图片报告样式调优

config_juxiting.py

@@ -142,7 +142,7 @@ data = {
 ClassifyId = 1161
 
 
-# # 变量定义--线上环境
+# 变量定义--线上环境
 # server_host = '10.200.32.39'
 # login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
 # upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"

config_juxiting_yuedu.py

@@ -225,6 +225,7 @@ ClassifyId = 1161
 # query_data_list_item_nos_url = f"http://{server_host}/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos"
 # # 上传数据项值
 # push_data_value_list_url = f"http://{server_host}/jingbo-api/api/dw/dataValue/pushDataValueList"
+# push_png_report_url = f"http://{server_host}/jingbo-api/api/analysis/reportInfo/priceForecastImg"
 
 # login_data = {
 #     "data": {
@@ -298,6 +299,21 @@ ClassifyId = 1161
 #          }
 #     ]
 # }
+
+# push_png_report_data = {
+#     "funcModule": '聚烯烃图片报告',
+#     "funcOperation": '上传聚烯烃PP价格预测图片报告',
+#     "data": {
+#         "groupNo": "000211",
+#         "updateTime": "2024-09-06 15:01:29",
+#         "fileBase64": '',  # 文件内容base64
+#         "title": '2025年8月5日日度周度预测结果',
+#         "billNo": '',
+#     }
+# }
+
+
+
 # # 八大维度数据项编码
 # bdwd_items = {
 #     'ciri': '原油大数据预测|FORECAST|PRICE|T',
@@ -422,6 +438,7 @@ push_png_report_data = {
 }
 
 
+
 # 八大维度数据项编码
 bdwd_items = {
     'ciri': 'jxtppbdwdcr',

lib/dataread.py

@@ -2520,6 +2520,8 @@ def push_market_data(data):
     }
 ]
     '''
+    # from lib.tools import NumpyEncoder
+
     # 获取token
     token = get_head_auth_report()
     # 定义请求参数
@@ -2527,6 +2529,9 @@ def push_market_data(data):
     # 发送请求
     headers = {"Authorization": token}
     config.logger.info('上传数据中...')
+    config.logger.info(f'上传数据URL：{config.push_data_value_list_url}')
+    config.logger.info(f'上传数据数据：{config.push_data_value_list_data}')
+
     items_res = requests.post(url=config.push_data_value_list_url, headers=headers,
                               json=config.push_data_value_list_data, timeout=(3, 35))
     json_data = json.loads(items_res.text)

lib/tools.py

@@ -36,6 +36,8 @@ import numpy as np
 import os
 import time
 import logging
+import json
+
 from dotenv import load_dotenv
 
 from lib.pydantic_models import PredictionResult, PpPredictionResult
@@ -93,6 +95,22 @@ class BinanceAPI:
         # return self.signature
 
 
+# 自定义JSON编码器，处理NumPy类型
+class NumpyEncoder(json.JSONEncoder):
+    def default(self, obj):
+        # 处理int64类型
+        if isinstance(obj, np.integer):
+            return int(obj)
+        # 处理float64等其他类型
+        elif isinstance(obj, np.floating):
+            return float(obj)
+        # 处理ndarray类型
+        elif isinstance(obj, np.ndarray):
+            return obj.tolist()
+        # 其他类型交给默认编码器处理
+        return super(NumpyEncoder, self).default(obj)
+
+
 class Graphs:
     '''
     pdf生成类
@@ -828,6 +846,8 @@ def convert_df_to_pydantic_pp(df_predict, model_id_name_dict, global_config):
     results = []
     data = global_config['DEFAULT_CONFIG'].copy()
     data['data_date'] = df_predict['created_dt'].values[0]
+    data['update_date'] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+
     if isinstance(data['data_date'], np.datetime64):
         data['data_date'] = pd.Timestamp(
             data['data_date']).to_pydatetime()
@@ -1130,6 +1150,11 @@ def plot_pp_predict_result(y_hat, global_config,wd='yuedu'):
 
     # 绘制 y 的折线图，颜色为蓝色
     sns.lineplot(x=y['ds'], y=y['y'], color='blue', label='真实值', ax=ax)
+    # 月度日期每月显示一个
+    import matplotlib.dates as mdates
+    if wd == 'yuedu':
+        ax.xaxis.set_major_locator(mdates.MonthLocator())
+        ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
 
     # date_str = pd.Timestamp(y_hat["ds"].iloc[0]).strftime('%Y-%m-%d')
     ax.set_title(f'{datetime.datetime.now().strftime("%Y-%m-%d")} PP期货十一大维度 预测价格走势', fontsize=24)

main_juxiting.py

@@ -128,28 +128,28 @@ def push_market_value():
             "dataItemNo": global_config['bdwd_items']['ciri'],
             "dataDate": global_config['end_time'].replace('-', ''),
             "dataStatus": "add",
-            "dataValue": five_days_predict_price.loc['次日','predictresult'].round(2)
+            "dataValue": five_days_predict_price.loc['次日','predictresult'].round(2).item()
         },{
             "dataItemNo": global_config['bdwd_items']['cierri'],
             "dataDate": global_config['end_time'].replace('-', ''),
             "dataStatus": "add",
-            "dataValue": five_days_predict_price.loc['次二日','predictresult'].round(2)
+            "dataValue": five_days_predict_price.loc['次二日','predictresult'].round(2).item()
         },{
             "dataItemNo": global_config['bdwd_items']['cisanri'],
             "dataDate": global_config['end_time'].replace('-', ''),
             "dataStatus": "add",
-            "dataValue": five_days_predict_price.loc['次三日','predictresult'].round(2)
+            "dataValue": five_days_predict_price.loc['次三日','predictresult'].round(2).item()
         },{
             "dataItemNo": global_config['bdwd_items']['cisiri'],
             "dataDate": global_config['end_time'].replace('-', ''),
             "dataStatus": "add",
-            "dataValue": five_days_predict_price.loc['次四日','predictresult'].round(2)
+            "dataValue": five_days_predict_price.loc['次四日','predictresult'].round(2).item()
         },
         {
             "dataItemNo": global_config['bdwd_items']['benzhou'],
             "dataDate": global_config['end_time'].replace('-', ''),
             "dataStatus": "add",
-            "dataValue": five_days_predict_price.loc['次五日','predictresult'].round(2)
+            "dataValue": five_days_predict_price.loc['次五日','predictresult'].round(2).item()
         }
     ]
 
@@ -558,17 +558,17 @@ def predict_main():
 if __name__ == '__main__':
     # global end_time
     # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
-    for i_time in pd.date_range('2025-8-1', '2025-8-11', freq='B'):
+    # for i_time in pd.date_range('2025-8-1', '2025-8-11', freq='B'):
-        try:
+    #     try:
-            global_config['end_time'] = i_time.strftime('%Y-%m-%d')
+    #         global_config['end_time'] = i_time.strftime('%Y-%m-%d')
-            global_config['db_mysql'].connect()
+    #         global_config['db_mysql'].connect()
-            predict_main()
-        except Exception as e:
-            logger.info(f'预测失败：{e}')
-            continue
-    # global_config['end_time'] = '2025-08-05'
     #         predict_main()
+    #     except Exception as e:
+    #         logger.info(f'预测失败：{e}')
+    #         continue
+    # global_config['end_time'] = '2025-08-05'
+    predict_main()
 
-    # global_config['end_time'] = '2025-08-01'
+    # global_config['end_time'] = '2025-08-12'
     # push_market_value()
     # sql_inset_predict(global_config)

main_juxiting_yuedu.py

@@ -113,13 +113,14 @@ def push_market_value():
 
     # 准备要推送的数据
     ciyue_mean = four_month_predict_price[best_bdwd_price['next_month_price']
-                                          ['model_name']].iloc[0]
+                                          ['model_name']].iloc[0].item()
+
     cieryue_mean = four_month_predict_price[best_bdwd_price['next_february_price']
-                                            ['model_name']].iloc[1]
+                                            ['model_name']].iloc[1].item()
     cisanyue_mean = four_month_predict_price[best_bdwd_price['next_march_price']
-                                             ['model_name']].iloc[2]
+                                             ['model_name']].iloc[2].item()
     cisieryue_mean = four_month_predict_price[best_bdwd_price['next_april_price']
-                                              ['model_name']].iloc[3]
+                                              ['model_name']].iloc[3].item()
     # # 保留两位小数
     ciyue_mean = round(ciyue_mean, 2)
     cieryue_mean = round(cieryue_mean, 2)
@@ -551,13 +552,13 @@ if __name__ == '__main__':
     #         logger.info(f'预测失败：{e}')
     #         continue
 
-    # global_config['end_time'] = '2025-07-25'
+    # global_config['end_time'] = '2025-08-13'
     # predict_main()
     # push_market_value()
     # sql_inset_predict(global_config)
 
     #  图片报告
-    global_config['end_time'] = '2025-08-12'
+    # global_config['end_time'] = '2025-08-12'
     logger.info('图片报告ing')
     pp_bdwd_png(global_config=global_config)
     logger.info('图片报告end')

main_juxiting_zhoudu.py

@@ -122,8 +122,8 @@ def push_market_value():
     first_mean = weeks_predict_price[best_bdwd_price['second_week_price']['model_name']].iloc[0]
     last_mean = weeks_predict_price[best_bdwd_price['next_week_price']['model_name']].iloc[-1]
     # 保留两位小数
-    first_mean = round(first_mean, 2)
+    first_mean = float(round(first_mean, 2))
-    last_mean = round(last_mean, 2)
+    last_mean = float(round(last_mean, 2))
 
     predictdata = [
         {
@@ -140,8 +140,6 @@ def push_market_value():
         }
     ]
 
-    print(predictdata)
-
     # 推送数据到市场信息平台
     try:
         push_market_data(predictdata)
@@ -512,18 +510,18 @@ def predict_main():
 if __name__ == '__main__':
     # global end_time
     # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
-    for i_time in pd.date_range('2025-7-24', '2025-8-12', freq='B'):
+    # for i_time in pd.date_range('2025-3-3', '2025-5-30', freq='B'):
-        try:
+    #     try:
-            global_config['end_time'] = i_time.strftime('%Y-%m-%d')
+    #         global_config['end_time'] = i_time.strftime('%Y-%m-%d')
-            global_config['db_mysql'].connect()
+    #         global_config['db_mysql'].connect()
-            predict_main()
-        except Exception as e:
-            logger.info(f'预测失败：{e}')
-            continue
-
-
-    # global_config['end_time'] = '2025-08-05'
     #         predict_main()
+    #     except Exception as e:
+    #         logger.info(f'预测失败：{e}')
+    #         continue
+
+
+    # global_config['end_time'] = '2025-08-12'
+    predict_main()
 
 
     # push_market_value()