134 lines
No EOL
3.7 KiB
Python
Executable file
134 lines
No EOL
3.7 KiB
Python
Executable file
import numpy as np
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import scipy.signal as signal
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from scipy.fftpack import fft, fftshift, ifft
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import matplotlib.pyplot as plt
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from matplotlib.widgets import Slider
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N = 256
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def gaussian_filter1d(size,sigma):
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filter_range = np.linspace(-int(size/2),int(size/2),size)
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gaussian_filter = [1 / (sigma * np.sqrt(2*np.pi)) * np.exp(-x**2/(2*sigma**2)) for x in filter_range]
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return gaussian_filter
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def generate_signal(N:int) -> np.ndarray:
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x = np.arange(1, N)
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y = np.zeros((N))
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for i in range(len(x)):
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y[i] = np.random.normal(scale=1) + (y[i-1] if i > 1 else 0)
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return np.convolve(y,gaussian_filter1d(N,1),'same')
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if __name__ == '__main__':
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np.random.seed(42)
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f_full = generate_signal(2048)
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x = np.arange(-180,180,360/N)
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f = f_full[1024:1024+N]
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h = f_full[1024:1024+N]
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g = signal.gaussian(N, std=10,sym=True)
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F = fft(f)
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F_ = np.conjugate(F)
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G = fft(g)
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K_ = (G*F_)/(F*F_)
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H = fft(h)
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r = ifft(H*K_)
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# ==========================================
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fig, (ax1, ax2, ax3, ax4, ax5, ax6, ax7) = plt.subplots(7, 1, gridspec_kw={'height_ratios':[4,4,4,1,1,1,1]})
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ax1.set_title('Terrain')
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plt_f, = ax1.plot(x,f)
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plt_h, = ax1.plot(x,h)
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ax2.set_title('MOSSE response signal')
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ax2.set_ylim([0, 1.2])
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line_r = ax2.axvline(x=-N//2+np.argmax(abs(r)), color='r')
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plt_r, = ax2.plot(x,abs(r))
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plt_r2, = ax2.plot(x,abs(r))
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ax3.set_title('Gaussian')
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plt_g, = ax3.plot(x,g)
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ax1.set_xlim([-180,180])
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ax2.set_xlim([-180,180])
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ax3.set_xlim([-180,180])
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slider1 = Slider(ax4, 'sigma', 0.3, 10, valinit=0.1)
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slider2 = Slider(ax5, 'shift', -N//2 , N//2, valinit=0, valstep=1)
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slider3 = Slider(ax6, 'seed', 0 , 50, valinit=0, valstep=1)
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slider4 = Slider(ax7, 'N', 128 , 1024, valinit=256, valstep=8)
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sigma = 0.3
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shift = 0
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def update():
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# K_ = (G*F_)/(F*F_)
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window = np.ones((N)) #signal.windows.hamming(N)
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H = fft(h*window)
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F = fft(f*window)
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R = H*G/F
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r = ifft(R)
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s = np.argmax(abs(r))
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r2 = np.copy(r)
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r2[s-5:s+5] = 0
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plt_g.set_data(x,g)
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plt_r.set_data(x,abs(r))
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plt_r2.set_data(x,abs(r2))
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plt_h.set_data(x,h)
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plt_f.set_data(x,f)
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ax1.set_ylim([min(np.min(h),np.min(f))-1, max(np.max(h),np.max(f))+1])
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ax2.set_ylim([0, np.max(r)+0.2])
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line_r.set_xdata(round((np.argmax(abs(r))/N-0.5)*360))
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fig.canvas.draw_idle()
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def update_sigma(val):
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global g, G, sigma, N
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sigma = val
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g = signal.gaussian(N, std=sigma,sym=True)
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G = fft(g)
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update()
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def update_shift(val):
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global shift, H, h
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shift = -val
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h = f_full[1024+round(shift*(N/360)):1024+N+round(shift*(N/360))]
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noise = np.random.normal(0,0.5, N)
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h = h+noise
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update()
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def update_seed(val):
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global f_full, f, h, F, H, F_, shift
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np.random.seed(val)
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f_full = generate_signal(2048)
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f = f_full[1024:1024+N]
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h = f_full[1024+round(shift*(N/360)):1024+N+round(shift*(N/360))]
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noise = np.random.normal(0,0.5, N)
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h = h+noise
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update()
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def update_n(val):
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global g, G, N, f_full, f, h, F, H, F_, shift, x, sigma, slider2
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N = val
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x = np.arange(-180,180,360/N)
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g = signal.gaussian(N, std=sigma,sym=True)
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G = fft(g)
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f = f_full[1024:1024+N]
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h = f_full[1024+shift:1024+N+shift]
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noise = np.random.normal(0,0.5, N)
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h = h+noise
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update()
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slider1.on_changed(update_sigma)
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slider2.on_changed(update_shift)
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slider3.on_changed(update_seed)
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slider4.on_changed(update_n)
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plt.subplots_adjust(hspace=0.5)
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plt.show() |