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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_5/build.svg)](https://gitlab.mpcdf.mpg.de/ift/NIFTy/commits/NIFTy_5)
[![coverage report](https://gitlab.mpcdf.mpg.de/ift/NIFTy/badges/NIFTy_5/coverage.svg)](https://gitlab.mpcdf.mpg.de/ift/NIFTy/commits/NIFTy_5)
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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
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### 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 3](https://www.python.org/) (3.5.x or later)
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- [SciPy](https://www.scipy.org/)
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Optional dependencies:
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- [pyFFTW](https://pypi.python.org/pypi/pyFFTW) for faster Fourier transforms
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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)
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### Sources
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The current version of Nifty5 can be obtained by cloning the repository and
switching to the NIFTy_5 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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NIFTy5 and its mandatory dependencies can be installed via:
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    sudo apt-get install git python3 python3-pip python3-dev
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    pip3 install --user git+https://gitlab.mpcdf.mpg.de/ift/NIFTy.git@NIFTy_5
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Plotting support is added via:

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    sudo apt-get install python3-matplotlib
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NIFTy uses Numpy's FFT implementation by default. For large problems FFTW may be
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used because of its higher performance. It can be installed via:
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    sudo apt-get install libfftw3-dev
    pip3 install --user pyfftw

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To enable FFTW usage in NIFTy, call
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    nifty5.fft.enable_fftw()
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at the beginning of your code.

(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.)

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Support for spherical harmonic transforms is added via:
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    pip3 install --user git+https://gitlab.mpcdf.mpg.de/ift/pyHealpix.git
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MPI support is added via:

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    sudo apt-get install python3-mpi4py
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### Running the tests

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To run the tests, additional packages are required:

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    sudo apt-get install python3-pytest-cov
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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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    pytest-3 --cov=nifty5 test
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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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    python3 demos/getting_started_1.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:

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> "Some of the results in this publication have been derived using the
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> NIFTy package [(https://gitlab.mpcdf.mpg.de/ift/NIFTy)](https://gitlab.mpcdf.mpg.de/ift/NIFTy)"

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and a citation to one of the [publications](http://ift.pages.mpcdf.de/NIFTy/citations.html).
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### 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*.