68 lines
2.5 KiB
Python
68 lines
2.5 KiB
Python
import sys
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sys.path.append("..")
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from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, gsrFeatureNames
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from CalculatingFeatures.calculate_features import calculateFeatures
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from CalculatingFeatures import gsr
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from eda_explorer.load_files import butter_lowpass_filter
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from eda_explorer.EDA_Peak_Detection_Script import calcPeakFeatures
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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pd.set_option('display.max_rows', None)
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pd.set_option('display.max_columns', None)
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pathToEDACsv = "../example_data/S2_E4_Data/EDA.csv"
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# get an array of values from EDA empatica file
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eda_data, startTimeStamp_EDA, sampleRate_EDA = convert1DEmpaticaToArray(pathToEDACsv)
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eda_data = eda_data[:int(300000//sampleRate_EDA)]
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windowLength_EDA = int(sampleRate_EDA*120)
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# Convert the HRV data into 2D array
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eda_data_2D = convertInputInto2d(eda_data, windowLength_EDA)
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calculatedFeatures_EDA = calculateFeatures(eda_data_2D, fs=int(sampleRate_EDA), featureNames=gsrFeatureNames)
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print(calculatedFeatures_EDA)
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# df_EDA = pd.DataFrame()
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# for row in eda_data_2D:
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# current_result = {}
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# current_result.update(gsr.extractGsrFeatures(row, sampleRate=int(sampleRate_EDA),featureNames=gsrFeatureNames))
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# df_EDA = df_EDA.append(current_result, ignore_index=True)
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# no_interest = 131
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# current_row = eda_data_2D[no_interest,]
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# filtered_EDA = butter_lowpass_filter(current_row, 1.0, int(sampleRate_EDA), 6)
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# plt.figure()
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# plt.plot(current_row, color='blue')
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# plt.plot(filtered_EDA, color='red')
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# plt.savefig('output_images/EDA_exa1.png')
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# gsr_data = pd.DataFrame(current_row, columns=["EDA"])
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# startTime = pd.to_datetime(0, unit="s")
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# gsr_data.index = pd.date_range(start=startTime, periods=len(gsr_data), freq=str(1000/sampleRate_EDA) + 'L')
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# gsr_data['filtered_eda'] = filtered_EDA
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# peakData = calcPeakFeatures(gsr_data, offset=1, thresh=.02, start_WT=4, end_WT=4, sampleRate=int(sampleRate_EDA))
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# peaks = np.where(peakData.peaks == 1.0)[0]
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# peak_starts = np.where(peakData.peak_start == 1.0)[0]
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# peak_ends = np.where(peakData.peak_end == 1.0)[0]
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# print(peaks)
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# print(peak_starts)
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# print(peak_ends)
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# plt.figure()
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# plt.plot(filtered_EDA, color='red')
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# plt.scatter(peaks, filtered_EDA[peaks], color="green")
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# plt.scatter(peak_starts, filtered_EDA[peak_starts], color="green", marker=">", alpha=0.5)
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# plt.scatter(peak_ends, filtered_EDA[peak_ends], color="green", marker="s", alpha=0.5)
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# plt.savefig('output_images/EDA_exa2.png')
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# print(df_EDA.loc[no_interest, df_EDA.columns.str.contains('Peak')])
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