Cleaning existing CF mains' and preparing src script for ACC.

config.yaml

@@ -477,9 +477,20 @@ EMPATICA_ACCELEROMETER:
   CONTAINER: ACC
   PROVIDERS:
     DBDP:
-      COMPUTE: False
+      COMPUTE: True
       FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
       SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py
+    CF:
+      COMPUTE: True
+      FEATURES: ["meanLow", "areaLow", "totalAbsoluteAreaBand", "totalMagnitudeBand", "entropyBand", "skewnessBand", "kurtosisBand", 
+                  "postureDistanceLow", "absoluteMeanBand", "absoluteAreaBand", "quartilesBand", "interQuartileRangeBand", "varianceBand", 
+                  "coefficientOfVariationBand", "amplitudeBand", "totalEnergyBand", "dominantFrequencyEnergyBand", "meanCrossingRateBand", 
+                  "correlationBand", "quartilesMagnitudesBand", "interQuartileRangeMagnitudesBand", "areaUnderAccelerationMagnitude", 
+                  "peaksDataLow", "sumPerComponentBand", "velocityBand", "meanKineticEnergyBand", "totalKineticEnergyBand", "squareSumOfComponent", 
+                  "sumOfSquareComponents", "averageVectorLength", "averageVectorLengthPower", "rollAvgLow", "pitchAvgLow", "rollStdDevLow", 
+                  "pitchStdDevLow", "rollMotionAmountLow", "rollMotionRegularityLow", "manipulationLow", "rollPeaks", "pitchPeaks", "rollPitchCorrelation"]
+      SRC_SCRIPT: src/features/empatica_accelerometer/cf/main.py
+
 
 # See https://www.rapids.science/latest/features/empatica-heartrate/
 EMPATICA_HEARTRATE:
@@ -495,11 +506,11 @@ EMPATICA_TEMPERATURE:
   CONTAINER: TEMP
   PROVIDERS:
     DBDP:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"]
       SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py
     CF:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["autocorrelations", "countAboveMean", "countBelowMean", "maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", 
                   "longestStrikeBelowMean", "stdDev", "median", "meanChange", "numberOfZeroCrossings", "absEnergy", "linearTrendSlope", 
                   "ratioBeyondRSigma", "binnedEntropy", "numOfPeaksAutocorr", "numberOfZeroCrossingsAutocorr", "areaAutocorr",
@@ -512,16 +523,16 @@ EMPATICA_ELECTRODERMAL_ACTIVITY:
   CONTAINER: EDA
   PROVIDERS:
     DBDP:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"]
       SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py
     CF:
-      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',
-                   'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'maxPeakResponseSlopeBefore', 'maxPeakResponseSlopeAfter', 'signalOverallChange',
+                  'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'maxPeakResponseSlopeBefore', 'maxPeakResponseSlopeAfter', 
-                   'changeDuration', 'changeRate', 'significantIncrease', 'significantDecrease']
+                  '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_accelerometer/cf/main.py

@@ -0,0 +1,86 @@
+import pandas as pd
+from scipy.stats import entropy
+
+from CalculatingFeatures.helper_functions import convert3DEmpaticaToArray, convertInputInto2d, gsrFeatureNames
+from CalculatingFeatures.calculate_features import calculateFeatures
+
+
+def getSampleRate(data):
+    try:
+        timestamps_diff = data['timestamp'].iloc[1] - data['timestamp'].iloc[0]
+    except:
+        raise Exception("Error occured while trying to get the sample rate from the first two sequential timestamps.")
+    
+    return 1000/timestamps_diff
+
+def extractAccFeaturesFromIntradayData(acc_intraday_data, features, time_segment, filter_data_by_segment):
+    acc_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
+
+    if not acc_intraday_data.empty:   
+        sample_rate = getSampleRate(acc_intraday_data)   
+     
+        acc_intraday_data = filter_data_by_segment(acc_intraday_data, time_segment)
+
+        if not acc_intraday_data.empty:
+
+            acc_intraday_features = pd.DataFrame()
+
+            # apply methods from calculate features module 
+            acc_intraday_features = \
+                acc_intraday_data.groupby('local_segment').apply(\
+                lambda x: calculateFeatures(convertInputInto2d(x['accelerometer'], x.shape[0]), fs=sample_rate, featureNames=features)) 
+
+            acc_intraday_features.reset_index(inplace=True)
+
+    return acc_intraday_features
+
+
+def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
+    eda_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
+
+    requested_intraday_features = provider["FEATURES"]
+    # name of the features this function can compute
+    base_intraday_features_names = gsrFeatureNames
+    # the subset of requested features this function can compute
+    intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))
+
+    # extract features from intraday data
+    eda_intraday_features = extractAccFeaturesFromIntradayData(eda_intraday_data,
+                                                                    intraday_features_to_compute, time_segment,
+                                                                    filter_data_by_segment)
+
+    return eda_intraday_features
+
+    def dbdp_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
+
+        acc_data = pd.read_csv(sensor_data_files["sensor_data"])
+        requested_features = provider["FEATURES"]
+        # name of the features this function can compute
+        base_features_names = ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
+        # the subset of requested features this function can compute
+        features_to_compute = list(set(requested_features) & set(base_features_names))
+
+        acc_features = pd.DataFrame(columns=["local_segment"] + features_to_compute)
+        if not acc_data.empty:
+            acc_data = filter_data_by_segment(acc_data, time_segment)
+            
+            if not acc_data.empty:
+                acc_features = pd.DataFrame()
+                # get magnitude related features: magnitude = sqrt(x^2+y^2+z^2)
+                magnitude = acc_data.apply(lambda row: np.sqrt(row["double_values_0"] ** 2 + row["double_values_1"] ** 2 + row["double_values_2"] ** 2), axis=1)
+                acc_data = acc_data.assign(magnitude = magnitude.values)
+                
+                if "maxmagnitude" in features_to_compute:
+                    acc_features["maxmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].max()
+                if "minmagnitude" in features_to_compute:
+                    acc_features["minmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].min()
+                if "avgmagnitude" in features_to_compute:
+                    acc_features["avgmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].mean()
+                if "medianmagnitude" in features_to_compute:
+                    acc_features["medianmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].median()
+                if "stdmagnitude" in features_to_compute:
+                    acc_features["stdmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].std()
+                
+                acc_features = acc_features.reset_index()
+
+        return acc_features

src/features/empatica_electrodermal_activity/cf/main.py

@@ -4,8 +4,6 @@ from scipy.stats import entropy
 from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, gsrFeatureNames
 from CalculatingFeatures.calculate_features import calculateFeatures
 
-pd.set_option('display.max_columns', None)
-
 
 def getSampleRate(data):
     try:
@@ -30,7 +28,7 @@ def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, time_segment
             # apply methods from calculate features module 
             eda_intraday_features = \
                 eda_intraday_data.groupby('local_segment').apply(\
-                lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], x.shape[0]), fs=sample_rate, featureNames=features)) # To-Do: Hardcoded f/s ... 
+                lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], x.shape[0]), fs=sample_rate, featureNames=features)) 
 
             eda_intraday_features.reset_index(inplace=True)
 

src/features/empatica_temperature/cf/main.py

@@ -1,7 +1,7 @@
 import pandas as pd
 from scipy.stats import entropy
 
-from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, genericFeatureNames
+from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, accelerometerFeatureNames
 from CalculatingFeatures.calculate_features import calculateFeatures
 
 
@@ -13,7 +13,7 @@ def getSampleRate(data):
     
     return 1000/timestamps_diff
 
-def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, time_segment, filter_data_by_segment):
+def extractAccFeaturesFromIntradayData(temperature_intraday_data, features, time_segment, filter_data_by_segment):
     temperature_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
 
     if not temperature_intraday_data.empty:
@@ -28,7 +28,7 @@ def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, tim
             # apply methods from calculate features module  
             temperature_intraday_features = \
                 temperature_intraday_data.groupby('local_segment').apply(\
-                lambda x: calculateFeatures(convertInputInto2d(x['temperature'], x.shape[0]), fs=sample_rate, featureNames=features)) # To-Do: Hardcoded f/s ... 
+                lambda x: calculateFeatures(convertInputInto2d(x['temperature'], x.shape[0]), fs=sample_rate, featureNames=features))
 
             temperature_intraday_features.reset_index(inplace=True)