Patching IBI with BVP. WIP
Primoz
parent
1471c86c62
commit
2a8f58f5c8
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@ -359,6 +359,8 @@ for provider in config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"].keys():
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if config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"][provider]["COMPUTE"]:
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files_to_compute.extend(expand("data/raw/{pid}/empatica_inter_beat_interval_raw.csv", pid=config["PIDS"]))
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files_to_compute.extend(expand("data/raw/{pid}/empatica_inter_beat_interval_with_datetime.csv", pid=config["PIDS"]))
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files_to_compute.extend(expand("data/raw/{pid}/empatica_blood_volume_pulse_raw.csv", pid=config["PIDS"]))
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files_to_compute.extend(expand("data/raw/{pid}/empatica_blood_volume_pulse_with_datetime.csv", pid=config["PIDS"]))
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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()))
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files_to_compute.extend(expand("data/processed/features/{pid}/empatica_inter_beat_interval.csv", pid=config["PIDS"]))
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files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
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25
config.yaml
25
config.yaml
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@ -3,7 +3,7 @@
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########################################################################################################################
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# See https://www.rapids.science/latest/setup/configuration/#participant-files
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PIDS: [p01, p02]
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PIDS: [p01] #p02, p03]
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# See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files
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CREATE_PARTICIPANT_FILES:
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@ -477,11 +477,11 @@ EMPATICA_ACCELEROMETER:
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CONTAINER: ACC
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PROVIDERS:
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DBDP:
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COMPUTE: True
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COMPUTE: False
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FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
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SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py
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CR:
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COMPUTE: True
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COMPUTE: False
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FEATURES: ["fqHighestPeakFreqs", "fqHighestPeaks", "fqEnergyFeat", "fqEntropyFeat", "fqHistogramBins","fqAbsMean", "fqSkewness", "fqKurtosis", "fqInterquart", # Freq features
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"meanLow", "areaLow", "totalAbsoluteAreaBand", "totalMagnitudeBand", "entropyBand", "skewnessBand", "kurtosisBand",
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"postureDistanceLow", "absoluteMeanBand", "absoluteAreaBand", "quartilesBand", "interQuartileRangeBand", "varianceBand",
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@ -502,7 +502,7 @@ EMPATICA_HEARTRATE:
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CONTAINER: HR
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PROVIDERS:
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DBDP:
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COMPUTE: True
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COMPUTE: False
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FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr"]
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SRC_SCRIPT: src/features/empatica_heartrate/dbdp/main.py
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@ -511,11 +511,11 @@ EMPATICA_TEMPERATURE:
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CONTAINER: TEMP
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PROVIDERS:
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DBDP:
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COMPUTE: True
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COMPUTE: False
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FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"]
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SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py
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CR:
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COMPUTE: True
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COMPUTE: False
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FEATURES: ["autocorrelations", "countAboveMean", "countBelowMean", "maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean",
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"longestStrikeBelowMean", "stdDev", "median", "meanChange", "numberOfZeroCrossings", "absEnergy", "linearTrendSlope",
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"ratioBeyondRSigma", "binnedEntropy", "numOfPeaksAutocorr", "numberOfZeroCrossingsAutocorr", "areaAutocorr",
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@ -532,11 +532,11 @@ EMPATICA_ELECTRODERMAL_ACTIVITY:
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CONTAINER: EDA
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PROVIDERS:
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DBDP:
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COMPUTE: True
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COMPUTE: False
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FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"]
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SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py
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CR:
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COMPUTE: True
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COMPUTE: False
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FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic',
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'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore',
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'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio',
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@ -553,11 +553,11 @@ EMPATICA_BLOOD_VOLUME_PULSE:
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CONTAINER: BVP
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PROVIDERS:
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DBDP:
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COMPUTE: True
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COMPUTE: False
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FEATURES: ["maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"]
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SRC_SCRIPT: src/features/empatica_blood_volume_pulse/dbdp/main.py
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CR:
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COMPUTE: True
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COMPUTE: False
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FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features
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'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features
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WINDOWS:
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@ -571,13 +571,14 @@ EMPATICA_INTER_BEAT_INTERVAL:
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CONTAINER: IBI
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PROVIDERS:
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DBDP:
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COMPUTE: True
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COMPUTE: False
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FEATURES: ["maxibi", "minibi", "avgibi", "medianibi", "modeibi", "stdibi", "diffmaxmodeibi", "diffminmodeibi", "entropyibi"]
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SRC_SCRIPT: src/features/empatica_inter_beat_interval/dbdp/main.py
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CR:
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COMPUTE: True
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FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features
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'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features
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'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features
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PATCH_WITH_BVP: True
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WINDOWS:
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COMPUTE: True
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WINDOW_LENGTH: 300 # specify window length in seconds
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@ -1,3 +1,3 @@
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label,start_time,length,repeats_on,repeats_value
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daily,00:00:00,23H 59M 59S,every_day,0
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night,00:00:00,5H 59M 59S,every_day,0
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E4baseline,01:00:00,3H,every_day,0
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@ -899,6 +899,7 @@ rule empatica_blood_volume_pulse_r_features:
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rule empatica_inter_beat_interval_python_features:
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input:
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sensor_data = "data/raw/{pid}/empatica_inter_beat_interval_with_datetime.csv",
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bvp_sensor_data = "data/raw/{pid}/empatica_blood_volume_pulse_with_datetime.csv",
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time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv"
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params:
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provider = lambda wildcards: config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"][wildcards.provider_key.upper()],
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@ -1,10 +1,17 @@
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import pandas as pd
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import numpy as np
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from scipy.stats import entropy
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from cr_features.helper_functions import convert_to2d, gsr_features
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from cr_features.calculate_features import calculate_features
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from cr_features_helper_methods import extract_second_order_features
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import sys
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#pd.set_option('display.max_columns', None)
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#pd.set_option('display.max_rows', None)
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#np.seterr(invalid='ignore')
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def extract_eda_features_from_intraday_data(eda_intraday_data, features, window_length, time_segment, filter_data_by_segment):
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eda_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
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@ -14,7 +21,7 @@ def extract_eda_features_from_intraday_data(eda_intraday_data, features, window_
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eda_intraday_data = filter_data_by_segment(eda_intraday_data, time_segment)
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if not eda_intraday_data.empty:
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if not eda_intraday_data.empty:
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eda_intraday_features = pd.DataFrame()
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@ -28,7 +35,6 @@ def extract_eda_features_from_intraday_data(eda_intraday_data, features, window_
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eda_intraday_data.groupby('local_segment').apply(\
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lambda x: calculate_features(convert_to2d(x['electrodermal_activity'], window_length*sample_rate), fs=sample_rate, feature_names=features))
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eda_intraday_features.reset_index(inplace=True)
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return eda_intraday_features
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@ -58,6 +64,7 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
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if calc_windows:
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so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"]
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eda_second_order_features = extract_second_order_features(eda_intraday_features, so_features_names)
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return eda_intraday_features, eda_second_order_features
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return eda_intraday_features
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@ -2,14 +2,14 @@ import pandas as pd
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import numpy as np
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from cr_features.helper_functions import convert_ibi_to2d_time, hrv_features, hrv_freq_features
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from cr_features.hrv import extract_hrv_features_2d_wrapper
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from cr_features.hrv import extract_hrv_features_2d_wrapper, get_HRV_features
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from cr_features_helper_methods import extract_second_order_features
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import math
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import sys
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pd.set_option('display.max_rows', 1000)
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pd.set_option('display.max_columns', None)
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#pd.set_option('display.max_columns', None)
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def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_length, time_segment, filter_data_by_segment):
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@ -48,9 +48,21 @@ def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_
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return ibi_intraday_features
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def patch_IBI_with_BVP(bvp_intraday_data):
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# get features method is used because
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hrv_time_and_freq_features, sample, rr, timings, peak_indx = \
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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'])
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def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
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print(sensor_data_files)
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ibi_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
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if provider["PATCH_WITH_BVP"]:
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bvp_intraday_data = pd.read_csv(sensor_data_files["bvp_sensor_data"])
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patch_IBI_with_BVP(bvp_intraday_data)
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# sys.exit()
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requested_intraday_features = provider["FEATURES"]
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calc_windows = kwargs.get('calc_windows', False)
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@ -1,6 +1,8 @@
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import pandas as pd
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from utils.utils import fetch_provider_features, run_provider_cleaning_script
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import sys
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sensor_data_files = dict(snakemake.input)
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provider = snakemake.params["provider"]
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@ -29,7 +31,6 @@ else:
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elif "empatica" in sensor_key:
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pd.DataFrame().to_csv(snakemake.output[1], index=False)
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sensor_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=False)
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if not calc_windows:
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@ -169,3 +169,7 @@ def run_provider_cleaning_script(provider, provider_key, sensor_key, sensor_data
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sensor_features = cleaning_function(sensor_data_files, provider)
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return sensor_features
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def empatica_patch_IBI_with_BVP(bvp_data):
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pass
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