EasyPlot 1.0.0
A matplotlib wrapper for fast and easy generation of reusable plots
License: MIT
Keywords: matplotlib wrapper plot easyplot

easyplot
=========

**easyplot:** A matplotlib wrapper written in Python to enable fast and
easy creation of reusable plots.

The EasyPlot module provides a thin wrapper to matplotlib, enabling fast and
easy generation of beautiful, annotated plots with minimal code. It also enables
the reuse of EasyPlot instances to generate new plots that maintain state from
previous plots allowing for quick and easy generation of multiple plots of the
same type. EasyPlot supports all commonly used plot parameters and allows access
to the underlying figure and axes instances to allow the user to further customize
the generated plots if necessary.

For more details and examples please see the included package documentation and
the IPython Notebook containing documentation and an extensive list of examples:
http://nbviewer.ipython.org/github/HamsterHuey/easyplot/blob/master/docs/easyplot_docs.ipynb


Requirements
---------------
- Python 2.7.2+
- matplotlib

Use of the `IPython <http://ipython.org/>`__ shell is strongly
recommended with this library (and matplotlib plotting in general). The
``%matplotlib`` magic command in IPython (or starting IPython using
``ipython --matplotlib``) implements a number of backend tweaks to
enable robust, interactive plotting using matplotlib.


Installation
==============

You can use the following commands to install EasyPlot:

``pip install easyplot``

or

``easy_install easyplot``

Alternatively, you could download the package manually from the Python
Package Index: https://pypi.python.org/pypi/EasyPlot, unzip it, navigate
into the package, and use the command:

``python setup.py install``

or

``pip install .``


Motivation and background
===========================

Setting up aesthetically pleasing plots with plot titles, axes labels, etc
requires several lines of boilerplate code in vanilla matplotlib. As an example,
creating a basic plot in matplotlib requires the following lines of code:

.. code:: python

    fig, ax = plt.subplots()
    ax.plot(x, x**2, 'b-o', label="y = x**2")
    ax.plot(x, x**3, 'r--s', label="y = x**3")
    ax.legend(loc='best')
    ax.grid()
    ax.set_xlabel('x')
    ax.set_ylabel('y')
    ax.set_title('title')

.. image:: https://raw.githubusercontent.com/HamsterHuey/easyplot/master/images/ep_motivation_1.png
   :align: center

Pylab alleviates some of this, but still requires calls to a number of
different functions that are commonly used (such as xlabel, xlim, etc.).
More complicated plots can require several more lines of code. Typing
all this code every time to generate plots gets tedious very quickly.
This situation is further exacerbated when working in an IPython
Notebook where all plots typically need to be labeled, annotated and
looking their best. Having several lines of code preceeding every plot
in a notebook can break the flow of the document and distract from the
code/concepts being presented by the author. Furthermore, oftentimes,
plots with similar labels and formatting need to be generated repeatedly
with different datasets. Generating these sets of plots would require
retyping these same lines of boilerplate code across different sections
of your code/notebook.

Easyplot is my attempt to address these issues and make generating
quick, pleasant looking, annotated plots a bit easier. In keeping with
`DRY
philosophy <http://en.wikipedia.org/wiki/Don%27t_repeat_yourself>`__,
``easyplot`` enables the creation of an ``EasyPlot`` object that
maintains state information of all plot parameters passed to it in order
to generate a plot. This can then be easily reused to generate new plots
with the user only having to supply any additional plot parameters, or
those parameters he or she wishes to override from the previous plot.

Easyplot supports a large number of standard plot parameters that most
users typically deal with when plotting in matplotlib. Additionally, it
provides methods to access the figure and axes instance for the latest
plot, enabling users to perform more custom plot modifications that are
not directly supported by easyplot. It also supports interactive
plotting where additional plot parameters can be passed to the current
plot using the ``update_plot`` method. The plot above can be generated
using ``easyplot`` as follows:

.. code:: python

    eplot = EasyPlot(x, x**2, 'b-o', label='y = x**2', showlegend=True,
                     xlabel='x', ylabel='y', title='title', grid='on')
    eplot.add_plot(x, x**3, 'r--s', label='y = x**3')

Along with the reduced typing, easyplot enables the consolidation and
passing of all plot parameters into a single plot call. This is already
quite handy, but the real benefit is evident when one needs to generate
a new plot with the same plot parameters (such as axis labels and title)
but with new data:

.. code:: python

    eplot.new_plot(x, 1/x, 'g-D', label='y = 1/x')

.. image:: https://raw.githubusercontent.com/HamsterHuey/easyplot/master/images/ep_motivation_2.png
   :align: center

``EasyPlot`` also provides an ``iter_plot()`` method that iterates
through x, y data and plot parameters that are provided in a list or
dictionary format to automatically generate an annotated, multi-line
plot with a single statement:

.. code:: python

    eplot = EasyPlot(xlabel=r'$x$', ylabel='$y$', fontsize=16,
                     colorcycle=["#66c2a5","#fc8d62","#8da0cb"], figsize=(8,5))
    eplot.iter_plot(x, y_dict, linestyle=linestyle_dict, marker=marker_dict,
                    label=labels_dict, linewidth=3, ms=10, showlegend=True, grid='on')

.. image:: https://raw.githubusercontent.com/HamsterHuey/easyplot/master/images/ep_motivation_3.png
   :align: center


Features
========

- Access to a large number of the most used matplotlib plot parameters under a 
  unified wrapper class
- Plot parameter aliases supported. Can be extended by user for
  arbitrary alias definitions for various plot parameters 
- Ability to use ``EasyPlot`` objects as templates to rapidly generate annotated
  plots of a similar type 
- ``iter_plot()`` method to easily iterate through x, y datasets and plot 
  multiple plots with a single method call
- Draggable legend when using GUI backends (eg: qt, wx, etc.)
- Provides access to underlying figure, axes and line2D objects for advanced plot
  customization


Usage and Examples
==================

For more details and examples, please view the online IPython Notebook containing 
easyplot documentation and an extensive list of examples:
http://nbviewer.ipython.org/github/HamsterHuey/easyplot/blob/master/docs/easyplot_docs.ipynb

Contact
=============

I'd love to hear your comments and/or suggestions. You can get in touch
with me via:

- eMail: sudeepmandal@gmail.com
- Twitter: https://twitter.com/hamsterhuey
- Google+: https://plus.google.com/u/0/105292596991480463202