Stereonets for matplotlib
mplstereonet provides lower-hemisphere equal-area and equal-angle stereonets for matplotlib.
Major changes in mplstereonet-v0.5 are:
- ax.cone method to plot small circles
- Various functions to fit poles and planes to a distribution of observations (e.g. mplstereonet.fit_girdle, mplstereonet.fit_pole, and mplstereonet.eigenvectors)
- Mean vector calculation and Fisher statistics for the mean vector (mplstereonet.find_mean_vector and mplstereonet.find_fisher_stats) (Many thanks to Tobias Schönberg!)
In most cases, you’ll want to import mplstereonet and then make an axes with projection="stereonet" (By default, this is an equal-area stereonet). Alternately, you can use mplstereonet.subplots, which functions identically to matplotlib.pyplot.subplots, but creates stereonet axes.
As an example:
import matplotlib.pyplot as plt import mplstereonet fig = plt.figure() ax = fig.add_subplot(111, projection='stereonet') strike, dip = 315, 30 ax.plane(strike, dip, 'g-', linewidth=2) ax.pole(strike, dip, 'g^', markersize=18) ax.rake(strike, dip, -25) ax.grid() plt.show()
Planes, lines, poles, and rakes can be plotted using axes methods (e.g. ax.line(plunge, bearing) or ax.rake(strike, dip, rake_angle)).
All planar measurements are expected to follow the right-hand-rule to indicate dip direction. As an example, 315/30S would be 135/30 follwing the right-hand rule.
mplstereonet also provides a few different methods of producing contoured orientation density diagrams.
The ax.density_contour and ax.density_contourf axes methods provide density contour lines and filled density contours, respectively. “Raw” density grids can be produced with the mplstereonet.density_grid function.
As a basic example:
import matplotlib.pyplot as plt import numpy as np import mplstereonet fig, ax = mplstereonet.subplots() strike, dip = 90, 80 num = 10 strikes = strike + 10 * np.random.randn(num) dips = dip + 10 * np.random.randn(num) cax = ax.density_contourf(strikes, dips, measurement='poles') ax.pole(strikes, dips) ax.grid(True) fig.colorbar(cax) plt.show()
By default, a modified Kamb method with exponential smoothing [Vollmer1995] is used to estimate the orientation density distribution. Other methods (such as the “traditional” Kamb [Kamb1956] and “Schmidt” (a.k.a. 1%) methods) are available as well. The method and expected count (in standard deviations) can be controlled by the method and sigma keyword arguments, respectively.
mplstereonet also includes a number of utilities to parse structural measurements in either quadrant or azimuth form such that they follow the right-hand-rule.
For an example, see parsing_example.py:
Parse quadrant azimuth measurements "N30E" --> 30.0 "E30N" --> 60.0 "W10S" --> 260.0 "N 10 W" --> 350.0 Parse quadrant strike/dip measurements. Note that the output follows the right-hand-rule. "215/10" --> Strike: 215.0, Dip: 10.0 "215/10E" --> Strike: 35.0, Dip: 10.0 "215/10NW" --> Strike: 215.0, Dip: 10.0 "N30E/45NW" --> Strike: 210.0, Dip: 45.0 "E10N 20 N" --> Strike: 260.0, Dip: 20.0 "W30N/46.7 S" --> Strike: 120.0, Dip: 46.7 Similarly, you can parse rake measurements that don't follow the RHR. "N30E/45NW 10NE" --> Strike: 210.0, Dip: 45.0, Rake: 170.0 "210 45 30N" --> Strike: 210.0, Dip: 45.0, Rake: 150.0 "N30E/45NW raking 10SW" --> Strike: 210.0, Dip: 45.0, Rake: 10.0
|[Kamb1956]||Kamb, 1959. Ice Petrofabric Observations from Blue Glacier, Washington, in Relation to Theory and Experiment. Journal of Geophysical Research, Vol. 64, No. 11, pp. 1891–1909.|
|[Vollmer1995]||Vollmer, 1995. C Program for Automatic Contouring of Spherical Orientation Data Using a Modified Kamb Method. Computers & Geosciences, Vol. 21, No. 1, pp. 31–49.|
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size mplstereonet-0.5.tar.gz (1.1 MB)||File type Source||Python version None||Upload date||Hashes View hashes|