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<!DOCTYPE html>

<html class="no-js" lang="en" >

<head>

<title><no title> — probscale 0.2.3 documentation</title>

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<a href="index.html" class="icon icon-home"> probscale

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0.2.3

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<ul>

<li class="toctree-l1"><a class="reference internal" href="tutorial/getting_started.html">Getting started with <code class="docutils literal"><span class="pre">mpl-probscale</span></code></a></li>

<li class="toctree-l1"><a class="reference internal" href="tutorial/closer_look_at_viz.html">A closer look at probability plots</a></li>

<li class="toctree-l1"><a class="reference internal" href="tutorial/closer_look_at_plot_pos.html">Using different formulations of plotting positions</a></li>

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<p># mpl-probscale

Real probability scales for matplotlib</p>

<p>[![Build Status](<a class="reference external" href="https://travis-ci.org/matplotlib/mpl-probscale.svg)](https://travis-ci.org/matplotlib/mpl-probscale">https://travis-ci.org/matplotlib/mpl-probscale.svg)](https://travis-ci.org/matplotlib/mpl-probscale</a>)

[![codecov](<a class="reference external" href="https://codecov.io/gh/matplotlib/mpl-probscale/branch/master/graph/badge.svg)](https://codecov.io/gh/matplotlib/mpl-probscale">https://codecov.io/gh/matplotlib/mpl-probscale/branch/master/graph/badge.svg)](https://codecov.io/gh/matplotlib/mpl-probscale</a>)</p>

<p>[Sphinx Docs](<a class="reference external" href="http://matplotlib.org/mpl-probscale/">http://matplotlib.org/mpl-probscale/</a>)</p>

<p>## Installation</p>

<p>### Official releases</p>

<p>Official releases are available through the conda-forge channel or pip</p>

<p><cite>conda install mpl-probscale –channel=conda-forge</cite></p>

<p><cite>pip install probscale</cite></p>

<p>### Development builds</p>

<p>This is a pure-python package, so building from source is easy on all platforms:</p>

<span class="pre">git</span> <span class="pre">clone</span> <span class="pre">git@github.com:matplotlib/mpl-probscale.git</span>

<span class="pre">cd</span> <span class="pre">mpl-probscale</span>

<span class="pre">pip</span> <span class="pre">install</span> <span class="pre">-e</span> <span class="pre">.</span>

<p>## Quick start</p>

<p>Simply importing <cite>probscale</cite> lets you use probability scales in your matplotlib figures:</p>

<p><a href="#id1"><span class="problematic" id="id2">``</span></a><a href="#id3"><span class="problematic" id="id4">`</span></a>python

import matplotlib.pyplot as plt

import probscale

import seaborn

clear_bkgd = {‘axes.facecolor’:’none’, ‘figure.facecolor’:’none’}

seaborn.set(style=’ticks’, context=’notebook’, rc=clear_bkgd)</p>

<p>fig, ax = plt.subplots(figsize=(8, 4))

ax.set_ylim(1e-2, 1e2)

ax.set_yscale(‘log’)</p>

<p>ax.set_xlim(0.5, 99.5)

ax.set_xscale(‘prob’)

seaborn.despine(fig=fig)

<p>![Alt text](docs/img/example.png “Example axes”)</p>

<p>## Testing</p>

<p>Testing is generally done via the <code class="docutils literal"><span class="pre">pytest</span></code> and <code class="docutils literal"><span class="pre">numpy.testing</span></code> modules.

The best way to run the tests is in an interactive python session:</p>

<p><code class="docutils literal"><span class="pre">`python</span>

<span class="pre">import</span> <span class="pre">matplotlib</span>

<span class="pre">matplotib.use('agg')</span>

<span class="pre">import</span> <span class="pre">probscale</span>

<span class="pre">probscale.test()</span>

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