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ift
NIFTy
Commits
a77eadc7
Commit
a77eadc7
authored
Aug 03, 2018
by
Philipp Arras
Browse files
Implement first working version of regridding operator
parent
cd89b1aa
Changes
2
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Inline
Side-by-side
nifty5/__init__.py
View file @
a77eadc7
...
...
@@ -46,6 +46,7 @@ from .operators.multi_adaptor import MultiAdaptor
from
.operators.null_operator
import
NullOperator
from
.operators.power_distributor
import
PowerDistributor
from
.operators.qht_operator
import
QHTOperator
from
.operators.regridding_operator
import
RegriddingOperator
from
.operators.sampling_enabler
import
SamplingEnabler
from
.operators.sandwich_operator
import
SandwichOperator
from
.operators.scaling_operator
import
ScalingOperator
...
...
nifty5/operators/regridding_operator.py
0 → 100644
View file @
a77eadc7
# 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) 2013-2018 Max-Planck-Society
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
# and financially supported by the Studienstiftung des deutschen Volkes.
from
__future__
import
absolute_import
,
division
,
print_function
import
numpy
as
np
from
scipy.sparse
import
csr_matrix
from
scipy.sparse.linalg
import
aslinearoperator
from
..
import
dobj
,
utilities
from
..compat
import
*
from
..domain_tuple
import
DomainTuple
from
..domains.rg_space
import
RGSpace
from
..domains.unstructured_domain
import
UnstructuredDomain
from
..field
import
Field
from
..sugar
import
from_global_data
from
.linear_operator
import
LinearOperator
class
RegriddingOperator
(
LinearOperator
):
def
__init__
(
self
,
domain
,
target
):
super
(
RegriddingOperator
,
self
).
__init__
()
self
.
_domain
=
DomainTuple
.
make
(
domain
)
self
.
_target
=
DomainTuple
.
make
(
target
)
# domain: fine domain
# target: coarse domain
distances_dom
=
tuple
([
domain
[
0
].
distances
[
0
],
domain
[
1
].
distances
[
0
]])
distances_tgt
=
tuple
([
target
[
0
].
distances
[
0
],
target
[
1
].
distances
[
0
]])
# index arrays
dom_indices
=
np
.
arange
(
self
.
domain
.
size
).
reshape
(
self
.
domain
.
shape
)
tgt_indices
=
np
.
arange
(
self
.
target
.
size
).
reshape
(
self
.
target
.
shape
)
# Input for sparse matrix
foo
=
self
.
domain
.
size
*
2
**
len
(
self
.
domain
.
shape
)
rs
,
cs
,
ws
=
np
.
zeros
(
foo
),
np
.
zeros
(
foo
),
np
.
zeros
(
foo
)
ind
=
0
print
(
'Initializing...'
)
# Loop through all points in fine grid (domain) and compute weights
for
xx
in
range
(
domain
.
shape
[
0
]):
for
yy
in
range
(
domain
.
shape
[
1
]):
# Find neighbours
xx_in_tgt
=
xx
*
distances_dom
[
0
]
/
distances_tgt
[
0
]
yy_in_tgt
=
yy
*
distances_dom
[
1
]
/
distances_tgt
[
1
]
p
=
np
.
array
([
xx
,
yy
])
p_in_tgt
=
np
.
array
([
xx_in_tgt
,
yy_in_tgt
])
xx_neigh
=
int
(
xx
*
distances_dom
[
0
]
/
distances_tgt
[
0
])
yy_neigh
=
int
(
yy
*
distances_dom
[
1
]
/
distances_tgt
[
1
])
neighbours
=
[
np
.
array
([
xx_neigh
,
yy_neigh
]),
np
.
array
([
xx_neigh
+
1
,
yy_neigh
]),
np
.
array
([
xx_neigh
,
yy_neigh
+
1
]),
np
.
array
([
xx_neigh
+
1
,
yy_neigh
+
1
])
]
for
n
in
neighbours
:
ws
[
ind
]
=
np
.
prod
(
1
-
np
.
abs
(
n
-
p_in_tgt
))
if
any
(
n
==
self
.
target
.
shape
):
rs
[
ind
],
cs
[
ind
]
=
-
1
,
-
1
else
:
rs
[
ind
]
=
tgt_indices
[
tuple
(
n
)]
cs
[
ind
]
=
dom_indices
[
tuple
(
p
)]
ind
+=
1
print
(
'{}%'
.
format
(
np
.
round
(
xx
/
domain
.
shape
[
0
]
*
100
,
1
)))
# FIXME?
mask
=
np
.
logical_and
(
rs
!=
-
1
,
ws
!=
0
)
rs
,
cs
,
ws
=
rs
[
mask
],
cs
[
mask
],
ws
[
mask
]
smat
=
csr_matrix
(
(
ws
,
(
rs
,
cs
)),
shape
=
(
self
.
target
.
size
,
self
.
domain
.
size
))
self
.
_smat
=
aslinearoperator
(
smat
)
def
apply
(
self
,
x
,
mode
):
self
.
_check_input
(
x
,
mode
)
inp
=
x
.
to_global_data
()
if
mode
==
self
.
TIMES
:
res
=
self
.
_smat
.
matvec
(
inp
.
reshape
(
-
1
))
else
:
res
=
self
.
_smat
.
rmatvec
(
inp
.
reshape
(
-
1
))
res
*=
self
.
target
.
size
/
self
.
domain
.
size
tgt
=
self
.
_tgt
(
mode
)
return
Field
.
from_global_data
(
tgt
,
res
.
reshape
(
tgt
.
shape
))
@
property
def
domain
(
self
):
return
self
.
_domain
@
property
def
target
(
self
):
return
self
.
_target
@
property
def
capability
(
self
):
return
self
.
TIMES
|
self
.
ADJOINT_TIMES
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