Testing different EDA findPeaks parameters.

.gitignore

@@ -118,3 +118,6 @@ site/
 
 # Docker container and other files
 .devcontainer
+
+# Calculating features module
+calculatingfeatures/

config.yaml

@@ -3,7 +3,7 @@
 ########################################################################################################################
 
 # See https://www.rapids.science/latest/setup/configuration/#participant-files
-PIDS: [p01] #p01, p02, p03]
+PIDS: [p02] #p01, p02, p03]
 
 # See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files
 CREATE_PARTICIPANT_FILES:
@@ -536,7 +536,7 @@ EMPATICA_ELECTRODERMAL_ACTIVITY:
                   'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore', 
                   'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio',
                   'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'signalOverallChange', 'changeDuration', 'changeRate', 'significantIncrease', 
-                  'significantDecrease', maxPeakResponseSlopeBefore, maxPeakResponseSlopeAfter]
+                  'significantDecrease']
       WINDOWS:
         COMPUTE: True
         WINDOW_LENGTH: 60 # specify window length in seconds

src/features/empatica_electrodermal_activity/cr/main.py

@@ -30,11 +30,13 @@ 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: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], x.shape[0]), sampleRate=sample_rate, featureNames=features)) 
+                    lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], x.shape[0]), sampleRate=sample_rate, featureNames=features,
+                    threshold=.01, offset=1, riseTime=5, decayTime=15)) 
             else:
                 eda_intraday_features = \
                     eda_intraday_data.groupby('local_segment').apply(\
-                    lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], window_length*sample_rate), sampleRate=sample_rate, featureNames=features)) 
+                    lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], window_length*sample_rate), sampleRate=sample_rate, featureNames=features,
+                    threshold=.01, offset=1, riseTime=5, decayTime=15)) 
 
             eda_intraday_features.reset_index(inplace=True)
 

tests/scripts/missing_vals.py

@@ -2,22 +2,27 @@ import pandas as pd
 import seaborn as sns
 import matplotlib.pyplot as plt
 
+participant = "p02"
 
 # 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/p01/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_cr_windows.csv" 
+path = f"/rapids/data/interim/{participant}/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()
+is_NaN = df.isnull()
-row_has_NaN = is_NaN. any(axis=1)
+row_has_NaN = is_NaN.any(axis=1)
 rows_with_NaN = df[row_has_NaN]
-print(rows_with_NaN.size)
+
+print("All rows:", len(df.index))
+print("\nCount NaN vals:", rows_with_NaN.size)
+print("\nDf mean:")
+print(df.mean())
 
 sns.heatmap(df.isna(), cbar=False)
-plt.savefig('eda_windows_p02_window_60_thresh_default.png', bbox_inches='tight')
+plt.savefig(f'eda_{participant}_windows_NaN.png', bbox_inches='tight')
 
 

tests/scripts/zero_vals.py

@@ -3,9 +3,11 @@ import seaborn as sns
 import matplotlib.pyplot as plt
 from itertools import compress
 
+participant = "p02"
+
 # 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/p01/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_cr_windows.csv" 
+path = f"/rapids/data/interim/{participant}/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" 
@@ -17,9 +19,6 @@ columns_num_peaks_zero = df_num_peaks_zero.columns[df_num_peaks_zero.isna().any(
 df_num_peaks_non_zero = df[df["empatica_electrodermal_activity_cr_numPeaks"] != 0]
 df_num_peaks_non_zero = df_num_peaks_non_zero[columns_num_peaks_zero]
 
-print(df_num_peaks_non_zero[df_num_peaks_non_zero["empatica_electrodermal_activity_cr_maxPeakAmplitudeChangeBefore"] != 0])
-
-
 pd.set_option('display.max_columns', None)
 
 df_q = pd.DataFrame()
@@ -27,7 +26,7 @@ for col in df_num_peaks_non_zero:
     df_q[col] = pd.to_numeric(pd.cut(df_num_peaks_non_zero[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
 
 sns.heatmap(df_q)
-plt.savefig('eda_windows_p01_window_values_non_zero_peak_distribution_0thresh.png', bbox_inches='tight')
+plt.savefig(f'eda_{participant}_window_non_zero_peak_other_vals.png', bbox_inches='tight')
 plt.close()
 
 # Filter columns that do not contain 0