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

nifty5/__init__.py

@@ -19,7 +19,6 @@ from .field import Field
 from .multi_field import MultiField
 
 from .operators.operator import Operator
-from .operators.central_zero_padder import CentralZeroPadder
 from .operators.diagonal_operator import DiagonalOperator
 from .operators.distributors import DOFDistributor, PowerDistributor
 from .operators.domain_tuple_field_inserter import DomainTupleFieldInserter

nifty5/logger.py

@@ -21,6 +21,7 @@ def _logger_init():
     from . import dobj
     res = logging.getLogger('NIFTy5')
     res.setLevel(logging.DEBUG)
+    res.propagate = False
     if dobj.rank == 0:
         ch = logging.StreamHandler()
         ch.setLevel(logging.DEBUG)

nifty5/operators/central_zero_padder.py

-# 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 itertools
-
-import numpy as np
-
-from .. import dobj, utilities
-from ..domain_tuple import DomainTuple
-from ..domains.rg_space import RGSpace
-from ..field import Field
-from .linear_operator import LinearOperator
-
-
-# MR FIXME: for even axis lengths, we probably should split the value at the
-#           highest frequency.
-class CentralZeroPadder(LinearOperator):
-    """Operator that enlarges a fields domain by adding zeros from the middle.
-
-    Parameters
-    ---------
-
-    domain: Domain, tuple of Domains or DomainTuple
-            The domain of the data that is input by "times" and output by
-            "adjoint_times"
-    new_shape: tuple
-               Shape of the target domain.
-    space: int, optional
-           The index of the subdomain on which the operator should act
-           If None, it is set to 0 if `domain` contains exactly one space.
-           `domain[space]` must be an RGSpace.
-
-    """
-
-    def __init__(self, domain, new_shape, space=0):
-        self._domain = DomainTuple.make(domain)
-        self._space = utilities.infer_space(self._domain, space)
-        dom = self._domain[self._space]
-
-        # verify domains
-        if not isinstance(dom, RGSpace):
-            raise TypeError("RGSpace required")
-        if len(new_shape) != len(dom.shape):
-            raise ValueError("Shape mismatch")
-        if any([a < b for a, b in zip(new_shape, dom.shape)]):
-            raise ValueError("New shape must be larger than old shape")
-
-        # make target space
-        tgt = RGSpace(new_shape, dom.distances, harmonic=dom.harmonic)
-        self._target = list(self._domain)
-        self._target[self._space] = tgt
-        self._target = DomainTuple.make(self._target)
-
-        self._capability = self.TIMES | self.ADJOINT_TIMES
-
-        # define the axes along which the input field is sliced
-        slicer = []
-        axes = self._target.axes[self._space]
-        for i in range(len(self._domain.shape)):
-            if i in axes:
-                slicer_fw = slice(0, (self._domain.shape[i]+1)//2)
-                slicer_bw = slice(-1, -1-(self._domain.shape[i]//2), -1)
-                slicer.append((slicer_fw, slicer_bw))
-        self.slicer = list(itertools.product(*slicer))
-
-        for i in range(len(self.slicer)):
-            for j in range(len(self._domain.shape)):
-                if j not in axes:
-                    tmp = list(self.slicer[i])
-                    tmp.insert(j, slice(None))
-                    self.slicer[i] = tuple(tmp)
-        self.slicer = tuple(self.slicer)
-
-    def apply(self, x, mode):
-        self._check_input(x, mode)
-        v = x.val
-        shp_out = self._tgt(mode).shape
-        v, x = dobj.ensure_not_distributed(v, self._target.axes[self._space])
-        curax = dobj.distaxis(v)
-
-        if mode == self.TIMES:
-            # slice along each axis and copy the data to an
-            # array of zeros which has the shape of the target domain
-            y = np.zeros(dobj.local_shape(shp_out, curax), dtype=x.dtype)
-            for i in self.slicer:
-                y[i] = x[i]
-        else:
-            # slice along each axis and copy the data to an array of zeros
-            # which has the shape of the input domain to remove excess zeros
-            y = np.empty(dobj.local_shape(shp_out, curax), dtype=x.dtype)
-            for i in self.slicer:
-                y[i] = x[i]
-        v = dobj.from_local_data(shp_out, y, distaxis=dobj.distaxis(v))
-        return Field(self._tgt(mode), dobj.ensure_default_distributed(v))

test/test_operators/test_adjoint.py

@@ -216,16 +216,6 @@ def testZeroPadder(space, factor, dtype, central):
     ift.extra.consistency_check(op, dtype, dtype)
 
 
-@pmp('space', [0, 2])
-@pmp('factor', [2, 2.7])
-def testZeroPadder2(space, factor, dtype):
-    dom = (ift.RGSpace(10), ift.UnstructuredDomain(13), ift.RGSpace(7, 12),
-           ift.HPSpace(4))
-    newshape = [int(factor*l) for l in dom[space].shape]
-    op = ift.CentralZeroPadder(dom, newshape, space)
-    ift.extra.consistency_check(op, dtype, dtype)
-
-
 @pmp('args',
      [(ift.RGSpace(10, harmonic=True), 4, 0), (ift.RGSpace(
          (24, 31), distances=(0.4, 2.34), harmonic=True), (4, 3), 0),