Added cf provider for EDA feature processing.

.gitignore

@@ -93,6 +93,7 @@ packrat/*
 
 # exclude data from source control by default
 data/external/*
+!/data/external/empatica/empatica1/E4 Data.zip
 !/data/external/.gitkeep
 !/data/external/stachl_application_genre_catalogue.csv
 !/data/external/timesegments*.csv

config.yaml

@@ -509,7 +509,14 @@ EMPATICA_ELECTRODERMAL_ACTIVITY:
       SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py
     CF:
       COMPUTE: True
-      FEATURES: ['mean', 'std', 'q25', 'q75', 'qd'] # To-Do add remaining features from CF helper file. 
+      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',
+                   'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'maxPeakResponseSlopeBefore',
+                   'maxPeakResponseSlopeAfter', 'signalOverallChange', 'changeDuration', 'changeRate',
+                   'significantIncrease', 'significantDecrease']
       SRC_SCRIPT: src/features/empatica_electrodermal_activity/cf/main.py
 
 # See https://www.rapids.science/latest/features/empatica-blood-volume-pulse/

src/features/empatica_electrodermal_activity/cf/main.py

@@ -1,57 +1,29 @@
 import pandas as pd
 from scipy.stats import entropy
 
+import sys
+sys.path.insert(1, '/workspaces/rapids/calculatingfeatures')
+from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, gsrFeatureNames
+from CalculatingFeatures.calculate_features import calculateFeatures
 
-def statsFeatures(eda_data, features, eda_features):
+pd.set_option('display.max_columns', None)
-    col_name = "electrodermal_activity"
-    if "sumeda" in features:
-        eda_features["sumeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
-            col_name].sum()
-    if "maxeda" in features:
-        eda_features["maxeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
-            col_name].max()
-    if "mineda" in features:
-        eda_features["mineda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
-            col_name].min()
-    if "avgeda" in features:
-        eda_features["avgeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
-            col_name].mean()
-    if "medianeda" in features:
-        eda_features["medianeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
-            col_name].median()
-    if "modeeda" in features:
-        eda_features["modeeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
-            col_name].agg(lambda x: pd.Series.mode(x)[0])
-    if "stdeda" in features:
-        eda_features["stdeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
-            col_name].std()
-    if "diffmaxmodeeda" in features:
-        eda_features["diffmaxmodeeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
-                                                  col_name].max() - \
-                                              eda_data[["local_segment", col_name]].groupby(["local_segment"])[
-                                                  col_name].agg(lambda x: pd.Series.mode(x)[0])
-    if "diffminmodeeda" in features:
-        eda_features["diffminmodeeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
-                                                  col_name].agg(lambda x: pd.Series.mode(x)[0]) - \
-                                              eda_data[["local_segment", col_name]].groupby(["local_segment"])[
-                                                  col_name].min()
-    if "entropyeda" in features:
-        eda_features["entropyeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
-            col_name].agg(entropy)
 
-    return eda_features
 
 
 def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, time_segment, filter_data_by_segment):
     eda_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
-    if not eda_intraday_data.empty:
+
+    if not eda_intraday_data.empty:        
         eda_intraday_data = filter_data_by_segment(eda_intraday_data, time_segment)
 
         if not eda_intraday_data.empty:
+
             eda_intraday_features = pd.DataFrame()
 
-            # get stats of eda
+            # apply a method from calculate features module 
-            eda_intraday_features = statsFeatures(eda_intraday_data, features, eda_intraday_features)
+            eda_intraday_features = \
+                eda_intraday_data.groupby('local_segment').apply(\
+                lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], x.shape[0]), fs=4, featureNames=features))
 
             eda_intraday_features.reset_index(inplace=True)
 
@@ -63,8 +35,7 @@ def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
 
     requested_intraday_features = provider["FEATURES"]
     # name of the features this function can compute
-    base_intraday_features_names = ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda",
+    base_intraday_features_names = gsrFeatureNames
-                                    "diffminmodeeda", "entropyeda"]
     # the subset of requested features this function can compute
     intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))