updated loggers

app.py

@@ -24,20 +24,19 @@ start_date, end_date, start_timestamp, end_timestamp = KairosStartEndDate().star
 def get_tag_details():
     try:
         df_raw_tags, df_predicted_tags = get_raw_predicted_tags()
+        logger.info(f'raw tags dataframe shape - {df_raw_tags.shape}')
+        logger.info(f'predicted tags dataframe shape - {df_predicted_tags.shape}')
 
-        df = get_tags_data(mppt_tags=df_raw_tags,
-                           start_timestamp=start_timestamp,
-                           end_timestamp=end_timestamp)
-
+        df = get_tags_data(tags=df_raw_tags, start_timestamp=start_timestamp, end_timestamp=end_timestamp)
         logger.info(f'Shape of final df - {df.shape}')
 
         mppt_data = GetData()
         df_mppt = mppt_data.current_voltage_mppt_data(df=df)
 
         data_preprocessing = DataPreprocessing()
-
         df_mppt = data_preprocessing.remove_outliers(df=df_mppt, param_list=['tilt_irradiance', 'voltage_mppt',
                                                                              'current_mppt'])
+
         df_mppt, df_train, df_test = data_preprocessing.train_test_split(df=df_mppt)
         get_training_inference = TrainingInference(df=df_mppt, df_train=df_train, df_test=df_test)
         ai_modelling(df_train=df_train, get_training_inference=get_training_inference,

scripts/core/engine/inv_and_mppt_level.py

@@ -20,6 +20,8 @@ class TrainingInference:
             x_train = df_train_mppt[['datetime', 'inv_id', 'mppt_id', 'hour', 'tilt_irradiance', 'voltage_mppt']]
             y_train = df_train_mppt[['current_mppt']]
 
+            logger.debug(f'shape of x_train for {inv_id} & {mppt_id} - {x_train.shape}')
+            logger.debug(f'shape of y_train for {inv_id} & {mppt_id} - {y_train.shape}')
             x_train_std, scaler_x = data_preprocessing.get_standardized_data(df=x_train,
                                                                              param_list=['datetime', 'inv_id',
                                                                                          'mppt_id'])
@@ -42,6 +44,9 @@ class TrainingInference:
             df_test_mppt.reset_index(drop=True, inplace=True)
             x_test = df_test_mppt[['datetime', 'inv_id', 'mppt_id', 'hour', 'tilt_irradiance', 'voltage_mppt']]
             y_test = df_test_mppt[['current_mppt']]
+
+            logger.debug(f'shape of x_test for {inv_id} & {mppt_id} - {x_test.shape}')
+            logger.debug(f'shape of y_test for {inv_id} & {mppt_id} - {y_test.shape}')
             data_preprocessing = DataPreprocessing()
             x_test_std = data_preprocessing.get_transform_std_data(df=x_test,
                                                                    param_list=['datetime', 'inv_id', 'mppt_id'],

scripts/core/engine/raw_predicted_tags.py

@@ -11,15 +11,19 @@ def get_raw_predicted_tags():
     try:
         mongo_conn = MongoConnect(uri=Mongo.mongo_uri, database=MongoConstants.db,
                                   collection=MongoConstants.collection)
+        logger.debug(f'mongo conn - {mongo_conn}')
         raw_tags_dict = mongo_conn.find_one({"$and": [{"id": "dalmia_string_level_tags"}, {"city": "ariyalur"},
                                                       {"tags_property": "raw"}]})
         req_tags = raw_tags_dict['input_data']
+        logger.info(f'raw tags dict - {req_tags}')
         df_raw_tags = pd.DataFrame.from_dict(req_tags, orient='index')
 
         predicted_tags_dict = mongo_conn.find_one({"$and": [{"id": "dalmia_string_level_tags"}, {"city": "ariyalur"},
                                                             {"tags_property": "predicted"}]})
         predicted_tags = predicted_tags_dict['input_data']
+        logger.info(f'predicted tags dict - {predicted_tags}')
         df_predicted_tags = pd.DataFrame.from_dict(predicted_tags, orient='index')
+
         df_raw_tags.reset_index(inplace=True)
         df_raw_tags.rename(columns={'index': 'tag_name'}, inplace=True)
         df_predicted_tags.reset_index(inplace=True)

scripts/core/engine/tags_data.py

@@ -6,12 +6,12 @@ from scripts.utils.reading_tags import GetTags
 base_path = 'data_folder'
 
 
-def get_tags_data(mppt_tags, start_timestamp, end_timestamp):
+def get_tags_data(tags, start_timestamp, end_timestamp):
     try:
         get_tags = GetTags(base_path=base_path)
         df_merged = pd.DataFrame()
-        for inv_id in list(mppt_tags['inv_id'].unique()):
-            df_tags_id = get_tags.get_tags_id(df=mppt_tags, inv_id=inv_id)
+        for inv_id in list(tags['inv_id'].unique()):
+            df_tags_id = get_tags.get_tags_id(df=tags, inv_id=inv_id)
             tags_dict = df_tags_id[['tag_id', 'parameter_name']].set_index('tag_id').T.to_dict(orient="records")[0]
             tags_dict['site_107$dept_140$line_371$equipment_4115$tag_15828'] = 'tilt_irradiance'
             df_data = KairosQuery(start_timestamp=start_timestamp,