log_normal_wiener_filter.py 3.56 KB
 Theo Steininger committed Jul 30, 2017 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 # -*- coding: utf-8 -*- from nifty import * if __name__ == "__main__": nifty_configuration['default_distribution_strategy'] = 'fftw' # Setting up parameters |\label{code:wf_parameters}| correlation_length = 1. # Typical distance over which the field is correlated field_variance = 2. # Variance of field in position space response_sigma = 0.05 # Smoothing length of response (in same unit as L) signal_to_noise = 1.5 # The signal to noise ratio np.random.seed(43) # Fixing the random seed def power_spectrum(k): # Defining the power spectrum a = 4 * correlation_length * field_variance**2 return a / (1 + k * correlation_length) ** 4 # Setting up the geometry |\label{code:wf_geometry}| L = 2. # Total side-length of the domain N_pixels = 512 # Grid resolution (pixels per axis) signal_space = RGSpace([N_pixels, N_pixels], distances=L/N_pixels) harmonic_space = FFTOperator.get_default_codomain(signal_space) fft = FFTOperator(harmonic_space, target=signal_space, target_dtype=np.float) power_space = PowerSpace(harmonic_space) # Creating the mock signal |\label{code:wf_mock_signal}| S = create_power_operator(harmonic_space, power_spectrum=power_spectrum) mock_power = Field(power_space, val=power_spectrum) mock_signal = fft(mock_power.power_synthesize(real_signal=True)) # Setting up an exemplary response mask = Field(signal_space, val=1.) N10 = int(N_pixels/10) mask.val[N10*5:N10*9, N10*5:N10*9] = 0. R = ResponseOperator(signal_space, sigma=(response_sigma,), exposure=(mask,)) #|\label{code:wf_response}| data_domain = R.target[0] R_harmonic = ComposedOperator([fft, R], default_spaces=[0, 0]) # Setting up the noise covariance and drawing a random noise realization N = DiagonalOperator(data_domain, diagonal=mock_signal.var()/signal_to_noise, bare=True) noise = Field.from_random(domain=data_domain, random_type='normal', std=mock_signal.std()/np.sqrt(signal_to_noise), mean=0) data = R(exp(mock_signal)) + noise #|\label{code:wf_mock_data}| # Wiener filter m0 = Field(harmonic_space, val=0.j) energy = library.LogNormalWienerFilterEnergy(m0, data, R_harmonic, N, S) minimizer = VL_BFGS(convergence_tolerance=0, iteration_limit=50, #callback=convergence_measure, max_history_length=3) minimizer = RelaxedNewton(convergence_tolerance=0, iteration_limit=1, #callback=convergence_measure ) # # Probing the variance # class Proby(DiagonalProberMixin, Prober): pass # proby = Proby(signal_space, probe_count=100) # proby(lambda z: fft(wiener_curvature.inverse_times(fft.inverse_times(z)))) # # sm = SmoothingOperator(signal_space, sigma=0.02) # variance = sm(proby.diagonal.weight(-1)) #Plotting #|\label{code:wf_plotting}| plotter = plotting.RG2DPlotter(color_map=plotting.colormaps.PlankCmap()) plotter.figure.xaxis = plotting.Axis(label='Pixel Index') plotter.figure.yaxis = plotting.Axis(label='Pixel Index') #plotter.plot.zmax = exp(mock_signal.max()); plotter.plot.zmin = 0 # plotter(variance, path = 'variance.html') #plotter.plot.zmin = exp(mock_signal.min()); plotter(mock_signal, path='mock_signal.html') plotter(Field(signal_space, val=np.log(data.val.get_full_data()).reshape(signal_space.shape)), path = 'data.html') # plotter(m, path = 'map.html')