def local_kaiser3(vec):
return(kaiser(len(vec),3*pi))
python类kaiser()的实例源码
def iFFT(Y, output_length=None, window=False):
""" Inverse real-valued Fourier Transform
Parameters
----------
Y : array_like
Frequency domain data [Nsignals x Nbins]
output_length : int, optional
Lenght of returned time-domain signal (Default: 2 x len(Y) + 1)
win : boolean, optional
Weights the resulting time-domain signal with a Hann
Returns
-------
y : array_like
Reconstructed time-domain signal
"""
Y = _np.atleast_2d(Y)
y = _np.fft.irfft(Y, n=output_length)
if window:
if window not in {'hann', 'hamming', 'blackman', 'kaiser'}:
raise ValueError('Selected window must be one of hann, hamming, blackman or kaiser')
no_of_signals, no_of_samples = y.shape
if window == 'hann':
window_array = _np.hanning(no_of_samples)
elif window == 'hamming':
window_array = _np.hamming(no_of_samples)
elif window == 'blackman':
window_array = _np.blackman(no_of_samples)
elif window == 'kaiser':
window_array = _np.kaiser(no_of_samples, 3)
y = window_array * y
return y
def smooth_curve(x):
"""?????????????????????
???http://glowingpython.blogspot.jp/2012/02/convolution-with-numpy.html
"""
window_len = 11
s = np.r_[x[window_len - 1:0:-1], x, x[-1:-window_len:-1]]
w = np.kaiser(window_len, 2)
y = np.convolve(w / w.sum(), s, mode='valid')
return y[5:len(y) - 5]
def smooth_curve(x):
"""?????????????????????
???http://glowingpython.blogspot.jp/2012/02/convolution-with-numpy.html
"""
window_len = 11
s = np.r_[x[window_len-1:0:-1], x, x[-1:-window_len:-1]]
w = np.kaiser(window_len, 2)
y = np.convolve(w/w.sum(), s, mode='valid')
return y[5:len(y)-5]
def smooth_curve(x):
""" Used to smooth the graph of the loss function
reference:http://glowingpython.blogspot.jp/2012/02/convolution-with-numpy.html
"""
window_len = 11
s = np.r_[x[window_len-1:0:-1], x, x[-1:-window_len:-1]]
w = np.kaiser(window_len, 2)
y = np.convolve(w/w.sum(), s, mode='valid')
return y[5:len(y)-5]
def smooth_curve(x):
"""?????????????????????
???http://glowingpython.blogspot.jp/2012/02/convolution-with-numpy.html
"""
window_len = 11
s = np.r_[x[window_len-1:0:-1], x, x[-1:-window_len:-1]]
w = np.kaiser(window_len, 2)
y = np.convolve(w/w.sum(), s, mode='valid')
return y[5:len(y)-5]
def smooth_curve(x):
"""?????????????????????
???http://glowingpython.blogspot.jp/2012/02/convolution-with-numpy.html
"""
window_len = 11
s = np.r_[x[window_len-1:0:-1], x, x[-1:-window_len:-1]]
w = np.kaiser(window_len, 2)
y = np.convolve(w/w.sum(), s, mode='valid')
return y[5:len(y)-5]
