def Translation_analytical_raypath(
velocity_translation=2.01 * 1221.0 / 1.0e6, direction=positions.CartesianPoint(1, 0, 0)
):
# Initialize map
m, fig = plot_data.setting_map()
cm = plt.cm.get_cmap("RdYlBu")
# seismic data set (from Lauren's file)
data_points = read_write.read_from_file(
"results.dat",
slices=[
"PKIKP-PKiKP travel time residual",
"turn lat",
"turn lon",
"turn depth",
"in lat",
"in lon",
"out lat",
"out lon",
],
)
nlines, ncolumns = data_points.shape
print data_points.info
# translate it in the correct format
dataset = []
translation_dataset = []
phi, theta, age, dt = [], [], [], []
print nlines, "points to write."
for i in range(nlines):
if i % 100 == 0: # print every 100 values
print "Writing point ", i, ". Coordinates: ", data_points.ix[i]
# because it's analytical solution, we don't need to define a grid. model is calulated exactly.
translation_dataset.append(
geodynamic.Translation(
positions.SeismoPoint(
1221.0 - data_points.ix[i, "turn depth"],
data_points.ix[i, "turn lat"],
data_points.ix[i, "turn lon"],
)
)
)
translation_dataset[i].analytical(velocity_translation, direction)
phi.append(translation_dataset[i].initial_position.phi)
theta.append(translation_dataset[i].initial_position.theta)
age.append(translation_dataset[i].exact_solution)
x, y = m(phi, theta)
m.scatter(x, y, c=age, zorder=10, cmap=plt.cm.RdYlGn)
m.colorbar()
fig, ax = plt.subplots()
ax.plot(phi, age / max(age), ".")
dt = data_points["PKIKP-PKiKP travel time residual"]
print dt.shape
ax.plot(phi, dt, ".r")
plt.show()
def map_plot(self, geodyn_model=''):
""" plot data on a map."""
# user should plot on map in the main code.
m, fig = plot_data.setting_map()
colormap = plt.cm.get_cmap('RdYlBu')
_, theta, phi = self.extract_rtp("bottom_turning_point")
x, y = m(phi, theta)
proxy = np.array([self.proxy]).T.astype(float)
sc = m.scatter(x, y, c=proxy, zorder=10, cmap=colormap)
# TO DO : make a function to plot great circles correctly!
#r1, theta1, phi1 = self.extract_in() #use extract_rtp()
#r2, theta2, phi2 = self.extract_out()
# for i, t in enumerate(theta1):
# z, w = m.gcpoints(phi1[i], theta1[i], phi2[i], theta2[i], 200)#
# m.plot(z, w, zorder=5, c="black")
# m.drawgreatcircle(phi1[i], theta1[i], phi2[i], theta2[i], zorder=5, c="black")
title = "Dataset: {},\n geodynamic model: {}".format(
self.name, geodyn_model)
plt.title(title)
plt.colorbar(sc)
data_set_random.method = "bt_point"
proxy_random = geodyn.evaluate_proxy(data_set_random, geodynModel, verbose=False)
r, t, p = data_set_random.extract_rtp("bottom_turning_point")
dist = positions.angular_distance_to_point(t, p, *velocity_center)
#data_set_random.map_plot(geodynModel.name)
#data_set_random.phi_plot(geodynModel.name)
#data_set_random.distance_plot(geodynModel.name, positions.SeismoPoint(1., 0., -80.))
# In[7]:
## map
m, fig = plot_data.setting_map()
cm = plt.cm.get_cmap('RdYlBu')
x, y = m(p, t)
sc = m.scatter(x, y, c=proxy_random, zorder=10, cmap=cm)
plt.title("Dataset: {},\n geodynamic model: {}".format(data_set_random.name, geodynModel.name))
plt.colorbar(sc)
# In[8]:
## phi and distance plots
fig, ax = plt.subplots(1,2, sharey=True)
cm2 = plt.cm.get_cmap('winter')
sc1 = ax[0].scatter(p, proxy_random, c=abs(t), cmap=cm2, vmin =-0, vmax =90)
ax[0].set_xlabel("longitude")
ax[0].set_ylabel("age (Myears)")
