# 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) 2013-2019 Max-Planck-Society
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.
import numpy as np
from ..domain_tuple import DomainTuple
from ..domains.dof_space import DOFSpace
from ..domains.power_space import PowerSpace
from ..field import Field
from ..utilities import infer_space, special_add_at
from .linear_operator import LinearOperator
[docs]
class DOFDistributor(LinearOperator):
"""Operator which distributes actual degrees of freedom (dof) according to
some distribution scheme into a higher dimensional space. This distribution
scheme is defined by the dofdex, a degree of freedom index, which
associates the entries within the operator's domain to locations in its
target. This operator's domain is a DOFSpace, which is defined according to
the distribution scheme.
Parameters
----------
dofdex: :class:`nifty8.field.Field` of integers
An integer Field on exactly one Space establishing the association
between the locations in the operator's target and the underlying
degrees of freedom in its domain.
It has to start at 0 and it increases monotonically, no empty bins are
allowed.
target: Domain, tuple of Domain, or DomainTuple, optional
The target of the operator. If not specified, the domain of the dofdex
is used.
space: int, optional:
The index of the sub-domain on which the operator acts.
Can be omitted if `target` only has one sub-domain.
"""
[docs]
def __init__(self, dofdex, target=None, space=None):
if target is None:
target = dofdex.domain
self._target = DomainTuple.make(target)
space = infer_space(self._target, space)
partner = self._target[space]
if not isinstance(dofdex, Field):
raise TypeError("dofdex must be a Field")
if not len(dofdex.domain) == 1:
raise ValueError("dofdex must be defined on exactly one Space")
if not np.issubdtype(dofdex.dtype, np.integer):
raise TypeError("dofdex must contain integer numbers")
if partner != dofdex.domain[0]:
raise ValueError("incorrect dofdex domain")
ldat = dofdex.val
if ldat.size == 0: # can happen for weird configurations
nbin = 0
else:
nbin = ldat.max()
nbin = nbin + 1
if partner.scalar_dvol is not None:
wgt = np.bincount(dofdex.val.ravel(), minlength=nbin)
wgt = wgt*partner.scalar_dvol
else:
dvol = Field.from_raw(partner, partner.dvol).val
wgt = np.bincount(dofdex.val.ravel(),
minlength=nbin, weights=dvol)
# The explicit conversion to float64 is necessary because bincount
# sometimes returns its result as an integer array, even when
# floating-point weights are present ...
wgt = wgt.astype(np.float64, copy=False)
if (wgt == 0).any():
raise ValueError("empty bins detected")
self._init2(dofdex.val, space, DOFSpace(wgt))
def _init2(self, dofdex, space, other_space):
self._space = space
dom = list(self._target)
dom[self._space] = other_space
self._domain = DomainTuple.make(dom)
self._capability = self.TIMES | self.ADJOINT_TIMES
self._dofdex = dofdex.ravel()
firstaxis = self._target.axes[self._space][0]
lastaxis = self._target.axes[self._space][-1]
arrshape = self._target.shape
presize = np.prod(arrshape[0:firstaxis], dtype=np.int64)
postsize = np.prod(arrshape[lastaxis+1:], dtype=np.int64)
self._hshape = (presize, self._domain[self._space].shape[0], postsize)
self._pshape = (presize, self._dofdex.size, postsize)
def _adjoint_times(self, x):
arr = x.val
arr = arr.reshape(self._pshape)
oarr = np.zeros(self._hshape, dtype=x.dtype)
oarr = special_add_at(oarr, 1, self._dofdex, arr)
oarr = oarr.reshape(self._domain.shape)
res = Field.from_raw(self._domain, oarr)
return res
def _times(self, x):
arr = x.val
arr = arr.reshape(self._hshape)
oarr = np.empty(self._pshape, dtype=x.dtype)
oarr[()] = arr[(slice(None), self._dofdex, slice(None))]
return Field(self._target, oarr.reshape(self._target.shape))
[docs]
def apply(self, x, mode):
self._check_input(x, mode)
return self._times(x) if mode == self.TIMES else self._adjoint_times(x)
[docs]
class PowerDistributor(DOFDistributor):
"""Operator which transforms between a PowerSpace and a harmonic domain.
Parameters
----------
target: Domain, tuple of Domain, or DomainTuple
the total *target* domain of the operator.
power_space: PowerSpace, optional
the input sub-domain on which the operator acts.
If not supplied, a matching PowerSpace with natural binbounds will be
used.
space: int, optional:
The index of the sub-domain on which the operator acts.
Can be omitted if `target` only has one sub-domain.
"""
[docs]
def __init__(self, target, power_space=None, space=None):
# Initialize domain and target
self._target = DomainTuple.make(target)
self._space = infer_space(self._target, space)
hspace = self._target[self._space]
if not hspace.harmonic:
raise ValueError("Operator requires harmonic target space")
if power_space is None:
power_space = PowerSpace(hspace)
else:
if not isinstance(power_space, PowerSpace):
raise TypeError("power_space argument must be a PowerSpace")
if power_space.harmonic_partner != hspace:
raise ValueError("power_space does not match its partner")
self._init2(power_space.pindex, self._space, power_space)
