Add folder structure for CF testing and EDA test.

calculatingfeatures/.gitignore

@@ -219,3 +219,6 @@ fabric.properties
 # Android studio 3.1+ serialized cache file
 .idea/caches/build_file_checksums.ser
 
+# Output images
+cf_tests/output_images/
+

calculatingfeatures/cf_tests/calculate_EDA_features_test.py

@@ -0,0 +1,63 @@
+import sys
+sys.path.append("..")
+from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, gsrFeatureNames
+from CalculatingFeatures.calculate_features import calculateFeatures
+from CalculatingFeatures import gsr
+from eda_explorer.load_files import butter_lowpass_filter
+from eda_explorer.EDA_Peak_Detection_Script import calcPeakFeatures
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+
+pathToEDACsv = "../example_data/EDA.csv"
+
+# get an array of values from EDA empatica file
+eda_data, startTimeStamp_EDA, sampleRate_EDA = convert1DEmpaticaToArray(pathToEDACsv)
+
+windowLength_EDA = int(sampleRate_EDA*120)
+
+# Convert the HRV data into 2D array
+eda_data_2D = convertInputInto2d(eda_data, windowLength_EDA)
+
+df_EDA = pd.DataFrame()
+for row in eda_data_2D:
+    current_result = {}
+    current_result.update(gsr.extractGsrFeatures(row, sampleRate=int(sampleRate_EDA),featureNames=gsrFeatureNames))
+    df_EDA = df_EDA.append(current_result, ignore_index=True)
+
+no_interest = 131
+current_row = eda_data_2D[no_interest,]
+filtered_EDA = butter_lowpass_filter(current_row, 1.0, int(sampleRate_EDA), 6)
+
+plt.figure()
+plt.plot(current_row, color='blue')
+plt.plot(filtered_EDA, color='red')
+plt.savefig('output_images/EDA_exa1.png')
+
+gsr_data = pd.DataFrame(current_row, columns=["EDA"])
+startTime = pd.to_datetime(0, unit="s")
+gsr_data.index = pd.date_range(start=startTime, periods=len(gsr_data), freq=str(1000/sampleRate_EDA) + 'L')
+gsr_data['filtered_eda'] = filtered_EDA
+peakData = calcPeakFeatures(gsr_data, offset=1, thresh=.02, start_WT=4, end_WT=4, sampleRate=int(sampleRate_EDA))
+
+peaks = np.where(peakData.peaks == 1.0)[0]
+peak_starts = np.where(peakData.peak_start == 1.0)[0]
+peak_ends = np.where(peakData.peak_end == 1.0)[0]
+print(peaks)
+print(peak_starts)
+print(peak_ends)
+
+plt.figure()
+plt.plot(filtered_EDA, color='red')
+plt.scatter(peaks, filtered_EDA[peaks], color="green")
+plt.scatter(peak_starts, filtered_EDA[peak_starts], color="green", marker=">", alpha=0.5)
+plt.scatter(peak_ends, filtered_EDA[peak_ends], color="green", marker="s", alpha=0.5)
+plt.savefig('output_images/EDA_exa2.png')
+
+print(df_EDA.loc[no_interest, df_EDA.columns.str.contains('Peak')])
+
+# calculatedFeatures_EDA = calculateFeatures(eda_data_2D, fs=int(sampleRate_EDA), featureNames=gsrFeatureNames)
+# print(calculatedFeatures_EDA)
+
+

calculatingfeatures/cf_tests/calculate_HR_features_test.py

@@ -1,8 +1,12 @@
-from calculatingfeatures.CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, frequencyFeatureNames, hrvFeatureNames
-from calculatingfeatures.CalculatingFeatures.calculate_features import calculateFeatures
+import sys
+sys.path.append("..")
+from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, frequencyFeatureNames, hrvFeatureNames
+from CalculatingFeatures.calculate_features import calculateFeatures
 import pandas as pd
 
-pathToHrvCsv = "calculatingfeatures/example_data/S2_E4_Data/BVP.csv"
+
+
+pathToHrvCsv = "../example_data/S2_E4_Data/BVP.csv"
 windowLength = 500
 
 # get an array of values from HRV empatica file