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_a(self, theano_f):
"""
2 Horizontal layers with drift one
"""
data_interp = theano_f[0]
compiled_f = theano_f[1]
# Importing the data from csv files and settign extent and resolution
geo_data = gempy.import_data([0, 10, 0, 10, -10, 0], [50, 50, 50],
path_f="./GeoModeller/test_a/test_a_Foliations.csv",
path_i="./GeoModeller/test_a/test_a_Points.csv")
# rescaled_data = gempy.rescale_data(geo_data)
#
# data_interp.interpolator._data_scaled = rescaled_data
# data_interp.interpolator._grid_scaled = rescaled_data.grid
# data_interp.interpolator.order_table()
# data_interp.interpolator.set_theano_shared_parameteres()
#
# # Prepare the input data (interfaces, foliations data) to call the theano function.
# # Also set a few theano shared variables with the len of formations series and so on
# input_data_P = data_interp.interpolator.data_prep(u_grade=[3])
# # Compile the theano function.
data_interp.set_interpolator(geo_data)
i = data_interp.get_input_data(u_grade=[3])
sol = compiled_f(*i)
real_sol = np.load('test_a_sol.npy')
np.testing.assert_array_almost_equal(sol[:, :2, :], real_sol, decimal=3)
gempy.plot_section(geo_data, 25, block=sol[0, 0, :], direction='y', plot_data=True)
gempy.plot_potential_field(geo_data, sol[0, 1, :], 25)
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 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_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_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 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 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_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_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_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_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_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_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()
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_rgeomod_integration(theano_f):
geo_data=gp.create_data(extent=[612000, 622000, 2472000, 2480000, -1000, 1000],
resolution=[50, 50, 50],
path_f=input_path+"/gempy_foliations.csv",
path_i=input_path+"/gempy_interfaces.csv")
formation_order = ["Unit4", "Unit3", "Unit2", "Unit1"]
gp.set_series(geo_data, {"Default series": formation_order},
order_formations = formation_order, verbose=1)
gp.plot_data(geo_data, direction="z")
#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)
print("3-D geological model calculated.")
gp.plot_section(geo_data, lith_block[0], 25, direction='y', plot_data=False)
#plt.savefig("../data/cross_section_NS_25.pdf", bbox_inches="tight")
gp.plot_section(geo_data, lith_block[0], 25, direction='x', plot_data=False)
#plt.savefig("../data/cross_section_EW_25.pdf", bbox_inches="tight")
vertices, simplices = gp.get_surfaces(interp_data, potential_lith=lith_block[1], step_size=2)
fig = plt.figure(figsize=(13,10))
ax = fig.add_subplot(111, projection='3d')
cs = ["lightblue", "pink", "lightgreen", "orange"]
for i in range(4):
surf = ax.plot_trisurf(vertices[i][:,0], vertices[i][:,1], vertices[i][:,2],
color=cs[i], linewidth=0, alpha=0.65, shade=False)
#plt.savefig("../data/surfaces_3D.pdf", bbox_inches="tight")
# try:
# gp.plot_surfaces_3D(geo_data, vertices, simplices)
# except NameError:
# print("3-D visualization library vtk not installed.")
# load the digital elevation model
geotiff_filepath = input_path+"/dome_sub_sub_utm.tif"
raster = gdal.Open(geotiff_filepath)
dtm = raster.ReadAsArray()
dtmp = plt.imshow(dtm, origin='upper', cmap="viridis");
plt.title("Digital elevation model");
plt.colorbar(dtmp, label="Elevation [m]");
plt.savefig(input_path+"/DTM.pdf")
# To be able to use gempy plotting functionality we need to create a dummy geo_data object with the
# resoluion we want. In this case resolution=[339, 271, 1]
import copy
geo_data_dummy = copy.deepcopy(geo_data)
geo_data_dummy.resolution = [339, 271, 1]
# convert the dtm to a gempy-suitable raveled grid
points = rgeomod.convert_dtm_to_gempy_grid(raster, dtm)
# Now we can use the function `compute_model_at` to get the lithology values at a specific location:
# In[17]:
# interp_data_geomap = gp.InterpolatorInput(geo_data, dtype="float64")
lith_block, fault_block = gp.compute_model_at(points, interp_data)
# <div class="alert alert-info">
# **Your task:** Create a visual representation of the geological map in a 2-D plot (note: result is also again saved to the `../data`-folder):
# </div>
#
# And here **the geological map**:
# In[18]:
gp.plot_section(geo_data_dummy, lith_block[0], 0, direction='z', plot_data=False)
plt.title("Geological map");
#plt.savefig("../geological_map.pdf")
# ### Export the map for visualization in GoogleEarth
# <div class="alert alert-info">
# **Your task:** Execute the following code to export a GeoTiff of the generated geological map, as well as `kml`-files with your picked points inside the data folder. Open these files in GoogleEarth and inspect the generated map:
# </div>
#
#
# <div class="alert alert-warning">
# **Note (1)**: Use the normal `File -> Open..` dialog in GoogleEarth to open the data - no need to use the `Import` method, as the GeoTiff contains the correct coordinates in the file.
# </div>
#
#
# <div class="alert alert-warning">
# **Note (2)**: For a better interpretation of the generated map, use the transparency feature (directly after opening the map, or using `right-click -> Get Info` on the file).
# </div>
# In[19]:
geo_map = lith_block[0].copy().reshape((339,271))
geo_map = geo_map.astype('int16') # change to int for later use
# In[20]:
rgeomod.export_geotiff(input_path+"/geomap.tif", geo_map, gp.plotting.colors.cmap, geotiff_filepath)
# Export the interface data points:
# In[21]:
t = input_path+"/templates/ge_template_raw_interf.xml"
pt = input_path+"/templates/ge_placemark_template_interf.xml"
rgeomod.gempy_export_points_to_kml(input_path, geo_data, pt, t, gp.plotting.colors.cmap)
# Export the foliation data:
t = input_path+"/templates/ge_template_raw_fol.xml"
pt = input_path+"/templates/ge_placemark_template_fol.xml"
rgeomod.gempy_export_fol_to_kml(input_path+"/dips.kml", geo_data, pt, t)
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()
# In[ ]: lith_block, fault_block = gp.compute_model(interp_data) # ## 3.4 - Model visualization # # ### 3.4.1 - 2D Sections # In[ ]: gp.plot_section(geo_data, lith_block[0], 25, direction='y') # In[ ]: gp.plot_section(geo_data, lith_block[0], 25, direction='x') # ### 3.4.2 - Pseudo-3D surfaces # In[ ]: v_l, s_l = gp.get_surfaces(interp_data, potential_lith=lith_block[1], step_size=2)
