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        "author": "Liran Funaro",
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            "License :: OSI Approved :: GNU General Public License (GPL)",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 3",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "# objsize\n\nTraversal over Python's objects sub-tree and calculating\nthe total size of the sub-tree (deep size).\n\nThis module uses python internal GC implementation\nto traverse all decedent objects.\nIt ignores type objects (i.e., `isinstance(o, type)`)\nsuch as classes and modules, as they are common among all objects.\nIt is implemented without recursive calls for best performance.\n\n\n# Features\n\n- Calculate single/multiple object(s) deep size.\n- Exclude non exclusive objects.\n- Traverse single/multiple objects(s) sub tree.\n\n[Pympler](https://pythonhosted.org/Pympler/) also supports determening an object deep size via `pympler.asizeof()`.\nThere are two main differences between `objsize` and `pympler`.\n\n1. `objsize` has additional features:\n   * Traversing the object sub-tree: iterating all of the object's descendants one by one.\n   * Excluding non-exclusive objects. That is, objects that are also referenced from somewhere else in the program. This is true for calculating the object's deep size and for traversing its descendants.\n2. `objsize` has a simple and robust implementation with significantly fewer lines of code, compared to `pympler`.\n   The Pympler implementation uses recursion, and thus have to use a maximal depth argument to avoid reaching Python's max depth.\n   `objsize`, however, uses BFS which is more efficient and simple to follow.\n   Moreover, the Pympler implementation carefully takes care of any object type.\n   `objsize` archives the same goal with a simple and generic implementation, which has fewer lines of code.\n\n\n# Install\n\n```bash\npip install objsize\n```\n\n\n# Basic Usage\n\nCalculate an object size including all its members.\n\n```python\n>>> import objsize\n>>> objsize.get_deep_size(dict(arg1='hello', arg2='world'))\n348\n```\n\nIt is possible to calculate the deep size of multiple objects by passing multiple arguments:\n\n```python\n>>> objsize.get_deep_size(['hello', 'world'], dict(arg1='hello', arg2='world'), {'hello', 'world'})\n652\n```\n\n# Complex Data\n\n`objsize` can calculate the size of an object's entire sub-tree\nregardless of the type of objects in it, and its depth.\n\nHere is a complex data structure, for example, that include a self reference:\n\n```python\nmy_data = (list(range(3)), list(range(3,6)))\n\nclass MyClass:\n    def __init__(self, x, y):\n        self.x = x\n        self.y = y\n        self.d = {'x': x, 'y': y, 'self': self}\n\n    def __repr__(self):\n        return \"MyClass\"\n\nmy_obj = MyClass(*my_data)\n```\n\nWe can calculate `my_obj` deep size, including its stored data.\n\n```python\n>>> objsize.get_deep_size(my_obj)\n796\n```\n\nWe might want to ignore non exclusive objects such as the ones stored in `my_data`.\n\n```python\n>>> objsize.get_exclusive_deep_size(my_obj)\n408\n```\n\n# Traversal\n\nA user can implement its own function over the entire sub tree\nusing the traversal method, which traverse all the objects in the sub tree.\n\n```python\n>>> for o in objsize.traverse_bfs(my_obj):\n...     print(o)\n... \nMyClass\n{'x': [0, 1, 2], 'y': [3, 4, 5], 'd': {'x': [0, 1, 2], 'y': [3, 4, 5], 'self': MyClass}}\n[0, 1, 2]\n[3, 4, 5]\n{'x': [0, 1, 2], 'y': [3, 4, 5], 'self': MyClass}\n2\n1\n0\n5\n4\n3\n```\n\nSimilirarly to before, non exclusive objects can be ignored.\n\n```python\n>>> for o in objsize.traverse_exclusive_bfs(my_obj):\n...     print(o)\n... \nMyClass\n{'x': [0, 1, 2], 'y': [3, 4, 5], 'd': {'x': [0, 1, 2], 'y': [3, 4, 5], 'self': MyClass}}\n{'x': [0, 1, 2], 'y': [3, 4, 5], 'self': MyClass}\n```\n\n# License\n[GPL](LICENSE.txt)\n\n\n",
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