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        "author": "Weitang Li",
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        "description": "# Remember Me\n[![Build Status](https://travis-ci.org/liwt31/remember-me.svg?branch=master)](https://travis-ci.org/liwt31/remember-me)\n\nRememberMe is a handy tool for memory problems in Python. It computes the total memory usage of\nPython objects.\n\n## RememberMe is a replacement for `sys.getsizeof`\n`sys.getsizeof` is almost confusing in Python:\n```python\nimport sys\na = [1, 2, 3]\nb = [a, a, a]\nprint(sys.getsizeof(a) == sys.getsizeof(b))  # Can you believe the result is `True`?\n```\nWhile `rememberme` gives you a clear idea how large an object is.\n```python\nfrom rememberme import memory\na = [1, 2, 3]\nb = [a, a, a]\nprint(memory(a))  # 172 bytes!\nprint(memory(b))  # 260 bytes!\n```\n\n## Installation\n```bash\npip install rememberme\n```\n\n## More features\nCheck out memory usage in the current frame:\n```python\nfrom rememberme import memory\ndef foo():\n    a = [1, 2, 3]\n    b = [a, a, a]\n    print memory()\nfoo()  # 260 bytes. Note `a` is included in `b`.\n```\nCheck out top memory consumers:\n```python\nfrom rememberme import top\ndef foo():\n    a = [1, 2, 3]\n    b = [a, a, a]\n    mem_top = top()  # with no args, check current frame\n    print(mem_top[0])  # `b` and its memory usage\n    print(mem_top[1])  # `a` and its memory usage\n```\nEven pretty print the result!\n```python\nfrom rememberme import mem_print\ndef foo():\n    a = [1, 2, 3]\n    b = [a, a, a]\n    mem_print(b)\nfoo()\n```\nOutput:\n```\n             \u2001             \u250cint (28.0B)\n             \u250clist (172.0B)\u253cint (28.0B)\n             \u2502             \u2514int (28.0B)\n             \u2502             \u250cint (28.0B)\nlist (260.0B)\u253clist (172.0B)\u253cint (28.0B)\n             \u2502             \u2514int (28.0B)\n             \u2502             \u250cint (28.0B)\n             \u2514list (172.0B)\u253cint (28.0B)\n             \u2001             \u2514int (28.0B)\n```\n\n## Known issues and limitations\n* For better performance (and making better sense), the global dict, as well as modules, \nare not included in the memory usage of any objects.\n* We essentially relies on [`tp_traverse`](https://docs.python.org/3/c-api/typeobj.html#c.PyTypeObject.tp_traverse) \nto traverse the object graph. For C extensions, memory usage might be underestimated under\nvarious circumstances. For the most common `numpy.ndarray`, a specific procedure is defined to\nprobe the memory usage correctly, but no correctness is guaranteed for other C extensions,\nwhich may have undetectable momery leaks within themselves.",
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