def test_kriging_mutation(interpolator, map_sequential_pile):
geo_model = map_sequential_pile
geo_model.set_theano_graph(interpolator)
gp.compute_model(geo_model, compute_mesh=False)
gp.plot.plot_2d(geo_model, cell_number=25, show_scalar=True, series_n=1, N=15,
direction='y', show_data=True)
print(geo_model.solutions.lith_block, geo_model._additional_data)
#plt.savefig(os.path.dirname(__file__)+'/figs/test_kriging_mutation')
geo_model.modify_kriging_parameters('range', 1)
geo_model.modify_kriging_parameters('drift equations', [0, 3])
print(geo_model.solutions.lith_block, geo_model._additional_data)
# copy dataframe before interpolator is calculated
pre = geo_model._additional_data.kriging_data.df.copy()
gp.set_interpolator(geo_model, compile_theano=True,
theano_optimizer='fast_compile', update_kriging=False)
gp.compute_model(geo_model, compute_mesh=False)
gp.plot.plot_2d(geo_model, cell_number=25, series_n=1, N=15, show_boundaries=False,
direction='y', show_data=True, show_lith=True)
print(geo_model.solutions.lith_block, geo_model._additional_data)
plt.savefig(os.path.dirname(__file__)+'/../figs/test_kriging_mutation2')
assert geo_model._additional_data.kriging_data.df['range'][0] == pre['range'][0]
def test_kriging_mutation(interpolator_islith_isfault, map_sequential_pile):
geo_model = map_sequential_pile
geo_model.set_theano_graph(interpolator_islith_isfault)
gp.compute_model(geo_model, compute_mesh=False)
gp.plot.plot_scalar_field(geo_model,
cell_number=25,
series=1,
N=15,
direction='y',
show_data=True)
print(geo_model.solutions.lith_block, geo_model.additional_data)
plt.savefig('figs/test_kriging_mutation')
geo_model.modify_kriging_parameters('range', 1)
geo_model.modify_kriging_parameters('drift equations', [0, 3])
print(geo_model.solutions.lith_block, geo_model.additional_data)
gp.compute_model(geo_model, compute_mesh=False)
gp.plot.plot_scalar_field(geo_model,
cell_number=25,
series=1,
N=15,
direction='y',
show_data=True)
print(geo_model.solutions.lith_block, geo_model.additional_data)
plt.savefig('figs/test_kriging_mutation2')
def test_magnetics_api():
# TODO add the check
geo_model = gp.create_model('test_center_grid_slicing')
geo_model.set_default_surfaces()
geo_model.add_surface_points(X=-1, Y=0, Z=0, surface='surface1')
geo_model.add_surface_points(X=1, Y=0, Z=0, surface='surface1')
geo_model.add_orientations(X=0,
Y=0,
Z=0,
surface='surface1',
pole_vector=(0, 0, 1))
geo_model.surfaces.add_surfaces_values([0.037289, 0.0297],
['susceptibility'])
# needed constants
mu_0 = 4.0 * np.pi * 10e-7 # magnetic permeability in free space [N/A^2]
cm = mu_0 / (4.0 * np.pi) # constant for SI unit
incl = 77.0653 # NOAA
decl = 6.8116
B_ext = 52819.8506939139e-9 # T
geo_model.set_regular_grid(extent=[-5, 5, -5, 5, -5, 5],
resolution=[5, 5, 5])
geo_model.set_centered_grid(np.array([0, 0, 0]),
resolution=[10, 10, 15],
radio=5000)
gp.set_interpolator(geo_model, output=['magnetics'], incl=incl, decl=decl)
gp.compute_model(geo_model)
print(geo_model.interpolator.theano_graph.lg0.get_value())
return geo_model
def test_gravity():
geo_model = gp.create_model('2-layers')
gp.init_data(geo_model,
extent=[0, 12, -2, 2, 0, 4],
resolution=[500, 1, 500])
geo_model.add_surfaces('surface 1')
geo_model.add_surfaces('surface 2')
geo_model.add_surfaces('basement')
dz = geo_model.grid.regular_grid.dz
geo_model.surfaces.add_surfaces_values([dz, 0, 0], ['dz'])
geo_model.surfaces.add_surfaces_values([2.6, 2.4, 3.2], ['density'])
geo_model.add_surface_points(3, 0, 3.05, 'surface 1')
geo_model.add_surface_points(9, 0, 3.05, 'surface 1')
geo_model.add_surface_points(3, 0, 1.02, 'surface 2')
geo_model.add_surface_points(9, 0, 1.02, 'surface 2')
geo_model.add_orientations(6, 0, 4, 'surface 1', [0, 0, 1])
device_loc = np.array([[6, 0, 4]])
geo_model.set_centered_grid(device_loc,
resolution=[10, 10, 100],
radius=16000)
gp.set_interpolator(geo_model,
output=['gravity'],
pos_density=2,
gradient=True,
theano_optimizer='fast_compile')
gp.compute_model(geo_model, set_solutions=True, compute_mesh=False)
print(geo_model.solutions.fw_gravity)
np.testing.assert_almost_equal(geo_model.solutions.fw_gravity,
np.array([-9291.8003]),
decimal=4)
def one_fault_model_topo_solution(one_fault_model):
one_fault_model.update_additional_data()
one_fault_model.update_to_interpolator()
one_fault_model.set_topography(d_z=(800, 1800))
gp.compute_model(one_fault_model)
return one_fault_model
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 update(self, sb_params: dict):
frame = sb_params.get('frame')
extent = sb_params.get('extent')
ax = sb_params.get('ax')
marker = sb_params.get('marker')
self.lock = sb_params.get('lock_thread')
self.frame = frame # Store the current frame
self.vmin = frame.min()
self.vmax = frame.max()
scale_frame = self.grid.scale_frame(frame)
_ = self.grid.update_grid(scale_frame)
self.geo_model._grid.topography.values = self.grid.depth_grid
data = self.grid.depth_grid[:, 2].reshape(
self.geo_model._grid.topography.resolution)
self.geo_model._grid.topography.values_2d[:, :, 2] = data
_ = self.geo_model._grid.update_grid_values()
_ = self.geo_model.update_from_grid()
gempy.compute_model(self.geo_model, compute_mesh=False)
if len(marker) > 0:
self.modelspace_arucos = self._compute_modelspace_arucos(marker)
self.set_aruco_dict(self.modelspace_arucos)
ax, cmap = self.plot(ax, self.geo_model, self._model_extent)
sb_params['ax'] = ax
sb_params['frame'] = scale_frame
sb_params['cmap'] = cmap
sb_params['marker'] = self.modelspace_arucos
sb_params['active_cmap'] = False
sb_params['active_shading'] = False
sb_params['extent'] = self._model_extent
sb_params['del_contour'] = not self.show_boundary
return sb_params
def update_surfaces(self, recompute=True):
import time
if recompute is True:
try:
gp.compute_model(self.model, sort_surfaces=False, compute_mesh=True)
except IndexError:
t = time.localtime()
current_time = time.strftime("[%H:%M:%S]", t)
print(current_time + 'IndexError: Model not computed. Laking data in some surface')
except AssertionError:
t = time.localtime()
current_time = time.strftime("[%H:%M:%S]", t)
print(current_time + 'AssertionError: Model not computed. Laking data in some surface')
self.remove_actor(self.regular_grid_actor)
surfaces = self.model._surfaces
# TODO add the option of update specific surfaces
try:
for idx, val in surfaces.df[['vertices', 'edges', 'surface']].dropna().iterrows():
self.surface_poly[val['surface']].points = val['vertices']
self.surface_poly[val['surface']].faces = np.insert(val['edges'], 0, 3, axis=1).ravel()
except KeyError:
self.plot_surfaces()
return True
def test_magnetics_no_regular_grid(interpolator_magnetics):
# TODO add the check
geo_model = gp.create_model('test_center_grid_slicing')
geo_model.set_default_surfaces()
geo_model.add_surface_points(X=-1, Y=0, Z=0, surface='surface1')
geo_model.add_surface_points(X=1, Y=0, Z=0, surface='surface1')
geo_model.add_orientations(X=0, Y=0, Z=0, surface='surface1', pole_vector=(0, 0, 1))
geo_model._surfaces.add_surfaces_values([0.037289, 0.0297], ['susceptibility'])
# needed constants
mu_0 = 4.0 * np.pi * 10e-7 # magnetic permeability in free space [N/A^2]
cm = mu_0 / (4.0 * np.pi) # constant for SI unit
incl = 77.0653 # NOAA
decl = 6.8116
B_ext = 52819.8506939139e-9 # T
geo_model.set_centered_grid(np.array([0, 0, 0]), resolution=[10, 10, 15], radius=5000)
Vmodel = MagneticsPreprocessing(geo_model._grid.centered_grid).set_Vs_kernel()
# gp.set_interpolator(geo_model, output=['magnetics'])
geo_model.set_theano_function(interpolator_magnetics)
geo_model._interpolator.set_theano_shared_magnetics(V= Vmodel, pos_magnetics=1,
incl= incl, decl=decl, B_ext=B_ext)
# geo_model.interpolator.theano_graph.V.set_value(Vmodel)
# geo_model.interpolator.theano_graph.incl.set_value(incl)
# geo_model.interpolator.theano_graph.decl.set_value(decl)
# geo_model.interpolator.theano_graph.B_ext.set_value(B_ext)
gp.compute_model(geo_model)
np.testing.assert_almost_equal(geo_model.solutions.fw_magnetics,
np.array([473.7836]), decimal=4)
gp.compute_model(geo_model)
return geo_model
def test_pile_geomodel_2():
ve = 3
extent = [451e3, 456e3, 6.7820e6, 6.7840e6, -2309 * ve, -1651 * ve]
geo_model = gp.create_model('Topology-Gullfaks')
gp.init_data(geo_model,
extent, [30, 30, 30],
path_o=input_path + "/filtered_orientations.csv",
path_i=input_path + "/filtered_surface_points.csv",
default_values=True)
series_distribution = {
"fault3": "fault3",
"fault4": "fault4",
"unconformity": "BCU",
"sediments": ("tarbert", "ness", "etive"),
}
gp.map_series_to_surfaces(geo_model,
series_distribution,
remove_unused_series=True)
geo_model.reorder_series(
["unconformity", "fault3", "fault4", "sediments", "Basement"])
geo_model.set_is_fault(["fault3"])
geo_model.set_is_fault(["fault4"])
rel_matrix = np.array([[0, 0, 0, 0, 0], [0, 0, 0, 1, 1], [0, 0, 0, 1, 1],
[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]])
geo_model.set_fault_relation(rel_matrix)
surf_groups = pd.read_csv(input_path +
"/filtered_surface_points.csv").group
geo_model.surface_points.df["group"] = surf_groups
orient_groups = pd.read_csv(input_path +
"/filtered_orientations.csv").group
geo_model.orientations.df["group"] = orient_groups
geo_model.surface_points.df.reset_index(inplace=True, drop=True)
geo_model.orientations.df.reset_index(inplace=True, drop=True)
gp.set_interpolator(geo_model,
verbose=['mask_matrix_loop', 'mask_e', 'nsle'])
gp.compute_model(geo_model)
gp.plot.plot_section(geo_model,
cell_number=25,
direction='y',
show_data=True)
from gempy.plot.plot_api import plot_2d
p = plot_2d(geo_model, cell_number=[25])
plt.savefig(os.path.dirname(__file__) + '/../figs/test_pile_lith_block')
return geo_model
def test_magnetics_api(interpolator_magnetics):
# TODO add the check
geo_model = gp.create_model('test_center_grid_slicing')
geo_model.set_default_surfaces()
geo_model.add_surface_points(X=-1, Y=0, Z=0, surface='surface1')
geo_model.add_surface_points(X=1, Y=0, Z=0, surface='surface1')
geo_model.add_orientations(X=0, Y=0, Z=0, surface='surface1', pole_vector=(0, 0, 1))
geo_model._surfaces.add_surfaces_values([0.037289, 0.0297], ['susceptibility'])
# needed constants
mu_0 = 4.0 * np.pi * 10e-7 # magnetic permeability in free space [N/A^2]
cm = mu_0 / (4.0 * np.pi) # constant for SI unit
incl = 77.0653 # NOAA
decl = 6.8116
B_ext = 52819.8506939139e-9 # T
geo_model.set_regular_grid(extent=[-5, 5, -5, 5, -5, 5], resolution=[5, 5, 5])
geo_model.set_centered_grid(np.array([[0, 0, 0]]), resolution=[10, 10, 15], radius=5000)
geo_model.set_theano_function(interpolator_magnetics)
geo_model._interpolator.set_theano_shared_magnetics(V='auto', pos_magnetics=1,
incl= incl, decl=decl, B_ext=B_ext)
gp.compute_model(geo_model)
print(geo_model._interpolator.theano_graph.lg0.get_value())
print(geo_model.solutions.fw_magnetics)
np.testing.assert_almost_equal(geo_model.solutions.fw_magnetics,
np.array([473.7836]), decimal=4)
return geo_model
def test_gravity(interpolator_gravity):
geo_model = gp.create_model('2-layers')
gp.init_data(geo_model,
extent=[0, 12, -2, 2, 0, 4],
resolution=[500, 1, 500])
geo_model.add_surfaces('surface 1')
geo_model.add_surfaces('surface 2')
geo_model.add_surfaces('basement')
dz = geo_model._grid.regular_grid.dz
geo_model._surfaces.add_surfaces_values([dz, 0, 0], ['dz'])
geo_model._surfaces.add_surfaces_values([2.6, 2.4, 3.2], ['density'])
geo_model.add_surface_points(3, 0, 3.05, 'surface 1')
geo_model.add_surface_points(9, 0, 3.05, 'surface 1')
geo_model.add_surface_points(3, 0, 1.02, 'surface 2')
geo_model.add_surface_points(9, 0, 1.02, 'surface 2')
geo_model.add_orientations(6, 0, 4, 'surface 1', [0, 0, 1])
device_loc = np.array([[6, 0, 4]])
geo_model.set_centered_grid(device_loc,
resolution=[10, 10, 100],
radius=16000)
geo_model.set_theano_function(interpolator_gravity)
geo_model._interpolator.set_theano_shared_gravity(pos_density=2)
print(geo_model._additional_data)
gp.compute_model(geo_model, set_solutions=True, compute_mesh=False)
print(geo_model.solutions.fw_gravity)
np.testing.assert_almost_equal(geo_model.solutions.fw_gravity,
np.array([-1624.1714]),
decimal=4)
def test_compute_model(interpolator_islith_isfault, map_sequential_pile):
geo_model = map_sequential_pile
geo_model.set_theano_graph(interpolator_islith_isfault)
gp.compute_model(geo_model, compute_mesh=False)
test_values = [45, 150, 2500]
if False:
np.save(input_path + '/test_integration_lith_block.npy',
geo_model.solutions.lith_block[test_values])
# Load model
real_sol = np.load(input_path + '/test_integration_lith_block.npy')
# We only compare the block because the absolute pot field I changed it
np.testing.assert_array_almost_equal(np.round(
geo_model.solutions.lith_block[test_values]),
real_sol,
decimal=0)
gp.plot.plot_section(geo_model,
cell_number=25,
direction='y',
show_data=True)
plt.savefig(
os.path.dirname(__file__) + '/../figs/test_integration_lith_block')
gp.plot.plot_scalar_field(geo_model,
cell_number=25,
series=1,
N=15,
direction='y',
show_data=True)
plt.savefig(os.path.dirname(__file__) + '/../figs/test_integration_scalar')
def test_load_model_compressed_remote2():
model_file = pooch.retrieve(url="https://github.com/cgre-aachen/gempy_data/raw/master/"
"data/gempy_models/Onlap_relations.zip",
known_hash=None)
geo_model = gp.load_model(name='Onlap_relations', path=model_file, recompile=True)
gp.compute_model(geo_model)
gp.plot_3d(geo_model, image=True)
def test_center_grid_slicing(test_magnetics_no_regular_grid):
geo_model = test_magnetics_no_regular_grid
geo_model.set_centered_grid(np.array([[0, 0, 0],
[1, 1, 1]]), resolution=[10, 10, 15], radius=5000)
gp.compute_model(geo_model)
print(geo_model._interpolator.theano_graph.lg0.get_value())
def __init__(self,
model,
extent=None,
associated_calibration=None,
xy_isometric=True,
lock=None):
self.id = next(self._ids)
self.__class__._instances.append(weakref.proxy(self))
self.xy_isometric = xy_isometric
self.scale = [None, None, None]
self.pixel_size = [None, None]
self.output_res = None
self.legend = True
self.model = model
gempy.compute_model(self.model)
self.empty_depth_grid = None
self.depth_grid = None
self.cmap = None
self.norm = None
self.lot = None
self.contours = True
self.main_contours = numpy.arange(0, 5000, 100)
self.sub_contours = numpy.arange(0, 5000, 25)
self.scalar_contours = False
self.scalar_main_contours = numpy.arange(0.0, 1.0, 0.1)
self.scalar_sub_contours = numpy.arange(0.0, 1.0, 0.02)
self.show_faults = True
self.fault_contours = numpy.arange(0.5, 50.5, 1.0)
self.stop_threat = False
self.lock = lock
self.delay = 0.0
self.show_framerate = False
if associated_calibration is None:
try:
self.associated_calibration = Calibration._instances[-1]
print(
"no calibration specified, using last calibration instance created: ",
self.associated_calibration)
except:
print(
"ERROR: no calibration instance found. please create a calibration"
)
# parameters from the model:
else:
self.associated_calibration = associated_calibration
if extent is None: # extent should be array with shape (6,) or convert to list?
self.extent = self.model.grid.extent
else:
self.extent = extent # check: array with 6 entries!
def model_horizontal_two_layers(interpolator):
geo_model = gp.create_model('interpolator')
# Importing the data from csv files and settign extent and resolution
gp.init_data(geo_model, [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_model.set_theano_function(interpolator)
gp.compute_model(geo_model)
return geo_model
def section_model(one_fault_model_topo_solution):
geo_model = one_fault_model_topo_solution
section_dict = {'section_SW-NE': ([250, 250], [1750, 1750], [100, 100]),
'section_NW-SE': ([250, 1750], [1750, 250], [100, 100])}
geo_model.set_section_grid(section_dict)
geo_model.set_active_grid('sections', reset=False)
one_fault_model_topo_solution.update_additional_data()
one_fault_model_topo_solution.update_to_interpolator()
gp.compute_model(geo_model, sort_surfaces=False)
return geo_model
def vista_object_computed_topo(self, one_fault_model_solution):
"""
Args:
one_fault_model_solution:
"""
from gempy.plot.vista import GemPyToVista
one_fault_model_solution.update_additional_data()
one_fault_model_solution.update_to_interpolator()
one_fault_model_solution.set_topography()
gp.compute_model(one_fault_model_solution)
return GemPyToVista(one_fault_model_solution, plotter_type='basic', off_screen=True)
def test_ch3_b(theano_f):
geo_data = gp.read_pickle(os.path.dirname(__file__)+"/ch3-pymc2_tutorial_geo_data.pickle")
# Check the stratigraphic pile for correctness:
gp.get_sequential_pile(geo_data)
# Then we can then compile the GemPy modeling function:
#interp_data = gp.InterpolatorData(geo_data, u_grade=[1])
interp_data = theano_f
interp_data.update_interpolator(geo_data)
# Now we can reproduce the original model:
lith_block, fault_block = gp.compute_model(interp_data)
gp.plot_section(geo_data, lith_block[0], 0)
# But of course we want to look at the perturbation results. We have a class for that:
import gempy.posterior_analysis
dbname = os.path.dirname(__file__)+"/ch3-pymc2.hdf5"
post = gempy.posterior_analysis.Posterior(dbname)
post.change_input_data(interp_data, 80)
lith_block, fault_block = gp.compute_model(interp_data)
gp.plot_section(interp_data.geo_data_res, lith_block[0], 2, plot_data=True)
post.change_input_data(interp_data, 15)
lith_block, fault_block = gp.compute_model(interp_data)
gp.plot_section(interp_data.geo_data_res, lith_block[0], 2, plot_data=True)
post.change_input_data(interp_data, 95)
lith_block, fault_block = gp.compute_model(interp_data)
gp.plot_section(geo_data, lith_block[0], 2)
ver, sim = gp.get_surfaces(interp_data, lith_block[1], None, original_scale= True)
def test_2d_from_gempy_to_NURBS_NOT_WORKING():
geo_model = create_gempy_model(resolution=[5, 5, 5])
extent = geo_model.grid.regular_grid.extent
section_dict = {
'section1': ([extent[0], extent[2] / 2], [extent[1],
extent[3] / 2], [50, 50])
}
geo_model.set_section_grid(section_dict)
gp.compute_model(geo_model)
plot_pathdict(geo_model, 'section1')
from pygeoiga.nurb.gempy_to_NURBS import extract_control_points_from_cross_section
control_points = extract_control_points_from_cross_section(
geo_model, 'section1')
plot_2d_control_points(control_points)
def test_compute_model(self, interpolator_islith_isfault, geo_model):
geo_model.set_theano_function(interpolator_islith_isfault)
sol = gp.compute_model(geo_model)
print(interpolator_islith_isfault)
print(sol)
return sol
def compute_posterior_models_all(db, interp_data, indices, u_grade=None, get_potential_at_interfaces=False):
"""Computes block models from stored input parameters for all iterations.
Args:
db (): loaded pymc database (e.g. hdf5)
interp_data (gp.data_management.InterpolatorData): GemPy interpolator object
indices (list or np.array): Trace indices specifying which models from the database will be calculated.
u_grade (list, optional):
get_potential_at_interfaces:
Returns:
"""
for i in tqdm.tqdm(indices):
interp_data_loop = change_input_data(db, interp_data, i)
lb, fb = gp.compute_model(interp_data_loop, output="geology", u_grade=u_grade, get_potential_at_interfaces=get_potential_at_interfaces)
if i == 0 or i == indices[0]:
lbs = np.expand_dims(lb, 0)
fbs = np.expand_dims(fb, 0)
else:
lbs = np.concatenate((lbs, np.expand_dims(lb, 0)), axis=0)
fbs = np.concatenate((fbs, np.expand_dims(fb, 0)), axis=0)
return lbs, fbs
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_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 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)
def geo_model(self, model_horizontal_two_layers):
geo_data = model_horizontal_two_layers
# Compute model
sol = gempy.compute_model(geo_data, compute_mesh_options={'rescale': True})
return geo_data
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 update_surfaces_real_time(self, delete=True):
try:
gp.compute_model(self.model,
sort_surfaces=False,
compute_mesh=True)
except IndexError:
print(
'IndexError: Model not computed. Laking data in some surface')
except AssertionError:
print(
'AssertionError: Model not computed. Laking data in some surface'
)
self.update_surfaces()
return True
def plot_section(self, iteration=1, block='lith', cell_number=3, **kwargs):
'''kwargs: gempy.plotting.plot_section keyword arguments'''
self._change_input_data(iteration)
lith_block, fault_block = gp.compute_model(self.interp_data)
if 'topography' not in kwargs:
if self.topography:
topography = self.topography
else:
topography = None
if block == 'lith':
gp.plot_section(self.geo_data,
lith_block[0],
cell_number=cell_number,
topography=topography,
**kwargs)
else:
gp.plot_section(self.geo_data,
block,
cell_number=cell_number,
topography=topography,
**kwargs)
else:
if block == 'lith':
gp.plot_section(self.geo_data,
lith_block[0],
cell_number=cell_number,
**kwargs)
else:
gp.plot_section(self.geo_data,
block,
cell_number=cell_number,
**kwargs)
def compute_posterior_models_all(db,
interp_data,
indices,
u_grade=None,
get_potential_at_interfaces=False):
"""Computes block models from stored input parameters for all iterations.
Args:
db (): loaded pymc database (e.g. hdf5)
interp_data (gp.data_management.InterpolatorData): GemPy interpolator object
indices (list or np.array): Trace indices specifying which models from the database will be calculated.
u_grade (list, optional):
get_potential_at_interfaces:
Returns:
"""
for i in tqdm.tqdm(indices):
interp_data_loop = change_input_data(db, interp_data, i)
lb, fb = gp.compute_model(
interp_data_loop,
output="geology",
u_grade=u_grade,
get_potential_at_interfaces=get_potential_at_interfaces)
if i == 0 or i == indices[0]:
lbs = np.expand_dims(lb, 0)
fbs = np.expand_dims(fb, 0)
else:
lbs = np.concatenate((lbs, np.expand_dims(lb, 0)), axis=0)
fbs = np.concatenate((fbs, np.expand_dims(fb, 0)), axis=0)
return lbs, fbs
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_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 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_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 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)
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)
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()
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)
