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xQML
xQML
Commits
51676738
Commit
51676738
authored
Jan 28, 2020
by
Matthieu Tristram
Browse files
Change verbosity
parent
bbb7236b
Changes
2
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Inline
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2 changed files
with
14 additions
and
6 deletions
+14
-6
src/libcov.c
src/libcov.c
+6
-6
xqml/xqml.py
xqml/xqml.py
+8
-0
No files found.
src/libcov.c
View file @
51676738
...
...
@@ -30,15 +30,15 @@ void build_dSdC( int nside, int nstokes, int npix, int nbin, long *ispec, long *
// const int nspec = nstokes2nspec(nstokes);
const
int64_t
npixtot
=
npix
*
nstokes
;
fprintf
(
stdout
,
"lmax=%d
\n
"
,
lmax
);
fprintf
(
stdout
,
"npix=%d
\n
"
,
npix
);
fprintf
(
stdout
,
"nstokes=%d
\n
"
,
nstokes
);
fprintf
(
stdout
,
"nspec=%d
\n
"
,
nspec
);
//
fprintf( stdout, "lmax=%d\n", lmax);
//
fprintf( stdout, "npix=%d\n", npix);
//
fprintf( stdout, "nstokes=%d\n", nstokes);
//
fprintf( stdout, "nspec=%d\n", nspec);
int64_t
ntot
=
nspec
*
nbin
*
npixtot
*
npixtot
;
fprintf
(
stdout
,
"memset (%d,%d,%d,%d = %lld)...
\n
"
,
nspec
,
nbin
,
nstokes
,
npix
,
ntot
);
//
fprintf( stdout, "memset (%d,%d,%d,%d = %lld)...\n", nspec,nbin,nstokes,npix,ntot);
memset
(
dSdC
,
0
.,
ntot
*
sizeof
(
double
));
fprintf
(
stdout
,
"ispec = (%d,%d,%d,%d,%d,%d)
\n
"
,
ispec
[
0
],
ispec
[
1
],
ispec
[
2
],
ispec
[
3
],
ispec
[
4
],
ispec
[
5
]);
//
fprintf( stdout, "ispec = (%d,%d,%d,%d,%d,%d)\n", ispec[0],ispec[1],ispec[2],ispec[3],ispec[4],ispec[5]);
#pragma omp parallel default(none) shared(stdout,nbin,nside,npix,nstokes,dSdC,ipix,bl,ellbins,ispec)
{
...
...
xqml/xqml.py
View file @
51676738
...
...
@@ -91,6 +91,14 @@ class xQML(object):
clth
=
np
.
asarray
(
clth
)
if
len
(
clth
)
==
4
:
clth
=
np
.
concatenate
((
clth
,
clth
[
0
:
2
]
*
0.
))
nbin
=
len
(
self
.
ellbins
)
nmem
=
self
.
nspec
*
nbin
*
(
self
.
nstokes
*
self
.
npix
)
**
2
toGb
=
1024.
*
1024.
*
1024.
print
(
"xQML"
)
print
(
"spec: "
,
spec
)
print
(
"nbin: "
,
nbin
)
print
(
"Memset: %.2f Gb (%d,%d,%d,%d)"
%
(
8.
*
nmem
/
toGb
,
self
.
nspec
,
nbin
,
self
.
nstokes
,
self
.
npix
))
# If Pl is given by the user, just load it, and then compute the signal
# covariance using the fiducial model.
...
...
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