Added CF for HRV and shortened test data

config.yaml

@@ -541,9 +541,14 @@ EMPATICA_BLOOD_VOLUME_PULSE:
   CONTAINER: BVP
   PROVIDERS:
     DBDP:
-      COMPUTE: False
-      FEATURES: ["maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"]
+      COMPUTE: True
+      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:
+      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
 
 # See https://www.rapids.science/latest/features/empatica-inter-beat-interval/
 EMPATICA_INTER_BEAT_INTERVAL:

src/features/empatica_blood_volume_pulse/cf/main.py

@@ -0,0 +1,54 @@
+import pandas as pd
+from scipy.stats import entropy
+
+from CalculatingFeatures.helper_functions import convertInputInto2d, hrvFeatureNames, frequencyFeatureNames
+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 extractBVPFeaturesFromIntradayData(bvp_intraday_data, features, time_segment, filter_data_by_segment):
+    bvp_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
+
+    if not bvp_intraday_data.empty:   
+        sample_rate = getSampleRate(bvp_intraday_data)   
+     
+        bvp_intraday_data = filter_data_by_segment(bvp_intraday_data, time_segment)
+
+        if not bvp_intraday_data.empty:
+
+            bvp_intraday_features = pd.DataFrame()
+
+            # apply methods from calculate features module 
+            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)) 
+
+            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):
+    bvp_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 = 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,
+                                                                    intraday_features_to_compute, time_segment,
+                                                                    filter_data_by_segment)
+
+    return bvp_intraday_features