### resurrect wiener_filter_easy

parent d018c524
Pipeline #22323 passed with stage
in 4 minutes and 44 seconds
 import numpy as np import nifty2go as ift # Note that the constructor of PropagatorOperator takes as arguments the # response R and noise covariance N operating on signal space and signal # covariance operating on harmonic space. class PropagatorOperator(ift.EndomorphicOperator): def __init__(self, R, N, Sh): super(PropagatorOperator, self).__init__() self.R = R self.N = N self.Sh = Sh self.fft = ift.FFTOperator(R.domain, target=Sh.domain) def _inverse_times(self, x): return self.R.adjoint_times(self.N.inverse_times(self.R(x))) \ + self.fft.adjoint_times(self.Sh.inverse_times(self.fft(x))) @property def domain(self): return self.R.domain @property def unitary(self): return False @property def self_adjoint(self): return True if __name__ == "__main__": # Set up physical constants # Total length of interval or volume the field lives on, e.g. in meters L = 2. # Typical distance over which the field is correlated (in same unit as L) correlation_length = 0.1 # Variance of field in position space sqrt(<|s_x|^2>) (in unit of s) field_variance = 2. # Smoothing length of response (in same unit as L) response_sigma = 0.01 # Define resolution (pixels per dimension) N_pixels = 256 # Set up derived constants k_0 = 1./correlation_length # Note that field_variance**2 = a*k_0/4. for this analytic form of power # spectrum a = field_variance**2/k_0*4. pow_spec = (lambda k: a / (1 + k/k_0) ** 4) pixel_width = L/N_pixels # Set up the geometry s_space = ift.RGSpace([N_pixels, N_pixels], distances=pixel_width) fft = ift.FFTOperator(s_space) h_space = fft.target p_space = ift.PowerSpace(h_space) # Create mock data Sh = ift.create_power_operator(h_space, power_spectrum=pow_spec) sp = ift.Field(p_space, val=pow_spec(p_space.k_lengths)) sh = ift.power_synthesize(sp, real_signal=True) ss = fft.inverse_times(sh) R = ift.FFTSmoothingOperator(s_space, sigma=response_sigma) signal_to_noise = 1 diag = ift.Field(s_space, ss.var()/signal_to_noise).weight(1) N = ift.DiagonalOperator(diag) n = ift.Field.from_random(domain=s_space, random_type='normal', std=ss.std()/np.sqrt(signal_to_noise), mean=0) d = R(ss) + n # Wiener filter j = R.adjoint_times(N.inverse_times(d)) IC = ift.GradientNormController(iteration_limit=500, tol_abs_gradnorm=0.1) inverter = ift.ConjugateGradient(controller=IC) D = ift.InversionEnabler(PropagatorOperator(Sh=Sh, N=N, R=R), inverter=inverter) m = D(j)
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