ms762wu/EMG_Biometrics_2021
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fix: change to right naming conventions

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This commit is contained in:
Skudalen 2021-07-02 10:26:05 +02:00
parent c36ddf1609
commit 99277bc101
5 changed files with 65 additions and 61 deletions
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6
Handle_emg_data.py
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@ -628,6 +628,7 @@ class DL_data_handler:
with open(json_path, "w") as fp: with open(json_path, "w") as fp:
json.dump(data, fp, indent=4) json.dump(data, fp, indent=4)
''' '''
# HELP FUNCTIONS: ------------------------------------------------------------------------: # HELP FUNCTIONS: ------------------------------------------------------------------------:
# Help: gets the str from emg nr # Help: gets the str from emg nr
@ -669,7 +670,10 @@ class DL_data_handler:
# Slightly modified mfcc with inputs like below. # Slightly modified mfcc with inputs like below.
# Returns N (x_values from original df) and mfcc_y_values # Returns N (x_values from original df) and mfcc_y_values
def mfcc_custom(df:DataFrame, samplesize, windowsize, stepsize, nr_coefficients, nr_mel_filters): def mfcc_custom(df:DataFrame, samplesize, windowsize=MFCC_WINDOWSIZE,
stepsize=MFCC_STEPSIZE,
nr_coefficients=NR_COEFFICIENTS,
nr_mel_filters=NR_MEL_BINS):
N = get_xory_from_df('x', df) N = get_xory_from_df('x', df)
y = get_xory_from_df('y', df) y = get_xory_from_df('y', df)
return N, base.mfcc(y, samplesize, windowsize, stepsize, nr_coefficients, nr_mel_filters) return N, base.mfcc(y, samplesize, windowsize, stepsize, nr_coefficients, nr_mel_filters)

38
Present_data.py
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@ -42,7 +42,7 @@ def plot_mfcc(mfcc_data, data_label:str):
fig, ax = plt.subplots() fig, ax = plt.subplots()
mfcc_data= np.swapaxes(mfcc_data, 0 ,1) mfcc_data= np.swapaxes(mfcc_data, 0 ,1)
ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x * mfcc_stepsize)) ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x * MFCC_STEPSIZE))
ax.xaxis.set_major_formatter(ticks_x) ax.xaxis.set_major_formatter(ticks_x)
ax.imshow(mfcc_data, interpolation='nearest', cmap=cm.coolwarm, origin='lower') ax.imshow(mfcc_data, interpolation='nearest', cmap=cm.coolwarm, origin='lower')
@ -55,7 +55,7 @@ def plot_3_mfcc(mfcc_data1, data_label1:str, mfcc_data2, data_label2:str, mfcc_d
fig, axes = plt.subplots(nrows=3) fig, axes = plt.subplots(nrows=3)
plt.subplots_adjust(hspace=1.4, wspace=0.4) plt.subplots_adjust(hspace=1.4, wspace=0.4)
ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x * mfcc_stepsize)) ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x * MFCC_STEPSIZE))
data_list = [mfcc_data1, mfcc_data2, mfcc_data3] data_list = [mfcc_data1, mfcc_data2, mfcc_data3]
label_list = [data_label1, data_label2, data_label3] label_list = [data_label1, data_label2, data_label3]
@ -74,7 +74,7 @@ def plot_3_mfcc(mfcc_data1, data_label1:str, mfcc_data2, data_label2:str, mfcc_d
def plot_all_emg_mfcc(data_list:list, label_list:list): def plot_all_emg_mfcc(data_list:list, label_list:list):
fig, axes = plt.subplots(nrows=4, ncols=2) fig, axes = plt.subplots(nrows=4, ncols=2)
plt.subplots_adjust(hspace=1.4, wspace=0.4) plt.subplots_adjust(hspace=1.4, wspace=0.4)
ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x * mfcc_stepsize)) ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x * MFCC_STEPSIZE))
plt.autoscale() plt.autoscale()
d_list = np.array([ [data_list[0], data_list[4]], d_list = np.array([ [data_list[0], data_list[4]],
@ -115,7 +115,7 @@ def pretty(dict):
# Takes in handler and detailes to denoise. # Takes in handler and detailes to denoise.
# Returns arrays and df # Returns arrays and df
def denoice_dataset(handler:Handler.CSV_handler, subject_nr, which_arm, round, emg_nr): 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) df = handler.get_df_from_data_dict(subject_nr, which_arm, round, emg_nr)
N = get_xory_from_df('x', df) N = get_xory_from_df('x', df)
@ -123,7 +123,7 @@ def denoice_dataset(handler:Handler.CSV_handler, subject_nr, which_arm, round, e
cA_filt, cD_filt = soft_threshold_filter(cA, cD) cA_filt, cD_filt = soft_threshold_filter(cA, cD)
y_values = inverse_wavelet(df, cA_filt, cD_filt) y_values = inverse_wavelet(df, cA_filt, cD_filt)
df_new = Handler.make_df_from_xandy(N, y_values, emg_nr) df_new = make_df_from_xandy(N, y_values, emg_nr)
return df_new return df_new
@ -157,9 +157,9 @@ def mfcc_3_plots_1_1_2(csv_handler:CSV_handler):
#print(df1.head, samplerate1) #print(df1.head, samplerate1)
#print(df2.head, samplerate2) #print(df2.head, samplerate2)
#print(df3.head, samplerate3) #print(df3.head, samplerate3)
N1, mfcc_feat1 = mfcc_custom(df1, samplerate1, mfcc_windowsize, mfcc_stepsize) N1, mfcc_feat1 = mfcc_custom(df1, samplerate1, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
N2, mfcc_feat2 = mfcc_custom(df2, samplerate2, 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) N3, mfcc_feat3 = mfcc_custom(df3, samplerate3, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
label_1 = 'Subject 1, session 1, left arm, emg nr. 1' label_1 = 'Subject 1, session 1, left arm, emg nr. 1'
label_2 = 'Subject 1, session 2, 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' label_3 = 'Subject 2, session 1, left arm, emg nr. 1'
@ -176,9 +176,9 @@ def mfcc_3_plots_3_3_4(csv_handler:CSV_handler):
#print(df1.head, samplerate1) #print(df1.head, samplerate1)
#print(df2.head, samplerate2) #print(df2.head, samplerate2)
#print(df3.head, samplerate3) #print(df3.head, samplerate3)
N1, mfcc_feat1 = mfcc_custom(df1, samplerate1, mfcc_windowsize, mfcc_stepsize) N1, mfcc_feat1 = mfcc_custom(df1, samplerate1, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
N2, mfcc_feat2 = mfcc_custom(df2, samplerate2, 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) N3, mfcc_feat3 = mfcc_custom(df3, samplerate3, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
label_1 = 'Subject 3, session 1, left arm, emg nr. 1' label_1 = 'Subject 3, session 1, left arm, emg nr. 1'
label_2 = 'Subject 3, session 2, 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' label_3 = 'Subject 4, session 1, left arm, emg nr. 1'
@ -194,14 +194,14 @@ def mfcc_all_emg_plots(csv_handler:CSV_handler):
df6, samplerate6 = csv_handler.get_data( 1, 'left', 1, 6) df6, samplerate6 = csv_handler.get_data( 1, 'left', 1, 6)
df7, samplerate7 = csv_handler.get_data( 1, 'left', 1, 7) df7, samplerate7 = csv_handler.get_data( 1, 'left', 1, 7)
df8, samplerate8 = csv_handler.get_data( 1, 'left', 1, 8) df8, samplerate8 = csv_handler.get_data( 1, 'left', 1, 8)
N1, mfcc_feat1 = csv_handler.mfcc_custom(df1, samplerate1, MFCC_WINDOWSIZE, MFCC_STEPSIZE) N1, mfcc_feat1 = mfcc_custom(df1, samplerate1, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
N2, mfcc_feat2 = csv_handler.mfcc_custom(df2, samplerate2, MFCC_WINDOWSIZE, MFCC_STEPSIZE) N2, mfcc_feat2 = mfcc_custom(df2, samplerate2, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
N3, mfcc_feat3 = csv_handler.mfcc_custom(df3, samplerate3, MFCC_WINDOWSIZE, MFCC_STEPSIZE) N3, mfcc_feat3 = mfcc_custom(df3, samplerate3, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
N4, mfcc_feat4 = csv_handler.mfcc_custom(df4, samplerate4, MFCC_WINDOWSIZE, MFCC_STEPSIZE) N4, mfcc_feat4 = mfcc_custom(df4, samplerate4, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
N5, mfcc_feat5 = csv_handler.mfcc_custom(df5, samplerate5, MFCC_WINDOWSIZE, MFCC_STEPSIZE) N5, mfcc_feat5 = mfcc_custom(df5, samplerate5, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
N6, mfcc_feat6 = csv_handler.mfcc_custom(df6, samplerate6, MFCC_WINDOWSIZE, MFCC_STEPSIZE) N6, mfcc_feat6 = mfcc_custom(df6, samplerate6, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
N7, mfcc_feat7 = csv_handler.mfcc_custom(df7, samplerate7, MFCC_WINDOWSIZE, MFCC_STEPSIZE) N7, mfcc_feat7 = mfcc_custom(df7, samplerate7, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
N8, mfcc_feat8 = csv_handler.mfcc_custom(df8, samplerate8, 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] 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_1 = 'Subject 1, session 1, left arm, emg nr. 1'
label_2 = 'Subject 1, session 1, left arm, emg nr. 2' label_2 = 'Subject 1, session 1, left arm, emg nr. 2'

4
Signal_prep.py
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@ -3,7 +3,7 @@ from pandas.core.frame import DataFrame
from scipy.fft import fft, fftfreq from scipy.fft import fft, fftfreq
import pywt import pywt
import sys import sys
import Handle_emg_data as Handler from Handle_emg_data import *
@ -17,7 +17,7 @@ def fft_of_df(df:DataFrame):
y_values = get_xory_from_df('y', df) y_values = get_xory_from_df('y', df)
N = y_values.size N = y_values.size
norm = normalize_wave(y_values) norm = normalize_wave(y_values)
N_trans = fftfreq(N, 1 / Handler.get_samplerate(df)) N_trans = fftfreq(N, 1 / get_samplerate(df))
y_f = fft(norm) y_f = fft(norm)
return N_trans, y_f return N_trans, y_f

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__pycache__/Handle_emg_data.cpython-38.pyc
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__pycache__/Signal_prep.cpython-38.pyc
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