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)
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)
def test_fit_girdle_noisy(self):
np.random.seed(1)
for strike in range(0, 370, 10):
for dip in range(0, 100, 10):
lon, lat = mplstereonet.plane(strike, dip)
lon += np.radians(np.random.normal(0, 1, lon.shape))
lat += np.radians(np.random.normal(0, 1, lat.shape))
s_noisy, d_noisy = mplstereonet.geographic2pole(lon, lat)
s, d = mplstereonet.fit_girdle(s_noisy, d_noisy)
ang_dist = self.cos_distance(strike, dip, s, d)
assert ang_dist < 2 or (180 - ang_dist) < 2
def test_fit_girdle_noisy(self):
np.random.seed(1)
for strike in range(0, 370, 10):
for dip in range(0, 100, 10):
lon, lat = mplstereonet.plane(strike, dip)
lon += np.radians(np.random.normal(0, 1, lon.shape))
lat += np.radians(np.random.normal(0, 1, lat.shape))
s_noisy, d_noisy = mplstereonet.geographic2pole(lon, lat)
s, d = mplstereonet.fit_girdle(s_noisy, d_noisy)
ang_dist = self.cos_distance(strike, dip, s, d)
assert ang_dist < 2 or (180 - ang_dist) < 2
This example simulates finding the plunge and bearing of a cylindrical fold axis from strike/dip measurements of bedding in the fold limbs. """ 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)
This example simulates finding the plunge and bearing of a cylindrical fold axis from strike/dip measurements of bedding in the fold limbs. """ 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)
