def test_create_formations():
formations = gp.Surfaces(values_array=np.arange(1, 8).reshape(-1, 1),
properties_names=np.array(['density']))
# formations.set_surfaces_names(['MainFault', 'SecondaryReservoir','Seal',
# 'Reservoir', 'Overlying'])
return formations
def test_restricting_wrapper():
from gempy.core.model import RestrictingWrapper
surface = gp.Surfaces(gp.core.data_modules.stack.Series(gp.core.data_modules.stack.Faults()))
s = RestrictingWrapper(surface)
print(s)
with pytest.raises(AttributeError):
print(s.add_surfaces)
def test_set_formation_names(self, test_create_formations):
test_create_formations.set_surfaces_names(['MainFault', 'SecondaryReservoir', 'Seal',
'Reservoir', 'Overlying'])
print(test_create_formations)
formations = gp.Surfaces(values_array=np.arange(1, 8).reshape(-1, 1),
properties_names=np.array(['density']))
formations.set_surfaces_names(['MainFault', 'SecondaryReservoir', 'Seal',
'Reservoir', 'Overlying'])
print(formations)
formations = gp.Surfaces(values_array=np.arange(1, 2).reshape(-1, 1),
properties_names=np.array(['density']))
formations.set_surfaces_names(['MainFault', 'SecondaryReservoir', 'Seal',
'Reservoir', 'Overlying'])
print(formations)
# voxel will have after discretization. This may be useful for example in # the case we want to map a specific geophysical property (such as # density) to a given unit. By default both are the same since to # discretize lithological units the value is arbitrary. # # %% # From an empty df # ^^^^^^^^^^^^^^^^ # # The Surfaces class needs to have an associate series object. This will # limit the name of the series since they are a ``pandas.Categorical``\ . # # %% surfaces = gp.Surfaces(series) # %% # We can set any number of formations by passing a list with the names. By # default they will take the name or the first series. # # %% surfaces.set_surfaces_names(['foo', 'foo2', 'foo5']) # %% series # %% # We can add new formations: #
def loop2gempy_(test_data_name, tmp_path, vtk_path, orientations_file, contacts_file, groups_file, dtm_reproj_file,
bbox, model_base, model_top, vtk):
import gempy as gp
from gempy import plot
geo_model = gp.create_model(test_data_name)
# If depth coordinates are much smaller than XY the whole system of equations becomes very unstable. Until
# I fix it properly in gempy this is a handcrafted hack
ve = (bbox[0] - bbox[2]) / (model_base - model_top)
if ve < 3:
ve = 0
else:
print('The vertical exageration is: ', ve)
gp.init_data(geo_model, extent=[bbox[0], bbox[2], bbox[1], bbox[3], model_base*ve, model_top*ve],
resolution = (50,50,50),
path_o = orientations_file,
path_i = contacts_file, default_values=True);
# Show example lithological points
#gp.get_data(geo_model, 'surface_points').head()
# Show example orientations
#gp.get_data(geo_model, 'orientations').head()
# Plot some of this data
#gp.plot.plot_data(geo_model, direction='z')
geo_model.modify_surface_points(geo_model.surface_points.df.index, Z=geo_model.surface_points.df['Z']*ve)
# Load reprojected topgraphy to model
fp = dtm_reproj_file
geo_model.set_topography(source='gdal',filepath=fp)
contents=np.genfromtxt(groups_file,delimiter=',',dtype='U100')
ngroups=len(contents)
faults = gp.Faults()
series = gp.Series(faults)
#series.df
#display(ngroups,contents)
groups = []
for i in range (0,ngroups):
groups.append(contents[i].replace("\n",""))
series.add_series(contents[i].replace("\n",""))
print(contents[i].replace("\n",""))
series.delete_series('Default series')
#series
# Load surfaces and assign to series
surfaces = gp.Surfaces(series)
print(ngroups,groups)
for i in range(0,ngroups):
contents=np.genfromtxt(tmp_path+groups[i]+'.csv',delimiter=',',dtype='U100')
nformations=len(contents.shape)
if(nformations==1):
for j in range (1,len(contents)):
surfaces.add_surface(str(contents[j]).replace("\n",""))
d={groups[i]:str(contents[j]).replace("\n","")}
surfaces.map_series({groups[i]:(str(contents[j]).replace("\n",""))}) #working but no gps
else:
for j in range (1,len(contents[0])):
surfaces.add_surface(str(contents[0][j]).replace("\n",""))
d={groups[i]:str(contents[0][j]).replace("\n","")}
surfaces.map_series({groups[i]:(str(contents[0][j]).replace("\n",""))}) #working but no gps
# Set Interpolation Data
id_only_one_bool = geo_model.surface_points.df['id'].value_counts() == 1
id_only_one = id_only_one_bool.index[id_only_one_bool]
single_vals = geo_model.surface_points.df[geo_model.surface_points.df['id'].isin(id_only_one)]
for idx, vals in single_vals.iterrows():
geo_model.add_surface_points(vals['X'], vals['Y'], vals['Z'], vals['surface'])
geo_model.update_structure()
gp.set_interpolation_data(geo_model,
compile_theano=True,
theano_optimizer='fast_compile',
verbose=[])
# Provide summary data on model
#geo_model.additional_data.structure_data
#Calculate Model
gp.compute_model(geo_model)
# Extract surfaces to visualize in 3D renderers
#gp.plot.plot_section(geo_model, 49, direction='z', show_data=False)
ver, sim = gp.get_surfaces(geo_model)
# import winsound
# duration = 700 # milliseconds
# freq = 1100 # Hz
# winsound.Beep(freq, duration)
# winsound.Beep(freq, duration)
# winsound.Beep(freq, duration)
#Visualise Model
gp.plot.plot_3D(geo_model, render_data=False)
#Save model as vtk
if(vtk):
gp.plot.export_to_vtk(geo_model, path=vtk_path, name=test_data_name+'.vtk', voxels=False, block=None, surfaces=True)
return geo_model
def test_map_formations_from_series2(self, test_create_series):
formations = gp.Surfaces()
formations.map_formations_from_series(test_create_series)
print(formations)
def create_surfaces(create_series):
series = create_series
surfaces = gp.Surfaces(series)
surfaces.set_surfaces_names(['foo', 'foo2', 'foo5'])
print(series)
# We can add new surfaces:
surfaces.add_surface(['feeeee'])
print(surfaces)
# The column surface is also a pandas.Categories.
# This will be important for the Data clases (SurfacePoints and Orientations)
print(surfaces.df['surface'])
### Set values
# To set the values we do it with the following method
surfaces.set_surfaces_values([2, 2, 2, 5])
print(surfaces)
# #### Set values with a given name:
# We can give specific names to the properties (i.e. density)
surfaces.add_surfaces_values([[2, 2, 2, 6], [2, 2, 1, 8]],
['val_foo', 'val2_foo'])
print(surfaces)
### Delete surfaces values
#
# To delete a full propery:
surfaces.delete_surface_values(['val_foo', 'value_0'])
# #### One of the surfaces must be set be the basement:
surfaces.set_basement()
print(surfaces)
# #### Set surface values
#
# We can also use set values instead adding. This will delete the previous properties and add the new one
surfaces.set_surfaces_values([[2, 2, 2, 6], [2, 2, 1, 8]],
['val_foo', 'val2_foo'])
print(surfaces)
# The last property is the correspondant series that each surface belong to. `series` and `surface`
# are pandas categories. To get a overview of what this mean
# check https://pandas.pydata.org/pandas-docs/stable/categorical.html.
print(surfaces.df['series'])
print(surfaces.df['surface'])
# ### Map series to surface
# To map a series to a surface we can do it by passing a dict:
# If a series does not exist in the `Series` object, we rise a warning and we set those surfaces to nans
d = {"foo7": 'foo', "booX": ('foo2', 'foo5', 'fee')}
surfaces.map_series(d)
surfaces.map_series({"foo7": 'foo', "boo": ('foo2', 'foo5', 'fee')})
print(surfaces)
# An advantage of categories is that they are order so no we can tidy the df by series and surface
surfaces.df.sort_values(by='series', inplace=True)
# If we change the basement:
surfaces.set_basement()
# Only one surface can be the basement:
print(surfaces)
# ### Modify surface name
surfaces.rename_surfaces({'foo2': 'lala'})
print(surfaces)
surfaces.df.loc[2, 'val_foo'] = 22
print(surfaces)
# We can use `set_is_fault` to choose which of our series are faults:
return surfaces