Modifications, mostly imports, after changes in cr-features module.
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parent
075c64d1e5
commit
8c8fe1fec7
16
config.yaml
16
config.yaml
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@ -491,7 +491,7 @@ EMPATICA_ACCELEROMETER:
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"sumOfSquareComponents", "averageVectorLength", "averageVectorLengthPower", "rollAvgLow", "pitchAvgLow", "rollStdDevLow",
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"pitchStdDevLow", "rollMotionAmountLow", "rollMotionRegularityLow", "manipulationLow", "rollPeaks", "pitchPeaks", "rollPitchCorrelation"] # Acc features
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WINDOWS:
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COMPUTE: False
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COMPUTE: True
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WINDOW_LENGTH: 10 # specify window length in seconds
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SRC_SCRIPT: src/features/empatica_accelerometer/cr/main.py
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@ -521,7 +521,7 @@ EMPATICA_TEMPERATURE:
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"calcMeanCrossingRateAutocorr", "countAboveMeanAutocorr", "sumPer", "sumSquared", "squareSumOfComponent",
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"sumOfSquareComponents"]
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WINDOWS:
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COMPUTE: False
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COMPUTE: True
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WINDOW_LENGTH: 90 # specify window length in seconds
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SRC_SCRIPT: src/features/empatica_temperature/cr/main.py
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@ -541,7 +541,7 @@ EMPATICA_ELECTRODERMAL_ACTIVITY:
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'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'maxPeakResponseSlopeBefore', 'maxPeakResponseSlopeAfter',
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'signalOverallChange', 'changeDuration', 'changeRate', 'significantIncrease', 'significantDecrease']
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WINDOWS:
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COMPUTE: False
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COMPUTE: True
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WINDOW_LENGTH: 80 # specify window length in seconds
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SRC_SCRIPT: src/features/empatica_electrodermal_activity/cr/main.py
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@ -550,11 +550,11 @@ 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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@ -567,17 +567,17 @@ 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: False
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COMPUTE: True
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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: False
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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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WINDOWS:
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COMPUTE: True
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WINDOW_LENGTH: 4 # specify window length in seconds
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SRC_SCRIPT: src/features/inter_beat_interval/cr/main.py
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SRC_SCRIPT: src/features/empatica_inter_beat_interval/cr/main.py
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# See https://www.rapids.science/latest/features/empatica-tags/
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EMPATICA_TAGS:
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@ -1,24 +1,24 @@
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import pandas as pd
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from scipy.stats import entropy
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from CalculatingFeatures.helper_functions import convert3DEmpaticaToArray, convertInputInto2d, accelerometerFeatureNames, frequencyFeatureNames
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from CalculatingFeatures.calculate_features import calculateFeatures
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from cr_features.helper_functions import convert_to2d, accelerometer_features, frequency_features
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from cr_features.calculate_features import calculate_features
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import sys
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def getSampleRate(data):
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def get_sample_rate(data):
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try:
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timestamps_diff = data['timestamp'].diff().dropna().mean()
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except:
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raise Exception("Error occured while trying to get the mean sample rate from the data.")
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return 1000/timestamps_diff
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return int(1000/timestamps_diff)
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def extractAccFeaturesFromIntradayData(acc_intraday_data, features, window_length, time_segment, filter_data_by_segment):
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def extract_acc_features_from_intraday_data(acc_intraday_data, features, window_length, time_segment, filter_data_by_segment):
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acc_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
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if not acc_intraday_data.empty:
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sample_rate = getSampleRate(acc_intraday_data)
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sample_rate = get_sample_rate(acc_intraday_data)
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acc_intraday_data = filter_data_by_segment(acc_intraday_data, time_segment)
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@ -29,18 +29,18 @@ def extractAccFeaturesFromIntradayData(acc_intraday_data, features, window_lengt
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# apply methods from calculate features module
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if window_length is None:
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acc_intraday_features = \
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acc_intraday_data.groupby('local_segment').apply(lambda x: calculateFeatures( \
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convertInputInto2d(x['double_values_0'], x.shape[0]), \
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convertInputInto2d(x['double_values_1'], x.shape[0]), \
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convertInputInto2d(x['double_values_2'], x.shape[0]), \
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fs=int(sample_rate), featureNames=features))
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acc_intraday_data.groupby('local_segment').apply(lambda x: calculate_features( \
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convert_to2d(x['double_values_0'], x.shape[0]), \
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convert_to2d(x['double_values_1'], x.shape[0]), \
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convert_to2d(x['double_values_2'], x.shape[0]), \
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fs=sample_rate, feature_names=features))
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else:
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acc_intraday_features = \
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acc_intraday_data.groupby('local_segment').apply(lambda x: calculateFeatures( \
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convertInputInto2d(x['double_values_0'], window_length*int(sample_rate)), \
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convertInputInto2d(x['double_values_1'], window_length*int(sample_rate)), \
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convertInputInto2d(x['double_values_2'], window_length*int(sample_rate)), \
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fs=int(sample_rate), featureNames=features))
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acc_intraday_data.groupby('local_segment').apply(lambda x: calculate_features( \
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convert_to2d(x['double_values_0'], window_length*sample_rate), \
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convert_to2d(x['double_values_1'], window_length*sample_rate), \
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convert_to2d(x['double_values_2'], window_length*sample_rate), \
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fs=sample_rate, feature_names=features))
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acc_intraday_features.reset_index(inplace=True)
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@ -61,12 +61,12 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
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requested_window_length = None
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# name of the features this function can compute
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base_intraday_features_names = accelerometerFeatureNames + frequencyFeatureNames
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base_intraday_features_names = accelerometer_features + frequency_features
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# the subset of requested features this function can compute
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intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))
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# extract features from intraday data
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acc_intraday_features = extractAccFeaturesFromIntradayData(acc_intraday_data, intraday_features_to_compute,
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acc_intraday_features = extract_acc_features_from_intraday_data(acc_intraday_data, intraday_features_to_compute,
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requested_window_length, time_segment, filter_data_by_segment)
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return acc_intraday_features
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@ -1,13 +1,13 @@
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import pandas as pd
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from scipy.stats import entropy
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from CalculatingFeatures.helper_functions import convertInputInto2d, hrvFeatureNames, hrvFreqFeatureNames
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from CalculatingFeatures.hrv import extractHrvFeatures, extractHrvFeatures2D, extractHrvFeatures2DWrapper
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from cr_features.helper_functions import convert_to2d, hrv_features, hrv_freq_features
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from cr_features.hrv import extract_hrv_features_2d_wrapper
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import sys
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def getSampleRate(data):
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def get_sample_rate(data):
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try:
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timestamps_diff = data['timestamp'].diff().dropna().mean()
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except:
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@ -15,11 +15,11 @@ def getSampleRate(data):
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return int(1000/timestamps_diff)
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def extractBVPFeaturesFromIntradayData(bvp_intraday_data, features, window_length, time_segment, filter_data_by_segment):
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def extract_bvp_features_from_intraday_data(bvp_intraday_data, features, window_length, time_segment, filter_data_by_segment):
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bvp_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
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if not bvp_intraday_data.empty:
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sample_rate = getSampleRate(bvp_intraday_data)
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sample_rate = get_sample_rate(bvp_intraday_data)
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print(bvp_intraday_data.shape)
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@ -29,24 +29,25 @@ def extractBVPFeaturesFromIntradayData(bvp_intraday_data, features, window_lengt
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bvp_intraday_features = pd.DataFrame()
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print(features)
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# apply methods from calculate features module
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if window_length is None:
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bvp_intraday_features = \
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bvp_intraday_data.groupby('local_segment').apply(\
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lambda x:
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extractHrvFeatures2DWrapper(
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convertInputInto2d(x['blood_volume_pulse'], x.shape[0]),
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sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, featureNames=features))
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extract_hrv_features_2d_wrapper(
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convert_to2d(x['blood_volume_pulse'], x.shape[0]),
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sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=False, feature_names=features))
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else:
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bvp_intraday_features = \
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bvp_intraday_data.groupby('local_segment').apply(\
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lambda x:
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extractHrvFeatures2DWrapper(
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convertInputInto2d(x['blood_volume_pulse'], window_length*sample_rate),
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sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, featureNames=features))
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print(sample_rate)
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print(bvp_intraday_features)
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extract_hrv_features_2d_wrapper(
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convert_to2d(x['blood_volume_pulse'], window_length*sample_rate),
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sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features))
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bvp_intraday_features.reset_index(inplace=True)
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return bvp_intraday_features
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@ -65,12 +66,12 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
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requested_window_length = None
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# name of the features this function can compute
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base_intraday_features_names = hrvFeatureNames + hrvFreqFeatureNames
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base_intraday_features_names = hrv_features + hrv_freq_features
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# the subset of requested features this function can compute
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intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))
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# extract features from intraday data
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bvp_intraday_features = extractBVPFeaturesFromIntradayData(bvp_intraday_data, intraday_features_to_compute,
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bvp_intraday_features = extract_bvp_features_from_intraday_data(bvp_intraday_data, intraday_features_to_compute,
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requested_window_length, time_segment, filter_data_by_segment)
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return bvp_intraday_features
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@ -1,8 +1,8 @@
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import pandas as pd
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from scipy.stats import entropy
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from CalculatingFeatures.helper_functions import convertInputInto2d, gsrFeatureNames
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from CalculatingFeatures.calculate_features import calculateFeatures
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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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def getSampleRate(data):
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@ -11,9 +11,9 @@ def getSampleRate(data):
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except:
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raise Exception("Error occured while trying to get the mean sample rate from the data.")
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return 1000/timestamps_diff
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return int(1000/timestamps_diff)
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def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, window_length, time_segment, filter_data_by_segment):
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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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if not eda_intraday_data.empty:
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@ -29,11 +29,11 @@ def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, window_lengt
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if window_length is None:
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eda_intraday_features = \
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eda_intraday_data.groupby('local_segment').apply(\
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lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], x.shape[0]), fs=int(sample_rate), featureNames=features))
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lambda x: calculate_features(convert_to2d(x['electrodermal_activity'], x.shape[0]), fs=sample_rate, feature_names=features))
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else:
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eda_intraday_features = \
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eda_intraday_data.groupby('local_segment').apply(\
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lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], window_length*int(sample_rate)), fs=int(sample_rate), featureNames=features))
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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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@ -54,12 +54,12 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
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requested_window_length = None
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# name of the features this function can compute
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base_intraday_features_names = gsrFeatureNames
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base_intraday_features_names = gsr_features
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# the subset of requested features this function can compute
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intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))
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# extract features from intraday data
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eda_intraday_features = extractEDAFeaturesFromIntradayData(eda_intraday_data, intraday_features_to_compute,
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eda_intraday_features = extract_eda_features_from_intraday_data(eda_intraday_data, intraday_features_to_compute,
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requested_window_length, time_segment, filter_data_by_segment)
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return eda_intraday_features
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@ -1,25 +1,27 @@
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import pandas as pd
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from scipy.stats import entropy
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from CalculatingFeatures.helper_functions import convertInputInto2dTime, convert2DEmpaticaToArray hrvFeatureNames, hrvFreqFeatureNames
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from CalculatingFeatures.calculate_features import calculateFeatures
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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.calculate_features import calculate_features
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import sys
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pd.set_option('display.max_rows', 1000)
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def getSampleRate(data):
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def get_sample_rate(data):
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try:
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timestamps_diff = data['timestamp'].diff().dropna().mean()
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except:
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raise Exception("Error occured while trying to get the mean sample rate from the data.")
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return 1000/timestamps_diff
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return int(1000/timestamps_diff)
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def extractIBIFeaturesFromIntradayData(ibi_intraday_data, features, window_length, time_segment, filter_data_by_segment):
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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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ibi_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
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if not ibi_intraday_data.empty:
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sample_rate = getSampleRate(ibi_intraday_data)
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sample_rate = get_sample_rate(ibi_intraday_data)
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ibi_intraday_data = filter_data_by_segment(ibi_intraday_data, time_segment)
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@ -27,15 +29,22 @@ def extractIBIFeaturesFromIntradayData(ibi_intraday_data, features, window_lengt
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ibi_intraday_features = pd.DataFrame()
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print(ibi_intraday_data.head(100))
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sys.exit()
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# apply methods from calculate features module
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# if window_length is None:
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# ibi_intraday_features = \
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# ibi_intraday_data.groupby('local_segment').apply(\
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# lambda x: calculateFeatures(convertInputInto2d(x['blood_volume_pulse'], x.shape[0]), fs=int(sample_rate), featureNames=features))
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# else:
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# ibi_intraday_features = \
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# ibi_intraday_data.groupby('local_segment').apply(\
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# lambda x: calculateFeatures(convertInputInto2d(x['blood_volume_pulse'], window_length*int(sample_rate)), fs=int(sample_rate), featureNames=features))
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if window_length is None:
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ibi_intraday_features = \
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ibi_intraday_data.groupby('local_segment').apply(\
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extract_hrv_features_2d_wrapper(
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convert_to2d(x['inter_beat_interval'], window_length*sample_rate),
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sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features))
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else:
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ibi_intraday_features = \
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ibi_intraday_data.groupby('local_segment').apply(\
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extract_hrv_features_2d_wrapper(
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convert_to2d(x['blood_volume_pulse'], window_length*sample_rate),
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sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features))
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ibi_intraday_features.reset_index(inplace=True)
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@ -55,12 +64,12 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
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requested_window_length = None
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# name of the features this function can compute
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base_intraday_features_names = hrvFeatureNames + hrvFreqFeatureNames
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base_intraday_features_names = hrv_features + hrv_freq_features
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# the subset of requested features this function can compute
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intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))
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# extract features from intraday data
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ibi_intraday_features = extractBVPFeaturesFromIntradayData(ibi_intraday_data, intraday_features_to_compute,
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ibi_intraday_features = extract_ibi_features_from_intraday_data(ibi_intraday_data, intraday_features_to_compute,
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requested_window_length, time_segment, filter_data_by_segment)
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return ibi_intraday_features
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@ -1,24 +1,24 @@
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import pandas as pd
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from scipy.stats import entropy
|
||||
|
||||
from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, genericFeatureNames
|
||||
from CalculatingFeatures.calculate_features import calculateFeatures
|
||||
from cr_features.helper_functions import convert_to2d, generic_features
|
||||
from cr_features.calculate_features import calculate_features
|
||||
|
||||
import sys
|
||||
|
||||
def getSampleRate(data):
|
||||
def get_sample_rate(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
|
||||
return int(1000/timestamps_diff)
|
||||
|
||||
def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, window_length, time_segment, filter_data_by_segment):
|
||||
def extract_temp_features_from_intraday_data(temperature_intraday_data, features, window_length, time_segment, filter_data_by_segment):
|
||||
temperature_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
|
||||
|
||||
if not temperature_intraday_data.empty:
|
||||
sample_rate = getSampleRate(temperature_intraday_data)
|
||||
sample_rate = get_sample_rate(temperature_intraday_data)
|
||||
|
||||
temperature_intraday_data = filter_data_by_segment(temperature_intraday_data, time_segment)
|
||||
|
||||
|
|
@ -30,11 +30,11 @@ def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, win
|
|||
if window_length is None:
|
||||
temperature_intraday_features = \
|
||||
temperature_intraday_data.groupby('local_segment').apply(\
|
||||
lambda x: calculateFeatures(convertInputInto2d(x['temperature'], x.shape[0]), fs=int(sample_rate), featureNames=features))
|
||||
lambda x: calculate_features(convert_to2d(x['temperature'], x.shape[0]), fs=sample_rate, feature_names=features))
|
||||
else:
|
||||
temperature_intraday_features = \
|
||||
temperature_intraday_data.groupby('local_segment').apply(\
|
||||
lambda x: calculateFeatures(convertInputInto2d(x['temperature'], window_length*int(sample_rate)), fs=int(sample_rate), featureNames=features))
|
||||
lambda x: calculate_features(convert_to2d(x['temperature'], window_length*sample_rate), fs=sample_rate, feature_names=features))
|
||||
|
||||
temperature_intraday_features.reset_index(inplace=True)
|
||||
|
||||
|
|
@ -54,11 +54,11 @@ 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 = genericFeatureNames
|
||||
base_intraday_features_names = generic_features
|
||||
# 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
|
||||
temperature_intraday_features = extractTempFeaturesFromIntradayData(temperature_intraday_data, intraday_features_to_compute,
|
||||
temperature_intraday_features = extract_temp_features_from_intraday_data(temperature_intraday_data, intraday_features_to_compute,
|
||||
requested_window_length, time_segment, filter_data_by_segment)
|
||||
return temperature_intraday_features
|
||||
Loading…
Reference in New Issue