def test_c(self, interpolator_islith_nofault):
"""
Two layers a bit curvy, drift degree 0
"""
# Importing the data from csv files and settign extent and resolution
geo_data = gempy.create_data([0, 10, 0, 10, -10, 0], [50, 50, 50],
path_o=input_path+"/GeoModeller/test_c/test_c_Foliations.csv",
path_i=input_path+"/GeoModeller/test_c/test_c_Points.csv")
geo_data.set_theano_function(interpolator_islith_nofault)
# Compute model
sol = gempy.compute_model(geo_data)
gempy.plot.plot_section(geo_data, 25, direction='y', show_data=True)
plt.savefig(os.path.dirname(__file__)+'/../figs/test_c.png', dpi=200)
if update_sol:
np.save(input_path + '/test_c_sol.npy', sol.lith_block[test_values])
# Load model
real_sol = np.load(input_path + '/test_c_sol.npy')
# Checking that the plots do not rise errors
gempy.plot.plot_section(geo_data, 25, direction='y', show_data=True)
gempy.plot.plot_scalar_field(geo_data, 25)
# We only compare the block because the absolute pot field I changed it
np.testing.assert_array_almost_equal(np.round(sol.lith_block[test_values]), real_sol, decimal=0)
def test_e(self, interpolator):
"""
Two layers a bit curvy, 1 fault
"""
# Importing the data from csv files and settign extent and resolution
geo_data = gempy.create_data([0, 10, 0, 10, -10, 0], [50, 50, 50],
path_o=input_path+"/GeoModeller/test_e/test_e_Foliations.csv",
path_i=input_path+"/GeoModeller/test_e/test_e_Points.csv")
gempy.set_series(geo_data, {'fault1': 'f1','series': ('A', 'B')})
geo_data.set_is_fault('fault1')
geo_data.set_theano_function(interpolator)
# Compute model
sol = gempy.compute_model(geo_data)
if update_sol:
np.save(input_path + '/test_e_sol.npy', sol.lith_block[test_values])
gempy.plot.plot_section(geo_data, 25, direction='y', show_data=True)
plt.savefig(os.path.dirname(__file__)+'/../figs/test_e.png', dpi=200)
# Load model
real_sol = np.load(input_path + '/test_e_sol.npy')
# We only compare the block because the absolute pot field I changed it
np.testing.assert_array_almost_equal(np.round(sol.lith_block[test_values]), real_sol, decimal=0)
def create_model(resolution=[50, 50, 50]):
geo_data = gp.create_data(
'fault',
extent=[0, 1000, 0, 1000, 0, 1000],
resolution=resolution,
path_o=path_to_data + "model5_orientations.csv",
path_i=path_to_data + "model5_surface_points.csv")
geo_data.get_data()
gp.map_stack_to_surfaces(geo_data, {
"Fault_Series": 'fault',
"Strat_Series": ('rock2', 'rock1')
})
geo_data.set_is_fault(['Fault_Series'])
interp_data = gp.set_interpolator(geo_data,
theano_optimizer='fast_compile')
sol = gp.compute_model(geo_data)
geo = gp.plot_2d(geo_data,
direction='y',
show_data=True,
show_lith=True,
show_boundaries=False)
geo.axes[0].set_title("")
plt.tight_layout()
plt.close()
return geo.axes[0].figure
def vista_obj(self) -> vs.Vista:
"""Return a GemPy Vista instance with basic geomodel attached."""
from gempy.plot import vista as vs
geo_model = gp.create_data(
[0, 2000, 0, 2000, 0, 2000], [50, 50, 50],
path_o=input_path + '/input_data/tut_chapter1'
'/simple_fault_model_orientations.csv',
path_i=input_path + '/input_data/tut_chapter1'
'/simple_fault_model_points.csv')
gp.set_series(
geo_model, {
"Fault_Series":
'Main_Fault',
"Strat_Series":
('Sandstone_2', 'Siltstone', 'Shale', 'Sandstone_1')
})
geo_model.set_is_fault(['Fault_Series'])
gp.set_interpolator(geo_model)
gp.compute_model(geo_model)
# with open(os.path.dirname(__file__)+"input_data/geomodel_fabian_sol.p", "rb") as f:
# geo_model.solutions = load(f)
return vs.Vista(geo_model)
def test_set_orientation_from_neighbours_all():
data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/'
path_to_data = data_path + "/data/input_data/jan_models/"
geo_data = gp.create_data('fault',
extent=[0, 1000, 0, 1000, 0, 1000],
resolution=[50, 50, 50],
path_o=path_to_data + "model5_orientations.csv",
path_i=path_to_data +
"model5_surface_points.csv")
# count orientations before orientation calculation
length_pre = geo_data._orientations.df.shape[0]
# find neighbours
neighbours = gp.select_nearest_surfaces_points(geo_data,
geo_data._surface_points.df,
2)
# calculate all fault orientations
gp.set_orientation_from_neighbours_all(geo_data, neighbours)
# count orientations after orientation calculation
length_after = geo_data._orientations.df.shape[0]
assert np.array_equal(geo_data._surface_points.df.shape[0],
length_after - length_pre)
def test_b(self, theano_f):
"""
Two layers a bit curvy, drift degree 1
"""
# Importing the data from csv files and settign extent and resolution
geo_data = gempy.create_data([0, 10, 0, 10, -10, 0], [50, 50, 50],
path_o=input_path+"/GeoModeller/test_b/test_b_Foliations.csv",
path_i=input_path+"/GeoModeller/test_b/test_b_Points.csv")
interp_data = theano_f
# Updating the interp data which has theano compiled
interp_data.update_interpolator(geo_data, u_grade=[1])
gempy.get_kriging_parameters(interp_data, verbose=1)
# Compute model
sol = gempy.compute_model(interp_data)
gempy.plot_section(geo_data, sol[0][0, :], 25, direction='y', plot_data=True)
plt.savefig(os.path.dirname(__file__)+'/figs/test_b.png', dpi=200)
if False:
np.save(input_path + '/test_b_sol.npy', sol)
# Load model
real_sol = np.load(input_path + '/test_b_sol.npy')
# Checking that the plots do not rise errors
gempy.plot_section(geo_data, sol[0][0, :], 25, direction='y', plot_data=True)
gempy.plot_scalar_field(geo_data, sol[0][1, :], 25)
# We only compare the block because the absolute pot field I changed it
np.testing.assert_array_almost_equal(np.round(sol[0][0, :]), real_sol[0][0, :], decimal=0)
def test_a(self, theano_f):
"""
2 Horizontal layers with drift 0
"""
# Importing the data from csv files and settign extent and resolution
geo_data = gempy.create_data([0, 10, 0, 10, -10, 0], [50, 50, 50],
path_o=os.path.dirname(__file__)+"/GeoModeller/test_a/test_a_Foliations.csv",
path_i=os.path.dirname(__file__)+"/GeoModeller/test_a/test_a_Points.csv")
interp_data = theano_f
# Updating the interp data which has theano compiled
interp_data.update_interpolator(geo_data)
# Compute model
sol = gempy.compute_model(interp_data, u_grade=[1])
if False:
np.save(os.path.dirname(__file__)+'/test_a_sol.npy', sol)
# Load model
real_sol = np.load(os.path.dirname(__file__)+'/test_a_sol.npy')
# We only compare the block because the absolute pot field I changed it
np.testing.assert_array_almost_equal(sol[0][0, :], real_sol[0][0, :], decimal=3)
# Checking that the plots do not rise errors
gempy.plot_section(geo_data, sol[0][0, :], 25, direction='y', plot_data=True)
plt.savefig(os.path.dirname(__file__)+'/figs/test_a.png', dpi=100)
gempy.plot_scalar_field(geo_data, sol[0][1, :], 25)
def test_e(self, theano_f_1f):
"""
Two layers a bit curvy, 1 fault
"""
# Importing the data from csv files and settign extent and resolution
geo_data = gempy.create_data([0, 10, 0, 10, -10, 0], [50, 50, 50],
path_o=input_path+"/GeoModeller/test_e/test_e_Foliations.csv",
path_i=input_path+"/GeoModeller/test_e/test_e_Points.csv")
gempy.set_series(geo_data, {'series': ('A', 'B'),
'fault1': 'f1'}, order_series=['fault1', 'series'],
order_formations=['f1','A','B'],
verbose=0)
interp_data = theano_f_1f
# Updating the interp data which has theano compiled
interp_data.update_interpolator(geo_data, u_grade=[1, 1])
# Compute model
sol = gempy.compute_model(interp_data)
if False:
np.save(input_path + '/test_e_sol.npy', sol)
gempy.plot_section(geo_data, sol[0][0, :], 25, direction='y', plot_data=True)
plt.savefig(os.path.dirname(__file__)+'/figs/test_e.png', dpi=200)
# Load model
real_sol = np.load(input_path + '/test_e_sol.npy')
# We only compare the block because the absolute pot field I changed it
np.testing.assert_array_almost_equal(np.round(sol[0][0, :]), real_sol[0][0, :], decimal=0)
def test_custom_grid_solution(interpolator_islith_nofault):
"""
Integration test for a gempy model using a custom grid
2 Horizontal layers with drift 0
:param interpolator_islith_nofault:
:return:
"""
# Importing the data from csv files and settign extent and resolution
geo_model = gempy.create_data([0, 10, 0, 10, -10, 0], [10, 10, 10],
path_o=input_path + "/GeoModeller/test_a/test_a_Foliations.csv",
path_i=input_path + "/GeoModeller/test_a/test_a_Points.csv")
# add a custom grid
cg = np.array([[5, 5, -9],
[5, 5, -5],
[5, 5, -5.1],
[5, 5, -5.2],
[5, 5, -1]])
values = geo_model.set_custom_grid(cg)
assert geo_model.grid.active_grids[1]
# set the theano function
geo_model.set_theano_function(interpolator_islith_nofault)
# Compute model
sol = gempy.compute_model(geo_model, compute_mesh=False)
assert sol.custom.shape == (2,1,5)
def test_set_orientations():
# Importing the data from CSV-files and setting extent and resolution
geo_data = gp.create_data(
[0, 2000, 0, 2000, 0, 2000], [50, 50, 50],
path_o=input_path +
'/input_data/tut_chapter1/simple_fault_model_orientations.csv',
path_i=input_path +
'/input_data/tut_chapter1/simple_fault_model_points.csv')
gp.get_data(geo_data)
# Assigning series to formations as well as their order (timewise)
gp.set_series(
geo_data, {
"Fault_Series": 'Main_Fault',
"Strat_Series":
('Sandstone_2', 'Siltstone', 'Shale', 'Sandstone_1')
},
order_series=["Fault_Series", 'Strat_Series'],
order_formations=[
'Main_Fault',
'Sandstone_2',
'Siltstone',
'Shale',
'Sandstone_1',
],
verbose=0)
gp.set_orientation_from_interfaces(geo_data, [0, 1, 2])
def test_compute_model_multiple_ranges(self, interpolator):
# Importing the data from csv files and settign extent and resolution
geo_data = gempy.create_data(
extent=[0, 2000, 0, 2000, -2000, 0],
resolution=[50, 50, 50],
path_o=input_path + "/GeoModeller/test_f/test_f_Foliations.csv",
path_i=input_path + "/GeoModeller/test_f/test_f_Points.csv")
gempy.map_stack_to_surfaces(
geo_data,
{
'fault1':
'MainFault',
'series': ('Reservoir', 'Seal', 'SecondaryReservoir',
'NonReservoirDeep'),
},
)
geo_data.set_theano_function(interpolator)
geo_data.set_is_fault('fault1')
geo_data.modify_kriging_parameters('range', [3000, 3500, 0])
geo_data._additional_data.kriging_data.set_default_c_o()
# Compute model
sol = gempy.compute_model(geo_data, sort_surfaces=True)
gempy.plot.plot_2d(geo_data,
cell_number=25,
direction='y',
show_data=True)
plt.show()
def theano_f():
# Importing the data from csv files and settign extent and resolution
geo_data = gempy.create_data([0, 10, 0, 10, -10, 0], [50, 50, 50],
path_o=input_path + "/GeoModeller/test_a/test_a_Foliations.csv",
path_i=input_path + "/GeoModeller/test_a/test_a_Points.csv")
interp_data = gempy.InterpolatorData(geo_data, dtype='float64', compile_theano=True,
verbose=[])
return interp_data
def theano_f_1f(self):
# Importing the data from csv files and settign extent and resolution
geo_data = gempy.create_data([0, 10, 0, 10, -10, 0], [50, 50, 50],
path_o=os.path.dirname(__file__)+"/GeoModeller/test_d/test_d_Foliations.csv",
path_i=os.path.dirname(__file__)+"/GeoModeller/test_d/test_d_Points.csv")
gempy.set_series(geo_data, {'series': ('A', 'B'),
'fault1': 'f1'}, order_series=['fault1', 'series'])
interp_data = gempy.InterpolatorData(geo_data, dtype='float64', compile_theano=True)
return interp_data
def test_ch2(theano_f):
# Importing the data from csv files and settign extent and resolution
geo_data = gp.create_data([696000,747000,6863000,6930000,-20000, 200], [50, 50, 50],
path_o=input_path+"/input_data/tut_SandStone/SandStone_Foliations.csv",
path_i=input_path+"/input_data/tut_SandStone/SandStone_Points.csv")
gp.plotting.plot_data(geo_data, direction='z')
# Assigning series to formations as well as their order (timewise)
gp.set_series(geo_data, {"EarlyGranite_Series": 'EarlyGranite',
"BIF_Series":('SimpleMafic2', 'SimpleBIF'),
"SimpleMafic_Series":'SimpleMafic1'},
order_series = ["EarlyGranite_Series",
"BIF_Series",
"SimpleMafic_Series"],
order_formations= ['EarlyGranite', 'SimpleMafic2', 'SimpleBIF', 'SimpleMafic1'],
verbose=1)
# interp_data = gp.InterpolatorData(geo_data, theano_optimizer='fast_run',
# compile_theano=True, verbose=[])
interp_data = theano_f
interp_data.update_interpolator(geo_data)
lith_block, fault_block = gp.compute_model(interp_data)
import matplotlib.pyplot as plt
gp.plot_section(geo_data, lith_block[0], -2, plot_data=True, direction='z')
fig = plt.gcf()
fig.set_size_inches(18.5, 10.5)
gp.plot_section(geo_data, lith_block[0],25, plot_data=True, direction='x')
fig = plt.gcf()
fig.set_size_inches(18.5, 10.5)
# In[14]:
gp.plot_scalar_field(geo_data, lith_block[1], 11, cmap='viridis', N=100)
import matplotlib.pyplot as plt
plt.colorbar(orientation='horizontal')
vertices, simplices = gp.get_surfaces(interp_data, lith_block[1], None, original_scale=False)
pyevtk = pytest.importorskip("pyevtk")
gp.export_to_vtk(geo_data, path=os.path.dirname(__file__)+'/vtk_files', lith_block=lith_block[0], vertices=vertices, simplices=simplices)
def theano_f(self):
# Importing the data from csv files and settign extent and resolution
geo_data = gempy.create_data([0, 10, 0, 10, -10, 0], [50, 50, 50],
path_o=os.path.dirname(__file__)+"/GeoModeller/test_a/test_a_Foliations.csv",
path_i=os.path.dirname(__file__)+"/GeoModeller/test_a/test_a_Points.csv")
interp_data = gempy.InterpolatorData(geo_data, dtype='float64', u_grade=[1], compile_theano=True,
verbose=['cov_gradients', 'cov_interfaces',
'solve_kriging', 'sed_dips_dips', 'slices'])
return interp_data
def test_f(self, theano_f_1f):
"""
Two layers a bit curvy, 1 fault. Checked with geomodeller
"""
# Importing the data from csv files and settign extent and resolution
geo_data = gempy.create_data(
[0, 2000, 0, 2000, -2000, 0], [50, 50, 50],
path_o=input_path + "/GeoModeller/test_f/test_f_Foliations.csv",
path_i=input_path + "/GeoModeller/test_f/test_f_Points.csv")
gempy.set_series(geo_data, {
'series':
('Reservoir', 'Seal', 'SecondaryReservoir', 'NonReservoirDeep'),
'fault1':
'MainFault'
},
order_series=['fault1', 'series'],
order_formations=[
'MainFault', 'SecondaryReservoir', 'Seal',
'Reservoir', 'NonReservoirDeep'
],
verbose=0)
interp_data = theano_f_1f
# Updating the interp data which has theano compiled
interp_data.update_interpolator(geo_data, u_grade=[1, 1])
# Compute model
sol = gempy.compute_model(interp_data)
if False:
np.save(input_path + '/test_f_sol.npy', sol)
real_sol = np.load(input_path + '/test_f_sol.npy')
gempy.plot_section(geo_data,
sol[0][0, :],
25,
direction='y',
plot_data=True)
plt.savefig(os.path.dirname(__file__) + '/figs/test_f.png', dpi=200)
# We only compare the block because the absolute pot field I changed it
np.testing.assert_array_almost_equal(np.round(sol[0][0, :]),
real_sol[0][0, :],
decimal=0)
ver, sim = gempy.get_surfaces(interp_data,
sol[0][1],
sol[1][1],
original_scale=True)
def test_find_interfaces():
block = np.load(input_path+'/noddy_block.npy')
bool_block = im.find_interfaces_from_block(block, 1)
geo_data = gp.create_data(extent=[0, 6000,
0, 6000,
0, 500], resolution=[60, 60 ,6])
p_df = im.interfaces_from_interfaces_block(bool_block, geo_data._grid.values)
im.set_interfaces_from_block(geo_data, block)
def theano_f_grav():
# Importing the data from csv files and settign extent and resolution
geo_data = gempy.create_data([0, 10, 0, 10, -10, 0], [50, 50, 50],
path_o=input_path + "/GeoModeller/test_a/test_a_Foliations.csv",
path_i=input_path + "/GeoModeller/test_a/test_a_Points.csv")
gempy.set_series(geo_data, {'series': ('A', 'B'),
'fault1': 'f1'}, order_series=['fault1', 'series'])
interp_data = gempy.InterpolatorData(geo_data, dtype='float64', compile_theano=True, output='gravity', verbose=[])
return interp_data
def theano_f():
# Importing the data from csv files and settign extent and resolution
geo_data = gempy.create_data(
[0, 10, 0, 10, -10, 0], [50, 50, 50],
path_o=input_path + "/GeoModeller/test_a/test_a_Foliations.csv",
path_i=input_path + "/GeoModeller/test_a/test_a_Points.csv")
interp_data = gempy.InterpolatorData(geo_data,
dtype='float64',
compile_theano=True,
verbose=[])
return interp_data
def test_set_section_twice(self):
geo_data = gp.create_data(extent=[0, 1000, 0, 1000, 0, 1000],
resolution=[10, 10, 10])
section_dict = {
'section1': ([0, 0], [1000, 1000], [100, 80]),
'section2': ([800, 0], [800, 1000], [150, 100]),
'section3': ([50, 200], [100, 500], [200, 150])
}
geo_data.set_section_grid(section_dict)
geo_data.set_section_grid(section_dict)
print(geo_data._grid.sections)
def create_gempy_model(resolution=[20, 20, 20], type=2):
if type == 1:
data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/'
path_to_data = data_path + "/data/input_data/jan_models/"
geo_data = gp.create_data(
'fold',
extent=[0, 1000, 0, 1000, 0, 1000],
resolution=resolution,
path_o=path_to_data + "model2_orientations.csv",
path_i=path_to_data + "model2_surface_points.csv")
gp.map_stack_to_surfaces(geo_data, {
"Strat_Series": ('rock2', 'rock1'),
"Basement_Series": ('basement')
})
interp_data = gp.set_interpolator(geo_data,
theano_optimizer='fast_compile')
sol = gp.compute_model(geo_data)
elif type == 2:
data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/'
path_to_data = data_path + "/data/input_data/jan_models/"
geo_data = gp.create_data(
'unconformity',
extent=[0, 1000, 0, 1000, 0, 1000],
resolution=[50, 50, 50],
path_o=path_to_data + "model6_orientations.csv",
path_i=path_to_data + "model6_surface_points.csv")
gp.map_stack_to_surfaces(
geo_data, {
"Strat_Series1": ('rock3'),
"Strat_Series2": ('rock2', 'rock1'),
"Basement_Series": ('basement')
})
interp_data = gp.set_interpolator(geo_data,
theano_optimizer='fast_compile')
sol = gp.compute_model(geo_data)
return geo_data
def one_fault_model_no_interp():
"""This only makes sense for running small test fast"""
model = gp.create_data('one_fault_model', [0, 2000, 0, 2000, 0, 2000], [50, 50, 50],
path_o=input_path2 + 'tut_chapter1/simple_fault_model_orientations.csv',
path_i=input_path2 + 'tut_chapter1/simple_fault_model_points.csv')
# Assigning series to surface as well as their order (timewise)
gp.map_stack_to_surfaces(model, {"Fault_Series": 'Main_Fault',
"Strat_Series": ('Sandstone_2', 'Siltstone',
'Shale', 'Sandstone_1')},
)
model.set_is_fault(['Fault_Series'])
return model
def geo_model(self, interpolator_islith_nofault):
"""
2 Horizontal layers with drift 0
"""
# Importing the data from csv files and settign extent and resolution
geo_data = gempy.create_data([0, 10, 0, 10, -10, 0], [50, 50, 50],
path_o=input_path + "/GeoModeller/test_a/test_a_Foliations.csv",
path_i=input_path + "/GeoModeller/test_a/test_a_Points.csv")
geo_data.set_theano_function(interpolator_islith_nofault)
# Compute model
sol = gempy.compute_model(geo_data, compute_mesh_options={'rescale': True})
return geo_data
def test_section_grid(self):
geo_data = gp.create_data([0, 1000, 0, 1000, 0, 1000],
resolution=[10, 10, 10])
geo_data.set_topography()
section_dict = {
'section1': ([0, 0], [1000, 1000], [100, 80]),
'section2': ([800, 0], [800, 1000], [150, 100]),
'section3': ([50, 200], [100, 500], [200, 150])
}
geo_data.set_section_grid(section_dict)
print(geo_data.grid.sections)
np.testing.assert_almost_equal(
geo_data.grid.sections.df.loc['section3', 'dist'],
304.138127,
decimal=4)
def test_rescaled_marching_cube(interpolator):
"""
2 Horizontal layers with drift 0
"""
# Importing the data from csv files and setting extent and resolution
geo_data = gempy.create_data(
'Simple interpolator', [0, 10, 0, 10, -10, 0], [50, 50, 50],
path_o=input_path + "/GeoModeller/test_a/test_a_Foliations.csv",
path_i=input_path + "/GeoModeller/test_a/test_a_Points.csv")
geo_data.set_theano_function(interpolator)
# Compute model
sol = gempy.compute_model(geo_data, compute_mesh_options={'rescale': True})
print(sol.vertices)
return geo_data
def test_simple_model_gempy_engine():
import numpy
numpy.set_printoptions(precision=3, linewidth=200)
g = gempy.create_data("test_engine",
extent=[-4, 4, -4, 4, -4, 4],
resolution=[4, 1, 4])
sp = np.array([[-3, 0, 0], [0, 0, 0], [2, 0, 0.5], [2.5, 0, 1.2],
[3, 0, 2], [1, 0, .2], [2.8, 0, 1.5]])
g.set_default_surfaces()
for i in sp:
g.add_surface_points(*i, surface="surface1")
g.add_orientations(-3, 0, 2, pole_vector=(0, 0, 1), surface="surface1")
g.add_orientations(2, 0, 3, pole_vector=(-.2, 0, .8), surface="surface1")
g.modify_orientations([0, 1], smooth=0.000000000001)
g.modify_surface_points(g._surface_points.df.index, smooth=0.0000000001)
gempy.set_interpolator(g,
verbose=[
"n_surface_op_float_sigmoid",
"scalar_field_iter", "compare", "sigma"
])
g.modify_kriging_parameters("range", 50)
# g.modify_kriging_parameters("$C_o$", 5 ** 2 / 14 / 3)
g.modify_kriging_parameters("drift equations", [0])
import theano
dtype = "float32"
g._interpolator.theano_graph.i_reescale.set_value(np.cast[dtype](1.))
g._interpolator.theano_graph.gi_reescale.set_value(np.cast[dtype](1.))
gempy.compute_model(g)
print(g.additional_data)
print(g.solutions.scalar_field_matrix)
gempy.plot_2d(g)
print(g.grid.values)
print(g.solutions.weights_vector)
def test_ch6(theano_f_1f):
# initialize geo_data object
geo_data = gp.create_data([0, 3000, 0, 20, 0, 2000],
resolution=[50, 3, 67])
# import data points
geo_data.import_data_csv(
input_path + "/input_data/tut_chapter6/ch6_data_interf.csv",
input_path + "/input_data/tut_chapter6/ch6_data_fol.csv")
gp.set_series(
geo_data, {
"fault":
geo_data.get_formations()[np.where(
geo_data.get_formations() == "Fault")[0][0]],
"Rest":
np.delete(geo_data.get_formations(),
np.where(geo_data.get_formations() == "Fault")[0][0])
},
order_series=["fault", "Rest"],
verbose=0,
order_formations=['Fault', 'Layer 2', 'Layer 3', 'Layer 4', 'Layer 5'])
gp.plot_data(geo_data)
plt.xlim(0, 3000)
plt.ylim(0, 2000)
interp_data = gp.InterpolatorData(geo_data, u_grade=[0, 1])
lith_block, fault_block = gp.compute_model(interp_data)
gp.plot_section(geo_data, lith_block[0], 0)
G, centroids, labels_unique, lith_to_labels_lot, labels_to_lith_lot = gp.topology_compute(
geo_data, lith_block[0], fault_block)
gp.plot_section(geo_data, lith_block[0], 0, direction='y')
gp.plot_topology(geo_data, G, centroids)
lith_to_labels_lot["4"].keys()
gp.topology.check_adjacency(G, 8, 3)
G.adj[8]
G.adj[8][2]["edge_type"]
def unconformity_model(interpolator):
geo_model = gp.create_data(
'unconformity_model', [0, 1000, 0, 1000, 0, 1000],
resolution=[50, 42, 33],
path_o=input_path2 + "jan_models/model6_orientations.csv",
path_i=input_path2 + "jan_models/model6_surface_points.csv")
gp.map_stack_to_surfaces(
geo_model, {
"Strat_Series1": ('rock3'),
"Strat_Series2": ('rock2', 'rock1'),
"Basement_Series": ('basement')
})
# with open("input_data/geomodel_jan_sol.p", "rb") as f:
# geo_model.solutions = load(f)
geo_model.set_theano_function(interpolator)
gp.compute_model(geo_model)
return geo_model
def test_f_sort_surfaces(self, interpolator):
"""
Two layers a bit curvy, 1 fault. Checked with geomodeller
"""
# Importing the data from csv files and settign extent and resolution
geo_data = gempy.create_data(
[0, 2000, 0, 2000, -2000, 0], [50, 50, 50],
path_o=input_path + "/GeoModeller/test_f/test_f_Foliations.csv",
path_i=input_path + "/GeoModeller/test_f/test_f_Points.csv")
gempy.set_series(
geo_data,
{
'fault1':
'MainFault',
'series': ('Reservoir', 'Seal', 'SecondaryReservoir',
'NonReservoirDeep'),
},
)
geo_data.set_theano_function(interpolator)
geo_data.set_is_fault('fault1')
# Compute model
sol = gempy.compute_model(geo_data, sort_surfaces=True)
if update_sol:
np.save(input_path + '/test_f_sol.npy',
sol.lith_block[test_values])
real_sol = np.load(input_path + '/test_f_sol.npy')
gempy.plot.plot_section(geo_data, 25, direction='y', show_data=True)
plt.savefig(os.path.dirname(__file__) + '/../figs/test_f.png', dpi=200)
# We only compare the block because the absolute pot field I changed it
np.testing.assert_array_almost_equal(np.round(
sol.lith_block[test_values]),
real_sol,
decimal=0)
ver, sim = gempy.get_surfaces(geo_data)
print(ver, sim)
def geo_model(self):
model = gp.create_data(
[0, 2000, 0, 2000, 0, 2000], [50, 50, 50],
path_o=input_path +
'/input_data/tut_chapter1/simple_fault_model_orientations.csv',
path_i=input_path +
'/input_data/tut_chapter1/simple_fault_model_points.csv')
# Assigning series to surface as well as their order (timewise)
gp.set_series(
model,
{
"Fault_Series":
'Main_Fault',
"Strat_Series":
('Sandstone_2', 'Siltstone', 'Shale', 'Sandstone_1')
},
)
return model
def theano_f_grav():
# Importing the data from csv files and settign extent and resolution
geo_data = gempy.create_data(
[0, 10, 0, 10, -10, 0], [50, 50, 50],
path_o=input_path + "/GeoModeller/test_a/test_a_Foliations.csv",
path_i=input_path + "/GeoModeller/test_a/test_a_Points.csv")
gempy.set_series(geo_data, {
'series': ('A', 'B'),
'fault1': 'f1'
},
order_series=['fault1', 'series'])
interp_data = gempy.InterpolatorData(geo_data,
dtype='float64',
compile_theano=True,
output='gravity',
verbose=[])
return interp_data
def test_f(self, theano_f_1f):
"""
Two layers a bit curvy, 1 fault. Checked with geomodeller
"""
# Importing the data from csv files and settign extent and resolution
geo_data = gempy.create_data([0, 2000, 0, 2000, -2000, 0], [50, 50, 50],
path_o=input_path+"/GeoModeller/test_f/test_f_Foliations.csv",
path_i=input_path+"/GeoModeller/test_f/test_f_Points.csv")
gempy.set_series(geo_data, {'series': ('Reservoir',
'Seal',
'SecondaryReservoir',
'NonReservoirDeep'
),
'fault1': 'MainFault'},
order_series=['fault1', 'series'],
order_formations=['MainFault', 'SecondaryReservoir', 'Seal', 'Reservoir', 'NonReservoirDeep'],
verbose=0)
interp_data = theano_f_1f
# Updating the interp data which has theano compiled
interp_data.update_interpolator(geo_data, u_grade=[1, 1])
# Compute model
sol = gempy.compute_model(interp_data)
if False:
np.save(input_path + '/test_f_sol.npy', sol)
real_sol = np.load(input_path + '/test_f_sol.npy')
gempy.plot_section(geo_data, sol[0][0, :], 25, direction='y', plot_data=True)
plt.savefig(os.path.dirname(__file__)+'/figs/test_f.png', dpi=200)
# We only compare the block because the absolute pot field I changed it
np.testing.assert_array_almost_equal(np.round(sol[0][0, :]), real_sol[0][0, :], decimal=0)
ver, sim = gempy.get_surfaces(interp_data, sol[0][1], sol[1][1], original_scale=True)
def topology_jan_unconf():
geo_model = gp.create_data(
[0, 1000, 0, 1000, 0, 1000],
resolution=[50, 50, 50],
path_o=data_path / "jan_models/model6_orientations.csv",
path_i=data_path / "jan_models/model6_surface_points.csv")
gp.map_series_to_surfaces(
geo_model, {
"Strat_Series1": ('rock3'),
"Strat_Series2": ('rock2', 'rock1'),
"Basement_Series": ('basement')
})
# with open("input_data/geomodel_jan_sol.p", "rb") as f:
# geo_model.solutions = load(f)
gp.set_interpolator(geo_model)
gp.compute_model(geo_model)
edges, centroids = tp.compute_topology(geo_model, voxel_threshold=1)
return edges, centroids
def test_ch6(theano_f_1f):
# initialize geo_data object
geo_data = gp.create_data([0, 3000, 0, 20, 0, 2000], resolution=[50, 3, 67])
# import data points
geo_data.import_data_csv(input_path+"/input_data/tut_chapter6/ch6_data_interf.csv",
input_path+"/input_data/tut_chapter6/ch6_data_fol.csv")
gp.set_series(geo_data, {"fault":geo_data.get_formations()[np.where(geo_data.get_formations()=="Fault")[0][0]],
"Rest":np.delete(geo_data.get_formations(), np.where(geo_data.get_formations()=="Fault")[0][0])},
order_series = ["fault", "Rest"], verbose=0, order_formations=['Fault','Layer 2', 'Layer 3', 'Layer 4', 'Layer 5'])
gp.plot_data(geo_data)
plt.xlim(0,3000)
plt.ylim(0,2000);
interp_data = gp.InterpolatorData(geo_data, u_grade=[0,1])
lith_block, fault_block = gp.compute_model(interp_data)
gp.plot_section(geo_data, lith_block[0], 0)
G, centroids, labels_unique, lith_to_labels_lot, labels_to_lith_lot = gp.topology_compute(
geo_data, lith_block[0], fault_block)
gp.plot_section(geo_data, lith_block[0], 0, direction='y')
gp.plot_topology(geo_data, G, centroids)
lith_to_labels_lot["4"].keys()
gp.topology.check_adjacency(G, 8, 3)
G.adj[8]
G.adj[8][2]["edge_type"]
def test_ch1(theano_f_1f):
# Importing the data from CSV-files and setting extent and resolution
geo_data = gp.create_data([0, 2000, 0, 2000, 0, 2000], [50, 50, 50],
path_o=input_path+'/input_data/tut_chapter1/simple_fault_model_orientations.csv',
path_i=input_path+'/input_data/tut_chapter1/simple_fault_model_points.csv')
gp.get_data(geo_data)
# Assigning series to formations as well as their order (timewise)
gp.set_series(geo_data, {"Fault_Series":'Main_Fault',
"Strat_Series": ('Sandstone_2','Siltstone',
'Shale', 'Sandstone_1')},
order_series = ["Fault_Series", 'Strat_Series'],
order_formations=['Main_Fault',
'Sandstone_2','Siltstone',
'Shale', 'Sandstone_1',
], verbose=0)
gp.get_sequential_pile(geo_data)
print(gp.get_grid(geo_data))
gp.get_data(geo_data, 'interfaces').head()
gp.get_data(geo_data, 'orientations')
gp.plot_data(geo_data, direction='y')
# interp_data = gp.InterpolatorData(geo_data, u_grade=[1,1],
# output='geology', compile_theano=True,
# theano_optimizer='fast_compile',
# verbose=[])
interp_data = theano_f_1f
interp_data.update_interpolator(geo_data)
gp.get_kriging_parameters(interp_data) # Maybe move this to an extra part?
lith_block, fault_block = gp.compute_model(interp_data)
gp.plot_section(geo_data, lith_block[0], cell_number=25, direction='y', plot_data=True)
gp.plot_scalar_field(geo_data, lith_block[1], cell_number=25, N=15,
direction='y', plot_data=False)
gp.plot_scalar_field(geo_data, lith_block[1], cell_number=25, N=15,
direction='z', plot_data=False)
gp.plot_section(geo_data, fault_block[0], cell_number=25, plot_data=True, direction='y')
gp.plot_scalar_field(geo_data, fault_block[1], cell_number=25, N=20,
direction='y', plot_data=False)
ver, sim = gp.get_surfaces(interp_data,lith_block[1], fault_block[1], original_scale=True)
# Cropping a cross-section to visualize in 2D #REDO this part?
bool_b = np.array(ver[1][:,1] > 999)* np.array(ver[1][:,1] < 1001)
bool_r = np.array(ver[1][:,1] > 1039)* np.array(ver[1][:,1] < 1041)
# Plotting section
gp.plot_section(geo_data, lith_block[0], 25, plot_data=True)
ax = plt.gca()
# Adding grid
ax.set_xticks(np.linspace(0, 2000, 100, endpoint=False))
ax.set_yticks(np.linspace(0, 2000, 100, endpoint=False))
plt.grid()
plt.ylim(1000,1600)
plt.xlim(500,1100)
# Plotting vertices
ax.plot(ver[1][bool_r][:, 0], ver[1][bool_r][:, 2], '.', color='b', alpha=.9)
ax.get_xaxis().set_ticklabels([])
ver_s, sim_s = gp.get_surfaces(interp_data,lith_block[1],
fault_block[1],
original_scale=True)
import importlib
from operator import itemgetter
from mpl_toolkits.mplot3d import Axes3D
import vtk
import evtk
from scipy.interpolate import griddata
import decision_making as dm
# Importing the data from csv files and setting extent and resolution
geo_data = gp.create_data([0,2000,0,2000,0,2000],[50,50,50],
path_o = "./reservoir_model_orientations.csv",
path_i = "./reservoir_model_interfaces.csv")
geo_data.n_faults = 1
gp.set_series(geo_data, {"fault":'MainFault',
"Rest":('Base_Top', 'Res_Top', 'Seal_Top', 'SecRes_Top')},
order_series = ["fault","Rest",], order_formations=['MainFault',
'SecRes_Top', 'Seal_Top', 'Res_Top','Base_Top',
])
# DECLARING SOME MODEL VARIABLES
resolution = geo_data.resolution[1] #resolution, standard: 50
model_size = geo_data.extent[:2][1] # 'real' model extent, here: 2000 m - cubic (what if not cubic?)
scale_factor = (model_size/resolution) # scale factor used for calculating voxel volumes in [m]
# here: 2000/50 = 40
#rescale_f = interp_data.rescaling_factor # rescaling factor from geo_data to interp_data
def test_ch3_a(theano_f):
# set cube size and model extent
cs = 50
extent = (3000, 200, 2000) # (x, y, z)
res = (120, 4, 80)
# initialize geo_data object
geo_data = gp.create_data([0, extent[0],
0, extent[1],
0, extent[2]],
resolution=[res[0], # number of voxels
res[1],
res[2]])
geo_data.set_interfaces(pn.read_csv(input_path+"/input_data/tut_chapter3/tutorial_ch3_interfaces",
index_col="Unnamed: 0"), append=True)
geo_data.set_orientations(pn.read_csv(input_path+"/input_data/tut_chapter3/tutorial_ch3_foliations",
index_col="Unnamed: 0"))
# let's have a look at the upper five interface data entries in the dataframe
gp.get_data(geo_data, 'interfaces', verbosity=1).head()
# Original pile
gp.get_sequential_pile(geo_data)
# Ordered pile
gp.set_order_formations(geo_data, ['Layer 2', 'Layer 3', 'Layer 4','Layer 5'])
gp.get_sequential_pile(geo_data)
# and at all of the foliation data
gp.get_data(geo_data, 'orientations', verbosity=0)
gp.plot_data(geo_data, direction="y")
plt.xlim(0,3000)
plt.ylim(0,2000);
gp.data_to_pickle(geo_data, os.path.dirname(__file__)+"/ch3-pymc2_tutorial_geo_data")
#interp_data = gp.InterpolatorData(geo_data, u_grade=[1], compile_theano=True)
interp_data = theano_f
interp_data.update_interpolator(geo_data)
# Afterwards we can compute the geological model
lith_block, fault_block = gp.compute_model(interp_data)
# And plot a section:
gp.plot_section(geo_data, lith_block[0], 2, plot_data = True)
import pymc
# Checkpoint in case you did not execute the cells above
geo_data = gp.read_pickle(os.path.dirname(__file__)+"/ch3-pymc2_tutorial_geo_data.pickle")
gp.get_data(geo_data, 'orientations', verbosity=1).head()
# So let's assume the vertical location of our layer interfaces is uncertain, and we want to represent this
# uncertainty by using a normal distribution. To define a normal distribution, we need a mean and a measure
# of deviation (e.g. standard deviation). For convenience the input data is already grouped by a "group_id" value,
# which allows us to collectively modify data that belongs together. In this example we want to treat the vertical
# position of each layer interface, on each side of the anticline, as uncertain. Therefore, we want to perturbate
# the respective three points on each side of the anticline collectively.
# These are our unique group id's, the number representing the layer, and a/b the side of the anticline.
group_ids = geo_data.interfaces["group_id"].dropna().unique()
print(group_ids)
# As a reminder, GemPy stores data in two main objects, an InputData object (called geo_data in the tutorials) and
# a InpterpolatorInput object (interp_data) in tutorials. geo_data contains the original data while interp_data the
# data prepared (and compiled) to compute the 3D model.
#
# Since we do not want to compile our code at every new stochastic realization, from here on we will need to work
# with thte interp_data. And remember that to improve float32 to stability we need to work with rescaled data
# (between 0 and 1). Therefore all the stochastic data needs to be rescaled accordingly. The object interp_data
# contains a property with the rescale factor (see below. As default depends on the model extent), or it is
# possible to add the stochastic data to the pandas dataframe of the geo_data---when the InterpolatorInput object
# is created the rescaling happens under the hood.
interface_Z_modifier = []
# We rescale the standard deviation
std = 20./interp_data.rescaling_factor
# loop over the unique group id's and create a pymc.Normal distribution for each
for gID in group_ids:
stoch = pymc.Normal(gID+'_stoch', 0, 1./std**2)
interface_Z_modifier.append(stoch)
# Let's have a look at one:
# sample from a distribtion
samples = [interface_Z_modifier[3].rand() for i in range(10000)]
# plot histogram
plt.hist(samples, bins=24, normed=True);
plt.xlabel("Z modifier")
plt.vlines(0, 0, 0.01)
plt.ylabel("n");
# Now we need to somehow sample from these distribution and put them into GemPy
# ## Input data handling
#
# First we need to write a function which modifies the input data for each iteration of the stochastic simulation.
# As this process is highly dependant on the simulation (e.g. what input parameters you want modified in which way),
# this process generally can't be automated.
#
# The idea is to change the column Z (in this case) of the rescaled dataframes in our interp_data object (which can
# be found in interp_data.geo_data_res). First we simply create the pandas Dataframes we are interested on:
import copy
# First we extract from our original intep_data object the numerical data that is necessary for the interpolation.
# geo_data_stoch is a pandas Dataframe
# This is the inital model so it has to be outside the stochastic frame
geo_data_stoch_init = copy.deepcopy(interp_data.geo_data_res)
gp.get_data(geo_data_stoch_init, numeric=True).head()
@pymc.deterministic(trace=True)
def input_data(value = 0,
interface_Z_modifier = interface_Z_modifier,
geo_data_stoch_init = geo_data_stoch_init,
verbose=0):
# First we extract from our original intep_data object the numerical data that is necessary for the interpolation.
# geo_data_stoch is a pandas Dataframe
geo_data_stoch = gp.get_data(geo_data_stoch_init, numeric=True)
# Now we loop each id which share the same uncertainty variable. In this case, each layer.
for e, gID in enumerate(group_ids):
# First we obtain a boolean array with trues where the id coincide
sel = gp.get_data(interp_data.geo_data_res, verbosity=2)['group_id'] == gID
# We add to the original Z value (its mean) the stochastic bit in the correspondant groups id
geo_data_stoch.loc[sel, 'Z'] += np.array(interface_Z_modifier[e])
if verbose > 0:
print(geo_data_stoch)
# then return the input data to be input into the modeling function. Due to the way pymc2 stores the traces
# We need to save the data as numpy arrays
return [geo_data_stoch.xs('interfaces')[["X", "Y", "Z"]].values, geo_data_stoch.xs('orientations').values]
# ## Modeling function
@pymc.deterministic(trace=False)
def gempy_model(value=0,
input_data=input_data, verbose=True):
# modify input data values accordingly
interp_data.geo_data_res.interfaces[["X", "Y", "Z"]] = input_data[0]
# Gx, Gy, Gz are just used for visualization. The theano function gets azimuth dip and polarity!!!
interp_data.geo_data_res.orientations[["G_x", "G_y", "G_z", "X", "Y", "Z", 'dip', 'azimuth', 'polarity']] = input_data[1]
try:
# try to compute model
lb, fb = gp.compute_model(interp_data)
if True:
gp.plot_section(interp_data.geo_data_res, lb[0], 0, plot_data=True)
return lb, fb
except np.linalg.linalg.LinAlgError as err:
# if it fails (e.g. some input data combinations could lead to
# a singular matrix and thus break the chain) return an empty model
# with same dimensions (just zeros)
if verbose:
print("Exception occured.")
return np.zeros_like(lith_block), np.zeros_like(fault_block)
# We then create a pymc model with the two deterministic functions (*input_data* and *gempy_model*), as well as all
# the prior parameter distributions stored in the list *interface_Z_modifier*:
params = [input_data, gempy_model, *interface_Z_modifier]
model = pymc.Model(params)
# Then we set the number of iterations:
# Then we create an MCMC chain (in pymc an MCMC chain without a likelihood function is essentially a Monte Carlo
# forward simulation) and specify an hdf5 database to store the results in
RUN = pymc.MCMC(model, db="hdf5", dbname=os.path.dirname(__file__)+"/ch3-pymc2.hdf5")
# and we are finally able to run the simulation:
RUN.sample(iter=100, verbose=0)
def test_ch5(theano_f_grav, theano_f):
# Importing the data from csv files and settign extent and resolution
geo_data = gp.create_data([696000,747000,6863000,6950000,-20000, 200],[50, 50, 50],
path_o = input_path+"/input_data/tut_SandStone/SandStone_Foliations.csv",
path_i = input_path+"/input_data/tut_SandStone/SandStone_Points.csv")
# Assigning series to formations as well as their order (timewise)
gp.set_series(geo_data, {"EarlyGranite_Series": 'EarlyGranite',
"BIF_Series":('SimpleMafic2', 'SimpleBIF'),
"SimpleMafic_Series":'SimpleMafic1'},
order_series = ["EarlyGranite_Series",
"BIF_Series",
"SimpleMafic_Series"],
order_formations= ['EarlyGranite', 'SimpleMafic2',
'SimpleBIF', 'SimpleMafic1'],
verbose=1)
gp.plot_data(geo_data)
#interp_data = gp.InterpolatorData(geo_data, compile_theano=True)
interp_data = theano_f
interp_data.update_interpolator(geo_data)
lith_block, fault_block = gp.compute_model(interp_data)
import matplotlib.pyplot as plt
gp.plot_section(geo_data, lith_block[0], 10, plot_data=True, direction='y')
fig = plt.gcf()
fig.set_size_inches(18.5, 10.5)
from matplotlib.patches import Rectangle
currentAxis = plt.gca()
currentAxis.add_patch(Rectangle((7.050000e+05, 6863000),
747000 - 7.050000e+05,
6925000 - 6863000,
alpha=0.3, fill='none', color ='green' ))
ver_s, sim_s = gp.get_surfaces(interp_data, lith_block[1],
None,
original_scale=True)
# gp.plot_surfaces_3D_real_time(interp_data, ver_s, sim_s)
# Importing the data from csv files and settign extent and resolution
geo_data_extended = gp.create_data([696000-10000,
747000 + 20600,
6863000 - 20600,6950000 + 20600,
-20000, 600],
[50, 50, 50],
path_o=input_path + "/input_data/tut_SandStone/SandStone_Foliations.csv",
path_i=input_path + "/input_data/tut_SandStone/SandStone_Points.csv")
# Assigning series to formations as well as their order (timewise)
gp.set_series(geo_data_extended, {"EarlyGranite_Series": 'EarlyGranite',
"BIF_Series":('SimpleMafic2', 'SimpleBIF'),
"SimpleMafic_Series":'SimpleMafic1'},
order_series = ["EarlyGranite_Series",
"BIF_Series",
"SimpleMafic_Series"],
order_formations= ['EarlyGranite', 'SimpleMafic2',
'SimpleBIF', 'SimpleMafic1'],
verbose=1)
# interp_data_extended = gp.InterpolatorData(geo_data_extended, output='geology',
# compile_theano=True)
interp_data_extended = interp_data
interp_data_extended.update_interpolator(geo_data_extended)
geo_data_extended.set_formations(formation_values=[2.61,2.92,3.1,2.92,2.61],
formation_order=['EarlyGranite', 'SimpleMafic2',
'SimpleBIF', 'SimpleMafic1',
'basement'])
lith_ext, fautl = gp.compute_model(interp_data_extended)
import matplotlib.pyplot as plt
gp.plot_section(geo_data_extended, lith_ext[0], -1, plot_data=True, direction='z')
fig = plt.gcf()
fig.set_size_inches(18.5, 10.5)
from matplotlib.patches import Rectangle
currentAxis = plt.gca()
currentAxis.add_patch(Rectangle((7.050000e+05, 6863000), 747000 - 7.050000e+05,
6925000 - 6863000,
alpha=0.3, fill='none', color ='green' ))
interp_data_grav = theano_f_grav
interp_data_grav.update_interpolator(geo_data_extended)
gp.set_geophysics_obj(interp_data_grav, [7.050000e+05,747000,6863000,6925000,-20000, 200],
[10, 10],)
gp.precomputations_gravity(interp_data_grav, 10)
lith, fault, grav = gp.compute_model(interp_data_grav, 'gravity')
import matplotlib.pyplot as plt
plt.imshow(grav.reshape(10, 10), cmap='viridis', origin='lower',
extent=[7.050000e+05,747000,6863000,6950000] )
plt.colorbar()
