Commit c330e089 authored by Theo Steininger's avatar Theo Steininger
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Merge branch 'coverage' into 'master'

Enable coverage report

See merge request !41
parents cd593c77 9aa58afa
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......@@ -15,3 +15,4 @@
build
.idea
.coverage
......@@ -64,7 +64,11 @@ test_mpi_fftw_hdf5:
- ci/install_libsharp.sh
- ci/install_pyfftw.sh
- python setup.py build_ext --inplace
- nosetests -vv
- >
nosetests -vv --with-coverage --cover-package=nifty --cover-inclusive
--cover-branches
- >
coverage report | grep TOTAL | awk '{ print "TOTAL: "$6; }'
release_image_master:
image: docker:latest
......
NIFTY - Numerical Information Field Theory
==========================================
[![build status](https://gitlab.mpcdf.mpg.de/ift/NIFTy/badges/master/build.svg)](https://gitlab.mpcdf.mpg.de/ift/NIFTy/commits/master)
[![coverage report](https://gitlab.mpcdf.mpg.de/ift/NIFTy/badges/master/coverage.svg)](https://gitlab.mpcdf.mpg.de/ift/NIFTy/commits/master)
**NIFTY** project homepage: `<http://www.mpa-garching.mpg.de/ift/nifty/>`_
**NIFTY** project homepage:
[http://www.mpa-garching.mpg.de/ift/nifty/](http://www.mpa-garching.mpg.de/ift/nifty/)
Summary
-------
Description
...........
**NIFTY**, "\ **N**\umerical **I**\nformation **F**\ield **T**\heor\ **y**\ ",
is a versatile library designed to enable the development of signal inference
algorithms that operate regardless of the underlying spatial grid and its
resolution. Its object-oriented framework is written in Python, although it
accesses libraries written in Cython, C++, and C for efficiency.
NIFTY offers a toolkit that abstracts discretized representations of continuous
spaces, fields in these spaces, and operators acting on fields into classes.
Thereby, the correct normalization of operations on fields is 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
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 constructed as
combinations of those.
Class & Feature Overview
........................
### Description
**NIFTY**, "**N**umerical **I**nformation **F**ield **T**heor**y**", is
a versatile library designed to enable the development of signal
inference algorithms that operate regardless of the underlying spatial
grid and its resolution. Its object-oriented framework is written in
Python, although it accesses libraries written in Cython, C++, and C for
efficiency.
NIFTY offers a toolkit that abstracts discretized representations of
continuous spaces, fields in these spaces, and operators acting on
fields into classes. Thereby, the correct normalization of operations on
fields is 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 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 constructed as combinations of
those.
### Class & Feature Overview
The NIFTY library features three main classes: **spaces** that represent
certain grids, **fields** that are defined on spaces, and **operators** that
apply to fields.
* `Spaces <http://www.mpa-garching.mpg.de/ift/nifty/space.html>`_
* ``point_space`` - unstructured list of points
* ``rg_space`` - *n*-dimensional regular Euclidean grid
* ``lm_space`` - spherical harmonics
* ``gl_space`` - Gauss-Legendre grid on the 2-sphere
* ``hp_space`` - `HEALPix <http://sourceforge.net/projects/healpix/>`_
certain grids, **fields** that are defined on spaces, and **operators**
that apply to fields.
- [Spaces](http://www.mpa-garching.mpg.de/ift/nifty/space.html)
- `point_space` - unstructured list of points
- `rg_space` - *n*-dimensional regular Euclidean grid
- `lm_space` - spherical harmonics
- `gl_space` - Gauss-Legendre grid on the 2-sphere
- `hp_space` - [HEALPix](http://sourceforge.net/projects/healpix/)
grid on the 2-sphere
* ``nested_space`` - arbitrary product of grids
* `Fields <http://www.mpa-garching.mpg.de/ift/nifty/field.html>`_
- `nested_space` - arbitrary product of grids
- [Fields](http://www.mpa-garching.mpg.de/ift/nifty/field.html)
- `field` - generic class for (discretized) fields
* ``field`` - generic class for (discretized) fields
::
<!-- -->
field.cast_domain field.hat field.power field.smooth
field.conjugate field.inverse_hat field.pseudo_dot field.tensor_dot
field.dim field.norm field.set_target field.transform
field.dot field.plot field.set_val field.weight
* `Operators <http://www.mpa-garching.mpg.de/ift/nifty/operator.html>`_
* ``diagonal_operator`` - purely diagonal matrices in a specified basis
* ``projection_operator`` - projections onto subsets of a specified basis
* ``vecvec_operator`` - matrices derived from the outer product of a
- [Operators](http://www.mpa-garching.mpg.de/ift/nifty/operator.html)
- `diagonal_operator` - purely diagonal matrices in a specified
basis
- `projection_operator` - projections onto subsets of a specified
basis
- `vecvec_operator` - matrices derived from the outer product of a
vector
* ``response_operator`` - exemplary responses that include a convolution,
masking and projection
* ``propagator_operator`` - information propagator in Wiener filter theory
* ``explicit_operator`` - linear operators with an explicit matrix
- `response_operator` - exemplary responses that include a
convolution, masking and projection
- `propagator_operator` - information propagator in Wiener filter
theory
- `explicit_operator` - linear operators with an explicit matrix
representation
* (and more)
- (and more)
- (and more)
* (and more)
*Parts of this summary are taken from* [1]_ *without marking them explicitly as
quotations.*
*Parts of this summary are taken from* [1] *without marking them
explicitly as quotations.*
Installation
------------
Requirements
............
* `Python <http://www.python.org/>`_ (v2.7.x)
### Requirements
* `NumPy <http://www.numpy.org/>`_
* `SciPy <http://www.scipy.org/>`_
* `Cython <http://cython.org/>`_
* `matplotlib <http://matplotlib.org/>`_
* `GFFT <https://github.com/mrbell/gfft>`_ (v0.1.0) - Generalized Fast
- [Python](http://www.python.org/) (v2.7.x)
- [NumPy](http://www.numpy.org/)
- [SciPy](http://www.scipy.org/)
- [Cython](http://cython.org/)
- [matplotlib](http://matplotlib.org/)
- [GFFT](https://github.com/mrbell/gfft) (v0.1.0) - Generalized Fast
Fourier Transformations for Python - **optional**
- [HEALPy](https://github.com/healpy/healpy) (v1.8.1 without openmp) -
A Python wrapper for
[HEALPix](http://sourceforge.net/projects/healpix/) -**optional,
only needed for spherical spaces**
- [libsharp-wrapper](https://github.com/mselig/libsharp-wrapper)
(v0.1.2 without openmp) - A Python wrapper for the
[libsharp](http://sourceforge.net/projects/libsharp/) library
-**optional, only needed for spherical spaces**
* `HEALPy <https://github.com/healpy/healpy>`_ (v1.8.1 without openmp) - A
Python wrapper for `HEALPix <http://sourceforge.net/projects/healpix/>`_ -
**optional, only needed for spherical spaces**
* `libsharp-wrapper <https://github.com/mselig/libsharp-wrapper>`_ (v0.1.2
without openmp) - A Python wrapper for the
`libsharp <http://sourceforge.net/projects/libsharp/>`_ library -
**optional, only needed for spherical spaces**
### Download
Download
........
The latest release is tagged **v1.0.7** and is available as a source package
at `<https://gitlab.mpcdf.mpg.de/ift/NIFTy/tags>`_. The current
version can be obtained by cloning the repository::
The latest release is tagged **v1.0.7** and is available as a source
package at [](https://gitlab.mpcdf.mpg.de/ift/NIFTy/tags). The current
version can be obtained by cloning the repository:
git clone https://gitlab.mpcdf.mpg.de/ift/NIFTy.git
Installation on Ubuntu
......................
### Installation on Ubuntu
This is for you if you want to install NIFTy on your personal computer
running with an Ubuntu-like linux system were you have root priviledges.
Starting with a fresh Ubuntu installation move to a folder like
`~/Downloads`:
This is for you if you want to install NIFTy on your personal computer running
with an Ubuntu-like linux system were you have root priviledges. Starting with
a fresh Ubuntu installation move to a folder like ``~/Downloads``:
- Install basic packages like python, python-dev, gsl and others:
* Install basic packages like python, python-dev, gsl and others::
sudo apt-get install curl git autoconf
sudo apt-get install python-dev python-pip gsl-bin libgsl0-dev libfreetype6-dev libpng-dev libatlas-base-dev gfortran
* Install matplotlib::
- Install matplotlib:
sudo apt-get install python-matplotlib
* Using pip install numpy, scipy, etc...::
- Using pip install numpy, scipy, etc...:
sudo pip install numpy scipy cython pyfits healpy
* Now install the 'non-standard' dependencies. First of all gfft::
- Now install the 'non-standard' dependencies. First of all gfft:
curl -LOk https://github.com/mrbell/gfft/tarball/master
tar -xzf master
......@@ -135,7 +131,7 @@ a fresh Ubuntu installation move to a folder like ``~/Downloads``:
sudo python setup.py install
cd ..
* Libsharp::
- Libsharp:
git clone http://git.code.sf.net/p/libsharp/code libsharp-code
cd libsharp-code
......@@ -144,7 +140,7 @@ a fresh Ubuntu installation move to a folder like ``~/Downloads``:
sudo make
cd ..
* Libsharpwrapper::
- Libsharpwrapper:
git clone http://github.com/mselig/libsharp-wrapper.git libsharp-wrapper
cd libsharp-wrapper
......@@ -152,50 +148,57 @@ a fresh Ubuntu installation move to a folder like ``~/Downloads``:
sudo python setup.py install
cd ..
* Finally, NIFTy::
- Finally, NIFTy:
git clone https://gitlab.mpcdf.mpg.de/ift/NIFTy.git
cd nifty
sudo python setup.py install
cd ..
Installation on a linux cluster
...............................
### Installation on a linux cluster
This is for you if you want to install NIFTy on a HPC machine or cluster that is hosted by your university or institute. Most of the dependencies will most likely already be there, but you won't have superuser priviledges. In this case, instead::
This is for you if you want to install NIFTy on a HPC machine or cluster
that is hosted by your university or institute. Most of the dependencies
will most likely already be there, but you won't have superuser
priviledges. In this case, instead:
sudo python setup.py install
use::
use:
python setup.py install --user
or::
or:
python setup.py install --install-lib=/SOMEWHERE
in the instruction above. This will install the python packages into
your local user directory.
in the instruction above. This will install the python packages into your local user directory.
### Installation on OS X 10.11
Installation on OS X 10.11
..........................
We advice to install the following packages in the order as they appear
below. We strongly recommend to install all needed packages via
MacPorts. Please be aware that not all packages are available on
MacPorts, missing ones need to be installed manually. It may also be
mentioned that one should only use one package manager, as multiple ones
may cause trouble.
We advice to install the following packages in the order as they appear below. We strongly recommend to install all needed packages via MacPorts. Please be aware that not all packages are available on MacPorts, missing ones need to be installed manually. It may also be mentioned that one should only use one package manager, as multiple ones may cause trouble.
- Install basic packages python, scipy, matplotlib and cython:
* Install basic packages python, scipy, matplotlib and cython::
sudo port install py27-numpy
sudo port install py27-scipy
sudo port install py27-matplotlib
sudo port install py27-cython
sudo port install py27-numpy
sudo port install py27-scipy
sudo port install py27-matplotlib
sudo port install py27-cython
* Install gfft. **Depending where you installed GSL you may need to change the path in setup.py!**::
- Install gfft. **Depending where you installed GSL you may need to
change the path in setup.py!**:
sudo port install gsl
git clone https://github.com/mrbell/gfft.git}{https://github.com/mrbell/gfft.git
sudo python setup.py install
* Install healpy::
- Install healpy:
sudo port install py27-pyfits
git clone https://github.com/healpy/healpy.git
......@@ -203,37 +206,37 @@ We advice to install the following packages in the order as they appear below. W
sudo python setup.py install
cd ..
* Install libsharp and therefore autoconf, automake and libtool.
Installations instructions for libsharp may be found here:
https://sourceforge.net/p/libsharp/code/ci/master/tree/::
- Install libsharp and therefore autoconf, automake and libtool.
Installations instructions for libsharp may be found here:
<https://sourceforge.net/p/libsharp/code/ci/master/tree/>:
curl -OL http://ftpmirror.gnu.org/autoconf/autoconf-2.69.tar.gz
tar -xzf autoconf-2.69.tar.gz
cd autoconf-2.69
./configure && make && sudo make install
cd ..
curl -OL http://ftpmirror.gnu.org/automake/automake-1.14.tar.gz
tar -xzf automake-1.14.tar.gz
cd automake-1.14
./configure && make && sudo make install
cd ..
curl -OL http://ftpmirror.gnu.org/libtool/libtool-2.4.2.tar.gz
tar -xzf libtool-2.4.2.tar.gz
cd libtool-2.4.2
./configure && make && sudo make install
cd ..
git clone http://git.code.sf.net/p/libsharp/code libsharp-code
cd libsharp-code
sudo autoconf
./configure --enable-pic --disable-openmp
sudo make
cd ..
* Install libsharp-wrapper.
**Adopt the path of the libsharp installation in setup.py** ::
- Install libsharp-wrapper. **Adopt the path of the libsharp
installation in setup.py** :
sudo port install gcc
sudo port select gcc mp-gcc5
......@@ -242,60 +245,56 @@ We advice to install the following packages in the order as they appear below. W
sudo python setup.py install
cd ..
* Install NIFTy::
- Install NIFTy:
git clone https://gitlab.mpcdf.mpg.de/ift/NIFTy.git
cd nifty
sudo python setup.py install
cd ..
Installation using pypi
.......................
### Installation using pypi
NIFTY can be installed using `PyPI <https://pypi.python.org/pypi>`_ and **pip**
by running the following command::
NIFTY can be installed using [PyPI](https://pypi.python.org/pypi) and
**pip** by running the following command:
pip install ift_nifty
Alternatively, a private or user specific installation can be done by::
Alternatively, a private or user specific installation can be done by:
pip install --user ift_nifty
### First Steps
First Steps
...........
For a quickstart, you can browse through the [informal
introduction](http://www.mpa-garching.mpg.de/ift/nifty/start.html) or
dive into NIFTY by running one of the demonstrations, e.g.:
For a quickstart, you can browse through the
`informal introduction <http://www.mpa-garching.mpg.de/ift/nifty/start.html>`_
or dive into NIFTY by running one of the demonstrations, e.g.::
>>> run -m nifty.demos.demo_wf1
>>> run -m nifty.demos.demo_wf1
Acknowledgement
---------------
Please, acknowledge the use of NIFTY in your publication(s) by using a phrase
such as the following:
*"Some of the results in this publication have been derived using the NIFTY
package [Selig et al., 2013]."*
Please, acknowledge the use of NIFTY in your publication(s) by using a
phrase such as the following:
References
..........
> *"Some of the results in this publication have been derived using the
> NIFTY package [Selig et al., 2013]."*
.. [1] Selig et al., "NIFTY - Numerical Information Field Theory - a
versatile Python library for signal inference",
`A&A, vol. 554, id. A26 <http://dx.doi.org/10.1051/0004-6361/201321236>`_,
2013; `arXiv:1301.4499 <http://www.arxiv.org/abs/1301.4499>`_
### References
Release Notes
-------------
The NIFTY package is licensed under the
`GPLv3 <http://www.gnu.org/licenses/gpl.html>`_ and is distributed *without any
warranty*.
[GPLv3](http://www.gnu.org/licenses/gpl.html) and is distributed
*without any warranty*.
----
* * * * *
**NIFTY** project homepage: `<http://www.mpa-garching.mpg.de/ift/nifty/>`_
**NIFTY** project homepage:
[](http://www.mpa-garching.mpg.de/ift/nifty/)
[1] Selig et al., "NIFTY - Numerical Information Field Theory - a
versatile Python library for signal inference", [A&A, vol. 554, id.
A26](http://dx.doi.org/10.1051/0004-6361/201321236), 2013;
[arXiv:1301.4499](http://www.arxiv.org/abs/1301.4499)
......@@ -3,6 +3,7 @@ scipy
cython
nose
nose-parameterized
coverage
git+https://gitlab.mpcdf.mpg.de/ift/mpi_dummy.git
git+https://gitlab.mpcdf.mpg.de/ift/keepers.git
git+https://gitlab.mpcdf.mpg.de/ift/D2O.git
......@@ -55,7 +55,7 @@ from spaces import *
from operators import *
from probing import *
#from probing import *
from sugar import *
......
# -*- coding: utf-8 -*-
from prober import Prober
from diagonal_prober import *
from trace_prober import *
# -*- coding: utf-8 -*-
from prober import Prober
class DiagonalProber(Prober):
# ---Mandatory properties and methods---
def finish_probe(self, probe, pre_result):
return probe[1].conjugate()*pre_result
# from prober import Prober
#
#
# class DiagonalProber(Prober):
#
# # ---Mandatory properties and methods---
# def finish_probe(self, probe, pre_result):
# return probe[1].conjugate()*pre_result
# -*- coding: utf-8 -*-
from prober import Prober
class TraceProber(Prober):
# ---Mandatory properties and methods---
def finish_probe(self, probe, pre_result):
return probe[1].conjugate().weight(power=-1).dot(pre_result)
# from prober import Prober
#
#
# class TraceProber(Prober):
#
# # ---Mandatory properties and methods---
# def finish_probe(self, probe, pre_result):
# return probe[1].conjugate().weight(power=-1).dot(pre_result)
......@@ -150,8 +150,7 @@ from keepers import Loggable,\
Versionable
class Space(Versionable, Loggable, Plottable, object):
class Space(Versionable, Loggable, object):
"""
.. __ __
.. /__/ / /_
......
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