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ift
NIFTy
Commits
615ff81a
Commit
615ff81a
authored
Nov 11, 2017
by
Martin Reinecke
Browse files
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tweaks
parent
0db9e4a6
Pipeline
#21398
failed with stage
in 3 minutes and 53 seconds
Changes
5
Pipelines
1
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Showing
5 changed files
with
33 additions
and
21 deletions
+33
-21
demos/wiener_filter_via_curvature.py
demos/wiener_filter_via_curvature.py
+1
-1
demos/wiener_filter_via_hamiltonian.py
demos/wiener_filter_via_hamiltonian.py
+4
-3
nifty/data_objects/distributed_do.py
nifty/data_objects/distributed_do.py
+23
-14
nifty/operators/power_projection_operator.py
nifty/operators/power_projection_operator.py
+1
-1
nifty/plotting/plot.py
nifty/plotting/plot.py
+4
-2
No files found.
demos/wiener_filter_via_curvature.py
View file @
615ff81a
...
...
@@ -44,7 +44,7 @@ if __name__ == "__main__":
np
.
random
.
seed
(
43
)
mock_power
=
ift
.
Field
(
power_space
,
val
=
power_spectrum
(
power_space
.
k_lengths
))
val
=
ift
.
dobj
.
from_global_data
(
power_spectrum
(
power_space
.
k_lengths
)
))
mock_harmonic
=
ift
.
power_synthesize
(
mock_power
,
real_signal
=
True
)
mock_harmonic
=
mock_harmonic
.
real
mock_signal
=
fft
(
mock_harmonic
)
...
...
demos/wiener_filter_via_hamiltonian.py
View file @
615ff81a
...
...
@@ -50,14 +50,15 @@ if __name__ == "__main__":
S
=
ift
.
create_power_operator
(
h_space
,
power_spectrum
=
p_spec
)
# Drawing a sample sh from the prior distribution in harmonic space
sp
=
ift
.
Field
(
p_space
,
val
=
p_spec
(
p_space
.
k_lengths
))
sp
=
ift
.
Field
(
p_space
,
ift
.
dobj
.
from_global_data
(
p_spec
(
p_space
.
k_lengths
)
))
sh
=
ift
.
power_synthesize
(
sp
,
real_signal
=
True
)
ss
=
fft
.
adjoint_times
(
sh
)
# Choosing the measurement instrument
# Instrument = ift.FFTSmoothingOperator(s_space, sigma=0.05)
diag
=
ift
.
Field
.
ones
(
s_space
)
diag
.
val
[
20
:
80
,
20
:
80
]
=
0
diag
=
np
.
ones
(
s_space
.
shape
)
diag
[
20
:
80
,
20
:
80
]
=
0
diag
=
ift
.
Field
(
s_space
,
ift
.
dobj
.
from_global_data
(
diag
))
Instrument
=
ift
.
DiagonalOperator
(
diag
)
# Adding a harmonic transformation to the instrument
...
...
nifty/data_objects/distributed_do.py
View file @
615ff81a
...
...
@@ -17,7 +17,7 @@ def shareRange(nwork, nshares, myshare):
hi
=
lo
+
nbase
+
(
1
if
myshare
<
additional
else
0
)
return
lo
,
hi
def
get_loc
shape
(
shape
,
distaxis
):
def
local_
shape
(
shape
,
distaxis
):
if
len
(
shape
)
==
0
:
distaxis
=
-
1
if
distaxis
==-
1
:
...
...
@@ -25,8 +25,6 @@ def get_locshape(shape, distaxis):
shape2
=
list
(
shape
)
shape2
[
distaxis
]
=
shareSize
(
shape
[
distaxis
],
ntask
,
rank
)
return
tuple
(
shape2
)
def
local_shape
(
shape
,
distaxis
):
return
get_locshape
(
shape
,
distaxis
)
class
data_object
(
object
):
def
__init__
(
self
,
shape
,
data
,
distaxis
):
...
...
@@ -35,7 +33,7 @@ class data_object(object):
if
len
(
self
.
_shape
)
==
0
:
distaxis
=
-
1
self
.
_distaxis
=
distaxis
lshape
=
get_loc
shape
(
self
.
_shape
,
self
.
_distaxis
)
lshape
=
local_
shape
(
self
.
_shape
,
self
.
_distaxis
)
self
.
_data
=
data
def
sanity_checks
(
self
):
...
...
@@ -123,6 +121,14 @@ class data_object(object):
def
sum
(
self
,
axis
=
None
):
return
self
.
_contraction_helper
(
"sum"
,
MPI
.
SUM
,
axis
)
# FIXME: to be improved!
def
mean
(
self
):
return
self
.
sum
()
/
self
.
size
def
std
(
self
):
return
np
.
sqrt
(
self
.
var
())
def
var
(
self
):
return
(
abs
(
self
-
self
.
mean
())
**
2
).
mean
()
def
_binary_helper
(
self
,
other
,
op
):
a
=
self
if
isinstance
(
other
,
data_object
):
...
...
@@ -173,6 +179,9 @@ class data_object(object):
def
__rdiv__
(
self
,
other
):
return
self
.
_binary_helper
(
other
,
op
=
'__rdiv__'
)
def
__idiv__
(
self
,
other
):
return
self
.
_binary_helper
(
other
,
op
=
'__idiv__'
)
def
__truediv__
(
self
,
other
):
return
self
.
_binary_helper
(
other
,
op
=
'__truediv__'
)
...
...
@@ -214,19 +223,19 @@ class data_object(object):
def
full
(
shape
,
fill_value
,
dtype
=
None
,
distaxis
=
0
):
return
data_object
(
shape
,
np
.
full
(
get_loc
shape
(
shape
,
distaxis
),
fill_value
,
dtype
),
distaxis
)
return
data_object
(
shape
,
np
.
full
(
local_
shape
(
shape
,
distaxis
),
fill_value
,
dtype
),
distaxis
)
def
empty
(
shape
,
dtype
=
None
,
distaxis
=
0
):
return
data_object
(
shape
,
np
.
empty
(
get_loc
shape
(
shape
,
distaxis
),
dtype
),
distaxis
)
return
data_object
(
shape
,
np
.
empty
(
local_
shape
(
shape
,
distaxis
),
dtype
),
distaxis
)
def
zeros
(
shape
,
dtype
=
None
,
distaxis
=
0
):
return
data_object
(
shape
,
np
.
zeros
(
get_loc
shape
(
shape
,
distaxis
),
dtype
),
distaxis
)
return
data_object
(
shape
,
np
.
zeros
(
local_
shape
(
shape
,
distaxis
),
dtype
),
distaxis
)
def
ones
(
shape
,
dtype
=
None
,
distaxis
=
0
):
return
data_object
(
shape
,
np
.
ones
(
get_loc
shape
(
shape
,
distaxis
),
dtype
),
distaxis
)
return
data_object
(
shape
,
np
.
ones
(
local_
shape
(
shape
,
distaxis
),
dtype
),
distaxis
)
def
empty_like
(
a
,
dtype
=
None
):
...
...
@@ -277,9 +286,9 @@ def from_object(object, dtype=None, copy=True):
def
from_random
(
random_type
,
shape
,
dtype
=
np
.
float64
,
distaxis
=
0
,
**
kwargs
):
generator_function
=
getattr
(
Random
,
random_type
)
lshape
=
get_loc
shape
(
shape
,
distaxis
)
return
data_object
(
shape
,
generator_function
(
dtype
=
dtype
,
shape
=
lshape
,
**
kwargs
),
distaxis
=
distaxis
)
#lshape = local_
shape(shape, distaxis)
#
return data_object(shape, generator_function(dtype=dtype, shape=lshape, **kwargs), distaxis=distaxis)
return
from_global_data
(
generator_function
(
dtype
=
dtype
,
shape
=
shape
,
**
kwargs
),
distaxis
=
distaxis
)
def
local_data
(
arr
):
return
arr
.
_data
...
...
@@ -368,8 +377,8 @@ def redistribute (arr, dist=None, nodist=None):
ssz
=
np
.
empty
(
ntask
,
dtype
=
np
.
int
)
rsz
=
np
.
empty
(
ntask
,
dtype
=
np
.
int
)
for
i
in
range
(
ntask
):
ssz
[
i
]
=
s
labsize
*
tmp
.
shape
[
1
]
*
shareSize
(
arr
.
shape
[
dist
],
ntask
,
i
)
rsz
[
i
]
=
s
labsize
*
shareSize
(
arr
.
shape
[
dist
],
ntask
,
rank
)
*
shareSize
(
arr
.
shape
[
arr
.
_distaxis
],
ntask
,
i
)
ssz
[
i
]
=
s
hareSize
(
arr
.
shape
[
dist
],
ntask
,
i
)
*
tmp
.
shape
[
1
]
*
slabsize
rsz
[
i
]
=
s
hareSize
(
arr
.
shape
[
dist
],
ntask
,
rank
)
*
shareSize
(
arr
.
shape
[
arr
.
_distaxis
],
ntask
,
i
)
*
slabsize
sdisp
=
np
.
empty
(
ntask
,
dtype
=
np
.
int
)
rdisp
=
np
.
empty
(
ntask
,
dtype
=
np
.
int
)
sdisp
[
0
]
=
0
...
...
@@ -377,7 +386,7 @@ def redistribute (arr, dist=None, nodist=None):
sdisp
[
1
:]
=
np
.
cumsum
(
ssz
[:
-
1
])
rdisp
[
1
:]
=
np
.
cumsum
(
rsz
[:
-
1
])
tmp
=
tmp
.
flatten
()
out
=
np
.
empty
(
np
.
prod
(
get_loc
shape
(
arr
.
shape
,
dist
)),
dtype
=
arr
.
dtype
)
out
=
np
.
empty
(
np
.
prod
(
local_
shape
(
arr
.
shape
,
dist
)),
dtype
=
arr
.
dtype
)
s_msg
=
[
tmp
,
(
ssz
,
sdisp
),
MPI
.
BYTE
]
r_msg
=
[
out
,
(
rsz
,
rdisp
),
MPI
.
BYTE
]
comm
.
Alltoallv
(
s_msg
,
r_msg
)
...
...
nifty/operators/power_projection_operator.py
View file @
615ff81a
...
...
@@ -77,7 +77,7 @@ class PowerProjectionOperator(LinearOperator):
oarr
=
oarr
.
reshape
(
self
.
_target
.
shape
)
res
=
Field
(
self
.
_target
,
dobj
.
from_global_data
(
oarr
))
else
:
oarr
=
oarr
.
reshape
(
dobj
.
get_loc
shape
(
self
.
_target
.
shape
,
dobj
.
distaxis
(
x
.
val
)))
oarr
=
oarr
.
reshape
(
dobj
.
local_
shape
(
self
.
_target
.
shape
,
dobj
.
distaxis
(
x
.
val
)))
res
=
Field
(
self
.
_target
,
dobj
.
from_local_data
(
self
.
_target
.
shape
,
oarr
,
dobj
.
default_distaxis
()))
return
res
.
weight
(
-
1
,
spaces
=
self
.
_space
)
...
...
nifty/plotting/plot.py
View file @
615ff81a
from
__future__
import
division
import
numpy
as
np
from
..
import
Field
,
RGSpace
,
HPSpace
,
GLSpace
,
PowerSpace
from
..
import
Field
,
RGSpace
,
HPSpace
,
GLSpace
,
PowerSpace
,
dobj
import
os
# relevant properties:
...
...
@@ -45,6 +45,8 @@ def _find_closest(A, target):
def
_makeplot
(
name
):
import
matplotlib.pyplot
as
plt
if
dobj
.
rank
!=
0
:
return
if
name
is
None
:
plt
.
show
()
return
...
...
@@ -185,7 +187,7 @@ def plot(f, **kwargs):
dy
=
dom
.
distances
[
1
]
xc
=
np
.
arange
(
nx
,
dtype
=
np
.
float64
)
*
dx
yc
=
np
.
arange
(
ny
,
dtype
=
np
.
float64
)
*
dy
im
=
ax
.
imshow
(
f
.
val
,
extent
=
[
xc
[
0
],
xc
[
-
1
],
yc
[
0
],
yc
[
-
1
]],
im
=
ax
.
imshow
(
dobj
.
to_global_data
(
f
.
val
)
,
extent
=
[
xc
[
0
],
xc
[
-
1
],
yc
[
0
],
yc
[
-
1
]],
vmin
=
kwargs
.
get
(
"zmin"
),
vmax
=
kwargs
.
get
(
"zmax"
),
cmap
=
cmap
,
origin
=
"lower"
)
# from mpl_toolkits.axes_grid1 import make_axes_locatable
...
...
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