32 lines
1.1 KiB
Python
32 lines
1.1 KiB
Python
import sys
|
|
sys.path.append("..")
|
|
from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, accelerometerFeatureNames, frequencyFeatureNames
|
|
from CalculatingFeatures.helper_functions import convert3DEmpaticaToArray
|
|
from CalculatingFeatures.calculate_features import calculateFeatures
|
|
|
|
import pandas as pd
|
|
|
|
pathToAccCsv = "../example_data/S2_E4_Data_shortened/ACC.csv"
|
|
windowLength = 500
|
|
|
|
#np.seterr(all='raise')
|
|
|
|
# get an array of values from ACC empatica file
|
|
acc_data, startTimeStamp, sampleRate = convert3DEmpaticaToArray(pathToAccCsv)
|
|
acc_data = acc_data[:, :int(300000//sampleRate)]
|
|
|
|
# Convert the ACC data into 2D array
|
|
x_2D = convertInputInto2d(acc_data[0], windowLength)
|
|
y_2D = convertInputInto2d(acc_data[1], windowLength)
|
|
z_2D = convertInputInto2d(acc_data[2], windowLength)
|
|
|
|
# Create a list with feature names
|
|
featureNames = []
|
|
featureNames.extend(accelerometerFeatureNames)
|
|
featureNames.extend(frequencyFeatureNames)
|
|
|
|
pd.set_option('display.max_columns', None)
|
|
|
|
# Calculate features
|
|
calculatedFeatures = calculateFeatures(x_2D, y_2D, z_2D, fs=int(sampleRate), featureNames=featureNames)
|
|
print(calculatedFeatures) |