Metadata-Version: 2.1
Name: perfplot
Version: 0.8.0
Summary: Performance plots for Python code snippets
Home-page: https://github.com/nschloe/perfplot
Author: Nico Schlömer
License: GPLv3
Project-URL: Code, https://github.com/nschloe/perfplot
Project-URL: Issues, https://github.com/nschloe/perfplot/issues
Description: <p align="center">
          <img alt="perfplot" src="https://nschloe.github.io/perfplot/logo-perfplot.svg" width="60%">
        </p>
        
        [![CircleCI](https://img.shields.io/circleci/project/github/nschloe/perfplot/master.svg?style=flat-square)](https://circleci.com/gh/nschloe/perfplot/tree/master)
        [![codecov](https://img.shields.io/codecov/c/github/nschloe/perfplot.svg?style=flat-square)](https://codecov.io/gh/nschloe/perfplot)
        [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg?style=flat-square)](https://github.com/psf/black)
        [![PyPI pyversions](https://img.shields.io/pypi/pyversions/perfplot.svg?style=flat-square)](https://pypi.org/pypi/perfplot/)
        [![PyPi Version](https://img.shields.io/pypi/v/perfplot.svg?style=flat-square)](https://pypi.org/project/perfplot)
        [![GitHub stars](https://img.shields.io/github/stars/nschloe/perfplot.svg?style=flat-square&logo=github&label=Stars&logoColor=white)](https://github.com/nschloe/perfplot)
        [![PyPi downloads](https://img.shields.io/pypi/dm/perfplot.svg?style=flat-square)](https://pypistats.org/packages/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=lambda n: numpy.random.rand(n),  # or simply 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)",
            # logx=False,
            # logy=False,
            # More optional arguments with their default values:
            # title=None,
            # logx="auto",  # set to True or False to force scaling
            # logy="auto",
            # equality_check=numpy.allclose,  # set to None to disable "correctness" assertion
            # colors=None,
            # target_time_per_measurement=1.0,
            # time_unit="s",  # set to one of ("auto", "s", "ms", "us", or "ns") to force plot units
            # relative_to=1,  # plot the timings relative to one of the measurements
            # flops=lambda n: 3*n,  # FLOPS plots
        )
        ```
        produces
        
        ![](https://nschloe.github.io/perfplot/concat.svg) | ![](https://nschloe.github.io/perfplot/relative.svg)
        | --- | --- |
        
        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", transparent=True, bbox_inches="tight")
        ```
        
        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 perfplot
        ```
        to install or upgrade.
        
        ### Testing
        
        To run the perfplot unit tests, check out this repository and type
        ```
        pytest
        ```
        
        ### License
        This software is published under the [GPLv3 license](https://www.gnu.org/licenses/gpl-3.0.en.html).
        
Platform: any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Software Development
Classifier: Topic :: Utilities
Requires-Python: >=3.5
Description-Content-Type: text/markdown
