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name: mplstereonet description: | Stereonet plots for structural geology using matplotlib. Create lower-hemisphere stereographic projections for orientation data. Use when the agent needs to: (1) Create stereonet plots for structural data, (2) Plot planes as great circles or poles, (3) Plot lineations with trend/plunge, (4) Generate density contours for orientations, (5) Calculate mean orientations and statistics, (6) Analyze fold axes with pi-diagrams, (7) Convert between strike/dip and trend/plunge formats. license: MIT metadata: version: 1.0.1 author: Geoscience Skills tags: '["Structural Geology", "Stereonet", "Orientation Data", "Matplotlib", "Visualization"]' dependencies: '["mplstereonet>=0.6", "matplotlib", "numpy"]' complements: '[]' workflow_role: visualization skill_type: domain

mplstereonet - Stereonets for Matplotlib

Quick Reference

import mplstereonet
import matplotlib.pyplot as plt

# Create stereonet
fig, ax = mplstereonet.subplots()

# Plot plane and pole (strike/dip, right-hand rule)
ax.plane(315, 45, 'b-')          # Great circle
ax.pole(315, 45, 'ko')           # Pole to plane

# Plot lineation (trend/plunge)
ax.line(120, 30, 'r^')

ax.grid()
plt.savefig('stereonet.png', dpi=150)

Key Functions

Function	Purpose
mplstereonet.subplots()	Create stereonet figure and axes
ax.plane(strike, dip)	Plot great circle
ax.pole(strike, dip)	Plot pole to plane
ax.line(trend, plunge)	Plot lineation point
ax.density_contourf()	Filled density contours
mplstereonet.fit_girdle()	Best-fit great circle
mplstereonet.find_mean_vector()	Mean orientation

Essential Operations

Multiple Measurements with Contours

import numpy as np

strikes = [45, 52, 38, 48, 55, 41, 50, 43]
dips = [25, 30, 22, 28, 35, 24, 32, 27]

fig, ax = mplstereonet.subplots()

# Density contour of poles
ax.density_contourf(strikes, dips, measurement='poles', cmap='Reds')
ax.pole(strikes, dips, 'k.', markersize=5)

ax.grid()
ax.set_title('Bedding Orientations')
plt.savefig('density.png', dpi=150)

Calculate Mean Orientation

# Fit best-fit plane (girdle)
mean_strike, mean_dip = mplstereonet.fit_girdle(strikes, dips)

# Or calculate mean pole for clustered data
lon, lat = mplstereonet.pole(strikes, dips)
mean_lon, mean_lat = mplstereonet.find_mean_vector(lon, lat)
mean_s, mean_d = mplstereonet.pole2strike(mean_lon, mean_lat)

Pi-Diagram (Fold Axis)

# Bedding measurements around a fold
strikes = np.array([20, 35, 50, 70, 90, 110, 130, 150, 165, 180])
dips = np.array([45, 40, 35, 30, 25, 30, 35, 40, 45, 50])

fig, ax = mplstereonet.subplots()
ax.pole(strikes, dips, 'ko', markersize=6)

# Fit girdle to poles - fold axis is pole to girdle
girdle_strike, girdle_dip = mplstereonet.fit_girdle(strikes, dips)
ax.plane(girdle_strike, girdle_dip, 'r-', linewidth=2)

fold_trend, fold_plunge = mplstereonet.pole(girdle_strike, girdle_dip)
ax.line(fold_trend, fold_plunge, 'r^', markersize=12, label='Fold axis')

ax.grid()
ax.legend()

Fault Plane with Slip Vector

fault_strike, fault_dip = 45, 60
rake = 30  # Degrees from strike

# Convert rake to trend/plunge
slip_trend, slip_plunge = mplstereonet.rake(fault_strike, fault_dip, rake)

fig, ax = mplstereonet.subplots()
ax.plane(fault_strike, fault_dip, 'r-', linewidth=2)
ax.line(slip_trend, slip_plunge, 'r>', markersize=10)
ax.grid()

Multiple Joint Sets

set1 = {'strikes': [45, 50, 42, 48], 'dips': [70, 75, 68, 72]}
set2 = {'strikes': [135, 140, 130, 138], 'dips': [60, 65, 58, 62]}

fig, ax = mplstereonet.subplots()
ax.pole(set1['strikes'], set1['dips'], 'ro', label='Set 1')
ax.pole(set2['strikes'], set2['dips'], 'bs', label='Set 2')
ax.grid()
ax.legend()

Measurement Conventions

Format	Description	Example
Strike/Dip	Right-hand rule (dip to right of strike)	045/60
Dip Direction/Dip	Azimuth of dip direction	135/60
Trend/Plunge	Linear orientation	180/30

Format Conversions

# Strike/dip to dip direction
strike, dip = 45, 60
dip_direction = (strike + 90) % 360

# Pole to strike/dip
lon, lat = mplstereonet.pole(strike, dip)
back_strike, back_dip = mplstereonet.pole2strike(lon, lat)

Contouring Methods

Method	Description
kamb	Statistical significance (default)
schmidt	Point counting
exponential_kamb	Smoothed Kamb

When to Use vs Alternatives

Tool	Best For	Limitations
mplstereonet	Quick stereonets in Python, matplotlib integration, scripted workflows	No interactive rotation, limited 3D
apsg	Advanced structural analysis, tensors, orientation statistics	Steeper learning curve
JTOPO	Interactive GUI exploration, teaching	Java-based, not scriptable

Use mplstereonet when you need programmatic stereonet generation integrated with matplotlib, batch processing of orientation datasets, or reproducible structural plots for publications.

Consider alternatives when you need interactive 3D visualization of orientations (use apsg), a GUI for teaching or quick inspection (use JTOPO), or advanced tensor statistics beyond what mplstereonet provides.

Common Workflows

Analyze bedding orientations and determine fold axis

• Load strike/dip measurements from CSV or array
• Create stereonet with mplstereonet.subplots()
• Plot poles to bedding with ax.pole(strikes, dips)
• Generate density contours with ax.density_contourf()
• Fit girdle to poles with mplstereonet.fit_girdle()
• Calculate fold axis as pole to girdle with mplstereonet.pole()
• Plot fold axis with ax.line(trend, plunge)
• Add grid, legend, and save figure

References

• Projection Types - Equal-area vs equal-angle projections
• Plotting Options - Customization and styling

Scripts

• scripts/structural_analysis.py - Analyze structural data and generate plots