# NIFTy
# Copyright (C) 2017 Theo Steininger
#
# Author: Theo Steininger
#
# 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 .
from nifty import PowerSpace,\
Field,\
DiagonalOperator,\
FFTOperator,\
sqrt
__all__ = ['create_power_operator']
def create_power_operator(domain, power_spectrum, power_domain=None, dtype=None,
distribution_strategy='not'):
if not domain.harmonic:
fft = FFTOperator(domain)
domain = fft.target[0]
if isinstance(power_spectrum, Field):
power_domain = power_spectrum.domain
elif power_domain is None:
power_domain = PowerSpace(domain,
distribution_strategy=distribution_strategy)
fp = Field(power_domain,
val=power_spectrum, dtype=dtype,
distribution_strategy=distribution_strategy)
f = fp.power_synthesize(mean=1, std=0, real_signal=False)
power_operator = DiagonalOperator(domain, diagonal=f, bare=True)
return power_operator
def generate_posterior_sample(mean, covariance):
S = covariance.S
R = covariance.R
N = covariance.N
power = sqrt(S.diagonal().power_analyze())
mock_signal = power.power_synthesize(real_signal=True)
noise = N.diagonal().val
mock_noise = Field.from_random(random_type="normal", domain=N.domain,
std = sqrt(noise), dtype = noise.dtype)
mock_data = R.derived_times(mock_signal, mean) + mock_noise
mock_j = R.derived_adjoint_times(N.inverse_times(mock_data), mean)
mock_m = covariance.inverse_times(mock_j)
sample = mock_signal - mock_m + mean
return sample