{ "info": { "author": "Olivier Le Thanh Duong", "author_email": "olivier@lethanh.be", "bugtrack_url": null, "classifiers": [], "description": "('Tim: Timed iteration monitor\\n============================\\n\\nA common pattern I have when writing code to import or treat large chunk of data is to do some printing to track the progress and some timing to monitor the speed.\\nSo I often end up with some variation of this in my code:\\n\\n```python\\nfrom datetime import datetime\\nstart = datetime.now()\\nn = 0\\nfor i in my_iter:\\n n+=1\\n print \"\\\\r\", i,\\n # do stuff\\nprint start - datetime.now()\\nprint (start - datetime.now()) / n / 100\\n```\\n\\n\\nwhich is a lot to remember and type which in turn make it easy to introduce bugs in some edge case which you need then to debug.\\n\\nSo I wrote tim to help me do just that, so I have this pattern ready for use and working and can spend my time coding my data treatment instead of debbugging my helper code.\\n\\n*WARNING* : This is alpha level stuff, I\\'m still working out the proper way to do stuff so the API may still change a bit.\\n\\nHow to use tim\\n--------------\\n\\nCommon usage pattern would probably be:\\n\\n```python\\nimport tim\\n\\ntim.start()\\nfor i in my_iter:\\n tim.pulse_print() # print current progress\\n # do stuff\\ntim.stop() # print overall stats and reset counter\\n```\\n\\nIf you just need to monitor start and stop you can use:\\n\\n```python\\ntim.start()\\n# calculation\\ntim.stop() # print elapsed time and reset\\n```\\n\\n\\nInstallation\\n------------\\n\\n pip install tim\\n',)", "description_content_type": null, "docs_url": null, "download_url": "UNKNOWN", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/olethanh/tim", "keywords": "", "license": "WTFPL", "maintainer": null, "maintainer_email": null, "name": "tim", "package_url": "https://pypi.org/project/tim/", "platform": "UNKNOWN", "project_url": 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