def update_plot(self):
if self.plot is None:
self.plot = self.setup_plot()
azi = np.radians(90 - self.azimuth)
dx, dy = self.slip * np.cos(azi), self.slip * np.sin(azi)
dx, dy = 1000 * dx, 1000 * dy
moved = inclined_shear(self.faultxyz, self.horxyz, (dx, dy), self.alpha,
remove_invalid=False)
x, y, z = moved.T
z *= self.ve
self.plot.mlab_source.set(x=x, y=y, z=z, scalars=z)
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_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 update_plot(self):
if self.plot is None:
self.plot = self.setup_plot()
azi = np.radians(90 - self.azimuth)
dx, dy = self.slip * np.cos(azi), self.slip * np.sin(azi)
dx, dy = 1000 * dx, 1000 * dy
moved = inclined_shear(self.faultxyz,
self.horxyz, (dx, dy),
self.alpha,
remove_invalid=False)
x, y, z = moved.T
z *= self.ve
self.plot.mlab_source.set(x=x, y=y, z=z, scalars=z)
fault = data.to_world(fault)
models = [[0, 0]]
i = 0
for hor in data.horizons[::-1]:
print hor.name
# Downsample horizon for faster solution...
xyz = data.to_xyz(hor)[::50,:]
xyz = data.to_world(xyz)
# Move this horizon to the last horizon's best fit offset.
# Following the path of all previous horizons...
for model in models:
dx, dy = model
xyz = inclined_shear(fault, xyz, (dx,dy), data.alpha)
model = invert_slip(fault, xyz, data.alpha, guess=(0,0))
models.append(model)
i += 1
models = np.array(models)
movement = models[:,:2].cumsum(axis=0)
x, y = movement.T
plt.plot(x, y, marker='o')
utilities.plot_plate_motion(time=3e5)
plt.axis('equal')
import geoprobe
import data
import utilities
from fault_kinematics import homogeneous_simple_shear
from process_bootstrap_results import get_result, load
fault = data.to_world(data.to_xyz(data.fault))
alpha = data.alpha
f, group = load()
for hor in data.horizons:
print hor.name
xyz = data.to_world(data.to_xyz(hor))
slip = get_result(hor.name, group)
restored = homogeneous_simple_shear.inclined_shear(fault, xyz, slip, alpha)
print 'Resampling...'
restored = data.to_model(restored)
restored = utilities.grid_xyz(restored)
new_hor = geoprobe.horizon(*restored.T)
new_hor.write('restored_horizons/' + hor.name + '.hzn')
f.close()
fault = data.to_world(fault)
models = [[0, 0]]
i = 0
for hor in data.horizons[::-1]:
print hor.name
# Downsample horizon for faster solution...
xyz = data.to_xyz(hor)[::50, :]
xyz = data.to_world(xyz)
# Move this horizon to the last horizon's best fit offset.
# Following the path of all previous horizons...
for model in models:
dx, dy = model
xyz = inclined_shear(fault, xyz, (dx, dy), data.alpha)
model = invert_slip(fault, xyz, data.alpha, guess=(0, 0))
models.append(model)
i += 1
models = np.array(models)
movement = models[:, :2].cumsum(axis=0)
x, y = movement.T
plt.plot(x, y, marker='o')
utilities.plot_plate_motion(time=3e5)
plt.axis('equal')
