55 lines
1.6 KiB
Python
55 lines
1.6 KiB
Python
from . import tau
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import numpy as np
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def sweep(amp, length, begin=20, end=20480, method='linear'):
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method = method or 'linear'
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xs = np.arange(length)/length
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if method in ('linear', 'quadratic', 'logarithmic', 'hyperbolic'):
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ys = amp*sig.chirp(xs, begin, 1, end, method=method)
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elif method is 'sinesweep':
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ang = lambda f: tau*f
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# because xs ranges from 0:1, length is 1 and has been simplified out
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domain = np.log(ang(end)/ang(begin))
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ys = amp*np.sin(ang(begin)/domain*(np.exp(xs*domain) - 1))
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return ys
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def tsp(N, m=0.5):
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"""
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OATSP(Optimized Aoshima's Time-Stretched Pulse) generator
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x = tsp( N, m )
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N : length of the time-stretched pulse
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m : ratio of the swept sine (0 < m < 1)
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Author(s): Seigo UTO 8-23-95
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Reference:
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Yoiti SUZUKI, Futoshi ASANO, Hack-Yoon KIM and Toshio SONE,
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"Considerations on the Design of Time-Stretched Pulses,"
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Techical Report of IEICE, EA92-86(1992-12)
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"""
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# http://www.sound.sie.dendai.ac.jp/dsp/e-21.html
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if m < 0 or m > 1:
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raise Exception("sdfgsdfgsdg")
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if N < 0:
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raise Exception("The number of length must be the positive number")
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NN = 2**np.floor(np.log2(N)) # nearest
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NN2 = NN//2
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M = np.round(NN2*m)
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nn2 = np.arange(NN2 + 1)**2
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j = np.complex(0, 1)
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H = np.exp(j*4*M*np.pi*nn2/NN**2)
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H2 = np.hstack([H, np.conj(H[1:NN2][::-1])])
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x = np.fft.ifft(H2)
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x = np.hstack([x[NN2 - M:NN + 1], x[0:NN2 - M + 1]])
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x = np.hstack([x.real, np.zeros(1, N - NN)])
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return x
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