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        "author": "Leo Dirac",
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        "description": "# timebudget\n### A stupidly-simple tool to see where your time is going in Python programs\n\nTrying to figure out where the time's going in your python code?  Tired of writing `elapsed = time.time() - start_time`?  You can find out with just a few lines of code after you\n\n```\npip install timebudget\n```\n\n## The simplest way\n\nWith just two lines of code (one is the import), you can see how long something takes...\n\n```python\nfrom timebudget import timebudget\n\nwith timebudget(\"Loading and processing the file\"):\n    raw = open(filename,'rt').readlines()\n    lines = [line.rstrip() for line in raw]\n```\n\nwill print\n\n```\nLoading and processing the file took 1.453sec\n```\n\n\n## Record times and print a report\n\nTo get a report on the total time from functions you care about, just annotate those functions:\n\n```python\nfrom timebudget import timebudget\ntimebudget.set_quiet()  # don't show measurements as they happen\ntimebudget.report_at_exit()  # Generate report when the program exits\n\n@timebudget  # Record how long this function takes\ndef possibly_slow():\n    ...\n\n@timebudget  # ... and this function too\ndef should_be_fast():\n    ...\n```\n\nAnd now when you run your program, you'll see how much time was spent in each annotated function:\n\n```\ntimebudget report...\n            possibly_slow:  901.12ms for      3 calls\n           should_be_fast:   61.35ms for      2 calls\n```\n\nOr instead of calling `report_at_exit()` you can manually call \n\n```python\ntimebudget.report(reset=True)  # print out the report now, and reset the statistics\n```\n\nIf you don't set `reset=True` then the statistics will accumulate into the next report.\n\nYou can also wrap specific blocks of code to be recorded in the report, and optionally override\nthe default `set_quiet` choice for any block:\n\n```python\nwith timebudget(\"load-file\", quiet=False):\n    text = open(filename,'rt').readlines()\n```\n\n\n## Percent of time in a loop\n\nIf you are doing something repeatedly, and want to know the percent of time doing different things, time the loop itself, and pass the name to report.  That is, add a timebudget annotation or wrapper onto the thing which is happening repeatedly.  Each time this method or code-block is entered will now be considered one \"cycle\" and your report will tell you what fraction of time things take within this cycle.\n\n\n```python\n@timebudget\ndef outer_loop():\n    if sometimes():\n        possibly_slow()\n    should_be_fast()\n    should_be_fast()\n\nfor _ in range(NUM_CYCLES):\n    outer_loop()\n\ntimebudget.report('outer_loop')\n```\n\nThen the report looks like:\n\n```\ntimebudget report per outer_loop cycle...\n               outer_loop: 100.0%   440.79ms/cyc @     1.0 calls/cyc\n            possibly_slow:  40.9%   180.31ms/cyc @     0.6 calls/cyc\n           should_be_fast:  13.7%    60.19ms/cyc @     2.0 calls/cyc\n```\n\nHere, the times in milliseconds are the totals (averages per cycle), not the average time per call.  So in the above example, `should_be_fast` is taking about 30ms per call, but being called twice per loop.  Similarly, `possibly_slow` is still about 300ms each time it's called, but it's only getting called on 60% of the cycles on average, so on average it's using 41% of the time in `outer_loop` or 180ms.\n\n\n## Requirements\n\nNeeds Python 3.6 or higher.  Other libraries are in `requirements.txt` and can be installed like\n\n```\npip install -r requirements.txt  # only needed for developing timebudget.\n```\n\n## Testing\n\nTo run tests:\n\n```\npytest\n```\n\n## Inspiration\n\nThis tool is inspired by [TQDM](https://github.com/tqdm/tqdm), the awesome progress bar.  TQDM is stupidly simple to add to your code, and just makes it better.  I aspire to imitate that.\n\n\n\n",
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