diff --git a/array_temp/.cache.sqlite b/array_temp/.cache.sqlite new file mode 100644 index 0000000..f968b26 Binary files /dev/null and b/array_temp/.cache.sqlite differ diff --git a/array_temp/Control_Model.ipynb b/array_temp/Control_Model.ipynb new file mode 100644 index 0000000..46c7f5f --- /dev/null +++ b/array_temp/Control_Model.ipynb @@ -0,0 +1,2595 @@ +{ + "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 (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": {}, + "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", + "- the RNN does not need to distinguish between laps" + ], + "id": "3d1eb59ff0e6ef7a" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-17T16:00:18.731717Z", + "start_time": "2026-03-17T16:00:16.397284Z" + } + }, + "cell_type": "code", + "source": [ + "#necessary imports\n", + "\n", + "\n", + "from data_tools import query\n", + "from data_tools.collections import TimeSeries\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import dill\n", + "import os\n", + "import pytz\n", + "from datetime import datetime, time, date" + ], + "id": "8672b9d1bf8a74aa", + "outputs": [], + "execution_count": 1 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-17T16:00:26.825458Z", + "start_time": "2026-03-17T16:00:21.630980Z" + } + }, + "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": "2026-03-17T16:00:30.880356Z", + "start_time": "2026-03-17T16:00:28.523652Z" + } + }, + "cell_type": "code", + "source": "speed_kph, mech_brake_pressed, accel_position, position = make_df(source = \"ingress\", event = \"FSGP_2024_Day_1\")", + "id": "9d99a3359b312d65", + "outputs": [], + 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(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": [ + { + "ename": "MergeError", + "evalue": "incompatible merge keys [0] dtype(' \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 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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 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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 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\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.._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('\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
curvature
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\n", + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 12 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-17T16:09:03.476022Z", + "start_time": "2026-03-17T16:09:03.466206Z" + } + }, + "cell_type": "code", + "source": [ + "def make_single_df():\n", + " day_dfs = []\n", + "\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", + " # 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": "bb38aa41d1ed86e5", + "outputs": [], + "execution_count": 15 + }, + { + "metadata": { + "ExecuteTime": { + "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": "8b3b3c33c24dc4ed", + "outputs": [ + { + "ename": "TypeError", + "evalue": "'list' object is not callable", + "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[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": 16 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-17T16:10:18.161376Z", + "start_time": "2026-03-17T16:10:18.153889Z" + } + }, + "cell_type": "code", + "source": [ + "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", + "\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": "e0b515f4c0c30bed", + "outputs": [], + "execution_count": 19 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-17T16:10:25.165599Z", + "start_time": "2026-03-17T16:10:19.221313Z" + } + }, + "cell_type": "code", + "source": "df = make_single_df()", + "id": "361b88fadb02073b", + "outputs": [], + "execution_count": 20 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-17T16:10:32.879730Z", + "start_time": "2026-03-17T16:10:32.857461Z" + } + }, + "cell_type": "code", + "source": "df.head()", + "id": "f5bd86fed9fb06c2", + "outputs": [ + { + "data": { + "text/plain": [ + " 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": [ + "
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brake_pressedaccel_positionspeedROC
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 22 + }, + { + "metadata": { + "ExecuteTime": { + "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": [], + "execution_count": null + }, + { + "metadata": {}, + "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": null + }, + { + "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", + "source": "pos.isna()", + "id": "4e81190f5287d2f5", + "outputs": [], + "execution_count": null + }, + { + "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": {}, + "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": "initial_id", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": "# speed is most likely in m/s", + "id": "5d30f3a75f7e9149", + "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": "9f8652589e173b01", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "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": "31de4e6237f9a41e", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "mech_brake_pressed.granularity\n", + "#granularity is at 0.1 seconds" + ], + "id": "467ef53567154e4a", + "outputs": [], + "execution_count": null + }, + { + "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": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "\n", + "plt.plot(accel_position.datetime_x_axis, accel_position, label = \"Accelerator Position\")\n" + ], + "id": "21f820a7985b2d39", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "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", + "# # remove this\n", + "#\n" + ], + "id": "6a0eed52a654e483", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "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": [], + "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": {}, + "cell_type": "code", + "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", + "outputs": [], + "execution_count": null + }, + { + "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)\n", + "plt.xlim(start_time, stop_time)\n", + "plt.title(\"speed kph\")\n", + "\n", + "# what is up with the whack units\n", + "\n" + ], + "id": "d41febe9473c8559", + "outputs": [], + "execution_count": null + }, + { + "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": null + }, + { + "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)" + ], + "id": "8f3d0704205b4567", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "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": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "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", + "outputs": [], + "execution_count": null + }, + { + "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", + "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", + " [ 37.00283342, 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-86.37126821],\n", + " [ 37.00188646, -86.37112396],\n", + " [ 37.0019441 , -86.37095574],\n", + " [ 37.00197296, -86.370787 ],\n", + " [ 37.00202945, -86.37067751],\n", + " [ 37.00209668, -86.37048098],\n", + " [ 37.00218305, -86.37031655],\n", + " [ 37.00228558, -86.37016005],\n", + " [ 37.00238818, -86.37002764],\n", + " [ 37.00249377, -86.36991909],\n", + " [ 37.00259639, -86.36983868],\n", + " [ 37.00270864, -86.36976266],\n", + " [ 37.00281411, -86.3697033 ],\n", + " [ 37.00292264, -86.36964769],\n", + " [ 37.00303847, -86.369599 ],\n", + " [ 37.00316942, -86.36952703],\n", + " [ 37.00332578, -86.36939126],\n", + " [ 37.00342743, -86.36921198],\n", + " [ 37.00346211, -86.36901195],\n", + " [ 37.00343231, -86.36879509],\n", + " [ 37.00336736, -86.36861758],\n", + " [ 37.00327983, -86.36847602],\n", + " [ 37.00316589, -86.36828932],\n", + " [ 37.00305696, -86.36810259],\n", + " [ 37.00296937, -86.36793662],\n", + " [ 37.00293802, -86.36773416],\n", + " [ 37.00295792, -86.36753614],\n", + " [ 37.00301643, -86.36739089],\n", + " [ 37.00307192, -86.36724611],\n", + " [ 37.00312788, -86.36711056],\n", + " [ 37.00320856, -86.36698638],\n", + " [ 37.00331523, -86.36684361],\n", + " [ 37.00340519, -86.36672019],\n", + " [ 37.00350264, -86.36658791],\n", + " [ 37.00361135, -86.36649982],\n", + " [ 37.00374245, -86.36645177],\n", + " [ 37.00387034, -86.3664758 ],\n", + " [ 37.00395668, -86.36655587],\n", + " [ 37.00402368, -86.36670753],\n", + " [ 37.00407807, -86.36686779],\n", + " [ 37.00412607, -86.36701603],\n", + " [ 37.00418686, -86.36718831],\n", + " [ 37.00427315, -86.36742386],\n", + " [ 37.00436296, -86.3676927 ],\n", + " [ 37.00442144, -86.36790543],\n", + " [ 37.00445858, -86.36815496],\n", + " [ 37.00447715, -86.36847342],\n", + " [ 37.00444541, -86.36882644],\n", + " [ 37.00434238, -86.36911386],\n", + " [ 37.00425654, -86.36927291],\n", + " [ 37.00418289, -86.36939303],\n", + " [ 37.00410293, -86.36950912],\n", + " [ 37.00399731, -86.36962489],\n", + " [ 37.00390147, -86.36973254],\n", + " [ 37.00379293, -86.36984054],\n", + " [ 37.00369701, -86.36994104],\n", + " [ 37.00359474, -86.37004514],\n", + " [ 37.0034838 , -86.3701598 ],\n", + " [ 37.00338477, -86.37024376],\n", + " [ 37.00327609, -86.37032788],\n", + " [ 37.00318982, -86.37040789],\n", + " [ 37.00306205, -86.37049992],\n", + " [ 37.00294389, -86.37059192],\n", + " [ 37.00283177, -86.37070006],\n", + " [ 37.00272312, -86.37083897],\n", + " [ 37.00264003, -86.37094293],\n", + " [ 37.00255683, -86.37103895],\n", + " [ 37.00249914, -86.37115127],\n", + " [ 37.00243497, -86.37126771],\n", + " [ 37.00236746, -86.3713964 ],\n", + " [ 37.00230001, -86.37152106],\n", + " [ 37.00223561, -86.37164596],\n", + " [ 37.00217132, -86.37175865],\n", + " [ 37.0021135 , -86.37187932],\n", + " [ 37.00204601, -86.37200003],\n", + " [ 37.00196791, -86.37211434],\n", + " [ 37.00188761, -86.3722312 ],\n", + " [ 37.00180726, -86.37233202],\n", + " [ 37.00173651, -86.37241676],\n", + " [ 37.00165284, -86.37250555],\n", + " [ 37.00155067, -86.3726109 ],\n", + " [ 37.00144762, -86.37271211],\n", + " [ 37.00136704, -86.37279282],\n", + " [ 37.00126713, -86.37288565],\n", + " [ 37.00115123, -86.37299929],\n", + " [ 37.00103518, -86.37310829],\n", + " [ 37.0009191 , -86.37321738],\n", + " [ 37.00082561, -86.37329809],\n", + " [ 37.00072863, -86.37339546],\n", + " [ 37.00061364, -86.37350099],\n", + " [ 37.00051698, -86.37360214],\n", + " [ 37.00042667, -86.37368311],\n", + " [ 37.00032338, -86.37378434],\n", + " [ 37.00022342, -86.37387665],\n", + " [ 37.00012018, -86.3739737 ],\n", + " [ 37.00002051, -86.37405916],\n", + " [ 36.99991404, -86.37415222],\n", + " [ 36.99981076, -86.37423248],\n", + " [ 36.99970432, -86.37430523],\n", + " [ 36.99958169, -86.37432571],\n", + " [ 36.99946928, -86.37430399],\n", + " [ 36.99934964, -86.37428072],\n", + " [ 36.99922365, -86.37425686],\n", + " [ 36.99908793, -86.37422888],\n", + " [ 36.99899069, -86.37413638],\n", + " [ 36.99896465, -86.37397888],\n", + " [ 36.9989808 , -86.37380904],\n", + " [ 36.99900664, -86.37362303],\n", + " [ 36.99904589, -86.37342887],\n", + " [ 36.9990885 , -86.37323096],\n", + " [ 36.99912743, -86.3730292 ],\n", + " [ 36.99916641, -86.37284378],\n", + " [ 36.99921127, -86.37261758],\n", + " [ 36.99924349, -86.37245211],\n", + " [ 36.99932082, -86.37230251],\n", + " [ 36.99932082, -86.37230251],\n", + " [ 36.99940337, -86.37214797],\n", + " [ 36.99948814, -86.37199725],\n", + " [ 36.99958754, -86.37189043],\n", + " [ 36.99970528, -86.37178738],\n", + " [ 36.99982625, -86.37169183],\n", + " [ 36.99997073, -86.37159181],\n", + " [ 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" + ], + "id": "2741c957d09edb03", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "\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", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "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": [], + "execution_count": null + }, + { + "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": null + }, + { + "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": {}, + "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": [], + "execution_count": null + }, + { + "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": {}, + "cell_type": "code", + "source": [ + "from data_tools import *\n", + "from data_tools.collections.time_series import TimeSeries\n", + "# from data_tools.query.postgresql_query import PostgresClient\n", + "#from data_tools.fsgp_2024_laps import FSGPDayLaps" + ], + "id": "84e31e6326fbd121", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": "from datetime import *", + "id": "8bfb296ce8b6d5f6", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "from scipy import integrate as intg\n", + "from datetime import datetime\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": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": "", + "id": "62f1a76206e8d1d8", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "plt.plot(all_positions)\n", + "#plt.plot(pd.DataFrame(speed_kph))" + ], + "id": "73543cfa882ed428", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "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": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": "plt.plot(pos)", + "id": "50b7ed46a96a8b21", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "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": [], + "execution_count": null + }, + { + "metadata": {}, + "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", + " dist\n", + " )\n", + "\n", + "pos_df = pd.DataFrame(pos)" + ], + "id": "25a88d24f23e2b2d", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": "calculated_speeds, calculated_position = state_array", + "id": "f4fb71b02da66f3e", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "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 = 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": null + }, + { + "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": {}, + "cell_type": "code", + "source": "plt.plot(file.datetime_x_axis, file)", + "id": "90dbd2d15d06f417", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "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": null + }, + { + "metadata": {}, + "cell_type": "code", + "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", + " 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": "aed81af65ec0feda", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "pos = []\n", + "pos_df = make_df(source = \"localization\", name = \"TrackIndex\")" + ], + "id": "198c0fb7383fad4a", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": "plt.plot(pos_df)", + "id": "18aceaa5d2fb1956", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "# 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)" + ], + "id": "c6edb2a9f27ba9ba", + "outputs": [], + "execution_count": null + }, + { + "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", + "source": [ + "plt.plot(calculated_position)\n", + "plt.plot(calculated_speeds, color = 'red') # this seems not right." + ], + "id": "d543d0eca7887c24", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": "#how do i align this with time?", + "id": "75a36cec8ab0ffb9", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": "len(calculated_position)", + "id": "cd71ca48061bb80b", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "#preprocessing - convert everything to pandas dataframes.\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", + "#df_speed_kph = pd.DataFrame(speed_kph)\n", + "\n", + "\n" + ], + "id": "a6707eebb9ccd8c9", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "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": "3e63d74e16a13193", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": "pos_df.head()", + "id": "67452406a84c903d", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "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": [], + "execution_count": null + }, + { + "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": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": "final_df.head()", + "id": "2c397a74782cbde3", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": "combined_df.head()", + "id": "b1208bd2410e08fc", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "plt.plot(combined_df[\"position\"])\n", + "plt.plot(combined_df[\"speed\"])" + ], + "id": "42e9fe006fc1b367", + "outputs": [], + "execution_count": null + }, + { + "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", + "# preprocessing pipeline: dataset - rescaling - sequences - tensors - RNN" + ], + "id": "e4585f807a9c575a", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "\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": "c00f5becff80fe17", + "outputs": [], + "execution_count": null + }, + { + "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", + "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", + "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": "573dc63af8846209" + }, + { + "metadata": {}, + "cell_type": "markdown", + "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" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "\n", + "from control_model.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": null + }, + { + "metadata": {}, + "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", + " 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", + " 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", + " 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": "329e1f0797e61e90", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": "train_model(model, train_loader, test_loader, epochs = 30)", + "id": "205d60e11f150705", + "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 = 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", + " 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": "55763d08b5868893", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "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": [], + "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": "f66c02f2a3933818", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "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", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "import seaborn as sns\n", + "\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", + " # 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", + " all_errors.extend(error.numpy())\n", + " all_speeds.extend(speeds.numpy())\n", + " all_targets.extend(targets.numpy())\n", + "\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", + " # 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", + " 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": "9e3d7d9c0f207973", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": "plot_error_heatmap(model, test_loader, device)", + "id": "4c0e45388dee0647", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": "", + "id": "c08265494dbc609e", + "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/array_temp/coefficient_fitting.ipynb b/array_temp/coefficient_fitting.ipynb new file mode 100644 index 0000000..04e1181 --- /dev/null +++ b/array_temp/coefficient_fitting.ipynb @@ -0,0 +1,809 @@ +{ + "cells": [ + { + "metadata": { + "ExecuteTime": { + "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", + "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(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", + "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" + ], + "id": "initial_id", + "outputs": [], + "execution_count": 59 + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": 19, + "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" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-14T03:52:16.302993Z", + "start_time": "2026-01-14T03:52:03.032092Z" + } + }, + "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(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) # 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_aliter = client.query_time_series(start_utc, stop_utc, \"MotorRotatingSpeed\")\n" + ], + "id": "856e8e887bd73795", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Start UTC: 2025-07-01 07:00:00+00:00\n", + "Stop UTC: 2025-07-06 07:00:00+00:00\n" + ] + } + ], + "execution_count": 70 + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": 21, + "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" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-14T03:52:21.645631Z", + "start_time": "2026-01-14T03:52:20.313563Z" + } + }, + "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 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 36.774658\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" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "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": 71 + }, + { + "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": "2026-01-14T03:52:24.802595Z", + "start_time": "2026-01-14T03:52:24.782459Z" + } + }, + "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 -2.213 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 -2.013 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", + "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": 72 + }, + { + "metadata": { + "ExecuteTime": { + "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": 73 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-14T03:52:26.159701Z", + "start_time": "2026-01-14T03:52:26.147935Z" + } + }, + "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": 74 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-14T03:52:26.874448Z", + "start_time": "2026-01-14T03:52:26.856107Z" + } + }, + "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 1.137 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", + "341 22.902834 \n", + "342 2.313184 \n", + "343 0.000000 \n", + "344 0.000000 " + ], + "text/html": [ + "
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" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 75 + }, + { + "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": "2026-01-14T03:52:29.773018Z", + "start_time": "2026-01-14T03:52:29.412010Z" + } + }, + "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", + "\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": [ + "
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" 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 14, + "source": "plt.plot(minutely_15_dataframe['date'], minutely_15_dataframe['shortwave_radiation_instant'])", + "id": "3d07f9ce1c4e6bb4" + }, + { + "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": { + "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": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "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": { + "end_time": "2026-01-07T03:57:44.426038Z", + "start_time": "2026-01-07T03:57:44.421879Z" + } + }, + "cell_type": "code", + "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 + } + ], + "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/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 new file mode 100644 index 0000000..6946d41 --- /dev/null +++ b/array_temp/faiman_coefficients.ipynb @@ -0,0 +1,1472 @@ +{ + "cells": [ + { + "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": "markdown", + "source": "From InfluxDB, query data over the first 4 days of FSGP 2025.", + "id": "b59eb1d3085bfd98" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-04T02:59:06.874190Z", + "start_time": "2026-02-04T02:58:53.887072Z" + } + }, + "cell_type": "code", + "source": [ + "from data_tools import query\n", + "from data_tools.collections import TimeSeries\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", + "import dill\n", + "import os\n", + "\n", + "\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", + "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" + ], + "id": "8b2b4006671bdc31", + "outputs": [], + "execution_count": 1 + }, + { + "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": { + "ExecuteTime": { + "end_time": "2026-01-24T19:09:59.449997Z", + "start_time": "2026-01-24T19:09:59.331692Z" + } + }, + "cell_type": "code", + "source": [ + "from datetime import datetime, date, time, timedelta, timezone\n", + "import pytz\n", + "\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 = 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", + "\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", + "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": "2026-02-04T02:32:02.997314Z", + "start_time": "2026-02-04T02:32:02.955553Z" + } + }, + "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_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_fsgp]):\n", + " with open(filepath, 'wb') as f:\n", + " dill.dump(data, f)\n", + "\n", + "\n", + "\n", + "#time zone matching conventions?? - add factor of 8 to open meteo maybe?" + ], + "id": "a807d5de05d7c8b0", + "outputs": [], + "execution_count": 2 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-04T02:59:08.603709Z", + "start_time": "2026-02-04T02:59:08.182291Z" + } + }, + "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\": 36.976,\n", + "\t\"longitude\": 86.4491,\n", + "\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\": \"auto\",\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: {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", + "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)\n" + ], + "id": "f718cf3615289b31", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Coordinates: 37.0°N 86.5°E\n", + "Elevation: 5139.0 m asl\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 2025-07-01 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 2025-07-07 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 8.825508 \n", + "1 10.594036 \n", + "2 13.138765 \n", + "3 15.391840 \n", + "4 16.873980 \n", + ".. ... \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": 2 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-04T02:59:09.779360Z", + "start_time": "2026-02-04T02:59:09.575856Z" + } + }, + "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": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 3 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "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" + ], + "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": "2026-02-04T03:54:19.443686Z", + "start_time": "2026-02-04T03:54:17.120886Z" + } + }, + "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": { + 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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=" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 8 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-04T03:54:23.281676Z", + "start_time": "2026-02-04T03:54:20.710545Z" + } + }, + "cell_type": "code", + "source": [ + "\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)\")" + ], + "id": "c5ccc00464a923b0", + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 9 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-04T03:54:26.094168Z", + "start_time": "2026-02-04T03:54:23.316319Z" + } + }, + "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(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", + "\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": "a3fa42c24abb970c", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 10 + }, + { + "metadata": { + "ExecuteTime": { + "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": 11 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-04T03:54:28.834441Z", + "start_time": "2026-02-04T03:54:27.570631Z" + } + }, + "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", + "df_15m[\"value_shifted\"] = df_15m[\"value\"].shift(-8)\n", + "\n", + "print(df_15m.head())\n" + ], + "id": "203be30192c9302a", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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": "stderr", + "output_type": "stream", + "text": [ + "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": 12 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-04T03:54:28.996598Z", + "start_time": "2026-02-04T03:54:28.963950Z" + } + }, + "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": 13 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-04T03:54:29.099264Z", + "start_time": "2026-02-04T03:54:29.079366Z" + } + }, + "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": 14 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-04T03:54:29.810420Z", + "start_time": "2026-02-04T03:54:29.733653Z" + } + }, + "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", + "df_15m_copy.index = df_15m_copy.index.floor(\"15T\")\n", + "weather_df.index = weather_df.index.floor(\"15T\")\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(\"no of aligned points:\", len(common_index))\n", + "\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", + ")\n" + ], + "id": "8d042560a87a9f85", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "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_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_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": 15 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-04T03:54:30.494999Z", + "start_time": "2026-02-04T03:54:30.364340Z" + } + }, + "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": [ + "
" + ], + "image/png": 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" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 17 + }, + { + "metadata": { + "ExecuteTime": { + "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": 18 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-04T03:54:32.075553Z", + "start_time": "2026-02-04T03:54:32.059616Z" + } + }, + "cell_type": "code", + "source": "merged_df.head()", + "id": "3d1f52f37c17fa80", + "outputs": [ + { + "data": { + "text/plain": [ + " array_temperature temperature_2m \\\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", + "2025-07-02 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": [ + "
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array_temperaturetemperature_2mshortwave_radiation_instantwind_speed_10m
2025-07-02 00:45:00+00:0028.002427-2.063243.1040504.680000
2025-07-02 01:00:00+00:0028.375842-1.963276.8082584.680000
2025-07-02 01:15:00+00:0028.749256-1.863309.4313354.680000
2025-07-02 01:30:00+00:0029.122671-1.713344.1488955.154415
2025-07-02 01:45:00+00:0029.496086-1.513386.1459665.692100
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" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 19 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-04T03:54:32.696232Z", + "start_time": "2026-02-04T03:54:32.557803Z" + } + }, + "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", + "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": "1624b6be9409b4fe", + "outputs": [ + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 20 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "import the Physics Array Temperature Model for validation/sanity checks\n", + "id": "11a5c363e4b5b953" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-04T03:54:34.499123Z", + "start_time": "2026-02-04T03:54:33.722289Z" + } + }, + "cell_type": "code", + "source": [ + "from v4.array_temperature.arrayTemperatureModel import arrayTemperatureModel\n", + "import numpy as np\n", + "from scipy.optimize import curve_fit\n", + "\n", + "\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", + "\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" + ], + "id": "1aa707313aeb5993", + "outputs": [], + "execution_count": 21 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-04T03:54:34.528212Z", + "start_time": "2026-02-04T03:54:34.523803Z" + } + }, + "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))" + ], + "id": "492ea4964124380d", + "outputs": [], + "execution_count": 22 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-04T03:54:34.972812Z", + "start_time": "2026-02-04T03:54:34.881477Z" + } + }, + "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+27, color=\"green\", label=\"Modelled Temperature\")" + ], + "id": "5e9c99851878c7d3", + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 23 + }, + { + "metadata": {}, + "cell_type": "markdown", + "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": "2026-02-04T03:54:36.513943Z", + "start_time": "2026-02-04T03:54:36.056474Z" + } + }, + "cell_type": "code", + "source": [ + "params = [22, 5.8]\n", + "xdata = np.stack([merged_df['shortwave_radiation_instant'], merged_df['temperature_2m'], merged_df['wind_speed_10m']])\n", + "popt = fit_faiman(faiman_model,\n", + " xdata,\n", + " merged_df['array_temperature'], params)" + ], + "id": "9839f97f4cfe5300", + "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[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": 24 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-04T03:54:37.402308Z", + "start_time": "2026-02-04T03:54:37.386806Z" + } + }, + "cell_type": "code", + "source": "popt\n", + "id": "fd0a96ba439544cc", + "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[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": 25 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-04T03:54:38.203268Z", + "start_time": "2026-02-04T03:54:38.001412Z" + } + }, + "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", + "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", + "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": "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": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 26 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-04T03:54:38.567073Z", + "start_time": "2026-02-04T03:54:38.559323Z" + } + }, + "cell_type": "code", + "source": "", + "id": "a48e41b4efacee1c", + "outputs": [], + "execution_count": null + }, + { + "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": "2026-02-04T03:54:39.771515Z", + "start_time": "2026-02-04T03:54:39.524900Z" + } + }, + "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": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "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": [ + "
" + ], + "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": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 30 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-15T02:36:25.324516Z", + "start_time": "2026-01-15T02:36:25.233184Z" + } + }, + "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": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 26 + }, + { + "metadata": { + "ExecuteTime": { + "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": 27 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-15T02:36:25.519458Z", + "start_time": "2026-01-15T02:36:25.405067Z" + } + }, + "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) + 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()" + ], + "id": "2374477e05f92f37", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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ee+89pk6dipOTEzNmzDBw03fs2JHff/+dr7/+mokTJ1KpUiW++eYbQkNDC2yE2NvbM3bsWPbu3cumTZvQaDRUrVqVn3/+mffee0/cb+TIkdjb2/P1118zbdo0HBwc6N+/P998802RNo+bOXMmnp6eLFq0iEmTJuHm5sbbb7/NV199hZWVVZHNo8/atWuZMGECixcvRhAEunbtyq5du/D19S2W+SxlbmPw9vbmxIkTTJs2jZ9++omUlBTq1avHtm3bDDxT9vb2HDhwgAkTJvDTTz9hb2/PsGHD6NGjB927dzcY86WXXuLkyZOiJyYhIQEfHx+aN2/OO++8U9KnKPEcIhMKorqTkHhBGTlyJP/880+O7mkJCQkJCdOQNCESEhISEhISZkEyQiQkJCQkJCTMgmSESEhISEhISJgFSRMiISEhISEhYRYkT4iEhISEhISEWZCMEAkJCQkJCQmzYHF1QjQaDQ8ePMDJycnoXiASEhISEhIS5kUQBOLj4/H19TXorp0XFmeEPHjwIFtHSwkJCQkJCYnSwb179yhfvrxR+1qcEeLk5ARoT8LYdtcSEhISEhIS5iUuLo4KFSqI13FjsDgjRBeCcXZ2lowQCQkJCQmJUoYpUgpJmCohISEhISFhFiQjREJCQkJCQsIsSEaIhISEhISEhFmwOE2IMQiCQHp6Omq12txLkZCQMAKFQoFSqZTS7iUkJAwodUaISqXi4cOHJCUlmXspEhISJmBvb0/ZsmWxtrY291IkJCQshFJlhGg0GkJCQlAoFPj6+mJtbS3dWUlIWDiCIKBSqXj06BEhISFUq1bN6EJGEhISzzelyghRqVRoNBoqVKiAvb29uZcjISFhJHZ2dlhZWREWFoZKpcLW1tbcS5KQkLAASuXtiHQXJSFR+pA+txISElmRvhUkJCQkJCQkzIJkhEhISEhISEiYBckIeU65desWLVq0wNbWlgYNGph7ORISEhISEtmQjJASYuTIkchkMt59991sj40bNw6ZTMbIkSOLbL4ZM2bg4OBAYGAgBw4cMHqN/fr1y3MfmUyW58/MmTMLv3gLw9/fnwULFph7GRISEhLPHZIRUoJUqFCB9evXk5ycLG5LSUlh7dq1VKxYkZQ0Nap0TZHMFRwcTJs2bfDz88Pd3b1IxgR4+PCh+LNgwQKcnZ0Ntk2ZMqXI5ipOdAXvShKVSlWi80lISEhYOqXeCBEEgSRVull+BEEwaa2NGjWiQoUKbNq0Sdy2adMmKlasSM3a9UhWqbn7KIHouBSu33vChAkT8PLywtbWljZt2nD27FnxuGfPnjFs2DA8PT2xs7OjWrVqrFixAtB6K86fP8/s2bMNvBP37t1j8ODBuLq64ubmRt++fQkNDQVg5syZrFq1iq1bt4pejcOHD2c7Bx8fH/HHxcUFmUxmsG39+vUEBARga2tLzZo1+fnnn8VjQ0NDkclk/PXXX7Rt2xY7OzuaNm3K7du3OXv2LE2aNMHR0ZEePXrw6NEj8Tidh2bWrFl4enri7OzMu+++a3BR12g0zJ07l0qVKmFnZ0f9+vX5559/xMcPHz6MTCZj165dNG7cGBsbG/777z+Cg4Pp27cv3t7eODo60rRpU/bv3y8e99JLLxEWFsakSZPE50X3fGUNcy1YsAB/f/9s654zZw6+vr7UqFEj39dBQkJC4kWiVNUJyYnkNDW1Pt9jlrlvzO6GvbVpT+Gbb77JihUrGDZsGADLly9n1KhRbNutvfCp1Boi41L4ZuYnHNy1jVWrVuHn58e8efPo1q0bQUFBuLm58dlnn3Hjxg127dqFh4cHQUFBoofl4cOHdO7cme7duzNlyhQcHR1JS0ujW7dutGzZkmPHjqFUKvnyyy/p3r07V65cYcqUKdy8eZO4uDjRmHFzczPp3NasWcPnn3/OokWLaNiwIRcvXmTMmDE4ODgwYsQIcb8ZM2awYMECKlasyJtvvsnQoUNxcnJi4cKF2NvbM3jwYD7//HN++eUX8ZgDBw5ga2vL4cOHCQ0NZdSoUbi7uzNnzhwA5s6dy59//smSJUuoVq0aR48e5fXXX8fT05P27duL43z00UfMnz+fypUrU6ZMGe7du0fPnj2ZM2cONjY2rF69mt69exMYGEjFihXZtGkT9evX5+2332bMmDEmPR+6dTs7O7Nv3z6AfF8HqZqohITEi0SpN0JKG6+//jrTp08nLCwMgOPHj7N+/Xq27868+05KSuSvP5azaMlv9OjRA4DffvuNffv28fvvvzN16lTCw8Np2LAhTZo0ATC4A/fx8UGpVOLo6IiPjw8Af/75JxqNhmXLlol38ytWrMDV1ZXDhw/TtWtX7OzsSE1NFY8xlRkzZvDdd9/xyiuvAFCpUiVu3LjB0qVLDYyQKVOm0K1bNwA++OADXnvtNQ4cOEDr1q0BGD16NCtXrjQY29ramuXLl2Nvb0/t2rWZPXs2U6dO5YsvviAtLY2vvvqK/fv307JlSwAqV67Mf//9x9KlSw2MkNmzZ9OlSxfxfzc3N+rXry/+/8UXX7B582b+/fdfxo8fj5ubGwqFAicnpwI9Lw4ODixbtkw0Lox5HSQkJCReFEq9EWJnpeDG7G5mm9tUPD09efnll1m5ciWCIPDyyy/j4eGBfvX5+2EhpKel0bJVK3GblZUVzZo14+bNmwC89957DBgwgAsXLtC1a1f69etHK739s3L58mWCgoJwcnIy2J6SkkJwcLDJ55GVxMREgoODGT16tIHHID09HRcXF4N969WrJ/7t7e0NQN26dQ22RUdHGxxTv359gyq5LVu2JCEhgXv37pGQkEBSUpKBcQFaDUbDhg0NtumMNh0JCQnMnDmTHTt28PDhQ9LT00lOTiY8PNyU08+VunXrGng3ivt10IUIpXYGEhISpYFSb4TIZDKTQyLm5s0332T8+PEALF68GDD9otGjRw/CwsLYuXMn+/bto1OnTowbN4758+fnuH9CQgKNGzdmzZo12R7z9PQ08QxyHh+0HpvmzZsbPKZQGBprVlZW4t+68866TaMxXqCrm3vHjh2UK1fO4DEbGxuD/x0cHAz+nzJlCvv27WP+/PlUrVoVOzs7Bg4cmK+IVC6XZ9MEpaWlZdsv63zF+ToIgkBQdAIaAap7O0qGiISEhMVTuq7ezwndu3dHpVIhk8nEsIT+5aK8XyWsrK05dfIE9WpWA7QXuLNnzzJx4kRxP09PT0aMGMGIESNo27YtU6dOzdUIadSoERs2bMDLywtnZ+cc97G2tkatVhfonLy9vfH19eXu3bui3qUouXz5MsnJydjZ2QFw6tQpHB0dqVChAm5ubtjY2BAeHm4QejGG48ePM3LkSPr37w9ojYSsItGcnhdPT08iIyMRBEG82F+6dCnf+Yx5HQqKRtBqpADS1BqslaZ76iQkJCRKklKfHVMaUSgU3Lx5kxs3bmTzEgDY2zswePibzPjkY3bv3s2NGzcYM2YMSUlJjB49GoDPP/+crVu3EhQUxPXr19m+fTsBAQG5zjls2DA8PDzo27cvx44dIyQkhMOHD/P+++9z//59QKsruXLlCoGBgTx+/DjHO/u8mDVrFnPnzuXHH3/k9u3bXL16lRUrVvD999+bNE5OqFQqRo8ezY0bN9i5cyczZsxg/PjxyOVynJycmDJlCpMmTWLVqlUEBwdz4cIFfvrpJ1atWpXnuNWqVWPTpk1cunSJy5cvM3To0GxeGH9/f44ePUpERASPHz8GtFkzjx49Yt68eQQHB7N48WJ27dqV73kY8zoUFH3PjMa0xC0JCQkJsyAZIWbC2dk5zzvhDz6aQa8+fRk+fDiNGjUiKCiIPXv2UKZMGUB7dz59+nTq1atHu3btUCgUrF+/Ptfx7O3tOXr0KBUrVuSVV14hICCA0aNHk5KSIq5jzJgx1KhRgyZNmuDp6cnx48dNOqe33nqLZcuWsWLFCurWrUv79u1ZuXIllSpVMmmcnOjUqRPVqlWjXbt2DBkyhD59+hgURvviiy/47LPPmDt3LgEBAXTv3p0dO3bkO/f3339PmTJlaNWqFb1796Zbt240atTIYJ/Zs2cTGhpKlSpVxJBJQEAAP//8M4sXL6Z+/fqcOXPGqBopxrwOBUXf8NCYmD4uISEhYQ5kgqnFLoqZuLg4XFxciI2NzfalnJKSQkhICJUqVXruWoGHPUkkNtnQ8+DtbIu38/N1ngVh5MiRxMTEsGXLFnMvxaJJTVMTGBUPQCUPB5xsrfI5omR5nj+/EhISeV+/c0PyhFgIOZmClmUeSlg6kidEQkKitCEZIRZCTpcMC3NSSVg4gt67SF001f8lJCQkihUpO8ZCyMngkMSFWrIWLpPIGf33i1p680hISJQCJE+IhZBzOEa6kEgYj2F2jPTekZCQsHwkI8RCEHIIyEgedQlTkDwhEhISpQ3JCLEQcrpmSJ4QCVMw8IRIRoiEhEQpQDJCLISc7A3pOiJhCgaeEMmAlZCQKAVIRoiFoAvHeDvb4uWk7XcieUIkTEH//SKFYyQkJEoDUnaMhaC7fjjZKklTC0Cq5AmRMAnDOiHmW4eEhISEsUieEAvh/JlTDOjcChcHO4YNHgDAudMnqVu3LlZWVvTr188s67p16xYtWrTA1taWBg0aGHXMzJkzjd5XougoLk/IsyQVEc+SJM+chEQRMW/3LSZvuCR9ppCMEIth3qyPqVG7LjdvB7Fk2XIA5s6YToMGDQgJCSmSWhkjR4402ZiZMWMGDg4OBAYGcuDAAaOOmTJlitH7mpOXXnrJoCtxaScvTUhhDMPI2BSeJKpIUhWsw7KEhEQmqnQNPx8OZtPFCIIfJZp7OWZHMkIshHuhITRr1Y6KFcrj5uoKQHhoCB07dqR8+fK4ZmwraYKDg2nTpg1+fn64u7sbdYyjo6PR+0oUDpVKJf6tn+ZdVNkxGkEgLaP8arpGQBAE0tPTi2RsCYkXkai4FHMvwbIQLIzY2FgBEGJjY7M9lpycLNy4cUNITk4Wt2k0GiEhNcEsPxqNxujzSklJESZMmCB4enoKNjY2QuvWrYUzZ84IISEhAtqq7eLP0t9+z7ZtxYoVwtOnT4WhQ4cKHh4egq2trVC1alVh+fLl4hzh4eHCoEGDBBcXF6FMmTJCnz59hJCQEEEQBGHGjBnZxjx06FCea866/4wZMwRBEIQPP/xQqFatmmBnZydUqlRJ+PTTTwWVSiUeN2PGDKF+/fri/yNGjBD69u0rzJkzR/Dy8hJcXFyEWbNmCWlpacKUKVOEMmXKCOXKlTM4F1PmWb16teDn5yc4OzsLQ4YMEeLi4vJ9PUaMGJHt/HTP1dWrV4Xu3bsLDg4OgpeXl/D6668Ljx49Eo9t3769MH78eOGDDz4QXF1dBS8vL+HXX38VEhIShJEjRwqOjo5ClSpVhJ07d4rHHDp0SACE7du3C3Xr1hVsbGyE5s2bC1evXjVY17Fjx4Q2bdoItra2Qvny5YUJEyYICQkJ4uN+fn7C7NmzheHDhwtOTk7CiBEjxOeqUpWqgq2tnVCuop8w5v0pQkpqqiAIgrBixYoc30+6997FixfF8Z89e2bw3ti7/4AACItX/yXUa9BQsLKyEg4dOiSo1Wrhq6++Evz9/QVbW1uhXr16wt9//53nc57T51dC4kXjTMgTwW/adsFv2nbhWkSMuZdTpOR1/c6NUi9MTUpLwnGuo1nmTpiegIO1g1H7fvjhh2zcuJFVq1bh5+fHvHnz6NatG3fu3OHBgwdUr1GDsf/7mAlvvYG9gyNVG7Wm70vN+PKL2QwZMgQXFxemTp3KjRs32LVrFx4eHgQFBZGcnAxAWloa3bp1o2XLlhw7dgylUsmXX35J9+7duXLlClOmTOHmzZvExcWxYsUKANzc3PJc88OHD+ncuTPdu3dnypQpODpqn2cnJydWrlyJr68vV69eZcyYMTg5OfHhhx/mOtbBgwcpX748R48e5fjx44wePZoTJ07Qrl07Tp8+zYYNG3jnnXfo0qUL5cuXN3qe4OBgtmzZwvbt23n27BmDBw/m66+/Zs6cOXme28KFC7l9+zZ16tRh9uzZAHh6ehITE0PHjh156623+OGHH0hOTmbatGkMHjyYgwcPisevWrWKDz/8kDNnzrBhwwbee+89Nm/eTP/+/fn444/54YcfGD58OOHh4djb24vHTZ06lYULF+Lj48PHH39M7969uX37NlZWVgQHB9O9e3e+/PJLli9fzqNHjxg/fjzjx48XXzOA+fPn8/nnnzNjxgxxm5OTE98vWoqNiwd3bl1n9rSJVC7rwUcfTWPIkCFcu3aN3bt3s3//fgBcXFyIiorK8zkCSFdrPSoL587ii6++oXHdmpQpU4a5c+fy559/smTJEqpVq8bRo0d5/fXX8fT0pH379vmOKyHxovIwNtMTkpImlaQs9Z6QhNQEgZmY5SchNSHbGnMiISFBsLKyEtasWSNuU6lUgq+vrzBv3jxBrdEITs7OwuzvFgvparWQmqYWLt97Jjg5OwsrVqwQj+ndu7cwatSoHOf4448/hBo1ahh4Z1JTUwU7Ozthz549giBkeiRMoX79+qIHJDe+/fZboXHjxuL/OXlC/Pz8BLVaLW6rUaOG0LZtW/H/9PR0wcHBQVi3bp1J89jb2xt4PqZOnSo0b97cmFMT2rdvL3zwwQcG27744guha9euBtvu3bsnAEJgYKB4XJs2bbKtffjw4eK2hw8fCoBw8uRJQRAyPSHr168X93ny5IlgZ2cnbNiwQRAEQRg9erTw9ttvG8x97NgxQS6Xi+95Pz8/oV+/fjmeT9iTROHyvWfC5XvPhMmfzBYaNcr9NREEwShPyLZdewVAWLBsjRDxLEkQBK1Xz97eXjhx4oTBeKNHjxZee+21HNcmCJInREJCEATh1yPBoifk+J1H+R9QinghPSH2VvYkTE8w29zGEBwcTFpaGq1btxa3WVlZ0axZM27evGlQqEyGDJksc4Og9+B7773HgAEDuHDhAl27dqVfv360atUKgMuXLxMUFISTk5PB3CkpKQQHBxfk9HJlw4YN/PjjjwQHB5OQkEB6ejrOzs55HlO7dm3k8kwJkre3N3Xq1BH/VygUuLu7Ex0dbdI8/v7+BudctmxZgzFM5fLlyxw6dEj0+ugTHBxM9erVAahXr162tdetW9fg/IBsa2nZsqX4t5ubGzVq1ODmzZvi3FeuXGHNmjXiPoIgoNFoCAkJISAgAIAmTZpkW9uGDRv49vsFhIbcJSkxEbU6HZd8XhNjSM/QltSq10D0igQFBZGUlESXLl0M9lWpVDRs2LDQc0pIPM8YeELSJbF3qTdCZDKZ0SERS0Xf0JDJDNXC+gZKjx49CAsLY+fOnezbt49OnToxbtw45s+fT0JCAo0bNza4gOnw9PQssrWePHmSYcOGMWvWLLp164aLiwvr16/nu+++y/M4Kysrg/9lMlmO2zQajUnz5DVGQUhISKB3795888032R4rW7as0ecjk8kATFpLQkIC77zzDu+//362xypWrCj+7eBg+H7XPVcTP/yExq1fwtHZmd1bN7Fm2eI859MZhfrvv7S0NIN9dEaInb0D6RnnkpCgNfp37NhBuXLlDPa3sbHJc04JiRedyLhk8W8pHPMcGCGlgSpVqmBtbc3x48fx8/MDtF/2Z8+eZeLEiWJOgwztxUumd2zWxnaenp6MGDGCESNG0LZtW6ZOncr8+fNp1KgRGzZswMvLK1evhLW1NWp14SzvEydO4OfnxyeffCJuCwsLK9SY5ponp+ejUaNGbNy4EX9/f5TKov94nDp1SjQonj17xu3bt0UPR6NGjbhx4wZVq1Y1aUzdczV20lQSUrWZKw8j7qERBBJS0nG0VeZ4rjrj9OHDh6IH49KlSwb7pKszvyRFr0itWtjY2BAeHi7pPyQkTMRQEyJ5QqQU3RLAwcGB9957j6lTp7J7925u3LjBmDFjSEpKYvTo0ZneDlnmL1kO43z++eds3bqVoKAgrl+/zvbt28UL2LBhw/Dw8KBv374cO3aMkJAQDh8+zPvvv8/9+/cBbejiypUrBAYG8vjx42x3vcZQrVo1wsPDWb9+PcHBwfz4449s3rzZ9CfFAubx9/fn9OnThIaG8vjxYzQaDePGjePp06e89tprnD17luDgYPbs2cOoUaMKbcABzJ49mwMHDnDt2jVGjhyJh4eHWLtl2rRpnDhxgvHjx3Pp0iXu3LnD1q1bGT9+fJ5j6p6rfzf9w73QENYsX8rB3dsBiI5PEc81JCSES5cu8fjxY1JTU7Gzs6NFixZ8/fXX3Lx5kyNHjvDpp58ajK1ng4jhGCcnJ6ZMmcKkSZNYtWoVwcHBXLhwgZ9++olVq1YV+jmSkHieiZSEqQaYZITMnDlTe6eu91OzZk3x8ZSUFMaNG4e7uzuOjo4MGDDAKAX+i8DXX3/NgAEDGD58OI0aNSIoKIg9e/ZQpkwZ0R2uMzx0zy0YhmOsra2ZPn069erVo127digUCtavXw+Avb09R48epWLFirzyyisEBAQwevRoUlJSRM/ImDFjqFGjBk2aNMHT05Pjx4+bfB59+vRh0qRJjB8/ngYNGnDixAk+++yzgj8xZpxnypQpKBQKatWqhaenJ+Hh4fj6+nL8+HHUajVdu3albt26TJw4EVdXVwNNS0H5+uuv+eCDD2jcuDGRkZFs27YNa2trQKszOXLkCLdv36Zt27Y0bNiQzz//HF9f3zzH1D1Xsz7+H4O7t+PmpXO8/cFUAFLTtV9yAwYMoHv37nTo0AFPT0/WrVsHwPLly0lPT6dx48ZMnDiRL7/80mDsdL1wkjqjTgjAF198wWeffcbcuXMJCAige/fu7Nixg0qVKhX6OZKQeF5RawSi41PF/yVPCMgEwfi6sTNnzuSff/4R0/wAlEolHh4egFY4uWPHDlauXImLiwvjx49HLpebdLGLi4vDxcWF2NjYbGGFlJQUQkJCqFSpEra2tkaPaemkpKm5HRWPUi6nlq/2nG88iCVdI1Dd2wlbK4WZVyhRWA4fPkyHDh149uxZsRWeC4yMJzVdTWVPR2yVcm48jAOgjq8LcnlOvrW8EQSBaxFxBiHBWmWdUSoKZow9r59fCQljiYpLoflXmdWkP+xeg7EvmRZ+tWTyun7nhslBb6VSiY+PT7btsbGx/P7776xdu5aOHTsCsGLFCgICAjh16hQtWrQwdaoXBtEToned0HpCBKm3gITR6N4rckCpkKOQy1BrBFLTNdhZm27IpqsFBARkyJDLtKXg0zUCSskmlpAoEPp6EIAUqRWC6ZqQO3fu4OvrS+XKlRk2bBjh4eEAnD9/nrS0NDp37izuW7NmTSpWrMjJkydzHS81NZW4uDiDnxeNLJIQ7d8Z/xRnN9SvvvoKR0fHHH969OhRfBOXAOHh4bmem6Ojo/i+fZ7QvVd0oTybDGtBVcA0wLSMUIxSIRO9H0XZGE9C4kXjkV4oBiAlXdKEmOQJad68OStXrqRGjRo8fPiQWbNm0bZtW65du0ZkZCTW1tbZXM3e3t5ERkbmOubcuXOZNWtWgRb/vCBkuXgAyEVNSPF96b/77rsMHjw4x8fs7OyKbd6SwNfXN1umR9bHS5KXXnqp2L1aurCJLvJio5STpMrUhZiKzuBQyGXi+1E/W0ZCQsI0ElINkwEkTYiJRoj+3XG9evVo3rw5fn5+/PXXXwW+aE2fPp3JkyeL/8fFxVGhQoUCjVVayTkco/1dnDeebm5u+ZZuL60olUqTU11LO1k9IdZKrfeioEaILiVXqW+ESJ4QCYkCk5BqaHRIRkghU3RdXV2pXr06QUFB+Pj4oFKpiImJMdgnKioqRw2JDhsbG5ydnQ1+XjR0lwj9cIyc4veESDw/CEKmfkjfEwLa1uEFQa3O9IQoFZIRIiFRWJJSDTtQSym6hTRCEhISCA4OpmzZsjRu3BgrKysOHMhU/gYGBhIeHm5QqloiOzmFY0RPiBnWI1H6MCj9XwyeEGWGZSNpQiQkCk5ihhGiyPg8SZ4QE8MxU6ZMoXfv3vj5+fHgwQNmzJiBQqHgtddew8XFhdGjRzN58mTc3NxwdnZmwoQJtGzZUsqMyYecwjHFpQlJU2tITddgo5BjpZRq1T0vaPTeJzpPiHWGmDRdo0EjCOJ7ylgyNSFydFm5khEiIVFwdOEYdwdrouNTJWEqJhoh9+/f57XXXuPJkyd4enrSpk0bTp06JZZ//uGHH5DL5QwYMIDU1FS6devGzz//XCwLf54oqeyYpNR0gh5p+34o5XJqlnUy+cIkYZmI3jQyC90p9GqDaDQCcoVpr7WuUJlCLhPHksIxEhIFJ0ml9YS46YwQyRNimhGiq86ZG7a2tixevJjFi/NunCVhSEllxyToxSPTNRpU6RqpENpzgoaca83IZDJtJ94CvI10Xg+lIlOYKnlCJCQKju472MPRBognVTJCpN4xJYUgCLz99tu4ubkhk8kM0kezlm0HuH8vjPoVynDl8uUiW0NW119BBYvmZObMmTRo0ED8f+TIkWLvlZIkNDQ02+toTnS2albPllz0qJluPOin6CokTYiERKHRaULcHbWtGiRhqtRFt8TYvXs3K1eu5PDhw1SuXFksdQ964Ri960f58hU4cP4W1f2Krp6FzvWnuztWPQc1HxYuXGi0tyg0NJRKlSpx8eJFA0PmeUCTg64ItEaJuoCVd3NK0ZWMEAmJgpOYoQlxc8gwQgpYSPB5QjJCSghdFlGrVq2yPZbTXaxSqcDDyxu5omheIkEQxCwJZ1slsclpZvOEqFQqsWlbYXFxcSmScUo7uXtCtP8XJhyjMDBCNAiCYBA6lJCQMI5ElX44RsqOgechHCMIkJhonh8j7y5HjhzJhAkTCA8PRyaT4e/vz+7du2nTpg2urq7U8PNl/MghhIXcFY+5Hx5O/QpluHr5EqBtgCaTydizZw8NGzbEzs6Ojh07Eh0dza5duwgICMDZ2ZmhQ4eSlJQkjqObp0yZMrStU4kJI18lOiIM0IZjdGGFTZs20aFDB+zt7alfv36epfb1WblyJa6urmzZsoVq1apha2tLt27duHfvnriPLoSybNkyg+ZlMTExvPXWW3h6euLs7EzHjh25nCX89PXXX+Pt7Y2Tk5PYFTjrc6sfjtFoNMybN4+qVatiY2NDxYoVmTNnDoDY4bVhw4bIZDJeeukl8bhly5YREBCAra0tNWvWzCaoPnPmDA0bNsTW1pYmTZpw8eJFo56fkiJ3T4jh40aPpxHEY5RyGQqdRqkAY0lISGgRwzEORRuOSUlTl1ovZek3QpKSwNHRPD96F/u8WLhwIbNnz6Z8+fI8fPiQs2fPkpiYyOTJkzl37hx//7sTuUzOmDdeRaPr1yGmRBqONXPmTBYtWsSJEye4d+8egwcPZsGCBaxdu5YdO3awd+9efvrpJ3F/3TyHjp3g1/VbUSrkjBo2BI1GYxCO+eSTT5gyZQqXLl2ievXqvPbaa6SnGxbWyf0lSGLOnDmsXr2a48ePExMTw6uvvmqwT1BQEBs3bmTTpk2ijmLQoEGiEXX+/HkaNWpEp06dePr0KQB//fUXM2fO5KuvvuLcuXOULVs232yr6dOn8/XXX/PZZ59x48YN1q5di7e3N6A1JAD279/Pw4cP2bRpEwBr1qzh888/Z86cOdy8eZOvvvqKzz77jFWrVgHaeji9evWiVq1anD9/npkzZzJlyhSjnpuSIl9PiIlfULpQjLZ5nQy5FJKRkCg0YopuEXpCElPTaTvvEMOWnSr0WGZBsDBiY2MFQIiNjc32WHJysnDjxg0hOTk5c2NCgiBov4NL/ichwejz+uGHHwQ/P78cH3sYkyQcvhwkAMLVq1cFQRCEyzdvC4Cw9cBxQRAE4dChQwIg7N+/Xzxu7ty5AiAEBweL29555x2hW7du2eaIjE0WLt97Jly6HSYAwj/7jgtX78cId+/eFQBh2bJl4r7Xr18XAOHmzZv5nteKFSsEQDh16pS47ebNmwIgnD59WhAEQZgxY4ZgZWUlREdHi/scO3ZMcHZ2FlJSUgzGq1KlirB06VJBEAShZcuWwtixYw0eb968uVC/fn3x/xEjRgh9+/YVBEEQ4uLiBBsbG+G3337Lca0hISECIFy8eDHbnGvXrjXY9sUXXwgtW7YUBEEQli5dKri7uxu873755ZccxzIXzxJThcv3nglB0fEG24Oj44XL954JTxNSTRovKTVNuHzvmXA9IvNzeONBrHD53jMhMTWtQGvM8fMrIfECEfDZLsFv2nbhXOhTwW/adsFv2nZBo9EUaswr92IEv2nbhaof7yj0WIUlr+t3bpR+TYi9PSQkmG/uAnLnzh0+//xzTp8+zaPHj1FneCXCw8OpU6cOSr0YvD716tUT//b29sbe3p7KlSsbbNPd8evPc/zkKZ4+eYIgaMeLfHCfajVriXe8+uOWLVsWgOjoaGrWrJnvuSiVSpo2bSr+X7NmTVxdXbl58ybNmjUDwM/PT6wnA3D58mUSEhJwd3c3GCs5OZng4GAAbt68ybvvvmvweMuWLTl06FCO67h58yapqal06tQp3zXrSExMJDg4mNGjRzNmzBhxe3p6uqg3uXnzJvXq1RPDSLp1WBI650RWT4guq8XUEIpaT5SqP1aaWvKESEgUBI1GIEml9Xx4OGZq4lILWSrhcYK2M2+aWij0WOag9BshMhk4OJh7FSajqzz722+/oXR040lCCgM6t0KlUgHkmhJpZWUl/i2TyQz+123T6Bkuunnmfv8TDmU88XK0oXWzhggZoZa0DHFq1nEBg3EKi0OW1yghIYGyZcty+PDhbPtm7cRsLAVpopiQYcD+9ttvNG/e3OAxhaL0fJj1RaT6FFSYmp7DeFKaroREwdGJUiEzHAPakExhDIdHGUYIQHxKeqkzQkq/JqQU8uTJEwIDA/n000/p1KkTVavXIC42xmAfXcMwjWB6PD+neVq2fYnK1WoQG/vMYPyiSNNNT0/n3Llz4v+BgYHExMQQEBCQ6zGNGjUiMjJS7Har/6NLXw4ICOD06dMGx506lXvcs1q1atjZ2Rn0L9JHl5GjVmfGYb29vfH19eXu3bvZ1qETsgYEBHDlyhUDUWxe6zAHuRkhsgIKU3MaTyFpQiQkCozOC6KQy3CwVuj1jyncd/BjAyMkrVBjmQPJCDEDZcqUwd3dnSVLlnLnzh3+O3KY+bM/NdhH362eXkCPhG6eX3/9lZC7wZw+fpRPP/oQKHxzM32srKyYMGECp0+f5vz584wcOZIWLVqIoZic6Ny5My1btqRfv37s3buX0NBQTpw4wSeffCIaNB988AHLly9nxYoV3L59mxkzZnD9+vVcx7S1tWXatGl8+OGHrF69muDgYE6dOsXvv/8OgJeXF3Z2duzevZuoqChiY2MBmDVrFnPnzuXHH3/k9u3bXL16lRUrVvD9998DMHToUGQyGWPGjOHGjRvs3LmT+fPnF/p5K0rUGUaGItcU3YIJU/XDMVITOwmJgqOrlmpvrUAmk2Gb8R1cWHHqkwSV+Hd8inHJBJaEZISYAblczu+r/uTU2XPUrVuXWZ9OY/Insw32kRkYIQX70pfL5axfv57z58/Ts31z5s/6mC+++hrIbG6Woiq8Otve3p5p06YxdOhQWrdujaOjIxs2bMjzGJlMxs6dO2nXrh2jRo2ievXqvPrqq4SFhYnZLEOGDOGzzz7jww8/pHHjxoSFhfHee+/lOe5nn33G//73Pz7//HMCAgIYMmQI0dHRgFa78uOPP7J06VJ8fX3p27cvAG+99RbLli1jxYoV1K1bl/bt27Ny5UrRE+Lo6Mi2bdu4evUqDRs25JNPPuGbb74p7NNWpBR1OEZntMj1PSEKyQiRkCgouvRcRxutCsLOWhs2KWzBMn1PiH5rjtKCTBAsK+k/Li4OFxcXYmNjcXZ2NngsJSWFkJAQg1oTpZXo+BQiY1NwtbdGEARik9PwdbUTi9gA3ImKJzlNjb+7A852VnmMlj+3o+JJSVNTycMBJ1srUtLU3I6KRy6TUdvXucDFp1auXMnEiROJiYkp1PokCkfo40TiUtIoX8YON4fM95DufVbG3poKbsYLqSOeJfEkUYWXky0+LtrPWnRcCpFxpo+l43n6/EpImMqJ4McM/e00Vb0c2T+5Pa2/PkhETDJbxrWmQQXXAo87bNkpjgc9AWDJ643oXqdsEa3YdPK6fueG5AkxEzrTTxAEvQ6ohijFVuyFtxOzNsmzUcqRy2Ro9CqpSpReRE9IEYVjxGwbvW8ISZgqIVFwkjJqhDhkeEBsrYomHPM4XgrHSBQA/WtCZu8YwwuIUmyfXngjQefw0nnXZTKZqKJOzuND0KNHDxwdHXP8+eqrrwq9LomiQdSEZAvHaH+b6u8UwzF670nJCJGQKDi67BiHjHCM7vu30EZIluyY0kbpT9EtpQgZpoe26pmhgaBDl8GiVhf+S19nxsj0/C321gqSVOkkq9SUycW7vmzZMpKTk3N8zM3NDTc3N0aOHFno9UkUjvw1IQX0hMhyEKZaVgRXQqJUoNNrFKURotYIPE0q3Z4QyQgxEzl6QrLsk+kJKYpwTPbeIqInJA9xarly5Qo9t0TxozNC5EUtTNUbTvKESEgUHF04xlE0QnThmIJ7up8mqgyuJQmpUopuiWBhWtpCIZBdr6FDIS96TYj+RcXeOjMc8zw9py8aGiGz2Vx2TUjmPiaNqck9HFPQ96P0HpN4kdFP0QWwVRbeE6IfioHS6QkpVUaIrqpnkpGN4ywZ3UVBK0zNWZmqC8ekF0FBsUxPSOYkkjj1+UC/mF32YmUFa2CXUzhGZxQLglCgAnq6z23WKr8SEi8CWVN0iyIc8zwYIaUqHKNQKHB1dRXrPtjb2xc4tdTcpKemIqSnkS5Xo9aAkK4mLVVBCplvSI0qHSFdRapGTnKyssDnKggCmnRt3DA1JQW1ItP2tCKdlHQ1sQmJYGed2xASFowqTY2QrkIuk5GaavillJbxWLpGblDxNT/SVakIGg1pqhRSMr4mBEGA9DQEBBKTkrFSGncPIwgCSUlJREdH4+rqWqrK4UtIFBWJGWFvnSZEZ4wUprZHNiOkFNYJKVVGCICPjw+AaIiUVp4mqkhSqbFRytEIAmlqASHOGhu9uv+CIPAoJkWrGYm3zXaXaywaQSA6RnsBUibZGtzdxiSpSEhVk/RUiWsha5FImAdVuobo+FSUchnKJMP6G+lqDdFxqchloEg0vrdOVEwyagFkCTZY6Rmtj3PZbgyurq7i51dC4kUjMUs4xslWe/ktjPdCVy3VzkpBcpq6VJZtL3VGiEwmo2zZsnh5eZGWVvqecB1/br/O4cBH1CrrTFxKOvefJbHg1QbULOdqsN+clWcIfZLEnP51aFHJo0BzxSWreHvzCQD2TGwn1h8B2H3tId8eCqRueRcWDGlY4PORMB9nQ54y89AVKnk4sGyEYb+ep4kq3t16AmSwf1J7o71pYxccRaXW8Ofo5pR1zTReZq44w/2nSXw3uD4NKpQxeo1WVlaSB0TihSZrOMbJVnvTF1cIwyExQ+xa1sWWu48TSZDCMSWHQqEo1V9qj5MFIuLVeLhoeJakJiJejcLKJlslSa8yzhwPjefKw2ReqlWwKpOxKrTjy2U4Ohjm4tYs705EvJqY4FisrW2yZVdIWD6xadrXt5yHMtv7x1mmJCI+I8SnsMbWOv/PjFojEBKj/WJ0drTH1jazAmuaoNC+X1JlUtVTCQkTyJqi62yn/R1XCMNBV+PJw8mGu48TS6UmpFQJU58n0jNqf6gFQUx5VOZgANT21Za+vRYRW+C5VBmiU+sc3OdVPR2xtZKTqFJz93FigeeQMB+xyVqDwSWHcJqdXngvr6J0+ujvZ29teJ/iam+dMacKCQkJ49F10c3qCSmM4aATtXo62WSMVfqiA5IRYiZUGRkv6WpBrIiak+ajTjkXAK4/iCvwXLrMF+schIRKhZyAsoU3dCTMR1yy9kvM2Ta7EaKQy7DJeN2TVMZ92en2k8kyaxno0OmGYpJK35edhIQ5yaoJcc7QhMQlF/yzpDNCvDKMkESVutTV8ZGMEDMhekI0+p6Q7C9HrQxPSERMMs8SC3b3mZrRpdEml2yGuhmGzlXJCCmV6GLKOvduVsR6MEZ2TNbtZ2elyKYhcbHPMEIK8cUpIfEikjUck+kJKbwRovOE6M9TWpCMEDORluEJUQuCWPwpp2QDZ1sr/N21Oo4rBTQSVHl4QkAyQko7ujupnDwhkBmSSTLSCNHtZ5+DfsQ1I41b8oRISJhG9nBM4bNjdKFTZ1srMdwuGSESRpGm0fOEqHVGSM4vR/2MNs+XwmMKNFe+Rkh5rRFy40FcgYpQSZgXnbDNOZcUazvr/BsV6qP7srTLyQjJ8IRImhAJCeMRBEFsYGdvo/1c6TRchcmO0ZV8t7NS6Bk1pesGQTJCzERaur4mJHdhKkADnRFy71mB5tLpT2yUOWdG6MSpCanphDyRxKmljczUv5xfX5241NRwjL1V9vBOphFSur7oJCTMSZJKLbbOyOoJSUnTiJ5xU9HdWNgaGCGSJ0TCCHRiVI1edkxuxcgyjZCYAvXfSE3L2xMiiVNLNzohqZ11zpoQnUcj0UhhqvjFloMnxFkSpkpImEyinthbFx7VGSNQcMMhNU3ntZTj7qjVhUTFGV8Z2RKQjBAzkZYRgknXZGbH5OYJqeXrjLVCzrOkNMKfmt43R/SE5FHhUtSF3JeMkNJGsp5LNidMzWjRGTX2OYwnZcdISJiOrqiYg3Vm+w2lQo5DhqFf0AwZ8YZBqcAvQzsYWspKLUhGiJlIE1N0NWKzsNw8ITZKBQEZWTKX7sWYPFd+mhDITAWWxKmlj2SVYepfVnR3SLoSz/mPl4cwVawTIhkhEhLGkihmxhh+pgpbK0SnCbG1VlDJ3QGAkMelq8GrZISYCZ0RotLrXptTiq6OhhkhmYsFEKfml6ILmZ6Q65I4tdSRnJa7kBTAw1FrODxJTM3x8azkKUzN8IQkpKYXOI4tIfGikTU9V4dOx1FQcaq+J8TfQ2uEhJYyXZ9khJgJXZ0Qld4XuUKRe8l0fV2IqRjjCanm5YiNUitOLW1v4hcd/boeOeHuoDVCsnbczHW8tNw9IfoZOJI3RELCOHQhTscsRoju81TQjJYUvRuQSh46T0jp+v6WjBAzoTM+dNoQyF0TAplGyI0HcaJnw1jyqpgqzq0nTpVCMqWLvOp6gLavBMBjI8MxoiYkB6GrQi4TKz1KuhAJCeNISM35M5rpCSloOEaXHSMXPSFPE1Wl6gZBMkLMRLo6e8gjN00IgJ+7PWXsrVCpNdx8GG/SXDqDJ6feMfroQjJShkzpQRCEfMMx7g46TUjhwzEg9Y+RkDCVrB10dYiddAtgNKSrNeJNrJ2VAkcbpVg5tTSJUyUjxEzkFE9X5NFmXSaT6RUtM61eiC5F18bKOCNE8oSUHlLTNWL9gdzCMZmaEBOFqbll29hLGTISEqaQmIsmxLkQtT1S9PSEthmfVZ04tTSF1CUjxAwIeqXadchlIM/DEwIF14VkekLybuMuNsuLkMSppQX9AmQ5hU8gMzsmJinNKDFpfp4VFylNN08EQeBQyCHWXl3L3uC9PE56bO4lSZiZRDEck7MnpCBGiP5nX5d04O+hTdO9+6j0GCE5f2tJFCtpOYRi8sqM0VFgI8QITQhANW9HrJVy4lPTCXuaJAqdJCyXpAyDwVopzzWc52pnhUIuQ60ReJqowtvZNu8xjQ7HSEZIVu48ucPIrSM5ce+EuE2GjFENRjG/63zK2JUx4+okzEWiKueqxoXJjtHXg+hqj1T2dAQgKDqhwGstaSRPiBnIMRSTjxcEMo2Q0CdJJnXUNSZFF8BKEqeWOnQ1QnILxYDWw+ZmQoZMXnVCQK9gmWSEGHDx4UVaL2/NiXsnsFPa0cG/A9XcqiEgsPzSclr+3pJHiY/MvUwJM5BrOKYQ2TFiZozeZ7+mjxMANyPjCrROcyAZIWbAVFGqDld7a9E7cel+jNHzGesJAahbLsMIMWF8CfORrNK+trkZDDp0abrGFCwTy8Dn0DsGMsMxsUmSMFVHdGI0Pdf25FHSIxqVbcTtCbc5OOIgtyfc5tioY1RwrkDgk0B6rOlBoqr0uMoligbRCLHOWRMSl1wATYiuUJmeEVIr4yYy9HGi0b2izI1khJgBVQE9IaAXkjGhaJnOCMnPEwKSOLW0kdk3Jm8jxMNRl6abvyckv5RfUZgqeUIA0AgaRm4ZSWRCJLU8a3FoxCHKO5cXH29TsQ37hu/Dw96D8w/PM3LryAL1gJIovehSdLN6Qkz5XGYlOQdPiKeTDe4O1mgECIwyLYvSXEhGiBnQ9YrRJ68aIfoURBciClONMkK040vi1NJBUg5fRDkhZsgY4QnR1ZWRhKnGseDUAnYF7cJWacuGgRtwtnHOtk8NjxpsGbIFK7kV/9z4h0VnFplhpRLmIrey7d7OBW86pwvH2Oh99mUymRhSv/WwdIRkJCPEDKSlFywcA9CwoisAl+8b31FXTNE1wgjJKk6VsGxS8vFa6NBlyDw2onR7il4p6JzQCVMlTwicf3Cej/Z/BMAP3X6gjledXPdtXbE133X9DoDZR2cTl1o6LhIShSe3iqleGSLxuJT0fMMnj+JTOXQrWvzez/SEGH6vB5TN0IVIRohEbqQVwhNS08cZa6WcmKQ0Qp8YZySY4gmRxKmlC13oxDYfT4h7hifkcbzxnpDc6srowjEvuiYkUZXI0E1DSdOk8UrAK7zT+J18j3mv6XvUcK/B46THzD8xvwRWKWEJ6HrHZEvRtVGKXszo+Ly9If0WH2fUyrNsvhgB6GfHGH72a/pov79NLWppLgplhHz99dfIZDImTpwobktJSWHcuHG4u7vj6OjIgAEDiIqKKuw6nytyzI7Jo2+MPtZKOXXEjrrGFS0Ty7bnUydEh06cKlVOtXzy6vOij5eT9o7LGLdvvp4QKTsGgP/t/R+3n9ymnFM5fuv9m5gmmRdKuZI5HecA8OPpHyWR6guCrk5IVk+ITCbTC8nk7aWMiEkGYNvlB0DO2TGAeBN5MzKuVGiPCmyEnD17lqVLl1KvXj2D7ZMmTWLbtm38/fffHDlyhAcPHvDKK68UeqHPEzllxxhTJ0RHgwraWgPGilON6R2jjyhOvS8ZIZZOZjpt3iV/ypexAzK/yPIiP0+Ii84Tkpz2wuqG/g38l6XnlwKwqt8q3OzcjD62f0B/qpSpQmxqLGuvri2uJUpYELo6IfY22Q17XUjGWF2IzqDJKTsGoKqXI1YKGfEp6dx/lv/n3dwUyAhJSEhg2LBh/Pbbb5Qpk1l8JzY2lt9//53vv/+ejh070rhxY1asWMGJEyc4depUkS26tFOY7BiABhm6kItGilNNyY6BzMqp1x7ElgpL+kXG2HCMaIQ8S87TcEhTa1BnPJ7b+0UnTBWEglV6LO08S37G6H9HA/C/lv+jU+VOJh0vl8l5r8l7ACw+u1j6jL0A5PUd7G2iEaIL7STnEo6xVsqpklG07Fak5YdkCmSEjBs3jpdffpnOnTsbbD9//jxpaWkG22vWrEnFihU5efJkjmOlpqYSFxdn8PO8k7MnxHgjpGFGhszNh3GiSy4vVOmZVTWNobq3k1acmpJOmJG6EwnzYGw4xsfZFoVchkqt4VEe6YCpOfSjyIqNUiHOF/MCNrH76thXPE56TIBHgBhaMZVRDUdhp7TjctRlzj44W8QrlLAkBEHIU5fnndF0LjreuDRdnVdFv2JqVnT1QkqDONVkI2T9+vVcuHCBuXPnZnssMjISa2trXF1dDbZ7e3sTGRmZ43hz587FxcVF/KlQoYKpSyp1FLRiqo7yZexwd7AmTS1w/UH+bzJThKmQIU7NqLwniVMtG13F1PyMEKVCTlkX7R3X/We5G5apekZtXl2XdbqQF610e1hMGD+d+QmA+V3nY6O0KdA4bnZu9KvZD4B1V9cV1fIkLBC1RhCbTOb0mcrqCXkYmyx6TnToe8sSs3hCckrPr1mKMmRMMkLu3bvHBx98wJo1a7C1zbv/hLFMnz6d2NhY8efevXtFMq4lk5MRYoonRCaTmVQvRJeim9dFJStiSEYyQiya3FyyOaELyeQVJ07R0w/l1VDRRZem+wLVChEEgXe2v0OqOpUO/h3oUbVHocZ7rc5rAKy/vh61pnRUt5QwHf3we043gl56tUJOBD2m1dcH+XTLVYN99D2UunBMai6aENATpz5vRsj58+eJjo6mUaNGKJVKlEolR44c4ccff0SpVOLt7Y1KpSImJsbguKioKHx8fHIc08bGBmdnZ4Of552cGtiZ4gkB04qW6T4EObntckOqnFo6yK+6qT7lXLUdNvMyQnSekPz0Qy52WiHsi5Qh8+v5X9kTvAdbpS0/v/yzUdkwedGtajfK2JYhMiGSw6GHi2aREhaHfl0oqzw8IdFxqWw4dw9BgM0XIwyqqOq8H6AVpKapNaIoPaeigjojJOxpksGxlohJRkinTp24evUqly5dEn+aNGnCsGHDxL+trKw4cOCAeExgYCDh4eG0bNmyyBdfWknP0RNiWmRMJ041Jk1XZWKKLhh6QiThnOWSX7M5fTI9IbmHY3JT3GfF1S6jk+4LUivk7rO7/G/v/wCY22kuNT1qFnpMa4U1g2oNAmDdNSkk87yi7wnJyeOtM0LuPUviwM1oQHujuvlChLhPUpZCZo8TUknJozGph6MNnk42CKWgfLtJVz4nJyfq1Klj8OPg4IC7uzt16tTBxcWF0aNHM3nyZA4dOsT58+cZNWoULVu2pEWLFsV1DqWOwmbHANQr7wrAvafJPMmn74ApDex0VPd2wlohJy4lnXCpcqrFUtThGGM7Lov9Y16AcIxao2bklpEkpiXS3q897zd/v8jGHlp3KAD/3PiH1HTT+4dIWD76mrycvGe6OiFpakEMtQCsPxsu3gDqxKg6ouNS8/SEQOkJyRR5xdQffviBXr16MWDAANq1a4ePjw+bNm0q6mmKlI03NlLmmzL0WNODY2HHin2+gnbR1cfFzooqnhkddfMIyag1Aun5pFzmhLVSLoqbpJBMzsSlxjHz8Ez6re/HyksrUalL3iuQZGSdEIDyZbThmIi8NCFGlvh3eYGa2C08vZBj4cdwtHZkRd8VyGVF97XZ1q8t5ZzKEZsay66gXUU2roTlkJaetybP3lrJq00zEzIGNCqPTAbBjxJ5nNHrSVcbREd0fKqo38qtqKAuueC5N0IOHz7MggULxP9tbW1ZvHgxT58+JTExkU2bNuWqB7EEUtJTmLhnIjEpMewO2k2XP7pwNOxovsdpBA2bbm5i2r5pLDqziDS18V/Ghc2O0SEWLcvDCNFP4TXmblmfOpIuJFfuxd6j3i/1mHVkFlsDtzJq6yg6rOrAk6QnJbqOgoVjksVaIFnReUKMDcc8756Qy5GX+fjAxwB83/V7KpWpVKTjy2VyXq3zKiCFZJ5X0ozITvyqf10md6lOTR8nxnaogq+L9rMa/lRbUTcpqyckPkXsG5W/J+Q5Csc8jyw5t4T7cfcp71yel6u9TKo6lT7r+vAg/kGux0TERdBpdScG/DWAeSfmMWHXBDqu7mj0BSgthwuAKdkxOjJ1ITG57qNvhJjiCYFMcaqUIWNIbEosPdb0ICw2DH9Xfz5q/REuNi6cuHeCJr81YcftHSW2FlPCMb6udthbK1CpNYQ8zrlceKqRhe3E/jHPcZ2QoKdBdPuzG6nqVHpU7cFbjd4qlnl0IZl/A/8lPtWyLxgSpqP7TFnl0ZpDLpfxfqdq7J7YjiqejlR003otdXWasnpCouJSiUvR3gA42OTsBdUZIYGR8RZd2fiFNkLS1Gl8e+JbAD5v9zl/D/qbxmUbE5say5dHv8zxmIi4CNqvbM/h0MM4WDkwuuFonKyd+C/8P0ZtHZWniPN69HX+vPInQU+vZHusIJ6QhnoZMrm9yVL0XIF5pVzmhH75dkmcmslnhz7j+qPr+Dr5cmTkEeZ2nsvxN4/j5+JHaEwovdb1os+6Puy4vYOdd3ay5NwSfjz9IwdDDpKSbnrL7rwwJTtGIZdRM8NFeyMXF21uTbGyIvaPeU49IRFxEXRe3ZmoxCjqeddjzStrCp0NkxsNfRpSw70GKekpbLm1pVjmkDAfOk+IQp7Oz2d/psGSBvh+50u7Fe04G5FzoTo/96xGSFZNSAr3MrR6Og9nVip7OmCtkJOQatnl219oI2Tb7W08iH+Al4MXIxqMwM7KTmy1/duF37j77K7B/lEJUXRa3YngZ8FUcq3ExXcusqzPMo6MPIK1wpptt7ex+OzibPM8TX7K0I1DqfNLHYZvHs4XZ/sSq/wLgcwLu9LIBnb61PBxwiajsund3O5sdSmXJqTn6pDEqdm5Hn2dn8/+DMDqfqup6FIRgNpetbn63lWmtpqKUq5k2+1t9FrXi5fXvsx7O97jg90f0Gl1J2otrsXFhxeLbD0pRlZM1aG7O7qRS5E7Yz0hz7MmJEGVQLc/uxEWG0ZVt6rsfX0vZezK5H9gAZHJZGLNkLXXpF4yzxuqdA0qWShX0sYxbuc4Lkdd5mHCQ46FH6PF7y34YNcHxKUafh4rZhghoU/iiE+NJzHV8HN26V4MiSo1MhmUc83ZCLFSyKnmrS3fnttNhyXwQhshv5z7BYDRDUdjrdDGuNv7t6dL5S6ka9IZ8s8QElQJgDY23Gl1JwKfBFLBuQIHRxykmns1ABqWbci3XbQelSl7p3AlKtPTEZ8aT6fVnVh3bR0KmYKGPg0REIixWk2M8k9xP4WJKbqgfZPpvBW5hWSMTbnMCUmcaoggCEzcMxG1oKZ/zf7ZeoY42Tgxr8s8Lr97mVENRhHgEUBtz9r0rdGX/jX742nvSUhMCK2Wt+LPK3/mMovxpKSpxVhxbnHhrNTK6MCc25dSqtGekIwU3efMCNEVJLv+6DplHcuyb/g+vB29i33e1+pqjZB9wfsIjw0v9vkkSgZBEFh/4zcibSaTqLmLl4MXP/X4ibNjzjKs7jA0goYfz/xIxR8q8s62d5h1eBZvbH6DOee7EW77Cj8GNsX5a2emHHuJGOUfVPDQfp/resJ4O9nm+Vmt6WP5GTIvrBFy89FN9t/djwwZbzd+2+CxxT0X427nzrkH56j7S11a/d6Khksbii74gyMO4u/qb3DMhGYTRE3JoL8HERoTSmxKLAP/HsilyEt4OXhxYvQJLrxzgf6VPgUgzmoD8YqdQME0IaBftCzneiEpotCwYC+1JE7N5N/Af9l/dz/WCmvmd52f6361PGuxvO9yboy7wbWx19jy6hY2DdlE4PhAelTtQUp6CsM3D2favmlohOwiZWM5E/IUjaDtC+PpaFz58FpF5AkRNSFJaQahuttR8WKsujSy6vIq1l5di0Km4O9Bf2f7nBcX1d2r08G/A2pBzfQD00tkToniJTIhkj7r+7Dg/HQEmQovqxZcefcK45uNp4lvE/585U/2vr6Xmh41iU2N5dcLvzLzyEz+uPIHDxLuIsgy9VaPUsKJtdrA2eRhxCr/QoM2nVunHcmNgFJQvv2FNULmHNM2nupXs1+2L5pq7tXYMXQHZWzLEBoTysn7JxEQGFJ7CCdHn6SqW9Vs48lkMlb0XUE5p3LcfnKber/Uo/qi6uwN3oud0o7tr22nWblmALT0HoZr2nAAnln9TposokCaEMhfnCrG+HNJ48oPSZyqJTU9lcl7JwPazqmVy1Q2eYwydmXY9to2Pm2rNULnnZjH0I1Ds7lijeXI7UcAtK/uabReoaaPM3KZtthRdHx2fUqmEZKPJyTDCFGpNaI49njQY7otOMqEtUUXbipJ7sfd54PdHwDwRYcvaF2xdYnOP7/rfGTIWHt1LUdCj5To3BJFyy9nf6HaT9XYfns7Srk1ZVRv09p1fjavWpcqXbg+9jrbX9vOR60/YnTD0XzR4Qv+HriVcim/Uz55LeEToxlU6VusNBVJ1cQTY7WaaOtP0ZBEhXyMEN1NhyV3030hjZDbT26L6XCftfssx32al29O+KRwNg7eyPI+ywmbGMb6getFDUBOeDp4cnL0SRqVbUS8Kp7oxGj8XPw4NOIQTcs1FfdLVws4pw/GVt0AQZbKE6sFyGUFuyPWeUJuPYzPsaOurr9AQTQhoG+ExL3Q4tQFpxZw99ldyjqWZXqbgt+pKuQKvuj4BSv7rkQhU7Dh+gbqL6nPyksrSdeYVl5ZNEJqeBp9jJ21gkoe2voyORmWeXXmNBjHSiGq/XXi1CVHghEEOHbnETGlrJLq3uC9tFnehrjUOJqVa8bU1lNLfA2NyjbizYZvAtB/Q39uPLpR4muQKDzfn/yesTvHkqBKoFm5ZnzXfjvO6j65pujKZXJerv4yczvPZVmfZXza7lMG1u6Dl30FFDgTn2hDFceulE39iderz8Na5kSq4ibR1jMo65r3zULNDCMk/GkS8RbqoXwhjZAl55agETT0qt6LhmUb5rqfo7UjrwS8wqiGo/I0PvSp4FKB02+d5tToU+wbvo9rY6/RvHxzg31Uag0yZLinvY9MsCNVcZMLT9cU6FzKudrh6WRDukbI+6JSQE+ITpwam5zGvaeWq7AuTiLiIvjymDZb6pvO3+Bk41ToMUc0GMGRkUeo6FKR0JhQRm0dRbsV7Qh5FmLU8cGPEgiKTkAug9ZVPEyau4mfGwDHg7KnlIuekHw0ITKZDBe9WiG3o+I5ducxABoBjmb8XRq4Hn2dXmt7ERYbhp+LH3/0/wOlPP/ib8XBwu4LaVG+Bc9SntFmeRt2B+02yzokCsbPZ38Wy/t/3u5zTo4+STmHAACsTfwO1oVaQp8kkpCqRoaCDhUGMrzqMuSCI6mKm2wL/zzPm0M3B2uxImughXpDXjgjRBAEMQ3uzQZvFsscSrmS5uWb07lyZxytHbM9rquYqhS8KJM2GoDDkT9xLfqayXPl11FXd1EpiDAVtOLUGj4vrjhVI2gYuXUkCaoEmpdrzrB6w4ps7NYVW3N97HXmdZ6Hs40zJ++fpO2KtkQmROZ53Om7Txi05CQAzSq5iZkqxtKuutZzcjTDk6JPptGa/1eDWLo9WcWGs9ru17qw4uFb0SatyVxoBA1vb3+bNE0a3ap04/rY61R3r2629ThYO7D9te00L9ecZynP6LmmJ18d++qF9kKWFpZfXM64neMAmN5mOjNfmolcJs8sVmZiBmQN74x0+gdxJGWk6DrYKGjr3wwP1XQQ5ByN2MyyC8vyHMfSy7e/cEbItehrhMSEYKu0pWuVrmZZg37FVEd1N2zVjVELKgb9PUjMxjEFnRFyMQcjxFj3el68yOLU7058x/67+7G3smdlv5VFWrIbtN62qa2ncvndy9Rwr0FEfAQD/hqQax+RaxGxjF51jqeJKmr7OvPtwPomz9mmqgdyGdyJTuBBjKF3KzN8l7/RqqsVEpuUJhY/e7luWUAbKrLkAkk6Nt7YyIl7J3C0duS33r/hYO1g7iXhbu/OkZFHeLvR2wgIfHLwEwb8NaDA2iGJ4uePy3/w1r/aYnaTWkxiTsc5ok7LmIqpOVGvgvZ79/L9GLF3jL21kqpejthp6uOaPhKA/+39X54ZVaIRInlCLIOtgVsB6FK5i9m+cNL0esfIkOGhmoSTlRe3Ht/i3e3vmnzXIxYtC4/J9liKWCekYJ4QeHHFqecenOPjg9qS3Qu7LyySzqm54e/qz7+v/StWXp2wa0KO74OJGy6RkJpOi8pubHyvVb7CtJxwsbeifsZ7Jqs3JK/OnFlx1asVEhmrFbn2qlcWB2sFTxJV3Ik23aAuaX698CsAHzT/gAouFfLZu+SwUdqwtPdSfuv9G9YKazbf2kyn1Z0kQ8TMaAQNv53/jRFbRvD6ptdZfGYxE3dP5I0tbyAg8F6T9/iu63cGQvHMiqmmXW7rZzQpvXI/VqyY6mCjoKaPE1YKGZWsB9GyfEviVfG8ve3tXK8bkifEwtCFYvrW6Gu2NWTtHaPAlaFVv0MhU7Dm6pp83WtZqVveBZkMImKSeRRveAedYmTKZZ7j63lCXhS3cHxqPK9tfI10TToDaw1kdMPRxT5ndffqrBuwDhkyfrvwG0vOLTF4XBAEgh9pL+zzB9UvcIgNoF01bUjm1F1DXYgpnhB9TUhUnNYIKV/GntqlxHN299ldMU2/uEqyF5a3Gr3FsVHH8LD34NyDc/RZ10fqtmsmBEFg4u6JvL39bVZfXs2aq2sYv2s8C08vBGByi8ks6rkoW6aa7qbTVCOkho8T1kqtHk9nQNhbK3F3tGHdmBasfbsVy/sux0Zhw57gPay4tCLHcWplpOlaavn2F8oIuR93n/MPzyNDRq/qvcy2jnRN9kyYaq5N+arTVwBM2DWBS5GXjB7PydaKal5a7UlWXYixZbjzorqPI1YKGbHJaRZd/tdUBEFg552djPl3DCO3jDQomT1h1wSCngZRwbkCv/b6tdhKdmelR7UezO00F4D3d79v0EwxNV2DzgZ0sTNNB5KV2hlFy7J6K1IL4Al5FJ/Kk0RtNoyPi22p8ZytuKj90u5SpUuJ1QMpCM3KNWPP63twtnHmSNgR3t1hurdUovD8dOYnfjrzEwAftvqQGe1n8HK1lxlSewh/DfyL77p9l2O4VpVesHCMlUIufk51ndAdMrplN/F3o6aPMzU9avJFhy8AmLxnMvfj7mcbx9/dAWulnCSVmjALrHz9Qhkh/wb+C0DLCi1LpApibqjSs3+ByGUyprSaQq/qvUhVpzLwr4HEpMQYPWZuRctS82n3bAw2SsVzJ06NTYml0+pOvLz2ZZZdXMaqy6vov6E/wzYNY8BfA1h1eRVymZw1r6wp1pLdOfFh6w95tc6rWi/MXwPFeK+uYy5oU2QLQ5UMo/Xuo0SDuyNTKuzqNCG3o7SxZmulnDL2VgaeM0tm863NAIyoP8LMK8mfRmUb8fegv1HIFKy8tFKs9ixRMtyPu88nBz8BtN2Uv+nyDTNfmsn2odtZP3A9g2oPyvXYTGGq6ZdbXUhGh71N9s/l5JaTaVauGbGpsbyz/Z1sBqpSIRdFrpYYknmhjBCdHsScoRjQb2iUeXetlMuQy+Ss6rcKPxc/gp8FM/jvwaSpjcvtblBBe6G8mEUXUhTCVKDUXFiMIT41no6rO3Io9BAOVg683+x9xjYZC8Daq2vZdHMTCpmC77t+T1u/tiW+PplMxu99fqeBTwMeJT2i3/p+JKUlkZTxWlor5CgL8IWmT0U3e6wUMpLT1DyIzfRuFcQToiuE5ONsi0wmE4XMNx7EobZA9y9AeGw41x9dRy6T071qd3Mvxyi6VunKvC7zAG17iMDHgeJjaWoNS48Es/F89jthicIzcfdEElQJtCzfkg9afGDSsQUVpgI0r+Rm8L/OE6KPQq5gRd8VWCus2XlnJ39d/yvbPrrKqbckI8R8xKbEcijkEKCtkmpOdOEY/btZRUb6lpudG1te3YKDlQP77u5j6KahRnVe1XlCrtyPNfjiL0zvGH3qlBIXuzF8fOBjLjy8gKe9J/+9+R8Leyxk8cuLOTziMJNbTGZMozGcHXPW5C+bosTeyp4tQ7bgYe/BxciLvPXvW2KanrF9YvLCSiHHz10rzA7SC8mY8n5xsddqQh4naDUKPs62AFT2cMDBWkFymlrUsFgau+7sAqBl+Za42bnls7flMLHFRDpX7kxyejKj/x2NIAgkpqbz6q+nmLvrFlP/uVzqCsVZOjvv7GTjzY0oZAqW9FpicoZcpjDV9JBu19o+VPHMTKBwyMETAtpWER+30Yrop+6bSlKaYdhFbFz50PIyZF4YI+RS5CXkMjk1PWqatQ4AQFpGOEbfO6HfO6aBTwPWDViHldyKf278Q4dVHQiLCctzzOrejthZKUhITTf44he76BZCmArPjzj15L2TYqfjtQPW0sCngfhYe//2fNftO37t/WueRexKCj9XP/4Z9A9KuZJ119bxy/kFQOFDMTqqempDMsGPMjswm+QJyaJL8XbRGiFyuUxslHf1vmUarTuDtD2belbraeaVmIZcJmd5n+XYKe05fu84a66uYdOF+5wP04ZhNUJ2sbE+ao1AQqpplXlfZJLSkhi/czygTb2t513P5DF0nhBThamg9ZZ/3ru2+L99Dp4QHVNbT6WiS0Xuxd3j2+PfGjxmyRkyL4wR0t6/PY8/fMw/g/4x91J4nKi9c9QXF2btotu7Rm/2vL4HFxsXTt0/Rd1f6jJp9yQ+3PchtRbXQjlbicc8Dwb+NZDj4cdRKuTULZ/RUVcvJJPZwK5wF64aGWlhMUmlV5wqCAKT905GQGBE/RF0rtzZ3EvKl/b+7VnQbQEAC87OIFl+Hvsi8IQAVM3QhRTUE1Lb1xl9va6Pc2YTPUuuLaNSqzhw9wAAPar2MPNqTOfYLQGb5IGANixzIcLwBiWnSrgAcSlpDF56kgaz9jJ909XnrgNycTDn6BxCYkKo4FyBGS/NKNAYhQnHgLY31JLXG7FyVNM8e4zZW9kzv4u2seY3x78xqB0SkNFNNyIm2eJe9xfGCAFtYajaXrXz37EYSVapCc0o7FTb10XcnlMX3Q6VOnDxnYs0L9eceFU8C04v4NsT33Lz8U3UgponyU/YeHMjHVd3ZFvgNrFeiH7RssyLSuFean1xan4hGVW6xiK1ADvu7ODU/VPYKe3EDJTSwNimY3mr4VsICDyynodG8aBIxq3ipXXzGnjOTEjpdne0Ed9zAD4uduLflpwhc/r+aRLTEvFy8KK+j+nF3sxJyONEvth+A+f0/lgLFYhKjGJ90DQENHSrrRXbHw/OXjI/Ta1h1IqznA97RrpGYN2ZcD7aeKWkl1+quPHoBt+e0HoUfuzxY47Vr41BzI4phI6re52yvFTDK9/9BtYaSHu/9iSnJ/Phvg/F7S72VpRz1X4+Lc0b8kIZIZbA7ah4NAJ4OFrjk+G+BnK1cCuVqcSJ0SfYMmQLIxuMZFzTcazqt4qwiWGcHH2SPjX6oFKrGPDXAFyctRcn/TTd1CLyhIBx4tSouBQaf7mPD9ZbXifVWUdmAfB+8/cp61TWzKsxHplMxqKeiwhwa4wgS+RC4idFUrSqqqfWqAyO1jdCTAvfdQrIzDLTaUIg871y3QLFqfvv7gegY6WORV4BtzhJV2uYtOESyWlqZFjhnjoNG4UdkamniVP+w9Dmfshl2oynh7GG3spDt6I5H/YMJ1slc/rXQS6DXdciOR/21ExnY9mo1Cre2PwGaZo0elXvVahkBl2dkIJ6QkxBJpOxoPsC5DI5G65vMEjxr1NO6w2xtBuD0vMJfE64Fam9eNT0cc6WHZMbcpmcvjX7sqLvChb1XMQb9d+goktFWpRvwcbBG+lZrSdpmjSWXp2KgJrAyDiSMsr86jwh+bVmNwZjXOyn7j4hPiWdPdcjxQuaJRD4OJBzD86hlCuZ0mqKuZdjMjZKGyY3WopCcCNeHcrrm15HIxSs87KOyhmCtyeJKp5l1PlINVHI3Ckg8+7M3dFab2xH7DPEqSGPLUucuj9Ea4R0rmT54Th9fjkczKV7MTjZKulSyxtrwZ9W7tpuvzHKP0kQroiVcOfvuW2g3fonI2vmtWYVGdbcj0GNtdVh5+y4Wao1XsXF7COzOf/wPG52bizttbRQdYIKWjG1oDTwacCYRmMA+GD3B6g12u9h3Y3BFQvTaUlGSAlzM0OdXNPHycDwyCvWlxdKuZJlvZfhauvKleiL4LAHjZApCCyqFF0wTpyq0xekqQWL6tr4942/AehcuTMe9qZ1nbUUbBQeeKZ+gkJmzbbb25hzdE6hxnOwUeKb4Y0LfpSARiOgUptWYbeGtxN1y7ng5mAtilFB+36ulSGGsyRdSFxqHKfvnwYoFZogHUmqdH46FATA7L61eaOlHwDBYU1xSO8AMg3DtwxiQDMNchlsvHCfL3fcJDY5jScJqRzMaCg4oFF5ACZ3rY6dlYIL4THsvhbJs0QVA385wcx/r7/wRsnx8OPM/U8brl3aaym+Tr6FGq8wwtSC8mXHL3G1deVS5CVWX14NQN2MmiOSJ+QFRxePq1nWGbmeda0sQPqWjrJOZUWNwyPZejQkiyEZsXdMEXhCjBGn3onKvOu1JItbZ4QMqpV7USFLJ0WlxkaoQUdvbdGkmUdmimnnBUVXtCz4UYJ4xwbGe0JkMhl/vdOSox92wNnWMFtG9Jzdt5wY9NGwo6gFNVXKVMHP1c/cyzGai+ExqNI1+LrY0q9BOZpXcsfdwRoZMtzSxuFuVYdnKc+YdvRV3uuoLcn/+38h9Fx4jM//vU66RqBuORdR1+XtbMuYtpUA+Gb3LebtCeRc2DNWnghl1YlQc52m2YlLjWP45uFoBA1v1H+DgbUGFnrMwgpTC4KHvQeftv0UgM8OfUZyWrJ4E3n3cSJxKZYjTpWMkBJEEASxsFN2T0jhXorRDUdTpUwVktVPiVdu0zNCikaYClpDprp33uLUO9GZ3o8r92MKPWdRcOPRDa5EXUEpV5q9RkxhSMqomNrYsz+jGoxCI2gYumkoUQlRBR6zimdmhox++MyUlG47awWONtlTBy1RnKrTg5QmLwjAmRCtdqNpJTdkMhnWSjnDM7whcmwZXm0JAR4BPIh/wKHomSx5vSEV3eyJiElmx5WHyGQwoWNVgzHfbl8FD0cbQp8kse5MZibFnJ03uRBuWHn5ReGD3R8QEhOCv6s/P/X4qUjGzBSmlkzrBx3jmo2joktFIuIjWHh6IW4O1qI41ZI+k5IRUoIcCowmNjkNa4Wcat6OyI3UhBiDlcKKWS9phZdxyo2cC9fGgMWy7UVUWyIvcaoqXUPok8wiOZbiCfn57M8AvFzt5VJVmCorSWm6dt4KFvVcRG3P2kQmRDJs0zAx7msqujTdwKgEdlx9CGjfi4WtyAqIKePXH8RaTOOs0mqEnA3NMEL8M9+/w1tkenLKOrmzYeAGbJW27AraxY349Wwe24p65V1QymV8N6g+XWv7GIzpaKNk6fDGYuXbNlU9eLluWdLUAuPWXOBpYmbRM0sTFxcH666uY+WllciQsbrfapxtnPM/yAjM4QkBsFXa8mWHLwH4+r+veZL0RPz+/mL7Tf45fz9bM1VzIBkhJURCajqfbr4GwBst/bBRKopEE6LPq3VeJcCjNhpZIreT1hEVlyIWKysqIyQvcWrok0TUGkGsDHg7Kt6g34k5iEuNY9XlVQBMaDbBrGspLCkZz6W9tQJ7K3v+HvQ39lb2HAg5wJdHvyzQmDpPyNHbj/gk4/2ZXkQXnCqe2gJ6iSo1dx8n5n9AMfMw/iHXH11HhowO/h3MvRyjSVNrxHYMzfTKeLs72jC1Ww28nGwY3KQCdb3rijVlPtr/EXdjr7BlbGvOf9qFVzK0IFlp7FeGLWNbM7FzNb4fUp+vB9SlsocDD2NT+GD9RfZej2TgLyeo/ukutl6KYN+NKJYdu5utW3dp59yDc4z+V9spe3qb6UXarqGkhan6DKs3jPre9YlNjeXLo1/Sr6EvSrmMmw/jmLf7VomvJyckI6SE+Hb3LR7EplDBzY7JXbUVW43NjjEWhVzBV520F6N45VYO3Q7SK1ZWNC+1vos9q4BNpwep7euCp5MNGgFumDkn/c8rf5KgSiDAI4COlTqadS2FRReO0RmUAZ4BLHl5CaBNP94TtMfkMXWeEH101RULi0KvcqoluH8PhGgLlDUs2xB3e3czr8Z4rkXEkpymxsXOSqxyq2Nch6qc+aQz/h7aTKe3G7/NwFoDSdOk8erGV4lXxeFin3fHZX8PByZ2ro6Xky1Otlb88npjbK3kHLvzmLf/OM+5sGeoNQIfbbzKmNXn+HLHTVp/c5DDgdHFds4lyZ9X/uSllS+RnJ5Mj6o9mN1hdpGObw5hqg65TM43nb8BtF2AvdwfcmJ6R6Z2q8G4DlXNsqasmH8FLwDnw56y+pS2quHc/vXE0rv6Roi8CIwQ0DbnK2tXG0GWwk/nvhVz1ItCmApacapSLuNZUhoRMYbiVF0H3+rejhZTJvhw6GEAhtcbXqg0O0tA18BOv2Lq8PrDxUJmg/4exLXoayaN6aGXVmtrJefwlJdYN6Z50SwYy2p8qDNCSltq7l/ntKHVVlXc8/2ekMlk/Nb7N/xd/bn77G6OXVXzo4aPE1/1rwtoQwjvtq9CvfIuJGe8/7ydbVCla/ho41XiLUjgaCrJacmM+XcMwzcPJzEtkY6VOrJuwDoU8qL5rtRRknVCcqJb1W4MqjUItaBm1NZR2NukM65DVUa08jfLerIiGSHFTGq6mmkbryIIMLBxedpUy0wPVRaxJwS0X0Ij63wEwKmo9aTLHgFF5wmxtcpZnBoUHc+qE1pDq311L7Fro7mNkLMPzgLQvHzRXVjNhX44Rp9FPRfRzq8d8ap4hvwzhNR0413lMpmMbrW9UchlLB/RFH8PB1ztrfM/0EgspXy7IAilUg/yKD6VjRe0RshIIy8arraurB+wHqVcyYbrG/jzyp8mz/tKo/Jsn9CGYx924KMeNZk/qD4V3ewZ3KQ8B//3En7u9kTGpfDtnsD8B7NA7jy5Q/NlzVl2cRkyZMxsP5O9r+/FxdYl/4NNpCgqphaWRT0X4WHvwZWoK7Rd0ZaIuAizrSUrkhFSzPx8KJig6AQ8HK35pGeAwWP6GTFFoQnR8UbDPtio66BBxVOrpQgI2BaRJwRyvrud+e8NVGoNHWp40rOuj1gjwpxGyKPER4TGhALQuGxjs62jqMgajtFho7Rh4+CNeDl4cePRDb45/o1J4/4wpAGHp7xEq6pFXz9F91658SDOrOLU209ucz/uPtYKa1pXbG22dZjK7/+FoErX0KCCq4EeJD+al2/OzPYzARi3cxwhz0JMnrtOORe8M6rgVvd24sjUl5g3sD4ONkrRU/LHqTCxeV5p4dbjW7Rd0Zar0VfxcvBi3/B9zHhpRpF7QHSYS5iqj5eDFzuG7sDLwYtLkZdo8XsLLkdeNtt69JGMkGLkdlQ8Px/WFhia0bs2ZRwM7zD1DWNlIVN09anm7UQ5xoKgJFlxilSrw0UW7gGoU15nhGgNjJQ0tdi587NetZDJZGI45lZkvNkuPucfngegunv1YrnDKWkywzHZ02E97D1Y2H0hAF8e/ZIT904YPa69tZIKbvZFs8gsVPF0wNZKTkJqOiFPzCdO1XlBWldojb1V8ZxrUaDRCMQmpSEIAtcfxLLs2F1Aq/0wNZz4UZuPaFOxDfGqeIZvHk66pnDdc/Xnb13Vg4GNyyMIMH3TFfFu39J5lPiIzqs7E5UYRT3velx65xKdKncq1jlVZtSE6NOsXDNOjT5FgEcA9+Pu035le54k5d5xuaSQjJBiQq0RmLbxCmlqgc4BXvSql71XSXF5QhRyGc3KN8Q1fSgA0YpFnHtwrsjGzypOvR0VT7pGoIy9FZUyBHKVPRywVspJUqkJf5qU13DFxtkIbSimqW9Ts8xf1OQWjtExpPYQBtUaRJomjQF/DeBBfNE0uisMSoVcNEjNKU4V9SAWHor5/b8Q6s/eS/cFx3jj9zOkawR61PGhc0D+zcuyopAr+KP/HzhZO3H83nHmHZ9XpGv9pGcA7g7W3I5KYMmR4CIduzgQBIFRW0cRER9BDfca7B++v0R6SKnE7Bjza9IqlanE8TeP08G/AzPaz7AIgbZkhBQTf5wM5WJ4DI42Sr7oVyfHu5ji0IToaFbJDef0AdiqGyPIUumzrg/34+4Xydi6QmtPE1U8iE0RwzJ1yrmI56lUyKnurVXymyskc+6h1vBq4tvELPMXNbo6IbmlW8tkMpb3XU4drzpEJkQy4K8BJulDigsxfGemujHpmnQOhhwEoFOl4r3rLSyHb2szTgKj4nmSqKKcqx2z++b8/WEM/q7+LOq5CNBmUN16XHRpmWUcrPm8dy0AFh0MEls2WCo/n/2ZHXd2YKOw4e9Bf+Pp4Fki81pCOEafMnZl2Dt8LxNbTDT3UgDJCCkWImKSmZch2JrWvQZl9Vqc6yMv4joh+rSr7oEMBZ6qadjJ/HmY8JDe63qToCr8F4W+OPXq/ViuZYRldCJEHTpdyLUH5rn4PG+ekOR8PCEAjtaObBmyBVdbV07dP8WEXeavjWJucerp+6eJTY3Fzc7N4g3SkEfakNWw5hVZOaop+ye3x9PJplBjDq83nB5Ve6BSq3jr37cK3fhQnz71fWlf3ROVWsPHm65aTFG6rNx+cpup+7TN/r7t8i11veuWyLzpag26p8ScwtSsKOVKi8kWtJxn5TlBEAQ+2XyVJJWaJn5lGNY89/4UBp6QInbV1c9oViTHHrfkz/C09+RS5CUG/DUAlVqV98FGoB+S0bnZ62YxQhpUKANgFuFaRFwEDxMeopApaFi2YYnPXxwkGWGEAFRxq8K6AeuQIeO3C7+x9NzSklherujeF9fNJE7dFbQLgK5Vuhab+LAoSFapeRCbAsD/utbgpRpe2OXzWhuDTCZjSa8lOFo7cvzecX45+0uhx9Qf+8t+dbCzUnAm9Ckbzt0rsrGLinRNOm9sfoPk9GQ6V+7MuGbjSmxuXXouWI4nxNKQnpUi5t/LDzgc+AhrhZyvB9TNUxBa1MXK9FEq5NhluO2VgjfbXtuGg5UDe4P38sbmNwpc5luHTpx68d4zsVtuHV9DI6Spv9YIuXQvpsTLA5+JOANAba/aFi1ENBZBEMQ6DcZcmLpX7c5Xnb4CYPyu8Ry4e6BY15cX1bwcsVFqxamhZhCn7g7aDUD3Kt1LfG5TCHuqfW5c7Kwok0+BMVOp6FKRrzt9DcBHBz7i5qObRTZ2BTd7/pdRgPGrnTeJjkspsrGLgm/++4bTEadxsXFheZ/lyGUld9lT6X3vmVuYaqlIz0oR8jRRxaxtNwAY37EqVb2c8txfIdMPxxT9S/Fqswri383LN2fzkM1Yya3YcH0D43eOL1TLbt3d7fGgJ6jUGpxtlVRwMww7VfF0xMXOipQ0DTcelKwuRFcf5HkJxaSma9C9XHZGluCf1noar9Z5lXRNOgP+GsD16OvFuMLc0Ren6kIyNx7E8f2+28Ve1j8qIUrMkupWtVuxzlVYdKGYSh4OxeIqf6/pe7Tza0eCKoGuf3blXmzReS1Gta5EvfIuxKekM3Obed5nOREWE8bso9oKqIt6LqKCS4V8jiha9LOGivpG83lBMkKKkC+23+Bpoooa3k68275KvvsrFMXnCQGY1r0mb7erzN/vtgSgS5UurHllDTJkLDm/hBmHZxR47KxdgDvX8s72xSmXy2jsp/WGnCvhkMzzZoQk6V2sc0rRzQmZTMaKvitoU7ENsamx9Fzbk8iEyOJaYp5kzaia/Nclfjxwh1+KOatib/BeABr6NMTH0Sefvc2Lrr9O5YwMs6JGLpOzcfBGanrU5H7cffqu70tyWnL+BxqBQi7j61fqoZDL2Hk1kn03Ct7ZuSiZdWQWKrWKjpU6MqzusBKfX1+UaikaDEtDMkKKiMOB0Wy+GIFMBl8PqGtU/M/QE1L0b1BbKwUf9www6Lw5qPYgfnlZGxP+4ugXjNo6iqfJTws0todjpmDujZb+Oe4nGiGhps9RUARBEFOSm5VrVmLzFie6UIy1Um7Se8VWacuWIVuo5laN8Nhweq3tRaKq5EMi+gXurkbEcisjhLfmVBgpacXnDdHpQXpU7VFscxQVoRlGiH8xGSGgrSeze9huPOw9uBh5kTf/fbPIhKq1fJ0Z07YyAJ9tuWb2ku63Ht8Sm1d+1fErsxgBllAt1dKRnpkiIDE1XexAOrKVPw0rljHquOJM0c2Ld5q8w/wu85HL5Ky8tJLy35dn9NbRnIk4Y5JWxMfFVvy7QQXXHPdpoucJKUz4xxSCngYRkxKDrdKWOl51SmTO4iZZpU3PNTYUo4+7vTs7h+3Ew96D8w/PM+jvQSVuiOgyZK5HxLH+bGYY4Emiii0Xi6eEtFqjFj0h3atath4EIORxZjimOPFz9ePvQX+jkClYf20943aMK7LP5gedqlHRzfwl3QVBYOLuiWgEDX1r9DVb2wZLS8+1RKRnpgiYvzeQiJhkyrnaMaVrDaOPUxRjim5+/K/V/zg68igNfBqQnJ7M8kvLab6sOS5fuzB883D2BO3hWXLeIZTZfWtTr7wL699ukes+9Su4YqWQ8Sg+lXtPi8b1mx86UWoDnwZYKYpW4GcujM2MyY2qblXZ+upWbJW27AraRdsVbQl6GlSUS8yTat6OWCvlxKems/Z0OIBYgOv3/0KKxUA99+AcT5Kf4GLjQssKLYt8/KLkSUKqKPAubiME4CX/l/ij/x9iaPZ/e/9XJK+BnbXCIkq6b7u9jT3Be7BWWDO/63yzrAH0q6VKoZjckIyQQnIx/BkrT4QCMKd/HRxsjIvXg3mNEIDWFVtz4e0L/DfqP16r8xqO1o4kpiXy55U/6b6mO27z3Kj+U3U+OfAJ16OvZ3Pb1ivvyr/j29Cicu5V92ytFOJd8LmwkgnJPG96EMisEVKYlM1WFVqxb/g+0RVff0l9pu+fzvXo64XOlsoPKz1xKmjLuX83uAGONkruRCdw9M7jIp9TlxXTuXJnlHLjP5cljVojMHHDJeJT06ni6UBNn7wF7UXFa3VfY1mfZQD8cOoHJuyaUOjS7gBtqnkwoJH5SrqrNWo+2q9t4jm5xWSqulUt0fn1yayWKl1qc0N6ZgqBrp21IED/huV4qYZppZXNbYSAVrzYumJr1g5YS8y0GE6NPsXohqOpUkYrrL3z9A5f/fcVdX6pg+vXrnRY1YFp+6bxz41/CIsJM+ruqUkJi1OfRyMk7Im29L2TCUZuTrSp2Ibzb5+nY6WOJKUl8fXxr7Wv7Tfa1/bDfR+a9NqaQhW9O/wJHavhYmfFkKbabAVdj5SipLToQRYdDOLYncfYWSn45fXGKEvwgvVmwzdZ3HMxAIvPLqbf+n5FUmX305czS7ovLeGS7n9d/4ubj2/iauvKR20+KtG5s6KrEyKFY3JHemYKwZIjwQRGxePmYM1nvWqZfLx+07qibGBXUBRyBc3LN2dZn2UEvR/E46mP+WvgX3St0hV7K3viVfEcDj3MvBPzGPT3IPwX+lP2u7JM3D2R209u5zpuYz+tMPZ8aPEbIemadC4+vAhA03LPhxEiCAJ/nAoDoGvtwmd4VHSpyL7h+9g0eBPdqnTD3sqeBFUCh0MP8+2Jbw1e2w92fUDg46KJ7et3gdX1UhrZyh+5DI7deSyGI4qCJ0lPxLCcJetB/rvzmAUHtJ+dOf3riJWIS5KxTceyafAm7JR27Lizg9c2vlZoz5h+SfefDgYR/KhkSrqnqdOYdWQWAP9r+T+zN67Uia4lYWruSM9MAQmKjmfRQV2H3Fq4ZemQawz6doe5PCF54W7vzqDag9jz+h5iP4rlyrtXWNZ7Ge80foeGPg1RypVEJUax8PRCaiyqQdc/urL11tZsYRtdhkxgVDyxScWrmL8efZ3k9GScbZyp7l69WOcqKc6HPeNqRCzWSjmvNi2aOgdymZz+Af3Z/fpu8bX9vc/vvNP4HRqVbSS+tj+e+ZGai2vS5Y8ubLm1pVCZFAMal2da95rsn9xOvNuv4GZP9zpaw2r5f6a3m8+NfXf3ISBQ16su5ZzLFdm4RUlkbAofrL+IIMBrzSrwSqPyZltL/4D+bH11K9YKazbf2swnBz8p9Jh96vvSLqOk+3QjS7rfiYonMrbgxc4WnVlE4JNAPOw9eL/5+wUep6iIyijcVtjS+88zJhkhv/zyC/Xq1cPZ2RlnZ2datmzJrl27xMdTUlIYN24c7u7uODo6MmDAAKKiLCNfvCjRaAQ+2ngVlVrDSzU86VPft0DjGHpCLM8I0UcpV1LXuy6jG41mSa8lXHjnAnEfxbFz6E56V++NDBn77u6j34Z+tFneRuzbAtoPoL+7tmrpxXvF6w3R3f028W1SopURiwNBENh3I4p3/7wAQL8Gvrg7Fv2Xme61fbPhmyzptYTzb58nfnq8wWu7/+5++m/oT+vlrbkWfa1A81gp5Lz3UpVsRfxGt9GmdW6+FMGj+KJpuGfpoZg0tYYJ6y7wJFFFrbLOzOhd29xLokuVLqzutxqAb45/w1/X/yrUeDKZjDm6ku4h+Zd0fxCTTK+f/mPoslMFCgVGJUQx88hMAOZ2mouzjXPeB5QAD2K0RohvLv3DJEw0QsqXL8/XX3/N+fPnOXfuHB07dqRv375cv66tkDdp0iS2bdvG33//zZEjR3jw4AGvvPJKsSzcnKw5E865sGfYWyuY079ugfPPdd4PmYw8y7tbKnZWdvSo1oN/X/uXux/cZVrraThZO3Hy/kmaLWtG82XNWX15NWnqNBpleEMuFLMu5MT9EwA0L2eelLyiICVNzdrT4XT54ShjVp/jcUIqNbydmNzF+MyrwmKrtDV4bT9q/RFO1k6cun+K5suas/nm5iKbq7FfGRpUcEWVruHPjLBTYVBr1Jml2i00FDN/TyBnQ5/hZKPkl9cb5doZuaQZUmcIU1tpG72N2jqKK1FXjDpOEAS2395Or7W9qLGoBvWX1GfElhGcj97LxM5aIzO/ku5nQ5+Smq7h7qNEImJMz6SbfmA6calxNC7bmFENRpl8fHHwMFZ7HmVdbfPZ88VFJhRSfebm5sa3337LwIED8fT0ZO3atQwcOBCAW7duERAQwMmTJ2nRIvc0Tn3i4uJwcXEhNjYWZ2fzW7JZeRibTJfvj5KQms7M3rUY2bpSgccKeZxIh/mHsVLIuDOnZxGu0nzcj7vPxwc+Zv219aRptKGX+t716ec/k5WHrWhT1YM/3yo+A6HywsqExISwa9gui70A5UZUXAqrT4ay9nQ4zzLCVg7WCoa18GNS5+pF0sysMETERTBq6yj23d0HwJyOc5jeZnqRFIHafuUB49dexN3BmuMfdSzURflo2FHar2xPGdsyRE2Jsrg07X03ohizWltMb8nrjehep6yZV2SIWqOmx5oe7Lu7jwrOFTg26hh+rrk34kxNT+W9He+x4tKKHB93tXVFnl6RxFTwcLSmurcT9lb2NPRpyKDag6jnXQ+AWduus+J4KACLhzbi5XrGPy+n75+mxe/aa8yJN09YTEr2G8vPcPT2I+YNrMfgJiVbMt4cFOT6XWB/tVqtZv369SQmJtKyZUvOnz9PWloanTt3FvepWbMmFStW5OTJk7mOk5qaSlxcnMGPpSIIAp9uvkZCajoNK7oyPJcqocaiq/lQkAJUlkp55/Ks7r+a+5PvM6fjHNzs3LgcdZnZp1/hidUiToVfRV1MnVTvxd4jJCYEuUxOqwqtimWO4uDq/VgmbbhEm28OsvhQMM+S0ihfxo5PXw7g5Med+LhngNkNEIByzuXYOWwn7zfTxto/OfgJXf/sWiT1RrrX9qGcqx1PElVsvZR78bKg6ATWng4XOzfnxKabmwDoU6OPxRkg4U+S+N9flwAY3aaSxRkgoBWorx+4nhruNbgXd4+OqzvmKjx/lPiIzn90ZsWlFchlcia1mMThEYfZ9to2/tfyf7jbuROTEsPT9CukKq4QkXyOQ6GH2HFnB18e+5L6S+rTdkVb1l9bz8XwR+K4l0wI20YmRDLknyEAvFH/DYsxQAAeZnh0pHBM7pic73f16lVatmxJSkoKjo6ObN68mVq1anHp0iWsra1xdXU12N/b25vIyNz7VcydO5dZs2aZvHBzsOPqQw7cisZKIeObAfUKLSb1drblk54BeLs8f646LwcvPm77MWMajWHKvimsvryaBOVuEthNu+VrmdrmfXpV71Wk9RuOhR8DoFHZRhYRD84LtUar91j+Xwhn9EraN/Uvw+g2lehSy8cixcpKuZKFPRZSy7MWH+z+gP1391Pn5zp81OYjxjcbj4e9R8HGVcgZ2cqfOTtvsuxYCIObVMjmYdlx5SHj1mr1MV5ONpz+uFO2fQRBEI2QAQEDCrSW4iIlTc3YteeJS0mnUUVXPupR09xLyhU3OzcOvHGAdivbcffZXZova870NtMZWGsgtkpbzkac5WjYUVZdXsWT5Cc42zizYeAGA+9jr+q9mNtpLlejrxL0NIjNF++z/2YUrnZWvNfRmxP3j7A1cCv/hf/Hf+H/oRDK4KjsimN6Dy7dy6w6/f2+25y++4SfhzXKpolKSkuiz7o+hMWGUdWtKt93/b7EniNjeJghspXCMbljcjhGpVIRHh5ObGws//zzD8uWLePIkSNcunSJUaNGkZpqKCxr1qwZHTp04JtvvslxvNTUVINj4uLiqFChgsWFY54lqujywxEeJ6j4oFM1JnV5PjIvSoojoUcYtuETIpJPgEz7lqvgXIF3m7zLW43ewsvBtBorOfHOtnf49cKvTG4xme+6fVfo8YqD+JQ0/j53n5UnQgl/qq39oZTL6FWvLG+2qUS98q7mXaAJBD0NYuyOsWJ4xkZhw5A6QxjXdFyBevbEpaTR8qsDJKrUrHqzGe2rexo8/tmWa2KqMsCRqS/h525YXVTnlne0duTR1EfYKi3ny/+TzVdZczqcMvZW7Hi/Lb6uln93HJkQSf8N/Tl1/1Su+9Rwr8HmIZsJ8AzIc6xklZpuC44S/jSJES39mNW3Dg/iH/Db+d9YfHYJj5IyblYFOQ5CEz7t/AovV+tP7wV3AG2F3WUjtGn3giAQm5LKqH9fY8utLbjbuXNy9EmquVcrmhMvAuJS0qg3U9s24Obs7hbhzSxuChKOMfk21NramqpVtRXoGjduzNmzZ1m4cCFDhgxBpVIRExNj4A2JiorCxyf32gY2NjbY2Fh++tKcnTd5nKCiqpcjYzvk3yFXwpD2/u2Z3mwlc/cexc3rCA/SdnAv7h6fHPyEmYdnMqj2IMY1HUfL8i0LpDHQCBr2BO8R57I07j1NYuWJUP46e4/4VG1VSld7K4Y2q8gbLf0N+vCUFqq6VWXP63v46/pffHviW84/PM/qy6tZfXk1TXybMLbJWF6t8yp2VsZdbJ1trRjStCLLj4fw+38h2YyQsAyjTcelezHZjJB119YB0Lt6b4syQLZeimDN6XBkMvhhSINSYYAA+Dj6cGTkEf64/AfLLy3nbMRZ1IKaWp61aOrblL41+vJy9ZeN8mjaWSuY078Ow38/w+pTYfRpUI7Gfr7MeGkGftbDmLr9d+ROe4hWXSRRdobpB8/w8cHpWFvXwFqoxpbbVSm37zJB0Qkcvf2IJ8I+khVnUcisGFRpIQevyblofw8Xeytc7axwtbemjL0VLvZW2ChL3gB4mJEZ42pv9UIYIAWl0L5wjUZDamoqjRs3xsrKigMHDjBggNYNGhgYSHh4OC1bWk6MriD8d+cx/5y/j0wG3wyoZ5Y39PNAx5pefLvHm9Qnr3Ln45/ZdmcjP5/9mdMRp1l7dS1rr66lrlddOlbqSL+a/Wjv195og+RgyEHCYsNwsXGhS+UuxXwmxiEIAufCnvH7sRD23ohEJ4Wp4unAm20q8UrD8qX+y0kmkzGkzhAG1x7MmYgzLD67mA3XN3DuwTne/PdNpuybQo+qPejg34EhdYbgaO2Y53ijWvuz8kQIR28/IjAynhp6JczDn2gbvNX0ceJWZDyX78XSt0FmDRC1Rs36a+sBzNK2PTeCouOZvukqAOM7VDW5srK5sVZYM7rRaEY3Go1KrSJdk469lX2BxmpbzZNXGpVj04UIpm+6wvYJbbFWyrkWkYiDpg0TGo/kUPAFjkfso7zPHa49+Y9UxS1SuQXA7BMZA2WoGWWCNW6p09h13pVdGfvkhJ2VIsMgsc4wULRGimuGwVLG3lo0Xso4aPcprPHyIEMPUlbSg+SJSUbI9OnT6dGjBxUrViQ+Pp61a9dy+PBh9uzZg4uLC6NHj2by5Mm4ubnh7OzMhAkTaNmypdGZMZZIkiqd6Zu1aWpvtPATC29JmE5NHyfKudoREZPM+dAE3qj/Bm/Uf4PzD86z+Oxi1l1bx9Xoq1yNvsrC0wup4FyBFuVb0NS3KU3LNaVx2cY42eRcUXLZBW0PjGF1hxl9511cqNI17Lz6kOXHQ7hyP1NA2baaB2+2qUT7ap6lMiU7L2QyGc3LN6d5+eZ81/U7ll9czi/nfiEsNow1V9ew5uoaJu+dzMj6IxnbdCw1PHJON67gZk+32j7suhbJ8v9C+GagNnMiXa3h/jPtl3rv+r7cigzk8v0Yg2MPhhwkKjEKdzt3ulbpWqznayxJqnTe+/MCSSo1raq4M7Fz6Q7jWiussVaYXphRn09frsXhwEdiSfcJnapx+V4MoO3GLQiNuBbmTFev8mieXiEq9TLNqj/jUPA5BLTF8pxslTQs70v7sm/jKKtFTLKKmKQ0YpJUxCSniX/HJqehESA5TU1yrJoHJhZCy2q8lHGwwsXOOOPlQaxOlGo5HjlLxCQjJDo6mjfeeIOHDx/i4uJCvXr12LNnD126aO88f/jhB+RyOQMGDCA1NZVu3brx888/F8vCS4of9t3m3tNkfF1smdrdcoVkpQGZTEanAC9Wnwxj/80oOtfyBqCxb2OW913O/K7z2XVnF4dCD7H+2nruxd3j3o17/H3jb+3xyKjnXY9RDUbRq3ovKpepjEwmIywmjM23tHUr3mr0ltnO71miirVnwll9MpSoOK3OyVop55WG5XizTSWzlOQ2B54OnkxrM40praZwOPQwx8KPsebqGoKeBvHjmR/58cyPdK7cmbFNxtK7Ru9srvy32lZi17VINl+KYGr3Gng42vAwNoV0jYC1Uk632j58uyeQaxGxpKk1YnOwXy/8CsDg2oMtIitGEASmbbzKnegEvJxsWPhqQ4sUG5c0bg7WfN6rFhM3XOKng0G0r+FJUEZZ93rlXUWP4fYrD0hNd8PXtgtbX+/CqBVn+S9I2+jw+371jaowq9EIxKekZxopyRmGSpLWUHmWYajEJKl4lpQm/l1Y48XeWoHulZZEqXlT6DohRY0l1Qm5cj+GfouPoxFg+cgmdKzpbdb1PA8cu/OI4b+fwcXOitMfd8q1HkSCKoEzEWc4G3GWMw+0v+/FGVZcrORaibcavcXuoN0cCz9G24ptOTrqaEmchgFB0QksPx7Cpgv3SUnT3ql5OtnwRgs/hjavWCxVTksbGkHDvuB9LD67mO23tyOQKU7uWa0nnSp1om/NvlgrrBEEgf4/n+DSvRgmdq7GxM7V+e/OY17//TRVPB3YN6k9DWbvJS4lne0T2lCnnAuBjwMJWByAgMDV965Sx6uOmc8Ylh4JZu6uWyjlMtaOaWHQO+dFRxAERqw4y9Hbj/BysiE6PpVyrnYc/6gj0fEpNJtzQNz3tWYVmPtKPf69/ID3113EyUbJmU86F2soM6vxkmmsaP+O0TNYcjJe9Jk3oB6Di6jdgqVTIsLUF4U0tYYP/7mCRtD2QJAMkKKhVRUPfF1seRCbwp7rkfRtUI5klZqp/1xGAH56tSFyuQxHa0c6VupIx0odxWMjEyLZfHMzq6+s5sLDC4TEhIg9LuyUDvzYfWmJnYcgCBy785jlx0M4HJhZ36C2rzOj21Ti5XplJe2QHnKZnG5Vu9GtajdCY0JZem4pyy4u417cPZaeX8rS80vxsPegVYVWNPNtRqPqlTl/T84fJ8N4t30Vwp5q9SB+7g7I5TLqV3Dl2J3HXL4fQ51yLsw7Pg8Bgd7Ve1uEAXL09iO+2a3VKMzoXUsyQLKgK+ne9YejRGeU6m9QwRUALydbMWwLMCDD4/Fy3bKEPU6kTnmXYtdSyeUyXDJErX7uxh+nb7w8S0pDBtQtZ94mepaOZITkwq9H73IrMp4y9lbM6G16h1yJnFHIZQxqUoGFB+6w4ew9etYty9g15zmUcSF/p13lXNNUfRx9eK/pewyt8xY9f9zPreTdJCvOkC6LwCFpKB+uj2bvpLzTBAtLSpqaLRcjWH48hNtRWheyTAadA7wZ3aYSzSu5FUkF0ecZf1d/5naey4yXZrDrzi6O3zvOmqtriEyI5N/Af/k38F8AZHYKnqpaMnn7Vaw11RAQqOimFUTWL681Qi6Fx+BX9p5YrdPcrdsBYpPSmLDuIhoBBjcpz+stcq82+iJTwc2eyV2qM2fnTQDqV8i8WDeo4EpETDL+7vaiDk8hlzGhk+Wk4OZEQY2XFxnJCMmBu48SWHhAm5v+Wa9akju9iBnUpDw/HrzDieAnDPzlBJf1xJsngp/kWyvju72BRDzTUN2lD4Maj2XXtUjuRCdwOyqBZ4kqyhSgo3F+RMel8MepMNacDudpogrQllQf1KQCo1r7Z0sVlcgfW6Ut/QP60z+gP3M7zdWG3x6c5UzEGc5EnCH4WTBJyv/45cp/AMhs7dgQ2gCfc29QxVvrITsbHsqWyCkICLgKXXGW12bALydQyGUMblKBgY1LvjPt0TuPiE1Ow9/dntl960hGaR6Mau3PzmsPuXwvhnZ6Kdm96pVlx9WHvNO+ivT8PedImpAsaDQCr/52ijMhT2lbzYPVbzaTPgTFwDe7b/HL4WBAW6yrY00v9t6Iom01D+YNrIeHo40oONTnxoM4ev10DI0Aa99qTquq2gqdrb8+SERMMn+/25Km/kXn+r7+IJb/t3ff4VFVWxvA30lIQkIaJY2eAIJIUQFDRJESKaKCFcGrgAKCWEFUvCJXr4rtcm1YPlSwUeQqYEEEqQohFIkQQCQQCC0BgiQB0md9fywnw0ASQjIzZ8r7e548DFPX7DkzZ5199l7741/T8d3vh1Fcql+VRuGBGNGtOe7s0gShtY0fAOmp1mZswsCPX8Ep+QNFPmkQk20hxFrmRhBTEUpNx+BrboCGhe+icVgDm0GEPz56LS6Nce7vyKRvtmHOhgzc1y0Wz7EX9YIKiktxLK8QTerZTvvNLyp1+yns3oZjQuxg7sYD2JB+AoF+vni5BivkUuWe7NsaIsD8TQfw/MDL0CoyBEt3ZOGX3ceRMHUFbr2yEabdefl5j/v6t4Mwi641YklAAKBVVDAOnczHn1l5NU5CSs2C5Tuz8PGv6UhOt5ZU79ysLu67JhZ92kahVjkJEtlXt6ad8XDn5zFz7T4IStEs6gRuvOo4ZqV8gp3Hd6LER9eY8TVHIaroX/BB8HmzGNamHXd6EpK0R2dwdGvJ/viqqO3ne14CAoAJiJdgEnKWrNwCTP37/OQTfVuX+8Ug+zCZTHi6fxs81a81TCYTzu2QW5RyGBP6tEajcypLrvlTx47c2NF24a9WkcFYtesYdv89TqM6ThWWYP6mA5i1bh/2Z1tLqt/QXkuqWwbOkfPc1y0WczZkoI6/P2YPvxON6wbhyW5P4PiZ47j389nYkLEHgaXxuDSqEQ6cyEd+cSlq+/lg9LVxeHtFGtbvzcbIa+OcFu+hk/nYl30Gvj4mDkYlqgIezv1NRDB5YSryCkvQsUk4hl/d3OiQvIKlp8lkMuHu+KYAgNDatVBqFnxx1johgFYg3H30FHxMwDUtbRdKaxWpNTjSjl58EnLgxBm89MMOJExdjue/24H92WcQFuiHsT1a4JeneuLtIVcwATFIk3pBWPrYdfjp8e5oXNd6UNAgqAFGX3Ubgkv74Kb2rTF3dAL6XKYz2G5oH1NWgyZ57wks3Z6JjOwz5T5/ZVIOnMTUH3fi1N9l9i8kt6AYb/+sY8naNwpDCE/VEV0Qe0L+tiQ1E0t3ZKGWjwmv3taeRYUM8NxNbXHfNbHYnXUKY77YjDkbMvBo71ZltUR+2a29IB2bhCM8yHbwacsoLQe++2helV5LRPBbxl/4+Nd0LEm1llSPi6iD+7rF4tYrGyHIn18PV9C0fvk9koOuaITr20ahToB+Tv+84VI0rhuIEd1iUTfIH7X9fJBXWILRn29GSEAtfHhPJ5tTeJURETwx//eypHZS/8pnXRWVmDF0xnqkHsoFANx6ZaNK709Eir+y0Cl1z327HQAwtkcLtIl2ndV7vUlALV+0iAhG8/p1yuoELEo5hMFdtIdkzW491969VcR5j20VqUlIVm4hcvKLERZY/lFocamlpPq+slLRgPas3H9NLK67xPNKqnsySwICAJGhtTGxr7WqcXxsfaz++/RdXmEJhn6UjEtjQvGfOzqibcPKv+O/ZZwsS0C+SNqPMd1bVDrravrKNKQeykV4kB9eva0D+rRlXSGiquDpGAAvL96JY3mFiIuog3E9Wxodjtfz9TFh2NVaW2Hm2n1l40W2H9KpvOWdaw+p7YeYv9do+DPr/N6Qk2eK8P6qPej+2ko8OjcFvx84Cf9aPhjcuQl+eqw7vhgZj55tIpmAeJCxPVrgqth6+L97OuHWKxrB18eEnUdycf+nG3Esr7DSx87fZK3Oe7qoFDPX7avwvpk5BZi+Mg0A8O+B7dD3smgOaCeqIq9PQtalHce8v39wXr2tQ4VlxMm5BnduikA/X/yRmYfk9BMoKC5Fxt9LuVt6Pc51WUMtdpSScbLsuj3HTuHZhduQMHUFXl3yB47kFKBBcADGX38J1j3dC6/e3sFmpVbyHF3j6uOrBxLQ57JoTBt8OZKf6Y24iDo4klOAsV9sRlGJudzHLdhyEAu26Mybe/4uNDY7OaPC+y//IwslZkHHJuG4sUNMufchovJ5dRJSUFyKSQt0ie1/dG1q1/oSVDNhQX645e/z6rPW7sO+7NMwiw5ajQgpv3icpbLipv0nsGnfCYyYuQG9/7MaX6zPQH5xKS6NCcUbd3TE2qd74pHerdCARei8SoPgAMy4tzNCatfCpv1/Ycq3qefNyvpi/X48Pu93FJaY0aN1BCbf2BYRIQE4fqoQy3Zklfu8K/84CgC4/tJI9oAQXSSvTkL++/Of2J99BtGhtfEUV8h1OZYZSkt3ZJatz9IyMrjCH/rOzTUJ+Wl7Fgb/33qs3HWsrKT6nFFdsfiRa3B7p8Zc08WLtYgIxttDroDJBMzZcMBmBtamfSfw/Hc6NuyB6+Lw8bAuZafsAGD2hv3nPV9BcSnWpmUDAHq2iXTCOyDyLF6bhKQeysFHv6QDAP49qB2n07mgS6JC0K1lfZgFeHeFnnO3TMUtT/tGYfD/u4hYqVnQNa4eVkzogY+GdUZCi/o8SiUAQM/WkWUHHc9/twNJe7Lx9eaDuPujZBSXCvq3i8bT/dqUzZC766omMJmAtWnZSD9+uux5cs4U472VacgvLkV0aG20dXJRNCJP4JWzY4pLzZj4v60oNQsGdIjB9RzJ7rKGXx2LtWnZZbUaWlYwHgTQyovhQX5lq3I+O6AtYhtwTRc63wPd47DzSC4WpRzG/Z9uxJmiUgBArzaReP2OjjYJa+O6QejZOhIr/jiKORsy0KN1BOZtPIAfUzPLxonc1DGGSS5RNXhlT8iHq/dg55Fc1A3yw/M3X2Z0OFSJXm0i0aSetWpqZUkIAPS+VLvEI0IC0I5LaFMFTCYTXr2tA9o1Ci1LQB7s0QIf3dsZwQHnH5sNvUqnif/fmr0YOiMZi1IOo6jEjDbRIZhyU1s8ydO5RNXidT0hu7Py8PZy7dqfctNlHJzo4nx9TBiW0Bwv/qDl9C+UhEzs2wZhgf74R9emzgiP3FhtP1/MuLczXvphJ65t1aCsHk15eraJRMOw2mVr0wyNb4q7ujRB+0Zh7AEhqgGvWkW31Cy444N1+C3jJHq2jsAnw7vwB8QN5OQXo9+ba1AnoBaWPtadtTzIEBvST2DxtiO4N6EZ4iIqT4aJvBFX0b2AT9ftw28ZJxEcUAsvcYVctxEW6Idl469DLR8TExAyzFWx9bgoHZGdeU0SkpF9Bq//tAsAMOmGNmh4zuqs5NrKO09PRETuzWt+2U/mFyEyNAAxYbUxpJJzv0REROQcXpOEdGgcjiWPdkdeQTG79ImIiFyA1yQhABDo74tAf1bLJCIicgVeWSeEiIiIjMckhIiIiAzBJISIiIgMwSSEiIiIDMEkhIiIiAzBJISIiIgMwSSEiIiIDMEkhIiIiAzBJISIiIgMwSSEiIiIDMEkhIiIiAzBJISIiIgMwSSEiIiIDMEkhIiIiAzBJISIiIgMwSSEiIiIDMEkhIiIiAzBJISIiIgMwSSEiIiIDMEkhIiIiAzBJISIiIgMcVFJyNSpU9GlSxeEhIQgMjISgwYNwq5du2zuU1BQgHHjxqF+/foIDg7GbbfdhqysLLsGTURERO7vopKQ1atXY9y4cVi/fj2WLVuG4uJi9OnTB6dPny67z+OPP47vvvsO8+fPx+rVq3H48GHceuutdg+ciIiI3JtJRKS6Dz527BgiIyOxevVqdO/eHTk5OYiIiMDs2bNx++23AwD++OMPXHrppUhKSkLXrl0v+Jy5ubkICwtDTk4OQkNDqxsaEREROVF19t81GhOSk5MDAKhXrx4AYPPmzSguLkZiYmLZfdq0aYOmTZsiKSmp3OcoLCxEbm6uzR8RERF5vmonIWazGY899hi6deuGdu3aAQAyMzPh7++P8PBwm/tGRUUhMzOz3OeZOnUqwsLCyv6aNGlS3ZCIiIjIjVQ7CRk3bhxSU1Mxd+7cGgUwadIk5OTklP0dOHCgRs9HRERE7qFWdR700EMP4fvvv8eaNWvQuHHjsuujo6NRVFSEkydP2vSGZGVlITo6utznCggIQEBAQHXCICIiIjd2UT0hIoKHHnoICxYswIoVKxAbG2tze6dOneDn54fly5eXXbdr1y5kZGQgISHBPhETERGRR7ionpBx48Zh9uzZWLRoEUJCQsrGeYSFhSEwMBBhYWG4//77MX78eNSrVw+hoaF4+OGHkZCQUKWZMUREROQ9LmqKrslkKvf6mTNnYvjw4QC0WNmECRMwZ84cFBYWom/fvnjvvfcqPB1zLk7RJSIicj/V2X/XqE6IIzAJISIicj9OrxNCREREVF1MQoiIiMgQTEKIiIjIEExCiIiIyBBMQoiIiMgQTEKIiIjIEExCiIiIyBBMQoiIiMgQTEKIiIjIEExCiIiIyBBMQoiIiMgQTEKIiIjIEExCiIiIyBBMQoiIiMgQTEKIiIjIEExCiIiIyBBMQoiIiMgQTEKIiIjIEExCiIiIyBBMQoiIiMgQTEKIiIjIEExCiIiIyBBMQoiIiMgQTEKIiIjIEExCiIiIyBBMQoiIiMgQTEKIiIjIEExCiIiIyBBMQoiIiMgQTEKIiIjIEExCiIiIyBBMQoiIiMgQTEKIiIjIEExCiIiIyBBMQoiIiMgQTEKIiIjIEExCiIiIyBBMQoiIiMgQTEKIiIjIEExCiIiIyBBMQoiIiMgQTEKIiIjIEExCiIiIyBBMQoiIiMgQF52ErFmzBjfddBMaNmwIk8mEhQsX2twuInjuuecQExODwMBAJCYmYvfu3faKl4iIiDzERSchp0+fRseOHTF9+vRyb3/ttdfw9ttv44MPPkBycjLq1KmDvn37oqCgoMbBEhERkeeodbEP6N+/P/r371/ubSKCN998E88++ywGDhwIAPjss88QFRWFhQsX4q677qpZtEREROQx7DomJD09HZmZmUhMTCy7LiwsDPHx8UhKSir3MYWFhcjNzbX5IyIiIs9n1yQkMzMTABAVFWVzfVRUVNlt55o6dSrCwsLK/po0aWLPkIiIiMhFGT47ZtKkScjJySn7O3DggNEhERERkRPYNQmJjo4GAGRlZdlcn5WVVXbbuQICAhAaGmrzR0RERJ7PrklIbGwsoqOjsXz58rLrcnNzkZycjISEBHu+FBEREbm5i54dc+rUKaSlpZX9Pz09HSkpKahXrx6aNm2Kxx57DC+++CJatWqF2NhYTJ48GQ0bNsSgQYPsGTcRkevasAE4cgSIiAA6dgTq1DE6IiKXdNFJyKZNm9CzZ8+y/48fPx4AMGzYMMyaNQtPPvkkTp8+jdGjR+PkyZO45pprsGTJEtSuXdt+URMRuaL9+4FRo4Bly6zX1aoFPPgg8NJLQHCwcbERuSCTiIjRQZwtNzcXYWFhyMnJ4fgQInIf27cDffoAhw8Dfn7AFVcABw/q/wGgfXtg9Wqgbl1j4yRykOrsvw2fHUNE5PZOnAD699eE47LLgJ07geRk4NAhYMkSIDoa2LYNuPFGID/f6GiJXAaTECKimhAB7r8fOHAAaNUKWLMGaNHCenvfvsDSpUB4OLBuHTB6tD6GiJiEEBHVyPvvAwsX6imYuXOBevXOv0/79sDXXwO+vsAXXwAffeT0MIlcEZMQIqLq2rYN+HtwPl57Dbjyyorv26sXMHWqXp48GTh92vHxEbk4JiFERNVRUAAMHQoUFgIDBgCPPnrhxzz6KBAXB2RlAW++6fAQiVwdkxAioup4+mkgNRWIjAQ++QQwmS78GH9/4MUX9fK0aRykSl6PSQgR0cX66Sfgrbf08syZmohU1Z13As2a6YyaefMcEx+Rm2ASQkR0MXJygOHD9fK4ccANN1zc4319gTFj9PK773KmDHk1JiFERBfjtdeAzEydjvvaa9V7jvvvBwICgM2bgS1b7BsfkRthEkJEVFWHDgH//a9efu01ICioes8TEQHcfLNenjPHPrERuSEmIUREVSGia8Dk5wPdugEDB9bs+YYM0X/nzAHM5prHR+SGmIQQEVXFp58C336rRcnee69qs2Eq078/EBamvSu//GKfGIncDJMQIqILyciw1gF54QWgQ4eaP2ft2sBtt+llnpIhL8UkhIioMmazDiTNzQW6dgUmTrTfcw8dqv/Onw8UFdnveYncBJMQAPj+e6BxY+D224GNG42OhtxFfj7w+uvA3XfrTqS01OiIyBHefx/4+WcgMBD47DOdYmsvPXroCrsnTugid0RehklIURHw8MN6Xvbrr/VHITn5wo8T0YJFL7ygxYq4A/IumZlAx47Ak08Cs2drAaq+fbWGBHmOnTv1MwZ0NkyrVvZ9fl9fYPBgvTx7tn2fm8gNMAn5+GNg3z49GundGzhzBrjxRl3boSJHj+p9+vUDpkwB7rtPCxZxB+QdTp3Sz3v3bqBhQ01i69QBli8H4uOBlSuNjpDsISMD6NNHfxN69dKZMY5gOSWzaBEXtSOv491JSGkp8MorevnZZ3U57g4dgOPHratdnuvoUe0tWbxYiw3ddZd20y5dCowcWXn1w/R0YMECPboi9/Wvf2mBqYgIYM0a4O239d+YGGDXLt1hDR0KrF6t13/+uSa7yclASYnR0VNVHD0KXH89cPAg0KaNllf3cdDPZZcuQIsWmux8+61jXoOMV1ysM6y6ddMetf79de0hbycuJicnRwBITk6O419s0SIRQKR+fZH8fL1u6VK9zt9fJCPD9v7Hj4u0b6+3N24ssm2bXp+UJFKrll4/Y8b5r5ObKzJypN4OiPj6irz1lojZ7Nj3R/b3xx/Wz/qHH2xvy84WGTtWxGSyftbn/l16qciOHcbETlVz5ozIlVfq59W0qciBA45/zcmT9fVuvNHxr0XOt3u3SJcu5/8e1KolMmmSyOnTFT+2tNR5cdZQdfbf3p2E9O+vG8LEidbrzGaR667T66+5Rn+QRHTnY/lhio4W+fNP2+d69VW9LTDQdidz+rTIVVfpbSaTyCWXWDfAF190+FskOxswQD+7G26o+D6bN4vceadIkyYizZuLJCaK9OkjEhamjw0OFvnmG6eFTBfpvvv0c4qIENm1yzmvuWOHdad06JBzXpMcz2wWmTlTpE4d/Xzr1hV55RWRVatEBg2y7gsiIkQef1zk9df1QOaKK/Tg2N9fb+/YUR+Xl2f0O6oUk5CLsXu39Yh1927b27Zvt+4w2rTRnYjl6DciQm8/V2mp3g8Quewy/SE5dUrkppv0unr1dMMzm0WmTrVufDNnOvZ9kv0sXmzdUVRn55SZaU1wAZHnn2dvmKuZPdt6wLB8uXNf+5pr9LWHD3fu65JjZGfrwYjl+96jx/m96wsW6IFKRT2n5/5FRoq8/bZIYaEhb+lCmIRcjOHD9UMdMKD821ev1iPWszeAm246P2E52+HDIlFR1qSjSRPrqZ1ffrG97zPP6G116ojs3Wu/90WOUVgo0rq1fmYTJlT/eYqL9YjHsk2NGFF5Vyw5z5Ej+r0FRJ57zvmvv369NQFat875r0/2M2uW9npYTr+//LJISUn59y0qEpk7V+Thh0Xuukvk6adFvvpKJDVVZP9+PR340UciLVpYfzd69dKDXBfDJKSq0tJ0wwD0i1+R7GyRzz4Teeedqp/HT0vTnhDLxhIToz0g5yotFbn2Wr1Pz55udd7PK02bZu0JO3my5s83fbq1J65NG/3R4TZgnDVrrKdKr7hCdwxG+Mc/rEe8lR3wkOt6/33r73+7dpXvYy5GUZHIBx+IhIRYExEX6xFhElJVEyfqh9i3r2OePz9f5OeftavtxImK77d7t0hQkMby7ruOiYVqLjPTenquvIHH1bVsmY4vOvvo5uBB+z0/Vc2uXSIBAdbxXqmpxsWSk6NJkCURKe8AhlzXzJnW7/OECdrzaW/r1lkTkZEjXeqULpOQqjCbRVq21A9w/nzHvMbFeOcdjSUoyHmD4KjqzGbrYNQrr6y4S7W6TpwQmTLFmow2by5y7Jh9X4MqZjbruXrLOXt79HLV1JEjOhDR0pX/5psutaOhCsyeLeLjo5/bI4849jNbvNj6Wp9+6rjXuUhMQqpi+3brOI3cXMe8xsUoLdXTMZYR0JbZOOQa3n5bP5uAAJGtWx33On/8IRIXZz09Z9TpAG+zYIF1Vpsrjc06fVrk7rutR9V3382xQ67sf/+znuJ/4AHnJI3//re+Xni4jkd0AdXZf3tfsbJFi/Tf3r2BkBBjYwG0ANIXX2jhq99/t67UScbbutW6WNkbbwDt2zvutVq31m2zTh2tuPrEE457LbL68EP995FHgNhYY2M5W1CQFrl7800t7f7ll1q9lRVVjSWi5fUffBB44AFg1izgn/8E7rhDi18OHw689x5gMjk+lqefBjp1Ak6eBMaM0djckQOTompxeE9IfLxmjx984Jjnr65ly6wDFT/7zOho6PRpLSxmKSDlrO7wb76xHv1+8olzXtNb7dtn/c6lpRkdTcVWrdKjXcs4NvaSGcNsFnnqqYqnzz7wgP1P117I1q0ifn76+l9+6dzXLgd7Qi7kyBHr4nQ332xsLOdKTNRy4IBmtTt2GBqOVxDR0uqPPQaMG2e7iun48VpePyYG+OQT5xzZAMAtt+h6RIBuB1VZTJGqZ9Ys3QZ69dKy6a7quut0mYigIF0085FH3Peo153NmAG8+qpeHjdOeyt79NB1xGbO1NWW7bnCclW0bw8895xefvhhXVjT3TgwKaoWh/aEfPCBZozx8fZ/bnsoKRG5/npreW9XGLPiqfLyrANOz/4bNUrknnus9RqWLXN+bKWl1mqKMTGsoOkoHTq4V8/jokXWnpuPPzY6Gu9y5Ih1htxLLxkdja2iIuuMqltuMXQQMwemXoilTPvLL9v/ue0lK0ukYUNrcTRnd+95g9OnRRISrANO77tPZNgw22TEZDJ2O8nNtdab6dpVpKDAuFg80cGD1s/ZnWYjvfSSxh0cLLJnj9HReI+hQ7XdO3d2zd/klBRrVe8FCwwLg0lIZXJzrXX4yyu77krWr7fWLbjnHpcrSOP2nnhC27ZuXdtCQj/8oOd1hwwRWbvWuPgsdu+2jgUYMYLTNO1pxgxrgudOSkpsixxym3C8Zcu0vX18RDZtMjqailmqcMfFWRdkdTImIZVZsUKnULVq5R5f3HnzrFO+evTQ7kCquU2brPPrv//e6Ggu7KefrPG+/bbR0XiOW27RNn3+eaMjuXh794rUrq3xz5tndDSeLT9f9xmAllV3ZXl51l50g3pxOTC1Mj17AllZwJw5zhtkWBN33gl8+61O2Vy1CmjXDpg8GXj+eaBLFyAsDGjeHBg2DNi82eho3YOIDjg1m4G77gIGDDA6ogvr08c6GO7xx3X6LtVMcTHw8896uX9/Y2OpjthYnZ4J6PZ84oSx8Xiy114Ddu8GoqOBf//b6GgqFxxs/a146SXg8GFj46kik4hrDbPOzc1FWFgYcnJyEBoaanQ4xvvjD2DwYK1ZUZHAQE1YEhOdF5c7WroU6NsXCAjQH5YmTYyOqGpEgHvu0VoR9esDmzZpAkrVs24d0K2btuXRo1qrx93k5+vMiD17dKbfwoXucXDlTnbv1jYuLATmztXfYVcnott2UpL+Znz2mVNfvjr7bzf89nmZNm20p+PLL7UgjqUYzo4dejR3/fX6g3TTTTqllCpmmQL94IPuk4AAunOZMUMLE2VnA4MGsWhVTVh6QXr1cs8EBNADj3nzAH9/PQB5802jI/IsJSXay1xYqL+xd95pdERVYzIBb72llz//HFi/3th4qsJhJ4eqySkL2HmSggKRxEQ9D5iQ4Jojt11BWpp1LY7MTKOjqZ6MDF3UDBC54w73GNvkiiwDOz/80OhIau7dd/W91KolkpRkdDSe44UXtF1DQ7WonbsZMULj79LFqatzc0yINwoI0GJaISHaBTdjhtERuab58/Xfnj2BqChjY6muJk2Ar78G/Pz0/fznP0ZH5H5OndLvCeAZpy8ffBC4/XY9cr/5ZmDXLqMjcn8bN+rYOwCYPh1o1szYeKrj5Zd1n7Bxo46DdGFMQjxBkybAiy/q5RdeAM6cMTYeV2RJQtylW7Ui11xj7W59+mkd30BVt2aN7rCbNwfi4oyOpuZMJuDjj4ErrwSOHdMxTxyoWn2nTwP/+IeuAzN4MHD33UZHVD3R0cCkSXr52Wf1tJKLYhLiKcaM0Yz9yBEdM0JWf/4J/PabllS+5Rajo6m5MWN0do/lhzI72+iI3Mfy5fqvJ/SCWISGAkuWaOn5/fuBkSNZ1r26Jk7U34tGjbQMuzsP9n30UX0f+/a59D6BSYin8Pe3rjnyyitAbq6x8biS99/Xf/v1Axo0MDYWezCZgP/7P+CSS4CDB3UAndlsdFTuwTIo1ZOSEEBX4Z43T0/VLVhg3eap6hYutLbbp58CdesaGk6NBQVZTyu9+KKutuuCmIR4knvu0SXhs7M5Wt7i1CkdMwMADz1kbCz2FBKip5hq1wZ++AF44w2jI3J9WVnWqe69ehkbiyN06gS8/rpeHj8eSEkxNBy3sm2b/n4C2na9exsbj70MGwa0baun6F55xehoysUkxJPUqqVjQgDdKbGbHpg9W3uFWrXSwl+epEMH4O239fIzz+iKwFSxFSv0344dtefAEz3yiK7qWliop+xOnTI6Itf3zTfAtddqW/Xs6bI762qpVcv6fqZNA1JTjY2nHExCPM3tt+uPbF6eVvvzdpYdz733um9NiMqMHGkdSHfrrXo+m8rnieNBzmUy6bLyjRrpTJmHHzY6ItdVVKTjJm67DcjJARIStHfRz8/oyOzrxht15lRxMTBihNaVciEO+1WePn06mjdvjtq1ayM+Ph4bNmxw1EvR2Xx8rDNl3nlHB6p6s40b9d+uXY2Nw1Es40Pi47XL9c479ceGbIkAy5bpZU9OQgAd9zR7tv4WzJoFfPWV0RG5nv37daaZpSfxiSe0J7F+fWPjcgSTCfjgAyA8XKst9+qllYJdhEOSkHnz5mH8+PGYMmUKfvvtN3Ts2BF9+/bFURd64x5twADN6vPzgcceMzoa45w4Aezdq5c7dTI2FkcKDNRBdfXqAb//rt2uZGvPHiAjQ49yr73W6Ggcr3t3PUUHAA88oAOYSe3dq9vAxo06+PTbb3Usjaf1gJwtJkbfZ926WkW1a1ddEsQFOCQJmTZtGkaNGoURI0agbdu2+OCDDxAUFIRPLAMEybFMJu0F8fXVo6B584yOyBibNum/LVu6/0j3C4mOtiYfU6ZwUcNzWWbFJCToopDe4LnngKuu0lkRnEGl/vpLe8IOHNAlMbZs0SUvvMG112qhvrg4ID1d15hxgXGDdk9CioqKsHnzZiSe1eXp4+ODxMREJFkqFZ6lsLAQubm5Nn9kB506WY+ERo6sfAE8T2VJQrp0MTYOZ7n3Xv1BLSzU8SHHjhkdkevwhvEg5/LzA774QqdqrljBGXMi+luYnq474hUr3LMaak20bq09IQkJwIQJLnH6ye5JyPHjx1FaWoqoc0pjR0VFITMz87z7T506FWFhYWV/TdxpYTFXN3mynv87dUp3TuW0v0ezjAfp3NnYOJzFZNJVM1u10lMPHB+iSkutSYinTL2sqlatgP/+Vy8/+yyQlmZsPEb6+GOdCePnpz3EMTFGR2SMiAhg5UprRVWDGT5dYNKkScjJySn7O3DggNEheQ4/P+B//9OiVhkZuvqqi42MdihLEuItPSGADj5buBAIDgZWrdIKkN5u40bthg8L09MT3mbUKE2+8vOB0aO9s5pqejrw+ON6+aWXPHuMWFUEBLhMNVi7JyENGjSAr68vsrKybK7PyspCdHT0efcPCAhAaGiozR/ZUd26wPff67/JyVo7oKTE6Kgc78gR4NAhnSFw5ZVGR+NcbdvqMt6ArjPz2WfGxmO0H3/Uf6+/XusmeBvLDKrAQD0C/vhjoyNyrtJSHRNz6pSOixg/3uiI6Cx2T0L8/f3RqVMnLLd0fwIwm81Yvnw5EhIS7P1yVBWtWunRce3aOkJ65EjPH6Rm6QVp29Z7BiKebdAgPR0H6NHv2rWGhmOoJUv03/79jY3DSHFx1qn7EybobCFv8eabwC+/aO/grFk6YJ9chkNOx4wfPx4zZszAp59+ip07d2Ls2LE4ffo0RowY4YiXo6ro3l1nyfj66roIEyd6dresN56KOde//qXJSGEhMHAgsHu30RE537Fj1m2hb19jYzHao49qPZncXK0e7A1jxA4d0rEwgI6N8YSVkz2MQ5KQwYMH44033sBzzz2Hyy+/HCkpKViyZMl5g1XJyW6+2bqOyrRpwKuvGhuPI1mK43lzEuLjo7MjunTRqXg33AAcP250VM61bJkm2x06aBVRb+brq4vbxcVprYxbbnHpJd7t4t//BgoKdDrq/fcbHQ2Vw2EDUx966CHs378fhYWFSE5ORnx8vKNeii7Gvfda60lMmqSLuuXlGRuTvYl43/TcitSpA3z3HdC8uc6MGDhQf5S9hWU8iDefijlbTAzw0086gHn9emDsWM/tEd2zxzr+ZepUlxmISbYMnx1DBnj8ce2qB4Dp04HGjXXhq+3bPeMHae9erZbq769HwN4uKgpYvFh3POvWAUOHekciYjbrDhcA+vUzNhZX0rIlMGeO9pTNnKljRDzhe3+u8eN1EH6/ft5RJddNMQnxVlOmAEuX6qDV3FytsNquHRAZCYwZA/z6q/tO57WMAejYURMRAi691FojYcECrR+TkWF0VI61ZYuOCQkJAa6+2uhoXEu/fsCMGXr5v//V6qqeZMkSHYRfqxaXMXBxTEK82fXX6/oBP/2k40X8/HTMwIcf6pFDSAhwxRXAyy8D+/a5z9GSZTyIN9aEqEzPntYekaQkoH17nTHhTp/txbCciundm8loee67D3j3Xb384ovAU095xqw5s1nfC6CrCF96qbHxUKVMIq7165Obm4uwsDDk5OSwZoizFRbqVM4ZM7TQ1bmj5yMjdYzFVVfpv1266Iqdrubaa7UnZ9YsrQ9AttLSdGzQ2csoRERYP1fLv6742V6Ma67R7fnDD3WaMpXvjTesRe3uuAP48kv3Xszt66+B22/Xg6h9+3RhR3KK6uy/mYRQ+UR0etvPPwMffaSFzsorchYXpwXQHngAaNrU+XGeq6REK2OeOaNjXNq2NToi11RaquMCPvxQByiW99nGxupnO2aMa3y2F+OvvzSJMpt12XZ3i9/ZvvxSe0aKinTM0Oef65gRd1NSAlx+uX73J08GXnjB6Ii8CpMQcpz8fF0mfsMGHXOxYQPw55/W23189JTOuHHa/W3USPRt23QwanCwrh7KwkQXVlAApKRYP9eNG4Fdu6y3+/jo2kPjxukCcO4wy2D+fF07p21b3SHRhX33nS58WFKiA9enTDE6oov33nu6ndatq7NjPH31bBdTrf23uJicnBwBIDk5OUaHQhfy118i8+eL9Owpon0n+te7t0hqqjExffyxxnDddca8vqewfLa9etl+tr16ifz5p9HRXdiIERrv+PFGR+JeZs60ftaLFhkdzcU5dkykbl2Nffp0o6PxStXZf7thfxu5jPBwPfe6YoUebY4bp6Xhly/XmTa9e+v5WWcOdrOUJ2ddmpqxfLbLlwM7dmg9mdq19bPu3NlaCt0Vmc3WQamcmntxhg/XzxoA/vEPHbheVStXAoMH61pNV1+tvweWYnHOMHmynobr0IFjgNwIT8eQfe3ZAzz5pK5VY0k+4uN1AS1n1Oxo1UoHXn7/PTBggONfz5vs3asDfX/9VU/RTJum9WVc7fTMunVaITM0FDh6VFcMpaorLtYDiF9+0TFfv/wCNGxY8f1LSrT2kGWmzbliYvS7f/Zg16AgnXl32236na2pLVt0ZVwRHVR/3XU1f066aDwdQ65j/36RZ54RCQnR7lFfX5HHH9frHeXQIX0tk0lPJ5D9FRaK3Heftcv+5ptFMjKMjsrW+PEa2913Gx2J+8rMFImN1XZs00YkPb38+/31l0ifPtbtYfRoke+/F5kzR2TMGOv3v7K/vn1Fvv1WpKSkerEeOybSsqU+1+DB1X3HZAfV2X+zJ4Qc69AhXTjr66/1/44c5Dh3LjBkiB5h/fab/Z6XbIlogaunntKj4OBgHcQ4erT2PhgdW1ycTs385htdH4WqJz1dF748eFCncE+ZoqfoAgJ0WYQ1a7Qs+uHD2rPxxRfnt/eZM8Dmzdo7efauJjtbT+399JP1+mbNdCbW/ffr61VFQYH+jqxdq49PTtYKwWQI9oSQ61q8+PwBrJdcIvLmm/brtRg7Vp/30Uft83xUudRUkauvtn6ewcEiDz5o3KBkEZFNmzSWoCCR06eNi8NTZGSIXH555T0ZzZqJ/PZb9Z5/zx6RJ54QqVfP+nz+/tqj8e67IocPV/zY0lKRIUP0MWFhxm53JCLsCSF3sHOnTqP79FPrwnlBQToIbty46o8bEdE1Mfbu5RGwM5nNWhTu9ddtBzFed51+noMGObfw1RNPAP/5jxbd+uor572uJztzRmsFffIJsHWrftdiY7Wg3cCBOq6jpuNu8vOBefN0LSvL4pOAll3v0UML6F15pf5WWMycqVOxa9XSgdK9e9csBqox1gkh95GXp92306fb1nFISNDy4rfcorMwqmrVKn1cSIh2DwcH2z1kqoSIzo6YPh1YtEiLoQE6oPHGG62fqSMHiZaWalGyw4d1YPTAgY57LW+Vm6sDV+vXd9xrbNwI/PCDnqpZv77y+/r5aWG1wYMdFw9VGZMQcj8iem55+nRdWO3syp1t2gBdu1rLiHfoUPEaIPfco0nN6NFaBZSMc/CgfgYzZgBZWdbrIyKAkSMdV4F15UpdmK9uXV1ygOvFuL8dO3R2zsaNQGqq7e9DaKguvNejh2HhkS0mIeTeDh/WqbUrV+oplaIi29v9/TUZGT5cj66jo62Pa9FCB6klJ3PhOldRVKT1On79VUvEHzqk11sGJz/4oA4qtFd58KFD9XVGjrSuEEtETsMkhDxHdrbWe7CUEt+wQQsRna1dO93hfPedFtWKj9dF2VytbgXpEey332qP14oV1utbtdKEJDER6NOn+mX29+7V5zKbtWbE5ZfbJWwiqjomIeS5RLQQ2sKFwGefadfs2Ztu7do6LZfLdru+nTuB99/Xwcm5udbrGzfWFZAtp9+uuMJ2IGJlxo4FPvhAK6RaqqUSkVMxCSHvcfKkJiPffacL6b3wglbzJPdx6pQmlb/+qjNZzu3pql1bV/EdMECTkqZNy+/l2rhRxw6ZzayWSWQgJiFE5J7y860DEC2n3zIzbe8TGam9JHfeqUWzAgM1Gb36au1dGTIEmD3bkPCJiEkIEXkKEZ2e+fnnOth461bbmRG1agHt2+vpnD17tErm9u2OnTpKRJWqzv67loNjIiK6eCaT1oxJSND/FxQAKSm6KutHHwEZGToAFdBaJIsXMwEhckPsCSEi9yICHDigp2yOHNGKnZWt8kpETsGeECLyfCaTDlJ1RMEzInIqO1UJIiIiIro4TEKIiIjIEExCiIiIyBBMQoiIiMgQTEKIiIjIEExCiIiIyBBMQoiIiMgQTEKIiIjIEExCiIiIyBBMQoiIiMgQTEKIiIjIEExCiIiIyBBMQoiIiMgQLreKrogA0CWBiYiIyD1Y9tuW/XhVuFwSkpeXBwBo0qSJwZEQERHRxcrLy0NYWFiV7muSi0lZnMBsNuPw4cMICQmByWSy63Pn5uaiSZMmOHDgAEJDQ+363O6E7aDYDortoNgOiu1gxbZQVW0HEUFeXh4aNmwIH5+qjfZwuZ4QHx8fNG7c2KGvERoa6tUblAXbQbEdFNtBsR0U28GKbaGq0g5V7QGx4MBUIiIiMgSTECIiIjKEVyUhAQEBmDJlCgICAowOxVBsB8V2UGwHxXZQbAcrtoVyZDu43MBUIiIi8g5e1RNCREREroNJCBERERmCSQgREREZgkkIERERGYJJCBERERnCa5KQ6dOno3nz5qhduzbi4+OxYcMGo0NyqH/9618wmUw2f23atCm7vaCgAOPGjUP9+vURHByM2267DVlZWQZGbB9r1qzBTTfdhIYNG8JkMmHhwoU2t4sInnvuOcTExCAwMBCJiYnYvXu3zX1OnDiBu+++G6GhoQgPD8f999+PU6dOOfFd2MeF2mL48OHnbSP9+vWzuY+7t8XUqVPRpUsXhISEIDIyEoMGDcKuXbts7lOV70JGRgYGDBiAoKAgREZGYuLEiSgpKXHmW6mRqrRDjx49ztsexowZY3Mfd28HAHj//ffRoUOHsuqfCQkJ+PHHH8tu94btAbhwOzhtexAvMHfuXPH395dPPvlEtm/fLqNGjZLw8HDJysoyOjSHmTJlilx22WVy5MiRsr9jx46V3T5mzBhp0qSJLF++XDZt2iRdu3aVq6++2sCI7WPx4sXyz3/+U7755hsBIAsWLLC5/ZVXXpGwsDBZuHCh/P7773LzzTdLbGys5Ofnl92nX79+0rFjR1m/fr388ssv0rJlSxkyZIiT30nNXagthg0bJv369bPZRk6cOGFzH3dvi759+8rMmTMlNTVVUlJS5IYbbpCmTZvKqVOnyu5zoe9CSUmJtGvXThITE2XLli2yePFiadCggUyaNMmIt1QtVWmH6667TkaNGmWzPeTk5JTd7gntICLy7bffyg8//CB//vmn7Nq1S5555hnx8/OT1NRUEfGO7UHkwu3grO3BK5KQq666SsaNG1f2/9LSUmnYsKFMnTrVwKgca8qUKdKxY8dybzt58qT4+fnJ/Pnzy67buXOnAJCkpCQnReh45+54zWazREdHy+uvv1523cmTJyUgIEDmzJkjIiI7duwQALJx48ay+/z4449iMpnk0KFDTovd3ipKQgYOHFjhYzyxLY4ePSoAZPXq1SJSte/C4sWLxcfHRzIzM8vu8/7770toaKgUFhY69w3YybntIKI7nUcffbTCx3hiO1jUrVtXPvroI6/dHiws7SDivO3B40/HFBUVYfPmzUhMTCy7zsfHB4mJiUhKSjIwMsfbvXs3GjZsiLi4ONx9993IyMgAAGzevBnFxcU2bdKmTRs0bdrUo9skPT0dmZmZNu87LCwM8fHxZe87KSkJ4eHh6Ny5c9l9EhMT4ePjg+TkZKfH7GirVq1CZGQkWrdujbFjxyI7O7vsNk9si5ycHABAvXr1AFTtu5CUlIT27dsjKiqq7D59+/ZFbm4utm/f7sTo7efcdrD48ssv0aBBA7Rr1w6TJk3CmTNnym7zxHYoLS3F3Llzcfr0aSQkJHjt9nBuO1g4Y3twuVV07e348eMoLS21aSgAiIqKwh9//GFQVI4XHx+PWbNmoXXr1jhy5Aief/55XHvttUhNTUVmZib8/f0RHh5u85ioqChkZmYaE7ATWN5beduC5bbMzExERkba3F6rVi3Uq1fP49qmX79+uPXWWxEbG4s9e/bgmWeeQf/+/ZGUlARfX1+Pawuz2YzHHnsM3bp1Q7t27QCgSt+FzMzMcrcZy23uprx2AIChQ4eiWbNmaNiwIbZu3YqnnnoKu3btwjfffAPAs9ph27ZtSEhIQEFBAYKDg7FgwQK0bdsWKSkpXrU9VNQOgPO2B49PQrxV//79yy536NAB8fHxaNasGb766isEBgYaGBm5irvuuqvscvv27dGhQwe0aNECq1atQu/evQ2MzDHGjRuH1NRU/Prrr0aHYqiK2mH06NFll9u3b4+YmBj07t0be/bsQYsWLZwdpkO1bt0aKSkpyMnJwf/+9z8MGzYMq1evNjosp6uoHdq2beu07cHjT8c0aNAAvr6+541uzsrKQnR0tEFROV94eDguueQSpKWlITo6GkVFRTh58qTNfTy9TSzvrbJtITo6GkePHrW5vaSkBCdOnPDotgGAuLg4NGjQAGlpaQA8qy0eeughfP/991i5ciUaN25cdn1VvgvR0dHlbjOW29xJRe1Qnvj4eACw2R48pR38/f3RsmVLdOrUCVOnTkXHjh3x1ltved32UFE7lMdR24PHJyH+/v7o1KkTli9fXnad2WzG8uXLbc59ebpTp05hz549iImJQadOneDn52fTJrt27UJGRoZHt0lsbCyio6Nt3ndubi6Sk5PL3ndCQgJOnjyJzZs3l91nxYoVMJvNZV9CT3Xw4EFkZ2cjJiYGgGe0hYjgoYcewoIFC7BixQrExsba3F6V70JCQgK2bdtmk5AtW7YMoaGhZV3Xru5C7VCelJQUALDZHty9HSpiNptRWFjoNdtDRSztUB6HbQ/VHETrVubOnSsBAQEya9Ys2bFjh4wePVrCw8NtRvV6mgkTJsiqVaskPT1d1q5dK4mJidKgQQM5evSoiOg0tKZNm8qKFStk06ZNkpCQIAkJCQZHXXN5eXmyZcsW2bJliwCQadOmyZYtW2T//v0iolN0w8PDZdGiRbJ161YZOHBguVN0r7jiCklOTpZff/1VWrVq5VbTUi0qa4u8vDx54oknJCkpSdLT0+Xnn3+WK6+8Ulq1aiUFBQVlz+HubTF27FgJCwuTVatW2Uw1PHPmTNl9LvRdsExF7NOnj6SkpMiSJUskIiLCraZkXqgd0tLS5IUXXpBNmzZJenq6LFq0SOLi4qR79+5lz+EJ7SAi8vTTT8vq1aslPT1dtm7dKk8//bSYTCZZunSpiHjH9iBSeTs4c3vwiiREROSdd96Rpk2bir+/v1x11VWyfv16o0NyqMGDB0tMTIz4+/tLo0aNZPDgwZKWllZ2e35+vjz44INSt25dCQoKkltuuUWOHDliYMT2sXLlSgFw3t+wYcNERKfpTp48WaKioiQgIEB69+4tu3btsnmO7OxsGTJkiAQHB0toaKiMGDFC8vLyDHg3NVNZW5w5c0b69OkjERER4ufnJ82aNZNRo0adl5i7e1uU9/4ByMyZM8vuU5Xvwr59+6R///4SGBgoDRo0kAkTJkhxcbGT3031XagdMjIypHv37lKvXj0JCAiQli1bysSJE23qQoi4fzuIiNx3333SrFkz8ff3l4iICOndu3dZAiLiHduDSOXt4MztwSQiUvV+EyIiIiL78PgxIUREROSamIQQERGRIZiEEBERkSGYhBAREZEhmIQQERGRIZiEEBERkSGYhBAREZEhmIQQERGRIZiEEBERkSGYhBAREZEhmIQQERGRIf4fkY6KIzVy9MIAAAAASUVORK5CYII=" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 28 + }, + { + "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", + "\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": [], + "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": 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Has multiple helper functions for: +# scaling data +# creating a testing/training split +# Making individual sequences into a format feedable to 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): + """ + 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() + for name in ["VehicleVelocity", "MechBrakePressed", "AcceleratorPosition"]: + file = client.get_file( + origin="production", + event=event, + source=source, + name=name + ).unwrap().data + files.append(file) + + + 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]) # remember to align twice. + 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(): + 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. +#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, + control_cols, + seq_len, + stride=50, + 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_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) + + # 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, num_workers=0, + shuffle=False, pin_memory = True + ) + + test_loader = DataLoader( + test_dataset, + 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/control_model/RNN.py b/control_model/RNN.py new file mode 100644 index 0000000..5d72ffe --- /dev/null +++ b/control_model/RNN.py @@ -0,0 +1,66 @@ +# necessary imports: +import torch +from torch import nn + +# 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): + + 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 + 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, 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. diff --git a/control_model/RNN_Dataset.py b/control_model/RNN_Dataset.py new file mode 100644 index 0000000..1ec9430 --- /dev/null +++ b/control_model/RNN_Dataset.py @@ -0,0 +1,46 @@ +import os +import torch +from torch import nn +from torch.utils.data import Dataset, TensorDataset, DataLoader + + +class RNN_Dataset(torch.utils.data.Dataset): + """ + + 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 + # 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