From 075c64d1e50bd78d130875e918c6aa5e46c4a128 Mon Sep 17 00:00:00 2001 From: = <=> Date: Thu, 14 Apr 2022 11:51:53 +0000 Subject: [PATCH] HRV: changed wrapper calcFeat method with specialized one. --- config.yaml | 28 +++++--- .../empatica_blood_volume_pulse/cr/main.py | 28 +++++--- .../empatica_inter_beat_interval/cr/main.py | 66 +++++++++++++++++++ src/features/entry.py | 2 +- 4 files changed, 104 insertions(+), 20 deletions(-) create mode 100644 src/features/empatica_inter_beat_interval/cr/main.py diff --git a/config.yaml b/config.yaml index 12e70b42..8a1516de 100644 --- a/config.yaml +++ b/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 - "maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"] # HRV features + FEATURES: ["maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"] 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: diff --git a/src/features/empatica_blood_volume_pulse/cr/main.py b/src/features/empatica_blood_volume_pulse/cr/main.py index 6d8afcd4..bdf26f75 100644 --- a/src/features/empatica_blood_volume_pulse/cr/main.py +++ b/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.calculate_features import calculateFeatures +from CalculatingFeatures.helper_functions import convertInputInto2d, hrvFeatureNames, hrvFreqFeatureNames +from CalculatingFeatures.hrv import extractHrvFeatures, extractHrvFeatures2D, extractHrvFeatures2DWrapper import sys @@ -13,13 +13,15 @@ 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) - if not bvp_intraday_data.empty: - sample_rate = getSampleRate(bvp_intraday_data) + 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) @@ -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)) diff --git a/src/features/empatica_inter_beat_interval/cr/main.py b/src/features/empatica_inter_beat_interval/cr/main.py new file mode 100644 index 00000000..f8d38b66 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/src/features/entry.py b/src/features/entry.py index 761235f1..f7127f35 100644 --- a/src/features/entry.py +++ b/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)