829 lines
47 KiB
Python
829 lines
47 KiB
Python
from typing import List
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from numpy.core.arrayprint import IntegerFormat
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from numpy.lib import math
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import pandas as pd
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from pathlib import Path
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import numpy as np
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from pandas.core.frame import DataFrame
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import sys
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sys.path.insert(0, '/Users/Markus/Prosjekter git/Slovakia 2021/python_speech_features/python_speech_features')
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from python_speech_features.python_speech_features import mfcc
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import json
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# Global variables for MFCC
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MFCC_STEPSIZE = 0.5 # Seconds
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MFCC_WINDOWSIZE = 2 # Seconds
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NR_COEFFICIENTS = 13 # Number of coefficients
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NR_MEL_BINS = 40 # Number of mel-filter-bins
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class Data_container:
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# Initiates personal data container for each subject. Dict for each session with keys 'left' and 'right',
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# and values equal to lists of EMG data indexed 0-7
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# NB! More sessions has to be added here in the future
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def __init__(self, subject_nr:int, subject_name:str):
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self.subject_nr = subject_nr
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self.subject_name = subject_name
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self.data_dict_round1 = {'left': [None]*8, 'right': [None]*8}
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self.data_dict_round2 = {'left': [None]*8, 'right': [None]*8}
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self.data_dict_round3 = {'left': [None]*8, 'right': [None]*8}
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self.data_dict_round4 = {'left': [None]*8, 'right': [None]*8}
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self.dict_list = [self.data_dict_round1,
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self.data_dict_round2,
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self.data_dict_round3,
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self.data_dict_round4
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]
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class CSV_handler:
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# Initiates object to store all datapoints in the experiment
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def __init__(self):
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self.working_dir = str(Path.cwd())
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self.data_container_dict = {} # Dict with keys equal subject numbers and values equal to its respective datacontainer
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self.data_type = None # String describing which type of data is stored in the object
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# Makes dataframe from the csv files in the working directory
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# Input: filename of a csv-file
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# Output: DataFrame
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def make_df(self, filename):
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filepath = self.working_dir + str(filename)
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df = pd.read_csv(filepath)
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return df
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# Extracts out the timestamp and the selected emg signal into a new dataframe
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# Input: filename of a csv-file, EMG nr
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# Output: DataFrame(timestamp/EMG)
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def get_time_emg_table(self, filename:str, emg_nr:int):
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tot_data_frame = self.make_df(filename)
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emg_str = 'emg' + str(emg_nr)
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filtered_df = tot_data_frame[["timestamp", emg_str]]
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return filtered_df
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# Takes in a df and stores the information in a Data_container object
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# Input: filename of a csv-file, EMG nr, left/right arm, subject's data_container, session nr
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# Output: None -> stores EMG data in data container
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def store_df_in_container(self, filename:str, emg_nr:int, which_arm:str, data_container:Data_container, session:int):
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df = self.get_time_emg_table(filename, emg_nr+1)
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if df.isnull().values.any():
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print('NaN in: subject', data_container.subject_nr, 'arm:', which_arm, 'session:', session, 'emg nr:', emg_nr)
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# Places the data correctly:
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if session == 1:
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if which_arm == 'left':
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data_container.data_dict_round1['left'][emg_nr] = df # Zero indexed emg_nr in the dict
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else:
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data_container.data_dict_round1['right'][emg_nr] = df
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elif session == 2:
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if which_arm == 'left':
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data_container.data_dict_round2['left'][emg_nr] = df
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else:
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data_container.data_dict_round2['right'][emg_nr] = df
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elif session == 3:
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if which_arm == 'left':
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data_container.data_dict_round3['left'][emg_nr] = df
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else:
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data_container.data_dict_round3['right'][emg_nr] = df
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elif session == 4:
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if which_arm == 'left':
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data_container.data_dict_round4['left'][emg_nr] = df
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else:
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data_container.data_dict_round4['right'][emg_nr] = df
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else:
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raise IndexError('Not a valid index')
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# Links the data container for a subject to the csv_handler object
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# Input: the subject's data_container
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# Output: None -> places the data container correctly in the CSV_handler data_container_dict
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def link_container_to_handler(self, data_container:Data_container):
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# Links the retrieved data with the subjects data_container
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subject_nr = data_container.subject_nr
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self.data_container_dict[subject_nr] = data_container
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# Retrieves df via the data_dict in the CSV_handler object
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# Input: Experiment detailes
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# Output: DataFrame
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def get_df_from_data_dict(self, subject_nr, which_arm, session, emg_nr):
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container:Data_container = self.data_container_dict.get(subject_nr)
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df = container.dict_list[session - 1].get(which_arm)[emg_nr - 1]
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return df
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# Loads the data from the csv files into the storing system of the CSV_handler object
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# Input: None(CSV_handler)
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# Output: None -> load and stores data
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def load_hard_PP_emg_data(self):
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# CSV data from subject 1
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file1_subject1_left = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1810/myoLeftEmg.csv"
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file2_subject1_left = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1830/myoLeftEmg.csv"
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file3_subject1_left = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1845/myoLeftEmg.csv"
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file4_subject1_left = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1855/myoLeftEmg.csv"
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subject1_left_files = [file1_subject1_left, file2_subject1_left, file3_subject1_left, file4_subject1_left]
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file1_subject1_rigth = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1810/myoRightEmg.csv"
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file2_subject1_rigth = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1830/myoRightEmg.csv"
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file3_subject1_rigth = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1845/myoRightEmg.csv"
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file4_subject1_rigth = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1855/myoRightEmg.csv"
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subject1_right_files = [file1_subject1_rigth, file2_subject1_rigth, file3_subject1_rigth, file4_subject1_rigth]
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# CSV data from subject 2
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file1_subject2_left = "/Exp20201205_2myo_hardTypePP/HaluskaMaros_20201205_2010/myoLeftEmg.csv"
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file2_subject2_left = "/Exp20201205_2myo_hardTypePP/HaluskaMaros_20201205_2025/myoLeftEmg.csv"
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file3_subject2_left = "/Exp20201205_2myo_hardTypePP/HaluskaMaros_20201205_2035/myoLeftEmg.csv"
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file4_subject2_left = "/Exp20201205_2myo_hardTypePP/HaluskaMaros_20201205_2045/myoLeftEmg.csv"
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subject2_left_files = [file1_subject2_left, file2_subject2_left, file3_subject2_left, file4_subject2_left]
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file1_subject2_rigth = "/Exp20201205_2myo_hardTypePP/HaluskaMaros_20201205_2010/myoRightEmg.csv"
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file2_subject2_rigth = "/Exp20201205_2myo_hardTypePP/HaluskaMaros_20201205_2025/myoRightEmg.csv"
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file3_subject2_rigth = "/Exp20201205_2myo_hardTypePP/HaluskaMaros_20201205_2035/myoRightEmg.csv"
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file4_subject2_rigth = "/Exp20201205_2myo_hardTypePP/HaluskaMaros_20201205_2045/myoRightEmg.csv"
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subject2_right_files = [file1_subject2_rigth, file2_subject2_rigth, file3_subject2_rigth, file4_subject2_rigth]
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# CSV data from subject 3
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file1_subject3_left = "/Exp20201205_2myo_hardTypePP/HaluskovaBeata_20201205_1700/myoLeftEmg.csv"
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file2_subject3_left = "/Exp20201205_2myo_hardTypePP/HaluskovaBeata_20201205_1715/myoLeftEmg.csv"
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file3_subject3_left = "/Exp20201205_2myo_hardTypePP/HaluskovaBeata_20201205_1725/myoLeftEmg.csv"
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file4_subject3_left = "/Exp20201205_2myo_hardTypePP/HaluskovaBeata_20201205_1735/myoLeftEmg.csv"
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subject3_left_files = [file1_subject3_left, file2_subject3_left, file3_subject3_left, file4_subject3_left]
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file1_subject3_rigth = "/Exp20201205_2myo_hardTypePP/HaluskovaBeata_20201205_1700/myoRightEmg.csv"
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file2_subject3_rigth = "/Exp20201205_2myo_hardTypePP/HaluskovaBeata_20201205_1715/myoRightEmg.csv"
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file3_subject3_rigth = "/Exp20201205_2myo_hardTypePP/HaluskovaBeata_20201205_1725/myoRightEmg.csv"
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file4_subject3_rigth = "/Exp20201205_2myo_hardTypePP/HaluskovaBeata_20201205_1735/myoRightEmg.csv"
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subject3_right_files = [file1_subject3_rigth, file2_subject3_rigth, file3_subject3_rigth, file4_subject3_rigth]
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# CSV data from subject 4
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file1_subject4_left = "/Exp20201205_2myo_hardTypePP/KelisekDavid_20201209_1900/myoLeftEmg.csv"
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file2_subject4_left = "/Exp20201205_2myo_hardTypePP/KelisekDavid_20201209_1915/myoLeftEmg.csv"
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file3_subject4_left = "/Exp20201205_2myo_hardTypePP/KelisekDavid_20201209_1925/myoLeftEmg.csv"
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file4_subject4_left = "/Exp20201205_2myo_hardTypePP/KelisekDavid_20201209_1935/myoLeftEmg.csv"
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subject4_left_files = [file1_subject4_left, file2_subject4_left, file3_subject4_left, file4_subject4_left]
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file1_subject4_rigth = "/Exp20201205_2myo_hardTypePP/KelisekDavid_20201209_1900/myoRightEmg.csv"
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file2_subject4_rigth = "/Exp20201205_2myo_hardTypePP/KelisekDavid_20201209_1915/myoRightEmg.csv"
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file3_subject4_rigth = "/Exp20201205_2myo_hardTypePP/KelisekDavid_20201209_1925/myoRightEmg.csv"
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file4_subject4_rigth = "/Exp20201205_2myo_hardTypePP/KelisekDavid_20201209_1935/myoRightEmg.csv"
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subject4_right_files = [file1_subject4_rigth, file2_subject4_rigth, file3_subject4_rigth, file4_subject4_rigth]
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# CSV data from subject 5
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file1_subject5_left = "/Exp20201205_2myo_hardTypePP/KelisekRichard_20201209_2030/myoLeftEmg.csv"
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file2_subject5_left = "/Exp20201205_2myo_hardTypePP/KelisekRichard_20201209_2040/myoLeftEmg.csv"
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file3_subject5_left = "/Exp20201205_2myo_hardTypePP/KelisekRichard_20201209_2050/myoLeftEmg.csv"
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file4_subject5_left = "/Exp20201205_2myo_hardTypePP/KelisekRichard_20201209_2100/myoLeftEmg.csv"
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subject5_left_files = [file1_subject5_left, file2_subject5_left, file3_subject5_left, file4_subject5_left]
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file1_subject5_rigth = "/Exp20201205_2myo_hardTypePP/KelisekRichard_20201209_2030/myoRightEmg.csv"
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file2_subject5_rigth = "/Exp20201205_2myo_hardTypePP/KelisekRichard_20201209_2040/myoRightEmg.csv"
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file3_subject5_rigth = "/Exp20201205_2myo_hardTypePP/KelisekRichard_20201209_2050/myoRightEmg.csv"
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file4_subject5_rigth = "/Exp20201205_2myo_hardTypePP/KelisekRichard_20201209_2100/myoRightEmg.csv"
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subject5_right_files = [file1_subject5_rigth, file2_subject5_rigth, file3_subject5_rigth, file4_subject5_rigth]
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left_list = [subject1_left_files, subject2_left_files, subject3_left_files, subject4_left_files, subject5_left_files]
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right_list = [subject1_right_files, subject2_right_files, subject3_right_files, subject4_right_files, subject5_right_files]
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subject1_data_container = Data_container(1, 'HaluskaMarek')
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subject2_data_container = Data_container(2, 'HaluskaMaros')
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subject3_data_container = Data_container(3, 'HaluskovaBeata')
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subject4_data_container = Data_container(4, 'KelisekDavid')
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subject5_data_container = Data_container(5, 'KelisekRichard')
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subject_data_container_list = [subject1_data_container, subject2_data_container, subject3_data_container,
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subject4_data_container, subject5_data_container]
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for subject_nr in range(5):
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data_container = subject_data_container_list[subject_nr]
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# left variant proccessed here
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for round in range(4):
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for emg_nr in range(8):
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filename = left_list[subject_nr][round]
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self.store_df_in_container(filename, emg_nr, 'left', data_container, round+1)
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# right variant proccessed here
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for round in range(4):
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for emg_nr in range(8):
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filename = right_list[subject_nr][round]
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self.store_df_in_container(filename, emg_nr, 'right', data_container, round+1)
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# Links the stored data in the data_container to the Handler
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self.link_container_to_handler(data_container)
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self.data_type = 'hardPP'
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return self.data_container_dict
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def load_soft_PP_emg_data(self):
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# CSV data from subject 1
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file1_subject1_left = "/Exp20201205_2myo_softTypePP/HaluskaMarek_20201207_1910/myoLeftEmg.csv"
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file2_subject1_left = "/Exp20201205_2myo_softTypePP/HaluskaMarek_20201207_1920/myoLeftEmg.csv"
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file3_subject1_left = "/Exp20201205_2myo_softTypePP/HaluskaMarek_20201207_1935/myoLeftEmg.csv"
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file4_subject1_left = "/Exp20201205_2myo_softTypePP/HaluskaMarek_20201207_1945/myoLeftEmg.csv"
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subject1_left_files = [file1_subject1_left, file2_subject1_left, file3_subject1_left, file4_subject1_left]
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file1_subject1_rigth = "/Exp20201205_2myo_softTypePP/HaluskaMarek_20201207_1910/myoRightEmg.csv"
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file2_subject1_rigth = "/Exp20201205_2myo_softTypePP/HaluskaMarek_20201207_1920/myoRightEmg.csv"
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file3_subject1_rigth = "/Exp20201205_2myo_softTypePP/HaluskaMarek_20201207_1935/myoRightEmg.csv"
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file4_subject1_rigth = "/Exp20201205_2myo_softTypePP/HaluskaMarek_20201207_1945/myoRightEmg.csv"
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subject1_right_files = [file1_subject1_rigth, file2_subject1_rigth, file3_subject1_rigth, file4_subject1_rigth]
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# CSV data from subject 2
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file1_subject2_left = "/Exp20201205_2myo_softTypePP/HaluskaMaros_20201205_2055/myoLeftEmg.csv"
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file2_subject2_left = "/Exp20201205_2myo_softTypePP/HaluskaMaros_20201205_2110/myoLeftEmg.csv"
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file3_subject2_left = "/Exp20201205_2myo_softTypePP/HaluskaMaros_20201205_2125/myoLeftEmg.csv"
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file4_subject2_left = "/Exp20201205_2myo_softTypePP/HaluskaMaros_20201205_2145/myoLeftEmg.csv"
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subject2_left_files = [file1_subject2_left, file2_subject2_left, file3_subject2_left, file4_subject2_left]
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file1_subject2_rigth = "/Exp20201205_2myo_softTypePP/HaluskaMaros_20201205_2055/myoRightEmg.csv"
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file2_subject2_rigth = "/Exp20201205_2myo_softTypePP/HaluskaMaros_20201205_2110/myoRightEmg.csv"
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file3_subject2_rigth = "/Exp20201205_2myo_softTypePP/HaluskaMaros_20201205_2125/myoRightEmg.csv"
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file4_subject2_rigth = "/Exp20201205_2myo_softTypePP/HaluskaMaros_20201205_2145/myoRightEmg.csv"
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subject2_right_files = [file1_subject2_rigth, file2_subject2_rigth, file3_subject2_rigth, file4_subject2_rigth]
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# CSV data from subject 3
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file1_subject3_left = "/Exp20201205_2myo_softTypePP/HaluskovaBeata_20201205_1745/myoLeftEmg.csv"
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file2_subject3_left = "/Exp20201205_2myo_softTypePP/HaluskovaBeata_20201205_1755/myoLeftEmg.csv"
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file3_subject3_left = "/Exp20201205_2myo_softTypePP/HaluskovaBeata_20201205_1810/myoLeftEmg.csv"
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file4_subject3_left = "/Exp20201205_2myo_softTypePP/HaluskovaBeata_20201205_1825/myoLeftEmg.csv"
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subject3_left_files = [file1_subject3_left, file2_subject3_left, file3_subject3_left, file4_subject3_left]
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file1_subject3_rigth = "/Exp20201205_2myo_softTypePP/HaluskovaBeata_20201205_1745/myoRightEmg.csv"
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file2_subject3_rigth = "/Exp20201205_2myo_softTypePP/HaluskovaBeata_20201205_1755/myoRightEmg.csv"
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file3_subject3_rigth = "/Exp20201205_2myo_softTypePP/HaluskovaBeata_20201205_1810/myoRightEmg.csv"
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file4_subject3_rigth = "/Exp20201205_2myo_softTypePP/HaluskovaBeata_20201205_1825/myoRightEmg.csv"
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subject3_right_files = [file1_subject3_rigth, file2_subject3_rigth, file3_subject3_rigth, file4_subject3_rigth]
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# CSV data from subject 4
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file1_subject4_left = "/Exp20201205_2myo_softTypePP/KelisekDavid_20201209_1945/myoLeftEmg.csv"
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file2_subject4_left = "/Exp20201205_2myo_softTypePP/KelisekDavid_20201209_1955/myoLeftEmg.csv"
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file3_subject4_left = "/Exp20201205_2myo_softTypePP/KelisekDavid_20201209_2010/myoLeftEmg.csv"
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file4_subject4_left = "/Exp20201205_2myo_softTypePP/KelisekDavid_20201209_2025/myoLeftEmg.csv"
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subject4_left_files = [file1_subject4_left, file2_subject4_left, file3_subject4_left, file4_subject4_left]
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file1_subject4_rigth = "/Exp20201205_2myo_softTypePP/KelisekDavid_20201209_1945/myoRightEmg.csv"
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file2_subject4_rigth = "/Exp20201205_2myo_softTypePP/KelisekDavid_20201209_1955/myoRightEmg.csv"
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file3_subject4_rigth = "/Exp20201205_2myo_softTypePP/KelisekDavid_20201209_2010/myoRightEmg.csv"
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file4_subject4_rigth = "/Exp20201205_2myo_softTypePP/KelisekDavid_20201209_2025/myoRightEmg.csv"
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subject4_right_files = [file1_subject4_rigth, file2_subject4_rigth, file3_subject4_rigth, file4_subject4_rigth]
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# CSV data from subject 5
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file1_subject5_left = "/Exp20201205_2myo_softTypePP/KelisekRichard_20201209_2110/myoLeftEmg.csv"
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file2_subject5_left = "/Exp20201205_2myo_softTypePP/KelisekRichard_20201209_2120/myoLeftEmg.csv"
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file3_subject5_left = "/Exp20201205_2myo_softTypePP/KelisekRichard_20201209_2130/myoLeftEmg.csv"
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file4_subject5_left = "/Exp20201205_2myo_softTypePP/KelisekRichard_20201209_2140/myoLeftEmg.csv"
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subject5_left_files = [file1_subject5_left, file2_subject5_left, file3_subject5_left, file4_subject5_left]
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file1_subject5_rigth = "/Exp20201205_2myo_softTypePP/KelisekRichard_20201209_2110/myoRightEmg.csv"
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file2_subject5_rigth = "/Exp20201205_2myo_softTypePP/KelisekRichard_20201209_2120/myoRightEmg.csv"
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file3_subject5_rigth = "/Exp20201205_2myo_softTypePP/KelisekRichard_20201209_2130/myoRightEmg.csv"
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file4_subject5_rigth = "/Exp20201205_2myo_softTypePP/KelisekRichard_20201209_2140/myoRightEmg.csv"
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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
|
|
|
|
# Loads data the to the CSV_handler(general load func). Choose data_type: hard, hardPP, soft og softPP as str.
|
|
# Input: String(datatype you want)
|
|
# Output: None -> load and stores data
|
|
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.
|
|
# Input: Experiment detailes
|
|
# Output: DataFrame, samplerate:int
|
|
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
|
|
|
|
# NOT IMPLEMENTED
|
|
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: pass
|
|
pass
|
|
|
|
|
|
class NN_handler:
|
|
|
|
# Paths for data storage in json to later use in Neural_Network_Analysis.py
|
|
JSON_PATH_REG = "reg_data.json"
|
|
JSON_PATH_MFCC = "mfcc_data.json"
|
|
|
|
# Class to manipulate data from the CSV_handler and store it for further analysis
|
|
# NB! More subject needs to be added manually
|
|
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 this: [sample_df, samplerate]
|
|
self.reg_samples_per_subject = {1: [],
|
|
2: [],
|
|
3: [],
|
|
4: [],
|
|
5: []
|
|
}
|
|
# Should med 4 sessions * (~150, 208) of mfcc samples per person. One [DataFrame, session_length_list] per subject
|
|
self.mfcc_samples_per_subject = {1: [],
|
|
2: [],
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|
3: [],
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|
4: [],
|
|
5: []
|
|
}
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|
|
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# GET method for reg_samples_dict
|
|
def get_reg_samples_dict(self) -> dict:
|
|
return self.reg_samples_per_subject
|
|
|
|
# GET method for mfcc_samples_dict
|
|
def get_mfcc_samples_dict(self) -> dict:
|
|
return self.mfcc_samples_per_subject
|
|
|
|
# Retrieves all EMG data from one subject and one session, and makes a list of the DataFrames
|
|
# Input: Subject nr, Session nr (norm, not 0-indexed)
|
|
# Output: List(df_1, ..., df_16)
|
|
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
|
|
|
|
# Creates one Dataframe of all EMG data(one session, one subject). One column for each EMG array
|
|
# Input: List(emg1, ..., emg16)
|
|
# Output: DataFrame(shape[1]=16)
|
|
def make_subj_sample(self, list_of_emgs_):
|
|
# Test and fix if the left/right 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
|
|
|
|
# Takes in all EMG session Dataframe and merges the EMG data into one column, creating one signal
|
|
# Input: DataFrame(shape[1]=16, EMG data)
|
|
# Output: DataFrame(signal), samplerate of it
|
|
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
|
|
|
|
# Stores split, merged signals in the NN-handler's reg_samples_per_subject
|
|
# Input: Split_nr:int(how many times to split this merged signal)
|
|
# Output: None -> stores in NN_handler
|
|
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.reg_samples_per_subject[subject_nr+1] = subj_samples
|
|
|
|
# Takes in all EMG session Dataframe and creates DataFrame of MFCC samples
|
|
# Input: DataFrame(shape[1]=16, EMG data)
|
|
# Output: DataFrame(merged MFCC data, shape: (n, 13*16)), length of session datapoints
|
|
def make_mfcc_df_from_session_df(self, session_df):
|
|
session_df.rename(columns={0:'timestamp'}, inplace=True)
|
|
samplerate = get_samplerate(session_df)
|
|
attach_func = lambda list_1, list_2: list_1.extend(list_2)
|
|
|
|
signal = session_df[1]
|
|
mfcc_0 = mfcc_custom(signal, samplerate, MFCC_WINDOWSIZE, MFCC_STEPSIZE, NR_COEFFICIENTS, NR_MEL_BINS)
|
|
df = DataFrame(mfcc_0).dropna()
|
|
df['combined'] = df.values.tolist()
|
|
result_df = df['combined']
|
|
|
|
for i in range(2, 17):
|
|
signal_i = session_df[i]
|
|
mfcc_i = mfcc_custom(signal_i, samplerate, MFCC_WINDOWSIZE, MFCC_STEPSIZE, NR_COEFFICIENTS, NR_MEL_BINS)
|
|
mfcc_i = DataFrame(mfcc_i).dropna()
|
|
mfcc_i['combined'] = mfcc_i.values.tolist()
|
|
df = result_df.combine(mfcc_i['combined'], attach_func)
|
|
|
|
session_length = (len(result_df.index)) # Add the length of session data points
|
|
|
|
return result_df, session_length
|
|
|
|
# Merges MFCC data from all sessions and stores the sample data in
|
|
# the NN_handler's mfcc_samples_per_subject dict
|
|
# Input: None(NN_handler)
|
|
# Output: None -> stores in NN_handler [samples, session_length_list] for each subject
|
|
def store_mfcc_samples(self) -> None:
|
|
for subject_nr in range(5):
|
|
subj_samples = []
|
|
session_length_list = []
|
|
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')
|
|
|
|
mfcc_df_i, session_length = self.make_mfcc_df_from_session_df(tot_session_df)
|
|
subj_samples.append(mfcc_df_i)
|
|
session_length_list.append(session_length)
|
|
|
|
result_df = pd.concat(subj_samples, axis=0, ignore_index=True)
|
|
self.mfcc_samples_per_subject[subject_nr+1] = [result_df, session_length_list]
|
|
|
|
|
|
# Makes MFCC data from reg_samples_per_subject and stores it in a json file
|
|
# Input: Path to the json file
|
|
# Output: None -> stores in json
|
|
def save_json_reg(self, json_path=JSON_PATH_REG):
|
|
|
|
# Dictionary to store mapping, labels, and MFCCs
|
|
data = {
|
|
"mapping": [],
|
|
"labels": [],
|
|
"mfcc": []
|
|
}
|
|
|
|
raw_data_dict = self.get_reg_samples_dict()
|
|
|
|
# Loop through all subjects to get samples
|
|
mfcc_list = []
|
|
mfcc_frame_list = []
|
|
|
|
for key, value in raw_data_dict.items():
|
|
|
|
|
|
# save subject label in the mapping
|
|
subject_label = 'Subject ' + str(key)
|
|
data["mapping"].append(subject_label)
|
|
print("\nProcessing: {}".format(subject_label))
|
|
|
|
# process all samples per subject
|
|
for i, (sample) in enumerate(value):
|
|
|
|
# load signal from sample
|
|
signal, sample_rate = sample[0], sample[1]
|
|
signal = signal['emg'].to_numpy()
|
|
test_df_for_bugs(signal, key, i)
|
|
#print(sample_rate)
|
|
|
|
# extract mfcc
|
|
mfcc = mfcc_custom(signal, sample_rate, MFCC_WINDOWSIZE, MFCC_STEPSIZE, NR_COEFFICIENTS, NR_MEL_BINS)
|
|
|
|
mfcc_list.append(mfcc.tolist())
|
|
mfcc_frame_list.append(mfcc.shape[0])
|
|
|
|
#data["mfcc"].append(mfcc.tolist())
|
|
data["labels"].append(key-1)
|
|
print("sample:{} is done".format(i+1))
|
|
|
|
minimum = min(mfcc_frame_list)
|
|
|
|
for mfcc_data in mfcc_list:
|
|
|
|
data["mfcc"].append(mfcc_data[:minimum])
|
|
print(np.array(mfcc_data[:minimum]).shape)
|
|
|
|
# save MFCCs to json file
|
|
with open(json_path, "w") as fp:
|
|
json.dump(data, fp, indent=4)
|
|
|
|
# Stores MFCC data from mfcc_samples_per_subject in a json file
|
|
# Input: Path to the json file
|
|
# Output: None -> stores in json
|
|
def save_json_mfcc(self, json_path=JSON_PATH_MFCC):
|
|
|
|
# dictionary to store mapping, labels, and MFCCs
|
|
data = {
|
|
"mapping": [],
|
|
"labels": [],
|
|
"mfcc": [],
|
|
|
|
"session_lengths": []
|
|
}
|
|
|
|
raw_data_dict = self.get_mfcc_samples_dict()
|
|
|
|
# loop through all subjects to get samples
|
|
for key, value in raw_data_dict.items():
|
|
|
|
# save subject label in the mapping
|
|
subject_label = 'Subject ' + str(key)
|
|
print("\nProcessing: {}".format(subject_label))
|
|
data["mapping"].append(subject_label) # Subject label
|
|
data["session_lengths"].append(value[1]) # List[subject][session_length_list]
|
|
|
|
# process all samples per subject
|
|
for i, sample in enumerate(value[0]):
|
|
|
|
data["labels"].append(key-1) # Subject nr
|
|
data["mfcc"].append(sample[0]) # MFCC sample on same index
|
|
|
|
print("sample:{} is done".format(i+1))
|
|
#print(np.array(mfcc_data).shape)
|
|
|
|
# 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)
|
|
|
|
# Help: 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)
|
|
|
|
# Help: slightly modified mfcc with inputs like below. Returns N (x_values from original df) and mfcc_y_values
|
|
def mfcc_custom(signal, samplerate, windowsize=MFCC_WINDOWSIZE,
|
|
stepsize=MFCC_STEPSIZE,
|
|
nr_coefficients=NR_COEFFICIENTS,
|
|
nr_mel_filters=NR_MEL_BINS):
|
|
|
|
return mfcc(signal, samplerate, windowsize, stepsize, nr_coefficients, nr_mel_filters)
|
|
|
|
# Help: test for unregularities in DataFrame obj
|
|
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)
|
|
|
|
|
|
|