From a5f581b321aedd97682db44adec6b8ae34016fc6 Mon Sep 17 00:00:00 2001
From: Marco Selig
Date: Thu, 21 Mar 2013 14:14:54 +0100
Subject: [PATCH] version update.

README.rst  2 +
nifty_core.py  2 +
nifty_power.py  34 +++++++++++++++++
setup.py  2 +
4 files changed, 20 insertions(+), 20 deletions()
diff git a/README.rst b/README.rst
index 750c4b0f..5846a74e 100644
 a/README.rst
+++ b/README.rst
@@ 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 ``_. The current version can be
obtained by cloning the repository::
diff git a/nifty_core.py b/nifty_core.py
index d3870e7b..a2ae9310 100644
 a/nifty_core.py
+++ b/nifty_core.py
@@ 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)
diff git a/nifty_power.py b/nifty_power.py
index 3c08f87b..dc88c909 100644
 a/nifty_power.py
+++ b/nifty_power.py
@@ 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
 Specifices 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=1E42,alpha=1,perception=(1,0),smoothness=False,var=100,bare=True,**kwargs):
"""
 Inferes the power spectrum.
+ Infers the power spectrum.
 Given a map the infered power spectrum is equal to ``m.power()``; given
+ Given a map the inferred 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 of which the power spectrum is inferred.
+ Map for 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 inverseGamme 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 inferred 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 zeromean and unitvariance)
@@ 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

diff git a/setup.py b/setup.py
index 959d7980..fdda91a6 100644
 a/setup.py
+++ b/setup.py
@@ 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@mpagarching.mpg.de",

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