162 lines
8.7 KiB
Python
162 lines
8.7 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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from pandas.core.frame import DataFrame
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from scipy.fft import fft, fftfreq
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import pywt
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from scipy.signal import wavelets
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#import pyyawt
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import Handle_emg_data as Handler
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SAMPLE_RATE = 200
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def load_user_emg_data():
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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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csv_handler = Handler.CSV_handler
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subject1_data_container = Handler.Data_container(1, 'HaluskaMarek')
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subject2_data_container = Handler.Data_container(1, 'HaluskaMaros')
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subject3_data_container = Handler.Data_container(1, 'HaluskovaBeata')
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subject4_data_container = Handler.Data_container(1, 'KelisekDavid')
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subject5_data_container = Handler.Data_container(1, '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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# 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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csv_handler.store_df(left_list[subject_nr][round], emg_nr+1, 'left', subject_data_container_list[subject_nr])
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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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csv_handler.store_df(left_list[subject_nr][round], emg_nr+1, 'right', subject_data_container_list[subject_nr])
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return csv_handler.data_container_dict
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# Takes in a df and outputs np arrays for x and y values
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def prep_df(df:DataFrame):
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min, duration = Handler.get_min_max_timestamp(df)
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y = df.iloc[:,1].to_numpy()
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return y, duration
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# Normalizes a ndarray of a signal to the scale of int16(32767)
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def normalize_wave(y_values):
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y = np.int16((y_values / y_values.max()) * 32767)
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return y
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# Takes the FFT of a DataFrame object
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def fft_of_df(df:DataFrame):
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y_values, duration = prep_df(df)
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N = y_values.size
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norm = normalize_wave(y_values)
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N_trans = fftfreq(N, 1 / SAMPLE_RATE)
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y_f = fft(norm)
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return N_trans, y_f, duration
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# Removes noise with db4 wavelet function
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def wavelet_db4_denoising(df:DataFrame):
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y_values, duration = prep_df(df)
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#y_values = normalize_wave(y_values)
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wavelet = pywt.Wavelet('db4')
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cA, cD = pywt.dwt(y_values, wavelet)
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N_trans = np.array(range(int(np.floor((y_values.size + wavelet.dec_len - 1) / 2))))
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return N_trans, cA, cD
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# Filters signal accordning to Stein's Unbiased Risk Estimate(SURE)
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def sure_threshold_filter(cA, cD):
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cA_filtered = pyyawt.theselect(cA, 'rigrsure')
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return cA_filtered, cD
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def soft_threshold_filter(cA, cD):
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cA_filtered = pywt.threshold(cA, 0.25 * cA.max())
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return cA_filtered, cD
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# Plots DataFrame objects
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def plot_df(df:DataFrame):
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lines = df.plot.line(x='timestamp')
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plt.show()
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# Plots ndarrays after transformations
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def plot_trans(N_trans, y_trans):
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plt.plot(N_trans, np.abs(y_trans))
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plt.show()
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def inverse_wavelet(N, cA, cD):
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return None
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#'''
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handler = Handler.CSV_handler()
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file = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1810/myoLeftEmg.csv"
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df = handler.get_time_emg_table(file, 1)
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N = df.size
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#plot_df(df)
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x, cA, cD = wavelet_db4_denoising(df)
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#plot_trans(x, cA)
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cA_filtered, cD = soft_threshold_filter(cA, cD)
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plot_trans(x, cA_filtered) |