Metadata-Version: 2.1
Name: perfplot
Version: 0.8.2
Summary: Performance plots for Python code snippets
Home-page: https://github.com/nschloe/perfplot
Author: Nico Schlömer
License: GPL-3.0-or-later
Project-URL: Code, https://github.com/nschloe/perfplot
Project-URL: Issues, https://github.com/nschloe/perfplot/issues
Platform: UNKNOWN
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 :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Software Development
Classifier: Topic :: Utilities
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: dufte
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: tqdm
Requires-Dist: termtables
Requires-Dist: importlib-metadata ; python_version < "3.8"

<p align="center">
  <img alt="perfplot" src="https://nschloe.github.io/perfplot/logo-perfplot.svg" width="60%">
</p>

[![PyPi Version](https://img.shields.io/pypi/v/perfplot.svg?style=flat-square)](https://pypi.org/project/perfplot)
[![PyPI pyversions](https://img.shields.io/pypi/pyversions/perfplot.svg?style=flat-square)](https://pypi.org/pypi/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)

[![gh-actions](https://img.shields.io/github/workflow/status/nschloe/perfplot/ci?style=flat-square)](https://github.com/nschloe/perfplot/actions?query=workflow%3Aci)
[![codecov](https://img.shields.io/codecov/c/github/nschloe/perfplot.svg?style=flat-square)](https://codecov.io/gh/nschloe/perfplot)
 [![LGTM](https://img.shields.io/lgtm/grade/python/github/nschloe/perfplot.svg?style=flat-square)](https://lgtm.com/projects/g/nschloe/perfplot)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg?style=flat-square)](https://github.com/psf/black)

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 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:
<!--exdown-skip-->
```python
out = perfplot.bench(
    # same arguments as above (except the plot-related ones, like time_unit or log*)
    )
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.

### 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).


