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NIFTy - Numerical Information Field Theory
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==========================================
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[![build status](https://gitlab.mpcdf.mpg.de/ift/NIFTy/badges/NIFTy_4/build.svg)](https://gitlab.mpcdf.mpg.de/ift/NIFTy/commits/NIFTy_4)
[![coverage report](https://gitlab.mpcdf.mpg.de/ift/NIFTy/badges/NIFTy_4/coverage.svg)](https://gitlab.mpcdf.mpg.de/ift/NIFTy/commits/NIFTy_4)
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**NIFTy** project homepage:
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[http://ift.pages.mpcdf.de/NIFTy](http://ift.pages.mpcdf.de/NIFTy)
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Summary
-------

### Description

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**NIFTy**, "**N**umerical **I**nformation **F**ield **T**heor<strong>y</strong>", is
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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
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Python, although it accesses libraries written in C++ and C for
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efficiency.

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NIFTy offers a toolkit that abstracts discretized representations of
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continuous spaces, fields in these spaces, and operators acting on
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fields into classes. The correct normalization of operations on
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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.
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Thus, NIFTy permits its user to rapidly prototype algorithms in 1D, and
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then apply the developed code in higher-dimensional settings of real
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world problems. The set of spaces on which NIFTy operates comprises
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point sets, *n*-dimensional regular grids, spherical spaces, their
harmonic counterparts, and product spaces constructed as combinations of
those.


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

### Requirements

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- [Python](https://www.python.org/) (v2.7.x or 3.5.x)
- [NumPy](https://www.numpy.org/)
- [pyFFTW](https://pypi.python.org/pypi/pyFFTW)
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Optional dependencies:
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- [pyHealpix](https://gitlab.mpcdf.mpg.de/ift/pyHealpix) (for harmonic
    transforms involving domains on the sphere)
- [mpi4py](https://mpi4py.scipy.org) (for MPI-parallel execution)
- [matplotlib](https://matplotlib.org/) (for field plotting)
- [SciPy](https://www.scipy.org/) (for additional minimization algorithms)
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### Sources
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The current version of Nifty4 can be obtained by cloning the repository and
switching to the NIFTy_4 branch:
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    git clone https://gitlab.mpcdf.mpg.de/ift/NIFTy.git

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### Installation
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In the following, we assume a Debian-based distribution. For other
distributions, the "apt" lines will need slight changes.
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NIFTy4 and its mandatory dependencies can be installed via:

    sudo apt-get install git libfftw3-dev python python-pip python-dev
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    pip install --user git+https://gitlab.mpcdf.mpg.de/ift/NIFTy.git@NIFTy_4

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(Note: If you encounter problems related to `pyFFTW`, make sure that you are
using a pip-installed `pyFFTW` package. Unfortunately, some distributions are
shipping an incorrectly configured `pyFFTW` package, which does not cooperate
with the installed `FFTW3` libraries.)

Plotting support is added via:

    pip install --user matplotlib

Support for spherical harmonic transforms is added via:
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    pip install --user git+https://gitlab.mpcdf.mpg.de/ift/pyHealpix.git
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MPI support is added via:

    sudo apt-get install openmpi-bin libopenmpi-dev
    pip install --user mpi4py

Scipy-based minimizers are enabled via:

    pip install --user scipy

### Installation for Python 3

If you want to run NIFTy with Python 3, you need to make the following changes
to the instructions above:

- in all `apt-get` commands, replace `python-*` by `python3-*`
- in all `pip` commands, replace `pip` by `pip3`

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### Running the tests

In oder to run the tests one needs two additional packages:

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    pip install --user nose parameterized coverage
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Afterwards the tests (including a coverage report) can be run using the
following command in the repository root:
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    nosetests -x --with-coverage --cover-html --cover-package=nifty4
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### First Steps

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For a quick start, you can browse through the [informal
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introduction](http://ift.pages.mpcdf.de/NIFTy/code.html) or
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dive into NIFTy by running one of the demonstrations, e.g.:
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    python demos/wiener_filter_via_curvature.py
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Acknowledgement
---------------

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Please acknowledge the use of NIFTy in your publication(s) by using a
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phrase such as the following:

> *"Some of the results in this publication have been derived using the
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> NIFTy package [Steininger et al., 2017]."[1]
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Or one of the following BibTeX entries:
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    @software{nifty,
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      author = {{Martin Reinecke, Theo Steininger, Marco Selig}},
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      title = {NIFTy -- Numerical Information Field TheorY},
      url = {https://gitlab.mpcdf.mpg.de/ift/NIFTy},
      version = {nifty4},
      date = {2018-04-05},
    }
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    @ARTICLE{2013A&A...554A..26S,
      author = {{Selig}, M. and {Bell}, M.~R. and {Junklewitz}, H. and {Oppermann}, N. and {Reinecke}, M. and {Greiner}, M. and {Pachajoa}, C. and {En{\ss}lin}, T.~A.},
      title = "{NIFTY - Numerical Information Field Theory. A versatile PYTHON library for signal inference}",
      journal = {\aap},
      archivePrefix = "arXiv",
      eprint = {1301.4499},
      primaryClass = "astro-ph.IM",
      keywords = {methods: data analysis, methods: numerical, methods: statistical, techniques: image processing},
      year = 2013,
      month = jun,
      volume = 554,
      eid = {A26},
      pages = {A26},
      doi = {10.1051/0004-6361/201321236},
      adsurl = {http://cdsads.u-strasbg.fr/abs/2013A%26A...554A..26S},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
    }
    
    @ARTICLE{2017arXiv170801073S,
      author = {{Steininger}, T. and {Dixit}, J. and {Frank}, P. and {Greiner}, M. and {Hutschenreuter}, S. and {Knollm{\"u}ller}, J. and {Leike}, R. and {Porqueres}, N. and {Pumpe}, D. and {Reinecke}, M. and {{\v S}raml}, M. and {Varady}, C. and {En{\ss}lin}, T.},
      title = "{NIFTy 3 - Numerical Information Field Theory - A Python framework for multicomponent signal inference on HPC clusters}",
      journal = {ArXiv e-prints},
      archivePrefix = "arXiv",
      eprint = {1708.01073},
      primaryClass = "astro-ph.IM",
      keywords = {Astrophysics - Instrumentation and Methods for Astrophysics},
      year = 2017,
      month = aug,
      adsurl = {http://cdsads.u-strasbg.fr/abs/2017arXiv170801073S},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
    }
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### References

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

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The NIFTy package is licensed under the terms of the
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[GPLv3](https://www.gnu.org/licenses/gpl.html) and is distributed
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*without any warranty*.

* * * * *

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[1] Steininger et al., "NIFTy 3 - Numerical Information Field Theory - A Python framework for multicomponent signal inference on HPC clusters", 2017, submitted to PLOS One;
[arXiv:1708.01073](https://arxiv.org/abs/1708.01073)