def main():
# Load data from Angelier, 1979
strikes, dips, rakes = parse_angelier_data.load()
params = dict(projection='stereonet', azimuth_ticks=[])
fig, (ax1, ax2) = plt.subplots(ncols=2, subplot_kw=params)
fault_and_striae_plot(ax1, strikes, dips, rakes)
ax1.set_title('Fault-and-Striae Diagram')
ax1.set_xlabel('Lineation direction plotted\nat rake location on plane')
tangent_lineation_plot(ax2, strikes, dips, rakes)
ax2.set_title('Tangent Lineation Diagram')
ax2.set_xlabel('Lineation direction plotted\nat pole location of plane')
fig.suptitle('Fault-slip data from Angelier, 1979', y=0.05)
fig.tight_layout()
plt.show()
"""
Reproduce Figure 5 from Vollmer, 1995 to illustrate different density contouring
methods.
"""
import matplotlib.pyplot as plt
import mplstereonet
import parse_angelier_data
def plot(ax, strike, dip, rake, **kwargs):
ax.rake(strike, dip, rake, 'ko', markersize=2)
ax.density_contour(strike, dip, rake, measurement='rakes', **kwargs)
# Load data from Angelier, 1979
strike, dip, rake = parse_angelier_data.load()
# Setup a subplot grid
fig, axes = mplstereonet.subplots(nrows=3, ncols=4)
# Hide azimuth tick labels
for ax in axes.flat:
ax.set_azimuth_ticks([])
contours = [range(2, 18, 2), range(1,21,2), range(1,22,2)]
# "Standard" Kamb contouring with different confidence levels.
for sigma, ax, contour in zip([3, 2, 1], axes[:,0], contours):
# We're reducing the gridsize to more closely match a traditional
# hand-contouring grid, similar to Kamb's original work and Vollmer's
# Figure 5. `gridsize=10` produces a 10x10 grid of density estimates.
import parse_angelier_data
def plot(ax, strike, dip, rake, **kwargs):
ax.rake(strike, dip, rake, 'ko', markersize=2)
ax.density_contour(strike,
dip,
rake,
measurement='rakes',
linewidths=1,
cmap='jet',
**kwargs)
# Load data from Angelier, 1979
strike, dip, rake = parse_angelier_data.load()
# Setup a subplot grid
fig, axes = mplstereonet.subplots(nrows=3, ncols=4)
# Hide azimuth tick labels
for ax in axes.flat:
ax.set_azimuth_ticks([])
contours = [range(2, 18, 2), range(1, 21, 2), range(1, 22, 2)]
# "Standard" Kamb contouring with different confidence levels.
for sigma, ax, contour in zip([3, 2, 1], axes[:, 0], contours):
# We're reducing the gridsize to more closely match a traditional
# hand-contouring grid, similar to Kamb's original work and Vollmer's
# Figure 5. `gridsize=10` produces a 10x10 grid of density estimates.
