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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/](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

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/)
        grid on the 2-sphere
    -   `nested_space` - arbitrary product of grids
-   [Fields](http://www.mpa-garching.mpg.de/ift/nifty/field.html)
    -   `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
        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
        representation
    -   (and more)
-   (and more)

*Parts of this summary are taken from* [1] *without marking them
explicitly as quotations.*

Installation
------------

### Requirements

-   [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**

### 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:

    git clone https://gitlab.mpcdf.mpg.de/ift/NIFTy.git

### 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`:

-   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:

        sudo apt-get install python-matplotlib

-   Using pip install numpy, scipy, etc...:

        sudo pip install numpy scipy cython pyfits healpy

-   Now install the 'non-standard' dependencies. First of all gfft:

        curl -LOk https://github.com/mrbell/gfft/tarball/master 
        tar -xzf master 
        cd mrbell-gfft* 
        sudo python setup.py install 
        cd ..

-   Libsharp:

        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 ..

-   Libsharpwrapper:

        git clone http://github.com/mselig/libsharp-wrapper.git libsharp-wrapper 
        cd libsharp-wrapper 
        sudo python setup.py build_ext 
        sudo python setup.py install 
        cd ..

-   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

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:

    python setup.py install --user

or:

    python setup.py install --install-lib=/SOMEWHERE

in the instruction above. This will install the python packages into
your local user directory.

### 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.

-   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

-   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:

        sudo port install py27-pyfits
        git clone https://github.com/healpy/healpy.git
        cd healpy 
        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/>:

        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** :

        sudo port install gcc
        sudo port select gcc  mp-gcc5
        git clone https://github.com/mselig/libsharp-wrapper.git
        cd libsharp-wrapper
        sudo python setup.py install
        cd ..

-   Install NIFTy:

        git clone https://gitlab.mpcdf.mpg.de/ift/NIFTy.git
        cd nifty
        sudo python setup.py install 
        cd .. 

### Installation using pypi

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:

    pip install --user ift_nifty

### 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.:

    >>> 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]."*

### References

Release Notes
-------------

The NIFTY package is licensed under the
[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/)

[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)