models = [geodyn_trg.TranslationGrowth(), geodyn_trg.TranslationGrowthRotation()]
geodynModel = geodyn_trg.PureTranslation()
geodynModel = geodyn_trg.TranslationRotation()
geodynModel = geodyn_trg.PureGrowth()
geodynModel = geodyn_trg.TranslationGrowth()
# geodynModel = geodyn_trg.TranslationGrowthRotation()
# geodynModel = geodyn_static.Hemispheres()
for geodynModel in models:
parameters = { 'rICB': rICB,
'tau_ic':age_ic,
'vt': velocity,
'exponent_growth': 0.3,
'omega': omega,
'proxy_type': "age"}
geodynModel.set_parameters(parameters)
## perfect sampling equator
npoints = 20 #number of points in the x direction for the data set.
data_set = data.PerfectSamplingEquator(npoints, rICB = 1.)
data_set.method = "bt_point"
proxy = geodyn.evaluate_proxy(data_set, geodynModel)
data_set.proxy = proxy #evaluate_proxy(data_set, geodynModel)
data_set.plot_c_vec(geodynModel)
#data_set.plot_scatter()
plt.show()
'omega': omega,
'proxy_type': proxy_type}
geodynModel.set_parameters(parameters)
geodynModel.define_units()
# ## Different data set and visualisations
# ### Perfect sampling at the equator (to visualise the flow lines)
# In[5]:
npoints = 50 #number of points in the x direction for the data set.
data_set = data.PerfectSamplingEquator(npoints, rICB = 1.)
data_set.method = "bt_point"
proxy = geodyn.evaluate_proxy(data_set, geodynModel, verbose = False)
data_set.plot_c_vec(geodynModel, proxy=proxy)
# ### Random data set, with "realistic" repartition
# In[6]:
# random data set
data_set_random = data.RandomData(3000)
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)