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ift
NIFTy
Commits
58d4b593
Commit
58d4b593
authored
Jun 18, 2018
by
Philipp Arras
Browse files
Rename nifty4 > nifty5
parent
02f153a5
Pipeline
#31301
passed with stages
in 8 minutes and 24 seconds
Changes
130
Pipelines
2
Show whitespace changes
Inline
Sidebyside
.gitlabci.yml
View file @
58d4b593
...
...
@@ 34,7 +34,7 @@ test_python2_with_coverage:
script
:

python setup.py install user f

mpiexec n 2 bindto none nosetests q 2> /dev/null

nosetests q withcoverage coverpackage=nifty
4
covererase

nosetests q withcoverage coverpackage=nifty
5
covererase

>
coverage report omit "*plotting*,*distributed_do*"

>
...
...
README.md
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58d4b593
...
...
@@ 49,8 +49,8 @@ Optional dependencies:
### Sources
The current version of Nifty
4
can be obtained by cloning the repository and
switching to the NIFTy_
4
branch:
The current version of Nifty
5
can be obtained by cloning the repository and
switching to the NIFTy_
5
branch:
git clone https://gitlab.mpcdf.mpg.de/ift/NIFTy.git
...
...
@@ 59,7 +59,7 @@ switching to the NIFTy_4 branch:
In the following, we assume a Debianbased distribution. For other
distributions, the "apt" lines will need slight changes.
NIFTy
4
and its mandatory dependencies can be installed via:
NIFTy
5
and its mandatory dependencies can be installed via:
sudo aptget install git libfftw3dev python pythonpip pythondev
pip install user git+https://gitlab.mpcdf.mpg.de/ift/NIFTy.git@NIFTy_4
...
...
@@ 99,7 +99,7 @@ In oder to run the tests one needs two additional packages:
Afterwards the tests (including a coverage report) can be run using the
following command in the repository root:
nosetests x withcoverage coverhtml coverpackage=nifty
4
nosetests x withcoverage coverhtml coverpackage=nifty
5
### First Steps
...
...
demos/Wiener_Filter.ipynb
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58d4b593
...
...
@@ 81,11 +81,11 @@
%% Cell type:code id: tags:
```
python
import
numpy
as
np
np
.
random
.
seed
(
40
)
import
nifty
4
as
ift
import
nifty
5
as
ift
import
matplotlib.pyplot
as
plt
%
matplotlib
inline
```
%% Cell type:markdown id: tags:
...
...
demos/critical_filtering.py
View file @
58d4b593
import
nifty
4
as
ift
from
nifty
4
.library.nonlinearities
import
Linear
,
Tanh
,
Exponential
import
nifty
5
as
ift
from
nifty
5
.library.nonlinearities
import
Linear
,
Tanh
,
Exponential
import
numpy
as
np
np
.
random
.
seed
(
42
)
...
...
demos/nonlinear_critical_filter.py
View file @
58d4b593
import
nifty
4
as
ift
from
nifty
4
.library.nonlinearities
import
Exponential
import
nifty
5
as
ift
from
nifty
5
.library.nonlinearities
import
Exponential
import
numpy
as
np
np
.
random
.
seed
(
42
)
...
...
demos/nonlinear_wiener_filter.py
View file @
58d4b593
import
nifty
4
as
ift
from
nifty
4
.library.nonlinearities
import
Linear
,
Exponential
,
Tanh
import
nifty
5
as
ift
from
nifty
5
.library.nonlinearities
import
Linear
,
Exponential
,
Tanh
import
numpy
as
np
np
.
random
.
seed
(
20
)
...
...
demos/paper_demos/cartesian_wiener_filter.py
View file @
58d4b593
import
numpy
as
np
import
nifty
4
as
ift
import
nifty
5
as
ift
if
__name__
==
"__main__"
:
signal_to_noise
=
0.5
# The signal to noise ratio
...
...
demos/paper_demos/wiener_filter.py
View file @
58d4b593
import
nifty
4
as
ift
import
nifty
5
as
ift
import
numpy
as
np
...
...
demos/poisson_demo.py
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58d4b593
...
...
@@ 4,7 +4,7 @@
# to get exact figure of paper comment out lines marked by "COMMENT OUT"
import
numpy
as
np
import
nifty
4
as
ift
import
nifty
5
as
ift
import
matplotlib.pyplot
as
plt
...
...
demos/probing.py
View file @
58d4b593
import
nifty
4
as
ift
import
nifty
5
as
ift
import
numpy
as
np
...
...
demos/sampling.py
View file @
58d4b593
import
nifty
4
as
ift
import
nifty
5
as
ift
import
numpy
as
np
import
matplotlib.pyplot
as
plt
from
nifty
4
.sugar
import
create_power_operator
from
nifty
5
.sugar
import
create_power_operator
np
.
random
.
seed
(
42
)
...
...
demos/tomography.py
View file @
58d4b593
import
nifty
4
as
ift
import
nifty
5
as
ift
import
numpy
as
np
...
...
demos/wiener_filter_data_space_noiseless.py
View file @
58d4b593
import
numpy
as
np
import
nifty
4
as
ift
import
nifty
5
as
ift
# TODO: MAKE RESPONSE MPI COMPATIBLE OR USE LOS RESPONSE INSTEAD
...
...
demos/wiener_filter_easy.py
View file @
58d4b593
import
numpy
as
np
import
nifty
4
as
ift
import
nifty
5
as
ift
if
__name__
==
"__main__"
:
...
...
demos/wiener_filter_via_curvature.py
View file @
58d4b593
import
numpy
as
np
import
nifty
4
as
ift
import
nifty
5
as
ift
import
numericalunits
as
nu
...
...
demos/wiener_filter_via_hamiltonian.py
View file @
58d4b593
import
nifty
4
as
ift
import
nifty
5
as
ift
import
numpy
as
np
np
.
random
.
seed
(
42
)
...
...
docs/generate.sh
View file @
58d4b593
rm
rf
docs/build docs/source/mod
sphinxapidoc
l
e
d
2
o
docs/source/mod nifty
4
sphinxapidoc
l
e
d
2
o
docs/source/mod nifty
5
sphinxbuild
b
html docs/source/ docs/build/
docs/source/citations.rst
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58d4b593
...
...
@@ 7,7 +7,7 @@ NIFTyrelated publications
author = {{Martin Reinecke, Theo Steininger, Marco Selig}},
title = {NIFTy  Numerical Information Field TheorY},
url = {https://gitlab.mpcdf.mpg.de/ift/NIFTy},
version = {nifty
4
},
version = {nifty
5
},
date = {20180405},
}
...
...
docs/source/code.rst
View file @
58d4b593
.. currentmodule:: nifty
4
.. currentmodule:: nifty
5
=============
Code Overview
...
...
@@ 35,7 +35,7 @@ Domains
Abstract base class

One of the fundamental building blocks of the NIFTy
4
framework is the *domain*.
One of the fundamental building blocks of the NIFTy
5
framework is the *domain*.
Its required capabilities are expressed by the abstract :class:`Domain` class.
A domain must be able to answer the following queries:
...
...
@@ 151,7 +151,7 @@ performance benefits.
Linear Operators
================
A linear operator (represented by NIFTy
4
's abstract :class:`LinearOperator`
A linear operator (represented by NIFTy
5
's abstract :class:`LinearOperator`
class) can be interpreted as an (implicitly defined) matrix.
It can be applied to :class:`Field` instances, resulting in other :class:`Field`
instances that potentially live on other domains.
...
...
@@ 245,7 +245,7 @@ highdimensional functions, which are often nonlinear.
Energy functionals

In NIFTy
4
such functions are represented by objects of type :class:`Energy`.
In NIFTy
5
such functions are represented by objects of type :class:`Energy`.
These hold the prescription how to calculate the function's
:attr:`~Energy.value`, :attr:`~Energy.gradient` and
(optionally) :attr:`~Energy.curvature` at any given :attr:`~Energy.position`
...
...
@@ 256,9 +256,9 @@ linear operator objects.
Energies are classes that typically have to be provided by the user when
tackling new IFT problems.
Some examples of concrete energy classes delivered with NIFTy
4
are
Some examples of concrete energy classes delivered with NIFTy
5
are
:class:`QuadraticEnergy` (with positionindependent curvature, mainly used with
conjugate gradient minimization) and :class:`~nifty
4
.library.WienerFilterEnergy`.
conjugate gradient minimization) and :class:`~nifty
5
.library.WienerFilterEnergy`.
Iteration control
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...
@@ 269,7 +269,7 @@ checking the quality of the current solution estimate and stopping once
it is sufficiently accurate. In case of numerical problems, the iteration needs
to be terminated as well, returning a suitable error description.
In NIFTy
4
, this functionality is encapsulated in the abstract
In NIFTy
5
, this functionality is encapsulated in the abstract
:class:`IterationController` class, which is provided with the initial energy
object before starting the minimization, and is updated with the improved
energy after every iteration. Based on this information, it can either continue
...
...
docs/source/conf.py
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58d4b593
...
...
@@ 73,7 +73,7 @@ source_suffix = '.rst'
master_doc
=
'index'
# General information about the project.
project
=
u
'NIFTy
4
'
project
=
u
'NIFTy
5
'
copyright
=
u
'20132018, MaxPlanckSociety'
author
=
u
'Theo Steininger / Martin Reinecke'
...
...
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