replot/README.md

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Replot
======
This repo is an attempt for a better API to plot graphs with
[Matplotlib](http://matplotlib.org/) in Python.
`Matplotlib` is a wonderful Python modules to plot data series, functions and
so on. However, I think the API is quite verbose. This is an attempt at
providing a better frontend API on top of `matplotlib` for easy and fast
plotting, especially at prototyping time.
## Features
These are the current features. I will extend the module whenever I feel the
need to introduce new functions and methods. Please let me know about any bad
design in the API, or required feature!
<dl>
<dt>Saner default plots</dt>
<dd>Matplotlib plots are quite ugly by default, colors are not really
suited for optimal black and white print, or ease reading for colorblind
people. This module defines a clean default colorscheme to solve it.</dd>
<dt>Support <code>with</code> statement</dt>
<dd>Ever got tired of having to start any figure with a call to
<code>matplotlib.pyplot.subplots()</code>? This module abstracts it using
<code>with</code> statement. New figures are defined by a
<code>with</code> statement, and are <code>show</code>n automatically (or
<code>save</code>d) upon leaving the <code>with</code> context.
<dt>Plot functions</dt>
<dd>Ever got annoyed by the fact that <code>matplotlib</code> can only
plot point series and not evaluate a function <em>à la</em> Mathematica?
This module let you do things like <code>plot(sin, (-10, 10))</code> to
plot a sine function between -10 and 10, using adaptive sampling.
<dt>Order of call of methods is no longer important</dt>
<dd>When calling a method from <code>matplotlib</code>, it is directly
applied to the figure, and not deferred to the final render call. Then, if
calling <code>matplotlib.pyplot.legend()</code> <strong>before</strong>
having actually <code>plot</code>ted anything, it will fail. This is not
the case with this module, as it abstracts on top of
<code>matplotlib</code> and do the actual render only when the figure is
to be <code>show</code>n. Even after having called the <code>show</code>
method, you can still change everything in your figure!</dd>
<dt>Does not interfere with <code>matplotlib</code></dt>
<dd>You can still use the default <code>matplotlib</code> if you want, as
<code>matplotlib</code> state and parameters are not directly affected by
this module, contrary to what <code>seaborn</code> do when you import it
for instance.</dd>
<dt>Useful aliases</dt>
<dd>You think <code>loc="top left"</code> is easier to remember than
<code>loc="upper left"</code> in a <code>matplotlib.pyplot.legend()</code>
call? No worry, this module aliases it for you! (same for "bottom" with
respect to "lower")</dd>
<dt>Automatic legend</dt>
<dd>If any of your plots contains a <code>label</code> keyword, a legend
will be added automatically on your graph (you can still explicitly tell
it not to add a legend by setting the <code>legend</code> attribute to
<code>False</code>).</dd>
<dt>Use <code>LaTeX</code> rendering in <code>matplotlib</code>, if
available.</dt>
<dd>If <code>replot</code> finds <code>LaTeX</code> installed on your
machine, it will overload <code>matplotlib</code> settings to use
<code>LaTeX</code> rendering.</dd>
<dt>Handle subplots more easily</dt>
<dd>Have you ever struggled with <code>matplotlib</code> to define a subplot
grid and arrange your plot? <code>replot</code> lets you describe your
grid visually using ascii art!</dd>
<dt>"Gridify"</dt>
<dd>You have some plots that you would like to arrange into a grid, to
compare them easily, but you do not want to waste time setting up a grid
and placing your plots at the correct place? <code>replot</code> handles
it for you out of the box!</dd>
<dt>Easy plotting in log scale</dt>
<dd><code>replot</code> defines <code>logplot</code> and
<code>loglogplot</code> shortcuts functions to plot in <em>log</em> scale
or <em>loglog</em> scale.</dd>
</dl>
## Examples
A more up to date doc is still to be written, but you can have a look at the
<a href="https://github.com/Phyks/replot/blob/master/Examples.ipynb">`Examples.ipynb`</a>
[Jupyter](https://github.com/jupyter/notebook/) notebook for
examples, which should cover most of the use cases.
## License
This Python module is released under MIT license. Feel free to contribute and
reuse. For more details, see `LICENSE.txt` file.
## Thanks
* [Matplotlib](http://matplotlib.org/) for their really good backend.
* [Seaborn](https://github.com/mwaskom/seaborn) and
[prettyplotlib](http://blog.olgabotvinnik.com/prettyplotlib/) which gave me
the original idea.
* [This code](http://central.scipy.org/item/53/1/adaptive-sampling-of-1d-functions)
from scipy central for a base code for adaptive sampling.
* [Palettable](https://jiffyclub.github.io/palettable/) for palettes.
* [Cubehelix](http://www.ifweassume.com/2013/05/cubehelix-or-how-i-learned-to-love.html)
colorscheme for nice black and white printing and desaturation compatibility.