Exception handling in case of empty ibi. Changes of the method EDA uses in main.py. Other small corrections.

config.yaml

@@ -3,7 +3,7 @@
 ########################################################################################################################
 
 # See https://www.rapids.science/latest/setup/configuration/#participant-files
-PIDS: [p03] #p01, p02, p03]
+PIDS: [p02] #p01, p02, p03]
 
 # See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files
 CREATE_PARTICIPANT_FILES:
@@ -484,7 +484,7 @@ EMPATICA_ACCELEROMETER:
       FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
       SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py
     CR:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["totalMagnitudeBand", "absoluteMeanBand", "varianceBand"] # Acc features
       WINDOWS:
         COMPUTE: True
@@ -511,7 +511,7 @@ EMPATICA_TEMPERATURE:
       FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"]
       SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py
     CR:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["countAboveMean", "countBelowMean", "maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", "longestStrikeBelowMean", 
                   "stdDev", "median", "meanChange", "sumSquared", "squareSumOfComponent", "sumOfSquareComponents"]
       WINDOWS:
@@ -529,7 +529,7 @@ EMPATICA_ELECTRODERMAL_ACTIVITY:
       FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"]
       SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py
     CR:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic', 
                   'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore', 
                   'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio',

src/data/streams/empatica_zip/container.py

@@ -129,19 +129,18 @@ def patch_ibi_with_bvp(ibi_data, bvp_data):
     ibi_data_file = BytesIO(ibi_data).getvalue().decode('utf-8')
     ibi_data_file = StringIO(ibi_data_file)
 
-
-
     # Begin with the cr-features part
     try:
         ibi_data, ibi_start_timestamp = empatica2d_to_array(ibi_data_file)
-    except IndexError:
+    except IndexError as e:
         # Checks whether IBI.csv is empty
         df_test = pd.read_csv(ibi_data_file, names=['timings', 'inter_beat_interval'], header=None)
-        print(df_test)
         if df_test.empty:
             df_test['timestamp'] = df_test['timings']
             df_test = df_test.set_index('timestamp')
             return df_test
+        else:
+            raise IndexError("Something went wrong with indices. Error that was previously caught:\n", repr(e))
 
     bvp_data_file = BytesIO(bvp_data).getvalue().decode('utf-8')
     bvp_data_file = StringIO(bvp_data_file)
@@ -154,7 +153,7 @@ def patch_ibi_with_bvp(ibi_data, bvp_data):
                         winsorize_value=25, hampel_fiter=False, median_filter=False, 
                         mod_z_score_filter=True, sampling=64, feature_names=['meanHr'])
     
-    ibi_timings, ibi_rr = get_patched_ibi_with_bvp(ibi_data[0], ibi_data[1], bvp_timings, bvp_rr, min_length=None)
+    ibi_timings, ibi_rr = get_patched_ibi_with_bvp(ibi_data[0], ibi_data[1], bvp_timings, bvp_rr)
 
     df = \
         pd.DataFrame(np.array([ibi_timings, ibi_rr]).transpose(), columns=['timestamp', 'inter_beat_interval'])

src/features/empatica_blood_volume_pulse/cr/main.py

@@ -1,7 +1,7 @@
 import pandas as pd
 from scipy.stats import entropy
 
-from cr_features.helper_functions import convert_to2d, hrv_features, hrv_freq_features
+from cr_features.helper_functions import convert_to2d, hrv_features
 from cr_features.hrv import extract_hrv_features_2d_wrapper
 from cr_features_helper_methods import extract_second_order_features
 
@@ -55,7 +55,7 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
         requested_window_length = None
 
     # name of the features this function can compute
-    base_intraday_features_names = hrv_features + hrv_freq_features
+    base_intraday_features_names = hrv_features
     # the subset of requested features this function can compute
     intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))
 

src/features/empatica_electrodermal_activity/cr/main.py

@@ -4,6 +4,7 @@ from scipy.stats import entropy
 
 from cr_features.helper_functions import convert_to2d, gsr_features
 from cr_features.calculate_features import calculate_features
+from cr_features.gsr import extractGsrFeatures2D
 from cr_features_helper_methods import extract_second_order_features
 
 import sys
@@ -29,11 +30,11 @@ def extract_eda_features_from_intraday_data(eda_intraday_data, features, window_
             if window_length is None:
                 eda_intraday_features = \
                     eda_intraday_data.groupby('local_segment').apply(\
-                    lambda x: calculate_features(convert_to2d(x['electrodermal_activity'], x.shape[0]), fs=sample_rate, feature_names=features)) 
+                    lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], x.shape[0]), sampleRate=sample_rate, threshold=0, featureNames=features)) 
             else:
                 eda_intraday_features = \
                     eda_intraday_data.groupby('local_segment').apply(\
-                    lambda x: calculate_features(convert_to2d(x['electrodermal_activity'], window_length*sample_rate), fs=sample_rate, feature_names=features)) 
+                    lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], window_length*sample_rate), sampleRate=sample_rate, threshold=0, featureNames=features)) 
 
             eda_intraday_features.reset_index(inplace=True)
 

src/features/empatica_inter_beat_interval/cr/main.py

@@ -1,7 +1,7 @@
 import pandas as pd
 import numpy as np
 
-from cr_features.helper_functions import convert_ibi_to2d_time, hrv_features, hrv_freq_features
+from cr_features.helper_functions import convert_ibi_to2d_time, hrv_features
 from cr_features.hrv import extract_hrv_features_2d_wrapper, get_HRV_features
 from cr_features_helper_methods import extract_second_order_features
 
@@ -61,7 +61,7 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
         requested_window_length = None
 
     # name of the features this function can compute
-    base_intraday_features_names = hrv_features + hrv_freq_features
+    base_intraday_features_names = hrv_features
     # the subset of requested features this function can compute
     intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))
 

tests/scripts/missing_values_heatmap.py

@@ -5,14 +5,19 @@ import matplotlib.pyplot as plt
 
 # path = "/rapids/data/processed/features/all_participants/all_sensor_features.csv" # all features all participants
 # path = "/rapids/data/interim/p03/empatica_accelerometer_features/empatica_accelerometer_python_cr_windows.csv" 
-path = "/rapids/data/interim/p02/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_cr_windows.csv" 
+path = "/rapids/data/interim/p03/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_cr_windows.csv" 
 # path = "/rapids/data/interim/p02/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_cr_windows.csv" 
 # path = "/rapids/data/interim/p02/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_cr_windows.csv" 
 # path = "/rapids/data/interim/p02/empatica_temperature_features/empatica_temperature_python_cr_windows.csv" 
 
 df = pd.read_csv(path)
 print(df)
+is_NaN = df. isnull()
+row_has_NaN = is_NaN. any(axis=1)
+rows_with_NaN = df[row_has_NaN]
+print(rows_with_NaN.size)
+
 sns.heatmap(df.isna(), cbar=False)
-plt.savefig('eda_windows_p02_window_60_more_peaks.png', bbox_inches='tight')
+plt.savefig('eda_windows_p03_window_60_thresh_default.png', bbox_inches='tight')