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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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[https://www.mpa-garching.mpg.de/ift/nifty/](https://www.mpa-garching.mpg.de/ift/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.

### Class & Feature Overview

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The NIFTy library features three main classes: **Space**s that represent
certain grids, **Field**s that are defined on spaces, and **LinearOperator**s
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that apply to fields.

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-   [Spaces](https://www.mpa-garching.mpg.de/ift/nifty/space.html)
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    -   `RGSpace` - *n*-dimensional regular Euclidean grid
    -   `LMSpace` - spherical harmonics
    -   `GLSpace` - Gauss-Legendre grid on the 2-sphere
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    -   `HPSpace` - [HEALPix](https://sourceforge.net/projects/healpix/)
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        grid on the 2-sphere
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-   [Fields](https://www.mpa-garching.mpg.de/ift/nifty/field.html)
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    -   `Field` - generic class for (discretized) fields
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<!-- -->

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    Field.conjugate     Field.dim          Field.norm
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    Field.vdot          Field.weight
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-   [Operators](https://www.mpa-garching.mpg.de/ift/nifty/operator.html)
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    -   `DiagonalOperator` - purely diagonal matrices in a specified
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        basis
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    -   `FFTOperator` - conversion between spaces and their harmonic
                        counterparts
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    -   (and more)
-   (and more)

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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    git checkout NIFTy_4
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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! Some distributions are unfortunately shipping
an incorrectly configured pyFFTW package, which does not cooperate with the
installed FFTW3 libraries)
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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](https://www.mpa-garching.mpg.de/ift/nifty/start.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]."*
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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;
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[arXiv:1708.01073](https://arxiv.org/abs/1708.01073)