An attempt at an easier API to plot graphs using Matplotlib.
Phyks (Lucas Verney) 5d77471f51 Fix DeprecationWarning due to ellipsis in adaptive sampling. 5 years ago
docs Close issue #21 5 years ago
replot Fix DeprecationWarning due to ellipsis in adaptive sampling. 5 years ago
.gitignore Add a file 5 years ago
Examples.ipynb Emphasize title. Closes #22 5 years ago
LICENSE Add a file 5 years ago Rework colorscheme, use Colorbrewer Q10 palette by default. Closes #8. 5 years ago
requirements.txt Better colorscheme 5 years ago Fix ImportError when importing after system-wide installation 5 years ago


This repo is an attempt for a better API to plot graphs with Matplotlib 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.


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!

Saner default plots
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 (based on Colorbrewer Q10 palette). It also provides direct access to the Tableau10 and Colorbrewer Q9 palettes.
Support with statement
Ever got tired of having to start any figure with a call to matplotlib.pyplot.subplots()? This module abstracts it using with statement. New figures are defined by a with statement, and are shown automatically (or saved) upon leaving the with context.
Plot functions
Ever got annoyed by the fact that matplotlib can only plot point series and not evaluate a function à la Mathematica? This module let you do things like plot(sin, (-10, 10)) to plot a sine function between -10 and 10, using adaptive sampling.
Order of call of methods is no longer important
When calling a method from matplotlib, it is directly applied to the figure, and not deferred to the final render call. Then, if calling matplotlib.pyplot.legend() before having actually plotted anything, it will fail. This is not the case with this module, as it abstracts on top of matplotlib and do the actual render only when the figure is to be shown. Even after having called the show method, you can still change everything in your figure!
Does not interfere with matplotlib
You can still use the default matplotlib if you want, as matplotlib state and parameters are not directly affected by this module, contrary to what seaborn do when you import it for instance.
Useful aliases
You think loc="top left" is easier to remember than loc="upper left" in a matplotlib.pyplot.legend() call? No worry, this module aliases it for you! (same for "bottom" with respect to "lower"). Similarly, you can use xrange or xlim arguments to specify axes ranges (respectively yrange / ylim).
Automatic legend
If any of your plots contains a label keyword, a legend will be added automatically on your graph (you can still explicitly tell it not to add a legend by setting the legend attribute to False).
Use LaTeX rendering in matplotlib, if available.
If replot finds LaTeX installed on your machine, it will overload matplotlib settings to use LaTeX rendering.
Handle subplots more easily
Have you ever struggled with matplotlib to define a subplot grid and arrange your plot? replot lets you describe your grid visually using ascii art!
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? replot handles it for you out of the box!
Easy plotting in log scale, orthonormal axis etc
replot defines logplot and loglogplot shortcuts functions to plot in log scale or loglog scale. Use orthonormal=True on a plot command to plot using orthonormal axes.


A more up to date doc is still to be written, but you can have a look at the Examples.ipynb Jupyter notebook for examples, which should cover most of the use cases.


This Python module is released under MIT license. Feel free to contribute and reuse. For more details, see LICENSE.txt file.