Patching IBI with BVP. WIP

Snakefile

@@ -359,6 +359,8 @@ for provider in config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"].keys():
     if config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"][provider]["COMPUTE"]:
         files_to_compute.extend(expand("data/raw/{pid}/empatica_inter_beat_interval_raw.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/raw/{pid}/empatica_inter_beat_interval_with_datetime.csv", pid=config["PIDS"]))
+        files_to_compute.extend(expand("data/raw/{pid}/empatica_blood_volume_pulse_raw.csv", pid=config["PIDS"]))
+        files_to_compute.extend(expand("data/raw/{pid}/empatica_blood_volume_pulse_with_datetime.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_{language}_{provider_key}.csv", pid=config["PIDS"], language=get_script_language(config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"][provider]["SRC_SCRIPT"]), provider_key=provider.lower()))
         files_to_compute.extend(expand("data/processed/features/{pid}/empatica_inter_beat_interval.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))

config.yaml

@@ -3,7 +3,7 @@
 ########################################################################################################################
 
 # See https://www.rapids.science/latest/setup/configuration/#participant-files
-PIDS: [p01, p02]
+PIDS: [p01] #p02, p03]
 
 # See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files
 CREATE_PARTICIPANT_FILES:
@@ -477,11 +477,11 @@ 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
     CR:
-      COMPUTE: True
+      COMPUTE: False
       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", 
@@ -502,7 +502,7 @@ EMPATICA_HEARTRATE:
   CONTAINER: HR
   PROVIDERS:
     DBDP:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr"]
       SRC_SCRIPT: src/features/empatica_heartrate/dbdp/main.py
 
@@ -511,11 +511,11 @@ 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
     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",
@@ -532,11 +532,11 @@ 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',
@@ -553,11 +553,11 @@ EMPATICA_BLOOD_VOLUME_PULSE:
   CONTAINER: BVP
   PROVIDERS:
     DBDP:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"]
       SRC_SCRIPT: src/features/empatica_blood_volume_pulse/dbdp/main.py
     CR:
-      COMPUTE: True
+      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:
@@ -571,13 +571,14 @@ EMPATICA_INTER_BEAT_INTERVAL:
   CONTAINER: IBI
   PROVIDERS:
     DBDP:
-      COMPUTE: True
+      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: True
       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
+                  'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features            
+      PATCH_WITH_BVP: True
       WINDOWS:
         COMPUTE: True
         WINDOW_LENGTH: 300 # specify window length in seconds

data/external/timesegments_periodic.csv

@@ -1,3 +1,3 @@
 label,start_time,length,repeats_on,repeats_value
 daily,00:00:00,23H 59M 59S,every_day,0
-night,00:00:00,5H 59M 59S,every_day,0
+E4baseline,01:00:00,3H,every_day,0

rules/features.smk

@@ -899,6 +899,7 @@ rule empatica_blood_volume_pulse_r_features:
 rule empatica_inter_beat_interval_python_features:
     input:
         sensor_data = "data/raw/{pid}/empatica_inter_beat_interval_with_datetime.csv",
+        bvp_sensor_data = "data/raw/{pid}/empatica_blood_volume_pulse_with_datetime.csv",
         time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv"
     params:
         provider = lambda wildcards: config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"][wildcards.provider_key.upper()],

src/features/empatica_electrodermal_activity/cr/main.py

@@ -1,10 +1,17 @@
 import pandas as pd
+import numpy as np
 from scipy.stats import entropy
 
 from cr_features.helper_functions import convert_to2d, gsr_features
 from cr_features.calculate_features import calculate_features
 from cr_features_helper_methods import extract_second_order_features
 
+import sys
+
+#pd.set_option('display.max_columns', None)
+#pd.set_option('display.max_rows', None)
+#np.seterr(invalid='ignore')
+
 
 def extract_eda_features_from_intraday_data(eda_intraday_data, features, window_length, time_segment, filter_data_by_segment):
     eda_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
@@ -14,7 +21,7 @@ def extract_eda_features_from_intraday_data(eda_intraday_data, features, window_
      
         eda_intraday_data = filter_data_by_segment(eda_intraday_data, time_segment)
 
-        if not eda_intraday_data.empty:
+        if not eda_intraday_data.empty: 
 
             eda_intraday_features = pd.DataFrame()
 
@@ -28,7 +35,6 @@ def extract_eda_features_from_intraday_data(eda_intraday_data, features, window_
                     eda_intraday_data.groupby('local_segment').apply(\
                     lambda x: calculate_features(convert_to2d(x['electrodermal_activity'], window_length*sample_rate), fs=sample_rate, feature_names=features)) 
 
-
             eda_intraday_features.reset_index(inplace=True)
 
     return eda_intraday_features
@@ -58,6 +64,7 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
     if calc_windows:
         so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"]
         eda_second_order_features = extract_second_order_features(eda_intraday_features, so_features_names)
+    
         return eda_intraday_features, eda_second_order_features
 
     return eda_intraday_features

src/features/empatica_inter_beat_interval/cr/main.py

@@ -2,14 +2,14 @@ import pandas as pd
 import numpy as np
 
 from cr_features.helper_functions import convert_ibi_to2d_time, hrv_features, hrv_freq_features
-from cr_features.hrv import extract_hrv_features_2d_wrapper
+from cr_features.hrv import extract_hrv_features_2d_wrapper, get_HRV_features
 from cr_features_helper_methods import extract_second_order_features
 
 import math
 import sys
 
 pd.set_option('display.max_rows', 1000)
-pd.set_option('display.max_columns', None)
+#pd.set_option('display.max_columns', None)
 
 
 def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_length, time_segment, filter_data_by_segment):
@@ -48,9 +48,21 @@ def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_
     return ibi_intraday_features
 
 
+def patch_IBI_with_BVP(bvp_intraday_data):
+    # get features method is used because 
+    hrv_time_and_freq_features, sample, rr, timings, peak_indx = \
+        get_HRV_features(bvp_intraday_data['blood_volume_pulse'].to_numpy(), hampel_fiter=False, median_filter=False, mod_z_score_filter=True, sampling=64, feature_names=['meanHr'])
+    
+
 def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
+    print(sensor_data_files)
     ibi_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
 
+    if provider["PATCH_WITH_BVP"]:
+        bvp_intraday_data = pd.read_csv(sensor_data_files["bvp_sensor_data"])
+        patch_IBI_with_BVP(bvp_intraday_data)
+        # sys.exit()
+
     requested_intraday_features = provider["FEATURES"]
     
     calc_windows = kwargs.get('calc_windows', False)

src/features/entry.py

@@ -1,6 +1,8 @@
 import pandas as pd
 from utils.utils import fetch_provider_features, run_provider_cleaning_script
 
+import sys
+
 sensor_data_files = dict(snakemake.input)
 
 provider = snakemake.params["provider"]
@@ -29,7 +31,6 @@ else:
     elif "empatica" in sensor_key: 
         pd.DataFrame().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)
 
 if not calc_windows:

src/features/utils/utils.py

@@ -169,3 +169,7 @@ def run_provider_cleaning_script(provider, provider_key, sensor_key, sensor_data
     sensor_features = cleaning_function(sensor_data_files, provider)
 
     return sensor_features
+
+
+def empatica_patch_IBI_with_BVP(bvp_data):
+    pass