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        "author": "TU Wien",
        "author_email": "christoph.paulik@geo.tuwien.ac.at",
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        "description": "=====\nmemon\n=====\n\nVery simple memory monitor that records the percent of memory used. This can be\nuseful if you want to dump data to disk if memory consumptions becomes too high.\n\nDescription and Usage\n=====================\n\nThe MemoryMonitor class takes an interval and a memory_limit in percent.\n\nTo start recording memory usage:\n\n.. code::\n\n    from memon import MemoryMonitor\n    import time\n    memmon = MemoryMonitor(interval=0.1)\n    memmon.start()\n    memmon.start_recording()\n    time.sleep(1)\n    memmon.stop_recording()\n    assert len(memmon.history) == 10\n\nIf historical data is recorded this can be used to query if memory usage will\nkeep under the memory limit. This is done by calling:\n\n.. code::\n\n    memmon.memory_available()\n\nThis function makes some assumptions:\n\n- The Python process is the main memory user on the system.\n- Any big fluctuations in memory usage are because of memory\n  allocation/deallocation of the process running the memon.\n- We want to fit the average fluctuation that occurs during processing under the\n  memory limit.\n\nBecause of these assumptions the ``memory_available()`` function calculates:\n\n.. code::\n\n   delta = max(history) - min(history)\n   level = mean(history) + delta\n   level < memory_limit\n\nNote\n====\n\nThis project has been set up using PyScaffold 2.5.7. For details and usage\ninformation on PyScaffold see http://pyscaffold.readthedocs.org/.\n\n\n\n",
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