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        "author": "Alistair Miles",
        "author_email": "alimanfoo@googlemail.com",
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        "classifiers": [
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "vcfnp\n=====\n\nLoad data from a VCF (variant call format) file into numpy arrays, and\n(optionally) from there into an HDF5 file.\n\nInstallation\n------------\n\nInstallation requires numpy and cython::\n\n    $ pip install cython\n    $ pip install numpy\n    $ pip install vcfnp\n\n...or::\n\n\t$ git clone --recursive git://github.com/alimanfoo/vcfnp.git\n\t$ cd vcfnp\n\t$ python setup.py build_ext --inplace\n\nUsage\n-----\n\nFor usage from Python, see the `IPython notebook example\n<http://nbviewer.ipython.org/github/alimanfoo/vcfnp/blob/master/example.ipynb>`_,\nor try::\n\n    >>> from __future__ import print_function, division\n    >>> import numpy as np\n    >>> import matplotlib\n    >>> matplotlib.use('TkAgg')\n    >>> import matplotlib.pyplot as plt\n    >>> import vcfnp\n    >>> vcfnp.__version__\n    '2.0.0'\n    >>> filename = 'fixture/sample.vcf'\n    >>> # load data from fixed fields (including INFO)\n    ... v = vcfnp.variants(filename, cache=True).view(np.recarray)\n    [vcfnp] 2015-01-23 11:10:46.670723 :: caching is enabled\n    [vcfnp] 2015-01-23 11:10:46.670830 :: cache file available\n    [vcfnp] 2015-01-23 11:10:46.670866 :: loading from cache file fixture/sample.vcf.vcfnp_cache/variants.npy\n    >>> # print some simple variant metrics\n    ... print('found %s variants (%s SNPs)' % (v.size, np.count_nonzero(v.is_snp)))\n    found 9 variants (5 SNPs)\n    >>> print('QUAL mean (std): %s (%s)' % (np.mean(v.QUAL), np.std(v.QUAL)))\n    QUAL mean (std): 25.0667 (22.816)\n    >>> # plot a histogram of variant depth\n    ... fig = plt.figure(1)\n    >>> ax = fig.add_subplot(111)\n    >>> ax.hist(v.DP)\n    (array([ 4.,  0.,  0.,  0.,  0.,  0.,  1.,  2.,  0.,  2.]), array([  0. ,   1.4,   2.8,   4.2,   5.6,   7. ,   8.4,   9.8,  11.2,\n            12.6,  14. ]), <a list of 10 Patch objects>)\n    >>> ax.set_title('DP histogram')\n    <matplotlib.text.Text object at 0x7f28f18f5c50>\n    >>> ax.set_xlabel('DP')\n    <matplotlib.text.Text object at 0x7f28f207c3c8>\n    >>> plt.show()\n    >>> # load data from sample columns\n    ... c = vcfnp.calldata_2d(filename, cache=True).view(np.recarray)\n    >>> # print some simple genotype metrics\n    ... count_phased = np.count_nonzero(c.is_phased)\n    >>> count_variant = np.count_nonzero(np.any(c.genotype > 0, axis=2))\n    >>> count_missing = np.count_nonzero(~c.is_called)\n    >>> print('calls (phased, variant, missing): %s (%s, %s, %s)'\n    ...     % (c.flatten().size, count_phased, count_variant, count_missing))\n    calls (phased, variant, missing): 27 (14, 12, 2)\n    >>> # plot a histogram of genotype quality\n    ... fig = plt.figure(2)\n    >>> ax = fig.add_subplot(111)\n    >>> ax.hist(c.GQ.flatten())\n    (array([ 15.,   0.,   1.,   1.,   0.,   1.,   2.,   4.,   2.,   1.]), array([  0. ,   6.1,  12.2,  18.3,  24.4,  30.5,  36.6,  42.7,  48.8,\n            54.9,  61. ]), <a list of 10 Patch objects>)\n    >>> ax.set_title('GQ histogram')\n    <matplotlib.text.Text object at 0x7f28f1eb1cc0>\n    >>> ax.set_xlabel('GQ')\n    <matplotlib.text.Text object at 0x7f28f18d4fd0>\n    >>> plt.show()\n\nCommand line scripts are also provided to facilitate parallelizing the\nconversion of a VCF file to NPY arrays split by genome region. For\nexample, the following command will create an NPY file containing a\nvariants array for the second 100kb on chromosome 2::\n\n    $ vcf2npy \\\n        --vcf /path/to/my.vcf \\\n        --fasta /path/to/ref.fa \\\n        --output-dir /path/to/npy/output \\\n        --array-type variants \\\n        --chromosome chr20 \\\n        --task-size 100000 \\\n        --task-index 2 \\\n        --progress 1000\n\nFor those with access to a cluster running Sun Grid Engine a script is\nprovided to submit a job array parallelizing the conversion, e.g.::\n\n    $ qsub_vcf2npy \\\n        --vcf /path/to/my.vcf \\\n        --fasta /path/to/ref.fa \\\n        --output-dir /path/to/npy/output \\\n        --array-type variants \\\n        --chromosome chr20 \\\n        --task-size 100000 \\\n        --progress 1000 \\\n        -l h_vmem=1G \\\n        -N test_vcfnp \\\n        -j y \\\n        -o /path/to/sge/logs \\\n        -q shortrun.q\n\nIt should be straightforward to adapt this script to run on other\nparallel computing platforms, see the `scripts\n<https://github.com/alimanfoo/vcfnp/tree/master/scripts>`_ folder for\nthe source code.\n\nA script is also provided to load data from multiple NPY files into a\nsingle HDF5 file. 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