from numpy.core.arrayprint import IntegerFormat from numpy.lib import math import pandas as pd from pathlib import Path import numpy as np from pandas.core.frame import DataFrame import sys sys.path.insert(0, '/Users/Markus/Prosjekter git/Slovakia 2021/python_speech_features/python_speech_features') from python_speech_features.python_speech_features import mfcc import json # Global variables for MFCC MFCC_STEPSIZE = 0.5 # Seconds MFCC_WINDOWSIZE = 2 # Seconds NR_COEFFICIENTS = 13 # Number of coefficients NR_MEL_BINS = 40 # Number of mel-filter-bins class Data_container: def __init__(self, subject_nr:int, subject_name:str): self.subject_nr = subject_nr self.subject_name = subject_name self.data_dict_round1 = {'left': [None]*8, 'right': [None]*8} self.data_dict_round2 = {'left': [None]*8, 'right': [None]*8} self.data_dict_round3 = {'left': [None]*8, 'right': [None]*8} self.data_dict_round4 = {'left': [None]*8, 'right': [None]*8} self.dict_list = [self.data_dict_round1, self.data_dict_round2, self.data_dict_round3, self.data_dict_round4 ] class CSV_handler: def __init__(self): self.working_dir = str(Path.cwd()) self.data_container_dict = {} # Dict with keys equal subject numbers and values equal the relvant datacontainer self.data_type = None # Makes dataframe from the csv files in the working directory def make_df(self, filename): filepath = self.working_dir + str(filename) df = pd.read_csv(filepath) return df # Extracts out the timestamp and the selected emg signal into a new dataframe and stores the data on the subject def get_time_emg_table(self, filename:str, emg_nr:int): tot_data_frame = self.make_df(filename) emg_str = 'emg' + str(emg_nr) filtered_df = tot_data_frame[["timestamp", emg_str]] return filtered_df # Takes in a df and stores the information in a Data_container object def store_df_in_container(self, filename:str, emg_nr:int, which_arm:str, data_container:Data_container, round:int): df = self.get_time_emg_table(filename, emg_nr+1) if df.isnull().values.any(): print('NaN in: subject', data_container.subject_nr, 'arm:', which_arm, 'session:', round, 'emg nr:', emg_nr) # Places the data correctly: if round == 1: if which_arm == 'left': data_container.data_dict_round1['left'][emg_nr] = df # Zero indexed emg_nr in the dict else: data_container.data_dict_round1['right'][emg_nr] = df elif round == 2: if which_arm == 'left': data_container.data_dict_round2['left'][emg_nr] = df else: data_container.data_dict_round2['right'][emg_nr] = df elif round == 3: if which_arm == 'left': data_container.data_dict_round3['left'][emg_nr] = df else: data_container.data_dict_round3['right'][emg_nr] = df elif round == 4: if which_arm == 'left': data_container.data_dict_round4['left'][emg_nr] = df else: data_container.data_dict_round4['right'][emg_nr] = df else: raise IndexError('Not a valid index') # Links the data container for a subject to the handler object def link_container_to_handler(self, data_container:Data_container): # Links the retrieved data with the subjects data_container subject_nr = data_container.subject_nr self.data_container_dict[subject_nr] = data_container # Loads the data from the csv files into a storing system in an CSV_handler object # (hard, hardPP, soft and softPP) def load_hard_PP_emg_data(self): # CSV data from subject 1 file1_subject1_left = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1810/myoLeftEmg.csv" file2_subject1_left = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1830/myoLeftEmg.csv" file3_subject1_left = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1845/myoLeftEmg.csv" file4_subject1_left = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1855/myoLeftEmg.csv" subject1_left_files = [file1_subject1_left, file2_subject1_left, file3_subject1_left, file4_subject1_left] file1_subject1_rigth = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1810/myoRightEmg.csv" file2_subject1_rigth = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1830/myoRightEmg.csv" file3_subject1_rigth = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1845/myoRightEmg.csv" file4_subject1_rigth = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1855/myoRightEmg.csv" subject1_right_files = [file1_subject1_rigth, file2_subject1_rigth, file3_subject1_rigth, file4_subject1_rigth] # CSV data from subject 2 file1_subject2_left = "/Exp20201205_2myo_hardTypePP/HaluskaMaros_20201205_2010/myoLeftEmg.csv" file2_subject2_left = "/Exp20201205_2myo_hardTypePP/HaluskaMaros_20201205_2025/myoLeftEmg.csv" file3_subject2_left = "/Exp20201205_2myo_hardTypePP/HaluskaMaros_20201205_2035/myoLeftEmg.csv" file4_subject2_left = "/Exp20201205_2myo_hardTypePP/HaluskaMaros_20201205_2045/myoLeftEmg.csv" subject2_left_files = [file1_subject2_left, file2_subject2_left, file3_subject2_left, file4_subject2_left] file1_subject2_rigth = "/Exp20201205_2myo_hardTypePP/HaluskaMaros_20201205_2010/myoRightEmg.csv" file2_subject2_rigth = "/Exp20201205_2myo_hardTypePP/HaluskaMaros_20201205_2025/myoRightEmg.csv" file3_subject2_rigth = "/Exp20201205_2myo_hardTypePP/HaluskaMaros_20201205_2035/myoRightEmg.csv" file4_subject2_rigth = "/Exp20201205_2myo_hardTypePP/HaluskaMaros_20201205_2045/myoRightEmg.csv" subject2_right_files = [file1_subject2_rigth, file2_subject2_rigth, file3_subject2_rigth, file4_subject2_rigth] # CSV data from subject 3 file1_subject3_left = "/Exp20201205_2myo_hardTypePP/HaluskovaBeata_20201205_1700/myoLeftEmg.csv" file2_subject3_left = "/Exp20201205_2myo_hardTypePP/HaluskovaBeata_20201205_1715/myoLeftEmg.csv" file3_subject3_left = "/Exp20201205_2myo_hardTypePP/HaluskovaBeata_20201205_1725/myoLeftEmg.csv" file4_subject3_left = "/Exp20201205_2myo_hardTypePP/HaluskovaBeata_20201205_1735/myoLeftEmg.csv" subject3_left_files = [file1_subject3_left, file2_subject3_left, file3_subject3_left, file4_subject3_left] file1_subject3_rigth = "/Exp20201205_2myo_hardTypePP/HaluskovaBeata_20201205_1700/myoRightEmg.csv" file2_subject3_rigth = "/Exp20201205_2myo_hardTypePP/HaluskovaBeata_20201205_1715/myoRightEmg.csv" file3_subject3_rigth = "/Exp20201205_2myo_hardTypePP/HaluskovaBeata_20201205_1725/myoRightEmg.csv" file4_subject3_rigth = "/Exp20201205_2myo_hardTypePP/HaluskovaBeata_20201205_1735/myoRightEmg.csv" subject3_right_files = [file1_subject3_rigth, file2_subject3_rigth, file3_subject3_rigth, file4_subject3_rigth] # CSV data from subject 4 file1_subject4_left = "/Exp20201205_2myo_hardTypePP/KelisekDavid_20201209_1900/myoLeftEmg.csv" file2_subject4_left = "/Exp20201205_2myo_hardTypePP/KelisekDavid_20201209_1915/myoLeftEmg.csv" file3_subject4_left = "/Exp20201205_2myo_hardTypePP/KelisekDavid_20201209_1925/myoLeftEmg.csv" file4_subject4_left = "/Exp20201205_2myo_hardTypePP/KelisekDavid_20201209_1935/myoLeftEmg.csv" subject4_left_files = [file1_subject4_left, file2_subject4_left, file3_subject4_left, file4_subject4_left] file1_subject4_rigth = "/Exp20201205_2myo_hardTypePP/KelisekDavid_20201209_1900/myoRightEmg.csv" file2_subject4_rigth = "/Exp20201205_2myo_hardTypePP/KelisekDavid_20201209_1915/myoRightEmg.csv" file3_subject4_rigth = "/Exp20201205_2myo_hardTypePP/KelisekDavid_20201209_1925/myoRightEmg.csv" file4_subject4_rigth = "/Exp20201205_2myo_hardTypePP/KelisekDavid_20201209_1935/myoRightEmg.csv" subject4_right_files = [file1_subject4_rigth, file2_subject4_rigth, file3_subject4_rigth, file4_subject4_rigth] # CSV data from subject 5 file1_subject5_left = "/Exp20201205_2myo_hardTypePP/KelisekRichard_20201209_2030/myoLeftEmg.csv" file2_subject5_left = "/Exp20201205_2myo_hardTypePP/KelisekRichard_20201209_2040/myoLeftEmg.csv" file3_subject5_left = "/Exp20201205_2myo_hardTypePP/KelisekRichard_20201209_2050/myoLeftEmg.csv" file4_subject5_left = "/Exp20201205_2myo_hardTypePP/KelisekRichard_20201209_2100/myoLeftEmg.csv" subject5_left_files = [file1_subject5_left, file2_subject5_left, file3_subject5_left, file4_subject5_left] file1_subject5_rigth = "/Exp20201205_2myo_hardTypePP/KelisekRichard_20201209_2030/myoRightEmg.csv" file2_subject5_rigth = "/Exp20201205_2myo_hardTypePP/KelisekRichard_20201209_2040/myoRightEmg.csv" file3_subject5_rigth = "/Exp20201205_2myo_hardTypePP/KelisekRichard_20201209_2050/myoRightEmg.csv" file4_subject5_rigth = "/Exp20201205_2myo_hardTypePP/KelisekRichard_20201209_2100/myoRightEmg.csv" subject5_right_files = [file1_subject5_rigth, file2_subject5_rigth, file3_subject5_rigth, file4_subject5_rigth] left_list = [subject1_left_files, subject2_left_files, subject3_left_files, subject4_left_files, subject5_left_files] right_list = [subject1_right_files, subject2_right_files, subject3_right_files, subject4_right_files, subject5_right_files] subject1_data_container = Data_container(1, 'HaluskaMarek') subject2_data_container = Data_container(2, 'HaluskaMaros') subject3_data_container = Data_container(3, 'HaluskovaBeata') subject4_data_container = Data_container(4, 'KelisekDavid') subject5_data_container = Data_container(5, 'KelisekRichard') subject_data_container_list = [subject1_data_container, subject2_data_container, subject3_data_container, subject4_data_container, subject5_data_container] for subject_nr in range(5): data_container = subject_data_container_list[subject_nr] # left variant proccessed here for round in range(4): for emg_nr in range(8): filename = left_list[subject_nr][round] self.store_df_in_container(filename, emg_nr, 'left', data_container, round+1) # right variant proccessed here for round in range(4): for emg_nr in range(8): filename = right_list[subject_nr][round] self.store_df_in_container(filename, emg_nr, 'right', data_container, round+1) # Links the stored data in the data_container to the Handler self.link_container_to_handler(data_container) self.data_type = 'hardPP' return self.data_container_dict def load_soft_PP_emg_data(self): # CSV data from subject 1 file1_subject1_left = "/Exp20201205_2myo_softTypePP/HaluskaMarek_20201207_1910/myoLeftEmg.csv" file2_subject1_left = "/Exp20201205_2myo_softTypePP/HaluskaMarek_20201207_1920/myoLeftEmg.csv" file3_subject1_left = "/Exp20201205_2myo_softTypePP/HaluskaMarek_20201207_1935/myoLeftEmg.csv" file4_subject1_left = "/Exp20201205_2myo_softTypePP/HaluskaMarek_20201207_1945/myoLeftEmg.csv" subject1_left_files = [file1_subject1_left, file2_subject1_left, file3_subject1_left, file4_subject1_left] file1_subject1_rigth = "/Exp20201205_2myo_softTypePP/HaluskaMarek_20201207_1910/myoRightEmg.csv" file2_subject1_rigth = "/Exp20201205_2myo_softTypePP/HaluskaMarek_20201207_1920/myoRightEmg.csv" file3_subject1_rigth = "/Exp20201205_2myo_softTypePP/HaluskaMarek_20201207_1935/myoRightEmg.csv" file4_subject1_rigth = "/Exp20201205_2myo_softTypePP/HaluskaMarek_20201207_1945/myoRightEmg.csv" subject1_right_files = [file1_subject1_rigth, file2_subject1_rigth, file3_subject1_rigth, file4_subject1_rigth] # CSV data from subject 2 file1_subject2_left = "/Exp20201205_2myo_softTypePP/HaluskaMaros_20201205_2055/myoLeftEmg.csv" file2_subject2_left = "/Exp20201205_2myo_softTypePP/HaluskaMaros_20201205_2110/myoLeftEmg.csv" file3_subject2_left = "/Exp20201205_2myo_softTypePP/HaluskaMaros_20201205_2125/myoLeftEmg.csv" file4_subject2_left = "/Exp20201205_2myo_softTypePP/HaluskaMaros_20201205_2145/myoLeftEmg.csv" subject2_left_files = [file1_subject2_left, file2_subject2_left, file3_subject2_left, file4_subject2_left] file1_subject2_rigth = "/Exp20201205_2myo_softTypePP/HaluskaMaros_20201205_2055/myoRightEmg.csv" file2_subject2_rigth = "/Exp20201205_2myo_softTypePP/HaluskaMaros_20201205_2110/myoRightEmg.csv" file3_subject2_rigth = "/Exp20201205_2myo_softTypePP/HaluskaMaros_20201205_2125/myoRightEmg.csv" file4_subject2_rigth = "/Exp20201205_2myo_softTypePP/HaluskaMaros_20201205_2145/myoRightEmg.csv" subject2_right_files = [file1_subject2_rigth, file2_subject2_rigth, file3_subject2_rigth, file4_subject2_rigth] # CSV data from subject 3 file1_subject3_left = "/Exp20201205_2myo_softTypePP/HaluskovaBeata_20201205_1745/myoLeftEmg.csv" file2_subject3_left = "/Exp20201205_2myo_softTypePP/HaluskovaBeata_20201205_1755/myoLeftEmg.csv" file3_subject3_left = "/Exp20201205_2myo_softTypePP/HaluskovaBeata_20201205_1810/myoLeftEmg.csv" file4_subject3_left = "/Exp20201205_2myo_softTypePP/HaluskovaBeata_20201205_1825/myoLeftEmg.csv" subject3_left_files = [file1_subject3_left, file2_subject3_left, file3_subject3_left, file4_subject3_left] file1_subject3_rigth = "/Exp20201205_2myo_softTypePP/HaluskovaBeata_20201205_1745/myoRightEmg.csv" file2_subject3_rigth = "/Exp20201205_2myo_softTypePP/HaluskovaBeata_20201205_1755/myoRightEmg.csv" file3_subject3_rigth = "/Exp20201205_2myo_softTypePP/HaluskovaBeata_20201205_1810/myoRightEmg.csv" file4_subject3_rigth = "/Exp20201205_2myo_softTypePP/HaluskovaBeata_20201205_1825/myoRightEmg.csv" subject3_right_files = [file1_subject3_rigth, file2_subject3_rigth, file3_subject3_rigth, file4_subject3_rigth] # CSV data from subject 4 file1_subject4_left = "/Exp20201205_2myo_softTypePP/KelisekDavid_20201209_1945/myoLeftEmg.csv" file2_subject4_left = "/Exp20201205_2myo_softTypePP/KelisekDavid_20201209_1955/myoLeftEmg.csv" file3_subject4_left = "/Exp20201205_2myo_softTypePP/KelisekDavid_20201209_2010/myoLeftEmg.csv" file4_subject4_left = "/Exp20201205_2myo_softTypePP/KelisekDavid_20201209_2025/myoLeftEmg.csv" subject4_left_files = [file1_subject4_left, file2_subject4_left, file3_subject4_left, file4_subject4_left] file1_subject4_rigth = "/Exp20201205_2myo_softTypePP/KelisekDavid_20201209_1945/myoRightEmg.csv" file2_subject4_rigth = "/Exp20201205_2myo_softTypePP/KelisekDavid_20201209_1955/myoRightEmg.csv" file3_subject4_rigth = "/Exp20201205_2myo_softTypePP/KelisekDavid_20201209_2010/myoRightEmg.csv" file4_subject4_rigth = "/Exp20201205_2myo_softTypePP/KelisekDavid_20201209_2025/myoRightEmg.csv" subject4_right_files = [file1_subject4_rigth, file2_subject4_rigth, file3_subject4_rigth, file4_subject4_rigth] # CSV data from subject 5 file1_subject5_left = "/Exp20201205_2myo_softTypePP/KelisekRichard_20201209_2110/myoLeftEmg.csv" file2_subject5_left = "/Exp20201205_2myo_softTypePP/KelisekRichard_20201209_2120/myoLeftEmg.csv" file3_subject5_left = "/Exp20201205_2myo_softTypePP/KelisekRichard_20201209_2130/myoLeftEmg.csv" file4_subject5_left = "/Exp20201205_2myo_softTypePP/KelisekRichard_20201209_2140/myoLeftEmg.csv" subject5_left_files = [file1_subject5_left, file2_subject5_left, file3_subject5_left, file4_subject5_left] file1_subject5_rigth = "/Exp20201205_2myo_softTypePP/KelisekRichard_20201209_2110/myoRightEmg.csv" file2_subject5_rigth = "/Exp20201205_2myo_softTypePP/KelisekRichard_20201209_2120/myoRightEmg.csv" file3_subject5_rigth = "/Exp20201205_2myo_softTypePP/KelisekRichard_20201209_2130/myoRightEmg.csv" file4_subject5_rigth = "/Exp20201205_2myo_softTypePP/KelisekRichard_20201209_2140/myoRightEmg.csv" subject5_right_files = [file1_subject5_rigth, file2_subject5_rigth, file3_subject5_rigth, file4_subject5_rigth] left_list = [subject1_left_files, subject2_left_files, subject3_left_files, subject4_left_files, subject5_left_files] right_list = [subject1_right_files, subject2_right_files, subject3_right_files, subject4_right_files, subject5_right_files] subject1_data_container = Data_container(1, 'HaluskaMarek') subject2_data_container = Data_container(2, 'HaluskaMaros') subject3_data_container = Data_container(3, 'HaluskovaBeata') subject4_data_container = Data_container(4, 'KelisekDavid') subject5_data_container = Data_container(5, 'KelisekRichard') subject_data_container_list = [subject1_data_container, subject2_data_container, subject3_data_container, subject4_data_container, subject5_data_container] for subject_nr in range(5): data_container = subject_data_container_list[subject_nr] # left variant proccessed here for round in range(4): for emg_nr in range(8): filename = left_list[subject_nr][round] self.store_df_in_container(filename, emg_nr, 'left', data_container, round+1) # right variant proccessed here for round in range(4): for emg_nr in range(8): filename = right_list[subject_nr][round] self.store_df_in_container(filename, emg_nr, 'right', data_container, round+1) # Links the stored data in the data_container to the Handler self.link_container_to_handler(data_container) self.data_type = 'softPP' return self.data_container_dict def load_hard_original_emg_data(self): # CSV data from subject 1 file1_subject1_left = "/Exp20201205_2myo_hardType/HaluskaMarek_20201207_1810/myoLeftEmg.csv" file2_subject1_left = "/Exp20201205_2myo_hardType/HaluskaMarek_20201207_1830/myoLeftEmg.csv" file3_subject1_left = "/Exp20201205_2myo_hardType/HaluskaMarek_20201207_1845/myoLeftEmg.csv" file4_subject1_left = "/Exp20201205_2myo_hardType/HaluskaMarek_20201207_1855/myoLeftEmg.csv" subject1_left_files = [file1_subject1_left, file2_subject1_left, file3_subject1_left, file4_subject1_left] file1_subject1_rigth = "/Exp20201205_2myo_hardType/HaluskaMarek_20201207_1810/myoRightEmg.csv" file2_subject1_rigth = "/Exp20201205_2myo_hardType/HaluskaMarek_20201207_1830/myoRightEmg.csv" file3_subject1_rigth = "/Exp20201205_2myo_hardType/HaluskaMarek_20201207_1845/myoRightEmg.csv" file4_subject1_rigth = "/Exp20201205_2myo_hardType/HaluskaMarek_20201207_1855/myoRightEmg.csv" subject1_right_files = [file1_subject1_rigth, file2_subject1_rigth, file3_subject1_rigth, file4_subject1_rigth] # CSV data from subject 2 file1_subject2_left = "/Exp20201205_2myo_hardType/HaluskaMaros_20201205_2010/myoLeftEmg.csv" file2_subject2_left = "/Exp20201205_2myo_hardType/HaluskaMaros_20201205_2025/myoLeftEmg.csv" file3_subject2_left = "/Exp20201205_2myo_hardType/HaluskaMaros_20201205_2035/myoLeftEmg.csv" file4_subject2_left = "/Exp20201205_2myo_hardType/HaluskaMaros_20201205_2045/myoLeftEmg.csv" subject2_left_files = [file1_subject2_left, file2_subject2_left, file3_subject2_left, file4_subject2_left] file1_subject2_rigth = "/Exp20201205_2myo_hardType/HaluskaMaros_20201205_2010/myoRightEmg.csv" file2_subject2_rigth = "/Exp20201205_2myo_hardType/HaluskaMaros_20201205_2025/myoRightEmg.csv" file3_subject2_rigth = "/Exp20201205_2myo_hardType/HaluskaMaros_20201205_2035/myoRightEmg.csv" file4_subject2_rigth = "/Exp20201205_2myo_hardType/HaluskaMaros_20201205_2045/myoRightEmg.csv" subject2_right_files = [file1_subject2_rigth, file2_subject2_rigth, file3_subject2_rigth, file4_subject2_rigth] # CSV data from subject 3 file1_subject3_left = "/Exp20201205_2myo_hardType/HaluskovaBeata_20201205_1700/myoLeftEmg.csv" file2_subject3_left = "/Exp20201205_2myo_hardType/HaluskovaBeata_20201205_1715/myoLeftEmg.csv" file3_subject3_left = "/Exp20201205_2myo_hardType/HaluskovaBeata_20201205_1725/myoLeftEmg.csv" file4_subject3_left = "/Exp20201205_2myo_hardType/HaluskovaBeata_20201205_1735/myoLeftEmg.csv" subject3_left_files = [file1_subject3_left, file2_subject3_left, file3_subject3_left, file4_subject3_left] file1_subject3_rigth = "/Exp20201205_2myo_hardType/HaluskovaBeata_20201205_1700/myoRightEmg.csv" file2_subject3_rigth = "/Exp20201205_2myo_hardType/HaluskovaBeata_20201205_1715/myoRightEmg.csv" file3_subject3_rigth = "/Exp20201205_2myo_hardType/HaluskovaBeata_20201205_1725/myoRightEmg.csv" file4_subject3_rigth = "/Exp20201205_2myo_hardType/HaluskovaBeata_20201205_1735/myoRightEmg.csv" subject3_right_files = [file1_subject3_rigth, file2_subject3_rigth, file3_subject3_rigth, file4_subject3_rigth] # CSV data from subject 4 file1_subject4_left = "/Exp20201205_2myo_hardType/KelisekDavid_20201209_1900/myoLeftEmg.csv" file2_subject4_left = "/Exp20201205_2myo_hardType/KelisekDavid_20201209_1915/myoLeftEmg.csv" file3_subject4_left = "/Exp20201205_2myo_hardType/KelisekDavid_20201209_1925/myoLeftEmg.csv" file4_subject4_left = "/Exp20201205_2myo_hardType/KelisekDavid_20201209_1935/myoLeftEmg.csv" subject4_left_files = [file1_subject4_left, file2_subject4_left, file3_subject4_left, file4_subject4_left] file1_subject4_rigth = "/Exp20201205_2myo_hardType/KelisekDavid_20201209_1900/myoRightEmg.csv" file2_subject4_rigth = "/Exp20201205_2myo_hardType/KelisekDavid_20201209_1915/myoRightEmg.csv" file3_subject4_rigth = "/Exp20201205_2myo_hardType/KelisekDavid_20201209_1925/myoRightEmg.csv" file4_subject4_rigth = "/Exp20201205_2myo_hardType/KelisekDavid_20201209_1935/myoRightEmg.csv" subject4_right_files = [file1_subject4_rigth, file2_subject4_rigth, file3_subject4_rigth, file4_subject4_rigth] # CSV data from subject 5 file1_subject5_left = "/Exp20201205_2myo_hardType/KelisekRichard_20201209_2030/myoLeftEmg.csv" file2_subject5_left = "/Exp20201205_2myo_hardType/KelisekRichard_20201209_2040/myoLeftEmg.csv" file3_subject5_left = "/Exp20201205_2myo_hardType/KelisekRichard_20201209_2050/myoLeftEmg.csv" file4_subject5_left = "/Exp20201205_2myo_hardType/KelisekRichard_20201209_2100/myoLeftEmg.csv" subject5_left_files = [file1_subject5_left, file2_subject5_left, file3_subject5_left, file4_subject5_left] file1_subject5_rigth = "/Exp20201205_2myo_hardType/KelisekRichard_20201209_2030/myoRightEmg.csv" file2_subject5_rigth = "/Exp20201205_2myo_hardType/KelisekRichard_20201209_2040/myoRightEmg.csv" file3_subject5_rigth = "/Exp20201205_2myo_hardType/KelisekRichard_20201209_2050/myoRightEmg.csv" file4_subject5_rigth = "/Exp20201205_2myo_hardType/KelisekRichard_20201209_2100/myoRightEmg.csv" subject5_right_files = [file1_subject5_rigth, file2_subject5_rigth, file3_subject5_rigth, file4_subject5_rigth] left_list = [subject1_left_files, subject2_left_files, subject3_left_files, subject4_left_files, subject5_left_files] right_list = [subject1_right_files, subject2_right_files, subject3_right_files, subject4_right_files, subject5_right_files] subject1_data_container = Data_container(1, 'HaluskaMarek') subject2_data_container = Data_container(2, 'HaluskaMaros') subject3_data_container = Data_container(3, 'HaluskovaBeata') subject4_data_container = Data_container(4, 'KelisekDavid') subject5_data_container = Data_container(5, 'KelisekRichard') subject_data_container_list = [subject1_data_container, subject2_data_container, subject3_data_container, subject4_data_container, subject5_data_container] for subject_nr in range(5): data_container = subject_data_container_list[subject_nr] # left variant proccessed here for round in range(4): for emg_nr in range(8): filename = left_list[subject_nr][round] self.store_df_in_container(filename, emg_nr, 'left', data_container, round+1) # right variant proccessed here for round in range(4): for emg_nr in range(8): filename = right_list[subject_nr][round] self.store_df_in_container(filename, emg_nr, 'right', data_container, round+1) # Links the stored data in the data_container to the Handler self.link_container_to_handler(data_container) self.data_type = 'hard' return self.data_container_dict def load_soft_original_emg_data(self): # CSV data from subject 1 file1_subject1_left = "/Exp20201205_2myo_softType/HaluskaMarek_20201207_1910/myoLeftEmg.csv" file2_subject1_left = "/Exp20201205_2myo_softType/HaluskaMarek_20201207_1920/myoLeftEmg.csv" file3_subject1_left = "/Exp20201205_2myo_softType/HaluskaMarek_20201207_1935/myoLeftEmg.csv" file4_subject1_left = "/Exp20201205_2myo_softType/HaluskaMarek_20201207_1945/myoLeftEmg.csv" subject1_left_files = [file1_subject1_left, file2_subject1_left, file3_subject1_left, file4_subject1_left] file1_subject1_rigth = "/Exp20201205_2myo_softType/HaluskaMarek_20201207_1910/myoRightEmg.csv" file2_subject1_rigth = "/Exp20201205_2myo_softType/HaluskaMarek_20201207_1920/myoRightEmg.csv" file3_subject1_rigth = "/Exp20201205_2myo_softType/HaluskaMarek_20201207_1935/myoRightEmg.csv" file4_subject1_rigth = "/Exp20201205_2myo_softType/HaluskaMarek_20201207_1945/myoRightEmg.csv" subject1_right_files = [file1_subject1_rigth, file2_subject1_rigth, file3_subject1_rigth, file4_subject1_rigth] # CSV data from subject 2 file1_subject2_left = "/Exp20201205_2myo_softType/HaluskaMaros_20201205_2055/myoLeftEmg.csv" file2_subject2_left = "/Exp20201205_2myo_softType/HaluskaMaros_20201205_2110/myoLeftEmg.csv" file3_subject2_left = "/Exp20201205_2myo_softType/HaluskaMaros_20201205_2125/myoLeftEmg.csv" file4_subject2_left = "/Exp20201205_2myo_softType/HaluskaMaros_20201205_2145/myoLeftEmg.csv" subject2_left_files = [file1_subject2_left, file2_subject2_left, file3_subject2_left, file4_subject2_left] file1_subject2_rigth = "/Exp20201205_2myo_softType/HaluskaMaros_20201205_2055/myoRightEmg.csv" file2_subject2_rigth = "/Exp20201205_2myo_softType/HaluskaMaros_20201205_2110/myoRightEmg.csv" file3_subject2_rigth = "/Exp20201205_2myo_softType/HaluskaMaros_20201205_2125/myoRightEmg.csv" file4_subject2_rigth = "/Exp20201205_2myo_softType/HaluskaMaros_20201205_2145/myoRightEmg.csv" subject2_right_files = [file1_subject2_rigth, file2_subject2_rigth, file3_subject2_rigth, file4_subject2_rigth] # CSV data from subject 3 file1_subject3_left = "/Exp20201205_2myo_softType/HaluskovaBeata_20201205_1745/myoLeftEmg.csv" file2_subject3_left = "/Exp20201205_2myo_softType/HaluskovaBeata_20201205_1755/myoLeftEmg.csv" file3_subject3_left = "/Exp20201205_2myo_softType/HaluskovaBeata_20201205_1810/myoLeftEmg.csv" file4_subject3_left = "/Exp20201205_2myo_softType/HaluskovaBeata_20201205_1825/myoLeftEmg.csv" subject3_left_files = [file1_subject3_left, file2_subject3_left, file3_subject3_left, file4_subject3_left] file1_subject3_rigth = "/Exp20201205_2myo_softType/HaluskovaBeata_20201205_1745/myoRightEmg.csv" file2_subject3_rigth = "/Exp20201205_2myo_softType/HaluskovaBeata_20201205_1755/myoRightEmg.csv" file3_subject3_rigth = "/Exp20201205_2myo_softType/HaluskovaBeata_20201205_1810/myoRightEmg.csv" file4_subject3_rigth = "/Exp20201205_2myo_softType/HaluskovaBeata_20201205_1825/myoRightEmg.csv" subject3_right_files = [file1_subject3_rigth, file2_subject3_rigth, file3_subject3_rigth, file4_subject3_rigth] # CSV data from subject 4 file1_subject4_left = "/Exp20201205_2myo_softType/KelisekDavid_20201209_1945/myoLeftEmg.csv" file2_subject4_left = "/Exp20201205_2myo_softType/KelisekDavid_20201209_1955/myoLeftEmg.csv" file3_subject4_left = "/Exp20201205_2myo_softType/KelisekDavid_20201209_2010/myoLeftEmg.csv" file4_subject4_left = "/Exp20201205_2myo_softType/KelisekDavid_20201209_2025/myoLeftEmg.csv" subject4_left_files = [file1_subject4_left, file2_subject4_left, file3_subject4_left, file4_subject4_left] file1_subject4_rigth = "/Exp20201205_2myo_softType/KelisekDavid_20201209_1945/myoRightEmg.csv" file2_subject4_rigth = "/Exp20201205_2myo_softType/KelisekDavid_20201209_1955/myoRightEmg.csv" file3_subject4_rigth = "/Exp20201205_2myo_softType/KelisekDavid_20201209_2010/myoRightEmg.csv" file4_subject4_rigth = "/Exp20201205_2myo_softType/KelisekDavid_20201209_2025/myoRightEmg.csv" subject4_right_files = [file1_subject4_rigth, file2_subject4_rigth, file3_subject4_rigth, file4_subject4_rigth] # CSV data from subject 5 file1_subject5_left = "/Exp20201205_2myo_softType/KelisekRichard_20201209_2110/myoLeftEmg.csv" file2_subject5_left = "/Exp20201205_2myo_softType/KelisekRichard_20201209_2120/myoLeftEmg.csv" file3_subject5_left = "/Exp20201205_2myo_softType/KelisekRichard_20201209_2130/myoLeftEmg.csv" file4_subject5_left = "/Exp20201205_2myo_softType/KelisekRichard_20201209_2140/myoLeftEmg.csv" subject5_left_files = [file1_subject5_left, file2_subject5_left, file3_subject5_left, file4_subject5_left] file1_subject5_rigth = "/Exp20201205_2myo_softType/KelisekRichard_20201209_2110/myoRightEmg.csv" file2_subject5_rigth = "/Exp20201205_2myo_softType/KelisekRichard_20201209_2120/myoRightEmg.csv" file3_subject5_rigth = "/Exp20201205_2myo_softType/KelisekRichard_20201209_2130/myoRightEmg.csv" file4_subject5_rigth = "/Exp20201205_2myo_softType/KelisekRichard_20201209_2140/myoRightEmg.csv" subject5_right_files = [file1_subject5_rigth, file2_subject5_rigth, file3_subject5_rigth, file4_subject5_rigth] left_list = [subject1_left_files, subject2_left_files, subject3_left_files, subject4_left_files, subject5_left_files] right_list = [subject1_right_files, subject2_right_files, subject3_right_files, subject4_right_files, subject5_right_files] subject1_data_container = Data_container(1, 'HaluskaMarek') subject2_data_container = Data_container(2, 'HaluskaMaros') subject3_data_container = Data_container(3, 'HaluskovaBeata') subject4_data_container = Data_container(4, 'KelisekDavid') subject5_data_container = Data_container(5, 'KelisekRichard') subject_data_container_list = [subject1_data_container, subject2_data_container, subject3_data_container, subject4_data_container, subject5_data_container] for subject_nr in range(5): data_container = subject_data_container_list[subject_nr] # left variant proccessed here for round in range(4): for emg_nr in range(8): filename = left_list[subject_nr][round] self.store_df_in_container(filename, emg_nr, 'left', data_container, round+1) # right variant proccessed here for round in range(4): for emg_nr in range(8): filename = right_list[subject_nr][round] self.store_df_in_container(filename, emg_nr, 'right', data_container, round+1) # Links the stored data in the data_container to the Handler self.link_container_to_handler(data_container) self.data_type = 'soft' return self.data_container_dict # Retrieves df via the data_dict in the handler object def get_df_from_data_dict(self, subject_nr, which_arm, session, emg_nr): container:Data_container = self.data_container_dict.get(subject_nr) df = container.dict_list[session - 1].get(which_arm)[emg_nr - 1] return df # Loads in data to a CSV_handler. Choose data_type: hard, hardPP, soft og softPP as str. # Returns None. def load_data(self, data_type): if data_type == 'hard': self.load_hard_original_emg_data() elif data_type == 'hardPP': self.load_hard_PP_emg_data() elif data_type == 'soft': self.load_soft_original_emg_data() elif data_type == 'softPP': self.load_soft_PP_emg_data() else: raise Exception('Wrong input') # Retrieved data. Send in loaded csv_handler and data detailes you want. # Returns DataFrame and samplerate def get_data(self, subject_nr, which_arm, session, emg_nr): data_frame = self.get_df_from_data_dict(subject_nr, which_arm, session, emg_nr) samplerate = get_samplerate(data_frame) return data_frame, samplerate ''' def get_keyboard_data(self, filename:str, pres_or_release:str='pressed'): filepath = self.working_dir + str(filename) df = pd.read_csv(filepath) if pres_or_release == 'pressed': df = df[(df['event'] == 'KeyPressed') and (df['event'] == 'KeyPressed')] else ''' class DL_data_handler: JSON_PATH = "mfcc_data.json" def __init__(self, csv_handler:CSV_handler) -> None: self.csv_handler = csv_handler # Should med 4 sessions * split nr of samples per person. Each sample is structured like [sample_df, samplerate] self.samples_per_subject = {1: [], 2: [], 3: [], 4: [], 5: [] } def get_samples_dict(self): return self.samples_per_subject def get_emg_list(self, subject_nr, session_nr) -> list: list_of_emgs = [] df, _ = self.csv_handler.get_data(subject_nr, 'left', session_nr, 1) list_of_emgs.append(df) for emg_nr in range(7): df, _ = self.csv_handler.get_data(subject_nr, 'left', session_nr, emg_nr+2) list_of_emgs.append(DataFrame(df[get_emg_str(emg_nr+2)])) for emg_nr in range(8): df, _ = self.csv_handler.get_data(subject_nr, 'right', session_nr, emg_nr+1) list_of_emgs.append(DataFrame(df[get_emg_str(emg_nr+1)])) return list_of_emgs # list of emg data where first element also has timestamp column def make_subj_sample(self, list_of_emgs_): # Test and fix if the emgs have different size list_of_emgs = [] length_left_emgs = int(len(list_of_emgs_[0].index)) length_right_emgs = int(len(list_of_emgs_[-1].index)) if length_left_emgs < length_right_emgs: for i in range(16): new_emg_df = list_of_emgs_[i].head(length_left_emgs) list_of_emgs.append(new_emg_df) elif length_right_emgs < length_left_emgs: for i in range(16): new_emg_df = list_of_emgs_[i].head(length_right_emgs) list_of_emgs.append(new_emg_df) else: list_of_emgs = list_of_emgs_ tot_session_df_list = [] for i in range(8): df = list_of_emgs[i] tot_session_df_list.append(df) for i in range(1, 9): emg_str_old = get_emg_str(i) emg_str_new = get_emg_str(8+i) df = list_of_emgs[7+i].rename(columns={emg_str_old: emg_str_new}) tot_session_df_list.append(df) tot_session_df = pd.concat(tot_session_df_list, axis=1, ignore_index=True) return tot_session_df def store_samples(self, split_nr) -> None: for subject_nr in range(5): subj_samples = [] for session_nr in range(4): list_of_emg = self.get_emg_list(subject_nr+1, session_nr+1) tot_session_df = self.make_subj_sample(list_of_emg) # TESTING FOR NAN if tot_session_df.isnull().values.any(): print('NaN in: subject', subject_nr+1, 'session:', session_nr+1, 'where? HERE') samples = np.array_split(tot_session_df.to_numpy(), split_nr) for array in samples: df = DataFrame(array).rename(columns={0:'timestamp'}) df_finished, samplerate = self.reshape_session_df_to_signal(df) subj_samples.append([df_finished, samplerate]) self.samples_per_subject[subject_nr+1] = subj_samples def reshape_session_df_to_signal(self, df:DataFrame): main_df = df[['timestamp', 1]].rename(columns={1: 'emg'}) for i in range(2, 17): adding_df = df[['timestamp', i]].rename(columns={i: 'emg'}) main_df = pd.concat([main_df, adding_df], ignore_index=True) samplerate = get_samplerate(main_df) return main_df, samplerate def save_mfcc(self, json_path=JSON_PATH): # dictionary to store mapping, labels, and MFCCs data = { "mapping": [], "labels": [], "mfcc": [] } raw_data_dict = self.get_samples_dict() # loop through all subjects to get samples for key, value in raw_data_dict.items(): # save genre label (i.e., sub-folder name) in the mapping subject_label = 'Subject ' + str(key) data["mapping"].append(subject_label) print("\nProcessing: {}".format(subject_label)) # process all audio files in genre sub-dir for i, (sample) in enumerate(value): # load audio file signal, sample_rate = sample[0], sample[1] signal = signal['emg'].to_numpy() test_df_for_bugs(signal, key, i) # extract mfcc #n_fft = MFCC_WINDOWSIZE * sample_rate #hop_length = MFCC_STEPSIZE * sample_rate #mfcc = librosa.feature.mfcc(signal, sample_rate, n_mfcc=NR_COEFFICIENTS, n_fft=n_fft, hop_length=hop_length) mfcc = mfcc_custom(signal, sample_rate, MFCC_WINDOWSIZE, MFCC_STEPSIZE, NR_COEFFICIENTS, NR_MEL_BINS) mfcc = mfcc.T #print(len(mfcc)) # store only mfcc feature with expected number of vectors #if len(mfcc) == num_mfcc_vectors_per_segment: data["mfcc"].append(mfcc.tolist()) data["labels"].append(key) print("sample:{}".format(i+1)) # save MFCCs to json file with open(json_path, "w") as fp: json.dump(data, fp, indent=4) # HELP FUNCTIONS: ------------------------------------------------------------------------: # Help: gets the str from emg nr def get_emg_str(emg_nr): return 'emg' + str(emg_nr) # Help: gets the min/max of a df def get_min_max_timestamp(df:DataFrame): #min = int(np.floor(df['timestamp'].min())) min = df['timestamp'].min() max = df['timestamp'].max() return min, max # Help: returns df_time_emg def make_df_from_xandy(x, y, emg_nr): dict = {'timestamp': x, get_emg_str(emg_nr): y} df = DataFrame(dict) #print(df) return df # Help: returns the samplerate of a df def get_samplerate(df:DataFrame): min, max = get_min_max_timestamp(df) if max > 60 and min < 60: seconds = max - 60 - min else: seconds = max - min samples = len(df.index) samplerate = samples / seconds return int(samplerate) # Takes in a df and outputs np arrays for x and y values def get_xory_from_df(x_or_y, df:DataFrame): swither = { 'x': df.iloc[:,0].to_numpy(), 'y': df.iloc[:,1].to_numpy() } return swither.get(x_or_y, 0) # Slightly modified mfcc with inputs like below. # Returns N (x_values from original df) and mfcc_y_values def mfcc_custom(signal, samplesize, windowsize=MFCC_WINDOWSIZE, stepsize=MFCC_STEPSIZE, nr_coefficients=NR_COEFFICIENTS, nr_mel_filters=NR_MEL_BINS): return mfcc(signal, samplesize, windowsize, stepsize, nr_coefficients, nr_mel_filters) def test_df_for_bugs(signal, key, placement_index): df = DataFrame(signal) if df.isnull().values.any(): print('NaN in subject', key, 'in sample', placement_index) if df.shape[1] != (1): print('Shape:', df.shape[1], 'at subject', key, 'in sample', placement_index)