ms762wu/EMG_Biometrics_2021
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chore: change naming convention from

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x/x_trans to N/N_trans
This commit is contained in:
Skudalen 2021-06-24 14:24:58 +02:00
parent 78f4e2ffc4
commit bca743d651
1 changed files with 11 additions and 8 deletions
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19
Signal_prep.py
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@ -115,18 +115,18 @@ def fft_of_df(df:DataFrame):
y_values, duration = prep_df(df) y_values, duration = prep_df(df)
N = y_values.size N = y_values.size
norm = normalize_wave(y_values) norm = normalize_wave(y_values)
x_f = fftfreq(N, 1 / SAMPLE_RATE) N_trans = fftfreq(N, 1 / SAMPLE_RATE)
y_f = fft(norm) y_f = fft(norm)
return x_f, y_f, duration return N_trans, y_f, duration
# Removes noise with db4 wavelet function # Removes noise with db4 wavelet function
def denoise_signal_pywt(df:DataFrame): def wavelet_db4_denoising(df:DataFrame):
y_values, duration = prep_df(df) y_values, duration = prep_df(df)
#y_values = normalize_wave(y_values) #y_values = normalize_wave(y_values)
wavelet = pywt.Wavelet('db4') wavelet = pywt.Wavelet('db4')
cA, cD = pywt.dwt(y_values, wavelet) cA, cD = pywt.dwt(y_values, wavelet)
x = np.array(range(int(np.floor((y_values.size + wavelet.dec_len - 1) / 2)))) N_trans = np.array(range(int(np.floor((y_values.size + wavelet.dec_len - 1) / 2))))
return x, cA, cD return N_trans, cA, cD
# Filters signal accordning to Stein's Unbiased Risk Estimate(SURE) # Filters signal accordning to Stein's Unbiased Risk Estimate(SURE)
def sure_threshold_filter(cA, cD): def sure_threshold_filter(cA, cD):
@ -143,17 +143,20 @@ def plot_df(df:DataFrame):
plt.show() plt.show()
# Plots ndarrays after transformations # Plots ndarrays after transformations
def plot_trans(x_f, y_f): def plot_trans(N_trans, y_trans):
plt.plot(x_f, np.abs(y_f)) plt.plot(N_trans, np.abs(y_trans))
plt.show() plt.show()
def inverse_wavelet(N, cA, cD):
return None
#''' #'''
handler = Handler.CSV_handler() handler = Handler.CSV_handler()
file = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1810/myoLeftEmg.csv" file = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1810/myoLeftEmg.csv"
df = handler.get_time_emg_table(file, 1) df = handler.get_time_emg_table(file, 1)
N = df.size
#plot_df(df) #plot_df(df)
x, cA, cD = denoise_signal_pywt(df) x, cA, cD = wavelet_db4_denoising(df)
#plot_trans(x, cA) #plot_trans(x, cA)
cA_filtered, cD = soft_threshold_filter(cA, cD) cA_filtered, cD = soft_threshold_filter(cA, cD)
plot_trans(x, cA_filtered) plot_trans(x, cA_filtered)