ms762wu/EMG_Biometrics_2021
2
1
Fork 0
Code Issues Pull Requests Releases Wiki Activity

feat: make plot func for three mfcc analysis

Browse Source
This commit is contained in:
Skudalen 2021-06-28 19:26:42 +02:00
parent c5d2e48771
commit f3e0cff363
1 changed files with 43 additions and 11 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

54
Present_data.py
View File

@ -44,17 +44,38 @@ def plot_mfcc(mfcc_data, data_label:str):
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_title('MFCC: ' + data_label)
ax.set_ylabel('Cepstral Coefficients')
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):
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: ' + label)
ax.set_ylabel('Cepstral Coefficients')
ax.set_xlabel('Time(s)')
plt.show()
# DATA FUNCTIONS: --------------------------------------------------------------:
# The CSV_handler takes in data_type, but only for visuals.
# E.g. handler = CSV_handler('soft')
# Loads in data. Choose data_type: hard, hardPP, soft og softPP as str. Returns None
# Loads in data to a CSV_handler. Choose data_type: hard, hardPP, soft og softPP as str.
# Returns None.
def load_data(csv_handler:CSV_handler, data_type):
if data_type == 'hard':
csv_handler.load_hard_original_emg_data()
@ -67,13 +88,15 @@ def load_data(csv_handler:CSV_handler, data_type):
else:
raise Exception('Wrong input')
# Retrieved data. Send in loaded csv_handler and data detailes you want. Returns DataFrame and samplerate
# Retrieved data. Send in loaded csv_handler and data detailes you want.
# Returns DataFrame and samplerate
def get_data(csv_handler:CSV_handler, subject_nr, which_arm, session, emg_nr):
data_frame = csv_handler.get_df_from_data_dict(subject_nr, which_arm, session, emg_nr)
samplerate = get_samplerate(data_frame)
return data_frame, samplerate
#Takes in handler and detailes to denoise. Returns arrays and df
# Takes in handler and detailes to denoise.
# Returns arrays and df
def denoice_dataset(handler:Handler.CSV_handler, subject_nr, which_arm, round, emg_nr):
df = handler.get_df_from_data_dict(subject_nr, which_arm, round, emg_nr)
@ -85,6 +108,7 @@ 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
def mfcc_custom(df:DataFrame, samplesize, windowsize, stepsize):
N = get_xory_from_df('x', df)
y = get_xory_from_df('y', df)
@ -92,6 +116,8 @@ def mfcc_custom(df:DataFrame, samplesize, windowsize, stepsize):
# 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)
@ -108,13 +134,19 @@ def main():
csv_handler = CSV_handler()
load_data(csv_handler, 'hard')
data_frame, samplerate = get_data(csv_handler, 2, 'left', 1, 1)
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_feat = mfcc_custom(data_frame[:5000], samplerate, mfcc_windowsize, mfcc_stepsize)
plot_mfcc(mfcc_feat)
#print(mfcc_feat)
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)
return None
main()