Example #1
0
 def test_fit_girdle(self):
     for strike in range(0, 370, 10):
         for dip in range(0, 100, 10):
             lon, lat = mplstereonet.plane(strike, dip)
             strikes, dips = mplstereonet.geographic2pole(lon, lat)
             s, d = mplstereonet.fit_girdle(strikes, dips)
             self.compare_strikedip(strike, dip, s, d)
Example #2
0
 def test_fit_pole_noisy(self):
     np.random.seed(1)
     for strike in range(0, 370, 10):
         for dip in range(0, 100, 10):
             lon, lat = mplstereonet.pole(strike, dip)
             lon = lon + np.radians(np.random.normal(0, 1, 100))
             lat = lat + np.radians(np.random.normal(0, 1, 100))
             s_noisy, d_noisy = mplstereonet.geographic2pole(lon, lat)
             s, d = mplstereonet.fit_pole(s_noisy, d_noisy)
             ang_dist = self.cos_distance(strike, dip, s, d)
             assert ang_dist < 2 or (180 - ang_dist) < 2
Example #3
0
import mplstereonet

import parse_angelier_data

# Load data from Angelier, 1979
strike, dip, rake = parse_angelier_data.load()

# Plot the raw data and contour it:
fig, ax = mplstereonet.subplots()
ax.density_contour(strike, dip, rake, measurement='rakes', cmap='gist_earth',
                    sigma=1.5)
ax.rake(strike, dip, rake, marker='.', color='black')

# Find the two modes
centers = mplstereonet.kmeans(strike, dip, rake, num=2, measurement='rakes')
strike_cent, dip_cent = mplstereonet.geographic2pole(*zip(*centers))
ax.pole(strike_cent, dip_cent, 'ro', ms=12)

# Fit a girdle to the two modes
# The pole of this plane will be the plunge of the fold axis
axis_s, axis_d = mplstereonet.fit_girdle(*zip(*centers), measurement='radians')
ax.plane(axis_s, axis_d, color='green')
ax.pole(axis_s, axis_d, color='green', marker='o', ms=15)

# Now we'll find the midpoint. We could project the centers as rakes on the
# plane we just fit, but it's easier to get their mean vector instead.
mid, _ = mplstereonet.find_mean_vector(*zip(*centers), measurement='radians')
midx, midy = mplstereonet.line(*mid)

# Now let's find the axial plane by fitting another girdle to the midpoint
# and the pole of the plunge axis.
"""
import numpy as np
import matplotlib.pyplot as plt
import mplstereonet
np.random.seed(1)

# Generate a random girdle distribution from the plunge/bearing of a fold hinge
# In the end, we'll have strikes and dips as measured from bedding in the fold.
# *strike* and *dip* below would normally be your input.
num_points = 200
real_bearing, real_plunge = 300, 5
s, d = mplstereonet.plunge_bearing2pole(real_plunge, real_bearing)
lon, lat = mplstereonet.plane(s, d, segments=num_points)
lon += np.random.normal(0, np.radians(15), lon.shape)
lat += np.random.normal(0, np.radians(15), lat.shape)
strike, dip = mplstereonet.geographic2pole(lon, lat)

# Plot the raw data and contour it:
fig, ax = mplstereonet.subplots()
ax.density_contourf(strike, dip, cmap='gist_earth')
ax.density_contour(strike, dip, colors='black')
ax.pole(strike, dip, marker='.', color='black')

# Fit a plane to the girdle of the distribution and display it.
fit_strike, fit_dip = mplstereonet.fit_girdle(strike, dip)
ax.plane(fit_strike, fit_dip, color='red', lw=2)
ax.pole(fit_strike, fit_dip, marker='o', color='red', markersize=14)

# Add some annotation of the result
lon, lat = mplstereonet.pole(fit_strike, fit_dip)
(plunge,), (bearing,) = mplstereonet.pole2plunge_bearing(fit_strike, fit_dip)