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    "info": {
        "author": "Simon Walker",
        "author_email": "s.r.walker101@googlemail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5"
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        "description": "===============================\nfitsiochunked\n===============================\n\n.. image:: https://img.shields.io/travis/mindriot101/fitsiochunked.svg\n        :target: https://travis-ci.org/mindriot101/fitsiochunked\n.. image:: https://codecov.io/gh/mindriot101/fitsiochunked/branch/master/graph/badge.svg\n  :target: https://codecov.io/gh/mindriot101/fitsiochunked\n\nPackage to *sequentially* efficiently read large fits arrays in object by object\n\n* Free software: MIT license\n\nFeatures\n--------\n\n* *Sequentially* read in large fits files, within a given fixed memory limit\n\nQuick usage\n-----------\n\nThe following example shows an example of reading in a large fits\nhdu within a memory limit of 2048MB, assuming light curves are\nstored in rows:\n\n.. code:: python\n\n  import numpy as np\n  import fitsio\n  import fitsiochunked as fc\n\n  with fitsio.FITS(filename) as infile:\n      hdu = infile['flux']\n      napertures = hdu.get_info()['ndim'][0]\n      mean_flux = np.zeros(napertures)\n\n      for chunk in fc.chunks(hdu, memory_limit_mb=2048):\n\n          # `chunk` is a namedtuple with `.data` and `.slice` properties\n          chunk_data = chunk.data\n          print('Data shape:', chunk_data.shape)\n          print('Data dtype:', chunk_data.dtype)\n\n\n          chunk_slice = chunk.slice\n          print('Chunk starting from aperture:', chunk_slice.start)\n          print('Chunk up to:', chunk_slice.stop)\n\n          chunk_mean = np.average(chunk_data, axis=1)\n          mean_flux[chunk_slice] = chunk_mean\n\nThe library copes with an aribtrary number of hdus:\n\n.. code:: python\n\n  import numpy as np\n  import fitsio\n  import fitsiochunked as fc\n\n  with fitsio.FITS(filename) as infile:\n      hjd_hdu = infile['hjd']\n      flux_hdu = infile['flux']\n      fluxerr_hdu = infile['fluxerr']\n\n      napertures = flux_hdu.get_info()['ndim'][0]\n      mean_flux = np.zeros(napertures)\n\n      for chunks in fc.chunks(hjd_hdu, flux_hdu, fluxerr_hdu, memory_limit_mb=2048):\n          # chunks is a tuple of chunks\n          hjd_chunk, flux_chunk, fluxerr_chunk = chunks\n\n          # `chunk` is a namedtuple with `.data` and `.slice` properties\n          flux_chunk_data = flux_chunk.data\n          print('Data shape:', flux_chunk_data.shape)\n          print('Data dtype:', flux_chunk_data.dtype)\n\n          # and so on\n\nNote: if multiple hdus are supplied, then the ``memory_limit_mb`` and\n``chunksize`` arguments to ``chunks`` apply to **each** HDU i.e. three HDUs and\na memory limit of 2048MB will lead to 3x2048 = 6144MB of memory used.\n\nInstallation\n------------\n\nInstall with ``pip``:\n\n.. code:: bash\n\n    pip install fitsiochunked\n    # or get the latest development version from github\n    pip install git+https://github.com/mindriot101/fitsiochunked\n\nor download and run the setup file:\n\n.. code:: bash\n\n    git clone https://github.com/mindriot101/fitsiochunked\n    cd fitsiochunked\n    python setup.py install\n\nDetails\n-------\n\nThe high level interface is the ``chunks`` function, which builds a\n``ChunkedAdapter`` object wrapping a ``fitsio.ImageHDU`` object.\n\nThe ``ChunkedAdapter`` wraps a ``fitsio`` HDU object. When constructed,\nit becomes a callable which yields the image data in that hdu in chunks.\n\nThe chunksize can be set either with with the parameter\n``chunksize`` which simply yields ``chunksize`` rows each time,\nor with ``memory_limit_mb`` which *tries* (no promises!) to\nautomatically calculate the number of lightcurves that will fit into\n``memory_limit_mb`` megabytes of memory.",
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