Cleaning existing CF mains' and preparing src script for ACC.
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parent
ca59a54d8f
commit
8b76c96e47
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config.yaml
37
config.yaml
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@ -477,9 +477,20 @@ EMPATICA_ACCELEROMETER:
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CONTAINER: ACC
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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: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
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SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py
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CF:
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COMPUTE: True
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FEATURES: ["meanLow", "areaLow", "totalAbsoluteAreaBand", "totalMagnitudeBand", "entropyBand", "skewnessBand", "kurtosisBand",
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"postureDistanceLow", "absoluteMeanBand", "absoluteAreaBand", "quartilesBand", "interQuartileRangeBand", "varianceBand",
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"coefficientOfVariationBand", "amplitudeBand", "totalEnergyBand", "dominantFrequencyEnergyBand", "meanCrossingRateBand",
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"correlationBand", "quartilesMagnitudesBand", "interQuartileRangeMagnitudesBand", "areaUnderAccelerationMagnitude",
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"peaksDataLow", "sumPerComponentBand", "velocityBand", "meanKineticEnergyBand", "totalKineticEnergyBand", "squareSumOfComponent",
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"sumOfSquareComponents", "averageVectorLength", "averageVectorLengthPower", "rollAvgLow", "pitchAvgLow", "rollStdDevLow",
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"pitchStdDevLow", "rollMotionAmountLow", "rollMotionRegularityLow", "manipulationLow", "rollPeaks", "pitchPeaks", "rollPitchCorrelation"]
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SRC_SCRIPT: src/features/empatica_accelerometer/cf/main.py
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# See https://www.rapids.science/latest/features/empatica-heartrate/
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EMPATICA_HEARTRATE:
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@ -495,16 +506,16 @@ 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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CF:
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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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"calcMeanCrossingRateAutocorr", "countAboveMeanAutocorr", "sumPer", "sumSquared", "squareSumOfComponent",
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"sumOfSquareComponents"]
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"longestStrikeBelowMean", "stdDev", "median", "meanChange", "numberOfZeroCrossings", "absEnergy", "linearTrendSlope",
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"ratioBeyondRSigma", "binnedEntropy", "numOfPeaksAutocorr", "numberOfZeroCrossingsAutocorr", "areaAutocorr",
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"calcMeanCrossingRateAutocorr", "countAboveMeanAutocorr", "sumPer", "sumSquared", "squareSumOfComponent",
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"sumOfSquareComponents"]
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SRC_SCRIPT: src/features/empatica_temperature/cf/main.py
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# See https://www.rapids.science/latest/features/empatica-electrodermal-activity/
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@ -512,16 +523,16 @@ 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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CF:
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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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'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'maxPeakResponseSlopeBefore', 'maxPeakResponseSlopeAfter', 'signalOverallChange',
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'changeDuration', 'changeRate', 'significantIncrease', 'significantDecrease']
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'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore',
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'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio',
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'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'maxPeakResponseSlopeBefore', 'maxPeakResponseSlopeAfter',
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'signalOverallChange', 'changeDuration', 'changeRate', 'significantIncrease', 'significantDecrease']
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SRC_SCRIPT: src/features/empatica_electrodermal_activity/cf/main.py
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# See https://www.rapids.science/latest/features/empatica-blood-volume-pulse/
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@ -0,0 +1,86 @@
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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, gsrFeatureNames
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from CalculatingFeatures.calculate_features import calculateFeatures
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def getSampleRate(data):
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try:
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timestamps_diff = data['timestamp'].iloc[1] - data['timestamp'].iloc[0]
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except:
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raise Exception("Error occured while trying to get the sample rate from the first two sequential timestamps.")
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return 1000/timestamps_diff
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def extractAccFeaturesFromIntradayData(acc_intraday_data, features, 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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acc_intraday_data = filter_data_by_segment(acc_intraday_data, time_segment)
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if not acc_intraday_data.empty:
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acc_intraday_features = pd.DataFrame()
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# apply methods from calculate features module
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acc_intraday_features = \
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acc_intraday_data.groupby('local_segment').apply(\
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lambda x: calculateFeatures(convertInputInto2d(x['accelerometer'], x.shape[0]), fs=sample_rate, featureNames=features))
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acc_intraday_features.reset_index(inplace=True)
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return acc_intraday_features
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def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
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eda_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
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requested_intraday_features = provider["FEATURES"]
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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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# 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 = extractAccFeaturesFromIntradayData(eda_intraday_data,
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intraday_features_to_compute, time_segment,
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filter_data_by_segment)
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return eda_intraday_features
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def dbdp_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
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acc_data = pd.read_csv(sensor_data_files["sensor_data"])
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requested_features = provider["FEATURES"]
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# name of the features this function can compute
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base_features_names = ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
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# the subset of requested features this function can compute
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features_to_compute = list(set(requested_features) & set(base_features_names))
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acc_features = pd.DataFrame(columns=["local_segment"] + features_to_compute)
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if not acc_data.empty:
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acc_data = filter_data_by_segment(acc_data, time_segment)
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if not acc_data.empty:
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acc_features = pd.DataFrame()
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# get magnitude related features: magnitude = sqrt(x^2+y^2+z^2)
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magnitude = acc_data.apply(lambda row: np.sqrt(row["double_values_0"] ** 2 + row["double_values_1"] ** 2 + row["double_values_2"] ** 2), axis=1)
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acc_data = acc_data.assign(magnitude = magnitude.values)
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if "maxmagnitude" in features_to_compute:
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acc_features["maxmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].max()
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if "minmagnitude" in features_to_compute:
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acc_features["minmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].min()
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if "avgmagnitude" in features_to_compute:
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acc_features["avgmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].mean()
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if "medianmagnitude" in features_to_compute:
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acc_features["medianmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].median()
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if "stdmagnitude" in features_to_compute:
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acc_features["stdmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].std()
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acc_features = acc_features.reset_index()
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return acc_features
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@ -4,8 +4,6 @@ from scipy.stats import entropy
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from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, gsrFeatureNames
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from CalculatingFeatures.calculate_features import calculateFeatures
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pd.set_option('display.max_columns', None)
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def getSampleRate(data):
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try:
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@ -30,7 +28,7 @@ def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, time_segment
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# apply methods from calculate features module
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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=sample_rate, featureNames=features)) # To-Do: Hardcoded f/s ...
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lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], x.shape[0]), fs=sample_rate, featureNames=features))
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eda_intraday_features.reset_index(inplace=True)
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@ -1,7 +1,7 @@
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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 convert1DEmpaticaToArray, convertInputInto2d, genericFeatureNames
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from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, accelerometerFeatureNames
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from CalculatingFeatures.calculate_features import calculateFeatures
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@ -13,7 +13,7 @@ def getSampleRate(data):
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return 1000/timestamps_diff
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def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, time_segment, filter_data_by_segment):
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def extractAccFeaturesFromIntradayData(temperature_intraday_data, features, time_segment, filter_data_by_segment):
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temperature_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
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if not temperature_intraday_data.empty:
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@ -28,7 +28,7 @@ def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, tim
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# apply methods from calculate features module
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temperature_intraday_features = \
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temperature_intraday_data.groupby('local_segment').apply(\
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lambda x: calculateFeatures(convertInputInto2d(x['temperature'], x.shape[0]), fs=sample_rate, featureNames=features)) # To-Do: Hardcoded f/s ...
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lambda x: calculateFeatures(convertInputInto2d(x['temperature'], x.shape[0]), fs=sample_rate, featureNames=features))
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temperature_intraday_features.reset_index(inplace=True)
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