63 lines
2.1 KiB
Python
63 lines
2.1 KiB
Python
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from datetime import datetime
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num_time_step = 201
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num_iter = 10
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env_data = pd.DataFrame(pd.read_excel('env_data.xlsx', engine='openpyxl', sheet_name=0))
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assert env_data.shape[0] == num_iter * (num_time_step + 1), f"{env_data.shape[0]}, {num_iter * (num_time_step + 1)}"
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lst_df = []
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for i in range(num_iter):
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df_tmp = env_data.iloc[i * (num_time_step + 1): (i + 1) * (num_time_step + 1), 1:]
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lst_df.append(df_tmp)
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lst_column = lst_df[0].columns
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# print(lst_column)
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# lst_column = [lst_column[2], lst_column[0]]
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dct_y = {
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'out_w_avg_salary': 'Avg. salary of workers',
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'out_w_gini_salary': "Gini index of workers' salaries (unit: 1)",
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'out_f_avg_profit': "Avg. profit of firms",
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'out_f_avg_yield': "Avg. yield of firms (unit: 1)",
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'out_f_gini_profit': "Gini index of firms' profit (unit: 1)",
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'out_w_percent_hired': "% hired workers (unit: %)"
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}
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dct_file_name = {
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'out_w_avg_salary': 'd',
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'out_w_gini_salary': "e",
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'out_f_avg_profit': "a",
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'out_f_avg_yield': "c",
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'out_f_gini_profit': "b",
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'out_w_percent_hired': "f"
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}
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for str_col in lst_column:
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x = np.arange(num_time_step+1)
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for df in lst_df:
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y = np.array(df[str_col]).flatten()
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if str_col == 'out_f_avg_profit':
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y /= 10000000000
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plt.ylabel(dct_y[str_col] + r' (unit: $1 \times 10^{10}$ CNY)')
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elif str_col == 'out_w_avg_salary':
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y /= 1000000000
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plt.ylabel(dct_y[str_col] + r' (unit: $1 \times 10^{9}$ CNY)')
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elif str_col == 'out_w_percent_hired':
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from matplotlib.ticker import PercentFormatter
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plt.gca().yaxis.set_major_formatter(PercentFormatter(1, decimals=0))
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plt.ylabel(dct_y[str_col])
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else:
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plt.ylabel(dct_y[str_col])
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plt.xlabel('Time step')
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plt.plot(x, y)
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# plt.show()
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# plt.close()
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# plt.savefig(f"{str_col}-{datetime.today().strftime('%Y-%m-%d')}.pdf", bbox_inches="tight")
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plt.savefig(f"Fig3{dct_file_name[str_col]}.pdf", bbox_inches="tight")
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plt.close()
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