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ift
NIFTy
Commits
a5f581b3
Commit
a5f581b3
authored
Mar 21, 2013
by
Marco Selig
Browse files
version update.
parent
56bbb7ad
Changes
4
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README.rst
View file @
a5f581b3
...
...
@@ -96,7 +96,7 @@ Requirements
Download
........
The latest release is tagged **v0.
2.1
** and is available as a source package
The latest release is tagged **v0.
3.0
** and is available as a source package
at `<https://github.com/mselig/nifty/tags>`_. The current version can be
obtained by cloning the repository::
...
...
nifty_core.py
View file @
a5f581b3
...
...
@@ -480,7 +480,7 @@ class _about(object): ## nifty support class for global settings
"""
## version
self
.
_version
=
"0.
2.1
"
self
.
_version
=
"0.
3.0
"
## switches and notifications
self
.
_errors
=
notification
(
default
=
True
,
ccode
=
notification
.
_code
)
...
...
nifty_power.py
View file @
a5f581b3
...
...
@@ -112,14 +112,11 @@ def smooth_power(power,kindex,mode="2s",exclude=1,sigma=-1):
The array specifying the coordinate indices in conjugate space.
mode : string
Specifi
c
es the smoothing mode (default: "2s") :
Specifies the smoothing mode (default: "2s") :
- "ff" (smoothing in the harmonic basis using fast Fourier
transformations)
- "ff" (smoothing in the harmonic basis using fast Fourier transformations)
- "bf" (smoothing in the position basis by brute force)
- "2s" (smoothing in the position basis restricted to a 2-`sigma`
interval)
- "2s" (smoothing in the position basis restricted to a 2-`sigma` interval)
exclude : scalar
Excludes the first power spectrum entries from smoothing, indicated by
...
...
@@ -192,9 +189,9 @@ def _calc_inverse(tk,var,kindex,rho,b1,Amem): ## > computes the inverse Hessian
def
infer_power
(
m
,
domain
=
None
,
Sk
=
None
,
D
=
None
,
pindex
=
None
,
pundex
=
None
,
kindex
=
None
,
rho
=
None
,
q
=
1E-42
,
alpha
=
1
,
perception
=
(
1
,
0
),
smoothness
=
False
,
var
=
100
,
bare
=
True
,
**
kwargs
):
"""
Infer
e
s the power spectrum.
Infers the power spectrum.
Given a map the infered power spectrum is equal to ``m.power()``; given
Given a map the infer
r
ed power spectrum is equal to ``m.power()``; given
an uncertainty a power spectrum is inferred according to the "critical"
filter formula, which can be extended by a smoothness prior. For
details, see references below.
...
...
@@ -202,7 +199,7 @@ def infer_power(m,domain=None,Sk=None,D=None,pindex=None,pundex=None,kindex=None
Parameters
----------
m : field
Map
o
f which the power spectrum is inferred.
Map f
or
which the power spectrum is inferred.
domain : space
The space wherein the power spectrum is defined, can be retrieved
from `Sk.domain` (default: None).
...
...
@@ -230,7 +227,8 @@ def infer_power(m,domain=None,Sk=None,D=None,pindex=None,pundex=None,kindex=None
Spectral shape parameter of the assumed inverse-Gamme prior
(default: 1).
perception : {tuple, list, array}, *optional*
Tuple specifying the filter perception (default: (1,0)).
Tuple specifying the filter perception (delta,epsilon)
(default: (1,0)).
smoothness : bool, *optional*
Indicates whether the smoothness prior is used or not
(default: False).
...
...
@@ -244,13 +242,13 @@ def infer_power(m,domain=None,Sk=None,D=None,pindex=None,pundex=None,kindex=None
Returns
-------
pk : numpy.ndarray
The infered power spectrum, weighted according to the `bare` flag.
The infer
r
ed power spectrum, weighted according to the `bare` flag.
Other Parameters
----------------
random : string, *optional*
The distribution from which the probes
are drawn, supported
distributions are (default: "pm1"):
The distribution from which the probes
for the diagonal probing are
drawn, supported
distributions are (default: "pm1"):
- "pm1" (uniform distribution over {+1,-1} or {+1,+i,-1,-i})
- "gau" (normal distribution with zero-mean and unit-variance)
...
...
@@ -280,10 +278,12 @@ def infer_power(m,domain=None,Sk=None,D=None,pindex=None,pundex=None,kindex=None
Notes
-----
The general approach to inference of unknown power spectra is detailed
in [#]_, where the "critical" filter formula used here is derived. The
further incorporation of a smoothness prior is detailed in [#]_, where
the underlying formulas of this implementation are derived and
discussed in terms of their applicability.
in [#]_, where the "critical" filter formula, Eq.(37b), used here is
derived, and the implications of a certain choise of the perception
tuple (delta,epsilon) are discussed.
The further incorporation of a smoothness prior as detailed in [#]_,
where the underlying formula(s), Eq.(27), of this implementation are
derived and discussed in terms of their applicability.
References
----------
...
...
setup.py
View file @
a5f581b3
...
...
@@ -23,7 +23,7 @@ from distutils.core import setup
import
os
setup
(
name
=
"nifty"
,
version
=
"0.
2.1
"
,
version
=
"0.
3.0
"
,
description
=
"Numerical Information Field Theory"
,
author
=
"Marco Selig"
,
author_email
=
"mselig@mpa-garching.mpg.de"
,
...
...
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