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89 lines
4.3 KiB
ReStructuredText
Executable file
Introduction
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============
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matplotlib is a library for making 2D plots of arrays in `Python
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<http://www.python.org>`_. Although it has its origins in emulating
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the `MATLAB™ <http://www.mathworks.com>`_ graphics commands, it is
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independent of MATLAB, and can be used in a Pythonic, object oriented
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way. Although matplotlib is written primarily in pure Python, it
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makes heavy use of `NumPy <http://www.numpy.org>`_ and other extension
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code to provide good performance even for large arrays.
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matplotlib is designed with the philosophy that you should be able to
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create simple plots with just a few commands, or just one! If you
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want to see a histogram of your data, you shouldn't need to
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instantiate objects, call methods, set properties, and so on; it
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should just work.
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For years, I used to use MATLAB exclusively for data analysis and
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visualization. MATLAB excels at making nice looking plots easy. When
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I began working with EEG data, I found that I needed to write
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applications to interact with my data, and developed and EEG analysis
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application in MATLAB. As the application grew in complexity,
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interacting with databases, http servers, manipulating complex data
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structures, I began to strain against the limitations of MATLAB as a
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programming language, and decided to start over in Python. Python
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more than makes up for all of MATLAB's deficiencies as a programming
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language, but I was having difficulty finding a 2D plotting package
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(for 3D `VTK <http://www.vtk.org/>`_ more than exceeds all of my
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needs).
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When I went searching for a Python plotting package, I had several
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requirements:
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* Plots should look great - publication quality. One important
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requirement for me is that the text looks good (antialiased, etc.)
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* Postscript output for inclusion with TeX documents
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* Embeddable in a graphical user interface for application
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development
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* Code should be easy enough that I can understand it and extend
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it
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* Making plots should be easy
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Finding no package that suited me just right, I did what any
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self-respecting Python programmer would do: rolled up my sleeves and
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dived in. Not having any real experience with computer graphics, I
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decided to emulate MATLAB's plotting capabilities because that is
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something MATLAB does very well. This had the added advantage that
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many people have a lot of MATLAB experience, and thus they can
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quickly get up to steam plotting in python. From a developer's
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perspective, having a fixed user interface (the pylab interface) has
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been very useful, because the guts of the code base can be redesigned
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without affecting user code.
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The matplotlib code is conceptually divided into three parts: the
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*pylab interface* is the set of functions provided by
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:mod:`matplotlib.pylab` which allow the user to create plots with code
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quite similar to MATLAB figure generating code
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(:ref:`pyplot-tutorial`). The *matplotlib frontend* or *matplotlib
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API* is the set of classes that do the heavy lifting, creating and
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managing figures, text, lines, plots and so on
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(:ref:`artist-tutorial`). This is an abstract interface that knows
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nothing about output. The *backends* are device dependent drawing
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devices, aka renderers, that transform the frontend representation to
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hardcopy or a display device (:ref:`what-is-a-backend`). Example
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backends: PS creates `PostScript®
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<http://http://www.adobe.com/products/postscript/>`_ hardcopy, SVG
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creates `Scalable Vector Graphics <http://www.w3.org/Graphics/SVG/>`_
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hardcopy, Agg creates PNG output using the high quality `Anti-Grain
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Geometry <http://www.antigrain.com>`_ library that ships with
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matplotlib, GTK embeds matplotlib in a `Gtk+ <http://www.gtk.org/>`_
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application, GTKAgg uses the Anti-Grain renderer to create a figure
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and embed it a Gtk+ application, and so on for `PDF
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<http://www.adobe.com/products/acrobat/adobepdf.html>`_, `WxWidgets
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<http://www.wxpython.org/>`_, `Tkinter
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<http://docs.python.org/lib/module-Tkinter.html>`_ etc.
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matplotlib is used by many people in many different contexts. Some
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people want to automatically generate PostScript files to send
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to a printer or publishers. Others deploy matplotlib on a web
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application server to generate PNG output for inclusion in
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dynamically-generated web pages. Some use matplotlib interactively
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from the Python shell in Tkinter on Windows™. My primary use is to
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embed matplotlib in a Gtk+ EEG application that runs on Windows, Linux
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and Macintosh OS X.
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