Metadata-Version: 2.1
Name: about-time
Version: 2.0.5
Summary: Small tool to track time of Python code blocks.
Home-page: https://github.com/rsalmei/about-time
Author: Rogério Sampaio de Almeida
Author-email: rsalmei@gmail.com
License: MIT
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        # about-time
        ## Small tool to track time of Python code blocks.
        
        
        ## What does it do?
        
        Sometimes I need something to log code execution, or to extract metrics to send to influx, so I've created this tool.
        
        A simple `start = time.time()` and `end = time.time() - start` does not cut it when you need to track two or more lines/blocks at the same time, or simultaneously whole/part relationship of blocks.
        
        This tool measures the execution time of blocks of code, and can even count iterations and the system throughput, with beautiful "human" representations.
        
        
        ## How do I install it?
        
        Just do in your python venv:
        
        ```bash
        $ pip install about-time
        ```
        
        
        ## How to use it?
        
        There's three modes of operation: context manager, callable handler and iterator metrics.
        
        
        ### 1. Use it like a context manager:
        
        ```python
        from about_time import about_time
        
        with about_time() as t_whole:
            with about_time() as t_1:
                func_1()
            with about_time() as t_2:
                func_2('params')
        ```
        
        Then, get the timings like this:
        
        ```python
        # python 3.7 example, using f strings.
        print(f'func_1 time: {t_1.duration_human}')
        print(f'func_2 time: {t_2.duration_human}')
        print(f'total time: {t_whole.duration_human}')
        ```
        
        There's also the `duration` property, which returns the actual float time in seconds.
        
        ```python
        secs = t_whole.duration
        ```
        
        
        ### 2. Use it like a callable handler:
        
        ```python
        t_1 = about_time(func_1)
        t_2 = about_time(lambda: func_2('params'))
        ```
        
        If you use the callable handler syntax, there will be a new field called `result` to get the outcome of the function!
        
        ```python
        results = t_1.result, t_2.result
        ```
        
        Or you can mix and match both:
        
        ```python
        with about_time() as t_whole:
            t_1 = about_time(func_1)
            t_2 = about_time(lambda: func_2('params'))
        ```
        
        
        ### 3. Use it to count iterations and measure throughput:
        
        Wrap your iterable and just iterate it! Since we internally have duration information, it can calculate the throughput of the whole block. Specially useful in generators, which do not have length (but you can use it with any iterables):
        
        This mode requires a small callback function (which can be an inner function or a lambda) to allow you to use a `for` loop any way you want, and the callback will be called automatically when the iterable is exhausted.
        
        ```python
        def callback(t_func):
            logger.info('func: size=%d throughput=%s', t_func.count,
                                                       t_func.throughput_human)
        items = filter(...)
        for item in about_time(callback, items):
            # use item any way you want.
            process(item)
        
        # the callback is already called upon getting here.
        ```
        
        
        ## Some nice features
        
        ### Humans are first class citizens :)
        
        I've considered two key concepts in designing the human friendly functions: `3.44s` is more meaningful than `3.43584783784s`, and `14.12us` is much nicer than `.0000141233333s`. So saying it another way, I round values to two decimal places at most, and finds the smaller unit to represent it, minimizing values smaller than `1`.
        
        Note that it dynamically finds the best unit to represent the value, considering even the rounding been applied. So if a value is for example `0.999999`, it would end up like `1000.0ms` after rounded, but it is auto-upgraded to the next unit `1.0s`.
        
        The `duration_human` changes seamlessly from nanoseconds to hours. Values smaller than 60 seconds are rendered with two digits precision at most (zeros to the right of the decimal point are not shown), and starting from 1 minute, an "hours:minutes:seconds.M" milliseconds (with only one digit precision). Some examples directly from the unit tests:
        
        duration (float seconds) | duration_human
        :---: | :---:
        .00000000185 | '1.85ns'
        .000000999996 | '1.0us'
        .00001 | '10.0us'
        .0000156 | '15.6us'
        .01 | '10.0ms'
        .0141233333333 | '14.12ms'
        .1099999 | '110.0ms'
        .1599999 | '160.0ms'
        .8015 | '801.5ms'
        3.434999 | '3.43s'
        59.999 | '0:01:00'
        68.5 | '0:01:08.5'
        125.825 | '0:02:05.8'
        4488.395 | '1:14:48.4'
        
        The `throughput_human` has similar logic, and to human brain it is much trickier to figure out: If it took `1165263` seconds to handle `123` items, how fast it did? Even dividing to find out the time per item `9473` seconds don't mean much. Dividing by `3600` we get `2.63` hours per item, and the throughput is returned nicely as `0.38/h`. The tool has per-second, per-minute and per-hour. Some examples:
        
        duration (float seconds) | number of elements | throughput_human
        :---: | :---: | :---:
        1\. | 10 | '10.0/s'
        1\. | 2500 | '2500.0/s'
        2\. | 1 | '30.0/m'
        2\. | 10 | '5.0/s'
        1.981981981981982 | 11 | '5.55/s'
        100\. | 10 | '6.0/m'
        1600\. | 3 | '6.75/h'
        .99 | 1 | '1.01/s'
        1165263\. | 123 | '0.38/h'
        
        
        ### Accuracy
        
        This tool supports all versions of python, but in pythons >= `3.3`, the code uses the new `time.perf_counter` to gain from the higher resolution and smaller propagating of errors. In older versions, it uses `time.time`.
        
        
        ## License
        This software is licensed under the MIT License. See the LICENSE file in the top distribution directory for the full license text.
        
        
        ## Nice huh?
        
        Thanks for your interest!
        
        I wish you have fun using this tool! :)
        
Keywords: python,track,tracker,time,code,blocks,statistics,analytics
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Environment :: Console
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
