Commit 9b0d8b76 authored by Martin Reinecke's avatar Martin Reinecke
Browse files

first case with apparently working dimensional analysis

parent 92442049
Pipeline #19982 passed with stage
in 4 minutes and 10 seconds
import numpy as np import numpy as np
import nifty2go as ift import nifty2go as ift
import numericalunits as nu
if __name__ == "__main__": if __name__ == "__main__":
dimensionality = 2
np.random.seed(43) np.random.seed(43)
# Setting up variable parameters # Setting up variable parameters
# Typical distance over which the field is correlated # Typical distance over which the field is correlated
correlation_length = 0.05 correlation_length = 0.05*nu.m
# Variance of field in position space sqrt(<|s_x|^2>) # sigma of field in position space sqrt(<|s_x|^2>)
field_variance = 2. field_sigma = 2.* nu.K
# smoothing length of response (in same unit as L) # smoothing length of response
response_sigma = 0.01 response_sigma = 0.01*nu.m
# The signal to noise ratio # The signal to noise ratio
signal_to_noise = 0.7 signal_to_noise = 0.7
# note that field_variance**2 = a*k_0/4. for this analytic form of power # note that field_variance**2 = a*k_0/4. for this analytic form of power
# spectrum # spectrum
def power_spectrum(k): def power_spectrum(k):
a = 4 * correlation_length * field_variance**2 cldim = correlation_length**(2*dimensionality)
return a / (1 + k * correlation_length) ** 4 a = 4/(2*np.pi) * cldim * field_sigma**2
return a / (1 + (k*correlation_length)**(2*dimensionality)) ** 2 # to be integrated over spherical shells later on
# Setting up the geometry # Setting up the geometry
# Total side-length of the domain # Total side-length of the domain
L = 2. L = 2.*nu.m
# Grid resolution (pixels per axis) # Grid resolution (pixels per axis)
N_pixels = 512 N_pixels = 512
shape = [N_pixels]*dimensionality
signal_space = ift.RGSpace([N_pixels, N_pixels], distances=L/N_pixels) signal_space = ift.RGSpace(shape, distances=L/N_pixels)
harmonic_space = signal_space.get_default_codomain() harmonic_space = signal_space.get_default_codomain()
fft = ift.FFTOperator(harmonic_space, target=signal_space) fft = ift.FFTOperator(harmonic_space, target=signal_space)
power_space = ift.PowerSpace(harmonic_space) power_space = ift.PowerSpace(harmonic_space)
...@@ -43,7 +46,8 @@ if __name__ == "__main__": ...@@ -43,7 +46,8 @@ if __name__ == "__main__":
mock_harmonic = mock_harmonic.real mock_harmonic = mock_harmonic.real
mock_signal = fft(mock_harmonic) mock_signal = fft(mock_harmonic)
R = ift.ResponseOperator(signal_space, sigma=(response_sigma,)) exposure = 1.
R = ift.ResponseOperator(signal_space, sigma=(response_sigma,),exposure=(exposure,))
data_domain = R.target[0] data_domain = R.target[0]
R_harmonic = ift.ComposedOperator([fft, R]) R_harmonic = ift.ComposedOperator([fft, R])
...@@ -57,14 +61,16 @@ if __name__ == "__main__": ...@@ -57,14 +61,16 @@ if __name__ == "__main__":
# Wiener filter # Wiener filter
j = R_harmonic.adjoint_times(N.inverse_times(data)) j = R_harmonic.adjoint_times(N.inverse_times(data))
ctrl = ift.GradientNormController(verbose=True,tol_abs_gradnorm=1e-2) ctrl = ift.GradientNormController(verbose=True,tol_abs_gradnorm=1e-4/nu.K)
inverter = ift.ConjugateGradient(controller=ctrl) inverter = ift.ConjugateGradient(controller=ctrl)
wiener_curvature = ift.library.WienerFilterCurvature(S=S, N=N, R=R_harmonic, inverter=inverter) wiener_curvature = ift.library.WienerFilterCurvature(S=S, N=N, R=R_harmonic, inverter=inverter)
m = wiener_curvature.inverse_times(j) m = wiener_curvature.inverse_times(j)
m_s = fft(m) m_s = fft(m)
ift.plotting.plot(mock_signal.real,name="mock_signal.pdf") sspace2=ift.RGSpace(shape, distances=L/N_pixels/nu.m)
ift.plotting.plot(ift.Field(signal_space,
val=data.val.real.reshape(signal_space.shape)), name="data.pdf") ift.plotting.plot(ift.Field(sspace2,mock_signal.real.val)/nu.K,name="mock_signal.pdf")
ift.plotting.plot(m_s.real, name="map.pdf") ift.plotting.plot(ift.Field(sspace2,
val=data.val.real.reshape(signal_space.shape))/nu.K, name="data.pdf")
ift.plotting.plot(ift.Field(sspace2,m_s.real.val)/nu.K, name="map.pdf")
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment