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    "info": {
        "author": "Appier Inc.",
        "author_email": "",
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        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Information Technology",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Database",
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        "description": "Please visit  the `Github repository <https://github.com/appier/h5sparse>`_\nfor more information.\n\nh5sparse\n========\n.. image:: https://img.shields.io/travis/appier/h5sparse/master.svg\n   :target: https://travis-ci.org/appier/h5sparse\n.. image:: https://img.shields.io/pypi/v/h5sparse.svg\n   :target: https://pypi.python.org/pypi/h5sparse\n.. image:: https://img.shields.io/pypi/l/h5sparse.svg\n   :target: https://pypi.python.org/pypi/h5sparse\n\nScipy sparse matrix in HDF5.\n\n\nInstallation\n------------\n.. code:: bash\n\n   pip install h5sparse\n\n\nTesting\n-------\n- for single environment:\n\n  .. code:: bash\n\n     python setup.py test\n\n- for all environments:\n\n  .. code:: bash\n\n     tox\n\n\nExamples\n--------\n\nCreate dataset\n**************\n.. code:: python\n\n   In [1]: import scipy.sparse as ss\n      ...: import h5sparse\n      ...: import numpy as np\n      ...:\n\n   In [2]: sparse_matrix = ss.csr_matrix([[0, 1, 0],\n      ...:                                [0, 0, 1],\n      ...:                                [0, 0, 0],\n      ...:                                [1, 1, 0]],\n      ...:                               dtype=np.float64)\n\n   In [3]: # create dataset from scipy sparse matrix\n      ...: with h5sparse.File(\"test.h5\") as h5f:\n      ...:     h5f.create_dataset('sparse/matrix', data=sparse_matrix)\n\n   In [4]: # you can also create dataset from another dataset\n      ...: with h5sparse.File(\"test.h5\") as h5f:\n      ...:     h5f.create_dataset('sparse/matrix2', data=h5f['sparse/matrix'])\n\n   In [5]: # you can also create dataset using the formats that original h5py accepts\n      ...: with h5sparse.File(\"test.h5\") as h5f:\n      ...:     h5f.create_dataset('sparse/matrix3', data=[1,2,3])\n\nRead dataset\n************\n.. code:: python\n\n   In [6]: h5f = h5sparse.File(\"test.h5\")\n\n   In [7]: h5f['sparse/matrix'][1:3]\n   Out[7]:\n   <2x3 sparse matrix of type '<class 'numpy.float64'>'\n           with 1 stored elements in Compressed Sparse Row format>\n\n   In [8]: h5f['sparse/matrix'][1:3].toarray()\n   Out[8]:\n   array([[ 0.,  0.,  1.],\n          [ 0.,  0.,  0.]])\n\n   In [9]: h5f['sparse']['matrix'][1:3].toarray()\n   Out[9]:\n   array([[ 0.,  0.,  1.],\n          [ 0.,  0.,  0.]])\n\n   In [10]: h5f['sparse']['matrix'][2:].toarray()\n   Out[10]:\n   array([[ 0.,  0.,  0.],\n          [ 1.,  1.,  0.]])\n\n   In [11]: h5f['sparse']['matrix'][:2].toarray()\n   Out[11]:\n   array([[ 0.,  1.,  0.],\n          [ 0.,  0.,  1.]])\n\n   In [12]: h5f['sparse']['matrix'][-2:].toarray()\n   Out[12]:\n   array([[ 0.,  0.,  0.],\n          [ 1.,  1.,  0.]])\n\n   In [13]: h5f['sparse']['matrix'][:-2].toarray()\n   Out[13]:\n   array([[ 0.,  1.,  0.],\n          [ 0.,  0.,  1.]])\n\n   In [14]: h5f['sparse']['matrix'][()].toarray()\n   Out[14]:\n   array([[ 0.,  1.,  0.],\n          [ 0.,  0.,  1.],\n          [ 0.,  0.,  0.],\n          [ 1.,  1.,  0.]])\n\n   In [15]: import h5py\n\n   In [16]: h5py_h5f = h5py.File(\"test.h5\")\n\n   In [17]: h5sparse.Group(h5py_h5f.id)['sparse/matrix'][()]\n   Out[17]:\n   <4x3 sparse matrix of type '<class 'numpy.float64'>'\n           with 4 stored elements in Compressed Sparse Row format>\n\n   In [18]: h5sparse.Group(h5py_h5f['sparse'].id)['matrix'][()]\n   Out[18]:\n   <4x3 sparse matrix of type '<class 'numpy.float64'>'\n           with 4 stored elements in Compressed Sparse Row format>\n\n   In [19]: h5sparse.Dataset(h5py_h5f['sparse/matrix'])[()]\n   Out[19]:\n   <4x3 sparse matrix of type '<class 'numpy.float64'>'\n           with 4 stored elements in Compressed Sparse Row format>\n\nAppend dataset\n**************\n.. code:: python\n\n   In [20]: to_append = ss.csr_matrix([[0, 1, 1],\n       ...:                            [1, 0, 0]],\n       ...:                           dtype=np.float64)\n\n   In [21]: h5f.create_dataset('matrix', data=sparse_matrix, chunks=(100000,),\n       ...:                    maxshape=(None,))\n\n   In [22]: h5f['matrix'].append(to_append)\n\n   In [23]: h5f['matrix'][()]\n   Out[23]:\n   <6x3 sparse matrix of type '<class 'numpy.float64'>'\n           with 7 stored elements in Compressed Sparse Row format>\n\n   In [24]: h5f['matrix'][()].toarray()\n   Out[24]:\n   array([[ 0.,  1.,  0.],\n          [ 0.,  0.,  1.],\n          [ 0.,  0.,  0.],\n          [ 1.,  1.,  0.],\n          [ 0.,  1.,  1.],\n          [ 1.,  0.,  0.]])\n\n\n",
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