wiener_filter.py 3.55 KB
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import nifty4 as ift
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import numpy as np


if __name__ == "__main__":
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    # Setting up parameters
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    L = 2.                         # Total side-length of the domain
    N_pixels = 512                 # Grid resolution (pixels per axis)
    correlation_length_scale = 1.  # Typical distance over which the field is
                                   # correlated
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    fluctuation_scale = 2.         # Variance of field in position space
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    response_sigma = 0.05          # Smoothing length of response
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    signal_to_noise = 1.5          # The signal to noise ratio
    np.random.seed(43)             # Fixing the random seed
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    def power_spectrum(k):         # Defining the power spectrum
        a = 4 * correlation_length_scale * fluctuation_scale**2
        return a / (1 + (k * correlation_length_scale)**2) ** 2

    signal_space = ift.RGSpace([N_pixels, N_pixels], distances=L/N_pixels)
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    harmonic_space = signal_space.get_default_codomain()
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    fft = ift.HarmonicTransformOperator(harmonic_space, target=signal_space)
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    power_space = ift.PowerSpace(
        harmonic_space, binbounds=ift.PowerSpace.useful_binbounds(
            harmonic_space, logarithmic=True))
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    # Creating the mock signal
    S = ift.create_power_operator(harmonic_space,
                                  power_spectrum=power_spectrum)
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    mock_power = ift.PS_field(power_space, power_spectrum)
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    mock_signal = ift.power_synthesize(mock_power, real_signal=True)
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    # Setting up an exemplary response
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    mask = np.ones(signal_space.shape)
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    N10 = int(N_pixels/10)
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    mask[N10*5:N10*9, N10*5:N10*9] = 0.
    mask = ift.Field(signal_space, ift.dobj.from_global_data(mask))
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    R = ift.GeometryRemover(signal_space)
    R = R*ift.DiagonalOperator(mask)
    R = R*fft
    R = R * ift.create_harmonic_smoothing_operator((harmonic_space,),0,response_sigma)
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    data_domain = R.target[0]

    # Setting up the noise covariance and drawing a random noise realization
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    ndiag = 1e-8*ift.Field.full(data_domain, fft(mock_signal).var()/signal_to_noise)
    N = ift.DiagonalOperator(ndiag)
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    noise = ift.Field.from_random(
        domain=data_domain, random_type='normal',
        std=mock_signal.std()/np.sqrt(signal_to_noise), mean=0)
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    data = R(mock_signal) + noise
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    # Wiener filter
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    j = R.adjoint_times(N.inverse_times(data))
    ctrl = ift.GradientNormController(name="inverter", tol_abs_gradnorm=1e-6)
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    inverter = ift.ConjugateGradient(controller=ctrl)
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    wiener_curvature = ift.library.WienerFilterCurvature(
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        S=S, N=N, R=R, inverter=inverter)
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    m_k = wiener_curvature.inverse_times(j)
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    m = fft(m_k)

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    plotdict = {"xlabel": "Pixel index", "ylabel": "Pixel index",
                "colormap": "Planck-like"}
    ift.plot(mock_signal, name="mock_signal.png", **plotdict)
    ift.plot(ift.Field(signal_space, val=data.val),
             name="data.png", **plotdict)
    ift.plot(m, name="map.png", **plotdict)
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    # Probing the uncertainty
    class Proby(ift.DiagonalProberMixin, ift.Prober):
        pass
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    proby = Proby(harmonic_space, probe_count=1, ncpu=1)
    proby(lambda z: wiener_curvature.inverse_times(z))
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    sm = ift.FFTSmoothingOperator(signal_space, sigma=0.03)
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    variance = ift.sqrt(sm(proby.diagonal.weight(-1)))
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    # Plotting
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    plotdict = {"xlabel": "Pixel index", "ylabel": "Pixel index",
                "colormap": "Planck-like"}
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    ift.plot(variance, name="uncertainty.png", **plotdict)
    ift.plot(mock_signal, name="mock_signal.png", **plotdict)
    ift.plot(ift.Field(signal_space, val=data.val),
             name="data.png", **plotdict)
    ift.plot(m, name="map.png", **plotdict)