146 lines
3.2 KiB
Python
146 lines
3.2 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Mon Jan 15 19:45:33 2018
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@author: anne
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"""
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import numpy as np
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from lasp import PowerSpectra, Window
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import matplotlib.pyplot as plt
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plt.close('all')
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# def test_ps():
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nfft = 8
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t = np.linspace(0, 1.0, nfft, endpoint=False)
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ps = PowerSpectra(nfft, Window.Rectangular)
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sig = np.random.randn(nfft)
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freq = 4
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omg = 2*np.pi*freq
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# sig = 8*np.cos(omg*t)
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cps = ps.compute(sig)
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pow1 = np.sum(sig**2)/sig.size
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pow2 = np.sum((cps[:,0,0]).real)
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# print(pow1)
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# print(pow2)
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plt.plot(cps[:,0,0])
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assert np.isclose(pow2 - pow1,0, atol=1e-1)
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# test_ps()
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# plt.plot(res_lasp.real-res_npy.real)
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# plt.plot(res_lasp.imag-res_npy.imag)
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# plt.plot(res_npy.real)
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# plt.plot(res_npy.imag)
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# plt.plot(t, sig)
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# print('nfft:',nfft)
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# #print(nfft)
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# nchannels = 2
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# t = np.linspace(0,1,nfft+1)
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# # print(t)
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# x1 = (np.cos(4*np.pi*t[:-1])+3.2*np.sin(6*np.pi*t[:-1]))[:,np.newaxis]+10
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# x = np.vstack([x1.T]*nchannels).T
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# # Using transpose to get the strides right
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# x = np.random.randn(nchannels,nfft).T
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# print("strides: ",x.strides)
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# # x.strides = (8,nfft*8)x
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# # print("signal:",x)
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# xms = np.sum(x**2,axis=0)/nfft
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# print('Total signal power time domain: ', xms)
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# X = np.fft.rfft(x,axis=0)
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# # X =np.fft.fft(x)
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# #X =np.fft.rfft(x)
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# # print(X)
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# Xs = 2*X/nfft
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# Xs[np.where(np.abs(Xs) < 1e-10)] = 0
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# Xs[0] /= np.sqrt(2)
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# Xs[-1] /= np.sqrt(2)
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# # print('single sided amplitude spectrum:\n',Xs)
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# power = Xs*np.conj(Xs)/2
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# # print('Frequency domain signal power\n', power)
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# print('Total signal power', np.sum(power,axis=0).real)
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# pstest = PowerSpectra(nfft,nchannels)
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# ps = pstest.compute(x)
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# fft = Fft(nfft,nchannels)
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# fft.fft(x)
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# ps[np.where(np.abs(ps) < 1e-10)] = 0+0j
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# print('our ps: \n' , ps)
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# print('Our total signal power: ',np.sum(ps,axis=0).real)
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# if __name__ == '__main__':
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# nfft=2048
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# fs = 2048
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# ps = PowerSpectra(nfft, Window.Rectangular)
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# t = np.linspace(0, 1.0, nfft, endpoint=False)
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# freq = 10
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# omg = 2*np.pi*freq
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# sig = np.sin(omg*t)
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# res = ps.compute(sig)
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# plt.plot(res[:,0,0])
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# # plt.plot(t, sig)
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# print('nfft:',nfft)
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# #print(nfft)
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# nchannels = 2
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# t = np.linspace(0,1,nfft+1)
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# # print(t)
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# x1 = (np.cos(4*np.pi*t[:-1])+3.2*np.sin(6*np.pi*t[:-1]))[:,np.newaxis]+10
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# x = np.vstack([x1.T]*nchannels).T
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# # Using transpose to get the strides right
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# x = np.random.randn(nchannels,nfft).T
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# print("strides: ",x.strides)
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# # x.strides = (8,nfft*8)x
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# # print("signal:",x)
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# xms = np.sum(x**2,axis=0)/nfft
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# print('Total signal power time domain: ', xms)
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# X = np.fft.rfft(x,axis=0)
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# # X =np.fft.fft(x)
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# #X =np.fft.rfft(x)
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# # print(X)
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# Xs = 2*X/nfft
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# Xs[np.where(np.abs(Xs) < 1e-10)] = 0
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# Xs[0] /= np.sqrt(2)
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# Xs[-1] /= np.sqrt(2)
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# # print('single sided amplitude spectrum:\n',Xs)
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# power = Xs*np.conj(Xs)/2
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# # print('Frequency domain signal power\n', power)
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# print('Total signal power', np.sum(power,axis=0).real)
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# pstest = PowerSpectra(nfft,nchannels)
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# ps = pstest.compute(x)
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# fft = Fft(nfft,nchannels)
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# fft.fft(x)
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# ps[np.where(np.abs(ps) < 1e-10)] = 0+0j
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# print('our ps: \n' , ps)
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# print('Our total signal power: ',np.sum(ps,axis=0).real)
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