120 lines
3.9 KiB
Python
120 lines
3.9 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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# 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. Choose data_type: hard, hardPP, soft og softPP as str. 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. 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. 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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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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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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# MAIN: ------------------------------------------------------------------------:
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def main():
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csv_handler = CSV_handler()
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load_data(csv_handler, 'hard')
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data_frame, samplerate = get_data(csv_handler, 2, 'left', 1, 1)
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N, mfcc_feat = mfcc_custom(data_frame[:5000], samplerate, mfcc_windowsize, mfcc_stepsize)
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plot_mfcc(mfcc_feat)
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#print(mfcc_feat)
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return None
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main() |