Stereonets for matplotlib

## Project description

`mplstereonet` provides lower-hemisphere equal-area and equal-angle stereonets
for matplotlib.

## What’s New

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!)

## Basic Usage

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.

## Density Contouring

`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.

## Utilities

`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

Additionally, you can find plane intersections and make other calculations by combining utility functions. See plane_intersection.py and parse_anglier_data.py for examples.

## References

[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. |

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