Implementation of the second order features.
Primoz
parent
66451160e9
commit
5638367999
19
config.yaml
19
config.yaml
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@ -493,6 +493,7 @@ EMPATICA_ACCELEROMETER:
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WINDOWS:
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COMPUTE: True
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WINDOW_LENGTH: 10 # specify window length in seconds
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SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min']
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SRC_SCRIPT: src/features/empatica_accelerometer/cr/main.py
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@ -510,11 +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: False
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COMPUTE: True
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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: False
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COMPUTE: True
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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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@ -523,6 +524,7 @@ EMPATICA_TEMPERATURE:
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WINDOWS:
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COMPUTE: True
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WINDOW_LENGTH: 90 # specify window length in seconds
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SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min']
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SRC_SCRIPT: src/features/empatica_temperature/cr/main.py
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# See https://www.rapids.science/latest/features/empatica-electrodermal-activity/
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@ -530,11 +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: False
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COMPUTE: True
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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: False
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COMPUTE: True
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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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@ -543,6 +545,7 @@ EMPATICA_ELECTRODERMAL_ACTIVITY:
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WINDOWS:
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COMPUTE: True
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WINDOW_LENGTH: 80 # specify window length in seconds
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SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min']
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SRC_SCRIPT: src/features/empatica_electrodermal_activity/cr/main.py
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# See https://www.rapids.science/latest/features/empatica-blood-volume-pulse/
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@ -550,16 +553,17 @@ 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: False
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COMPUTE: True
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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: 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: 10 # specify window length in seconds
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SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min']
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SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cr/main.py
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# See https://www.rapids.science/latest/features/empatica-inter-beat-interval/
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@ -576,7 +580,8 @@ EMPATICA_INTER_BEAT_INTERVAL:
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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: 120 # specify window length in seconds
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WINDOW_LENGTH: 2000 # specify window length in seconds
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SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min']
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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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@ -110,12 +110,13 @@ dependencies:
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- bidict==0.22.0
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- biosppy==0.8.0
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- cached-property==1.5.2
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- calculatingfeatures==0.1.1
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- configargparse==0.15.1
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- cr-features==0.1.8
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- cycler==0.11.0
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- decorator==4.4.2
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- fonttools==4.31.2
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- fonttools==4.33.2
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- h5py==3.6.0
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- hmmlearn==0.2.7
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- ipython-genutils==0.2.0
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- jupyter-core==4.6.3
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- kiwisolver==1.4.2
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@ -124,7 +125,7 @@ dependencies:
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- opencv-python==4.5.5.64
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- packaging==21.3
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- peakutils==1.3.3
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- pillow==9.0.1
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- pillow==9.1.0
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- pulp==2.4
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- pyparsing==2.4.7
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- pyrsistent==0.15.5
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@ -134,5 +135,5 @@ dependencies:
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- snakemake==5.30.2
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- toposort==1.5
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- traitlets==4.3.3
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- typing-extensions==4.1.1
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- typing-extensions==4.2.0
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prefix: /opt/conda/envs/rapids
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@ -771,7 +771,8 @@ rule empatica_accelerometer_python_features:
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sensor_key = "empatica_accelerometer"
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output:
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"data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_python_{provider_key}.csv",
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"data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_python_{provider_key}_windows.csv"
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"data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_python_{provider_key}_windows.csv",
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"data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_python_{provider_key}_windows_SO_features.csv"
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script:
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"../src/features/entry.py"
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@ -798,7 +799,8 @@ rule empatica_heartrate_python_features:
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sensor_key = "empatica_heartrate"
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output:
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"data/interim/{pid}/empatica_heartrate_features/empatica_heartrate_python_{provider_key}.csv",
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"data/interim/{pid}/empatica_heartrate_features/empatica_heartrate_python_{provider_key}_windows.csv"
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"data/interim/{pid}/empatica_heartrate_features/empatica_heartrate_python_{provider_key}_windows.csv",
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"data/interim/{pid}/empatica_heartrate_features/empatica_heartrate_python_{provider_key}_windows_SO_features.csv"
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script:
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"../src/features/entry.py"
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@ -825,7 +827,8 @@ rule empatica_temperature_python_features:
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sensor_key = "empatica_temperature"
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output:
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"data/interim/{pid}/empatica_temperature_features/empatica_temperature_python_{provider_key}.csv",
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"data/interim/{pid}/empatica_temperature_features/empatica_temperature_python_{provider_key}_windows.csv"
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"data/interim/{pid}/empatica_temperature_features/empatica_temperature_python_{provider_key}_windows.csv",
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"data/interim/{pid}/empatica_temperature_features/empatica_temperature_python_{provider_key}_windows_SO_features.csv"
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script:
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"../src/features/entry.py"
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@ -852,7 +855,8 @@ rule empatica_electrodermal_activity_python_features:
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sensor_key = "empatica_electrodermal_activity"
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output:
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"data/interim/{pid}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_{provider_key}.csv",
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"data/interim/{pid}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_{provider_key}_windows.csv"
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"data/interim/{pid}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_{provider_key}_windows.csv",
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"data/interim/{pid}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_{provider_key}_windows_SO_features.csv"
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script:
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"../src/features/entry.py"
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@ -879,7 +883,8 @@ rule empatica_blood_volume_pulse_python_features:
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sensor_key = "empatica_blood_volume_pulse"
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output:
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"data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}.csv",
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"data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}_windows.csv"
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"data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}_windows.csv",
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"data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}_windows_SO_features.csv"
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script:
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"../src/features/entry.py"
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@ -906,7 +911,8 @@ rule empatica_inter_beat_interval_python_features:
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sensor_key = "empatica_inter_beat_interval"
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output:
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"data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_{provider_key}.csv",
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"data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_{provider_key}_windows.csv"
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"data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_{provider_key}_windows.csv",
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"data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_{provider_key}_windows_SO_features.csv"
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script:
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"../src/features/entry.py"
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@ -0,0 +1,30 @@
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import pandas as pd
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def extract_second_order_features(intraday_features, so_features_names):
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if not intraday_features.empty:
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so_features = pd.DataFrame()
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if "mean" in so_features_names:
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so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).mean().add_suffix("_SO_mean")], axis=1)
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if "median" in so_features_names:
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so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).mean().add_suffix("_SO_median")], axis=1)
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if "sd" in so_features_names:
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so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).std().add_suffix("_SO_sd")], axis=1)
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if "max" in so_features_names:
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so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).max().add_suffix("_SO_max")], axis=1)
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if "min" in so_features_names:
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so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).min().add_suffix("_SO_min")], axis=1)
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so_features.reset_index(inplace=True)
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else:
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so_features = pd.DataFrame(columns=["local_segment"])
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return so_features
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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 int(1000/timestamps_diff)
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@ -3,6 +3,7 @@ from scipy.stats import entropy
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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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from cr_features_helper_methods import get_sample_rate, extract_second_order_features
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import sys
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@ -69,4 +70,9 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
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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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if calc_windows:
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so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"]
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acc_second_order_features = extract_second_order_features(acc_intraday_features, so_features_names)
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return acc_intraday_features, acc_second_order_features
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return acc_intraday_features
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@ -3,6 +3,7 @@ from scipy.stats import entropy
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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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from cr_features_helper_methods import get_sample_rate, extract_second_order_features
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import sys
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@ -74,4 +75,9 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
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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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if calc_windows:
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so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"]
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bvp_second_order_features = extract_second_order_features(bvp_intraday_features, so_features_names)
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return bvp_intraday_features, bvp_second_order_features
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return bvp_intraday_features
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@ -3,6 +3,7 @@ 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 get_sample_rate, extract_second_order_features
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def getSampleRate(data):
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@ -62,4 +63,9 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
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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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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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@ -3,6 +3,7 @@ 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_helper_methods import get_sample_rate, extract_second_order_features
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import math
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import sys
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@ -11,14 +12,6 @@ pd.set_option('display.max_rows', 1000)
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pd.set_option('display.max_columns', None)
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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 int(1000/timestamps_diff)
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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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@ -30,12 +23,6 @@ def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_
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if not ibi_intraday_data.empty:
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ibi_intraday_features = pd.DataFrame()
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# np.set_printoptions(threshold=sys.maxsize)
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# print(ibi_intraday_data.groupby('local_segment').apply(lambda x: math.ceil(x['timings'].iloc[-1])))
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# nekaj = ibi_intraday_data.groupby('local_segment').apply(lambda x: \
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# convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], window_length)[1])
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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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@ -82,5 +69,10 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
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# extract features from intraday data
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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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if calc_windows:
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so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"]
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ibi_second_order_features = extract_second_order_features(ibi_intraday_features, so_features_names)
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return ibi_intraday_features, ibi_second_order_features
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return ibi_intraday_features
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@ -3,6 +3,7 @@ from scipy.stats import entropy
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from cr_features.helper_functions import convert_to2d, generic_features
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from cr_features.calculate_features import calculate_features
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from cr_features_helper_methods import get_sample_rate, extract_second_order_features
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import sys
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@ -61,4 +62,10 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
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# extract features from intraday data
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temperature_intraday_features = extract_temp_features_from_intraday_data(temperature_intraday_data, intraday_features_to_compute,
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requested_window_length, time_segment, filter_data_by_segment)
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if calc_windows:
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so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"]
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temperature_second_order_features = extract_second_order_features(temperature_intraday_features, so_features_names)
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return temperature_intraday_features, temperature_second_order_features
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return temperature_intraday_features
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@ -22,13 +22,17 @@ else:
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del sensor_data_files["time_segments_labels"]
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time_segments_file = snakemake.input["time_segments_labels"]
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sensor_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=calc_windows)
|
||||
# Calculation over multiple windows in case of Empatica's CR-features
|
||||
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)
|
||||
first_order_features, second_order_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=calc_windows)
|
||||
first_order_features.to_csv(snakemake.output[1], index=False)
|
||||
second_order_features.to_csv(snakemake.output[2], index=False)
|
||||
calc_windows = False
|
||||
elif "empatica" in sensor_key:
|
||||
pd.DataFrame().to_csv(snakemake.output[1], index=False)
|
||||
pd.DataFrame().to_csv(snakemake.output[2], index=False)
|
||||
|
||||
|
||||
sensor_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=calc_windows)
|
||||
|
||||
|
||||
sensor_features.to_csv(snakemake.output[0], index=False)
|
||||
|
|
|
|||
|
|
@ -93,6 +93,8 @@ def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_file
|
|||
from importlib import import_module, util
|
||||
|
||||
sensor_features = pd.DataFrame(columns=["local_segment"])
|
||||
sensor_fo_features = pd.DataFrame(columns=["local_segment"])
|
||||
sensor_so_features = pd.DataFrame(columns=["local_segment"])
|
||||
time_segments_labels = pd.read_csv(time_segments_file, header=0)
|
||||
if "FEATURES" not in provider:
|
||||
raise ValueError("Provider config[{}][PROVIDERS][{}] is missing a FEATURES attribute in config.yaml".format(sensor_key.upper(), provider_key.upper()))
|
||||
|
|
@ -106,23 +108,57 @@ def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_file
|
|||
time_segments_labels["label"] = [""]
|
||||
for time_segment in time_segments_labels["label"]:
|
||||
print("{} Processing {} {} {}".format(rapids_log_tag, sensor_key, provider_key, time_segment))
|
||||
|
||||
features = feature_function(sensor_data_files, time_segment, provider, filter_data_by_segment=filter_data_by_segment, chunk_episodes=chunk_episodes, calc_windows=calc_windows)
|
||||
if not "local_segment" in features.columns:
|
||||
raise ValueError("The dataframe returned by the " + sensor_key + " provider '" + provider_key + "' is missing the 'local_segment' column added by the 'filter_data_by_segment()' function. Check the provider script is using such function and is not removing 'local_segment' by accident (" + provider["SRC_SCRIPT"] + ")\n The 'local_segment' column is used to index a provider's features (each row corresponds to a different time segment instance (e.g. 2020-01-01, 2020-01-02, 2020-01-03, etc.)")
|
||||
features.columns = ["{}{}".format("" if col.startswith("local_segment") else (sensor_key + "_"+ provider_key + "_"), col) for col in features.columns]
|
||||
sensor_features = pd.concat([sensor_features, features], axis=0, sort=False)
|
||||
|
||||
# In case of calc_window = True
|
||||
if isinstance(features, tuple):
|
||||
if not "local_segment" in features[0].columns or not "local_segment" in features[1].columns:
|
||||
raise ValueError("The dataframe returned by the " + sensor_key + " provider '" + provider_key + "' is missing the 'local_segment' column added by the 'filter_data_by_segment()' function. Check the provider script is using such function and is not removing 'local_segment' by accident (" + provider["SRC_SCRIPT"] + ")\n The 'local_segment' column is used to index a provider's features (each row corresponds to a different time segment instance (e.g. 2020-01-01, 2020-01-02, 2020-01-03, etc.)")
|
||||
features[0].columns = ["{}{}".format("" if col.startswith("local_segment") else (sensor_key + "_"+ provider_key + "_"), col) for col in features[0].columns]
|
||||
features[1].columns = ["{}{}".format("" if col.startswith("local_segment") else (sensor_key + "_"+ provider_key + "_"), col) for col in features[1].columns]
|
||||
if not features[0].empty:
|
||||
sensor_fo_features = pd.concat([sensor_features, features[0]], axis=0, sort=False)
|
||||
if not features[1].empty:
|
||||
sensor_so_features = pd.concat([sensor_features, features[1]], axis=0, sort=False)
|
||||
else:
|
||||
if not "local_segment" in features.columns:
|
||||
raise ValueError("The dataframe returned by the " + sensor_key + " provider '" + provider_key + "' is missing the 'local_segment' column added by the 'filter_data_by_segment()' function. Check the provider script is using such function and is not removing 'local_segment' by accident (" + provider["SRC_SCRIPT"] + ")\n The 'local_segment' column is used to index a provider's features (each row corresponds to a different time segment instance (e.g. 2020-01-01, 2020-01-02, 2020-01-03, etc.)")
|
||||
features.columns = ["{}{}".format("" if col.startswith("local_segment") else (sensor_key + "_"+ provider_key + "_"), col) for col in features.columns]
|
||||
sensor_features = pd.concat([sensor_features, features], axis=0, sort=False)
|
||||
else:
|
||||
for feature in provider["FEATURES"]:
|
||||
sensor_features[feature] = None
|
||||
segment_colums = pd.DataFrame()
|
||||
sensor_features['local_segment'] = sensor_features['local_segment'].str.replace(r'_RR\d+SS', '')
|
||||
split_segemnt_columns = sensor_features["local_segment"].str.split(pat="(.*)#(.*),(.*)", expand=True)
|
||||
new_segment_columns = split_segemnt_columns.iloc[:,1:4] if split_segemnt_columns.shape[1] == 5 else pd.DataFrame(columns=["local_segment_label", "local_segment_start_datetime","local_segment_end_datetime"])
|
||||
segment_colums[["local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]] = new_segment_columns
|
||||
for i in range(segment_colums.shape[1]):
|
||||
sensor_features.insert(1 + i, segment_colums.columns[i], segment_colums[segment_colums.columns[i]])
|
||||
|
||||
return sensor_features
|
||||
if calc_windows:
|
||||
segment_colums = pd.DataFrame()
|
||||
sensor_fo_features['local_segment'] = sensor_fo_features['local_segment'].str.replace(r'_RR\d+SS', '')
|
||||
split_segemnt_columns = sensor_fo_features["local_segment"].str.split(pat="(.*)#(.*),(.*)", expand=True)
|
||||
new_segment_columns = split_segemnt_columns.iloc[:,1:4] if split_segemnt_columns.shape[1] == 5 else pd.DataFrame(columns=["local_segment_label", "local_segment_start_datetime","local_segment_end_datetime"])
|
||||
segment_colums[["local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]] = new_segment_columns
|
||||
for i in range(segment_colums.shape[1]):
|
||||
sensor_fo_features.insert(1 + i, segment_colums.columns[i], segment_colums[segment_colums.columns[i]])
|
||||
|
||||
segment_colums = pd.DataFrame()
|
||||
sensor_so_features['local_segment'] = sensor_so_features['local_segment'].str.replace(r'_RR\d+SS', '')
|
||||
split_segemnt_columns = sensor_so_features["local_segment"].str.split(pat="(.*)#(.*),(.*)", expand=True)
|
||||
new_segment_columns = split_segemnt_columns.iloc[:,1:4] if split_segemnt_columns.shape[1] == 5 else pd.DataFrame(columns=["local_segment_label", "local_segment_start_datetime","local_segment_end_datetime"])
|
||||
segment_colums[["local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]] = new_segment_columns
|
||||
for i in range(segment_colums.shape[1]):
|
||||
sensor_so_features.insert(1 + i, segment_colums.columns[i], segment_colums[segment_colums.columns[i]])
|
||||
|
||||
return sensor_fo_features, sensor_so_features
|
||||
|
||||
else:
|
||||
segment_colums = pd.DataFrame()
|
||||
sensor_features['local_segment'] = sensor_features['local_segment'].str.replace(r'_RR\d+SS', '')
|
||||
split_segemnt_columns = sensor_features["local_segment"].str.split(pat="(.*)#(.*),(.*)", expand=True)
|
||||
new_segment_columns = split_segemnt_columns.iloc[:,1:4] if split_segemnt_columns.shape[1] == 5 else pd.DataFrame(columns=["local_segment_label", "local_segment_start_datetime","local_segment_end_datetime"])
|
||||
segment_colums[["local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]] = new_segment_columns
|
||||
for i in range(segment_colums.shape[1]):
|
||||
sensor_features.insert(1 + i, segment_colums.columns[i], segment_colums[segment_colums.columns[i]])
|
||||
|
||||
return sensor_features
|
||||
|
||||
def run_provider_cleaning_script(provider, provider_key, sensor_key, sensor_data_files):
|
||||
from importlib import import_module, util
|
||||
|
|
|
|||
Loading…
Reference in New Issue