原油月度预测报告添加八大维度

config_jingbo_yuedu.py

@@ -304,3 +304,4 @@ logger.addHandler(file_handler)
 logger.addHandler(console_handler)
 
 # logger.info('当前配置：'+settings)
+

lib/dataread.py

@@ -1186,6 +1186,10 @@ class Config:
     def push_data_value_list_data(
         self): return global_config['push_data_value_list_data']
 
+    @property
+    def bdwd_items(self): return global_config['bdwd_items']
+
+
     # 字段映射
     @property
     def offsite_col(self): return global_config['offsite_col']
@@ -2325,4 +2329,84 @@ def push_market_value():
 
 
 def get_bdwd_predict_data():
-    pass
+    # 获取认证头部信息
+    token = get_head_auth_report()
+
+    # 设置请求头部
+    headers = {"Authorization": token}
+
+    # 打印日志，显示正在上传预警数据
+    config.logger.info("获取八大维度数据...")
+
+    # 打印日志，显示上传的URL
+    config.logger.info(f"query_data_list_item_nos_url:{config.query_data_list_item_nos_url}")
+
+    # 打印日志，显示认证头部信息
+    config.logger.info(f"token:{token}")
+
+
+
+    # 打印日志，显示要查询的数据项
+    config.logger.info(f"query_data_list_item_nos_data:{query_data_list_item_nos_data}")
+
+    # 发送POST请求，上传预警数据
+    respose = requests.post(
+        url=config.upload_warning_url, headers=headers, json=query_data_list_item_nos_data, timeout=(3, 15))
+
+
+    # 如果上传成功，返回响应对象
+    if respose:
+        # 处理返回结果为df
+        df = pd.DataFrame(respose.json()['data'])
+
+        return df
+    # 如果上传失败，打印日志并返回None
+    else:
+        config.logger.info("预警上传失败")
+        return None
+    pass
+
+
+def get_bdwd_predict_data():
+    """
+    获取市场数据，拼接到df中
+    """
+    # 获取token
+    token = get_head_auth_report()
+    # 定义请求参数
+    query_data_list_item_nos_data = config.query_data_list_item_nos_data.copy()
+    query_data_list_item_nos_data['data']['dateStart'] = config.end_time.replace(
+        '-', '')
+    query_data_list_item_nos_data['data']['dateEnd'] = config.end_time.replace(
+        '-', '')
+    query_data_list_item_nos_data['data']['dataItemNoList'] = list(
+        config.bdwd_items.values())
+    # 发送请求
+    headers = {"Authorization": token}
+    config.logger.info('获取数据中...')
+    items_res = requests.post(url=config.query_data_list_item_nos_url, headers=headers,
+                              json=query_data_list_item_nos_data, timeout=(3, 35))
+    json_data = json.loads(items_res.text)
+    config.logger.info(f"获取到的数据:{json_data}")
+    df3 = pd.DataFrame(json_data['data'])
+    # 按照dataItemNo 分组 得到多个dataframe ，最后根据dataDate merge 成一个dataframe
+    df2 = pd.DataFrame()
+    for i in df3['dataItemNo'].unique():
+        df1 = df3[df3['dataItemNo'] == i]
+        df1 = df1[['dataDate', 'dataValue']]
+        df1 = df1.rename(columns={'dataValue': i})
+        if len(df2) == 0:
+            df2 = df1
+            continue
+        df2 = pd.merge(df2, df1, how='left')
+    df2 = df2.rename(columns={'dataDate': 'date'})
+    # 20240101 转换为 2024-01-01
+    df2['date'] = pd.to_datetime(df2['date'], format='%Y%m%d')
+    df2['date'] = df2['date'].dt.strftime('%Y-%m-%d')
+    # df = pd.merge(df, df2, how='left', on='date')
+    # 更改列名：
+    df2.rename(columns={'yyycbdwdbz':'本周','yyycbdwdcey':'次二月','yyycbdwdcr':'次日','yyycbdwdcsiy':'次四月','yyycbdwdcsy':'次三月','yyycbdwdcy':'次月','yyycbdwdcz':'次周','yyycbdwdgz':'隔周',}, inplace=True)
+    # 更改显示顺序
+    df2 = df2[['date','次日','本周','次周','隔周','次月','次二月','次三月','次四月']]
+    print(df2)
+    return df2

main_yuanyou_yuedu.py

@@ -76,6 +76,7 @@ global_config.update({
 
     # 数据库配置
     'sqlitedb': sqlitedb,
+    'is_bdwd': is_bdwd,
 })
 
 
@@ -463,3 +464,4 @@ if __name__ == '__main__':
     #         continue
 
     predict_main()
+    

models/nerulforcastmodels.py

@@ -2289,9 +2289,16 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in
     if config.is_bdwd:
         # 添加八大维度数据
         content.append(Graphs.draw_text('八大维度预测：'))
-        bdwd_df = pd.read_csv(os.path.join(
-            config.dataset, '八大维度数据.csv'), encoding='utf-8')
-        # 
+        df_bdwd = get_bdwd_predict_data()
+        # df转置
+        df_bdwd = df_bdwd.T
+        df_bdwd = df_bdwd.T
+        # df重置索引
+        df_bdwd = df_bdwd.reset_index()
+        # 添加预测值表格
+        data = df_bdwd.values.tolist()
+        col_width = 500/len(df.columns)
+        content.append(Graphs.draw_table(col_width, *data))
         pass
     content.append(Graphs.draw_little_title('二、上一预测周期偏差率分析：'))
     df = pd.read_csv(os.path.join(