Patching IBI with BVP - completed.
Primoz
parent
bb62497ba6
commit
5532043b1f
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@ -899,7 +899,6 @@ rule empatica_blood_volume_pulse_r_features:
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rule empatica_inter_beat_interval_python_features:
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input:
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sensor_data = "data/raw/{pid}/empatica_inter_beat_interval_with_datetime.csv",
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bvp_sensor_data = "data/raw/{pid}/empatica_blood_volume_pulse_with_datetime.csv",
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time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv"
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params:
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provider = lambda wildcards: config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"][wildcards.provider_key.upper()],
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@ -2,13 +2,16 @@ from zipfile import ZipFile
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import warnings
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from pathlib import Path
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import pandas as pd
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import numpy as np
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from pandas.core import indexing
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import yaml
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import csv
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from collections import OrderedDict
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from io import BytesIO, StringIO
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import sys, os
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from cr_features.hrv import get_HRV_features
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from cr_features.hrv import get_HRV_features, get_patched_ibi_with_bvp
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from cr_features.helper_functions import empatica1d_to_array, empatica2d_to_array
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def processAcceleration(x, y, z):
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x = float(x)
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@ -88,6 +91,10 @@ def pull_data(data_configuration, device, sensor, container, columns_to_download
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participant_data = pd.DataFrame(columns=columns_to_download.values())
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participant_data.set_index('timestamp', inplace=True)
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with open('config.yaml', 'r') as stream:
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config = yaml.load(stream, Loader=yaml.FullLoader)
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cr_ibi_provider = config['EMPATICA_INTER_BEAT_INTERVAL']['PROVIDERS']['CR']
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available_zipfiles = list((Path(data_configuration["FOLDER"]) / Path(device)).rglob("*.zip"))
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if len(available_zipfiles) == 0:
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warnings.warn("There were no zip files in: {}. If you were expecting data for this participant the [EMPATICA][DEVICE_IDS] key in their participant file is missing the pid".format((Path(data_configuration["FOLDER"]) / Path(device))))
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@ -96,17 +103,15 @@ def pull_data(data_configuration, device, sensor, container, columns_to_download
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print("Extracting {} data from {} for {}".format(sensor, zipfile, device))
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with ZipFile(zipfile, 'r') as zipFile:
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listOfFileNames = zipFile.namelist()
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if sensor == "EMPATICA_INTER_BEAT_INTERVAL":
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extracted_bvp_data = extract_empatica_data(zipFile.read('BVP.csv'), "EMPATICA_BLOOD_VOLUME_PULSE")
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hrv_time_and_freq_features, sample, bvp_rr, bvp_timings, peak_indx = \
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get_HRV_features(extracted_bvp_data['blood_volume_pulse'].to_numpy(), ma=False, detrend=False, m_deternd=False,
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low_pass=False, winsorize=True, winsorize_value=25,
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hampel_fiter=False, median_filter=False, mod_z_score_filter=True,
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sampling=64, feature_names=['meanHr'])
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print(bvp_rr, bvp_timings)
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for fileName in listOfFileNames:
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if fileName == sensor_csv:
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if sensor == "EMPATICA_INTER_BEAT_INTERVAL" and cr_ibi_provider.get('PATCH_WITH_BVP', False):
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participant_data = \
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pd.concat([participant_data, patch_ibi_with_bvp(zipFile.read('IBI.csv'), zipFile.read('BVP.csv'))], axis=0)
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#print("patch with ibi")
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else:
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participant_data = pd.concat([participant_data, extract_empatica_data(zipFile.read(fileName), sensor)], axis=0)
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#print("no patching")
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warning = False
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if warning:
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warnings.warn("We could not find a zipped file for {} in {} (we tried to find {})".format(sensor, zipFile, sensor_csv))
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@ -117,4 +122,42 @@ def pull_data(data_configuration, device, sensor, container, columns_to_download
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participant_data["device_id"] = device
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return(participant_data)
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def patch_ibi_with_bvp(ibi_data, bvp_data):
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ibi_data_file = BytesIO(ibi_data).getvalue().decode('utf-8')
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ibi_data_file = StringIO(ibi_data_file)
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ibi_data, ibi_start_timestamp = empatica2d_to_array(ibi_data_file)
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bvp_data_file = BytesIO(bvp_data).getvalue().decode('utf-8')
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bvp_data_file = StringIO(bvp_data_file)
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bvp_data, bvp_start_timestamp, sample_rate = empatica1d_to_array(bvp_data_file)
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hrv_time_and_freq_features, sample, bvp_rr, bvp_timings, peak_indx = \
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get_HRV_features(bvp_data, ma=False,
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detrend=False, m_deternd=False, low_pass=False, winsorize=True,
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winsorize_value=25, hampel_fiter=False, median_filter=False,
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mod_z_score_filter=True, sampling=64, feature_names=['meanHr'])
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ibi_timings, ibi_rr = get_patched_ibi_with_bvp(ibi_data[0], ibi_data[1], bvp_timings, bvp_rr, min_length=10)
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df = \
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pd.DataFrame(np.array([ibi_timings, ibi_rr]).transpose(), columns=['timestamp', 'inter_beat_interval'])
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df.loc[-1] = [ibi_start_timestamp, 'IBI'] # adding a row
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df.index = df.index + 1 # shifting index
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df = df.sort_index() # sorting by index
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# Repeated as in extract_empatica_data for IBI
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df['timings'] = df['timestamp']
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timestampstart = float(df['timestamp'][0])
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df['timestamp'] = (df['timestamp'][1:len(df)]).astype(float) + timestampstart
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df = df.drop([0])
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df['inter_beat_interval'] = df['inter_beat_interval'].astype(float)
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df = df.set_index('timestamp')
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# format timestamps
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df.index *= 1000
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df.index = df.index.astype(int)
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return(df)
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# print(pull_data({'FOLDER': 'data/external/empatica'}, "e01", "EMPATICA_accelerometer", {'TIMESTAMP': 'timestamp', 'DEVICE_ID': 'device_id', 'DOUBLE_VALUES_0': 'x', 'DOUBLE_VALUES_1': 'y', 'DOUBLE_VALUES_2': 'z'}))
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@ -48,21 +48,9 @@ def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_
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return ibi_intraday_features
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def patch_IBI_with_BVP(bvp_intraday_data):
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# get features method is used because
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hrv_time_and_freq_features, sample, rr, timings, peak_indx = \
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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'])
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def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
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print(sensor_data_files)
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ibi_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
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if provider["PATCH_WITH_BVP"]:
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bvp_intraday_data = pd.read_csv(sensor_data_files["bvp_sensor_data"])
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patch_IBI_with_BVP(bvp_intraday_data)
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# sys.exit()
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requested_intraday_features = provider["FEATURES"]
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calc_windows = kwargs.get('calc_windows', False)
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@ -169,7 +169,3 @@ def run_provider_cleaning_script(provider, provider_key, sensor_key, sensor_data
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sensor_features = cleaning_function(sensor_data_files, provider)
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return sensor_features
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def empatica_patch_IBI_with_BVP(bvp_data):
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pass
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