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doc: add doc for mfcc case func

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Skudalen 2021-06-28 20:24:45 +02:00
parent f3e0cff363
commit e0a1dfee71
1 changed files with 23 additions and 17 deletions
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40
Present_data.py
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@ -49,7 +49,7 @@ def plot_mfcc(mfcc_data, data_label:str):
ax.set_xlabel('Time(s)')
plt.show()
def plot_3_mfcc(mfcc_data1, mfcc_data2, mfcc_data3, data_label1:str, data_label2:str, data_label3:str):
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)
@ -63,7 +63,7 @@ def plot_3_mfcc(mfcc_data1, mfcc_data2, mfcc_data3, data_label1:str, data_label2
ax.xaxis.set_major_formatter(ticks_x)
ax.imshow(mfcc_data, interpolation='nearest', cmap=cm.coolwarm, origin='lower')
ax.set_title('MFCC: ' + label)
ax.set_title('MFCC: ' + str(label))
ax.set_ylabel('Cepstral Coefficients')
ax.set_xlabel('Time(s)')
@ -108,7 +108,8 @@ def denoice_dataset(handler:Handler.CSV_handler, subject_nr, which_arm, round, e
df_new = Handler.make_df_from_xandy(N, y_values, emg_nr)
return df_new
# Slightly modified mfcc with inputs like below.
# Returns N (x_values from original df) and mfcc_y_values
def mfcc_custom(df:DataFrame, samplesize, windowsize, stepsize):
N = get_xory_from_df('x', df)
y = get_xory_from_df('y', df)
@ -127,26 +128,31 @@ def compare_with_wavelet_filter(data_frame):
plot_compare_two_df(data_frame_freq, 'Original data', data_frame_freq_filt, 'Analyzed data')
def compare_mfcc_3_plots(csv_handler:CSV_handler):
df1, samplerate1 = get_data(csv_handler, 1, 'left', 1, 1)
df2, samplerate2 = get_data(csv_handler, 1, 'left', 2, 1)
df3, samplerate3 = get_data(csv_handler, 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'
label_2 = 'Subject 1, session 2'
label_3 = 'Subject 2, session 1'
plot_3_mfcc(mfcc_feat1, label_1, mfcc_feat2, label_2, mfcc_feat3, label_3)
# MAIN: ------------------------------------------------------------------------:
def main():
csv_handler = CSV_handler()
load_data(csv_handler, 'hard')
df1, samplerate1 = get_data(csv_handler, 1, 'left', 1, 1)
df2, samplerate2 = get_data(csv_handler, 1, 'left', 2, 1)
df3, samplerate3 = get_data(csv_handler, 2, 'left', 1, 1)
N, mfcc_feat1 = mfcc_custom(df1[:5000], samplerate1, mfcc_windowsize, mfcc_stepsize)
N, mfcc_feat2 = mfcc_custom(df2[:5000], samplerate2, mfcc_windowsize, mfcc_stepsize)
N, mfcc_feat3 = mfcc_custom(df3[:5000], samplerate3, mfcc_windowsize, mfcc_stepsize)
label_1 = 'Subject 1, session 1'
label_2 = 'Subject 1, session 2'
label_3 = 'Subject 2, session 1'
plot_3_mfcc(mfcc_feat1, mfcc_feat2, mfcc_feat3, label_1, label_2, label_3)
load_data(csv_handler, 'soft')
compare_mfcc_3_plots(csv_handler)
main()