58 lines
1.7 KiB
Python
58 lines
1.7 KiB
Python
import numpy as np
|
|
from pandas.core.frame import DataFrame
|
|
from scipy.fft import fft, fftfreq
|
|
import pywt
|
|
import sys
|
|
from Handle_emg_data import *
|
|
|
|
|
|
|
|
# Normalizes a ndarray of a signal to the scale of int16(32767)
|
|
def normalize_wave(y_values):
|
|
y = np.int16((y_values / y_values.max()) * 32767)
|
|
return y
|
|
|
|
# Takes the FFT of a DataFrame object
|
|
def fft_of_df(df:DataFrame):
|
|
y_values = get_xory_from_df('y', df)
|
|
N = y_values.size
|
|
norm = normalize_wave(y_values)
|
|
N_trans = fftfreq(N, 1 / get_samplerate(df))
|
|
y_f = fft(norm)
|
|
return N_trans, y_f
|
|
|
|
# Removes noise with db4 wavelet function
|
|
def wavelet_db4(df:DataFrame):
|
|
y_values = get_xory_from_df('y', df)
|
|
#y_values = normalize_wave(y_values)
|
|
wavelet = pywt.Wavelet('db4')
|
|
cA, cD = pywt.dwt(y_values, wavelet)
|
|
N_trans = np.array(range(int(np.floor((y_values.size + wavelet.dec_len - 1) / 2))))
|
|
return N_trans, cA, cD
|
|
|
|
# soft filtering of wavelet trans with the a 1/2 std filter
|
|
def soft_threshold_filter(cA, cD):
|
|
cA_filt = pywt.threshold(cA, np.std(cA)/2)
|
|
cD_filt = pywt.threshold(cD, np.std(cD)/2)
|
|
return cA_filt, cD_filt
|
|
|
|
# Inverse dwt for brining denoise signal back to the time domainfi
|
|
def inverse_wavelet(df, cA_filt, cD_filt):
|
|
wavelet = pywt.Wavelet('db4')
|
|
y_new_values = pywt.idwt(cA_filt, cD_filt, wavelet)
|
|
new_len = len(y_new_values)
|
|
old_len = len(get_xory_from_df('y', df))
|
|
if new_len > old_len:
|
|
while new_len > old_len:
|
|
y_new_values = y_new_values[:-1]
|
|
new_len = len(y_new_values)
|
|
old_len = len(get_xory_from_df('y', df))
|
|
return y_new_values
|
|
|
|
# NOT FINISHED
|
|
def cepstrum(df:DataFrame):
|
|
N = get_xory_from_df('x', df)
|
|
y = get_xory_from_df('y', df)
|
|
return None
|
|
|