234 lines
9.4 KiB
Python
234 lines
9.4 KiB
Python
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
|
|
def plot_array(N, y):
|
|
plt.plot(N, np.abs(y))
|
|
plt.show()
|
|
|
|
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()
|
|
|
|
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()
|
|
|
|
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()
|
|
|
|
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 data_type, but only for visuals.
|
|
# E.g. handler = CSV_handler('soft')
|
|
|
|
# Takes in handler and detailes to denoise.
|
|
# Returns arrays and 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
|
|
|
|
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
|
|
# Returns None. Plots the two
|
|
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_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_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)
|
|
|
|
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)
|
|
|
|
# MAIN: ------------------------------------------------------------------------:
|
|
|
|
def main():
|
|
|
|
csv_handler = CSV_handler()
|
|
csv_handler.load_data('soft')
|
|
dl_data_handler = DL_data_handler(csv_handler)
|
|
emg_list = dl_data_handler.get_emg_list(1, 1)
|
|
session_df = dl_data_handler.make_subj_sample(emg_list)
|
|
print(session_df)
|
|
df = dl_data_handler.make_mfcc_df_from_session_df(session_df)
|
|
print(df)
|
|
print(len(df.iloc[0]))
|
|
|
|
|
|
|
|
|
|
main() |