Commit 3928db97 authored by Martin Reinecke's avatar Martin Reinecke
Browse files

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[0]
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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