Cr-feat window length for all empaticas sensors.

config.yaml

@@ -477,10 +477,10 @@ EMPATICA_ACCELEROMETER:
   CONTAINER: ACC
   PROVIDERS:
     DBDP:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
       SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py
-    CF:
+    CR:
       COMPUTE: True
       FEATURES: ["fqHighestPeakFreqs", "fqHighestPeaks", "fqEnergyFeat", "fqEntropyFeat", "fqHistogramBins","fqAbsMean", "fqSkewness", "fqKurtosis", "fqInterquart", # Freq features
                   "meanLow", "areaLow", "totalAbsoluteAreaBand", "totalMagnitudeBand", "entropyBand", "skewnessBand", "kurtosisBand", 
@@ -490,7 +490,10 @@ EMPATICA_ACCELEROMETER:
                   "peaksDataLow", "sumPerComponentBand", "velocityBand", "meanKineticEnergyBand", "totalKineticEnergyBand", "squareSumOfComponent", 
                   "sumOfSquareComponents", "averageVectorLength", "averageVectorLengthPower", "rollAvgLow", "pitchAvgLow", "rollStdDevLow", 
                   "pitchStdDevLow", "rollMotionAmountLow", "rollMotionRegularityLow", "manipulationLow", "rollPeaks", "pitchPeaks", "rollPitchCorrelation"] # Acc features
-      SRC_SCRIPT: src/features/empatica_accelerometer/cf/main.py
+      WINDOWS:
+        COMPUTE: False
+        WINDOW_LENGTH: 10 # specify window length in seconds
+      SRC_SCRIPT: src/features/empatica_accelerometer/cr/main.py
 
 
 # See https://www.rapids.science/latest/features/empatica-heartrate/
@@ -507,48 +510,57 @@ 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:
+    CR:
       COMPUTE: True
       FEATURES: ["autocorrelations", "countAboveMean", "countBelowMean", "maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", 
                   "longestStrikeBelowMean", "stdDev", "median", "meanChange", "numberOfZeroCrossings", "absEnergy", "linearTrendSlope", 
                   "ratioBeyondRSigma", "binnedEntropy", "numOfPeaksAutocorr", "numberOfZeroCrossingsAutocorr", "areaAutocorr",
                   "calcMeanCrossingRateAutocorr", "countAboveMeanAutocorr", "sumPer", "sumSquared", "squareSumOfComponent", 
                   "sumOfSquareComponents"]
-      SRC_SCRIPT: src/features/empatica_temperature/cf/main.py
+      WINDOWS:
+        COMPUTE: False
+        WINDOW_LENGTH: 90 # specify window length in seconds
+      SRC_SCRIPT: src/features/empatica_temperature/cr/main.py
 
 # See https://www.rapids.science/latest/features/empatica-electrodermal-activity/
 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:
+    CR:
       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',
                   'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'maxPeakResponseSlopeBefore', 'maxPeakResponseSlopeAfter', 
                   'signalOverallChange', 'changeDuration', 'changeRate', 'significantIncrease', 'significantDecrease']
-      SRC_SCRIPT: src/features/empatica_electrodermal_activity/cf/main.py
+      WINDOWS:
+        COMPUTE: False
+        WINDOW_LENGTH: 80 # specify window length in seconds
+      SRC_SCRIPT: src/features/empatica_electrodermal_activity/cr/main.py
 
 # See https://www.rapids.science/latest/features/empatica-blood-volume-pulse/
 EMPATICA_BLOOD_VOLUME_PULSE:
   CONTAINER: BVP
   PROVIDERS:
     DBDP:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["fqHighestPeakFreqs", "fqHighestPeaks", "fqEnergyFeat", "fqEntropyFeat", "fqHistogramBins","fqAbsMean", "fqSkewness", "fqKurtosis", "fqInterquart", # Freq features
                   "maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"] # HRV features
       SRC_SCRIPT: src/features/empatica_blood_volume_pulse/dbdp/main.py
-    CF:
+    CR:
       COMPUTE: True
       FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR']
-      SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cf/main.py
+      WINDOWS:
+        COMPUTE: False
+        WINDOW_LENGTH: 4 # specify window length in seconds
+      SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cr/main.py
 
 # See https://www.rapids.science/latest/features/empatica-inter-beat-interval/
 EMPATICA_INTER_BEAT_INTERVAL:

src/features/empatica_accelerometer/cr/main.py

@@ -14,7 +14,7 @@ def getSampleRate(data):
     
     return 1000/timestamps_diff
 
-def extractAccFeaturesFromIntradayData(acc_intraday_data, features, time_segment, filter_data_by_segment):
+def extractAccFeaturesFromIntradayData(acc_intraday_data, features, window_length, time_segment, filter_data_by_segment):
     acc_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
 
     if not acc_intraday_data.empty:   
@@ -27,12 +27,20 @@ def extractAccFeaturesFromIntradayData(acc_intraday_data, features, time_segment
             acc_intraday_features = pd.DataFrame()
 
             # apply methods from calculate features module 
-            acc_intraday_features = \
+            if window_length is None:
-                acc_intraday_data.groupby('local_segment').apply(lambda x: calculateFeatures( \
+                acc_intraday_features = \
-                convertInputInto2d(x['double_values_0'], x.shape[0]), \
+                    acc_intraday_data.groupby('local_segment').apply(lambda x: calculateFeatures( \
-                convertInputInto2d(x['double_values_1'], x.shape[0]), \
+                    convertInputInto2d(x['double_values_0'], x.shape[0]), \
-                convertInputInto2d(x['double_values_2'], x.shape[0]), \
+                    convertInputInto2d(x['double_values_1'], x.shape[0]), \
-                fs=int(sample_rate), featureNames=features)) 
+                    convertInputInto2d(x['double_values_2'], x.shape[0]), \
+                    fs=int(sample_rate), featureNames=features)) 
+            else:
+                acc_intraday_features = \
+                    acc_intraday_data.groupby('local_segment').apply(lambda x: calculateFeatures( \
+                    convertInputInto2d(x['double_values_0'], window_length*int(sample_rate)), \
+                    convertInputInto2d(x['double_values_1'], window_length*int(sample_rate)), \
+                    convertInputInto2d(x['double_values_2'], window_length*int(sample_rate)), \
+                    fs=int(sample_rate), featureNames=features)) 
 
             acc_intraday_features.reset_index(inplace=True)
 
@@ -40,18 +48,23 @@ def extractAccFeaturesFromIntradayData(acc_intraday_data, features, time_segment
 
 
 
-def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
+def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
     acc_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
 
     requested_intraday_features = provider["FEATURES"]
+    
+    if provider["WINDOWS"]["COMPUTE"]:
+        requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"]
+    else:
+        requested_window_length = None
+
     # name of the features this function can compute
     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))
 
     # extract features from intraday data
-    acc_intraday_features = extractAccFeaturesFromIntradayData(acc_intraday_data,
+    acc_intraday_features = extractAccFeaturesFromIntradayData(acc_intraday_data, intraday_features_to_compute, 
-                                                                    intraday_features_to_compute, time_segment,
+                                                                requested_window_length, time_segment, filter_data_by_segment)
-                                                                    filter_data_by_segment)
 
     return acc_intraday_features

src/features/empatica_blood_volume_pulse/cr/main.py

@@ -15,7 +15,7 @@ def getSampleRate(data):
 
     return 1000/timestamps_diff
 
-def extractBVPFeaturesFromIntradayData(bvp_intraday_data, features, time_segment, filter_data_by_segment):
+def extractBVPFeaturesFromIntradayData(bvp_intraday_data, features, window_length, time_segment, filter_data_by_segment):
     bvp_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
 
     if not bvp_intraday_data.empty:   
@@ -28,27 +28,37 @@ def extractBVPFeaturesFromIntradayData(bvp_intraday_data, features, time_segment
             bvp_intraday_features = pd.DataFrame()
 
             # apply methods from calculate features module
-            bvp_intraday_features = \
+            if window_length is None:
-                bvp_intraday_data.groupby('local_segment').apply(\
+                bvp_intraday_features = \
-                lambda x: calculateFeatures(convertInputInto2d(x['blood_volume_pulse'], x.shape[0]), fs=int(sample_rate), featureNames=features)) 
+                    bvp_intraday_data.groupby('local_segment').apply(\
+                    lambda x: calculateFeatures(convertInputInto2d(x['blood_volume_pulse'], x.shape[0]), fs=int(sample_rate), featureNames=features)) 
+            else:
+                bvp_intraday_features = \
+                    bvp_intraday_data.groupby('local_segment').apply(\
+                    lambda x: calculateFeatures(convertInputInto2d(x['blood_volume_pulse'], window_length*int(sample_rate)), fs=int(sample_rate), featureNames=features)) 
 
             bvp_intraday_features.reset_index(inplace=True)
 
     return bvp_intraday_features
 
 
-def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
+def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
     bvp_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
 
     requested_intraday_features = provider["FEATURES"]
+    
+    if provider["WINDOWS"]["COMPUTE"]:
+        requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"]
+    else:
+        requested_window_length = None
+
     # name of the features this function can compute
     base_intraday_features_names = hrvFeatureNames + frequencyFeatureNames
     # 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
-    bvp_intraday_features = extractBVPFeaturesFromIntradayData(bvp_intraday_data,
+    bvp_intraday_features = extractBVPFeaturesFromIntradayData(bvp_intraday_data, intraday_features_to_compute, 
-                                                                    intraday_features_to_compute, time_segment,
+                                                                requested_window_length, time_segment, filter_data_by_segment)
-                                                                    filter_data_by_segment)
 
     return bvp_intraday_features

src/features/empatica_electrodermal_activity/cr/main.py

@@ -13,7 +13,7 @@ def getSampleRate(data):
 
     return 1000/timestamps_diff
 
-def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, time_segment, filter_data_by_segment):
+def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, window_length, time_segment, filter_data_by_segment):
     eda_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
 
     if not eda_intraday_data.empty:   
@@ -26,27 +26,38 @@ def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, time_segment
             eda_intraday_features = pd.DataFrame()
 
             # apply methods from calculate features module 
-            eda_intraday_features = \
+            if window_length is None:
-                eda_intraday_data.groupby('local_segment').apply(\
+                eda_intraday_features = \
-                lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], x.shape[0]), fs=int(sample_rate), featureNames=features)) 
+                    eda_intraday_data.groupby('local_segment').apply(\
+                    lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], x.shape[0]), fs=int(sample_rate), featureNames=features)) 
+            else:
+                eda_intraday_features = \
+                    eda_intraday_data.groupby('local_segment').apply(\
+                    lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], window_length*int(sample_rate)), fs=int(sample_rate), featureNames=features)) 
+
 
             eda_intraday_features.reset_index(inplace=True)
 
     return eda_intraday_features
 
 
-def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
+def cr_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"]
+    
+    if provider["WINDOWS"]["COMPUTE"]:
+        requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"]
+    else:
+        requested_window_length = None
+
     # 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 = extractEDAFeaturesFromIntradayData(eda_intraday_data,
+    eda_intraday_features = extractEDAFeaturesFromIntradayData(eda_intraday_data, intraday_features_to_compute, 
-                                                                    intraday_features_to_compute, time_segment,
+                                                                requested_window_length, time_segment, filter_data_by_segment)
-                                                                    filter_data_by_segment)
 
     return eda_intraday_features

src/features/empatica_temperature/cr/main.py

@@ -4,6 +4,7 @@ from scipy.stats import entropy
 from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, genericFeatureNames
 from CalculatingFeatures.calculate_features import calculateFeatures
 
+import sys
 
 def getSampleRate(data):
     try:
@@ -13,7 +14,7 @@ def getSampleRate(data):
 
     return 1000/timestamps_diff
 
-def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, time_segment, filter_data_by_segment):
+def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, window_length, time_segment, filter_data_by_segment):
     temperature_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
 
     if not temperature_intraday_data.empty:
@@ -26,27 +27,35 @@ def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, tim
             temperature_intraday_features = pd.DataFrame()
 
             # apply methods from calculate features module
-            temperature_intraday_features = \
+            if window_length is None:
-                temperature_intraday_data.groupby('local_segment').apply(\
+                temperature_intraday_features = \
-                lambda x: calculateFeatures(convertInputInto2d(x['temperature'], x.shape[0]), fs=int(sample_rate), featureNames=features))
+                    temperature_intraday_data.groupby('local_segment').apply(\
+                    lambda x: calculateFeatures(convertInputInto2d(x['temperature'], x.shape[0]), fs=int(sample_rate), featureNames=features))
+            else:
+                temperature_intraday_features = \
+                    temperature_intraday_data.groupby('local_segment').apply(\
+                    lambda x: calculateFeatures(convertInputInto2d(x['temperature'], window_length*int(sample_rate)), fs=int(sample_rate), featureNames=features))
 
             temperature_intraday_features.reset_index(inplace=True)
 
     return temperature_intraday_features
 
 
-def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
+def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
     temperature_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
 
     requested_intraday_features = provider["FEATURES"]
+    if provider["WINDOWS"]["COMPUTE"]:
+        requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"]
+    else:
+        requested_window_length = None
+    
     # name of the features this function can compute
     base_intraday_features_names = genericFeatureNames
     # 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
-    temperature_intraday_features = extractTempFeaturesFromIntradayData(temperature_intraday_data,
+    temperature_intraday_features = extractTempFeaturesFromIntradayData(temperature_intraday_data, intraday_features_to_compute, 
-                                                                    intraday_features_to_compute, time_segment,
+                                                                        requested_window_length, time_segment, filter_data_by_segment)
-                                                                    filter_data_by_segment)
-
     return temperature_intraday_features