Metadata-Version: 2.1
Name: perfplot
Version: 0.4.0
Summary: Performance plots for Python code snippets
Home-page: https://github.com/nschloe/perfplot
Author: Nico Schlömer
Author-email: nico.schloemer@gmail.com
License: License :: OSI Approved :: MIT License
Description: # perfplot
        
        [![CircleCI](https://img.shields.io/circleci/project/github/nschloe/perfplot/master.svg)](https://circleci.com/gh/nschloe/perfplot/tree/master)
        [![codecov](https://img.shields.io/codecov/c/github/nschloe/perfplot.svg)](https://codecov.io/gh/nschloe/perfplot)
        [![Codacy grade](https://img.shields.io/codacy/grade/32994ce499db42059777d42edcfce900.svg)](https://app.codacy.com/app/nschloe/perfplot/dashboard)
        [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
        [![PyPi Version](https://img.shields.io/pypi/v/perfplot.svg)](https://pypi.org/project/perfplot)
        [![GitHub stars](https://img.shields.io/github/stars/nschloe/perfplot.svg?logo=github&label=Stars)](https://github.com/nschloe/perfplot)
        
        perfplot extends Python's
        [timeit](https://docs.python.org/3/library/timeit.html) by testing snippets
        with input parameters (e.g., the size of an array) and plotting the results.
        (By default, perfplot asserts the equality of the output of all snippets, too.)
        
        For example, to compare different NumPy array concatenation methods, the script
        ```python
        import numpy
        import perfplot
        
        perfplot.show(
            setup=numpy.random.rand,
            kernels=[
                lambda a: numpy.c_[a, a],
                lambda a: numpy.stack([a, a]).T,
                lambda a: numpy.vstack([a, a]).T,
                lambda a: numpy.column_stack([a, a]),
                lambda a: numpy.concatenate([a[:, None], a[:, None]], axis=1)
                ],
            labels=['c_', 'stack', 'vstack', 'column_stack', 'concat'],
            n_range=[2**k for k in range(15)],
            xlabel='len(a)'
            )
        ```
        produces
        
        ![](https://nschloe.github.io/perfplot/concat.png)
        
        Clearly, `stack` and `vstack` are the best options for large arrays.
        
        Benchmarking and plotting can be separated, too. This allows multiple plots of
        the same data, for example:
        ```python
        out = perfplot.bench(
            # same arguments as above
            )
        out.show()
        out.save('perf.png')
        ```
        
        Other examples:
        
          * [Making a flat list out of list of lists in Python](https://stackoverflow.com/a/45323085/353337)
          * [Most efficient way to map function over numpy array](https://stackoverflow.com/a/46470401/353337)
          * [numpy: most efficient frequency counts for unique values in an array](https://stackoverflow.com/a/43096495/353337)
          * [Most efficient way to reverse a numpy array](https://stackoverflow.com/a/44921013/353337)
          * [How to add an extra column to an numpy array](https://stackoverflow.com/a/40218298/353337)
          * [Initializing numpy matrix to something other than zero or one](https://stackoverflow.com/a/45006691/353337)
        
        ### Installation
        
        perfplot is [available from the Python Package
        Index](https://pypi.org/project/perfplot/), so simply do
        ```
        pip install -U perfplot
        ```
        to install or upgrade.
        
        ### Testing
        
        To run the perfplot unit tests, check out this repository and type
        ```
        pytest
        ```
        
        ### Distribution
        To create a new release
        
        1. bump the `__version__` number,
        
        2. publish to PyPi and tag on GitHub:
            ```
            $ make publish
            ```
        
        ### License
        
        perfplot is published under the [MIT license](https://en.wikipedia.org/wiki/MIT_License).
        
Platform: any
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Software Development
Classifier: Topic :: Utilities
Description-Content-Type: text/markdown
Provides-Extra: all
Provides-Extra: print
