a great journey to construct RTK2(Romance of The Three Kingdoms II, KOEI, 1989) ERP
- Read the Save Data in a Linked List Structure (2025.07.06)
- Get Portraits from
KAODATA.DAT(Trial 2) (2024.08.05) - Get Portraits from
KAODATA.DAT(Trial 1) (2023.03.09) - Get Generals' Data from
TAIKI.DAT2 (2021.03.18) - Get Generals' Data from
TAIKI.DAT1 (2020.03.01) - Get Provinces' Data from the Save File with
Pandas(2019.08.12) - Get Provinces' Data from the Save File (2019.07.23)
- Get Provinces Data's Offset from the Save File (2019.07.22)
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A long-standing goal has been achieved!
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Successfully analyzed the linked list structure of the save data and sorted ruler, province, and general data accordingly.
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For practical use in gameplay, migration to VBA is required.
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Code Structure :
RTK2_SaveData_Extractor.pyFlowchart
Loadingflowchart TD A[Start: Main] --> B[read_binary_file] B --> C[extract_generals_from_save] C --> D[extract_provinces_with_generals] D --> E[extract_rulers_with_provinces_and_generals] E --> F[link_provinces_by_ruler] F --> G[link_generals_by_province] G --> H[summarize_province_with_generals] H --> I[summarize_ruler_with_provinces_and_generals] I --> J[Print sample outputs] J --> K[save_dataframes_to_csv] K --> L[End] subgraph Extraction B C D E end subgraph Arrangement F G end subgraph Aggregation H I end subgraph Output J K end -
Results
Console Output
General DataFrame (first 5 rows): general_idx next_gen_idx name int war cha fai vir amb ruler_idx loy exp syn soldiers weapons trainning birth face prov_idx prov_governor prov_ruler 0 0 60 Cao Cao 95 91 95 60 65 99 0 0 1 1 10000 1000 80 155 103 17 Cao Cao Cao Cao 1 60 76 Sima Yi 98 67 93 88 73 98 0 95 1 2 1000 100 80 179 79 17 Cao Cao Cao Cao 2 76 86 Cao Pi 76 70 80 82 84 83 0 100 1 1 1000 100 80 187 104 17 Cao Cao Cao Cao 3 86 87 Cao Zhang 60 92 72 86 78 76 0 100 1 1 1000 100 80 190 98 17 Cao Cao Cao Cao 4 87 88 Cao Zhi 80 15 80 82 82 18 0 100 1 1 1000 100 80 192 99 17 Cao Cao Cao Cao Province DataFrame 2 (first 5 rows): prov_idx next_prov_idx governor_idx governor gold food pop ruler_idx loy land flood horses forts rate merch state ruler_name soldiers_sum gen_cnt free_cnt 0 17 18 0 Cao Cao 3000 70000 200000 0 73 74 67 10 4 55 True 9 Cao Cao 20000 11 0 1 18 13 18 Zhang Liao 2500 45000 250000 0 72 66 66 10 3 57 False 9 Cao Cao 12000 8 0 2 13 8 14 Zhang Lu 2500 30000 240000 0 70 80 75 10 3 52 False 6 Cao Cao 5000 1 0 3 8 29 9 Xiahou Dun 2500 35000 80000 0 65 67 72 10 2 55 False 3 Cao Cao 6000 2 0 4 29 11 27 Xiahou Yuan 2500 45000 300000 0 65 81 67 5 3 48 True 12 Cao Cao 10000 6 0 Ruler DataFrame (first 5 rows): ruler_idx ruler_name capital_idx advisor_idx advisor_name trust prov_cnt gold_sum food_sum pop_sum soldiers_sum gen_cnt free_cnt 0 0 Cao Cao 17 60 Sima Yi 50 18 45500 680000 4560000 143000 66 0 1 1 Liu Bei 33 40 Zhuge Liang 50 7 4000 210000 2830000 87000 54 0 2 2 Sun Quan 24 54 Lu Su 50 12 6500 200000 2790000 92000 39 0 3 3 Meng Huo 36 -1 50 1 1000 35000 85000 17000 8 0 4 -1 -1 0 0 0 0 0 0 0 0 DataFrames have been saved to ./Data as CSV files (sep=',').
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Although it is not completely finished, some progress has been made
- Confirm the correct identification of the portrait image data positions for each character in
KAODATA.DAT - Separate the palette data into a separate JSON file
- Reference ☞ gcjjyy > koei_viewer
- Confirm the correct identification of the portrait image data positions for each character in
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Future tasks
- Consider endianness when extracting 3-bit palette data Ensure accurate color mapping
- Integrate the images into a 2D grid structure instead of a vertical layout
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Code(
RTK2_Portraits_2.py) and Console OutputImport modules and declare constants
import os import json from PIL import Image # Pillow
# Parameters IS_TEST = True # True : Test Mode LOAD_PATH = "./KAODATA.DAT" PALETTE_PATH = "./RTK2_Palette.json" PALETTE_NAME = "palette_rtk2_3" SAVE_PATH = "./Images/RTK2_Portraits2.gif" TEST_PATH = "./Images/RTK2_Portraits2_Test.gif" WIDTH = 64
Read data and palette files
def read_data_file(_path): """ Reads binary data from the specified file. Args: _path (str): The path to the file to be read. Returns: bytes: The binary data read from the file. """ if os.path.isfile(_path): with open(_path, "rb") as f: _data = f.read() if IS_TEST: for i in range(3): print(f" data[{i}] : {chr(_data[i])} {_data[i]:3d} {bin(_data[i])}") print(" ……") return _data else: print(" There's no target file.") exit()
def read_palette_file(_path): """ Reads the palette data from the specified JSON file. Args: _path (str): The path to the JSON file. Returns: list of list of int: The palette data read from the JSON file. """ if os.path.isfile(_path): with open(_path, "r", encoding="utf-8") as f: palette_data = json.load(f) if IS_TEST: for el in palette_data[PALETTE_NAME]: print(f" palette : {el}") return palette_data[PALETTE_NAME] else: print(" There's no palette file.") exit()
Extracts 3-bit palette indices
def extract_3_bit_palette_indices(byte_data): """ Extracts 3-bit palette indices from the given byte data. Args: data (bytes): The byte data to extract 3-bit palette indices from. Returns: list of int: The extracted 3-bit palette indices. """ bit_list = [] for index, byte in enumerate(byte_data): for bit_position in range(8): bit = (byte >> (7 - bit_position)) & 1 bit_list.append(bit) # Extract individual bits if IS_TEST: if index < 3: print(f" data[{index}] : {byte:3d} {bin(byte):10s} {bit_list[-8:]}") elif index == 3: print(" ……") # Extract 3-bit palette indices palette_indices = [] for index in range(0, len(bit_list), 3): if index + 2 < len(bit_list): palette_index = (bit_list[index] << 2) | (bit_list[index + 1] << 1) | bit_list[index + 2] palette_indices.append(palette_index) if IS_TEST: if index < 24: print(f" palette_index[{int(index/3)}] : {bit_list[index:index+3]} {bin(palette_index):5s} {palette_index}") elif index == 24: print(" ……") return palette_indices
Converts palette indices to RGB values
def convert_colors_to_rgb(_palette_indices, _palette): """ Converts palette indices to RGB values using the specified palette. Args: _palette_indices (list of int): The palette indices to convert. _palette (list of list of int): The palette to use for conversion. Returns: list of tuple: The converted RGB values. """ _rgb_colors = [] for palette_index in _palette_indices: _rgb_color = tuple(_palette[palette_index]) _rgb_colors.append(_rgb_color) if IS_TEST: for i in range(8): print(f" converted_color[{i}] : {_palette_indices[i]} {_rgb_colors[i]}") print(" ……") return _rgb_colors
Saves the image data as a GIF file
def save_image(_image_data): """ Saves the image data as a GIF file. Args: _image_data (list of tuple): The RGB image data to save. """ width = WIDTH height = int(len(_image_data) / width) im = Image.new(mode="RGB", size=(width, height)) im.putdata(_image_data) if IS_TEST: crop_box = (0, 0, width, min(200, height)) im.crop(crop_box).save(TEST_PATH) print(f" The file saved as {TEST_PATH}.") else: im.save(SAVE_PATH) print(f" The file saved as {SAVE_PATH}.")
Run
# Run if __name__ == "__main__": print("Reading data file ……") data = read_data_file(LOAD_PATH) print("Reading palette file ……") palette = read_palette_file(PALETTE_PATH) print("Extracting 3-bit palette indices ……") extracted_palette_indices = extract_3_bit_palette_indices(data) print("Converting palette indices to RGB ……") converted_colors = convert_colors_to_rgb(extracted_palette_indices, palette) print("Saving image ……") save_image(converted_colors)
Console Output
Reading data file …… data[0] : U 85 0b1010101 data[1] : » 187 0b10111011 data[2] : » 187 0b10111011 ……
Reading palette file …… palette : [47, 31, 0] palette : [31, 63, 127] palette : [175, 63, 31] palette : [191, 127, 79] palette : [63, 111, 31] palette : [63, 127, 143] palette : [255, 175, 127] palette : [207, 207, 175]
Extracting 3-bit palette indices …… data[0] : 85 0b1010101 [0, 1, 0, 1, 0, 1, 0, 1] data[1] : 187 0b10111011 [1, 0, 1, 1, 1, 0, 1, 1] data[2] : 187 0b10111011 [1, 0, 1, 1, 1, 0, 1, 1] …… palette_index[0] : [0, 1, 0] 0b10 2 palette_index[1] : [1, 0, 1] 0b101 5 palette_index[2] : [0, 1, 1] 0b11 3 palette_index[3] : [0, 1, 1] 0b11 3 palette_index[4] : [1, 0, 1] 0b101 5 palette_index[5] : [1, 1, 0] 0b110 6 palette_index[6] : [1, 1, 1] 0b111 7 palette_index[7] : [0, 1, 1] 0b11 3 ……
Converting palette indices to RGB …… converted_color[0] : 2 (175, 63, 31) converted_color[1] : 5 (63, 127, 143) converted_color[2] : 3 (191, 127, 79) converted_color[3] : 3 (191, 127, 79) converted_color[4] : 5 (63, 127, 143) converted_color[5] : 6 (255, 175, 127) converted_color[6] : 7 (207, 207, 175) converted_color[7] : 3 (191, 127, 79) ……
Saving image …… The file saved as ./Images/RTK2_Portraits2_Test.gif.
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Try to extract portraits from binary data
- Known that each 3-bits chunk indicates a pixel of 8 colored
GIFimage - But the exact data pattern is not discovered yet
- Assumption : All data would be entirely sequential
- Use temporary palette
- Known that each 3-bits chunk indicates a pixel of 8 colored
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Results & Next Tasks
- Failed
- Seems to need understanding about the data structure
- Maybe the best way is to analyse other existing codes; aaidee/RTK2face
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Code
RTK2_Portraits_1.py
import os from PIL import Image
# Parameters test = True # True : Test Mode path = "C:\Game\KOEI\RTK2\KAODATA.DAT" palette = [ (0, 0, 0), # Black (255, 255, 255), # White (255, 0, 0), # Red (0, 255, 0), # Green (0, 0, 255), # Blue (255, 255, 0), # Yellow (255, 0, 255), # Magenta (0, 255, 255), # Cyan ]
def ReadPath(path): if (os.path.isfile(path)): with open(path, "rb") as f: data = f.read() if test: print("test : ", data[0], type(data[0]), bin(data[0])) # OK : 0 85 <class 'int'> 0b1010101 …… return data else: print("There's no target file.") exit()
def Extract3Bits(data): pixels = [] for byte in data: for i in range(8): # Iterate over 8 bits (== 1 byte) pixel_value = (byte >> (3*i)) & 0b111 # Extract 3-bit data and guarantee always between 0 and 7 by adding `& 0b111` pixels.append(pixel_value) if test: print("pixels : ", pixels[:5]) # OK : [5, 2, 1, 0, 0] return pixels
def ConvertColors(pixels): image_data = [palette[pixel_value] for pixel_value in pixels] if test: print("converted colors : ", image_data[:5]) # OK : [(255, 255, 0), (255, 0, 0), (255, 255, 255), (0, 0, 0), (0, 0, 0)] return image_data
def SaveImage(image_data): width = 64 height = int(len(image_data) / width) im = Image.new("RGB", (width, height)) im.putdata(image_data) if test: crop_box = (0, 0, width, min(200, height)) # (x, y, width, height) image_cropped = im.crop(crop_box) image_cropped.save("./Images/RTK2_Portraits_Cropped.gif") else: im.save("./Images/RTK2_Portraits.gif")
# Run if __name__ == "__main__": # 1. Read data or do exit() if not exists data = ReadPath(path) # 2. Extract data in 3-bit chunks pixels = Extract3Bits(data) # 3. Convert each pixel value to a color from the palette image_data = ConvertColors(pixels) # 4. Save into a gif file SaveImage(image_data)
Output (Not entire but partially cropped)
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Call and print outside generals' data from
TAIKI.DAT -
Use
osbytes() -
Not a large size data but still is open to faster enhancement
RTK2_General_Taiki_2.py : Mainly added/changed part
# 4. Read The Data readlocation = (0, 2, 1, 28) + tuple(list(range(7, 13))) + (18,) # print(readlocation) # (0, 2, 1, 7, 8, 9, 10, 11, 12, 18) print("이름", "출현연도", "출현지역", "혈연", "출생연도", "지력", "무력", "매력", "의리", "인덕", "야망", "상성") # for i in list(range(0, 10)) : # test for i in list(range(0, len(general_offset_init) - 2)) : # The last two rows are empty general_data[i][2] += 1 # province# : 0~40 → 1~41 print(bytes(general_data[i][31:46]).decode('utf-8').ljust(15), " ", end='') # name : [31:46] for j in readlocation : # other values print(str(general_data[i][j]).rjust(3), " ", end='') print(" ") # line replacement
이름 출현연도 출현지역 혈연 출생연도 지력 무력 매력 의리 인덕 야망 상성
Gan Ning 190 21 255 175 51 92 52 79 56 71 98
Wang Zhong 190 9 255 167 34 52 53 60 47 52 10
Han Hao 190 21 255 153 25 31 15 16 18 32 25
Zhao Yue 190 3 255 156 85 99 92 90 88 70 50
Chun Yuqiong 190 6 255 146 65 76 68 66 63 71 45
Bao Xin 190 9 255 153 30 42 41 47 36 48 10
Gong Zhi 190 20 255 158 46 42 52 59 41 44 70
Yuan Pu 190 29 255 163 80 33 42 55 53 57 20
Man Chong 190 10 255 170 81 40 92 84 88 63 10
Ma Wan 190 14 255 175 23 52 26 13 38 39 20
……
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Call outside generals' data from
TAIKI.DAT -
Succeed in separating each general's data, but they should convert from
ASCII Code(int)tostring -
Use
os# Each Geneal's Data Length : 46 bytes # Header Data : 6 bytes
1. Check If TAIKI.DAT Exists and get the file's length
import os path = "C:\Game\KOEI\RTK2\TAIKI.DAT"
os.path.isfile(path)
True
filelenth = os.path.getsize(path) num = int((filelenth - 6) / 46)
print(num) # There're 420 General's Data
420
2. Make Offset Initial Information
- Generate an Arithmetic Progression : a1 = 7, d = 46
- make (i. j) list from 1)
len(general_offset_init) len(general_offset_data) print(general_offset_init[0:10]) print(general_offset_data[0:2])
420
420
[6, 52, 98, 144, 190, 236, 282, 328, 374, 420]
[[6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51], [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97]]3. Call TAIKI.DAT
with open(path,'rb') as f: general_raw_data = f.read() general_data = [] for i in list(range(0,num)) : general_data_row = [] for j in list(range(0,distance)) : general_data_row.append(general_raw_data[general_offset_data[i][j]]) general_data.append(general_data_row)
print(general_data[0:3])
[[190, 255, 20, 0, 0, 0, 0, 51, 92, 52, 79, 56, 71, 255, 0, 0, 255, 0, 98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 175, 39, 0, 71, 97, 110, 32, 78, 105, 110, 103, 0, 0, 0, 0, 0, 0, 0], [190, 255, 8, 0, 0, 0, 0, 34, 52, 53, 60, 47, 52, 255, 0, 0, 255, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 167, 83, 145, 87, 97, 110, 103, 32, 90, 104, 111, 110, 103, 0, 0, 0, 0, 0], [190, 255, 20, 0, 0, 0, 0, 25, 31, 15, 16, 18, 32, 255, 0, 0, 255, 0, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 153, 31, 152, 72, 97, 110, 32, 72, 97, 111, 0, 0, 0, 0, 0, 0, 0, 0]]
chr(general_data[0][0]) # Should Convert The Whole List from ASCII Code(int) to string
'¾'
Practice
for i in range(1,10) : print(i)
1
2
3
……
9
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Upgrade : Adopt
Numpy&Pandasand convert to aclass -
The parameter
lordof the defdataloaddoesn't work yet. -
The columns aren't named yet, too.
Codes : Rtk2 (Class)
# Class using NumPy & Pandas import numpy as np import pandas as pd class Rtk2 : # province_offset_data def __init__(self) : self.province_offset_init = [] self.province_offset_data = [] for i in list(range(0,41)) : self.province_offset_init.append(11668 + 35*i) self.province_offset_data.append(list(range(self.province_offset_init[i], self.province_offset_init[i]+35))) # call the save data on each offset location def dataload(self, path, lord) : self.path = path self.lord = lord with open(self.path,'rb') as self.f: self.province_law_data = self.f.read() self.province_data = [] for i in list(range(0,41)) : self.province_data_row = [] for j in list(range(0,35)) : self.province_data_row.append(self.province_law_data[self.province_offset_data[i][j]]) self.province_data.append(self.province_data_row) self.province_data_array = np.array(self.province_data) # calculate pop, gold and food self.province_pop = [] self.province_gold = [] self.province_food = [] for i in list(range(0,41)) : self.province_pop.append((self.province_data_array[i][6] + self.province_data_array[i][7]*(2**8))*100) self.province_gold.append(self.province_data_array[i][0] + self.province_data_array[i][1]*(2**8)) self.province_food.append(self.province_data_array[i][2] + self.province_data_array[i][3]*(2**8) + self.province_data_array[i][4]*(2**16)) # merge the dataframes self.province_gold_array = pd.DataFrame(self.province_gold, columns=['Gold']) self.province_food_array = pd.DataFrame(self.province_food, columns=['Food']) self.province_pop_array = pd.DataFrame(self.province_pop, columns=['Pop']) self.province_data_df = pd.DataFrame(self.province_data) return pd.concat([ self.province_pop_array, self.province_gold_array, self.province_food_array, self.province_data_df.iloc[:, 8], self.province_data_df.iloc[:, 14:20] ], axis=1)
rtk2 = Rtk2() save = rtk2.dataload('path', 15) # the parameter lord('15') doesn't work yet save.head()
Pop Gold Food 8 14 15 16 17 18 19
0 274400 4080 4364 3 9 75 4 4 1 27
1 225900 29450 2700000 15 50 100 92 0 2 62
2 253300 29950 2700000 15 100 100 100 28 4 70
3 198500 30000 2699000 15 100 100 100 79 0 66
4 268000 30000 2700000 15 100 100 100 16 1 48
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Call each province's data of population, gold, food and so on from a save file
Codes : RTK2_Province.py
# province_offset_data - from Offset.py (2019.07.22) province_offset_init = [] province_offset_data = [] for i in list(range(0,41)) : province_offset_init.append(11668 + 35*i) province_offset_data.append(list(range(province_offset_init[i], province_offset_init[i]+35)))
# call the save data on each offset location with open('Documents/신랑/개발/Python/SAVE','rb') as f: province_law_data = f.read() province_data = [] for i in list(range(0,41)) : province_data_row = [] for j in list(range(0,35)) : province_data_row.append(province_law_data[province_offset_data[i][j]]) province_data.append(province_data_row) print(province_data[0:3])
[[182, 0, 8, 1, 0, 0, 240, 9, 3, 255, 128, 48, 255, 255, 7, 79, 4, 4, 1, 34, 8, 1, 55, 0, 6, 0, 0, 196, 45, 217, 0, 0, 0, 0, 0],
[20, 10, 172, 74, 4, 0, 20, 9, 3, 255, 128, 50, 255, 2, 56, 100, 52, 0, 2, 64, 221, 0, 67, 0, 5, 0, 0, 150, 46, 11, 26, 12, 5, 0, 0],
[48, 117, 96, 54, 42, 0, 61, 9, 15, 255, 0, 0, 255, 255, 100, 99, 100, 33, 4, 55, 174, 0, 73, 0, 4, 1, 0, 0, 0, 182, 4, 0, 0, 0, 0]]# test : gold province_gold = [] for i in list(range(0,41)) : province_gold.append(province_data[i][0] + province_data[i][1]*256) print(province_gold)
[182, 2580, 30000, 30000, 30000, 7139, 30000, 1783, 29880, 30000, 29988, 30000, 130, 73, 51, 339, 30000, 0, 30000, 11841, 311, 2542, 12033, 0, 100, 100, 100, 605, 3697, 8908, 30000, 22452, 30000, 6341, 7482, 3649, 2528, 574, 4451, 8050, 12206]
# all province data province_gold = [] province_food = [] province_pop = [] province_rate = [] province_horses = [] province_loy = [] province_land = [] province_flood = [] province_forts = [] for i in list(range(0,41)) : province_gold.append(province_data[i][0] + province_data[i][1]*(2**8)) province_food.append(province_data[i][2] + province_data[i][3]*(2**8) + province_data[i][4]*(2**16)) province_pop.append((province_data[i][6] + province_data[i][7]*(2**8))*100) province_rate.append(province_data[i][19]) province_horses.append(province_data[i][17]) province_loy.append(province_data[i][15]) province_land.append(province_data[i][14]) province_flood.append(province_data[i][16]) province_forts.append(province_data[i][18]) print("Province", "Pop\t\t", "Gold\t", "Food\t\t", "Rate Horses Loy Land Flood Forts") for i in list(range(0,10)) : print(i+1, "\t", province_pop[i], "\t", province_gold[i], "\t", province_food[i], "\t", end =' ') print(province_rate[i], province_horses[i], province_loy[i], province_land[i], province_flood[i], province_forts[i])
Province Pop Gold Food Rate Horses Loy Land Flood Forts
1 254400 182 264 34 4 79 7 4 1
2 232400 2580 281260 64 0 100 56 52 2
3 236500 30000 2766432 55 33 99 100 100 4
4 179300 30000 1732260 46 82 99 93 96 0
5 246800 30000 2666060 57 19 96 100 100 1
6 499500 7139 233937 50 42 98 79 64 3
7 269800 30000 2730580 37 85 94 100 100 3
8 173600 1783 329476 41 49 100 83 83 2
9 276300 29880 2694902 30 39 95 47 79 2
10 1010800 30000 3000000 33 83 96 100 100 6
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Make offset locations' list before call the save data
""" the initial data offset addresses of the each province (hexadecimal) 1 - 2d94 2 - 2db7 3 - 2dda …… 41 - 330c """
# 각 영토별 데이터는 35바이트 단위임을 확인 int('2db7', 16) - int('2d94', 16) int('2dda', 16) - int('2db7', 16)
35
35Codes and Results
# 영토별 첫번째 값의 offset 위치를 10진수로 확인 0x2d94 0x330c type(0x330c) # 이 자체로 int type
11668
13068
int# 35바이트 간격 리스트 생성하기(*꼭 16진수로 할 필요없다) province_offset_init = [11668] for i in list(range(1,41)) : province_offset_init.append(province_offset_init[0] + 35*i) print(province_offset_init) len(province_offset_init)
[11668, 11703, 11738, 11773, 11808, 11843, 11878, 11913, 11948, 11983, 12018, 12053, 12088, 12123, 12158, 12193, 12228, 12263, 12298, 12333, 12368, 12403, 12438, 12473, 12508, 12543, 12578, 12613, 12648, 12683, 12718, 12753, 12788, 12823, 12858, 12893, 12928, 12963, 12998, 13033, 13068]
41# offset : gold province_offset_gold = [] for i in list(range(0,41)) : province_offset_gold.append([province_offset_init[i], province_offset_init[i]+1]) print(province_offset_gold) # offset : food # offset : loyalty # an so on ……
[[11668, 11669], [11703, 11704], [11738, 11739], [11773, 11774], [11808, 11809], [11843, 11844], [11878, 11879], [11913, 11914], [11948, 11949], [11983, 11984], [12018, 12019], [12053, 12054], [12088, 12089], [12123, 12124], [12158, 12159], [12193, 12194], [12228, 12229], [12263, 12264], [12298, 12299], [12333, 12334], [12368, 12369], [12403, 12404], [12438, 12439], [12473, 12474], [12508, 12509], [12543, 12544], [12578, 12579], [12613, 12614], [12648, 12649], [12683, 12684], [12718, 12719], [12753, 12754], [12788, 12789], [12823, 12824], [12858, 12859], [12893, 12894], [12928, 12929], [12963, 12964], [12998, 12999], [13033, 13034], [13068, 13069]]
# province_offset_data (more efficient way) province_offset_data = [] for i in list(range(0,41)) : province_offset_data.append(list(range(province_offset_init[i], province_offset_init[i]+35))) print(province_offset_data[0:2])
[[11668, 11669, 11670, 11671, 11672, 11673, 11674, 11675, 11676, 11677, 11678, 11679, 11680, 11681, 11682, 11683, 11684, 11685, 11686, 11687, 11688, 11689, 11690, 11691, 11692, 11693, 11694, 11695, 11696, 11697, 11698, 11699, 11700, 11701, 11702], [11703, 11704, 11705, 11706, 11707, 11708, 11709, 11710, 11711, 11712, 11713, 11714, 11715, 11716, 11717, 11718, 11719, 11720, 11721, 11722, 11723, 11724, 11725, 11726, 11727, 11728, 11729, 11730, 11731, 11732, 11733, 11734, 11735, 11736, 11737]]
# province_offset_data (final) province_offset_init = [] province_offset_data = [] for i in list(range(0,41)) : province_offset_init.append(11668 + 35*i) province_offset_data.append(list(range(province_offset_init[i], province_offset_init[i]+35))) print(province_offset_init) print(province_offset_data[0:2])
[11668, 11703, 11738, 11773, 11808, 11843, 11878, 11913, 11948, 11983, 12018, 12053, 12088, 12123, 12158, 12193, 12228, 12263, 12298, 12333, 12368, 12403, 12438, 12473, 12508, 12543, 12578, 12613, 12648, 12683, 12718, 12753, 12788, 12823, 12858, 12893, 12928, 12963, 12998, 13033, 13068]
[[11668, 11669, 11670, 11671, 11672, 11673, 11674, 11675, 11676, 11677, 11678, 11679, 11680, 11681, 11682, 11683, 11684, 11685, 11686, 11687, 11688, 11689, 11690, 11691, 11692, 11693, 11694, 11695, 11696, 11697, 11698, 11699, 11700, 11701, 11702], [11703, 11704, 11705, 11706, 11707, 11708, 11709, 11710, 11711, 11712, 11713, 11714, 11715, 11716, 11717, 11718, 11719, 11720, 11721, 11722, 11723, 11724, 11725, 11726, 11727, 11728, 11729, 11730, 11731, 11732, 11733, 11734, 11735, 11736, 11737]]