Commit 00691967 authored by Martin Reinecke's avatar Martin Reinecke

Merge remote-tracking branch 'origin/NIFTy_5' into improving_consistency_checks

parents b82dc0dc 156c9d79
Pipeline #62109 passed with stages
in 7 minutes and 3 seconds
......@@ -2,6 +2,7 @@
git_version.py
# custom
*.txt
setup.cfg
.idea
.DS_Store
......
......@@ -10,11 +10,11 @@ RUN apt-get update && apt-get install -y \
# Testing dependencies
python3-pytest-cov jupyter \
# Optional NIFTy dependencies
libfftw3-dev python3-mpi4py python3-matplotlib python3-pynfft \
python3-mpi4py python3-matplotlib \
# more optional NIFTy dependencies
&& pip3 install pyfftw \
&& pip3 install git+https://gitlab.mpcdf.mpg.de/ift/pyHealpix.git \
&& pip3 install git+https://gitlab.mpcdf.mpg.de/ift/nifty_gridder.git \
&& pip3 install git+https://gitlab.mpcdf.mpg.de/mtr/pypocketfft.git \
&& pip3 install jupyter \
&& rm -rf /var/lib/apt/lists/*
......
......@@ -47,9 +47,9 @@ Installation
- [Python 3](https://www.python.org/) (3.5.x or later)
- [SciPy](https://www.scipy.org/)
- [pypocketfft](https://gitlab.mpcdf.mpg.de/mtr/pypocketfft)
Optional dependencies:
- [pyFFTW](https://pypi.python.org/pypi/pyFFTW) for faster Fourier transforms
- [pyHealpix](https://gitlab.mpcdf.mpg.de/ift/pyHealpix) (for harmonic
transforms involving domains on the sphere)
- [nifty_gridder](https://gitlab.mpcdf.mpg.de/ift/nifty_gridder) (for radio
......@@ -73,35 +73,19 @@ NIFTy5 and its mandatory dependencies can be installed via:
sudo apt-get install git python3 python3-pip python3-dev
pip3 install --user git+https://gitlab.mpcdf.mpg.de/ift/nifty.git@NIFTy_5
pip3 install --user git+https://gitlab.mpcdf.mpg.de/mtr/pypocketfft
Plotting support is added via:
sudo apt-get install python3-matplotlib
NIFTy uses Numpy's FFT implementation by default. For large problems FFTW may be
used because of its higher performance. It can be installed via:
sudo apt-get install libfftw3-dev
pip3 install --user pyfftw
To enable FFTW usage in NIFTy, call
nifty5.fft.enable_fftw()
at the beginning of your code.
(Note: If you encounter problems related to `pyFFTW`, make sure that you are
using a pip-installed `pyFFTW` package. Unfortunately, some distributions are
shipping an incorrectly configured `pyFFTW` package, which does not cooperate
with the installed `FFTW3` libraries.)
Support for spherical harmonic transforms is added via:
pip3 install --user git+https://gitlab.mpcdf.mpg.de/ift/pyHealpix.git
Support for the radio interferometry gridder is added via:
pip3 install git+https://gitlab.mpcdf.mpg.de/ift/nifty_gridder.git
pip3 install --user git+https://gitlab.mpcdf.mpg.de/ift/nifty_gridder.git
MPI support is added via:
......
......@@ -5,13 +5,11 @@ import numpy as np
import nifty5 as ift
ift.fft.enable_fftw()
np.random.seed(40)
N0s, a0s, b0s, c0s = [], [], [], []
N1s, a1s, b1s, c1s = [], [], [], []
for ii in range(10, 23):
for ii in range(10, 26):
nu = 1024
nv = 1024
N = int(2**ii)
......@@ -29,34 +27,20 @@ for ii in range(10, 23):
img = ift.from_global_data(uvspace, img)
t0 = time()
GM = ift.GridderMaker(uvspace, eps=1e-7)
idx = GM.getReordering(uv)
uv = uv[idx]
vis = vis[idx]
GM = ift.GridderMaker(uvspace, eps=1e-7, uv=uv)
vis = ift.from_global_data(visspace, vis)
op = GM.getFull(uv).adjoint
op = GM.getFull().adjoint
t1 = time()
op(img).to_global_data()
t2 = time()
op.adjoint(vis).to_global_data()
t3 = time()
print(t2-t1, t3-t2)
N0s.append(N)
a0s.append(t1 - t0)
b0s.append(t2 - t1)
c0s.append(t3 - t2)
t0 = time()
op = ift.NFFT(uvspace, uv)
t1 = time()
op(img).to_global_data()
t2 = time()
op.adjoint(vis).to_global_data()
t3 = time()
N1s.append(N)
a1s.append(t1 - t0)
b1s.append(t2 - t1)
c1s.append(t3 - t2)
print('Measure rest operator')
sc = ift.StatCalculator()
op = GM.getRest().adjoint
......@@ -68,10 +52,9 @@ t_fft = sc.mean
print('FFT shape', res.shape)
plt.scatter(N0s, a0s, label='Gridder mr')
plt.scatter(N1s, a1s, marker='^', label='NFFT')
plt.legend()
# no idea why this is necessary, but if it is omitted, the range is wrong
plt.ylim(min(a0s+a1s), max(a0s+a1s))
plt.ylim(min(a0s), max(a0s))
plt.ylabel('time [s]')
plt.title('Initialization')
plt.loglog()
......@@ -79,9 +62,7 @@ plt.savefig('bench0.png')
plt.close()
plt.scatter(N0s, b0s, color='k', marker='^', label='Gridder mr times')
plt.scatter(N1s, b1s, color='r', marker='^', label='NFFT times')
plt.scatter(N0s, c0s, color='k', label='Gridder mr adjoint times')
plt.scatter(N1s, c1s, color='r', label='NFFT adjoint times')
plt.axhline(sc.mean, label='FFT')
plt.axhline(sc.mean + np.sqrt(sc.var))
plt.axhline(sc.mean - np.sqrt(sc.var))
......
......@@ -103,13 +103,15 @@ if __name__ == '__main__':
data = signal_response(mock_position) + N.draw_sample()
# Minimization parameters
ic_sampling = ift.GradientNormController(iteration_limit=100)
ic_newton = ift.GradInfNormController(
name='Newton', tol=1e-7, iteration_limit=35)
ic_sampling = ift.AbsDeltaEnergyController(
name='Sampling', deltaE=0.05, iteration_limit=100)
ic_newton = ift.AbsDeltaEnergyController(
name='Newton', deltaE=0.5, iteration_limit=35)
minimizer = ift.NewtonCG(ic_newton)
# Set up likelihood and information Hamiltonian
likelihood = ift.GaussianEnergy(mean=data, covariance=N)(signal_response)
likelihood = ift.GaussianEnergy(mean=data,
inverse_covariance=N.inverse)(signal_response)
H = ift.StandardHamiltonian(likelihood, ic_sampling)
initial_mean = ift.MultiField.full(H.domain, 0.)
......
......@@ -2,6 +2,12 @@ NIFTy-related publications
==========================
::
@article{asclnifty5,
title={NIFTy5: Numerical Information Field Theory v5},
author={Arras, Philipp and Baltac, Mihai and Ensslin, Torsten A and Frank, Philipp and Hutschenreuter, Sebastian and Knollmueller, Jakob and Leike, Reimar and Newrzella, Max-Niklas and Platz, Lukas and Reinecke, Martin and others},
journal={Astrophysics Source Code Library},
year={2019}
}
@software{nifty,
author = {{Martin Reinecke, Theo Steininger, Marco Selig}},
......@@ -11,7 +17,7 @@ NIFTy-related publications
date = {2018-04-05},
}
@ARTICLE{2013A&A...554A..26S,
@article{2013A&A...554A..26S,
author = {{Selig}, M. and {Bell}, M.~R. and {Junklewitz}, H. and {Oppermann}, N. and {Reinecke}, M. and {Greiner}, M. and {Pachajoa}, C. and {En{\ss}lin}, T.~A.},
title = "{NIFTY - Numerical Information Field Theory. A versatile PYTHON library for signal inference}",
journal = {\aap},
......@@ -29,7 +35,7 @@ NIFTy-related publications
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@ARTICLE{2017arXiv170801073S,
@article{2017arXiv170801073S,
author = {{Steininger}, T. and {Dixit}, J. and {Frank}, P. and {Greiner}, M. and {Hutschenreuter}, S. and {Knollm{\"u}ller}, J. and {Leike}, R. and {Porqueres}, N. and {Pumpe}, D. and {Reinecke}, M. and {{\v S}raml}, M. and {Varady}, C. and {En{\ss}lin}, T.},
title = "{NIFTy 3 - Numerical Information Field Theory - A Python framework for multicomponent signal inference on HPC clusters}",
journal = {ArXiv e-prints},
......
......@@ -9,35 +9,19 @@ NIFTy5 and its mandatory dependencies can be installed via::
sudo apt-get install git python3 python3-pip python3-dev
pip3 install --user git+https://gitlab.mpcdf.mpg.de/ift/nifty.git@NIFTy_5
pip3 install --user git+https://gitlab.mpcdf.mpg.de/mtr/pypocketfft
Plotting support is added via::
sudo apt-get install python3-matplotlib
NIFTy uses Numpy's FFT implementation by default. For large problems FFTW may be
used because of its higher performance. It can be installed via::
sudo apt-get install libfftw3-dev
pip3 install --user pyfftw
To enable FFTW usage in NIFTy, call::
nifty5.fft.enable_fftw()
at the beginning of your code.
(Note: If you encounter problems related to `pyFFTW`, make sure that you are
using a pip-installed `pyFFTW` package. Unfortunately, some distributions are
shipping an incorrectly configured `pyFFTW` package, which does not cooperate
with the installed `FFTW3` libraries.)
Support for spherical harmonic transforms is added via::
pip3 install --user git+https://gitlab.mpcdf.mpg.de/ift/pyHealpix.git
Support for the radio interferometry gridder is added via:
Support for the radio interferometry gridder is added via::
pip3 install git+https://gitlab.mpcdf.mpg.de/ift/nifty_gridder.git
pip3 install --user git+https://gitlab.mpcdf.mpg.de/ift/nifty_gridder.git
MPI support is added via::
......
......@@ -45,20 +45,22 @@ from .operators.block_diagonal_operator import BlockDiagonalOperator
from .operators.outer_product_operator import OuterProduct
from .operators.simple_linear_operators import (
VdotOperator, ConjugationOperator, Realizer,
FieldAdapter, ducktape, GeometryRemover, NullOperator)
FieldAdapter, ducktape, GeometryRemover, NullOperator,
MatrixProductOperator, PartialExtractor)
from .operators.value_inserter import ValueInserter
from .operators.energy_operators import (
EnergyOperator, GaussianEnergy, PoissonianEnergy, InverseGammaLikelihood,
BernoulliEnergy, StandardHamiltonian, AveragedEnergy)
BernoulliEnergy, StandardHamiltonian, AveragedEnergy, QuadraticFormOperator,
Squared2NormOperator)
from .operators.convolution_operators import FuncConvolutionOperator
from .probing import probe_with_posterior_samples, probe_diagonal, \
StatCalculator
StatCalculator, approximation2endo
from .minimization.line_search import LineSearch
from .minimization.iteration_controllers import (
IterationController, GradientNormController, DeltaEnergyController,
GradInfNormController)
GradInfNormController, AbsDeltaEnergyController)
from .minimization.minimizer import Minimizer
from .minimization.conjugate_gradient import ConjugateGradient
from .minimization.nonlinear_cg import NonlinearCG
......@@ -86,7 +88,6 @@ from .library.wiener_filter_curvature import WienerFilterCurvature
from .library.correlated_fields import CorrelatedField, MfCorrelatedField
from .library.adjust_variances import (make_adjust_variances_hamiltonian,
do_adjust_variances)
from .library.nfft import NFFT
from .library.gridder import GridderMaker
from . import extra
......@@ -97,6 +98,8 @@ from .logger import logger
from .linearization import Linearization
from .operator_spectrum import operator_spectrum
from . import internal_config
_scheme = internal_config.parallelization_scheme()
if _scheme == "Samples":
......
......@@ -103,7 +103,9 @@ class GLSpace(StructuredDomain):
The partner domain
"""
from ..domains.lm_space import LMSpace
return LMSpace(lmax=self._nlat-1, mmax=self._nlon//2)
mmax = self._nlon//2
lmax = max(mmax, self._nlat-1)
return LMSpace(lmax=lmax, mmax=mmax)
def check_codomain(self, codomain):
"""Raises `TypeError` if `codomain` is not a matching partner domain
......
......@@ -50,6 +50,8 @@ class LogRGSpace(StructuredDomain):
self._bindistances = tuple(bindistances)
self._t_0 = tuple(t_0)
if min(self._bindistances) <= 0:
raise ValueError('Non-positive bindistances encountered')
self._dim = int(reduce(lambda x, y: x*y, self._shape))
self._dvol = float(reduce(lambda x, y: x*y, self._bindistances))
......@@ -80,8 +82,8 @@ class LogRGSpace(StructuredDomain):
return np.array(self._t_0)
def __repr__(self):
return ("LogRGSpace(shape={}, harmonic={})".format(
self.shape, self.harmonic))
return ("LogRGSpace(shape={}, bindistances={}, t_0={}, harmonic={})".format(
self.shape, self.bindistances, self.t_0, self.harmonic))
def get_default_codomain(self):
"""Returns a :class:`LogRGSpace` object representing the (position or
......@@ -91,10 +93,10 @@ class LogRGSpace(StructuredDomain):
Returns
-------
LogRGSpace
The parter domain
The partner domain
"""
codomain_bindistances = 1./(self.bindistances*self.shape)
return LogRGSpace(self.shape, codomain_bindistances, self._t_0, True)
return LogRGSpace(self.shape, codomain_bindistances, self._t_0, not self.harmonic)
def get_k_length_array(self):
"""Generates array of distances to origin of the space.
......
......@@ -165,6 +165,8 @@ class PowerSpace(StructuredDomain):
if binbounds is not None:
binbounds = tuple(binbounds)
if min(binbounds) < 0:
raise ValueError('Negative binbounds encountered')
key = (harmonic_partner, binbounds)
if self._powerIndexCache.get(key) is None:
......
......@@ -54,6 +54,8 @@ class RGSpace(StructuredDomain):
if np.isscalar(shape):
shape = (shape,)
self._shape = tuple(int(i) for i in shape)
if min(self._shape) < 0:
raise ValueError('Negative number of pixels encountered')
if distances is None:
if self.harmonic:
......@@ -66,6 +68,8 @@ class RGSpace(StructuredDomain):
temp = np.empty(len(self.shape), dtype=np.float64)
temp[:] = distances
self._distances = tuple(temp)
if min(self._distances) <= 0:
raise ValueError('Non-positive distances encountered')
self._dvol = float(reduce(lambda x, y: x*y, self._distances))
self._size = int(reduce(lambda x, y: x*y, self._shape))
......@@ -177,7 +181,7 @@ class RGSpace(StructuredDomain):
Returns
-------
RGSpace
The parter domain
The partner domain
"""
distances = 1. / (np.array(self.shape)*np.array(self.distances))
return RGSpace(self.shape, distances, not self.harmonic)
......
......@@ -17,8 +17,10 @@
import numpy as np
from .domain_tuple import DomainTuple
from .field import Field
from .linearization import Linearization
from .multi_domain import MultiDomain
from .operators.linear_operator import LinearOperator
from .sugar import from_random
......@@ -68,14 +70,25 @@ def _full_implementation(op, domain_dtype, target_dtype, atol, rtol,
def _check_linearity(op, domain_dtype, atol, rtol):
needed_cap = op.TIMES
if (op.capability & needed_cap) != needed_cap:
return
fld1 = from_random("normal", op.domain, dtype=domain_dtype)
fld2 = from_random("normal", op.domain, dtype=domain_dtype)
alpha = np.random.random()
alpha = np.random.random() # FIXME: this can break badly with MPI!
val1 = op(alpha*fld1+fld2)
val2 = alpha*op(fld1)+op(fld2)
_assert_allclose(val1, val2, atol=atol, rtol=rtol)
def _domain_check(op):
for dd in [op.domain, op.target]:
if not isinstance(dd, (DomainTuple, MultiDomain)):
raise TypeError(
'The domain and the target of an operator need to',
'be instances of either DomainTuple or MultiDomain.')
def consistency_check(op, domain_dtype=np.float64, target_dtype=np.float64,
atol=0, rtol=1e-7, only_r_linear=False):
"""
......@@ -109,7 +122,11 @@ def consistency_check(op, domain_dtype=np.float64, target_dtype=np.float64,
"""
if not isinstance(op, LinearOperator):
raise TypeError('This test tests only linear operators.')
_domain_check(op)
_check_linearity(op, domain_dtype, atol, rtol)
_check_linearity(op.adjoint, target_dtype, atol, rtol)
_check_linearity(op.inverse, target_dtype, atol, rtol)
_check_linearity(op.adjoint.inverse, domain_dtype, atol, rtol)
_full_implementation(op, domain_dtype, target_dtype, atol, rtol,
only_r_linear)
_full_implementation(op.adjoint, target_dtype, domain_dtype, atol, rtol,
......@@ -180,6 +197,7 @@ def check_jacobian_consistency(op, loc, tol=1e-8, ntries=100):
tol : float
Tolerance for the check.
"""
_domain_check(op)
for _ in range(ntries):
lin = op(Linearization.make_var(loc))
loc2, lin2 = _get_acceptable_location(op, loc, lin)
......
......@@ -15,152 +15,28 @@
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.
from .utilities import iscomplextype
import numpy as np
import pypocketfft
_nthreads = 1
_use_fftw = False
_fftw_prepped = False
_fft_extra_args = {}
def nthreads():
return _nthreads
def enable_fftw():
global _use_fftw
_use_fftw = True
def disable_fftw():
global _use_fftw
_use_fftw = False
def _init_pyfftw():
global _fft_extra_args, _fftw_prepped
if not _fftw_prepped:
import pyfftw
from pyfftw.interfaces.numpy_fft import fftn, rfftn, ifftn
pyfftw.interfaces.cache.enable()
pyfftw.interfaces.cache.set_keepalive_time(1000.)
# Optional extra arguments for the FFT calls
# if exact reproducibility is needed,
# set "planner_effort" to "FFTW_ESTIMATE"
import os
nthreads = int(os.getenv("OMP_NUM_THREADS", "1"))
_fft_extra_args = dict(planner_effort='FFTW_ESTIMATE',
threads=nthreads)
_fftw_prepped = True
def set_nthreads(nthr):
global _nthreads
_nthreads = int(nthr)
def fftn(a, axes=None):
if _use_fftw:
from pyfftw.interfaces.numpy_fft import fftn
_init_pyfftw()
return fftn(a, axes=axes, **_fft_extra_args)
else:
return np.fft.fftn(a, axes=axes)
def rfftn(a, axes=None):
if _use_fftw:
from pyfftw.interfaces.numpy_fft import rfftn
_init_pyfftw()
return rfftn(a, axes=axes, **_fft_extra_args)
else:
return np.fft.rfftn(a, axes=axes)
return pypocketfft.c2c(a, axes=axes, nthreads=_nthreads)
def ifftn(a, axes=None):
if _use_fftw:
from pyfftw.interfaces.numpy_fft import ifftn
_init_pyfftw()
return ifftn(a, axes=axes, **_fft_extra_args)
else:
return np.fft.ifftn(a, axes=axes)
return pypocketfft.c2c(a, axes=axes, inorm=2, forward=False,
nthreads=_nthreads)
def hartley(a, axes=None):
# Check if the axes provided are valid given the shape
if axes is not None and \
not all(axis < len(a.shape) for axis in axes):
raise ValueError("Provided axes do not match array shape")
if iscomplextype(a.dtype):
raise TypeError("Hartley transform requires real-valued arrays.")
tmp = rfftn(a, axes=axes)
def _fill_array(tmp, res, axes):
if axes is None:
axes = tuple(range(tmp.ndim))
lastaxis = axes[-1]
ntmplast = tmp.shape[lastaxis]
slice1 = (slice(None),)*lastaxis + (slice(0, ntmplast),)
np.add(tmp.real, tmp.imag, out=res[slice1])
def _fill_upper_half(tmp, res, axes):
lastaxis = axes[-1]
nlast = res.shape[lastaxis]
ntmplast = tmp.shape[lastaxis]
nrem = nlast - ntmplast
slice1 = [slice(None)]*lastaxis + [slice(ntmplast, None)]
slice2 = [slice(None)]*lastaxis + [slice(nrem, 0, -1)]
for i in axes[:-1]:
slice1[i] = slice(1, None)
slice2[i] = slice(None, 0, -1)
slice1 = tuple(slice1)
slice2 = tuple(slice2)
np.subtract(tmp[slice2].real, tmp[slice2].imag, out=res[slice1])
for i, ax in enumerate(axes[:-1]):
dim1 = (slice(None),)*ax + (slice(0, 1),)
axes2 = axes[:i] + axes[i+1:]
_fill_upper_half(tmp[dim1], res[dim1], axes2)
_fill_upper_half(tmp, res, axes)
return res
return _fill_array(tmp, np.empty_like(a), axes)
# Do a real-to-complex forward FFT and return the _full_ output array
def my_fftn_r2c(a, axes=None):
# Check if the axes provided are valid given the shape
if axes is not None and \
not all(axis < len(a.shape) for axis in axes):
raise ValueError("Provided axes do not match array shape")
if iscomplextype(a.dtype):
raise TypeError("Transform requires real-valued input arrays.")
tmp = rfftn(a, axes=axes)
def _fill_complex_array(tmp, res, axes):
if axes is None:
axes = tuple(range(tmp.ndim))
lastaxis = axes[-1]
ntmplast = tmp.shape[lastaxis]
slice1 = [slice(None)]*lastaxis + [slice(0, ntmplast)]
res[tuple(slice1)] = tmp
def _fill_upper_half_complex(tmp, res, axes):
lastaxis = axes[-1]
nlast = res.shape[lastaxis]
ntmplast = tmp.shape[lastaxis]
nrem = nlast - ntmplast
slice1 = [slice(None)]*lastaxis + [slice(ntmplast, None)]
slice2 = [slice(None)]*lastaxis + [slice(nrem, 0, -1)]
for i in axes[:-1]:
slice1[i] = slice(1, None)
slice2[i] = slice(None, 0, -1)
# np.conjugate(tmp[slice2], out=res[slice1])
res[tuple(slice1)] = np.conjugate(tmp[tuple(slice2)])
for i, ax in enumerate(axes[:-1]):
dim1 = tuple([slice(None)]*ax + [slice(0, 1)])
axes2 = axes[:i] + axes[i+1:]
_fill_upper_half_complex(tmp[dim1], res[dim1], axes2)
_fill_upper_half_complex(tmp, res, axes)
return res
return _fill_complex_array(tmp, np.empty_like(a, dtype=tmp.dtype), axes)
def my_fftn(a, axes=None):
return fftn(a, axes=axes)
return pypocketfft.genuine_hartley(a, axes=axes, nthreads=_nthreads)
......@@ -20,109 +20,92 @@ import numpy as np
from ..domain_tuple import DomainTuple
from ..domains.rg_space import RGSpace
from ..domains.unstructured_domain import UnstructuredDomain
from ..fft import hartley
from ..operators.linear_operator import LinearOperator
from ..sugar import from_global_data, makeDomain
class GridderMaker(object):
def __init__(self, domain, eps=1e-15):
domain = makeDomain(domain)
if (len(domain) != 1 or not isinstance(domain[0], RGSpace) or
not len(domain.shape) == 2):
raise ValueError("need domain with exactly one 2D RGSpace")
nu, nv = domain.shape
if nu % 2 != 0 or nv % 2 != 0:
raise ValueError("dimensions must be even")
rat = 3 if eps < 1e-11 else 2
nu2, nv2 = rat*nu, rat*nv
nspread = int(-np.log(eps)/(np.pi*(rat-1)/(rat-.5)) + .5) + 1
nu2 = max([nu2, 2*nspread])
nv2 = max([nv2, 2*nspread])
r2lamb = rat*rat*nspread/(rat*(rat-.5))
oversampled_domain = RGSpace(
[nu2, nv2], distances=[1, 1], harmonic=False)
self._nspread = nspread
self._r2lamb = r2lamb
self._rest = _RestOperator(domain, oversampled_domain, r2lamb)
def getReordering(self, uv):
from nifty_gridder import peanoindex
nu2, nv2 = self._rest._domain.shape
return peanoindex(uv, nu2, nv2)
def getGridder(self, uv):
return RadioGridder(self._rest.domain, self._nspread, self._r2lamb, uv)
def __init__(self, dirty_domain, uv, eps=2e-13):
import nifty_gridder
dirty_domain = makeDomain(dirty_domain)
if (len(dirty_domain) != 1 or not isinstance(dirty_domain[0], RGSpace)
or not len(dirty_domain.shape) == 2):
raise ValueError("need dirty_domain with exactly one 2D RGSpace")
if uv.ndim != 2:
raise ValueError("uv must be a 2D array")
if uv.shape[1] != 2:
raise ValueError("second dimension of uv must have length 2")
dstx, dsty = dirty_domain[0].distances
# wasteful hack to adjust to shape required by nifty_gridder
uvw = np.empty((uv.shape[0], 3), dtype=np.float64)
uvw[:, 0:2] = uv
uvw[:, 2] = 0.
# Scale uv such that 0<uv<=1 which is assumed by nifty_gridder
uvw[:, 0] = uvw[:, 0]*dstx
uvw[:, 1] = uvw[:, 1]*dsty
speedOfLight = 299792458.
bl = nifty_gridder.Baselines(uvw, np.array([speedOfLight]))
nxdirty, nydirty = dirty_domain.shape
gconf = nifty_gridder.GridderConfig(nxdirty, nydirty, eps, 1., 1.)
nu, nv = gconf.Nu(), gconf.Nv()
self._idx = nifty_gridder.getIndices(
bl, gconf, np.zeros((uv.shape[0], 1), dtype=np.bool))
self._bl = bl
du, dv = 1./(nu*dstx), 1./(nv*dsty)
grid_domain = RGSpace([nu, nv], distances=[du, dv], harmonic=True)
self._rest = _RestOperator(dirty_domain, grid_domain, gconf)
self._gridder = RadioGridder(grid_domain, bl, gconf, self._idx)
def getGridder(self):
return self._gridder
def getRest(self):
return self._rest
def getFull(self, uv):