Commit c3c4a8c4 authored by Martin Reinecke's avatar Martin Reinecke

Merge branch 'NIFTy_5' into misc_tweaks

parents 9b7b4d94 8cf302cb
import nifty5 as ift
import numpy as np
if __name__ == '__main__':
# ABOUT THIS CODE
np.random.seed(41)
# Set up the position space of the signal
#
# # One dimensional regular grid with uniform exposure
# position_space = ift.RGSpace(1024)
# exposure = np.ones(position_space.shape)
# Two-dimensional regular grid with inhomogeneous exposure
position_space = ift.RGSpace([512, 512])
# # Sphere with with uniform exposure
# position_space = ift.HPSpace(128)
# exposure = ift.Field.full(position_space, 1.)
# Defining harmonic space and transform
harmonic_space = position_space.get_default_codomain()
HT = ift.HarmonicTransformOperator(harmonic_space, position_space)
domain = ift.MultiDomain.make({'xi': harmonic_space})
position = ift.from_random('normal', domain)
# Define power spectrum and amplitudes
def sqrtpspec(k):
return 1. / (20. + k**2)
p_space = ift.PowerSpace(harmonic_space)
pd = ift.PowerDistributor(harmonic_space, p_space)
a = ift.PS_field(p_space, sqrtpspec)
A = pd(a)
# Set up a sky model
xi = ift.Variable(position)['xi']
logsky_h = xi * A
logsky = HT(logsky_h)
sky = ift.PointwisePositiveTanh(logsky)
GR = ift.GeometryRemover(position_space)
# Set up instrumental response
R = GR
# Generate mock data
d_space = R.target[0]
p = R(sky)
mock_position = ift.from_random('normal', p.position.domain)
pp = p.at(mock_position).value
data = np.random.binomial(1, pp.to_global_data().astype(np.float64))
data = ift.Field.from_global_data(d_space, data)
# Compute likelihood and Hamiltonian
position = ift.from_random('normal', p.position.domain)
likelihood = ift.BernoulliEnergy(p, data)
ic_cg = ift.GradientNormController(iteration_limit=50)
ic_newton = ift.GradientNormController(name='Newton', iteration_limit=30,
tol_abs_gradnorm=1e-3)
minimizer = ift.RelaxedNewton(ic_newton)
ic_sampling = ift.GradientNormController(iteration_limit=100)
# Minimize the Hamiltonian
H = ift.Hamiltonian(likelihood, ic_sampling)
H = H.makeInvertible(ic_cg)
# minimizer = ift.SteepestDescent(ic_newton)
H, convergence = minimizer(H)
reconstruction = sky.at(H.position).value
ift.plot(reconstruction, title='reconstruction', name='reconstruction.pdf')
ift.plot(GR.adjoint_times(data), title='data', name='data.pdf')
ift.plot(sky.at(mock_position).value, title='truth', name='truth.pdf')
......@@ -85,6 +85,7 @@ from .library.wiener_filter_curvature import WienerFilterCurvature
from .library.wiener_filter_energy import WienerFilterEnergy
from .library.correlated_fields import (make_correlated_field,
make_mf_correlated_field)
from .library.bernoulli_energy import BernoulliEnergy
from . import extra
......
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# Copyright(C) 2013-2018 Max-Planck-Society
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
# and financially supported by the Studienstiftung des deutschen Volkes.
from numpy import inf, isnan
from ..minimization.energy import Energy
from ..operators.sandwich_operator import SandwichOperator
from ..sugar import log, makeOp
class BernoulliEnergy(Energy):
def __init__(self, p, d):
"""
p: Model object
"""
super(BernoulliEnergy, self).__init__(p.position)
self._p = p
self._d = d
p_val = self._p.value
self._value = -self._d.vdot(log(p_val)) - (1. - d).vdot(log(1.-p_val))
if isnan(self._value):
self._value = inf
metric = makeOp(1./((p_val) * (1.-p_val)))
self._gradient = self._p.jacobian.adjoint_times(metric(p_val-d))
self._metric = SandwichOperator.make(self._p.jacobian, metric)
def at(self, position):
return self.__class__(self._p.at(position), self._d)
@property
def value(self):
return self._value
@property
def gradient(self):
return self._gradient
@property
def metric(self):
return self._metric
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