Circular diffraction pattern using matplotlib
|Original author(s)||John Hunter|
|Developer(s)||Michael Droettboom, et al.|
|Stable release||1.4.3 (26 October 2014[±])|
matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. 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. SciPy makes use of matplotlib.
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.
Comparison with Gnuplot
Both Gnuplot and Matplotlib are mature open source projects. They both can produce enormous types of different plots. While it is hard to specify a type of figure that one can do and the other can not, they still have different advantages and disadvantages:
>>> import matplotlib.pyplot 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 >>> import matplotlib.pyplot as plt >>> import numpy as np >>> 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)
- Plotly - for interactive, online matplotlib and Python graphs
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- "PyCha". bitbucket.org.
- "PyX". pyx.sourceforge.net/.
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