Agent skill
mplstereonet
Stereonet plots for structural geology using matplotlib. Create lower-hemisphere stereographic projections for orientation data. Use when Claude 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.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/mplstereonet
SKILL.md
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
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