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        "author": "Alexandre Boucaud",
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            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Topic :: Scientific/Engineering :: Astronomy"
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        "description": "====================================================\nPyPHER - Python-based PSF Homogenization kERnels\n====================================================\n\n|pypi| |docs| |license| |doi| |travis|\n\nCompute an homogenization kernel between two PSFs.\n\nThis code is well suited for PSF matching applications in both an astronomical or microscopy context.\n\nIt has been developed as part of the ESA `Euclid <http://www.cosmos.esa.int/web/euclid>`_ mission and is currently being used for multi-band photometric studies of `HST <https://www.spacetelescope.org/>`_ (visible) and `Herschel <http://www.cosmos.esa.int/web/herschel/home>`_ (IR) data.\n\n:Paper: http://arxiv.org/abs/1609.02006\n:Documentation: https://pypher.readthedocs.io\n\nFeatures\n========\n\n1. **Warp** (rotation + resampling) the PSF images (if necessary),\n2. **Filter** images in Fourier space using a regularized Wiener filter,\n3. **Produce** a homogenization kernel.\n\n**Note:** ``pypher`` needs the pixel scale information to be present in the FITS files. If not, use the provided ``addpixscl`` method to add this missing info.\n\n**Warning:** This code **does not**\n\n    * interpolate NaN values (replaced by 0 instead),\n    * center PSF images,\n    * minimize the kernel size.\n\n\nInstallation\n============\n\nPyPHER works both with Python 2.7 and 3.4 or later and relies on `numpy <http://www.numpy.org/>`_, `scipy <http://www.scipy.org/>`_ and `astropy <http://www.astropy.org/>`_ libraries.\n\nOption 1: `Pip <https://pypi.python.org/pypi/pypher>`_\n------------------------------------------------------\n\n.. code:: bash\n\n    $ pip install pypher\n\nOption 2: from `source <https://git.ias.u-psud.fr/aboucaud/pypher>`_\n--------------------------------------------------------------------\n\n.. code:: bash\n\n    $ git clone https://git.ias.u-psud.fr/aboucaud/pypher.git\n    $ cd pypher\n    $ python setup.py install\n\n\nBasic example\n=============\n\n.. code:: bash\n\n    $ pypher psf_a.fits psf_b.fits kernel_a_to_b.fits -r 1.e-5\n\nThis will create the desired kernel ``kernel_a_to_b.fits`` and a short\nlog ``kernel_a_to_b.log`` with information about the processing.\n\n\nAcknowledging\n=============\n\nIf you make use of any product of this code in a scientific publication,\nplease consider acknowledging the work by citing the paper |arxiv| as\nwell as the code itself |doi|.\n\n\n.. |pypi| image:: https://img.shields.io/pypi/v/pypher.svg\n    :alt: Latest Version\n    :scale: 100%\n    :target: https://pypi.python.org/pypi/pypher\n\n.. |docs| image:: https://readthedocs.org/projects/pypher/badge/?version=latest\n    :alt: Documentation Status\n    :scale: 100%\n    :target: https://pypher.readthedocs.org/en/latest/?badge=latest\n\n.. |license| image:: https://img.shields.io/badge/license-BSD-blue.svg?style=flat\n    :alt: License type\n    :scale: 100%\n    :target: https://git.ias.u-psud.fr/aboucaud/pypher/blob/master/LICENSE\n\n.. |doi| image:: https://zenodo.org/badge/21241/aboucaud/pypher.svg\n    :alt: DOI number\n    :scale: 100%\n    :target: https://zenodo.org/badge/latestdoi/21241/aboucaud/pypher\n\n.. |arxiv| image:: http://img.shields.io/badge/arXiv-1609.02006-yellow.svg?style=flat\n     :alt: arXiv paper\n     :scale: 100%\n     :target: https://arxiv.org/abs/1609.02006\n\n.. |travis| image:: https://travis-ci.org/aboucaud/pypher.svg?branch=master\n    :alt: Travis CI\n    :scale: 100%\n    :target: https://travis-ci.org/aboucaud/pypher",
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