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Get a sample rate from two sequential timestamps.

sociality-task
= 2022-03-28 13:50:08 +00:00
parent 393dab72f5
commit ca59a54d8f
2 changed files with 22 additions and 5 deletions
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11
src/features/empatica_electrodermal_activity/cf/main.py
View File

@ -7,11 +7,20 @@ from CalculatingFeatures.calculate_features import calculateFeatures
pd.set_option('display.max_columns', None)
def getSampleRate(data):
try:
timestamps_diff = data['timestamp'].iloc[1] - data['timestamp'].iloc[0]
except:
raise Exception("Error occured while trying to get the sample rate from the first two sequential timestamps.")
return 1000/timestamps_diff
def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, time_segment, filter_data_by_segment):
eda_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
if not eda_intraday_data.empty:
sample_rate = getSampleRate(eda_intraday_data)
eda_intraday_data = filter_data_by_segment(eda_intraday_data, time_segment)
if not eda_intraday_data.empty:
@ -21,7 +30,7 @@ def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, time_segment
# apply methods from calculate features module
eda_intraday_features = \
eda_intraday_data.groupby('local_segment').apply(\
lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], x.shape[0]), fs=4, featureNames=features))
lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], x.shape[0]), fs=sample_rate, featureNames=features)) # To-Do: Hardcoded f/s ...
eda_intraday_features.reset_index(inplace=True)

12
src/features/empatica_temperature/cf/main.py
View File

@ -4,13 +4,21 @@ from scipy.stats import entropy
from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, genericFeatureNames
from CalculatingFeatures.calculate_features import calculateFeatures
import sys
def getSampleRate(data):
try:
timestamps_diff = data['timestamp'].iloc[1] - data['timestamp'].iloc[0]
except:
raise Exception("Error occured while trying to get the sample rate from the first two sequential timestamps.")
return 1000/timestamps_diff
def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, time_segment, filter_data_by_segment):
temperature_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
if not temperature_intraday_data.empty:
sample_rate = getSampleRate(temperature_intraday_data)
temperature_intraday_data = filter_data_by_segment(temperature_intraday_data, time_segment)
if not temperature_intraday_data.empty:
@ -20,7 +28,7 @@ def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, tim
# apply methods from calculate features module
temperature_intraday_features = \
temperature_intraday_data.groupby('local_segment').apply(\
lambda x: calculateFeatures(convertInputInto2d(x['temperature'], x.shape[0]), fs=4, featureNames=features))
lambda x: calculateFeatures(convertInputInto2d(x['temperature'], x.shape[0]), fs=sample_rate, featureNames=features)) # To-Do: Hardcoded f/s ...
temperature_intraday_features.reset_index(inplace=True)