From 8648a6b4bf2adda80b75f3e9d30098c3b8bd218e Mon Sep 17 00:00:00 2001
From: Martin Reinecke <martin@mpa-garching.mpg.de>
Date: Sat, 14 Jul 2018 21:10:44 +0200
Subject: [PATCH] generalize FieldZeroPadder
---
nifty5/operators/field_zero_padder.py | 24 +++++++++++------------
nifty5/operators/qht_operator.py | 1 -
nifty5/operators/symmetrizing_operator.py | 1 -
test/test_operators/test_adjoint.py | 2 +-
4 files changed, 13 insertions(+), 15 deletions(-)
diff --git a/nifty5/operators/field_zero_padder.py b/nifty5/operators/field_zero_padder.py
index 5074aade5..267f59110 100644
--- a/nifty5/operators/field_zero_padder.py
+++ b/nifty5/operators/field_zero_padder.py
@@ -19,12 +19,11 @@ class FieldZeroPadder(LinearOperator):
dom = self._domain[self._space]
if not isinstance(dom, RGSpace):
raise TypeError("RGSpace required")
- if not len(dom.shape) == 1:
- raise TypeError("RGSpace must be one-dimensional")
if dom.harmonic:
raise TypeError("RGSpace must not be harmonic")
- tgt = RGSpace((int(factor*dom.shape[0]),), dom.distances)
+ newshp = tuple(factor*s for s in dom.shape)
+ tgt = RGSpace(newshp, dom.distances)
self._target = list(self._domain)
self._target[self._space] = tgt
self._target = DomainTuple.make(self._target)
@@ -47,20 +46,21 @@ class FieldZeroPadder(LinearOperator):
dax = dobj.distaxis(x)
shp_in = x.shape
shp_out = self._tgt(mode).shape
- ax = self._target.axes[self._space][0]
- if dax == ax:
- x = dobj.redistribute(x, nodist=(ax,))
+ axbefore = self._target.axes[self._space][0]
+ axes = self._target.axes[self._space]
+ if dax in axes:
+ x = dobj.redistribute(x, nodist=axes)
curax = dobj.distaxis(x)
if mode == self.ADJOINT_TIMES:
newarr = np.empty(dobj.local_shape(shp_out, curax), dtype=x.dtype)
- newarr[()] = dobj.local_data(x)[(slice(None),)*ax +
- (slice(0, shp_out[ax]),)]
+ sl = tuple(slice(0, shp_out[axis]) for axis in axes)
+ newarr[()] = dobj.local_data(x)[(slice(None),)*axbefore + sl]
else:
newarr = np.zeros(dobj.local_shape(shp_out, curax), dtype=x.dtype)
- newarr[(slice(None),)*ax +
- (slice(0, shp_in[ax]),)] = dobj.local_data(x)
+ sl = tuple(slice(0, shp_in[axis]) for axis in axes)
+ newarr[(slice(None),)*axbefore + sl] = dobj.local_data(x)
newarr = dobj.from_local_data(shp_out, newarr, distaxis=curax)
- if dax == ax:
- newarr = dobj.redistribute(newarr, dist=ax)
+ if dax in axes:
+ newarr = dobj.redistribute(newarr, dist=dax)
return Field(self._tgt(mode), val=newarr)
diff --git a/nifty5/operators/qht_operator.py b/nifty5/operators/qht_operator.py
index 3eebc3381..76d7b1d66 100644
--- a/nifty5/operators/qht_operator.py
+++ b/nifty5/operators/qht_operator.py
@@ -80,7 +80,6 @@ class QHTOperator(LinearOperator):
n = self._domain.axes[self._space]
rng = n if mode == self.TIMES else reversed(n)
ax = dobj.distaxis(x)
- globshape = x.shape
for i in rng:
sl = (slice(None),)*i + (slice(1, None),)
if i == ax:
diff --git a/nifty5/operators/symmetrizing_operator.py b/nifty5/operators/symmetrizing_operator.py
index 8a2aa881e..a8c9e49ed 100644
--- a/nifty5/operators/symmetrizing_operator.py
+++ b/nifty5/operators/symmetrizing_operator.py
@@ -43,7 +43,6 @@ class SymmetrizingOperator(EndomorphicOperator):
self._check_input(x, mode)
tmp = x.val.copy()
ax = dobj.distaxis(tmp)
- globshape = tmp.shape
for i in self._domain.axes[self._space]:
lead = (slice(None),)*i
if i == ax:
diff --git a/test/test_operators/test_adjoint.py b/test/test_operators/test_adjoint.py
index f6f9e9542..82064f7ca 100644
--- a/test/test_operators/test_adjoint.py
+++ b/test/test_operators/test_adjoint.py
@@ -119,7 +119,7 @@ class Consistency_Tests(unittest.TestCase):
@expand(product([0, 2], [2, 2.7], [np.float64, np.complex128]))
def testZeroPadder(self, space, factor, dtype):
- dom = (ift.RGSpace(10), ift.UnstructuredDomain(13), ift.RGSpace(7),
+ dom = (ift.RGSpace(10), ift.UnstructuredDomain(13), ift.RGSpace(7, 12),
ift.HPSpace(4))
op = ift.FieldZeroPadder(dom, factor, space)
ift.extra.consistency_check(op, dtype, dtype)
--
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