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Exception handling in case of empty ibi. Changes of the method EDA uses in main.py. Other small corrections.

sociality-task
primoz 2022-06-03 12:34:36 +00:00
parent d300f0f8f0
commit 2acf6ff9fb
6 changed files with 22 additions and 17 deletions
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8
config.yaml
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@ -3,7 +3,7 @@
########################################################################################################################
# See https://www.rapids.science/latest/setup/configuration/#participant-files
PIDS: [p03] #p01, p02, p03]
PIDS: [p02] #p01, p02, p03]
# See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files
CREATE_PARTICIPANT_FILES:
@ -484,7 +484,7 @@ EMPATICA_ACCELEROMETER:
FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py
CR:
COMPUTE: True
COMPUTE: False
FEATURES: ["totalMagnitudeBand", "absoluteMeanBand", "varianceBand"] # Acc features
WINDOWS:
COMPUTE: True
@ -511,7 +511,7 @@ EMPATICA_TEMPERATURE:
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: ["countAboveMean", "countBelowMean", "maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", "longestStrikeBelowMean",
"stdDev", "median", "meanChange", "sumSquared", "squareSumOfComponent", "sumOfSquareComponents"]
WINDOWS:
@ -529,7 +529,7 @@ EMPATICA_ELECTRODERMAL_ACTIVITY:
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',

9
src/data/streams/empatica_zip/container.py
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@ -129,19 +129,18 @@ def patch_ibi_with_bvp(ibi_data, bvp_data):
ibi_data_file = BytesIO(ibi_data).getvalue().decode('utf-8')
ibi_data_file = StringIO(ibi_data_file)
# Begin with the cr-features part
try:
ibi_data, ibi_start_timestamp = empatica2d_to_array(ibi_data_file)
except IndexError:
except IndexError as e:
# Checks whether IBI.csv is empty
df_test = pd.read_csv(ibi_data_file, names=['timings', 'inter_beat_interval'], header=None)
print(df_test)
if df_test.empty:
df_test['timestamp'] = df_test['timings']
df_test = df_test.set_index('timestamp')
return df_test
else:
raise IndexError("Something went wrong with indices. Error that was previously caught:\n", repr(e))
bvp_data_file = BytesIO(bvp_data).getvalue().decode('utf-8')
bvp_data_file = StringIO(bvp_data_file)
@ -154,7 +153,7 @@ def patch_ibi_with_bvp(ibi_data, bvp_data):
winsorize_value=25, hampel_fiter=False, median_filter=False,
mod_z_score_filter=True, sampling=64, feature_names=['meanHr'])
ibi_timings, ibi_rr = get_patched_ibi_with_bvp(ibi_data[0], ibi_data[1], bvp_timings, bvp_rr, min_length=None)
ibi_timings, ibi_rr = get_patched_ibi_with_bvp(ibi_data[0], ibi_data[1], bvp_timings, bvp_rr)
df = \
pd.DataFrame(np.array([ibi_timings, ibi_rr]).transpose(), columns=['timestamp', 'inter_beat_interval'])

4
src/features/empatica_blood_volume_pulse/cr/main.py
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@ -1,7 +1,7 @@
import pandas as pd
from scipy.stats import entropy
from cr_features.helper_functions import convert_to2d, hrv_features, hrv_freq_features
from cr_features.helper_functions import convert_to2d, hrv_features
from cr_features.hrv import extract_hrv_features_2d_wrapper
from cr_features_helper_methods import extract_second_order_features
@ -55,7 +55,7 @@ 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 = hrv_features + hrv_freq_features
base_intraday_features_names = hrv_features
# the subset of requested features this function can compute
intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))

5
src/features/empatica_electrodermal_activity/cr/main.py
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@ -4,6 +4,7 @@ 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.gsr import extractGsrFeatures2D
from cr_features_helper_methods import extract_second_order_features
import sys
@ -29,11 +30,11 @@ def extract_eda_features_from_intraday_data(eda_intraday_data, features, window_
if window_length is None:
eda_intraday_features = \
eda_intraday_data.groupby('local_segment').apply(\
lambda x: calculate_features(convert_to2d(x['electrodermal_activity'], x.shape[0]), fs=sample_rate, feature_names=features))
lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], x.shape[0]), sampleRate=sample_rate, threshold=0, featureNames=features))
else:
eda_intraday_features = \
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))
lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], window_length*sample_rate), sampleRate=sample_rate, threshold=0, featureNames=features))
eda_intraday_features.reset_index(inplace=True)

4
src/features/empatica_inter_beat_interval/cr/main.py
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@ -1,7 +1,7 @@
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.helper_functions import convert_ibi_to2d_time, hrv_features
from cr_features.hrv import extract_hrv_features_2d_wrapper, get_HRV_features
from cr_features_helper_methods import extract_second_order_features
@ -61,7 +61,7 @@ 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 = hrv_features + hrv_freq_features
base_intraday_features_names = hrv_features
# the subset of requested features this function can compute
intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))

9
tests/scripts/missing_values_heatmap.py
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@ -5,14 +5,19 @@ import matplotlib.pyplot as plt
# path = "/rapids/data/processed/features/all_participants/all_sensor_features.csv" # all features all participants
# path = "/rapids/data/interim/p03/empatica_accelerometer_features/empatica_accelerometer_python_cr_windows.csv"
path = "/rapids/data/interim/p02/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_cr_windows.csv"
path = "/rapids/data/interim/p03/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_cr_windows.csv"
# path = "/rapids/data/interim/p02/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_cr_windows.csv"
# path = "/rapids/data/interim/p02/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_cr_windows.csv"
# path = "/rapids/data/interim/p02/empatica_temperature_features/empatica_temperature_python_cr_windows.csv"
df = pd.read_csv(path)
print(df)
is_NaN = df. isnull()
row_has_NaN = is_NaN. any(axis=1)
rows_with_NaN = df[row_has_NaN]
print(rows_with_NaN.size)
sns.heatmap(df.isna(), cbar=False)
plt.savefig('eda_windows_p02_window_60_more_peaks.png', bbox_inches='tight')
plt.savefig('eda_windows_p03_window_60_thresh_default.png', bbox_inches='tight')