HRV: changed wrapper calcFeat method with specialized one.

config.yaml

@@ -510,18 +510,18 @@ 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
     CR:
-      COMPUTE: True
+      COMPUTE: False
       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"]
       WINDOWS:
-        COMPUTE: True
+        COMPUTE: False
         WINDOW_LENGTH: 90 # specify window length in seconds
       SRC_SCRIPT: src/features/empatica_temperature/cr/main.py
 
@@ -530,18 +530,18 @@ 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
     CR:
-      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', 'changeDuration', 'changeRate', 'significantIncrease', 'significantDecrease']
       WINDOWS:
-        COMPUTE: True
+        COMPUTE: False
         WINDOW_LENGTH: 80 # specify window length in seconds
       SRC_SCRIPT: src/features/empatica_electrodermal_activity/cr/main.py
 
@@ -551,15 +551,15 @@ EMPATICA_BLOOD_VOLUME_PULSE:
   PROVIDERS:
     DBDP:
       COMPUTE: True
-      FEATURES: ["fqHighestPeakFreqs", "fqHighestPeaks", "fqEnergyFeat", "fqEntropyFeat", "fqHistogramBins","fqAbsMean", "fqSkewness", "fqKurtosis", "fqInterquart", # Freq features
+      FEATURES: ["maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"]
-                  "maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"] # HRV features
       SRC_SCRIPT: src/features/empatica_blood_volume_pulse/dbdp/main.py
     CR:
       COMPUTE: True
-      FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR']
+      FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features
+                  'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features
       WINDOWS:
         COMPUTE: True
-        WINDOW_LENGTH: 4 # specify window length in seconds
+        WINDOW_LENGTH: 10 # 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/
@@ -570,6 +570,14 @@ EMPATICA_INTER_BEAT_INTERVAL:
       COMPUTE: False
       FEATURES: ["maxibi", "minibi", "avgibi", "medianibi", "modeibi", "stdibi", "diffmaxmodeibi", "diffminmodeibi", "entropyibi"]
       SRC_SCRIPT: src/features/empatica_inter_beat_interval/dbdp/main.py
+    CR:
+      COMPUTE: False
+      FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features
+                  'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features
+      WINDOWS:
+        COMPUTE: True
+        WINDOW_LENGTH: 4 # specify window length in seconds
+      SRC_SCRIPT: src/features/inter_beat_interval/cr/main.py
 
 # See https://www.rapids.science/latest/features/empatica-tags/
 EMPATICA_TAGS:

src/features/empatica_blood_volume_pulse/cr/main.py

@@ -1,8 +1,8 @@
 import pandas as pd
 from scipy.stats import entropy
 
-from CalculatingFeatures.helper_functions import convertInputInto2d, hrvFeatureNames, frequencyFeatureNames
+from CalculatingFeatures.helper_functions import convertInputInto2d, hrvFeatureNames, hrvFreqFeatureNames
-from CalculatingFeatures.calculate_features import calculateFeatures
+from CalculatingFeatures.hrv import extractHrvFeatures, extractHrvFeatures2D, extractHrvFeatures2DWrapper
 
 import sys
 
@@ -13,7 +13,7 @@ def getSampleRate(data):
     except:
         raise Exception("Error occured while trying to get the mean sample rate from the data.")
 
-    return 1000/timestamps_diff
+    return int(1000/timestamps_diff)
 
 def extractBVPFeaturesFromIntradayData(bvp_intraday_data, features, window_length, time_segment, filter_data_by_segment):
     bvp_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
@@ -21,6 +21,8 @@ def extractBVPFeaturesFromIntradayData(bvp_intraday_data, features, window_lengt
     if not bvp_intraday_data.empty:
         sample_rate = getSampleRate(bvp_intraday_data)  
 
+        print(bvp_intraday_data.shape) 
+     
         bvp_intraday_data = filter_data_by_segment(bvp_intraday_data, time_segment)
 
         if not bvp_intraday_data.empty:
@@ -31,12 +33,20 @@ def extractBVPFeaturesFromIntradayData(bvp_intraday_data, features, window_lengt
             if window_length is None:
                 bvp_intraday_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)) 
+                    lambda x: 
+                        extractHrvFeatures2DWrapper(
+                            convertInputInto2d(x['blood_volume_pulse'], x.shape[0]), 
+                            sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, 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)) 
+                    lambda x: 
-
+                        extractHrvFeatures2DWrapper(
+                            convertInputInto2d(x['blood_volume_pulse'], window_length*sample_rate), 
+                            sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, featureNames=features)) 
+            print(sample_rate)
+            print(bvp_intraday_features)
             bvp_intraday_features.reset_index(inplace=True)
 
     return bvp_intraday_features
@@ -55,7 +65,7 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
         requested_window_length = None
 
     # name of the features this function can compute
-    base_intraday_features_names = hrvFeatureNames + frequencyFeatureNames
+    base_intraday_features_names = hrvFeatureNames + hrvFreqFeatureNames
     # the subset of requested features this function can compute
     intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))
 

src/features/empatica_inter_beat_interval/cr/main.py

@@ -0,0 +1,66 @@
+import pandas as pd
+from scipy.stats import entropy
+
+from CalculatingFeatures.helper_functions import convertInputInto2dTime, convert2DEmpaticaToArray hrvFeatureNames, hrvFreqFeatureNames
+from CalculatingFeatures.calculate_features import calculateFeatures
+
+import sys
+
+
+def getSampleRate(data):
+    try:
+        timestamps_diff = data['timestamp'].diff().dropna().mean()
+    except:
+        raise Exception("Error occured while trying to get the mean sample rate from the data.")
+
+    return 1000/timestamps_diff
+
+def extractIBIFeaturesFromIntradayData(ibi_intraday_data, features, window_length, time_segment, filter_data_by_segment):
+    ibi_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
+
+    if not ibi_intraday_data.empty:   
+        sample_rate = getSampleRate(ibi_intraday_data)   
+     
+        ibi_intraday_data = filter_data_by_segment(ibi_intraday_data, time_segment)
+
+        if not ibi_intraday_data.empty:
+
+            ibi_intraday_features = pd.DataFrame()
+
+            # apply methods from calculate features module
+            # if window_length is None:
+            #     ibi_intraday_features = \
+            #         ibi_intraday_data.groupby('local_segment').apply(\
+            #         lambda x: calculateFeatures(convertInputInto2d(x['blood_volume_pulse'], x.shape[0]), fs=int(sample_rate), featureNames=features)) 
+            # else:
+            #     ibi_intraday_features = \
+            #         ibi_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)) 
+
+            ibi_intraday_features.reset_index(inplace=True)
+
+    return ibi_intraday_features
+
+
+def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
+    ibi_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
+
+    requested_intraday_features = provider["FEATURES"]
+    
+    calc_windows = kwargs.get('calc_windows', False)
+
+    if provider["WINDOWS"]["COMPUTE"] and calc_windows:
+        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 + hrvFreqFeatureNames
+    # 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
+    ibi_intraday_features = extractBVPFeaturesFromIntradayData(ibi_intraday_data, intraday_features_to_compute, 
+                                                                requested_window_length, time_segment, filter_data_by_segment)
+
+    return ibi_intraday_features

src/features/entry.py

@@ -27,7 +27,7 @@ else:
     if calc_windows:
         sensor_features.to_csv(snakemake.output[1], index=False)
         sensor_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=False)
-    elif "empatica" in sensor_key and provider_key == "dbdp": 
+    elif "empatica" in sensor_key: 
         pd.DataFrame().to_csv(snakemake.output[1], index=False)