def istft(X, fftsize=128, step="half", wsola=False, mean_normalize=True,
real=False, compute_onesided=True):
"""
Compute ISTFT for STFT transformed X
"""
if real:
local_ifft = fftpack.irfft
X_pad = np.zeros((X.shape[0], X.shape[1] + 1)) + 0j
X_pad[:, :-1] = X
X = X_pad
else:
local_ifft = fftpack.ifft
if compute_onesided:
X_pad = np.zeros((X.shape[0], 2 * X.shape[1])) + 0j
X_pad[:, :fftsize // 2 + 1] = X
X_pad[:, fftsize // 2 + 1:] = 0
X = X_pad
X = local_ifft(X).astype("float64")
if step == "half":
X = invert_halfoverlap(X)
else:
X = overlap_add(X, step, wsola=wsola)
if mean_normalize:
X -= np.mean(X)
return X
python类irfft()的实例源码
def istft(X, fftsize=128, step="half", wsola=False, mean_normalize=True,
real=False, compute_onesided=True):
"""
Compute ISTFT for STFT transformed X
"""
if real:
local_ifft = fftpack.irfft
X_pad = np.zeros((X.shape[0], X.shape[1] + 1)) + 0j
X_pad[:, :-1] = X
X = X_pad
else:
local_ifft = fftpack.ifft
if compute_onesided:
X_pad = np.zeros((X.shape[0], 2 * X.shape[1])) + 0j
X_pad[:, :fftsize // 2 + 1] = X
X_pad[:, fftsize // 2 + 1:] = 0
X = X_pad
X = local_ifft(X).astype("float64")
if step == "half":
X = invert_halfoverlap(X)
else:
X = overlap_add(X, step, wsola=wsola)
if mean_normalize:
X -= np.mean(X)
return X
def istft(X, fftsize=128, step="half", wsola=False, mean_normalize=True,
real=False, compute_onesided=True):
"""
Compute ISTFT for STFT transformed X
"""
if real:
local_ifft = fftpack.irfft
X_pad = np.zeros((X.shape[0], X.shape[1] + 1)) + 0j
X_pad[:, :-1] = X
X = X_pad
else:
local_ifft = fftpack.ifft
if compute_onesided:
X_pad = np.zeros((X.shape[0], 2 * X.shape[1])) + 0j
X_pad[:, :fftsize // 2] = X
X_pad[:, fftsize // 2:] = 0
X = X_pad
X = local_ifft(X).astype("float64")
if step == "half":
X = invert_halfoverlap(X)
else:
X = overlap_add(X, step, wsola=wsola)
if mean_normalize:
X -= np.mean(X)
return X
def istft(X, fftsize=128, step="half", wsola=False, mean_normalize=True,
real=False, compute_onesided=True):
"""
Compute ISTFT for STFT transformed X
"""
if real:
local_ifft = fftpack.irfft
X_pad = np.zeros((X.shape[0], X.shape[1] + 1)) + 0j
X_pad[:, :-1] = X
X = X_pad
else:
local_ifft = fftpack.ifft
if compute_onesided:
X_pad = np.zeros((X.shape[0], 2 * X.shape[1])) + 0j
X_pad[:, :fftsize // 2] = X
X_pad[:, fftsize // 2:] = 0
X = X_pad
X = local_ifft(X).astype("float64")
if step == "half":
X = invert_halfoverlap(X)
else:
X = overlap_add(X, step, wsola=wsola)
if mean_normalize:
X -= np.mean(X)
return X
def istft(X, fftsize=128, step="half", wsola=False, mean_normalize=True,
real=False, compute_onesided=True):
"""
Compute ISTFT for STFT transformed X
"""
if real:
local_ifft = fftpack.irfft
X_pad = np.zeros((X.shape[0], X.shape[1] + 1)) + 0j
X_pad[:, :-1] = X
X = X_pad
else:
local_ifft = fftpack.ifft
if compute_onesided:
X_pad = np.zeros((X.shape[0], 2 * X.shape[1])) + 0j
X_pad[:, :fftsize // 2] = X
X_pad[:, fftsize // 2:] = 0
X = X_pad
X = local_ifft(X).astype("float64")
if step == "half":
X = invert_halfoverlap(X)
else:
X = overlap_add(X, step, wsola=wsola)
if mean_normalize:
X -= np.mean(X)
return X
def istft(X, fftsize=128, step="half", wsola=False, mean_normalize=True,
real=False, compute_onesided=True):
"""
Compute ISTFT for STFT transformed X
"""
if real:
local_ifft = fftpack.irfft
X_pad = np.zeros((X.shape[0], X.shape[1] + 1)) + 0j
X_pad[:, :-1] = X
X = X_pad
else:
local_ifft = fftpack.ifft
if compute_onesided:
X_pad = np.zeros((X.shape[0], 2 * X.shape[1])) + 0j
X_pad[:, :fftsize // 2] = X
X_pad[:, fftsize // 2:] = 0
X = X_pad
X = local_ifft(X).astype("float64")
if step == "half":
X = invert_halfoverlap(X)
else:
X = overlap_add(X, step, wsola=wsola)
if mean_normalize:
X -= np.mean(X)
return X
def istft(X, fftsize=128, step="half", wsola=False, mean_normalize=True,
real=False, compute_onesided=True):
"""
Compute ISTFT for STFT transformed X
"""
if real:
local_ifft = fftpack.irfft
X_pad = np.zeros((X.shape[0], X.shape[1] + 1)) + 0j
X_pad[:, :-1] = X
X = X_pad
else:
local_ifft = fftpack.ifft
if compute_onesided:
X_pad = np.zeros((X.shape[0], 2 * X.shape[1])) + 0j
X_pad[:, :fftsize // 2] = X
X_pad[:, fftsize // 2:] = 0
X = X_pad
X = local_ifft(X).astype("float64")
if step == "half":
X = invert_halfoverlap(X)
else:
X = overlap_add(X, step, wsola=wsola)
if mean_normalize:
X -= np.mean(X)
return X
def istft(X, fftsize=128, step="half", wsola=False, mean_normalize=True,
real=False, compute_onesided=True):
"""
Compute ISTFT for STFT transformed X
"""
if real:
local_ifft = fftpack.irfft
X_pad = np.zeros((X.shape[0], X.shape[1] + 1)) + 0j
X_pad[:, :-1] = X
X = X_pad
else:
local_ifft = fftpack.ifft
if compute_onesided:
X_pad = np.zeros((X.shape[0], 2 * X.shape[1])) + 0j
X_pad[:, :fftsize // 2] = X
X_pad[:, fftsize // 2:] = 0
X = X_pad
X = local_ifft(X).astype("float64")
if step == "half":
X = invert_halfoverlap(X)
else:
X = overlap_add(X, step, wsola=wsola)
if mean_normalize:
X -= np.mean(X)
return X
def istft(X, fftsize=128, step="half", wsola=False, mean_normalize=True,
real=False, compute_onesided=True):
"""
Compute ISTFT for STFT transformed X
"""
if real:
local_ifft = fftpack.irfft
X_pad = np.zeros((X.shape[0], X.shape[1] + 1)) + 0j
X_pad[:, :-1] = X
X = X_pad
else:
local_ifft = fftpack.ifft
if compute_onesided:
X_pad = np.zeros((X.shape[0], 2 * X.shape[1])) + 0j
X_pad[:, :fftsize // 2] = X
X_pad[:, fftsize // 2:] = 0
X = X_pad
X = local_ifft(X).astype("float64")
if step == "half":
X = invert_halfoverlap(X)
else:
X = overlap_add(X, step, wsola=wsola)
if mean_normalize:
X -= np.mean(X)
return X
def istft(X, fftsize=128, step="half", wsola=False, mean_normalize=True,
real=False, compute_onesided=True):
"""
Compute ISTFT for STFT transformed X
"""
if real:
local_ifft = fftpack.irfft
X_pad = np.zeros((X.shape[0], X.shape[1] + 1)) + 0j
X_pad[:, :-1] = X
X = X_pad
else:
local_ifft = fftpack.ifft
if compute_onesided:
X_pad = np.zeros((X.shape[0], 2 * X.shape[1])) + 0j
X_pad[:, :fftsize // 2] = X
X_pad[:, fftsize // 2:] = 0
X = X_pad
X = local_ifft(X).astype("float64")
if step == "half":
X = invert_halfoverlap(X)
else:
X = overlap_add(X, step, wsola=wsola)
if mean_normalize:
X -= np.mean(X)
return X
def istft(X, fftsize=128, step="half", wsola=False, mean_normalize=True,
real=False, compute_onesided=True):
"""
Compute ISTFT for STFT transformed X
"""
if real:
local_ifft = fftpack.irfft
X_pad = np.zeros((X.shape[0], X.shape[1] + 1)) + 0j
X_pad[:, :-1] = X
X = X_pad
else:
local_ifft = fftpack.ifft
if compute_onesided:
X_pad = np.zeros((X.shape[0], 2 * X.shape[1])) + 0j
X_pad[:, :fftsize // 2] = X
X_pad[:, fftsize // 2:] = 0
X = X_pad
X = local_ifft(X).astype("float64")
if step == "half":
X = invert_halfoverlap(X)
else:
X = overlap_add(X, step, wsola=wsola)
if mean_normalize:
X -= np.mean(X)
return X
def istft(X, fftsize=128, step="half", wsola=False, mean_normalize=True,
real=False, compute_onesided=True):
"""
Compute ISTFT for STFT transformed X
"""
if real:
local_ifft = fftpack.irfft
X_pad = np.zeros((X.shape[0], X.shape[1] + 1)) + 0j
X_pad[:, :-1] = X
X = X_pad
else:
local_ifft = fftpack.ifft
if compute_onesided:
X_pad = np.zeros((X.shape[0], 2 * X.shape[1])) + 0j
X_pad[:, :fftsize // 2] = X
X_pad[:, fftsize // 2:] = 0
X = X_pad
X = local_ifft(X).astype("float64")
if step == "half":
X = invert_halfoverlap(X)
else:
X = overlap_add(X, step, wsola=wsola)
if mean_normalize:
X -= np.mean(X)
return X
def istft(X, fftsize=128, step="half", wsola=False, mean_normalize=True,
real=False, compute_onesided=True):
"""
Compute ISTFT for STFT transformed X
"""
if real:
local_ifft = fftpack.irfft
X_pad = np.zeros((X.shape[0], X.shape[1] + 1)) + 0j
X_pad[:, :-1] = X
X = X_pad
else:
local_ifft = fftpack.ifft
if compute_onesided:
X_pad = np.zeros((X.shape[0], 2 * X.shape[1])) + 0j
X_pad[:, :fftsize // 2] = X
X_pad[:, fftsize // 2:] = 0
X = X_pad
X = local_ifft(X).astype("float64")
if step == "half":
X = invert_halfoverlap(X)
else:
X = overlap_add(X, step, wsola=wsola)
if mean_normalize:
X -= np.mean(X)
return X
def bandpass(data_list, min_hz, max_hz):
fft_list = rfft(data_list)
# Filter
for i in range(len(fft_list)):
if not (min_hz < i/2+1 < max_hz): fft_list[i] = 0
result_vals = irfft(fft_list)
return result_vals
def remove_freq_range(data_list, min_hz, max_hz):
fft_list = rfft(data_list)
# Filter
for i in range(len(fft_list)):
if (min_hz < i / 2 + 1 < max_hz): fft_list[i] = 0
result_vals = irfft(fft_list)
return result_vals
data_load.py 文件源码
项目：Automatic-feature-extraction-from-signal
作者: VVVikulin
项目源码
文件源码
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def calc_ifft(data):
return irfft(data)
data_load.py 文件源码
项目：Automatic-feature-extraction-from-signal
作者: VVVikulin
项目源码
文件源码
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def high_filter(data, sample_rate=1000):
f_signal = rfft(data)
l = int(len(f_signal)*50.0/sample_rate);
cut_f_signal = f_signal.copy()
cut_f_signal[l:len(f_signal)-1] = 0
cut_signal = irfft(cut_f_signal)
return cut_signal
data_load.py 文件源码
项目：Automatic-feature-extraction-from-signal
作者: VVVikulin
项目源码
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def low_filter(data, sample_rate=1000):
f_signal = rfft(data)
l = int(len(f_signal)*5.0/sample_rate);
cut_f_signal = f_signal.copy()
cut_f_signal[0:l+1] = 0
cut_f_signal[len(f_signal) + 1 - l :] = 0
cut_signal = irfft(cut_f_signal)
return cut_signal
data_load.py 文件源码
项目：Automatic-feature-extraction-from-signal
作者: VVVikulin
项目源码
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def simple_lf_filter(signal, sample_rate=1000):
W = fftfreq(signal.size, d=1/float(sample_rate))
f_signal = rfft(signal)
cut_f_signal = f_signal.copy()
cut_f_signal[(W<5)] = 0
return irfft(cut_f_signal).astype('int16')
data_load.py 文件源码
项目：Automatic-feature-extraction-from-signal
作者: VVVikulin
项目源码
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def simple_hf_filter(signal, sample_rate=1000):
W = fftfreq(signal.size, d=1/float(sample_rate))
f_signal = rfft(signal)
cut_f_signal = f_signal.copy()
cut_f_signal[(W>70)] = 0
return irfft(cut_f_signal).astype('int16')
