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Patching IBI with BVP. WIP

sociality-task
Primoz 2022-05-20 13:18:45 +00:00
parent 1471c86c62
commit 2a8f58f5c8
8 changed files with 46 additions and 18 deletions
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2
Snakefile
View File

@ -359,6 +359,8 @@ for provider in config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"].keys():
if config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"][provider]["COMPUTE"]:
files_to_compute.extend(expand("data/raw/{pid}/empatica_inter_beat_interval_raw.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/raw/{pid}/empatica_inter_beat_interval_with_datetime.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/raw/{pid}/empatica_blood_volume_pulse_raw.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/raw/{pid}/empatica_blood_volume_pulse_with_datetime.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_{language}_{provider_key}.csv", pid=config["PIDS"], language=get_script_language(config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"][provider]["SRC_SCRIPT"]), provider_key=provider.lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/empatica_inter_beat_interval.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))

23
config.yaml
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@ -3,7 +3,7 @@
########################################################################################################################
# See https://www.rapids.science/latest/setup/configuration/#participant-files
PIDS: [p01, p02]
PIDS: [p01] #p02, p03]
# See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files
CREATE_PARTICIPANT_FILES:
@ -477,11 +477,11 @@ EMPATICA_ACCELEROMETER:
CONTAINER: ACC
PROVIDERS:
DBDP:
COMPUTE: True
COMPUTE: False
FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py
CR:
COMPUTE: True
COMPUTE: False
FEATURES: ["fqHighestPeakFreqs", "fqHighestPeaks", "fqEnergyFeat", "fqEntropyFeat", "fqHistogramBins","fqAbsMean", "fqSkewness", "fqKurtosis", "fqInterquart", # Freq features
"meanLow", "areaLow", "totalAbsoluteAreaBand", "totalMagnitudeBand", "entropyBand", "skewnessBand", "kurtosisBand",
"postureDistanceLow", "absoluteMeanBand", "absoluteAreaBand", "quartilesBand", "interQuartileRangeBand", "varianceBand",
@ -502,7 +502,7 @@ EMPATICA_HEARTRATE:
CONTAINER: HR
PROVIDERS:
DBDP:
COMPUTE: True
COMPUTE: False
FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr"]
SRC_SCRIPT: src/features/empatica_heartrate/dbdp/main.py
@ -511,11 +511,11 @@ 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",
@ -532,11 +532,11 @@ 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',
@ -553,11 +553,11 @@ EMPATICA_BLOOD_VOLUME_PULSE:
CONTAINER: BVP
PROVIDERS:
DBDP:
COMPUTE: True
COMPUTE: False
FEATURES: ["maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"]
SRC_SCRIPT: src/features/empatica_blood_volume_pulse/dbdp/main.py
CR:
COMPUTE: True
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:
@ -571,13 +571,14 @@ EMPATICA_INTER_BEAT_INTERVAL:
CONTAINER: IBI
PROVIDERS:
DBDP:
COMPUTE: True
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: True
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
PATCH_WITH_BVP: True
WINDOWS:
COMPUTE: True
WINDOW_LENGTH: 300 # specify window length in seconds

2
data/external/timesegments_periodic.csv vendored
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@ -1,3 +1,3 @@
label,start_time,length,repeats_on,repeats_value
daily,00:00:00,23H 59M 59S,every_day,0
night,00:00:00,5H 59M 59S,every_day,0
E4baseline,01:00:00,3H,every_day,0

1 label start_time length repeats_on repeats_value
2 daily 00:00:00 23H 59M 59S every_day 0
3 night E4baseline 00:00:00 01:00:00 5H 59M 59S 3H every_day 0

1
rules/features.smk
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@ -899,6 +899,7 @@ rule empatica_blood_volume_pulse_r_features:
rule empatica_inter_beat_interval_python_features:
input:
sensor_data = "data/raw/{pid}/empatica_inter_beat_interval_with_datetime.csv",
bvp_sensor_data = "data/raw/{pid}/empatica_blood_volume_pulse_with_datetime.csv",
time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv"
params:
provider = lambda wildcards: config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"][wildcards.provider_key.upper()],

9
src/features/empatica_electrodermal_activity/cr/main.py
View File

@ -1,10 +1,17 @@
import pandas as pd
import numpy as np
from scipy.stats import entropy
from cr_features.helper_functions import convert_to2d, gsr_features
from cr_features.calculate_features import calculate_features
from cr_features_helper_methods import extract_second_order_features
import sys
#pd.set_option('display.max_columns', None)
#pd.set_option('display.max_rows', None)
#np.seterr(invalid='ignore')
def extract_eda_features_from_intraday_data(eda_intraday_data, features, window_length, time_segment, filter_data_by_segment):
eda_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
@ -28,7 +35,6 @@ def extract_eda_features_from_intraday_data(eda_intraday_data, features, window_
eda_intraday_data.groupby('local_segment').apply(\
lambda x: calculate_features(convert_to2d(x['electrodermal_activity'], window_length*sample_rate), fs=sample_rate, feature_names=features))
eda_intraday_features.reset_index(inplace=True)
return eda_intraday_features
@ -58,6 +64,7 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen
if calc_windows:
so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"]
eda_second_order_features = extract_second_order_features(eda_intraday_features, so_features_names)
return eda_intraday_features, eda_second_order_features
return eda_intraday_features

16
src/features/empatica_inter_beat_interval/cr/main.py
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@ -2,14 +2,14 @@ import pandas as pd
import numpy as np
from cr_features.helper_functions import convert_ibi_to2d_time, hrv_features, hrv_freq_features
from cr_features.hrv import extract_hrv_features_2d_wrapper
from cr_features.hrv import extract_hrv_features_2d_wrapper, get_HRV_features
from cr_features_helper_methods import extract_second_order_features
import math
import sys
pd.set_option('display.max_rows', 1000)
pd.set_option('display.max_columns', None)
#pd.set_option('display.max_columns', None)
def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_length, time_segment, filter_data_by_segment):
@ -48,9 +48,21 @@ def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_
return ibi_intraday_features
def patch_IBI_with_BVP(bvp_intraday_data):
# get features method is used because
hrv_time_and_freq_features, sample, rr, timings, peak_indx = \
get_HRV_features(bvp_intraday_data['blood_volume_pulse'].to_numpy(), hampel_fiter=False, median_filter=False, mod_z_score_filter=True, sampling=64, feature_names=['meanHr'])
def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
print(sensor_data_files)
ibi_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
if provider["PATCH_WITH_BVP"]:
bvp_intraday_data = pd.read_csv(sensor_data_files["bvp_sensor_data"])
patch_IBI_with_BVP(bvp_intraday_data)
# sys.exit()
requested_intraday_features = provider["FEATURES"]
calc_windows = kwargs.get('calc_windows', False)

3
src/features/entry.py
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@ -1,6 +1,8 @@
import pandas as pd
from utils.utils import fetch_provider_features, run_provider_cleaning_script
import sys
sensor_data_files = dict(snakemake.input)
provider = snakemake.params["provider"]
@ -29,7 +31,6 @@ else:
elif "empatica" in sensor_key:
pd.DataFrame().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)
if not calc_windows:

4
src/features/utils/utils.py
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@ -169,3 +169,7 @@ def run_provider_cleaning_script(provider, provider_key, sensor_key, sensor_data
sensor_features = cleaning_function(sensor_data_files, provider)
return sensor_features
def empatica_patch_IBI_with_BVP(bvp_data):
pass