exponential(*args, **kwargs)
use scipy.signal.windows.exponential instead.
The Exponential window is defined as
$$w(n) = e^{-|n-center| / \tau}$$Number of points in the output window. If zero or less, an empty array is returned.
Parameter defining the center location of the window function. The default value if not given is center = (M-1) / 2
. This parameter must take its default value for symmetric windows.
Parameter defining the decay. For center = 0
use tau = -(M-1) / ln(x)
if x
is the fraction of the window remaining at the end.
When True (default), generates a symmetric window, for use in filter design. When False, generates a periodic window, for use in spectral analysis.
The window, with the maximum value normalized to 1 (though the value 1 does not appear if M is even and :None:None:`sym` is True).
Return an exponential (or Poisson) window.
Plot the symmetric window and its frequency response:
>>> from scipy import signal
... from scipy.fft import fft, fftshift
... import matplotlib.pyplot as plt
>>> M = 51
... tau = 3.0
... window = signal.windows.exponential(M, tau=tau)
... plt.plot(window)
... plt.title("Exponential Window (tau=3.0)")
... plt.ylabel("Amplitude")
... plt.xlabel("Sample")
>>> plt.figure()
... A = fft(window, 2048) / (len(window)/2.0)
... freq = np.linspace(-0.5, 0.5, len(A))
... response = 20 * np.log10(np.abs(fftshift(A / abs(A).max())))
... plt.plot(freq, response)
... plt.axis([-0.5, 0.5, -35, 0])
... plt.title("Frequency response of the Exponential window (tau=3.0)")
... plt.ylabel("Normalized magnitude [dB]")
... plt.xlabel("Normalized frequency [cycles per sample]")
This function can also generate non-symmetric windows:
>>> tau2 = -(M-1) / np.log(0.01)See :
... window2 = signal.windows.exponential(M, 0, tau2, False)
... plt.figure()
... plt.plot(window2)
... plt.ylabel("Amplitude")
... plt.xlabel("Sample")
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