Added ACC calculated features and shorter version of ACC data.

config.yaml

@@ -482,13 +482,14 @@ EMPATICA_ACCELEROMETER:
       SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py
     CF:
       COMPUTE: True
-      FEATURES: ["meanLow", "areaLow", "totalAbsoluteAreaBand", "totalMagnitudeBand", "entropyBand", "skewnessBand", "kurtosisBand", 
+      FEATURES: ["fqHighestPeakFreqs", "fqHighestPeaks", "fqEnergyFeat", "fqEntropyFeat", "fqHistogramBins","fqAbsMean", "fqSkewness", "fqKurtosis", "fqInterquart", # Freq 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"]
+                  "pitchStdDevLow", "rollMotionAmountLow", "rollMotionRegularityLow", "manipulationLow", "rollPeaks", "pitchPeaks", "rollPitchCorrelation"] # Acc features
       SRC_SCRIPT: src/features/empatica_accelerometer/cf/main.py
 
 
@@ -506,11 +507,11 @@ EMPATICA_TEMPERATURE:
   CONTAINER: TEMP
   PROVIDERS:
     DBDP:
-      COMPUTE: False
+      COMPUTE: True
       FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"]
       SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py
     CF:
-      COMPUTE: False
+      COMPUTE: True
       FEATURES: ["autocorrelations", "countAboveMean", "countBelowMean", "maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", 
                   "longestStrikeBelowMean", "stdDev", "median", "meanChange", "numberOfZeroCrossings", "absEnergy", "linearTrendSlope", 
                   "ratioBeyondRSigma", "binnedEntropy", "numOfPeaksAutocorr", "numberOfZeroCrossingsAutocorr", "areaAutocorr",
@@ -523,11 +524,11 @@ EMPATICA_ELECTRODERMAL_ACTIVITY:
   CONTAINER: EDA
   PROVIDERS:
     DBDP:
-      COMPUTE: False
+      COMPUTE: True
       FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"]
       SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py
     CF:
-      COMPUTE: False
+      COMPUTE: True
       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',
@@ -559,6 +560,7 @@ EMPATICA_TAGS:
   PROVIDERS:  # None implemented yet
 
 
+
 ########################################################################################################################
 #                                                 PLOTS                                                                #
 ########################################################################################################################

src/features/empatica_accelerometer/cf/main.py

@@ -1,9 +1,10 @@
 import pandas as pd
 from scipy.stats import entropy
 
-from CalculatingFeatures.helper_functions import convert3DEmpaticaToArray, convertInputInto2d, gsrFeatureNames
+from CalculatingFeatures.helper_functions import convert3DEmpaticaToArray, convertInputInto2d, accelerometerFeatureNames, frequencyFeatureNames
 from CalculatingFeatures.calculate_features import calculateFeatures
 
+import sys
 
 def getSampleRate(data):
     try:
@@ -17,7 +18,7 @@ def extractAccFeaturesFromIntradayData(acc_intraday_data, features, time_segment
     acc_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
 
     if not acc_intraday_data.empty:   
-        sample_rate = getSampleRate(acc_intraday_data)   
+        sample_rate = getSampleRate(acc_intraday_data)
      
         acc_intraday_data = filter_data_by_segment(acc_intraday_data, time_segment)
 
@@ -27,20 +28,24 @@ def extractAccFeaturesFromIntradayData(acc_intraday_data, features, time_segment
 
             # apply methods from calculate features module 
             acc_intraday_features = \
-                acc_intraday_data.groupby('local_segment').apply(\
+                acc_intraday_data.groupby('local_segment').apply(lambda x: calculateFeatures( \
-                lambda x: calculateFeatures(convertInputInto2d(x['accelerometer'], x.shape[0]), fs=sample_rate, featureNames=features)) 
+                convertInputInto2d(x['double_values_0'], x.shape[0]), \
+                convertInputInto2d(x['double_values_1'], x.shape[0]), \
+                convertInputInto2d(x['double_values_2'], x.shape[0]), \
+                fs=int(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
+    base_intraday_features_names = accelerometerFeatureNames + frequencyFeatureNames
     # the subset of requested features this function can compute
     intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))
 
@@ -49,38 +54,4 @@ def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
                                                                     intraday_features_to_compute, time_segment,
                                                                     filter_data_by_segment)
 
-    return eda_intraday_features
+    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

@@ -1,7 +1,7 @@
 import pandas as pd
 from scipy.stats import entropy
 
-from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, gsrFeatureNames
+from CalculatingFeatures.helper_functions import convertInputInto2d, gsrFeatureNames
 from CalculatingFeatures.calculate_features import calculateFeatures
 
 
@@ -28,7 +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)) 
+                lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], x.shape[0]), fs=int(sample_rate), featureNames=features)) 
 
             eda_intraday_features.reset_index(inplace=True)
 

src/features/empatica_temperature/cf/main.py

@@ -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))
+                lambda x: calculateFeatures(convertInputInto2d(x['temperature'], x.shape[0]), fs=int(sample_rate), featureNames=features))
 
             temperature_intraday_features.reset_index(inplace=True)