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Commit 9aa58afa authored by Jait Dixit's avatar Jait Dixit
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    .gitignore
    View file @ 9aa58afa
    ......@@ -15,3 +15,4 @@
    build
    .idea
    .coverage
    .gitlab-ci.yml
    View file @ 9aa58afa
    ......@@ -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
    ......
    README.rst → README.md
    View file @ 9aa58afa
    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)
    ci/requirements_base.txt
    View file @ 9aa58afa
    ......@@ -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
    nifty/__init__.py
    View file @ 9aa58afa
    ......@@ -55,7 +55,7 @@ from spaces import *
    from operators import *
    from probing import *
    #from probing import *
    from sugar import *
    ......
    nifty/operators/probing_operator/__init__.py
    View file @ 9aa58afa
    # -*- coding: utf-8 -*-
    from prober import Prober
    from diagonal_prober import *
    from trace_prober import *
    nifty/operators/probing_operator/diagonal_prober.py
    View file @ 9aa58afa
    # -*- 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
    nifty/operators/probing_operator/trace_prober.py
    View file @ 9aa58afa
    # -*- 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)
    nifty/spaces/space/space.py
    View file @ 9aa58afa
    ......@@ -150,8 +150,7 @@ from keepers import Loggable,\
    Versionable
    class Space(Versionable, Loggable, Plottable, object):
    class Space(Versionable, Loggable, object):
    """
    .. __ __
    .. /__/ / /_
    ......
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