Upload tool.py

tool.py

@@ -4,7 +4,6 @@ import pandas as pd
 import os
 import random
 from matplotlib.ticker import MaxNLocator
-import matplotlib.font_manager as fm
 #from prettytable import PrettyTable
 #from blessed import Terminal
 import time
@@ -17,11 +16,14 @@ import matplotlib.font_manager as fm
 from matplotlib.lines import Line2D
 from typing import Union, Any
 from sklearn.linear_model import LinearRegression
-plt.rcParams['font.sans-serif'] = ['Arial Unicode MS']
-plt.rcParams['axes.unicode_minus'] = False
 
 
+# plt.rcParams['font.sans-serif'] = ['Arial Unicode MS']
+# plt.rcParams['axes.unicode_minus'] = False
+
 
+font_path = './fonts/SimHei.ttf'
+font_prop = fm.FontProperties(fname=font_path)
 
 
 tushare_token = os.getenv('TUSHARE_TOKEN')
@@ -382,30 +384,30 @@ def plot_stock_data(stock_data: pd.DataFrame, ax: Optional[plt.Axes] = None, fig
 
         ax.annotate(unchanged_var + ':' + str(round(start_y, 2)) + ' @' + start_x_dim, xy=(start_x_dim, start_y),
                     xytext=(start_x_dim, start_y),
-                    textcoords='data', fontsize=9,color=color, horizontalalignment='right')
+                    textcoords='data', fontsize=14,color=color, horizontalalignment='right')
 
         ax.annotate(unchanged_var + ':' + str(round(end_y, 2)) +' @' + end_x_dim, xy=(end_x_dim, end_y),
                     xytext=(end_x_dim, end_y),
-                    textcoords='data', fontsize=9, color=color, horizontalalignment='left')
+                    textcoords='data', fontsize=14, color=color, horizontalalignment='left')
 
 
     elif figure_type == 'bar':
         ax.bar(x_dim, stock_data.iloc[:, 2], label = unchanged_var + '_' + index_name, width=0.3, color=color)
         ax.annotate(unchanged_var + ':' + str(round(start_y, 2)) + ' @' + start_x_dim, xy=(start_x_dim, start_y),
                     xytext=(start_x_dim, start_y),
-                    textcoords='data', fontsize=9, color=color, horizontalalignment='right')
+                    textcoords='data', fontsize=14, color=color, horizontalalignment='right')
 
         ax.annotate(unchanged_var + ':' + str(round(end_y, 2)) + ' @' + end_x_dim, xy=(end_x_dim, end_y),
                     xytext=(end_x_dim, end_y),
-                    textcoords='data', fontsize=9, color=color, horizontalalignment='left')
+                    textcoords='data', fontsize=14, color=color, horizontalalignment='left')
 
     plt.xticks(x_dim,rotation=45)                                                  #
     ax.xaxis.set_major_locator(MaxNLocator( integer=True, prune=None, nbins=100))  #
 
 
-    plt.xlabel(x_name)
-    plt.ylabel(f'{index_name}')
-    ax.set_title(title_name )
+    plt.xlabel(x_name, fontproperties=font_prop,fontsize=18)
+    plt.ylabel(f'{index_name}', fontproperties=font_prop,fontsize=16)
+    ax.set_title(title_name , fontproperties=font_prop,fontsize=16)
     plt.legend()  # 显示图例
     fig = plt.gcf()
     fig.set_size_inches(18, 12)
@@ -1759,7 +1761,7 @@ if __name__ == "__main__":
     # ------------step1 数据查询层  获取股票代码
     # start_last_year = get_last_year_date(start)
     # end_last_year = get_last_year_date(end)
-    #stock_code = get_stock_code(stock_name)
+    stock_code = get_stock_code(stock_name)
     # name = get_stock_name_from_code(stock_code)
     # print(name)
     # print(stock_code)
@@ -1795,8 +1797,8 @@ if __name__ == "__main__":
     # df_macro = get_cpi_ppi_currency_supply_data('200101','202304','cpi','nt_yoy')
     # df_macro = get_cpi_ppi_currency_supply_data('200101','202304','ppi','ppi_yoy')
     # df_macro = get_cpi_ppi_currency_supply_data('200101','202304','currency_supply','m2_yoy')
-    df_gdp = get_GDP_data('2001Q1','2023Q1','gdp_yoy')
-    df_gdp = predict_next_value(df_gdp, 'gdp_yoy', 4)
+    # df_gdp = get_GDP_data('2001Q1','2023Q1','gdp_yoy')
+    # df_gdp = predict_next_value(df_gdp, 'gdp_yoy', 4)
     #company_df = get_company_info('贵州茅台')
     #print_save_table(company_df, '贵州茅台公司信息')
     #fin_df = get_Financial_data_from_time_range(stock_code, '20200101', '20230526','roe')