Preparation for the EDA features integration from CF.

calculatingfeatures/cf_tests/calculate_EDA_features_test.py

@@ -9,55 +9,59 @@ import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 
+pd.set_option('display.max_rows', None)
+pd.set_option('display.max_columns', None)
 
-pathToEDACsv = "../example_data/EDA.csv"
+
+pathToEDACsv = "../example_data/S2_E4_Data/EDA.csv"
 
 # get an array of values from EDA empatica file
 eda_data, startTimeStamp_EDA, sampleRate_EDA = convert1DEmpaticaToArray(pathToEDACsv)
+eda_data = eda_data[:int(300000//sampleRate_EDA)]
 
 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)
 
+# 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')])
+
 

config.yaml

@@ -504,12 +504,12 @@ EMPATICA_ELECTRODERMAL_ACTIVITY:
   CONTAINER: EDA
   PROVIDERS:
     DBDP:
-      COMPUTE: False
+      COMPUTE: True
       FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"]
       SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py
     CF:
       COMPUTE: True
-      FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"]
+      FEATURES: ['mean', 'std', 'q25', 'q75', 'qd'] # To-Do add remaining features from CF helper file. 
       SRC_SCRIPT: src/features/empatica_electrodermal_activity/cf/main.py
 
 # See https://www.rapids.science/latest/features/empatica-blood-volume-pulse/