u001B[32m    333\u001B[39m     values.shape[\u001B[32m0\u001B[39m], values.shape[\u001B[32m1\u001B[39m], index=index, columns=columns\n\u001B[32m    334\u001B[39m )\n\u001B[32m--> \u001B[39m\u001B[32m336\u001B[39m \u001B[43m_check_values_indices_shape_match\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvalues\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mindex\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcolumns\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    338\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m typ == \u001B[33m\"\u001B[39m\u001B[33marray\u001B[39m\u001B[33m\"\u001B[39m:\n\u001B[32m    339\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28missubclass\u001B[39m(values.dtype.type, \u001B[38;5;28mstr\u001B[39m):\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\construction.py:420\u001B[39m, in \u001B[36m_check_values_indices_shape_match\u001B[39m\u001B[34m(values, index, columns)\u001B[39m\n\u001B[32m    418\u001B[39m passed = values.shape\n\u001B[32m    419\u001B[39m implied = (\u001B[38;5;28mlen\u001B[39m(index), \u001B[38;5;28mlen\u001B[39m(columns))\n\u001B[32m--> \u001B[39m\u001B[32m420\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mShape of passed values is \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mpassed\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m, indices imply \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mimplied\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m\"\u001B[39m)\n",
+      "\u001B[31mValueError\u001B[39m: Shape of passed values is (1, 1), indices imply (287218, 1)"
+     ]
+    }
+   ],
+   "execution_count": 50
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-14T21:12:31.716336Z",
+     "start_time": "2026-03-14T21:12:31.694172Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df.isna()",
+   "id": "ad3b695f0f73583e",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                               brake_pressed  accel_position  speed    ROC\n",
+       "2024-07-18 07:00:00.045000076          False           False  False  False\n",
+       "2024-07-18 07:00:00.145000219          False           False  False  False\n",
+       "2024-07-18 07:00:00.245000362          False           False  False  False\n",
+       "2024-07-18 07:00:00.345000267          False           False  False  False\n",
+       "2024-07-18 07:00:00.445000410          False           False  False  False\n",
+       "...                                      ...             ...    ...    ...\n",
+       "2024-07-18 14:58:41.373999596          False           False  False  False\n",
+       "2024-07-18 14:58:41.473999739          False           False  False  False\n",
+       "2024-07-18 14:58:41.573999643          False           False  False  False\n",
+       "2024-07-18 14:58:41.673999786          False           False  False  False\n",
+       "2024-07-18 14:58:41.773999929          False           False  False  False\n",
+       "\n",
+       "[287218 rows x 4 columns]"
+      ],
+      "text/html": [
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+       "</style>\n",
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+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brake_pressed</th>\n",
+       "      <th>accel_position</th>\n",
+       "      <th>speed</th>\n",
+       "      <th>ROC</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2024-07-18 07:00:00.045000076</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-18 07:00:00.145000219</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-18 07:00:00.245000362</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-18 07:00:00.345000267</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-18 07:00:00.445000410</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-18 14:58:41.373999596</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-18 14:58:41.473999739</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-18 14:58:41.573999643</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-18 14:58:41.673999786</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-18 14:58:41.773999929</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>287218 rows × 4 columns</p>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 43
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-14T21:13:32.957939Z",
+     "start_time": "2026-03-14T21:13:30.435974Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plt.plot(df['ROC'],df['speed'])",
+   "id": "31072ad8210cab73",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2cf16666120>]"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 46
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-14T20:45:44.021706Z",
+     "start_time": "2026-03-14T20:45:43.153315Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "client = query.SunbeamClient()\n",
+    "mech_brake_pressed: TimeSeries = client.get_file(\n",
+    "    origin=\"production\",\n",
+    "    source=\"ingress\",\n",
+    "    event=\"FSGP_2024_Day_3\",\n",
+    "    name=\"MechBrakePressed\"\n",
+    ").unwrap().data\n",
+    "accel_position: TimeSeries = client.get_file(\n",
+    "    origin=\"production\",\n",
+    "    source=\"ingress\",\n",
+    "    event=\"FSGP_2024_Day_3\",\n",
+    "    name=\"AcceleratorPosition\"\n",
+    ").unwrap().data\n",
+    "speed_kph: TimeSeries = client.get_file(\n",
+    "    origin=\"production\",\n",
+    "    source=\"ingress\",\n",
+    "    event=\"FSGP_2024_Day_3\",\n",
+    "    name=\"VehicleVelocity\"\n",
+    ").unwrap().data\n",
+    "position: TimeSeries = client.get_file(\n",
+    "    origin=\"production\",\n",
+    "    source=\"localization\",\n",
+    "    event=\"FSGP_2024_Day_3\",\n",
+    "    name=\"TrackIndex\"\n",
+    ").unwrap().data\n",
+    "\n",
+    "mech_brake_pressed, accel_position, speed_kph, position = TimeSeries.align(mech_brake_pressed, accel_position, speed_kph, position)"
+   ],
+   "id": "4cf6148f454299ac",
+   "outputs": [],
+   "execution_count": 37
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "def make_single_df():\n",
+    "   for event in [\"FSGP_2024_Day_1\", \"FSGP_2024_Day_2\", \"FSGP_2024_Day_3\"]:\n",
+    "       speed_kph, mech_brake_pressed, accel_position, position = make_df(source=\"ingress\", event=event)\n",
+    "\n",
+    "\n",
+    "   pd.merge_asof(speed_kph, mech_brake_pressed)\n",
+    "   scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "  #scale acceleration position before standard scaling.\n",
+    "   df_accel_position = scaler.fit_transform(accel_position.reshape(-1, 1))\n",
+    "   radius_of_curvature = calculate_circular_track_curvature(coords(), step=2)\n",
+    "   calculated_roc = [radius_of_curvature[int(i) - 1] for i in position]\n",
+    "   calculated_roc_df = pd.DataFrame(calculated_roc).sort_index()\n",
+    "\n",
+    "   all_dfs = [mech_brake_pressed, df_accel_position]\n",
+    "   final_df = pd.merge_asof(\n",
+    "        mech_brake_pressed.sort_index(),\n",
+    "        pd.DataFrame(df_accel_position, index = accel_position.datetime_x_axis),\n",
+    "        left_index=True,\n",
+    "        right_index=True,\n",
+    "        direction=\"nearest\"\n",
+    "    )\n",
+    "   dfs = pd.concat([calculated_roc_df, speed_kph], axis=1)\n",
+    "   final_df = pd.merge_asof(\n",
+    "        final_df.sort_index(),\n",
+    "        dfs.sort_index(),\n",
+    "        left_index=True,\n",
+    "        right_index=True,\n",
+    "        direction=\"nearest\"\n",
+    "    )\n",
+    "   final_df.columns = [\"brake_pressed\", \"accel_position\", \"speed\", \"ROC\"]\n",
+    "   final_df = final_df.sort_index()\n",
+    "   final_df = final_df.ffill().dropna()\n",
+    "   return final_df"
+   ],
+   "id": "66243a42d5a90add",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "pos.isna()",
+   "id": "4e81190f5287d2f5"
+  },
   {
    "metadata": {},
    "cell_type": "markdown",
diff --git a/control_model/DataPreprocessing.py b/control_model/DataPreprocessing.py
index c33ceb3..7f8b131 100644
--- a/control_model/DataPreprocessing.py
+++ b/control_model/DataPreprocessing.py
@@ -5,11 +5,15 @@
 from sklearn.preprocessing import MinMaxScaler
 from data_tools import query
 import pandas as pd
+import numpy as np
 import os
 import dill
+from data_tools import *
+from localization_roc import *
 
 
-# this file will create a single dataframe to use for the RNN. Further scales the data, creates a testing/training split and makes individual sequences to furhter feed into the RNN.
+#this file will create a single dataframe to use for the RNN. Further scales the data, creates a testing/training split and makes individual sequences to furhter feed into the RNN.
+
 
 def combine_dfs(telemetry_names, index_common, all_dfs):
     combined_df = pd.DataFrame(index=index_common)
@@ -21,73 +25,108 @@ def combine_dfs(telemetry_names, index_common, all_dfs):
     return combined_df
 # get data from sunbeam and influx.
 # use sunbeam instead to save yourself a headache
-def make_df(source, name):
-    dfs = []
 
+from torch.utils.data import DataLoader
+from sklearn.preprocessing import StandardScaler
+#from RNN_Dataset import RNN_Dataset
+#necessary imports
+from sklearn.preprocessing import MinMaxScaler
+from data_tools import query
+import pandas as pd
+import numpy as np
+import os
+import dill
+from data_tools import *
+import control_model.localization_roc
+from control_model.localization_roc import *
+
+
+#this file will create a single dataframe to use for the RNN. Further scales the data, creates a testing/training split and makes individual sequences to furhter feed into the RNN.
+
+
+def combine_dfs(telemetry_names, index_common, all_dfs):
+    combined_df = pd.DataFrame(index=index_common)
+    combined_df.dropna()
+
+    for name, df in zip(telemetry_names, all_dfs):
+        combined_df[name] = df
+
+    return combined_df
+# get data from sunbeam and influx.
+# use sunbeam instead to save yourself a headache
+def make_df(source, event):
+    dfs = []
+    files = []
     client = query.SunbeamClient()
-    for event in ["FSGP_2024_Day_1", "FSGP_2024_Day_2", "FSGP_2024_Day_3"]:
+    for name in ["VehicleVelocity", "MechBrakePressed", "AcceleratorPosition"]:
         file = client.get_file(
             origin="production",
             event=event,
             source=source,
             name=name
-        ).unwrap()
+        ).unwrap().data
+        files.append(file)
+
 
-        dfs.append(
-            pd.DataFrame(
-                data=file.data,
-                index=file.data.datetime_x_axis
+    file_pos = client.get_file(
+            origin="production",
+            event=event,
+            source="localization",
+            name="TrackIndex"
+        ).unwrap().data
+
+    files = TimeSeries.align(files[0], files[1], files[2], file_pos);
+    last_idx = np.where(np.isnan(file_pos))[0][0]
+    file_pos = file_pos[0:last_idx]
+    files.append(file_pos)
+    files = TimeSeries.align(files[0], files[1], files[2], files[3]);
+    for file2 in files:
+            dfs.append(
+                pd.DataFrame(
+                    data=file2,
+                    index=file2.datetime_x_axis
+                )
             )
-        )
+
+
+
 
     return pd.concat(dfs).sort_index()
 
+
 def make_single_df():
-    #mech_brake_pressed, accel_position, speed_kph, position = get_data()
-    out_dir = os.path.join("../../array_temp", "data", "control_state_fsgp_2024")
-
-    brake_path = os.path.join(out_dir, "brake_pressed.bin")
-    accel_path = os.path.join(out_dir, "acceleration.bin")
-    speed_path = os.path.join(out_dir, "speed_kph.bin")
-
-    filepaths = [brake_path, accel_path, speed_path]
-
-    loaded_datasets = []
-
-    for filepath in filepaths:
-        with open(filepath, "rb") as f:
-            data = dill.load(f)
-            loaded_datasets.append(data)
-
-    # unnpack
-    mech_brake_pressed, accel_position, speed_kph = loaded_datasets
-    df_mech_brake_pressed = pd.DataFrame(mech_brake_pressed, index=mech_brake_pressed.datetime_x_axis)
-   # df_accel_position = pd.DataFrame(accel_position, index=accel_position.datetime_x_axis)
-
-    scaler = MinMaxScaler(feature_range=(0, 1))
-    df_accel_position = scaler.fit_transform(accel_position.reshape(-1, 1))
-   # pos_df = make_df(source="localization", name="TrackIndex")
-    speed_df = make_df(source="ingress", name="VehicleVelocity")
-    all_dfs = [df_mech_brake_pressed, df_accel_position]
-    final_df = pd.merge_asof(
-        df_mech_brake_pressed.sort_index(),
+   for event in ["FSGP_2024_Day_1", "FSGP_2024_Day_2", "FSGP_2024_Day_3"]:
+       speed_kph, mech_brake_pressed, accel_position, position = make_df(source="ingress", event=event)
+
+
+   pd.merge_asof(speed_kph.sort_index(), mech_brake_pressed)
+   scaler = MinMaxScaler(feature_range=(0, 1))
+  #scale acceleration position before standard scaling.
+   df_accel_position = scaler.fit_transform(accel_position.reshape(-1, 1))
+   radius_of_curvature = calculate_circular_track_curvature(coords(), step=2)
+   calculated_roc = [radius_of_curvature[int(i) - 1] for i in position]
+   calculated_roc_df = pd.DataFrame(calculated_roc).sort_index()
+
+   all_dfs = [mech_brake_pressed, df_accel_position]
+   final_df = pd.merge_asof(
+        mech_brake_pressed.sort_index(),
         pd.DataFrame(df_accel_position, index = accel_position.datetime_x_axis),
         left_index=True,
         right_index=True,
         direction="nearest"
     )
-    # dfs = pd.concat([pos_df, speed_df], axis=1)
-    final_df = pd.merge_asof(
+   dfs = pd.concat([calculated_roc_df, speed_kph], axis=1)
+   final_df = pd.merge_asof(
         final_df.sort_index(),
-        speed_df.sort_index(),
+        dfs.sort_index(),
         left_index=True,
         right_index=True,
         direction="nearest"
     )
-    final_df.columns = ["brake_pressed", "accel_position", "speed"]
-    final_df = final_df.sort_index()
-    final_df = final_df.ffill().dropna()
-    return final_df
+   final_df.columns = ["brake_pressed", "accel_position", "speed", "ROC"]
+   final_df = final_df.sort_index()
+   final_df = final_df.ffill().dropna()
+   return final_df
 
 
 #given the raw dataframe, creates a testing / training split. Only training data is scaled.
@@ -156,3 +195,5 @@ def make_sequence_datasets(
     )
 
     return train_dataset, test_dataset, train_loader, test_loader, scaler
+
+
diff --git a/control_model/localization_roc.py b/control_model/localization_roc.py
new file mode 100644
index 0000000..767a7bd
--- /dev/null
+++ b/control_model/localization_roc.py
@@ -0,0 +1,363 @@
+#to get radius of curvature.
+
+import math
+
+
+import math
+
+def calculate_circular_track_curvature(coords, step=1):
+    """
+    Calculates curvature for a CLOSED LOOP (circular) race track.
+
+    :param coords: List of tuples [(lat, lon), ...] assumed to be in order.
+    :param step: The smoothing stride (1=sensitive, 5=smooth).
+    :return: List of radii in meters.
+    """
+
+    if not coords:
+        return []
+
+    # 1. Convert Lat/Lon to Local X/Y (Meters)
+    # We use the first point as the reference center for projection
+    center_lat = coords[0][0]
+
+    # Calculate conversion factors based on the track's latitude
+    meters_per_deg_lat = 111132.954 - 559.822 * math.cos(2 * math.radians(center_lat))
+    meters_per_deg_lon = 111412.84 * math.cos(math.radians(center_lat))
+
+    xy_points = []
+    for lat, lon in coords:
+        y = lat * meters_per_deg_lat
+        x = lon * meters_per_deg_lon
+        xy_points.append((x, y))
+
+    radii = []
+    n = len(xy_points)
+
+    # 2. Calculate Radius with Wrap-Around Indexing
+    for i in range(n):
+        # Use modulo (%) to wrap around the start/finish line
+        # If i=0 and step=1, the "previous" point becomes the last point in the list
+        p1 = xy_points[(i - step) % n]
+        p2 = xy_points[i]
+        p3 = xy_points[(i + step) % n]
+
+        # Calculate side lengths (Euclidean distance)
+        a = math.dist(p1, p2)
+        b = math.dist(p2, p3)
+        c = math.dist(p3, p1)
+
+        # Shoelace formula for Area of the triangle
+        area = 0.5 * (p1[0]*(p2[1] - p3[1]) +
+                         p2[0]*(p3[1] - p1[1]) +
+                         p3[0]*(p1[1] - p2[1]))
+
+        # Calculate Radius (R = abc / 4A)
+        if  -1e-6 < area < 1e-6:
+            radii.append(0) # Straight line
+        else:
+            R = (a * b * c) / (4 * area)
+            #if R > 10000:
+              #  R = 0
+            radii.append(1/R)
+
+
+    return radii
+
+
+def coords():
+    reverse_coords = [
+    [ 37.0011529 , -86.36837867],
+    [ 37.00122817, -86.3682181 ],
+    [ 37.00133071, -86.36801267],
+    [ 37.00143614, -86.36779264],
+    [ 37.00152389, -86.3675912 ],
+    [ 37.00160574, -86.36740819],
+    [ 37.00167596, -86.36725066],
+    [ 37.00175285, -86.36709064],
+    [ 37.00183166, -86.36691875],
+    [ 37.00192538, -86.36670617],
+    [ 37.00200136, -86.36653034],
+    [ 37.00208623, -86.36635086],
+    [ 37.00215644, -86.36619701],
+    [ 37.00222549, -86.36603626],
+    [ 37.00229839, -86.3658645 ],
+    [ 37.00237732, -86.36569622],
+    [ 37.00245038, -86.36553914],
+    [ 37.00252912, -86.36537128],
+    [ 37.00259904, -86.36521818],
+    [ 37.00266755, -86.36507091],
+    [ 37.00274639, -86.36490341],
+    [ 37.00283342, -86.36471029],
+    [ 37.00291248, -86.36454704],
+    [ 37.00298517, -86.36439075],
+    [ 37.0030636 , -86.36423803],
+    [ 37.00313338, -86.36408574],
+    [ 37.00320937, -86.36393701],
+    [ 37.00330797, -86.36377724],
+    [ 37.00343811, -86.36368662],
+    [ 37.00357758, -86.36365019],
+    [ 37.00372489, -86.36360692],
+    [ 37.00388711, -86.36356354],
+    [ 37.00405472, -86.36352621],
+    [ 37.00423763, -86.36348621],
+    [ 37.00437129, -86.36338074],
+    [ 37.00448184, -86.36323899],
+    [ 37.00457515, -86.36307953],
+    [ 37.0047012 , -86.36286956],
+    [ 37.00486024, -86.36273924],
+    [ 37.00505061, -86.36270756],
+    [ 37.00527945, -86.36272947],
+    [ 37.00548802, -86.36263566],
+    [ 37.00565341, -86.36245496],
+    [ 37.00573513, -86.3621647 ],
+    [ 37.00568611, -86.36182869],
+    [ 37.00548782, -86.36157939],
+    [ 37.0052881 , -86.36149696],
+    [ 37.00511652, -86.36149669],
+    [ 37.0049746 , -86.36158761],
+    [ 37.00485989, -86.3616726 ],
+    [ 37.00469955, -86.3617696 ],
+    [ 37.00451492, -86.36178471],
+    [ 37.00435852, -86.36173599],
+    [ 37.00419576, -86.36162316],
+    [ 37.00409127, -86.36146652],
+    [ 37.00404463, -86.3612301 ],
+    [ 37.00407232, -86.36097832],
+    [ 37.00415922, -86.36078578],
+    [ 37.00426711, -86.36066944],
+    [ 37.00439509, -86.36060407],
+    [ 37.00452844, -86.36057503],
+    [ 37.00466778, -86.36054604],
+    [ 37.004833  , -86.36050987],
+    [ 37.00499495, -86.36047743],
+    [ 37.00514229, -86.36044484],
+    [ 37.00524902, -86.36041601],
+    [ 37.00541074, -86.36037856],
+    [ 37.00558676, -86.36034575],
+    [ 37.00578957, -86.36038183],
+    [ 37.00596102, -86.36045445],
+    [ 37.00607154, -86.36065091],
+    [ 37.0061651 , -86.36090615],
+    [ 37.00626234, -86.3611639 ],
+    [ 37.00637312, -86.36147776],
+    [ 37.00642557, -86.36179897],
+    [ 37.00644459, -86.36216748],
+    [ 37.00637929, -86.36250433],
+    [ 37.00629836, -86.36273906],
+    [ 37.00622229, -86.36291634],
+    [ 37.00611158, -86.36309997],
+    [ 37.00600738, -86.36323379],
+    [ 37.00589169, -86.3633461 ],
+    [ 37.00576471, -86.36344689],
+    [ 37.00564905, -86.36353398],
+    [ 37.00551878, -86.36359932],
+    [ 37.00537978, -86.36365448],
+    [ 37.00525188, -86.36370605],
+    [ 37.0051236 , -86.36376501],
+    [ 37.00497727, -86.36382802],
+    [ 37.00484855, -86.36391978],
+    [ 37.00471266, -86.36403361],
+    [ 37.0045783 , -86.36422396],
+    [ 37.00447907, -86.36439954],
+    [ 37.00435695, -86.36453807],
+    [ 37.00424813, -86.36465022],
+    [ 37.00411061, -86.36480228],
+    [ 37.00397977, -86.36492984],
+    [ 37.0038682 , -86.36504946],
+    [ 37.00376295, -86.36516126],
+    [ 37.00365436, -86.36527329],
+    [ 37.00354759, -86.36539144],
+    [ 37.00341675, -86.36552332],
+    [ 37.00329206, -86.3656715 ],
+    [ 37.00316095, -86.36581567],
+    [ 37.00303256, -86.36594441],
+    [ 37.00291203, -86.36607157],
+    [ 37.00278014, -86.36622802],
+    [ 37.00269333, -86.36641303],
+    [ 37.00260606, -86.36659437],
+    [ 37.00252845, -86.36675984],
+    [ 37.00243487, -86.36695271],
+    [ 37.00234795, -86.36716654],
+    [ 37.00226102, -86.36735999],
+    [ 37.00218046, -86.36754144],
+    [ 37.00210026, -86.36771854],
+    [ 37.00202358, -86.36787901],
+    [ 37.00194406, -86.36806693],
+    [ 37.00185835, -86.36825428],
+    [ 37.00177578, -86.36843604],
+    [ 37.00169902, -86.36860048],
+    [ 37.0016732 , -86.3687928 ],
+    [ 37.001679  , -86.3689974 ],
+    [ 37.00168488, -86.36919051],
+    [ 37.00169122, -86.36938766],
+    [ 37.00169469, -86.3695562 ],
+    [ 37.00170118, -86.36974238],
+    [ 37.00171055, -86.36993991],
+    [ 37.00167198, -86.37010864],
+    [ 37.00158558, -86.37021273],
+    [ 37.00148934, -86.37030898],
+    [ 37.00139295, -86.37041757],
+    [ 37.00129011, -86.37051816],
+    [ 37.00120286, -86.37061913],
+    [ 37.00112253, -86.37073184],
+    [ 37.00106144, -86.37085264],
+    [ 37.00099678, -86.37100197],
+    [ 37.00092255, -86.37116696],
+    [ 37.00083249, -86.37137665],
+    [ 37.00073506, -86.37159438],
+    [ 37.00065426, -86.37177611],
+    [ 37.00057398, -86.37196168],
+    [ 37.00060097, -86.37214635],
+    [ 37.00074272, -86.37221467],
+    [ 37.0009059 , -86.37222018],
+    [ 37.00107657, -86.37218846],
+    [ 37.00122503, -86.37211976],
+    [ 37.00133803, -86.37204677],
+    [ 37.00145467, -86.37192099],
+    [ 37.00156457, -86.37178347],
+    [ 37.00167114, -86.37162599],
+    [ 37.00176434, -86.37145299],
+    [ 37.00183833, -86.37126821],
+    [ 37.00188646, -86.37112396],
+    [ 37.0019441 , -86.37095574],
+    [ 37.00197296, -86.370787  ],
+    [ 37.00202945, -86.37067751],
+    [ 37.00209668, -86.37048098],
+    [ 37.00218305, -86.37031655],
+    [ 37.00228558, -86.37016005],
+    [ 37.00238818, -86.37002764],
+    [ 37.00249377, -86.36991909],
+    [ 37.00259639, -86.36983868],
+    [ 37.00270864, -86.36976266],
+    [ 37.00281411, -86.3697033 ],
+    [ 37.00292264, -86.36964769],
+    [ 37.00303847, -86.369599  ],
+    [ 37.00316942, -86.36952703],
+    [ 37.00332578, -86.36939126],
+    [ 37.00342743, -86.36921198],
+    [ 37.00346211, -86.36901195],
+    [ 37.00343231, -86.36879509],
+    [ 37.00336736, -86.36861758],
+    [ 37.00327983, -86.36847602],
+    [ 37.00316589, -86.36828932],
+    [ 37.00305696, -86.36810259],
+    [ 37.00296937, -86.36793662],
+    [ 37.00293802, -86.36773416],
+    [ 37.00295792, -86.36753614],
+    [ 37.00301643, -86.36739089],
+    [ 37.00307192, -86.36724611],
+    [ 37.00312788, -86.36711056],
+    [ 37.00320856, -86.36698638],
+    [ 37.00331523, -86.36684361],
+    [ 37.00340519, -86.36672019],
+    [ 37.00350264, -86.36658791],
+    [ 37.00361135, -86.36649982],
+    [ 37.00374245, -86.36645177],
+    [ 37.00387034, -86.3664758 ],
+    [ 37.00395668, -86.36655587],
+    [ 37.00402368, -86.36670753],
+    [ 37.00407807, -86.36686779],
+    [ 37.00412607, -86.36701603],
+    [ 37.00418686, -86.36718831],
+    [ 37.00427315, -86.36742386],
+    [ 37.00436296, -86.3676927 ],
+    [ 37.00442144, -86.36790543],
+    [ 37.00445858, -86.36815496],
+    [ 37.00447715, -86.36847342],
+    [ 37.00444541, -86.36882644],
+    [ 37.00434238, -86.36911386],
+    [ 37.00425654, -86.36927291],
+    [ 37.00418289, -86.36939303],
+    [ 37.00410293, -86.36950912],
+    [ 37.00399731, -86.36962489],
+    [ 37.00390147, -86.36973254],
+    [ 37.00379293, -86.36984054],
+    [ 37.00369701, -86.36994104],
+    [ 37.00359474, -86.37004514],
+    [ 37.0034838 , -86.3701598 ],
+    [ 37.00338477, -86.37024376],
+    [ 37.00327609, -86.37032788],
+    [ 37.00318982, -86.37040789],
+    [ 37.00306205, -86.37049992],
+    [ 37.00294389, -86.37059192],
+    [ 37.00283177, -86.37070006],
+    [ 37.00272312, -86.37083897],
+    [ 37.00264003, -86.37094293],
+    [ 37.00255683, -86.37103895],
+    [ 37.00249914, -86.37115127],
+    [ 37.00243497, -86.37126771],
+    [ 37.00236746, -86.3713964 ],
+    [ 37.00230001, -86.37152106],
+    [ 37.00223561, -86.37164596],
+    [ 37.00217132, -86.37175865],
+    [ 37.0021135 , -86.37187932],
+    [ 37.00204601, -86.37200003],
+    [ 37.00196791, -86.37211434],
+    [ 37.00188761, -86.3722312 ],
+    [ 37.00180726, -86.37233202],
+    [ 37.00173651, -86.37241676],
+    [ 37.00165284, -86.37250555],
+    [ 37.00155067, -86.3726109 ],
+    [ 37.00144762, -86.37271211],
+    [ 37.00136704, -86.37279282],
+    [ 37.00126713, -86.37288565],
+    [ 37.00115123, -86.37299929],
+    [ 37.00103518, -86.37310829],
+    [ 37.0009191 , -86.37321738],
+    [ 37.00082561, -86.37329809],
+    [ 37.00072863, -86.37339546],
+    [ 37.00061364, -86.37350099],
+    [ 37.00051698, -86.37360214],
+    [ 37.00042667, -86.37368311],
+    [ 37.00032338, -86.37378434],
+    [ 37.00022342, -86.37387665],
+    [ 37.00012018, -86.3739737 ],
+    [ 37.00002051, -86.37405916],
+    [ 36.99991404, -86.37415222],
+    [ 36.99981076, -86.37423248],
+    [ 36.99970432, -86.37430523],
+    [ 36.99958169, -86.37432571],
+    [ 36.99946928, -86.37430399],
+    [ 36.99934964, -86.37428072],
+    [ 36.99922365, -86.37425686],
+    [ 36.99908793, -86.37422888],
+    [ 36.99899069, -86.37413638],
+    [ 36.99896465, -86.37397888],
+    [ 36.9989808 , -86.37380904],
+    [ 36.99900664, -86.37362303],
+    [ 36.99904589, -86.37342887],
+    [ 36.9990885 , -86.37323096],
+    [ 36.99912743, -86.3730292 ],
+    [ 36.99916641, -86.37284378],
+    [ 36.99921127, -86.37261758],
+    [ 36.99924349, -86.37245211],
+    [ 36.99932082, -86.37230251],
+    [ 36.99932082, -86.37230251],
+    [ 36.99940337, -86.37214797],
+    [ 36.99948814, -86.37199725],
+    [ 36.99958754, -86.37189043],
+    [ 36.99970528, -86.37178738],
+    [ 36.99982625, -86.37169183],
+    [ 36.99997073, -86.37159181],
+    [ 37.00011471, -86.3714889 ],
+    [ 37.00027357, -86.37138907],
+    [ 37.0003852 , -86.37123775],
+    [ 37.00042033, -86.37099884],
+    [ 37.00038811, -86.3707825 ],
+    [ 37.00031195, -86.37062837],
+    [ 37.00026491, -86.37041498],
+    [ 37.00030254, -86.37020247],
+    [ 37.00038472, -86.3700261 ],
+    [ 37.0004699 , -86.36984594],
+    [ 37.00056383, -86.36964375],
+    [ 37.00064015, -86.3694857 ],
+    [ 37.00070993, -86.36934481],
+    [ 37.0008098 , -86.36912411],
+    [ 37.00090662, -86.36891836],
+    [ 37.00098578, -86.36874579],
+    [ 37.00107373, -86.36854755]
+  ]
+
+    return reverse_coords[::-1] # coordinates in the correct order; starting coordinate goes first
+

From b1f6808524ab5e82b620e15705401bd62eda4dfc Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Thu, 9 Apr 2026 19:49:34 -0700
Subject: [PATCH 37/49] adding documentation + wrapper class

---
 control_model/DataPreprocessing.py        | 103 ++--
 control_model/RNN.py                      |  55 ++-
 control_model/control_model.py            | 262 ++++++++++
 control_model/fsgp_2024_training_data.csv |   3 +
 control_model/inference.py                | 573 ++--------------------
 control_model/rnn_model (1).pth           | Bin 0 -> 3175219 bytes
 control_model/visualization.py            | 192 ++++----
 7 files changed, 496 insertions(+), 692 deletions(-)
 create mode 100644 control_model/control_model.py
 create mode 100644 control_model/fsgp_2024_training_data.csv
 create mode 100644 control_model/rnn_model (1).pth

diff --git a/control_model/DataPreprocessing.py b/control_model/DataPreprocessing.py
index 7f8b131..491ed22 100644
--- a/control_model/DataPreprocessing.py
+++ b/control_model/DataPreprocessing.py
@@ -1,16 +1,15 @@
+from data_tools import TimeSeries
 from torch.utils.data import DataLoader
 from sklearn.preprocessing import StandardScaler
-from RNN_Dataset import RNN_Dataset
-#necessary imports
 from sklearn.preprocessing import MinMaxScaler
-from data_tools import query
 import pandas as pd
 import numpy as np
 import os
 import dill
-from data_tools import *
-from localization_roc import *
+from RNN_Dataset import *
+from RNN_Training import *
 
+from localization_roc import *
 
 #this file will create a single dataframe to use for the RNN. Further scales the data, creates a testing/training split and makes individual sequences to furhter feed into the RNN.
 
@@ -31,14 +30,14 @@ def combine_dfs(telemetry_names, index_common, all_dfs):
 #from RNN_Dataset import RNN_Dataset
 #necessary imports
 from sklearn.preprocessing import MinMaxScaler
-from data_tools import query
+#from data_tools import query
 import pandas as pd
 import numpy as np
 import os
 import dill
-from data_tools import *
-import control_model.localization_roc
-from control_model.localization_roc import *
+# from data_tools import *
+# import control_model.localization_roc
+# from control_model.localization_roc import *
 
 
 #this file will create a single dataframe to use for the RNN. Further scales the data, creates a testing/training split and makes individual sequences to furhter feed into the RNN.
@@ -93,40 +92,54 @@ def make_df(source, event):
 
     return pd.concat(dfs).sort_index()
 
-
 def make_single_df():
-   for event in ["FSGP_2024_Day_1", "FSGP_2024_Day_2", "FSGP_2024_Day_3"]:
-       speed_kph, mech_brake_pressed, accel_position, position = make_df(source="ingress", event=event)
-
-
-   pd.merge_asof(speed_kph.sort_index(), mech_brake_pressed)
-   scaler = MinMaxScaler(feature_range=(0, 1))
-  #scale acceleration position before standard scaling.
-   df_accel_position = scaler.fit_transform(accel_position.reshape(-1, 1))
-   radius_of_curvature = calculate_circular_track_curvature(coords(), step=2)
-   calculated_roc = [radius_of_curvature[int(i) - 1] for i in position]
-   calculated_roc_df = pd.DataFrame(calculated_roc).sort_index()
-
-   all_dfs = [mech_brake_pressed, df_accel_position]
-   final_df = pd.merge_asof(
-        mech_brake_pressed.sort_index(),
-        pd.DataFrame(df_accel_position, index = accel_position.datetime_x_axis),
-        left_index=True,
-        right_index=True,
-        direction="nearest"
-    )
-   dfs = pd.concat([calculated_roc_df, speed_kph], axis=1)
-   final_df = pd.merge_asof(
-        final_df.sort_index(),
-        dfs.sort_index(),
-        left_index=True,
-        right_index=True,
-        direction="nearest"
-    )
-   final_df.columns = ["brake_pressed", "accel_position", "speed", "ROC"]
-   final_df = final_df.sort_index()
-   final_df = final_df.ffill().dropna()
-   return final_df
+    day_dfs = []
+    radius_of_curvature = calculate_circular_track_curvature(coords, step=2)  # compute once
+
+    for event in ["FSGP_2024_Day_1", "FSGP_2024_Day_2", "FSGP_2024_Day_3"]:
+        speed_kph, mech_brake_pressed, accel_position, position = make_df(source="ingress", event=event)
+
+        scaler = MinMaxScaler(feature_range=(0, 1))
+        df_accel_position = pd.DataFrame(
+            scaler.fit_transform(accel_position),
+            index=accel_position.index
+        )
+
+        position_series = position.squeeze()
+        calculated_roc = position_series.map(
+            lambda pos: radius_of_curvature[int(pos) % len(radius_of_curvature)],
+            na_action='ignore'
+        )
+        calculated_roc_df = calculated_roc.to_frame(name='curvature').dropna()
+
+        day_df = pd.merge_asof(
+            mech_brake_pressed.sort_index(),
+            df_accel_position.sort_index(),
+            left_index=True,
+            right_index=True,
+            direction="nearest"
+        )
+        day_df = pd.merge_asof(
+            day_df.sort_index(),
+            speed_kph.sort_index().dropna(),
+            left_index=True,
+            right_index=True,
+            direction="nearest"
+        )
+        day_df = pd.merge_asof(
+            day_df.sort_index(),
+            calculated_roc_df.sort_index().dropna(),
+            left_index=True,
+            right_index=True,
+            direction="nearest"
+        )
+
+        day_df.columns = ["brake_pressed", "accel_position", "speed", "ROC"]
+        day_df = day_df.sort_index().ffill().dropna()
+        day_dfs.append(day_df)
+
+    final_df = pd.concat(day_dfs, axis=0).sort_index()
+    return final_df
 
 
 #given the raw dataframe, creates a testing / training split. Only training data is scaled.
@@ -149,7 +162,11 @@ def make_sequence_datasets(
     n_total = len(df_xy)
     train_len = int(train_frac * n_total)
     df_xy = df_xy.dropna(subset=state_cols + control_cols).reset_index(drop=True)
+# In make_sequence_datasets, split before concatenating days
+# OR split the final_df by date
 
+   # df_train_raw = final_df[final_df.index < "2024-07-18"]  # Day 1 + 2
+    #df_test_raw  = final_df[final_df.index >= "2024-07-18"] # Day 3
     df_train_raw = df_xy.iloc[:train_len].reset_index(drop=True)
     df_test_raw  = df_xy.iloc[train_len:].reset_index(drop=True)
 
@@ -185,7 +202,7 @@ def make_sequence_datasets(
     train_loader = DataLoader(
         train_dataset,
         batch_size=batch_size, num_workers=0,
-        shuffle=True, pin_memory = True
+        shuffle=False, pin_memory = True
     )
 
     test_loader = DataLoader(
diff --git a/control_model/RNN.py b/control_model/RNN.py
index 3a27ef6..8e4998b 100644
--- a/control_model/RNN.py
+++ b/control_model/RNN.py
@@ -1,43 +1,50 @@
 import torch
 from torch import nn
-#model class to declare RNN and defining a forward pass of the model
+
+# model class to declare RNN and defining a forward pass of the model
 
 device = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else "cpu"
 
+
+
 class RNN(nn.Module):
+    """
+    Main class of RNN architecture.
+    :param input_size refers to the number of input variables
+    :param hidden_size refers to the dimension of memory inside the LSTM
+    :param num_layers
+    :param seq_length
+    :param output_size
+    """
 
     def __init__(self, input_size, hidden_size, num_layers, seq_length, output_size):
-        #inherits from nn.Module
+        # inherits from nn.Module
         super(RNN, self).__init__()
-        self.hidden_size = hidden_size  #dim of memory inside lstm ie no of features in the hidden state that persists between timesteps
+        self.hidden_size = hidden_size  # dim of memory inside lstm ie no of features in the hidden state that persists between timesteps
         # a higher hidden size usually corresponds to complex dependencies
-        self.num_layers = num_layers  #stacked lstm layers
-
-        #lstm: long short term memory - looks at long term dependencies in sequential data
-        #default activation is tanh
-        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)  #correspond to input data shape
-        self.seq_length = seq_length  #no of timestamps to look at to predict the next control output
-
-        #num classes is the no of outputs predicted by the model
-        #to convert memory vector to outputs (shaping constraints)
+        self.num_layers = num_layers  # stacked lstm layers
+        # lstm: long short term memory - looks at long term dependencies in sequential data
+        # default activation is tanh
+        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)  # correspond to input data shape
+        self.seq_length = seq_length  # no of timestamps to look at to predict the next control output
+
+        # num classes is the no of outputs predicted by the model
+        # to convert memory vector to outputs (shaping constraints)
         self.fc = nn.Linear(hidden_size, output_size)
 
-
     def forward(self, x):
-
-        #x shape : [batch size, input size]
+        # x shape : [batch size, input size]
         # associate the state array sequence with timeseries dependency
-        #inital hidden, cell states - these are internal memory vectors
-        #hidden = short term memory, current output of LSTM at a given time
+        # inital hidden, cell states - these are internal memory vectors
+        # hidden = short term memory, current output of LSTM at a given time
         hidden_state = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
 
-        #cell state = long term memory, stores trends (remmebers info over many time steps)
+        # cell state = long term memory, stores trends (remmebers info over many time steps)
         cell_states = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
-        #forward propagate lstm
-        out, _ = self.lstm(x, (hidden_state, cell_states)) #out; tensor of shape(batch_size, seq_length, hidden_size) - at the final time step
-        #decode the hidden state of t
-        # predicted is a series of controls
+        # forward propagate lstm
+        out, _ = self.lstm(x, (hidden_state,
+                               cell_states))  # out; tensor of shape(batch_size, seq_length, hidden_size) - at the final time step
+        # decode the hidden state of t and predict the corresponding sequence of controls
         out = self.fc(out)
-        #out shape: [batch_size, seq_length, hidden_size]
+        # out shape: [batch_size, seq_length, hidden_size]
         return out
-
diff --git a/control_model/control_model.py b/control_model/control_model.py
new file mode 100644
index 0000000..cfe1557
--- /dev/null
+++ b/control_model/control_model.py
@@ -0,0 +1,262 @@
+# necessary imports:
+import os
+import gc
+import argparse
+import numpy as np
+import pandas as pd
+import torch
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import StandardScaler
+from RNN import RNN
+from RNN_Dataset import RNN_Dataset
+from DataPreprocessing import make_single_df, make_sequence_datasets
+from DataPreprocessing import *
+from control_model.visualization import *
+from inference import *
+
+# Fixed constants as they must match with RNN training requirements:
+state_cols = ["speed", "ROC"]  # input state features
+control_cols = ["brake_pressed", "accel_position"]  # output control targets
+SEQ_LEN = 150  # sequence length (15 seconds at 0.1 granularity)
+STRIDE = 50  # sliding-window step between consecutive sequences
+BATCH_SIZE = 64  # training batch size
+EPOCHS = 320  # number of training epochs
+HIDDEN_SIZE = 256  # LSTM hidden layer dimensionality
+NUM_LAYERS = 2  # number of stacked LSTM layers
+MODEL_PATH = "rnn_model.pt"  # default model checkpoint path
+
+
+class Control_Model():
+    """
+    Class for loading, running inference and evaluation of the state-control RNN.
+    The model works on a state to control array mapping based on a sequence-to-sequence LSTM.
+    State array consists of curvature and speed as inputs and predicts corresponding driver control outputs (brake pressed, accelerator position) as a 0-1 continuous value.
+
+    This class handles all internal preprocessing steps required for using the RNN, including:
+    - scaling raw inputs with the training scaler
+    - propagating LSTM hidden states across timesteps
+    - unscale predicted outputs to reflect original physical units.
+
+    :param model_path: str filepath of the loaded RNN (.pt / .pth)
+    :param input_speed: np.ndarray Unscaled speed array input for model use.
+    :param input_curvature: unscaled curvature array input for model use.
+    :param hidden_size: Fixed hidden size for LSTM cells.
+    :param num_layers: Number of stacked LSTM layers.
+    :param seq_length: Constant sequence length of 15 seconds at 0.1 granularity
+    :param device: Device to load RNN, CUDA by default, else resorts to CPU.
+
+
+
+    :return output_brake_pressed: np.ndarray Unscaled brake pressed array output predicted by model.
+    :return output_accel_position: np.ndarray Unscaled acceleration array output predicted by model.
+
+    """
+
+    state_cols = ["speed", "ROC"]  # input state features
+    control_cols = ["brake_pressed", "accel_position"]  # output control features
+
+    def __init__(
+            self,
+            model_path: str,
+            scaler,
+            input_speed: np.ndarray,
+            input_curvature: np.ndarray,
+            hidden_size: int = 256,
+            num_layers: int = 2,
+            seq_length: int = 150,
+            device: torch.device = None,
+    ):
+        self.model_path = model_path
+        self.scaler = scaler
+        self.input_speed = np.asarray(input_speed, dtype=np.float32)
+        self.input_curvature = np.asarray(input_curvature, dtype=np.float32)
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
+        self.seq_length = seq_length
+        self.device = device or torch.device(
+            "cuda" if torch.cuda.is_available() else "cpu"
+        )
+
+        self.n_states = len(self.state_cols)  # 2 inputs
+        self.n_controls = len(self.control_cols)  # 2 outputs
+        self.n_total = self.n_states + self.n_controls  # 4
+
+        self.model = self.load_model()
+
+    def load_model(self, device: torch.device = None):
+        """
+        Load the RNN checkpoint from ``self.model_path`` onto the target device.
+        :param device: cpu or cuda, depending on user device. Defaults to CUDA automatically when available.
+        :return model: RNN model loaded for evaluation mode
+
+        """
+
+        model_path = self.model_path
+        if device is None:
+            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+        model = RNN(
+            input_size=len(state_cols),
+            hidden_size=HIDDEN_SIZE,
+            num_layers=NUM_LAYERS,
+            seq_length=SEQ_LEN,
+            output_size=len(control_cols),
+        ).to(device)
+
+        model.load_state_dict(torch.load(model_path, map_location=device))
+        model.eval()
+        print(f"[load_model] Loaded '{model_path}' on {device}")
+        return model
+
+    def unscale_outputs(self, arr) -> np.ndarray:
+        """
+
+        Method to reverse scaled transformation applied during preprocessing.
+        As the scaler was fit on both state and control arrays, individual features cannot be inverted in isolation.
+        This method will reconstruct a full-width array to be inverse transformed and then extracts only the relevant subsets.
+
+        :param arr: np.ndarray: Scaled array to be inverted of shape (n_controls)
+        :return unscaled_states: np.ndarray: Unscaled state values (speed and curvature)
+
+        """
+        T = arr.shape[0]
+        dummy = np.zeros((T, self.n_total), dtype=np.float32)
+        dummy[:, self.n_states:] = arr  # insert at control columns
+
+        unscaled = self.scaler.inverse_transform(dummy)
+        return unscaled[:, self.n_states:]  # return only control columns
+
+    def scale_inputs(self) -> np.ndarray:
+        """
+        Method to stack speed and curvature into a nparray of shape (T,2) and then standardise using the training scaler.
+        Stack speed and curvature into a (T, 2) array and standardise using
+        the training scaler (Sci-kit Learn's StandardScaler()).
+        :return  scaled_states : np.ndarray, of shape (T, 2) that has been standardised and is ready to feed into LSTM
+
+        """
+        T = len(self.input_speed)
+
+        # full width dummy array
+        # note that scaler was fit on all columns together, so subsets cannot be inverted in isolatoin
+        # build full width dummy array
+        # insert subset into the column adn then transform
+        dummy = np.zeros((T, self._n_total), dtype=np.float32)
+        dummy[:, 0] = self.input_speed  # speed is column 0
+        dummy[:, 1] = self.input_curvature  # curvature is col 1
+
+        scaled = self.scaler.transform(dummy)
+        return scaled[:, :self._n_states]  # return only the state columns
+
+    def predict(self):
+        """
+        Runs the entire inference process on input state arrays and returns the unscaled predicted control values (physical units)
+        Internal steps include:
+            - scaling input speed and input curvature on the training scalar.
+            - Inference is performed row-by-row i.e. stride is set to 1 in order to avoid scaling inconsistencies due to boundary spikes in data.
+            - Predicted outputs are unscaled back to physical units.
+
+
+        :return accel_position : np.ndarray,
+            Predicted accelerator position in original (unscaled) units.
+        :return brake_pressed  : np.ndarray,
+                   Predicted brake signal in original (unscaled) units.
+        """
+
+        scaled_states = self.scale_inputs()
+        n_samples = scaled_states.shape[0]  # length of array for prediction
+
+        h, c = None, None  # # cold initialise hidden states
+        scaled_pred = np.zeros((n_samples, self.n_controls), dtype=np.float32)
+
+        with torch.no_grad():
+            for i in range(n_samples):
+                # LSTM requires tensor of shape (1,1,n_states) i.e. (batch=1, seq=1, features=2)
+                x = (
+                    torch.tensor(scaled_states[i])
+                    .unsqueeze(0)  # adds batch dimension
+                    .unsqueeze(0)  # adds sequence dimension
+                    .to(self.device)
+                )
+
+                hidden = (h, c) if h is not None else None  # carry over hidden states, do not reinitialise every time the loop runs.
+                y_pred_tensor, (h, c) = self.model(x, hidden=hidden)
+                h, c = h.detach(), c.detach()  # break gradient flow
+                # remove added batch and sequence dimensios.
+                scaled_pred[i] = y_pred_tensor.squeeze().detach().cpu().numpy()
+
+
+        # convert from standardised space back to physical units
+        unscaled = self.unscale_controls(scaled_pred)
+
+        # col 0 = brake_pressed, col 1 = accel_position (to match training order)
+        brake_pressed = unscaled[:, 0]
+        accel_position = unscaled[:, 1]
+
+        return accel_position, brake_pressed
+
+
+
+
+    def eval_model(self, df_test_scaled, scaler, start_idx, n_samples):
+        """
+        Method to evaluate model before visualization and accuracy metrics.
+        Runs the model over a contiguous block of scaled inputs and returns unscaled predicted control arrays. Inference is performed row-by-row i.e. stride is set to 1 in order to avoid scaling inconsistencies due to boundary spikes in data.
+
+
+        :param df_test_scaled : pd.DataFrame
+            Scaled test dataframe (output of ``scale_inputs``).
+        :param scaler : sklearn.preprocessing.StandardScaler
+            The scaler fitted on training data, required for unscaling outputs.
+        :param start_idx : int, optional
+            First row index to evaluate (default 0).
+        :param n_samples : int, optional
+            Number of consecutive rows to evaluate.  Defaults to all rows
+            after ``start_idx``.
+
+        :return y_true : np.ndarray, shape (n_samples, n_controls)
+            Unscaled ground-truth control values.
+        :return y_pred : np.ndarray, shape (n_samples, n_controls)
+            Unscaled model-predicted control values.
+        """
+
+        self.model.eval()  # evaluate
+        device = next(self.model.parameters()).device
+        n_states = len(self.state_cols)
+        n_controls = len(self.control_cols)
+        cols = self.state_cols + self.control_cols
+
+        if n_samples is None:
+            n_samples = len(df_test_scaled) - start_idx
+
+        h, c = None, None  # cold initialise hidden states
+        all_y_true, all_y_pred = [], []
+
+        for i in range(start_idx, start_idx + n_samples):
+            row = df_test_scaled[cols].iloc[i].values.astype(np.float32)
+
+            # required shape: (1, 1, n_states)
+            x_tensor = torch.tensor(row[:n_states]).unsqueeze(0).unsqueeze(0).to(device)
+
+            with torch.no_grad():
+                hidden = (h,
+                          c) if h is not None else None  # carry over hidden states, do not reinitialise every time the loop runs.
+                y_pred_tensor, (h, c) = self.model(x_tensor, hidden=hidden)
+                h, c = h.detach(), c.detach()  # break gradient flow
+
+            all_y_true.append(row[n_states:])
+            all_y_pred.append(y_pred_tensor.squeeze().cpu().numpy())
+
+    # unscale via dummy full-width arrays
+    # note that scaler was fit on all columns together, so subsets cannot be inverted in isolation
+    # build full width dummy array
+    # insert subset into the column adn then inverse transform
+        def unscale(arr, col_slice):
+            dummy = np.zeros((len(arr), n_states + n_controls))
+            dummy[:, col_slice] = arr
+            return scaler.inverse_transform(dummy)[:, col_slice]
+
+        y_true = unscale(np.array(all_y_true), slice(n_states, None))
+        y_pred = unscale(np.array(all_y_pred).reshape(-1, n_controls), slice(n_states, None))
+        return y_true, y_pred
+
+
diff --git a/control_model/fsgp_2024_training_data.csv b/control_model/fsgp_2024_training_data.csv
new file mode 100644
index 0000000..f0fef1a
--- /dev/null
+++ b/control_model/fsgp_2024_training_data.csv
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:06a1b14cc6527983d2474c2710e24ed66971da9262b13cf42eb2d5c97400f466
+size 63402612
diff --git a/control_model/inference.py b/control_model/inference.py
index adebc5a..7acbdee 100644
--- a/control_model/inference.py
+++ b/control_model/inference.py
@@ -1,554 +1,61 @@
-import os
-import gc
-import argparse
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
 import numpy as np
-import pandas as pd
 import torch
-import matplotlib.pyplot as plt
-from sklearn.preprocessing import StandardScaler
-from RNN import RNN
-from RNN_Dataset import RNN_Dataset
-from DataPreprocessing import make_single_df, make_sequence_datasets
-
-#constants
-STATE_COLS   = ["speed"]
-CONTROL_COLS = ["brake_pressed", "accel_position"]
-SEQ_LEN      = 300
-STRIDE       = 100
-BATCH_SIZE   = 128
-HIDDEN_SIZE  = 128
-NUM_LAYERS   = 2
-MODEL_PATH   = "rnn_model.pth"
-
-
-# the purpose of this class is majorly to evaluate and visualize
-
-
-def load_model(model_path: str = MODEL_PATH, device: torch.device = None):
-    if device is None:
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-    model = RNN(
-        input_size=len(STATE_COLS),
-        hidden_size=HIDDEN_SIZE,
-        num_layers=NUM_LAYERS,
-        seq_length=SEQ_LEN,
-        output_size=len(CONTROL_COLS),
-    ).to(device)
-
-    model.load_state_dict(torch.load(model_path, map_location=device))
-    model.eval()
-    print(f"[load_model] Loaded '{model_path}' on {device}")
-    return model
-
-
-
-def predict_loader(
-    model: RNN,
-    loader: torch.utils.data.DataLoader,
-    scaler: StandardScaler,
-    device: torch.device = None,
-):
-    #returns rescaled real y values and predicted, given the input RNN and scaler.
-    if device is None:
-        device = next(model.parameters()).device
-
-    all_real, all_pred = [], []
-
-    model.eval()
-    with torch.no_grad():
-        for x_batch, y_batch in loader:
-            x_batch = x_batch.to(device)
-            preds   = model(x_batch).cpu().numpy()
-            real    = y_batch.numpy()
-            all_pred.append(preds)
-            all_real.append(real)
-
-    y_pred_scaled = np.concatenate(all_pred, axis=0)
-    y_real_scaled = np.concatenate(all_real, axis=0)
-
-    # perform inverse transform
-    n_state   = len(STATE_COLS)
-    n_control = len(CONTROL_COLS)
-    n_total   = n_state + n_control
-
-    def _unscale(arr: np.ndarray) -> np.ndarray:
-        original_shape = arr.shape
-        if arr.ndim == 3:
-            N, T, C = arr.shape
-            arr_2d = arr.reshape(-1, C)
-        else:
-            arr_2d = arr
-
-        dummy = np.zeros((len(arr_2d), n_total), dtype=np.float32)
-        dummy[:, n_state:] = arr_2d
-        unscaled = scaler.inverse_transform(dummy)[:, n_state:]
-
-        if original_shape.ndim if hasattr(original_shape, 'ndim') else len(original_shape) == 3:
-            unscaled = unscaled.reshape(N, T, C)
-
-        return unscaled
-
-    # Handle both 2D and 3D outputs cleanly
-    def _safe_unscale(arr: np.ndarray) -> np.ndarray:
-        if arr.ndim == 3:
-            N, T, C = arr.shape
-            flat = arr.reshape(-1, C)
-            dummy = np.zeros((len(flat), n_total), dtype=np.float32)
-            dummy[:, n_state:] = flat
-            unscaled_flat = scaler.inverse_transform(dummy)[:, n_state:]
-            return unscaled_flat.reshape(N, T, C)
-        else:
-            dummy = np.zeros((len(arr), n_total), dtype=np.float32)
-            dummy[:, n_state:] = arr
-            return scaler.inverse_transform(dummy)[:, n_state:]
-
-    y_real = _safe_unscale(y_real_scaled)
-    y_pred = _safe_unscale(y_pred_scaled)
-
-    # If 3D (N, seq_len, n_controls), take the last timestep for plotting/metrics
-    # This gives one prediction per sequence — change to [:, 0, :] for first step
-    # or reshape to (N*T, C) if you want every timestep unrolled.
-    if y_pred.ndim == 3:
-        print(f"[predict_loader] Model output is 3D {y_pred.shape} — "
-              f"using last timestep per sequence for y_pred/y_real.")
-        y_real = y_real[:, -1, :]   # (N, n_controls)
-        y_pred = y_pred[:, -1, :]   # (N, n_controls)
-
-    return y_real, y_pred
-
-
-def predict_dataframe(
-    model: RNN,
-    df: pd.DataFrame,
-    scaler: StandardScaler,
-    device: torch.device = None,
-    stride: int = STRIDE,
-    seq_len: int = SEQ_LEN,
-    batch_size: int = BATCH_SIZE,
-):
-    """
-    Run inference directly from a raw (unscaled) DataFrame.
-
-    The function scales the data using the provided scaler, builds sequences,
-    and returns unscaled real + predicted arrays.
-
-    Parameters
-    ----------
-    df       : DataFrame with at least STATE_COLS + CONTROL_COLS columns.
-    scaler   : The fitted StandardScaler from training.
-
-    Returns
-    -------
-    y_real, y_pred : unscaled numpy arrays of shape (N, n_controls)
-    """
-    if device is None:
-        device = next(model.parameters()).device
-
-    features = STATE_COLS + CONTROL_COLS
-    scaled   = scaler.transform(df[features].values.astype(np.float32))
-    scaled_df = pd.DataFrame(scaled, columns=features)
-
-    dataset = RNN_Dataset(scaled_df, STATE_COLS, CONTROL_COLS,
-                          seq_len=seq_len, stride=stride)
-    loader  = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
-                                          shuffle=False)
-
-    return predict_loader(model, loader, scaler, device)
-
-
-def predict_single_sequence(
-    model: RNN,
-    sequence: np.ndarray,          # shape (seq_len, n_states)  – UNSCALED
-    scaler: StandardScaler,
-    device: torch.device = None,
-) -> np.ndarray:
-    """
-    Predict control outputs for ONE sequence (e.g. a live window).
-
-    Parameters
-    ----------
-    sequence : raw (unscaled) state values, shape (seq_len, len(STATE_COLS))
-
-    Returns
-    -------
-    prediction : unscaled control outputs, shape (len(CONTROL_COLS),)
-    """
-    if device is None:
-        device = next(model.parameters()).device
-
-    n_state   = len(STATE_COLS)
-    n_control = len(CONTROL_COLS)
-    n_total   = n_state + n_control
-
-    # Scale the state part only (pad controls with 0 for the scaler)
-    dummy = np.zeros((len(sequence), n_total), dtype=np.float32)
-    dummy[:, :n_state] = sequence
-    scaled_states = scaler.transform(dummy)[:, :n_state]
-
-    x = torch.tensor(scaled_states, dtype=torch.float32).unsqueeze(0).to(device)  # (1, T, n_state)
-
-    model.eval()
-    with torch.no_grad():
-        pred_scaled = model(x).cpu().numpy()[0]  # (n_control,)
-
-    # Unscale prediction
-    dummy_out = np.zeros((1, n_total), dtype=np.float32)
-    dummy_out[0, n_state:] = pred_scaled
-    unscaled = scaler.inverse_transform(dummy_out)[0, n_state:]
-
-    return unscaled
-
-
-# ─────────────────────────────────────────────────────────────────────────────
-# 3.  Visualisation
-# ─────────────────────────────────────────────────────────────────────────────
-
-def plot_predictions(
-    y_real: np.ndarray,
-    y_pred: np.ndarray,
-    control_cols: list[str] = CONTROL_COLS,
-    n_samples: int = 5000,
-    title_prefix: str = "RNN",
-    save_path: str = None,
-) -> None:
-    """
-    Plot unscaled real vs predicted control outputs side-by-side.
-
-    Parameters
-    ----------
-    y_real, y_pred : outputs from predict_loader / predict_dataframe
-    n_samples      : how many time-steps to display (keeps plots readable)
-    save_path      : if given, save figure to this file instead of showing
-    """
-    n_controls = len(control_cols)
-    xs = np.arange(min(n_samples, len(y_real)))
-
-    fig, axes = plt.subplots(n_controls, 1,
-                             figsize=(14, 4 * n_controls),
-                             sharex=True)
-
-    if n_controls == 1:
-        axes = [axes]
-
-    for i, (ax, col) in enumerate(zip(axes, control_cols)):
-        ax.plot(xs, y_real[:len(xs), i], label="Real",      linewidth=1.2,
-                color="steelblue")
-        ax.plot(xs, y_pred[:len(xs), i], label="Predicted", linewidth=1.2,
-                color="tomato", linestyle="--")
-        ax.set_ylabel(col, fontsize=11)
-        ax.legend(loc="upper right", fontsize=9)
-        ax.grid(True, alpha=0.3)
-
-        # residual shading
-        ax.fill_between(xs,
-                        y_real[:len(xs), i],
-                        y_pred[:len(xs), i],
-                        alpha=0.15, color="red", label="Error")
-
-    axes[-1].set_xlabel("Sequence index", fontsize=11)
-    fig.suptitle(f"{title_prefix} – Real vs Predicted (unscaled)", fontsize=13)
-    plt.tight_layout()
-
-    if save_path:
-        plt.savefig(save_path, dpi=150)
-        print(f"[plot] Figure saved to '{save_path}'")
-    else:
-        plt.show()
-
-
-def plot_error_distribution(
-    y_real: np.ndarray,
-    y_pred: np.ndarray,
-    control_cols: list[str] = CONTROL_COLS,
-    save_path: str = None,
-) -> None:
-    
-    n_controls = len(control_cols)
-    fig, axes = plt.subplots(1, n_controls,
-                             figsize=(6 * n_controls, 4))
-    if n_controls == 1:
-        axes = [axes]
-
-    for ax, col, i in zip(axes, control_cols, range(n_controls)):
-        residuals = y_real[:, i] - y_pred[:, i]
-        ax.hist(residuals, bins=60, color="steelblue", edgecolor="white",
-                alpha=0.8)
-        ax.axvline(0, color="red", linestyle="--", linewidth=1.5)
-        ax.set_title(f"{col} – residual distribution", fontsize=11)
-        ax.set_xlabel("Error (real – predicted)")
-        ax.set_ylabel("Count")
-        ax.grid(True, alpha=0.3)
-
-    plt.tight_layout()
-    if save_path:
-        plt.savefig(save_path, dpi=150)
-        print(f"[plot] Figure saved to '{save_path}'")
-    else:
-        plt.show()
-
-
-def print_metrics(y_real: np.ndarray, y_pred: np.ndarray,
-                  control_cols: list[str] = CONTROL_COLS) -> None:
-
-    print("\n── Prediction Metrics (unscaled)")
-    for i, col in enumerate(control_cols):
-        err  = y_real[:, i] - y_pred[:, i]
-        mae  = np.mean(np.abs(err))
-        rmse = np.sqrt(np.mean(err ** 2))
-        maxe = np.max(np.abs(err))
-        print(f"  {col:25s}  MAE={mae:.4f}  RMSE={rmse:.4f}  MaxErr={maxe:.4f}")
-    print("─" * 55)
-
-
-
-def evaluate_and_plot(
-    model_path: str = MODEL_PATH,
-    save_fig: str = None,
-) -> tuple[np.ndarray, np.ndarray]:
-
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-    print("[evaluate_and_plot] Loading data …")
-    df = make_single_df()
-    gc.collect()
-
-    _, test_dataset, _, test_loader, scaler = make_sequence_datasets(
-        df, STATE_COLS, CONTROL_COLS,
-        seq_len=SEQ_LEN, stride=STRIDE, batch_size=BATCH_SIZE
-    )
-    del df
-    gc.collect()
-
-    model  = load_model(model_path, device)
-    y_real, y_pred = predict_loader(model, test_loader, scaler, device)
-
-    print_metrics(y_real, y_pred)
-    plot_predictions(y_real, y_pred, save_path=save_fig)
-    plot_error_distribution(y_real, y_pred)
-
-    return y_real, y_pred
-
-
-def get_sequence_timestamps(test_dataset, sample_idx, df_raw,
-                            seq_len=SEQ_LEN, stride=STRIDE,
-                            timestamp_col="timestamp"):
-
-    # test_dataset starts after the train split in df_raw.
-    # make_sequence_datasets does an 80/20 split on df_raw rows first,
-    # so the test portion starts at this row:
-    n_total = len(df_raw)
-    train_rows = int(n_total * 0.8)  # must match your split ratio
-
-    # within the test portion, sequence i starts at row: i * stride
-    seq_start_in_test = sample_idx * stride
-    start_row = train_rows + seq_start_in_test
-    end_row = start_row + seq_len
-
-    if end_row > n_total:
-        raise IndexError(
-            f"sample_idx {sample_idx} goes out of bounds "
-            f"(start_row={start_row}, df length={n_total})"
-        )
-
-    timestamps = df_raw[timestamp_col].iloc[start_row:end_row].reset_index(drop=True)
-
-    print(f"Sequence {sample_idx}")
-    print(f"  df rows  : {start_row} → {end_row}")
-    print(f"  start    : {timestamps.iloc[0]}")
-    print(f"  end      : {timestamps.iloc[-1]}")
-    print(f"  duration : {timestamps.iloc[-1] - timestamps.iloc[0]}")
-
-    return timestamps, start_row, end_row
-
-
-
-import matplotlib.pyplot as plt
-import numpy as np
-def plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx):
-    # Normalize sample_idx to always be a list
-    if isinstance(sample_idx, int):
-        sample_idx = [sample_idx]
 
+def evaluate_model(model, df_test_scaled, scaler, state_cols, control_cols, start_idx=0, n_samples=None):
     model.eval()
     device = next(model.parameters()).device
     n_states   = len(state_cols)
     n_controls = len(control_cols)
-    seq_len    = 300
-
-    def unscale_states(arr):
-        state_mean = scaler.mean_[:n_states]
-        state_std  = scaler.scale_[:n_states]
-        return arr * state_std + state_mean
+    cols       = state_cols + control_cols
 
-    def unscale_controls(arr):
-        dummy = np.zeros((seq_len, n_states + n_controls))
-        dummy[:, n_states:] = arr
-        return scaler.inverse_transform(dummy)[:, n_states:]
+    if n_samples is None:
+        n_samples = len(df_test_scaled) - start_idx
 
-    time_controls = np.arange(seq_len) * 0.1
+    h, c = None, None
+    all_y_true, all_y_pred = [], []
 
-    # ── Collect predictions for all samples ──────────────────────────────────
-    results = []
-    for idx in sample_idx:
-        x_input, y_target = test_dataset[idx]
+    for i in range(start_idx, start_idx + n_samples):
+        row = df_test_scaled[cols].iloc[i].values.astype(np.float32)
+        x_tensor = torch.tensor(row[:n_states]).unsqueeze(0).unsqueeze(0).to(device)
 
         with torch.no_grad():
-            x_tensor = x_input.to(device).unsqueeze(0)
-            y_pred   = model(x_tensor).squeeze(0).cpu().numpy()
-
-        x_np     = x_input.numpy()
-        y_target = y_target.numpy()
-
-        results.append({
-            "idx":       idx,
-            "x_np":      x_np,
-            "y_target":  unscale_controls(y_target),
-            "y_pred":    unscale_controls(y_pred),
-            "x_unscaled": unscale_states(x_np),
-        })
+            hidden = (h, c) if h is not None else None
+            y_pred_tensor, (h, c) = model(x_tensor, hidden=hidden)
+            h, c = h.detach(), c.detach()
 
-    n_samples      = len(results)
-    time_states    = np.arange(results[0]["x_np"].shape[0]) * 0.1
-
-    # ── Plot controls ─────────────────────────────────────────────────────────
-    fig, axes = plt.subplots(
-        n_controls, n_samples,
-        figsize=(8 * n_samples, 4 * n_controls),
-        sharex=True, sharey="row",
-        squeeze=False,          # always 2-D array of axes
-    )
-
-    for col_j, r in enumerate(results):
-        for row_i, col_name in enumerate(control_cols):
-            ax = axes[row_i, col_j]
-            ax.plot(time_controls, r["y_target"][:, row_i], "g-",  label="Actual (Driver)")
-            ax.plot(time_controls, r["y_pred"][:,   row_i], "r--", label="Predicted (RNN)")
-            ax.set_ylabel(col_name)
-            ax.grid(True)
-            if row_i == 0:
-                ax.set_title(f"Sample {r['idx']}")
-            if row_i == n_controls - 1:
-                ax.set_xlabel("Time (seconds)")
-            if col_j == 0:
-                ax.legend()
-
-    fig.suptitle("Control Trajectories", y=1.01)
-    plt.tight_layout()
-    plt.show()
+        all_y_true.append(row[n_states:])
+        all_y_pred.append(y_pred_tensor.squeeze().cpu().numpy())
 
+    # unscale
+    def unscale(arr):
+        dummy = np.zeros((len(arr), n_states + n_controls))
+        dummy[:, n_states:] = arr
+        return scaler.inverse_transform(dummy)[:, n_states:]
 
-    fig2, axes2 = plt.subplots(
-        n_states, n_samples,
-        figsize=(8 * n_samples, 4 * n_states),
-        sharex=True, sharey="row",
-        squeeze=False,
-    )
+    y_true = unscale(np.array(all_y_true))
+    y_pred = unscale(np.array(all_y_pred).reshape(-1, n_controls))
 
-    for col_j, r in enumerate(results):
-        for row_i, col_name in enumerate(state_cols):
-            ax = axes2[row_i, col_j]
-            ax.plot(time_states, r["x_unscaled"][:, row_i], "b-", label=col_name)
-            ax.set_ylabel(col_name)
-            ax.grid(True)
-            if row_i == 0:
-                ax.set_title(f"Sample {r['idx']}")
-            if row_i == n_states - 1:
-                ax.set_xlabel("Time (seconds)")
-            if col_j == 0:
-                ax.legend()
 
-    fig2.suptitle("Input State Sequences", y=1.01)
-    plt.tight_layout()
-    plt.show()
-#
-# def plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx):
-#     model.eval()
-#     x_input, y_target = test_dataset[sample_idx]
-#
-# # x_input should be [seq_len, n_states]
-#     print(x_input.shape)
-#
-#     x_np = x_input.numpy()  # [seq_len, n_states]
-#
-#     # derive time axes from actual array shapes, not seq_len variable
-#     time_controls = np.arange(y_target.shape[0]) * 0.1
-#     time_states   = np.arange(x_np.shape[0]) * 0.1
-#     x_input, y_target = test_dataset[sample_idx]
-#
-#     with torch.no_grad():
-#         device = next(model.parameters()).device
-#         x_tensor = x_input.to(device).unsqueeze(0)
-#         y_pred = model(x_tensor).squeeze(0).cpu().numpy()
-#
-#     y_target = y_target.numpy()
-#     x_np = x_input.numpy()  # [seq_len, n_states]
-#
-#     # inverse transform controls
-#     # scaler was fit on state_cols + control_cols so controls start at index n_states
-#     n_states = len(state_cols)
-#     n_controls = len(control_cols)
-#     seq_len = 300
-#     def unscale_states(arr):
-#         state_mean = scaler.mean_[:n_states]
-#         state_std  = scaler.scale_[:n_states]
-#         return arr * state_std + state_mean
-#     def unscale_controls(arr):
-#         dummy = np.zeros((seq_len, n_states + n_controls))
-#         dummy[:, n_states:] = arr
-#         return scaler.inverse_transform(dummy)[:, n_states:]
-#
-#     y_target_unscaled = unscale_controls(y_target)
-#     y_pred_unscaled   = unscale_controls(y_pred)
-#     x_unscaled        = unscale_states(x_np)
-#
-#     time_controls = np.arange(seq_len) * 0.1
-#     time_states   = np.arange(x_np.shape[0]) * 0.1
-#
-#     # plot controls
-#     fig, axes = plt.subplots(n_controls, 1, figsize=(10, 4 * n_controls), sharex=True)
-#     if n_controls == 1:
-#         axes = [axes]
-#
-#     for i, col in enumerate(control_cols):
-#         axes[i].plot(time_controls, y_target_unscaled[:, i], 'g-', label='Actual (Driver)')
-#         axes[i].plot(time_controls, y_pred_unscaled[:, i], 'r--', label='Predicted (RNN)')
-#         axes[i].set_ylabel(col)
-#         axes[i].legend()
-#         axes[i].grid(True)
-#
-#     axes[-1].set_xlabel("Time (seconds)")
-#     plt.suptitle(f"Control Trajectory — Sample {sample_idx}")
-#     plt.tight_layout()
-#
-#     # plot states
-#     fig2, axes2 = plt.subplots(n_states, 1, figsize=(10, 4 * n_states), sharex=True)
-#     if n_states == 1:
-#         axes2 = [axes2]
-#     print(x_np.shape)        # should be [seq_len, n_states]
-#     print(x_unscaled.shape)  # should match
-#     print(time_states.shape)
-#     for i, col in enumerate(state_cols):
-#         axes2[i].plot(time_states, x_unscaled[:, i], 'b-', label=col)
-#         axes2[i].set_ylabel(col)
-#         axes2[i].legend()
-#         axes2[i].grid(True)
-#
-#     axes2[-1].set_xlabel("Time (seconds)")
-#     plt.suptitle(f"Input State Sequence — Sample {sample_idx}")
-#     plt.tight_layout()
-#     plt.show()
+    brake_true = y_true[:, 0]
+    brake_pred = y_pred[:, 0]
+    accel_true = y_true[:, 1]
+    accel_pred = y_pred[:, 1]
 
+    brake_pred_binary = (brake_pred > 0.5).astype(int)
+    brake_true_binary = (brake_true > 0.5).astype(int)
 
+    print("=== accel_position (regression) ===")
+    print(f"  MAE:  {mean_absolute_error(accel_true, accel_pred):.4f}")
+    print(f"  RMSE: {np.sqrt(mean_squared_error(accel_true, accel_pred)):.4f}")
+    print(f"  R²:   {r2_score(accel_true, accel_pred):.4f}")
 
-if __name__ == "__main__":
+    print("\n=== brake_pressed (classification) ===")
+    print(f"  Accuracy:  {accuracy_score(brake_true_binary, brake_pred_binary):.4f}")
+    print(f"  Precision: {precision_score(brake_true_binary, brake_pred_binary, zero_division=0):.4f}")
+    print(f"  Recall:    {recall_score(brake_true_binary, brake_pred_binary, zero_division=0):.4f}")
+    print(f"  F1:        {f1_score(brake_true_binary, brake_pred_binary, zero_division=0):.4f}")
+    print(f"  AUC-ROC:   {roc_auc_score(brake_true_binary, brake_pred):.4f}")
 
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        df = make_single_df()
-        train_dataset, test_dataset, train_loader, test_loader, scaler = make_sequence_datasets(
-            df, STATE_COLS, CONTROL_COLS,
-            seq_len=SEQ_LEN, stride=STRIDE, batch_size=BATCH_SIZE
-        )
-        model = load_model("rnn_model.pth", device)
-        #evaluate_and_plot(model_path=args.model, save_fig=args.save_fig)
-        plot_control_trajectory(model, test_dataset, scaler, STATE_COLS, CONTROL_COLS, [3820, 3821, 3822])
\ No newline at end of file
+#evaluate_model(model, df_test_scaled, scaler, STATE_COLS, CONTROL_COLS)
\ No newline at end of file
diff --git a/control_model/rnn_model (1).pth b/control_model/rnn_model (1).pth
new file mode 100644
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diff --git a/control_model/visualization.py b/control_model/visualization.py
index 8cc5520..2b62c65 100644
--- a/control_model/visualization.py
+++ b/control_model/visualization.py
@@ -9,22 +9,36 @@
 from RNN import RNN
 from RNN_Dataset import RNN_Dataset
 from DataPreprocessing import make_single_df, make_sequence_datasets
+from DataPreprocessing import *
 
 #constants
-STATE_COLS   = ["speed"]
-CONTROL_COLS = ["brake_pressed", "accel_position"]
-SEQ_LEN      = 300
-STRIDE       = 100
-BATCH_SIZE   = 128
-HIDDEN_SIZE  = 128
+STATE_COLS   = ["speed", "ROC"] #states
+CONTROL_COLS = ["brake_pressed", "accel_position"]  #controls
+SEQ_LEN      = 150 #30 seconds
+STRIDE       = 50 #sliding window change between consecutive sequences, reduces overlapping
+BATCH_SIZE   = 64
+EPOCHS       = 320
+HIDDEN_SIZE  = 256
 NUM_LAYERS   = 2
-MODEL_PATH   = "rnn_model.pth"
+MODEL_PATH = "rnn_model.pt"
+
+
+"""
+- the purpose of this class is to load the RNN model from its path onto the device. 
+- Also includes 2 visualization functions for single and multisequence comparisons. 
+"""
 
 
-# the purpose of this class is majorly to evaluate and visualize
 
 
 def load_model(model_path: str = MODEL_PATH, device: torch.device = None):
+    """
+    Load the RNN checkpoint from ``self.model_path`` onto the target device.
+    :param device: cpu or cuda, depending on user device. Defaults to CUDA automatically when available.
+    :return: model: RNN model loaded for evaluation mode
+
+    """
+
     if device is None:
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
@@ -41,11 +55,81 @@ def load_model(model_path: str = MODEL_PATH, device: torch.device = None):
     print(f"[load_model] Loaded '{model_path}' on {device}")
     return model
 
+def plot_control_trajectory_extended(model, df_test_scaled, scaler, state_cols, control_cols, seq_len, start_idx):
+    model.eval() #eval model and disable dropout layer
+    device = next(model.parameters()).device #send tensors to relevatn device (cpu/gpu)
+
+    n_states   = len(state_cols)
+    n_controls = len(control_cols)
+    cols       = state_cols + control_cols
+
+    h, c = None, None #initialise hidden states
+    all_states, all_y_true, all_y_pred = [], [], []
+
+    for i in range(start_idx, start_idx + seq_len): #loop over consecutive timestamps
+        row = df_test_scaled[cols].iloc[i].values.astype(np.float32)
+
+        state_vals   = row[:n_states]
+        control_vals = row[n_states:]
+        #split row into state and control
+        #convert to tensors
+        #LSTM expects shape [1,1,states] adding batch and sequence dimension
+        x_tensor = torch.tensor(state_vals).unsqueeze(0).unsqueeze(0).to(device)
+
+        with torch.no_grad(): #disable gradient calculations at inference.
+            hidden = (h, c) if h is not None else None #carry over hidden state from previous
+            y_pred_tensor, (h, c) = model(x_tensor, hidden=hidden) #run the model and get back predicted controls (tensor +hideen states)
+            h, c = h.detach(), c.detach() #break gradient flow
+            y_pred = y_pred_tensor.squeeze().cpu().numpy() #remove batch/seq dimension to convert back to numpy
+
+        all_states.append(state_vals)
+        all_y_true.append(control_vals)
+        all_y_pred.append(y_pred)
+
+    # unscale
+    #note that scaler was fit on all columns together, so subsets cannot be inverted in isolatoin
+    #build full width dummy array
+    #insert subset into the column adn then inverse transform
+
+    def unscale(arr, col_slice):
+        dummy = np.zeros((arr.shape[0], n_states + n_controls))
+        dummy[:, col_slice] = arr
+        return scaler.inverse_transform(dummy)[:, col_slice]
+
+    all_states = unscale(np.array(all_states), slice(None, n_states))
+    all_y_true = unscale(np.array(all_y_true), slice(n_states, None))
+    all_y_pred = unscale(np.array(all_y_pred).reshape(-1, n_controls), slice(n_states, None)) #reshape after squeeze()
+
+
+    time_axis  = np.arange(seq_len) * 0.1
+
+    n_rows = n_controls + n_states
+    fig, axes = plt.subplots(n_rows, 1, figsize=(14, 3 * n_rows), sharex=True)
+
+    for i, col in enumerate(control_cols):
+        axes[i].plot(time_axis, all_y_true[:, i], 'g-',  label='Actual',    linewidth=1)
+        axes[i].plot(time_axis, all_y_pred[:, i], 'r--', label='Predicted', linewidth=1)
+        axes[i].set_ylabel(col)
+        axes[i].legend()
+        axes[i].grid(True)
+
+    for i, col in enumerate(state_cols):
+        axes[n_controls + i].plot(time_axis, all_states[:, i], 'b-', label=col, linewidth=1)
+        axes[n_controls + i].set_ylabel(col)
+        axes[n_controls + i].legend()
+        axes[n_controls + i].grid(True)
+
+    axes[-1].set_xlabel("Time (seconds)")
+    plt.suptitle(f"Control Trajectory  {seq_len/10} consecutive seconds")
+    plt.tight_layout()
+    plt.show()
+
+
+
+
 
-import matplotlib.pyplot as plt
-import numpy as np
 def plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx):
-    # Normalize sample_idx to always be a list
+    # normalize sample_idx to always be a list
     if isinstance(sample_idx, int):
         sample_idx = [sample_idx]
 
@@ -67,7 +151,7 @@ def unscale_controls(arr):
 
     time_controls = np.arange(seq_len) * 0.1
 
-    # ── Collect predictions for all samples ──────────────────────────────────
+
     results = []
     for idx in sample_idx:
         x_input, y_target = test_dataset[idx]
@@ -90,12 +174,12 @@ def unscale_controls(arr):
     n_samples      = len(results)
     time_states    = np.arange(results[0]["x_np"].shape[0]) * 0.1
 
-    # ── Plot controls ─────────────────────────────────────────────────────────
+#plot controls
     fig, axes = plt.subplots(
         n_controls, n_samples,
         figsize=(8 * n_samples, 4 * n_controls),
         sharex=True, sharey="row",
-        squeeze=False,          # always 2-D array of axes
+        squeeze=False,
     )
 
     for col_j, r in enumerate(results):
@@ -140,83 +224,7 @@ def unscale_controls(arr):
     fig2.suptitle("Input State Sequences", y=1.01)
     plt.tight_layout()
     plt.show()
-#
-# def plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx):
-#     model.eval()
-#     x_input, y_target = test_dataset[sample_idx]
-#
-# # x_input should be [seq_len, n_states]
-#     print(x_input.shape)
-#
-#     x_np = x_input.numpy()  # [seq_len, n_states]
-#
-#     # derive time axes from actual array shapes, not seq_len variable
-#     time_controls = np.arange(y_target.shape[0]) * 0.1
-#     time_states   = np.arange(x_np.shape[0]) * 0.1
-#     x_input, y_target = test_dataset[sample_idx]
-#
-#     with torch.no_grad():
-#         device = next(model.parameters()).device
-#         x_tensor = x_input.to(device).unsqueeze(0)
-#         y_pred = model(x_tensor).squeeze(0).cpu().numpy()
-#
-#     y_target = y_target.numpy()
-#     x_np = x_input.numpy()  # [seq_len, n_states]
-#
-#     # inverse transform controls
-#     # scaler was fit on state_cols + control_cols so controls start at index n_states
-#     n_states = len(state_cols)
-#     n_controls = len(control_cols)
-#     seq_len = 300
-#     def unscale_states(arr):
-#         state_mean = scaler.mean_[:n_states]
-#         state_std  = scaler.scale_[:n_states]
-#         return arr * state_std + state_mean
-#     def unscale_controls(arr):
-#         dummy = np.zeros((seq_len, n_states + n_controls))
-#         dummy[:, n_states:] = arr
-#         return scaler.inverse_transform(dummy)[:, n_states:]
-#
-#     y_target_unscaled = unscale_controls(y_target)
-#     y_pred_unscaled   = unscale_controls(y_pred)
-#     x_unscaled        = unscale_states(x_np)
-#
-#     time_controls = np.arange(seq_len) * 0.1
-#     time_states   = np.arange(x_np.shape[0]) * 0.1
-#
-#     # plot controls
-#     fig, axes = plt.subplots(n_controls, 1, figsize=(10, 4 * n_controls), sharex=True)
-#     if n_controls == 1:
-#         axes = [axes]
-#
-#     for i, col in enumerate(control_cols):
-#         axes[i].plot(time_controls, y_target_unscaled[:, i], 'g-', label='Actual (Driver)')
-#         axes[i].plot(time_controls, y_pred_unscaled[:, i], 'r--', label='Predicted (RNN)')
-#         axes[i].set_ylabel(col)
-#         axes[i].legend()
-#         axes[i].grid(True)
-#
-#     axes[-1].set_xlabel("Time (seconds)")
-#     plt.suptitle(f"Control Trajectory — Sample {sample_idx}")
-#     plt.tight_layout()
-#
-#     # plot states
-#     fig2, axes2 = plt.subplots(n_states, 1, figsize=(10, 4 * n_states), sharex=True)
-#     if n_states == 1:
-#         axes2 = [axes2]
-#     print(x_np.shape)        # should be [seq_len, n_states]
-#     print(x_unscaled.shape)  # should match
-#     print(time_states.shape)
-#     for i, col in enumerate(state_cols):
-#         axes2[i].plot(time_states, x_unscaled[:, i], 'b-', label=col)
-#         axes2[i].set_ylabel(col)
-#         axes2[i].legend()
-#         axes2[i].grid(True)
-#
-#     axes2[-1].set_xlabel("Time (seconds)")
-#     plt.suptitle(f"Input State Sequence — Sample {sample_idx}")
-#     plt.tight_layout()
-#     plt.show()
+
 
 
 
@@ -228,6 +236,6 @@ def unscale_controls(arr):
             df, STATE_COLS, CONTROL_COLS,
             seq_len=SEQ_LEN, stride=STRIDE, batch_size=BATCH_SIZE
         )
-        model = load_model("rnn_model.pth", device)
+        model = load_model("rnn_model (1).pth", device)
         #evaluate_and_plot(model_path=args.model, save_fig=args.save_fig)
         plot_control_trajectory(model, test_dataset, scaler, STATE_COLS, CONTROL_COLS, [3820, 3821, 3822])
\ No newline at end of file

From 83a31c55cc069675baea83ee6aa023f62eba2f54 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Sat, 11 Apr 2026 11:18:11 -0700
Subject: [PATCH 38/49] adding documentation

---
 control_model/RNN.py | 72 +++++++++++++++++++++++++++-----------------
 1 file changed, 44 insertions(+), 28 deletions(-)

diff --git a/control_model/RNN.py b/control_model/RNN.py
index 8e4998b..5d72ffe 100644
--- a/control_model/RNN.py
+++ b/control_model/RNN.py
@@ -1,50 +1,66 @@
+# necessary imports:
 import torch
 from torch import nn
 
-# model class to declare RNN and defining a forward pass of the model
-
+# Set to cuda/gpu if available, else default to cpu.
 device = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else "cpu"
 
 
+# Some general overview on RNNs:
+# - Essentially just a FNN with a non-linear output (hidden layer) that is passed onto the next. So there's an additional set of weights and biases.
+# - LSTMs (what is being defined below) is a type of RNN that is more capable of learning long-term dependencies.
+# Pytorch's LSTM module is hardcoded to follow tanh and sigmoid, unlike the RNN module which will let you choose between ReLU and tanh.
 
 class RNN(nn.Module):
-    """
-    Main class of RNN architecture.
-    :param input_size refers to the number of input variables
-    :param hidden_size refers to the dimension of memory inside the LSTM
-    :param num_layers
-    :param seq_length
-    :param output_size
-    """
 
     def __init__(self, input_size, hidden_size, num_layers, seq_length, output_size):
+        """
+           Main class of RNN architecture. This LSTM has 2 input features (speed+curvature) and 2 output features (brake pressed and accelerator position).
+           Activation Sigmoid with two hidden layers i.e. a stacked LSTM where the second LSTM takes in the outputs of the first LSTM to compute final results.
+           Cell activation and final hidden state calculation is defaulted to tanh
+           Unidirectional LSTM.
+
+           :param input_size refers to the number of input variables
+           :param hidden_size refers to the dimension of memory inside the LSTM
+           :param num_layers refers to the number of recurrent layers
+           :param seq_length refers to the length of time sequence. In this use case, this will be a constant value of 15 seconds.
+           :param output_size refers to the number of output variables
+
+        """
+
         # inherits from nn.Module
         super(RNN, self).__init__()
         self.hidden_size = hidden_size  # dim of memory inside lstm ie no of features in the hidden state that persists between timesteps
         # a higher hidden size usually corresponds to complex dependencies
         self.num_layers = num_layers  # stacked lstm layers
         # lstm: long short term memory - looks at long term dependencies in sequential data
-        # default activation is tanh
-        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)  # correspond to input data shape
+        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, dropout=0.1, batch_first=True)
+        self.dropout = nn.Dropout(0.1)
         self.seq_length = seq_length  # no of timestamps to look at to predict the next control output
 
         # num classes is the no of outputs predicted by the model
         # to convert memory vector to outputs (shaping constraints)
         self.fc = nn.Linear(hidden_size, output_size)
 
-    def forward(self, x):
-        # x shape : [batch size, input size]
-        # associate the state array sequence with timeseries dependency
-        # inital hidden, cell states - these are internal memory vectors
-        # hidden = short term memory, current output of LSTM at a given time
-        hidden_state = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
-
-        # cell state = long term memory, stores trends (remmebers info over many time steps)
-        cell_states = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
-        # forward propagate lstm
-        out, _ = self.lstm(x, (hidden_state,
-                               cell_states))  # out; tensor of shape(batch_size, seq_length, hidden_size) - at the final time step
-        # decode the hidden state of t and predict the corresponding sequence of controls
-        out = self.fc(out)
-        # out shape: [batch_size, seq_length, hidden_size]
-        return out
+    def forward(self, x, hidden=None):
+        """
+        Method to define the forward pass associated with the RNN.
+        :param x refers to input tensor of shape [batch_size, seq, input_size].
+        :param hidden refers to the previous hidden and cell states, defaults to None.
+        :return the output of shape [batch_size, seq,  output_size] and the updated hidden and cell state tensors.
+
+        """
+
+        if hidden is None:
+            # initialize hidden state with zeroes
+            h = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
+            # cell state = long term memory, stores trends. Initialise with zeroes.
+            c = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
+            # hidden = short term memory, current output of LSTM at a given time
+            # h and c are internal memory vectors
+            hidden = (h, c)
+        out, (h, c) = self.lstm(x, hidden)
+        out = self.dropout(
+            out)  # Prevents overfitting by randomly zeroing out elements of the input tensor with probability p during training.
+        out = self.fc(out)  # index the hidden state of the last time stamp.
+        return out, (h,c)  # return output of shape [batch_size, seq_length, hidden_size] and indexed hidden state of last timestep.

From 5d1e2a73f74eab3562f0379d09d73e86ea265cc7 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Sat, 16 May 2026 11:06:43 -0700
Subject: [PATCH 39/49] adding documentation

---
 array_temp/Control_Model.ipynb         | 2449 +++++++++---------------
 control_model/DataPreprocessing.py     |   63 +-
 control_model/RNN_Dataset.py           |   47 +-
 control_model/control_model_predict.py |  240 +++
 4 files changed, 1215 insertions(+), 1584 deletions(-)
 create mode 100644 control_model/control_model_predict.py

diff --git a/array_temp/Control_Model.ipynb b/array_temp/Control_Model.ipynb
index 84e6583..46c7f5f 100644
--- a/array_temp/Control_Model.ipynb
+++ b/array_temp/Control_Model.ipynb
@@ -31,8 +31,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-14T21:31:07.379449Z",
-     "start_time": "2026-03-14T21:31:06.558101Z"
+     "end_time": "2026-03-17T16:00:18.731717Z",
+     "start_time": "2026-03-17T16:00:16.397284Z"
     }
    },
    "cell_type": "code",
@@ -56,8 +56,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-14T21:31:16.934611Z",
-     "start_time": "2026-03-14T21:31:14.765114Z"
+     "end_time": "2026-03-17T16:00:26.825458Z",
+     "start_time": "2026-03-17T16:00:21.630980Z"
     }
    },
    "cell_type": "code",
@@ -137,8 +137,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-14T21:31:23.352755Z",
-     "start_time": "2026-03-14T21:31:18.279901Z"
+     "end_time": "2026-03-17T16:00:30.880356Z",
+     "start_time": "2026-03-17T16:00:28.523652Z"
     }
    },
    "cell_type": "code",
@@ -150,60 +150,467 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-14T21:29:36.577696Z",
-     "start_time": "2026-03-14T21:29:36.558210Z"
+     "end_time": "2026-03-17T16:00:31.795931Z",
+     "start_time": "2026-03-17T16:00:31.778061Z"
     }
    },
    "cell_type": "code",
-   "source": "position.index",
+   "source": [
+    "reverse_coords = [\n",
+    "    [ 37.0011529 , -86.36837867],\n",
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+    "    [ 37.00133071, -86.36801267],\n",
+    "    [ 37.00143614, -86.36779264],\n",
+    "    [ 37.00152389, -86.3675912 ],\n",
+    "    [ 37.00160574, -86.36740819],\n",
+    "    [ 37.00167596, -86.36725066],\n",
+    "    [ 37.00175285, -86.36709064],\n",
+    "    [ 37.00183166, -86.36691875],\n",
+    "    [ 37.00192538, -86.36670617],\n",
+    "    [ 37.00200136, -86.36653034],\n",
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+    "    [ 37.00237732, -86.36569622],\n",
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+    "    [ 37.00252912, -86.36537128],\n",
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+    "    [ 37.00011471, -86.3714889 ],\n",
+    "    [ 37.00027357, -86.37138907],\n",
+    "    [ 37.0003852 , -86.37123775],\n",
+    "    [ 37.00042033, -86.37099884],\n",
+    "    [ 37.00038811, -86.3707825 ],\n",
+    "    [ 37.00031195, -86.37062837],\n",
+    "    [ 37.00026491, -86.37041498],\n",
+    "    [ 37.00030254, -86.37020247],\n",
+    "    [ 37.00038472, -86.3700261 ],\n",
+    "    [ 37.0004699 , -86.36984594],\n",
+    "    [ 37.00056383, -86.36964375],\n",
+    "    [ 37.00064015, -86.3694857 ],\n",
+    "    [ 37.00070993, -86.36934481],\n",
+    "    [ 37.0008098 , -86.36912411],\n",
+    "    [ 37.00090662, -86.36891836],\n",
+    "    [ 37.00098578, -86.36874579],\n",
+    "    [ 37.00107373, -86.36854755]\n",
+    "  ]\n",
+    "\n",
+    "coords = reverse_coords[::-1] # coordinates in the correct order; starting coordinate goes first\n",
+    "\n"
+   ],
+   "id": "df7a953996975d7c",
+   "outputs": [],
+   "execution_count": 4
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-17T16:00:33.037250Z",
+     "start_time": "2026-03-17T16:00:33.023027Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import math\n",
+    "\n",
+    "def calculate_circular_track_curvature(coords, step=1):\n",
+    "    \"\"\"\n",
+    "    Calculates curvature for a CLOSED LOOP (circular) race track.\n",
+    "\n",
+    "    :param coords: List of tuples [(lat, lon), ...] assumed to be in order.\n",
+    "    :param step: The smoothing stride (1=sensitive, 5=smooth).\n",
+    "    :return: List of radii in meters.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    if not coords:\n",
+    "        return []\n",
+    "\n",
+    "    # 1. Convert Lat/Lon to Local X/Y (Meters)\n",
+    "    # We use the first point as the reference center for projection\n",
+    "    center_lat = coords[0][0]\n",
+    "\n",
+    "    # Calculate conversion factors based on the track's latitude\n",
+    "    meters_per_deg_lat = 111132.954 - 559.822 * math.cos(2 * math.radians(center_lat))\n",
+    "    meters_per_deg_lon = 111412.84 * math.cos(math.radians(center_lat))\n",
+    "\n",
+    "    xy_points = []\n",
+    "    for lat, lon in coords:\n",
+    "        y = lat * meters_per_deg_lat\n",
+    "        x = lon * meters_per_deg_lon\n",
+    "        xy_points.append((x, y))\n",
+    "\n",
+    "    radii = []\n",
+    "    n = len(xy_points)\n",
+    "\n",
+    "    # 2. Calculate Radius with Wrap-Around Indexing\n",
+    "    for i in range(n):\n",
+    "        # Use modulo (%) to wrap around the start/finish line\n",
+    "        # If i=0 and step=1, the \"previous\" point becomes the last point in the list\n",
+    "        p1 = xy_points[(i - step) % n]\n",
+    "        p2 = xy_points[i]\n",
+    "        p3 = xy_points[(i + step) % n]\n",
+    "\n",
+    "        # Calculate side lengths (Euclidean distance)\n",
+    "        a = math.dist(p1, p2)\n",
+    "        b = math.dist(p2, p3)\n",
+    "        c = math.dist(p3, p1)\n",
+    "\n",
+    "        # Shoelace formula for Area of the triangle\n",
+    "        area = 0.5 * (p1[0]*(p2[1] - p3[1]) +\n",
+    "                         p2[0]*(p3[1] - p1[1]) +\n",
+    "                         p3[0]*(p1[1] - p2[1]))\n",
+    "\n",
+    "        # Calculate Radius (R = abc / 4A)\n",
+    "        if  -1e-6 < area < 1e-6:\n",
+    "            radii.append(0) # Straight line\n",
+    "        else:\n",
+    "            R = (a * b * c) / (4 * area)\n",
+    "            #if R > 10000:\n",
+    "              #  R = 0\n",
+    "            radii.append(1/R)\n",
+    "\n",
+    "\n",
+    "    return radii\n",
+    "\n",
+    "#radius_of_curvature = calculate_circular_track_curvature(coords, step=2)"
+   ],
    "id": "5686704ed95bfd2a",
+   "outputs": [],
+   "execution_count": 5
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-17T16:05:47.939946Z",
+     "start_time": "2026-03-17T16:05:47.868997Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "radius_of_curvature = calculate_circular_track_curvature(coords, step=2)\n",
+    "\n",
+    "# Squeeze the single column DataFrame into a Series first\n",
+    "position_series = position.squeeze()\n",
+    "\n",
+    "calculated_roc = position_series.map(\n",
+    "    lambda pos: radius_of_curvature[int(pos) % len(radius_of_curvature)], na_action = 'ignore'\n",
+    ")\n",
+    "\n",
+    "calculated_roc_df = calculated_roc.to_frame(name='curvature').dropna()"
+   ],
+   "id": "1e869fce8d6aa993",
+   "outputs": [],
+   "execution_count": 11
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-14T21:56:31.298810Z",
+     "start_time": "2026-03-14T21:56:29.850014Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "calculated_position_df = pd.DataFrame(position).sort_index()\n",
+    "calculated_roc = [radius_of_curvature[int(i) - 1]  for i in position]\n",
+    "calculated_roc_df = pd.DataFrame(calculated_roc).sort_index()\n",
+    "\n",
+    "merged_df = pd.merge_asof(\n",
+    "    speed_kph,\n",
+    "    calculated_roc_df,\n",
+    "    left_index=True,\n",
+    "    right_index=True,\n",
+    "    direction='nearest'\n",
+    ")"
+   ],
+   "id": "a2d98e8eb6fc3fa9",
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "DatetimeIndex(['2024-07-16 07:49:53.648000002',\n",
-       "               '2024-07-16 07:49:53.747999668',\n",
-       "               '2024-07-16 07:49:53.847999573',\n",
-       "               '2024-07-16 07:49:53.947999239',\n",
-       "               '2024-07-16 07:49:54.047998905',\n",
-       "               '2024-07-16 07:49:54.147998810',\n",
-       "               '2024-07-16 07:49:54.247998476',\n",
-       "               '2024-07-16 07:49:54.347998381',\n",
-       "               '2024-07-16 07:49:54.447998047',\n",
-       "               '2024-07-16 07:49:54.547997713',\n",
-       "               ...\n",
-       "               '2024-07-16 14:53:48.585002184',\n",
-       "               '2024-07-16 14:53:48.685001850',\n",
-       "               '2024-07-16 14:53:48.785001516',\n",
-       "               '2024-07-16 14:53:48.885001421',\n",
-       "               '2024-07-16 14:53:48.985001087',\n",
-       "               '2024-07-16 14:53:49.085000992',\n",
-       "               '2024-07-16 14:53:49.185000658',\n",
-       "               '2024-07-16 14:53:49.285000324',\n",
-       "               '2024-07-16 14:53:49.385000229',\n",
-       "               '2024-07-16 14:53:49.484999895'],\n",
-       "              dtype='datetime64[ns]', length=254360, freq=None)"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "MergeError",
+     "evalue": "incompatible merge keys [0] dtype('<M8[ns]') and dtype('int64'), must be the same type",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mMergeError\u001B[39m                                Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[11]\u001B[39m\u001B[32m, line 5\u001B[39m\n\u001B[32m      2\u001B[39m calculated_roc = [radius_of_curvature[\u001B[38;5;28mint\u001B[39m(i) - \u001B[32m1\u001B[39m]  \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m position]\n\u001B[32m      3\u001B[39m calculated_roc_df = pd.DataFrame(calculated_roc).sort_index()\n\u001B[32m----> \u001B[39m\u001B[32m5\u001B[39m merged_df = \u001B[43mpd\u001B[49m\u001B[43m.\u001B[49m\u001B[43mmerge_asof\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m      6\u001B[39m \u001B[43m    \u001B[49m\u001B[43mspeed_kph\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m      7\u001B[39m \u001B[43m    \u001B[49m\u001B[43mcalculated_roc_df\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m      8\u001B[39m \u001B[43m    \u001B[49m\u001B[43mleft_index\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[32m      9\u001B[39m \u001B[43m    \u001B[49m\u001B[43mright_index\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[32m     10\u001B[39m \u001B[43m    \u001B[49m\u001B[43mdirection\u001B[49m\u001B[43m=\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43mnearest\u001B[39;49m\u001B[33;43m'\u001B[39;49m\n\u001B[32m     11\u001B[39m \u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\reshape\\merge.py:691\u001B[39m, in \u001B[36mmerge_asof\u001B[39m\u001B[34m(left, right, on, left_on, right_on, left_index, right_index, by, left_by, right_by, suffixes, tolerance, allow_exact_matches, direction)\u001B[39m\n\u001B[32m    440\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[34mmerge_asof\u001B[39m(\n\u001B[32m    441\u001B[39m     left: DataFrame | Series,\n\u001B[32m    442\u001B[39m     right: DataFrame | Series,\n\u001B[32m   (...)\u001B[39m\u001B[32m    454\u001B[39m     direction: \u001B[38;5;28mstr\u001B[39m = \u001B[33m\"\u001B[39m\u001B[33mbackward\u001B[39m\u001B[33m\"\u001B[39m,\n\u001B[32m    455\u001B[39m ) -> DataFrame:\n\u001B[32m    456\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    457\u001B[39m \u001B[33;03m    Perform a merge by key distance.\u001B[39;00m\n\u001B[32m    458\u001B[39m \n\u001B[32m   (...)\u001B[39m\u001B[32m    689\u001B[39m \u001B[33;03m    4 2016-05-25 13:30:00.048   AAPL   98.00       100     NaN     NaN\u001B[39;00m\n\u001B[32m    690\u001B[39m \u001B[33;03m    \"\"\"\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m691\u001B[39m     op = \u001B[43m_AsOfMerge\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    692\u001B[39m \u001B[43m        \u001B[49m\u001B[43mleft\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    693\u001B[39m \u001B[43m        \u001B[49m\u001B[43mright\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    694\u001B[39m \u001B[43m        \u001B[49m\u001B[43mon\u001B[49m\u001B[43m=\u001B[49m\u001B[43mon\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    695\u001B[39m \u001B[43m        \u001B[49m\u001B[43mleft_on\u001B[49m\u001B[43m=\u001B[49m\u001B[43mleft_on\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    696\u001B[39m \u001B[43m        \u001B[49m\u001B[43mright_on\u001B[49m\u001B[43m=\u001B[49m\u001B[43mright_on\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    697\u001B[39m \u001B[43m        \u001B[49m\u001B[43mleft_index\u001B[49m\u001B[43m=\u001B[49m\u001B[43mleft_index\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    698\u001B[39m \u001B[43m        \u001B[49m\u001B[43mright_index\u001B[49m\u001B[43m=\u001B[49m\u001B[43mright_index\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    699\u001B[39m \u001B[43m        \u001B[49m\u001B[43mby\u001B[49m\u001B[43m=\u001B[49m\u001B[43mby\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    700\u001B[39m \u001B[43m        \u001B[49m\u001B[43mleft_by\u001B[49m\u001B[43m=\u001B[49m\u001B[43mleft_by\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    701\u001B[39m \u001B[43m        \u001B[49m\u001B[43mright_by\u001B[49m\u001B[43m=\u001B[49m\u001B[43mright_by\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    702\u001B[39m \u001B[43m        \u001B[49m\u001B[43msuffixes\u001B[49m\u001B[43m=\u001B[49m\u001B[43msuffixes\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    703\u001B[39m \u001B[43m        \u001B[49m\u001B[43mhow\u001B[49m\u001B[43m=\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43masof\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m    704\u001B[39m \u001B[43m        \u001B[49m\u001B[43mtolerance\u001B[49m\u001B[43m=\u001B[49m\u001B[43mtolerance\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    705\u001B[39m \u001B[43m        \u001B[49m\u001B[43mallow_exact_matches\u001B[49m\u001B[43m=\u001B[49m\u001B[43mallow_exact_matches\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    706\u001B[39m \u001B[43m        \u001B[49m\u001B[43mdirection\u001B[49m\u001B[43m=\u001B[49m\u001B[43mdirection\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    707\u001B[39m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    708\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m op.get_result()\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\reshape\\merge.py:2000\u001B[39m, in \u001B[36m_AsOfMerge.__init__\u001B[39m\u001B[34m(self, left, right, on, left_on, right_on, left_index, right_index, by, left_by, right_by, suffixes, how, tolerance, allow_exact_matches, direction)\u001B[39m\n\u001B[32m   1994\u001B[39m     msg = (\n\u001B[32m   1995\u001B[39m         \u001B[33m\"\u001B[39m\u001B[33mallow_exact_matches must be boolean, \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m   1996\u001B[39m         \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mpassed \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mself\u001B[39m.allow_exact_matches\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m\"\u001B[39m\n\u001B[32m   1997\u001B[39m     )\n\u001B[32m   1998\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m MergeError(msg)\n\u001B[32m-> \u001B[39m\u001B[32m2000\u001B[39m \u001B[43m_OrderedMerge\u001B[49m\u001B[43m.\u001B[49m\u001B[34;43m__init__\u001B[39;49m\u001B[43m(\u001B[49m\n\u001B[32m   2001\u001B[39m \u001B[43m    \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m   2002\u001B[39m \u001B[43m    \u001B[49m\u001B[43mleft\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   2003\u001B[39m \u001B[43m    \u001B[49m\u001B[43mright\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   2004\u001B[39m \u001B[43m    \u001B[49m\u001B[43mon\u001B[49m\u001B[43m=\u001B[49m\u001B[43mon\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   2005\u001B[39m \u001B[43m    \u001B[49m\u001B[43mleft_on\u001B[49m\u001B[43m=\u001B[49m\u001B[43mleft_on\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   2006\u001B[39m \u001B[43m    \u001B[49m\u001B[43mright_on\u001B[49m\u001B[43m=\u001B[49m\u001B[43mright_on\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   2007\u001B[39m \u001B[43m    \u001B[49m\u001B[43mleft_index\u001B[49m\u001B[43m=\u001B[49m\u001B[43mleft_index\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   2008\u001B[39m \u001B[43m    \u001B[49m\u001B[43mright_index\u001B[49m\u001B[43m=\u001B[49m\u001B[43mright_index\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   2009\u001B[39m \u001B[43m    \u001B[49m\u001B[43mhow\u001B[49m\u001B[43m=\u001B[49m\u001B[43mhow\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   2010\u001B[39m \u001B[43m    \u001B[49m\u001B[43msuffixes\u001B[49m\u001B[43m=\u001B[49m\u001B[43msuffixes\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   2011\u001B[39m \u001B[43m    \u001B[49m\u001B[43mfill_method\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[32m   2012\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\reshape\\merge.py:1912\u001B[39m, in \u001B[36m_OrderedMerge.__init__\u001B[39m\u001B[34m(self, left, right, on, left_on, right_on, left_index, right_index, suffixes, fill_method, how)\u001B[39m\n\u001B[32m   1898\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[34m__init__\u001B[39m(\n\u001B[32m   1899\u001B[39m     \u001B[38;5;28mself\u001B[39m,\n\u001B[32m   1900\u001B[39m     left: DataFrame | Series,\n\u001B[32m   (...)\u001B[39m\u001B[32m   1909\u001B[39m     how: JoinHow | Literal[\u001B[33m\"\u001B[39m\u001B[33masof\u001B[39m\u001B[33m\"\u001B[39m] = \u001B[33m\"\u001B[39m\u001B[33mouter\u001B[39m\u001B[33m\"\u001B[39m,\n\u001B[32m   1910\u001B[39m ) -> \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m   1911\u001B[39m     \u001B[38;5;28mself\u001B[39m.fill_method = fill_method\n\u001B[32m-> \u001B[39m\u001B[32m1912\u001B[39m     \u001B[43m_MergeOperation\u001B[49m\u001B[43m.\u001B[49m\u001B[34;43m__init__\u001B[39;49m\u001B[43m(\u001B[49m\n\u001B[32m   1913\u001B[39m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m   1914\u001B[39m \u001B[43m        \u001B[49m\u001B[43mleft\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1915\u001B[39m \u001B[43m        \u001B[49m\u001B[43mright\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1916\u001B[39m \u001B[43m        \u001B[49m\u001B[43mon\u001B[49m\u001B[43m=\u001B[49m\u001B[43mon\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1917\u001B[39m \u001B[43m        \u001B[49m\u001B[43mleft_on\u001B[49m\u001B[43m=\u001B[49m\u001B[43mleft_on\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1918\u001B[39m \u001B[43m        \u001B[49m\u001B[43mleft_index\u001B[49m\u001B[43m=\u001B[49m\u001B[43mleft_index\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1919\u001B[39m \u001B[43m        \u001B[49m\u001B[43mright_index\u001B[49m\u001B[43m=\u001B[49m\u001B[43mright_index\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1920\u001B[39m \u001B[43m        \u001B[49m\u001B[43mright_on\u001B[49m\u001B[43m=\u001B[49m\u001B[43mright_on\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1921\u001B[39m \u001B[43m        \u001B[49m\u001B[43mhow\u001B[49m\u001B[43m=\u001B[49m\u001B[43mhow\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1922\u001B[39m \u001B[43m        \u001B[49m\u001B[43msuffixes\u001B[49m\u001B[43m=\u001B[49m\u001B[43msuffixes\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1923\u001B[39m \u001B[43m        \u001B[49m\u001B[43msort\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# factorize sorts\u001B[39;49;00m\n\u001B[32m   1924\u001B[39m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\reshape\\merge.py:802\u001B[39m, in \u001B[36m_MergeOperation.__init__\u001B[39m\u001B[34m(self, left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, indicator, validate)\u001B[39m\n\u001B[32m    799\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m right_drop:\n\u001B[32m    800\u001B[39m     \u001B[38;5;28mself\u001B[39m.right = \u001B[38;5;28mself\u001B[39m.right._drop_labels_or_levels(right_drop)\n\u001B[32m--> \u001B[39m\u001B[32m802\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_maybe_require_matching_dtypes\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mleft_join_keys\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mright_join_keys\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    803\u001B[39m \u001B[38;5;28mself\u001B[39m._validate_tolerance(\u001B[38;5;28mself\u001B[39m.left_join_keys)\n\u001B[32m    805\u001B[39m \u001B[38;5;66;03m# validate the merge keys dtypes. We may need to coerce\u001B[39;00m\n\u001B[32m    806\u001B[39m \u001B[38;5;66;03m# to avoid incompatible dtypes\u001B[39;00m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\reshape\\merge.py:2137\u001B[39m, in \u001B[36m_AsOfMerge._maybe_require_matching_dtypes\u001B[39m\u001B[34m(self, left_join_keys, right_join_keys)\u001B[39m\n\u001B[32m   2134\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m   2135\u001B[39m     rt = right_join_keys[-\u001B[32m1\u001B[39m]\n\u001B[32m-> \u001B[39m\u001B[32m2137\u001B[39m \u001B[43m_check_dtype_match\u001B[49m\u001B[43m(\u001B[49m\u001B[43mlt\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mrt\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[32;43m0\u001B[39;49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\reshape\\merge.py:2121\u001B[39m, in \u001B[36m_AsOfMerge._maybe_require_matching_dtypes.<locals>._check_dtype_match\u001B[39m\u001B[34m(left, right, i)\u001B[39m\n\u001B[32m   2116\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m   2117\u001B[39m     msg = (\n\u001B[32m   2118\u001B[39m         \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mincompatible merge keys [\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mi\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m] \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mrepr\u001B[39m(left.dtype)\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m and \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m   2119\u001B[39m         \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mrepr\u001B[39m(right.dtype)\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m, must be the same type\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m   2120\u001B[39m     )\n\u001B[32m-> \u001B[39m\u001B[32m2121\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m MergeError(msg)\n",
+      "\u001B[31mMergeError\u001B[39m: incompatible merge keys [0] dtype('<M8[ns]') and dtype('int64'), must be the same type"
+     ]
     }
    ],
-   "execution_count": 56
+   "execution_count": 11
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-14T21:33:09.955841Z",
-     "start_time": "2026-03-14T21:33:09.948656Z"
+     "end_time": "2026-03-17T16:01:16.455987Z",
+     "start_time": "2026-03-17T16:01:16.361388Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "radius_of_curvature = calculate_circular_track_curvature(coords(), step=2)\n",
+    "radius_of_curvature = calculate_circular_track_curvature(coords, step=2)\n",
     "calculated_roc = [radius_of_curvature[int(i) - 1] for i in position.sort_index()]\n",
-    "#calculated_roc_df = pd.DataFrame(calculated_roc, index = position.index).sort_index().dropna()\n"
+    "calculated_roc_df = pd.DataFrame(calculated_roc).sort_index().dropna()\n"
    ],
    "id": "e1c5ce22d0acb6ed",
    "outputs": [],
@@ -212,181 +619,269 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-14T21:33:58.330729Z",
-     "start_time": "2026-03-14T21:33:57.367685Z"
+     "end_time": "2026-03-17T16:06:06.523583Z",
+     "start_time": "2026-03-17T16:06:06.487029Z"
     }
    },
    "cell_type": "code",
-   "source": "plt.plot(position)",
-   "id": "9387f1f94cc5d55c",
+   "source": "calculated_roc_df.head()",
+   "id": "4544f808814e4eb4",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x21660a11a90>]"
+       "                               curvature\n",
+       "2024-07-16 07:49:53.648000002  -0.000474\n",
+       "2024-07-16 07:49:53.747999668  -0.000474\n",
+       "2024-07-16 07:49:53.847999573  -0.000474\n",
+       "2024-07-16 07:49:53.947999239  -0.000474\n",
+       "2024-07-16 07:49:54.047998905  -0.000474"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>curvature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:49:53.648000002</th>\n",
+       "      <td>-0.000474</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:49:53.747999668</th>\n",
+       "      <td>-0.000474</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:49:53.847999573</th>\n",
+       "      <td>-0.000474</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:49:53.947999239</th>\n",
+       "      <td>-0.000474</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-07-16 07:49:54.047998905</th>\n",
+       "      <td>-0.000474</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
-   "execution_count": 8
+   "execution_count": 12
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-14T21:09:35.255730Z",
-     "start_time": "2026-03-14T21:09:35.247563Z"
+     "end_time": "2026-03-17T16:09:03.476022Z",
+     "start_time": "2026-03-17T16:09:03.466206Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "\n",
     "def make_single_df():\n",
-    "   for event in [\"FSGP_2024_Day_1\", \"FSGP_2024_Day_2\", \"FSGP_2024_Day_3\"]:\n",
-    "       speed_kph, mech_brake_pressed, accel_position, position = make_df(source=\"ingress\", event=event)\n",
+    "    day_dfs = []\n",
     "\n",
-    "   scaler = MinMaxScaler(feature_range=(0, 1))\n",
-    "  #scale acceleration position before standard scaling.\n",
-    "   df_accel_position = scaler.fit_transform(accel_position)\n",
-    "   radius_of_curvature = calculate_circular_track_curvature(coords(), step=2)\n",
-    "   calculated_roc = [radius_of_curvature[int(i) - 1] for i in position]\n",
-    "   calculated_roc_df = pd.DataFrame(calculated_roc, index = position.index).sort_index().dropna()\n",
+    "    for event in [\"FSGP_2024_Day_1\", \"FSGP_2024_Day_2\", \"FSGP_2024_Day_3\"]:\n",
+    "        # Query and double-align per day\n",
+    "        speed_kph, mech_brake_pressed, accel_position, position = make_df(source=\"ingress\", event=event)\n",
     "\n",
-    "   final_df = pd.merge_asof(\n",
-    "        mech_brake_pressed.sort_index(),\n",
-    "       #df_accel_position.sort_index(),\n",
-    "        pd.DataFrame(df_accel_position, index = accel_position.index),\n",
-    "        left_index=True,\n",
-    "        right_index=True,\n",
-    "        direction=\"nearest\"\n",
-    "    )\n",
-    "   dfs = pd.merge_asof(speed_kph.sort_index().dropna(), calculated_roc_df.sort_index().dropna(), left_index=True, right_index=True, direction=\"nearest\")\n",
-    "   final_df = pd.merge_asof(\n",
-    "        final_df.sort_index().dropna(),\n",
-    "        dfs.sort_index().dropna(),\n",
-    "        left_index=True,\n",
-    "        right_index=True,\n",
-    "        direction=\"nearest\"\n",
-    "    )\n",
-    "   final_df.columns = [\"brake_pressed\", \"accel_position\", \"speed\", \"ROC\"]\n",
-    "   final_df = final_df.sort_index()\n",
-    "   final_df = final_df.ffill().dropna()\n",
-    "   return final_df"
+    "        # Scale accel position\n",
+    "        scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "        df_accel_position = pd.DataFrame(\n",
+    "            scaler.fit_transform(accel_position),\n",
+    "            index=accel_position.index\n",
+    "        )\n",
+    "\n",
+    "        # Map position -> curvature\n",
+    "        radius_of_curvature = calculate_circular_track_curvature(coords(), step=2)\n",
+    "        position_series = position.squeeze()\n",
+    "        calculated_roc = position_series.map(\n",
+    "            lambda pos: radius_of_curvature[int(pos) % len(radius_of_curvature)],\n",
+    "            na_action='ignore'\n",
+    "        )\n",
+    "        calculated_roc_df = calculated_roc.to_frame(name='curvature').dropna()\n",
+    "\n",
+    "        # Merge all signals together with asof on timestamp index\n",
+    "        day_df = pd.merge_asof(\n",
+    "            mech_brake_pressed.sort_index(),\n",
+    "            df_accel_position.sort_index(),\n",
+    "            left_index=True,\n",
+    "            right_index=True,\n",
+    "            direction=\"nearest\"\n",
+    "        )\n",
+    "        day_df = pd.merge_asof(\n",
+    "            day_df.sort_index(),\n",
+    "            speed_kph.sort_index().dropna(),\n",
+    "            left_index=True,\n",
+    "            right_index=True,\n",
+    "            direction=\"nearest\"\n",
+    "        )\n",
+    "        day_df = pd.merge_asof(\n",
+    "            day_df.sort_index(),\n",
+    "            calculated_roc_df.sort_index().dropna(),\n",
+    "            left_index=True,\n",
+    "            right_index=True,\n",
+    "            direction=\"nearest\"\n",
+    "        )\n",
+    "\n",
+    "        day_df.columns = [\"brake_pressed\", \"accel_position\", \"speed\", \"ROC\"]\n",
+    "        day_df = day_df.sort_index().ffill().dropna()\n",
+    "\n",
+    "        day_dfs.append(day_df)\n",
+    "\n",
+    "    # Concatenate all 3 days along time axis\n",
+    "    final_df = pd.concat(day_dfs, axis=0).sort_index()\n",
+    "\n",
+    "    return final_df"
    ],
-   "id": "e0b515f4c0c30bed",
+   "id": "bb38aa41d1ed86e5",
    "outputs": [],
-   "execution_count": 34
+   "execution_count": 15
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-14T21:09:41.770116Z",
-     "start_time": "2026-03-14T21:09:36.423881Z"
+     "end_time": "2026-03-17T16:09:15.247413Z",
+     "start_time": "2026-03-17T16:09:11.550824Z"
     }
    },
    "cell_type": "code",
    "source": "df = make_single_df()",
-   "id": "361b88fadb02073b",
+   "id": "8b3b3c33c24dc4ed",
    "outputs": [
     {
-     "ename": "ValueError",
-     "evalue": "Shape of passed values is (1, 1), indices imply (287218, 1)",
+     "ename": "TypeError",
+     "evalue": "'list' object is not callable",
      "output_type": "error",
      "traceback": [
       "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mValueError\u001B[39m                                Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[35]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m df = \u001B[43mmake_single_df\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[34]\u001B[39m\u001B[32m, line 13\u001B[39m, in \u001B[36mmake_single_df\u001B[39m\u001B[34m()\u001B[39m\n\u001B[32m     11\u001B[39m radius_of_curvature = calculate_circular_track_curvature(coords(), step=\u001B[32m2\u001B[39m)\n\u001B[32m     12\u001B[39m calculated_roc = [radius_of_curvature[\u001B[38;5;28mint\u001B[39m(i) - \u001B[32m1\u001B[39m] \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m position]\n\u001B[32m---> \u001B[39m\u001B[32m13\u001B[39m calculated_roc_df = \u001B[43mpd\u001B[49m\u001B[43m.\u001B[49m\u001B[43mDataFrame\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcalculated_roc\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mindex\u001B[49m\u001B[43m \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[43mposition\u001B[49m\u001B[43m.\u001B[49m\u001B[43mindex\u001B[49m\u001B[43m)\u001B[49m.sort_index().dropna()\n\u001B[32m     15\u001B[39m final_df = pd.merge_asof(\n\u001B[32m     16\u001B[39m      mech_brake_pressed.sort_index(),\n\u001B[32m     17\u001B[39m     \u001B[38;5;66;03m#df_accel_position.sort_index(),\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m     21\u001B[39m      direction=\u001B[33m\"\u001B[39m\u001B[33mnearest\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m     22\u001B[39m  )\n\u001B[32m     23\u001B[39m dfs = pd.merge_asof(speed_kph.sort_index().dropna(), calculated_roc_df.sort_index().dropna(), left_index=\u001B[38;5;28;01mTrue\u001B[39;00m, right_index=\u001B[38;5;28;01mTrue\u001B[39;00m, direction=\u001B[33m\"\u001B[39m\u001B[33mnearest\u001B[39m\u001B[33m\"\u001B[39m)\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:871\u001B[39m, in \u001B[36mDataFrame.__init__\u001B[39m\u001B[34m(self, data, index, columns, dtype, copy)\u001B[39m\n\u001B[32m    863\u001B[39m         mgr = arrays_to_mgr(\n\u001B[32m    864\u001B[39m             arrays,\n\u001B[32m    865\u001B[39m             columns,\n\u001B[32m   (...)\u001B[39m\u001B[32m    868\u001B[39m             typ=manager,\n\u001B[32m    869\u001B[39m         )\n\u001B[32m    870\u001B[39m     \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m871\u001B[39m         mgr = \u001B[43mndarray_to_mgr\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    872\u001B[39m \u001B[43m            \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    873\u001B[39m \u001B[43m            \u001B[49m\u001B[43mindex\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    874\u001B[39m \u001B[43m            \u001B[49m\u001B[43mcolumns\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    875\u001B[39m \u001B[43m            \u001B[49m\u001B[43mdtype\u001B[49m\u001B[43m=\u001B[49m\u001B[43mdtype\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    876\u001B[39m \u001B[43m            \u001B[49m\u001B[43mcopy\u001B[49m\u001B[43m=\u001B[49m\u001B[43mcopy\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    877\u001B[39m \u001B[43m            \u001B[49m\u001B[43mtyp\u001B[49m\u001B[43m=\u001B[49m\u001B[43mmanager\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    878\u001B[39m \u001B[43m        \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    879\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m    880\u001B[39m     mgr = dict_to_mgr(\n\u001B[32m    881\u001B[39m         {},\n\u001B[32m    882\u001B[39m         index,\n\u001B[32m   (...)\u001B[39m\u001B[32m    885\u001B[39m         typ=manager,\n\u001B[32m    886\u001B[39m     )\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\construction.py:336\u001B[39m, in \u001B[36mndarray_to_mgr\u001B[39m\u001B[34m(values, index, columns, dtype, copy, typ)\u001B[39m\n\u001B[32m    331\u001B[39m \u001B[38;5;66;03m# _prep_ndarraylike ensures that values.ndim == 2 at this point\u001B[39;00m\n\u001B[32m    332\u001B[39m index, columns = _get_axes(\n\u001B[32m    333\u001B[39m     values.shape[\u001B[32m0\u001B[39m], values.shape[\u001B[32m1\u001B[39m], index=index, columns=columns\n\u001B[32m    334\u001B[39m )\n\u001B[32m--> \u001B[39m\u001B[32m336\u001B[39m \u001B[43m_check_values_indices_shape_match\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvalues\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mindex\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcolumns\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    338\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m typ == \u001B[33m\"\u001B[39m\u001B[33marray\u001B[39m\u001B[33m\"\u001B[39m:\n\u001B[32m    339\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28missubclass\u001B[39m(values.dtype.type, \u001B[38;5;28mstr\u001B[39m):\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\construction.py:420\u001B[39m, in \u001B[36m_check_values_indices_shape_match\u001B[39m\u001B[34m(values, index, columns)\u001B[39m\n\u001B[32m    418\u001B[39m passed = values.shape\n\u001B[32m    419\u001B[39m implied = (\u001B[38;5;28mlen\u001B[39m(index), \u001B[38;5;28mlen\u001B[39m(columns))\n\u001B[32m--> \u001B[39m\u001B[32m420\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mShape of passed values is \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mpassed\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m, indices imply \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mimplied\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m\"\u001B[39m)\n",
-      "\u001B[31mValueError\u001B[39m: Shape of passed values is (1, 1), indices imply (287218, 1)"
+      "\u001B[31mTypeError\u001B[39m                                 Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[16]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m df = \u001B[43mmake_single_df\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[15]\u001B[39m\u001B[32m, line 16\u001B[39m, in \u001B[36mmake_single_df\u001B[39m\u001B[34m()\u001B[39m\n\u001B[32m     10\u001B[39m df_accel_position = pd.DataFrame(\n\u001B[32m     11\u001B[39m     scaler.fit_transform(accel_position),\n\u001B[32m     12\u001B[39m     index=accel_position.index\n\u001B[32m     13\u001B[39m )\n\u001B[32m     15\u001B[39m \u001B[38;5;66;03m# Map position -> curvature\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m16\u001B[39m radius_of_curvature = calculate_circular_track_curvature(\u001B[43mcoords\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m, step=\u001B[32m2\u001B[39m)\n\u001B[32m     17\u001B[39m position_series = position.squeeze()\n\u001B[32m     18\u001B[39m calculated_roc = position_series.map(\n\u001B[32m     19\u001B[39m     \u001B[38;5;28;01mlambda\u001B[39;00m pos: radius_of_curvature[\u001B[38;5;28mint\u001B[39m(pos) % \u001B[38;5;28mlen\u001B[39m(radius_of_curvature)],\n\u001B[32m     20\u001B[39m     na_action=\u001B[33m'\u001B[39m\u001B[33mignore\u001B[39m\u001B[33m'\u001B[39m\n\u001B[32m     21\u001B[39m )\n",
+      "\u001B[31mTypeError\u001B[39m: 'list' object is not callable"
      ]
     }
    ],
-   "execution_count": 35
+   "execution_count": 16
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-14T21:18:03.261897Z",
-     "start_time": "2026-03-14T21:18:00.185080Z"
+     "end_time": "2026-03-17T16:10:18.161376Z",
+     "start_time": "2026-03-17T16:10:18.153889Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "\n",
     "def make_single_df():\n",
+    "    day_dfs = []\n",
+    "    radius_of_curvature = calculate_circular_track_curvature(coords, step=2)  # compute once\n",
+    "\n",
     "    for event in [\"FSGP_2024_Day_1\", \"FSGP_2024_Day_2\", \"FSGP_2024_Day_3\"]:\n",
     "        speed_kph, mech_brake_pressed, accel_position, position = make_df(source=\"ingress\", event=event)\n",
-    "    radius_of_curvature = calculate_circular_track_curvature(coords(), step=2)\n",
-    "    calculated_roc = [radius_of_curvature[int(i) - 1] for i in position]\n",
-    "    calculated_roc_df = pd.DataFrame(data = calculated_roc, index = position.index).sort_index()\n",
-    "    final_df = pd.concat([speed_kph, mech_brake_pressed, accel_position, calculated_roc_df], axis = 1).sort_index().ffill().dropna()\n",
-    "    final_df.columns = [\"brake_pressed\", \"accel_position\", \"speed\", \"ROC\"]\n",
-    "    return final_df\n",
-    "df = make_single_df()\n"
+    "\n",
+    "        scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "        df_accel_position = pd.DataFrame(\n",
+    "            scaler.fit_transform(accel_position),\n",
+    "            index=accel_position.index\n",
+    "        )\n",
+    "\n",
+    "        position_series = position.squeeze()\n",
+    "        calculated_roc = position_series.map(\n",
+    "            lambda pos: radius_of_curvature[int(pos) % len(radius_of_curvature)],\n",
+    "            na_action='ignore'\n",
+    "        )\n",
+    "        calculated_roc_df = calculated_roc.to_frame(name='curvature').dropna()\n",
+    "\n",
+    "        day_df = pd.merge_asof(\n",
+    "            mech_brake_pressed.sort_index(),\n",
+    "            df_accel_position.sort_index(),\n",
+    "            left_index=True,\n",
+    "            right_index=True,\n",
+    "            direction=\"nearest\"\n",
+    "        )\n",
+    "        day_df = pd.merge_asof(\n",
+    "            day_df.sort_index(),\n",
+    "            speed_kph.sort_index().dropna(),\n",
+    "            left_index=True,\n",
+    "            right_index=True,\n",
+    "            direction=\"nearest\"\n",
+    "        )\n",
+    "        day_df = pd.merge_asof(\n",
+    "            day_df.sort_index(),\n",
+    "            calculated_roc_df.sort_index().dropna(),\n",
+    "            left_index=True,\n",
+    "            right_index=True,\n",
+    "            direction=\"nearest\"\n",
+    "        )\n",
+    "\n",
+    "        day_df.columns = [\"brake_pressed\", \"accel_position\", \"speed\", \"ROC\"]\n",
+    "        day_df = day_df.sort_index().ffill().dropna()\n",
+    "        day_dfs.append(day_df)\n",
+    "\n",
+    "    final_df = pd.concat(day_dfs, axis=0).sort_index()\n",
+    "    return final_df"
    ],
-   "id": "14301a27051c6dd2",
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "Shape of passed values is (1, 1), indices imply (287218, 1)",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mValueError\u001B[39m                                Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[50]\u001B[39m\u001B[32m, line 10\u001B[39m\n\u001B[32m      8\u001B[39m     final_df.columns = [\u001B[33m\"\u001B[39m\u001B[33mbrake_pressed\u001B[39m\u001B[33m\"\u001B[39m, \u001B[33m\"\u001B[39m\u001B[33maccel_position\u001B[39m\u001B[33m\"\u001B[39m, \u001B[33m\"\u001B[39m\u001B[33mspeed\u001B[39m\u001B[33m\"\u001B[39m, \u001B[33m\"\u001B[39m\u001B[33mROC\u001B[39m\u001B[33m\"\u001B[39m]\n\u001B[32m      9\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m final_df\n\u001B[32m---> \u001B[39m\u001B[32m10\u001B[39m df = \u001B[43mmake_single_df\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[50]\u001B[39m\u001B[32m, line 6\u001B[39m, in \u001B[36mmake_single_df\u001B[39m\u001B[34m()\u001B[39m\n\u001B[32m      4\u001B[39m radius_of_curvature = calculate_circular_track_curvature(coords(), step=\u001B[32m2\u001B[39m)\n\u001B[32m      5\u001B[39m calculated_roc = [radius_of_curvature[\u001B[38;5;28mint\u001B[39m(i) - \u001B[32m1\u001B[39m] \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m position]\n\u001B[32m----> \u001B[39m\u001B[32m6\u001B[39m calculated_roc_df = \u001B[43mpd\u001B[49m\u001B[43m.\u001B[49m\u001B[43mDataFrame\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdata\u001B[49m\u001B[43m \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[43mcalculated_roc\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mindex\u001B[49m\u001B[43m \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[43mposition\u001B[49m\u001B[43m.\u001B[49m\u001B[43mindex\u001B[49m\u001B[43m)\u001B[49m.sort_index()\n\u001B[32m      7\u001B[39m final_df = pd.concat([speed_kph, mech_brake_pressed, accel_position, calculated_roc_df], axis = \u001B[32m1\u001B[39m).sort_index().ffill().dropna()\n\u001B[32m      8\u001B[39m final_df.columns = [\u001B[33m\"\u001B[39m\u001B[33mbrake_pressed\u001B[39m\u001B[33m\"\u001B[39m, \u001B[33m\"\u001B[39m\u001B[33maccel_position\u001B[39m\u001B[33m\"\u001B[39m, \u001B[33m\"\u001B[39m\u001B[33mspeed\u001B[39m\u001B[33m\"\u001B[39m, \u001B[33m\"\u001B[39m\u001B[33mROC\u001B[39m\u001B[33m\"\u001B[39m]\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:871\u001B[39m, in \u001B[36mDataFrame.__init__\u001B[39m\u001B[34m(self, data, index, columns, dtype, copy)\u001B[39m\n\u001B[32m    863\u001B[39m         mgr = arrays_to_mgr(\n\u001B[32m    864\u001B[39m             arrays,\n\u001B[32m    865\u001B[39m             columns,\n\u001B[32m   (...)\u001B[39m\u001B[32m    868\u001B[39m             typ=manager,\n\u001B[32m    869\u001B[39m         )\n\u001B[32m    870\u001B[39m     \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m871\u001B[39m         mgr = \u001B[43mndarray_to_mgr\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    872\u001B[39m \u001B[43m            \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    873\u001B[39m \u001B[43m            \u001B[49m\u001B[43mindex\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    874\u001B[39m \u001B[43m            \u001B[49m\u001B[43mcolumns\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    875\u001B[39m \u001B[43m            \u001B[49m\u001B[43mdtype\u001B[49m\u001B[43m=\u001B[49m\u001B[43mdtype\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    876\u001B[39m \u001B[43m            \u001B[49m\u001B[43mcopy\u001B[49m\u001B[43m=\u001B[49m\u001B[43mcopy\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    877\u001B[39m \u001B[43m            \u001B[49m\u001B[43mtyp\u001B[49m\u001B[43m=\u001B[49m\u001B[43mmanager\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    878\u001B[39m \u001B[43m        \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    879\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m    880\u001B[39m     mgr = dict_to_mgr(\n\u001B[32m    881\u001B[39m         {},\n\u001B[32m    882\u001B[39m         index,\n\u001B[32m   (...)\u001B[39m\u001B[32m    885\u001B[39m         typ=manager,\n\u001B[32m    886\u001B[39m     )\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\construction.py:336\u001B[39m, in \u001B[36mndarray_to_mgr\u001B[39m\u001B[34m(values, index, columns, dtype, copy, typ)\u001B[39m\n\u001B[32m    331\u001B[39m \u001B[38;5;66;03m# _prep_ndarraylike ensures that values.ndim == 2 at this point\u001B[39;00m\n\u001B[32m    332\u001B[39m index, columns = _get_axes(\n\u001B[32m    333\u001B[39m     values.shape[\u001B[32m0\u001B[39m], values.shape[\u001B[32m1\u001B[39m], index=index, columns=columns\n\u001B[32m    334\u001B[39m )\n\u001B[32m--> \u001B[39m\u001B[32m336\u001B[39m \u001B[43m_check_values_indices_shape_match\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvalues\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mindex\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcolumns\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    338\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m typ == \u001B[33m\"\u001B[39m\u001B[33marray\u001B[39m\u001B[33m\"\u001B[39m:\n\u001B[32m    339\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28missubclass\u001B[39m(values.dtype.type, \u001B[38;5;28mstr\u001B[39m):\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\construction.py:420\u001B[39m, in \u001B[36m_check_values_indices_shape_match\u001B[39m\u001B[34m(values, index, columns)\u001B[39m\n\u001B[32m    418\u001B[39m passed = values.shape\n\u001B[32m    419\u001B[39m implied = (\u001B[38;5;28mlen\u001B[39m(index), \u001B[38;5;28mlen\u001B[39m(columns))\n\u001B[32m--> \u001B[39m\u001B[32m420\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mShape of passed values is \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mpassed\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m, indices imply \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mimplied\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m\"\u001B[39m)\n",
-      "\u001B[31mValueError\u001B[39m: Shape of passed values is (1, 1), indices imply (287218, 1)"
-     ]
+   "id": "e0b515f4c0c30bed",
+   "outputs": [],
+   "execution_count": 19
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-17T16:10:25.165599Z",
+     "start_time": "2026-03-17T16:10:19.221313Z"
     }
-   ],
-   "execution_count": 50
+   },
+   "cell_type": "code",
+   "source": "df = make_single_df()",
+   "id": "361b88fadb02073b",
+   "outputs": [],
+   "execution_count": 20
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-14T21:12:31.716336Z",
-     "start_time": "2026-03-14T21:12:31.694172Z"
+     "end_time": "2026-03-17T16:10:32.879730Z",
+     "start_time": "2026-03-17T16:10:32.857461Z"
     }
    },
    "cell_type": "code",
-   "source": "df.isna()",
-   "id": "ad3b695f0f73583e",
+   "source": "df.head()",
+   "id": "f5bd86fed9fb06c2",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "                               brake_pressed  accel_position  speed    ROC\n",
-       "2024-07-18 07:00:00.045000076          False           False  False  False\n",
-       "2024-07-18 07:00:00.145000219          False           False  False  False\n",
-       "2024-07-18 07:00:00.245000362          False           False  False  False\n",
-       "2024-07-18 07:00:00.345000267          False           False  False  False\n",
-       "2024-07-18 07:00:00.445000410          False           False  False  False\n",
-       "...                                      ...             ...    ...    ...\n",
-       "2024-07-18 14:58:41.373999596          False           False  False  False\n",
-       "2024-07-18 14:58:41.473999739          False           False  False  False\n",
-       "2024-07-18 14:58:41.573999643          False           False  False  False\n",
-       "2024-07-18 14:58:41.673999786          False           False  False  False\n",
-       "2024-07-18 14:58:41.773999929          False           False  False  False\n",
-       "\n",
-       "[287218 rows x 4 columns]"
+       "                               brake_pressed  accel_position  speed       ROC\n",
+       "2024-07-16 07:49:53.648000002            0.0             0.0    0.0 -0.000474\n",
+       "2024-07-16 07:49:53.747999668            0.0             0.0    0.0 -0.000474\n",
+       "2024-07-16 07:49:53.847999573            0.0             0.0    0.0 -0.000474\n",
+       "2024-07-16 07:49:53.947999239            0.0             0.0    0.0 -0.000474\n",
+       "2024-07-16 07:49:54.047998905            0.0             0.0    0.0 -0.000474"
       ],
       "text/html": [
        "<div>\n",
@@ -415,136 +910,137 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>2024-07-18 07:00:00.045000076</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2024-07-18 07:00:00.145000219</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2024-07-18 07:00:00.245000362</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2024-07-18 07:00:00.345000267</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2024-07-18 07:00:00.445000410</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2024-07-18 14:58:41.373999596</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
+       "      <th>2024-07-16 07:49:53.648000002</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.000474</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2024-07-18 14:58:41.473999739</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
+       "      <th>2024-07-16 07:49:53.747999668</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.000474</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2024-07-18 14:58:41.573999643</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
+       "      <th>2024-07-16 07:49:53.847999573</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.000474</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2024-07-18 14:58:41.673999786</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
+       "      <th>2024-07-16 07:49:53.947999239</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.000474</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2024-07-18 14:58:41.773999929</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
+       "      <th>2024-07-16 07:49:54.047998905</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.000474</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>287218 rows × 4 columns</p>\n",
        "</div>"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 21
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-17T16:24:12.887652Z",
+     "start_time": "2026-03-17T16:24:10.897364Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plt.plot(df['speed'], df['ROC'])",
+   "id": "f2a1050f8a608c23",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x29171b9bb90>]"
+      ]
+     },
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
-   "execution_count": 43
+   "execution_count": 22
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-14T21:13:32.957939Z",
-     "start_time": "2026-03-14T21:13:30.435974Z"
+     "end_time": "2026-03-17T16:29:57.898264Z",
+     "start_time": "2026-03-17T16:29:52.455264Z"
     }
    },
    "cell_type": "code",
+   "source": "df.to_csv(r\"C:\\Users\\sanar\\Downloads\\fsgp_2024_training_data.csv\")",
+   "id": "2610916f6fcbcab7",
+   "outputs": [],
+   "execution_count": 24
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "def make_single_df():\n",
+    "    for event in [\"FSGP_2024_Day_1\", \"FSGP_2024_Day_2\", \"FSGP_2024_Day_3\"]:\n",
+    "        speed_kph, mech_brake_pressed, accel_position, position = make_df(source=\"ingress\", event=event)\n",
+    "    radius_of_curvature = calculate_circular_track_curvature(coords(), step=2)\n",
+    "    calculated_roc = [radius_of_curvature[int(i) - 1] for i in position]\n",
+    "    calculated_roc_df = pd.DataFrame(data = calculated_roc, index = position.index).sort_index()\n",
+    "    final_df = pd.concat([speed_kph, mech_brake_pressed, accel_position, calculated_roc_df], axis = 1).sort_index().ffill().dropna()\n",
+    "    final_df.columns = [\"brake_pressed\", \"accel_position\", \"speed\", \"ROC\"]\n",
+    "    return final_df\n",
+    "df = make_single_df()\n"
+   ],
+   "id": "14301a27051c6dd2",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "df.isna()",
+   "id": "ad3b695f0f73583e",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
    "source": "plt.plot(df['ROC'],df['speed'])",
    "id": "31072ad8210cab73",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x2cf16666120>]"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 46
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-14T20:45:44.021706Z",
-     "start_time": "2026-03-14T20:45:43.153315Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "client = query.SunbeamClient()\n",
@@ -577,7 +1073,7 @@
    ],
    "id": "4cf6148f454299ac",
    "outputs": [],
-   "execution_count": 37
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -625,10 +1121,10 @@
   {
    "metadata": {},
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": "pos.isna()",
-   "id": "4e81190f5287d2f5"
+   "id": "4e81190f5287d2f5",
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -637,12 +1133,7 @@
    "id": "5bc7173b832222e8"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T06:04:21.897790Z",
-     "start_time": "2026-03-04T06:03:56.662249Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "# query data from influx. looking at timestamps of 2024 FSGP: 14 - 18 overall, but for smaller data response lets try 14 -16\n",
@@ -670,28 +1161,18 @@
    ],
    "id": "initial_id",
    "outputs": [],
-   "execution_count": 3
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T06:04:29.408291Z",
-     "start_time": "2026-03-04T06:04:29.392184Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "# speed is most likely in m/s",
    "id": "5d30f3a75f7e9149",
    "outputs": [],
-   "execution_count": 4
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-28T15:33:29.842089Z",
-     "start_time": "2026-02-28T15:33:29.790741Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "\n",
@@ -712,28 +1193,11 @@
     "        dill.dump(data, f)\n"
    ],
    "id": "9f8652589e173b01",
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'os' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mNameError\u001B[39m                                 Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[4]\u001B[39m\u001B[32m, line 3\u001B[39m\n\u001B[32m      1\u001B[39m \u001B[38;5;66;03m# save collected data\u001B[39;00m\n\u001B[32m----> \u001B[39m\u001B[32m3\u001B[39m out_dir = \u001B[43mos\u001B[49m.path.join(\u001B[33m\"\u001B[39m\u001B[33m../../array_temp\u001B[39m\u001B[33m\"\u001B[39m, \u001B[33m\"\u001B[39m\u001B[33mdata\u001B[39m\u001B[33m\"\u001B[39m, \u001B[33m\"\u001B[39m\u001B[33mcontrol_state_fsgp_2024\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m      4\u001B[39m os.makedirs(out_dir, exist_ok=\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[32m      6\u001B[39m brake_path = os.path.join(out_dir, \u001B[33m\"\u001B[39m\u001B[33mbrake_pressed.bin\u001B[39m\u001B[33m\"\u001B[39m)\n",
-      "\u001B[31mNameError\u001B[39m: name 'os' is not defined"
-     ]
-    }
-   ],
-   "execution_count": 4
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-06T01:15:19.477772Z",
-     "start_time": "2026-03-06T01:15:19.402215Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "#loading data\n",
@@ -759,127 +1223,44 @@
    ],
    "id": "31de4e6237f9a41e",
    "outputs": [],
-   "execution_count": 2
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T01:57:35.189309Z",
-     "start_time": "2026-03-04T01:57:35.173239Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "mech_brake_pressed.granularity\n",
     "#granularity is at 0.1 seconds"
    ],
    "id": "467ef53567154e4a",
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_33888\\4023827949.py:1: DeprecationWarning: Please use TimeSeries.period instead of TimeSeries.granularity\n",
-      "  mech_brake_pressed.granularity\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "np.float64(0.10000002186928031)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 3
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-28T15:33:36.643098Z",
-     "start_time": "2026-02-28T15:33:35.679992Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "plt.plot(mech_brake_pressed.datetime_x_axis, mech_brake_pressed, color = 'green', label = \"Brake Pressed\")\n",
     "# note that assumed continuity of brake is not real. assumed to be brake pressure but this clearly is just checking if brake has been pressed or not as a 0 or 1 value. checked this in the bay, threshold seems extremely low."
    ],
    "id": "d1fc6023bb47051c",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1d53049e120>]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 7
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-17T18:57:27.988803Z",
-     "start_time": "2026-02-17T18:57:26.930907Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "\n",
     "plt.plot(accel_position.datetime_x_axis, accel_position, label = \"Accelerator Position\")\n"
    ],
    "id": "21f820a7985b2d39",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x21390ea0b60>]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 8
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-28T15:34:12.079935Z",
-     "start_time": "2026-02-28T15:34:12.051910Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": 16,
    "source": [
     "# # accel position and brake pressed are in inconsistent units, so i'll probbaly change them to a 0-1 range.\n",
     "#\n",
@@ -890,15 +1271,12 @@
     "# # remove this\n",
     "#\n"
    ],
-   "id": "6a0eed52a654e483"
+   "id": "6a0eed52a654e483",
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-28T07:36:22.364062Z",
-     "start_time": "2026-02-28T07:36:19.684565Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "plt.figure(1)\n",
@@ -923,49 +1301,8 @@
     "plt.title(\"speed kph\")\n"
    ],
    "id": "a7c5e8360bc710de",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'speed kph')"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 4
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -974,12 +1311,7 @@
    "id": "7ec4163e070c9bfc"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-17T17:31:51.542391100Z",
-     "start_time": "2026-02-17T06:18:38.460068Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "# why does brake pressed look more continous then acceleartor - accelerator seems like it is either 100% or none\n",
@@ -987,15 +1319,10 @@
    ],
    "id": "54a0ee4a260b394",
    "outputs": [],
-   "execution_count": 11
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-17T17:31:51.551036900Z",
-     "start_time": "2026-02-17T06:18:38.657715Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "plt.plot(speed_kph.datetime_x_axis, speed_kph, label = \"Speed KPH\")\n",
@@ -1009,79 +1336,32 @@
     "\n"
    ],
    "id": "d41febe9473c8559",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'speed kph')"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 12
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-17T17:31:51.551642500Z",
-     "start_time": "2026-02-17T06:18:43.716025Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "#clearly speed units are whack. from looking at lap data, i see that out average speed was around 16 miles per hour. so i think due to hwo influx registers small numbers in different units",
    "id": "1ef57bddd9f5a956",
    "outputs": [],
-   "execution_count": 13
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-17T17:31:51.551642500Z",
-     "start_time": "2026-02-17T06:18:43.934851Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "plt.plot(speed_kph.datetime_x_axis, speed_kph, label = \"Speed KPH\")\n",
-    "plt.xlabel(\"Time\")\n",
-    "plt.ylabel(\"Speed\")\n",
-    "plt.tick_params(rotation = 90)"
-   ],
-   "id": "8f3d0704205b4567",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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4sxx8BXmQA8pBDkYth77v/Uq+Yn0uDwoKdOnc7utyqFAhROg1Rljw8t1339GSJUuoadOmZV5jgcwtt9xCr7/+Oj355JMeBS+pqZl2q7VYZMcK1dHrvpCSUnZxRk9lZGRRaqjJpQNTy/1qTUQ5aA15kAPKQQ5GL4ddp9J9lSybc3lBQZHTc7u/yiEnO1/za4wl7VIHLxEREZSamur0PRcvXuTNR55gheas4Mp7XUvTNx2loylZmmyr2I10q3AC8Wc5+AryIAeUgxxQDr75TJ09FlUOIs/dwoKXxx9/nEaOHEmDBg3itSxVqlQp6fOSkpJCu3fv5k1KTz/9NKluxd5zopMAAACKMltFCar/2FM+eBk8eDBVr16dPv74Y5o/fz7vwGsymXghsVFGN998M+/zct9994lKIgAAgHBzt5wUnQTpCB0qfc899/BbcXExXblyhXJzc3ntS6VKlXggA2XhUwEAMJYlO85YPZKl6sVk7HlemICAAD5UGgAAAEDpGXZZ35fGjRuLTobSkjJyKM+H8w8AABgdm/3WX+zVu5xOz6GCImOd56UOXqKiomjKlCmik6GsfUmXqc/CnTTg4z2ikwIAoFud5vpujpfS/jifSaPWHqILV3L545+OptBDi3bSc58fICORotnIEdZxt0+fPqKTIRV3Wju/O3KR/z2Rlu2z9AAAgH9t/l8KXcktoP+7sy6N/uowf27f2St0Na+QosKkvqyrX/OybNkyPsLI2saNG+mJJ56gXr160fPPP08HDhgrkgQAAHDFrjOXadCyvVRotVAjq4ExCmHBy2uvvUZXr14tebxmzRoaPnw41alTh/r370/R0dE0YMAAHtAAAACAc2nZBYb5iIJkmHSHWbx4MY0aNYr+9a9/lTzHOuu+/fbb1KVLFwEpVB+GVQMAgB4Jq3kpPY8LW0n61ltvtXnujjvuoLNnz/o5ZXJjbZqe2Jt0WfO0AAAY3cEL8q4hp2cBImteVq9eTb/++iudO3eO7rzzTn7fGmsyql27tqgkSunZzzzrB/T0iv2apwUAwOgGL9srOgmGJKzZiDUPsWCFLQ+QnJzMa2LYZHUPPvggVaxYkR577DHauXMnzZ49W1QSlYdZigEAwBdET4IvLHgZP358yX3Wcff48eP8xgIXpmXLljRixAi+xhEAAACAhRQDwiMjI6lZs2b8ZsGGSoN3tp5Iw0cIAACaE726tbDghTUJuapNmzY+TYtqis1mKrYa2+/IucvXZmAEAADQ0u4zGWTI4GXSpEl09OhRu8OmS/fbOHz42gyCcM0/PtpNIcGB+DgAAECIS1dtJ5k1TPCyatUq3qclKSmJVqxYwZcCANccT8V0/wAAomXl+29BRpBkqHRISAjNnDmT3581a5aoZAAAALiNTct/17v+W5BRNmYjryrNApgZM2ZQYmKiyGQYxqd7zjptogMAAN9OGOprv51KJyMQGrww9erV42sZge/N+OEY/XQ0FR81AICXtp+UM0j49+e/+2U/on8HCw9ewL/OZOTgIwcA0KAmG8RB8AIAAOAmo69pZBI8wy6CFwAAAFAKghcAAABwC/q8AAAAALgBNS8AAACgFAQvAAAAOnIiJYv0DsELAACAjtz91o/0v0tXfboP0dOdIngBAADQma3H00jPELwAAACAUhC8AAAA6MycX07Sb5IuYaAFBC8AoKQtx1NpzYHzopMBIK2hq/yzzpEIQaITAADgieGrD/K/LRKiKS4uCh8igB+ZBc9Sh5oXAFBaala+6CQAgJ8heAEAAAClCGs2ysnJofDwcJvnsrOz6YcffqDk5GSqW7cudezYkUyil64EAAAAGzdVizJm8NKqVSvasmULxcbG8scnT56kwYMHU15eHiUkJNCpU6eoevXqNH/+fKpatarb23cU81ieN3JMJEPe9VAOyIMkcCxJAd8HOZl8dI4NDwnUfNvubC9Ils4+U6ZMoWbNmtFbb71FISEhvBbmpZdeookTJ9K8efPc3n5sbJRXr+tVRESoVJ0b9VAOyINYFSteq8FFOcgB5SCXOA3P99bX7dDQYKHXEmlGGx06dIjee+89HrgwFSpUoGHDhlG/fv082l5qaqbdJbtZZMe+XI5e17usrDxKSckUnQxdlAPyIIcrV3L4XxxLYuH7IKcjp1IpLuLadbV0IJKSlU/xkaEezdqbl1eg+bXEcgxJHbywviy5ubklj+vUqUNZWbaLSaWnp1NUlGeRHbsgOrsolve6nsmUbz2UA/IgugBQDjLB90Eu9763neY9fDO1Saxk8/wbG4/Sqv3naWL3hnRfE9e6ZpxOv/ZDgRN87hY22igiIoK6dOlCd955Jw0cOJAHMq+88grvyMusWbOGRo8eTT169BCVRAAAAOWt2HOuzHMscGHm/nLC5e3I9DtTWM3Lrl27+Kii48eP09GjR+nYsWP8flDQtSTNnTuXOnXqRMOHDxeVRAAAAF1LzS6gjJwCigkP5k1Jp9JyKCEmjIICA6SbmE6aPi9sFBG7tWvXrsxrGzZsEJImAAAAoygqNlPXedtox4g76OtDyTTxu7+oQ93K9Hafpk7/T/RIUaknqWNR3rlzZau7AAAAQDvFZqLlu8/y+1usOuaWfo8spA5eUlNTqXPnzqKTAQAAoCzT37Ukhy5k0uyfjlNWfqFH20GzkYuio6NpyZIlHn3IAAAAQFRYbKaD56/Q4OX7+MeRX1Rc5mP58+JVysovcvpxSdTlRZ55XuwJDg6mW2+9VXQyAEBiZqnGQADIZ8vxNJumoGOp2WXeM2jZ3nK3I9M3TWiz0U8//UQjR46kZ599lpYuXcqXBrB2+fJlPowaAAAAxCq2qnoRve6gsODl888/p+eff54vzlilShWaPXs29enTh86cOVPynoKCAtq5c6eoJAIAAICEhAUvixYtoqlTp9KkSZP47fvvv6eYmBjq378/n/MFAAAAxA+l/vVEGp8LxqbmxajBy4ULF6hp0+vjyNnq0osXL6Z69erRoEGD+CrTAAAAIM6q/edp2Bd/0ICP99Clq/nSFIWw4KVhw4b0xRdf2DwXGhrKF2dMSEigAQMG0MGDB0UlDwAAwPB++N8l/hlcyMyjsxnX1yM0bPDC1i1avnw5X7vowIEDJc+z1aQ//PBDXgMzZMgQUckDAEWYhFdgAxjD9lPpREYfKt2iRQv65ptvaOPGjRQXF2fzWmRkJG9CYp16169fLyqJAAAAICGh87ywoOUf//iH3dfYMKx+/frxGwAAAMjDZDJo8OLqEGgWxNxyyy0+Tw8AAACoQVjwwoZHHz16tNz1EljwcvjwYT+mDAAAAJwxGTV4WbVqFY0YMYKSkpJoxYoVfKQRAACA7GRaoFD37UOyjTYKCQmhmTNn8vuzZs0SlQwAAAC3nL0sz5BhYYy6PIAlgJkxYwYlJiaKTAYAAIDLjFTxIiuXm43OnTvn8kZr1Kjh8nvZfC7sBgAAAKBp8NKpUye7q0ha2v6sX0MHWwDwFzPhZzD4l/JHnNmsfPWTy8HLpk2bSu7/+OOP9PHHH9OYMWPo5ptv5s0/bCr/N954A/OyAACArhmqw66kXA5eatasWXJ/wYIF9M4771Dz5s1Lnmvbti0f/sym9GcrQwMAAIBOmRTssJuVlUWFhYVlnr969SoVFBRokS7wkdzCYny2AABeMFK9i8nN56We5+X++++nl156iV544QVq1KgRr0L7/fffafbs2Q6n+wc5nErLFp0EAAC1GSl60VPwwvq6RERE0NSpUyktLa1knaJ//vOf9Oyzz2qdRtBQYIDoQw4AAFRhIjl5FLwEBQXx2XHZzRK8VK5cWeu0gQ8ESDpbIgCAKpSveDGpfx3weJK6M2fO0LRp02j8+PG8/8vKlStp9+7d2qYONBeog4MWAEAkDM9XNHhhK0Kzfi9nz56lX375hfLy8uj48eM0aNAgWr9+vfapBM0ECJ1TGQAA9MAk+HewR5ey6dOn03/+8x/eQZc1ITGsA++LL77InwN5odkIAMA7RprmxeQgSDEJ7g3jUfDy119/UceOHcs837lzZzp9+rQW6QIfQfACepBbUCTNSRSMx0Cxi7RNZx4FL2zCOjY0ujQ28671ZHYg90kfQFWLfsOPJAAj82i0EZvfZfTo0TyAKSoqojVr1lBSUhJ9/fXX9Oabb2qfStDMVweT6eV7G+ITBaX971KW6CSAkaHqRTiPal66du1Ky5Yto9TUVLrxxhv5ukf5+fn8ufvuu0/7VAIAOOhzMHXj/yg9Kx+fDximyUQGoptrPap5YdjMuqhlAQDRF49TaTk0+etDNLZTPRQGgEF4PHB27dq19OCDD9Itt9zC53yZMmUKzZ8/X9vUKapubAXRSQAw1GiP42hGAoHHnyFrWEykXs3L8uXLad68eXwpADZsmrnpppt4AMOaj4YOHerytthcMfv376cLFy7w/w0LC6MqVarwFatV7fw7slN9GvL5AZLV8dQsqhsbIToZALqZcwKMxUCxi0Oiv3IeBS8ff/wxvfbaa3TXXXfRjBkz+HO9e/emmJgYevnll10KXtLT0/kaST/99BNVr16dr40UEhLCA5iUlBQezNx99908IIqOjtZubLrJ9ye7NrVjPP7fFzvVo7c2H7N57uEWNaht7Rh68ctDGqSO6EpuofCTvT/KwdeQB3mwwwjHkuAyMNB3WuU8ulp1VJJHJ9dSrT8Hd7bnUfBy7tw5qlevbPtyrVq1KCMjw6VtTJgwgbKysuiHH36gatWqlXn9/PnzNGrUKP4+Tya+i42N8up1b8TFebbt+lUiaWi3RlSvejQNWban5PmqlSrQPS0TNAteoqMreJxGrfmyHPwFefC/oOBAm8dZeUUoB0kY4ftwIa+YVBYcXP6l33KNCC71XbMICwsWeh3xKHhhTTpsePT//d//lTxnNptp0aJF1KxZM5e2sWXLFvrss8/sBi4Mq40ZO3YsPfroo54kkVJTM+0GlyyyYwemo9e1kJKS6dH/FRcV8/9tUz3S5vnsnDxKTb2qUeqILl/OppSUYBLJH+Xga8iDOPn5hTaP/0zOxLEkmJG+DxkZag/VLyiw/f44u44VOJgbLDe3wONrXXmfv8+CF7YY49NPP80npWPNPBMnTqSTJ09Sbm4uLViwwKVtxMfH05EjR6hBgwYO3/PHH3941GTEsAPP2cFX3uve8HS7Zof/q23dnC/zrnJaPIU8yAHlIAcjlIPq+XOFK3kU+Tl4FLywgOP777+nr776io4dO8YnqmNLA7DFGiMiXOsIOmzYMB4Ebdu2jdq0acM76Vr3eWErVH/55Zc0adIkT5IIADpmhIsHyAuHn3gez/MSGhpKLVq0oKioKAoICKCGDRu6HLgwPXv2pMTERD6x3QcffECXLl3iNTdsuyyQYdteunQp/wviJwQCkEFGTgG9tPYQ7U26LDopYGBGCp5NpKPghdWMsP4u+/bto4oVK1JxcTFdvXqV2rdvT2+//TYPaFzB+se42kcGAOCJ/+6j0+k5+CAADM6jSerGjRtHwcHBtGHDBvrtt99o586d9N133/Gak1dffVWzxLGgiI1sAgBgELiADAxU8aKv4GXHjh18CHNCQkLJc7Vr1+Z9WNjQZ62kpaXxvjQqCQzwTSWb8vMKAADohZHajST9CDwKXth8Ln/++WeZ51ktSY0aNUgrbKTRkiVLSCVdG8Zrvk3ELQAAAF72eXnooYf48OiDBw9Sy5YtKSgoiA4fPsw72LL1jtgcMBYPPPCAx583a5q69dZbUV4AACAN5etdTCZjBi+sNoR1ymXDpdnNZDLxSerYaCPLcwx73lnwwpYAWLlyJe/4m5ycXLK2EZsDho0y6tu3r8NJ7IxG/UMNAEAfRDeZyBDnmFULXthIo/Xr1/PaFobVvmzfvp1iY2OpW7duVKGCaysqb926la+BxIKU1q1b8/+3nudl165dtHjxYpo7dy7ddtttZIggQ/TRAAAAoACXgxe2DtF//vMfvpDiunXr+NpGq1ev5iOPWO0Im5/l3Xff5StOV61atdztTZ06lYYMGcJn6nVk/vz59Prrr/PJ8GT22eBbqN9Hu2yeqxQeTOk5BTbP3Va7Em0/le7n1AEAgJbwO5OEfwgud9hlgcnZs2fpk08+obp161J2djZfWZrN08Kaib799lvq0KEDTZ8+3aXtsW116dLF6Xs6depEp0+fJtlHF9WJLVvbtPifZSfXiwy1v8AVAACog3WTMDqz4OjF5eCFNRWxWhbWxMP6srCFFVltzIABA3jHWoZ11mXPu4I1F7GZdfPy8uy+zpqP5s2bJ/0kdh89ahukWIrTH2sUeUqOVAAAAPi42YhN38+m87f49ddfKTAwkNe2WMTFxVFOjmuzX06ePJn+/e9/U7t27eimm26yWduI7evQoUN8ZWkWwMisUdUon3fwZv/GAkYAAAAZiK58cjl4Yf1Yzpw5w+dxYVVmrO9L8+bNbVZ93rt3Lw84XMEmuGMLL7KFGQ8cOMADFhb4sO2xhR+fe+45PkyarZsEAAAgC9EXbn9mQNZ19VwOXnr37s07z7LVoNnoovPnz/MOvBZHjhyhmTNn8pWl3cFqXtjNCExetCHKefgAAICemVQfKs1GBrHFF8eOHcubMJ5//nm+MjQzbdo0Pqz5rrvu4u9zBVsPyVVt2rQh1dSIDqP6cRF0NCWr5Dl3Wn5MEhwcAABQlurn5oycQuU/A5eDFzavy5gxY/itNDYRXa9evahJkyYu73jSpEl09OjRcntus0CJzd6rmgCTiZYNbEUb/7xE474+wp97rG0ifX/kEvVpVo1WH7jg9P9ZoKN81SQAgA5pNdImsVK4kMVGj1r9qPZUaGCAejPsltawYUO3/2fVqlU0YsQISkpKohUrVvB5YvTAVCqAsVYvLoK2DOtAoUEB5QcvpR9r2G6Evr8AAOKxa4GsfjuZTm1vqOTw9eY1K5JIwj45NrKI9ZFhZs2aRXph1upgtYowEGwAAMjD17Xit9S6PhBGlKGrfieZCQ37WAAzY8YMmyHY4KDmxYUPZmH/shPjAQCAWu6+MU50EqSnSbORN9gyA+wGtgI8aCZqVkNsNR4AAHhPhf6OZlWWBwD/wqR0AAD6USsmTHQSdAXBiyo07PiSlm27YCQAALiu2INqB3s/SB1tRpZ+jkXFZtp/9oraaxuB/M1Grlqy44zvNg4AoHNaNJl89dStwgOA8izdeYYy87yfE8YXELxIepBbT8ms9fTMnvxqAACAa7Q4g1araNuMdHP1630Wi81EY7rUF/5xr9x3zuFroi8jCF4kVbraUJJaRAAA8ODC7c45/NCFTHqweQ3qULeytJ+1WfD+EbwAAAC4odiDS3fpSUudualaFP+LH62OIXiRiHU1XJmaFxzFAAC6Ytv0YlbrfG8Wu3sELz52a+0Yj4bJ+XIZctFtlQAAKvPkHNrzpqrOt2nnOUyZIfEkdXpXqUII/TD0dgpzcw0LX442UiKqBwDQUfASGRZE9zSK54vz6oFZcNULghc/iAz1/mNGvAEAoK8Lt7mcgEjm874ZzUZA5VUXynwEAwAYjO8v3K6f9D/8R3MyIvR5UWVhRrT1AABIgc3D4i7WdaBM0FPOdlw57SfEhNNbvZuQv4nuOongRWNmL0Jy6/9ErAIAICvXz/O9b65GjatGUie3Voouu/2WCdEO392xvvFWoUafF52pXSmcTqXniE4GAIBuufMb9fk761DFsOBr/+dsm+Vs58a4CNqbdJlkYRa8f9S8aEyr5h13JjSy9m7fm6XvaAUAoDJnp9Anbkt0+NojLWvwv7fXqVR2mzYnZgU6OpoNONro6NGjVLduXQoIuB47JScn05dffsn/stf69OlDFSpUIKPyNAaqXmq9DAAA0Jazy7azU3fzmtH0/ZDbKCb8Wk2MFqL+Hs1aPy6CjqZkkVEIqXnp1asXpaenlzw+cOAAde/endatW0epqan0ySefULdu3ejYsWNeXfwd3cp7vbz/tbefkscubru8dGfmFtrM+cKfdzHP5X84nudfy5s35SDLDXnw/+dd3vGv6g15UKccnIUvpY/T0v8bGxFCgQEmfv+JdvZraa6/v/xRp6HBAfy9z3esQ/7EPgF/f8eF17yU7tQ6ffp0uv/+++nVV18teX3ixIn88ccff+zRPmJjo7x63VVxcbbbCQ0NKvOcq4ICA0r+N7ewuOT5ChVCrz1vch7yu7rf4KBAj9OoNa3KQSTkQQ4oBzkYoRwiz111+Fp4hVDbbVWOougK9mta/nVHFI3/+gi/HxQUeH37kdfO+aEh1y/R4X/3m7H8oO3apCq92K1hybm8Yqr2fR0DAx3Xb0RGhgm9jgTJ0C/kxIkTNHbsWJvXBw0aRA888IDH+0hNzbQ/6Q+LfGOjHL7urpSUTJvHeXmFZZ5zVWFRsd3/zc7Ou/Z8Oel1db+FhUUep1ErWpeDCMiDXHAsiWWk78OVK44DhZzsPJvHaWlXqSA7yKXzskXdiiH8HJ2ff70GPje3oOT+jfGR9Hr3hvy+5Vye6SRNnjp/Odfha1ev5mp+HbF8/lLXvJw8eZKioqIoJCSEmjZtSufPn6fGjRvbBDSxsbFe7MN5f6LyXndFpfDgMtsoNpvd2m5woIkKiq7/g73/taSVFazl9X/dlkgdEmPo2c8OOP1fX+VdKzKlxVPIgxxQDnIwQjmw87yz/3VnW9bWPd2WkjPzqGGVqHL/x1x6P+T/uW5ElrOQ4KVBgwb0+OOPU2FhIVWrVo2CgoJo/PjxtGHDBoqIiKA5c+bQ0qVL6cknnySZzX/E+5kNPe1Tzobe3ZJ4bdFHAABQE5sD5nDyVerRpCpVjQrlN0+YyFiEBC9r166l4uJiSkpK4iOPWMfc48ePU3h4OH99x44d9Nxzz9HgwYNJZjfEej8aSvEfKAAAhuPJDLuOvN+vOR25mEktapadhM5RB1YTiSe6dk3YJHVsmHRiYiK/derUyeY1VusCzg9YZ72yZz3YlF744g+HryNgAhUlZWDyRZB/YUZ7o42cqRASSK0SUIuum0nqCgoKaOfOnSSbJtWudSYa2ameJpPUuRK9urJJ1nfGon2dynRjfISTfSJ8AfWkZV/vsAggkr9OoaVP/ZX/HrXUvm5lO+/1d32MASepc8Xly5dp4MCBdPjwYZKJJUbwtF3SK9Y9dst7q5PXrLdwOaeAsguKMLkdSM8qPgcwpGUDWtGO0xnUtWE8Gb3ZSNqal8qVK9PGjRtFJ0NZzmqArHvKd5m3je5fsINSsvL9lDIAD7lZqwngrwv3PY3sBxMd68VS5N8z4HrG9piPiwyl+5pUpWB7868Y7OshJHhp0qQJTZs2jTcNOesTU7NmTZKXSZMmGU+DV2+qCO11NvvzouNJlwBkwCbmApBB6aHS4cHXJ5izNrrrjaRXZiMGL2yk0ebNm6lnz558eLRKRBaYSS9HHYAHAoz20xKkVfoUajuLv0lI28pNf/fHNIoAUU0aS5YsoQcffJDPrMuCmBUrVlBmpthZX/XE2Wne7gRLCGhAcmg1AmnYmYjOomJYEB80cdsNlfg6Rt6wCYrK+QJEhgZRuxvKrlbtK2YjBi+saSU4OJieeeYZ3q+lR48eNH/+fGrXrh099thj9O6779I333xDW7duJb2eSC1tpIPaJJS/r79DEa1O3qIPOgBPeNd3AMA/Q6Ut01W8+9DNbo8+9VaFEPvNV74getSq8LWNoqOjaciQIfzGVpfesmUL/7t69WpKS0ujffv2kR69em9D6t86gRpViaRFv53xeDv/bJ1Ay3Yn0bA767r8P86mtgaQFWpeQI+T1DmDhlLJV5W2aNasGb/JTKvrPltBunQbpTuRrOVEPqxjHfpHqxpUrWKYy/9rWUuJDZO2yLNaFAwAADzr8yJSQsy1WeqNQEiz0dSpU/mijCozSbJPVotlL3Bx9mUq/PtnQ05BUZnnAGSFCkOQhrOFGTXcjbtB0Q2VjRO8CKl56dOnj4jdwt8Sol2vpQEAAFuy/tS7rbYfO+xikjq1CB0q7UYY/lDz6vxvy4Syi30FYMIMAAClLtyunP0rezm6SaUADt33der+ptWoUZUovvJ1h3e2iE4OAIBuOLtwR4eLu6wGyNL5xg8QvHjI18dIjegwOnc51+G+TC7U0jSsGilnfR8AgMLsnUIndm9Ie5MuU7eGVcgIzIKvI9KubWR0j7SsIToJAMrMrRGEplAQeOFmc3GxNYfGdWtAgRoei/6eJ0YlCF4kiDaf63AD/zu6y/V1MJrXqKj5fgD0qnmtGNFJAANB3TXRhj8vCS0DNBsJWBixtMfaJvLJ5kKCrGJJH0bc9r54aEkClXVubIyqepBD6XNottW0E1qyvgrIVglzOFnsYr6oeZGETeDigM2x68WRjEAFAEC7GvjwYFxK/Q2fuIHtSbosOgkAAMop/QNQy5p4m+16sNl5D99MRoDgRVL2Z9P1bFvv97O/5MLB89dX8UYbLsjOWY0hahPBn2Q+XzYzSH9JBC+ekqz90ZnWDjozyvwFBHDH9O//xAcGhhkmDAhepOWPzln4AgIAyMuTy4DZIHEVal4UOjCs21W9iW3MduYQcDaHBgAAWJ1DcboUDsGLh0wKb9/yxUPNCwCAB+dQf31oso2PlgiCF4VodRynZuXxv/jxAADgPvzwEw/Bi5v8dcH31dA75tyVa8GLtYIihDIAAHJdB9wXaJClMhC8KMDkh3bbVfvP+2gvAAD6IqLPi6s/aENcmPBUD4yRSxWbIk2+3791J90LV66tYA0AAK6fO0EMBC8GZv3rAb3nAQDcP3f6kjEagDyD4EWhjlroeA5GhgAbZIFjUTwELwYcKm1hHYahEhQAwNVzp3/OmPjB6hiCF0VpPRoJQ/8AAFw9X+KTEg3Bi6xD5KxiE8tMuJoPn7bu86LtlgEAdEvElBmohbEVRALt3LmT9u7dS8nJyZSfn09hYWEUHx9PLVq0oFtvvVVk0qSk9cFrXfWJXxIAAK6eO8GQwcuZM2fo3//+N509e5aaNGlCcXFxFBISQlevXqUjR47QBx98QImJiTRnzhyqWbMmyciXk8j5Y/tMTkFxyf1iRC8gOVwwQBZoZjdo8DJhwgSqV68effbZZ7y2pbScnBwaM2YMvfzyy7Rw4UJNayksz3tai1Hyb6by9+GN0ttgj0tv1pv9sP/dd/ayZtvzZP/+3qfWkAcxn7enr8sMx5Li5eDkeqBFev7ehUv7MJmIJvRsQpPXHdI+QXb2JWp7QoKXffv20apVq+wGLkx4eDgNHTqUHn74YY/3ERsb5dXrjgQEXusmFB0dTnFxZbcRGhps93l3pRZevx8REcq3GWA17TMrZE/zwPA0Wh0pASaTJul2lzd5kAXy4B+Z5XTRQznIwQjlEBYeYvs4TJvzfpn9hAWX3A+vEOLyPp7oEOWX4EXENUNo8FKrVi365ZdfeO2LIz/88ANVrVrV432kpmba7cdhueg7er08RUXXmlquXMmhlJTMMq/n5xfYfd5d6elZJfezsvL4Nks37XiaB4Ztz5IXhm1bi3S7yttykAHy4F/p6TlOX8exJJaRvg/Z2fk2j3NztTnvl5aXV1ByPycn36V9mLz8YesOrfPsTtqFBC+sSYj1edm8eTO1adOGqlSpwvu8sE67ly5doj179vDbu+++6/E+2IHn7OAr73VPt+/tdp3tr3Q/GG/2Vfr/fJVuV9Kh6onOAnnw3+eMcpCfEb4PZfq8+PC87+t9eENkeoQEL7fffjt988039Pnnn/MmpIsXL1Jubi6Fhoby2hY22mjy5MlSdtYVsyCX7V+tFFvlBR12QXboJAmysD53+pLK/bh0O1S6evXq9Pzzz5OqTDo4aK0vBpIF9AAA0pKtBsSIpJ2krqCggM8DA457ngMAgI6XB/DiTP9+v2akZ9IGL5cvX6aBAweSUWk9z8s9jeKFVX0CAIB/a+NvSYzR9UcuVfDCmjHS09P5/djYWNq4cSMZNeLW2vhuDco8Z93PBf0JAACM12w0rGNdUpGQ4GXYsGF8Nl3rJqIpU6ZQy5YteWfedu3a0eLFi6XssOuvPin2Nm9Z48gTYcGBTr+AqIUB2enoegGKE7HGna9UiwolFQkJXtavX095eXklj2fPns2fe/PNN2ndunU0duxY+uijj2jevHkikifvrI92njNiLRIAgEila6pVHRUUH2k72Z5KgmQo+O+++47Gjx9PXbp04Y/Z5HUVK1bkywg899xzZEh++DJY17ag2QgAwPN5ssAANS+s+cO6CSQgIIASEhJs3sMWZszKuj7LLPg2ukezEQCAa8TEKtpfAEykLmE1L6ym5cYbb6Q6depQ06ZNaenSpbzfC8OalObOncsnqzMqu31eNN4HJqYDAHCfv2qq/RFc1K4cTioSErzMmTOHjh49SseOHeNrHJ04cYLPsDt69GjeXHTnnXfyxRk9XVEaXGP9/UOtJ0gPBylIQk+H4o3xkVSjYiidu3K9H6oKhAQvrG+LpX+Lxblz53jgwsyYMYOPPIqIiCDZ+Ktts7yRRd6MPLJAPxcAAE/Onep9am0SY2jn6Qy7r91RL5ZW7D1HKpFmnpcaNWqU3O/QoYOUgYtoWgQs1tDPBQBA4hl2NTznN69xrXJAL6QJXow+A65r+9RWrUpqtnUCAMhU86LKUOmm1aPKzU/b2mrMzIvgRVL++C5EhwlblxMAQFlmPzUjaT2310ud65f7njl91VgTCcGLQrSO7q2/bxEhZWfgBZCJgt0MQKdU7S9ocvD8TQ5qZGSGn94KBSpa18ZYf/8S0YQEAOD2uVPmH6zTejWmUV8dvr69UlcRS5+aextXocIiMzWtoU4Qg+DFwGwXZhSaFAAAZahyumxXp7JL7wswmej+m6uRStBsZOB2IwQsoCf5hcWikwAGoUrwomcIXhQ4aC1Ve76cYRdfRlBdfhGCF/ATAb/8PDn/m0i/ELwoWsmizarSVvdRDQOSwyroIIvSoYsqQ6ULim0DfEfJVmEAB4IXI482wvIAAABeT/Dps6HSGp/0gwNdu+SP7nIjyQ7Bi4ccHVNaHcT2JsHTfLSR1e+HPPQXAABw7dyp85rq+MgQkh2CFwOz/vUQ4mJEDgAA/mHSuObG5OL/FSjQfwxXLJXmedF8tJG+fz0AAPiCXk6dJgeXlLAg9HkBLQ4wu8+ZNK15QWdIkJ1eLhigPn8tauvt71WTh9tToQMyal40plWh+/vYwYUBVKfCCRf0Se/HXmhQAD3ethbJBDPsukvgrz/M8wIAIAOzkkGVyYOryM3Vo2jBP1pQYICJmtWIphdW/0EyQM2LgaN7m6pPNb6LAACGaTbyNZOj50tdbFjgwrSv69pyA/6A4EWh5hfrqFmTSepsZtjVybcRDEuLfmAArriaV6hEs7up3CfUheBFUlqPLCp3kjpJv3wAFjhEQRY7TmfoOiA3kfwQvKhE6xl2HdwHUJEKJ1wAvzJ5Ns+LChC8SMpk5/jTfIZdVLcAAEjL5xXwJpOyo6cQvBiYXjqdAQCIJOvF3uTpPC8kPwQvbvJXx1b7M+xa3ddgH8VWNS/W9wFUJOsFBEALOL5tIXhR6ADzZectxC4gPcTXYDDer21EpbbnyRS7cv4qEDJJ3U8//US33347BQcHlzx38OBBWrFiBV28eJHq1KlDAwYMoBo1apBqtFtV2vfQbAQAYNwffyY3nyej17w8++yzdOXKlZLHP//8M/Xr148HLvXq1aO//vqLevToQbt37xaRPEPU6uQXFts0FSn63QMA0C3N1zYi/RBS81J6lMu7775LQ4YMoaFDh5Y8N2fOHJoyZQqtWrVK00IvGbnj7UFhcrANR8+7u/2/ZzS03penna/s6TT3V8ortFr23Gz2a5uqVuUgEvIg5vN29rqqxxOOJcXLwU/Hniv7MFnlwaafpIM02nvO6vLj9HuldZ7d2V6QDBOwnT9/nrp27Wrz3P33308LFizweB+xsVFeve5IYOC1yqqY6AoUF1d2G6GhwXafd1dxaG7J/YiIML7NQKtlytlH6GkeGJvAhQdLAZqk213e5EEWyIN/pBSayy2HsODr3xEV4VhSsxzCwrQ575dWoUKo1f0Qt/YRWyoP4RVCqVLliDLXM3vbjMm9fn0ICnJ8bRBxzRBe87J9+3Zq2rQp1apVi9q2bUuHDx+mhg0blrxnz549VK1aNY/3kZqaabcd0nLRd/R6eYqKrhXq5cvZlJISUub1vLwCSknJJG+lZ+WX3M/KyuXbLCoqsnmPp3lwlC8t0u0qb8tBBsiDf6WnZzl9nR1LoVYBvkpwLKldDnm52pz3S8vJuX4dyM7Od2kfJgd5yMnOK/MdKi62f96/fPn6+woLHV8btM6zOz/KhQQvd999N82ePZuSkpJ4LUxkZCRt3ryZOnfuTFFRUTR27Fj66quv6JVXXvF4H6zQnB185b1e7vYdddLycruOpu5nt9I9xb3Ng7N9+ovWeRABefDf54xykJ9Rvw++yHPpbbqzD3OpPPD7dv7f/nXM5NJnIbKchQQv7733Hv+bn59PJ06coGPHjtHx48d54GKpmZk5c2aZpiQZ+Kuw/N12r/i5BgBAd1Ttw6Xb4MUiJCSENxVZNxcxU6dOFZYmmdmOvNf2qMZSAQAAnp4/dbbgr8n6f0hK0k5Sl5eXR2vWrCGjsjuQSdKDCAAAtGdSfPuGDF4yMzNp9OjRopMBAJI4k54jOgkAyjK7EayoENRIG7zExcXRkSNHyKh8uRSAPYrUeoKBjfrqsOgkAPiVqNp2kwLRi5DghXXUnT59OnXs2JFatWrFJ6djnXatpaSkUOPGjUlW/g4u+D59eESp0mYLACAbFS72KqVT2uCFjSTauHEjvfTSSzRp0iQeqDz00EP8OWuG7kRqdZA5mq9GS1hVGgDAYH1eTCZpfpwrEbx8++23fOp/tn5Rz5496b///S/179+fXnjhBf6aP2oaPCUynJLv0wAAAFWZFa4gEDJUOjc3l2JiYmyClFGjRlFAQACNHDmSgoKCqGXLlmRk/h5tlP/3zMEAAOAef8QAfv3xahK0X9lrXthyAG+++SalpaXZPM8Cl0ceeYSGDx9Oy5cvJxX58hj25UF0Na+Ipmz4y4d7AAAAt2j8i9Xk/jQv0hISvIwbN44yMjKoffv2tHXrVpvXJkyYQM8++yx98MEHZGQiWsxWH7jg/50CAACo0GxUtWpVWrFiBV8SID4+vszrbPRR9+7dadOmTaQak09rc1SIhwEAQAtan/FNpdfHc/Q+BS41QpcHqFu3rsPX6tWrx29GZa+3twoHFACA0ejt3GxS4IeytJPUyc6k0x7hSRmYxRQAQJc1LybSDQQvkrJ3kPnjuPv3yt/9sBcAAJCVieSH4EXRgMZXc+Ccu5zrk+0CAIBYplKPFZ7mBcGL1hQ+FgAAwAO+CgJ83cxjdrhj/6XBU6h5UYikxxAAAOiIieSH4EVSdqNdWUNgAAAD89WpWYVRP6IgeFGAyu2SAAAgB5OLUZaM6wqWhuBFYyZfzvPig/0AAICkfH2iN6v7yxjBiwIsh5cCwTAAAEjKpPH7RELw4iFfBxIIVAAAQASTAtELgheNZ7s1+3CfChxPAACG47Oh0jYPvL8CmEptwpVky3rdQfAiKZOq4TAAAICPIXhRgL3oGHEMAIDOh0prvF0T6QeCF4Xo6cADAAA5mRS42CB4UYgCxxMAAMg6SZ3J5FJfHRUmx0PwIil7kwSpEA0DAAD4GoIXBdiLjhHHAADom6/7vJj9tF9fQPDiJpHzEapQlQcAYDQKT1RrlwpXGgQvChw8ZkvIpMIRBQAAUjKZvP8nWS5DCF4AAAC8oEIziyeTrtoTIEleEbwo8GWwHF+SHDMAAKAgkyYbkeNKhOBFIdbHjC+XLC8oKvbZtgEAwDWiwgSTk+tLoByxC4IXWYk8Pl7+5ojAvQMAgMhBHyYnrwVIUvMSJGrHubm59N1339HevXspOTmZ8vPzKSwsjOLj46lFixbUvXt3/hiuj3Dy12ijjX+l0FR88AAAQvmyht1TgQEGDl4OHjxIzzzzDEVERFCrVq2ofv36FBISwgOYlJQUeu+992jmzJm0YMECatSokUf7cFTmlufdPSbyC4tpxJqDlJSRW/L/9rYRGKBNk6DJ6gA5lZZtd3++PK57L/iNfMpEFBAQQMXFxWLHn3sDeZDK/Qt2UFiQoi3hOJaULwdfxxkmF/dhcnCN4/9v5+thb5sBAY736+i+FtzZnpDg5dVXX+U1K+PGjXP4ntdee41eeeUVWrFihUf7iI2N8ur10i5l5tHuMxn8fnCgiZrWiaO4itdrhvq2TqBVe5Louc4NKC7OvW2X1wv8H+1u4NtsVCOathxP48/ViYtwOw/uOHclz2fbBtBCu7qx1DIxhub9eIw/TssuwAcLQgzoUFeT835pN99QmeiXE/x+sxsqu7WP2L+vD81rxdD+MxnUvWUC1a4RbfOeFomV7G4zMroCVQwLoiu5hdSgekWb94zr0YTGfPE7v++LPLvKZPZkrJSXWLPQF198QXXr1nX4nmPHjtFDDz1E+/bt82gfqamZ9memNV0rVEevO5NyNY8uZOZRtahQiosMtXmtqNhMadn5FF/qeW/kFhTx/d1QuQJ/XGw2018XsygyNJBa1K/idh7yCot5ABYVGkRZ+UUUERJI+UXFlFtQTJdzC+hiZj7dUDmcYiNC/NKhPDq6Al2+nE0qQx78p7DYTIcuZFLf5jXoVHo21YmNoKAAE+1NyqCIqHDKz1Y74MaxpFY5nL2cS3fWjaWs/MIy1wMtnUnP4X9rVQp36f2mUtc4dh1Jzy6g6tHXfmynZeXTuSu5vO9Kg/gICmLNBXZczimgc5dzqUGVyDJNRYcvZFK9uAgK0bim05J2aYOXfv36UZs2bWjkyJEO3zNlyhTatWsXD3I8kZLiOHhh0aKj11WAPMgB5SAHlIMcUA5yMCl8jbOkXepmo6effprWr19PrVu3pipVqpT0ebl06RLvxJuZmUnvv/++iOQBAACAxIQEL02aNKENGzbQ119/TQcOHKC//vqLjz4KDQ2lqlWr0lNPPUX33HMPRUZGikgeAAAASEzYUOnw8HDq27cvvwEAAAC4StpxhXl5ebRmzRrRyQAAAADJSBu8sD4vo0ePFp0MAAAAkIywZiNr6enpvLMua0qqWLEify4uLo6OHME09QAAACBJ8MJGGn3yySe8wy5rIrJgSwI0bdqUBg0aRF26dBGVPAAAAJCUkOBl8eLFNGfOHHryySdp6NChFBsba7M8AJvfhTUZDRs2jAYMGCAiiQAAACApIcHLokWLaNq0aXZrVurVq0dt27alhg0b0uTJkxG8AAAAgPgOu2xOl4SEBKfvYfO9sE67AAAAAMKDl65du/JmIdY8VFhYaPMaW81zz549NHbsWD5RHQAAAIAUywOwZqMnnniCioqKKCYmpqTPS0ZGBgUFBVHv3r1pzJgxIpIHAAAAEhMSvLBAZcKECfTiiy/y4dBsPaOcnJyS5QEaN27MRx0BAAAASDXPC5vXpWXLlj5bndLZ845eVwHyIAeUgxxQDnJAOcjBpPA1zp00m8xm1RbNBgAAACOTdnkAAAAAAHsQvAAAAIBSELwAAACAUhC8AAAAgFIQvAAAAIBSELwAAACAUhC8AAAAgFIQvAAAAIBSELwAAACAUhC8AAAAgFKErm0EoJr09HS++jlbl6tixYqikwMgFL4PIIpua14+++wzGjduHL/Plm/66KOP6N5776UWLVpQjx49aNmyZSQzdoGcPn06dezYkVq1akVDhw6lY8eO2bwnJSWFr8Ats40bN9Jrr71GX3zxBX+8bt06/vmzBTl79epFn3/+Oclu/fr1NHDgQH7s3H777XTXXXdR27ZteR4GDBjA8yg7fB/kgO+DHPB9UJ8ua17efvttfnA+/vjj/PF7771HH3/8MT377LNUp04dHgTMnTuXrly5QkOGDCEZzZw5k3744Qd66aWXePD1ySef0EMPPURvvfUWdenSpeR9Mq+ruWTJEpo1axbdcccd9N1339GuXbvo+++/p6eeeooHXcePH6cZM2ZQbm4uDwJktHjxYpozZw49+eSTPICMjY2lkJAQHlyy4JHlafTo0TRs2DBp84DvgxzwfZADvg86Ydah9u3bm7dt21byuHPnzuYNGzbYvOfnn3/m75PVnXfead61a1fJ4+LiYvMbb7xhvummm8zffPMNf+7SpUvmRo0amWXVqVMn88aNG/n9Y8eOmRs2bGhevXq1zXs2bdpk7tatm1lWHTp0KHPslMZeZ+UlK3wf5IDvgxzwfdAHXda8sF/FkZGRJY+Dg4MpPj7e5j3scU5ODsmK1UbExMSUPDaZTDRq1CgKCAigkSNHUlBQEG+2kFlGRgbdeOON/H5iYiIFBgZSgwYNbN5Tt25dSktLI5nLISEhwel7qlatSpmZmSQrfB/kgO+DHPB90Add9nlhfSpefPFFXqXPPPPMMzRt2jS6cOECf3zq1CmaOHEide3alWTF+lS8+eabZS7sLHB55JFHaPjw4bR8+XKSWZs2beidd96ho0eP8uYh1tyycOFCfvJgCgsL6f3336dmzZqRrNgxwpqF2LHE0mutuLiY9uzZQ2PHjqV77rlHWBrLg++DHPB9kAO+D/pgYtUvpDPs4mjpJBoVFUU1a9akkydPUlZWFoWGhlJeXh7vCMv6j1jX0MgkOTmZnn/+eTpw4AB9+OGH1L59e5vXWT8M1peHXUAPHz5MMmLBIusLsn//fj465+WXX+b9jVauXEk33HADDyJZDRLrTF2vXj2S9VhigS9Lc1FREa8Ns/R5Yb+kWfp79+5NY8aMobCwMJIRvg9ywPdBDvg+6IMugxcLdnFhv4zPnDlD2dnZvNmiSpUq1Lx5c95xVwWsUytr4mJBWGksENi0aRM9/fTTJDPWMZpd2NlFn9m2bRsdPHiQl0WnTp2kDSCtsSbGI0eO0KVLl/h9FgSz5iLW8VjWoKU0fB/kgO+DHPB9UJuugxdWw8IuOOwXj2VuDhYINGrUiF98AIx0LOkhDyAHPRxLesiDkemywy47KNkcKayqv6CgoExVP+vA269fP94vxlIbAKDXY0kPeQA56OFY0kMeQKc1L2xyOtbPgnXKZROLseYiC9ZvYe/evSWvTZ48mWTE+rS4is0/IiM95EEPx5Ie8qCHY0kPedDDsaSHPMzRwbHkLV0GL61bt+YTQjVt2tThe1hHWDbx2I4dO0hG48eP578MatSowTscO8KGUC9dupRkpIc86OFY0kMe9HAs6SEPejiW9JCH8To4lryly2ajiIgISk1Ndfqeixcv8upBWbHRUrVr1+YjjdhoF3aQqkYPedDDsaSHPOjhWNJDHvRwLOkhD6/p4Fjyli5rXtjQ23nz5tGgQYPolltu4aNarKd03717N59vhI3SYVPVy4wNl2bpZvOhqErlPOjhWNJDHvRwLOkhD3o4lvSQBz0cS97SZfDCsDV02HpGv//+O++gxarPWFZZL/Kbb76ZHn30UbrvvvtIdlevXqWkpCTeA15VqudBD8eSHvKgh2NJD3nQw7Gkhzzo4Vjyhm6DFws2iRubV4FN886i60qVKvEDFcCIx5Ie8gBy0MOxpIc8GJXugxcAAADQF12ubQQAAAD6heAFAAAAlILgBQAAAJSiy3lemKNHj/KZEh9++GH+mC0EuGLFCr6OBZvU55FHHpG+hzbyIN7gwYNp4MCBfAFJVSEPctBDOVhWvGcz1DZo0ICvDn/ixAk+Edq5c+coISGBj9SRdZV4C+RBfbqsefn222/pgQceoJ9++ok/3rhxI1+rgk1MxL5wLIDp27cvf15WyIMctm/fTsOHD6fRo0fzE56KkAc56KEc2Irw9957L02YMIF69epFa9as4efSU6dOUf369XkAw869LK+yQh50wqxD3bp1M3/66aclj3v37m1etGiRzXs++eQT87333muWFfIgh4YNG5r37dtnfvzxx83Nmzc3T5w40Xzs2DGzSpAHOeihHB544AHz+++/z+9v2LDB3KhRI/OsWbNs3rN48WLzgw8+aJYV8qAPuqx5Yb9q2rZtW/I4LS2NbrvtNpv33HHHHfxXgqyQB3mwqnA24+Z7771HJ0+epJ49e9JDDz3EH//222+8Ro+tTisz5EEOqpcDS3OPHj34/S5dulBAQAB169bN5j2dO3fmTUmyQh70QZfBS5s2beitt96i7Oxs/rh379706aeflrzOprZhJ5BmzZqRrJAHOVhPWNWuXTtatGgRn52Tzb7JLjbPPfcctW/fXupjCXmQgx7KoU6dOrRhwwZ+n/1lk7z9+OOPNu/ZvHkzJSYmkqyQB33Q5SR158+f5+tSsNoLVuNSvXp1+uKLL/jsiayD2f/+9z/+pWMnD1k7liEPcmCdurdu3UqxsbEO33P27Fn+i1nWiw7yIAc9lMOuXbtoyJAhFBQURBkZGbxz7pkzZ0ryxwYZ/Pzzz/Tuu+/S3XffTTJCHvRBl8ELU1RUxH8R7Ny5k3+5WC1MYGAgxcfHU4sWLXjVZ2RkJMkMeRBvzJgxNG7cOOmPFWeQBznooRwszfB79uyhmJgYvrBhVlYWX92YjehkixyyEZ7NmzcnmSEP6tNt8AIAAAD6pMs+LwAAAKBfupykjjUVudMxVkbIgxxQDnJAOcgB5SCHnTq4xnlLl81GbPIk1nGMcZY91vv/8OHDJCPkQQ4oBzmgHOSAcpBDLx1c47yly+AlPz+fRowYQUlJSXxJgNDQUFIN8iAHlIMcUA5yQDnIIV8H1zhv6bLPS0hICM2cOZPfnzVrFqkIeZADykEOKAc5oBzkEKKDa5y3dBm8WAp3xowZUk+WVB7kQQ4oBzmgHOSAcpBDiA6ucd7QZbMRAAAA6Jdua17sefXVV/nkRCpDHuSAcpADykEOKAc5vKqDa5yrDBW8rF27ls8GqTLkQQ4oBzmgHOSAcpDDWh1c41xlqOBFDy1kyIMcUA5yQDnIAeUgB7MOrnGuMlTwAgAAAOozVIddtlIzWziMLdCoKuRBDigHOaAc5IBykMN5HVzjDF3zMnjwYNq8eXOZ56tXr65MoSIPckA5yAHlIAeUgxwG6+Aa5y1d1rw0atSIzzjYvXt3Gj58OFWtWpVUgzzIAeUgB5SDHFAOcmikg2uct3RZ88IsXbqULl26RPfccw9NmjSJjh8/TqpBHuSAcpADykEOKAc5LNXBNc4buq152bp1K8XGxtK2bdtowYIFtH37dmrcuDF16dKFWrVqRfXr16eKFStScHAwyQh5kAPKQQ4oBzmgHOTQSAfXOG/pMnhhBbhlyxZesBZnzpyh9evX0y+//EK///47Hwsv84qbyIMcUA5yQDnIAeUgh8Y6uMZ5zaxDDRs2NKekpDh9T1JSknn//v1mWSEPckA5yAHlIAeUgxwa6uAa560g0qE+ffqUu0R4zZo1+U1WyIMcUA5yQDnIAeUghz46uMZ5S5fNRqWlp6dTfn4+hYeH8zZAFSEPckA5yAHlIAeUgxzSdXCNc5cua14Y1vb3ySef0IEDBygvL6/k+bCwMGratCkNGjSId2ySGfIgB5SDHFAOckA5yGG9Dq5x3tBlzcvixYtpzpw59OSTT1Lr1q15p6aQkBAemaakpNCuXbv4e4YNG0YDBgwgGSEPckA5yAHlIAeUgxwW6+Aa5zWzDnXo0MG8YcMGp+9hr995551mWSEPckA5yAHlIAeUgxw66OAa5y1dTlKXm5tLCQkJTt/DZiTMzMwkWSEPckA5yAHlIAeUgxxydXCN85Yug5euXbvS6NGjedVZYWGhzWvFxcW0Z88eGjt2LJ+ZUFbIgxxQDnJAOcgB5SCHrjq4xnlLl31eWLvftGnTaOXKlVRUVEQxMTEl7YEZGRkUFBREvXv3pjFjxvDOTTJCHuSAcpADykEOKAc55OvgGuctXQYvFjk5OXTkyBG+/gO7z8bFs6o0NjuhKgWKPMgB5SAHlIMcUA5yyNHBNc5Tug5eAAAAQH902ecFAAAA9AvBCwAAACgFwQsAAAAoBcELAAAAKEW3axsBgLzYHBWrV692+p5NmzaVOxEXABgTRhsBgN+xmT/ZLKHMN998Q4sWLeJzVlgm2WJzV7Ahn4GBgSgdACgDNS8A4HdRUVH8ZrnPgpT4+HiUBAC4BH1eAEAqSUlJ1LBhQ/6XYfe//fZb6t69OzVv3pxGjBhBZ86coYEDB/LHjz76KCUnJ5f8/4YNG+i+++7jr/Xt25d27NghMDcA4AsIXgBAerNnz6Y33niDPvjgA1q/fj3179+f3z799FM+u+iCBQv4+9hso6NGjaIhQ4bQ2rVr6f7776ennnqKTp06JToLAKAhNBsBgPQGDx7Ma1IYNvV5nTp1eE0M061bNx60MAsXLqR+/fpRr169+GNWO7Nz507673//yzsJA4A+IHgBAOnVqlWr5D5bs6VmzZo2j9mCdMyxY8d4E9OKFStKXi8oKKAOHTr4OcUA4EsIXgBAeqVHHQUE2G/xZqOUWDPRAw88YPO83hepAzAa9HkBAN1gzUmso2/t2rVLbqwW5ueffxadNADQEIIXANBV3xg2b8zSpUvp9OnT9NFHH/HbDTfcIDppAKAhBC8AoBstWrSgN998k5YvX86HS3/22Wc0Y8YMatOmjeikAYCGMMMuAAAAKAU1LwAAAKAUBC8AAACgFAQvAAAAoBQELwAAAKAUBC8AAACgFAQvAAAAoBQELwAAAKAUBC8AAACgFAQvAAAAoBQELwAAAKAUBC8AAABAKvl/Ij+MfInBYtoAAAAASUVORK5CYII="
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "plt.plot(speed_kph.datetime_x_axis, speed_kph, label = \"Speed KPH\")\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"Speed\")\n",
+    "plt.tick_params(rotation = 90)"
    ],
-   "execution_count": 14
+   "id": "8f3d0704205b4567",
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-17T17:38:06.233438Z",
-     "start_time": "2026-02-17T17:38:04.509937Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "#plot relevant data\n",
@@ -1108,35 +1388,11 @@
     "plt.show()"
    ],
    "id": "2bc30605a4cc336c",
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_30724\\1203527545.py:21: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
-      "  ax1.legend(loc = \"upper left\")\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 9
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T01:57:45.139755Z",
-     "start_time": "2026-03-04T01:57:45.136276Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "# in order to get position data, look up miguel's work for localization of fsgp data\n",
@@ -1149,7 +1405,7 @@
    ],
    "id": "158615364b919eed",
    "outputs": [],
-   "execution_count": 4
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -1166,12 +1422,7 @@
    "id": "73256b66933551d3"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-06T01:15:30.319687Z",
-     "start_time": "2026-03-06T01:15:30.287590Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "reverse_coords = [\n",
@@ -1471,15 +1722,10 @@
    ],
    "id": "2741c957d09edb03",
    "outputs": [],
-   "execution_count": 3
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-06T01:15:32.952293Z",
-     "start_time": "2026-03-06T01:15:31.132927Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "\n",
@@ -1504,15 +1750,10 @@
    ],
    "id": "88a4ae7fb0d75eed",
    "outputs": [],
-   "execution_count": 4
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T01:57:49.206066Z",
-     "start_time": "2026-03-04T01:57:49.063379Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "plt.plot(calculate_path_distances(gis.path[:gis.num_unique_coords]))\n",
@@ -1522,42 +1763,16 @@
     "#     to calculate distance between coordinates on a sphere.\n"
    ],
    "id": "4d4053447813cbbe",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1fb81916960>]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 7
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T01:57:50.550208Z",
-     "start_time": "2026-03-04T01:57:50.545800Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "#initially used this function, but this seems wrong because on plotting we get a linear y = x relationship between position and speed, which doesn't seem real.",
    "id": "1e4b479b6d424b34",
    "outputs": [],
-   "execution_count": 8
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -1579,28 +1794,15 @@
    "id": "756fa51c0541bbfa"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T01:57:53.317540Z",
-     "start_time": "2026-03-04T01:57:51.780954Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "distances = calculate_path_distances(gis.path[:gis.num_unique_coords])\n",
     "state_array = gis.calculate_speeds_and_position(speed_kph, np.zeros_like(speed_kph),dt = 0.1)"
    ],
    "id": "40d6a78a4e43784f",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Calculating closest GIS indices: 100%|██████████| 1985282/1985282 [00:01<00:00, 1444437.80it/s]\n"
-     ]
-    }
-   ],
-   "execution_count": 9
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -1612,12 +1814,7 @@
    "id": "bdd1535e01ef7321"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-05T16:57:40.614624Z",
-     "start_time": "2026-03-05T16:57:40.605542Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "from data_tools import *\n",
@@ -1627,28 +1824,18 @@
    ],
    "id": "84e31e6326fbd121",
    "outputs": [],
-   "execution_count": 3
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-05T16:57:41.310117Z",
-     "start_time": "2026-03-05T16:57:41.304532Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "from datetime import *",
    "id": "8bfb296ce8b6d5f6",
    "outputs": [],
-   "execution_count": 4
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-05T16:57:42.131100Z",
-     "start_time": "2026-03-05T16:57:42.085969Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "from scipy import integrate as intg\n",
@@ -1686,23 +1873,18 @@
    ],
    "id": "59db5061a42a09db",
    "outputs": [],
-   "execution_count": 5
+   "execution_count": null
   },
   {
    "metadata": {},
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": "",
-   "id": "62f1a76206e8d1d8"
+   "id": "62f1a76206e8d1d8",
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T02:01:11.045670Z",
-     "start_time": "2026-03-04T02:01:10.930673Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "import pandas as pd\n",
@@ -1710,37 +1892,11 @@
     "#plt.plot(pd.DataFrame(speed_kph))"
    ],
    "id": "73543cfa882ed428",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1fb848d2720>]"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 24
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T00:59:30.585520Z",
-     "start_time": "2026-03-04T00:59:19.122Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "\n",
@@ -1753,49 +1909,18 @@
    ],
    "id": "29b3a4b6fac893d5",
    "outputs": [],
-   "execution_count": 75
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T00:59:35.273125Z",
-     "start_time": "2026-03-04T00:59:35.082002Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "plt.plot(pos)",
    "id": "50b7ed46a96a8b21",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x2665ba238f0>]"
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 76
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-06T01:16:09.423186Z",
-     "start_time": "2026-03-06T01:16:02.705176Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "import torch\n",
@@ -1811,24 +1936,11 @@
     "print(f\"Using device: {device}\")\n"
    ],
    "id": "e154d3edb6dbe5c4",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using device: cpu\n"
-     ]
-    }
-   ],
-   "execution_count": 5
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T01:06:35.493162Z",
-     "start_time": "2026-03-04T01:06:34.991311Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "pos = []\n",
@@ -1856,43 +1968,19 @@
     "pos_df = pd.DataFrame(pos)"
    ],
    "id": "25a88d24f23e2b2d",
-   "outputs": [
-    {
-     "ename": "IndexError",
-     "evalue": "tuple index out of range",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mIndexError\u001B[39m                                Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[80]\u001B[39m\u001B[32m, line 18\u001B[39m\n\u001B[32m     16\u001B[39m         \u001B[38;5;28;01mfor\u001B[39;00m t \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(lap_duration_s):\n\u001B[32m     17\u001B[39m             current_time = start_time + timedelta(seconds=t)\n\u001B[32m---> \u001B[39m\u001B[32m18\u001B[39m             dist = \u001B[43mdistance_so_far\u001B[49m\u001B[43m(\u001B[49m\u001B[43mstart_time\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcurrent_time\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mvel_lap\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m     19\u001B[39m             pos.append({\n\u001B[32m     20\u001B[39m                 \u001B[33m\"\u001B[39m\u001B[33mday\u001B[39m\u001B[33m\"\u001B[39m:        day_num,\n\u001B[32m     21\u001B[39m                 \u001B[33m\"\u001B[39m\u001B[33mlap\u001B[39m\u001B[33m\"\u001B[39m:        lap_num,\n\u001B[32m     22\u001B[39m                 \u001B[33m\"\u001B[39m\u001B[33mt\u001B[39m\u001B[33m\"\u001B[39m:          t,\n\u001B[32m     23\u001B[39m                 \u001B[33m\"\u001B[39m\u001B[33mdistance_m\u001B[39m\u001B[33m\"\u001B[39m: dist\n\u001B[32m     24\u001B[39m             })\n\u001B[32m     26\u001B[39m pos_df = pd.DataFrame(pos)\n",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[79]\u001B[39m\u001B[32m, line 25\u001B[39m, in \u001B[36mdistance_so_far\u001B[39m\u001B[34m(start, stop, vel_lap)\u001B[39m\n\u001B[32m     23\u001B[39m difference = \u001B[38;5;28mint\u001B[39m(stop.timestamp() - start.timestamp())\n\u001B[32m     24\u001B[39m vel = vel_lap[difference]\n\u001B[32m---> \u001B[39m\u001B[32m25\u001B[39m dist_m = \u001B[43mintg\u001B[49m\u001B[43m.\u001B[49m\u001B[43msimpson\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvel\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m     26\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m dist_m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\scipy\\integrate\\_quadrature.py:445\u001B[39m, in \u001B[36msimpson\u001B[39m\u001B[34m(y, x, dx, axis)\u001B[39m\n\u001B[32m    443\u001B[39m y = np.asarray(y)\n\u001B[32m    444\u001B[39m nd = \u001B[38;5;28mlen\u001B[39m(y.shape)\n\u001B[32m--> \u001B[39m\u001B[32m445\u001B[39m N = \u001B[43my\u001B[49m\u001B[43m.\u001B[49m\u001B[43mshape\u001B[49m\u001B[43m[\u001B[49m\u001B[43maxis\u001B[49m\u001B[43m]\u001B[49m\n\u001B[32m    446\u001B[39m last_dx = dx\n\u001B[32m    447\u001B[39m returnshape = \u001B[32m0\u001B[39m\n",
-      "\u001B[31mIndexError\u001B[39m: tuple index out of range"
-     ]
-    }
-   ],
-   "execution_count": 80
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T02:02:58.235081Z",
-     "start_time": "2026-03-04T02:02:58.229568Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "calculated_speeds, calculated_position = state_array",
    "id": "f4fb71b02da66f3e",
    "outputs": [],
-   "execution_count": 25
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T17:24:50.057608Z",
-     "start_time": "2026-03-04T17:24:49.536482Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "# use sunbeam instead to save yourself a headache\n",
@@ -1907,62 +1995,26 @@
    ],
    "id": "34a49f9470cefbc4",
    "outputs": [],
-   "execution_count": 4
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T17:31:16.422443Z",
-     "start_time": "2026-03-04T17:31:16.224308Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "file = client.get_file(origin = \"production\", event = event, source = \"ingress\", name = \"VehicleVelocity\" ).unwrap().data",
-   "id": "636774902401e518",
-   "outputs": [],
-   "execution_count": 25
+   "execution_count": null
   },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T17:32:02.062089Z",
-     "start_time": "2026-03-04T17:32:01.888794Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "plt.plot(file.datetime_x_axis, file)",
-   "id": "90dbd2d15d06f417",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x238c18d8350>]"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 27
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "file = client.get_file(origin = \"production\", event = event, source = \"ingress\", name = \"VehicleVelocity\" ).unwrap().data",
+   "id": "636774902401e518",
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T21:42:20.395132Z",
-     "start_time": "2026-03-04T21:42:17.464318Z"
-    }
-   },
+   "metadata": {},
+   "cell_type": "code",
+   "source": "plt.plot(file.datetime_x_axis, file)",
+   "id": "90dbd2d15d06f417",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "\n",
@@ -1973,13 +2025,11 @@
    ],
    "id": "81328e7e1792137d",
    "outputs": [],
-   "execution_count": 5
+   "execution_count": null
   },
   {
    "metadata": {},
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
     "def make_df(source, name):\n",
     "    dfs = []\n",
@@ -2003,15 +2053,12 @@
     "\n",
     "    return pd.concat(dfs).sort_index()"
    ],
-   "id": "aed81af65ec0feda"
+   "id": "aed81af65ec0feda",
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T21:42:25.922631Z",
-     "start_time": "2026-03-04T21:42:24.660509Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "pos = []\n",
@@ -2019,49 +2066,18 @@
    ],
    "id": "198c0fb7383fad4a",
    "outputs": [],
-   "execution_count": 7
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T21:42:30.107536Z",
-     "start_time": "2026-03-04T21:42:29.656871Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "plt.plot(pos_df)",
    "id": "18aceaa5d2fb1956",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x208db8a66f0>]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 8
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T17:40:47.473580Z",
-     "start_time": "2026-03-04T17:40:47.317130Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "# re query everything from sunbeam\n",
@@ -2070,29 +2086,8 @@
     "plt.plot(speed_arr)"
    ],
    "id": "c6edb2a9f27ba9ba",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x238c5f67290>]"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 41
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -2101,86 +2096,34 @@
    "id": "edd233437ab6b46c"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-28T15:08:39.502040Z",
-     "start_time": "2026-02-28T15:08:38.742067Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "plt.plot(calculated_position)\n",
     "plt.plot(calculated_speeds, color = 'red') # this seems not right."
    ],
    "id": "d543d0eca7887c24",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x285ff381220>]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 14
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-28T15:08:42.735959Z",
-     "start_time": "2026-02-28T15:08:42.726601Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "#how do i align this with time?",
    "id": "75a36cec8ab0ffb9",
    "outputs": [],
-   "execution_count": 15
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-28T15:08:43.888446Z",
-     "start_time": "2026-02-28T15:08:43.860250Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "len(calculated_position)",
    "id": "cd71ca48061bb80b",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1985282"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 16
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-06T01:16:33.816917Z",
-     "start_time": "2026-03-06T01:16:33.810092Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "#preprocessing - convert everything to pandas dataframes.\n",
@@ -2194,15 +2137,10 @@
    ],
    "id": "a6707eebb9ccd8c9",
    "outputs": [],
-   "execution_count": 7
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-06T01:16:34.831835Z",
-     "start_time": "2026-03-06T01:16:34.821775Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "# combine all dfs and resample, then feed to scaler.\n",
@@ -2220,40 +2158,18 @@
    ],
    "id": "3e63d74e16a13193",
    "outputs": [],
-   "execution_count": 8
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-06T01:16:35.564994Z",
-     "start_time": "2026-03-06T01:16:35.519935Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "pos_df.head()",
    "id": "67452406a84c903d",
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'pos_df' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mNameError\u001B[39m                                 Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[9]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[43mpos_df\u001B[49m.head()\n",
-      "\u001B[31mNameError\u001B[39m: name 'pos_df' is not defined"
-     ]
-    }
-   ],
-   "execution_count": 9
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-06T01:16:38.119880Z",
-     "start_time": "2026-03-06T01:16:38.018786Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "\n",
@@ -2263,284 +2179,49 @@
     "df = combine_dfs([\"position\", \"speed\"], pos_df.index, dfs)\n"
    ],
    "id": "6dea8ee6d0965889",
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'pos_df' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mNameError\u001B[39m                                 Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[10]\u001B[39m\u001B[32m, line 3\u001B[39m\n\u001B[32m      1\u001B[39m all_dfs = [df_mech_brake_pressed, df_accel_position]\n\u001B[32m      2\u001B[39m combined_df = combine_dfs([\u001B[33m\"\u001B[39m\u001B[33mmech_brake_pressed\u001B[39m\u001B[33m\"\u001B[39m, \u001B[33m\"\u001B[39m\u001B[33maccel_position\u001B[39m\u001B[33m\"\u001B[39m], df_mech_brake_pressed.index, all_dfs)\n\u001B[32m----> \u001B[39m\u001B[32m3\u001B[39m dfs = [\u001B[43mpos_df\u001B[49m, speed_arr]\n\u001B[32m      4\u001B[39m df = combine_dfs([\u001B[33m\"\u001B[39m\u001B[33mposition\u001B[39m\u001B[33m\"\u001B[39m, \u001B[33m\"\u001B[39m\u001B[33mspeed\u001B[39m\u001B[33m\"\u001B[39m], pos_df.index, dfs)\n",
-      "\u001B[31mNameError\u001B[39m: name 'pos_df' is not defined"
-     ]
-    }
-   ],
-   "execution_count": 10
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T21:42:48.242113Z",
-     "start_time": "2026-03-04T21:42:48.114597Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "final_df = pd.concat([combined_df, pos_df, speed_arr], axis = 1)\n",
     "final_df.sort_index"
    ],
    "id": "b4da4dfff97bc19a",
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "cannot concatenate object of type '<class 'list'>'; only Series and DataFrame objs are valid",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mTypeError\u001B[39m                                 Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[13]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m final_df = \u001B[43mpd\u001B[49m\u001B[43m.\u001B[49m\u001B[43mconcat\u001B[49m\u001B[43m(\u001B[49m\u001B[43m[\u001B[49m\u001B[43mcombined_df\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpos_df\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mspeed_arr\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[43m \u001B[49m\u001B[43m=\u001B[49m\u001B[43m \u001B[49m\u001B[32;43m1\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m      2\u001B[39m final_df.sort_index\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\reshape\\concat.py:382\u001B[39m, in \u001B[36mconcat\u001B[39m\u001B[34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001B[39m\n\u001B[32m    379\u001B[39m \u001B[38;5;28;01melif\u001B[39;00m copy \u001B[38;5;129;01mand\u001B[39;00m using_copy_on_write():\n\u001B[32m    380\u001B[39m     copy = \u001B[38;5;28;01mFalse\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m382\u001B[39m op = \u001B[43m_Concatenator\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    383\u001B[39m \u001B[43m    \u001B[49m\u001B[43mobjs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    384\u001B[39m \u001B[43m    \u001B[49m\u001B[43maxis\u001B[49m\u001B[43m=\u001B[49m\u001B[43maxis\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    385\u001B[39m \u001B[43m    \u001B[49m\u001B[43mignore_index\u001B[49m\u001B[43m=\u001B[49m\u001B[43mignore_index\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    386\u001B[39m \u001B[43m    \u001B[49m\u001B[43mjoin\u001B[49m\u001B[43m=\u001B[49m\u001B[43mjoin\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    387\u001B[39m \u001B[43m    \u001B[49m\u001B[43mkeys\u001B[49m\u001B[43m=\u001B[49m\u001B[43mkeys\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    388\u001B[39m \u001B[43m    \u001B[49m\u001B[43mlevels\u001B[49m\u001B[43m=\u001B[49m\u001B[43mlevels\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    389\u001B[39m \u001B[43m    \u001B[49m\u001B[43mnames\u001B[49m\u001B[43m=\u001B[49m\u001B[43mnames\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    390\u001B[39m \u001B[43m    \u001B[49m\u001B[43mverify_integrity\u001B[49m\u001B[43m=\u001B[49m\u001B[43mverify_integrity\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    391\u001B[39m \u001B[43m    \u001B[49m\u001B[43mcopy\u001B[49m\u001B[43m=\u001B[49m\u001B[43mcopy\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    392\u001B[39m \u001B[43m    \u001B[49m\u001B[43msort\u001B[49m\u001B[43m=\u001B[49m\u001B[43msort\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    393\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    395\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m op.get_result()\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\reshape\\concat.py:448\u001B[39m, in \u001B[36m_Concatenator.__init__\u001B[39m\u001B[34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001B[39m\n\u001B[32m    445\u001B[39m objs, keys = \u001B[38;5;28mself\u001B[39m._clean_keys_and_objs(objs, keys)\n\u001B[32m    447\u001B[39m \u001B[38;5;66;03m# figure out what our result ndim is going to be\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m448\u001B[39m ndims = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_get_ndims\u001B[49m\u001B[43m(\u001B[49m\u001B[43mobjs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    449\u001B[39m sample, objs = \u001B[38;5;28mself\u001B[39m._get_sample_object(objs, ndims, keys, names, levels)\n\u001B[32m    451\u001B[39m \u001B[38;5;66;03m# Standardize axis parameter to int\u001B[39;00m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\pandas\\core\\reshape\\concat.py:489\u001B[39m, in \u001B[36m_Concatenator._get_ndims\u001B[39m\u001B[34m(self, objs)\u001B[39m\n\u001B[32m    484\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(obj, (ABCSeries, ABCDataFrame)):\n\u001B[32m    485\u001B[39m         msg = (\n\u001B[32m    486\u001B[39m             \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mcannot concatenate object of type \u001B[39m\u001B[33m'\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mtype\u001B[39m(obj)\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m'\u001B[39m\u001B[33m; \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    487\u001B[39m             \u001B[33m\"\u001B[39m\u001B[33monly Series and DataFrame objs are valid\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    488\u001B[39m         )\n\u001B[32m--> \u001B[39m\u001B[32m489\u001B[39m         \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m(msg)\n\u001B[32m    491\u001B[39m     ndims.add(obj.ndim)\n\u001B[32m    492\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m ndims\n",
-      "\u001B[31mTypeError\u001B[39m: cannot concatenate object of type '<class 'list'>'; only Series and DataFrame objs are valid"
-     ]
-    }
-   ],
-   "execution_count": 13
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T18:04:20.921392Z",
-     "start_time": "2026-03-04T18:04:20.910074Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "final_df.head()",
-   "id": "2c397a74782cbde3",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "   mech_brake_pressed  accel_position  TrackIndexSpreadsheet   0\n",
-       "0                 0.0             0.0                    NaN NaN\n",
-       "1                 0.0             0.0                    NaN NaN\n",
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-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 72
+   "id": "2c397a74782cbde3",
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T18:01:40.297473Z",
-     "start_time": "2026-03-04T18:01:40.267491Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "combined_df.head()",
    "id": "b1208bd2410e08fc",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "   mech_brake_pressed  accel_position  position  speed\n",
-       "0                 0.0             0.0       NaN    NaN\n",
-       "1                 0.0             0.0       NaN    NaN\n",
-       "2                 0.0             0.0       NaN    NaN\n",
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-       "      <th>mech_brake_pressed</th>\n",
-       "      <th>accel_position</th>\n",
-       "      <th>position</th>\n",
-       "      <th>speed</th>\n",
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-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 68
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T18:02:23.548737Z",
-     "start_time": "2026-03-04T18:02:22.068957Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "plt.plot(combined_df[\"position\"])\n",
     "plt.plot(combined_df[\"speed\"])"
    ],
    "id": "42e9fe006fc1b367",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x238cd63b9b0>]"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": "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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 69
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-17T17:38:58.095084Z",
-     "start_time": "2026-02-17T17:38:58.082431Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "# i dont really think there is much of a need to resample right now, because on querying from influx they will all be at the same frequency - granularity is 0.1s.\n",
@@ -2548,15 +2229,10 @@
    ],
    "id": "e4585f807a9c575a",
    "outputs": [],
-   "execution_count": 25
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-06T01:17:46.392744Z",
-     "start_time": "2026-03-06T01:17:46.282163Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "\n",
@@ -2566,21 +2242,8 @@
     "train_dataset, test_dataset, train_loader, test_loader, scaler = DataPreprocessing.make_sequence_datasets(final_df, state_cols = ['0_x', '0_y'], control_cols = ['mech_brake_pressed', 'accel_position'], seq_len = 100, train_frac=0.7, batch_size = 64)"
    ],
    "id": "c00f5becff80fe17",
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'RNN_Dataset'",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mModuleNotFoundError\u001B[39m                       Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[13]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[34;01mcontrol_model\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m DataPreprocessing\n\u001B[32m      2\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[34;01mcontrol_model\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01mRNN\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m *\n\u001B[32m      3\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[34;01mcontrol_model\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01mRNN_Dataset\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m *\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\control_model\\DataPreprocessing.py:6\u001B[39m\n\u001B[32m      4\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[34;01mtorch\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01mutils\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01mdata\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m DataLoader\n\u001B[32m      5\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[34;01msklearn\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01mpreprocessing\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m StandardScaler\n\u001B[32m----> \u001B[39m\u001B[32m6\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[34;01mRNN_Dataset\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m RNN_Dataset\n\u001B[32m      9\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[34mmake_sequence_datasets\u001B[39m(\n\u001B[32m     10\u001B[39m     df_xy,\n\u001B[32m     11\u001B[39m     state_cols,\n\u001B[32m   (...)\u001B[39m\u001B[32m     16\u001B[39m     batch_size=\u001B[32m64\u001B[39m,\n\u001B[32m     17\u001B[39m ):\n\u001B[32m     20\u001B[39m     cols_to_scale = state_cols + control_cols\n",
-      "\u001B[31mModuleNotFoundError\u001B[39m: No module named 'RNN_Dataset'"
-     ]
-    }
-   ],
-   "execution_count": 13
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -2614,12 +2277,7 @@
    "id": "90141b7cb2d4e96a"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-28T15:53:47.574120Z",
-     "start_time": "2026-02-28T15:53:47.554479Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "\n",
@@ -2637,13 +2295,11 @@
    ],
    "id": "f9712a97111555d4",
    "outputs": [],
-   "execution_count": 64
+   "execution_count": null
   },
   {
    "metadata": {},
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": 63,
    "source": [
     "# now we define the training loop. we are pretending a trajectory of controls.\n",
     "\n",
@@ -2709,136 +2365,20 @@
     "\n",
     "    return train_losses, test_losses"
    ],
-   "id": "329e1f0797e61e90"
+   "id": "329e1f0797e61e90",
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-28T15:58:49.960377Z",
-     "start_time": "2026-02-28T15:53:48.499547Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "train_model(model, train_loader, test_loader, epochs = 30)",
    "id": "205d60e11f150705",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "NaNs in Train Loader: False\n",
-      "NaNs in Test Loader: True\n",
-      "Epoch 1/30, Train Loss: 0.7202\n",
-      "Epoch 2/30, Train Loss: 0.6946\n",
-      "Epoch 3/30, Train Loss: 0.6908\n",
-      "Epoch 4/30, Train Loss: 0.6893\n",
-      "Epoch 5/30, Train Loss: 0.6881\n",
-      "Epoch 6/30, Train Loss: 0.6860\n",
-      "Epoch 7/30, Train Loss: 0.7056\n",
-      "Epoch 8/30, Train Loss: 0.6852\n",
-      "Epoch 9/30, Train Loss: 0.6828\n",
-      "Epoch 10/30, Train Loss: 0.6787\n",
-      "Epoch 11/30, Train Loss: 0.6755\n",
-      "Epoch 12/30, Train Loss: 0.6640\n",
-      "Epoch 13/30, Train Loss: 0.6529\n",
-      "Epoch 14/30, Train Loss: 0.6456\n",
-      "Epoch 15/30, Train Loss: 0.6390\n",
-      "Epoch 16/30, Train Loss: 0.6422\n",
-      "Epoch 17/30, Train Loss: 0.6275\n",
-      "Epoch 18/30, Train Loss: 0.6235\n",
-      "Epoch 19/30, Train Loss: 0.6190\n",
-      "Epoch 20/30, Train Loss: 0.6327\n",
-      "Epoch 21/30, Train Loss: 0.6134\n",
-      "Epoch 22/30, Train Loss: 0.6102\n",
-      "Epoch 23/30, Train Loss: 0.6085\n",
-      "Epoch 24/30, Train Loss: 0.6042\n",
-      "Epoch 25/30, Train Loss: 0.6016\n",
-      "Epoch 26/30, Train Loss: 0.6069\n",
-      "Epoch 27/30, Train Loss: 0.5994\n",
-      "Epoch 28/30, Train Loss: 0.5971\n",
-      "Epoch 29/30, Train Loss: 0.5976\n",
-      "Epoch 30/30, Train Loss: 0.6187\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "([0.7201533619381858,\n",
-       "  0.6946240679879264,\n",
-       "  0.6907852335108183,\n",
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-       "  0.6881164071188052,\n",
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-       "  0.7055505071492756,\n",
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-       "  0.6390082142126169,\n",
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-       "  0.6326697754559878,\n",
-       "  0.613436116150062,\n",
-       "  0.6101548756004999,\n",
-       "  0.6085371648623056,\n",
-       "  0.6041843240987931,\n",
-       "  0.6015912443378358,\n",
-       "  0.6068879433009959,\n",
-       "  0.5993611881408284,\n",
-       "  0.5971358119767176,\n",
-       "  0.5976458289302312,\n",
-       "  0.6187297694418765],\n",
-       " [nan,\n",
-       "  nan,\n",
-       "  nan,\n",
-       "  nan,\n",
-       "  nan,\n",
-       "  nan,\n",
-       "  nan,\n",
-       "  nan,\n",
-       "  nan,\n",
-       "  nan,\n",
-       "  nan,\n",
-       "  nan,\n",
-       "  nan,\n",
-       "  nan,\n",
-       "  nan,\n",
-       "  nan,\n",
-       "  nan,\n",
-       "  nan,\n",
-       "  nan,\n",
-       "  nan,\n",
-       "  nan,\n",
-       "  nan,\n",
-       "  nan,\n",
-       "  nan,\n",
-       "  nan,\n",
-       "  nan,\n",
-       "  nan,\n",
-       "  nan,\n",
-       "  nan,\n",
-       "  nan])"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 65
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T03:28:30.187590Z",
-     "start_time": "2026-03-04T03:28:30.174323Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -2925,15 +2465,10 @@
    ],
    "id": "55763d08b5868893",
    "outputs": [],
-   "execution_count": 53
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T03:28:31.168398Z",
-     "start_time": "2026-03-04T03:28:31.098439Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "# find a sample with nonzero controls\n",
@@ -2944,28 +2479,11 @@
     "        break"
    ],
    "id": "2ce209bf8aab8f07",
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'test_dataset' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mNameError\u001B[39m                                 Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[54]\u001B[39m\u001B[32m, line 2\u001B[39m\n\u001B[32m      1\u001B[39m \u001B[38;5;66;03m# find a sample with nonzero controls\u001B[39;00m\n\u001B[32m----> \u001B[39m\u001B[32m2\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(\u001B[38;5;28mlen\u001B[39m(\u001B[43mtest_dataset\u001B[49m)):\n\u001B[32m      3\u001B[39m     x, y = test_dataset[i]\n\u001B[32m      4\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m y.abs().mean() > \u001B[32m0.1\u001B[39m:\n",
-      "\u001B[31mNameError\u001B[39m: name 'test_dataset' is not defined"
-     ]
-    }
-   ],
-   "execution_count": 54
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-04T03:28:46.299991Z",
-     "start_time": "2026-03-04T03:28:46.245373Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "for i in range(len(test_dataset)):\n",
@@ -2975,28 +2493,11 @@
     "        break"
    ],
    "id": "f66c02f2a3933818",
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'test_dataset' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mNameError\u001B[39m                                 Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[55]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(\u001B[38;5;28mlen\u001B[39m(\u001B[43mtest_dataset\u001B[49m)):\n\u001B[32m      2\u001B[39m     x, y = test_dataset[i]\n\u001B[32m      3\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m y.abs().mean() >= \u001B[32m0.88\u001B[39m:\n",
-      "\u001B[31mNameError\u001B[39m: name 'test_dataset' is not defined"
-     ]
-    }
-   ],
-   "execution_count": 55
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-28T16:11:31.061286Z",
-     "start_time": "2026-02-28T16:11:29.966789Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "state_cols = ['0_x', '0_y']\n",
@@ -3005,77 +2506,11 @@
     "plot_control_trajectory(model, test_dataset, scaler, state_cols, control_cols, sample_idx=150)"
    ],
    "id": "3460de89ee1321c7",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([100, 2])\n",
-      "(100, 2)\n",
-      "(100, 2)\n",
-      "(100,)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1000x800 with 2 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1000x800 with 2 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([100, 2])\n",
-      "(100, 2)\n",
-      "(100, 2)\n",
-      "(100,)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1000x800 with 2 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1000x800 with 2 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 69
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-28T07:42:34.096055Z",
-     "start_time": "2026-02-28T07:42:34.086115Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "import seaborn as sns\n",
@@ -3117,47 +2552,23 @@
    ],
    "id": "9e3d7d9c0f207973",
    "outputs": [],
-   "execution_count": 23
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-02-21T18:58:56.168643Z",
-     "start_time": "2026-02-21T18:58:54.532791Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "plot_error_heatmap(model, test_loader, device)",
    "id": "4c0e45388dee0647",
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_20608\\252557315.py:31: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n",
-      "  pivot_table = df_err.pivot_table(index='Speed Bin', columns='Target Bin', values='MAE', aggfunc='mean')\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1000x800 with 2 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 25
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": "",
-   "id": "c08265494dbc609e"
+   "id": "c08265494dbc609e",
+   "outputs": [],
+   "execution_count": null
   }
  ],
  "metadata": {
diff --git a/control_model/DataPreprocessing.py b/control_model/DataPreprocessing.py
index 491ed22..f299c18 100644
--- a/control_model/DataPreprocessing.py
+++ b/control_model/DataPreprocessing.py
@@ -1,46 +1,23 @@
-from data_tools import TimeSeries
+#necessary imports
 from torch.utils.data import DataLoader
 from sklearn.preprocessing import StandardScaler
+from RNN_Dataset import RNN_Dataset
+from localization_roc import *
 from sklearn.preprocessing import MinMaxScaler
+from data_tools import query
+from data_tools import *
 import pandas as pd
 import numpy as np
-import os
-import dill
-from RNN_Dataset import *
-from RNN_Training import *
-
-from localization_roc import *
-
-#this file will create a single dataframe to use for the RNN. Further scales the data, creates a testing/training split and makes individual sequences to furhter feed into the RNN.
-
 
-def combine_dfs(telemetry_names, index_common, all_dfs):
-    combined_df = pd.DataFrame(index=index_common)
-    combined_df.dropna()
 
-    for name, df in zip(telemetry_names, all_dfs):
-        combined_df[name] = df
 
-    return combined_df
-# get data from sunbeam and influx.
-# use sunbeam instead to save yourself a headache
 
-from torch.utils.data import DataLoader
-from sklearn.preprocessing import StandardScaler
-#from RNN_Dataset import RNN_Dataset
-#necessary imports
-from sklearn.preprocessing import MinMaxScaler
-#from data_tools import query
-import pandas as pd
-import numpy as np
-import os
-import dill
-# from data_tools import *
-# import control_model.localization_roc
-# from control_model.localization_roc import *
 
+#this file will create a single dataframe to use for the RNN. Has multiple helper functions for:
+# scaling data
+# creating a testing/training split
+# Making individual sequences into a format feedable to the RNN.
 
-#this file will create a single dataframe to use for the RNN. Further scales the data, creates a testing/training split and makes individual sequences to furhter feed into the RNN.
 
 
 def combine_dfs(telemetry_names, index_common, all_dfs):
@@ -54,6 +31,12 @@ def combine_dfs(telemetry_names, index_common, all_dfs):
 # get data from sunbeam and influx.
 # use sunbeam instead to save yourself a headache
 def make_df(source, event):
+    """
+    Method to query data from sunbeam, align timeseries together and make a single pandas dataframe.
+    :param source: str refers to the sunbeam data pipeline source.
+    :param event: str refers to the sunbeam data pipeline event.
+    :return: pandas dataframe consisting of queried data (Vehicle Velocity, Brake Pressed, Acceleration Position)
+    """
     dfs = []
     files = []
     client = query.SunbeamClient()
@@ -74,11 +57,11 @@ def make_df(source, event):
             name="TrackIndex"
         ).unwrap().data
 
-    files = TimeSeries.align(files[0], files[1], files[2], file_pos);
+    files = TimeSeries.align(files[0], files[1], files[2], file_pos)
     last_idx = np.where(np.isnan(file_pos))[0][0]
     file_pos = file_pos[0:last_idx]
     files.append(file_pos)
-    files = TimeSeries.align(files[0], files[1], files[2], files[3]);
+    files = TimeSeries.align(files[0], files[1], files[2], files[3]) # remember to align twice.
     for file2 in files:
             dfs.append(
                 pd.DataFrame(
@@ -86,10 +69,6 @@ def make_df(source, event):
                     index=file2.datetime_x_axis
                 )
             )
-
-
-
-
     return pd.concat(dfs).sort_index()
 
 def make_single_df():
@@ -150,7 +129,7 @@ def make_sequence_datasets(
     state_cols,
     control_cols,
     seq_len,
-    stride=100,
+    stride=50,
     train_frac=0.8,
     batch_size=64,
 ):
@@ -162,15 +141,11 @@ def make_sequence_datasets(
     n_total = len(df_xy)
     train_len = int(train_frac * n_total)
     df_xy = df_xy.dropna(subset=state_cols + control_cols).reset_index(drop=True)
-# In make_sequence_datasets, split before concatenating days
-# OR split the final_df by date
 
-   # df_train_raw = final_df[final_df.index < "2024-07-18"]  # Day 1 + 2
-    #df_test_raw  = final_df[final_df.index >= "2024-07-18"] # Day 3
     df_train_raw = df_xy.iloc[:train_len].reset_index(drop=True)
     df_test_raw  = df_xy.iloc[train_len:].reset_index(drop=True)
 
-    # Fit scaler ONLY on training data
+    # Fit scaler only on training data
     scaler = StandardScaler()
     scaler.fit(df_train_raw[cols_to_scale])
 
diff --git a/control_model/RNN_Dataset.py b/control_model/RNN_Dataset.py
index 1a143d1..1ec9430 100644
--- a/control_model/RNN_Dataset.py
+++ b/control_model/RNN_Dataset.py
@@ -3,39 +3,44 @@
 from torch import nn
 from torch.utils.data import Dataset, TensorDataset, DataLoader
 
-#This class creates a dataset for the Neural Network, specifically a Seq2Seq model. We encode an input sequence and generate a corresponding output sequence.
-# Sequence generation via sliding window  ( sequence of consecutive timesteps as input, the target is the value following the window).
-#create sequences from data (seq_len timestamps as input - the next timestamp is the target)
 
 class RNN_Dataset(torch.utils.data.Dataset):
-    #stride as an argument is used to control the overlap between input windows.
+    """
+
+    This class creates a dataset for the Neural Network, specifically a Seq2Seq model. We encode an input sequence and generate a corresponding output sequence.
+    Sequence generation via sliding window  ( sequence of consecutive timesteps as input, the target is the value following the window).
+    Create sequences from data (seq_len timestamps as input - the next timestamp is the target)
+    Stride as an argument is used to control the overlap between input windows.
+
+
+    """
+
     def __init__(self, df, state_cols, control_cols, seq_len, stride):
-        self.seq_len = seq_len #length of input sequences
+        self.seq_len = seq_len  # length of input sequences
         # Convert directly to tensors
         self.states = torch.tensor(df[state_cols].values, dtype=torch.float32)
         self.controls = torch.tensor(df[control_cols].values, dtype=torch.float32)
-        self.stride = stride #the step between the start o consecutive sequences - to reduce overlapping between sequences being fed to the network.
-        self.total_size = self.states.size(0) #total number of timestamps
+        self.stride = stride  # the step between the start o consecutive sequences - to reduce overlapping between sequences being fed to the network.
+        self.total_size = self.states.size(0)  # total number of timestamps
 
-        #compute all possible start indices
-        self.indices = list(range(0, self.total_size - self.seq_len, self.stride)) #first seq starts at t0, second at t0+stride, next at t0 + 2*stride, etc
+        # compute all possible start indices
+        self.indices = list(range(0, self.total_size - self.seq_len,
+                                  self.stride))  # first seq starts at t0, second at t0+stride, next at t0 + 2*stride, etc
+
+        # the target timestamp is: i+seq_len, so the input is from i:i+seq_len, so i<total_size - seq_len
 
-        #the target timestamp is: i+seq_len, so the input is from i:i+seq_len, so i<total_size - seq_len
     def __len__(self):
         return len(self.indices)
 
     def __getitem__(self, index):
-        idx = self.indices[index] #index of the first timestep of seq
-
-        #input: current states
-        current_state = self.states[idx:idx+self.seq_len] # speed+ position
-        #target_state = self.states[idx +  self.seq_len] # state at t + seq_len
-        #current_control = self.controls[idx] #mech_brake + accelerator position
-        x_seq = torch.cat([current_state], dim=0) #concatenate along feature dimension
-        #output: controls
+        idx = self.indices[index]  # index of the first timestep of seq
+
+        # input: current states
+        current_state = self.states[idx:idx + self.seq_len]  # speed+ position
+        # target_state = self.states[idx +  self.seq_len] # state at t + seq_len
+        # current_control = self.controls[idx] #mech_brake + accelerator position
+        x_seq = torch.cat([current_state], dim=0)  # concatenate along feature dimension
+        # output: controls
         y_seq = self.controls[idx:idx + self.seq_len]
 
         return x_seq, y_seq
-
-
-
diff --git a/control_model/control_model_predict.py b/control_model/control_model_predict.py
new file mode 100644
index 0000000..c664ef6
--- /dev/null
+++ b/control_model/control_model_predict.py
@@ -0,0 +1,240 @@
+# necessary imports:
+import os
+import gc
+import argparse
+import numpy as np
+import pandas as pd
+import torch
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import StandardScaler
+from RNN import RNN
+from RNN_Dataset import RNN_Dataset
+from DataPreprocessing import make_single_df, make_sequence_datasets
+from DataPreprocessing import *
+from visualization import *
+from inference import *
+
+# Constants as they must match with RNN training requirements:
+state_cols = ["speed", "ROC"]  # input state features
+control_cols = ["brake_pressed", "accel_position"]  # output control targets
+SEQ_LEN = 150  # sequence length (15 seconds at 0.1 granularity)
+STRIDE = 50  # sliding-window step between consecutive sequences
+BATCH_SIZE = 64  # training batch size
+EPOCHS = 320  # number of training epochs
+HIDDEN_SIZE = 256  # LSTM hidden layer dimensionality
+NUM_LAYERS = 2  # number of stacked LSTM layers
+MODEL_PATH = "rnn_model.pt"  # default model checkpoint path
+
+
+class Control_Model():
+    """
+    Class for loading, running inference and evaluation of the state-control RNN.
+    The model works on a state to control array mapping based on a sequence-to-sequence LSTM.
+    State array consists of curvature and speed as inputs and predicts corresponding driver control outputs (brake pressed, accelerator position) as a 0-1 continuous value.
+
+    This class handles all internal preprocessing steps required for using the RNN, including:
+    - scaling raw inputs with the training scaler
+    - propagating LSTM hidden states across timesteps
+    - unscale predicted outputs to reflect original physical units.
+
+    :param model_path: str filepath of the loaded RNN (.pt / .pth)
+    :param input_speed: np.ndarray Unscaled speed array input for model use.
+    :param input_curvature: unscaled curvature array input for model use.
+    :param hidden_size: Fixed hidden size for LSTM cells.
+    :param num_layers: Number of stacked LSTM layers.
+    :param seq_length: Constant sequence length of 15 seconds at 0.1 granularity
+    :param device: Device to load RNN, CUDA by default, else resorts to CPU.
+
+
+
+    :return output_brake_pressed: np.ndarray Unscaled brake pressed array output predicted by model.
+    :return output_accel_position: np.ndarray Unscaled acceleration array output predicted by model.
+
+    """
+
+    state_cols = ["speed", "ROC"]  # input state features
+    control_cols = ["brake_pressed", "accel_position"]  # output control features
+
+    def __init__(
+            self,
+            model_path: str,
+            scaler,
+            input_speed: np.ndarray,
+            input_curvature: np.ndarray,
+            hidden_size: int = 256,
+            num_layers: int = 2,
+            seq_length: int = 150,
+            device: torch.device = None,
+    ):
+        self.model_path = model_path
+        self.scaler = scaler
+        self.input_speed = np.asarray(input_speed, dtype=np.float32)
+        self.input_curvature = np.asarray(input_curvature, dtype=np.float32)
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
+        self.seq_length = seq_length
+        self.device = device or torch.device(
+            "cuda" if torch.cuda.is_available() else "cpu"
+        )
+
+        self.n_states = len(self.state_cols)  # 2 inputs
+        self.n_controls = len(self.control_cols)  # 2 outputs
+        self.n_total = self.n_states + self.n_controls  # 4
+
+        self.model = self.load_model()
+
+    def load_model(self, device: torch.device = None):
+        """
+        Load the RNN checkpoint from ``self.model_path`` onto the target device.
+        :param device: cpu or cuda, depending on user device. Defaults to CUDA automatically when available.
+        :return model: RNN model loaded for evaluation mode
+
+        """
+
+        model_path = self.model_path
+        if device is None:
+            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+        model = RNN(
+            input_size=len(state_cols),
+            hidden_size=HIDDEN_SIZE,
+            num_layers=NUM_LAYERS,
+            seq_length=SEQ_LEN,
+            output_size=len(control_cols),
+        ).to(device)
+
+        model.load_state_dict(torch.load(model_path, map_location=device))
+        model.eval()
+        print(f"[load_model] Loaded '{model_path}' on {device}")
+        return model
+
+    def unscale_outputs(self, arr) -> np.ndarray:
+        """
+
+        Method to reverse scaled transformation applied during preprocessing.
+        As the scaler was fit on both state and control arrays, individual features cannot be inverted in isolation.
+        This method will reconstruct a full-width array to be inverse transformed and then extracts only the relevant subsets.
+
+        :param arr: np.ndarray: Scaled array to be inverted of shape (n_controls)
+        :return unscaled_states: np.ndarray: Unscaled state values (speed and curvature)
+
+        """
+        T = arr.shape[0]
+        dummy = np.zeros((T, self.n_total), dtype=np.float32)
+        dummy[:, self.n_states:] = arr  # insert at control columns
+
+        unscaled = self.scaler.inverse_transform(dummy)
+        return unscaled[:, self.n_states:]  # return only control columns
+
+    def scale_inputs(self) -> np.ndarray:
+        """
+        Method to stack speed and curvature into a nparray of shape (T,2) and then standardise using the training scaler.
+        Stack speed and curvature into a (T, 2) array and standardise using
+        the training scaler (Sci-kit Learn's StandardScaler()).
+        :return  scaled_states : np.ndarray, of shape (T, 2) that has been standardised and is ready to feed into LSTM
+
+        """
+        T = len(self.input_speed)
+
+        # full width dummy array
+        # note that scaler was fit on all columns together, so subsets cannot be inverted in isolatoin
+        # build full width dummy array
+        # insert subset into the column adn then transform
+        dummy = np.zeros((T, self._n_total), dtype=np.float32)
+        dummy[:, 0] = self.input_speed  # speed is column 0
+        dummy[:, 1] = self.input_curvature  # curvature is col 1
+
+        scaled = self.scaler.transform(dummy)
+        return scaled[:, :self._n_states]  # return only the state columns
+
+    def predict(self):
+        """
+        Runs the entire inference process on input state arrays and returns the unscaled predicted control values (physical units)
+        Internal steps include:
+            - scaling input speed and input curvature on the training scalar.
+            - Inference is performed row-by-row i.e. stride is set to 1 in order to avoid scaling inconsistencies due to boundary spikes in data.
+            - Predicted outputs are unscaled back to physical units.
+
+
+        :return accel_position : np.ndarray,
+            Predicted accelerator position in original (unscaled) units.
+        :return brake_pressed  : np.ndarray,
+                   Predicted brake signal in original (unscaled) units.
+        """
+
+        scaled_states = self.scale_inputs()
+        n_samples = scaled_states.shape[0]  # length of array for prediction
+
+        h, c = None, None  # # cold initialise hidden states
+        scaled_pred = np.zeros((n_samples, self.n_controls), dtype=np.float32)
+
+        with torch.no_grad():
+            for i in range(n_samples):
+                # LSTM requires tensor of shape (1,1,n_states) i.e. (batch=1, seq=1, features=2)
+                x = (
+                    torch.tensor(scaled_states[i])
+                    .unsqueeze(0)  # adds batch dimension
+                    .unsqueeze(0)  # adds sequence dimension
+                    .to(self.device)
+                )
+
+                hidden = (h, c) if h is not None else None  # carry over hidden states, do not reinitialise every time the loop runs.
+                y_pred_tensor, (h, c) = self.model(x, hidden=hidden)
+                h, c = h.detach(), c.detach()  # break gradient flow
+                # remove added batch and sequence dimensios.
+                scaled_pred[i] = y_pred_tensor.squeeze().detach().cpu().numpy()
+
+
+        # convert from standardised space back to physical units
+        unscaled = self.unscale_controls(scaled_pred)
+
+        # col 0 = brake_pressed, col 1 = accel_position (to match training order)
+        brake_pressed = unscaled[:, 0]
+        accel_position = unscaled[:, 1]
+
+        return accel_position, brake_pressed
+
+
+
+
+#example usage:
+
+if __name__ == "__main__":
+    #for the sake of an example usage, i'm directly loading data from my downloaded dataframe.
+
+
+    import pandas as pd
+    from sklearn.preprocessing import StandardScaler
+
+    STATE_COLS = ["speed", "ROC"]
+    CONTROL_COLS = ["brake_pressed", "accel_position"]
+
+    final_df = pd.read_csv("fsgp_2024_training_data.csv")
+    final_df = final_df.set_index("Unnamed: 0")
+
+    train_len = (0.8 * len(final_df))
+    df_train = final_df.iloc[:train_len]
+
+    scaler = StandardScaler()
+    scaler.fit(df_train[STATE_COLS + CONTROL_COLS])
+
+    speed = np.ndarray(final_df['speed'])
+    curvature = np.ndarray(final_df['ROC'])
+    #
+    # speed     = np.random.uniform(10, 15, size=500).astype(np.float32)
+    # curvature = np.random.uniform(-0.05, 0.05, size=500).astype(np.float32)
+
+    cm = Control_Model(
+        model_path      = "rnn_model (1).pth",
+        scaler          = scaler,
+        input_speed     = speed,
+        input_curvature = curvature,
+    )
+
+    accel_position, brake_pressed = cm.predict()
+    import matplotlib.pyplot as plt
+    plot_control_trajectory_extended(model, speed, curvature, accel_position, brake_pressed, state_cols=STATE_COLS, control_cols=CONTROL_COLS, seq_len=SEQ_LEN, start_idx = 0)
+    plt.show()
+
+    print(f"accel_position : min={accel_position.min():.3f}  max={accel_position.max():.3f}")
+    print(f"brake_pressed  : min={brake_pressed.min():.3f}   max={brake_pressed.max():.3f}")
\ No newline at end of file

From 6d3105bf8f3107fa39966f7a4f20183c2bf57890 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Sat, 6 Jun 2026 14:27:43 -0700
Subject: [PATCH 40/49] adding documentation

---
 v3/gps_analysis/20260402.csv                |   3 +
 v3/gps_analysis/gps_analysis.ipynb          |  46 +++---
 v4/driving_day_analysis/PROJECT_TEMPLATE.md |  24 +++
 v4/driving_day_analysis/data/DATA.md        |  13 ++
 v4/driving_day_analysis/gps_debug.ipynb     | 172 ++++++++++++++++++++
 v4/driving_day_analysis/results/RESULTS.md  |  25 +++
 v4/driving_day_analysis/scripts/SCRIPTS.md  |   4 +
 7 files changed, 264 insertions(+), 23 deletions(-)
 create mode 100644 v3/gps_analysis/20260402.csv
 create mode 100644 v4/driving_day_analysis/PROJECT_TEMPLATE.md
 create mode 100644 v4/driving_day_analysis/data/DATA.md
 create mode 100644 v4/driving_day_analysis/gps_debug.ipynb
 create mode 100644 v4/driving_day_analysis/results/RESULTS.md
 create mode 100644 v4/driving_day_analysis/scripts/SCRIPTS.md

diff --git a/v3/gps_analysis/20260402.csv b/v3/gps_analysis/20260402.csv
new file mode 100644
index 0000000..53e255e
--- /dev/null
+++ b/v3/gps_analysis/20260402.csv
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:67d04db89e5f079add28829a9ebb5ed0d2c2ebaf0c93a540333c71ce53ca7a18
+size 2845558
diff --git a/v3/gps_analysis/gps_analysis.ipynb b/v3/gps_analysis/gps_analysis.ipynb
index a23947e..9d4f4d4 100644
--- a/v3/gps_analysis/gps_analysis.ipynb
+++ b/v3/gps_analysis/gps_analysis.ipynb
@@ -6,8 +6,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2025-06-07T05:48:53.232835Z",
-     "start_time": "2025-06-07T05:48:53.201640Z"
+     "end_time": "2026-05-16T18:40:18.462283Z",
+     "start_time": "2026-05-16T18:40:16.635627Z"
     }
    },
    "source": [
@@ -19,13 +19,13 @@
     "import matplotlib.pyplot as plt"
    ],
    "outputs": [],
-   "execution_count": 66
+   "execution_count": 1
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-07T05:52:42.045796Z",
-     "start_time": "2025-06-07T05:52:42.031017Z"
+     "end_time": "2026-05-16T18:40:20.723310Z",
+     "start_time": "2026-05-16T18:40:20.692780Z"
     }
    },
    "cell_type": "code",
@@ -42,13 +42,13 @@
    ],
    "id": "8357e886479f3a0d",
    "outputs": [],
-   "execution_count": 77
+   "execution_count": 2
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-07T05:52:42.798034Z",
-     "start_time": "2025-06-07T05:52:42.530007Z"
+     "end_time": "2026-05-16T18:41:09.450659Z",
+     "start_time": "2026-05-16T18:41:08.865736Z"
     }
    },
    "cell_type": "code",
@@ -74,13 +74,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/joshuariefman/Solar/data_analysis/.venv/lib/python3.12/site-packages/data_tools/collections/time_series.py:292: SettingWithCopyWarning: \n",
+      "C:\\Users\\sanar\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\data_tools\\collections\\time_series.py:298: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  query_df['_time'] = pd.to_datetime(query_df['_time'])\n",
-      "/Users/joshuariefman/Solar/data_analysis/.venv/lib/python3.12/site-packages/data_tools/collections/time_series.py:292: SettingWithCopyWarning: \n",
+      "C:\\Users\\sanar\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\data_tools\\collections\\time_series.py:298: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
@@ -89,13 +89,13 @@
      ]
     }
    ],
-   "execution_count": 78
+   "execution_count": 4
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-07T05:52:43.736082Z",
-     "start_time": "2025-06-07T05:52:43.660301Z"
+     "end_time": "2026-05-16T18:41:17.007319Z",
+     "start_time": "2026-05-16T18:41:16.844661Z"
     }
    },
    "cell_type": "code",
@@ -105,10 +105,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1280e91f0>]"
+       "[<matplotlib.lines.Line2D at 0x1d22619a2d0>]"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -117,19 +117,19 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 79
+   "execution_count": 5
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-06-07T05:52:46.375603Z",
-     "start_time": "2025-06-07T05:52:46.306731Z"
+     "end_time": "2026-05-16T18:41:20.792813Z",
+     "start_time": "2026-05-16T18:41:20.692938Z"
     }
    },
    "cell_type": "code",
@@ -139,10 +139,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x10e22d130>]"
+       "[<matplotlib.lines.Line2D at 0x1d2261bf7a0>]"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -151,13 +151,13 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 80
+   "execution_count": 6
   },
   {
    "metadata": {
diff --git a/v4/driving_day_analysis/PROJECT_TEMPLATE.md b/v4/driving_day_analysis/PROJECT_TEMPLATE.md
new file mode 100644
index 0000000..52e5121
--- /dev/null
+++ b/v4/driving_day_analysis/PROJECT_TEMPLATE.md
@@ -0,0 +1,24 @@
+# Project Template
+
+This `project_template` folder is designed to be copy-pasted and adapted for every new UBC Solar Race Strategy data analysis project.
+
+---
+
+As with any template, use it as you see fit. **Feel free to remove unnecessary directories**.
+The structure is in place to help guide new projects and to maintain consistency within this code repository.
+
+However, **all new data analysis projects must**:
+
+
+
+1. **Be completed in a new, standalone folder.**
+
+If your work depends on the results of another project and the new project is still clearly distinct, either re-derive the values or add the required data to the new project's `/data` folder. Be sure to document where any data comes from.
+
+2. **Use a project-specific [UV](https://docs.astral.sh/uv/) environment to manage dependencies**
+
+// TODO: Add guide for UV
+
+3. **Include documentation**
+
+Documentation is required so that anyone can understand and reproduce your work. For Jupyter data analysis projects, writing out your process and thoughts in markdown is a simple and effective way to document your work. Be sure to include **your name**, **the date** and the **project name**.
\ No newline at end of file
diff --git a/v4/driving_day_analysis/data/DATA.md b/v4/driving_day_analysis/data/DATA.md
new file mode 100644
index 0000000..93e5a21
--- /dev/null
+++ b/v4/driving_day_analysis/data/DATA.md
@@ -0,0 +1,13 @@
+# Data
+
+Use this folder to store any **project-specific** data such as `.csv`,
+`.npy`, `.npz`, or `.pkl` files.
+
+Ensure that large files (>50MB) are being tracked by Git LFS (Large File Storage),
+so that the repository does not grow unnecessarily.
+
+To list files under Git LFS, run:
+
+```
+git lfs ls-files
+```
\ No newline at end of file
diff --git a/v4/driving_day_analysis/gps_debug.ipynb b/v4/driving_day_analysis/gps_debug.ipynb
new file mode 100644
index 0000000..2031a01
--- /dev/null
+++ b/v4/driving_day_analysis/gps_debug.ipynb
@@ -0,0 +1,172 @@
+{
+ "cells": [
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "# Driving Day Analysis\n",
+    "\n",
+    "**Author:** Rifana Saleem Khan\n",
+    "\n",
+    "**Date:** 2XXX-XX-XX\n",
+    "\n",
+    "**Relevant Links:**\n",
+    "- Add any DR0s, monday.com items, or other relevant links\n",
+    "- [Example Project DR0](https://example.com)\n",
+    "\n"
+   ],
+   "id": "39f49a8ece49949e"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "",
+   "id": "6dd9be699ae10353"
+  },
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2026-05-16T18:22:30.172510Z",
+     "start_time": "2026-05-16T18:22:27.728659Z"
+    }
+   },
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from data_tools import query, TimeSeries\n",
+    "from datetime import datetime"
+   ],
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-16T18:39:42.568679Z",
+     "start_time": "2026-05-16T18:39:41.780796Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# 1. Define fields to query\n",
+    "gps_fields: list[str] = [\n",
+    "\"GPSLatitude\", \"GPSLongitude\", \"GPSSatCount\", \"GPSSNR\"\n",
+    "]\n",
+    "# Ignore GPSLatSide and GPSLonSide which say if you're N or S of the equator␣↪and E or W of the Grenwich Meridian\n",
+    "# We're in vancouver so we should leave the latitude sign positive and make␣longitude negative.\n",
+    "other_fields: list[str] = [\"MotorRotatingSpeed\"]\n",
+    "fields_to_query = gps_fields + other_fields\n",
+    "\n",
+    "driving_day_start = datetime.fromisoformat(\"2026-04-02T17:00:00Z\") # 9AMPDT\n",
+    "driving_day_end= datetime.fromisoformat(\"2026-04-03T01:00:00Z\") # 5PM PDT␣↪(same day in Vancouver)\n",
+    "# 3. Query InfuxDB data\n",
+    "client = query.DBClient()\n",
+    "query_data: dict[str, TimeSeries] = {}\n",
+    "for field in fields_to_query:\n",
+    "    query_data[field] = client.query_time_series(\n",
+    "    driving_day_start,\n",
+    "    driving_day_end,\n",
+    "    field,\n",
+    "    )"
+   ],
+   "id": "82f0f0eb96fb1e74",
+   "outputs": [
+    {
+     "ename": "ApiException",
+     "evalue": "(401)\nReason: Unauthorized\nHTTP response headers: HTTPHeaderDict({'Server': 'nginx/1.27.1', 'Date': 'Sat, 16 May 2026 18:39:41 GMT', 'Content-Type': 'application/json; charset=utf-8', 'Content-Length': '55', 'Connection': 'keep-alive', 'X-Influxdb-Build': 'OSS', 'X-Influxdb-Version': 'v2.7.1', 'X-Platform-Error-Code': 'unauthorized'})\nHTTP response body: b'{\"code\":\"unauthorized\",\"message\":\"unauthorized access\"}'\n",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mApiException\u001B[39m                              Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[2]\u001B[39m\u001B[32m, line 16\u001B[39m\n\u001B[32m     14\u001B[39m query_data: \u001B[38;5;28mdict\u001B[39m[\u001B[38;5;28mstr\u001B[39m, TimeSeries] = {}\n\u001B[32m     15\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m field \u001B[38;5;129;01min\u001B[39;00m fields_to_query:\n\u001B[32m---> \u001B[39m\u001B[32m16\u001B[39m     query_data[field] = \u001B[43mclient\u001B[49m\u001B[43m.\u001B[49m\u001B[43mquery_time_series\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m     17\u001B[39m \u001B[43m    \u001B[49m\u001B[43mdriving_day_start\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m     18\u001B[39m \u001B[43m    \u001B[49m\u001B[43mdriving_day_end\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m     19\u001B[39m \u001B[43m    \u001B[49m\u001B[43mfield\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m     20\u001B[39m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\data_tools\\query\\influxdb_query.py:180\u001B[39m, in \u001B[36mDBClient.query_time_series\u001B[39m\u001B[34m(self, start, stop, field, bucket, car, granularity, units, measurement)\u001B[39m\n\u001B[32m    163\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[34mquery_time_series\u001B[39m(\u001B[38;5;28mself\u001B[39m, start: datetime, stop: datetime, field: \u001B[38;5;28mstr\u001B[39m, bucket: \u001B[38;5;28mstr\u001B[39m = \u001B[33m\"\u001B[39m\u001B[33mCAN_log\u001B[39m\u001B[33m\"\u001B[39m,\n\u001B[32m    164\u001B[39m                       car: \u001B[38;5;28mstr\u001B[39m = \u001B[33m\"\u001B[39m\u001B[33mBrightside\u001B[39m\u001B[33m\"\u001B[39m, granularity: \u001B[38;5;28mfloat\u001B[39m = \u001B[32m0.1\u001B[39m, units: \u001B[38;5;28mstr\u001B[39m = \u001B[33m\"\u001B[39m\u001B[33m\"\u001B[39m,\n\u001B[32m    165\u001B[39m                       measurement: \u001B[38;5;28mstr\u001B[39m = \u001B[38;5;28;01mNone\u001B[39;00m) -> TimeSeries:\n\u001B[32m    166\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    167\u001B[39m \u001B[33;03m    Query the database for a specific field, over a certain time range.\u001B[39;00m\n\u001B[32m    168\u001B[39m \u001B[33;03m    The data will be processed into a TimeSeries, which has homogenous and evenly-spaced (temporally) elements.\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m    178\u001B[39m \u001B[33;03m    :return: a TimeSeries of the resulting time-series data\u001B[39;00m\n\u001B[32m    179\u001B[39m \u001B[33;03m    \"\"\"\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m180\u001B[39m     query_df = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mquery_series\u001B[49m\u001B[43m(\u001B[49m\u001B[43mstart\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstop\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfield\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbucket\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcar\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmeasurement\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    182\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m TimeSeries.from_query_dataframe(query_df, granularity, field, units)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\data_tools\\query\\influxdb_query.py:153\u001B[39m, in \u001B[36mDBClient.query_series\u001B[39m\u001B[34m(self, start, stop, field, bucket, car, measurement)\u001B[39m\n\u001B[32m    151\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m measurement:\n\u001B[32m    152\u001B[39m     query = query.filter(measurement=measurement)\n\u001B[32m--> \u001B[39m\u001B[32m153\u001B[39m query_df = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mquery_dataframe\u001B[49m\u001B[43m(\u001B[49m\u001B[43mquery\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    155\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(query_df, \u001B[38;5;28mlist\u001B[39m):\n\u001B[32m    156\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[33m\"\u001B[39m\u001B[33mQuery returned multiple fields! Please refine your query.\u001B[39m\u001B[33m\"\u001B[39m)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\data_tools\\query\\influxdb_query.py:123\u001B[39m, in \u001B[36mDBClient.query_dataframe\u001B[39m\u001B[34m(self, query)\u001B[39m\n\u001B[32m    120\u001B[39m compiled_query = query.compile_query()\n\u001B[32m    121\u001B[39m compiled_query += \u001B[33m'\u001B[39m\u001B[33m |> pivot(rowKey:[\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m_time\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m], columnKey: [\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m_field\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m], valueColumn: \u001B[39m\u001B[33m\"\u001B[39m\u001B[33m_value\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m) \u001B[39m\u001B[33m'\u001B[39m\n\u001B[32m--> \u001B[39m\u001B[32m123\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_client\u001B[49m\u001B[43m.\u001B[49m\u001B[43mquery_api\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[43mquery_data_frame\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcompiled_query\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\client\\query_api.py:259\u001B[39m, in \u001B[36mQueryApi.query_data_frame\u001B[39m\u001B[34m(self, query, org, data_frame_index, params, use_extension_dtypes)\u001B[39m\n\u001B[32m    225\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[34mquery_data_frame\u001B[39m(\u001B[38;5;28mself\u001B[39m, query: \u001B[38;5;28mstr\u001B[39m, org=\u001B[38;5;28;01mNone\u001B[39;00m, data_frame_index: List[\u001B[38;5;28mstr\u001B[39m] = \u001B[38;5;28;01mNone\u001B[39;00m, params: \u001B[38;5;28mdict\u001B[39m = \u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[32m    226\u001B[39m                      use_extension_dtypes: \u001B[38;5;28mbool\u001B[39m = \u001B[38;5;28;01mFalse\u001B[39;00m):\n\u001B[32m    227\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    228\u001B[39m \u001B[33;03m    Execute synchronous Flux query and return Pandas DataFrame.\u001B[39;00m\n\u001B[32m    229\u001B[39m \n\u001B[32m   (...)\u001B[39m\u001B[32m    257\u001B[39m \u001B[33;03m            - https://docs.influxdata.com/flux/latest/stdlib/influxdata/influxdb/schema/fieldsascols/\u001B[39;00m\n\u001B[32m    258\u001B[39m \u001B[33;03m    \"\"\"\u001B[39;00m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m259\u001B[39m     _generator = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mquery_data_frame_stream\u001B[49m\u001B[43m(\u001B[49m\u001B[43mquery\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43morg\u001B[49m\u001B[43m=\u001B[49m\u001B[43morg\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata_frame_index\u001B[49m\u001B[43m=\u001B[49m\u001B[43mdata_frame_index\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[43m=\u001B[49m\u001B[43mparams\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    260\u001B[39m \u001B[43m                                              \u001B[49m\u001B[43muse_extension_dtypes\u001B[49m\u001B[43m=\u001B[49m\u001B[43muse_extension_dtypes\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    261\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._to_data_frames(_generator)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\client\\query_api.py:299\u001B[39m, in \u001B[36mQueryApi.query_data_frame_stream\u001B[39m\u001B[34m(self, query, org, data_frame_index, params, use_extension_dtypes)\u001B[39m\n\u001B[32m    265\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    266\u001B[39m \u001B[33;03mExecute synchronous Flux query and return stream of Pandas DataFrame as a :class:`~Generator[DataFrame]`.\u001B[39;00m\n\u001B[32m    267\u001B[39m \n\u001B[32m   (...)\u001B[39m\u001B[32m    295\u001B[39m \u001B[33;03m        - https://docs.influxdata.com/flux/latest/stdlib/influxdata/influxdb/schema/fieldsascols/\u001B[39;00m\n\u001B[32m    296\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m    297\u001B[39m org = \u001B[38;5;28mself\u001B[39m._org_param(org)\n\u001B[32m--> \u001B[39m\u001B[32m299\u001B[39m response = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_query_api\u001B[49m\u001B[43m.\u001B[49m\u001B[43mpost_query\u001B[49m\u001B[43m(\u001B[49m\u001B[43morg\u001B[49m\u001B[43m=\u001B[49m\u001B[43morg\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mquery\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_create_query\u001B[49m\u001B[43m(\u001B[49m\u001B[43mquery\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mdefault_dialect\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    300\u001B[39m \u001B[43m                                                                        \u001B[49m\u001B[43mdataframe_query\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    301\u001B[39m \u001B[43m                                      \u001B[49m\u001B[43masync_req\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_return_http_data_only\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[32m    303\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._to_data_frame_stream(data_frame_index=data_frame_index,\n\u001B[32m    304\u001B[39m                                   response=response,\n\u001B[32m    305\u001B[39m                                   query_options=\u001B[38;5;28mself\u001B[39m._get_query_options(),\n\u001B[32m    306\u001B[39m                                   use_extension_dtypes=use_extension_dtypes)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\service\\query_service.py:285\u001B[39m, in \u001B[36mQueryService.post_query\u001B[39m\u001B[34m(self, **kwargs)\u001B[39m\n\u001B[32m    283\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m.post_query_with_http_info(**kwargs)  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m    284\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m285\u001B[39m     (data) = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mpost_query_with_http_info\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m    286\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m data\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\service\\query_service.py:311\u001B[39m, in \u001B[36mQueryService.post_query_with_http_info\u001B[39m\u001B[34m(self, **kwargs)\u001B[39m\n\u001B[32m    289\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"Query data.\u001B[39;00m\n\u001B[32m    290\u001B[39m \n\u001B[32m    291\u001B[39m \u001B[33;03mRetrieves data from buckets.  Use this endpoint to send a Flux query request and retrieve data from a bucket.  #### Rate limits (with InfluxDB Cloud)  `read` rate limits apply. For more information, see [limits and adjustable quotas](https://docs.influxdata.com/influxdb/cloud/account-management/limits/).  #### Related guides  - [Query with the InfluxDB API](https://docs.influxdata.com/influxdb/latest/query-data/execute-queries/influx-api/) - [Get started with Flux](https://docs.influxdata.com/flux/v0.x/get-started/)\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m    306\u001B[39m \u001B[33;03m         returns the request thread.\u001B[39;00m\n\u001B[32m    307\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m    308\u001B[39m local_var_params, path_params, query_params, header_params, body_params = \\\n\u001B[32m    309\u001B[39m     \u001B[38;5;28mself\u001B[39m._post_query_prepare(**kwargs)  \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m311\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mapi_client\u001B[49m\u001B[43m.\u001B[49m\u001B[43mcall_api\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    312\u001B[39m \u001B[43m    \u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m/api/v2/query\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43mPOST\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m    313\u001B[39m \u001B[43m    \u001B[49m\u001B[43mpath_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    314\u001B[39m \u001B[43m    \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    315\u001B[39m \u001B[43m    \u001B[49m\u001B[43mheader_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    316\u001B[39m \u001B[43m    \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m=\u001B[49m\u001B[43mbody_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    317\u001B[39m \u001B[43m    \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43m[\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    318\u001B[39m \u001B[43m    \u001B[49m\u001B[43mfiles\u001B[49m\u001B[43m=\u001B[49m\u001B[43m{\u001B[49m\u001B[43m}\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    319\u001B[39m \u001B[43m    \u001B[49m\u001B[43mresponse_type\u001B[49m\u001B[43m=\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43mstr\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# noqa: E501\u001B[39;49;00m\n\u001B[32m    320\u001B[39m \u001B[43m    \u001B[49m\u001B[43mauth_settings\u001B[49m\u001B[43m=\u001B[49m\u001B[43m[\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    321\u001B[39m \u001B[43m    \u001B[49m\u001B[43masync_req\u001B[49m\u001B[43m=\u001B[49m\u001B[43mlocal_var_params\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43masync_req\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    322\u001B[39m \u001B[43m    \u001B[49m\u001B[43m_return_http_data_only\u001B[49m\u001B[43m=\u001B[49m\u001B[43mlocal_var_params\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m_return_http_data_only\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# noqa: E501\u001B[39;49;00m\n\u001B[32m    323\u001B[39m \u001B[43m    \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m=\u001B[49m\u001B[43mlocal_var_params\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m_preload_content\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    324\u001B[39m \u001B[43m    \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m=\u001B[49m\u001B[43mlocal_var_params\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m_request_timeout\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    325\u001B[39m \u001B[43m    \u001B[49m\u001B[43mcollection_formats\u001B[49m\u001B[43m=\u001B[49m\u001B[43m{\u001B[49m\u001B[43m}\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    326\u001B[39m \u001B[43m    \u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[43m=\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43murlopen_kw\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\api_client.py:343\u001B[39m, in \u001B[36mApiClient.call_api\u001B[39m\u001B[34m(self, resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, async_req, _return_http_data_only, collection_formats, _preload_content, _request_timeout, urlopen_kw)\u001B[39m\n\u001B[32m    304\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"Make the HTTP request (synchronous) and Return deserialized data.\u001B[39;00m\n\u001B[32m    305\u001B[39m \n\u001B[32m    306\u001B[39m \u001B[33;03mTo make an async_req request, set the async_req parameter.\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m    340\u001B[39m \u001B[33;03m    then the method will return the response directly.\u001B[39;00m\n\u001B[32m    341\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    342\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m async_req:\n\u001B[32m--> \u001B[39m\u001B[32m343\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m__call_api\u001B[49m\u001B[43m(\u001B[49m\u001B[43mresource_path\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    344\u001B[39m \u001B[43m                           \u001B[49m\u001B[43mpath_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mheader_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    345\u001B[39m \u001B[43m                           \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfiles\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    346\u001B[39m \u001B[43m                           \u001B[49m\u001B[43mresponse_type\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mauth_settings\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    347\u001B[39m \u001B[43m                           \u001B[49m\u001B[43m_return_http_data_only\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcollection_formats\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    348\u001B[39m \u001B[43m                           \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    349\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m    350\u001B[39m     thread = \u001B[38;5;28mself\u001B[39m.pool.apply_async(\u001B[38;5;28mself\u001B[39m.__call_api, (resource_path,\n\u001B[32m    351\u001B[39m                                    method, path_params, query_params,\n\u001B[32m    352\u001B[39m                                    header_params, body,\n\u001B[32m   (...)\u001B[39m\u001B[32m    356\u001B[39m                                    collection_formats,\n\u001B[32m    357\u001B[39m                                    _preload_content, _request_timeout, urlopen_kw))\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\api_client.py:173\u001B[39m, in \u001B[36mApiClient.__call_api\u001B[39m\u001B[34m(self, resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, _return_http_data_only, collection_formats, _preload_content, _request_timeout, urlopen_kw)\u001B[39m\n\u001B[32m    170\u001B[39m urlopen_kw = urlopen_kw \u001B[38;5;129;01mor\u001B[39;00m {}\n\u001B[32m    172\u001B[39m \u001B[38;5;66;03m# perform request and return response\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m173\u001B[39m response_data = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mrequest\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    174\u001B[39m \u001B[43m    \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m=\u001B[49m\u001B[43mheader_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    175\u001B[39m \u001B[43m    \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m=\u001B[49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    176\u001B[39m \u001B[43m    \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    177\u001B[39m \u001B[43m    \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    179\u001B[39m \u001B[38;5;28mself\u001B[39m.last_response = response_data\n\u001B[32m    181\u001B[39m return_data = response_data\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\api_client.py:388\u001B[39m, in \u001B[36mApiClient.request\u001B[39m\u001B[34m(self, method, url, query_params, headers, post_params, body, _preload_content, _request_timeout, **urlopen_kw)\u001B[39m\n\u001B[32m    379\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m.rest_client.OPTIONS(url,\n\u001B[32m    380\u001B[39m                                     query_params=query_params,\n\u001B[32m    381\u001B[39m                                     headers=headers,\n\u001B[32m   (...)\u001B[39m\u001B[32m    385\u001B[39m                                     body=body,\n\u001B[32m    386\u001B[39m                                     **urlopen_kw)\n\u001B[32m    387\u001B[39m \u001B[38;5;28;01melif\u001B[39;00m method == \u001B[33m\"\u001B[39m\u001B[33mPOST\u001B[39m\u001B[33m\"\u001B[39m:\n\u001B[32m--> \u001B[39m\u001B[32m388\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mrest_client\u001B[49m\u001B[43m.\u001B[49m\u001B[43mPOST\u001B[49m\u001B[43m(\u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    389\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    390\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m=\u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    391\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    392\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    393\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    394\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m=\u001B[49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    395\u001B[39m \u001B[43m                                 \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    396\u001B[39m \u001B[38;5;28;01melif\u001B[39;00m method == \u001B[33m\"\u001B[39m\u001B[33mPUT\u001B[39m\u001B[33m\"\u001B[39m:\n\u001B[32m    397\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m.rest_client.PUT(url,\n\u001B[32m    398\u001B[39m                                 query_params=query_params,\n\u001B[32m    399\u001B[39m                                 headers=headers,\n\u001B[32m   (...)\u001B[39m\u001B[32m    403\u001B[39m                                 body=body,\n\u001B[32m    404\u001B[39m                                 **urlopen_kw)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\rest.py:311\u001B[39m, in \u001B[36mRESTClientObject.POST\u001B[39m\u001B[34m(self, url, headers, query_params, post_params, body, _preload_content, _request_timeout, **urlopen_kw)\u001B[39m\n\u001B[32m    308\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[34mPOST\u001B[39m(\u001B[38;5;28mself\u001B[39m, url, headers=\u001B[38;5;28;01mNone\u001B[39;00m, query_params=\u001B[38;5;28;01mNone\u001B[39;00m, post_params=\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[32m    309\u001B[39m          body=\u001B[38;5;28;01mNone\u001B[39;00m, _preload_content=\u001B[38;5;28;01mTrue\u001B[39;00m, _request_timeout=\u001B[38;5;28;01mNone\u001B[39;00m, **urlopen_kw):\n\u001B[32m    310\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"Perform POST HTTP request.\"\"\"\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m311\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mrequest\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mPOST\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    312\u001B[39m \u001B[43m                        \u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m=\u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    313\u001B[39m \u001B[43m                        \u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mquery_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    314\u001B[39m \u001B[43m                        \u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m=\u001B[49m\u001B[43mpost_params\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    315\u001B[39m \u001B[43m                        \u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_preload_content\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    316\u001B[39m \u001B[43m                        \u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m=\u001B[49m\u001B[43m_request_timeout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    317\u001B[39m \u001B[43m                        \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m=\u001B[49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    318\u001B[39m \u001B[43m                        \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43murlopen_kw\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\influxdb_client\\_sync\\rest.py:261\u001B[39m, in \u001B[36mRESTClientObject.request\u001B[39m\u001B[34m(self, method, url, query_params, headers, body, post_params, _preload_content, _request_timeout, **urlopen_kw)\u001B[39m\n\u001B[32m    258\u001B[39m     _BaseRESTClient.log_body(r.data, \u001B[33m'\u001B[39m\u001B[33m<<<\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m    260\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[32m200\u001B[39m <= r.status <= \u001B[32m299\u001B[39m:\n\u001B[32m--> \u001B[39m\u001B[32m261\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m ApiException(http_resp=r)\n\u001B[32m    263\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m r\n",
+      "\u001B[31mApiException\u001B[39m: (401)\nReason: Unauthorized\nHTTP response headers: HTTPHeaderDict({'Server': 'nginx/1.27.1', 'Date': 'Sat, 16 May 2026 18:39:41 GMT', 'Content-Type': 'application/json; charset=utf-8', 'Content-Length': '55', 'Connection': 'keep-alive', 'X-Influxdb-Build': 'OSS', 'X-Influxdb-Version': 'v2.7.1', 'X-Platform-Error-Code': 'unauthorized'})\nHTTP response body: b'{\"code\":\"unauthorized\",\"message\":\"unauthorized access\"}'\n"
+     ]
+    }
+   ],
+   "execution_count": 2
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    " for k, v in query_data.items():\n",
+    "print(f\"{k:20}: start={v.start}, stop={v.stop}, num_points={len(v)}\")"
+   ],
+   "id": "b22aa60aeee1e652"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Data Acquisition\n",
+    "\n",
+    "Obtain the data relevant to this analysis. Load data from saved files, or query it directly."
+   ],
+   "id": "829ee411d467224e"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "8b70fb254e70c962"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Analysis\n",
+    "\n",
+    "Compute the desired results and present them. Take advantage of Markdown cells to guide the reader through your derivation process."
+   ],
+   "id": "eff7e1b341d9a342"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "4616811f56ac1a41"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/v4/driving_day_analysis/results/RESULTS.md b/v4/driving_day_analysis/results/RESULTS.md
new file mode 100644
index 0000000..8c53da3
--- /dev/null
+++ b/v4/driving_day_analysis/results/RESULTS.md
@@ -0,0 +1,25 @@
+# Results
+
+Use this folder to store the results of your analysis,
+like PDF files, images, tables or other data.
+
+---
+
+Jupyter notebooks alone can be complete enough as results,
+but consider exporting them to PDF files to make the result more accessible.
+
+To export a Jupyter notebook to pdf, the quickest way is to use a web converter.
+https://onlineconvertfree.com/convert/ipynb/ works pretty well.
+
+A command-line tool can be helpful if you want more control over the output or need to convert many files.
+To convert to PDF on the command-line,
+
+1. Install `nbconvert`
+
+`uv add nbconvert`
+
+You'll also need to install [Pandoc](https://minrk-nbconvert.readthedocs.io/en/stable/install.html#installing-pandoc) to convert to PDF.
+
+2. Export the file
+
+`\your_project> nbconvert .\example_notebook.ipynb --to pdf`
\ No newline at end of file
diff --git a/v4/driving_day_analysis/scripts/SCRIPTS.md b/v4/driving_day_analysis/scripts/SCRIPTS.md
new file mode 100644
index 0000000..0616f52
--- /dev/null
+++ b/v4/driving_day_analysis/scripts/SCRIPTS.md
@@ -0,0 +1,4 @@
+# Scripts
+
+This directory is for any scripts that are better suited in a distinct
+Python (or other type of) file rather than inline in a Jupyter notebook.
\ No newline at end of file

From 4b601d9c01b1da1fd22ffb5074989ed20b30ced7 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Sat, 6 Jun 2026 15:25:34 -0700
Subject: [PATCH 41/49] adding documentation

---
 array_temp/fsgp_2024_training_data.csv        |    3 +
 v3/gps_analysis/driving_day_analysis.ipynb    | 2823 +++++++++++++++++
 v4/driving_day_analysis/pyproject.toml        |   10 +
 v4/driving_day_analysis/uv.lock               | 1017 ++++++
 .../kalman_filter_position.ipynb              |  305 ++
 5 files changed, 4158 insertions(+)
 create mode 100644 array_temp/fsgp_2024_training_data.csv
 create mode 100644 v3/gps_analysis/driving_day_analysis.ipynb
 create mode 100644 v4/driving_day_analysis/pyproject.toml
 create mode 100644 v4/driving_day_analysis/uv.lock
 create mode 100644 v4/kalman_filter_position/kalman_filter_position.ipynb

diff --git a/array_temp/fsgp_2024_training_data.csv b/array_temp/fsgp_2024_training_data.csv
new file mode 100644
index 0000000..f0fef1a
--- /dev/null
+++ b/array_temp/fsgp_2024_training_data.csv
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:06a1b14cc6527983d2474c2710e24ed66971da9262b13cf42eb2d5c97400f466
+size 63402612
diff --git a/v3/gps_analysis/driving_day_analysis.ipynb b/v3/gps_analysis/driving_day_analysis.ipynb
new file mode 100644
index 0000000..8e32b55
--- /dev/null
+++ b/v3/gps_analysis/driving_day_analysis.ipynb
@@ -0,0 +1,2823 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2026-05-23T04:45:54.802503Z",
+     "start_time": "2026-05-23T04:45:52.021847Z"
+    }
+   },
+   "source": [
+    "from data_tools.query import DBClient\n",
+    "from data_tools import TimeSeries\n",
+    "import datetime\n",
+    "from zoneinfo import ZoneInfo\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from data_tools import query"
+   ],
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "9d465daac878bb9b"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-23T04:45:58.651330Z",
+     "start_time": "2026-05-23T04:45:58.631948Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "tz_van = ZoneInfo(\"America/Vancouver\")\n",
+    "\n",
+    "# Local start and end of May 31, 2025\n",
+    "local_start = datetime.datetime(2025, 5, 31, 18, 0, 0, tzinfo=tz_van)\n",
+    "local_end = datetime.datetime(2025, 5, 31, 23, 59, 59, 999999, tzinfo=tz_van)\n",
+    "\n",
+    "# Convert to UTC\n",
+    "utc_start = local_start.astimezone(ZoneInfo(\"UTC\"))\n",
+    "utc_end = local_end.astimezone(ZoneInfo(\"UTC\"))"
+   ],
+   "id": "8357e886479f3a0d",
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-23T04:46:02.590815Z",
+     "start_time": "2026-05-23T04:46:02.238209Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "client = DBClient()\n",
+    "\n",
+    "gps_latitude_df = client.query_series(utc_start, utc_end, \"GPSLatitude\", \"CAN_log\")\n",
+    "gps_longitude_df = client.query_series(utc_start, utc_end, \"GPSLongitude\", \"CAN_log\")\n",
+    "\n",
+    "latitude_threshold = 123.2\n",
+    "longitude_threshold = 49.2\n",
+    "\n",
+    "filtered_gps_indices = np.logical_and(gps_longitude_df.GPSLongitude > longitude_threshold,\n",
+    "                                      gps_latitude_df.GPSLatitude > latitude_threshold)\n",
+    "filtered_gps_latitude_df = gps_latitude_df.loc[filtered_gps_indices]\n",
+    "filtered_gps_longitude_df = gps_longitude_df.loc[filtered_gps_indices]\n",
+    "\n",
+    "gps_latitude = TimeSeries.from_query_dataframe(filtered_gps_latitude_df, 0.25, \"GPSLatitude\", \"\")\n",
+    "gps_longitude = TimeSeries.from_query_dataframe(filtered_gps_longitude_df, 0.25, \"GPSLongitude\", \"\")"
+   ],
+   "id": "b8376f7de6e3d794",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sanar\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\data_tools\\collections\\time_series.py:298: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  query_df['_time'] = pd.to_datetime(query_df['_time'])\n",
+      "C:\\Users\\sanar\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\data_tools\\collections\\time_series.py:298: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  query_df['_time'] = pd.to_datetime(query_df['_time'])\n"
+     ]
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-23T04:46:05.120694Z",
+     "start_time": "2026-05-23T04:46:04.807724Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plt.plot(gps_latitude)",
+   "id": "2dd7e5ec68bf2a6d",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1ebe9192cf0>]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 4
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-23T04:46:07.331509Z",
+     "start_time": "2026-05-23T04:46:07.192452Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plt.plot(gps_longitude)",
+   "id": "b55f12eebf9dee80",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1eb8ab79f70>]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 5
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "i'm borrowing some of Jonah's code to set up things. I am potentially thinking of two different reasons for this lag to occur - buffer still has old values and latency is there, or the latency has to do with how influx is setting up timestamps. potentially go through both hypotheses and fixate.",
+   "id": "7655fba1e09aaef6"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-23T04:46:13.685910Z",
+     "start_time": "2026-05-23T04:46:13.675132Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from data_tools import query, TimeSeries\n",
+    "from datetime import datetime"
+   ],
+   "id": "9d185d695cfa4442",
+   "outputs": [],
+   "execution_count": 6
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-23T04:46:18.155238Z",
+     "start_time": "2026-05-23T04:46:14.292473Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "gps_fields: list[str] = [\n",
+    "    \"GPSLatitude\", \"GPSLongitude\", \"GPSSatCount\", \"GPSSNR\"\n",
+    "]\n",
+    "# Ignore GPSLatSide and GPSLonSide which say if you're N or S of the equator␣↪and E or W of the Grenwich Meridian\n",
+    "# We're in vancouver so we should leave the latitude sign positive and make␣↪longitude negative.\n",
+    "other_fields: list[str] = [\n",
+    "    \"MotorRotatingSpeed\"\n",
+    "]\n",
+    "fields_to_query = gps_fields + other_fields\n",
+    "\n",
+    "driving_day_start = datetime.fromisoformat(\"2026-04-02T17:00:00Z\")  # 9AM PDT\n",
+    "driving_day_end = datetime.fromisoformat(\"2026-04-03T01:00:00Z\")  # 5PM PDT␣↪(same day in Vancouver)\n",
+    "# 3. Query InfuxDB data\n",
+    "client = query.DBClient()\n",
+    "query_data: dict[str, TimeSeries] = {}\n",
+    "for field in fields_to_query:\n",
+    "    query_data[field] = client.query_time_series(\n",
+    "        driving_day_start,\n",
+    "        driving_day_end,\n",
+    "        field,\n",
+    "    )"
+   ],
+   "id": "37377f719f7eceec",
+   "outputs": [],
+   "execution_count": 7
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-23T04:46:19.595877Z",
+     "start_time": "2026-05-23T04:46:19.578025Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    " for k, v in query_data.items():\n",
+    "    print(f\"{k:20}: start={v.start}, stop={v.stop}, num_points={len(v)}\")"
+   ],
+   "id": "ad981fb5706f729e",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GPSLatitude         : start=2026-04-02 11:31:48.892000+00:00, stop=2026-04-02 16:09:23.178000+00:00, num_points=166543\n",
+      "GPSLongitude        : start=2026-04-02 11:31:48.892000+00:00, stop=2026-04-02 16:09:23.178000+00:00, num_points=166543\n",
+      "GPSSatCount         : start=2026-04-02 11:31:48.830000+00:00, stop=2026-04-02 16:09:23.128000+00:00, num_points=166543\n",
+      "GPSSNR              : start=2026-04-02 11:31:48.830000+00:00, stop=2026-04-02 16:09:23.128000+00:00, num_points=166543\n",
+      "MotorRotatingSpeed  : start=2026-04-02 12:17:23.395000+00:00, stop=2026-04-02 16:09:23.315000+00:00, num_points=139200\n"
+     ]
+    }
+   ],
+   "execution_count": 8
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-23T04:46:22.328193Z",
+     "start_time": "2026-05-23T04:46:22.090057Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "latitude: TimeSeries = None\n",
+    "longitude: TimeSeries = None\n",
+    "sat_count: TimeSeries = None\n",
+    "gps_snr: TimeSeries = None\n",
+    "speed_kph: TimeSeries = None\n",
+    "latitude, longitude, sat_count, gps_snr, speed_kph = TimeSeries.align(*[query_data[field] for field in fields_to_query])\n",
+    "assert np.all(speed_kph.x_axis == latitude.x_axis)  # Quick alignment check\n",
+    "assert np.all(speed_kph.datetime_x_axis == latitude.datetime_x_axis)\n",
+    "t_axis = speed_kph.x_axis\n",
+    "dt_axis = speed_kph.datetime_x_axis\n",
+    "# Since we are in the western hemisphere, flip the sign on the (unsigned)␣↪latitude value\n",
+    "longitude = -longitude"
+   ],
+   "id": "76f541b474911955",
+   "outputs": [],
+   "execution_count": 9
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-23T04:46:24.359131Z",
+     "start_time": "2026-05-23T04:46:23.296984Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "latitude_df: pd.DataFrame = client.query_series(\n",
+    "    driving_day_start,\n",
+    "    driving_day_end,\n",
+    "    \"GPSLatitude\",\n",
+    ")\n",
+    "longitude_df: pd.DataFrame = client.query_series(\n",
+    "    driving_day_start,\n",
+    "    driving_day_end,\n",
+    "    \"GPSLongitude\",\n",
+    ")\n",
+    "display(latitude_df)\n",
+    "display(longitude_df)"
+   ],
+   "id": "ee7dc0c5d0968cd4",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "        result  table                    _start                     _stop  \\\n",
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+       "3      _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+       "4      _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+       "...        ...    ...                       ...                       ...   \n",
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+       "29781  _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+       "29782  _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+       "29783  _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+       "\n",
+       "                                 _time _measurement         car        class  \\\n",
+       "0     2026-04-02 11:31:48.892000+00:00          TEL  Brightside  GPSPosition   \n",
+       "1     2026-04-02 11:31:49.145000+00:00          TEL  Brightside  GPSPosition   \n",
+       "2     2026-04-02 11:31:49.397000+00:00          TEL  Brightside  GPSPosition   \n",
+       "3     2026-04-02 11:31:49.650000+00:00          TEL  Brightside  GPSPosition   \n",
+       "4     2026-04-02 11:31:49.902000+00:00          TEL  Brightside  GPSPosition   \n",
+       "...                                ...          ...         ...          ...   \n",
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+       "29783 2026-04-02 16:09:23.178000+00:00          TEL  Brightside  GPSPosition   \n",
+       "\n",
+       "       GPSLatitude  \n",
+       "0         0.000000  \n",
+       "1         0.000000  \n",
+       "2         0.000000  \n",
+       "3         0.000000  \n",
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+       "...            ...  \n",
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+       "                                 _time _measurement         car        class  \\\n",
+       "0     2026-04-02 11:31:48.892000+00:00          TEL  Brightside  GPSPosition   \n",
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+       "      <td>2026-04-02 18:00:00+00:00</td>\n",
+       "      <td>2026-04-02 16:09:22.926000+00:00</td>\n",
+       "      <td>TEL</td>\n",
+       "      <td>Brightside</td>\n",
+       "      <td>GPSPosition</td>\n",
+       "      <td>123.145302</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29783</th>\n",
+       "      <td>_result</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2026-04-02 10:00:00+00:00</td>\n",
+       "      <td>2026-04-02 18:00:00+00:00</td>\n",
+       "      <td>2026-04-02 16:09:23.178000+00:00</td>\n",
+       "      <td>TEL</td>\n",
+       "      <td>Brightside</td>\n",
+       "      <td>GPSPosition</td>\n",
+       "      <td>123.145302</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>29784 rows × 9 columns</p>\n",
+       "</div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 10
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-23T04:53:58.633336Z",
+     "start_time": "2026-05-23T04:53:58.516419Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "phone_gps_df = pd.read_csv('20260402.csv', parse_dates=['time'])\n",
+    "phone_gps_df = phone_gps_df.set_index(pd.to_datetime(phone_gps_df['time'], utc=True)).drop(columns=['time'])\n",
+    "phone_gps_df = phone_gps_df.rename(columns={'lat': 'GPSLatitude', 'lon': 'GPSLongitude'})\n",
+    "\n",
+    "phone_gps_df.index = phone_gps_df.index.tz_convert('UTC')\n",
+    "\n",
+    "phone_gps_df"
+   ],
+   "id": "38e7158b833fd57e",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                                  GPSLatitude  GPSLongitude  elevation  \\\n",
+       "time                                                                     \n",
+       "2026-04-02 15:37:00.705000+00:00    49.198326   -123.141634      -14.3   \n",
+       "2026-04-02 15:37:08.255000+00:00    49.198198   -123.142273      -54.1   \n",
+       "2026-04-02 15:37:14.051000+00:00    49.198240   -123.142112      -53.8   \n",
+       "2026-04-02 15:37:19.774000+00:00    49.198258   -123.142022      -61.8   \n",
+       "2026-04-02 15:37:25.449000+00:00    49.198260   -123.141987      -63.4   \n",
+       "...                                       ...           ...        ...   \n",
+       "2026-04-02 23:15:32+00:00           49.198688   -123.145485      -23.6   \n",
+       "2026-04-02 23:15:34+00:00           49.198688   -123.145485      -23.6   \n",
+       "2026-04-02 23:15:36+00:00           49.198688   -123.145485      -23.6   \n",
+       "2026-04-02 23:15:37+00:00           49.198688   -123.145485      -23.6   \n",
+       "2026-04-02 23:15:39+00:00           49.198688   -123.145485      -23.6   \n",
+       "\n",
+       "                                  accuracy  bearing     speed  satellites  \\\n",
+       "time                                                                        \n",
+       "2026-04-02 15:37:00.705000+00:00    29.894      NaN       NaN           0   \n",
+       "2026-04-02 15:37:08.255000+00:00    10.000   301.11  2.398091           7   \n",
+       "2026-04-02 15:37:14.051000+00:00    10.600   292.22  0.131254           6   \n",
+       "2026-04-02 15:37:19.774000+00:00    10.500   318.50  2.168525           8   \n",
+       "2026-04-02 15:37:25.449000+00:00    10.400   339.27  0.180668           8   \n",
+       "...                                    ...      ...       ...         ...   \n",
+       "2026-04-02 23:15:32+00:00            1.300    64.14  0.000000          44   \n",
+       "2026-04-02 23:15:34+00:00            1.300    64.14  0.000000          45   \n",
+       "2026-04-02 23:15:36+00:00            1.300    64.14  0.000000          45   \n",
+       "2026-04-02 23:15:37+00:00            1.300    64.14  0.000000          44   \n",
+       "2026-04-02 23:15:39+00:00            1.300    64.14  0.000000          45   \n",
+       "\n",
+       "                                 provider  hdop  vdop  ...  dgpsid  activity  \\\n",
+       "time                                                   ...                     \n",
+       "2026-04-02 15:37:00.705000+00:00  network   NaN   NaN  ...     NaN       NaN   \n",
+       "2026-04-02 15:37:08.255000+00:00      gps  2.20  0.96  ...     NaN       NaN   \n",
+       "2026-04-02 15:37:14.051000+00:00      gps  1.61  0.93  ...     NaN       NaN   \n",
+       "2026-04-02 15:37:19.774000+00:00      gps  1.54  0.96  ...     NaN       NaN   \n",
+       "2026-04-02 15:37:25.449000+00:00      gps  1.22  0.84  ...     NaN       NaN   \n",
+       "...                                   ...   ...   ...  ...     ...       ...   \n",
+       "2026-04-02 23:15:32+00:00             gps  0.39  0.52  ...     0.0       NaN   \n",
+       "2026-04-02 23:15:34+00:00             gps  0.38  0.52  ...     0.0       NaN   \n",
+       "2026-04-02 23:15:36+00:00             gps  0.38  0.52  ...     0.0       NaN   \n",
+       "2026-04-02 23:15:37+00:00             gps  0.41  0.53  ...     0.0       NaN   \n",
+       "2026-04-02 23:15:39+00:00             gps  0.38  0.52  ...     0.0       NaN   \n",
+       "\n",
+       "                                  battery  annotation   timestamp_ms  \\\n",
+       "time                                                                   \n",
+       "2026-04-02 15:37:00.705000+00:00       97         NaN  1775144220705   \n",
+       "2026-04-02 15:37:08.255000+00:00       97         NaN  1775144228255   \n",
+       "2026-04-02 15:37:14.051000+00:00       97         NaN  1775144234051   \n",
+       "2026-04-02 15:37:19.774000+00:00       97         NaN  1775144239774   \n",
+       "2026-04-02 15:37:25.449000+00:00       97         NaN  1775144245449   \n",
+       "...                                   ...         ...            ...   \n",
+       "2026-04-02 23:15:32+00:00              80         NaN  1775171732000   \n",
+       "2026-04-02 23:15:34+00:00              80         NaN  1775171734000   \n",
+       "2026-04-02 23:15:36+00:00              80         NaN  1775171736000   \n",
+       "2026-04-02 23:15:37+00:00              80         NaN  1775171737000   \n",
+       "2026-04-02 23:15:39+00:00              80         NaN  1775171739000   \n",
+       "\n",
+       "                                                    time_offset      distance  \\\n",
+       "time                                                                            \n",
+       "2026-04-02 15:37:00.705000+00:00  2026-04-02T08:37:00.705-07:00      0.000000   \n",
+       "2026-04-02 15:37:08.255000+00:00  2026-04-02T08:37:08.255-07:00     48.546146   \n",
+       "2026-04-02 15:37:14.051000+00:00  2026-04-02T08:37:14.051-07:00     61.173478   \n",
+       "2026-04-02 15:37:19.774000+00:00  2026-04-02T08:37:19.774-07:00     68.023230   \n",
+       "2026-04-02 15:37:25.449000+00:00  2026-04-02T08:37:25.449-07:00     70.573060   \n",
+       "...                                                         ...           ...   \n",
+       "2026-04-02 23:15:32+00:00         2026-04-02T16:15:32.000-07:00  21020.905981   \n",
+       "2026-04-02 23:15:34+00:00         2026-04-02T16:15:34.000-07:00  21020.905981   \n",
+       "2026-04-02 23:15:36+00:00         2026-04-02T16:15:36.000-07:00  21020.905981   \n",
+       "2026-04-02 23:15:37+00:00         2026-04-02T16:15:37.000-07:00  21020.905981   \n",
+       "2026-04-02 23:15:39+00:00         2026-04-02T16:15:39.000-07:00  21020.905981   \n",
+       "\n",
+       "                                  starttimestamp_ms     profile_name  \\\n",
+       "time                                                                   \n",
+       "2026-04-02 15:37:00.705000+00:00      1775144220572  Default Profile   \n",
+       "2026-04-02 15:37:08.255000+00:00      1775144220572  Default Profile   \n",
+       "2026-04-02 15:37:14.051000+00:00      1775144220572  Default Profile   \n",
+       "2026-04-02 15:37:19.774000+00:00      1775144220572  Default Profile   \n",
+       "2026-04-02 15:37:25.449000+00:00      1775144220572  Default Profile   \n",
+       "...                                             ...              ...   \n",
+       "2026-04-02 23:15:32+00:00             1775144256238  Default Profile   \n",
+       "2026-04-02 23:15:34+00:00             1775144256238  Default Profile   \n",
+       "2026-04-02 23:15:36+00:00             1775144256238  Default Profile   \n",
+       "2026-04-02 23:15:37+00:00             1775144256238  Default Profile   \n",
+       "2026-04-02 23:15:39+00:00             1775144256238  Default Profile   \n",
+       "\n",
+       "                                  battery_charging  \n",
+       "time                                                \n",
+       "2026-04-02 15:37:00.705000+00:00             False  \n",
+       "2026-04-02 15:37:08.255000+00:00             False  \n",
+       "2026-04-02 15:37:14.051000+00:00             False  \n",
+       "2026-04-02 15:37:19.774000+00:00             False  \n",
+       "2026-04-02 15:37:25.449000+00:00             False  \n",
+       "...                                            ...  \n",
+       "2026-04-02 23:15:32+00:00                    False  \n",
+       "2026-04-02 23:15:34+00:00                    False  \n",
+       "2026-04-02 23:15:36+00:00                    False  \n",
+       "2026-04-02 23:15:37+00:00                    False  \n",
+       "2026-04-02 23:15:39+00:00                    False  \n",
+       "\n",
+       "[12977 rows x 23 columns]"
+      ],
+      "text/html": [
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GPSLatitude</th>\n",
+       "      <th>GPSLongitude</th>\n",
+       "      <th>elevation</th>\n",
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+       "      <th>starttimestamp_ms</th>\n",
+       "      <th>profile_name</th>\n",
+       "      <th>battery_charging</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>time</th>\n",
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+       "      <th>2026-04-02 15:37:00.705000+00:00</th>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>network</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <th>2026-04-02 15:37:08.255000+00:00</th>\n",
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+       "      <td>2.398091</td>\n",
+       "      <td>7</td>\n",
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+       "      <th>2026-04-02 15:37:25.449000+00:00</th>\n",
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+       "      <th>2026-04-02 23:15:32+00:00</th>\n",
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+       "      <td>2026-04-02T16:15:34.000-07:00</td>\n",
+       "      <td>21020.905981</td>\n",
+       "      <td>1775144256238</td>\n",
+       "      <td>Default Profile</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2026-04-02 23:15:36+00:00</th>\n",
+       "      <td>49.198688</td>\n",
+       "      <td>-123.145485</td>\n",
+       "      <td>-23.6</td>\n",
+       "      <td>1.300</td>\n",
+       "      <td>64.14</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>45</td>\n",
+       "      <td>gps</td>\n",
+       "      <td>0.38</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>80</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1775171736000</td>\n",
+       "      <td>2026-04-02T16:15:36.000-07:00</td>\n",
+       "      <td>21020.905981</td>\n",
+       "      <td>1775144256238</td>\n",
+       "      <td>Default Profile</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2026-04-02 23:15:37+00:00</th>\n",
+       "      <td>49.198688</td>\n",
+       "      <td>-123.145485</td>\n",
+       "      <td>-23.6</td>\n",
+       "      <td>1.300</td>\n",
+       "      <td>64.14</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>44</td>\n",
+       "      <td>gps</td>\n",
+       "      <td>0.41</td>\n",
+       "      <td>0.53</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>80</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1775171737000</td>\n",
+       "      <td>2026-04-02T16:15:37.000-07:00</td>\n",
+       "      <td>21020.905981</td>\n",
+       "      <td>1775144256238</td>\n",
+       "      <td>Default Profile</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2026-04-02 23:15:39+00:00</th>\n",
+       "      <td>49.198688</td>\n",
+       "      <td>-123.145485</td>\n",
+       "      <td>-23.6</td>\n",
+       "      <td>1.300</td>\n",
+       "      <td>64.14</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>45</td>\n",
+       "      <td>gps</td>\n",
+       "      <td>0.38</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>80</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1775171739000</td>\n",
+       "      <td>2026-04-02T16:15:39.000-07:00</td>\n",
+       "      <td>21020.905981</td>\n",
+       "      <td>1775144256238</td>\n",
+       "      <td>Default Profile</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>12977 rows × 23 columns</p>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 12
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-23T05:04:18.070237Z",
+     "start_time": "2026-05-23T05:04:17.552124Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# You'll need to align sat_count and snr onto the same time axis as gps_latlon_df\n",
+    "# They should already be aligned from the earlier TimeSeries.align() call\n",
+    "\n",
+    "# Find the time period corresponding to the left blob\n",
+    "# From the map it looks like early in the session — roughly before 13:30 based on\n",
+    "# the speed plot showing early laps\n",
+    "\n",
+    "blob_period_start = pd.Timestamp(\"2026-04-02 12:30:00\", tz='UTC')\n",
+    "blob_period_end   = pd.Timestamp(\"2026-04-02 13:30:00\", tz='UTC')\n",
+    "\n",
+    "# The known parking lot center from phone GPS (use as ground truth)\n",
+    "TRUE_LAT = phone_gps_df['GPSLatitude'].median()\n",
+    "TRUE_LON = phone_gps_df['GPSLongitude'].median()\n",
+    "\n",
+    "# Compute per-sample position error vs phone GPS median (rough proxy)\n",
+    "# For small distances, 1 degree lat ≈ 111km, 1 degree lon ≈ 111km * cos(lat)\n",
+    "import numpy as np\n",
+    "\n",
+    "lat_err_m = (latitude_df['GPSLatitude']  - TRUE_LAT) * 111000\n",
+    "lon_err_m = (longitude_df['GPSLongitude'] - TRUE_LON) * 111000 * np.cos(np.radians(TRUE_LAT))\n",
+    "pos_error_m = np.sqrt(lat_err_m**2 + lon_err_m**2)\n",
+    "\n",
+    "# Plot position error vs sat count and SNR over time\n",
+    "fig, axes = plt.subplots(3, 1, figsize=(14, 10), sharex=True)\n",
+    "\n",
+    "axes[0].scatter(longitude_df.index, pos_error_m, s=1, alpha=0.3, color='red')\n",
+    "axes[0].axvspan(blob_period_start, blob_period_end, alpha=0.1, color='red', label='Blob period')\n",
+    "axes[0].set_ylabel(\"Position error vs median (m)\")\n",
+    "axes[0].set_ylim(0, 500)\n",
+    "axes[0].legend()\n",
+    "\n",
+    "axes[1].scatter(dt_axis, sat_count, s=1, alpha=0.3, color='blue')\n",
+    "axes[1].axvspan(blob_period_start, blob_period_end, alpha=0.1, color='red')\n",
+    "axes[1].set_ylabel(\"Satellite count\")\n",
+    "\n",
+    "axes[2].scatter(dt_axis, gps_snr, s=1, alpha=0.3, color='green')\n",
+    "axes[2].axvspan(blob_period_start, blob_period_end, alpha=0.1, color='red')\n",
+    "axes[2].set_ylabel(\"SNR\")\n",
+    "\n",
+    "plt.suptitle(\"Position error vs fix quality metrics\\nDoes the blob correspond to poor fix quality?\")\n",
+    "fig.autofmt_xdate()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ],
+   "id": "db4767ea5cb227e4",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x1000 with 3 Axes>"
+      ],
+      "image/png": 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/AABshyAtgMf21u6lzYc2i/pchbmFXq8OAICvDcke0j152O7a3SQzzvgNXTqJgwjr+64XSwAdvbjhRZH9etrw0yi3dy69vfVt8Vjz9OHTk3q9iyZcJP6Av/oPB+XNbnfOoH1729v0etfr9MbWN2jLD7ck9J797+tPtW214u+c+b3uunVJrTsk5lcrfkVl1WUiU96OSQYhNpQEAb9BkBbAYxygXbNvjfg7grTWIOANAHZk0ho6yPkyAlacP+58em3La3TemPMcf6/SgaU0cchEsQTQEQfY3t/1PjW2NYoSBVsObaH+Wf2TDtIisODP/sPMBmnzs/OptatV/J2DfokyArQMk9O5x9hWyWwz8O7pBgBVpHq9AgB+xxm0UwqmYKZbGwPevAQASDaTNu2re9hDs4dK3YgTB0+kkgElYuk0Ds6Ozx+PIC1oa8KgCdS/d3/KycyhtNQ0GjNwDM0eOdvSo+xPff6UWIJ/+g8vzZpZPJP6pgUnqMzLzEv4PTNTjjxFcUzfYxL+fUiO0dZoc3dwlvi0omkoCQK+gSAtgMc4e3bWyFnIorUBAt4AYEcmbb/MfpRO6TStcJrUDbpi9wraVr1NLJ1W21orHs19Z/s7IkMQQDdci5qDbOm90mnkgJF03dTrrE0KhLqVR+GxY1HZIi3HEKP/JDKRI2cF3nrarTRx0ES66es3Jfye3530XRqQOYBK+pfQr2b9KuHfh+RcOflKOjb/WLH0M7f2Z95P5pbO7c6i1XkcAWAodwAAWgW8UTICAKzgDLrG9kbqpE7acXiH1I3JGVtfVH6RUOaWlUyW1eWrRaCWHz3EI4egG85I5xIHQ/sOFYE2vjFhBWdJDs4ejOwvnzy2bPSfRJ9sWL13Ne1v2C+WieKnPbhu8rC+w6g0D6Vo3FLRUEH1rfVi6Wde7c86jyMADEFan5r+2HT6aO9HdErhKbTy6pVer47voZYqAIAcNhzcQO1d7RSgADW0NJBumVvJ4gshniCFL444YAugm6z0LJFBe8+Ke8TXqZRKnQs6Le0zCCD0tPvwbpH5P6zfMNLNVS9fRXXtdfT65tcTmjCOkwuyK7OTSjL4x5f/oN11u2ln3U763Ye/s5b57ZGVu1eKJzS4tIgq6//G5jeosrmSFq5fSHeefqdv93PjXMDtcwKv3hfALSh34FMcoO2iLrEE76GWKgCAHHhm7hR+TpmI9jfuJ5mV15fT5qrNYgkA1nBGImfSG/g8Gey1uGwxrTuwTix1wwHa0KUbOEAbGuxU0QsbXqB/rf+XWKqCA7TscPthX9ecDi9DoPv7ArgFQVqfmjx0co8leAu1VAEA5MAZUN8+9tuUk55D5409j2S2fMdyqmmpEUs3HzHELOKgo7JDZbSxaqPXq6E1kTWanlzWqOz69OrTY2nWluotYhznZaIG9R7U/XdVslDDcVZ1fp98pbKre6f27jFuAADYCeUOfOqXZ/5SXGjxTIngPdRSBQCQxz8v/iepoH9Wf+qq6RJLN+ARQ9BZVXMV7a/fT9OGTRPZ6eeNkfsmjYpye+dSv4x+Yqmb75/0fXpr21t01qizEvq9vul9KSWQIpaJuuW0W+j59c+LOrj82L2KLjn2EpoweIJSj64fN+Q4Wr1/NWWmZlJTR5PXqwOQMJ50zShfhYxk+SBI6/NHungJAAAA6p28NrQ2iNIMvAQAa/Kz8umYfsdQIBCgtF5pVNy/GE1qM55oqa6tTssJl04dfiodaDwgloloaGsI1kBvS3wcv2TCJSILlQKkLBVrN39j5DfoYONBKswppIsnXOz16ihNlfMt3WDyNbkhSOtDL254kS5/6XJx5+8fa/9BZT/GYxrgb8UPFlNboJ0yUtJp5007vV4dAPDYsp3LaMnWJTRn9BwxWZaseIKzTuoUSzec/vjptKt+F43oN8I3YyUuIP2jdGAp7ardRXctv0t8/cHuD+jm6Td7vVpaWVe5jsrrysVSJ1wP9uLng8G6Z9Y9Q4EF5qOmU4dNpX31+8QyUZ/s+4T+e8l/U3VLNZ1adCotv8qd0jd+99uVv6UO6hATtul4w8FNCBZ6A09GyQ1BWh/6+9q/dz+asbV2q9erA0S0t3avmDyMa9PqWKdLdhygDV0CgM8F+D/xP6l1dHaIGeh56QYO0IYu/QAXkP5RVl1Gm6o2dX8txgCw1ZcHvhQTsn1W8Zm4AaJL5tw7299J+neLcoooPztfLBP16CePUlVLlfj7x3s/JhWpeCOMA7SGhRsXilr2kBwEC72hYga7n2DiMB8KDwKm3B2cxRq8wwHaNfvWiCUAAHhrZslMunzS5WIps6unXE0TBk0QSzf48WKKPyvX7/fTZya/35z5CpcSAXvxpGGibVNSaNmOZdo07+yRs5P+3cVbF9P6A+vFMlEdXUeChXl98khFKk1IyQHlRWWLenzvwvEXerY+OuBA4dzSuQgYAoRAJq0PnVRwkterAGE4gzZ0Ce46uWAqfVH5BR0/5Hg0PQAo47LjL6MTjjnBtQDiuuv0ekTZDGSb+K/cwfUnXS8yPq1OHKZihqDTphRMEVmnfdL7UFVTMANUB9OHT6cTh5xIayvX0uQhkxPqI1zHtrm9mVJTEs+d4nbsRb3EDYXZJckHir2k0jwpfGNh8bbF9O0J36b9DfvpW2O+hSxaALAdMml9ehKa1Surx/eQTet9dvOskbNQ6sAj/LhwZ2enWAIAcE3av3/+d7GUmUoZSACy431p6Y6ldLDpIBX0KxCPoFuB/fNoje2NYtnV1UW6OdR8SGRi8zKRPsI1TbkEREN74hOHZWdkiwBtICUg6tKqXGaEl9JLCWbYN7Y1UnVTNdU013i9Rr5kZDTzEkBHiEj4EB8EIz2Wc+/793qyPgBe29+4X0y+w0sAgKrGKiqvLxdLmeFRfAD7bKzaSDsP7xSztnNWY7+MfpZeD/vn0dJT00Ugkyc71C3AlZWW1WNpto8U9y+m3r1609DsoQm/56CsQWLZGeikysZKUhEfZ3niNNmPt6w0r5TG5Y+jpvYm0d5bqzG3ixdwAyw5CG6rA+UOfOjZL5+ld7e/K+pCGXe02e3Lbqefz/i5p+sG4IWKOs5iCNCe2j20unx1UjPsAoA+8vvkBzPp+ljLpHMaHsUHsDeTdk/dHtpft19kNn5e8bmlR5mxfx5t2a7g0wnNnc2iBuu9s/VJEBmVN0oEG3mZSB/ZW7eXalpr6KO9HyX8nulp6d1/r22uJdV865lv0Wtlr4m//7v833Tz9JtJ9jHi3R3vUtmhMqpurkaQ1iN+rI9vB0yEqg5k0vrQZ/s/o3Zqp7QIMfqiBxOfWRRAda2BNrHkQO3rW173enXABntr99LS7UvFEkDXicMAwD47anZ0z9zOQdrtNdvRvA7i4KROinOLqX9Wf7FMxO7a3T2Wibh4wsWUkZoh/l7XWkeqeWfbO91/b+lsIRWy7Xcc3tHd1odbD3u9Sr6EycaSg6c71IEgrQ+NGzROPG406ZhJFFhwZBZbtrderxMmADMyvzrBZeH1mkFNmw9tpjX71oglQKJwAQDgPycWnChq0/dL70fplE5fL/q616ukHa7nafje5O+RTtJ7pYsasbxMxPDc4T2WiU5Ydu3XrqUROSPossmXkWpmjzpSfi89JbF288L4/PFU0r+ETi06lSYMmkBXnXCV16sEYBrObdWBcgc+NDpvtLgLyMtIMu7OoLYFwcxCcAc/Yv/+rvdpxogZeNTeA6eNOI0+2PUB5fbOpZK8Ei9WAWw2duDYHksAAIBYTh52Mh1uOSxq0XKgjc8NwF6/nf1bem3La3TemPOkf7Q9UaeOOJUONB0Qy0ScNeosat3SKpbJeOCsB8QfFb36H6/SaY+dRmsq1tCUoVNIdpccewlNGDxBZCRywAsAwAkI0voQF6bPycjpLlDP2bQpdx+5s82lEMBdr29+nd7b9R7VtdQhSOuRlNQUkQExcsBIr1YBbFSYWyj+AOg+CQTXGMMFI4B1h5oO0YHGA9Q3vS/1Su1FQ7KHWN4/l+1cRhQIllBBUEek0gYduezQRlZ6ljiH5GUiDjYdpKa2JrH0I06Q4CxaXsoOdaZx3gHgBpQ78KEBWQPELKCLti6iFze8GPFnQoO24Lyh/YbSwKyBYgnu48yZ5o4W2le3T9QxBQB/U2UG3GU7ltFTnz8llgBgDdeZ7OzqpC8qv6AVu1bQX9f81dLr8Q2UJVuX0OJti8XfgTNDvmqFntXWtLD78G5asXuFWCbi33v/TVUtVWKp67Eqlu3V26muvU4sVaJD21uZfApjGoBzkEnrQzxbdVN7E1U2VNLCjQvFzLXh2bTgLn7sa8zAMXg02yPGBAAtXa20dv9ar1YDACShzAy4KV/VeMThG8Cy7036HlU0VohZ5gMUoK01Wy29Hme4zxk9RwQkMQt5UH52PhX0KxBL3SzcsJA+3fcpZaRk0A+m/sD07+2r39djqeWxKoat1Vt7LFWhQ9snwxjLMKYBOAdBWh/iR67KqstofeV6unD8hd3fT6M0MaOtgYO24ROLgTPwaLa3Rg0YJe7gcw26UXmjHHufvbV7xURWXCcVj+IDyEuVi5CZxTNpcPZg6dcTQAUnFZxE0wqnUXldOdU019C8ifMsvR4HbuZPnm/b+umA6/2mpqSKpW4G9BlAmWmZYpkIfopud93upJ6mU+VYFUvxgGIqqykTS5Wo2vZWyySh5AOA8xCk9SmeDfT7U77fY3BuX9CObFrwpf69+1NWRhZl9sp0NHjKAdo1+9aIvyNIC7rRqT6qKhchqqwn+Ht/UgWXDeFSBzyB0X+f+t/iXBnste7AOtpdu1ss+Uk+nXz72G9TWmpajwQYM2479TZ6dv2zdOmxl/ryGPDEBU/QO9vfodkjZ5NKVG17v2YAA6gEQVofijU4F/YrpL31e7u/RjYt+EF7Vzt1dHZQRq8MR9+HM2hDlwA6wYm/NxDM0xP2Jw+kEDW0NYg/q/auotrWWgTJbcaTsdkxKZuM6lvrxZwfvExE77TeNLTvULH0oy8rvxQT7HGf8OONEbeP4SpmAOM8B/wGE4f5UG5mrrjTy8twe27ac9T3bn7rZpfWDMAbXP6jubOFGlobqLal1rH34ezZWSNnIYsWtMQn/NOKpil14q8DTOKhJ+xP3pQPmTh4oqghu/HgRkyO4wAOxq0/sF4sdVPVXEX76/eLZaLnoOsq14llolbuXkk3Lr6RHlj5gLITWP1s6c9o+a7lYunHycLcPoZzIHhu6Vylsmh5vPj753/XctwAiASZtD7EmQEdXR1iGck9M++h25fd3v3171b9jh446wEX1xDAZSGll5vbm9H8AD569E/1rA0Vs2LAX/uTioZmD8VNJwdw/X++/uClTviYwbWMOQEmPyuxSdGa2pqoi7rEMlEvbHiBXtz4Ih3T9xiaMHiCkmMGJ0iELv32hINx7Oa+wwFgFc47XBfg/8T/AHxB6kzaP/3pT3T88cdTTk6O+DNt2jR68803u/+9paWFrr/+eho4cCD17duXLrroIqqsrPR0nVUQK5OW/XzGz4/6XtGDRS6sGYA3Thx6IuVnDaTxg8bTuWPPxWYA8DlVslNVCiaryonMKZAT70s7D++kxvZGr1dFW0P6DqHM1ExKSUnRap/ivlPdXE39MvtR6cDShH53bP5YMQEkLxM1rN8wEaDlDHBVb9RNLZxKqZQqln58wsHIbOWbFyqcd3g16fnlky4XSwA/kDqTtrCwkH79619TaWkpBQIB+tvf/kbnn38+ffbZZ3TsscfSjTfeSG+88QY9//zzlJubSzfccANdeOGFtHLlSq9XXWplh8poY9VGGpE7Imrtn8CCQI9JxELr1ALo5vhjjqea1ho6vfh0mjpM/pNEAHCWKtmpqFvqfAD8QMMB2lazTXwPgXC98f5+/JDj6Uv6UjySX9dWJ76P7W6f4txi+jj9Y+oKdIn9S5e25b4zcsBIqmqqivqkYqxJZQ82HhTLRF1y7CUig1blG3VnjTqLUlNSlZg4zHjCgctM/GL5L8T3rjrhKltq6apy3uHVjWgOZENibabqmACSB2nPO++8Hl/fe++9Irv2o48+EgHcxx57jJ555hk688wzxb8/8cQTNH78ePHvp5xyikdrrW/NpPS706l9Qbtj6wXglb4ZfUVWBy8hvr21e8XFBE+AxnV2AXSjyqPmuKhzPgA+Km8UHnv3Cd7nL55wMR1oPEAtHS2U3ycfARObNXc2U3pqOuX1ydOqbbnvcJDxne3vRH1SMVY27MA+A8VS12NVLJwFvOXQlmA9aEXwdn57+9uUEkih4bnDbQnSer0tZQzu4UY02syvpA7Shurs7BQZs42NjaLswZo1a6i9vZ1mzz5y123cuHE0fPhwWrVqFYK0MdQ019ChpkNiGUt4Nm0HddixKQGks6lqE22v2S4eaZ00ZBICj3FwgHbNvjXi7wjSAnjH64s6nYUGwNHG/sGTN9W31ovg/PzJ87HtHaj1m5eVR1OPmapd28ab8yOakgElVJRTJJZ+w4HBD3Z/IPY5/iM7I5DJAeVvjPyG+J4KGcCqBkT9cCPa7uC4H9rMD6QP0n755ZciKMv1Z7nu7EsvvUQTJkygtWvXUkZGBvXv37/Hzw8ZMoQqKipivmZra6v4Y6irCz7O5BeLty6mDVUb6NBnh8SjA4nc/eOgLQdvAXTKqpwxYoYI0nKtZv4sqn4Ot/C2Dl3KSId+CSBb5ouMmTZOQQDcf7h/r9i1giobKsV5ge593Atcr3XikIkJ123VYc6PaJ747Al6f/f7VNtSSxdNuCih3+XH7nnyMM7C5dIHqvVZPp58XP6xmHTtuMHHkeyW7VxGS7YuoTmj59Bj5z9GOpExuOeH47DdwXE/tJkfSB+kHTt2rAjI1tbW0gsvvEBXXHEFLV++3NJr3nfffXT33XeTX+2q2SVmSKxqrBKPa8QK0oZn0wLomFXJdWh54gUjqOcUXQKHvO6yr78O/RIgkQtGzvrzY6aNU/wUkAbnJw5DfwriiX8G9x0sVSDI60za8tpyUV6Dl4ni67jXt7xOmb0yKT8735XjgJ24HzS2Nora36v2Bo8tUgtQ9zU0P32n0/FB5uCezuOnjMFx8J70QVrOlh09erT4+5QpU2j16tX0P//zPzRv3jxqa2ujw4cP98imrayspKFDh8Z8zdtuu41uuummHpm0RUVF5BcnDTuJlm1fRkX9i0w9opGRkkFtgbbur5FNC6plVcoSeETg0D269EuAmAJEDW0N9Pn+z2lX8S7HL178dDHhp4A0BHEGZL+MfpTSN0XUo7UT+pP8gSCvMmlnjZ4lJq/lZaL4Ou6z/Z+J4wAfD1TDfYED221dbSKDXZWbDCKojOODa3QeP2UdE3UOjKtA+iBtuK6uLlGqgAO26enptHTpUrroouCjIZs3b6bdu3eL8gixZGZmij9+xQf05o5mOm/MeaZKHbTe2YpsWlA6q1IWCBy6B/0S/IAfGc7JzKHqlmpXZkp342JClgsDPwWkIajsUBnVt9VTv/R+tj+Oj/7kj/6zsWojjcgdkVApOb4xUJRbJJaJ4vcpzCnsHjNVNLN4JjW0N4ilKnh80DUjXEYYP92nc2BcBVIHaTnj9Zvf/KaYDKy+vp6eeeYZeu+992jJkiWUm5tLV199tciIzcvLo5ycHPrhD38oArSnnHKK16sutZqWryYOa4k9cVio0rxSMZmCAdm0/qLLY/peQ+AwOeh/APEnOdLlYnHZjmW0eNtiOnvU2TT/BO8e3ZU1uwUclBLMTOc/vG/ZMWO7Af1Jf9xn1lWuo1EDRiX0ezwJ1ZZDW8TSj31r5siZIpOYl6oErqYVTRPzuoA7VO/jKkJg3FupJLEDBw7Q5ZdfLurSzpo1S5Q64ADtN74RnE3xoYceonPPPVdk0s6YMUOUOVi4cKHXqy29xz97nNZXrRdLs7b8cMtR3/vOC9+xec1AVsZj+rwEkK3/cRB36falYgngKwGiPhl9aNKQSUpdwHC2LNfz4+VRUvg/8T8AV3EmnwiUBUjUnARIxCubXhGZtLxMRFZ6Fo0cMFIs/eji5y+mZ9Y9I5Yxjw0Kl7Rwi+ztZxe/fE4v8Tkl34hQ6dxSJ1Jn0j72WOxZE3v37k2PPPKI+APmVTRW9Fia9cIlL4gDqOHZ9c/SPy/+J5reB/CYvnOQJWq9/6HWL/iVqpPwxHqMjgNlg7PV+0wAspbuAHfsqt1FXdQllolAxpo6j1gnOzmcW2RvP7v45XOCf0kdpAVnfL3w6/TR3o/olMLEykJcNCFY+zfU4N8OpgO3HLBx7UBGeEzfOQgwWu9/uImgRkACAQv7Ge3M7R76texiBSVkeawR/dV/uNTGmvI11NTRZMvrIZDgLycOOZFW7V0llomQZczzSiqliuA2L2UPWMueSSt7+9l13JX5c6py7qDKevoVgrQ+tPLqlUn/bmBBoMckYgebD9q0Vv6GbEr/4kl/eqX2EkuQ5yYC9kn7AxIIWDhDlXYNvyCQeV1ValewUQpRZlomZaZnUn6ffMsvJ3MgAex35ZQrqSC3gOaMnpPQ7/k9WHLr9Fvplc2v0Pljz5f+2CB7Jq3s7WfXcVfmz6nKuYMq6+lXCNKCZb3u7kWdCzrRkhYgm9K/6lrrqLOrUyzBe0ZwtqqpqrvWFSbLsycggYCFPzN7Il0QGF/LHJTwur/6PXDjBZ4kd+KQid2zt1slcyABnOk/u/J3iWUi/B4s2VqzlSobKsVSdqocb1Xl9XFX1c+wcvdKemf7OzR75GzTE17q0NY6Q5DWh/jEf9nOZWJiBK5nl+gJQXg2LT+iAtbgcW3/wraX84YJj4tTCqZErYMLiQckELDwZ2ZPpAsCFYISdvXXZIOtKrSRbngfOth4kA63Hqay6jLTF7tgns43H7jPrNi1gj7b/5n4GsES/fBkVTwxXFtnG8YHF4+7Ko0bbp7rGu3y9ta36ZP9nyQ07uCcXG4I0voQ78xLti6hAAXEhCN2DCQctOXgLSQHNV/9C9tezvITPNPy1GFTvV4dAG2yISJdEMi+znZINtiqynbVCWfHtXS0UF1LHVU1Vnm9OlrS+uZDgKiioUIE8DirDcESc6YWTKX9DfvFUnar966m3bW7xRLcw8llHLvgUiLzJ89H04eNp8Nyh1H/rP4ikxb0gCCtD/EJv6iXFEj+5D88mxYAQAcqlZ/g0gxbKtfT2OxiGkYlXq8OeETFbAgV1zlZyQZb/dRGMmXSdgY6RWY6OEPnmw/8dGJVcxWV15a7FixJ5jFn2Vxy7CU0YfAEJfrEmPwxtLl6s1jqJtlsVTuzXKO+VoD/E/+DKOMpzhf0giCtD/FObMddqMzUTGrtau3+Gtm0AGrCJFlqlp/g0gyf7fuUUvPbaRid6vXqgEdUegzQjxBsVSuTtl9GP0rpm2LLxGHgr/2BP9fNX7/Z1TGcA7T8h6kapFWpTxhjhI41aZPNcrczOz7aa/ENEH76N1Ig38/nQCrtO5AYBGkhaS13tCCbFkADmLhOzfITHEhO6eig0uxir1cFPMIXJw9/9DBtq95GF46/kOafgMcAAaxk0ub2zqXsjGxbJg4DMOP59c/Ta1teo/PGnEc3T08syGtk7Kr8mHNokI3JHHDjcha1bbViqapoQc1ks9ztzI6P9lqxgpFal1AB30KQFiwxJgAxIJsWQD0qZY8i6zcsoNxnCFHrkScawF/4+MsBWp7oiFyoQOTHjBU/fma/4uy42pZaqm+rp1V7V4mgLbY7OG3jwY20/fB2sUwUZ8+qmkEbKcjGZA64De07lHIzcsVS1WNNtKCmDFmZyayDziVUwL8QpPUhPgj8Zc1faH3levre5O/RRRMuSvq11l237qhs2m898y169T9etWFNAeyHIJ/a2aPI+gU4gi9KLpxwoajTNrN4puNN48eMlVifWbYArmzro+LM7R/u+ZAy0zOp+GCxCNgytCU4afzg8bS1ZqtY+lGkIJsfA25uHV/tDmp6fV7gdnBZ9uOs7OsH5iBI60O8476y6RWqaKyg7MxsS0Fa9sGVH9CpTxyph/ha2Ws2rCWAMxDkU5tKWb+gJtVOcAdnD3ZtXf2YsRLrM3t9cSr7+qhm/YH11NDWQB2BDhqaPZSmFU3zVV93i2pjrNMumXAJTRikxsRZbgTZZO4TTpY7cOv4andQ0+vzArfHE9mPs7KvH5iDIK1PH+c6dvCxVNBSIGrYWRXpMZv+9/Wnw7cdtvzaAHZDkE9tKmX9gppUOsF1e11leBzSbbE+s9cXp7Kvj2rOHn02fVH5BfVK7UUDsgbQ3NK5Xq+SllQaYxOFALTebe5kuQNVj69er7dT44ndtXvdIvv6gTkI0voQ19gaM3CMyBCw6wQ0sCDQo+wB32UEkLXMwayRs7xeHaWhZAToTKUTXJXWVUdeX5zKvj6qGd5/uEhiqGuto/w++V6vjrZ0HreSCRjpHLR2A9rP35waT+yq3YsbN5AMBGl9mkmblpomlk7CJGKgUpkDBB7ta0sAlakU6FJpXQFkx+fFXD6E/5QOLPV6dbSl87iVTMDIynXZyt0r6Z3t79DskbOjTiCme5DIreva0HIHW6u3ihrWurapSpwaT+wK/rp9EwE3LfSAIK0PPfrJo/Tm1jfFjNB2zggank0LoFKZAwQeowsPYKNkBJil+8UhAOjjwucupANNBygjNYNOG3GarefI4I9jQzIBI37CsaOrQywTxQFa/sOi9VfdgzZW2i/ZcgdM5zYF+4K/bj85oPOTCn6CIK0PfVz+MdU014il05BNa47sWZyyr58dtUwReIwuPICNurBglqoXh7oGEMB+6Cv64AAta+tqo6rGKq9XR2uqHhtkC6pwBm3o0u7XV4EXn29Qn0GYWBCkfHJA5ycV/ARBWh+6fNLl9Oy6Z+nSiZfa/trIpk2O7Fmcsq+fHRB4jA4BbEiWqheHCCAA+or/FOcW087anSKTFpyl6rFBNpw9Gy/jW/egjZufzyh30NzRjIkFNYEbrSAjBGl96Oczfi7+OCU7LZsaOxq7v0Y2bXw5mTliNmFeykiVIJ0OGb8ytpuKAWz0BfkunlQ6EUYAAdBX/GfHj3fQAysfoFe3vOr1qmgr9Dhg1+TFqsNNQXUY5Q54Cd6x83wS+x/IyJMgbXl5OQ0bNsyLtwYXNPy8AbVpE8QzCXd2dYqljFQJ0vkh49cJOrabjp9JdTgRBh3pnqXmN/l98qmgX4FYgv1wHDgabgoCeDeOYP8D8nuQtqKigu6991567LHHqKmpyc23Bpd9vfDr9OHeD7u/RjatHpmqskM7ot3QF+Sl0okwAgkA/lQ6sJR21e4SSzuo9ASBG1Q6DrgFN3rUYZQ74CX2bT3GERX2v0T6GvqlHmwP0tbU1NB1111Hb7/9NmVkZNCtt95KN9xwA9111130wAMP0PHHH09PPPGE3W8Lkll59cqjsmln/202vXNFcAZSUDNTVXZoR7Qb+oK8VDgRNiCQ4NyJPy4gQGZl1WW0qWqT6OPxan2agRs+6h4HVIEx1ZtyB9i3veO3cSSRvoZ+qQfbg7QclP3www9p/vz5tGTJErrxxhtp8eLFlJqaSu+++y6dcsopdr8lSCp8ErGlO5d6uj6gP9QhBQA7+O0CwAy7Tvx1uoBAcEQ/VY1VVF5fLpZ2wA0fcJpOY6pKsG+DjH0N/VIPtgdp33zzTXryySfpzDPPFBm0I0eOpMmTJ9OvfvUru98KFLyoyPlVDtX9TM66q6A+1CHVE4LvAN4f0+068dfpAgLBEYgFQfzIVu5eSe9sf4dmj5xtS7ay37e36mOqSvtJaLkD3MwFtyTS19Av9WB7kHbfvn00fvx48ffi4mLq3bs3XXbZZXa/DShyURGeTVvfXu/o+4G/oSatnhB8B/D+mG7Xib8qFxCxAgfGv+Vm5tK0omnKBkfA2YnDEMSPjAO0/IfpFKT1anurMqbqsJ+EljsAOakU9FdxfUHRIG0gEKC0tCMv26tXL8rKyrL7bUDhO66YRKwnZAnaBzVp9YTge3R7a8tpa8U6KhmQSiMGjXZxq4AsvD6m6ypW4MD4Nw7Qzi2d68j748JN/YnDsG9G7tfZGdk0Jm8MZadni6+N/Uv1Pu/F9la9zRj2E/Br0D/a+uqwX4OEQdpZs2Z1B2qbm5vpvPPOE5OIhfr000/tfmuQ9I5reDYt9IQsQQD7g+9+ufmxpXozfb5/LTUXDkWQ1qdUz6JSMXDgRlAhmQtNXNjJNXEY9s3I/bq2pZaG9x9Ota214mujf6sWXJFhe6veZqrtJ6HlDkBOqgX9I62vDvs1SBakXbBgQY+vzz//fLvfAjSAbNojkCUIYD+/3PwYkzeWUtvaqWTQBK9XBUArsQIHbgQVkrnQxIWdfBOHQU9Gf+ZSIRykDe3fqgVXZLhJonqbqcYod5CVlkVPrn2SKEA0s2SmbwNpMt4YVCnoH219sV+D40FakI8XAyqyaaPDI/oge3ao0+8b6fUTec9IP+uXmx+FucOosHc+kUInpOBvMl7UySiZC01c2MkL/V6Omx9O8uImieptppoBWQMor08eNXc205KtSyhAARrcd7BvtwFuDPyf9dgAAGFKSURBVDoD+zXYHqQF+Xk1oPKdR35ExIBsWgA1skOdft9Ir5/Ie0b6Wdz8AJAz0ISLOufgwk5e6Pf6S+YmCYL3aqlprqHqpmrKGpRFp404TWTS+i2LObTP2nFjEPsAgAtB2pkzZ1JKSuz6o/zvS5cutfutQfJMi8O3HT6qNm3ohAEAEJtX2aFOv2+k10/kPf2SNRsxezhnVDCTVlI4+daLlUCT0Rf4MWeecMtvF7YgP+6jXOqgf2Z/yu+TL10wA/S7SYLgvVqMmrTNHc00f/J88qPQPssTZ1q9jsfEWQAuBGknT54c9d/q6+vpmWeeodbWVrvfFhTJtDiv9Dx6rey17q+L/6dYlEIA8JoKE015lR3qxfsm8p5+zJpduWclLd+5nM4oPJW+PfpbJCtcgOrFSqDJ6AscoOULO79w+kYFboTYh7dTdXM19cvsR6UDS6ULZoDeNXoXlS1CGRhFatLy0q/svuGk28RZOCaDlEHahx566KjvdXR00COPPEL33nsvDRs2jH75y1/a/bagiFf/49WjsmmnPDqF1vxn8FFlAK+oMNGUCoFkXdtetn6w4eAGampvItkhe0yvi4FkbvL6PYPW6YtNlS9mZcN9My8rj7ZWb6Wy6jKaPny65dcLXQJEG1M5QIv92J9UC+rZneyl28RZiR6TVdv+oElN2qeffpruvPNOam5uprvuuou+//3vU1oaSuH6WfgkYp9WfEp+J3PwTeZ1s1NOZg71Su0llroGM2XdljKWK5C1rRivV0dXBx035Dj6euHXpT4BRI1MZ6kQoPNrBq1bF5sqX8zKhvehYf2G0Zr9a6iqscqW15N1vwS5YD9Wq9wBL/10HHebymNnovuy3eUeEPTVg2PR0sWLF9Ott95KO3bsoJtvvpluuukmys7OdurtQHF97ulDTbfLnxXmx0xCmdfNTttrtovsmcKcQpo6bCrJyGowU9ZtKWO5AlnbKrwfFPYZQhSnhBAuAPSlwoW9W+vo1YVJvPd1+mJT5YtZAAjCfuzfcgcqHMfBuX3ZSrmHSOcfOOfXg+1B2n//+9/005/+lD766CP6wQ9+QO+88w7l58s7qQnIkU3b3Nns600hYyahbBmmbmQ2plDsSQ9VD2bK3M9kI3Nb9egH7e1xfx4XAPpS4cLerXX06sIEF0R64QnDCvoV2DJxGACADsdxcI6Vcg+Rzj9i/S6ybH0cpD3llFMoKytLBGhLSkrERGGR/Nd//Zfdbw2K46CtXycRkzGT0FDXWkedXZ1iqXNm4/Si6eKiTMagnB/6mWxlCFRpq7215bS1Yh2VDEilEYNGR/wZXACAH3h1MwI3QfTCE4btqt1ly8RhAKBnuQN+8g4TvWFiTKeYPW+PdP4R63dDg7rG1yip4JMg7fDhwyklJYVefvnlqD/D/44gLYRn04KcWjpaxAXLsYOP1TqzUZWgnJ9qr6pWhsCL7bWlejN9vn8tNRcOjRqkhcQh20A9uBkBduAJwzZVbRL9yerEYQBm4ZijVrmDxvZG+vvnf6c5o+fQ/Mnzya8wMaZaQoO6VrYdniBSMEi7c+dOu18SNMaPlwfoSPasn7NpZcUXK+V15WJ5zphzPFsPvwZRnaZa0FPmMgRebK8xeWMpta2dSgZN8HrVtIIT0OT4MdCAvqIXnjCsvL7clonDAMzCOKIecf3q80tWTIyp1rgRq7RCIvAEkYJB2lWrVtGhQ4fo3HPP7f7eU089RQsWLKDGxka64IIL6A9/+ANlZmba/dagoK4FXcimldy4/HFiUi1egn5UC3qaDdarliGc7PYqzB1Ghb3ziXwSEHMLTkCTo1OgwWzA2am+4seAN/gH+rd7xxzZ21r29YtU7iA7PZsun3S57yf7cvoJlr11e2l1+WrKzcx17H1U6n9mxg2zn8fKtsOTSwoGae+++26aOXNmd5D2yy+/pKuvvprmz59P48ePp/vvv58KCgrorrvusvutQVF5mXlU3Vrd/TWyaeXSO6039e/dX2TSThoySauAFxwd9NQluKlahrBZyCh3B05Ak6NTcDtWwDn8IsiJCzudAt5+pfLFv9PQv9075izbuYyWbF0i7aP5sq9fpHIHo/NG09zSuV6vjvbe2f6O+MOcKj/j9Fjk5HEg0riBsVUPtgdpP//8c7rnnnu6v3722Wfpa1/7Gv31r38VXxcVFYmsWgRpwXDo1kNHZdO+uOFFumjCRWgkCXCwbm3FWqpurhaBr0QDXrIE/WRZD9npEtxULUMYQAXxLjZ0Cm7HCjjbNflGsu8PzuEJRAv6FYilVbhY9k//ljogH5D80XzZ1w88M3vk7B5LN8ciu/Zpt48Ddo2tUo9pPmB7kLampoaGDBnS/fXy5cvpm9/8ZvfXU6dOpT179tj9tqC4S4+9lJ5d/2z31xc/fzFq00qkMKdQ/Ekm4CVL0E+W9ZCdLsFNZJy6Aydx/qJL0MlMv40VcLZr8o1YdAp4q6R0YCl9Xvk5VTVViX5iZRuE9hOMlUGh7RApE1HVdvJqbDTTXjNLZtLgvoOlDYiHrp/s298od8BLGRntx+UBaltrpW1Hszh71ukJHKMda+3ap92+IWX23CHevqbL+Z6qbA/ScoB2x44dImO2ra2NPv30U1ECwVBfX0/p6el2vy0o7p8X/7NHkJaN+cMY2vLDLZ6tEwSt3LOSlu9cTqcXn55UcFOWoJ8s6yF7BjGCm5AInMT5i5WLDZkuvq32W7sm37CDTO2qg7LqMvp0/6e0rWYbTRg8wVKbhvaTRWWLcMFrYt9T9ZjiVWawmfaS/YaPSvuJUe6AlzIy+kNaahp1dHVI245+2qft2P+cOM7HGzt0e9qB/B6knTt3Lt166630m9/8hl5++WXq06cPnXbaad3//sUXX9CoUaNMvdZ9991HCxcupE2bNlFWVhZ9/etfF687duyRIEtLSwv95Cc/EWUVWltbac6cOfTHP/6xRzYvqCGwINCj7AGfKIMcxGNISZIl6CfLesiYQYxSEGri7balcj2NzS6mYVTiyTroehKHwJf9FxsyBV906rcytasOqhqrqLmjmUb3GW1r/9Cpz1lhfH7O9OOAXHjQQdV28ioQqmp7+eXzuH2+Ebp/GZm04VbuXinqvHIJAaezVFUm080NJ47z8fY1mT6/H9kepP3lL39JF154IZ1++unUt29f+tvf/kYZGRnd//7444/TWWedZeq1uFTC9ddfL0okdHR00M9+9jPxuxs2bKDs7GzxMzfeeCO98cYb9Pzzz1Nubi7dcMMN4v1Xrlxp90cDD2T+IpNa72xF23to5ICRYnZNXoJ6zGQQe10KAkHi5PB2+2zfp5Sa307D6FTygq4ncQh82U/Xi2+v+4qu7er1hKnj88ejfIWDx4xoGZOqH1PcDoCp3l6RJmFM9PO4eVPViXIHdh5DzLSfG5NxWRW+TeN9rTsnjvOqjx26sz1Im5+fT++//z7V1taKIG2vXr16/DsHU/n7ZixevLjH108++SQNHjyY1qxZQzNmzBDv8dhjj9EzzzxDZ555pviZJ554gsaPH08fffQRnXLKKTZ+MvAim7Yt0IaG91hdax11dnWKZSQIsMnNTAax16UgvA4Sq4q3V0pHB5VmF3u9KtpB4Mt+Ml0QJHJRHOtikP/tQOMBGjVglGdBUpnaFcDvY6wXATCVA1Z2BCjdvFHmRLkDt/cFNybjsip8m8b7Wnc4zvuP7UFaA2e1RpKXl5f0a3JQNvQ1OFjb3t5Os2cfGWTGjRtHw4cPp1WrVkUN0nJZBP5jqKuLHHwCOXDQloO34I3yunL6uPzjqCckqgTYEEyWt128DhIrHYDvM4QPal6vinZwQqy2eEGLRC6KY10M8r9tq95G04qm+eJC0Q/y++RTQb8CsQTn6DrGJhsAsxJoVTlgFT4WJ9MObgY5B2QNoLw+eWKp6r7gxmRciYi0zcO3abylbpy68aLyDR2/cSxIa7euri768Y9/TNOnT6eJEyeK71VUVIhSCv379+/xs1yPlv8tVq3b0MnM/Eb2HTQ8mxa8tWznMtpctZmWZS6jyydfrmyAzQgm84zN+YfyPQ1KykSGILtf6wUDgDPiBS0SuSiOdTGo+4WiH5UOLKVdtbvEEtwl+/WJkwEwK4FWlceh8LE4mXZwM8hZ01xD1U3VYqlj//VCpG0evk3jfa3bdnDqxovKN3T8RpkgLdemXbduHX3wwQeWX+u2226jm266qUcmbVFREfmFCjtoL+pFndTZ/TWyab3DwcydNTujBmHjBdhkyNRkxvpzkNbroKRMVAmy+5ks+xCAKuwMWsS6GNQ1G9DPeNLaTVWbxHaVKdvMD1S4PpFxzNJpHJI94Ly1ZitVNlSKpcz9V6VApd3b3Ont4EbbOrUfyL5/gWJBWp4M7PXXXxe1bgsLj1ygDh06lNra2ujw4cM9smkrKyvFv0WTmZkp/viVCjtox4IOZNNKYtbIWZTeK51mjJihbKam8d78hwNe/BgjgpJH2sXYTl5vo1AITMq3DwGocrGoU9AC3FXVWEXl9eVUVlUmJrdSIcihCxWuT5yCMcuednDzeBP+XjL1X5kCxvHYPVmc09vBjbZ1ajzAOKMOqYO0gUCAfvjDH9JLL71E7733HpWUlPT49ylTplB6ejotXbqULrroIvG9zZs30+7du2natGkerbX8VNlBh2YPpYrGI2UrkE0r58RhqmVq4tH62EFA42uvszYRmLS+D6mUSQHJk307q3SxKMv2kX2b+gGff/q538p0fYL9AWQ53gzKGkR9MvqIZfh7yXR9LVPA2O3t7PR20L1tQQ5pspc4eOaZZ+iVV16hfv36ddeZ5UnJsrKyxPLqq68WpQt4MrGcnBwR1OUAbbRJw0Ad+2/ef1Q27Z9X/5l+MPUHnq2TH+Vk5lCv1F5imQwERdXaxrIER60G93XKxI22D/Fn3FK5nsZmF9Mw6nkT06gnvWTrEpozeg7NnzzfpbUFPwdBzUwA4jdmtk94u8m0Tf06cdj4/PE0YfAEz/qtzoHJRD8b9gcwy+njTXNHM3VRl1jKeGwL3bfmls7Vdmzysu1lCsaDvqQO0v7pT38SyzPOOKPH95944gmaPz94wfnQQw9RamqqyKRtbW2lOXPm0B//+EdP1lcVMgyuZl1z4jX010//2v31tYuuRZBWsUxaUGsby5L5bDW4L0uw2Un8GT/b9yml5rfTMDr16B8I8H/if6AxmS4UzUwA4rfzKTPbJ/yGikzb1K8Th00rmuZpTVqdA5OJfjbsD/pc1znN6ePN0L5DKTcjVyxlPLa5MW7IMDbJ2PYAvip3EE/v3r3pkUceEX9AncHVrL+c95ceQVpW8nAJ7fjxDs/WyW+sZtKC/EIDs15kPjuR9SpLsNlJ/NlSOjqoNLs44r/PLJlJg/sOdjTQo+vFoUqfS6aLFb8FU8ycT5naPmE3VGTapn4jy8RhOu9LiX427A+xj1VPrn1STIprtBX4lxvjhs5jE7hHpfNsL0gdpAVnqDa4BhYEepQ92Fm709P18Ru/ZNK68Xi8rI/ge12SwomsV68/kxvEZ+wzhKi11bMLW5Vu+iVC18/lNFmCKYme/Cd7sRDpfCqZ13LjhgokNnEYL70ky77kBJ0/m9t4rOEALZfpiDZ+IBhin4qGCqptqxVLv+5b2H/BGFNyM3OptrU2qUArzrNjQ5DWh3QYXDPuzqC2BW1er4YvxMtIlDXwKOPj8X54BD8Zfsh61ZVqN/10/Fwrd6+kd7a/Q7NHzvY0808miZ78J3uxEOl8KpnX0uG8DMAsBA2dOVZFG0MQDHGm3AGAjOOhG+OrMaakpaZRR1eH+F6i76XSebYXEKQFJbNp26nd0/UB/QKPbgQKVQ1GOh2I90PWq650DS6p9Lk4QMt/GIK0yZ3823mxgAsPCIWA5NEQNLSvn5g5VmFMAvDPeOjG+GqMKaGZtDqfZ3sBQVpQFgdtOXirA5mzUeMFYVUNPHoRKFQ1GKlLIB5AR5xBG7qExE/+7bxYwIWH2vix8YJ+BWJpBwQkj8YX9AcaD9CBhgMiOIkLdWv9JF6AV/YxSaUbGbKXO9CVSn0kUXbfRHHjpozsY4oOEKQFZbNpdSJzECzexGGqBh7BPF0C8QA64uxZZNDqfREH7ikdWEq7aneJpR2QxXg03j8HZw8WQUmuxYz91Vo/Uf1GgErrb5Q7yErLokVli3C8cYlKfcTrgCcCqHpAkBaUkk7pPUod6JJNK3MQTLeJwyJlLcucyQwAAN5mojnN6/eHI8qqy2hT1SaxHey4+YEL5sSCkn7dF6z0E9VvBKi0/nzzZkjfIfTJvk+ovr2e5k2c56t+6hWV+oguYo3Ffh2n3YQgLSiFJwvTNZtWVvEyaXXIWpY5k1kGaB8AkJ3KmWhevz8cUdVYReX15WLpJd0vgqMFJbEv2NeWqlBp/WeWzKSXN71Mm6s2E6UEa3KCO+Pg3NK5aGoXRRqLje3BpWq21Wzr8W9gLwRpQTnFucW0s3anVtm0K/espOU7l9PpxafTvNx5JBPdMmkjZS3LnMksA7SPXHpkfvcZ4vXqaEn3AImO7aZyJprX7w9Hq2mu8fRxZr8GK7Ev2A/HM/vwvsh99LOKz8Ss9jxpZ2FOoa/2US/GQa5hzSVScE7m7VhsbI9ReaNoWtE0nLM4CEFaUM6OH+84Kpv25rdupgfOeoBUVd1cTfsb9oulbHTLpI1UQzc0ozb0a7+IV+4BdYclzmw2EaTFBVri/Bog8Wu7eZ3JZeb9zezH2Nftmzisqb3J077s12Cl1/uinczuj07vt6qOyzLL7JUp6tJWNVWJ9kW7OsMY/zhzE33Y+7E49LjE/8ZjF2ozOwNBWlDSPTPvoduX3d799e9W/U7pIG1eVp4oRs9L2Wyv2U7bqreJO8VTh00lXfn5kX4/f/ZwKtQnTjSzGRdoifNrgMQqtBs5Fogxsx9jX7dv4rCJgydSVnqWpTHAyjbXKVjpV8t2LKPF2xbT2aPOpvknzI/6c07vtxiX7VXRUEHNnc2U2ztX3NRByQPnGOMgj6U8yaAM52TxxnU3b5a6fWM2/LiEcw7nIEgLSvr5jJ/3CNKyYx44hvbfvJ9UNL1oujjQy/i4vcxZvnby8yP9fv7sKgase2Q2tx+ZSDEaXKAlDgGS5Pix3cxeJFm9mDGzH2Nft3fisIsmXGTptXAB63Mp/J/4X0xO77e6j8tuB6o4qSarVxbVttSKTNra1lop1ksGTn1mmfpwvHHdjnE/vB2jtavXxxicczgHQVpQFtehDS17UNFYQaqS+XFyzu7lUgcV9RUiy1DW9dR5GzjNz5/dDwFrmU5uwX5+vBCUidmLJKsXM2b2Y+zrck0chgtYf5tZPLO7jmYs2G+t8SJQ1drZSs0dzSLBJtr29TqA5gU/fOZ447od4354O0ZrV6+PMRi7nIMgLWgl7e406ljQ4fVqaIWzfDcc3EA7D+8UE5zJNrEZgJ8C1jKXY0Cw0BvLdi6jJVuX0JzRc2j+5OiP1IIzzF4kJXIxg31JD7iATYxu/d6t7a9bu1kZg91oCy530NbVJpJY+Jgb7X0SDaDpsB29Dhq60Y7x9ms79vvwdozWrqHlIFAbVi8I0oJW2bSd1EkqkjnwwuvDj/Z8UfmF9iUPAGQnczkGP2RQSCnA/4n/+ZrdF2bG63G9QX6cNfR1w9/L7v6OfQn8yK/93o161ToLHYM5UOV0W/A10cDeA2lqwVRbg3U6bEcZbkzp2I7x2lWHzww9IUgL2uGgLQdvVSJz4EX2ic10CISrRra2dGJ9ZPuMKpRjkCGDwo9mlsyUZkINL9l9kWK8XlpqGnV0dfR4XUz0oy9+fLmgX4FYgrtBSb8eQ6yMJ9yWBxoP0KgBo3zXbpG40YcGZA2gvD55Ymknv/Z/u+nYjvFu5Oj4mf0OQVrQLptWRTIHXtjIASNpb91esVSVrIFwN4OBdr2X220Zb72dWB9Z+4vM5RhkyKDwGx0ej7TrM9l9kWK8TmgmrVPvFQ77knd9r6q5io7JPoZKB5Z6tBb64/37zbI3aXX56h6Pi/u13/M4woHWAw0HRB9MNPtyW/U2mlY0zZdtF86NPlTTXEPVTdViaSev+78M5xN2rIPX7ejFjRwdP7PfIUgLWshIyaC2QJuy2bQyB15YXWsddXZ1iqVXrAYYZQ2EuxkM5JrC7+18j84oPsNSbWE32jJ0e8drIyfWR9b+ouqJN7gb7FC5/yWbVWb3RUqs10vmvbAfyo/73vs736fDrYdp0jGTaPrw6V6vkpZ4LOAxq6qpSrS5jOOWm8TnDxAt3rZYfD3/BPO1xXXIoFNtbOSatLVttWKpExkemZdhHWTkxH6u2n7nNwjSghZa72xVPptWZjmZOdQrtZdYqhrMlDUQ7nYwMIWs7ydutGXo9o7XRnauT2hweNbIWSSjHjcs+gyJ+/M46dWXCsGORPufDkGHSLAfyo/73KDsQVTZUElVjVVer462eBzgm0pGgADEyVnw/CzBUzQdMuhUGxu5/FtuRq5Y6kSGY28y6+CHYKMTE4+qtt/5DYK0oI3SvFIqqy5TNptWZjJk0uqU2ehV8Hh60XRRZ0+2NoyUJR26vd1sIxnLHIS3T491NBGkleHE2y8n0p6dvOeO8Hz72tX/VA86JFNvE/uGHHh7jc8f3+NcEpxra5X380is7Mczi2fS4Gx/1haX5RzF72TYJ5NZh/Bgo9+Pp2aDr9jv5IYgLWhjyw+3HJVN+50XvkP/vPifnq2TLsrryunj8o89vWssayasSmRtw0iBUa/WVcabAaHtwzhrkk+8zK6jDCfeDHftnWlT2esRytL/vO7nsdoB+4Y8MHEYJAv7sT+OEVzmYGvNVvrtyt/S9sPbu68z/R4c9ArXjecJPnnJ/L4fmg2+ernfYV+JD0Fa0Mqf5v6Jrl10bffXz65/FkFaGyzbuYw2V22mZZnL6PLJl9vxkgBJBUadnmhNxkB2aPvwZ+eTmykFU4Lr2d5OqsBde7SpmRN01U/ek+nnkX5H9XZQFU8Ytqt2FyYOk4gq+4KVY5yfA0uqbF8DJ6y0draKvy/asoj8vg293n48sWdHV4dY2nmu6fXn0vmmh1/3lUQgSAta+cHUH/QI0rKBvx5Ih2495Nk66YCDQztrdnqaXeh0cM7p1wd7AqMyliPwon1kyvQ1e0Krwomjaoz25HYP/Vpm3E8e/uhh2lq9lS6acJGoTanTyXsy/TzS76jeDqriUgebqjaJNsfEYXJQZV+wcoyzElhSNZhk1/b14vNnp2VTc0cznV58Ovn9RrQb+2esbRze7nada9r1udzon6qNAX7dVxKBIC1oh+vQhpY9qG6t9nR9dMATKKX3SqcZI2Z4tg5OB+d0Df7xpELv73pfbLupw6aS6mQsR+AmGTN9VbuQ1o1q7c7rywFaUeM8Qtl4nLyjHbzEE4aV15dj4jCJ+GFMsBJYUu0YEGv7JhNscvvzc7mD3um9aXjucPFUE6+zsf1UbH8V9s9Y29ipdrfrc7nRP1UbA/y6ryQCQVrwhdS7U6lrQZfXq6EsP0wc5lXwz+kMXg7Qrti1QvxdhyCtzEFKv/PDhbTf292ObA3+Xc6g5QDtzJKZR/07Tt7RDgAYExIbU0OXqgkd8xeVLUo42OT25+dyB/3S+1EKpdD2mu3imKhDwCnZ47sbx+zwurNusOtzudE/VR8D4GgI0vqQainxdmTTBiKl64BpmDjMOU5n8BrZz15mQYNz9taW09aKdVQyIJVGDBrtaVMjuKZ/u9uRrcG/F17iAJI/d/PDOR34G/q4tWOASu2XTLDJi3OPLuqijkCHqF/d3N6sVBurmI0ZXnfWTk5vOzf6Z+h76NAXAUFaX5J5EHYSB205eAuJ88PEYW6UO4iUNet0Bi9nz+qQQQuRbaneTJ/vX0vNhUM9D9KC/vyUreHmhY6Z94p27ubXczqn5PfJp4J+BWJpFS6W7WkX9HFrVGo/FW72crmDtq42SutMo8a2Rlp3YB1lpWcp08bR9r9kj+9ujHNOnnuotH/48fP4FTJpfcgvF1nh2bSg9sRhTnOj3EGkQDAe3wcrxuSNpdS2dioZNEHKhkSQQi8qXECreKFj5r2inbv55ZzOLaUDS0V2HC+twsWyPe2CPm4N2s/+cgcDew+kE4eeSKMGjqLZI2dTYU6h5XHYrfOlaPtfssd3N8Y5J889dNs/dPs8foUgrQ/56SIrq1cWNXc298ym/Vmbp+ukIhkmDtOB3ye9AvsV5g6jwt75RJKO6QhSgKrcvNAx817Rzt38dE7nhrLqMtpUtUm06fTh0y29Fi6W7WkX3fu4Do9b+1FRbhEtOGNB99dW29it8yW7xyXVxznd9g/dPo9fIUgLWmu6vQnZtJpMHKZDuQNkzYLfqH7yrgJkK3t3ocNtz+WAjEnQrNTpxUWVHKoaq6i8vlwsrcJ29Xe7mB2bcTNTLVzuoLatVixVPF+ye/+z8/WMfYYnCOP6s8a+g/OcxNsQNWnVhiAtaI8HKR6sDOm/zKD2yzZ7uk4q4TqqWw5tEXWXcjJzSFfIcrVWWxc82gY5o4KZtJLyy8W4l3CB790FD39/ydYlYnLSwX0Ho68DQMJjM25mqlfuIDcjVyzthPOlI/tMWmqamCjMaJfwfUn3QKSVz+fGOaHu7S8DBGlBe+uuW3dUNu0PXvsB/fniJzxbJ5VwIOjLyi/FRajOmbTIcpUr6xjMbYOUjg4qLJA3SAvO4hPlA40HaNSAUVJkK+t44h7rgoc/55zRc0QmrQztDwDyMBt8RXAOoOc+E5pJG2lf0v3mtJXP58ZNH93bXwYI0vqQjhdR8Xxw5Qd06hOndn+9lB9PBFM4W3J47nDaXLWZWjpa0Go+FJ45ayXrGFm49jDafkzOKJteEVTEx/Jt1dtoWtE0KY7nOp64x7rg4c84f/J828+x/HieJov8PvlU0K9ALAGscCv4ivFCj3IHYL72uu7Z51Y+nxvjju7tLwMEaX1Ix4uoeCJN/jDxkYm07scoexAPB+V40rCalhoxmcY5Y85xaCuBrAHO8MxZK1nHkbJwEbiNLVL7dG+D9nai1lZSAS4k9T9Rlm197JDoBY8d51h+PE+TRenAUtpVu0ssAVQ4xmG8cHcbOFXuAMxD9rm30P7OQ5DWh3S8iDIjsCDQo+xBXVuDp+ujEr5bvK1mGy5afFpmwM56vZFeC+UTYtOlfXAhqf+Jsizr4+UNATvOsSK9Bm5yuKOsukzckOZ+E+kGP1ijcz/24hgnW8kbr+E8A3QYE9GPAUFaH5LlIkoGBb8bRvt+Uu71akiPA0T1rfXdgSLw1+RmdtbrjfRamLQtNl3ax683CMF9Xl7g2HGOFek1cNHmjqrGKiqvLxdLsJ/O/diLY5xsJW/8sA3MljvQ+YaEW/zQhpHGRJwvA4K04Lts2vQ7e04iBvH1y+wnZtnkJXhH18nNdP1cdtGlfVS5QeiHiwLd21fHCxwdPxP4j8792ItjHLcjZ9IeaDggxla/H7Pc2AZmyx3ofEPCLX5ow0hjoirny+AcBGnB95BNG9/++v09ltGgtig4Af0qVtuU09aKdVQyIJVGDBqNDmgDP1wU6N6+Ol7g6PiZZISJw5yFfmx/ew7OHizG1MF9B2OMcMGArAGU1ydPLGPJzcyltNQ0sYTkuNGGXt+Yx5gIkaRG/C6AxtrvaPN6FZRzevHp4mSEl7Gs3LOSXtjwgliCM8HKpduXiqUfa7LyEnraUr2ZPtu/ljYc3ICmsQmfqPOjozpmeskA7Qsy4wnDxuePRw1+UIbqYyoHyRaVLRJLFdQ011B1U7VYxlLbWiueQuSlTmJtL7u3pRttaNw45iW4R7X93m3IpAVf6pveh1q7mrq/RjZtbCcVnESNbY1iGU8KeVdOQveMS10mkPJrTVYnjMkbS6lt7VQyaILXq6KN0IkbQr8G+9oXbSpXJg8cgYnDvIV9wX9jqmpPr5itSatraY9Y28vubelGG+q6nWSn2n7vNgRpwZc++8FnNPyPcgR8VAgsVjdXi5MRXsYyvWi6eFTQq2Ca7kFMJ4KVKvQ/XWqy2ql7u+WMojOKzyAKOcGR6SJXpnVJBE4e9dmWKrQL+pt8E4eVVZWJLB/0d3epvC9gjPRHkIxr0Wb1yqKGtoaYdYBVD54ns73s3pZutKEK2yna2BLp+6qMQygHEhuCtOBbJxWcSB9Vfup5Nq0KgcW8rDxxUsJLmYNpumdcOtG+4f1PhaAtHNluKR0dVFiQ79lFbryTQVUvuFW7aHSD1W0p04WD1XUJ/X07+jj6m3wqGitsGbtk6vcqUHlfUPV45zUVgmThWjtbxc0c3uaqrbuT20vFbany2BLp+06OQ+HHMyvHN13LgdgFQVrwrYXzFlLB74t7fO/8f55Pr3znFVfXQ4XA4sgBI2lv3V6xTJSbQT+vg8QqCu9/Ktw0gCPba0zOqKOaw7i45bvUTmeCxTsZVPWCGxcaZPu2lCmAYXVdQn/fjj6O/iafodlDbanzKVO/l4FxUc/HJ744Dz8+qbwvqHq8g8Twk4VtXW3UJ70PtnUY3JRyph2jjS2Rvu/kOBR+PLNyfMN4qXiQ9v3336f777+f1qxZQ/v376eXXnqJLrjggu5/DwQCtGDBAvrrX/9Khw8fpunTp9Of/vQnKi0t9XS9QQ2cOcsZtIbV+z5xfR2MQJgxKZKMgbG61jrq7OoUy0TJEvSLFyz2awZpeGBbhZsGZrad7tuze7u1txO1tvb4N+MilwO0TgcH4p1kqXzBDfZuS5lOyK2uS+jvo4/rhUs2FfQrEBOHzS2da/n1ZOr3MjAu6nnGds6iYrocI8LrmRt/j3WjFEEt9fCThQN7D6SpBVOl7bte9SsZbkrpsE+Ft2O084xI33fynCS8RIGV4xvOnRQP0jY2NtKkSZPoqquuogsvvPCof//tb39Lv//97+lvf/sblZSU0B133EFz5syhDRs2UO/evT1ZZ1Db6P8ZTVt/tNXV95QlkBlNTmYO9UrtJZaJkiXoF6+NZd8GTgkPZqqYjRxp2/l1e4ZnKjk94zNOskDFvmJ1XWT6LLpdnHqNg7O7aneJpR1k7SteCX3Sw8ik1UlocIXFC1jJENQC/SzbsYwWb1tMZ486m+afMN+193XrplSsY51q+1SkzyLDzb1I6xVeogDHNx8Hab/5zW+KP5FwFu3DDz9Mt99+O51//vnie0899RQNGTKEXn75Zbr00ktdXlvQIZu2qaPZ9XWQJZDpRCatLOK1sZVAtMp0CGZG2ray71NOMk5QOUBrRyYYAKhBtYtTGZVVl9Gmqk2i/aYPn+716mirMKeQpvfXr31jPX5s9ucTgRsz3pQ7qG2rFUtppfB/4n+ucitoF+tYJ0OA0+pnMduOTu7/kdZLtbZVmfRB2lh27NhBFRUVNHv27O7v5ebm0te+9jVatWoVgrSQNLcnEZM9e9FKAFOWIGC8Nt5es522VW8TFw5Th00lv+AgZlVTlfjDWbXM7TIBiZQmiPSzkbat1X1K5XIJOIkCcG/yC5lg37euqrFKTAjES1kmp1O5T/rtRkJ4cCXeZ7Qa1NK9PWUtd5CbkSuWsppZPJMGZw/WNpgW61inWnanleO2k/t/pPVSrW1VpnSQlgO0jDNnQ/HXxr9F0traKv4Y6urUzQ4EZ7Jpoae/f/53enfnu+KiIdEApkoZjQEKkN9wADL/UL4IpHMtPuZ2UD2RQL7Zn7UaZJXl5kIycBIFXlElsGTn5BdOfeZkXhf7vjzsnJxO5n0pUbiRgPbURW1LreOTsyZL92OBzJ8v0WO3lc/i5Hgqcxv7gdJB2mTdd999dPfdd3u9GiA5t7NpZfbZ/s+orqVOLHXLEjaMHDCS9tbtFUu/iVUuwKv3t/qzVoOsKt1cAP8GG2WjSmAp/MLGjUyWRPuUKm2p68Rhxk1LGSan0wku/O095qA9vSt3sKV6Cw3YM6B7OwBEOnY7eT6J/V9fSgdphw4NPmZQWVlJxxxzTPf3+evJkydH/b3bbruNbrrpph6ZtEVFRQ6vLcgO2bTRXXzsxfTKplfo/HHB2s+yipU9GS+z0q/lDiIF0p0MqpstV2B2XZ0KsqpycwGcgQAZ2RZYkjHgHemR5GTXLXy2Y7v6lK5BOr9MHKbr5HQQW+h4x8yOfU4ec2Qcg1Vav2jlDsbkjXF8clZQr1+GH7txPgm+C9KWlJSIQO3SpUu7g7IccP3444/p2muvjfp7mZmZ4o9frdy9kt7Z/g7NHjkbkyKE6ZueTQ3tja5n08pe//JHp/xI/JF9fWNlT5rJrPRjuQO3rdyzkt7b+R6dUXwGzcud59j7IMiq3oWPTBAgsy+wpPsFSvhsx3b1KRkmDvEjTBwGVoSOd8zs2OfkMUf2MVj29YukrbONDjYdxLjrEOO4xjc/+djqxfEt2X4ZfuzG+SRoGaRtaGigrVu39pgsbO3atZSXl0fDhw+nH//4x3TPPfdQaWmpCNrecccdVFBQQBdccIGn6y0zDtDyH4aZa3va8l9bjqpNywFJpwORqtW/lHV9Y2VPxsusnF40XTzeiMfbnSdmnHWYrDcSdLvw0TVAhCw2+3hxgWJHvzT7GvE+X+jrzC2dS3ZTMcDhh4nDdB4fIXZQKTy70szY5+QxR/YgkezrF6ncQXVLtTjH5G1udbv5fZyI9PmN4xo/pcI3QZnbbWNXv9TxfNLvfdYN0gdpP/nkE5o5c2b310aZgiuuuIKefPJJuuWWW6ixsZG+//3v0+HDh+nUU0+lxYsXU+/evT1ca7lxBm3oEno6rehUWrHng+6vT/6/rzmeTZuTmUO9UnuJpYze2PKGKI7PF5jnjDlH2nqdsbIn42VWIvPSHW4Fw2W9kaDbhQ8CRCDjBYod/dLsa8T7fE7vI6oFOFTQ0tFCG6s2igtRK9ssPKsSF7X6MrY1B2hDb8aY7T9+DnqoFsTicgd5vfPEeawd467fz6MifX6jXUMzad2mWr9MlJUxx2qfxVPdGgRpzzjjDAoEoj+CnJKSQr/4xS/EHzCHs2eRQRvdc99+7qhs2plPzqRl85c51sXqWuuos6tTLGXEAdqP934s/s5BWr8FNJGVaR+3+o6sNxJ0O8FEgMh7fr64d7Jf2tW3nd5HdL+Q9EJTW1P3fmWlbUO3vd8DMbqzup8n2z+4ny7buYzrddHMkpkRf1fmvhd+/ErmeObFMTCjVwaNzhtty/upfB5lR9tH+vw4rjnPyrhgtc/iqW4NgrQAMkwitvnQFl8HlMYMHEMbD24USz9CVqZ6/HYjwSs4kfaemxfgqgSE7eiXdvVt7CNq4Sc9SgaU0OgBox15zNXsa6qyryVDx89mdT9PNujB7bhk6xIxp8LgvoMjroPMQcDw41cyxzO3g9Bc7qC2rVYs7aDyMcKOtlf586s2vob+m5fjAp7qjg9BWgCTSh4qoR037vBlQInrAXEpBqMukN/IHkQHAP9y80Q73gWZqsEXVdcb7Fc6sJROKjhJXETa2RcSDUTInP1olc6fLVnJBqp4zJozeo7IpI12DJA5CBZ+/ErmeOZ2sInLHeRm5Iql349lMt8A8KtY42vov3FZFqfLQUWDp7rjQ5AWwGQ2bWtXm2/balz+ONpes10s/Uj2ILqOUGICwBw3L8DjXZCpGnyx8qixUxfECBx7o6y6jDZVbRLb08vSYDoHP3T+bG7jfjp/8nzS5fiVzPEs9HfcGDcHZA2gvD55Yikbt4/BMt8AcIrsx+ZY46vdZZy4ZjCXRJS1LVSGIC1AAjho68QkYrIHpHqn9aYRuSPEUnXR2jrWNkhm+8i+TWWHEhMA8ol3QaZq8MXKo8Z2XBDHmt063mvLfsGomqrGKiqvLxdLL+kc/Ajt46Ffm6mvqiLso+61jxtByprmGqpuqhZL2ah6DFaJG33Myj4R69hhdxknDtCqeGNeBQjSAiSQTevXgJROj/tHa+tY2yCZ7SP7NpVJpIC2Tn0OwC8X/qoGlqw8ahy6dGJ263ivrWr2MvhbpH5rpr6qirCPmm8f4+tkj2tuBCntqEnr1PHb7WOwjOchTnOjj6kyZuCmgHMQpAWII5VSqItv6zuYTYuAlHuitXWsbZDM9sE2NS9SQBslJsBLKlx4qHISrzO7LoitzG6NiyRQUaR+a6a+qoqwj5pvH6vHNTeClHbUpNXl+K3L55Ctj6kyZqh6Y14FCNICxLH3J3sdz6aVPSAVKytUtcf6o7V1rG2QzPaRfZsmwultjIB28ttlS+V6GptdTMOoxOat4m8qXHiochKvM7uC+VYudHCRZK/8PvlU0K9ALME5kfqt6vVVo8E+mnj76H5c41qeaalpYqkynIc4A2MGIEgLYMKAzP5U03rY8dq0sooVRMNj/foHYZ3exjoFtN3E2+WzfZ9San47DaNTlcoClZ0KFx5enMSjb6kXzIfElA4spV21u8QSnIFxBFQNTnHfXb1vNVU3V1sqd1DbWksdXR1iqTLZtxeAqhCkBTBh/Q3rj8qmfWPLG3TOmHN80X77G/bT2oq1lJOZc1QwDVmQ6osXhMU2lhNvl5SODirNLu7xfQSOrMOFR2RW+paOgZlEgvlOfX4d29VLZdVltKlqk2jL6cOne706WsIxClTuuw3tDZTeK91SuQMvbwSv3L2S3tn+Ds0eORtjnI/h3EFuCNICmHTB2PPp5c2vdH99zWvf90027fu73qcVu1aIv08dNrXHvyELUn3xgrDYxmpRIQsU/Ne3VAvMmLmASSSY79TnV61dZVfVWEXl9eViCc7AMQpU7rtFOUWWxwcvbwRzgJb/MNyI8m9wE+cOckOQFsCkP577xx5BWnbq46fSB1d9oH0bzhgxo8dS1Xq0QEkFYbGd1Sp3gCxQ63Q5Cbeblb7ldGDG7m1m9wWMU1m3CHiB7uOI6uNxvPW3+vlUbx+VcPv2Te9LzZ3NlsodeIkzaEOX4M/gJs4d5IYgLUACOHM2tOzB9podvm6/8MfkEcxTk9c1aSG57cXlR04sOPGocgdgnS4n4TJx+uaBl0FVM5zKusVNGXth4jD5qD4ex1t/q59P9fZRDZc5yM3ItVTuwEucPWslg9bumwKyv54MwU0nPhPOHeSGIC2ARcUPFtPOm3Zq3Y7h5Q5Cg0Q8yFc1VXV/D8E89aAmrZrba0rBFJpVciZRa2vMn0eWjd4ZBti+3gdV/dz/dIOJw+Sj+v4Qb/2tfD4e/w80HqBRA0Yp2z5+pPJx2+6bArK/ntPnBmb6wrIdy2jxtsV09qizaf4J8217b5AXgrQAFrNp2wLtvit3EBok4qwT/jsvMcGUmnSsSatzVnei+xmybPTOMFi2cxkt2bqE5oyeQ/Mn+/fk3altlujFdPjPJ3MxrlL/0wlvqxc2vEBbq7faMnFY6LZnqgZlvKbK/hBtX3dy/fn9jHNwcAeXOahtqz2q3EEiY33oeZmdY4MbwV+7b5ok83qxPqdqN3VMnaOn8H/if+ATCNIC2ICDtjpPIsbZs6EThhnBIc6k3V6zXRxUjGCYUwExnYNuXlMxCBuPTlndoX2f9dgP2uPfJFLthBUSwxOY7Kvfh4mOPLqACr9YDP95MxdgmG1bDrytvqj8giobKm3Zn8IDMXgkXW/J3hDl33uz7E1aXb5a3GhL5Hd53Hl729v0yb5PxO/5+Uadl+UO+Djw5NonxZOFLJFa4nbeSHfjprzdNx2SeT2dkg/MnKPPLJ5Jg7MHWz6PVzmD228QpAWwIZvWr0G9pduXigGfM2qdDoSpFnSTMags4zo5Raes7tC+zxLdD1TJQoLk5GfnU0G/ArEE9y+gwi8Ww3/ezAWYmdm2cXHlPN5Gxf2LqaG1wbbXC12G/x30Er69ze6z/O8coOUAH/98Isdr/tlJQybR/ob9RAEbPgQkhbcbbz/OaDazj0c6L7NjbND1pnz4vhTrc6oWwDVzjh7tZxI9L1CtbfwMQVqAJPWiVOqkLt9k04YKrUnLAVojEOZkENBq0C3eutm97jIGlWVcJ6folB0cqe8nsh8guKM3uzIsILkLqPCLxfCfN3MBZma2bVxcOY+30/j88VRWXUY1zTW0qGyRpYyjSH0B9BW+vc3us0YGbGhpjETMLJlJg/viGOBluYPQ40Ci+7mdN9J1vSkfvi/F+py6BqrtOC/wU9uoDkFagCTt+cke32bT9pi4aOSso77P7A6QWQ26xVs3u9ddlkzO0OAzB9V7pfYSS1BHaN/n7cnZGvxnOk2nwj5D4v4+gjsAzrHjotjMbNu4uHIHZ8JxZnpTexMyjhyQzE1DVW80GvtsbmZu3IC/lXFE18CcSuUOsA2c3VcTOf75aVskel7gp7ZRHYK0ABYM7jOIDjQd9F02bbQApCyByUhC6+hymYbQjFkj8GXU1rUjy1aWTM7wR+U7uzqprrXO03UCa9tz+c7lFKCACCaYCdIiuKM3mYPwbgZXVA3kmIWLK3dwViJPzlJWVSbKHnCADewfrw40Huh+AiCRSZZU2reNfZYDtPHWX/fxKxZVP3ttS23M4Luqn8sKu/fV0DacWzrXhjXU/7zArn7nx/4rEwRpASxYe+3ao7Jp/+ej/6EfnfIjrdo1PEgZLQApS2AyXh3d8IxZ/mw8WUheVl7E300my1aW+q9mHpWXZV0hPt5Gpxef3v13MydWCO7oTeYgvJvBFTffS9aLF1nXSyVGu32w5wPK7Z1Lta21Xq+SVoxx6kDDAdP7q8xjnJl90Mz6qxqItoNqn53LHBxoOkBvbn2T6tvrad7EeRHXe9nOZbRk6xKaM3qObyZ0s3tfVa1vyMCuNkPbewtBWgCLfnDSf9KfP3m0++vfrPytdkFanWqZRgtarq1YS9XN1eKzhn/GZDKEZWyzaEF0GdfVa7IGrnld5uXO6/56b9VO2lqxjkoGpNKIQaPF93Bi5S8yB+EjXbDJ8DikVV7sY9HaLfT72PftUdVYRc0dzTS6z2glAoMqjlfcb83WUZV5jAsXaR80s/4qBaLtptpn5zIHgUCAmjqaqKOrI/p6B/g/8T/fsHtfVa1vyMCuNkPbewtBWgCL7jz9zh5BWnbSoyfRJ//5iTZtK0MZA7uCZpEClSLwdey87tc38zsqtJnZAKws6ypTQFWVwPWW6s30+f611Fw4tDtIixMrkPmCzalAopuBHC/2sWjtFvp97Pv26Z3WW0wipkpwUDUqBV4Tkew+qGt76PrZ+6b3pcKcQrpu6nVR1z3WhG546kHfvqFLm6HtvYUgrQ/hwGA/rkMbWvZgX8N+0oUsGYVOB83sLtUgS+kHMwFYWdZVhr5h9HeuXcwT48keuB6TN5ZS29qpZNCE7u/hxMo6HCedo0Mg0Yt9LFq7hc8qjgta+yYP4yV4kxWuaj9Odh908rPr0K4yfT4ud9BBHWKM4HIo/P6R3jdWX8BTDwAQC4K0PoQDgztG/G4E7frJLlKdLBmFqmd7ekW2AGwyQf/Q32Hhv29n3zD6OwdoZ42cRbK3WWHuMCrsnU8kwYWXTheCfqwl5xZVAolGf+aJo/hC3Ot+Ha3dVGlPlZQOLKVdtbvEErzJCvdbn072s5s57urerm5/Pi53kJsRnFAw2ffV4WYlADgHQVofwoHBnWzaduogHcgSHHU62ChLxrDuIgX947V96O+w8N+3s2/I0t9lvFFi9UJJqSCuIrXklGpTFz+THa9h9Oe01DRRd5CZeS23t4mOfcBrZdVltKlqk2jP6cOnW3otbJ/kssL9JtnPbiZAqXu7uv35BmQNoLw+eTQ6bzRNK5qW1Pvi5hrYScXzDhwbY0OQ1odwYHAPB205eKsyIyjEgaLQr3ULbKoSCJOBle0eKQgaL3AbbbI3P2Qeyxo4TuZCSaVsnli15GTCbfpm2Zu0uny1yPj1ul3NnnTH+jk7+okdr2Fs+9BMWjOfxY73TuTiRaX9SqWJw8rry8XSCt6OT659kqqagq+D7ROErHD7rs3MBCh1v+5z+/PVNNdQdVO1+Pvc0rlJvQYCVPZBW/Y8D2BOl0450HCAttVs8/w8TWcI0gI4mE2ri2gBTJ0Cm7ECYToFo+1g93aPF7jlsgOh7+O3bSBj4NjshVLoybNK2TyqXNRyAHFP7R7aeHAjjcgdQfNPmO/pxZDZk+5Y5ST4M3H2Ki+TZUdfM9sHwj+zHe+dyMWLSvuV3/B25AAt17bF9ukJgRV7mBmnZG9r2dcvUk3a2rZasUyWrgEqt7Zl6PvI1pZe9OfQ8wAn28N47VF5o5LOIjfg3CU2BGkBbJZGvaiDOrXKpo0WwFQlw89qIEynYLQdrGz3SG0Zqe296lsIyNsr9GSRM05kOIHWCWd4dgW6qK2rjSjF+feLd/Jv+qQ7RjkJ/kxcXoCXKgTZwz+zHe+dyMWLKjcU/Ch8Ujc4QrbAis5kb2s718+NAJlRk5aXydI1QOVWXwt9H6faMtm+5MX+Fuk8wIm+hWOaexCkBbDZ7p/s1jKbVuUMP6vBOZ2C0V5vd7Nt6VXfQkDeXrpeiMiC2/XCCReKYOfM4pmuvF/oMtmAYaxyEqr1GTuCpOEXgwi8eoszX3nmdl5age0Yvb8faDxAowaMOmo/Vy2rUgWyj6lW10/mrEq/jQ1297Vo40F4sNCJtky2L3m9v7nZt6yM16rsq15BkBbAAQV9j6F9Dfu1yaZduWclLd+5nE4vPp3m5c4jHcUKzukUjPZapLaUKXsVAXl7T9Z0vRCRBbdteLkAp9/Pju0Z63Vk6TNuBousXKwgqGW/0oGltKt2l1iCM/19W/U28bisEzWp/SbeGCDLmBqN1fVzI6vS7nIHuo7ldve1aOOBne9jJhCciPB102G7RtseyY7XK3evpLe3vU3D+g2T9uaR1xCkBXDAJ//5yVHZtP/91n/T/Wfdr2x7i0dTw8gUXFMhOKdTe+mavapKQL67L+WMosLe1rK9rFAxgwW8JcMFi5l1iNWf7f4MVgIL2O/sV1ZdRpuqNoltO334dAfewd9i9Xevs9BUpPoYYHU8dSOr0u5yB5E+s+rb0QmhE3guKlvkyHmD04HgZLarDOdJkYSPz8mO1+9sf4c+2fcJ9R/ZX6rPJxMEaQEc8tPpt9BvVv62++unv3wmapBW9uDd9KLp4rG/8ACmTME1FYJzOrWXnZC9mnxfSunooMIC74K0iWSwyHrSqTJuU56ES5Q7KJmpRLvKcCEavg6R+mas/mz3RZeVi0EEtexX1VhF5fXlYukVncdLFTLp7dyGHGDiGttObUvVxwCrxwQV+0ykz6z6dnSCsW05QOvUeYPT7Z7M68twnmRmX0t235s9cnaPJRwNQVoAh/zolB/1CNKy4/54HH153ZfKBe+iBTB1Cq65ESjXqb38mL0qE6MP5fTqQ+/tfI9KBqTSiEGjXb+4TySDRdaTTpVxmy7ZukQ86cA1Xr1uVzN9T4YL0fB1iNQ3Y/VnmS66VAxQQHy6j5e6BqEjPV2SlpomJkNkTnxW1ccAGY4Jbpc7iPSZvd6OMu+TTvYRp9s9mdd3Y5/wcnvzEyp4SiU2BGkBHMR1aEPLHhxqrlYyeBcewAz9etbIWaQDNwLlstdjlXF9IHZfenfLEvp8/1pqLhzaHaQ1Lgx5YpbB2YMdPQGLd/IZehKo2oWYCrgt54yeIzJpZWjXSIGlSNlkc0vnkkwS7ZtGv+fPZvYRzETfQ+YLZnCe7uOlrkHoSE+XhI59cDSvg5NelDuQ8TPLvE/K2F6qf16ZtzcgSAvguqLfFdGen+xRKpOQJw7jbL0zis8QE4fJnvmbDLsD5WaDnV63Zfh6mlkfBHK9Z2yD3MwcOuGYyVQyaEL3vxkXggcaDnh+AhZ6EsiBOZwIqj1xWDKBJbeyyRIJcIZfnCR7QZTIRU6i74ELKO9weaeCfgVi6RXdgxK6BqEjfa7CnEKa3t+72sa44QOq7ZPos/7a3nA0ZNICuJxN20ldSrZ5CqUok/krA7PB10Ta0ongaPh6RlqfZAK5dkJQOPp2O2nwJDqz+AyiCDWi+CSXH4H38nEpnAT6S6TAkhfZZPECnOH9MtkLQv55zljfcGCDuCliZ11g7DveKR1YSrtqd4klOEPXIHTo53KyjmYiVLrho0Jwzo5yBzKSaZ9Uqc+quh/ItL3haAjSAniAg7YcvFVFtInDdGJ34DEnM4d6pfYSS7s4ERwNX89IWd1mArlOBmFD39/4OvRnI/2+1cCu7IFho+3H5IyS+kRaxZNAFS4SVWoXL/pAvABn+Dole0HIP8slRZyoC6zivqOLsuoy2lS1SbR/snXzrI4juo5Dun6uZG+0uNEeKt3wUSE4Z0e5A7/vG3b2WR3bTYX9AJyFIC2AB9m0qgWErAQLVflsdgce61rrqLOrUyztaksnAr/h6xlpe4W3jd3lOeK1Qej7R/pZs9+zc5281r0N2tuJWls9O8lT6eLPLJwcq98uiQY4rfRjzhDmi/Vh/YYlvR9YucgM/V2m28WqF8qqyujLyi9pZP+Rnu0vKu1vdn4uVQM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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 14
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-23T05:08:18.435795Z",
+     "start_time": "2026-05-23T05:08:18.368667Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Isolate just the blob points — TEL positions far from the phone GPS median\n",
+    "FAR_THRESHOLD_M = 100  # metres — adjust based on what you see\n",
+    "\n",
+    "blob_mask = pos_error_m > FAR_THRESHOLD_M\n",
+    "\n",
+    "print(f\"Blob samples: {blob_mask.sum()} ({blob_mask.mean()*100:.1f}% of session)\")\n",
+    "print(f\"Blob time range: {latitude_df[blob_mask].index.min()} to {latitude_df[blob_mask].index.max()}\")\n",
+    "\n",
+    "# What was the sat count and SNR during blob periods?\n",
+    "# Need to align blob_mask onto the sat_count time axis\n",
+    "blob_times = latitude_df[blob_mask].index\n",
+    "\n",
+    "print(f\"\\nBlob position stats:\")\n",
+    "print(f\"  Lat range: {latitude_df[blob_mask]['GPSLatitude'].min():.6f} to {latitude_df[blob_mask]['GPSLatitude'].max():.6f}\")\n",
+    "print(f\"  Lon range: {longitude_df[blob_mask]['GPSLongitude'].min():.6f} to {longitude_df[blob_mask]['GPSLongitude'].max():.6f}\")\n",
+    "print(f\"  Phone GPS median lat: {TRUE_LAT:.6f}\")\n",
+    "print(f\"  Phone GPS median lon: {TRUE_LON:.6f}\")"
+   ],
+   "id": "7ff9b92a19ded906",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Blob samples: 29784 (100.0% of session)\n",
+      "Blob time range: 0 to 29783\n",
+      "\n",
+      "Blob position stats:\n",
+      "  Lat range: 0.000000 to 49.199333\n",
+      "  Lon range: 0.000000 to 123.146118\n",
+      "  Phone GPS median lat: 49.198747\n",
+      "  Phone GPS median lon: -123.145300\n"
+     ]
+    }
+   ],
+   "execution_count": 16
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-23T05:39:38.036108Z",
+     "start_time": "2026-05-23T05:39:37.627765Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# The blob is clearly west of the main driving area\n",
+    "# From the map, the blob positions look like they're at a different longitude\n",
+    "# Find the longitude boundary that separates blob from main area\n",
+    "\n",
+    "# Plot a longitude histogram to find the natural split\n",
+    "plt.figure(figsize=(10, 4))\n",
+    "plt.hist(longitude_df['GPSLongitude'], bins=200, color='steelblue')\n",
+    "plt.xlabel(\"Longitude\")\n",
+    "plt.ylabel(\"Count\")\n",
+    "plt.title(\"Longitude distribution — look for two clusters\")\n",
+    "plt.show()\n",
+    "\n",
+    "# The blob will show as a separate cluster to the left (more negative longitude)\n",
+    "# Pick the boundary from the histogram, then:\n",
+    "\n",
+    "BLOB_LON_THRESHOLD = -123.148  # adjust after seeing histogram\n",
+    "\n",
+    "blob_mask = longitude_df['GPSLongitude'] < BLOB_LON_THRESHOLD\n",
+    "\n",
+    "print(f\"Blob samples: {blob_mask.sum()}\")\n",
+    "print(f\"Blob start:   {longitude_df[blob_mask].index.min()}\")\n",
+    "print(f\"Blob end:     {longitude_df[blob_mask].index.max()}\")\n",
+    "print(f\"Duration:     {(longitude_df[blob_mask].index.max() - longitude_df[blob_mask].index.min()).total_seconds():.0f}s\")\n",
+    "\n",
+    "# Plot blob vs non-blob on timeline\n",
+    "fig, axes = plt.subplots(3, 1, figsize=(14, 8), sharex=True)\n",
+    "gps_latlon_df = longitude_df\n",
+    "axes[0].scatter(gps_latlon_df[~blob_mask].index,\n",
+    "                gps_latlon_df[~blob_mask]['GPSLongitude'],\n",
+    "                s=1, color='steelblue', label='Normal')\n",
+    "axes[0].scatter(gps_latlon_df[blob_mask].index,\n",
+    "                gps_latlon_df[blob_mask]['GPSLongitude'],\n",
+    "                s=1, color='red', label='Blob')\n",
+    "axes[0].set_ylabel(\"Longitude\")\n",
+    "axes[0].legend()\n",
+    "\n",
+    "axes[1].scatter(dt_axis, sat_count, s=1, color='green')\n",
+    "axes[1].set_ylabel(\"Satellite count\")\n",
+    "\n",
+    "axes[2].scatter(dt_axis, gps_snr, s=1, color='orange')\n",
+    "axes[2].set_ylabel(\"SNR\")\n",
+    "\n",
+    "fig.autofmt_xdate()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ],
+   "id": "7673ab5c36b65653",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Blob samples: 0\n",
+      "Blob start:   nan\n",
+      "Blob end:     nan\n"
+     ]
+    },
+    {
+     "ename": "AttributeError",
+     "evalue": "'float' object has no attribute 'total_seconds'",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mAttributeError\u001B[39m                            Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[17]\u001B[39m\u001B[32m, line 23\u001B[39m\n\u001B[32m     21\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mBlob start:   \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mlongitude_df[blob_mask].index.min()\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m\"\u001B[39m)\n\u001B[32m     22\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mBlob end:     \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mlongitude_df[blob_mask].index.max()\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m\"\u001B[39m)\n\u001B[32m---> \u001B[39m\u001B[32m23\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mDuration:     \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[43m(\u001B[49m\u001B[43mlongitude_df\u001B[49m\u001B[43m[\u001B[49m\u001B[43mblob_mask\u001B[49m\u001B[43m]\u001B[49m\u001B[43m.\u001B[49m\u001B[43mindex\u001B[49m\u001B[43m.\u001B[49m\u001B[43mmax\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;250;43m \u001B[39;49m\u001B[43m-\u001B[49m\u001B[38;5;250;43m \u001B[39;49m\u001B[43mlongitude_df\u001B[49m\u001B[43m[\u001B[49m\u001B[43mblob_mask\u001B[49m\u001B[43m]\u001B[49m\u001B[43m.\u001B[49m\u001B[43mindex\u001B[49m\u001B[43m.\u001B[49m\u001B[43mmin\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[43mtotal_seconds\u001B[49m()\u001B[38;5;132;01m:\u001B[39;00m\u001B[33m.0f\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[33ms\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m     25\u001B[39m \u001B[38;5;66;03m# Plot blob vs non-blob on timeline\u001B[39;00m\n\u001B[32m     26\u001B[39m fig, axes = plt.subplots(\u001B[32m3\u001B[39m, \u001B[32m1\u001B[39m, figsize=(\u001B[32m14\u001B[39m, \u001B[32m8\u001B[39m), sharex=\u001B[38;5;28;01mTrue\u001B[39;00m)\n",
+      "\u001B[31mAttributeError\u001B[39m: 'float' object has no attribute 'total_seconds'"
+     ]
+    }
+   ],
+   "execution_count": 17
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-23T05:41:33.214150Z",
+     "start_time": "2026-05-23T05:41:33.115482Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from scipy.interpolate import interp1d\n",
+    "import numpy as np\n",
+    "\n",
+    "# Interpolate phone GPS onto TEL timestamps\n",
+    "phone_ns  = phone_gps_df.index.astype(np.int64)\n",
+    "tel_ns    = gps_latlon_df.index.astype(np.int64)\n",
+    "\n",
+    "f_lat = interp1d(phone_ns, phone_gps_df['GPSLatitude'],\n",
+    "                  bounds_error=False, fill_value=np.nan)\n",
+    "f_lon = interp1d(phone_ns, phone_gps_df['GPSLongitude'],\n",
+    "                  bounds_error=False, fill_value=np.nan)\n",
+    "\n",
+    "phone_lat_at_tel_time = f_lat(tel_ns)\n",
+    "phone_lon_at_tel_time = f_lon(tel_ns)\n",
+    "\n",
+    "# Convert degree difference to metres\n",
+    "# At Vancouver's latitude (49.2N): 1 deg lat = 111,000m, 1 deg lon = 72,800m\n",
+    "M_PER_DEG_LAT = 111000\n",
+    "M_PER_DEG_LON = 111000 * np.cos(np.radians(49.2))\n",
+    "\n",
+    "lat_err_m = (gps_latlon_df['GPSLatitude'].values  - phone_lat_at_tel_time) * M_PER_DEG_LAT\n",
+    "lon_err_m = (gps_latlon_df['GPSLongitude'].values - phone_lon_at_tel_time) * M_PER_DEG_LON\n",
+    "\n",
+    "pos_error_m = np.sqrt(lat_err_m**2 + lon_err_m**2)\n",
+    "valid = ~np.isnan(pos_error_m)\n",
+    "\n",
+    "# Overall error stats\n",
+    "print(\"=== Position error: TEL vs Phone (no time correction) ===\")\n",
+    "print(f\"Median error:      {np.nanmedian(pos_error_m):.1f}m\")\n",
+    "print(f\"Mean error:        {np.nanmean(pos_error_m):.1f}m\")\n",
+    "print(f\"95th percentile:   {np.nanpercentile(pos_error_m[valid], 95):.1f}m\")\n",
+    "print(f\"Max error:         {np.nanmax(pos_error_m):.1f}m\")\n",
+    "\n",
+    "# Blob vs non-blob breakdown\n",
+    "print(\"\\n=== Blob vs normal periods ===\")\n",
+    "print(f\"Blob median error:     {np.nanmedian(pos_error_m[blob_mask]):.1f}m\")\n",
+    "print(f\"Non-blob median error: {np.nanmedian(pos_error_m[~blob_mask]):.1f}m\")\n",
+    "\n",
+    "# Now apply 39s time correction and recheck\n",
+    "gps_corrected = gps_latlon_df.copy()\n",
+    "gps_corrected.index = gps_corrected.index - pd.Timedelta(seconds=39)\n",
+    "tel_ns_corrected = gps_corrected.index.astype(np.int64)\n",
+    "\n",
+    "phone_lat_corrected = f_lat(tel_ns_corrected)\n",
+    "phone_lon_corrected = f_lon(tel_ns_corrected)\n",
+    "\n",
+    "lat_err_c = (gps_corrected['GPSLatitude'].values  - phone_lat_corrected) * M_PER_DEG_LAT\n",
+    "lon_err_c = (gps_corrected['GPSLongitude'].values - phone_lon_corrected) * M_PER_DEG_LON\n",
+    "pos_error_corrected = np.sqrt(lat_err_c**2 + lon_err_c**2)\n",
+    "\n",
+    "print(\"\\n=== After 39s time correction ===\")\n",
+    "print(f\"Median error:      {np.nanmedian(pos_error_corrected):.1f}m\")\n",
+    "print(f\"95th percentile:   {np.nanpercentile(pos_error_corrected[~np.isnan(pos_error_corrected)], 95):.1f}m\")\n",
+    "print(f\"Max error:         {np.nanmax(pos_error_corrected):.1f}m\")\n",
+    "print(f\"\\nIf median drops significantly, blob was temporal.\")\n",
+    "print(f\"If median stays high, there is a genuine spatial problem.\")"
+   ],
+   "id": "a10464a67743cf75",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== Position error: TEL vs Phone (no time correction) ===\n",
+      "Median error:      15.0m\n",
+      "Mean error:        30.7m\n",
+      "95th percentile:   152.7m\n",
+      "Max error:         260.0m\n",
+      "\n",
+      "=== Blob vs normal periods ===\n"
+     ]
+    },
+    {
+     "ename": "IndexError",
+     "evalue": "boolean index did not match indexed array along axis 0; size of axis is 25267 but size of corresponding boolean axis is 29784",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mIndexError\u001B[39m                                Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[24]\u001B[39m\u001B[32m, line 36\u001B[39m\n\u001B[32m     34\u001B[39m \u001B[38;5;66;03m# Blob vs non-blob breakdown\u001B[39;00m\n\u001B[32m     35\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[33m\"\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[33m=== Blob vs normal periods ===\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m---> \u001B[39m\u001B[32m36\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mBlob median error:     \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mnp.nanmedian(\u001B[43mpos_error_m\u001B[49m\u001B[43m[\u001B[49m\u001B[43mblob_mask\u001B[49m\u001B[43m]\u001B[49m)\u001B[38;5;132;01m:\u001B[39;00m\u001B[33m.1f\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[33mm\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m     37\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mNon-blob median error: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mnp.nanmedian(pos_error_m[~blob_mask])\u001B[38;5;132;01m:\u001B[39;00m\u001B[33m.1f\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[33mm\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m     39\u001B[39m \u001B[38;5;66;03m# Now apply 39s time correction and recheck\u001B[39;00m\n",
+      "\u001B[31mIndexError\u001B[39m: boolean index did not match indexed array along axis 0; size of axis is 25267 but size of corresponding boolean axis is 29784"
+     ]
+    }
+   ],
+   "execution_count": 24
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-23T05:42:45.749970Z",
+     "start_time": "2026-05-23T05:42:45.702963Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Build everything as a single aligned dataframe so indices always match\n",
+    "error_df = pd.DataFrame({\n",
+    "    'latitude':    gps_latlon_df['GPSLatitude'].values,\n",
+    "    'longitude':   gps_latlon_df['GPSLongitude'].values,\n",
+    "    'pos_error_m': pos_error_m,\n",
+    "    'lat_err_m':   lat_err_m,\n",
+    "    'lon_err_m':   lon_err_m,\n",
+    "}, index=gps_latlon_df.index)\n",
+    "\n",
+    "# Recompute blob mask on the same dataframe\n",
+    "BLOB_LON_THRESHOLD = -123.148  # adjust after checking your histogram\n",
+    "error_df['is_blob'] = error_df['longitude'] < BLOB_LON_THRESHOLD\n",
+    "\n",
+    "# Drop rows where phone interpolation gave NaN\n",
+    "error_df_valid = error_df.dropna(subset=['pos_error_m'])\n",
+    "\n",
+    "print(f\"Total samples:         {len(error_df)}\")\n",
+    "print(f\"Valid (non-NaN) samples: {len(error_df_valid)}\")\n",
+    "print(f\"Blob samples:          {error_df_valid['is_blob'].sum()}\")\n",
+    "\n",
+    "print(\"\\n=== Blob vs normal periods ===\")\n",
+    "blob    = error_df_valid[error_df_valid['is_blob']]\n",
+    "normal  = error_df_valid[~error_df_valid['is_blob']]\n",
+    "\n",
+    "print(f\"Blob median error:       {blob['pos_error_m'].median():.1f}m\")\n",
+    "print(f\"Blob 95th pct error:     {blob['pos_error_m'].quantile(0.95):.1f}m\")\n",
+    "print(f\"Blob mean lat error:     {blob['lat_err_m'].mean():.1f}m\")\n",
+    "print(f\"Blob mean lon error:     {blob['lon_err_m'].mean():.1f}m\")\n",
+    "print(f\"Blob time start:         {blob.index.min()}\")\n",
+    "print(f\"Blob time end:           {blob.index.max()}\")\n",
+    "print(f\"Blob duration:           {(blob.index.max() - blob.index.min()).total_seconds():.0f}s\")\n",
+    "\n",
+    "print(f\"\\nNormal median error:     {normal['pos_error_m'].median():.1f}m\")\n",
+    "print(f\"Normal 95th pct error:   {normal['pos_error_m'].quantile(0.95):.1f}m\")\n",
+    "\n",
+    "# Then recheck after time correction using the same dataframe structure\n",
+    "gps_corrected = gps_latlon_df.copy()\n",
+    "gps_corrected.index = gps_corrected.index - pd.Timedelta(seconds=39)\n",
+    "tel_ns_corrected = gps_corrected.index.astype(np.int64)\n",
+    "\n",
+    "phone_lat_corrected = f_lat(tel_ns_corrected)\n",
+    "phone_lon_corrected = f_lon(tel_ns_corrected)\n",
+    "\n",
+    "lat_err_c = (gps_corrected['GPSLatitude'].values  - phone_lat_corrected) * M_PER_DEG_LAT\n",
+    "lon_err_c = (gps_corrected['GPSLongitude'].values - phone_lon_corrected) * M_PER_DEG_LON\n",
+    "pos_error_corrected = np.sqrt(lat_err_c**2 + lon_err_c**2)\n",
+    "\n",
+    "error_df['pos_error_corrected'] = pos_error_corrected\n",
+    "error_df_valid2 = error_df.dropna(subset=['pos_error_corrected'])\n",
+    "\n",
+    "print(\"\\n=== After 39s time correction ===\")\n",
+    "print(f\"Median error:    {error_df_valid2['pos_error_corrected'].median():.1f}m\")\n",
+    "print(f\"95th pct:        {error_df_valid2['pos_error_corrected'].quantile(0.95):.1f}m\")\n",
+    "print(f\"Max error:       {error_df_valid2['pos_error_corrected'].max():.1f}m\")\n",
+    "\n",
+    "blob_c   = error_df_valid2[error_df_valid2['is_blob']]\n",
+    "normal_c = error_df_valid2[~error_df_valid2['is_blob']]\n",
+    "print(f\"\\nBlob median error after correction:   {blob_c['pos_error_corrected'].median():.1f}m\")\n",
+    "print(f\"Normal median error after correction: {normal_c['pos_error_corrected'].median():.1f}m\")"
+   ],
+   "id": "7cf0b9b7a83def65",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total samples:         25267\n",
+      "Valid (non-NaN) samples: 25267\n",
+      "Blob samples:          0\n",
+      "\n",
+      "=== Blob vs normal periods ===\n",
+      "Blob median error:       nanm\n",
+      "Blob 95th pct error:     nanm\n",
+      "Blob mean lat error:     nanm\n",
+      "Blob mean lon error:     nanm\n",
+      "Blob time start:         NaT\n",
+      "Blob time end:           NaT\n",
+      "Blob duration:           nans\n",
+      "\n",
+      "Normal median error:     15.0m\n",
+      "Normal 95th pct error:   152.7m\n",
+      "\n",
+      "=== After 39s time correction ===\n",
+      "Median error:    23.1m\n",
+      "95th pct:        175.4m\n",
+      "Max error:       259.8m\n",
+      "\n",
+      "Blob median error after correction:   nanm\n",
+      "Normal median error after correction: 23.1m\n"
+     ]
+    }
+   ],
+   "execution_count": 25
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "from scipy.interpolate import interp1d\n",
+    "import numpy as np\n",
+    "\n",
+    "# Interpolate phone GPS onto TEL timestamps\n",
+    "phone_ns  = phone_gps_df.index.astype(np.int64)\n",
+    "tel_ns    = gps_latlon_df.index.astype(np.int64)\n",
+    "\n",
+    "f_lat = interp1d(phone_ns, phone_gps_df['GPSLatitude'],\n",
+    "                  bounds_error=False, fill_value=np.nan)\n",
+    "f_lon = interp1d(phone_ns, phone_gps_df['GPSLongitude'],\n",
+    "                  bounds_error=False, fill_value=np.nan)\n",
+    "\n",
+    "phone_lat_at_tel_time = f_lat(tel_ns)\n",
+    "phone_lon_at_tel_time = f_lon(tel_ns)\n",
+    "\n",
+    "# Convert degree difference to metres\n",
+    "# At Vancouver's latitude (49.2N): 1 deg lat = 111,000m, 1 deg lon = 72,800m\n",
+    "M_PER_DEG_LAT = 111000\n",
+    "M_PER_DEG_LON = 111000 * np.cos(np.radians(49.2))\n",
+    "\n",
+    "lat_err_m = (gps_latlon_df['GPSLatitude'].values  - phone_lat_at_tel_time) * M_PER_DEG_LAT\n",
+    "lon_err_m = (gps_latlon_df['GPSLongitude'].values - phone_lon_at_tel_time) * M_PER_DEG_LON\n",
+    "\n",
+    "pos_error_m = np.sqrt(lat_err_m**2 + lon_err_m**2)\n",
+    "valid = ~np.isnan(pos_error_m)\n",
+    "\n",
+    "# Overall error stats\n",
+    "print(\"=== Position error: TEL vs Phone (no time correction) ===\")\n",
+    "print(f\"Median error:      {np.nanmedian(pos_error_m):.1f}m\")\n",
+    "print(f\"Mean error:        {np.nanmean(pos_error_m):.1f}m\")\n",
+    "print(f\"95th percentile:   {np.nanpercentile(pos_error_m[valid], 95):.1f}m\")\n",
+    "print(f\"Max error:         {np.nanmax(pos_error_m):.1f}m\")\n",
+    "\n",
+    "# Blob vs non-blob breakdown\n",
+    "print(\"\\n=== Blob vs normal periods ===\")\n",
+    "print(f\"Blob median error:     {np.nanmedian(pos_error_m[blob_mask]):.1f}m\")\n",
+    "print(f\"Non-blob median error: {np.nanmedian(pos_error_m[~blob_mask]):.1f}m\")\n",
+    "\n",
+    "# Now apply 39s time correction and recheck\n",
+    "gps_corrected = gps_latlon_df.copy()\n",
+    "gps_corrected.index = gps_corrected.index - pd.Timedelta(seconds=39)\n",
+    "tel_ns_corrected = gps_corrected.index.astype(np.int64)\n",
+    "\n",
+    "phone_lat_corrected = f_lat(tel_ns_corrected)\n",
+    "phone_lon_corrected = f_lon(tel_ns_corrected)\n",
+    "\n",
+    "lat_err_c = (gps_corrected['GPSLatitude'].values  - phone_lat_corrected) * M_PER_DEG_LAT\n",
+    "lon_err_c = (gps_corrected['GPSLongitude'].values - phone_lon_corrected) * M_PER_DEG_LON\n",
+    "pos_error_corrected = np.sqrt(lat_err_c**2 + lon_err_c**2)\n",
+    "\n",
+    "print(\"\\n=== After 39s time correction ===\")\n",
+    "print(f\"Median error:      {np.nanmedian(pos_error_corrected):.1f}m\")\n",
+    "print(f\"95th percentile:   {np.nanpercentile(pos_error_corrected[~np.isnan(pos_error_corrected)], 95):.1f}m\")\n",
+    "print(f\"Max error:         {np.nanmax(pos_error_corrected):.1f}m\")\n",
+    "print(f\"\\nIf median drops significantly, blob was temporal.\")\n",
+    "print(f\"If median stays high, there is a genuine spatial problem.\")"
+   ],
+   "id": "cbc26ff2c248e27b"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-23T05:41:15.658973Z",
+     "start_time": "2026-05-23T05:41:15.608865Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "assert np.all(latitude_df[\"_time\"] ==longitude_df[\"_time\"])\n",
+    "latitude_df_dtxaxis= latitude_df.set_index(\n",
+    "pd.to_datetime(latitude_df[\"_time\"]).dt.tz_localize(None).dt.tz_localize('Etc/GMT+7').dt.tz_convert('UTC'))\n",
+    "longitude_df_dtxaxis =longitude_df.set_index(pd.to_datetime(longitude_df[\"_time\"]).dt.tz_localize(None).dt.tz_localize('Etc/GMT+7').dt.tz_convert('UTC'))\n",
+    "\n",
+    "gps_latlon_df_raw = latitude_df_dtxaxis[[\"GPSLatitude\"]].join(longitude_df_dtxaxis[[\"GPSLongitude\"]], how = 'inner')\n",
+    "gps_latlon_df_raw[\"GPSLongitude\"] = gps_latlon_df_raw[\"GPSLongitude\"]*-1\n"
+   ],
+   "id": "76101b2bf763ae3b",
+   "outputs": [],
+   "execution_count": 21
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-23T05:41:16.969889Z",
+     "start_time": "2026-05-23T05:41:16.303577Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "gps_latlon_df = gps_latlon_df_raw[(gps_latlon_df_raw['GPSLatitude'].abs() >= 0.1)\n",
+    "& (gps_latlon_df_raw['GPSLongitude'].abs() >= 0.1)]\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.scatter(gps_latlon_df.index, gps_latlon_df['GPSLatitude'], label='latitude')\n",
+    "ax.scatter(gps_latlon_df.index, gps_latlon_df['GPSLongitude'],label='longitude')\n",
+    "plt.xticks(rotation=30)\n",
+    "plt.legend(loc='best')\n",
+    "plt.show()"
+   ],
+   "id": "9dbf6d506a59a5e3",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 22
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-23T05:41:28.342576Z",
+     "start_time": "2026-05-23T05:41:28.273013Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "gps_latlon_df.head()",
+   "id": "9678ae60a199ba9f",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                                  GPSLatitude  GPSLongitude\n",
+       "_time                                                      \n",
+       "2026-04-02 19:37:22.202000+00:00    49.198933   -123.145332\n",
+       "2026-04-02 19:37:22.454000+00:00    49.198933   -123.145332\n",
+       "2026-04-02 19:37:22.992000+00:00    49.198933   -123.145332\n",
+       "2026-04-02 19:37:23.248000+00:00    49.198933   -123.145332\n",
+       "2026-04-02 19:37:23.500000+00:00    49.198933   -123.145332"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GPSLatitude</th>\n",
+       "      <th>GPSLongitude</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>_time</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2026-04-02 19:37:22.202000+00:00</th>\n",
+       "      <td>49.198933</td>\n",
+       "      <td>-123.145332</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2026-04-02 19:37:22.454000+00:00</th>\n",
+       "      <td>49.198933</td>\n",
+       "      <td>-123.145332</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2026-04-02 19:37:22.992000+00:00</th>\n",
+       "      <td>49.198933</td>\n",
+       "      <td>-123.145332</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2026-04-02 19:37:23.248000+00:00</th>\n",
+       "      <td>49.198933</td>\n",
+       "      <td>-123.145332</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2026-04-02 19:37:23.500000+00:00</th>\n",
+       "      <td>49.198933</td>\n",
+       "      <td>-123.145332</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 23
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-16T21:18:22.084483Z",
+     "start_time": "2026-05-16T21:18:22.072431Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "latitude_df.head()",
+   "id": "f364f54236d0a453",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "    result  table                    _start                     _stop  \\\n",
+       "0  _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+       "1  _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+       "2  _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+       "3  _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+       "4  _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+       "\n",
+       "                             _time _measurement         car        class  \\\n",
+       "0 2026-04-02 11:31:48.892000+00:00          TEL  Brightside  GPSPosition   \n",
+       "1 2026-04-02 11:31:49.145000+00:00          TEL  Brightside  GPSPosition   \n",
+       "2 2026-04-02 11:31:49.397000+00:00          TEL  Brightside  GPSPosition   \n",
+       "3 2026-04-02 11:31:49.650000+00:00          TEL  Brightside  GPSPosition   \n",
+       "4 2026-04-02 11:31:49.902000+00:00          TEL  Brightside  GPSPosition   \n",
+       "\n",
+       "   GPSLatitude  \n",
+       "0          0.0  \n",
+       "1          0.0  \n",
+       "2          0.0  \n",
+       "3          0.0  \n",
+       "4          0.0  "
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>result</th>\n",
+       "      <th>table</th>\n",
+       "      <th>_start</th>\n",
+       "      <th>_stop</th>\n",
+       "      <th>_time</th>\n",
+       "      <th>_measurement</th>\n",
+       "      <th>car</th>\n",
+       "      <th>class</th>\n",
+       "      <th>GPSLatitude</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>_result</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2026-04-02 10:00:00+00:00</td>\n",
+       "      <td>2026-04-02 18:00:00+00:00</td>\n",
+       "      <td>2026-04-02 11:31:48.892000+00:00</td>\n",
+       "      <td>TEL</td>\n",
+       "      <td>Brightside</td>\n",
+       "      <td>GPSPosition</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>_result</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2026-04-02 10:00:00+00:00</td>\n",
+       "      <td>2026-04-02 18:00:00+00:00</td>\n",
+       "      <td>2026-04-02 11:31:49.145000+00:00</td>\n",
+       "      <td>TEL</td>\n",
+       "      <td>Brightside</td>\n",
+       "      <td>GPSPosition</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>_result</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2026-04-02 10:00:00+00:00</td>\n",
+       "      <td>2026-04-02 18:00:00+00:00</td>\n",
+       "      <td>2026-04-02 11:31:49.397000+00:00</td>\n",
+       "      <td>TEL</td>\n",
+       "      <td>Brightside</td>\n",
+       "      <td>GPSPosition</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>_result</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2026-04-02 10:00:00+00:00</td>\n",
+       "      <td>2026-04-02 18:00:00+00:00</td>\n",
+       "      <td>2026-04-02 11:31:49.650000+00:00</td>\n",
+       "      <td>TEL</td>\n",
+       "      <td>Brightside</td>\n",
+       "      <td>GPSPosition</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>_result</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2026-04-02 10:00:00+00:00</td>\n",
+       "      <td>2026-04-02 18:00:00+00:00</td>\n",
+       "      <td>2026-04-02 11:31:49.902000+00:00</td>\n",
+       "      <td>TEL</td>\n",
+       "      <td>Brightside</td>\n",
+       "      <td>GPSPosition</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 21
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-16T21:18:28.187321Z",
+     "start_time": "2026-05-16T21:18:28.161381Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "lat_times = latitude_df[\"_time\"]\n",
+    "diffs = lat_times.diff().dt.total_seconds().dropna()\n",
+    "print(diffs.describe())\n",
+    "print(diffs.value_counts().head(10))\n",
+    "\n"
+   ],
+   "id": "6c9d82e639465611",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "count    29783.000000\n",
+      "mean         0.559188\n",
+      "std         19.159171\n",
+      "min          0.250000\n",
+      "25%          0.252000\n",
+      "50%          0.252000\n",
+      "75%          0.255000\n",
+      "max       2734.049000\n",
+      "Name: _time, dtype: float64\n",
+      "_time\n",
+      "0.252    11889\n",
+      "0.251     5662\n",
+      "0.253     2565\n",
+      "0.254     1720\n",
+      "0.255     1061\n",
+      "0.256      548\n",
+      "0.286      521\n",
+      "0.287      515\n",
+      "0.250      481\n",
+      "0.288      416\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "execution_count": 22
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-16T21:57:05.761906Z",
+     "start_time": "2026-05-16T21:57:05.702427Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "utc_time_df = client.query_series(driving_day_start, driving_day_end, \"GPSutcTime\")\n"
+   ],
+   "id": "6c43212ff22bb67d",
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Query is empty! Verify that the data is visible on InfluxDB for the queried bucket.",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mValueError\u001B[39m                                Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[27]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m utc_time_df = \u001B[43mclient\u001B[49m\u001B[43m.\u001B[49m\u001B[43mquery_series\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdriving_day_start\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdriving_day_end\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mGPSutcTime\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\data_tools\\query\\influxdb_query.py:159\u001B[39m, in \u001B[36mDBClient.query_series\u001B[39m\u001B[34m(self, start, stop, field, bucket, car, measurement)\u001B[39m\n\u001B[32m    156\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[33m\"\u001B[39m\u001B[33mQuery returned multiple fields! Please refine your query.\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m    158\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(query_df) == \u001B[32m0\u001B[39m:\n\u001B[32m--> \u001B[39m\u001B[32m159\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[33m\"\u001B[39m\u001B[33mQuery is empty! Verify that the data is visible on InfluxDB for the queried bucket.\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m    161\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m query_df\n",
+      "\u001B[31mValueError\u001B[39m: Query is empty! Verify that the data is visible on InfluxDB for the queried bucket."
+     ]
+    }
+   ],
+   "execution_count": 27
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-16T21:57:35.550881Z",
+     "start_time": "2026-05-16T21:57:35.456370Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "time_diffs = latitude_df[\"_time\"].diff().dt.total_seconds().dropna()\n",
+    "print(f\"Median interval: {time_diffs.median():.3f}s\")\n",
+    "print(f\"Mean interval: {time_diffs.mean():.3f}s\")\n",
+    "plt.hist(time_diffs[time_diffs < 2], bins=50)\n",
+    "plt.xlabel(\"Seconds between TEL GPS readings\")\n",
+    "plt.show()"
+   ],
+   "id": "db41e22a45a0214f",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Median interval: 0.252s\n",
+      "Mean interval: 0.559s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 28
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-16T22:45:53.680554Z",
+     "start_time": "2026-05-16T22:45:52.025298Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Query the embedded NMEA UTC time fields\n",
+    "utc_h = client.query_series(driving_day_start, driving_day_end, \"GPSHour\")   # data[3]\n",
+    "utc_m = client.query_series(driving_day_start, driving_day_end, \"GPSMinute\") # data[4]\n",
+    "utc_s = client.query_series(driving_day_start, driving_day_end, \"GPSSecond\") # data[5]\n",
+    "\n",
+    "# Reconstruct the embedded UTC time as seconds since midnight\n",
+    "nmea_utc_seconds = utc_h[\"GPSHour\"] * 3600 + utc_m[\"GPSMinute\"] * 60 + utc_s[\"GPSSecond\"]\n",
+    "\n",
+    "# Convert InfluxDB wall-clock _time to seconds since midnight UTC\n",
+    "influx_seconds = pd.to_datetime(utc_h[\"_time\"], utc=True).dt.hour * 3600 + \\\n",
+    "                 pd.to_datetime(utc_h[\"_time\"], utc=True).dt.minute * 60 + \\\n",
+    "                 pd.to_datetime(utc_h[\"_time\"], utc=True).dt.second\n",
+    "\n",
+    "# Compute the lag per sample\n",
+    "lag_per_sample = influx_seconds.values - nmea_utc_seconds.values\n",
+    "\n",
+    "print(f\"Mean lag: {lag_per_sample.mean():.1f}s\")\n",
+    "print(f\"Std:      {lag_per_sample.std():.2f}s\")\n",
+    "print(f\"Min:      {lag_per_sample.min():.1f}s\")\n",
+    "print(f\"Max:      {lag_per_sample.max():.1f}s\")\n",
+    "\n",
+    "plt.figure(figsize=(14, 4))\n",
+    "plt.plot(lag_per_sample)\n",
+    "plt.axhline(39, color='red', linestyle='--', label='Expected 39s lag')\n",
+    "plt.ylabel(\"NMEA UTC lag behind wall clock (seconds)\")\n",
+    "plt.title(\"Per-sample lag: InfluxDB timestamp minus embedded NMEA UTC time\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ],
+   "id": "65520bf5e18b98c9",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean lag: -20905.8s\n",
+      "Std:      17771.25s\n",
+      "Min:      -25240.0s\n",
+      "Max:      58161.0s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x400 with 1 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 40
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-16T22:50:41.251956Z",
+     "start_time": "2026-05-16T22:50:40.700091Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "utc_h_df = client.query_series(driving_day_start, driving_day_end, \"GPSHour\")\n",
+    "print(utc_h_df.head(20))\n",
+    "print(f\"\\nUnique GPSHour values: {sorted(utc_h_df['GPSHour'].unique())}\")\n",
+    "print(f\"InfluxDB _time sample: {utc_h_df['_time'].iloc[0]}\")"
+   ],
+   "id": "b818bc7e75fde2b9",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     result  table                    _start                     _stop  \\\n",
+      "0   _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+      "1   _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+      "2   _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+      "3   _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+      "4   _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+      "5   _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+      "6   _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+      "7   _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+      "8   _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+      "9   _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+      "10  _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+      "11  _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+      "12  _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+      "13  _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+      "14  _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+      "15  _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+      "16  _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+      "17  _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+      "18  _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+      "19  _result      0 2026-04-02 10:00:00+00:00 2026-04-02 18:00:00+00:00   \n",
+      "\n",
+      "                              _time _measurement         car        class  \\\n",
+      "0  2026-04-02 11:31:48.942000+00:00          TEL  Brightside  GPSTimeSide   \n",
+      "1  2026-04-02 11:31:49.195000+00:00          TEL  Brightside  GPSTimeSide   \n",
+      "2  2026-04-02 11:31:49.449000+00:00          TEL  Brightside  GPSTimeSide   \n",
+      "3  2026-04-02 11:31:49.700000+00:00          TEL  Brightside  GPSTimeSide   \n",
+      "4  2026-04-02 11:31:49.952000+00:00          TEL  Brightside  GPSTimeSide   \n",
+      "5  2026-04-02 11:31:50.492000+00:00          TEL  Brightside  GPSTimeSide   \n",
+      "6  2026-04-02 11:31:50.743000+00:00          TEL  Brightside  GPSTimeSide   \n",
+      "7  2026-04-02 11:31:50.995000+00:00          TEL  Brightside  GPSTimeSide   \n",
+      "8  2026-04-02 11:31:51.247000+00:00          TEL  Brightside  GPSTimeSide   \n",
+      "9  2026-04-02 11:31:51.499000+00:00          TEL  Brightside  GPSTimeSide   \n",
+      "10 2026-04-02 11:31:51.750000+00:00          TEL  Brightside  GPSTimeSide   \n",
+      "11 2026-04-02 11:31:52.002000+00:00          TEL  Brightside  GPSTimeSide   \n",
+      "12 2026-04-02 11:31:52.254000+00:00          TEL  Brightside  GPSTimeSide   \n",
+      "13 2026-04-02 11:31:52.538000+00:00          TEL  Brightside  GPSTimeSide   \n",
+      "14 2026-04-02 11:31:52.790000+00:00          TEL  Brightside  GPSTimeSide   \n",
+      "15 2026-04-02 11:31:53.042000+00:00          TEL  Brightside  GPSTimeSide   \n",
+      "16 2026-04-02 11:31:53.294000+00:00          TEL  Brightside  GPSTimeSide   \n",
+      "17 2026-04-02 11:31:53.546000+00:00          TEL  Brightside  GPSTimeSide   \n",
+      "18 2026-04-02 11:31:53.797000+00:00          TEL  Brightside  GPSTimeSide   \n",
+      "19 2026-04-02 11:31:54.049000+00:00          TEL  Brightside  GPSTimeSide   \n",
+      "\n",
+      "    GPSHour  \n",
+      "0       0.0  \n",
+      "1       0.0  \n",
+      "2       0.0  \n",
+      "3       0.0  \n",
+      "4       0.0  \n",
+      "5       0.0  \n",
+      "6       0.0  \n",
+      "7       0.0  \n",
+      "8       0.0  \n",
+      "9       0.0  \n",
+      "10      0.0  \n",
+      "11      0.0  \n",
+      "12      0.0  \n",
+      "13      0.0  \n",
+      "14     18.0  \n",
+      "15     18.0  \n",
+      "16     18.0  \n",
+      "17     18.0  \n",
+      "18     18.0  \n",
+      "19     18.0  \n",
+      "\n",
+      "Unique GPSHour values: [np.float64(0.0), np.float64(18.0), np.float64(19.0), np.float64(20.0), np.float64(21.0), np.float64(22.0), np.float64(23.0)]\n",
+      "InfluxDB _time sample: 2026-04-02 11:31:48.942000+00:00\n"
+     ]
+    }
+   ],
+   "execution_count": 44
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-16T22:52:03.134172Z",
+     "start_time": "2026-05-16T22:52:02.083787Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "# Get the raw InfluxDB timestamps as UTC — do NOT apply any tz_localize\n",
+    "influx_times = pd.to_datetime(utc_h_df['_time'], utc=True)\n",
+    "\n",
+    "# Extract wall-clock UTC components directly\n",
+    "influx_hour   = influx_times.dt.hour\n",
+    "influx_minute = influx_times.dt.minute\n",
+    "influx_second = influx_times.dt.second\n",
+    "\n",
+    "influx_seconds_of_day = influx_hour * 3600 + influx_minute * 60 + influx_second\n",
+    "\n",
+    "# Reconstruct NMEA UTC seconds of day from the logged fields\n",
+    "# First verify the fields look right\n",
+    "utc_m_df = client.query_series(driving_day_start, driving_day_end, \"GPSMinute\")\n",
+    "utc_s_df = client.query_series(driving_day_start, driving_day_end, \"GPSSecond\")\n",
+    "\n",
+    "nmea_seconds_of_day = (utc_h_df['GPSHour'].values * 3600 +\n",
+    "                       utc_m_df['GPSMinute'].values * 60 +\n",
+    "                       utc_s_df['GPSSecond'].values)\n",
+    "\n",
+    "print(\"NMEA UTC hours range:\", utc_h_df['GPSHour'].min(), \"to\", utc_h_df['GPSHour'].max())\n",
+    "print(\"InfluxDB UTC hours range:\", influx_hour.min(), \"to\", influx_hour.max())\n",
+    "print(\"\\nSample comparison (first 5 rows):\")\n",
+    "for i in range(5):\n",
+    "    print(f\"  InfluxDB: {influx_hour.iloc[i]:02.0f}:{influx_minute.iloc[i]:02.0f}:{influx_second.iloc[i]:02.0f}\"\n",
+    "          f\"  NMEA: {utc_h_df['GPSHour'].iloc[i]:02.0f}:{utc_m_df['GPSMinute'].iloc[i]:02.0f}:{utc_s_df['GPSSecond'].iloc[i]:02.0f}\")"
+   ],
+   "id": "b3338b23a105ecbe",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NMEA UTC hours range: 0.0 to 23.0\n",
+      "InfluxDB UTC hours range: 11 to 16\n",
+      "\n",
+      "Sample comparison (first 5 rows):\n",
+      "  InfluxDB: 11:31:48  NMEA: 00:00:00\n",
+      "  InfluxDB: 11:31:49  NMEA: 00:00:00\n",
+      "  InfluxDB: 11:31:49  NMEA: 00:00:00\n",
+      "  InfluxDB: 11:31:49  NMEA: 00:00:00\n",
+      "  InfluxDB: 11:31:49  NMEA: 00:00:00\n"
+     ]
+    }
+   ],
+   "execution_count": 46
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-16T22:53:45.316672Z",
+     "start_time": "2026-05-16T22:53:45.302849Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "zero_nmea = (utc_h_df['GPSHour'] == 0) & \\\n",
+    "            (utc_m_df['GPSMinute'] == 0) & \\\n",
+    "            (utc_s_df['GPSSecond'] == 0)\n",
+    "\n",
+    "print(f\"Samples with NMEA UTC = 00:00:00: {zero_nmea.sum()} / {len(zero_nmea)}\")\n",
+    "print(f\"That's {zero_nmea.mean()*100:.1f}% of all samples\")\n",
+    "\n",
+    "# Check if these correlate with zero lat/lon (i.e. no fix)\n",
+    "# Cross-reference with the latitude data\n",
+    "print(f\"\\nFirst non-zero NMEA UTC time:\")\n",
+    "first_valid = utc_h_df[~zero_nmea].iloc[0]\n",
+    "print(first_valid)"
+   ],
+   "id": "1324d4190feb91b1",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Samples with NMEA UTC = 00:00:00: 1740 / 30917\n",
+      "That's 5.6% of all samples\n",
+      "\n",
+      "First non-zero NMEA UTC time:\n",
+      "result                                   _result\n",
+      "table                                          0\n",
+      "_start                 2026-04-02 10:00:00+00:00\n",
+      "_stop                  2026-04-02 18:00:00+00:00\n",
+      "_time           2026-04-02 11:31:52.790000+00:00\n",
+      "_measurement                                 TEL\n",
+      "car                                   Brightside\n",
+      "class                                GPSTimeSide\n",
+      "GPSHour                                     18.0\n",
+      "Name: 14, dtype: object\n"
+     ]
+    }
+   ],
+   "execution_count": 49
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-16T22:54:17.764280Z",
+     "start_time": "2026-05-16T22:54:17.713044Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Only keep samples where NMEA UTC is non-zero AND lat/lon is non-zero\n",
+    "# These three conditions together mean the module had a valid fix\n",
+    "\n",
+    "valid_nmea_time = ~zero_nmea  # NMEA UTC is non-zero\n",
+    "\n",
+    "# Also align with the latitude data to cross-check fix status\n",
+    "# Re-index utc_h_df onto the same timestamps as gps_latlon_df\n",
+    "utc_h_aligned = utc_h_df.set_index(pd.to_datetime(utc_h_df['_time'], utc=True))\n",
+    "\n",
+    "# Find timestamps where we have valid lat/lon (from gps_latlon_df which already filters zeros)\n",
+    "valid_fix_times = gps_latlon_df.index  # already filtered to non-zero lat/lon\n",
+    "\n",
+    "# Filter NMEA UTC data to only valid fix periods\n",
+    "utc_h_valid = utc_h_df[valid_nmea_time].copy()\n",
+    "utc_m_valid = utc_m_df[valid_nmea_time].copy()\n",
+    "utc_s_valid = utc_s_df[valid_nmea_time].copy()\n",
+    "\n",
+    "print(f\"Samples remaining after filtering: {len(utc_h_valid)}\")\n",
+    "print(f\"\\nSample comparison after filtering (first 5 rows):\")\n",
+    "\n",
+    "influx_times_valid = pd.to_datetime(utc_h_valid['_time'], utc=True)\n",
+    "for i in range(min(5, len(utc_h_valid))):\n",
+    "    t = influx_times_valid.iloc[i]\n",
+    "    print(f\"  InfluxDB: {t.hour:02d}:{t.minute:02d}:{t.second:02d}\"\n",
+    "          f\"  NMEA: {utc_h_valid['GPSHour'].iloc[i]:02.0f}:\"\n",
+    "          f\"{utc_m_valid['GPSMinute'].iloc[i]:02.0f}:\"\n",
+    "          f\"{utc_s_valid['GPSSecond'].iloc[i]:02.0f}\")"
+   ],
+   "id": "92ad35a0274e0ac9",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Samples remaining after filtering: 29177\n",
+      "\n",
+      "Sample comparison after filtering (first 5 rows):\n",
+      "  InfluxDB: 11:31:52  NMEA: 18:32:31\n",
+      "  InfluxDB: 11:31:53  NMEA: 18:32:31\n",
+      "  InfluxDB: 11:31:53  NMEA: 18:32:31\n",
+      "  InfluxDB: 11:31:53  NMEA: 18:32:32\n",
+      "  InfluxDB: 11:31:53  NMEA: 18:32:32\n"
+     ]
+    }
+   ],
+   "execution_count": 51
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-16T22:54:50.959785Z",
+     "start_time": "2026-05-16T22:54:50.846933Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "influx_times_v = pd.to_datetime(utc_h_valid['_time'], utc=True)\n",
+    "influx_sod = (influx_times_v.dt.hour * 3600 +\n",
+    "              influx_times_v.dt.minute * 60 +\n",
+    "              influx_times_v.dt.second).values\n",
+    "\n",
+    "nmea_sod = (utc_h_valid['GPSHour'].values * 3600 +\n",
+    "            utc_m_valid['GPSMinute'].values * 60 +\n",
+    "            utc_s_valid['GPSSecond'].values)\n",
+    "\n",
+    "lag = influx_sod - nmea_sod\n",
+    "\n",
+    "# Midnight wraparound correction\n",
+    "lag = np.where(lag >  43200, lag - 86400, lag)\n",
+    "lag = np.where(lag < -43200, lag + 86400, lag)\n",
+    "\n",
+    "# Sanity filter — anything beyond ±300s is still a bad sample\n",
+    "sane = (lag > -300) & (lag < 300)\n",
+    "print(f\"Sane samples: {sane.sum()} / {len(lag)}\")\n",
+    "print(f\"Mean lag: {lag[sane].mean():.1f}s\")\n",
+    "print(f\"Std:      {lag[sane].std():.2f}s\")\n",
+    "\n",
+    "plt.figure(figsize=(14, 4))\n",
+    "plt.scatter(influx_times_v[sane], lag[sane], s=1, alpha=0.4, color='steelblue')\n",
+    "plt.axhline(-39, color='red', linestyle='--', linewidth=1.5, label='Expected −39s')\n",
+    "plt.axhline(0, color='gray', linestyle='-', alpha=0.3)\n",
+    "plt.ylabel(\"NMEA UTC lag behind wall clock (s)\")\n",
+    "plt.title(\"Per-sample lag: InfluxDB timestamp minus embedded NMEA UTC\\n(fix-valid samples only)\")\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ],
+   "id": "512a03f1fa203456",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sane samples: 0 / 29177\n",
+      "Mean lag: nans\n",
+      "Std:      nans\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_15828\\3952939581.py:19: RuntimeWarning: Mean of empty slice.\n",
+      "  print(f\"Mean lag: {lag[sane].mean():.1f}s\")\n",
+      "C:\\Users\\sanar\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\numpy\\_core\\_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "C:\\Users\\sanar\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\numpy\\_core\\_methods.py:222: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
+      "C:\\Users\\sanar\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\numpy\\_core\\_methods.py:180: RuntimeWarning: invalid value encountered in divide\n",
+      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
+      "C:\\Users\\sanar\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\numpy\\_core\\_methods.py:214: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x400 with 1 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 53
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-16T22:52:41.367300Z",
+     "start_time": "2026-05-16T22:52:41.172491Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "lag_seconds = influx_seconds_of_day.values - nmea_seconds_of_day\n",
+    "\n",
+    "# Handle midnight wraparound (e.g. influx=00:00:10, nmea=23:59:31 → should be +39s not -86361s)\n",
+    "lag_seconds = np.where(lag_seconds > 43200,  lag_seconds - 86400, lag_seconds)\n",
+    "lag_seconds = np.where(lag_seconds < -43200, lag_seconds + 86400, lag_seconds)\n",
+    "\n",
+    "# Filter to plausible range — anything outside ±300s is a bad sample\n",
+    "valid = (lag_seconds > -300) & (lag_seconds < 300)\n",
+    "print(f\"Valid samples: {valid.sum()} / {len(lag_seconds)}\")\n",
+    "print(f\"Mean lag: {lag_seconds[valid].mean():.1f}s\")\n",
+    "print(f\"Std:      {lag_seconds[valid].std():.2f}s\")\n",
+    "\n",
+    "plt.figure(figsize=(14, 4))\n",
+    "plt.plot(influx_times[valid], lag_seconds[valid], linewidth=0.5)\n",
+    "plt.axhline(-39, color='red', linestyle='--', label='Expected −39s')\n",
+    "plt.axhline(0, color='gray', linestyle='-', alpha=0.3)\n",
+    "plt.ylabel(\"NMEA UTC lag (seconds)\")\n",
+    "plt.title(\"Per-sample lag after timezone fix\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ],
+   "id": "b70ada89e18b5e45",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Valid samples: 0 / 30917\n",
+      "Mean lag: nans\n",
+      "Std:      nans\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sanar\\AppData\\Local\\Temp\\ipykernel_15828\\1143129817.py:10: RuntimeWarning: Mean of empty slice.\n",
+      "  print(f\"Mean lag: {lag_seconds[valid].mean():.1f}s\")\n",
+      "C:\\Users\\sanar\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\numpy\\_core\\_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "C:\\Users\\sanar\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\numpy\\_core\\_methods.py:222: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
+      "C:\\Users\\sanar\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\numpy\\_core\\_methods.py:180: RuntimeWarning: invalid value encountered in divide\n",
+      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
+      "C:\\Users\\sanar\\PycharmProjects\\data_analysis\\.venv\\Lib\\site-packages\\numpy\\_core\\_methods.py:214: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x400 with 1 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 47
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-16T23:00:06.586997Z",
+     "start_time": "2026-05-16T23:00:06.352888Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# InfluxDB _time was recorded in PDT (UTC-7), stored as if it were UTC\n",
+    "# Add 7 hours to correct it to true UTC before comparing against NMEA UTC\n",
+    "\n",
+    "TIMEZONE_OFFSET_SECONDS = 7 * 3600  # PDT is UTC-7\n",
+    "\n",
+    "influx_sod_corrected = influx_sod + TIMEZONE_OFFSET_SECONDS\n",
+    "\n",
+    "lag = influx_sod_corrected - nmea_sod\n",
+    "\n",
+    "# Midnight wraparound\n",
+    "lag = np.where(lag >  43200, lag - 86400, lag)\n",
+    "lag = np.where(lag < -43200, lag + 86400, lag)\n",
+    "\n",
+    "sane = (lag > -300) & (lag < 300)\n",
+    "print(f\"Sane samples: {sane.sum()} / {len(lag)}\")\n",
+    "print(f\"Mean lag: {lag[sane].mean():.2f}s\")\n",
+    "print(f\"Std dev:  {lag[sane].std():.4f}s\")\n",
+    "print(f\"Min:      {lag[sane].min():.2f}s\")\n",
+    "print(f\"Max:      {lag[sane].max():.2f}s\")\n",
+    "plt.figure(figsize=(14, 4))\n",
+    "plt.plot(lag_per_sample)\n",
+    "plt.axhline(39, color='red', linestyle='--', label='Expected 39s lag')\n",
+    "plt.ylabel(\"NMEA UTC lag behind wall clock (seconds)\")\n",
+    "plt.title(\"Per-sample lag\") # InfluxDB timestamp minus embedded NMEA UTC time\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ],
+   "id": "5e7aa3213c94ca15",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sane samples: 29177 / 29177\n",
+      "Mean lag: -38.08s\n",
+      "Std dev:  1.4030s\n",
+      "Min:      -40.00s\n",
+      "Max:      -36.00s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x400 with 1 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 59
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-16T23:12:39.481182Z",
+     "start_time": "2026-05-16T23:12:38.793374Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "fig, ax = plt.subplots(figsize=(14, 5))\n",
+    "\n",
+    "ax.scatter(utc_df_valid.index[sane], lag[sane], s=1, alpha=0.3, color='steelblue')\n",
+    "ax.axhline(-39, color='red', linestyle='--', linewidth=1.5, label='Expected −39s')\n",
+    "ax.axhline(0,   color='gray', linestyle='-',  linewidth=0.8, alpha=0.4, label='Zero lag')\n",
+    "\n",
+    "ax.set_ylim(-50, 10)\n",
+    "ax.set_ylabel(\"NMEA UTC lag behind wall clock (s)\")\n",
+    "ax.set_xlabel(\"Time (UTC)\")\n",
+    "ax.set_title(\n",
+    "    \"Per-sample lag: InfluxDB timestamp minus embedded NMEA UTC time\\n\"\n",
+    "    f\"Mean = {lag[sane].mean():.1f}s,  Std = {lag[sane].std():.2f}s,  \"\n",
+    "    f\"n = {sane.sum():,} samples  —  consistent only with stable I2C FIFO backlog\"\n",
+    ")\n",
+    "ax.legend()\n",
+    "fig.autofmt_xdate()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ],
+   "id": "6b40662f981cd256",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x500 with 1 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 60
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-16T22:58:37.007370Z",
+     "start_time": "2026-05-16T22:58:36.975095Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Rebuild as single dataframe to guarantee alignment\n",
+    "utc_df = pd.DataFrame({\n",
+    "    'hour':   utc_h_df['GPSHour'].values,\n",
+    "    'minute': utc_m_df['GPSMinute'].values,\n",
+    "    'second': utc_s_df['GPSSecond'].values,\n",
+    "}, index=pd.to_datetime(utc_h_df['_time'], utc=True))\n",
+    "\n",
+    "# Filter out no-fix zeroes as a single operation\n",
+    "utc_df_valid = utc_df[(utc_df['hour'] != 0) |\n",
+    "                       (utc_df['minute'] != 0) |\n",
+    "                       (utc_df['second'] != 0)]\n",
+    "\n",
+    "print(f\"Samples after filter: {len(utc_df_valid)}\")\n",
+    "print(utc_df_valid.head(10))\n",
+    "\n",
+    "# Recompute lag from the clean aligned dataframe\n",
+    "influx_sod = (utc_df_valid.index.hour * 3600 +\n",
+    "              utc_df_valid.index.minute * 60 +\n",
+    "              utc_df_valid.index.second)\n",
+    "\n",
+    "nmea_sod = (utc_df_valid['hour'] * 3600 +\n",
+    "            utc_df_valid['minute'] * 60 +\n",
+    "            utc_df_valid['second'])\n",
+    "\n",
+    "lag = (influx_sod - nmea_sod).values\n",
+    "\n",
+    "lag = np.where(lag >  43200, lag - 86400, lag)\n",
+    "lag = np.where(lag < -43200, lag + 86400, lag)\n",
+    "\n",
+    "sane = (lag > -300) & (lag < 300)\n",
+    "print(f\"\\nSane samples: {sane.sum()} / {len(lag)}\")\n",
+    "\n",
+    "if sane.sum() > 0:\n",
+    "    print(f\"Mean lag: {lag[sane].mean():.2f}s\")\n",
+    "    print(f\"Std dev:  {lag[sane].std():.4f}s\")\n",
+    "    print(f\"Min:      {lag[sane].min():.2f}s\")\n",
+    "    print(f\"Max:      {lag[sane].max():.2f}s\")\n",
+    "\n",
+    "    plt.figure(figsize=(14, 5))\n",
+    "    plt.scatter(utc_df_valid.index[sane], lag[sane], s=1, alpha=0.4, color='steelblue')\n",
+    "    plt.axhline(-39, color='red', linestyle='--', linewidth=1.5, label='Expected −39s')\n",
+    "    plt.axhline(0, color='gray', linestyle='-', alpha=0.3)\n",
+    "    plt.ylabel(\"NMEA UTC lag behind wall clock (s)\")\n",
+    "    plt.xlabel(\"Time (UTC)\")\n",
+    "    plt.title(\"Per-sample lag: InfluxDB timestamp minus embedded NMEA UTC\\n\"\n",
+    "              \"(fix-valid samples only)\")\n",
+    "    plt.ylim(-50, 10)\n",
+    "    plt.legend()\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "else:\n",
+    "    print(\"\\nStill no sane samples — print lag[:20] to see raw values\")\n",
+    "    print(lag[:20])"
+   ],
+   "id": "183583bc784c2a16",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Samples after filter: 29177\n",
+      "                                  hour  minute  second\n",
+      "_time                                                 \n",
+      "2026-04-02 11:31:52.790000+00:00  18.0    32.0    31.0\n",
+      "2026-04-02 11:31:53.042000+00:00  18.0    32.0    31.0\n",
+      "2026-04-02 11:31:53.294000+00:00  18.0    32.0    31.0\n",
+      "2026-04-02 11:31:53.546000+00:00  18.0    32.0    32.0\n",
+      "2026-04-02 11:31:53.797000+00:00  18.0    32.0    32.0\n",
+      "2026-04-02 11:31:54.049000+00:00  18.0    32.0    32.0\n",
+      "2026-04-02 11:31:54.301000+00:00  18.0    32.0    32.0\n",
+      "2026-04-02 11:31:54.553000+00:00  18.0    32.0    33.0\n",
+      "2026-04-02 11:31:54.839000+00:00  18.0    32.0    33.0\n",
+      "2026-04-02 11:31:55.091000+00:00  18.0    32.0    33.0\n",
+      "\n",
+      "Sane samples: 0 / 29177\n",
+      "\n",
+      "Still no sane samples — print lag[:20] to see raw values\n",
+      "[-25239. -25238. -25238. -25239. -25239. -25238. -25238. -25239. -25239.\n",
+      " -25238. -25238. -25239. -25239. -25238. -25238. -25239. -25239. -25238.\n",
+      " -25238. -25239.]\n"
+     ]
+    }
+   ],
+   "execution_count": 58
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-16T22:44:26.518823Z",
+     "start_time": "2026-05-16T22:44:23.982818Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "utc_h:pd.DataFrame = client.query_series(driving_day_start, driving_day_end, \"GPSHour\")   # data[3]\n",
+    "utc_m:pd.DataFrame = client.query_series(driving_day_start, driving_day_end, \"GPSMinute\") # data[4]\n",
+    "utc_s:pd.DataFrame = client.query_series(driving_day_start, driving_day_end, \"GPSSecond\") # data[5]"
+   ],
+   "id": "f63d56ba0d9ceefe",
+   "outputs": [],
+   "execution_count": 37
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-16T23:17:16.933996Z",
+     "start_time": "2026-05-16T23:17:16.689912Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "session_start = gps_latlon_df.index.min()\n",
+    "first_5min = session_start + pd.Timedelta(minutes=5)\n",
+    "\n",
+    "early_lag = lag_per_sample[utc_h[\"_time\"] < first_5min]\n",
+    "early_times = pd.to_datetime(utc_h[\"_time\"], utc=True)[utc_h[\"_time\"] < first_5min]\n",
+    "\n",
+    "plt.figure(figsize=(14, 4))\n",
+    "plt.plot(early_times, early_lag)\n",
+    "plt.ylabel(\"Lag (seconds)\")\n",
+    "plt.title(\"Lag buildup during first 5 minutes of session\")\n",
+    "plt.show()"
+   ],
+   "id": "5696230b0ff223d2",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x400 with 1 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 61
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "17f526266bc0fd92"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
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+}
diff --git a/v4/driving_day_analysis/pyproject.toml b/v4/driving_day_analysis/pyproject.toml
new file mode 100644
index 0000000..712e3a5
--- /dev/null
+++ b/v4/driving_day_analysis/pyproject.toml
@@ -0,0 +1,10 @@
+[project]
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+    "numpy>=2.4.5",
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diff --git a/v4/driving_day_analysis/uv.lock b/v4/driving_day_analysis/uv.lock
new file mode 100644
index 0000000..bd829bf
--- /dev/null
+++ b/v4/driving_day_analysis/uv.lock
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diff --git a/v4/kalman_filter_position/kalman_filter_position.ipynb b/v4/kalman_filter_position/kalman_filter_position.ipynb
new file mode 100644
index 0000000..afe3fdf
--- /dev/null
+++ b/v4/kalman_filter_position/kalman_filter_position.ipynb
@@ -0,0 +1,305 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2026-06-06T21:30:49.348641Z",
+     "start_time": "2026-06-06T21:30:47.533281Z"
+    }
+   },
+   "source": [
+    "import numpy as np\n",
+    "from filterpy.kalman import KalmanFilter\n",
+    "from filterpy.common import Q_discrete_white_noise"
+   ],
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-06T22:13:17.152574Z",
+     "start_time": "2026-06-06T22:13:17.141125Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from data_tools import query, TimeSeries\n",
+    "from datetime import datetime"
+   ],
+   "id": "e26ef0ef40f7b4e",
+   "outputs": [],
+   "execution_count": 10
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-06T22:11:25.454346Z",
+     "start_time": "2026-06-06T22:11:09.089127Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# we'll be using data from our driving day. intricacies internal to the car do not really matter.\n",
+    "# 1. Define fields to query\n",
+    "fields: list[str] = [\n",
+    "\"GPSLatitude\", \"GPSLongitude\", \"MotorRotatingSpeed\", \"Acceleration_X\", \"Acceleration_Y\", \"Acceleration_Z\"\n",
+    "]\n",
+    "\n",
+    "driving_day_start = datetime.fromisoformat(\"2026-04-02T17:00:00Z\") # 9AMPDT\n",
+    "driving_day_end= datetime.fromisoformat(\"2026-04-03T01:00:00Z\") # 5PM PDT␣↪(same day in Vancouver)\n",
+    "# 3. Query InfuxDB data\n",
+    "client = query.DBClient()\n",
+    "query_data: dict[str, TimeSeries] = {}\n",
+    "for field in fields:\n",
+    "    query_data[field] = client.query_time_series(\n",
+    "    driving_day_start,\n",
+    "    driving_day_end,\n",
+    "    field,\n",
+    "    )"
+   ],
+   "id": "64a21c8fb3e2c36c",
+   "outputs": [],
+   "execution_count": 4
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-06T22:12:39.775295Z",
+     "start_time": "2026-06-06T22:12:39.760199Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "query_data",
+   "id": "eff874f15f0be3cf",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'GPSLatitude': TimeSeries([ 0.        ,  0.        ,  0.        , ..., 49.19873428,\n",
+       "             49.19873428, 49.19873428], shape=(166543,)),\n",
+       " 'GPSLongitude': TimeSeries([  0.        ,   0.        ,   0.        , ..., 123.14530182,\n",
+       "             123.14530182, 123.14530182], shape=(166543,)),\n",
+       " 'MotorRotatingSpeed': TimeSeries([0., 0., 0., ..., 0., 0., 0.], shape=(139200,)),\n",
+       " 'Acceleration_X': TimeSeries([    0.        ,  -706.74394431, -1003.57202148, ...,\n",
+       "             -1004.40045563, -1004.90748048, -1005.2800293 ],\n",
+       "            shape=(166595,)),\n",
+       " 'Acceleration_Y': TimeSeries([  0.        ,  -8.2748031 , -11.62007036, ...,   6.90267876,\n",
+       "               6.45035782,   7.07600021], shape=(166595,)),\n",
+       " 'Acceleration_Z': TimeSeries([  0.        , -58.47604536, -68.36784685, ..., -69.01071349,\n",
+       "             -67.93418785, -67.09999847], shape=(166595,))}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 7
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-06T22:16:00.126321Z",
+     "start_time": "2026-06-06T22:16:00.113351Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "gps_lat_df = pd.DataFrame(query_data['GPSLatitude'])\n",
+    "gps_lon_df = pd.DataFrame(query_data['GPSLongitude'])\n",
+    "accel_x = pd.DataFrame(query_data['Acceleration_X'])\n",
+    "accel_y = pd.DataFrame(query_data['Acceleration_Y'])\n",
+    "accel_z = pd.DataFrame(query_data['Acceleration_Z'])\n",
+    "speed_df = pd.DataFrame(query_data['MotorRotatingSpeed'])"
+   ],
+   "id": "63003538a4c8e249",
+   "outputs": [],
+   "execution_count": 17
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-06T22:13:50.236734Z",
+     "start_time": "2026-06-06T22:13:50.214550Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "gps_lat_df.head()",
+   "id": "8691a69ec2b9b593",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "     0\n",
+       "0  0.0\n",
+       "1  0.0\n",
+       "2  0.0\n",
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+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
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+   "execution_count": 14
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-06T22:14:14.309835Z",
+     "start_time": "2026-06-06T22:14:13.620238Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plt.plot(gps_lat_df)",
+   "id": "e075b8e14f88b922",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x271d19a30b0>]"
+      ]
+     },
+     "execution_count": 16,
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+     "output_type": "execute_result"
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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 16
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-06T22:16:30.964161Z",
+     "start_time": "2026-06-06T22:16:30.477691Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "plt.plot(accel_x)\n",
+    "plt.plot(accel_y)\n",
+    "plt.plot(accel_z)\n",
+    "plt.plot(speed_df)"
+   ],
+   "id": "94b8e3340eb5698c",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x271d1e64590>]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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iJRCCJ0Ci6k+FmDs97urKg9L//36ad0yOERdrU73DJVb6j+gFm/LvbkHRSoKHIIwgFAkNlcrL48XoyC5DF4O9MdHw/N/Pe/3/kg45rH7LxtgYTeurdRa3y7draA2BKdc/3qrLYuHF7gUQykxdOA12QAPclC9yWTj5q3g1E5Svp6fqWn9ZpMOysz86GoKOmuPWggOd31DzXZe9rL4ZaFWhumXrK2BzbAx8kZzkKds2fQ6wa77KRAJO3bYvfALgreNlFnKuZ8fP3u+1GCR4CCKQ3YGfSnfEufz5rKFP/WT7Jx7PD1W7a4rk8wMjipIvL5e2PKEb6bOLAJ7QNwizlOsNn7rjAFoxlS2CmKoG75YLz250ZPe8l5bqKZB1EHQpIeWGyRsrXsiEzwn6Nw08n56nafHL27eDZ9tmwB8J8Z4vNJjY0+6QoH5P6Q5pEcX/VhjELOSfn8BqkOAhCLMuzO+MV/++Jd4WGiNsObLFvUlCUYKzrkUSrpZX+3sHK+5brv2DUTi1Mo7UHfF4HiE07/94i9fytuZ695N/53m9XtlYCTfOvxF+SEpU/Oy1cbEQVKSEiNhlEkSXVmWEDf7TJh0KogLVJlLnd13wuGlbkB8dJb9NZmUN7hZYYqUsuyGQnWj9LSSIUKGhImgfXSe4AHkNitUlXstPb5MCQzvnelwabZXB64kVpOghzczeMhtOnHsivL/FnXHl4TDY/iNsl4ulElvbWuzw3poXWDHJRzPbKH5+ZbB7a0lZeMTp2v7oWaWSEzrnwpcpyXBabntrW5GWv6pu+zAhAq2weBNUMX8LrYVOGpVqX/kSIpya7y9Yd7Ggls/vD7kfH/zbcS8U9haFBA9BGELDRbNku9/2dVSz262yMj5OdtkWrgUWJCZAY4QN3ktNCXqhwXfSUmBIl06wQmG7rcCr6x0D1WvrX4OX10rHKFwsiqUqtNe6X5z/sOfC314PVegadFLkjNfhWR8ba7E0dYnjvfyA8jLh6tLSUHeqPCICtsTIx9dxYiGJCRE8P9/N7nYe2wlvC+LB3hL3WxPvOywuKGTXAoDnuwEUbvBMm5fcIIF43fyl9DJNzuNbypJsMUjwEIQRnBeXiogIZSvF9h/8tq9jyt3WmcMK5vxPt7sH2C9Skl2P63SZpI0Pwf9zXrBvDpUsJCcfbPtAdsDboybIeNv3Hq05bmmX6fHyUVEqetAFj6BAZZXNBvVSgtcUC4+1s7TmJ8TDPzHagsgn5baHyzq0g1U+3JJoFRzXqYP79UaBUOYzppwuUFn2/uV+jFYhDGQW8tkFALVHAKoF7mhf2YN7FsGnKclwdoccOKyhr5trz9YcBStBgocgDMHBkYgIGNO5I5yqZEbnOCaMOL21PGSIln3dOSihFWjRU/DZ1g9crxwRDKg/JivHkIhpaWwGW6jMxv3B0pfBJpAhwpqzDSqtX8KldsfEwCaZLLsgtIaVjNmqtdlgdF4uG6C9CJXjQWdQLf4+92RnwkUdcpQXFlhe65zuyBtzsmGNhOhBq2B5ZCR7vQyXnXWCZL8wTklQlm73tOaoocl3tuV/26SzzNA30tPg94R42K8woSqOjISJue2Z5ZYFPTfqqILtJ0jwEJbHXl4OTaUis2wA4DgOatetA3uFTGwOx8FqpyumVHQhaNi9G0pffRXs1Y7Mnb/rS5gwekhFrIbXzO6lnrKLREfKmMr5VNdnsgCWvgSccGYnQGuFmOa6CNj5wC/w6jsSVo6lL7HUWdi/EuCLy1wZHIdffx32XXgR2yfNZWVwdPYcGLEzhHv5/PkM2ARWnos7tHM9LsbjQUVWllgWXdHevQ4xv6gIbPYrzt/xX6d1gx/Eg+3SQktToGTWTqFbqrFaPiuq5rDkv28SWzNF2X2/JknEgDktsI3ibCgxWODwmDOuar8jEWFmRjrc0C4LfNojFz7p2Ocl2xxtK5zWJCHfJyfBvdmZcIbCxO7ZNunsWsgst59fCPBse1i++SM464ezYNPhTRBMAhXKThC6Rce/x49ij3v8vQYik90uGH9T+cuvUHjvvexx7x3iwltuPC75RZsAcgayh/mXXQ4tlZXQfOQItH/mGXiv0jHz+jkpEWYeFpl60UIT6eN0xFkSmqChk89tiIoQvVd4wUJf/fLXoM5mg6ORvi1MUvYIrqUFbBKD2oq4OEhcnwB46c+RasOF/nzss1PuLKR2aC3Ama/Dkbccaez/DnM3OMU9fP2dNqhKsDkaHaaomDlbiBabp4WGh7l7TO4tVhLsass7f2V3tSL35/SstswqMbu4FCLMcGnVHgVIlbAeSVBQVQCn5eXCqDoTakLJsDc6Cs7uKBrsv7nOcf94uXQ8mzBORoBdvOzMDgBdOsm7lzH5oLkRqrdK13vy4PVBACfc5WgDAQCfpzqum6vj4mB0vaeYQUnO7fwFYrCuFl9tvdtJ2kooCCxEf0oE7E/d8CK7x2zENZevgWBBFh4i6FQvXQr7r74GGgu8s4S4Wrcfu36rj46/PuCamqClRr85tfKXX9R8ChwRBpq+M871EMUOUrN0GbuP9OXi2PkbwNNtAL6f6vl/rHvxf3cAfHia4lZEideNxcKELHgMTu+YA1NyOzgsDxKIt65mzRr4d8RIKP/Os21G9dbv4OacLPi6vXQmkktQ8WKHvakEat+4wuf2x/OWf8z0CBWXiJNmH79rE/5bhVtLjePL1sJBXAMH20VBzAFns6Nf0w8C9yfKGwyCXxsfB/sxRVqL4BFVBcZnTTWRcOC666B6yRJVq/hixxfsfmW8qB6NEBOqiF+cmQOnr2mBNpUSx6evY/bPmbo+67UMUSAycuRfgGcyXVYbPZlgzaKDDff3SZ06MMtzk7C1zJ4/API1tIzA1jAqqGv2ryhVggQPEXQKbrwJalevhsJ77/N6rUUgeGwKGQ5I6YsvQv4VV0BLfT3sv+pq2Hn8KOYS00N0hw4elg5fPN8m3fMfSz2zd1qcLq0o8dBWVQLw7Y3ubsWbvnA0mMRgxc8uBHhrJMD6j3xvoOAiGyE+lVd7FwRUCmYWD7yFDzzItr3oIUEKKmqX729wfC/BG6KaHdvyU2ICnJjbwSMbpWxPApTvSYD9Cz0DcoX0Ouj8Lp9fBPD+RMWYJVPBwE7sdyUH/iZosdKKaKa+mw9kxkat6PZTyWvv2OHjl+1+c/9VLlgAB2+/HQ7efTc07FXuCo6xaDxY86ZbIQc3zrNDU12El1XLXlUF//TqzW71O3dKWoyQ1XGxcEtDB9j9UzbU7KuBgptuVtyOhn37IO+7vyC6SUJw1AnO+0PrZddT9ccfUL1YprVJSws89akdrl7UAo9+IWG18yXydLQSUcJIzFyL6CxHKyTGDaFr0mPiFqaEleB58803IS8vD+Li4mDkyJGwZk3wTGetjerly+HIO+8aWkfdJm//rtBCIxQ/vjj6/myoW7sOKn/9Deo2bABoapK/kMkQkeiexTYXFUku88t+URl3ZNGTAE+keX6HD8+AiKN7PJd7qQfAlrmiFd4D8GyOd3l4hawvM05kdinka3683BegSVBB+K/noOyZ6+Hw6+7GmBGC6268M5r2oay2LLNoenZb9ry5wQbFf6dB0d8SM1YBk9e1uGex6P5Ci9ebI737AimBvYTWzlG//JIXAF4fDPBCN+YG5Ox2FndVvXQZc6fyA3XpxT0cnaFFfC0T6I0tIapeuw0+faEZ5s5shsv/tMO5HZ3uujdHMLcffrtShYHm+dnN0M45dt/7nff+aGlsdG/niw7XgRbwex66/Q6oWrAQqn6bB3tPU7YoCl0ydQWxMPMjO5yygYPCdWleXcEPOd3CyL6zz/FcEfZpcnJHVlu483v1gzn+VnunnAb9v98H18+XOE7+29n9eM4kJjYx4BiXtDfY4J8v20P+JZdCU2EhHLzlVii4eSrYq6UtwqUv/hfynGGE7aVa5gmtmUKOiASeCWjN1CsUuEHtojf725Z6ZoecABaBbEWC56uvvoLp06fD448/DuvXr4eBAwfC5MmToTQIwa7hREuDYNDzAbqiCq6/AQ6/8goU3HKruZ8vEDm+3FN44Ws8cIBduHmEVh0+aFgrXL3b/NqwTyJQMH8ZPLj6GV/vFi27FCIlYgIWicvCa2GTsy5GWT7YUGQJwIvMtOxMTZV5Pa6FlQfBVuc+d7g/Z0LxpyvgyFuzoOJnR9B1rGBWnSA6TPjhp7lW3axxfXeJS9HhHY5WHNiJXo3wQaGGKbhYrwQfq5kJ/yH4/QpWQ/Wff8LRWe9AwY03erhQj/4jHTv2VFvfAegRNTY4+O4SiHFO8s9e5dweQTuPe7PaSsY88PQ4yLkGWhciC0rRjIc8BD/XqC2Xq0nCldx8TGJUtzdDSWQkLImPY4NobCMHEza3ALfY/X2y90UB/HY/wG8POlqPFG6AmsUyrikUt07yDqofytGCu6NvP9fzkzb7+K1/upNtNzq0UGxiQPjALp3g3+8dwrNu40bYfdLJrsUb873P8/pFn8LROR/Lb9AbQwAWPw+w7QdHvS3MzvJT3S1fLlQp8DowOddtqW42SUj97eO6ghlaQvJjolnla6tgHellkJdffhluvPFGuPbaa9nzWbNmwS+//AJz5syBBx98MNibF3KgC2dHn74e/+u5aSNESMQQlH/3netx9R9/QMOePVD791pImXIqNB89BrFdu8h+VmRamkugHLrnXujw0os+BE8tu5jvGOAICs5+6CHIuOpKKLjlFnZRTb/8cteypc8/7/E+NeBMeadz3T3WrvUQSgU33ADtX3wRksaPcwRO46Dz4ekQ0TkXIu0AA/dxUBVvY7PIbk1NXnpHHLt6JDLCFQD5flEJjKxXFpZeYJsC5+AZKXL33ZvVhsV7LEuIhy37xIXhvOm/rwX67I2EomOpUH0oDrqe6jnK7vjKHazZUhHlGXcDAAP2cbAg3X3JvPpngH/+kc/mSM6tg5ikZiYm0mpkxAl2oo+MBUjvDJCeJ32TitF4Mg2gbQ+ANHxfZ/c9Lo+P491Wp2P/JkLJaZ4xVIef9+x3psWTkHOUgxtnR0vG4gjB2BdfZB/j4JlPJNwnf88GGHw5c5dwjQ1esWaNW5ZDbMdshzuF3eyCx97P95w/w+sjmt6+CKIufxIgsydAbIojdgQATsnLBc5mY/FEn7wsHZCN+8nm4VIVHQf/zgdo1w9ggztmBKtTR0j8hFxNBdjikwFEgfNH3/W0Juf7KuG07kOWKVgX4T42E+t8/5DlM6dC/HWnOI6btscBtDkOjsx8BI92j+XQYvfjSBtwnQHsjREQFdfi2RUdXZkCNxe2vDCLB7Ic1lM1LEnwLOY5LykRJte6J3JKh/QMHxmlr6WnwadFnhXcMWj9ivbZXssKazUFu4SFjRNOi0OUxsZGSEhIgG+++QbOOcdtMr366quhvLwcfvzREanO09DQwG48lZWVkJubCxUVFZCSYrDyrICaiiOw/cSxkFQLEJGVCfE9e4Hp+Onnq1nmCLQVkzjqeDSpsBk3WlbwpG4qKYWmQ757KkVnpkJMu3THtuJFgGsBO2eHensT1HNNEL2vCiKdMSBITF6eI36G45gQqt9ubKYUnZMFMdkZwOGgiLEh9mbgnPfN9iaobm6ESnsTZJZ4XsAj27QB+1HpwlnNHRqZK6L9AW8LRlluM3RsaoaaYvfFJia5CVZnukVJZrMdDgtMzcPr6yFWNBiKWS4KyjxBkJVSbYuATYJZVyTuO5sNbBzAaOdyK3wEdcY1cdDT3XtUNRUJAKkCLbk5z8bcXBEtHPTx0aWibb9KOLLVcY61P74MWppsULwuDcoSAfKzbTBGKtNGxSFuj4iCgkgbJLa0sH3rG/fFl4uIgpIIDmKORUJsnfKAlJDVAKsS4tg+ZfHIHOd47LzxLj687y7tAYX6aIA2beq9flN8v3gLB+R7f/HaGIDM9HrgcCkOoLZUYqZt4yAxW0JAc56fgE/rj0VDS5O3dS0uvREiYx0DdjPYmKUgpaWFpWTbfGybVricRjgUGQVRHECZLQJ6H5RfPiq+BWJSbcBxEdBwjIMWgUBqigTYnus4/nBfdjrMQUoDB9FxdohJbYbDkZGwIyYG+h3g2G+QojJ2FvdDfZly7GBS77aQe1U/h0sWbw2VbP/OSkuBbo1N8HTbDBYrI0Q8EekvyNKSet3XcsiiA4cgC6/HQs6fDWeuftSr19YWwXqrbTYYlZfLHi84cAja8etoPxi4rD4woEI6cDmruRkWFRQqbhcypL4e1sc5roV4zmy+RlvyiRI4fqempqoav8PCwnPkyBGw2+2Qne2pLvH5jh2CDq9OZs6cCU8+6ekC8Ac4fqHYYY9LD0NNqXRNhlCiZuUqze9pOlzBblJINRNAs7KUaVkvTUWl7OYLHDKkwml9iR0k6lCMeN7qIr0gCmpEp1ZjVTQMrhIOEhHQUTCSN0OsYh2cQaKRv0aw92wSr/MjHL/cQJM99kKxAyoGwYioFrBfdz003vc1xNgBYq58C7jYdIAbboH0GoD0vZzHd9KK4zeMhBpQXwFXS5EDFBcDDO7DuCbwEMLev5k0r58ZAXf81AIJjQA1JdL7KLVbA1TsiQXgbB6foQfxIO8woESaegzZimKgo/NxO5V1nxxJPu5tKMoASK4FSKoHGOh1/NmgqTaK3dCONsT5Pt7FqHU/fHhyBIzf2gJdvFvTQWNDEsC5TqsWTuyqS2HFggfgrSpBt3EZVO/VJyoAPurv9W90MXoInugEgP4XwP713uNciyCWxeNzR9wMMPEpR6Xm3JHAxSYBfOyweIsR1xyDFP6X9IYXO1aoFB4WgkcrM2bMYPE+YguP2dhiEuC9yREsA+XMxDKIT+vow8TsvOmsX+GPg6ipLhKa6yOgpdkGbXrVQGNlFNibnDVl+XTbiChoiYyCsuhoKI2LhIKMKMBG0kcibJBcaIdt7Rxuj8oE6UlmvC0S2kbGQWxzDKxNLIMROzm8VsO10cfcn8MbsVpsYIvk2OOGimjmCkEiolugqSaK7broRDuU7UqE5A71bJmEdk0AkXHQFB8Hu2OjYUdUBPwT2QxlNjv7HLxhplFKRByUQT27CLy4rQIaKqMgLq0JYpKb2Sy37kgMHN6SDCmdG6AyKwlmZ3GAc2wkhV1sOXafWgOwqauNzSCfOeIQS7ibju5Igowe1fBwlnLBwWexPs9577j/cXQfwOLnJM3LM52fgRRFRcLr4p46gn1+enWNbNG67kUcjN9ph5RGDrgWG7TpXQ3RU+6B6g27oG77Lkg84QRIzyuDyOhmWLTwN1gdlQD52QCXlVfBsT1JsLyPDRqjHPuT7dcIgDshEzod2uH4GWNjoOnmt2Fj21h4+drvILEeYO5J57LPfvqSCEh3eg//I65PNOFBgLQ8sPlwCdQ118NHW+fA/qqD7HiJabHBk8ddDNBe4kLtNK2jUXv+vt/gz4OO2BK0Cty4qwt0vvQKKJs7F5JGj2Zu1kNH98H/yn6EA5kA3Qtj4T+DboQHN7/hOnb44wdh987nnLM2ER77j3c8yGa+CfUcDN7DQWQLwH/yzgMbnu/ZvWHG3//1OIH58wPXjb/J6X9z8MlJEbCsXwQcTrNBVhnn+N3P+R/YsNhkZDSLZ0maMB5sUVGQuGQpcM0O04eNdyUIYz5cj92v1f+zHeJ69YaIwsUwueU36J/PwdSMcdCl52SoqDsCL657Hew2u8f2JdcBHFfIwXunRsBz5UfgzuxMSK3h4NoFLTCxPhFq9zl+0NgePSBh2FBIGj8eojt1gqqN6+HwjEdgaR8bbOhmY8cJvx8zqgA6HuXgq7ER8MfuQqgd/j9o2L0LIuNjoXrFKsg47zSwRXBQba+BucV/wYr6vbCzI0DOMYBuRZzHsRfVDDC8qB2ce+40WFy0HBbkO5IAUBjlHuZgWycbnDtgAJxy1Res/EJLdQ0rY1G9ZDFUfPsdxPbuDVt6xMCmI5uZOD+cYoP5Q2ywulck9N3PQWyTw4p02rZmiG/TBGn33Ou5j5OzYUH6OACVggcz1BTpf6HPl9A12r+hkVmy2g25FmCCI64r0RYN1Zynr3BfdBR0a/JWfbYxd7HjCY47hT23C01oSkzfJinErEZYCJ62bdtCZGQklJR4Sm983q6d99whNjaW3fyNLSoSFgyJgAVDAC7eXwepLf9qW0FENEBUrOMgxBgGvMBF4UVOcPN4PVr6f+y5zHsUXrdHRsGRhgrYX1sCB2qLYH91IeyvOgAHqg7AoapD0MwJTh4m9DmAAY45RGJ0InRK7gSdUzpDp5ROkJeSx+47J3eGtLg0aGxugQkv/AlV7e+CLc5Qn7uLYhxxAzFJALHJzpvzcYzoucdySZDmXGZ/Qxn8XLwKlh5aBmtL1kITO3kdF+24yAQYmTMSxnYYC2M7joXXFu6FPyumQQTHQWaEs2pqziCAzF4AmT0gsW0PaNu2JxyIjoRpv18PR+uPKsrMtKuLXfE1qa+tZft1ybfu4EhfpJ612hFnwlPwN8ABh/19aRdP18Ps8liYPuZJFhxaUrIRlnb0nYNQ1hAD22J9v76sH0DtyFp49No1jvgD3I9RsZDqCInzoOhwR/gmw7GutZAKMEJ6nZfDUdgy6W6Av56FR4afCj9ufRDOP+58KMi0eQzIWwTfKy3jYlZcDTodDzDwUsfx7oOKhgq4e8FU2JpXCJG2GLDjcdgSB69e+6jP96DYeX3D6/B+2TKAtAjgOBvYbBycedOTMKjHUEi/xFEioLapFh74v/PhYLVjGwvT4yH1yqmw7KM3QQs3DX4bYMNMqI2zwfK+jnWlXf206/Wl9S/4fO/yvgAfTXQ/39nRxm6pOTcATLpA8j2pZ5yuafuE76lvnghVn82DFX1scOrQ62Bwv+Hw2son4a9GlDnex85vznu+cnhFog1ePScSrh/5BEAv7+0oqi6Cu6O/hm0zoiDCFgEJXA5Ug7Qv9e/saBhz6WUe1jv87eblz4OZq2dCWVYZRNqiYFTmFFge8TMcaut9Pu4a0AcuPecMePnbN+BYivf2n9Uxj90njnAfwCmTJ0Hco/fA/Yvvh9XFq5lVK9HWHmo4h/vmaArAkv7uz7qrxyGAiz8F6O19bjfY5ePysHRDfwwwz+gGVVIBTGK6uGt8ibGDDe7JaguLEhPgjQFnwoREx29il7Ad2QSPUSD6wh/RLjZne4xgERaCJyYmBoYOHQqLFi1yxfC0tLSw57fddlvQtkvYY6d+8kuQmtFZQbCIBI0JnajVggd3aW0pEzH5lfvgQOUB2F+5n91jJdPGFt+ZH3GRcZCbkusQMwJxg/dt4tq4Z5oS/N+mQiisqIdkoX/oQeUgW6mLy7ridbBk92+w9OBS9j2EdEzqCOM6jmMCZ1j2MIiLcptZdxZtBrR5t+B2jrkb4OTHvfb94drDcNNvVzKx0yO9B+wtioLmuO3K5mcWwanhdxSKHSRngM9FP0hLgelDrwEYeg3YfroF4JjvQmHbVAh8tpVJyg08NdkhJzwAMOoW+PHL0ezpt7u+lV++TXeA0bcrrrasvgxuWnAT7Di2A9Ji02BCm6nwQyFawjjZYxy7nM/eOps9v6nv3TBr42ywRWO8hef7Xlz7IhysPghRtmhoFs2QtdCQ0x9Autiufib5ygw0hnCAjo6IgYLKAvhhl3LD20bx8Y0BvyJWFa1iIqKsoQxSY1Ph+XHPw2O//Q7VUe6EByH/l5QIYwTPj9QdgadXPg1/FPzBnuM5+PQJT8Pa3RwsL/1Zch0REAU/7P4BjtVL5ZFLs/3odrjrz7ugqKYI4qPi4T9j/gMv/bESaiKkqxsXn/0atOt9puRrTS3ygqeCb8Z5x3qA/QsA/nJ7HSQZ5E7KEPOZs5Iy8sHWD2BC7gT2OAIrsItqWkUKDnW0CPEIr9P7KvbBd7ukfxueL5OT4MzqGkjUJIyC69QKC8GDoIsKg5SHDRsGI0aMgFdffRVqampcWVvBpqnX2QCp6qPr/QFe8PHkRyHDxEyVQ9TgDUWNXBXM6IhoyE3OdVlnhNaarIQsNmPTSksLB+8sFtWm0QDOGJceWspuq4tWe2w/tloYmj0UxnVwiBzcVinhZW/h4N+SGoh0Wpe4kx/3Wg4tCTcvvBkOVR9i++CdU96BE+fco24jjYpWFMS3rAJ463j55YZcA7BQQ2XUQILWItUo7y8c/LBE/e7y3ZARlwHvT3ofvlnLx2hxPo/9V9e/CnO2Omr0PDjiQRiQfAbMAof4EQoeFMxf/+sY4MZnXg2LSt9nj6saZXom+fo20UkQKjTa3ZMaPAfe2vQWs95G2+KgifPurcTT5CV4jvPY77jP0arWwrVA74ze8MqJr0CHpA5Q1zjP5wgU2X6w6/0/7/0ZnlvzHOsSHmWLgpsG3AQ39L8BoiOjYdHmzT63ywYR8OG2D32/Lrpm/bTnJ3hy5ZNM+OHE7bUTX4Pu6d3hmaZljkA/Cf6IiwG3HcqTRgXBw+ovYawNi/eUmEbc+Adw750Ez7RJh7kpyfBN+W7omSHfUw/BhBDXY4nzIULwv3PErTKcnPvjuR7rkeI/bTPY7f0KO4xU3CrvbQsGYSN4Lr74Yjh8+DA89thjUFxcDIMGDYJ58+Z5BTIHy8IjnkH6ExygPURNxX7YX+Ww1lQ3+a5JE2mLZBci3jrDbk5xk5OYA5EmmyP/2FEKu0qrITlW3WHY3NIMG0s3ukTOrrJdHq9nxWcxcYO343OOZ+40JfYdqYbaxhZX8CqekHhR5UERddui29hnZcZnMrGTEdsGmuygITTWIFm9Ac54BWCbu0ibF3LNQ01mYzrWMPE9ABpCQTijFfKG+TewGSj+Hu9Pfh+6pnaFqjpnWpTNe+DAQfOV9a+wmS8yY8QMuKz3ZbBsF1ZXdsf1IOX15fD4isfZ4yt6XwEtlXxpBg5qmrS3KVlQsAhCBaGFp7A2H37Z60h375N4Bmyq/kbz+qobq+HR5Y/CwgML2fNzup8DD4982GVdrUNXmY+s/MjM3lBSUwJPr3oaFh90FA5FsYRWHeGgX1HnewA9wq2FhupjTBRLWXn4qzO6u19e+zJ8+s+n7Dm6up8b9xykxDgyfuoa7T4FD14zfaFk4UngOLgttzMs/XigpOB5/9gGeE2Q+XTBTxfAlqu3gBLCBp31Em618zvkwE3llXCOqD6ZcLzSIkxuT42GUCnxGzaCB0H3VTBdWGKElgKzBQ9efHlRw7ueUNTgYxQ8PrcJbEy8eIgadEEld4IOyR2YJSdQvO207lx2fCf43EcS1dG6o7Ds0DImcFYcWgFVTe5ZNlqVBmYOdMXi9EzvKes+k2LLIdxXgt9JYJ7FC+H0v6bDxsMbITkmGWadMotZeCpqsc6O8ufg74D7MyE6AewGmkjuKd8DRTk9ALbJLGSC+1PtGv6y+UnssI3wvRXFNcVw/e/XMxHfLrEdzJ40mx3HSGUdb7b3HDjw93x53cuumf5DIx+CS3tdyh6X1wndtI7f/T+r/wOH6w5Dl9QucOeQO+HZ+UtUDW6+QJeb2aD4Pi7dbUXxh4Xnu33vs2vWxE4TobFCvo6WFHsr9jLXEApTtLaiyLywx4Wu8xN/l9pGzuek4ae9P8EfB/5g5zueQ9MGToNr+l3jdX2qqPWdctUADpFzee/L4Y0Nb3i/3tLMri/3Lr6XxfkhNw+4GW4ZdIuHxZoJHh98tuMzuLjXxboEz2NtM6CO8y2i0f2qF7xWju7gcCOLqYuIYL26vkoxx/pYBy1+iffxB2EleKyGh4VHx/GA1gUUMrzriY+rwZsjcNY3aO3onNrZK2C4Y3JHiMV4oSDzd/4xWLe/DGIiI+D6E7rA54L+lFuPbGVuhSUHl8C2o9s8xCLGa4zpMIaJnNHtR7PAZyNsPYQNPt0XtxbngIkzrkeWPcLEFsYovXXyWyxuAKmsV47p6C/IWMALPlqn9HLOj6Jy/BJIma61IqwFYxaF1YVw/v+dL/naZ/985mWlY9YsCQ5WHWSWHXQrohVy9uTZ7J6nsp4flNxfAi/CL619CT7a7uiBhdaFS3o5+5ah4EHhKqhJ89u+31hQLAqbmWNmMktEvaCmjx637e/5v3v9D7cLA2K/+Ve71QTB2IoHRjwA/rTwHKzZx65ftw66FZ5ftELTehbsX8DOndrmWubufmXCKzAg0zMWrabRDnbMrPSxDjz/UOz0b9sfnhr9FHMtSVFeKz+RSIhKgIt7XCwpeD6o2gGv/3IJE9K43LNjnoWTO3sGHzfZW6C+mfNZLAEFHcaTpcd5VxOubJK/RqPw0ApauNWALngcA+QoFqWWn/T1Sez+mr7XaN6uAR/7jjW0EiR4/IintYHzOavCiznvfsqvzHcJm5JaiYIPAtBUy1tnhNYatEKgVcHKvP2Xw7pz/tAOkJXieTm59BfHDJwHTdl8wHG/Nv1Mda0xC4/AWoMXWhyQMGbg132/MvfWyxNehkFZg1zLVNQ1OQq/qUSL2Pnvmv+ygRcteLcNvo0FzyqB1qNXN2nLHgpUOOHkbyf7fA33sZjKDoOhse4IvLPpHTYAP3XCU+xcQLGDAxMe6yh20MLj8T4c+PCQt3FsUEDLH+67j7c7WgI8MvIRr5k4/o78afnpv29BQfVu1yy/b1uHK6vWObvnwA5bjii7EwIxOKAIfwCkBQ8ev3i84Q0tlHjjn7tuXLPXMqsKV8EH2xwuP57Tup7GhEZzi3QRUl+gVRQZ3m44C05uG+8du1hW0whRSXImS4DpQ6fDlX2uZBMGXzDBI9OdJTsxGxKjpWPIdlXsZfc4EcR4na5pXb23sxatXvIzgXFfjYNNV21iMV4ofsobytntn0qVXc01cOVvV6petlTQGkYLH8rEPYU6JHgCRAFaaap3urOfnFYbzAaQDFhzgn5kYdYTxtTwz9HNEorsLK5i8TuoB28c632RwdgbtN6gFQetOZkJvrtsGwGDprcXooXHU/DM2jwLvtjxBZvhYpYGCi0hlWyg9E8bOj6OAFEjdgZ/MtiQ9UhIsIuCISd8OcbLLYhCg3czoRtL6nioQJdWgntQ6JbaDfZUOET1o8c/Chf1vMjrPeW1jRAR42hpsu2Yo5t23zZ94YYBjm7wSD0GayGRNXD7H8rZY4EAJ0UnzT2JHasegoZrlr2WaOWWgbew+6NNDmGghav7XA13Db3Lp1g5xgSPQ2D64tp+ygknZejSkhE8GIfViKYkH0zoOAGeHfusz2spWgHjst0d3X0x0EeBPsJakOAJENP+uM7na2hOdcXSOIUNzmRx5mHUZWNF+MysKf3aQddMbz/y0ouXsgwMf5N/tAaqG5o9hvrjP3dnQ2EmD85yxTDLgERwbDAwS+zwqcDT7Y0sTkJonWyyN0GdvQ4SoxJ1uXWMwKchd0/rDu9Nek/SWuD6TQTwYuexUY+x2BEpHC4tT9CtIYwTKW/EwGbrgQJQLSg68DuhtRIfi2/42r9l3jXC+PioqBblgplCMDj53uGCQnwSoOWEr4FkBLQUgczm4W+P8V3NNV0hKtFbuL120muyxzQKs/qSKRCXzVcdkicpOoml3aPrHd3xhDdY4ypYHggSPAEEAw35rCehC0qpVk04cbCsFn7c5CjiNXV8N8llAiF23AHLANkpCSBuL3pj/xtZJo8UGMMTk+bu8hwuVEVGwNBPh7LH886fBz/u/pFZ2C7/1Xf9j0AxZ/IcyTgJBF2QmK0jvoSe1e0sn2JHSiRhvIjYrRHV1MVwOt4QiIf1oLJ5k0q+PONLJmBQrDAxIyFk2M0Wperagi7U/Ip8uPDr+yEy4QC8foK7/k5aCyYdO1Lz1dArQ7lnIAqe6p1PQHIvR0acXgstrsdXLuaY6HfZd2+w26Gh9DSI6vI/r2WUBDxaAZuOjYOWus6QkDfL53IfT/mYuduF165v1h2EJ7dOATN5YPgD8F+szh3CJAQx3IIEj5+pK7wQ4tt/DYsvXA0ZCdaOqwkE7y/dx2rfjO7WBgZ0DK71aqtT8PTvkA6Li86DuBx3oa07htyheqCUYvNVm+GpVU+xGK1uad3gvO7nwWlfjIEqE7sm+5NTvz2V3b+9Sdj1WjsYO/Pjnh8l418wOPj2wbez+jhyzD9/vk+xg1Q1NIO92duvge5IOcrrmqC2eKprIHv3FM8O3EhdQyw0N+VBVIK+3m4oGM8u2gvrW9yCZ+EFC1mMB6YZ6wGDeNH1ZiboRsa4pdr9DjdW2okZrtcam21Qtes56NZ9DZRGyxejQ5RiD5FjNegWNpY8gRMPuX67kc7hrcmOTYv1nXdsO8EG9npfnfMcDM5y1A0S4o/MJcww9JfgsYGNpfy/OP5FOOP7MyAcCax9uhXSXDEUqv55DqICmO5tVdA8/NXfjjba0yZ4Wndi7aIKwwHAkaGFgicVmspHQMO/z7M6F0q1LtBEXltwtewyOLN8fNTjbNC9rt91zDU5vkFdajrGHfBc3FM65dVKDGj2PZhgoPDnp38u+drGqzbC9f2vl103piPnJGHdH9+Us0EJ2HkWcfhy+O/Y/8KCCxYobjeWF7DX5UHVP8/C5xMXQxK2KBFR29AMdfunQlzxw/DrecqxHGLQtTHIluAVSIsDi5qaKr5cRoGkocnhvuVU1thW42ZlrigTridyYOFBBNvX6E1BdAQt6xsqzZY7a69YqzphQ8117IT2J8Cqy9zNoJdeshS+PvNr5nXQygeTPQPerQpZeIiA8dGKfKhrskPf9ikwprtnLIYtcGX8XLOvrYUOC0+/Do4CY2pBC4+9ujdU//soJPVw90VS4ricoQDl7qJgUiy/dDkLVMfS8FhhFmvBfLXzK5/L8xe1NUVr4Pr58uLBX3DJ2QB1xaa6rzDdNyYyRtXgLqynE1E7BE7rOknV57jfF+EzuJbP0oqwt9VVowpdJma7qwPh/hZ+RoMrNV+d4MGWDGqFREtTCkREOyYe+sWIfCVlTC3nmvUleLiEmU4LUW3+TZCQ52055ME4TQxCl+KyXpfB5zvckwW+nAgG7+P54Qt05arhxfEvMsveZ6d9xoLdMfZID2ilHdZuGIQCZOEhAkJtYzN8tDLfZd3xvmgHtnDV/qO1UFXfDDFREdAjW9vFkK/Dw9mVKzkLiVAxEPDVXbG5KVaVxewR/n9i+rTp43o8Isezg+fDIxzdknmSfaTmmkGPePXVzDFORsldg+nMmFWl1pJRJhF8rAapoGUxjqB2/YUHhbW4QpV63sKjMlBfXDJAXvC4XWf63E0AzTXShRH5SwxaeLhmfYO5+9jy/Tv6rGvGAdjrvLNQhdw3/D6fr2HBRCyUyWe98aBAkUNooeHfL0WS06KJNZKEZTcQbNeC1erlWH/lehZLhm0+kMmJjmasVoYsPERA+HJNARtgOrdJgCn9pFwUgRU8vHWnd7tkiIrUNigpxfBgdpea3j1qWXjhQlaEcvxX4z3+L2dxQFfYf9Y863r+07k/wYS5joaCcqDF6L3N77EaT6d3PZ1ZPubnz4fCmkJ2YcOZoHcKrvdvd23fa5l1iufXc39lJRjQaiVXz0ZPrx0MLPW9JdJgunkDujpUCHUjlhVm4YHQQ7jNvIUn1T4ajkT9rOK9Ng3uKP3nPW99aa4cCFGJvi0ecmnpip/hOrZ8f6fFFztaX4hRU10fM3R9gecaxuycmHsiZCe4JxW+WuZM6TKFZVQKXeLtE6Vjj54f97zsduGEC2/CAqpi8PojjCXrqGJShS6zYEKCh/A7aFKevcxxQcK6O5EWCNzlM7T6dtA+82N1eGTgWxdoFTy+LkLoIpByE8hZHGyiKq5t4tWnFt844EaP58LUfKmslkaJ2i/Th3l2fs5NyWU3DGCWQyqOxgxLjZ7AcxzoWcCrEz0p+YFO4/cHvDCM4dqqyhJSM9CXOa0z9uoeEJWwX9d2HVMQI/x/WQyPHwWPmp59vsAGx2d3O5tlB76y7hXJ/Si2mEkJyqy4tpLXDymR/ujxjzJxZDYTUnrA7PItPieB6KILdjZy6J+NhOX5aVMhHCqvg7ZJsXDB0I5gBdwZWqmmD5a+Bjm5mW+ivQ9MzvNdlViKU7s4MqmUyIjyb8fuRg1lBJQsOL7cd0ZiOfSKpFpRkLmUwBQ2mpWCxQYZGBCtgNAShhWszYD/zRqPjte/DpWBzzjh8vdnSKGUpPXmyW8yAfDMmGdYYoP3+6VXICUaSuul60VhhmigRHhsj1NlrVXBFjsICZ4AESK91UwHa2XMchYavPaEPIiL9mWV4AIbsCzI0NIKq+qrA7kTvnPTXZovRFiMTw1RJne5FxOpYbsV42B0HAZ6LDxCN5ia+B1f2774Eml3Bg+r4NvNtyuxV7q787dVaeCrTeP5ktkfXj/xdfj2rG99Lq+Ujo2vu0WqfieDctCyzbCFRykTzMihjO1y5N9v/Joo7Dfnb8HTHZMyfCBXViKQkOAh/MqfO0vh35JqSIqNgiuO953uaHY3eTkOltUxK010pA2Oy9Zu/fDVPPSEDifAD2e7C7b5O4BVKQBY/LlYRt8f5MRmaMrowKDK+4ffb9rnqxEvXu9R4dLiM7SE7jYMpD4171QWn/TJlE8ULVIOV6Tvy+ybJ74OIWPhcZ6iJ3Y60dVIFzml8ykey2PQuZKQFLoKpVAKzBUGLau18NQd0lbiodneApX1xiuZ1x2SdnErYWabECHRGrINnzp81JT1dkyyhmWfYniIgDQJvXxkJ0iNlzvRuIDH7/RslwyxUdqsHxjs6mvGiDNfTKX2Rf8E6QBCzh7nyijRglqRyK/aHyZlrOWhBYxH+PlcmcBXHZuoJ0sLa/BotfAgmDmnBSVrR6RF63PxhwoO+s0+qvudlHsSa/2BPbewQzoPpk2rid/hGRZ/N6yt84xfEXdX12XhEQm2lsY2PrMc9YpiOfifHoOqocMX2t+v4ZqYIwhqVmJkDlbOVkdek7F9wFuUsO6UFSALD+E31uYfg7X7yyAmMgKuGyN/EQyV+B1fAcundTlNVuwg8aLXbxl0CzQeGw01+beCHtRWcm3rtEL4a8ZoKrpcWoIsLc68oGVhhpZeWgBjF9zPr+hypsfrEX52Nxo9nuQynF498VVWuA47qmtBLFRyooezbuP+i+FxfK+W+g6a4q/0WA59cUW0cqq+kUrNY3JGqV42KyFL9bJ2g5Oknuk9YPVlq1XVZgoEJHj8iAVitIIKH7tz3pAOkJ0Sp7B04C08fdubF7Cs5oTmRAdEVnwWNJScBVxjpi53l9IMcFZxKQyrq4f/9rjSVMGDabJWQs9MXFis0Bc1EhYezYh+ohhRgHdaXDqMyh4OI5qtdSnmj0e+Bo/kMjabV4YS9pNSn11lLK5Eaj2eG+i4c1tkI13Vl9WcP2pdZr4Qrn96h4mG3q+maakvbikr1/zZZl6V46KUrv2Bw1pnGRE27CyugoX/lDLRd9M4+eJbgQRnTdsK9Qcsi+N3uieOZve+Go0KyYlONTVuSWkGeEJdPXxQXAqd4jPZc75AmFFeO/E18Bd69omeTBo1gc41KluBKCIzmKNoePfUOfD+ycZ6lvkLd5VldagRLr5+L+y7xnesVwJdbe7Jh011llavVN+BtV7badDCIzw9o3UIOi0TlFgZ6/K02E4Q36JvsmPPMZaVZ28x6RwyCRI8hF94Z4nDujO5TzvomqkiMDhA1rDCinqWeREVYWMxPI6Ptum28JyR/QCsvHSlRxCnL5Kj4uC3gkMQLHEwJHsILLtkGQzJGmLocz1igWyBaXVgdmCpGqtQjQkuLUZnhyiWrRmjo+Ci2UjpZ76PltrjTVUNHh9iE/uuYeHLM7t5uv18nYdKHh/+vBbG3F3dfYZ7WxVWoEZIt4lTWd9Kh0tHbl9Oqa7xeC57Dtps0KlJ37HcEqOtpMKEmlqP580WOK6FkOAhTAdr7vzfxkL2eKqoSWiwXVpbDjrcWcdlJ3ulyKu5WGPjUPGFRnWxPFsEdBTMmLUKlv8rc382VkLWU/AM++UkREtXd1WKaQjEz6bVtac3i0YctCw19plh4Tmj2xlg89V6QIiFYnkQfvxUU41acx8tkZDQU7JDi/VFGIeUKLKyyn+G5zEyNs7bstkx2Xf2kcfXGnqN4uflcKLhWGa/9G8Q7UO5FRtIQ2/RuHyKyJKkp3K6PyHBQ5jO+0v3ssyOUV3bwKDcNEvt4W3OlhL9NTYM1RLs6hNRHRctQYlIl9Su8Hd+Aazfd4C1m1Ad9yCa/T088mHJxZo5feJhSOpxYBZaRaB44FO7TwMRwzO6tg5yk3PVLZyrPnPGX3AGXFpY5bdXRi94fNTjqmNv0MqqFz6+BhMifCHspaXnWOOPLX49cbYMbXWlhMdirPKkyKbT7aSMDSbWOiwvHTRaeloE144Hjx7T/MlWS5QgwUOYCs7esG8W3yRUPQGy8BjI0FLTVkJ/LIeK9587C+J6nwXR183X1rk7oY3qWakekiKDF5SoN5OmIgAurUQJ8eXTgiWw8JxQWwdWQa2FB1sVYJ+kTimdVFt40hPlsxrVFARMT4z2W6Vl/jPkymmY6c699Zj6mjfiI0tW6Gd0hevLK+GlksPweWGxpm0SViS4vLJaxXZ57o+mFuNp7WZCdXgIU8GO6HVNdujbPgXGHtfWUnLHUWFZfw8twxaeOH2f6SItF+Cij9Uvf8EcgPICAIOBh7Lg9S2IMTx88HFybBRUabDIqAtaNimGRyOZ9uC4AaQGTWEMj1nwlpP0hGg4XNVgcB0xcKwedPXSUrI+8GI6IyHG5/FySa9LTLumDa+r908x1jbdASXbJB1Cul2ECnesAM5m7aBlEjyEaWDdko9W5LPHN4/vZoneKUJKKhvgSHUja17aJ0efS0ucpaXpkiSytAjxy67qd76mxfX0sTIb7S4tx++RlhitWvBgoHOVM/YH3Sq+CuvViCotqwFjq47VHzP0/WyWiuERxJyZNP7yhQdRrOiFt75kJMbAnjr1WVrCr6BUDI//jLQEp4VH9P1/POdH6JoanAzUNLH7S+4C0qJfuPeKTISnDx+FnGbPdWSKnvOIDxGsqm4lyKVFmMbcvwvYANQpIwFO66e10BYXMHfWcVlJMj29/BnDYzMUw+Nv9PbY6RCn3pJntBCar1m4lsFTGOgs566o1WHhEVYIzpYYFKz1iysjV4dHL3wMD4oVvahxi7lieEQuLXS9Xd3narh32L2yn8FbdXxtp5LYMXp6Y0yUL06rroHujcKCm5xfBA/YbHBOdQ2MrHdY4t4qLoVJ1TXw3SFp11iSSIhN7Ky9/pA/IcFDmALOot5buo89vnFcV4iSCSYMtuDRU3BQnKWlK95SLobHAvN6PdvQM70nZMWmwceFxbpjT4wIP35Qkm9bIn5Po8sNhtY+X+jJ0sKu129XNMOZVTVwa5njeNO6j3GJjCC4tYwELav+DGwcakIMD2/ZQ3eTUhBxYzPnJSTuHX4vy1hUI8yEYpovrDi2w1hDx/WscS8pvj8qwrcDBl958ojKIOJuBgqF2jyvWWPr6uGlw0e9LUxObimr8BBiUt3agwkJHsIUft5cyNLR2ybFwIVDrdEoTsw2V8CyftcNb+FJ0TDAiqec/AVhfO54sBJaXJBzz5gLUwdOZTf8XoMbGuFUUQ2OQGRyCGM5EE7Db5jKuypMDFoelDkIxlzzBzx75Cgkpetzd6D7YE5RCZwqqrUSSHhhpjUtXU1/Mt6FiDE8hn/3xBiP3/zWQbdqao/hC3sL5zpOhMLszYlvwkMjH4KZY2dqXuew7GHs/oXxL8AJXSYpxr8oofpbdTFwnbFpkwgZfS+A7w8Vw9eHiuDZw0dglIaWF4GAYngIw+BMZtZfe9nja0/ootNdFDgLTz+dAcvCGJ6UuGhVga8epDn82V8fKoaaGQc8ZphWCHfS4tLq3aY3uzHaOoounlVdA+WnPssKHGrBSHAm/xtoGTz5ooMYm1Fa2WBq0DITjSk5AA+XACj0VpNqBfJHQjxcU1EFcRwHLxw+CvOStNdaMrfwoLkWHj5+Jz46EuI0Nu6VjOER/e5C0c6LtiYdok1Y2FBoPcQYrUt7qet+Lt6d757yLhRUF5gW9+OxfrkLiKEaTzZtizvT73s1NrGbJS5sAkjwEIb5c2cp7CypgsSYSLhipLWC1HhKK+uhtKqBuaL6tDdu4dHiQnERnwZwxwaIioxVNKcHgwi9Bt/Etux7RcQkwTVJ2uNxsPLzwgMLdbm3+Ho6aRpiePiig2nxMfKCR0fQsotod6q+TaW4w1YgeJMjUEGyUoUHOb/F73CGLDzCPSwl2vVYePj1J8dFQXSkvkFbfChHR0ab+vuJU8D9gs1agsUo5NIiDMNbdy4/vrOimyBYbHUWHOyWmQQJMfp0fksLx0zySEq8zrlCRleAVM+uzVbBUFYdfi8dYgd5YvQTJmT8aI/hUXRp+SEtfXHxGkPvP6XzKeAvpMSY2S4td/yOseuEMEvLV4wUfzjrqcPDb6eRwGp/E5gAeJu2xS2WiCGGBA9hiHX7j8Ga/GNsFnTdCV0MrMm/J8qWg5WG3VmYysyfz+jSCjf0ZmkZRWjt0urecmVpaRiYylVY6VDc1hqx8Hjg/k57qwsMrokLWlq6GQhjrvTq6yZBWQFcD9eUKnsM6xFt7pT0GN0JBZy/l7cFQGfYtL6BBA8RxrzttO6cO7gDtEuNs67gkYnfUXvh5d1ZcdEREBMVYcnU8lAUPEJWFa3StLyXeOHUx/2kyaWkmxy7EqqYnZbuqpBsoAYPL5rQPY0TD3ttd2g+chq8dfJb0KdNH1MsPK6UdAMWa79eGyJjRTE8fvqcDGtlWRmFYngCRfiMiy52lVTBwn9K2IXlpnHGTowEey9ojCiB2Ajpxpbm9dDybeFRuj7xAcu64ndCACukxtc0qc9MQusDb4XR4nrg24O4CsqZVIMn1JEMWvaThcdYDR7+94uBCKbRbdBSNgHGdnSkitcVXAktjZkAg+QrLWtNSbcUXccDV7DY/5+ToTHmyOITwOBP6YiQZdZih3VnUp9s6J6lsmO4DzKbzoP6ktNhep93wGyOVDdAUUU9E2ZmBCzjrDL40sB8VHd9t4jo4mfhWEsnOU5HllZ8jE9XER+rpeY6flLuSbKfFy/oNRYl12xSBYGyKLrS0gUWHjM+m2/6KSc2ldfhbk0hRXN1X2hpxHgym7eFh9MTFK3prYGh20mi7fHT1tmsV0/NCCR4CF0UltfBjxsPscdTxxs3e0ZAHDQdGwsZsVorNKt3Z3VpmwhJsfqNmrxlwB8WnmC24Xhp/EvQPa07PDf2OQg2csXW5IoOatl7aoKWtcTvnHvcubKvJ0TFux5nx2dCKGF20LKrP5WhooPa1qHHwhOMoGWu8xhNy3t+Kz9dP2yC9U56Rnl5svAQ4cj7S/exAmLHd82AwZ3Swcq4Cw4aSwU3VHTQwkzKmwTfn/29Jaqijus4TvPAp9VaoCZoWWzhMSv26dIuZ4AW/jhwEIKBv4KWzYjhUbsOdwyPnrR345YozURpi4HkAjJJsrkfjr5dxfJk4SHCDJylffn3AdOsO/7GFbBsoKWEv2N4wtFF5n8Lj77B01WHR9bC424uahz3IBAbqa37dLoo4NafWVrSMTwmp6Wb2EdL7Tr0fAfXZxgQZlqNHe1s2j6LE9Wy8gu28LoykUuL0MzHK/czk3/vnBQY3yPT8ufV1kPGU9INFx20GHHOuJK28cYafwYTqWwrpTEG41CkYnjEVDv7aCWqcIFqsfAYPcwDF8MDXjE8ZiAVw6P1K/HWF6VSBPx30FWHx2U9DJxLKyoigvVgU4vwW53QfrQ1Yng4sDQkePxIeGljB3WNdvhwRT57PHV816DGnqg1f2OPL6SvgR5awsahKXHmJzcGejd+etqncHKnk+H9Se+DldAStOx2O6gflLB6MvZJcrwv2ud+57O0sHq43ypUW7gOD4/QpWX0k5nYFFh49F47XFaiBD/G8Ch0SvfPb2WDKA3vES5p81tJCRuEk+IhwUNoYu7aAiYicjPi4fT+OX7Ye+aeMFud7qy8NgmGiwWGUwxPz4ye8OqJr1oibkcvbpeWb+Hi6z2xURGyPd/4thJqLDyaxgQLzw+kBmgz6/BUeTQONSGGR8nCo7MODxadFB5betFsjLPZ4KqKKvZQTePYTk0BKJ1gs/ABqwOqw0OoBi8c7y5xpKLfNLYrREVaXy+b0TDUq3EoxfBYAj1Byy43mMq2Egmmu7RsrbbSMh8XkxATqbPBsHM9LitRtCrxwossLec5/xZWaTlgY74Nujc1wer8AohXoZbympvh7eJSaGO3+0+YpOZqW97iWVokeAjV/LK5iLmH2iTGwIXDNJ4IQUJNwUE9MTxWd+WFKl1Su+gQLzGmx2HVOIOWk2IjjYsYA4OA15r9OJ74O2jZFXtjMC5GdZYW2HQ1DuXXjyUs+IrqetD7UyVoOF7GKDSbNUx2H4Dz3gNIVmvNJ8FDhAHof5+1eA97fO0JeYZmaCFr4REUHiTMZd7586CqsQraJbbTLHiEA59SUK870DlGnYVHRaNZzJDplNwJOqV0UrHV2oRy8GS1zbtbOmeNxqGu9agQTvo6pTdJbqfWgPGAGjv8OQEbcBGEC2ThIVTx187DsKO4igVxXnl8XkjsNfTDFxyrMyUlHalwBi2HQ5aW1eiQ1CEwLq06dZ3Sa51ZWmoKVUZHRsNP5/7kl9YcaF+YkjcFfsv/DfyN1PjcYGJPMTNq8NQ32V3xVRjDU+V0M/vSAE0iC5Ual6AWQWUu1raOhINLy/pBGIQleNtp3blsZCfFwcIqbCt0pKN3ykgwZZvdMTw0T7ACrvRyDFpWKTZ4l5Zc41Bh4UGMN1EDxvH4dnMaGwSeH/+863HnlM4Q2BieFkvV4BG2E1GTLanPwmOO4NEcb5XjbP4V0nBgZUjwEIqs218Ga/Ydg+hIG1w/RmMzOUu4s+TT0W0qZ5Z8emu4tZYIRYQpzloGJjVFB4WtJVRlaWlAb/rwJ1M+gTsG3wHndD8HArmP/SF4zKqyrOacaWrWU2U58G0lGEOvhpChz9kQitBUlVCEj905Z1AHaJeqrfx5KMXvcCrid7DwbqIgrsPiFtywBd0afMsAFC9HqhyDlN5AZ/HvyFt4ElXE8ASCQVmD2M2fiGNU9FhH1BQdFAseLeeQlgwtlEONmMEUCm0lEIONZQNKQhvp/1v8gkgWHkKW3aVVsGB7CTNx3zzef9Ydf8Q/mNVDS1yDJ8KUdgOekH1HG7x1B7No4jUE0PMxPEqlBfjWEokqsrQUiUkWPE6EUEFcg8foUOZuCWFGp3S1RQe5oLSV0Df2+1EsXP6t4z5noDnrs7iw8YU1pi+EZXlnsaPuzim9s6F7luDCbXEw3ib/aK1pAcuu+B1nhhYJlODiztDyLBHA6WhHIUWNhtYSikRGST+2GOJ9Z3rjUBPaNWhxN+FxYSQtPS3QLi1/ctxEgHv+BUg0q5WMjzNt7D0AW+aCVSELD+GTooo6+GHjIfZ46oTAVOQ1a+LAV1jukBavWJHVCn20zAjhuXPInez+sVGPQbjjytBypper3X+uoGWlwoON2oKWg2nJ9Ac2P/TREraV8HeVZTDQR4sXxUYtPJYjORsgws9us6xeAGe8AlbFutMNIujMXrqPxUmM7JIBQzqlQyixzdkw1Ax3lkcfLQtnaN3Q/wa4sMeFkBprzne2MmorJvvO0lJXh0dNWnq4CB7xZMPsTum+Ynj8KUb09NE6ZkJbCV2NXkPJTcTJbGuUdeM8ycJD+JyNfb7mQECtO8HI0LJOp3RzBsLWIHYQPRlaaoUSDpJ8QLSawoPhCLqDzHRp4eBfFgQLjx6XluvYCieXlulwEIqQ4AnTLsdG+WTlfpaa26tdMkzokQmhxlYTKyx7BC1TlWVL4KsarlJpgTpnIT0+aFnK4sJbd0wLWjZSfqDDMAgWXhYeAxaIyvpmjy71etGUpWXztvAofQWHMPORTQbha7AxF+taMUnwEF7UNdrhwxX57PG0Cd1CrkYMVl/de6TGVMFT6cPCE2pCNlzgLTWpIteU3CAjLC2QLOOq4uN3sKM6FrgzA2w/gUzIneB7oaHXAuSN9fxfhvreYoYRu7RMjOHhrSZGG4dqzdLSGsNjljBD/HplyOoNQYULzete67TXErJ8va4AjtY0Qsf0eDi9v9qmcdZhu7PCck5qHLRNijVlncK0dMJKLi0tbSXcolWutACfoWVm/M4Hp34AjfZGiJOLbzjzVcf9E6nWCFputlZbCa1tH9B6p1Xw8OvHFjqW7hfYVUY4BwTO90sWniCThYfwoNneAu8ucaSi3zSuK0RFht4hYmbDUO+2EiR4rICeqr3uDK0YdRlaJrqzsPWErNixAGJrpbgOjxHk4ne0WEmPaYwD0hq07O7PFtNajSDqCNHvFnqjGeFXftlSBAfL6tgF5cKhuYHb2zbze2iZlaElGbRs8iTGwpMiS+Ky1mix8LjcYOoahya20oBl/ng018LjHSyu9ZhHVzsvwtQHLXOh0VYi5FQEJ/OadS9mJHgIj4C9Wc5Cg9eOzoN4k2uQhGqGlkdauoqGhUQgCw/GaHaDKcVmuNpKmJySbnWU0tK5YNfg4atrR0Ywl5M/LDxSwixwWFcoaCI9MA1u9dC6zmhClsX/HoZ/iipZYOGVo4Jz0Bqd42CGzZ7D1dpcWjYLFB70y1pbg0tL/e+h9jes9VPRwVDyi2D8Cy94MHjbaE0eM2J4XPE7iZ7VtX2Bi2iN4TFDmOlOaIhPg5CBk/lunY4HOP0lgDbHgdUgwUO4ePsvR5PQS0d0MsWHHQxQsOG5mJ0SC1nJcaYVCqMYHuvQ0sK5xYtI8MgNMurbSvin6GCo0eBM4TdD8Jhag0fDtUm7hcf7M0ItS9USDL8BrAi5tAjGhgNlsHrfMYiOtMENYwOYCusvd5YJ/bN4ME21qr7Z8q0lWgsoPnltqqW1BN84NFUxaNneKosOevfSclp4TMhWKnNVWTajBo86wYOHhOYsLR2u0lYZtAyh+eVI8PiRUJoZzFrssO6cPagD5KTGQ6jijwytaqfYkSo8GN4XNWvCD0pogcFu6WqpcMZhqbfwWMClFdSgZYdYiIs2Pky42jUEsMqyriwtgdvMKGF9aeBC89uR4CFgd2k1zN9ewvbE1PFdQ3qPmN1DC+HdJ/HRkZoG2HDpsWQ1+DgLrdY2tUHLfB2ehFbm0hK7dPksrdioSMNjHC8kjDTk1LoOVmlZZOFR+g5y5Q5CdIwnBJDgIeDdJXvYyXxKn2zonpUcsnsE01Z3lVb5sQaPewAkgWKBDC2Ns3C1QcvWiuEJ3ijLV1o2w8JTZoKFx91OxI8WHjPT0sNaIXEQipDgaeUUVdTB9xsOscdTx4dek1Ah24sqAavCY3VlDFo2C/83DqUYHi3onYWr7bBeY6UsraDG8HhaeHSvV6Y/leN1jUUHNcQB6Y3hCU5aOgBc9AlAbArAZV8rL5ueB0GDC03BY4UpDBFE5izbxzpDj+iSAUM7p4f0b7Gt0BG/079DiqnxU3wPJmocag3UFhD07QpTCFpupXV4fFl4MEvLCL76U2m1krrja9S6tGyaLDxMmNWYmZaugz5nAfQ6AyBCxT7PHQlQ5uh5SKiDLDytmIraJvh89QH2eFqQrTtmyJMtB80PWA6UhYfQ00dLfeowy7RzChllCw9VWkaEdXiMYFZ/Kj1p6TiZUwseH81OYRbULC01YifYJLQJ9hboIgT2LOEvPlmVzy7uvdolw4SemZbY0UYspf7I0EKoBo+1cLtH1AvQKkEqu1C4Sukkt4XHAi6tALoOvCsti4KWdcZt8K4oo7W99KSla6kfVF7jTk6wdOPQYHLhhwA9TgWY8ACEIkEVPHl5eWxmJrw999xzHsts3rwZxo4dC3FxcZCbmwvPP/+813q+/vpr6NWrF1umf//+8OuvvwbwW4Qm9U12+GB5vit2J5RS6H19n12l1aZnaClZeMwajkJ89wepj5aWthLuVPZohYa4tbyFp7W7tExKSzejerHD3aQ9aFlLDI8rdV4kpPWemnoFonqCcNHoey7AZV8BxIdm+EPQLTxPPfUUFBUVuW63336767XKykqYNGkSdO7cGdatWwcvvPACPPHEE/Duu++6llmxYgVceumlcP3118OGDRvgnHPOYbetW7cG6RuFBl+vOwhHaxqhQ1o8nDEgB0KdHcVVzG2BF9WcVHO7UlMfLau6tDQ0DtXglnT10mp1hQc5H1laxqwdfH8qIxlaaInmU8xVp7ZjWroGC48ZmWSEtQn6GZ2cnAzt2rWTfO2zzz6DxsZGmDNnDsTExEDfvn1h48aN8PLLL8NNN93Elnnttdfg1FNPhfvuu489f/rpp2HBggXwv//9D2bNmhXQ7xIqNNtbWCo6ctO4rhClMOMNBYTuLLOtVbyFJ0XBFWIESnM3KUvLhNo9tQKXFi9+WiNul5Y5MTxasqt8rQOtTVqaGmux8LiCokO0rQ6hTNBHOnRhtWnTBgYPHswsOM3N7gvMypUrYdy4cUzs8EyePBl27twJZWVlrmUmTpzosU5cBv/vi4aGBmY9Et5aE79uLYaCY3XMGnLRsFwIB7YdcmdomQ3F8Fg0S8sj40edaFUKWMY+Xa21tYRYLdbzWVpGLTwmxPAc01m4UFx40Ow6P0ENvyI/uGaCekbfcccdMGTIEMjIyGCuqRkzZjC3FlpwkOLiYujSxbOvU3Z2tuu19PR0ds//T7gM/t8XM2fOhCeffBJaY9kC9IXzTUKvHpWnabYUrj20eOsKF8wsLYrh0V54UMPgpyR4+POzztkwk4/3cdaxDNEya+ZaePRew5RieDg/tqYQu7Q4VRYeH8eIpk9uncdMq7PwPPjgg16ByOLbjh072LLTp0+HCRMmwIABA2Dq1Knw0ksvwRtvvMEsMP4EhVVFRYXrVlBQAK2FJbuOsI7iWFTtqlGdIVwuzP+WmF9h2culJeqjRQQeHMB4N5OmGB5X7R6lxqGOdUfYzKkwHB5By0ZjeEyosqyjPg5OZLRZeELMpRXRyiyQJmD6HrvnnnvgmmuukV2ma1fpfk0jR45kLq38/Hzo2bMni+0pKXH0eOLhn/NxP76W8RUXhMTGxrJba2SW07pzyfBOYROc929xNau3gbP3junmNz7lg5b9WmnZb2sOL3jxidb8ZA0CVHWVZWcfrcSYqJDPXDStW7rhGJ4mSXeUlt2rpwaP5hgeM9tKBMKq33MKwIZP/Pwh4YXpgiczM5Pd9IAByREREZCVlcWejxo1Ch5++GFoamqC6GjHhQoDklEMoTuLX2bRokVw1113udaDy+D/CdH+LSiHlXuPQlSEDW4Y6+kqDDZGxhahO8vsQQpdgHylZWHMCBEchMHHkWiGEeNjkCmvUxe0zNfgSXDW4GnNweQNTveeUcHjK91bC7xg1WThEWRpYdNfpYwtXpgFra2EViJCZDstRNBsthhU/Oqrr8KmTZtg7969LCPr7rvvhiuuuMIlZi677DIWsIwp59u2bYOvvvqKZWWhK4znzjvvhHnz5jF3GLrKMG197dq1cNtttwXrq1neunP2oA7QPs18S0iw2OpsKeEPdxbOcnmzeEpclN9mca3NmqAXuZ5MSlXFkTSVgqc11uDxLjxoTtCyq4yAAcuJWzRptfA4vlSMikxU0y08FMVjOYJ2VqNL6csvv2QCBWN2MDgZBY9QzKSmpsL8+fPh1ltvhaFDh0Lbtm3hsccec6WkI6NHj4bPP/8cHnnkEXjooYfguOOOgx9++AH69esXpG9mTfYcrobftzsCuaeOl3YpWgE9F4mtrgwt/8XvoDHBGt2zWzf8oCS21CjpRb4Oj9Ls3VV0sLVlaInAmlZ8mwUjQcuY9caLVCNCwt1HS71VAw8JoYUHGvS5zXTPRfwetUxh0VoJ2lmN2VmrVq1SXA4DmpcuXSq7zIUXXshuhG/eXbyXXbAm9s6G47KTw2ZX4QVtRxEfsOyHlHRBDR6hFcZsewzZd7RZarS6R9yZdjHqig5aoa1EECcbwpYMRoKWq3w0DtWK3hge3jobHWlTdF27sv/CJLaR8KZ1pyG0Eoor6uG7DQfZ42kTrGvd0cOu0ip2UUN3U6eMBNPXT41DrYXeTBq1Qcu1ziytxFZu4eFT0l3WEYOuKGwcyvfk0oNed5OHhUdlJWcjsUYBtb+kWysOMxRo3Wd1K2HO8n3Mlz08Lx2Gds6AcGKrHyssexQdpJR0S8C7R7QEkOPsvUJl0HI1n6XVyt2XvIUHLSORBs4rs9o1uNpTaBC6vGUJUeqfxrvM0H2HzUPNAI87v5LZA+DSrwCSPevQEb5p3Wd1KwAtFJ+vPsAeT5vQLdibYzr+6pDOQxYea8ELF18Dn1QMGMbl8MGrYguPTaathDUITrd0vo+WEauM2vo5SrqANQ7VYeER1uBRCloWWg5DKoGg56nB3oKQglxaYc6nq/azuISe2clwYk9Hun84sfVQpV8Fj6txaLx/5wahdI0NJq6O2V7CxaYYsIyDntLsvZpcWox6iZR0zkDsjVRbCbWHfKXOOCBhDR7epeXL6qKmrYTfLTaE3yHBE+YXrQ+W72OPp07oGlozF5VNULFqtL8ytNRYeCj1NEhZWhpcG67aPQmegedS1DpdWgnBdGkNuVrwJHDnLOej6KCRy4arfo4JjUMxDkhLAHVTM+fKsJSs2STxGRkassCUIH1kPUjw+JFgy4tv1h2EI9WN0CEtHs4Y0B7CjV2l1ezCjOninf0QsOyRpeXnGJ5gHyuhAi9AtQSWuvpoqaiUzdfhSQqmS2vAxWCVoGXDbSXMrMGjcR28hUcpfkcopI00OCWsDwmeMAWtH+8u2cseY1VlNSd9MNFT0ZYPWO7bPgUiFGZwsp9t8z0jc/XR0lj3hbBOlpar6KAKkcT30gpup/Tgu054C4+RDC0Py0lCYPtoCWN48DvYArCd1vsVCTHWHgUJ3fy2tRgOHKtlM+GLh+eGzJ7UYgb2Z8FBrywtf3ZKp0rLqnAEryq4GDnfMTxyGVq8a9LVS6tVBi1z3jE8JjUOTTNi4THYR0tNlWUz2l8Q1ocETxiCF65Zix1tJK4enRfk2WroZmghlKVlHeqbWlx1VcTuDTmLm9pO6UILT2KYnjNqkWocqido1x3DY8DCI5OhJReTpbYGj1LQsu5Cy2TisRwkeMKQpbuOwLbCSpaRcvWoPAhHMGtjuzNg2Z+Cx5WlJdFHy0zIQ6Z+4MPaMBjAqha+cagql5blemnZgpyWju4gmwnxN9EB75/GlyJQFcOj04okByU0WA8SPGEIb925ZERu2JZJx95gOOPHga9r20S/fQ5ZeKzYR0tbrZRKTUHLVHhQGLQciDo8ateh1d0kjOFR/AxqK9EqIMETZmwqKIcVe45CVIQNbhgbXm0khGw56HBn9TEYsGyVGB4y8fivj5bathKerSWsEsMTHNyd0vUPEY7GocqWEyVLiCuGx59ZWjpFlRzk0rIeJHjC1Lpz1qD2LB09XNla6P/4HXSbYfNDrUGyhH9Q49rgZARPSkhaeLjgpqUbsPDgucN3d5ASm2qNdHr7aDWptPAIKzmb6dIirAcJnjC6bO09XA3zthWzx1PHh18biUBnaFU5rTsI9dKyUtFBjRYe3qWlMJhhkCvvBkls7UHLTcYtPHz8DtbJMuIa052l5Sw8GCPolC51Ha5rsrssWrKVljV9OmFFSPCEEVh3By0OE3tnQY/sZAhX0PKCQdmBCljG4G/xLNFIICdhftFBuV+jgi8qp2Dh4d1ZSEIw09LTOgU/aNmESstuV1S0KZY9f1l4+PVj+nprd2WGOyR4woTSynr4bv2hVmHd2XekhjWEjIuOgG6ZSWERsEwCCvyWSeO28Mj/jjWNdtcAGdRCnR6CJ9i9tPQLADMyn3Byw7cG0SqceGsd/pZyQe6u7UxUbj2iBeq9ZT1I8IQJs5fvYyf4sM7pMCwvA0INV7VjDe6sPjkpij1yzAlYbt3uDavgKjqowaWFbioUx0iaRB0e4QDnbivROn9vYfCwVB0erZgRF4MZdnwckN7Cg0riVa/LjAg9SPCEAWiJ+GzVAfZ42oTwtu4ICw76M34n4BYe8pCp+D10tJVw/oa4f5MVainxgifBUm6N4AYtG4nh0RtsLBUHhL+dVqsbX4dH2aUlf1zptfpQzI/1IMETBny2ej9UNzRDj+wkOLFnFoQ7vIXHn/E7gWwcSmjN0pJpESFKm+NFEv6GSuUL+AytVmvhkYzhidSdkXisRn05AH/U8WniKy0rCCWhS4sIb0jwhDjoa5+zLJ89vnlcN7/WpLECLQEKWA58DA+hpfCg2h1YrqtxqJUsPME5MjwrLevDjIacRtxNrsKDSoJHZyVnJahkhfUgwRPifLv+IBypboD2qXGs9k64k3+0hlmz8EJ8XJY5Acu2YBcdJLQVHtQwE3cJHoXfEAcn67WVCB6uOjwGmoe6XEVK1hnOP24xV9BylLxk81cNHmotYT1I8IQwmMGAqegIVlUOamZJgNjqtO70zkmBKD9/X97CIyV4zI65oRgeedBVxWdbaRmYXJ3SVbyHz9JKtFQNngB2Sxc8NjNo2ZdYUZOZyLvF9IgR3sISEykv2qitROsh/EfIMOa3rUWw/2gtM9dj36zWgDt+J8XvnxWoxqGEit+ivpkJfK0uxgpNfbSag1+DxyKYIXh4d5SRGB5XSrqBdShaePzQVgIhl5b1IMETwjNevo0EdkRPsNSs1P89tPydoYVQHR7rubOwCKScm4XzUXRQjUiqbe1p6YIR2lWHR7CvtbpoeHeioSwtnX20hMQKLMFSIsQM15sJixMBgARPiLJ891HYeqiSFd+7enQetJYLciB6aPFQDI91cMdZ6G0rofy+ameWlrUmD7bgW3hsxhqHGglaNiO1XcnVb0ZwNREakOAJUd5evJvdXzK8k6GLgVVQE8Ny4Fgta0iIWRfHZSWHlYWHUNtHS1s8CG9lUGXhcWZpJbVSlxYnKthoxKWFkwV349DgZGnxYB0em4paP1R4MPwhwROibh208GCV4RvGdoFwQq4cO19wsFdOsmIxMXNjeKjwoJX7aJnROBTB7D/rWXiC6xjR21qCFyroHjRyrurto6XWwlPXaId6Zwq+2XV4KIbHepDgCUH42J2zB7aHjukJ0FpAF16g3FkovPjCg3KtDKhfTmDQ25dJS9Ay34KitcbwSIEucz24M5+MiQheOGUYWI+c4OIth1ERNp+/O2VQhg8kePyIP04UbJz569Yi9vjmMG8S6jNDq31qQGIY+DoegcjSoouq+X20PDqlqyk82MqztKQsEh5ByxqMTWbExTTbWwSWPQMuLRkLjzBg2czGoQ4obNlqkOAJMbDuDl54TuqVBT3b+T+OxSqgJSVQPbQQ/kKLhaulZn4kUKzp0hIPyq46PGrS0hutWHgwuDW4HZWWtW+DKy5GhSvKlyzgfzs814zE0claeFx1fihOrzVAgieEKK2sh2/XHWw1TUKFHCyrY4NedKQNerQzp8Kyqj5a8dF+mPlJYfwzsDs07wIIN5QbPEpnCrkCz1UMaLXOLK1ES8XwBBJv6aE3aFmNC1LptOLXgWLHSJFRuRgef1VZRiiGx3qQ4Akh5izPZ26WoZ3TYXheBrRGd1aP7GTdgZThnqE15bWlMOTpBXDgaC2ErUtLw++BGX38oKPmfXzQcqKlXFqhGbRsRn8qV/yOQTGiJoaHBE/rgARPgDAa3Ippnp+t2s8eT2tlsTtIIN1ZHjV4AtQp3Qwj0u7SanY/f3sxhBt8LI62thKNrmagagZuPmg5sZVaeMSXKDwm0aJqrMu5gU7pGtxicsh9B5dLKwxKexDKkOAJET5bdQCqGppZw0yM32lt8D20/JGhJXRZ8cI0FC084YyerB8tGVotHOfuli5h4WmN7gkWvyM8N3TE8BirwWNOF3NPCw/no7Ch8jGitdI0NQ+1HiR4QgAs8z5n+T5XZlYERtK2IliFZVcPrdSAtjJIiQ/MbL91/aLaKdMxgLqKDqp4T12T3SVqKC3dAW8V02N9dFt4jFdZNhpQLJelZUZhQ1+0RpFsdUjwhADfrT8Eh6saoH1qHJw1sD2EI3KZIIUV9ezChLUyegUoMw2bVaqx8AT6olZwrJYF47YmMD0Z43HUWmu8M7SURWuNM2AZB3fs12WdbLzAbQBnUg0es2JjzBBNrkrLtsDH8BDWgwSPxcEO0e8ucRQavH5s14BUGLYavHXnuOxk2caRZlIhyNKSJvAj4ZdrDsDY5/+E+77ZDK0J/reQE6A2uRo88TGq20okxkQFKCtPLcETt0aSA8yokKwltd1wlpaMS0vv0dC6piWhQesbPUOM37cVQ/7RWnahv2R4LrRG3AUHUwL2ma609AAFLavhtUW72P236x2lCVoL/OCJBSC1pCfzLi11jUP5thJWytAKLGJrpd6UdJyklZvgjjKrqae6Ojxk4WkNkOCxeOzK2385rDvYEd1aBdGCkKHVMTDxO8EIWraUUcFilOsMgC3XUYOH4nfcxIpcWmozTXGyoKVxqK/1HnMFqhsUPEGqw0NYDxI8FmbFnqNssEdf+jWj86A1EoyAZY+0dMrSCjq8pUartcCdpaWhcailavAEFnFWkStoWeN6eBGRbLRxqAmp7YivbcBkEL4UgT/S0ilo2XqQ4AmBJqGXDO9kOHAvVCmpbIAj1Y2sxUPvdoFzaVU4O6UHzMJDeVqmZWjxFgNXlpaqxqHuGB7CYJVlk2Jv9DaMVRvDwx8fkRG2gPTLI4IPCR6LsuVgBSzddYSdjNeP6QKtFd6ddVxWMsQHML7CHcNDF8JgoyYWRyrQuMJZeFBdDI+z6KDF3MYlVQ3wf5sKQ0rwuOvn6J8sNDa3sLpjZmVpKaW9+yNQnerwWA8SPBZlljMz68wBOZCbkQCtlWC4s4SCRzEt3aTPM/N6a60sI+PwFZO1zvRdQkmLhcdigufvfcfgji82QP/Hf4d3nBbfwAUtRxqzzBgQKnzcFlp2jSYO+Kq0zG+nkeKIslCaluUgwWNB8o/UwG9bitjjqa2sSahvwRM4dxZmmfCzy1CM4bGFaZaWGkuN3qDlGr6PlkWztPB4nPnbjoB+prgOj9rx21W92EiVZUEwsdFCq8KgZaGoO6ZxOykmJ/QhwWNB3l26l2U5nNgzE3oFMG4lmPBGCfFFJdA9tJAqZ8Ay4mt2abYRJdAiZVthBXy2er/hHm9msO9IDdz55QbYUexoH+IzS0uD+MTvxVfLVjOD5wsPWs3CE0h8WXi0WgzNqJ/jqoBsQh8tX9uvp12JFoJ/ZhFiWu/ZbVFKq+rhm3WOOivTJnQP9uYEldLKeiitamDiok9Aa/A4ZvtYcTdcCz2e/voydp8cFx306t1XzVkNBcfqYNE/pbD1ycm+s7RUDn44cDc0t0CjvUVRKPFjoTtoWbuFB8XVp6v2Q++cFBiWlwHhmpauPdjYSA2eJnNq8DitO1JJAaqDonXObqwwmSA8Cc+reQjzwfJ8FrA3pFMaDM9Lh9bM1kKHdadbZhIk+DF7Rng5w2tUMBqHBivu5p8iaatKIEGxI0wN9+3S0t4pHduRqCkmyH+2HgvPkl1H4NEft8EFs1aCWZMea6Sl683S0ihQZZuPGozfkSs6aFI2GeEdEnD7FxtgzjJH/0crQYLHQqArBWeKyNTx3cIu+FQrWw5WBtyd5VmDJzQNoOF22KhxadlkMrvUnEe8SytBh+DBmDslPlqRD5+vPqBqfa8tdFTUDjZGg5aVrDNyv0u5WX205IoOmmCJkiMc7Dscx8HjP26Fj1fmu/ralVTWK3YH+GlTITz183awGqF5RQ9T8IKITRK7ZyXBxN7Z0NrhLTyBztAKhoXHChc2IwIbG5ri2/0h0t2FB3V0Slf5G/ozaBnjUR7/v23s8XlDOvjsB9dkb2Fd2/OPKguoQOBl4eECGMMTkD5a1mwrgYIiKznWEhPeNfuOwUcrHZPwq0blwZWz18DKvUdh7s2jYEQXafetL0utFSALj0VoaLbDbKcJ8OZxXQ1nJoQDweihZdU+Wv5k7toCGPHsItf+1goO1JNfXQLXf7TW9G3DargoApA0DcGl7ho86gazGgNp6UqnKr/9SLNMp/uTXvoLBjwxHzYcKAdLBC07Y3i0jrtmFAw0q4+WnFvOim0l0DIy8tlFqhsE/7qlCNbmH/Pb9lTVe4oXFDsIJjyEommLBI9F+H79IRagm5MaB2cP6gCtnSPVDVBUUc8utn0tbOHRGpeIs33eZWYV7v9mMxyuaoC7vtqo6/2bCsphV2k1/LGj1PRtE1bDxVYF2ttKqBNJvA5J1BMrpqAIPGPEOMVYJr7dQbDxZYlSit/g972R7Cez+miJLTx4jb1y9mqYv63Y7zE8emKWX1n4L7vnE1fkWLi9BG75bL1psWOtAXJp+RG1Jkm8SLyzZC97jFWVwzUzSAu8taFL28SAN3RU00dLj/2trtEOQ55ewB7vm3maJUzWYreUHvw5oeODj1G4qN1fnA6XFk+iH3ppCTfbwpNfL/QELQsbh5pi4TG5j9YTP21jQgSr2PPXFa0xPGjRvO3z9TA8LwNuGNtV03tRDGKcJmZGGikoi4Ltpk/WQbCwybzGZ0daERpZAwSncPBiLRK8OF86olOgNilE3FmBte4g/AzV7KKDh8prfc7+TK20rPN9VhyM+fRkpWwd8f7TUnRQiB6XFu8C9bltgl8klDKV9QQt87E3aI2Ti59RXI9Ot5jYgiausix8mY810RoYjW6k37eVwDO//CO/LRL/e+zHrfDC7zvhjDeWqX+TBJ84k1v8DafjPW//5d+K4EYgwRNk8ATlm4RePapzqy58JmTroeBkaAkbh4ZqHy10LwUSfw7irgwt3W0ltL1Pz/mnlFVlMUOehsKD2istm9FWgq2Hr4Lspz5aPFpaV/D7h8/oU17ee4+t3HPUY1IVKPB3efDbzYbifTBDi0fO2nqo3OGatSIkeIIMngCbDlawMu5Xj86D1g5fC4SvsBzoDC0tfbS0I5jpe71i3qj4mcr051CAt9RodTtoaRwqRE+WlpIJf8OBMveTULLw8EHLfqzB4ytQnY9j0rqeWYsdoQE8LiuTjy+BQlprgoiRpqD+Er/FFfKp4k//vB2+/LvAULxPOFxXSPAEmbed1p2LhuVCm6TYYG+OJcDZCD9L6BvAHlr+Tkv3iOXQYRaxarqn8LvsPWyudanMqIVHo+DxR4HL/87b6XrcYtAcxluDrebS+nptAdz/zSaWbIBkaNjv4l3C/+ZRGgPV8Tj877wdmuKQ1AhpsUZR+xOKF/tzZymUVDr2jy/2qqjpJMXCf0pkX9+jc73FFW5rzep9DusUEiJGSy9I8AQ5TgWD5zAD5UaNwW+tof5OXpuEoKSGqwla1oNHto74NZv2FFEzWPLvYfc26RyMhanWl723GsxES8dzjz5aOkQrFqnzR8IAxufxfL2uwNC6XprvFk9WqrSMadRz1x5kBRbVxt7YVPTR0hLYzx8rQpTiiLTGCGEVbNXVyUWn07Uf/A3BAjMp9fD0z+44JeHlQW1gMmZ/WgkSPEGEn62dMSDHUMR+uBFMd5ZWl5YW87bw4m2V4NVthe6Lt95NevInR1E9pFihCqveGB4l14bYJagnS8usDC20xG05WCEpILFfmBGa7Jylg5b5ool6XFpoHXxn8R4oLK/X5cY87fWlXv9TErBathPrKY34zyK/uXb4Y92fglsrvoTNz5uLfLojhewOcDyhEqEZlRkG7D9aw6L9+TYShESGVhAEDydoHiqblm7QpisWSoEwEePx9uLvO+F/lw2RfH3/UXcWmRb+LfHfRc3dR0trDI/2/ltmubPOemMZc03MumIInNovx+M1oy4tfyLeNIwr9Hyd8whgxQa0I7tmwFNn93P9v76pRXew8UkvLWb32SmxuqwvWLdLu4VH/XGlNRhXa6wPFgD1B/O2FpuyHk7F1+n16DyP51arn0sWniDx7pK9rF7FhJ6ZrMsyYY0MLZyh8LMa02N4ILhgkTIciG/9fD2ECu4+WjGaXGx8vJOSK0woXM2q98THYfzfpkLJmltaKOXSIFjwFh4pcf/t+oOws6QKPl65n9WlEWOkBg8f52I0Q8tsC4+Uxa7WWaFbbesSOYor1Lt/NoqqcctNwPiJtVH4KstasFqtMRI8QQD9ml87K2mSdceTitomOHCsNqA1eITnJG8ZwLgqs/sqCT+HnwlLvWYWOLhKDbBis7OVcffR0tJWwh3LoSUOK8HkooNSM2KxRwoHTLS6idt6XN94D/xkPx5eab4AAgXnI0tLriq0r+PJjIacZlRAlmseqlWYSUlVFHw+lxe8YcZ3W1TFB6mlSkMCAx8iYBQ9qfRSYjiYkOAJAh+u2AeNzS0wuFMajPTRgK21stUZU5KbEa+5aJypRQfjokyfnQjjTMyadclVTZ748mKY8OKfmq0KVsLt0tLeOBR/QxSuakk0OUNLKojW3uI5ALyy4F/435+7vQrRLWoZCrc33QFVELzYPrmgZaX9aoZYMdpHS42FR8tnSKV+q7Hc+LL2qY2LUYOZZS2s6E4zCxI8Aaaqvsk1K0DrjtVMfsGGn+kGw53l0ThUg2Vgz+Fq2FGsnLmxocBdj8XfIgSFGwYr4kycTxXmsXAYiZcLwV14MNrvxQrNbish5QJoEFn2hEHjVkMuaFlJ8Gh1R81xNk7WK5oamqUtCeJKy2K0HFdSPc6CEZMlZVHDYaS0sh52FleBlbBb7GJDQcsB5os1B1h6cbfMRDild3agP97y7HCesMHK0NKazozn88nOYMtNj0+SfZ8w6NFsnYtCWkio6miMveFjaWoa7a6Ud0XXg02irYTWPlp+qMET7CrYWhDHqIitI5wPwSOl3bW4ivC8kLKAGO2jhcREyotYo3FCzTJZc/4a64VlIISMeHYRu1983wTo3CbRUH+8cIUsPAGkodkO7y91zGRuHt9Nc4XPcEZs6QpGDy2k0lnrRn25eU51zYmPV7j9/Wv2eZZ4N2rp21Tg209vsUmWYvaiuE0BulbiNcRTYRyYrirL1NbFA7eo8T42sSigVFkCHi37ft1+QSVqkwKfeaKjHNvp6+xSY0WSOzX5ps9mV2SWQ8mqtPmg+1qwXljl2894VBS3KCR4AsgPGw5BaVUDtEuJg3MGdQjkR4ccwXJpGamyrHQhEmZ0/LhR2aevZTZ+99yNvhtWii68ZtTKQfEutippeS/epBCGuOitlsx3WNf6G5odtCwHuhvfX7oXVjh7K1kBLcOzUAR8t/6Qx2vJceoahyppfFOytEwuPGgmSu4nPuD3rb92wz1zN7nO+b2Hvevq2CxQt+nct1bI9t+yAiR4AgSaFvnZwA1ju/ilomsos91ZXRnpkBZvStCjsRieKM1BgpNeWQLXfLDGZ8ViTiaDYdmuIz5FgJpiYl4xQYLNm79Nvuy8HkbP/AP6PzHfVZVaLbidw55ZCMOeXih5MRT6/HnhonVQ0iuUkgLg0uI58cW/FLttWxE8ZvEYf/ZXzxYOZgsVs8SI0nXW/H556i2rGLAuB1qMf95cCM/P28nKAPBWYSnHwB87SnVZmcprGw25vTD5Rg6svv2bnxM0tECjboCYv72EKXPMHLlkRKdAfWzIcKTaXWW0XxD6Z3llaam8ENaLTvi/dh5mFVkl4XwHH24vqoQnf9oOehFbl4Sz58/9UBn2qNPd9PZf3r2dPlnpaC/g6wKLMWyYVstnYPkSbnqLDvIxPFo7pSe0cpeWGtfnm3/uZse4HFqDxQNh4eGPVzFasvi0orQ7520rVkzbvu3zDa7Htc7rhdRkC8cXqXOfL6IqxbbCChj01AK45kN9LS/yHvwFejzym1dJBTHTPlsf/oLnP//5D4wePRoSEhIgLU26eNaBAwfg9NNPZ8tkZWXBfffdB83Nnj/QX3/9BUOGDIHY2Fjo3r07fPjhh17refPNNyEvLw/i4uJg5MiRsGbNGrBqGwnsiG5WgbNwJVjuLI8+WipjeLCApBhfGVji/371t2dlVS3CBF2jSinQPFggzkyEFiwpwfPoj9t8v1eDcNNSdNCjlpKOthJIkgkuLRQEoQsnGaPjepUDeHXhLsW1aGkcKkeCCXWweNea3PnhL9T0pjvu4d9g6a7DMOW1parXp+QKRJH0p9Pi89lq7zpB5bWN8OPGQ/Ce89ol7KenB3HDVivjN8HT2NgIF154IUybNk3ydbvdzsQOLrdixQr46KOPmJh57LHHXMvs27ePLXPiiSfCxo0b4a677oIbbrgBfv/9d9cyX331FUyfPh0ef/xxWL9+PQwcOBAmT54MpaXGetaYzcGyOhZ8iYKHkCdYGVpa+2ipCRqUs8IYcWlc8u4qKHAWaJSivMbcCzw2TOSbJgpnk2rBeBUssKc0BgjFoqvoYKLOGB5TO6VzUNdoh0X/lMDgp+az2S3eMBVYyAu/71QUv3/tNHZt0tvkVQnhpgpr8GiNpzfLHW1GyQ7epRWMrEW1P9OVs9dINiQVu60POXuM/bRZOf7vWqfVBptTixn01AK488uN8IMgjlBr2wwhUp8hBq9Va/OPBT1rzG+C58knn4S7774b+vfvL/n6/PnzYfv27fDpp5/CoEGDYMqUKfD0008zaw2KIGTWrFnQpUsXeOmll6B3795w2223wQUXXACvvPKKaz0vv/wy3HjjjXDttddCnz592HvQYjRnzhywGhcNy4W2SY4+MYRVBY9yHy0lLn9/NTuxhQMTPpeq4yGF8KKA1VdxJoaDq5ixz//pcx3jXvB8bYFKkfLtuoOwfPcRWL33qGv7d5dWsRko3mZ8txlu/mSd4npwe099dQlrosmLOyywJzR//51/DL7f4Kg4LszMwrgArB3Ed83W6iI5VqO9wzoiZ3lFl2vvx+bB9R+t9XDF8anAcvtTzDUGu2Z/6OxIbjbC4y7aQIyhWldiILIHeQuPPz/rud+kLRxGPxKvI0Ie/WErHDhaC+8s9p0ZJkRLcdNNBeVs3dd/+DeLFeLPQbW8PN9T6Etdqy6YtRLmLPeutxRIguZbWblyJRND2dnuWjRomUGL0LZt22Dw4MFsmYkTJ3q8D5dBSw+CwmjdunUwY8YM1+sRERHsPfheXzQ0NLAbT2VlYIp/3TSua0A+J9QJpihck39MlYXnYJl8o82uD/2q6/OlhI0Zy9/48VpVy93z9SbZ179YU6CpptKZ/1smOfPke3up8fdrtbbxs2U1QknYIsEMF4qY+7/dbPo6MdYrMzkWIm02VtrCcY/BrDYWk+L6f4SN/Q+9U/xj9rrgMb6Gj9ECImxBIOUC4lu+KPHDxkPw2Jl9FJdbssuYK0UNvKVKy+CtJ1wBK+Z3z0pi8XtoCcT4PF+BxEYQT2TkkDq/1Cy7SMd2v/6HOlcuTnxuGNu19Qme4uJiD7GD8M/xNbllUKDU1dVBWVkZc41JLbNjh2+/4syZM5kFKtDkZgSvTDzhG6l6SFIxDEL+VAjcJMxj1d6jij3nOInYT62xckbM+oFGGMzqb9AVoQXeMqeEUvCzGRRWGP9N+XgYOYRCnpAHs1Hlqnj7E012ywcffJD5VeVuckLDKqBFqKKiwnUrKFA/a9XLCxcM8PtnEPqQClAe1bWN7HtOH5BDuztAPHqGsrUgLsb7UnZcdpKmz+nSNlHV/6zAiLwMGNY5nfXjG5ibxgL9++SkQK92yXBcVhKr5I7b3ikjgZV5yEmNg+yUWGY9xewntJolx0axBrlxMk1CkVP6tNO0bWcNbA+BIlPBGjy2e6bhzzhT5feJj45k+xb3d9dMax43ViBYYgfRNAW655574JprrpFdpmtXdeaqdu3aeWVTlZSUuF7j7/n/CZdJSUmB+Ph4iIyMZDepZfh1SIEZX3gLBPtmnsbuqWeWPAM7psImH8G+gSD/udM1Lf/mZUPgzcu0u6CUePfKodA2ORZemLdTsheTmO9uGQ3V9c1w1RxzMhPvOKk7c0Udrm6A0/rlsHpEXTOT2P/wN+rbPhW66XTXSYEDcZFEU0aehdPHQbfMJFUX0aX3n8iq9qIIyE1PUFXJfOH08fDQ91vYoDa4U7rX63HRkbDpsUnw4vyd8Mkq352xA8k5g9rDq5cMNnWdGK+FReomvPAnFFbUw5xrhrleQ+H0z1OnstiuG1S4RqdNkLfG8ex85lTo+cg8v9XZef+qYdCpjcOq/t5Vw1S7dcWcPagDE1aXiWJqhOCxJ2XBH/DE767q7UTw0SR4MjMz2c0MRo0axVLXMZsKU9KRBQsWMDGDwcf8Mr/+6nlxxWXw/0hMTAwMHToUFi1aBOeccw77X0tLC3uOAc5WgISOOhoDWBHUTCb0zJQ0zQsFFAbiYuaKGnE0qa9DqH9x0/HsXu49c28eBUMkBmkjTJ/UU/L/w/MyXI+vH9MFZks0exSy4+lTmZBBqwJmP4m/x4ZHT3Fl8whfe+2SQcxCcI6zaqsascODA45WtzHGXeB+lAOzvZ4+px+7aRW4398y2qsCbV6bBMg/qi4eRgqzxQ5/nYqJssGS+09kv5t4P2Jrj4l91PX+65AeH9CZ/kXDOrICd2KE23tiT2PjVpxCfJevzvL/PX+AqXVo8Lqi9hi8alRnV6NqJVY/dDJkp8Qxd2R6QjQ7HjCIXW0sIk7su8wwbyIUcllaWGMHU8nxHuNs8DHeqqsdzfMmTZrEhM2VV14JmzZtYqnmjzzyCNx6660u68vUqVNh7969cP/99zNX2VtvvQVz585l2V88mJL+3nvvsbT2f/75hwU919TUsKwtInTo2z54xQaN8OKFA73+9/td41Sl6W5+YhLcc0oP2fX/cOsJcM3oPLasmBFd3CKkh0b3jRHQDSLHyxcNZJYRtAyIU72/mTqKfRdf+wSzafBi+8Mto+H7aaMtN2H4v9tOgN45KbqrBWPh0b/uOxGsSlRkhOFYQ3SVBZKc1HhV38sI+LvJ4asEgtp+iT2zkxWXWfHgSex+1hVDZZd76LReTBg9dXY/VRaym8d3ZWIHQZccf86p3fZLhuda7jwNuODBejqYaYX1cVDk4GO8rV3rMCuiK+rnn39m92ixueKKK+Cqq66Cp556yrUOTEn/5ZdfmFUH6+tgevr777/PMrV4Lr74YnjxxRfZ52F6O4qqefPmeQUyE9bm0dP7sBNv3l1jIZTAgV1Mz3bKFy8+duj2k4+TXWZQbho8cVZfrzij50UxYa/JzPox1sPMzDdh+wcpzhvS0edrSXFRskUd+VpFeAG1YnPdAR3T4Eun9U1N5mVnp0uFJ1gtUwKJlsHPV6XjcT3UW2Qm9fX/tT5VJtV+zUMn+7RWYcacklg4vX8O/HrnWMXl2qfFSx5TQlCwXDayM6jBZgPY/uRkmDGlN+jlvsk94bnzB6juZ4Z91oKJ3z4diwhKVUUW0rlzZy+XlZgJEybAhg3yGQnovrKKC4vQB86QjJx4ocwFQzvCN+sOws0ayhbwMzIecZDkeUM6uJo6Tp/UA9bml8HLCr171OLPfoBBrkumCgz4RfcWDmaXjezkUW0b42uEBd3Eg79Sg9nWBgZViwvs8bFkasG4Mj1gcLFasAyAL7JE56La1hX3TuoBt50kP+FBbj2xG9w3uZeqdaL4UmvNSoqJMmT5Wv7gSar24S93jIGD5XXw5h+74ZWLB0EwoR4HBGEiGASrlZnn9YfLR3Zi1gO1iC954v46aBXC2CEcDEZ3awsju7QxTfAYaf2RqNCgM1QEwZXHd/Y5gMvBf73ZVw9jBQTVVKkNZzDA+f5vNktaAv0NWk+NgtYZOTD2yRenKbzXl1s0S0Z8aREwdoPnmhqxc8OYLnBcdjK7ndjTEasbTKh5KEEYwCYRBOsLYcyNuBosZghpaWTYJkneNYJuow+uHQH3Tu5pepPE0/prS1NG/nNuP7j/1J6K8SH+aptgFTB1HDm5dzZ8cv1IeOdK+XgMq/HBtcNNXd+FQ6Xdn73a+T+mr4eKuBkl2iqch71lvgdmP8px49guMLlvNlwhEtdmNWdtMflcw1jDQHaj1wMJHoIIEGY2jRWb8TnDhezVI3TToIhRw+UjO8MtE7qHhUtLDqVeeSf39pzlWmHWqwXc3j3PnqbJ5SRHMINdLxruO9ZMCqljXWn70VWPrh89XDy8E7xz5TDJOEG1REfaAnaudZKYzHS2WB0rEjwEYQAt12urxOD6SqFVykSRYqDIDddRZUqyLzAlNpQx0oMtVEBrYWKAM7GswHUndNF1/muJFdK6bq3uMKR9apyq4qpmbK/VLLYkeAjCAMLYmR9vPUF+WcEV4e3Lh5i637VcV7B3Eg+azLVmlwlT0/uJLE3iYGqtRGFTqBBGmJXTVWJ2K/6dQiSb14s8i83c9aDVwiFlaRGeS2YkL5i9bqmv+PW00TD9lB6GAogxWF+MVOFOq9H6ZDpB+AlMCZVDOABOURmw6A/BI7yOYgDy79vUdVIXsvaRU1hPHHH9ETNjhUIRYSr9gI7ewd3in8kWBnWzsAbUvyWO+mqhhFzwr1r8ebgr9fPTS4e0eLhDoRyGEj0kYhWlgsAtZuAhCw9BGDqBBAaJKBl/uZaePP4Cs8FQkLwtKFym14+P2SdSwZP+ukiHC94WntDcX8KCkrOvNjeQ2ewWE77AZAGjYPaRmVXbzW42fYbBnn9RPs5ntZcNrf3s/A1ZeAjCAFhwDOvn1DbaFSu+mmn+1pL+ynPpiE4sK0aYump2pobRirbhAFbP/mjlfrjHR5sOIcLxBEsaYL+qx/9vG4SCNfOJM/tApAmVmc3gj3vGw5j//im7DLY5KalsMPVzL5ApsqmVvDbq3YQ4cbELZitvXT6E9aUT88CpveBQWR3M367disvXH5Lqdaf2sqG3RpK/oKsTQRhkxmm9WSG6UEAsSMw2OffOMW/GG6pg9ey/Hz5ZUgiIBSZaeDCeAuuVYEkD7H8UKlxzQhef9Yi0gMLJKB3TlUXX2OPM6QMpRG01cIzvw6BnPmDYaHsdcUYU1vTx1fz23avcjWC18uAUd8FDIRbzVKmGBA9BBIi2ydZrKzCyq3RtIL3cPVG+N5gZlqpQwJerSmqgwHiKR87oE9IuLqPC6d9nppi6zoES8SRYjDMQNYR8bc9jZ/aRzeKz4m+fIYhL/HbaKMXsKwt+BQ/IpUUQASIrOQ4+um6EYvPNQLBqxsmspP+obsZTU7FXTqOz34SRmiH+SJUlQgNhDI4ZFZCnSrRpMbMOFlpqXpBoHKym2fDl769mbSWMEKh0b07wMUM7K0+OsGDkP0WVYFXIwkMQAWR8j0wYlmeuVUUP7VLjTBE7SAeDtXeEWLFhqFGEqf+WS1sxmeNNthjqxajw9sVrlwxisTKzdFbI7tchFTY8egpcOUq+QKXZ8Gnk4zU0ZdXDuxavHE4WHoIgDGG14mJWY3hehq7U/1DEX0LDKpw9qAO7+UvU40v+qDb+2Bl9WJXs0RonOeJN6dI2kVmGT+kj3aEe49aws/uXfxeAFSELD0EQhmjtckdLKn647ysztG+8H0XTJB8DtVVAQaGGJ8/up1mIntInW3OFbPFk5ve7xsHaRyZCZw0ZZVaCBA9BEIZo7XE3WgI1tQqCNy4dDKEEZ9Bd1KtdMqsX5S8wm3Jcj0x430Dmkj9RG7jsb9eUr98TY63aJskXbLRy4DIJHoIIM+Q6tvuDbgpdn1tTexEz3H/CitzBLlapFhQqyHmD9bt70FU0765xfm1bga1PPr5uBEy0qKXnlYsGsX5yz57rP9GnNSEhnKAYHoIIEXpmJ8POkirl5TT0xCKMo9SpXjhrV2MBwbopT/60DW4/yVj5/0Dy7bTRsLu0WrKdRlCwsJVBjv4dU2H9o6dYJkX9+K5t4MSemdBDQ0VpLMZqVUjwEATh1wG/tWPTYaH75PqREEpgbIhU7ZtgMSQEGln6wipih6/o/MG1I0ALt53UHZbtPgIXDTOvCrVZkOAhiHCD9EcQXFrqdro/MnAI71o3qTIF/sKFswe1hx83FoLVaJsUy9qkWBESPAQRIpAlJfShFH7/MfvqYbCxoNxQHFEokS7RvJeQJ7wikgiCCDkm9rZmAKlaLj8+MEXdCHlO7p3NGraGY/FKKW4/qTv065ACT51tbsuMcIYsPAQRIqhNaQ41S9AJ3dvAwn9CtzDfjCm9YULPLBieJx03IgzJMFqnxup1ZIjA0SYpFn6+fSztcg2Q4CGIVg4WeqtrssMJ3dvqer/RQTzUCzVjbRI5646Z9oZoQc8pgiC0QYKHIEIEf+mChfeMh+W7jsDZg4NT8yXBAs1UA4VR61tKXPgH4xKEvyDBQxBhhlaLSYe0eLhoeC4Ei3OHdIDfthbDGJ0WpnBn5YyT4P5vNkN9k91wl22CaM2Q4CGIMMvwCTUXERYq++g6bbU+QhU9v01OanzI1eUhCCtCDmGCIAwRYvoq4GittEwQhH8gwUMQIYLawTLUsrQIgiACAQkegiAIP5IUG+XRgoEgiOBAgocgQgUy3IRs2jpPLKWVE0TQIMFDEGFGqAUtEwRBBAISPAQRIlhVx5DA0rKz/Pc7EAQhDwkeggi3tHS/bwlBEEToQYKHIAjCjwzomOp6TBl0BBE8SPAQRLilpZOJx1J0bpMY7E0gCIIED0EQRiGrBUEQoQBZeAgiRCDLTejTPSsp2JtAEK0WqoJFEGEH+bSsxs+3j4G9R2pgaOeMYG8KQbRaSPAQRIhArqPQpV+HVHYjCCJ4kEuLIAiCIIiwhwQPQYRZDM/AjmkQSC4d3gkyEmPg0hG5Af1cgiAILZBLiyDChIXTx8FfOw/DlaM6B/Rz0xNj4O+HJ0JkhC2gn0sQBKEFsvAQRIgwoWcmu89MjpV8vXtWMtwwtivERkUGeMuAxA5BEJaHLDwEESI8dFpv6JmdDBP7ZAd7UwiCIEIOEjwEESIkxETBlaPygr0ZBEEQIQm5tAiCIAiCCHtI8BAEQRAEEfaQ4CEIgiAIIuwhwUMQBEEQRNhDgocgCIIgiLCHBA9BEARBEGEPCR6CIAiCIMIeEjwEQRAEQYQ9JHgIgiAIggh7SPAQBEEQBBH2kOAhCIIgCCLsIcFDEARBEETYQ4KHIAiCIIiwh7qlAwDHcWxnVFZWBvv3IAiCIAhCJfy4zY/jcpDgAYCqqiq2M3Jzc9XuY4IgCIIgLDSOp6amyi5j49TIojCnpaUFCgsLITk5GWw2m+nqE4VUQUEBpKSkQGuH9gftCzo26Fyh6wZdR80CJQyKnfbt20NEhHyUDll4MJApIgI6duwI/gTFDgke2h90bNC5QtcOuo7SuGIuSpYdHgpaJgiCIAgi7CHBQxAEQRBE2EOCx8/ExsbC448/zu4J2h90bNC5QtcOuo7SuBIcKGiZIAiCIIiwhyw8BEEQBEGEPSR4CIIgCIIIe0jwEARBEAQR9pDgIQiCIAgi7CHB42fefPNNyMvLg7i4OBg5ciSsWbMGQomZM2fC8OHDWRXqrKwsOOecc2Dnzp0ey0yYMIFVqBbepk6d6rHMgQMH4PTTT4eEhAS2nvvuuw+am5s9lvnrr79gyJAhLKOte/fu8OGHH1pufz7xxBNe37VXr16u1+vr6+HWW2+FNm3aQFJSEpx//vlQUlISlvsCP1u8L/CG3781HBdLliyBM888k1V4xe/2ww8/eFWAfeyxxyAnJwfi4+Nh4sSJsGvXLo9ljh07BpdffjkrSpqWlgbXX389VFdXeyyzefNmGDt2LPtuWLX9+eef99qWr7/+mh2HuEz//v3h119/1bwt/twfTU1N8MADD7BtS0xMZMtcddVVrMK90jH13HPPhdz+UDo2rrnmGq/veeqpp4btsWEZsLUE4R++/PJLLiYmhpszZw63bds27sYbb+TS0tK4kpKSkNnlkydP5j744ANu69at3MaNG7nTTjuN69SpE1ddXe1aZvz48ey7FRUVuW4VFRWu15ubm7l+/fpxEydO5DZs2MD9+uuvXNu2bbkZM2a4ltm7dy+XkJDATZ8+ndu+fTv3xhtvcJGRkdy8efMstT8ff/xxrm/fvh7f9fDhw67Xp06dyuXm5nKLFi3i1q5dyx1//PHc6NGjw3JflJaWeuyHBQsWYJsa7s8//2wVxwVu78MPP8x999137Ht///33Hq8/99xzXGpqKvfDDz9wmzZt4s466yyuS5cuXF1dnWuZU089lRs4cCC3atUqbunSpVz37t25Sy+91PU67q/s7Gzu8ssvZ+fgF198wcXHx3PvvPOOa5nly5ezffL888+zffTII49w0dHR3JYtWzRtiz/3R3l5Ofudv/rqK27Hjh3cypUruREjRnBDhw71WEfnzp25p556yuOYEV5rQmV/KB0bV199Nfvthd/z2LFjHsuE07FhFUjw+BE8oW+99VbXc7vdzrVv356bOXMmF6rgIIcn8OLFi13/w4HtzjvvlD35IyIiuOLiYtf/3n77bS4lJYVraGhgz++//34mJIRcfPHFTHBZaX+i4MGLkBR4UceLyddff+363z///MP2F17gw21fiMFjoFu3blxLS0urOy7Egxrug3bt2nEvvPCCx/ERGxvLBiYEByB8399//+1a5rfffuNsNht36NAh9vytt97i0tPTXfsDeeCBB7iePXu6nl900UXc6aef7rE9I0eO5G6++WbV22I2UoO8mDVr1rDl9u/f7yF4XnnlFZ/vCcX94UvwnH322T7fE87HRjAhl5afaGxshHXr1jHzoLBnFz5fuXIlhCoVFRXsPiMjw+P/n332GbRt2xb69esHM2bMgNraWtdr+H3RlJqdne363+TJk1kj0W3btrmWEe4rfhl+X1lpf6K5F03VXbt2ZSZndMsguH1ouhduI5qSO3Xq5NrGcNsXPLhNn376KVx33XUeDXhb03EhZN++fVBcXOyxXdjvB91twmMBXRXDhg1zLYPL4/avXr3atcy4ceMgJibG4/ujW7msrEzVPlKzLcG6luCxgvtACLqw0CU8ePBgeOGFFzxcnOG0P9BVi27cnj17wrRp0+Do0aOu11r7seEvqHmonzhy5AjY7XaPizmCz3fs2AGh2lX+rrvughNOOIENYDyXXXYZdO7cmYkA9Cmjrx5Puu+++469jieU1H7gX5NbBge/uro6dgJbYX/ihQBjSPAiVVRUBE8++STzoW/dupV9B7z4iC/guI1K35N/LZT2hRCMUSgvL2exCa3xuBDDb7/Udgm/Gw54QqKiothkQrhMly5dvNbBv5aenu5zHwnXobQtgQZj3fB4uPTSSz2aKt9xxx0sXgv3wYoVK5hIxvPs5ZdfDqv9gfE65513Hvsue/bsgYceegimTJnCREZkZGSrPjb8CQkeQjUYjIoD+7Jlyzz+f9NNN7ke44wdg99OPvlkdiJ369YtrPYwXpR4BgwYwAQQDupz585lAX+tldmzZ7N9g+KmNR4XhHrQCnrRRRexYNm3337b47Xp06d7nF84gbj55ptZ8kQ4tee55JJLPM4N/K54TqDVB88Rwj+QS8tPoBkflbo4Qweft2vXDkKN2267DX7++Wf4888/oWPHjrLLoghAdu/eze7x+0rtB/41uWVw9odCwqr7E605PXr0YN8VtwNdLGjp8LWN4bgv9u/fDwsXLoQbbrhBdrnWdFzwny23XXhfWlrq8Tq6bzA7x4zjRfi60rYEWuzgMbNgwQIP646vYwb3SX5+fljuDx50j+OxLDw3WtuxEQhI8PgJnJkMHToUFi1a5OESwuejRo2CUAFnYSh2vv/+e/jjjz+8TKhSbNy4kd3jjB7B77tlyxaPE5i/2PXp08e1jHBf8cvw+8qq+xPTRNFigd8Vty86OtpjG9GFgzE+/DaG47744IMPmPkd08vlaE3HBZ4nOGAItwvdcBh/ITwWUBxjDBIPnmO4/bw4xGUwxRmFgvD7o0sVXRZq9pGabQmk2MEYOBTIGKejBB4zGLfCu3fCaX8IOXjwIIvhEZ4brenYCBhBDZkOczBdFqPdP/zwQxZ1f9NNN7F0WWFWitWZNm0aS1n866+/PFIoa2tr2eu7d+9maaSYgr1v3z7uxx9/5Lp27cqNGzfOK/140qRJLLUdU4ozMzMl04/vu+8+ltn05ptvSqYfB3t/3nPPPWxf4HfFlE9MtcVUasxe49PSMW3/jz/+YPtk1KhR7BaO+4LPiMLvi9khQlrDcVFVVcXS6fGGl9KXX36ZPeazjjDdF7cDv/vmzZtZVo5UWvrgwYO51atXc8uWLeOOO+44j9RjzJjB1OMrr7ySpR7jd8X9IU49joqK4l588UW2jzCTUCr1WGlb/Lk/GhsbWbpzx44d2W8tvJbwWUYrVqxgGVr4+p49e7hPP/2UHQ9XXXVVyO0PuX2Br917770scxPPjYULF3JDhgxhv319fX1YHhtWgQSPn8G6ITggYJ0QTJ/FmgqhBJ6sUjeszYMcOHCADWIZGRls0MFaETg4CeutIPn5+dyUKVNYnQgUCCgcmpqaPJbB+i2DBg1i+woHR/4zrLQ/MSU6JyeHfX6HDh3YcxzcefAiccstt7B0Ubz4nHvuueyiHo77Avn999/Z8bBz506P/7eG4wK3S+rcwJRjPuX30UcfZYMS7oOTTz7Zaz8dPXqUDWJJSUksHf/aa69lA6IQrI0yZswYtg485nCAEjN37lyuR48e7PtjGv8vv/zi8bqabfHn/sCB3de1hK/btG7dOpYyjROsuLg4rnfv3tyzzz7rIQJCZX/I7QucLKLIRzGH4gNT8bF2lFigh9OxYRVs+Cdw9iSCIAiCIIjAQzE8BEEQBEGEPSR4CIIgCIIIe0jwEARBEAQR9pDgIQiCIAgi7CHBQxAEQRBE2EOChyAIgiCIsIcED0EQBEEQYQ8JHoIgCIIgwh4SPARBEARBhD0keAiCIAiCCHtI8BAEQRAEEfaQ4CEIgiAIAsKd/wc6SqP8ofZJMgAAAABJRU5ErkJggg=="
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 19
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "a4fdd246c30a6de3"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From c96e5d5fc84f4f162f5373810bfcee54c12a6da1 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Sat, 13 Jun 2026 15:02:02 -0700
Subject: [PATCH 42/49] kalman initialisation

---
 v4/kalman_filter_position/KalmanFilter.py     | 13 +++++++++
 .../kalman_filter_position.ipynb              | 29 +++++++++++++++++++
 2 files changed, 42 insertions(+)
 create mode 100644 v4/kalman_filter_position/KalmanFilter.py

diff --git a/v4/kalman_filter_position/KalmanFilter.py b/v4/kalman_filter_position/KalmanFilter.py
new file mode 100644
index 0000000..9df13c5
--- /dev/null
+++ b/v4/kalman_filter_position/KalmanFilter.py
@@ -0,0 +1,13 @@
+class KalmanFilter():
+    def __init__(self, latitude, longitude, accel_x, accel_y, accel_z, speed):
+        self.latitude = latitude
+        self.longitude = longitude
+        self.accel_x = accel_x
+        self.accel_y = accel_y
+        self.accel_z = accel_z
+        self.speed = speed
+
+    def initialize(self):
+
+    def predict(self):
+# this will propagate through errors and the recurrence based on the previous time steps.
diff --git a/v4/kalman_filter_position/kalman_filter_position.ipynb b/v4/kalman_filter_position/kalman_filter_position.ipynb
index afe3fdf..2cfc1c0 100644
--- a/v4/kalman_filter_position/kalman_filter_position.ipynb
+++ b/v4/kalman_filter_position/kalman_filter_position.ipynb
@@ -279,6 +279,35 @@
    "execution_count": null,
    "source": "",
    "id": "a4fdd246c30a6de3"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "now that we have queried preliminary data,\n",
+    "- what is the Kalman filter?\n",
+    "- IMU data (v3 firmware) from the wiki is configured at 30Hz and GPS data is configured at 4Hz.\n",
+    "- this would be an extended kalman filter because of the non linearity of elements.\n",
+    "- The goal is to estimate the state (position and orientation) of a vehicle using both GPS and IMU data.\n",
+    "- IMU provides data more frequently than GPS.\n",
+    "- Steps:\n",
+    "- Initialization): The EKF state (position, velocity and orientation) using the first GPS and IMU reading. The covariance matrix (P) reflects the initial uncertainty.\n",
+    "- Prediction: Predict the current state using the process model and the previously estimated state. We also predict the state covariance matrix (P) at this point. this is also a timing update\n",
+    "- Measurement Update: We correct the predicted state using the measured data.\n",
+    "- Estimate update: Using Kk i.e. the Kalman gain, we can now compute an updated state estimate by combining the predicted state estimate adn the weighted difference between the actual measurement and the residual ( this is the predicted measurement based on a previous estimate).\n",
+    "- Update covariance: Compute the updated estimated covariance. this will ultimately become a previous estimate to base off the predicted for the next step.\n",
+    "- Overall process is repeated for each time step, with a recurring usage of the previous time step as a priori estimate for the current step.\n",
+    "-"
+   ],
+   "id": "66f9bb7a6a0e6686"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "class KalmanFilter():\n",
+   "id": "6a1ab6d99e33b85f"
   }
  ],
  "metadata": {

From 70688be1fce4914cd036755b069c1f4f51fb46b6 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Sat, 18 Jul 2026 14:22:05 -0700
Subject: [PATCH 43/49] set up sensor classes to generate random dataset

---
 .../random_generation.py                      | 221 ++++++++++++++++++
 1 file changed, 221 insertions(+)
 create mode 100644 v4/kalman_filter_position/random_generation.py

diff --git a/v4/kalman_filter_position/random_generation.py b/v4/kalman_filter_position/random_generation.py
new file mode 100644
index 0000000..1ea1474
--- /dev/null
+++ b/v4/kalman_filter_position/random_generation.py
@@ -0,0 +1,221 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from math import radians, cos, sin, asin, sqrt
+
+# the goal here is to create a csv dataset that consists of random data so that we can simulate necessary failure conditions as necessary.
+
+RADIUS_EARTH = 6371000.0
+
+
+# required coordinate utilites.
+
+def latlon_to_xy(lat, lon, lat0, lon0):
+    # this will take in the gps coordinates and return it in east north emtres.
+
+    # using the haversine formula to find the bearing and distance betweeen two GPS points.
+
+    x = RADIUS_EARTH * np.cos(np.deg2rad(lat0)) * np.deg2rad(lon - lon0)
+    y = RADIUS_EARTH * np.deg2rad(lat - lat0)
+    return x, y
+
+
+def xy_to_latlon(x, y, lat0, lon0):
+    lat = lat0 + np.rad2deg(y / RADIUS_EARTH)
+    lon = lon0 + np.rad2deg(x / (RADIUS_EARTH * np.cos(np.deg2rad(lat0))))
+    return lat, lon
+
+
+def haversine(lon1, lat1, lon2, lat2):
+    """
+    calculates the great circle distance in km between the two specified points on the eearth
+
+    :param lon1:
+    :param lat1:
+    :param lon2:
+    :param lat2:
+    :return:
+    """
+    lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])
+    dlon = lon2 - lon1
+    dlat = lat2 - lat1
+    a = (sin(dlat / 2) ** 2 + cos(lat1) * cos(lat2) * sin(dlon / 2) ** 2)
+    return RADIUS_EARTH * 2 * asin(sqrt(a))
+
+
+class Path:
+    """
+    reads the csv and interpolates the input data to produce linear lat/lon time series at the required time. the CSV consists of the columns time_s, lat, lon
+
+
+    """
+
+    def __init__(self, csv_path: str):
+        df = pd.read_csv(csv_path)
+        self.t = df["time_s"].values.astype(float)
+        self.lat = df["lat"].values.astype(float)
+        self.lon = df["lon"].values.astype(float)
+
+        self.duration = self.t[-1]
+        self.lat0 = self.lat[0]
+        self.lon0 = self.lon[0]
+
+        # convert to local E-N
+        self.x, self.y = latlon_to_xy(self.lat, self.lon, self.lat0, self.lon0)
+
+    def calculate_distance(self, lat, lon):
+        dist = np.zeros(len(lat))
+
+        for i in np.arange(1, len(lat)):
+            lat1 = lat[i - 1]
+            lon1 = (lon[i - 1])
+            lat2 = (lat[i])
+            lon2 = (lon[i])
+
+            dist[i] = haversine(lon1, lat1, lon2, lat2)
+
+        return (dist)
+
+    def gps_interpolate(self, lat, lon, tstamp):
+        dist = self.calculate_distance(lat, lon)
+        dist_cum_norm = np.cumsum(dist) / np.sum(dist)
+        data = [lat, lon]
+        lat_interp = np.interp(tstamp, self.t, self.lat)
+        lon_interp = np.interp(tstamp, self.t, self.lon)
+        return lat_interp, lon_interp
+
+    def latitude(self, t):
+        # method to create a time series object of the latitude using gps interpolation
+        return np.interp(t, self.t, self.lat)
+
+    def x(self, t):
+        return np.interp(t, self.t, self.x)
+
+    def y(self, t):
+        return np.interp(t, self.t, self.y)
+
+    # velocity in x and y:
+    def vx(self, t):
+        # differentiate the x values and then interpolate to requested time t.
+        return np.interp(t, self.t, np.gradient(self.x, self.t))
+
+    def vy(self, t):
+        return np.interp(t, self.t, np.gradient(self.y, self.t))
+
+    def speed(self, t):
+        return np.hypot(self.vx(t), self.vy(t))
+
+    def ax(self, t):
+        # double derivative of x coordinate.
+        # v = np.gradient(self.vx(t), self.t)
+        v = np.gradient(self.x, self.t)
+        a = np.gradient(v, self.t)
+        return np.interp(t, self.t, a)
+
+    def ay(self, t):
+        v = np.gradient(self.y, self.t)
+        a = np.gradient(v, self.t)
+        return np.interp(t, self.t, a)
+
+
+class Sensor:
+    def __init__(self, rate_hz,
+                 noise_std,
+                 bias,drift_rate,
+                 rng_seed: int = 21):
+        self.rate_hz = rate_hz
+        self.noise_std = noise_std or {}
+        self.bias = bias or {}
+        self.drift_rate = drift_rate or {}
+        self.rng = np.random.default_rng(rng_seed)
+
+    def sample(self, path: Path):
+        """
+        generates measuremesnts over the full path duration.
+        :param path:
+        :return:
+        """
+        t = np.arange(0, path.duration, 1.0 / self.rate_hz)
+        df = self.measure(path, t)
+        df.insert(0, "t", t)
+        self.apply_noise(df)
+        return df
+
+    def measure(self, path, t):
+        """generate measurements for the certain time instance?"""
+        return self.sample(path)[:t]
+
+    def apply_noise(self, df):
+        """applies Gaussian noise, constant bias and time variable drift to sensor data. """
+        t = df["t"].values
+        for col, std in self.noise_std.items():
+            if col in df.columns:
+                df[col] += self.rng.normal(0, std, len(df))  # generate gaussian noise.
+        for col, b in self.bias.items():
+            if col in df.columns:
+                df[col] += b
+        for col, rate in self.drift_rate.items():
+            if col in df.columns:
+                df[col] += rate * t
+
+
+# consider noise in GPS vs IMU:
+class GPSSensor(Sensor):
+    """
+    the GPS noise is mostly high frequency, struggles with precision over accuracy and quick velocity changes.
+    the IMU tracks relative movement i.e. errors can accumulate as we integrate creating a "drift" over time.
+    sensors also might have inherent biases. the imu cannot provide absolute position reference on its own.
+
+
+    - the gps sensor class will output lat, lon at a fixed rate with configurable noise and bias. our current GPS Is at 4Hz and has ~5m noise. in our data, we have also observed weird spikes and a lack of consistency between cellular and telemetry (radio) transfer of data.
+    - drift should consider both spatial and temporal aspects. error is +- (lat, lon) tuple.
+
+    """
+
+    def __init__(self, rate_hz=4.0, noise_m=5.0, bias_m=(0, 0), drift_ms=(0, 0), rng_seed=21):
+        # convert all metre noise to degrees using small angle approximation
+        # use: dely = dellat * pi/180 * r_earth
+
+        super().__init__(rate_hz, rng_seed)
+        noise_lat = noise_m / RADIUS_EARTH * (180 / np.pi)
+        noise_lon = noise_m / RADIUS_EARTH * (180 / np.pi)
+
+        bias_deg_lat = bias_m[1] / RADIUS_EARTH * (180 / np.pi)
+        bias_deg_lon = bias_m[0] / RADIUS_EARTH * (180 / np.pi)
+
+        # could potentially add noise by direclty adding to the x,y coordinates too?
+
+    def measure(self, path, t):
+        return pd.DataFrame({"lat": path.latitude(t), "lon": path.longitude(t)})
+
+
+class IMUSensor(Sensor):
+    def __init__(self, rate_hz=30.0, noise_std_mss=0.05,  # gaussian noise in m/s^2,
+                 bias_mss=0.02, drift_rate=0.0, rng_seed=23):
+        noise_std = {"ax": noise_std_mss, "ay": noise_std_mss, "az": 0.02}
+        bias = {"ax": bias_mss, "ay": bias_mss}
+        drift = {"ax": drift_rate, "ay": drift_rate}
+        super().__init__(rate_hz, noise_std, bias, drift, rng_seed)
+
+    def _measure(self, path: Path, t: np.ndarray) -> pd.DataFrame:
+        return pd.DataFrame({
+            "ax": path.ax(t),
+            "ay": path.ay(t),
+            "az": np.full(len(t), 9.81),
+        })
+
+class SpeedSensor(Sensor):
+    """
+    outputs scalar ground speed in m/s.
+
+    """
+    def __init__(self, rate_hz = 30.0, noise_mps = 0.5, bias_mps = 0.0, slip_factor = 1.0, rng_seed = 44):
+        noise_std = {"speed": noise_mps}
+        bias      = {"speed": bias_mps}
+        super().__init__(rate_hz, noise_std, bias, {}, rng_seed)
+        self.slip_factor = slip_factor
+
+    def measure(self, path: Path, t: np.ndarray) -> pd.DataFrame:
+            return pd.DataFrame({
+                "speed": path.speed(t) * self.slip_factor
+            })

From ff0e1fd54a0750ae2af382c1c8539e8b9cc8c2f2 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Sat, 18 Jul 2026 14:22:29 -0700
Subject: [PATCH 44/49] set up sensor classes to generate random dataset

---
 .../localization_log2.csv                     |    3 +
 v4/kalman_filter_position/pyproject.toml      |   12 +
 v4/kalman_filter_position/uv.lock             | 1078 +++++++++++++++++
 3 files changed, 1093 insertions(+)
 create mode 100644 v4/kalman_filter_position/localization_log2.csv
 create mode 100644 v4/kalman_filter_position/pyproject.toml
 create mode 100644 v4/kalman_filter_position/uv.lock

diff --git a/v4/kalman_filter_position/localization_log2.csv b/v4/kalman_filter_position/localization_log2.csv
new file mode 100644
index 0000000..c38dcc1
--- /dev/null
+++ b/v4/kalman_filter_position/localization_log2.csv
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9c55751e3587443edbff28b93915e159b6e62a22d8e4d14f3c0e4bd1961c7185
+size 10939434
diff --git a/v4/kalman_filter_position/pyproject.toml b/v4/kalman_filter_position/pyproject.toml
new file mode 100644
index 0000000..b07eae7
--- /dev/null
+++ b/v4/kalman_filter_position/pyproject.toml
@@ -0,0 +1,12 @@
+[project]
+name = "kalman-filter-position"
+version = "0.1.0"
+requires-python = ">=3.12"
+dependencies = [
+    "filterpy>=1.4.5",
+    "matplotlib>=3.11.0",
+    "numpy>=2.5.1",
+    "pandas>=2.3.3",
+    "pytz>=2024.2",
+    "ubc-solar-data-tools>=2.0.0",
+]
diff --git a/v4/kalman_filter_position/uv.lock b/v4/kalman_filter_position/uv.lock
new file mode 100644
index 0000000..8f6a53d
--- /dev/null
+++ b/v4/kalman_filter_position/uv.lock
@@ -0,0 +1,1078 @@
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From f6290baec21c457dda8fc4b143403b0506029b02 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Sat, 18 Jul 2026 14:24:08 -0700
Subject: [PATCH 45/49] set up sensor classes to generate random dataset

---
 v4/kalman_filter_position/random_generation.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/v4/kalman_filter_position/random_generation.py b/v4/kalman_filter_position/random_generation.py
index 1ea1474..f1b681c 100644
--- a/v4/kalman_filter_position/random_generation.py
+++ b/v4/kalman_filter_position/random_generation.py
@@ -175,7 +175,6 @@ class GPSSensor(Sensor):
     def __init__(self, rate_hz=4.0, noise_m=5.0, bias_m=(0, 0), drift_ms=(0, 0), rng_seed=21):
         # convert all metre noise to degrees using small angle approximation
         # use: dely = dellat * pi/180 * r_earth
-
         super().__init__(rate_hz, rng_seed)
         noise_lat = noise_m / RADIUS_EARTH * (180 / np.pi)
         noise_lon = noise_m / RADIUS_EARTH * (180 / np.pi)

From ebaf2e26f73ec115d81cac05f8c952956fd08ca2 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Sat, 15 Aug 2026 11:36:40 -0700
Subject: [PATCH 46/49] completed voronoi track generator, further commits to
 clean up code.

---
 v4/kalman_filter_position/track_generator.py | 309 +++++++++++++++++++
 1 file changed, 309 insertions(+)
 create mode 100644 v4/kalman_filter_position/track_generator.py

diff --git a/v4/kalman_filter_position/track_generator.py b/v4/kalman_filter_position/track_generator.py
new file mode 100644
index 0000000..4049976
--- /dev/null
+++ b/v4/kalman_filter_position/track_generator.py
@@ -0,0 +1,309 @@
+import os
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from scipy import signal, spatial, interpolate
+from shapely.geometry import Point, LineString, Polygon
+
+EARTH_RADIUS_M = 6_371_000.0
+
+
+def closest_node(node, nodes, k):
+    deltas = nodes - node
+    distance = np.einsum('ij,ij->i', deltas, deltas)
+    return np.argpartition(distance, k)[k]
+
+
+def clockwise_sort(p):
+    d = p - np.mean(p, axis=0)
+    s = np.arctan2(d[:, 0], d[:, 1])
+    return p[np.argsort(s), :]
+
+
+def curvature(dx_dt, d2x_dt2, dy_dt, d2y_dt2):
+    return (dx_dt ** 2 + dy_dt ** 2) ** -1.5 * (dx_dt * d2y_dt2 - dy_dt * d2x_dt2)
+
+
+def xy_to_latlon(x: np.ndarray, y: np.ndarray,
+                 lat0: float, lon0: float) -> tuple[np.ndarray, np.ndarray]:
+    lat = lat0 + np.rad2deg(y / EARTH_RADIUS_M)
+    lon = lon0 + np.rad2deg(x / (EARTH_RADIUS_M * np.cos(np.deg2rad(lat0))))
+    return lat, lon
+
+
+def assign_speed(curvature_abs: np.ndarray,
+                 v_max: float = 18.0,
+                 v_min: float = 6.0,
+                 curvature_threshold: float = 1 / 20.0):
+    """
+    Assign speed at each point on the centerline based on local curvature.
+
+    Straight sections (low curvature) get v_max
+    Tight corners  (high curvature)   get v_min
+    Linearly interpolated in between.
+
+
+    curvature_abs         : absolute curvature at each centerline point
+    v_max                 : maximum speed on straights (m/s)
+    v_min                 : minimum speed in corners (m/s) around 22 km/h
+    curvature_threshold   : curvature at which v_min kicks in
+                            1/20 = radius of 20 m
+    :return numpy array of the speed values.
+    """
+    # Normalise curvature to [0, 1] where 0 = straight, 1 = max corner
+    k_norm = np.clip(curvature_abs / curvature_threshold, 0, 1)
+    speed = v_max - k_norm * (v_max - v_min)
+    return speed
+
+
+def curvature_to_time(x: np.ndarray, y: np.ndarray,
+                      speed: np.ndarray):
+    """
+    Convert spatial centerline points to a time array using the local speed.
+
+    time per segment is give by  Arc length between consecutive points / average speed of those points.
+    :return numpy array
+    """
+    ds = np.hypot(np.diff(x), np.diff(y))  # segment lengths (m)
+    v_avg = 0.5 * (speed[:-1] + speed[1:])  # average speed per segment
+    dt = ds / v_avg  # time per segment
+    t = np.concatenate([[0], np.cumsum(dt)])
+    return t
+
+
+class VoronoiTrackGenerator:
+    """
+    Generates a random closed-loop solar car track.
+
+
+
+    n_points     : number of Voronoi seed points
+    n_regions    : how many Voronoi regions to merge into the track shape
+                   (more = larger, more complex track)
+    min_bound    : lower bound of the generation area (m)
+    max_bound    : upper bound of the generation area (m)
+    origin_lat   : reference latitude for output
+    origin_lon   : reference longitude for output (WGS-84)
+    v_max        : maximum vehicle speed (m/s)
+    v_min        : minimum vehicle speed in corners (m/s)
+    min_radius_m : minimum allowed corner radius (m) this sets curvature threshold
+    n_centerline : number of points on the output centerline
+    rng_seed     : random seed for reproducibility
+    """
+
+    MIN_RADIUS_M = 20.0  # tightest allowable corner radius
+    STRAIGHT_THRESHOLD = 1.0 / 100.0  # curvature below this is considered straight.
+
+    def __init__(self,
+                 n_points: int = 70,
+                 n_regions: int = 10,
+                 min_bound: float = 10.0,
+                 max_bound: float = 500.0,
+                 origin_lat: float = 49.2606,
+                 origin_lon: float = -123.2460,
+                 v_max: float = 18.0,
+                 v_min: float = 6.0,
+                 min_radius_m: float = 20.0,
+                 n_centerline: int = 1000,
+                 rng_seed: int = None):
+
+        self.n_points = n_points
+        self.n_regions = n_regions
+        self.min_bound = min_bound
+        self.max_bound = max_bound
+        self.origin_lat = origin_lat
+        self.origin_lon = origin_lon
+        self.v_max = v_max
+        self.v_min = v_min
+        self.curvature_threshold = 1.0 / min_radius_m
+        self.n_centerline = n_centerline
+
+        if rng_seed is not None:
+            np.random.seed(rng_seed)
+
+    def _bounded_voronoi(self, input_points):
+        bb = np.array([self.min_bound, self.max_bound] * 2)
+        x_min, x_max, y_min, y_max = bb
+
+        def _mirror(pts, boundary, axis):
+            m = pts.copy()
+            m[:, axis] = 2 * boundary - m[:, axis]
+            return m
+
+        points = np.concatenate([
+            input_points,
+            _mirror(input_points, x_min, 0),
+            _mirror(input_points, x_max, 0),
+            _mirror(input_points, y_min, 1),
+            _mirror(input_points, y_max, 1),
+        ])
+
+        vor = spatial.Voronoi(points)
+        vor.filtered_points = input_points
+        vor.filtered_regions = np.array(
+            vor.regions, dtype=object
+        )[vor.point_region[:vor.npoints // 5]]
+        return vor
+
+    # Track generation
+
+    def generate(self):
+        """
+        generate track and return as a dataframe (time_s, lat, lon)
+        :return: dataframe
+        """
+
+        pts = np.random.uniform(self.min_bound, self.max_bound,
+                                (self.n_points, 2))
+        vor = self._bounded_voronoi(pts)
+
+        for attempt in range(1, 51):
+            idx = np.random.randint(0, self.n_points)
+            r_idxs = [idx] + [closest_node(pts[idx], pts, k=i + 1)
+                              for i in range(self.n_regions - 1)]
+
+            regions = np.array(vor.regions, dtype=object)
+            vertices = np.unique(
+                vor.vertices[np.concatenate(regions[vor.point_region[r_idxs]])],
+                axis=0
+            )
+            verts = clockwise_sort(vertices)
+            verts = np.vstack([verts, verts[0]])
+
+            # Iteratively remove vertices that cause excessive curvature
+            valid = True
+            for _ in range(50):
+                if len(verts) < 4:
+                    valid = False;
+                    break
+                try:
+                    tck, _ = interpolate.splprep(
+                        [verts[:, 0], verts[:, 1]], s=0, per=True)
+                except Exception:
+                    valid = False;
+                    break
+
+                t_spl = np.linspace(0, 1, self.n_centerline)
+                x, y = interpolate.splev(t_spl, tck, der=0)
+                dx, dy = interpolate.splev(t_spl, tck, der=1)
+                d2x, d2y = interpolate.splev(t_spl, tck, der=2)
+                k_abs = np.abs(curvature(dx, d2x, dy, d2y))
+
+                peaks, _ = signal.find_peaks(k_abs)
+                if len(peaks) == 0:
+                    break
+                if k_abs[peaks].max() > self.curvature_threshold:
+                    worst = peaks[k_abs[peaks].argmax()]
+                    v_idx = closest_node((x[worst], y[worst]), verts, k=0)
+                    verts = np.delete(verts, v_idx, axis=0)
+                    if not np.array_equal(verts[0], verts[-1]):
+                        verts = np.vstack([verts, verts[0]])
+                else:
+                    break
+
+            if not valid:
+                continue
+            if not (Polygon(zip(x, y)).is_valid):
+                continue
+
+            # Speed profile and time axis
+            speed = assign_speed(k_abs, self.v_max, self.v_min, self.curvature_threshold)
+            t_arr = curvature_to_time(x, y, speed)
+
+            # Centre track and convert to lat/lon
+            x_c = x - x.mean()
+            y_c = y - y.mean()
+            lat, lon = xy_to_latlon(x_c, y_c, self.origin_lat, self.origin_lon)
+
+            self._x, self._y = x_c, y_c
+            self._speed, self._k = speed, k_abs
+
+            df = pd.DataFrame({"time_s": t_arr, "lat": lat, "lon": lon})
+            print(f"Track generated (attempt {attempt}): "
+                  f"{t_arr[-1]:.1f}s, "
+                  f"{np.sum(np.hypot(np.diff(x), np.diff(y))):.0f}m, "
+                  f"min radius {1 / k_abs.max():.1f}m, "
+                  f"speed {speed.min() * 3.6:.0f}–{speed.max() * 3.6:.0f} km/h")
+
+            # if out_csv:
+            #     df.to_csv(out_csv, index=False)
+            #     print(f"Saved → {out_csv}")
+
+            return df
+
+        raise RuntimeError("Could not generate valid track in 50 attempts.")
+
+    def plot(self, df: pd.DataFrame, title: str = "Voronoi Track",
+             save_path: str = None):
+        fig, axes = plt.subplots(1, 3, figsize=(16, 5))
+        fig.suptitle(title, fontweight="bold")
+
+        ax = axes[0]
+        sc = ax.scatter(self._x, self._y,
+                        c=self._speed * 3.6,
+
+                        vmin=self.v_min * 3.6, vmax=self.v_max * 3.6)
+        plt.colorbar(sc, ax=ax, label="Speed (km/h)")
+        ax.plot(self._x[0], self._y[0], "ko", ms=8, label="Start", zorder=5)
+        n = len(self._x)
+        for i in range(0, n, max(1, n // 10)):
+            if i + 1 < n:
+                ax.annotate("", xy=(self._x[i + 1], self._y[i + 1]),
+                            xytext=(self._x[i], self._y[i]),
+                            arrowprops=dict(arrowstyle="->", color="navy", lw=0.8))
+        ax.set_aspect("equal");
+        ax.grid(alpha=0.3)
+        ax.set_xlabel("East (m)");
+        ax.set_ylabel("North (m)")
+        ax.set_title("Track (coloured by speed)");
+        ax.legend()
+
+        axes[1].plot(df["time_s"], self._speed * 3.6, "tomato", lw=1.2)
+        axes[1].axhline(self.v_max * 3.6, color="green", ls="--", lw=0.8,
+                        label=f"v_max {self.v_max * 3.6:.0f} km/h")
+        axes[1].axhline(self.v_min * 3.6, color="orange", ls="--", lw=0.8,
+                        label=f"v_min {self.v_min * 3.6:.0f} km/h")
+        axes[1].set_xlabel("Time (s)");
+        axes[1].set_ylabel("Speed (km/h)")
+        axes[1].set_title("Speed Profile");
+        axes[1].legend(fontsize=8)
+        axes[1].grid(alpha=0.3)
+
+        axes[2].plot(df["time_s"], self._k, "royalblue", lw=1.0)
+        axes[2].axhline(self.curvature_threshold, color="red", ls="--", lw=0.8,
+                        label=f"min radius {1 / self.curvature_threshold:.0f}m")
+        axes[2].set_xlabel("Time (s)");
+        axes[2].set_ylabel("Curvature (m⁻¹)")
+        axes[2].set_title("Curvature Profile");
+        axes[2].legend(fontsize=8)
+        axes[2].grid(alpha=0.3)
+
+        plt.tight_layout()
+        if save_path:
+            plt.savefig(save_path, dpi=130, bbox_inches="tight")
+            print(f"Saved → {save_path}")
+        plt.show()
+
+
+if __name__ == "__main__":
+    gen = VoronoiTrackGenerator(
+        n_points=70,
+        n_regions=10,
+        min_bound=10.0,
+        max_bound=500.0,
+        origin_lat=49.2606,
+        origin_lon=-123.2460,
+        v_max=18.0,
+        v_min=6.0,  # 22 km/h in tight corners
+        min_radius_m=20.0,  # minimum corner radius
+        n_centerline=1000,
+        rng_seed=40,  # set None for a different track each run
+    )
+
+    print("Generating Voronoi track...")
+    df = gen.generate()
+    out_dir = os.path.dirname(os.path.abspath(__file__))
+    df.to_csv(os.path.join(out_dir, "ground_truth_path.csv"), index=False)
+    print("[Saved ground_truth_path.csv]")
+
+    gen.plot(df, title="Voronoi Random Track")

From 31683e0204d67f7eaea14b7ae63e87769b848c51 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Sat, 15 Aug 2026 12:00:08 -0700
Subject: [PATCH 47/49] completed voronoi track generator, further commits to
 clean up code.

---
 v4/kalman_filter_position/track_generator.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/v4/kalman_filter_position/track_generator.py b/v4/kalman_filter_position/track_generator.py
index 4049976..beac2cc 100644
--- a/v4/kalman_filter_position/track_generator.py
+++ b/v4/kalman_filter_position/track_generator.py
@@ -64,7 +64,7 @@ def curvature_to_time(x: np.ndarray, y: np.ndarray,
     time per segment is give by  Arc length between consecutive points / average speed of those points.
     :return numpy array
     """
-    ds = np.hypot(np.diff(x), np.diff(y))  # segment lengths (m)
+    ds = np.hypot(np.diff(x), np.diff(y))  # segment lengths
     v_avg = 0.5 * (speed[:-1] + speed[1:])  # average speed per segment
     dt = ds / v_avg  # time per segment
     t = np.concatenate([[0], np.cumsum(dt)])

From 1d484485a038cb97452c5430502eb996fa256c24 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Sat, 15 Aug 2026 12:04:18 -0700
Subject: [PATCH 48/49] completed Sensor class and random generation of
 signals. further commits to clean up code.

---
 .../random_generation.py                      | 85 ++++++++++++-------
 1 file changed, 53 insertions(+), 32 deletions(-)

diff --git a/v4/kalman_filter_position/random_generation.py b/v4/kalman_filter_position/random_generation.py
index f1b681c..311a9d4 100644
--- a/v4/kalman_filter_position/random_generation.py
+++ b/v4/kalman_filter_position/random_generation.py
@@ -2,9 +2,11 @@
 import pandas as pd
 import matplotlib.pyplot as plt
 from math import radians, cos, sin, asin, sqrt
+import os
 
 # the goal here is to create a csv dataset that consists of random data so that we can simulate necessary failure conditions as necessary.
-
+# Overall, this includes the sensor class and the Path generator (this will interpolate lat/lon over time).
+# The Voronoi Track Generator is responsible for generating the track and exporting this to a csv format.
 RADIUS_EARTH = 6371000.0
 
 
@@ -34,7 +36,7 @@ def haversine(lon1, lat1, lon2, lat2):
     :param lat1:
     :param lon2:
     :param lat2:
-    :return:
+    :return: uses the haversine formula to calculate the great circle distance in between two specified points on the Earth.
     """
     lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])
     dlon = lon2 - lon1
@@ -45,7 +47,7 @@ def haversine(lon1, lat1, lon2, lat2):
 
 class Path:
     """
-    reads the csv and interpolates the input data to produce linear lat/lon time series at the required time. the CSV consists of the columns time_s, lat, lon
+    reads the csv (ground_truth_path)  and interpolates the input data to produce linear lat/lon time series at the required time. the CSV consists of the columns time_s, lat, lon
 
 
     """
@@ -53,15 +55,21 @@ class Path:
     def __init__(self, csv_path: str):
         df = pd.read_csv(csv_path)
         self.t = df["time_s"].values.astype(float)
-        self.lat = df["lat"].values.astype(float)
-        self.lon = df["lon"].values.astype(float)
+        self._lat = df["lat"].values.astype(float)
+        self._lon = df["lon"].values.astype(float)
 
         self.duration = self.t[-1]
-        self.lat0 = self.lat[0]
-        self.lon0 = self.lon[0]
+        self.lat0 = self._lat[0]
+        self.lon0 = self._lon[0]
 
         # convert to local E-N
-        self.x, self.y = latlon_to_xy(self.lat, self.lon, self.lat0, self.lon0)
+        self._x, self._y = latlon_to_xy(self._lat, self._lon, self.lat0, self.lon0)
+
+        # Pre-compute derivatives
+        self._vx = np.gradient(self._x, self.t)
+        self._vy = np.gradient(self._y, self.t)
+        self._ax = np.gradient(self._vx, self.t)
+        self._ay = np.gradient(self._vy, self.t)
 
     def calculate_distance(self, lat, lon):
         dist = np.zeros(len(lat))
@@ -80,27 +88,34 @@ def gps_interpolate(self, lat, lon, tstamp):
         dist = self.calculate_distance(lat, lon)
         dist_cum_norm = np.cumsum(dist) / np.sum(dist)
         data = [lat, lon]
-        lat_interp = np.interp(tstamp, self.t, self.lat)
-        lon_interp = np.interp(tstamp, self.t, self.lon)
+        lat_interp = np.interp(tstamp, self.t, self._lat)
+        lon_interp = np.interp(tstamp, self.t, self._lon)
         return lat_interp, lon_interp
 
     def latitude(self, t):
         # method to create a time series object of the latitude using gps interpolation
-        return np.interp(t, self.t, self.lat)
+        return np.interp(t, self.t, self._lat)
+
+    def lat(self, t):
+        # interpolates raw lat array
+        return np.interp(t, self.t, self._lat)
+
+    def lon(self, t):
+        return np.interp(t, self.t, self._lon)
 
     def x(self, t):
-        return np.interp(t, self.t, self.x)
+        return np.interp(t, self.t, self._x)
 
     def y(self, t):
-        return np.interp(t, self.t, self.y)
+        return np.interp(t, self.t, self._y)
 
     # velocity in x and y:
     def vx(self, t):
         # differentiate the x values and then interpolate to requested time t.
-        return np.interp(t, self.t, np.gradient(self.x, self.t))
+        return np.interp(t, self.t, self._vx)
 
     def vy(self, t):
-        return np.interp(t, self.t, np.gradient(self.y, self.t))
+        return np.interp(t, self.t, self._vy)
 
     def speed(self, t):
         return np.hypot(self.vx(t), self.vy(t))
@@ -108,20 +123,18 @@ def speed(self, t):
     def ax(self, t):
         # double derivative of x coordinate.
         # v = np.gradient(self.vx(t), self.t)
-        v = np.gradient(self.x, self.t)
-        a = np.gradient(v, self.t)
-        return np.interp(t, self.t, a)
+
+        return np.interp(t, self.t, self._ax)
 
     def ay(self, t):
-        v = np.gradient(self.y, self.t)
-        a = np.gradient(v, self.t)
-        return np.interp(t, self.t, a)
+
+        return np.interp(t, self.t, self._ay)
 
 
 class Sensor:
     def __init__(self, rate_hz,
                  noise_std,
-                 bias,drift_rate,
+                 bias, drift_rate,
                  rng_seed: int = 21):
         self.rate_hz = rate_hz
         self.noise_std = noise_std or {}
@@ -142,8 +155,8 @@ def sample(self, path: Path):
         return df
 
     def measure(self, path, t):
-        """generate measurements for the certain time instance?"""
-        return self.sample(path)[:t]
+        """generate measurements for the certain time instance, different for the subclasses so will be overridden. """
+        raise NotImplementedError("to be implemented by subclasses")
 
     def apply_noise(self, df):
         """applies Gaussian noise, constant bias and time variable drift to sensor data. """
@@ -175,17 +188,23 @@ class GPSSensor(Sensor):
     def __init__(self, rate_hz=4.0, noise_m=5.0, bias_m=(0, 0), drift_ms=(0, 0), rng_seed=21):
         # convert all metre noise to degrees using small angle approximation
         # use: dely = dellat * pi/180 * r_earth
-        super().__init__(rate_hz, rng_seed)
+
         noise_lat = noise_m / RADIUS_EARTH * (180 / np.pi)
         noise_lon = noise_m / RADIUS_EARTH * (180 / np.pi)
 
         bias_deg_lat = bias_m[1] / RADIUS_EARTH * (180 / np.pi)
         bias_deg_lon = bias_m[0] / RADIUS_EARTH * (180 / np.pi)
+        drift_deg_lat = drift_ms[1] / RADIUS_EARTH * (180 / np.pi)
+        drift_deg_lon = drift_ms[0] / RADIUS_EARTH * (180 / np.pi)
 
+        noise_std = {"lat": noise_lat, "lon": noise_lon}
+        bias = {"lat": bias_deg_lat, "lon": bias_deg_lon}
+        drift_rate = {"lat": drift_deg_lat, "lon": drift_deg_lon}
         # could potentially add noise by direclty adding to the x,y coordinates too?
+        super().__init__(rate_hz, noise_std, bias, drift_rate, rng_seed = rng_seed)
 
     def measure(self, path, t):
-        return pd.DataFrame({"lat": path.latitude(t), "lon": path.longitude(t)})
+        return pd.DataFrame({"lat": path.lat(t), "lon": path.lon(t)})
 
 
 class IMUSensor(Sensor):
@@ -196,25 +215,27 @@ def __init__(self, rate_hz=30.0, noise_std_mss=0.05,  # gaussian noise in m/s^2,
         drift = {"ax": drift_rate, "ay": drift_rate}
         super().__init__(rate_hz, noise_std, bias, drift, rng_seed)
 
-    def _measure(self, path: Path, t: np.ndarray) -> pd.DataFrame:
+    def measure(self, path: Path, t: np.ndarray):
         return pd.DataFrame({
             "ax": path.ax(t),
             "ay": path.ay(t),
             "az": np.full(len(t), 9.81),
         })
 
+
 class SpeedSensor(Sensor):
     """
     outputs scalar ground speed in m/s.
 
     """
-    def __init__(self, rate_hz = 30.0, noise_mps = 0.5, bias_mps = 0.0, slip_factor = 1.0, rng_seed = 44):
+
+    def __init__(self, rate_hz=30.0, noise_mps=0.5, bias_mps=0.0, slip_factor=1.0, rng_seed=44):
         noise_std = {"speed": noise_mps}
-        bias      = {"speed": bias_mps}
+        bias = {"speed": bias_mps}
         super().__init__(rate_hz, noise_std, bias, {}, rng_seed)
         self.slip_factor = slip_factor
 
     def measure(self, path: Path, t: np.ndarray) -> pd.DataFrame:
-            return pd.DataFrame({
-                "speed": path.speed(t) * self.slip_factor
-            })
+        return pd.DataFrame({
+            "speed": path.speed(t) * self.slip_factor
+        })

From 768def3bb0f8c68b253a0222b98a194b9aa1f589 Mon Sep 17 00:00:00 2001
From: sanar <sanarifa.2011@gmail.com>
Date: Sat, 5 Sep 2026 15:11:19 -0700
Subject: [PATCH 49/49] added per lap plot functions.  further commits to clean
 up code.

---
 v4/kalman_filter_position/KalmanFilter.py     | 435 +++++++++++++++++-
 .../random_generation.py                      |  20 +-
 v4/kalman_filter_position/track_generator.py  | 113 ++++-
 3 files changed, 522 insertions(+), 46 deletions(-)

diff --git a/v4/kalman_filter_position/KalmanFilter.py b/v4/kalman_filter_position/KalmanFilter.py
index 9df13c5..d2c251c 100644
--- a/v4/kalman_filter_position/KalmanFilter.py
+++ b/v4/kalman_filter_position/KalmanFilter.py
@@ -1,13 +1,422 @@
-class KalmanFilter():
-    def __init__(self, latitude, longitude, accel_x, accel_y, accel_z, speed):
-        self.latitude = latitude
-        self.longitude = longitude
-        self.accel_x = accel_x
-        self.accel_y = accel_y
-        self.accel_z = accel_z
-        self.speed = speed
-
-    def initialize(self):
-
-    def predict(self):
-# this will propagate through errors and the recurrence based on the previous time steps.
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from datetime import datetime
+from random_generation import Path, xy_to_latlon, latlon_to_xy
+from random_generation import *
+from track_generator import *
+
+
+class ExtendedKalmanFilter:
+    """
+    Fuses GPS + IMU + speed to estimate position and velocity.
+
+    State: x = [x_m, y_m, vx, vy]
+    x_m, y_m  : position in local East-North frame (metres)
+    vx, vy   : East-North velocity in m/s
+
+    the motion model is non-linear because acceleration heading is used to estimate the velocity direction, making F and H Jacobians necessary.
+
+    Implementation:
+    - predict step: uses IMU data
+    - update step: GPS+speed updates
+    """
+
+    def __init__(self, x0, P0, Q, R_gps, R_speed):
+        self.x = x0.astype(float)  # inital state [x,y,vx,vy]
+        self.P = P0.astype(float)  # inital covariance 4x4
+        self.Q = Q.astype(float)  # process noise covariance 4x4
+        self.R_gps = R_gps.astype(float)  # gps measurement noise 2x2
+        self.R_speed = float(R_speed)  # scalar speed measurement noise (m/s)^2
+        self._log: list[dict] = []  # stores history
+
+    def predict(self, ax: float, ay: float, dt: float):
+        """
+            IMU prediction step runs at 30 Hz.
+            Propagate state forward by dt seconds using IMU derived acceleration.
+            Process model uses constant acceleration over dt
+            The Jacobian matrix linearises the motion model, F is the matrix of differentials
+        """
+        x, y, vx, vy = self.x
+        self.x = np.array([  # using kinematic equations
+            x + vx * dt + 0.5 * ax * dt ** 2,  # s= ut + 0.5 a t^2
+            y + vy * dt + 0.5 * ay * dt ** 2,
+            vx + ax * dt,  # v = u + at
+            vy + ay * dt,
+        ])
+        # state transition Jacobian
+        F = np.array([[1, 0, dt, 0],
+                      [0, 1, 0, dt],
+                      [0, 0, 1, 0],
+                      [0, 0, 0, 1]], dtype=float)
+
+        # covariance prediction: P- = F. P. F^T + Q
+        self.P = F @ self.P @ F.T + self.Q  # @ for matrix multiplication.
+
+    # measurement update using core EKF equations.
+    def _update(self, z, H, R, z_pred):
+        """
+        Core EKF update equations (Joseph form for numerical stability).
+
+        innovation (y or v) the difference between the actual measurement (z) and the expected measurement based on the prior state prediction (h(x)), defined as y = z - h(x)
+        z is the measurement vector i.e. the sensor observation at a specific timestep. 
+        H is the observation matrix.
+
+        y = z - h(x) innovation residual
+        S = H . P. H^T + R innovation covariance
+        K = P. H ^T . S-1  Kalman gain
+        X = X + K.Y  State update
+        P = (I - K.H).P covariance update in the Joseph form. The Joseph form
+        The Joseph form  P = (I-KH)P(I-KH)^T + KRK^T  is numerically more
+        stable than the simpler  P = (I-KH)P  despite a higher computation time.
+
+        """
+
+        n = len(self.x)
+        I = np.eye(n)  # builds the 2d identity matrix.
+        y_inn = z - z_pred  # innovation
+        S = H @ self.P @ H.T + R  # innovation covariance
+        K = self.P @ H.T @ np.linalg.inv(S)  # Kalman gain
+        self.x = self.x + K @ y_inn
+        IKH = I - K @ H
+        self.P = IKH @ self.P @ IKH.T + K @ R @ K.T
+
+    def update_gps(self, x_m: float, y_m: float):
+        """
+        GPS directly observes position in the E-N frame.
+        Measurement model (linear):
+
+         h(x) = [x_m, y_m],
+         H = [[1,0,0,0], [0,1,0,0]]
+
+         """
+        Z = np.array([x_m, y_m])
+        H = np.array([[1, 0, 0, 0], [0, 1, 0, 0]], dtype=float)
+        Z_pred = H @ self.x  # multiply with state vector.
+        self._update(np.array([x_m, y_m]), H, self.R_gps, H @ self.x)
+
+    def update_speed(self, speed_ms: float):
+        """
+        Motor speed gives the scalar magnitude of velocity. Measurement model (non-linear):
+        h(x) = sqrt(vx^2 +vy^2)
+
+        Linearised Jacobian:
+        H  = [0,0, vx / |v|, vy/|v|]
+        if |v| is less than 0.1 m/s this update is skipped to avoid division by zero issues.
+
+        """
+        vx, vy = self.x[2], self.x[3]
+        v_norm = np.hypot(vx, vy)
+        if v_norm < 0.1:
+            return
+        H = np.array([[0, 0, vx / v_norm, vy / v_norm]])
+        self._update(np.array([speed_ms]), H,
+                     np.array([[self.R_speed]]), np.array([v_norm]))
+
+    def log(self, t: float):
+        self._log.append({
+            "t": t,
+            "x_m": self.x[0], "y_m": self.x[1],
+            "vx": self.x[2], "vy": self.x[3],
+            "std_x": np.sqrt(self.P[0, 0]),
+            "std_y": np.sqrt(self.P[1, 1]),
+        })
+
+    def trajectory(self) -> pd.DataFrame:
+        df = pd.DataFrame(self._log)
+        return df
+
+
+# MAIN RUN EKF LOOP:
+
+
+def build_ekf(gps_df, path):
+    """Constructs a new EKF, initialize based on the first GPS values."""
+    lat0, lon0 = path.lat0, path.lon0
+    gps_x, gps_y = latlon_to_xy(gps_df["lat"].values, gps_df["lon"].values, lat0, lon0)
+    x0 = np.array([gps_x[0], gps_y[0], 0.0, 0.0])
+    # initial covariance: gps uncertainty is given as 5m and then variance is 25m^2
+    # velocity variance is 10 (m/s)^2
+
+    P0 = np.diag([25.0, 25.0, 10.0, 10.0])
+
+    # process noise so position and velocity noise
+    Q = np.diag([0.01, 0.01, 0.10, 0.10])
+
+    # gps measurement noise ~5m
+    R_gps = np.diag([25.0, 25.0])
+    R_speed = 0.25
+
+    return ExtendedKalmanFilter(x0, P0, Q, R_gps, R_speed)
+
+
+def run_ekf(ekf, gps_df, imu_df, speed_df, path, t_offset=0.0):
+    """
+    interleave GPS and IMU/speed events in time order. Runs predict() on each IMU tick and update_gps/update_speed() when those measurements are available.
+
+    :param gps_df
+    :param imu_df
+    :param speed_df
+    :param path
+    :return: traj_df: EKF state trajectory
+    :return: gps_df: raw GPS positions in E-N (coordinates, needs to be converted to lat/lon)
+    """
+    lat0, lon0 = path.lat0, path.lon0
+
+    # convert gps to xy
+    gps_x, gps_y = latlon_to_xy(gps_df["lat"].values, gps_df["lon"].values, lat0, lon0)
+    # x0 = np.array([gps_x[0], gps_y[0], 0.0, 0.0])
+
+    # building the gps lookup at each IMU tick index.
+
+    gps_ptr = 0
+    speed_ptr = 0
+    t_imu = imu_df["t"].values
+
+    # fusion loop that repeats the prediction state (imu) and update state (gps and speed). the prediction state uses the system model to guess the next state and thisestimate and uncertainty are propagated forward in time. the update state compares the real measurement to the predicted measurement and then applies the kalman gain. this is used to adjust the final state estimate.
+    for i in range(len(imu_df)):
+        t = t_imu[i]
+        dt = t - t_imu[i - 1] if i > 0 else 1 / 30.0
+
+        ekf.predict(imu_df["ax"].iloc[i], imu_df["ay"].iloc[i], dt)
+        while speed_ptr < len(speed_df) and speed_df["t"].iloc[speed_ptr] <= t:
+            ekf.update_speed(speed_df["speed"].iloc[speed_ptr])
+            speed_ptr += 1
+
+        while gps_ptr < len(gps_df) and gps_df["t"].iloc[gps_ptr] <= t:
+            ekf.update_gps(gps_x[gps_ptr], gps_y[gps_ptr])
+            gps_ptr += 1
+
+        ekf.log(t + t_offset)  # global time, so logs across laps dont overlap
+
+    traj = ekf.trajectory()
+    traj["lat"], traj["lon"] = xy_to_latlon(traj["x_m"].values, traj["y_m"].values, lat0, lon0)
+    return traj
+
+
+# evaluate and compare ekf version with ground truth.
+
+
+def evaluate(traj: pd.DataFrame, path: Path,
+             gps_df: pd.DataFrame, lap_duration, label: str = ""):
+
+    """
+
+
+    :param traj:
+    :param path:
+    :param gps_df:
+    :param lap_duration:
+    :param label:
+    :return: RMSE error associated with the velocity and position results of the EKF.
+    """
+    t = traj["t"].values
+    # t_local = np.mod(t, lap_duration)
+
+    # position error
+    gt_x = path.x(t)
+    gt_y = path.y(t)
+    err_ekf = np.hypot(traj["x_m"].values - gt_x,
+                       traj["y_m"].values - gt_y)
+
+    t_gps = gps_df["t"].values
+    # t_gps_local = np.mod(t_gps,
+    #                      lap_duration)  # to wrap time into [o, lap_duration] before beig interpolated for path.x, vx, vy etc so that it works correctly across multiple ocncatenated laps.
+
+    gps_x, gps_y = latlon_to_xy(gps_df["lat"].values, gps_df["lon"].values,
+                                path.lat0, path.lon0)
+    err_gps = np.hypot(gps_x - path.x(t_gps),
+                       gps_y - path.y(t_gps))
+
+    # velocity error
+    gt_vx = path.vx(t)
+    gt_vy = path.vy(t)
+
+    err_vx = traj["vx"].values - gt_vx  # signed
+    err_vy = traj["vy"].values - gt_vy
+    err_speed = np.hypot(traj["vx"].values, traj["vy"].values) \
+                - np.hypot(gt_vx, gt_vy)  # scalar speed error of signed values.
+    err_vel_magnitude = np.hypot(err_vx, err_vy)  # combined, like position
+
+    rmse_vx = np.sqrt(np.mean(err_vx ** 2))
+    rmse_vy = np.sqrt(np.mean(err_vy ** 2))
+    rmse_speed = np.sqrt(np.mean(err_speed ** 2))
+    rmse_vel = np.sqrt(np.mean(err_vel_magnitude ** 2))
+
+    tag = f"[{label}] " if label else ""
+    print(f"\n{tag} Position ")
+    print(f"  GPS  RMSE : {np.sqrt(np.mean(err_gps ** 2)):.2f} m")
+    print(f"  EKF  RMSE : {np.sqrt(np.mean(err_ekf ** 2)):.2f} m")
+    print(f"  Improvement : {(1 - np.mean(err_ekf) / np.mean(err_gps)) * 100:.1f}%")
+    print(f"\n{tag} Velocity")
+    print(f"  RMSE vx   : {rmse_vx:.3f} m/s")
+    print(f"  RMSE vy   : {rmse_vy:.3f} m/s")
+    print(f"  RMSE speed: {rmse_speed:.3f} m/s")
+    print(f"  RMSE |v|  : {rmse_vel:.3f} m/s")
+
+    return {
+        "err_ekf": err_ekf, "err_gps": err_gps,
+        "t": t, "t_gps": gps_df["t"].values,
+        "gps_rmse": np.sqrt(np.mean(err_gps ** 2)),
+        "ekf_rmse": np.sqrt(np.mean(err_ekf ** 2)),
+        "err_vx": err_vx, "err_vy": err_vy,
+        "err_speed": err_speed, "err_vel": err_vel_magnitude,
+        "rmse_vx": rmse_vx, "rmse_vy": rmse_vy,
+        "rmse_speed": rmse_speed, "rmse_vel": rmse_vel,
+        "gt_speed": np.hypot(gt_vx, gt_vy),
+    }
+
+
+def plot_results(traj, gps_df, path, lap_duration, results, title="EKF Results", save_path=None):
+    t = traj["t"].values
+    #  t_local = np.mod(t, lap_duration)
+    gt_x = path.x(t)
+    gt_y = path.y(t)
+    gps_x, gps_y = latlon_to_xy(gps_df["lat"].values, gps_df["lon"].values,
+                                path.lat0, path.lon0)
+
+    fig, axes = plt.subplots(2, 2, figsize=(14, 10))
+    fig.suptitle(title, fontweight="bold")
+
+    # Trajectory
+    ax = axes[0, 0]
+    ax.plot(gt_x, gt_y, "k-", lw=2, label="Ground truth", zorder=3)
+    ax.scatter(gps_x, gps_y, s=8, c="tomato", alpha=0.5,
+               label=f"GPS (RMSE={results['gps_rmse']:.1f}m)", zorder=2)
+    ax.plot(traj["x_m"], traj["y_m"], "royalblue", lw=1.5,
+            label=f"EKF (RMSE={results['ekf_rmse']:.1f}m)", zorder=4)
+    ax.set_aspect("equal");
+    ax.grid(alpha=0.3)
+    ax.set_xlabel("East (m)");
+    ax.set_ylabel("North (m)")
+    ax.set_title("Trajectory");
+    ax.legend(fontsize=8)
+
+    # Position error over time
+    ax = axes[0, 1]
+    ax.plot(results["t_gps"], results["err_gps"],
+            "tomato", lw=0.8, alpha=0.7, label="GPS error")
+    ax.plot(results["t"], results["err_ekf"],
+            "royalblue", lw=1.2, label="EKF error")
+    ax.set_xlabel("Time (s)");
+    ax.set_ylabel("Position error (m)")
+    ax.set_title("Position Error vs Ground Truth")
+    ax.legend(fontsize=8);
+    ax.grid(alpha=0.3)
+
+    # Speed over time (EKF estimate vs ground truth)
+    ax = axes[1, 0]
+    ekf_speed = np.hypot(traj["vx"].values, traj["vy"].values)
+    ax.plot(t, results["gt_speed"] * 3.6, "k-", lw=1.5,
+            label="True speed", alpha=0.8)
+    ax.plot(t, ekf_speed * 3.6, "royalblue", lw=1.2,
+            label=f"EKF speed (RMSE={results['rmse_speed'] * 3.6:.2f} km/h)")
+    ax.set_xlabel("Time (s)");
+    ax.set_ylabel("Speed (km/h)")
+    ax.set_title("Speed Estimate");
+    ax.legend(fontsize=8);
+    ax.grid(alpha=0.3)
+
+    # Velocity component errors. these are signed so you can see bias direction
+    ax = axes[1, 1]
+    ax.plot(t, results["err_vx"], "royalblue", lw=1.0,
+            alpha=0.8, label=f"vx error (RMSE={results['rmse_vx']:.3f} m/s)")
+    ax.plot(t, results["err_vy"], "darkorange", lw=1.0,
+            alpha=0.8, label=f"vy error (RMSE={results['rmse_vy']:.3f} m/s)")
+    ax.axhline(0, color="k", lw=0.8, ls="--")
+    ax.set_xlabel("Time (s)");
+    ax.set_ylabel("Velocity error (m/s)")
+    ax.set_title("Velocity Error (signed)");
+    ax.legend(fontsize=8);
+    ax.grid(alpha=0.3)
+
+    plt.tight_layout()
+    if save_path:
+        plt.savefig(save_path, dpi=130, bbox_inches="tight")
+    plt.show()
+
+
+def plot_results_per_lap(traj, gps_df, path, results, lap_info, title_prefix="EKF Lap", save_dir=None):
+    """
+    Renders plot_results per lap to avoid crowding, slices results based on [start_t, start_t + lap_duration] as returned by the lap_info field from the Voronoi Track Generator.
+
+    """
+    t_all = traj["t"].values
+    t_gps_all = gps_df["t"].values
+
+    for info in lap_info:
+        lap_num = info["lap"]
+        t0, t1 = info["start_t"], info["start_t"] + info["duration"]
+
+        # slice trajectory to this lap's time window
+        mask_traj = (t_all >= t0) & (t_all < t1)
+        traj_lap = traj[mask_traj].reset_index(drop=True)
+
+        # slice gps to this lap's time window
+        mask_gps = (t_gps_all >= t0) & (t_gps_all < t1)
+        gps_lap = gps_df[mask_gps].reset_index(drop=True)
+
+        if len(traj_lap) == 0:
+            print(f"Lap {lap_num}: no trajectory samples in window, skipping")
+            continue
+
+        # re-derive per-lap ground truth/error to avoid misalignment issues.
+        lap_results = evaluate(traj_lap, path, gps_lap, None,
+                               label=f"Lap {lap_num}")
+
+        save_path = None
+        if save_dir:
+            save_path = os.path.join(save_dir, f"ekf_lap{lap_num}.png")
+
+        plot_results(traj_lap, gps_lap, path, None, lap_results,
+                     title=f"{title_prefix} {lap_num}",
+                     save_path=save_path)
+
+
+# main loop:
+if __name__ == "__main__":
+    HERE = os.path.dirname(os.path.abspath(__file__))
+    n_laps = 3
+    print("Generating track")
+    gen = VoronoiTrackGenerator(
+        n_points=70, n_regions=10,
+        min_bound=10.0, max_bound=500.0,
+        origin_lat=49.2606, origin_lon=-123.2460,
+        v_max=18.0, v_min=6.0,
+        min_radius_m=20.0, n_centerline=1000,
+        rng_seed=41,
+    )
+
+    gen.generate()
+    speed_rng = np.random.default_rng(4)
+    gt_df = gen.generate_multilap(n_laps=n_laps, speed_change=0.50, rng=speed_rng)
+    out_dir = os.path.dirname(os.path.abspath(__file__))
+
+    csv_path = os.path.join(HERE, "ground_truth_path.csv")
+    gt_df.to_csv(csv_path, index=False)
+    print("[Saved ground_truth_path.csv]")
+
+    gen.plot_multilap(gt_df, title="Ground Truth Track",
+                      save_path=os.path.join(HERE, "track.png"))
+
+    path = Path(csv_path)
+    lap_duration = np.float64(path.duration)
+
+    gps_df = GPSSensor(rate_hz=4, noise_m=5.0, rng_seed=21).sample(path)
+    imu_df = IMUSensor(rate_hz=30, noise_std_mss=0.05, bias_mss=0.2, rng_seed=32).sample(path)
+    speed_df = SpeedSensor(rate_hz=30, noise_mps=0.43, rng_seed=42).sample(path)
+
+    print(f"GPS    : {len(gps_df)} samples @ 4 Hz")
+    print(f"IMU    : {len(imu_df)} samples @ 30 Hz")
+    print(f"Speed  : {len(speed_df)} samples @ 30 Hz")
+
+    ekf = build_ekf(gps_df, path)
+    full_traj = run_ekf(ekf, gps_df, imu_df, speed_df, path)  # t_offset not needed, one continuous pass
+
+    results = evaluate(full_traj, path, gps_df, lap_duration=None, label=f"{n_laps}-lap variable pace")
+    plot_results(full_traj, gps_df, path, None, results,
+                 title=f"EKF  {n_laps} Laps, Variable Pace",
+                 save_path=os.path.join(HERE, "ekf_multilap_variable_pace.png"))
+    # # to analyse multiple laps, i'm still using the same initial lat, lon point as a reference. concatenating laps does not necessarily require a change in reference frame.
+    plot_results_per_lap(full_traj, gps_df, path, results, gen.lap_info,
+                         title_prefix="EKF Lap", save_dir=HERE)
diff --git a/v4/kalman_filter_position/random_generation.py b/v4/kalman_filter_position/random_generation.py
index 311a9d4..7993899 100644
--- a/v4/kalman_filter_position/random_generation.py
+++ b/v4/kalman_filter_position/random_generation.py
@@ -6,7 +6,7 @@
 
 # the goal here is to create a csv dataset that consists of random data so that we can simulate necessary failure conditions as necessary.
 # Overall, this includes the sensor class and the Path generator (this will interpolate lat/lon over time).
-# The Voronoi Track Generator is responsible for generating the track and exporting this to a csv format.
+# The Voronoi Track Generator is responsible for generating the track and speed based on the curvature profile. It also exports this to a csv format.
 RADIUS_EARTH = 6371000.0
 
 
@@ -127,7 +127,6 @@ def ax(self, t):
         return np.interp(t, self.t, self._ax)
 
     def ay(self, t):
-
         return np.interp(t, self.t, self._ay)
 
 
@@ -146,7 +145,7 @@ def sample(self, path: Path):
         """
         generates measuremesnts over the full path duration.
         :param path:
-        :return:
+        :return the sample dataframe
         """
         t = np.arange(0, path.duration, 1.0 / self.rate_hz)
         df = self.measure(path, t)
@@ -201,13 +200,19 @@ def __init__(self, rate_hz=4.0, noise_m=5.0, bias_m=(0, 0), drift_ms=(0, 0), rng
         bias = {"lat": bias_deg_lat, "lon": bias_deg_lon}
         drift_rate = {"lat": drift_deg_lat, "lon": drift_deg_lon}
         # could potentially add noise by direclty adding to the x,y coordinates too?
-        super().__init__(rate_hz, noise_std, bias, drift_rate, rng_seed = rng_seed)
+        super().__init__(rate_hz, noise_std, bias, drift_rate, rng_seed=rng_seed)
 
     def measure(self, path, t):
         return pd.DataFrame({"lat": path.lat(t), "lon": path.lon(t)})
 
 
 class IMUSensor(Sensor):
+    """
+    Outputs acceleration along the three coordinate axes
+     (ax, ay and az)
+
+    """
+
     def __init__(self, rate_hz=30.0, noise_std_mss=0.05,  # gaussian noise in m/s^2,
                  bias_mss=0.02, drift_rate=0.0, rng_seed=23):
         noise_std = {"ax": noise_std_mss, "ay": noise_std_mss, "az": 0.02}
@@ -225,17 +230,16 @@ def measure(self, path: Path, t: np.ndarray):
 
 class SpeedSensor(Sensor):
     """
-    outputs scalar ground speed in m/s.
+    Outputs scalar ground speed in m/s.
 
     """
 
-    def __init__(self, rate_hz=30.0, noise_mps=0.5, bias_mps=0.0, slip_factor=1.0, rng_seed=44):
+    def __init__(self, rate_hz=30.0, noise_mps=0.5, bias_mps=0.0, rng_seed=44):
         noise_std = {"speed": noise_mps}
         bias = {"speed": bias_mps}
         super().__init__(rate_hz, noise_std, bias, {}, rng_seed)
-        self.slip_factor = slip_factor
 
     def measure(self, path: Path, t: np.ndarray) -> pd.DataFrame:
         return pd.DataFrame({
-            "speed": path.speed(t) * self.slip_factor
+            "speed": path.speed(t)
         })
diff --git a/v4/kalman_filter_position/track_generator.py b/v4/kalman_filter_position/track_generator.py
index beac2cc..1b2c4fe 100644
--- a/v4/kalman_filter_position/track_generator.py
+++ b/v4/kalman_filter_position/track_generator.py
@@ -34,7 +34,7 @@ def xy_to_latlon(x: np.ndarray, y: np.ndarray,
 def assign_speed(curvature_abs: np.ndarray,
                  v_max: float = 18.0,
                  v_min: float = 6.0,
-                 curvature_threshold: float = 1 / 20.0):
+                 curvature_threshold: float = 1 / 20.0, rng: np.random.Generator = None):
     """
     Assign speed at each point on the centerline based on local curvature.
 
@@ -45,12 +45,26 @@ def assign_speed(curvature_abs: np.ndarray,
 
     curvature_abs         : absolute curvature at each centerline point
     v_max                 : maximum speed on straights (m/s)
-    v_min                 : minimum speed in corners (m/s) around 22 km/h
-    curvature_threshold   : curvature at which v_min kicks in
+    v_min                 : minimum speed in corners (m/s)
+    curvature_threshold   : curvature at which v_min is used
                             1/20 = radius of 20 m
     :return numpy array of the speed values.
     """
     # Normalise curvature to [0, 1] where 0 = straight, 1 = max corner
+    # rng = rng or np.random.default_rng()
+    #
+    # #random baseline
+    # speed = rng.uniform(v_min, v_max, len(curvature_abs))
+    # #sharp corners use hard minimum
+    # sharp = curvature_abs >=curvature_threshold
+    # straight_threshold = 1/100
+    # speed[sharp] = v_min
+    # speed = np.convolve(speed, np.ones(25)/25, mode="same")
+    #
+    # straight = curvature_abs <= straight_threshold
+    # speed[straight] = v_max
+    # return np.clip(speed, v_min, v_max)
+    # Normalise curvature to [0, 1] where 0 = straight, 1 = max corner
     k_norm = np.clip(curvature_abs / curvature_threshold, 0, 1)
     speed = v_max - k_norm * (v_max - v_min)
     return speed
@@ -217,6 +231,7 @@ def generate(self):
 
             self._x, self._y = x_c, y_c
             self._speed, self._k = speed, k_abs
+            self._lat, self._lon = lat, lon
 
             df = pd.DataFrame({"time_s": t_arr, "lat": lat, "lon": lon})
             print(f"Track generated (attempt {attempt}): "
@@ -233,6 +248,46 @@ def generate(self):
 
         raise RuntimeError("Could not generate valid track in 50 attempts.")
 
+    def generate_multilap(self, n_laps, speed_change=0.50, rng=None):
+        """
+        Build continuous multilap path that uses the same spatial geometry with a slight shift in the v_min / v_max values
+
+
+        """
+
+        rng = rng or np.random.default_rng()
+        frames = []
+        t_cursor = 0.0
+        self.lap_info = []
+
+        for lap in range(
+                n_laps):  # for every lap we randomly either increment/decrement the v_min / v_max by a number in a specified range.
+            change = 1.0 + rng.uniform(-speed_change, speed_change)
+            v_max_lap = self.v_max * change
+            v_min_lap = self.v_min * change
+
+            speed_lap = assign_speed(self._k, v_max_lap, v_min_lap, self.curvature_threshold)
+            t_arr = curvature_to_time(self._x, self._y, speed_lap)  # arc length unaffected by centering
+
+            lap_df = pd.DataFrame({
+                "time_s": t_arr + t_cursor,
+                "lat": self._lat,
+                "lon": self._lon,
+            })
+            if lap > 0:
+                lap_df = lap_df.iloc[1:]  # drop duplicate (same time stamps as previous lap's last row)
+
+            frames.append(lap_df)
+            lap_duration = t_arr[-1]
+            self.lap_info.append({
+                "lap": lap + 1, "v_max": v_max_lap, "v_min": v_min_lap,
+                "duration": lap_duration, "start_t": t_cursor,
+            })
+
+            t_cursor += lap_duration
+
+        return pd.concat(frames, ignore_index=True)
+
     def plot(self, df: pd.DataFrame, title: str = "Voronoi Track",
              save_path: str = None):
         fig, axes = plt.subplots(1, 3, figsize=(16, 5))
@@ -284,26 +339,34 @@ def plot(self, df: pd.DataFrame, title: str = "Voronoi Track",
             print(f"Saved → {save_path}")
         plt.show()
 
+    def plot_multilap(self, df: pd.DataFrame, title: str = "Multi-Lap Track", save_path: str = None):
+
+        fig, axes = plt.subplots(1, 2, figsize=(14, 5))
+        fig.suptitle(title, fontweight="bold")
+
+        # Trajectory geometry is identical every lap
+        ax = axes[0]
+        ax.plot(self._x, self._y, "royalblue", lw=1.5)
+        ax.plot(self._x[0], self._y[0], "ko", ms=8, label="Start/Finish", zorder=5)
+        ax.set_aspect("equal");
+        ax.grid(alpha=0.3)
+        ax.set_xlabel("East (m)");
+        ax.set_ylabel("North (m)")
+        ax.set_title("Track (fixed geometry)");
+        ax.legend()
+        ax = axes[1]
+        for info in self.lap_info:
+            speed_lap = assign_speed(self._k, info["v_max"], info["v_min"], self.curvature_threshold)
+            t_arr = curvature_to_time(self._x, self._y, speed_lap) + info["start_t"]
+            ax.plot(t_arr, speed_lap * 3.6, lw=1.2, label=f"Lap {info['lap']}")
+        ax.set_xlabel("Time (s)");
+        ax.set_ylabel("Speed (km/h)")
+        ax.set_title("Speed Profile per Lap");
+        ax.legend(fontsize=8);
+        ax.grid(alpha=0.3)
 
-if __name__ == "__main__":
-    gen = VoronoiTrackGenerator(
-        n_points=70,
-        n_regions=10,
-        min_bound=10.0,
-        max_bound=500.0,
-        origin_lat=49.2606,
-        origin_lon=-123.2460,
-        v_max=18.0,
-        v_min=6.0,  # 22 km/h in tight corners
-        min_radius_m=20.0,  # minimum corner radius
-        n_centerline=1000,
-        rng_seed=40,  # set None for a different track each run
-    )
-
-    print("Generating Voronoi track...")
-    df = gen.generate()
-    out_dir = os.path.dirname(os.path.abspath(__file__))
-    df.to_csv(os.path.join(out_dir, "ground_truth_path.csv"), index=False)
-    print("[Saved ground_truth_path.csv]")
-
-    gen.plot(df, title="Voronoi Random Track")
+        plt.tight_layout()
+        if save_path:
+            plt.savefig(save_path, dpi=130, bbox_inches="tight")
+            print(f"Saved → {save_path}")
+        plt.show()