change.py
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import librosa
import numpy as np
import matplotlib.pyplot as plt
import librosa, librosa.display
import cv2
n_data = np.load('N_samples.npy')
s_data = np.load('S_samples.npy')
v_data = np.load('V_samples.npy')
f_data = np.load('F_samples.npy')
q_data = np.load('Q_samples.npy')
n_fft_n= 256
win_length_n=64
hp_length_n=2
sr = 360
data =n_data #데이터 종류
lst = [] #npy로 저장할 데이터들
length = len(data) #출력할 데이터 개수
for i in range(length):
#원래 ECG 그래프 그리기
#ax1 = fig1.add_subplot(length,2,2*(i+1)-1)
#ax1.plot(data[i,0,:])
# STFT 이미지 그리기
#ax2 = fig1.add_subplot(length,2,2*(i+1))
#STFT
D_highres = librosa.stft(data[i,0,:].flatten(), n_fft=n_fft_n, hop_length=hp_length_n, win_length=win_length_n)
#ampiltude로 변환
magnitude = np.abs(D_highres)
#amplitude를 db 스케일로 변환
log_spectrogram = librosa.amplitude_to_db(magnitude)
#화이트 노이즈 제거
log_spectrogram = log_spectrogram[:,10:150]
#128,128로 resize
log_spectrogram = cv2.resize(log_spectrogram, (128,128), interpolation = cv2.INTER_AREA)
#스펙트로그램 출력
#img = librosa.display.specshow(log_spectrogram, sr=sr, hop_length = hp_length_n, ax=ax2, y_axis="linear", x_axis="time")
#컬러바
#fig.colorbar(img, ax=ax2)# format="%+2.f dB")
#print(log_spectrogram.shape)
lst.append(log_spectrogram)
if i%30==0:
print(i,'/',length)
#npy로 저장
lst = np.array(lst)
output_filename = 'n_spectrogram'
print(lst.shape)
np.save(output_filename, lst)
##########
data =s_data #데이터 종류
lst = [] #npy로 저장할 데이터들
length = len(data) #출력할 데이터 개수
for i in range(length):
#원래 ECG 그래프 그리기
#ax1 = fig1.add_subplot(length,2,2*(i+1)-1)
#ax1.plot(data[i,0,:])
# STFT 이미지 그리기
#ax2 = fig1.add_subplot(length,2,2*(i+1))
#STFT
D_highres = librosa.stft(data[i,0,:].flatten(), n_fft=n_fft_n, hop_length=hp_length_n, win_length=win_length_n)
#ampiltude로 변환
magnitude = np.abs(D_highres)
#amplitude를 db 스케일로 변환
log_spectrogram = librosa.amplitude_to_db(magnitude)
#화이트 노이즈 제거
log_spectrogram = log_spectrogram[:,10:150]
#128,128로 resize
log_spectrogram = cv2.resize(log_spectrogram, (128,128), interpolation = cv2.INTER_AREA)
#스펙트로그램 출력
#img = librosa.display.specshow(log_spectrogram, sr=sr, hop_length = hp_length_n, ax=ax2, y_axis="linear", x_axis="time")
#컬러바
#fig.colorbar(img, ax=ax2)# format="%+2.f dB")
#print(log_spectrogram.shape)
lst.append(log_spectrogram)
if i%30==0:
print(i,'/',length)
#npy로 저장
lst = np.array(lst)
output_filename = 's_spectrogram'
print(lst.shape)
np.save(output_filename, lst)
##########
data =v_data #데이터 종류
lst = [] #npy로 저장할 데이터들
length = len(data) #출력할 데이터 개수
for i in range(length):
#원래 ECG 그래프 그리기
#ax1 = fig1.add_subplot(length,2,2*(i+1)-1)
#ax1.plot(data[i,0,:])
# STFT 이미지 그리기
#ax2 = fig1.add_subplot(length,2,2*(i+1))
#STFT
D_highres = librosa.stft(data[i,0,:].flatten(), n_fft=n_fft_n, hop_length=hp_length_n, win_length=win_length_n)
#ampiltude로 변환
magnitude = np.abs(D_highres)
#amplitude를 db 스케일로 변환
log_spectrogram = librosa.amplitude_to_db(magnitude)
#화이트 노이즈 제거
log_spectrogram = log_spectrogram[:,10:150]
#128,128로 resize
log_spectrogram = cv2.resize(log_spectrogram, (128,128), interpolation = cv2.INTER_AREA)
#스펙트로그램 출력
#img = librosa.display.specshow(log_spectrogram, sr=sr, hop_length = hp_length_n, ax=ax2, y_axis="linear", x_axis="time")
#컬러바
#fig.colorbar(img, ax=ax2)# format="%+2.f dB")
#print(log_spectrogram.shape)
lst.append(log_spectrogram)
if i%30==0:
print(i,'/',length)
#npy로 저장
lst = np.array(lst)
output_filename = 'v_spectrogram'
print(lst.shape)
np.save(output_filename, lst)
##########
data =f_data #데이터 종류
lst = [] #npy로 저장할 데이터들
length = len(data) #출력할 데이터 개수
for i in range(length):
#원래 ECG 그래프 그리기
#ax1 = fig1.add_subplot(length,2,2*(i+1)-1)
#ax1.plot(data[i,0,:])
# STFT 이미지 그리기
#ax2 = fig1.add_subplot(length,2,2*(i+1))
#STFT
D_highres = librosa.stft(data[i,0,:].flatten(), n_fft=n_fft_n, hop_length=hp_length_n, win_length=win_length_n)
#ampiltude로 변환
magnitude = np.abs(D_highres)
#amplitude를 db 스케일로 변환
log_spectrogram = librosa.amplitude_to_db(magnitude)
#화이트 노이즈 제거
log_spectrogram = log_spectrogram[:,10:150]
#128,128로 resize
log_spectrogram = cv2.resize(log_spectrogram, (128,128), interpolation = cv2.INTER_AREA)
#스펙트로그램 출력
#img = librosa.display.specshow(log_spectrogram, sr=sr, hop_length = hp_length_n, ax=ax2, y_axis="linear", x_axis="time")
#컬러바
#fig.colorbar(img, ax=ax2)# format="%+2.f dB")
#print(log_spectrogram.shape)
lst.append(log_spectrogram)
if i%30==0:
print(i,'/',length)
#npy로 저장
lst = np.array(lst)
output_filename = 'f_spectrogram'
print(lst.shape)
np.save(output_filename, lst)
##########
data =q_data #데이터 종류
lst = [] #npy로 저장할 데이터들
length = len(data) #출력할 데이터 개수
for i in range(length):
#원래 ECG 그래프 그리기
#ax1 = fig1.add_subplot(length,2,2*(i+1)-1)
#ax1.plot(data[i,0,:])
# STFT 이미지 그리기
#ax2 = fig1.add_subplot(length,2,2*(i+1))
#STFT
D_highres = librosa.stft(data[i,0,:].flatten(), n_fft=n_fft_n, hop_length=hp_length_n, win_length=win_length_n)
#ampiltude로 변환
magnitude = np.abs(D_highres)
#amplitude를 db 스케일로 변환
log_spectrogram = librosa.amplitude_to_db(magnitude)
#화이트 노이즈 제거
log_spectrogram = log_spectrogram[:,10:150]
#128,128로 resize
log_spectrogram = cv2.resize(log_spectrogram, (128,128), interpolation = cv2.INTER_AREA)
#스펙트로그램 출력
#img = librosa.display.specshow(log_spectrogram, sr=sr, hop_length = hp_length_n, ax=ax2, y_axis="linear", x_axis="time")
#컬러바
#fig.colorbar(img, ax=ax2)# format="%+2.f dB")
#print(log_spectrogram.shape)
lst.append(log_spectrogram)
if i%30==0:
print(i,'/',length)
#npy로 저장
lst = np.array(lst)
output_filename = 'q_spectrogram'
print(lst.shape)
np.save(output_filename, lst)