from logging import error from matplotlib.cbook import get_sample_data from Handle_emg_data import * from Signal_prep import * import matplotlib.pyplot as plt from matplotlib import cm import matplotlib.ticker as ticker # 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 # PLOT FUNCTIONS --------------------------------------------------------------: # Plots DataFrame objects def plot_df(df:DataFrame): lines = df.plot.line(x='timestamp') plt.show() # Plots ndarrays after transformations # Input: X-values and Y-values # Output: None --> Plot def plot_array(N, y): plt.plot(N, np.abs(y)) plt.show() # Plots two subplots with two dataframes in order to compare them # Input: Old dataframe, old title, new dataframe, new title # Output: None --> Plot def plot_compare_two_df(df_old, old_name, df_new, new_name): x = get_xory_from_df('x', df_old) y1 = get_xory_from_df('y', df_old) y2 = get_xory_from_df('y', df_new) figure, axis = plt.subplots(1, 2) axis[0].plot(x, y1) axis[0].set_title(old_name) axis[1].plot(x, y2) axis[1].set_title(new_name) plt.show() # Plots one set of MFCC data # Input: 2d array of MFCC data(frame, coefficients), data_label for description # Output: None -> Plot def plot_mfcc(mfcc_data, data_label:str): fig, ax = plt.subplots() mfcc_data= np.swapaxes(mfcc_data, 0 ,1) ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x * MFCC_STEPSIZE)) ax.xaxis.set_major_formatter(ticks_x) ax.imshow(mfcc_data, interpolation='nearest', cmap=cm.coolwarm, origin='lower') ax.set_title('MFCC: ' + data_label) ax.set_ylabel('Cepstral Coefficients') ax.set_xlabel('Time(s)') plt.show() # Plots three sets of MFCC data # Input: 3 x (2d array of MFCC data(frame, coefficients)), 3 x (data_label for description) # Output: None -> Plot def plot_3_mfcc(mfcc_data1, data_label1:str, mfcc_data2, data_label2:str, mfcc_data3, data_label3:str): fig, axes = plt.subplots(nrows=3) plt.subplots_adjust(hspace=1.4, wspace=0.4) ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x * MFCC_STEPSIZE)) data_list = [mfcc_data1, mfcc_data2, mfcc_data3] label_list = [data_label1, data_label2, data_label3] for ax, data, label in zip(axes, data_list, label_list): mfcc_data= np.swapaxes(data, 0 ,1) ax.xaxis.set_major_formatter(ticks_x) ax.imshow(mfcc_data, interpolation='nearest', cmap=cm.coolwarm, origin='lower') ax.set_title('MFCC: ' + str(label)) ax.set_ylabel('Coefficients') ax.set_xlabel('Time(s)') plt.show() # Plots eight subplots with all EMG data from Subject 1 and Session 1 # Input: list of 8 arrays of EMG data(datapoints), list of 8 data_labels for description # Output: None -> Plot def plot_all_emg_mfcc(data_list:list, label_list:list): fig, axes = plt.subplots(nrows=4, ncols=2) plt.subplots_adjust(hspace=1.4, wspace=0.4) ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x * MFCC_STEPSIZE)) plt.autoscale() d_list = np.array([ [data_list[0], data_list[4]], [data_list[1], data_list[5]], [data_list[2], data_list[6]], [data_list[3], data_list[7]] ]) l_list = np.array([ [label_list[0], label_list[4]], [label_list[1], label_list[5]], [label_list[2], label_list[6]], [label_list[3], label_list[7]] ]) for col in [0, 1]: for ax, data, label in zip(axes[:,col], d_list[:,col], l_list[:,col]): mfcc_data= np.swapaxes(data, 0 ,1) ax.xaxis.set_major_formatter(ticks_x) ax.imshow(mfcc_data, interpolation='nearest', cmap=cm.coolwarm, origin='lower') ax.set_title('MFCC: ' + str(label)) ax.set_ylabel('Coefficients') ax.set_xlabel('Time(s)') plt.show() def pretty(dict): for key, value in dict.items(): print('Subject', key, 'samples:') print('\t\t Number av samples:', len(value)) print('\t\t EX sample nr 1:') print('\t\t\t Type:', type(value[0][0]), type(value[0][1])) print('\t\t\t Sample:', value[0][0], value[0][1]) # DATA FUNCTIONS: --------------------------------------------------------------: # The CSV_handler takes in nr of subjects and nr of sessions in the experiment # E.g. handler = CSV_handler(nr_subjects=5, nr_sessions=4) # Needs to load data: handler.load_data(, ) # Denoices one set of EMG data # Input: CSV_handler and detailes for ID # Output: DataFrame(df) def denoice_dataset(handler:CSV_handler, subject_nr, which_arm, round, emg_nr): df = handler.get_df_from_data_dict(subject_nr, which_arm, round, emg_nr) N = get_xory_from_df('x', df) N_trans, cA, cD = wavelet_db4(df) cA_filt, cD_filt = soft_threshold_filter(cA, cD) y_values = inverse_wavelet(df, cA_filt, cD_filt) df_new = make_df_from_xandy(N, y_values, emg_nr) return df_new # Quick debug function for NN_handler dict # Input: NN_hanlder dict, nr of samples per person # Output: None -> prints if NaN def test_for_NaN(dict, samples_per_person): for key, value in dict.items(): for i in range(samples_per_person): df = value[i][0] #print(df) print(df.isnull()) # CASE FUNTIONS ----------------------------------------------------------------: # Takes in a df and compares the FFT and the wavelet denoising of the FFT # Input: timestamp/EMG Dataframe # Output: None --> Plot def compare_with_wavelet_filter(data_frame): N_trans, cA, cD = wavelet_db4(data_frame) data_frame_freq = make_df_from_xandy(N_trans, cA, 1) cA_filt, cD_filt = soft_threshold_filter(cA, cD) data_frame_freq_filt = make_df_from_xandy(N_trans, cD_filt, 1) plot_compare_two_df(data_frame_freq, 'Original data', data_frame_freq_filt, 'Analyzed data') # Loads three preset EMG nr 1 datasets(subj1:session1, subj1:session2, subj2:session1), calculates mfcc for each and plots them. # Input: CSV_handler # Output: None --> Plot def mfcc_3_plots_1_1_2(csv_handler:CSV_handler): df1, samplerate1 = csv_handler.get_data( 1, 'left', 1, 1) df2, samplerate2 = csv_handler.get_data( 1, 'left', 2, 1) df3, samplerate3 = csv_handler.get_data( 2, 'left', 1, 1) #print(df1.head, samplerate1) #print(df2.head, samplerate2) #print(df3.head, samplerate3) N1, mfcc_feat1 = mfcc_custom(df1, samplerate1, MFCC_WINDOWSIZE, MFCC_STEPSIZE) N2, mfcc_feat2 = mfcc_custom(df2, samplerate2, MFCC_WINDOWSIZE, MFCC_STEPSIZE) N3, mfcc_feat3 = mfcc_custom(df3, samplerate3, MFCC_WINDOWSIZE, MFCC_STEPSIZE) label_1 = 'Subject 1, session 1, left arm, emg nr. 1' label_2 = 'Subject 1, session 2, left arm, emg nr. 1' label_3 = 'Subject 2, session 1, left arm, emg nr. 1' plot_3_mfcc(mfcc_feat1, label_1, mfcc_feat2, label_2, mfcc_feat3, label_3) # Loads three preset EMG nr 1 datasets(subj3:session1, subj3:session2, subj4:session1), calculates mfcc for each and plots them. # Input: CSV_handler # Output: None --> Plot def mfcc_3_plots_3_3_4(csv_handler:CSV_handler): df1, samplerate1 = csv_handler.get_data(3, 'left', 1, 1) df2, samplerate2 = csv_handler.get_data(3, 'left', 2, 1) df3, samplerate3 = csv_handler.get_data(4, 'left', 1, 1) #print(df1.head, samplerate1) #print(df2.head, samplerate2) #print(df3.head, samplerate3) N1, mfcc_feat1 = mfcc_custom(df1, samplerate1, MFCC_WINDOWSIZE, MFCC_STEPSIZE) N2, mfcc_feat2 = mfcc_custom(df2, samplerate2, MFCC_WINDOWSIZE, MFCC_STEPSIZE) N3, mfcc_feat3 = mfcc_custom(df3, samplerate3, MFCC_WINDOWSIZE, MFCC_STEPSIZE) label_1 = 'Subject 3, session 1, left arm, emg nr. 1' label_2 = 'Subject 3, session 2, left arm, emg nr. 1' label_3 = 'Subject 4, session 1, left arm, emg nr. 1' plot_3_mfcc(mfcc_feat1, label_1, mfcc_feat2, label_2, mfcc_feat3, label_3) # Loads preset emg 1-8 datasets(subj1 and session1) and calculates mfcc for each and plots them. # Input: CSV_handler # Output: None --> Plot def mfcc_all_emg_plots(csv_handler:CSV_handler): df1, samplerate1 = csv_handler.get_data( 1, 'left', 1, 1) df2, samplerate2 = csv_handler.get_data( 1, 'left', 1, 2) df3, samplerate3 = csv_handler.get_data( 1, 'left', 1, 3) df4, samplerate4 = csv_handler.get_data( 1, 'left', 1, 4) df5, samplerate5 = csv_handler.get_data( 1, 'left', 1, 5) df6, samplerate6 = csv_handler.get_data( 1, 'left', 1, 6) df7, samplerate7 = csv_handler.get_data( 1, 'left', 1, 7) df8, samplerate8 = csv_handler.get_data( 1, 'left', 1, 8) N1, mfcc_feat1 = mfcc_custom(df1, samplerate1, MFCC_WINDOWSIZE, MFCC_STEPSIZE) N2, mfcc_feat2 = mfcc_custom(df2, samplerate2, MFCC_WINDOWSIZE, MFCC_STEPSIZE) N3, mfcc_feat3 = mfcc_custom(df3, samplerate3, MFCC_WINDOWSIZE, MFCC_STEPSIZE) N4, mfcc_feat4 = mfcc_custom(df4, samplerate4, MFCC_WINDOWSIZE, MFCC_STEPSIZE) N5, mfcc_feat5 = mfcc_custom(df5, samplerate5, MFCC_WINDOWSIZE, MFCC_STEPSIZE) N6, mfcc_feat6 = mfcc_custom(df6, samplerate6, MFCC_WINDOWSIZE, MFCC_STEPSIZE) N7, mfcc_feat7 = mfcc_custom(df7, samplerate7, MFCC_WINDOWSIZE, MFCC_STEPSIZE) N8, mfcc_feat8 = mfcc_custom(df8, samplerate8, MFCC_WINDOWSIZE, MFCC_STEPSIZE) feat_list = [mfcc_feat1, mfcc_feat2, mfcc_feat3, mfcc_feat4, mfcc_feat5, mfcc_feat6, mfcc_feat7, mfcc_feat8] label_1 = 'Subject 1, session 1, left arm, emg nr. 1' label_2 = 'Subject 1, session 1, left arm, emg nr. 2' label_3 = 'Subject 1, session 1, left arm, emg nr. 3' label_4 = 'Subject 1, session 1, left arm, emg nr. 4' label_5 = 'Subject 1, session 1, left arm, emg nr. 5' label_6 = 'Subject 1, session 1, left arm, emg nr. 6' label_7 = 'Subject 1, session 1, left arm, emg nr. 7' label_8 = 'Subject 1, session 1, left arm, emg nr. 8' label_list = [label_1, label_2, label_3, label_4, label_5, label_6, label_7, label_8] plot_all_emg_mfcc(feat_list, label_list) # Prints (and logs) max, min, mean, EMS and median of EMG data # Input: CSV_handler # Output: None --> Print def log_emg_characteristics(csv_handler:CSV_handler): min_values = [] max_values = [] mean_list = [] RMS_list = [] median_list = [] if csv_handler.data_type == 'soft': for subject_container in csv_handler.data_container_dict.values(): min_values_sub = [] max_values_sub = [] mean_list_sub = [] RMS_list_sub = [] median_list_sub = [] for session_dict in subject_container.dict_list: for emg_list in session_dict.values(): for emg_df in emg_list: df = emg_df.iloc[:,1] min_values_sub.append(df.min()) max_values_sub.append(df.max()) mean_list_sub.append(df.abs().mean()) RMS_list_sub.append(np.sqrt(np.mean(np.square(df.to_numpy())))) median_list_sub.append(df.abs().median()) min_values.append(df.min()) max_values.append(df.max()) mean_list.append(df.abs().mean()) RMS_list.append(np.sqrt(np.mean(np.square(df.to_numpy())))) median_list.append(df.abs().median()) subject_nr = subject_container.subject_nr #print('\n') print('Natural typing behavior, subject {}, minimum EMG value:'.format(subject_nr), min(min_values_sub)) print('Natural typing behavior, subject {}, maximum EMG value:'.format(subject_nr), max(max_values_sub)) print('Natural typing behavior, subject {}, mean EMG value:'.format(subject_nr), np.mean(mean_list_sub)) print('Natural typing behavior, subject {}, RMS EMG value:'.format(subject_nr), np.sqrt(np.mean(np.square(RMS_list_sub)))) print('Natural typing behavior, subject {}, median EMG value:'.format(subject_nr), np.median(median_list_sub)) print('\n') elif csv_handler.data_type == 'hard': for subject_container in csv_handler.data_container_dict.values(): min_values_sub = [] max_values_sub = [] mean_list_sub = [] RMS_list_sub = [] median_list_sub = [] for session_dict in subject_container.dict_list: for emg_list in session_dict.values(): for emg_df in emg_list: df = emg_df.iloc[:,1] min_values_sub.append(df.min()) max_values_sub.append(df.max()) mean_list_sub.append(df.abs().mean()) RMS_list_sub.append(np.sqrt(np.mean(np.square(df.to_numpy())))) median_list_sub.append(df.abs().median()) min_values.append(df.min()) max_values.append(df.max()) mean_list.append(df.abs().mean()) RMS_list.append(np.sqrt(np.mean(np.square(df.to_numpy())))) median_list.append(df.abs().median()) subject_nr = subject_container.subject_nr #print('\n') print('Strong typing behavior, subject {}, minimum EMG value:'.format(subject_nr), min(min_values_sub)) print('Strong typing behavior, subject {}, maximum EMG value:'.format(subject_nr), max(max_values_sub)) print('Strong typing behavior, subject {}, mean EMG value:'.format(subject_nr), np.mean(mean_list_sub)) print('Strong typing behavior, subject {}, RMS EMG value:'.format(subject_nr), np.sqrt(np.mean(np.square(RMS_list_sub)))) print('Strong typing behavior, subject {}, median EMG value:'.format(subject_nr), np.median(median_list_sub)) print('\n') else: raise Exception('Not available data type') print(min_values) print(max_values) print(mean_list) print(RMS_list) print(median_list) # MAIN: ------------------------------------------------------------------------: if __name__ == "__main__": NR_SUBJECTS = 5 NR_SESSIONS = 4 soft_dir_name = 'Exp20201205_2myo_softType' hard_dir_name = 'Exp20201205_2myo_hardType' JSON_FILE_SOFT = 'mfcc_data_soft.json' JSON_FILE_HARD = 'mfcc_data_hard.json' csv_handler = CSV_handler(NR_SUBJECTS, NR_SESSIONS) dict = csv_handler.load_data('soft', soft_dir_name) #nn_handler = NN_handler(csv_handler) #nn_handler.store_mfcc_samples() #nn_handler.save_json_mfcc(JSON_FILE_SOFT)