{
    "info": {
        "author": "Matthew Honnibal",
        "author_email": "matt@explosion.ai",
        "bugtrack_url": null,
        "classifiers": [
            "Environment :: Console",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: MacOS :: MacOS X",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX :: Linux",
            "Programming Language :: Cython",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering"
        ],
        "description": "cymem: A Cython Memory Helper\n********************\n\ncymem provides two small memory-management helpers for Cython. They make it\neasy to tie memory to a Python object's life-cycle, so that the memory is freed\nwhen the object is garbage collected.\n\n.. image:: https://img.shields.io/travis/explosion/cymem/master.svg?style=flat-square&logo=travis\n    :target: https://travis-ci.org/explosion/cymem\n    \n.. image:: https://img.shields.io/appveyor/ci/explosion/cymem/master.svg?style=flat-square&logo=appveyor\n    :target: https://ci.appveyor.com/project/explosion/cymem\n    :alt: Appveyor Build Status\n\n.. image:: https://img.shields.io/pypi/v/cymem.svg?style=flat-square  \n    :target: https://pypi.python.org/pypi/cymem\n    :alt: pypi Version\n    \n.. image:: https://img.shields.io/conda/vn/conda-forge/cymem.svg?style=flat-square\n    :target: https://anaconda.org/conda-forge/cymem\n    :alt: conda Version\n\n.. image:: https://img.shields.io/badge/wheels-%E2%9C%93-4c1.svg?longCache=true&style=flat-square&logo=python&logoColor=white\n    :target: https://github.com/explosion/wheelwright/releases\n    :alt: Python wheels\n\nOverview\n========\n\nThe most useful is ``cymem.Pool``, which acts as a thin wrapper around the calloc\nfunction:\n\n.. code:: python\n\n    from cymem.cymem cimport Pool\n    cdef Pool mem = Pool()\n    data1 = <int*>mem.alloc(10, sizeof(int))\n    data2 = <float*>mem.alloc(12, sizeof(float))\n\nThe ``Pool`` object saves the memory addresses internally, and frees them when the\nobject is garbage collected. Typically you'll attach the ``Pool`` to some cdef'd\nclass. This is particularly handy for deeply nested structs, which have\ncomplicated initialization functions. Just pass the ``Pool`` object into the\ninitializer, and you don't have to worry about freeing your struct at all \u2014\nall of the calls to ``Pool.alloc`` will be automatically freed when the ``Pool``\nexpires.\n\nInstallation\n============\n\nInstallation is via `pip <https://pypi.python.org/pypi/pip>`_, and requires `Cython <http://cython.org/>`_.\n\n.. code:: bash\n\n    pip install cymem\n\nExample Use Case: An array of structs\n=====================================\n\nLet's say we want a sequence of sparse matrices. We need fast access, and\na Python list isn't performing well enough. So, we want a C-array or C++\nvector, which means we need the sparse matrix to be a C-level struct \u2014 it\ncan't be a Python class.  We can write this easily enough in Cython:\n\n.. code:: cython\n\n    \"\"\"Example without Cymem\n\n    To use an array of structs, we must carefully walk the data structure when\n    we deallocate it.\n    \"\"\"\n\n    from libc.stdlib cimport calloc, free\n\n    cdef struct SparseRow:\n        size_t length\n        size_t* indices\n        double* values\n\n    cdef struct SparseMatrix:\n        size_t length\n        SparseRow* rows\n\n    cdef class MatrixArray:\n        cdef size_t length\n        cdef SparseMatrix** matrices\n\n        def __cinit__(self, list py_matrices):\n            self.length = 0\n            self.matrices = NULL\n\n        def __init__(self, list py_matrices):\n            self.length = len(py_matrices)\n            self.matrices = <SparseMatrix**>calloc(len(py_matrices), sizeof(SparseMatrix*))\n\n            for i, py_matrix in enumerate(py_matrices):\n                self.matrices[i] = sparse_matrix_init(py_matrix)\n\n        def __dealloc__(self):\n            for i in range(self.length):\n                sparse_matrix_free(self.matrices[i])\n            free(self.matrices)\n\n\n    cdef SparseMatrix* sparse_matrix_init(list py_matrix) except NULL:\n        sm = <SparseMatrix*>calloc(1, sizeof(SparseMatrix))\n        sm.length = len(py_matrix)\n        sm.rows = <SparseRow*>calloc(sm.length, sizeof(SparseRow))\n        cdef size_t i, j\n        cdef dict py_row\n        cdef size_t idx\n        cdef double value\n        for i, py_row in enumerate(py_matrix):\n            sm.rows[i].length = len(py_row)\n            sm.rows[i].indices = <size_t*>calloc(sm.rows[i].length, sizeof(size_t))\n            sm.rows[i].values = <double*>calloc(sm.rows[i].length, sizeof(double))\n            for j, (idx, value) in enumerate(py_row.items()):\n                sm.rows[i].indices[j] = idx\n                sm.rows[i].values[j] = value\n        return sm\n\n\n    cdef void* sparse_matrix_free(SparseMatrix* sm) except *:\n        cdef size_t i\n        for i in range(sm.length):\n            free(sm.rows[i].indices)\n            free(sm.rows[i].values)\n        free(sm.rows)\n        free(sm)\n\n\nWe wrap the data structure in a Python ref-counted class at as low a level as\nwe can, given our performance constraints.  This allows us to allocate and free\nthe memory in the ``__cinit__`` and ``__dealloc__`` Cython special methods.\n\nHowever, it's very easy to make mistakes when writing the ``__dealloc__`` and\n``sparse_matrix_free`` functions, leading to memory leaks. cymem prevents you from\nwriting these deallocators at all. Instead, you write as follows:\n\n.. code:: cython\n\n    \"\"\"Example with Cymem.\n\n    Memory allocation is hidden behind the Pool class, which remembers the\n    addresses it gives out.  When the Pool object is garbage collected, all of\n    its addresses are freed.\n\n    We don't need to write MatrixArray.__dealloc__ or sparse_matrix_free,\n    eliminating a common class of bugs.\n    \"\"\"\n    from cymem.cymem cimport Pool\n\n    cdef struct SparseRow:\n        size_t length\n        size_t* indices\n        double* values\n\n    cdef struct SparseMatrix:\n        size_t length\n        SparseRow* rows\n\n\n    cdef class MatrixArray:\n        cdef size_t length\n        cdef SparseMatrix** matrices\n        cdef Pool mem\n\n        def __cinit__(self, list py_matrices):\n            self.mem = None\n            self.length = 0\n            self.matrices = NULL\n\n        def __init__(self, list py_matrices):\n            self.mem = Pool()\n            self.length = len(py_matrices)\n            self.matrices = <SparseMatrix**>self.mem.alloc(self.length, sizeof(SparseMatrix*))\n            for i, py_matrix in enumerate(py_matrices):\n                self.matrices[i] = sparse_matrix_init(self.mem, py_matrix)\n\n    cdef SparseMatrix* sparse_matrix_init_cymem(Pool mem, list py_matrix) except NULL:\n        sm = <SparseMatrix*>mem.alloc(1, sizeof(SparseMatrix))\n        sm.length = len(py_matrix)\n        sm.rows = <SparseRow*>mem.alloc(sm.length, sizeof(SparseRow))\n        cdef size_t i, j\n        cdef dict py_row\n        cdef size_t idx\n        cdef double value\n        for i, py_row in enumerate(py_matrix):\n            sm.rows[i].length = len(py_row)\n            sm.rows[i].indices = <size_t*>mem.alloc(sm.rows[i].length, sizeof(size_t))\n            sm.rows[i].values = <double*>mem.alloc(sm.rows[i].length, sizeof(double))\n            for j, (idx, value) in enumerate(py_row.items()):\n                sm.rows[i].indices[j] = idx\n                sm.rows[i].values[j] = value\n        return sm\n\n\nAll that the ``Pool`` class does is remember the addresses it gives out. When the\n``MatrixArray`` object is garbage-collected, the ``Pool`` object will also be garbage\ncollected, which triggers a call to ``Pool.__dealloc__``. The ``Pool`` then frees all of\nits addresses. This saves you from walking back over your nested data structures\nto free them, eliminating a common class of errors.\n\nCustom Allocators\n=================\n\nSometimes external C libraries use private functions to allocate and free objects,\nbut we'd still like the laziness of the ``Pool``.\n\n.. code:: python\n\n    from cymem.cymem cimport Pool, WrapMalloc, WrapFree\n    cdef Pool mem = Pool(WrapMalloc(priv_malloc), WrapFree(priv_free))",
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