From 4af553cf80e7c2a207d832c20f6aaade98365ee8 Mon Sep 17 00:00:00 2001 From: Walker Arce Date: Sat, 30 Jul 2022 17:51:29 -0500 Subject: [PATCH] Wrapping up v1.2.7 - Cleaned up E4 metrics calculation - Updated user guide --- e4_utils.py | 281 +++++++++++------- menu_bar.py | 2 +- reference/Cometrics User Guide.pdf | Bin 1911509 -> 1949131 bytes ...e_v6.docx => Cometrics User Guide_v7.docx} | Bin 3949807 -> 3998729 bytes user_guide/Cometrics User Guide_v7.pdf | Bin 0 -> 1949131 bytes 5 files changed, 170 insertions(+), 113 deletions(-) rename user_guide/{Cometrics User Guide_v6.docx => Cometrics User Guide_v7.docx} (89%) create mode 100644 user_guide/Cometrics User Guide_v7.pdf diff --git a/e4_utils.py b/e4_utils.py index cc7fd4c..f524c75 100644 --- a/e4_utils.py +++ b/e4_utils.py @@ -1,8 +1,11 @@ +import _tkinter import csv import glob import json import os import pathlib +import threading +import tkinter from datetime import datetime from enum import IntEnum from tkinter import messagebox @@ -10,7 +13,11 @@ import numpy as np import pandas as pd import warnings + from pyempatica import EmpaticaE4 +from tkvideoutils import ImageLabel + +from tkinter_utils import center warnings.filterwarnings('ignore') @@ -40,6 +47,19 @@ class EmpaticaData(IntEnum): date_format = "%B %d, %Y" time_format = "%H:%M:%S" datetime_format = date_format + time_format +eda_header = ['SCR_Peaks_N', 'SCR_Peaks_Amplitude_Mean'] +ppg_header = ['PPG_Rate_Mean', 'HRV_MeanNN', 'HRV_SDNN', 'HRV_SDANN1', 'HRV_SDNNI1', + 'HRV_SDANN2', 'HRV_SDNNI2', 'HRV_SDANN5', 'HRV_SDNNI5', 'HRV_RMSSD', 'HRV_SDSD', 'HRV_CVNN', + 'HRV_CVSD', 'HRV_MedianNN', 'HRV_MadNN', 'HRV_MCVNN', 'HRV_IQRNN', 'HRV_Prc20NN', 'HRV_Prc80NN', + 'HRV_pNN50', 'HRV_pNN20', 'HRV_MinNN', 'HRV_MaxNN', 'HRV_HTI', 'HRV_TINN', 'HRV_ULF', 'HRV_VLF', + 'HRV_LF', 'HRV_HF', 'HRV_VHF', 'HRV_LFHF', 'HRV_LFn', 'HRV_HFn', 'HRV_LnHF', 'HRV_SD1', 'HRV_SD2', + 'HRV_SD1SD2', 'HRV_S', 'HRV_CSI', 'HRV_CVI', 'HRV_CSI_Modified', 'HRV_PIP', 'HRV_IALS', 'HRV_PSS', + 'HRV_PAS', 'HRV_GI', 'HRV_SI', 'HRV_AI', 'HRV_PI', 'HRV_C1d', 'HRV_C1a', 'HRV_SD1d', 'HRV_SD1a', + 'HRV_C2d', 'HRV_C2a', 'HRV_SD2d', 'HRV_SD2a', 'HRV_Cd', 'HRV_Ca', 'HRV_SDNNd', 'HRV_SDNNa', + 'HRV_DFA_alpha1', 'HRV_MFDFA_alpha1_Width', 'HRV_MFDFA_alpha1_Peak', 'HRV_MFDFA_alpha1_Mean', + 'HRV_MFDFA_alpha1_Max', 'HRV_MFDFA_alpha1_Delta', 'HRV_MFDFA_alpha1_Asymmetry', + 'HRV_MFDFA_alpha1_Fluctuation', 'HRV_MFDFA_alpha1_Increment', 'HRV_ApEn', 'HRV_SampEn', 'HRV_ShanEn', + 'HRV_FuzzyEn', 'HRV_MSEn', 'HRV_CMSEn', 'HRV_RCMSEn', 'HRV_CD', 'HRV_HFD', 'HRV_KFD', 'HRV_LZC'] def find_indices(search_list, search_item): @@ -84,124 +104,161 @@ def convert_timestamps(empatica_data): empatica_data[EmpaticaData.EDA_TIMESTAMPS] = [int(l) for l in empatica_data[EmpaticaData.EDA_TIMESTAMPS]] -def export_e4_metrics(prim_dir, reli_dir, time_period=20): +def export_e4_metrics(root, prim_dir, reli_dir, output_dir, time_period=20): prim_files = glob.glob(f'{prim_dir}/**/*.json', recursive=True) reli_files = glob.glob(f'{reli_dir}/**/*.json', recursive=True) prim_filepaths = [_ for _ in prim_files if _.split("\\")[0]] reli_filepaths = [_ for _ in reli_files if _.split("\\")[0]] - eda_header = ['SCR_Peaks_N', 'SCR_Peaks_Amplitude_Mean'] - ppg_header = ['PPG_Rate_Mean', 'HRV_MeanNN', 'HRV_SDNN', 'HRV_SDANN1', 'HRV_SDNNI1', - 'HRV_SDANN2', 'HRV_SDNNI2', 'HRV_SDANN5', 'HRV_SDNNI5', 'HRV_RMSSD', 'HRV_SDSD', 'HRV_CVNN', - 'HRV_CVSD', 'HRV_MedianNN', 'HRV_MadNN', 'HRV_MCVNN', 'HRV_IQRNN', 'HRV_Prc20NN', 'HRV_Prc80NN', - 'HRV_pNN50', 'HRV_pNN20', 'HRV_MinNN', 'HRV_MaxNN', 'HRV_HTI', 'HRV_TINN', 'HRV_ULF', 'HRV_VLF', - 'HRV_LF', 'HRV_HF', 'HRV_VHF', 'HRV_LFHF', 'HRV_LFn', 'HRV_HFn', 'HRV_LnHF', 'HRV_SD1', 'HRV_SD2', - 'HRV_SD1SD2', 'HRV_S', 'HRV_CSI', 'HRV_CVI', 'HRV_CSI_Modified', 'HRV_PIP', 'HRV_IALS', 'HRV_PSS', - 'HRV_PAS', 'HRV_GI', 'HRV_SI', 'HRV_AI', 'HRV_PI', 'HRV_C1d', 'HRV_C1a', 'HRV_SD1d', 'HRV_SD1a', - 'HRV_C2d', 'HRV_C2a', 'HRV_SD2d', 'HRV_SD2a', 'HRV_Cd', 'HRV_Ca', 'HRV_SDNNd', 'HRV_SDNNa', - 'HRV_DFA_alpha1', 'HRV_MFDFA_alpha1_Width', 'HRV_MFDFA_alpha1_Peak', 'HRV_MFDFA_alpha1_Mean', - 'HRV_MFDFA_alpha1_Max', 'HRV_MFDFA_alpha1_Delta', 'HRV_MFDFA_alpha1_Asymmetry', - 'HRV_MFDFA_alpha1_Fluctuation', 'HRV_MFDFA_alpha1_Increment', 'HRV_ApEn', 'HRV_SampEn', 'HRV_ShanEn', - 'HRV_FuzzyEn', 'HRV_MSEn', 'HRV_CMSEn', 'HRV_RCMSEn', 'HRV_CD', 'HRV_HFD', 'HRV_KFD', 'HRV_LZC'] + popup_root = tkinter.Toplevel(root) + popup_root.config(bd=-2) + popup_root.title("Processing") + popup_root.geometry("250x100") + popup_root.config(bg="white") + center(popup_root) + label_var = tkinter.StringVar(popup_root, value=f'Processing Session 0 / {len(prim_filepaths + reli_filepaths)}') + text_label = tkinter.Label(popup_root, textvariable=label_var, font=('Purisa', 11), bg="white") + text_label.place(x=125, y=50, anchor=tkinter.CENTER) + + prim_export = os.path.join(output_dir, "Primary") + reli_export = os.path.join(output_dir, "Reliability") + + # Create directories if they don't exist + if not os.path.exists(reli_export): + os.mkdir(reli_export) + if not os.path.exists(prim_export): + os.mkdir(prim_export) + + e4_metrics_thread = threading.Thread(target=__e4_metrics_thread, args=(prim_filepaths, reli_filepaths, prim_export, + reli_export, time_period, label_var, + output_dir, popup_root)) + e4_metrics_thread.daemon = True + e4_metrics_thread.start() + + +def __e4_metrics_thread(prim_filepaths, reli_filepaths, prim_export, reli_export, time_period, label_var, output_dir, + popup_root): + e4_data_found = False + file_count = 0 if prim_filepaths and reli_filepaths: - for file in prim_filepaths + reli_filepaths: - with open(file, 'r') as f: - json_file = json.load(f) - freq = json_file['KSF']['Frequency'] - freq_header = [] - for f_key in freq: - freq_header.append(f_key[1]) - dur = json_file['KSF']['Duration'] - dur_header = [] - for d_key in dur: - dur_header.append(d_key[1]) - with open(os.path.join(pathlib.Path(file).parent, f"{pathlib.Path(file).stem}_HR.csv"), 'w', - newline='') as ppg_file: - ksf_ppg_header = ['Session Time', 'E4 Time'] + freq_header + dur_header + ppg_header - ppg_f = csv.writer(ppg_file) - ppg_f.writerow([pathlib.Path(file).parts[-3]]) - ppg_f.writerow([pathlib.Path(file).parts[-2]]) - ppg_f.writerow([pathlib.Path(file).stem]) - ppg_f.writerow([str(datetime.now())]) - ppg_f.writerow(ksf_ppg_header) - - with open(os.path.join(pathlib.Path(file).parent, f"{pathlib.Path(file).stem}_EDA.csv"), 'w', - newline='') as eda_file: - ksf_eda_header = ['Session Time', 'E4 Time'] + freq_header + dur_header + eda_header - eda_f = csv.writer(eda_file) - eda_f.writerow([pathlib.Path(file).parts[-3]]) - eda_f.writerow([pathlib.Path(file).parts[-2]]) - eda_f.writerow([pathlib.Path(file).stem]) - eda_f.writerow([str(datetime.now())]) - eda_f.writerow(ksf_eda_header) - - e4_data = json_file['E4 Data'] - event_history = json_file['Event History'] - if e4_data: - if len(e4_data) > 19: - start_time_datetime = convert_timezone( - datetime.strptime(json_file['Session Date'] + json_file['Session Start Time'], - datetime_format)) - start_time = int(EmpaticaE4.get_unix_timestamp(start_time_datetime)) - end_time = int(start_time + int(json_file['Session Time'])) - e4_data = convert_legacy_e4_data(e4_data) - convert_legacy_events_e4(event_history, start_time) - else: - start_time = int(json_file['Session Start Timestamp']) - end_time = int(json_file['Session End Timestamp']) - convert_timestamps(e4_data) - for i in range(start_time + int(time_period / 2), end_time, time_period): - try: - data_time = i - int(time_period / 2) - session_time = data_time - start_time - data_range = (data_time, data_time + time_period) - timestamp_list = np.arange(*data_range) - - ppg_csv_data = [session_time, data_time] + len(freq_header) * [0] + len(dur_header) * [0] - eda_csv_data = [session_time, data_time] + len(freq_header) * [0] + len(dur_header) * [0] - - ppg_data_range = find_indices(e4_data[EmpaticaData.BVP_TIMESTAMPS], timestamp_list) - ppg_data = e4_data[EmpaticaData.BVP][ppg_data_range[0]:ppg_data_range[-1]] - - eda_data_range = find_indices(e4_data[EmpaticaData.EDA_TIMESTAMPS], timestamp_list) - eda_data = e4_data[EmpaticaData.EDA][eda_data_range[0]:eda_data_range[-1]] - - for event in event_history: - if type(event[1]) is list: - event_duration = np.arange(int(event[3][0]), int(event[3][1])) - if int(event[3][0]) in timestamp_list or int(event[3][1]) in timestamp_list: - ppg_csv_data[dur_header.index(event[0]) + 2 + len(freq_header)] = 1 - eda_csv_data[dur_header.index(event[0]) + 2 + len(freq_header)] = 1 - if i in event_duration: - ppg_csv_data[dur_header.index(event[0]) + 2 + len(freq_header)] = 1 - eda_csv_data[dur_header.index(event[0]) + 2 + len(freq_header)] = 1 - else: - if int(event[3]) in timestamp_list: - ppg_csv_data[freq_header.index(event[0]) + 2] = 1 - eda_csv_data[freq_header.index(event[0]) + 2] = 1 - try: - ppg_signals, _ = nk.ppg_process(ppg_data, sampling_rate=64) - ppg_results = nk.ppg_analyze(ppg_signals, sampling_rate=64) - ppg_csv_data.extend(ppg_results.values.ravel().tolist()) - except Exception as e: - print(f"INFO: Could not process PPG signal in {file}") - try: - eda_signals, _ = eda_custom_process(eda_data, sampling_rate=4) - eda_results = nk.eda_analyze(eda_signals, method='interval-related') - eda_csv_data.extend(eda_results.values.ravel().tolist()) - except Exception as e: - print(f"INFO: Could not process EDA signal in {file}") - except KeyError: - print(f"No E4 data found in {file}") - except Exception as e: - print(f"Something went wrong with {file}: {str(e)}") - finally: - eda_f.writerow(eda_csv_data) - ppg_f.writerow(ppg_csv_data) - messagebox.showinfo("E4 Metrics Computed", "E4 sessions have been successfully analyzed!\n" - "Check in raw data folders for output CSV files.") + for file in prim_filepaths: + file_count += 1 + label_var.set(f'Processing Session {file_count} / {len(prim_filepaths + reli_filepaths)}') + e4_data_found |= process_e4_data(file, prim_export, time_period) + for file in reli_filepaths: + file_count += 1 + label_var.set(f'Processing Session {file_count} / {len(prim_filepaths + reli_filepaths)}') + e4_data_found |= process_e4_data(file, reli_export, time_period) + if e4_data_found: + messagebox.showinfo("E4 Metrics Computed", "E4 sessions have been successfully analyzed!\n" + "Check in raw data folders for output CSV files.") + os.startfile(output_dir) + else: + messagebox.showwarning("Warning", "No E4 data found in sessions!") + else: + messagebox.showwarning("Warning", "No sessions found!") + popup_root.destroy() + + +def process_e4_data(file, output_dir, time_period): + with open(file, 'r') as f: + json_file = json.load(f) + e4_data = json_file['E4 Data'] + if e4_data: + freq = json_file['KSF']['Frequency'] + freq_header = [] + for f_key in freq: + freq_header.append(f_key[1]) + dur = json_file['KSF']['Duration'] + dur_header = [] + for d_key in dur: + dur_header.append(d_key[1]) + with open(os.path.join(output_dir, f"{pathlib.Path(file).stem}_HR.csv"), 'w', + newline='') as ppg_file: + ksf_ppg_header = ['Session Time', 'E4 Time'] + freq_header + dur_header + ppg_header + ppg_f = csv.writer(ppg_file) + ppg_f.writerow([pathlib.Path(file).parts[-3]]) + ppg_f.writerow([pathlib.Path(file).parts[-2]]) + ppg_f.writerow([pathlib.Path(file).stem]) + ppg_f.writerow([str(datetime.now())]) + ppg_f.writerow(ksf_ppg_header) + + with open(os.path.join(output_dir, f"{pathlib.Path(file).stem}_EDA.csv"), 'w', + newline='') as eda_file: + ksf_eda_header = ['Session Time', 'E4 Time'] + freq_header + dur_header + eda_header + eda_f = csv.writer(eda_file) + eda_f.writerow([pathlib.Path(file).parts[-3]]) + eda_f.writerow([pathlib.Path(file).parts[-2]]) + eda_f.writerow([pathlib.Path(file).stem]) + eda_f.writerow([str(datetime.now())]) + eda_f.writerow(ksf_eda_header) + + event_history = json_file['Event History'] + if len(e4_data) > 19: + start_time_datetime = convert_timezone( + datetime.strptime(json_file['Session Date'] + json_file['Session Start Time'], + datetime_format)) + start_time = int(EmpaticaE4.get_unix_timestamp(start_time_datetime)) + end_time = int(start_time + int(json_file['Session Time'])) + e4_data = convert_legacy_e4_data(e4_data) + convert_legacy_events_e4(event_history, start_time) + else: + start_time = int(json_file['Session Start Timestamp']) + end_time = int(json_file['Session End Timestamp']) + convert_timestamps(e4_data) + for i in range(start_time + int(time_period / 2), end_time, time_period): + try: + data_time = i - int(time_period / 2) + session_time = data_time - start_time + data_range = (data_time, data_time + time_period) + timestamp_list = np.arange(*data_range) + + ppg_csv_data = [session_time, data_time] + len(freq_header) * [0] + len(dur_header) * [ + 0] + eda_csv_data = [session_time, data_time] + len(freq_header) * [0] + len(dur_header) * [ + 0] + + ppg_data_range = find_indices(e4_data[EmpaticaData.BVP_TIMESTAMPS], timestamp_list) + ppg_data = e4_data[EmpaticaData.BVP][ppg_data_range[0]:ppg_data_range[-1]] + + eda_data_range = find_indices(e4_data[EmpaticaData.EDA_TIMESTAMPS], timestamp_list) + eda_data = e4_data[EmpaticaData.EDA][eda_data_range[0]:eda_data_range[-1]] + + for event in event_history: + if type(event[1]) is list: + event_duration = np.arange(int(event[3][0]), int(event[3][1])) + if int(event[3][0]) in timestamp_list or int(event[3][1]) in timestamp_list: + ppg_csv_data[dur_header.index(event[0]) + 2 + len(freq_header)] = 1 + eda_csv_data[dur_header.index(event[0]) + 2 + len(freq_header)] = 1 + if i in event_duration: + ppg_csv_data[dur_header.index(event[0]) + 2 + len(freq_header)] = 1 + eda_csv_data[dur_header.index(event[0]) + 2 + len(freq_header)] = 1 + else: + if int(event[3]) in timestamp_list: + ppg_csv_data[freq_header.index(event[0]) + 2] = 1 + eda_csv_data[freq_header.index(event[0]) + 2] = 1 + try: + ppg_signals, _ = nk.ppg_process(ppg_data, sampling_rate=64) + ppg_results = nk.ppg_analyze(ppg_signals, sampling_rate=64) + ppg_csv_data.extend(ppg_results.values.ravel().tolist()) + except Exception as e: + print(f"INFO: Could not process PPG signal in {file}") + try: + eda_signals, _ = eda_custom_process(eda_data, sampling_rate=4) + eda_results = nk.eda_analyze(eda_signals, method='interval-related') + eda_csv_data.extend(eda_results.values.ravel().tolist()) + except Exception as e: + print(f"INFO: Could not process EDA signal in {file}") + except KeyError: + print(f"No E4 data found in {file}") + except Exception as e: + print(f"Something went wrong with {file}: {str(e)}") + finally: + eda_f.writerow(eda_csv_data) + ppg_f.writerow(ppg_csv_data) + return True else: - messagebox.showwarning("Warning", "No E4 sessions found!") + return False def eda_custom_process(eda_signal, sampling_rate=4, method="neurokit"): @@ -228,4 +285,4 @@ def eda_custom_process(eda_signal, sampling_rate=4, method="neurokit"): # Store signals = pd.DataFrame({"EDA_Raw": eda_signal, "EDA_Clean": eda_cleaned}) signals = pd.concat([signals, eda_decomposed, peak_signal], axis=1) - return signals, info \ No newline at end of file + return signals, info diff --git a/menu_bar.py b/menu_bar.py index f5d694d..1d454be 100644 --- a/menu_bar.py +++ b/menu_bar.py @@ -40,7 +40,7 @@ def __init__(self, parent, caller, *args, **kwargs): menu.add_cascade(label="Help", menu=help_menu) def get_e4_metrics(self): - export_e4_metrics(self.caller.prim_dir, self.caller.reli_dir) + export_e4_metrics(self.caller.root, self.caller.prim_dir, self.caller.reli_dir, self.caller.export_dir) def config_popup(self): ConfigPopup(self.parent, self.caller.config) diff --git a/reference/Cometrics User Guide.pdf b/reference/Cometrics User Guide.pdf index adb49d7a0e5f03ef3d4a0df8a95bb2461926855c..5344a84a644cdccaed33e23929906b9b5fdf2b01 100644 GIT binary patch delta 274309 zcmb@tWmp_bv^ENZ4DK4-T?Y5y?(Xiv-NN8*!3GP#-8~Qp?ry=|f&~i>x$K?&-SeIM 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