Circular diffraction pattern using matplotlib
|Original author(s)||John Hunter|
|Developer(s)||Michael Droettboom, et al.|
|Stable release||1.3.1 (26 March 2013[±])|
matplotlib is a plotting library for the Python programming language and its NumPy numerical mathematics extension. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like wxPython, Qt, or GTK. There is also a procedural "pylab" interface based on a state machine (like OpenGL), designed to closely resemble that of MATLAB.
matplotlib was originally written by John Hunter, has an active development community, and is distributed under a BSD-style license. Michael Droettboom was nominated as matplotlib's lead developer shortly before John Hunter's death in 2012.
Comparison with MATLAB
The pylab interface makes matplotlib easy to learn for experienced MATLAB users, making it a viable alternative to MATLAB as a teaching tool for numerical mathematics and signal processing.
Some of the advantages of the combination of Python, NumPy, and matplotlib over MATLAB include:
- Based on Python, a full-featured modern object-oriented programming language suitable for large-scale software development
- Free, open source, no license servers
- Native SVG support
Typically pylab is imported to bring NumPy and matplotlib into a single global namespace for the most MATLAB like syntax, however a more explicit import style, which names both matplotlib and NumPy, is the preferred coding style.
>>> import matplotlib as plt >>> import numpy as np >>> a = np.linspace(0,10,100) >>> b = np.exp(-a) >>> plt.plot(a,b) >>> plt.show()
>>> from numpy.random import normal,rand >>> x = normal(size=200) >>> plt.hist(x,bins=30) >>> plt.show()
>>> a = rand(100) >>> b = rand(100) >>> plt.scatter(a,b) >>> plt.show()
>>> from matplotlib import cm >>> from mpl_toolkits.mplot3d import Axes3D >>> fig = plt.figure() >>> ax = fig.gca(projection='3d') >>> X = np.arange(-5, 5, 0.25) >>> Y = np.arange(-5, 5, 0.25) >>> X, Y = np.meshgrid(X, Y) >>> R = np.sqrt(X**2 + Y**2) >>> Z = np.sin(R) >>> surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm) >>> plt.show()
Several toolkits are available which extend matplotlib functionality. Some are separate downloads, others ship with the matplotlib source code but have external dependencies.
- Basemap: map plotting with various map projections, coastlines, and political boundaries
- Cartopy: a mapping library featuring object-oriented map projection definitions, and arbitrary point, line, polygon and image transformation capabilities. (matplotlib v1.2 and above)
- Excel tools: utilities for exchanging data with Microsoft Excel
- GTK tools: interface to the GTK+ library
- Qt interface
- Mplot3d: 3-D plots
- Natgrid: interface to the natgrid library for gridding irregularly spaced data.
- GNU Octave
- PLplot – Python bindings available
- PyCha – libcairo implementation
- PyPlotter – compatible with Jython
- Sage (mathematics software) – uses matplotlib to draw plots
- SciPy (modules plt and gplt)
- wxPython (module wx.lib.plot.py)
||This article includes a list of references, but its sources remain unclear because it has insufficient inline citations. (August 2009)|
- "Matplotlib github stats". matplotlib.org.
- "Announcing Michael Droettboom as the lead matplotlib developer". matplotlib.org.
- "New in matplotlib 1.2". Retrieved 2012-11-25.
- "Matplotlib coding styles". matplotlib.org.
- "Toolkits". matplotlib.org.
- Whitaker, Jeffrey. "The Matplotlib Basemap Toolkit User's Guide (v. 1.0.5)". Matplotlib Basemap Toolkit documentation. Retrieved 24 April 2013.
- Elson, Philip. "Cartopy". Retrieved 24 April 2013.
- "Bigglessimple, elegant python plotting". biggles.sourceforge.net. Retrieved 24 November 2010.
- "Chaco". code.enthought.com.
- "Gnuplot.py on". gnuplot-py.sourceforge.net. Retrieved 24 November 2010.
- "PyCha". bitbucket.org.
- "PyX". pyx.sourceforge.net/.
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