Commit aa0b1292 authored by Philipp Arras's avatar Philipp Arras
Browse files

Cosmetics

parent 69da894e
......@@ -18,9 +18,9 @@ def make_random_mask():
if __name__ == '__main__':
# # description of the tutorial ###
np.random.seed(42)
# FIXME description of the tutorial
# Choose problem geometry and masking
# One dimensional regular grid
......@@ -28,7 +28,7 @@ if __name__ == '__main__':
mask = np.ones(position_space.shape)
# # Two dimensional regular grid with chess mask
# position_space = ift.RGSpace([128,128])
# position_space = ift.RGSpace([128, 128])
# mask = make_chess_mask(position_space)
# # Sphere with half of its locations randomly masked
......@@ -38,7 +38,7 @@ if __name__ == '__main__':
harmonic_space = position_space.get_default_codomain()
HT = ift.HarmonicTransformOperator(harmonic_space, target=position_space)
# set correlation structure with a power spectrum and build
# Set correlation structure with a power spectrum and build
# prior correlation covariance
def power_spectrum(k):
return 100. / (20.+k**3)
......@@ -48,7 +48,7 @@ if __name__ == '__main__':
S = ift.DiagonalOperator(prior_correlation_structure)
# build instrument response consisting of a discretization, mask
# Build instrument response consisting of a discretization, mask
# and harmonic transformaion
GR = ift.GeometryRemover(position_space)
mask = ift.Field.from_global_data(position_space, mask)
......@@ -57,19 +57,19 @@ if __name__ == '__main__':
data_space = GR.target
# setting the noise covariance
# Set the noise covariance
noise = 5.
N = ift.ScalingOperator(noise, data_space)
# creating mock data
# Create mock data
MOCK_SIGNAL = S.draw_sample()
MOCK_NOISE = N.draw_sample()
data = R(MOCK_SIGNAL) + MOCK_NOISE
# building propagator D and information source j
# Build propagator D and information source j
j = R.adjoint_times(N.inverse_times(data))
D_inv = R.adjoint * N.inverse * R + S.inverse
# make it invertible
# Make it invertible
IC = ift.GradientNormController(iteration_limit=500, tol_abs_gradnorm=1e-3)
D = ift.InversionEnabler(D_inv, IC, approximation=S.inverse).inverse
......
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