EMG_Biometrics_2021/Handle_emg_data.py

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
from math import floor
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
#import librosa
#from Present_data import get_data

# 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)