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 model():
geo_model = gp.create_model('test_map2Loop')
gp.init_data(
geo_model,
extent=[bbox[0], bbox[2], bbox[1], bbox[3], model_base, model_top],
resolution=[50, 50, 80],
path_o=orientations_file,
path_i=contacts_file)
# Load Topology
geo_model.set_topography(source='gdal', filepath=fp)
# Stack Processing
contents = np.genfromtxt(series_file, delimiter=',', dtype='U100')[1:,
4:-1]
map_series_to_surfaces = {}
for pair in contents:
map_series_to_surfaces.setdefault(pair[1], []).append(pair[0])
gp.map_stack_to_surfaces(geo_model,
map_series_to_surfaces,
remove_unused_series=False)
gp.plot_3d(geo_model,
ve=None,
show_topography=False,
image=True,
show_lith=False,
kwargs_plot_data={'arrow_size': 300})
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_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 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
def test_issue_569(data_path):
surface_points_df = df = pd.read_csv(data_path + "/coordinates_mwe.csv")
orientations_df = pd.read_csv(data_path + "/orientations_mwe.csv")
geo_model = gp.create_model("Deltatest")
gp.init_data(geo_model, [
df.X.min() - 50,
df.X.max() + 50,
df.Y.min() - 50,
df.Y.max() + 50,
df.Z.min() - 50,
df.Z.max() + 50,
], [50, 50, 50],
surface_points_df=surface_points_df,
orientations_df=orientations_df,
default_values=True)
fault_list = []
series = {
"Strat_Series":
surface_points_df.loc[
~surface_points_df["formation"].str.contains("fault"),
"formation"].unique().tolist()
}
for fault in surface_points_df.loc[
surface_points_df["formation"].str.contains("fault"),
"formation"].unique():
series[fault] = fault
fault_list.append(fault)
gp.map_stack_to_surfaces(geo_model, series, remove_unused_series=True)
geo_model.set_is_fault(fault_list)
geo_model.reorder_features(['fault_a', 'fault_b', 'Strat_Series'])
geo_model.add_surfaces("basement")
plot = gp.plot_2d(geo_model,
show_lith=False,
show_boundaries=True,
direction=['z'])
plt.show(block=False)
gp.set_interpolator(
geo_model,
compile_theano=True,
theano_optimizer='fast_compile',
)
gp.get_data(geo_model, 'kriging')
sol = gp.compute_model(geo_model, sort_surfaces=True)
gp.plot_2d(geo_model, show_scalar=True, series_n=0)
gp.plot_2d(geo_model, series_n=0)
gp.plot_3d(geo_model, image=True)
def load_model():
geo_model = gp.create_model('Model_Tuto1-1')
# Importing the data from CSV-files and setting extent and resolution
gp.init_data(geo_model, [0, 2000., 0, 2000., 0, 2000.], [50 ,50 ,50],
path_o = input_path+"/simple_fault_model_orientations.csv",
path_i = input_path+"/simple_fault_model_points.csv", default_values=True)
gp.get_data(geo_model, 'surface_points').head()
return geo_model
def interpolator_islith_nofault():
geo_model = gp.create_model('interpolator_islith_isfault')
# 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")
interpolator = gempy.set_interpolation_data(geo_model, grid=None, compile_theano=True)
return interpolator
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)
return geo_model
def load_model(name=None, path=None, recompile=False):
"""
Loading model saved with model.save_model function.
Args:
name: name of folder with saved files
path (str): path to folder directory or the zip file
recompile (bool): if true, theano functions will be recompiled
Returns:
:class:`Project`
"""
# TODO: Divide each dataframe in its own function and move them as
# method of the class
# TODO: Include try except in case some of the datafiles is missing
#
# Default path
is_compressed = False
if path is None:
path = f'./{name}'
p = pathlib.Path(path)
# If the path includes .zip
if p.suffix == '.zip':
is_compressed, path = _unpack_model_if_compressed_includes_zip(
is_compressed, path)
# if the path does not include .zip but exist
elif os.path.isfile(f'{path}.zip'):
is_compressed = _unpack_model_if_compressed_no_zip(is_compressed, path)
# create model with extent and resolution from csv - check
geo_model = create_model()
init_data(geo_model, np.load(f'{path}/{name}_extent.npy'),
np.load(f'{path}/{name}_resolution.npy'))
_load_files_into_geo_model(geo_model, name, path)
if recompile is True:
from gempy.api_modules.setters import set_interpolator
set_interpolator(geo_model, verbose=[0])
# Cleaning temp files
if is_compressed:
shutil.rmtree(path)
return geo_model
def test_load_model_df():
verbose = True
df_i = pn.DataFrame(np.random.randn(6,3), columns='X Y Z'.split())
df_i['formation'] = ['surface_1' for _ in range(3)] + ['surface_2' for _ in range(3)]
df_o = pn.DataFrame(np.random.randn(6,6), columns='X Y Z azimuth dip polarity'.split())
df_o['formation'] = ['surface_1' for _ in range(3)] + ['surface_2' for _ in range(3)]
geo_model = gp.create_model('test')
# Importing the data directly from the dataframes
gp.init_data(geo_model, [0, 2000., 0, 2000., 0, 2000.], [50, 50, 50],
surface_points_df=df_i, orientations_df=df_o, default_values=True)
df_cmp_i = gp.get_data(geo_model, 'surface_points')
df_cmp_o = gp.get_data(geo_model, 'orientations')
if verbose:
print(df_cmp_i.head())
print(df_cmp_o.head())
assert not df_cmp_i.empty, 'data was not set to dataframe'
assert not df_cmp_o.empty, 'data was not set to dataframe'
assert df_cmp_i.shape[0] == 6, 'data was not set to dataframe'
assert df_cmp_o.shape[0] == 6, 'data was not set to dataframe'
# try without the default_values command
geo_model = gp.create_model('test')
# Importing the data directly from the dataframes
gp.init_data(geo_model, [0, 2000., 0, 2000., 0, 2000.], [50 ,50 ,50],
surface_points_df=df_i, orientations_df=df_o)
df_cmp_i2 = gp.get_data(geo_model, 'surface_points')
df_cmp_o2 = gp.get_data(geo_model, 'orientations')
if verbose:
print(df_cmp_i2.head())
print(df_cmp_o2.head())
assert not df_cmp_i2.empty, 'data was not set to dataframe'
assert not df_cmp_o2.empty, 'data was not set to dataframe'
assert df_cmp_i2.shape[0] == 6, 'data was not set to dataframe'
assert df_cmp_o2.shape[0] == 6, 'data was not set to dataframe'
return geo_model
def test_add_point():
extend = [0.0, 1.0, 0.0, 1.0, 0.0, 1.1]
discretization = [5, 20, 20]
x, y, z, f = 0.0, 0.0, 0.5, 'surface2'
# %%
geo_model = gp.create_model('test')
gp.init_data(geo_model, extend, discretization)
geo_model.set_default_surfaces()
geo_model.set_default_orientation()
strats = ['surface1', 'surface2', 'basement']
gp.map_stack_to_surfaces(geo_model, {'Strat_Series': strats})
geo_model.add_surface_points(x, y, z, f)
geo_model.add_orientations(x, y, z, f, pole_vector=(1,0,0))
def test_issue_564(data_path):
geo_model = gp.create_model('SBPM')
gp.init_data(geo_model, [550, 730, -200, 1000, 20, 55], [50, 50, 50],
path_i=data_path + "/564_Points.csv",
path_o=data_path + "/564_Orientations_.csv",
default_values=True)
gp.map_stack_to_surfaces(geo_model, {
"Q": 'Quartaer',
"vK": 'verwKeuper',
"Sa": 'Sandstein',
"Sc": 'Schluffstein',
"b": 'basement'
},
remove_unused_series=True)
gp.set_interpolator(geo_model,
compile_theano=True,
theano_optimizer='fast_compile')
sol = gp.compute_model(geo_model)
gp.plot_2d(geo_model)
gpv = gp.plot_3d(geo_model,
image=True,
plotter_type='basic',
ve=5,
show_lith=False)
geo_model.set_bottom_relation(
["Q", "vK", "Sa", "Sc", "b"],
["Onlap", "Onlap", "Onlap", "Onlap", "Onlap"])
sol = gp.compute_model(geo_model)
gp.plot_2d(geo_model)
gpv = gp.plot_3d(geo_model,
image=True,
plotter_type='basic',
ve=5,
show_lith=True)
def test_issue_566():
from pyvista import set_plot_theme
set_plot_theme('document')
geo_model = gp.create_model('Model1')
geo_model = gp.init_data(geo_model,
extent=[0, 791, 0, 200, -582, 0],
resolution=[100, 10, 100])
geo_model.set_default_surfaces()
geo_model.add_surface_points(X=223, Y=0.01, Z=-94, surface='surface1')
def test_map2loop_model_import_data():
geo_model = gp.create_model('test_map2Loop')
gp.init_data(
geo_model,
extent=[bbox[0], bbox[2], bbox[1], bbox[3], model_base, model_top],
resolution=[50, 50, 50],
path_o=orientations_file,
path_i=contacts_file
)
# Load Topology
geo_model.set_topography(source='gdal', filepath=fp)
gp.plot_2d(geo_model, ve=10, show_topography=True)
plt.show()
# Plot in 3D
gp.plot_3d(geo_model, ve=None, show_topography=False, image=True,
kwargs_plot_data={'arrow_size': 400}
)
print(geo_model.orientations)
def test_map2loop_model_import_aus():
geo_model = gp.create_model('test_map2Loop')
gp.init_data(
geo_model,
extent=extent_g,
resolution=[50, 50, 50],
path_o=orientations_file2,
path_i=contacts_file2
)
# Load Topology
geo_model.set_topography(source='gdal', array=fp2)
gp.plot_2d(geo_model, ve=10, show_topography=True)
plt.show()
# Plot in 3D
gp.plot_3d(geo_model, ve=None, show_topography=False, image=False,
kwargs_plot_data={'arrow_size': 40}
)
print(geo_model.orientations)
def geo_model(interpolator):
geo_model = gp.create_model('Test_uncomformities')
# Importing the data from CSV-files and setting extent and resolution
gp.init_data(
geo_model, [0, 10., 0, 2., 0, 5.], [100, 3, 100],
path_o=input_path + '/05_toy_fold_unconformity_orientations.csv',
path_i=input_path + '/05_toy_fold_unconformity_interfaces.csv',
default_values=True)
gp.map_stack_to_surfaces(
geo_model, {
"Flat_Series": 'Flat',
"Inclined_Series": 'Inclined',
"Fold_Series": ('Basefold', 'Topfold', 'basement')
})
# Create the theano model
geo_model.set_theano_function(interpolator)
return geo_model
def load_model():
verbose = False
geo_model = gp.create_model('Model_Tuto1-1')
# Importing the data from CSV-files and setting extent and resolution
gp.init_data(geo_model, [0, 2000., 0, 2000., 0, 2000.], [50 ,50 ,50],
path_o=input_path+"/simple_fault_model_orientations.csv",
path_i=input_path+"/simple_fault_model_points.csv", default_values=True)
df_cmp_i = gp.get_data(geo_model, 'surface_points')
df_cmp_o = gp.get_data(geo_model, 'orientations')
df_o = pn.read_csv(input_path + "/simple_fault_model_orientations.csv")
df_i = pn.read_csv(input_path + "/simple_fault_model_points.csv")
assert not df_cmp_i.empty, 'data was not set to dataframe'
assert not df_cmp_o.empty, 'data was not set to dataframe'
assert df_cmp_i.shape[0] == df_i.shape[0], 'data was not set to dataframe'
assert df_cmp_o.shape[0] == df_o.shape[0], 'data was not set to dataframe'
if verbose:
gp.get_data(geo_model, 'surface_points').head()
return geo_model
def test_reorder_series():
geo_model = gp.create_model('Geological_Model1')
geo_model = gp.init_data(geo_model,
extent=[0, 4000, 0, 2775, 200, 1200],
resolution=[100, 10, 100])
# Adding a fault
geo_model.rename_features(['Cycle1'])
geo_model.add_features(['Fault1'])
geo_model.set_is_fault(['Fault1'])
geo_model.reorder_features(['Fault1', 'Cycle1'])
assert (geo_model._stack.df['BottomRelation'] == ['Fault',
'Erosion']).all()
assert (geo_model._stack.df.index == ['Fault1', 'Cycle1']).all()
print(geo_model._stack.df)
def test_map2loop_model_no_faults():
# Location box
bbox = (500000, 7490000, 545000, 7520000)
model_base = -3200 # Original 3200
model_top = 800
# Input files
geo_model = gp.create_model('test_map2Loop')
gp.init_data(
geo_model,
extent=[bbox[0], bbox[2], bbox[1], bbox[3], model_base, model_top],
resolution=[50, 50, 80],
path_o=orientations_file,
path_i=contacts_file
)
gp.set_interpolator(geo_model)
# Load Topology
geo_model.set_topography(source='gdal', filepath=fp)
# Stack Processing
contents = np.genfromtxt(series_file,
delimiter=',', dtype='U100')[1:, 4:-1]
map_series_to_surfaces = {}
for pair in contents:
map_series_to_surfaces.setdefault(pair[1], []).append(pair[0])
gp.map_stack_to_surfaces(geo_model, map_series_to_surfaces,
remove_unused_series=False)
gp.plot_2d(geo_model, ve=10, show_topography=False)
plt.show()
# Plot in 3D
gp.plot_3d(geo_model, ve=10, show_topography=False, image=True)
# Stack Processing
contents = np.genfromtxt(series_file,
delimiter=',', dtype='U100')[1:, 4:-1]
map_series_to_surfaces = {}
for pair in contents:
map_series_to_surfaces.setdefault(pair[1], []).append(pair[0])
gp.map_stack_to_surfaces(geo_model, map_series_to_surfaces,
remove_unused_series=False)
# Adding axial rescale
#geo_model._rescaling.toggle_axial_anisotropy()
# Increasing nugget effect
geo_model.modify_surface_points(
geo_model.surface_points.df.index,
smooth=0.001
)
geo_model.modify_kriging_parameters('drift equations', [9, 9, 9, 9, 9])
gp.compute_model(geo_model)
gp.plot_2d(geo_model,
section_names=['topography'],
show_topography=True,
)
plt.show()
gp.plot_3d(geo_model, ve=10, show_topography=True,
image=True,
show_lith=False,
)
# %%
# Setup the model
# ---------------
#
# %%
geo_model = gp.create_model('Tutorial_ch1-1_Basics')
# Importing the data from CSV-files and setting extent and resolution
data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/'
gp.init_data(
geo_model, [0, 2000., 0, 2000., 0, 2000.], [5, 5, 5],
path_o=data_path +
"/data/input_data/tut_chapter1/simple_fault_model_orientations.csv",
path_i=data_path +
"/data/input_data/tut_chapter1/simple_fault_model_points.csv",
default_values=True)
gp.map_stack_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'])
# %%
# Add sections
# ~~~~~~~~~~~~
# representative space. As our model voxels are not cubes, but prisms, the
# resolution can take a different shape than the extent. We don't
# recommend going much higher than 100 cells in every direction (1,000,000
# voxels), as higher resolutions will become increasingly expensive to
# compute.
#
# %%
geo_model = gp.create_model('Tutorial_ch1_1_Basics')
# %%
data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/'
# Importing the data from CSV-files and setting extent and resolution
gp.init_data(geo_model, [0, 2000., 0, 2000., 0, 750.], [50, 50, 50],
path_o=data_path + "/data/input_data/getting_started/"
"simple_fault_model_orientations.csv",
path_i=data_path + "/data/input_data/getting_started/"
"simple_fault_model_points.csv",
default_values=True)
# %%
geo_model.surfaces
# %%
# The input data can then be listed using the command ``get_data``. Note
# that the order of formations and respective allocation to series is
# still completely arbitrary. We will fix this in the following.
#
# %%
gp.get_data(geo_model, 'surface_points').head()
# Importing auxiliary libraries
import matplotlib
matplotlib.rcParams['figure.figsize'] = (20.0, 10.0)
os.environ["THEANO_FLAGS"] = "mode=FAST_RUN,device=cuda"
# %%
data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/'
geo_model = gp.create_model('Perth_Basin')
# %%
gp.init_data(
geo_model,
extent=[337000, 400000, 6640000, 6710000, -18000, 1000],
resolution=[100, 100, 100],
path_i=data_path +
"/data/input_data/Perth_basin/Paper_GU2F_sc_faults_topo_Points.csv",
path_o=data_path +
"/data/input_data/Perth_basin/Paper_GU2F_sc_faults_topo_Foliations.csv")
# %%
geo_model.surfaces
# %%
del_surfaces = ['Cadda', 'Woodada_Kockatea', 'Cattamarra']
# %%
geo_model.delete_surfaces(del_surfaces, remove_data=True)
# %%
# %debug
# below but for this lets treat is a simple spatial interpolation in order
# to keep the focus on the constraction of the probabilistic model.
#
# %%
path_dir = os.getcwd() + '/examples/tutorials/ch5_probabilistic_modeling'
# %%
geo_model = gp.load_model(r'2-layers', path=path_dir, recompile=True)
# %%
# %%
geo_model = gp.create_model('2-layers')
gp.init_data(geo_model,
extent=[0, 12e3, -2e3, 2e3, 0, 4e3],
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(np.array([2.6, 2.4, 3.2]), ['density'])
# %%
geo_model.add_surface_points(3e3, 0, 3.05e3, 'surface 1')
geo_model.add_surface_points(9e3, 0, 3.05e3, 'surface 1')
geo_model.add_surface_points(3e3, 0, 1.02e3, 'surface 2')
if 'examples' not in cwd:
data_path = os.getcwd() + '/examples'
else:
data_path = cwd + '/../..'
path_interf = data_path + "/data/input_data/AlesModel/2018_interf.csv"
path_orient = data_path + "/data/input_data/AlesModel/2018_orient_clust_n_init5_0.csv"
path_dem = data_path + "/data/input_data/AlesModel/_cropped_DEM_coarse.tif"
# %%
resolution = [100, 100, 100]
extent = np.array([729550.0, 751500.0, 1913500.0, 1923650.0, -1800.0, 800.0])
geo_model = gp.create_model('Alesmodel')
gp.init_data(geo_model,
extent=extent,
resolution=resolution,
path_i=path_interf,
path_o=path_orient)
# %%
sdict = {'section1': ([732000, 1916000], [745000, 1916000], [200, 150])}
geo_model.set_section_grid(sdict)
# %%
# sorting of lithologies
gp.map_stack_to_surfaces(geo_model, {
'fault_left': ('fault_left'),
'fault_right': ('fault_right'),
'fault_lr': ('fault_lr'),
'Trias_Series': ('TRIAS', 'LIAS'),
'Carbon_Series': ('CARBO'),
def test_complex_model():
geo_model = gp.create_model('Geological_Model1')
geo_model = gp.init_data(geo_model,
extent=[0, 4000, 0, 2775, 200, 1500],
resolution=[100, 10, 100])
gp.set_interpolator(geo_model, theano_optimizer='fast_run', verbose=[])
geo_model.add_features(['Fault2', 'Cycle2', 'Fault1', 'Cycle1'])
geo_model.delete_features('Default series')
geo_model.add_surfaces(['F2', 'H', 'G', 'F1', 'D', 'C', 'B', 'A'])
gp.map_stack_to_surfaces(geo_model, {
'Fault1': 'F1',
'Fault2': 'F2',
'Cycle2': ['G', 'H']
})
geo_model.set_is_fault(['Fault1', 'Fault2'])
###Cycle 1
# surface B - before F1
geo_model.add_surface_points(X=584, Y=285, Z=500, surface='B')
geo_model.add_surface_points(X=494, Y=696, Z=500, surface='B')
geo_model.add_surface_points(X=197, Y=1898, Z=500, surface='B')
geo_model.add_surface_points(X=473, Y=2180, Z=400, surface='B')
geo_model.add_surface_points(X=435, Y=2453, Z=400, surface='B')
# surface C - before F1
geo_model.add_surface_points(X=946, Y=188, Z=600, surface='C')
geo_model.add_surface_points(X=853, Y=661, Z=600, surface='C')
geo_model.add_surface_points(X=570, Y=1845, Z=600, surface='C')
geo_model.add_surface_points(X=832, Y=2132, Z=500, surface='C')
geo_model.add_surface_points(X=767, Y=2495, Z=500, surface='C')
# Surface D - Before F1
geo_model.add_surface_points(X=967, Y=1638, Z=800, surface='D')
geo_model.add_surface_points(X=1095, Y=996, Z=800, surface='D')
# Adding orientation to Cycle 1
geo_model.add_orientations(X=832,
Y=2132,
Z=500,
surface='C',
orientation=[76, 17.88, 1])
# surface B - After F1
geo_model.add_surface_points(X=1447, Y=2554, Z=500, surface='B')
geo_model.add_surface_points(X=1511, Y=2200, Z=500, surface='B')
geo_model.add_surface_points(X=1549, Y=629, Z=600, surface='B')
geo_model.add_surface_points(X=1630, Y=287, Z=600, surface='B')
# surface C - After F1
geo_model.add_surface_points(X=1891, Y=2063, Z=600, surface='C')
geo_model.add_surface_points(X=1605, Y=1846, Z=700, surface='C')
geo_model.add_surface_points(X=1306, Y=1641, Z=800, surface='C')
geo_model.add_surface_points(X=1476, Y=979, Z=800, surface='C')
geo_model.add_surface_points(X=1839, Y=962, Z=700, surface='C')
geo_model.add_surface_points(X=2185, Y=893, Z=600, surface='C')
geo_model.add_surface_points(X=2245, Y=547, Z=600, surface='C')
# Surface D - After F1
geo_model.add_surface_points(X=2809, Y=2567, Z=600, surface='D')
geo_model.add_surface_points(X=2843, Y=2448, Z=600, surface='D')
geo_model.add_surface_points(X=2873, Y=876, Z=700, surface='D')
# Surface D - After F2
geo_model.add_surface_points(X=3056, Y=2439, Z=650, surface='D')
geo_model.add_surface_points(X=3151, Y=1292, Z=700, surface='D')
### Fault 1
# Surface F1
geo_model.add_surface_points(X=1203, Y=138, Z=600, surface='F1')
geo_model.add_surface_points(X=1250, Y=1653, Z=800, surface='F1')
# orientation to Fault 1
geo_model.add_orientations(X=1280,
Y=2525,
Z=500,
surface='F1',
orientation=[272, 90, 1])
### Cycle 2
# Surface G - Before F2
geo_model.add_surface_points(X=1012, Y=1493, Z=900, surface='G')
geo_model.add_surface_points(X=1002, Y=1224, Z=900, surface='G')
geo_model.add_surface_points(X=1579, Y=1376, Z=850, surface='G')
geo_model.add_surface_points(X=2489, Y=336, Z=750, surface='G')
geo_model.add_surface_points(X=2814, Y=1848, Z=750, surface='G')
# Surface H - Before F2
geo_model.add_surface_points(X=2567, Y=129, Z=850, surface='H')
geo_model.add_surface_points(X=3012, Y=726, Z=800, surface='H')
# Orientation to cycle 2
geo_model.add_orientations(X=1996,
Y=47,
Z=800,
surface='G',
orientation=[92, 5.54, 1])
# Surface G - After F2
geo_model.add_surface_points(X=3031, Y=2725, Z=800, surface='G')
geo_model.add_surface_points(X=3281, Y=2314, Z=750, surface='G')
geo_model.add_surface_points(X=3311, Y=1357, Z=750, surface='G')
geo_model.add_surface_points(X=3336, Y=898, Z=750, surface='G')
# Surface H - After F2
geo_model.add_surface_points(X=3218, Y=1818, Z=890, surface='H')
geo_model.add_surface_points(X=3934, Y=1207, Z=810, surface='H')
geo_model.add_surface_points(X=3336, Y=704, Z=850, surface='H')
### Fault 2
geo_model.add_surface_points(X=3232, Y=178, Z=1000, surface='F2')
geo_model.add_surface_points(X=2912, Y=2653, Z=700, surface='F2')
# Add orientation to Fault 2
geo_model.add_orientations(X=3132,
Y=951,
Z=700,
surface='F2',
orientation=[85, 90, 1])
gp.compute_model(geo_model, sort_surfaces=False)
module = GemPyModule(geo_model=geo_model,
extent=extent,
box=[1000, 800],
load_examples=False)
module.show_boundary = True
module.show_lith = True
module.show_hillshades = True
module.show_contour = True
module.show_fill_contour = False
sb_params = module.update(pytest.sb_params)
sb_params['fig'].show()
# %%
# Data initialization:
# ~~~~~~~~~~~~~~~~~~~~
#
# Suggested size of the axis-aligned modeling box: Origin: 0 -0.5 0
# Maximum: 16 0.5 4.5
#
# Suggested resolution: 0.05m (grid size 321 x 21 x 91)
#
# %%
geo_model = gp.create_model('Moureze')
geo_model = gp.init_data(geo_model,
extent=[0, 16, -0.5, 0.5, 0, 4.5],
resolution=[321, 21, 91],
surface_points_df=surface_points,
orientations_df=orientations,
surface_name='surface',
add_basement=True)
# %%
geo_model.orientations.df.at[5, 'surface']
# %%
geo_model.orientations.df
# %%
# We need an orientation per series/fault. The faults does not have
# orientation so the easiest is to create an orientation from the surface
# points availablle:
#
def extract_borehole(geo_model: gp.core.model.Project,
geo_data: gemgis.GemPyData, loc: List[Union[int, float]],
**kwargs):
"""
Extracting a borehole at a provided location from a recalculated GemPy Model
Args:
geo_model: gp.core.model.Project - previously calculated GemPy Model
geo_data: gemgis.GemPyData - GemGIS GemPy Data class used to calculate the previous model
loc: list of x and y point pair representing the well location
Kwargs:
zmax: int/float indicating the maximum depth of the well, default is minz of the previous model
res: int indicating the resolution of the model in z-direction
Returns:
"""
# Checking if geo_model is a GemPy geo_model
if not isinstance(geo_model, gp.core.model.Project):
raise TypeError('geo_model must be a GemPy geo_model')
# Checking if geo_data is a GemGIS GemPy Data Class
if not isinstance(geo_data, gemgis.GemPyData):
raise TypeError('geo_data must be a GemPy Data object')
# Checking if loc is of type list
if not isinstance(loc, list):
raise TypeError(
'Borehole location must be provided as a list of a x- and y- coordinate'
)
# Checking if elements of loc are of type int or float
if not all(isinstance(n, (int, float)) for n in loc):
raise TypeError(
'Location values must be provided as integers or floats')
# Selecting DataFrame columns and create deep copy of DataFrame
orientations_df = geo_model.orientations.df[[
'X', 'Y', 'Z', 'surface', 'dip', 'azimuth', 'polarity'
]].copy(deep=True)
interfaces_df = geo_model.surface_points.df[['X', 'Y', 'Z',
'surface']].copy(deep=True)
# Creating formation column
orientations_df['formation'] = orientations_df['surface']
interfaces_df['formation'] = interfaces_df['surface']
# Deleting surface column
del orientations_df['surface']
del interfaces_df['surface']
# Getting maximum depth and resolution
zmax = kwargs.get('zmax', geo_model.grid.regular_grid.extent[5])
res = kwargs.get('res', geo_model.grid.regular_grid.resolution[2])
# Checking if zmax is of type int or float
if not isinstance(zmax, (int, float)):
raise TypeError('Maximum depth must be of type int or float')
# Checking if res is of type int
if not isinstance(res, (int, float, np.int32)):
raise TypeError('Resolution must be of type int')
# Creating variable for maximum depth
z = geo_model.grid.regular_grid.extent[5] - zmax
# Prevent printing
# sys.stdout = open(os.devnull, 'w')
# Create GemPy Model
well_model = gp.create_model('Well_Model')
# Initiate Data for GemPy Model
gp.init_data(well_model,
extent=[
loc[0] - 5, loc[0] + 5, loc[1] - 5, loc[1] + 5,
geo_model.grid.regular_grid.extent[4],
geo_model.grid.regular_grid.extent[5] - z
],
resolution=[5, 5, res],
orientations_df=orientations_df.dropna(),
surface_points_df=interfaces_df.dropna(),
default_values=False)
# Map Stack to surfaces
gp.map_stack_to_surfaces(well_model,
geo_data.stack,
remove_unused_series=True)
# Add Basement surface
well_model.add_surfaces('basement')
# Change colors of surfaces
well_model.surfaces.colors.change_colors(geo_data.surface_colors)
# Set Interpolator
gp.set_interpolator(well_model,
compile_theano=True,
theano_optimizer='fast_run',
dtype='float64',
update_kriging=False,
verbose=[])
# Set faults active
for i in geo_model.surfaces.df[geo_model.surfaces.df['isFault'] ==
True]['surface'].values.tolist():
well_model.set_is_fault([i])
# Compute Model
sol = gp.compute_model(well_model, compute_mesh=False)
# Reshape lith_block
well = sol.lith_block.reshape(well_model.grid.regular_grid.resolution[0],
well_model.grid.regular_grid.resolution[1],
well_model.grid.regular_grid.resolution[2])
# Select colors for plotting
color_dict = well_model.surfaces.colors.colordict
surface = well_model.surfaces.df.copy(deep=True)
surfaces = surface[~surface['id'].isin(np.unique(np.round(sol.lith_block))
)]
for key in surfaces['surface'].values.tolist():
color_dict.pop(key)
cols = list(color_dict.values())
# Calculate boundaries
boundaries = np.where(
np.round(well.T[:, 1])[:-1] != np.round(well.T[:, 1])[1:]
)[0][::well_model.grid.regular_grid.resolution[0]]
# Create Plot
plt.figure(figsize=(3, 10))
plt.imshow(
np.rot90(np.round(well.T[:, 1]), 2),
cmap=ListedColormap(cols),
extent=(0, (well_model.grid.regular_grid.extent[5] -
well_model.grid.regular_grid.extent[4]) / 8,
well_model.grid.regular_grid.extent[4],
well_model.grid.regular_grid.extent[5]),
)
list_values = np.unique(np.round(well.T[:, 1])[:, 0]).tolist()
# Display depths of layer boundaries
for i in boundaries:
plt.text((well_model.grid.regular_grid.extent[5] -
well_model.grid.regular_grid.extent[4]) / 7,
i * geo_model.grid.regular_grid.dz +
geo_model.grid.regular_grid.extent[4] +
geo_model.grid.regular_grid.dz,
'%d m' % (i * geo_model.grid.regular_grid.dz +
geo_model.grid.regular_grid.extent[4]),
fontsize=14)
del list_values[list_values.index(np.round(well.T[:, 1])[:, 0][i + 1])]
# Plot last depth
plt.text(
(well_model.grid.regular_grid.extent[5] -
well_model.grid.regular_grid.extent[4]) / 7,
geo_model.grid.regular_grid.extent[4] + geo_model.grid.regular_grid.dz,
'%d m' % (geo_model.grid.regular_grid.extent[4]),
fontsize=14)
list_values = np.unique(np.round(well.T[:, 1])[:, 0]).tolist()
# Display lithology IDs
for i in boundaries:
plt.text((well_model.grid.regular_grid.extent[5] -
well_model.grid.regular_grid.extent[4]) / 24,
i * geo_model.grid.regular_grid.dz +
geo_model.grid.regular_grid.extent[4] +
2 * geo_model.grid.regular_grid.dz,
'ID: %d' % (np.round(well.T[:, 1])[:, 0][i + 1]),
fontsize=14)
del list_values[list_values.index(np.round(well.T[:, 1])[:, 0][i + 1])]
# Plot last ID
plt.text((well_model.grid.regular_grid.extent[5] -
well_model.grid.regular_grid.extent[4]) / 24,
geo_model.grid.regular_grid.extent[4] +
1 * geo_model.grid.regular_grid.dz,
'ID: %d' % (list_values[0]),
fontsize=14)
# Set legend handles
patches = [
mpatches.Patch(
color=cols[i],
label="{formation}".format(formation=surface[surface['id'].isin(
np.unique(np.round(sol.lith_block)))].surface.to_list()[i]))
for i in range(
len(surface[surface['id'].isin(np.unique(np.round(
sol.lith_block)))].surface.to_list()))
]
# Remove xticks
plt.tick_params(axis='x', labelsize=0, length=0)
# Set ylabel
plt.ylabel('Depth [m]')
# Set legend
plt.legend(handles=patches, bbox_to_anchor=(3, 1))
# Create depth dict
depth_dict = {
int(np.round(well.T[:, 1])[:, 0][i + 1]):
i * geo_model.grid.regular_grid.dz +
geo_model.grid.regular_grid.extent[4]
for i in boundaries
}
depth_dict[int(list_values[0])] = geo_model.grid.regular_grid.extent[4]
depth_dict = dict(sorted(depth_dict.items()))
return sol, well_model, depth_dict
#
# Suggested size of the axis-aligned modeling box: Origin: 548800 7816600
# -8400 Maximum: 552500 7822000 -11010
#
# Suggested resolution: 100m x 100m x -90m (grid size 38 x 55 x 30)
#
# %%
# Number of voxels:
np.array([38, 55, 30]).prod()
# %%
geo_model = gp.create_model('Claudius')
# Importing the data from csv files and settign extent and resolution
geo_model = gp.init_data(geo_model,
extent=[548800, 552500, 7816600, 7822000, -11010, -8400], resolution=[38, 55, 30],
surface_points_df=surface_points[::5], orientations_df=orientations, surface_name='surface',
add_basement=True)
# %%
# We are going to increase the smoothness (nugget) of the data to increase
# the conditional number of the matrix:
#
# %%
geo_model.modify_surface_points(geo_model.surface_points.df.index, smooth=0.1).df.tail()
# %%
# Also the original poles are pointing downwards. We can change the
# direction by calling the following:
#
#
# The first step to create a GemPy model is create a gempy.Model object
# that will contain all the other data structures and necessary
# functionality.
#
# In addition for this example we will define a regular grid since the
# beginning. This is the grid where we will interpolate the 3D geological
# model. GemPy comes with an array of different grids for different
# pourposes as we will see below. For visualization usually a regular grid
# is the one that makes more sense.
#
# %%
geo_model = gp.create_model('Model1')
geo_model = gp.init_data(geo_model,
extent=[0, 791, 0, 200, -582, 0],
resolution=[100, 10, 100])
# %%
# GemPy core code is written in Python. However for efficiency (and other
# reasons) most of heavy computations happend in optimize compile code,
# either C or CUDA for GPU. To do so, GemPy rely on the library theano. To
# guarantee maximum optimization theano requires to compile the code for
# every Python kernel. The compilation is done by calling the following
# line at any point (before computing the model):
#
# %%
gp.set_interpolator(geo_model, theano_optimizer='fast_compile', verbose=[])
# %%
