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sequential_restoration_cross_section.py
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sequential_restoration_cross_section.py
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import numpy as np
import matplotlib.pyplot as plt
import scipy.interpolate as interpolate
import geoprobe
from fault_kinematics.homogeneous_simple_shear import inclined_shear
import data
import process_bootstrap_results
class Section(object):
numpoints = 300
def __init__(self):
x, y = self.section_line()
self.x, self.y = x, y
diff = np.diff(np.r_[x[0], x]), np.diff(np.r_[y[0], y])
self.dist = np.cumsum(np.hypot(*diff))
def section_line(self):
def resample(item):
return np.linspace(item[0], item[1], self.numpoints)
x = [626538.9783601476, 659780.2625511164]
y = [3700356.4603633783, 3680243.543275068]
return resample(x), resample(y)
def xsec(self, xyz):
interp = interpolate.LinearNDInterpolator(xyz[:,:2], xyz[:,-1])
return interp(np.vstack([self.x, self.y]).T)
def plot(self, xyz, ax=None, **kwargs):
if ax is None:
ax = plt.gca()
z = self.xsec(xyz)
return ax.plot(self.dist/1000, z/1000, **kwargs)
ax.set_aspect(2)
def plot_horizons(ax, horizons, slip=(0,0)):
fault = data.world_xyz(data.fault)
sec = Section()
ax.set_ylim([-8, -1.5])
for hor in horizons:
xyz = data.world_xyz(hor)[::100]
xyz = inclined_shear(fault, xyz, slip, data.alpha)
sec.plot(xyz, ax)
sec.plot(fault, ax, color='red')
plt.setp(ax.get_xticklabels(), visible=False)
def plot_present_day(fig):
topax = fig.add_subplot(6,1,1, aspect=2, adjustable='box')
seafloor = geoprobe.horizon('/data/nankai/data/Horizons/jdk_seafloor.hzn')
plot_horizons(topax, data.horizons + [seafloor])
plt.setp(topax.get_xticklabels(), visible=True)
topax.set_ylabel('Depth (km bsl)')
topax.set_xlabel('Distance Along Section (km)')
return topax
def main():
# Use the mean slip vector from the bootstrapping results...
slip_vecs = process_bootstrap_results.mean_slip_vectors()
# Walk through things in reverse order, restoring things to lower slips
# then to progressively higher slips.
horizons, slip_vecs = data.horizons[::-1], slip_vecs[::-1]
fig, axes = plt.subplots(nrows=6, ncols=2, figsize=(12,12),
subplot_kw=dict(aspect=2, adjustable='datalim'))
# Hide the top two subplots and add a centered one with all horizons...
for ax in axes[0]:
ax.set_visible(False)
axes = axes[1:]
topax = plot_present_day(fig)
# Sequentially restore and plot horizons.
for i, (hor, slip, ax) in enumerate(zip(horizons, slip_vecs, axes.T.flat)):
horizons = data.horizons[:(len(data.horizons) - i)]
plot_horizons(ax, horizons, slip)
names = ['Present Day'] + ['Horizon {}'.format(x) for x in data.gulick_names[::-1]]
for name, ax in zip(names, [topax] + list(axes.T.flat)):
ax.annotate(name, (0,1), xytext=(6, -6), xycoords='axes fraction',
textcoords='offset points', va='top', ha='left')
ax.grid(True)
fig.subplots_adjust(hspace=0, wspace=0)
for ax in axes[-1]:
plt.setp(ax.get_xticklabels(), visible=True)
ax.set_xlabel('Distance Along Section (km)')
for ax in axes[:,1]:
ax.yaxis.tick_right()
ax.yaxis.label_position = 'right'
for i, ax in enumerate(axes.T.flat):
if (i % 2) == 0:
plt.setp(ax.get_yticklabels(), visible=True)
else:
plt.setp(ax.get_yticklabels(), visible=False)
fig.savefig('sequential_restoration_cross_section.pdf')
plt.show()
main()