240 lines
9.8 KiB
Python
240 lines
9.8 KiB
Python
from logging import error
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from matplotlib.cbook import get_sample_data
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from Handle_emg_data import *
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from Signal_prep import *
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import matplotlib.pyplot as plt
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from matplotlib import cm
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import matplotlib.ticker as ticker
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# Global variables for MFCC
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mfcc_stepsize = 0.5
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mfcc_windowsize = 2
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# PLOT FUNCTIONS --------------------------------------------------------------:
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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_array(N, y):
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plt.plot(N, np.abs(y))
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plt.show()
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def plot_compare_two_df(df_old, old_name, df_new, new_name):
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x = get_xory_from_df('x', df_old)
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y1 = get_xory_from_df('y', df_old)
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y2 = get_xory_from_df('y', df_new)
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figure, axis = plt.subplots(1, 2)
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axis[0].plot(x, y1)
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axis[0].set_title(old_name)
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axis[1].plot(x, y2)
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axis[1].set_title(new_name)
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plt.show()
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def plot_mfcc(mfcc_data, data_label:str):
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fig, ax = plt.subplots()
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mfcc_data= np.swapaxes(mfcc_data, 0 ,1)
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ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x * mfcc_stepsize))
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ax.xaxis.set_major_formatter(ticks_x)
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ax.imshow(mfcc_data, interpolation='nearest', cmap=cm.coolwarm, origin='lower')
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ax.set_title('MFCC: ' + data_label)
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ax.set_ylabel('Cepstral Coefficients')
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ax.set_xlabel('Time(s)')
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plt.show()
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def plot_3_mfcc(mfcc_data1, data_label1:str, mfcc_data2, data_label2:str, mfcc_data3, data_label3:str):
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fig, axes = plt.subplots(nrows=3)
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plt.subplots_adjust(hspace=1.4, wspace=0.4)
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ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x * mfcc_stepsize))
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data_list = [mfcc_data1, mfcc_data2, mfcc_data3]
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label_list = [data_label1, data_label2, data_label3]
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for ax, data, label in zip(axes, data_list, label_list):
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mfcc_data= np.swapaxes(data, 0 ,1)
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ax.xaxis.set_major_formatter(ticks_x)
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ax.imshow(mfcc_data, interpolation='nearest', cmap=cm.coolwarm, origin='lower')
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ax.set_title('MFCC: ' + str(label))
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ax.set_ylabel('Coefficients')
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ax.set_xlabel('Time(s)')
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plt.show()
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def plot_all_emg_mfcc(data_list:list, label_list:list):
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fig, axes = plt.subplots(nrows=4, ncols=2)
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plt.subplots_adjust(hspace=1.4, wspace=0.4)
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ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x * mfcc_stepsize))
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plt.autoscale()
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d_list = np.array([ [data_list[0], data_list[4]],
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[data_list[1], data_list[5]],
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[data_list[2], data_list[6]],
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[data_list[3], data_list[7]]
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])
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l_list = np.array([ [label_list[0], label_list[4]],
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[label_list[1], label_list[5]],
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[label_list[2], label_list[6]],
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[label_list[3], label_list[7]]
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])
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for col in [0, 1]:
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for ax, data, label in zip(axes[:,col], d_list[:,col], l_list[:,col]):
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mfcc_data= np.swapaxes(data, 0 ,1)
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ax.xaxis.set_major_formatter(ticks_x)
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ax.imshow(mfcc_data, interpolation='nearest', cmap=cm.coolwarm, origin='lower')
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ax.set_title('MFCC: ' + str(label))
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ax.set_ylabel('Coefficients')
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ax.set_xlabel('Time(s)')
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plt.show()
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# DATA FUNCTIONS: --------------------------------------------------------------:
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# The CSV_handler takes in data_type, but only for visuals.
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# E.g. handler = CSV_handler('soft')
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# Loads in data to a CSV_handler. Choose data_type: hard, hardPP, soft og softPP as str.
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# Returns None.
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def load_data(csv_handler:CSV_handler, data_type):
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if data_type == 'hard':
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csv_handler.load_hard_original_emg_data()
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elif data_type == 'hardPP':
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csv_handler.load_hard_PP_emg_data()
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elif data_type == 'soft':
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csv_handler.load_soft_original_emg_data()
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elif data_type == 'softPP':
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csv_handler.load_soft_PP_emg_data()
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else:
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raise Exception('Wrong input')
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# Retrieved data. Send in loaded csv_handler and data detailes you want.
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# Returns DataFrame and samplerate
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def get_data(csv_handler:CSV_handler, subject_nr, which_arm, session, emg_nr):
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data_frame = csv_handler.get_df_from_data_dict(subject_nr, which_arm, session, emg_nr)
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samplerate = get_samplerate(data_frame)
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return data_frame, samplerate
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# Takes in handler and detailes to denoise.
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# Returns arrays and df
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def denoice_dataset(handler:Handler.CSV_handler, subject_nr, which_arm, round, emg_nr):
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df = handler.get_df_from_data_dict(subject_nr, which_arm, round, emg_nr)
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N = get_xory_from_df('x', df)
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N_trans, cA, cD = wavelet_db4(df)
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cA_filt, cD_filt = soft_threshold_filter(cA, cD)
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y_values = inverse_wavelet(df, cA_filt, cD_filt)
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df_new = Handler.make_df_from_xandy(N, y_values, emg_nr)
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return df_new
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# Slightly modified mfcc with inputs like below.
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# Returns N (x_values from original df) and mfcc_y_values
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def mfcc_custom(df:DataFrame, samplesize, windowsize, stepsize):
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N = get_xory_from_df('x', df)
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y = get_xory_from_df('y', df)
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return N, base.mfcc(y, samplesize, windowsize, stepsize)
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# CASE FUNTIONS ----------------------------------------------------------------:
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# Takes in a df and compares the FFT and the wavelet denoising of the FFT
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# Returns None. Plots the two
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def compare_with_wavelet_filter(data_frame):
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N_trans, cA, cD = wavelet_db4(data_frame)
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data_frame_freq = make_df_from_xandy(N_trans, cA, 1)
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cA_filt, cD_filt = soft_threshold_filter(cA, cD)
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data_frame_freq_filt = make_df_from_xandy(N_trans, cD_filt, 1)
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plot_compare_two_df(data_frame_freq, 'Original data', data_frame_freq_filt, 'Analyzed data')
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# Loads three preset emg_1 datasets(subj1:session1, subj1:session2, subj2:session1), calculates mfcc for each and plots them.
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# Input: CSV_handler
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# Output: None --> Plot
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def mfcc_3_plots_1_1_2(csv_handler:CSV_handler):
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df1, samplerate1 = get_data(csv_handler, 1, 'left', 1, 1)
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df2, samplerate2 = get_data(csv_handler, 1, 'left', 2, 1)
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df3, samplerate3 = get_data(csv_handler, 2, 'left', 1, 1)
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#print(df1.head, samplerate1)
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#print(df2.head, samplerate2)
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#print(df3.head, samplerate3)
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N1, mfcc_feat1 = mfcc_custom(df1, samplerate1, mfcc_windowsize, mfcc_stepsize)
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N2, mfcc_feat2 = mfcc_custom(df2, samplerate2, mfcc_windowsize, mfcc_stepsize)
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N3, mfcc_feat3 = mfcc_custom(df3, samplerate3, mfcc_windowsize, mfcc_stepsize)
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label_1 = 'Subject 1, session 1, left arm, emg nr. 1'
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label_2 = 'Subject 1, session 2, left arm, emg nr. 1'
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label_3 = 'Subject 2, session 1, left arm, emg nr. 1'
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plot_3_mfcc(mfcc_feat1, label_1, mfcc_feat2, label_2, mfcc_feat3, label_3)
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# Loads three preset emg_1 datasets(subj3:session1, subj3:session2, subj4:session1), calculates mfcc for each and plots them.
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# Input: CSV_handler
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# Output: None --> Plot
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def mfcc_3_plots_3_3_4(csv_handler:CSV_handler):
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df1, samplerate1 = get_data(csv_handler, 3, 'left', 1, 1)
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df2, samplerate2 = get_data(csv_handler, 3, 'left', 2, 1)
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df3, samplerate3 = get_data(csv_handler, 4, 'left', 1, 1)
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#print(df1.head, samplerate1)
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#print(df2.head, samplerate2)
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#print(df3.head, samplerate3)
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N1, mfcc_feat1 = mfcc_custom(df1, samplerate1, mfcc_windowsize, mfcc_stepsize)
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N2, mfcc_feat2 = mfcc_custom(df2, samplerate2, mfcc_windowsize, mfcc_stepsize)
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N3, mfcc_feat3 = mfcc_custom(df3, samplerate3, mfcc_windowsize, mfcc_stepsize)
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label_1 = 'Subject 3, session 1, left arm, emg nr. 1'
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label_2 = 'Subject 3, session 2, left arm, emg nr. 1'
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label_3 = 'Subject 4, session 1, left arm, emg nr. 1'
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plot_3_mfcc(mfcc_feat1, label_1, mfcc_feat2, label_2, mfcc_feat3, label_3)
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def mfcc_all_emg_plots(csv_handler:CSV_handler):
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df1, samplerate1 = get_data(csv_handler, 1, 'left', 1, 1)
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df2, samplerate2 = get_data(csv_handler, 1, 'left', 1, 2)
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df3, samplerate3 = get_data(csv_handler, 1, 'left', 1, 3)
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df4, samplerate4 = get_data(csv_handler, 1, 'left', 1, 4)
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df5, samplerate5 = get_data(csv_handler, 1, 'left', 1, 5)
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df6, samplerate6 = get_data(csv_handler, 1, 'left', 1, 6)
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df7, samplerate7 = get_data(csv_handler, 1, 'left', 1, 7)
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df8, samplerate8 = get_data(csv_handler, 1, 'left', 1, 8)
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N1, mfcc_feat1 = mfcc_custom(df1, samplerate1, mfcc_windowsize, mfcc_stepsize)
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N2, mfcc_feat2 = mfcc_custom(df2, samplerate2, mfcc_windowsize, mfcc_stepsize)
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N3, mfcc_feat3 = mfcc_custom(df3, samplerate3, mfcc_windowsize, mfcc_stepsize)
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N4, mfcc_feat4 = mfcc_custom(df4, samplerate4, mfcc_windowsize, mfcc_stepsize)
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N5, mfcc_feat5 = mfcc_custom(df5, samplerate5, mfcc_windowsize, mfcc_stepsize)
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N6, mfcc_feat6 = mfcc_custom(df6, samplerate6, mfcc_windowsize, mfcc_stepsize)
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N7, mfcc_feat7 = mfcc_custom(df7, samplerate7, mfcc_windowsize, mfcc_stepsize)
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N8, mfcc_feat8 = mfcc_custom(df8, samplerate8, mfcc_windowsize, mfcc_stepsize)
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feat_list = [mfcc_feat1, mfcc_feat2, mfcc_feat3, mfcc_feat4, mfcc_feat5, mfcc_feat6, mfcc_feat7, mfcc_feat8]
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label_1 = 'Subject 1, session 1, left arm, emg nr. 1'
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label_2 = 'Subject 1, session 1, left arm, emg nr. 2'
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label_3 = 'Subject 1, session 1, left arm, emg nr. 3'
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label_4 = 'Subject 1, session 1, left arm, emg nr. 4'
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label_5 = 'Subject 1, session 1, left arm, emg nr. 5'
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label_6 = 'Subject 1, session 1, left arm, emg nr. 6'
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label_7 = 'Subject 1, session 1, left arm, emg nr. 7'
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label_8 = 'Subject 1, session 1, left arm, emg nr. 8'
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label_list = [label_1, label_2, label_3, label_4, label_5, label_6, label_7, label_8]
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plot_all_emg_mfcc(feat_list, label_list)
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# MAIN: ------------------------------------------------------------------------:
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def main():
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csv_handler = CSV_handler()
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load_data(csv_handler, 'soft')
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#mfcc_3_plots_1_1_2(csv_handler)
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#mfcc_3_plots_3_3_4(csv_handler)
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mfcc_all_emg_plots(csv_handler)
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main() |