{
    "info": {
        "author": "Ryan Hausen & Brant Robertson",
        "author_email": "rhausen@ucsc.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: POSIX :: Linux",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: PyPy",
            "Topic :: Scientific/Engineering"
        ],
        "description": "\n.. Variables to use the correct hyperlinks in the readmertd build\n.. |classifier| replace:: `morpheus.classifier.Classifier <https://morpheus-astro.readthedocs.io/en/latest/source/morpheus.html#morpheus.classifier.Classifier>`__\n.. |classify| replace:: `classify <https://morpheus-astro.readthedocs.io/en/latest/source/morpheus.html#morpheus.classifier.Classifier.classify>`__\n.. |segmap| replace:: `segmap <https://morpheus-astro.readthedocs.io/en/latest/source/morpheus.html#morpheus.classifier.Classifier.segmap_from_classifed>`__\n.. |catalog| replace:: `catalog <https://morpheus-astro.readthedocs.io/en/latest/source/morpheus.html#morpheus.classifier.Classifier.catalog_from_classifed>`__\n.. |colorize| replace:: `colorize <https://morpheus-astro.readthedocs.io/en/latest/source/morpheus.html#morpheus.classifier.Classifier.colorize_classifed>`__\n\nMorpheus\n========\n\n.. image:: https://travis-ci.com/morpheus-project/morpheus.svg?branch=master\n    :target: https://travis-ci.com/morpheus-project/morpheus\n\n.. image:: https://codecov.io/gh/morpheus-project/morpheus/branch/master/graph/badge.svg\n    :target: https://codecov.io/gh/morpheus-project/morpheus\n\n.. image:: https://img.shields.io/badge/code%20style-black-000000.svg\n    :target: https://github.com/ambv/black\n\n.. image:: https://img.shields.io/badge/python-3.6-blue.svg\n    :target: https://www.python.org/downloads/release/python-360/\n\n.. image:: https://readthedocs.org/projects/morpheus-astro/badge/?version=latest\n    :target: https://morpheus-astro.readthedocs.io\n\nMorpheus is a neural network model used to generate pixel level morphological\nclassifications for astronomical sources. This model can be used to generate\nsegmentation maps or to inform other photometric measurements with granular\nmorphological information.\n\nInstallation\n============\n\nMorpheus is implemented using `TensorFlow <https://www.tensorflow.org/>`_.\nTensorFlow is **not** listed in the dependencies for the package. So you need\nto install TensorFlow before you install Morpheus. It has to be done this way\nto support the GPU accelerated version of TensorFlow, which has a different\npackage name. For more information on installing TensorFlow visit the\n`TensorFlow website <https://www.tensorflow.org/install/>`_.\n\n\n.. code-block:: bash\n\n    pip install morpheus-astro\n\nDocker\n======\n\nMorpheus has two main flavors of Docker Image: ``gpu`` for the GPU enabled version\nof TensorFlow and ``cpu`` for the standard CPU implementation of TensorFlow.\nVisit the `Docker Hub <https://hub.docker.com/r/morpheusastro/morpheus>`_ page\nfor relevant tags.\n\nFor GPU support:\n\n.. code-block:: bash\n\n    docker run --runtime=nvidia -it morpheusastro/morpheus:0.4-gpu\n\nFor CPU only:\n\n.. code-block:: bash\n\n    docker run -it morpheusastro/morpheus:0.4-cpu\n\n\nUsage\n=====\n\nThere are two ways to use morpheus on your own images: the python API and the\ncommand line interface\n\nPython API\n----------\n\nThe |classifier| class is the interface to the various functionalities of\nMorpheus. The primary function and requirement before any other action\ncan be performed is to morphologically classify the pixels in an image using\n|classify|.\n\nMorphological classification\n****************************\n\nTo perform a pixel-level morphological classification the image needs to be\nprovided in the H, J, Z, and V bands. See |classify| for more information.\n\n.. code-block:: python\n\n    from morpheus.classifier import Classifier\n    from morpheus.data import example\n\n    h, j, v, z = example.get_sample()\n    classified = Classifier.classify(h=h, j=j, v=v, z=z)\n\nThe classify function returns a dictionary where the keys indicate the output\nfor example ``spheroid``, and the value is the corresponding numpy ndarray.\n\nUsing the output from |classify| you can:\n\n-   Make a segmap\n\n-   Make a morphgological catalog\n\n-   Make colorized version of the morphological classifications\n\nSegmentation Map\n****************\n\nTo create a segmentation map using Morphues, you need to provide the output\nfrom the |classify| function and a single flux band. In the below example we\nuse H. For more information see |segmap|\n\n.. code-block:: python\n\n    from morpheus.classifier import Classifier\n    from morpheus.data import example\n\n    h, j, v, z = example.get_sample()\n    classified = Classifier.classify(h=h, j=j, v=v, z=z)\n    segmap = Classifier.segmap_from_classified(classified, h)\n\nCatalog\n*******\n\nTo crete a catalog using Morpheus, you need to provide the output from the\n|classify| function, the flux in a single band (we use H), and a segmentation\nmap. The segmentation map doesn't have to be generated by Morpheus, but it\nmust be similar in form. It should assign pixels values greater than 0 for all\npixels that are associated with a source. Each source should be assigned a\nunique ID. Background should be set to 0 and excluded regions should be\nassigned -1. The catalog returned is a JSON compatible list of morphological\nclassifications for each source in the segmap. For more information see\n|catalog|.\n\n.. code-block:: python\n\n    from morpheus.classifier import Classifier\n    from morpheus.data import example\n\n    h, j, v, z = example.get_sample()\n    classified = Classifier.classify(h=h, j=j, v=v, z=z)\n    segmap = Classifier.segmap_from_classified(classified, h)\n    catalog = Classifier.catalog_from_classified(classified, h, segmap)\n\nColorized Classifications\n*************************\n\nA colorized classification is a way of making a single image to interpret the\npixel level morphological classifications. For more information see |colorize|.\n\n.. code-block:: python\n\n    from morpheus.classifier import Classifier\n    from morpheus.data import example\n\n    h, j, v, z = example.get_sample()\n    classified = Classifier.classify(h=h, j=j, v=v, z=z)\n    color_rgb = Classifier.colorize_classified(classified)\n\nParallezation\n*************\n\nMorpheus supports simple parallezation by breaking an image into equally sized\npieces along the y axis, classifying them in seperate processes, and stitching\nthem back into a single image. Parallezation can be split into CPU jobs or\nGPU jobs. Importantly, you cannot specify both at the same time.\n\n**GPUS**\n\nThe ``gpus`` argument should be a list of integers that are the ids assigned to\nthe GPUS to be used. These ids can be found by using ``nvidia-smi``.\n\n.. code-block:: python\n\n    from morpheus.classifier import Classifier\n    from morpheus.data import example\n\n    h, j, v, z = example.get_sample()\n\n    classified = Classifier.classify(h=h, j=j, v=v, z=z, gpus=[0,1])\n\n**CPUS**\n\nThe ``cpus`` argument should be an integer indicating how many processes to\nspin off.\n\n.. code-block:: python\n\n    from morpheus.classifier import Classifier\n    from morpheus.data import example\n\n    h, j, v, z = example.get_sample()\n\n    classified = Classifier.classify(h=h, j=j, v=v, z=z, cpus=2)\n\n\nCommand Line Interface\n----------------------\n\nMorpheus can be used from the terminal using the ``morpheus`` command. To\nclassify an image, it needs to be available in the H, J, V, and Z bands. From\nthe terminal the following actions can be performed:\n\n-   Per pixel morphological classification\n-   Make segmentation map\n-   Make a catalog of morphological classifications\n-   Make a colorized version of the morhological classifications\n\n\nMorphological classification\n****************************\n\n.. code-block:: bash\n\n    morpheus h.fits j.fits v.fits z.fits\n\nOrder is important when calling the Morpheus from the terminal. They files\nshould be in the order H, J, V, and Z, as displayed in the above example. The\nouput classification will be saved in the current working directory unless\notherwise indicated by the ``--out_dir`` optional argument.\n\nSegmentation Map\n****************\n\n.. code-block:: bash\n\n    morpheus h.fits j.fits v.fits z.fits --action segmap\n\nTo create a segmap, append the optional ``--action`` flag with the argument\n``segmap``. This will save both the classificaitons and the segmap to the\nsame directory.\n\nCatalog\n*******\n\n.. code-block:: bash\n\n    morpheus h.fits j.fits v.fits z.fits --action catalog\n\nThis will create a catalog by classifying the input images, creating a\nsegmap, and the using both of those to generate a morphological catalog. The\nmorphological classificaitons, segmap, and catalog are all saved to the same\nplace.\n\nColorized Classifications\n*************************\n\n.. code-block:: bash\n\n    morpheus h.fits j.fits v.fits z.fits --action colorize\n\nUsing ``--action colorize`` will classify the image and then generate a\ncolorized verision of that classification and save the classification and\ncolorized version to the same place.\n\nParallezation\n*************\n\nMorpheus supports simple parallezation by breaking an image into equally sized\npieces along the y axis, classifying them in seperate processes, and stitching\nthem back into a single image. Parallezation can be split into CPU jobs or\nGPU jobs. Importantly, you cannot specify both at the same time.\n\n**GPUS**\n\nThe ``gpus`` optional flag should be a comma-seperated list of ids for the\nGPUS to be used. These ids can be found by using ``nvidia-smi``.\n\n.. code-block:: bash\n\n    morpheus h.fits j.fits v.fits z.fits --gpus 0,1\n\n**CPUS**\n\nThe ``cpus`` optional flag should be an integer indicating how many processes\nto spin off.\n\n.. code-block:: bash\n\n    morpheus h.fits j.fits v.fits z.fits --cpus 2\n\n\nPython Demo\n=============\n\nTry it out on `Google Colab <https://colab.research.google.com/github/morpheus-project/morpheus/blob/master/examples/example_array.ipynb>`_!\n\nDocumentation\n=============\n\nhttps://morpheus-astro.readthedocs.io/\n\n\n",
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        "license": "MIT",
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        "name": "morpheus-astro",
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