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ift
NIFTy
Commits
238ce84f
Commit
238ce84f
authored
Jan 21, 2013
by
Marco Selig
Browse files
docstring corrections.
parent
3682c12b
Changes
4
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4 changed files
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46 additions
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46 deletions
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-46
README.rst
README.rst
+1
-1
nifty_cmaps.py
nifty_cmaps.py
+2
-2
nifty_core.py
nifty_core.py
+40
-40
nifty_power.py
nifty_power.py
+3
-3
No files found.
README.rst
View file @
238ce84f
...
...
@@ -67,7 +67,7 @@ apply to fields.
* (and more)
*Parts of this summary are taken* [1]_ *without marking them explicitly as
*Parts of this summary are taken
from
* [1]_ *without marking them explicitly as
quotations.*
Installation
...
...
nifty_cmaps.py
View file @
238ce84f
...
...
@@ -33,7 +33,7 @@
The visualization of fields is useful for obvious reasons, and therefore
some nice color maps are here to be found. Those are segmented color maps
that can be used in many settings, including the native ploting method for
that can be used in many settings, including the native plot
t
ing method for
fields. (Some of the color maps offered here are results from IFT
publications, cf. references below.)
...
...
@@ -237,4 +237,4 @@ class ncmap(object):
return
cm
(
"Plus Minus"
,
segmentdata
,
N
=
int
(
ncolors
),
gamma
=
1.0
)
##-----------------------------------------------------------------------------
\ No newline at end of file
##-----------------------------------------------------------------------------
nifty_core.py
View file @
238ce84f
...
...
@@ -42,7 +42,7 @@
taken care of automatically without concerning the user. This allows for an
abstract formulation and programming of inference algorithms, including
those derived within information field theory. Thus, NIFTY permits its user
to rapidly prototype algorithms in 1D
,
and then apply the developed code in
to rapidly prototype algorithms in 1D and then apply the developed code in
higher-dimensional settings of real world problems. The set of spaces on
which NIFTY operates comprises point sets, n-dimensional regular grids,
spherical spaces, their harmonic counterparts, and product spaces
...
...
@@ -5579,7 +5579,7 @@ class field(object):
controlled by kwargs.
target : space, *optional*
The space wherein the operator output lives (default: domain)
The space wherein the operator output lives (default: domain)
.
Other Parameters
...
...
@@ -5629,7 +5629,7 @@ class field(object):
controlled by the keyword arguments.
target : space, *optional*
The space wherein the operator output lives (default: domain)
The space wherein the operator output lives (default: domain)
.
"""
def
__init__
(
self
,
domain
,
val
=
None
,
target
=
None
,
**
kwargs
):
...
...
@@ -7482,10 +7482,10 @@ class operator(object):
Notes
-----
The ambiguity of `bare` or non-bare diagonal entries is based
on the choi
s
e of a matrix representation of the operator in
question. The naive choi
s
e of absorbing the volume weights
on the choi
c
e of a matrix representation of the operator in
question. The naive choi
c
e of absorbing the volume weights
into the matrix leads to a matrix-vector calculus with the
non-bare entries which seems intuitive, though. The choi
s
e of
non-bare entries which seems intuitive, though. The choi
c
e of
keeping matrix entries and volume weights separate deals with the
bare entries that allow for correct interpretation of the matrix
entries; e.g., as variance in case of an covariance operator.
...
...
@@ -7559,10 +7559,10 @@ class operator(object):
Notes
-----
The ambiguity of `bare` or non-bare diagonal entries is based
on the choi
s
e of a matrix representation of the operator in
question. The naive choi
s
e of absorbing the volume weights
on the choi
c
e of a matrix representation of the operator in
question. The naive choi
c
e of absorbing the volume weights
into the matrix leads to a matrix-vector calculus with the
non-bare entries which seems intuitive, though. The choi
s
e of
non-bare entries which seems intuitive, though. The choi
c
e of
keeping matrix entries and volume weights separate deals with the
bare entries that allow for correct interpretation of the matrix
entries; e.g., as variance in case of an covariance operator.
...
...
@@ -7657,10 +7657,10 @@ class operator(object):
Notes
-----
The ambiguity of `bare` or non-bare diagonal entries is based
on the choi
s
e of a matrix representation of the operator in
question. The naive choi
s
e of absorbing the volume weights
on the choi
c
e of a matrix representation of the operator in
question. The naive choi
c
e of absorbing the volume weights
into the matrix leads to a matrix-vector calculus with the
non-bare entries which seems intuitive, though. The choi
s
e of
non-bare entries which seems intuitive, though. The choi
c
e of
keeping matrix entries and volume weights separate deals with the
bare entries that allow for correct interpretation of the matrix
entries; e.g., as variance in case of an covariance operator.
...
...
@@ -7721,10 +7721,10 @@ class operator(object):
Notes
-----
The ambiguity of `bare` or non-bare diagonal entries is based
on the choi
s
e of a matrix representation of the operator in
question. The naive choi
s
e of absorbing the volume weights
on the choi
c
e of a matrix representation of the operator in
question. The naive choi
c
e of absorbing the volume weights
into the matrix leads to a matrix-vector calculus with the
non-bare entries which seems intuitive, though. The choi
s
e of
non-bare entries which seems intuitive, though. The choi
c
e of
keeping matrix entries and volume weights separate deals with the
bare entries that allow for correct interpretation of the matrix
entries; e.g., as variance in case of an covariance operator.
...
...
@@ -7784,10 +7784,10 @@ class operator(object):
Notes
-----
The ambiguity of `bare` or non-bare diagonal entries is based
on the choi
s
e of a matrix representation of the operator in
question. The naive choi
s
e of absorbing the volume weights
on the choi
c
e of a matrix representation of the operator in
question. The naive choi
c
e of absorbing the volume weights
into the matrix leads to a matrix-vector calculus with the
non-bare entries which seems intuitive, though. The choi
s
e of
non-bare entries which seems intuitive, though. The choi
c
e of
keeping matrix entries and volume weights separate deals with the
bare entries that allow for correct interpretation of the matrix
entries; e.g., as variance in case of an covariance operator.
...
...
@@ -7848,10 +7848,10 @@ class operator(object):
Notes
-----
The ambiguity of `bare` or non-bare diagonal entries is based
on the choi
s
e of a matrix representation of the operator in
question. The naive choi
s
e of absorbing the volume weights
on the choi
c
e of a matrix representation of the operator in
question. The naive choi
c
e of absorbing the volume weights
into the matrix leads to a matrix-vector calculus with the
non-bare entries which seems intuitive, though. The choi
s
e of
non-bare entries which seems intuitive, though. The choi
c
e of
keeping matrix entries and volume weights separate deals with the
bare entries that allow for correct interpretation of the matrix
entries; e.g., as variance in case of an covariance operator.
...
...
@@ -7902,10 +7902,10 @@ class diagonal_operator(operator):
Notes
-----
The ambiguity of `bare` or non-bare diagonal entries is based
on the choi
s
e of a matrix representation of the operator in
question. The naive choi
s
e of absorbing the volume weights
on the choi
c
e of a matrix representation of the operator in
question. The naive choi
c
e of absorbing the volume weights
into the matrix leads to a matrix-vector calculus with the
non-bare entries which seems intuitive, though. The choi
s
e of
non-bare entries which seems intuitive, though. The choi
c
e of
keeping matrix entries and volume weights separate deals with the
bare entries that allow for correct interpretation of the matrix
entries; e.g., as variance in case of an covariance operator.
...
...
@@ -7953,10 +7953,10 @@ class diagonal_operator(operator):
Notes
-----
The ambiguity of `bare` or non-bare diagonal entries is based
on the choi
s
e of a matrix representation of the operator in
question. The naive choi
s
e of absorbing the volume weights
on the choi
c
e of a matrix representation of the operator in
question. The naive choi
c
e of absorbing the volume weights
into the matrix leads to a matrix-vector calculus with the
non-bare entries which seems intuitive, though. The choi
s
e of
non-bare entries which seems intuitive, though. The choi
c
e of
keeping matrix entries and volume weights separate deals with the
bare entries that allow for correct interpretation of the matrix
entries; e.g., as variance in case of an covariance operator.
...
...
@@ -8240,10 +8240,10 @@ class diagonal_operator(operator):
Notes
-----
The ambiguity of `bare` or non-bare diagonal entries is based
on the choi
s
e of a matrix representation of the operator in
question. The naive choi
s
e of absorbing the volume weights
on the choi
c
e of a matrix representation of the operator in
question. The naive choi
c
e of absorbing the volume weights
into the matrix leads to a matrix-vector calculus with the
non-bare entries which seems intuitive, though. The choi
s
e of
non-bare entries which seems intuitive, though. The choi
c
e of
keeping matrix entries and volume weights separate deals with the
bare entries that allow for correct interpretation of the matrix
entries; e.g., as variance in case of an covariance operator.
...
...
@@ -8327,10 +8327,10 @@ class diagonal_operator(operator):
Notes
-----
The ambiguity of `bare` or non-bare diagonal entries is based
on the choi
s
e of a matrix representation of the operator in
question. The naive choi
s
e of absorbing the volume weights
on the choi
c
e of a matrix representation of the operator in
question. The naive choi
c
e of absorbing the volume weights
into the matrix leads to a matrix-vector calculus with the
non-bare entries which seems intuitive, though. The choi
s
e of
non-bare entries which seems intuitive, though. The choi
c
e of
keeping matrix entries and volume weights separate deals with the
bare entries that allow for correct interpretation of the matrix
entries; e.g., as variance in case of an covariance operator.
...
...
@@ -8408,7 +8408,7 @@ def identity(domain):
Returns an identity operator.
The identity operator is represented by a `diagonal_operator` instance,
which is applicable to a field-like object; i.e., a scalar
s
, list,
which is applicable to a field-like object; i.e., a scalar, list,
array or field. (The identity operator is unrelated to PYTHON's
built-in function :py:func:`id`.)
...
...
@@ -8492,10 +8492,10 @@ class power_operator(diagonal_operator):
Notes
-----
The ambiguity of `bare` or non-bare diagonal entries is based
on the choi
s
e of a matrix representation of the operator in
question. The naive choi
s
e of absorbing the volume weights
on the choi
c
e of a matrix representation of the operator in
question. The naive choi
c
e of absorbing the volume weights
into the matrix leads to a matrix-vector calculus with the
non-bare entries which seems intuitive, though. The choi
s
e of
non-bare entries which seems intuitive, though. The choi
c
e of
keeping matrix entries and volume weights separate deals with the
bare entries that allow for correct interpretation of the matrix
entries; e.g., as variance in case of an covariance operator.
...
...
@@ -9229,10 +9229,10 @@ class vecvec_operator(operator):
Notes
-----
The ambiguity of `bare` or non-bare diagonal entries is based
on the choi
s
e of a matrix representation of the operator in
question. The naive choi
s
e of absorbing the volume weights
on the choi
c
e of a matrix representation of the operator in
question. The naive choi
c
e of absorbing the volume weights
into the matrix leads to a matrix-vector calculus with the
non-bare entries which seems intuitive, though. The choi
s
e of
non-bare entries which seems intuitive, though. The choi
c
e of
keeping matrix entries and volume weights separate deals with the
bare entries that allow for correct interpretation of the matrix
entries; e.g., as variance in case of an covariance operator.
...
...
nifty_power.py
View file @
238ce84f
...
...
@@ -36,11 +36,11 @@
homogeneity and isotropy. Fields which are only statistically homogeneous
can also be created using the diagonal operator routine.
In
the moment, NIFTy offers one additional routine for power spectrum
At
the moment, NIFTy offers one additional routine for power spectrum
manipulation, the smooth_power function to smooth a power spectrum with a
Gaussian convolution kernel. This can be necessary in cases where power
spectra are reconstructed and reused in an i
n
terative algorithm, where
too much statistical variation might
effect
severely the results.
spectra are reconstructed and reused in an iterative algorithm, where
too much statistical variation might severely
effect
the results.
"""
...
...
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