def interpolator_islith_isfault():
geo_model = gp.create_model('interpolator_islith_isfault')
# Importing the data from CSV-files and setting extent and resolution
gp.init_data(geo_model,
path_o=input_path + "/simple_fault_model_orientations.csv",
path_i=input_path + "/simple_fault_model_points.csv",
default_values=True)
gp.map_series_to_surfaces(geo_model, {
"Fault_Series":
'Main_Fault',
"Strat_Series":
('Sandstone_2', 'Siltstone', 'Shale', 'Sandstone_1', 'basement')
},
remove_unused_series=True)
geo_model.set_is_fault(['Fault_Series'])
gp.set_interpolation_data(geo_model,
compile_theano=True,
theano_optimizer='fast_compile',
verbose=[])
return geo_model.interpolator
gp.map_series_to_surfaces(
geo_model,
{
"Fault_Series":'Main_Fault',
"Strat_Series": ('Sandstone_2','Siltstone', 'Shale', 'Sandstone_1', 'basement')
}, remove_unused_series=True)
# geo_model.series
geo_model.set_is_fault(['Fault_Series'])
geo_model.faults.faults_relations_df
# gp.plot.plot_data(geo_model, direction='y')
# gp.plot.plot_3D(geo_model)
gp.set_interpolation_data(geo_model,
compile_theano=True,
theano_optimizer='fast_compile',
verbose=[])
gp.get_data(geo_model, 'kriging')
geo_model.additional_data.structure_data
sol = gp.compute_model(geo_model, compute_mesh=True, sort_surfaces=False)
gp.plot.plot_section(geo_model, cell_number=25,
direction='y', show_data=True)
ver , sim = gp.get_surfaces(geo_model)
for i in range(len(ver)):
fig = plt.figure()
x, y, z = ver[i][:, 0], ver[i][:, 1], ver[i][:, 2]
X, Y = np.meshgrid(x, y)
def loop2gempy_(test_data_name, tmp_path, vtk_path, orientations_file, contacts_file, groups_file, dtm_reproj_file,
bbox, model_base, model_top, vtk):
import gempy as gp
from gempy import plot
geo_model = gp.create_model(test_data_name)
# If depth coordinates are much smaller than XY the whole system of equations becomes very unstable. Until
# I fix it properly in gempy this is a handcrafted hack
ve = (bbox[0] - bbox[2]) / (model_base - model_top)
if ve < 3:
ve = 0
else:
print('The vertical exageration is: ', ve)
gp.init_data(geo_model, extent=[bbox[0], bbox[2], bbox[1], bbox[3], model_base*ve, model_top*ve],
resolution = (50,50,50),
path_o = orientations_file,
path_i = contacts_file, default_values=True);
# Show example lithological points
#gp.get_data(geo_model, 'surface_points').head()
# Show example orientations
#gp.get_data(geo_model, 'orientations').head()
# Plot some of this data
#gp.plot.plot_data(geo_model, direction='z')
geo_model.modify_surface_points(geo_model.surface_points.df.index, Z=geo_model.surface_points.df['Z']*ve)
# Load reprojected topgraphy to model
fp = dtm_reproj_file
geo_model.set_topography(source='gdal',filepath=fp)
contents=np.genfromtxt(groups_file,delimiter=',',dtype='U100')
ngroups=len(contents)
faults = gp.Faults()
series = gp.Series(faults)
#series.df
#display(ngroups,contents)
groups = []
for i in range (0,ngroups):
groups.append(contents[i].replace("\n",""))
series.add_series(contents[i].replace("\n",""))
print(contents[i].replace("\n",""))
series.delete_series('Default series')
#series
# Load surfaces and assign to series
surfaces = gp.Surfaces(series)
print(ngroups,groups)
for i in range(0,ngroups):
contents=np.genfromtxt(tmp_path+groups[i]+'.csv',delimiter=',',dtype='U100')
nformations=len(contents.shape)
if(nformations==1):
for j in range (1,len(contents)):
surfaces.add_surface(str(contents[j]).replace("\n",""))
d={groups[i]:str(contents[j]).replace("\n","")}
surfaces.map_series({groups[i]:(str(contents[j]).replace("\n",""))}) #working but no gps
else:
for j in range (1,len(contents[0])):
surfaces.add_surface(str(contents[0][j]).replace("\n",""))
d={groups[i]:str(contents[0][j]).replace("\n","")}
surfaces.map_series({groups[i]:(str(contents[0][j]).replace("\n",""))}) #working but no gps
# Set Interpolation Data
id_only_one_bool = geo_model.surface_points.df['id'].value_counts() == 1
id_only_one = id_only_one_bool.index[id_only_one_bool]
single_vals = geo_model.surface_points.df[geo_model.surface_points.df['id'].isin(id_only_one)]
for idx, vals in single_vals.iterrows():
geo_model.add_surface_points(vals['X'], vals['Y'], vals['Z'], vals['surface'])
geo_model.update_structure()
gp.set_interpolation_data(geo_model,
compile_theano=True,
theano_optimizer='fast_compile',
verbose=[])
# Provide summary data on model
#geo_model.additional_data.structure_data
#Calculate Model
gp.compute_model(geo_model)
# Extract surfaces to visualize in 3D renderers
#gp.plot.plot_section(geo_model, 49, direction='z', show_data=False)
ver, sim = gp.get_surfaces(geo_model)
# import winsound
# duration = 700 # milliseconds
# freq = 1100 # Hz
# winsound.Beep(freq, duration)
# winsound.Beep(freq, duration)
# winsound.Beep(freq, duration)
#Visualise Model
gp.plot.plot_3D(geo_model, render_data=False)
#Save model as vtk
if(vtk):
gp.plot.export_to_vtk(geo_model, path=vtk_path, name=test_data_name+'.vtk', voxels=False, block=None, surfaces=True)
return geo_model
# Setting and ordering the units and series:
#
gp.map_stack_to_surfaces(geo_data, {
"Strat_Series": ('rock2', 'rock1'),
"Basement_Series": ('basement')
})
# %matplotlib inline
gp.plot.plot_data(geo_data, direction='y')
######################################################################
# Calculating the model:
#
interp_data = gp.set_interpolation_data(geo_data,
theano_optimizer='fast_compile')
geo_data.update_to_interpolator()
geo_data._orientations
geo_data._interpolator.theano_graph.nugget_effect_grad_T.get_value()
sol = gp.compute_model(geo_data)
######################################################################
# Displaying the result in y and x direction:
#
# %matplotlib inline
gp.plot.plot_section(geo_data, cell_number=15, direction='y', show_data=True)
