Add new daily segment and filter by segment in the cleaning script.

data/external/timesegments_daily.csv

@@ -1,2 +1,3 @@
 label,start_time,length,repeats_on,repeats_value
 daily,04:00:00,23H 59M 59S,every_day,0
+working_day,04:00:00,18H 00M 00S,every_day,0

src/features/all_cleaning_overall/straw/main.py

@@ -11,8 +11,10 @@ sys.path.append('/rapids/')
 from src.features import empatica_data_yield as edy
 
 def straw_cleaning(sensor_data_files, provider, target):
-    
+
     features = pd.read_csv(sensor_data_files["sensor_data"][0])
+
+    features = features[features['local_segment_label'] == 'working_day'] # Filtriranje ustreznih časovnih segmentov
     
     esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns
 
@@ -160,11 +162,11 @@ def straw_cleaning(sensor_data_files, provider, target):
         # plt.savefig(f'correlation_matrix.png', bbox_inches='tight')
         # plt.close()
 
-        s = corr_matrix.unstack()
-        so = s.sort_values(ascending=False)
+        # s = corr_matrix.unstack()
+        # so = s.sort_values(ascending=False)
 
-        pd.set_option('display.max_rows', None)
-        sorted_upper = upper.unstack().sort_values(ascending=False)
+        # pd.set_option('display.max_rows', None)
+        # sorted_upper = upper.unstack().sort_values(ascending=False)
         # print(sorted_upper[sorted_upper > drop_corr_features["CORR_THRESHOLD"]])
 
         features.drop(to_drop, axis=1, inplace=True)
@@ -196,7 +198,7 @@ def impute(df, method='zero'):
         'knn': k_nearest(df) 
     }[method]
 
-def graph_bf_af(features, phase_name, plt_flag=False):
+def graph_bf_af(features, phase_name, plt_flag=True):
     if plt_flag:
         sns.set(rc={"figure.figsize":(16, 8)})
         sns.heatmap(features.isna(), cbar=False) #features.select_dtypes(include=np.number)