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
#from Present_data import get_data

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:

    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)

        # TESTING FOR NAN
        if tot_session_df.isnull().values.any():
            print('NaN in: where? THERE')
            print(length_left_emgs, length_right_emgs)
            #print(tot_session_df_list)

        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? AFTER MAKE')

                samples = np.array_split(tot_session_df.to_numpy(), split_nr)
                for array in samples:
                    df = DataFrame(array).rename(columns={0:'timestamp'})
                    '''
                    # TESTING FOR NAN
                    if df.isnull().values.any():
                        print('NaN in: subject', subject_nr+1, 'session:', session_nr+1, 'where? AFTER SPLIT')
                    '''
                    df_finished, samplerate = self.reshape_session_df_to_signal(df)
                    '''
                    # TESTING FOR NAN
                    if df_finished.isnull().values.any():
                        print('NaN in: subject', subject_nr+1, 'session:', session_nr+1, 'where? AFTER RESHAPE')
                    '''
                    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


# 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:
            seconds = max - 60 - min
        else:
            seconds = max - min
        samples = len(df.index)
        samplerate = samples / seconds
        return samplerate