def add_fault_displacements(self, cmap='rainbow', **kwargs):
"""Add a block model painted by the fault displacement magnitude
Calls fault.displacementfeature.evaluate_value(points) for all faults
Parameters
----------
cmap : matplotlib cmap, optional
colourmap name or object from mpl
Notes
------
It is sensible to increase the viewer step sizes before running this function to
increase the resolution of the model as its not possible to interpolate a discrete
colourmap and this causes the model to look like a lego block.
You can update the model resolution by changing the attribute nsteps
>>> viewer.nsteps = np.array([100,100,100])
"""
name = kwargs.get('name', 'fault_displacements')
points, tri = create_box(self.bounding_box, self.nsteps)
surf = self.lv.triangles(name)
surf.vertices(self.model.rescale(points))
surf.indices(tri)
vals = self.model.evaluate_fault_displacements(points)
surf.values(vals, 'displacement')
surf["colourby"] = 'displacement'
vmin = kwargs.get('vmin', np.nanmin(vals))
vmax = kwargs.get('vmax', np.nanmax(vals))
surf.colourmap(cmap, range=(vmin, vmax))
def add_scalar_field(self, geological_feature, **kwargs):
"""
Parameters
----------
geological_feature : GeologicalFeature
the geological feature to colour the scalar field by
kwargs
kwargs for lavavu
Returns
-------
"""
name = kwargs.get('name', geological_feature.name + '_scalar_field')
points, tri = create_box(self.bounding_box, self.nsteps)
surf = self.lv.triangles(name)
surf.vertices(self.model.rescale(points))
surf.indices(tri)
val = geological_feature.evaluate_value(self.model.scale(points))
surf.values(val, geological_feature.name)
surf["colourby"] = geological_feature.name
cmap = kwargs.get('cmap', lavavu.cubehelix(100))
logger.info("Adding scalar field of %s to viewer. Min: %f, max: %f" %
(geological_feature.name, geological_feature.min(),
geological_feature.max()))
vmin = kwargs.get('vmin', np.nanmin(val))
vmax = kwargs.get('vmax', np.nanmax(val))
surf.colourmap(cmap, range=(vmin, vmax))
def add_model(self, cmap=None, **kwargs):
"""Add a block model painted by stratigraphic id to the viewer
Calls self.model.evaluate_model() for a cube surrounding the model.
Parameters
----------
cmap : matplotlib cmap, optional
colourmap name or object from mpl
Notes
------
It is sensible to increase the viewer step sizes before running this function to
increase the resolution of the model as its not possible to interpolate a discrete
colourmap and this causes the model to look like a lego block.
You can update the model resolution by changing the attribute nsteps
>>> viewer.nsteps = np.array([100,100,100])
"""
import matplotlib.colors as colors
from matplotlib import cm
name = kwargs.get('name', 'geological_model')
points, tri = create_box(self.bounding_box, self.nsteps)
surf = self.lv.triangles(name)
surf.vertices(self.model.rescale(points))
surf.indices(tri)
val = self.model.evaluate_model(points, scale=True)
surf.values(val, 'model')
surf["colourby"] = 'model'
if cmap is None:
import matplotlib.colors as colors
colours = []
boundaries = []
data = []
for g in self.model.stratigraphic_column.keys():
if g == 'faults':
continue
for u, v in self.model.stratigraphic_column[g].items():
data.append((v['id'], v['colour']))
colours.append(v['colour'])
boundaries.append(v['id']) #print(u,v)
cmap = colors.ListedColormap(colours).colors
# else:
# cmap = cm.get_cmap(cmap,n_units)
# logger.info("Adding scalar field of %s to viewer. Min: %f, max: %f" % (geological_feature.name,
# geological_feature.min(),
# geological_feature.max()))
vmin = kwargs.get('vmin', np.nanmin(val))
vmax = kwargs.get('vmax', np.nanmax(val))
surf.colourmap(cmap, range=(vmin, vmax))
def add_scalar_field(self,
geological_feature,
name=None,
cmap='rainbow',
vmin=None,
vmax=None,
**kwargs):
"""Add a block the size of the model area painted with the scalar field value
Parameters
----------
geological_feature : GeologicalFeature
the geological feature to colour the scalar field by
name : string, optional
Name of the object for lavavu, needs to be unique for the viewer object, by default uses feature name
cmap : str, optional
mpl colourmap reference, by default 'rainbow'
vmin : double, optional
minimum value of the colourmap, by default None
vmax : double, optional
maximum value of the colourmap, by default None
"""
if name == None:
if geological_feature is None:
name = 'unnamed scalar field'
else:
name = geological_feature.name + '_scalar_field'
points, tri = create_box(self.bounding_box, self.nsteps)
surf = self.lv.triangles(name)
surf.vertices(self.model.rescale(points))
surf.indices(tri)
val = geological_feature.evaluate_value(self.model.scale(points))
surf.values(val, geological_feature.name)
surf["colourby"] = geological_feature.name
logger.info("Adding scalar field of %s to viewer. Min: %f, max: %f" %
(geological_feature.name, geological_feature.min(),
geological_feature.max()))
if vmin == None:
vmin = np.nanmin(val)
if vmax == None:
vmax = np.nanmax(val)
surf.colourmap(cmap, range=(vmin, vmax))
def add_support_box(self, geological_feature, paint=False, **kwargs):
name = kwargs.get('name', geological_feature.name + '_support')
box = np.vstack([
geological_feature.interpolator.support.origin,
geological_feature.interpolator.support.maximum
])
points, tri = create_box(box, self.nsteps)
surf = self.lv.triangles(name)
surf.vertices(self.model.rescale(points))
surf.indices(tri)
if paint:
val = geological_feature.evaluate_value(self.model.scale(points))
surf.values(val, geological_feature.name)
surf["colourby"] = geological_feature.name
cmap = kwargs.get('cmap', lavavu.cubehelix(100))
logger.info(
"Adding scalar field of %s to viewer. Min: %f, max: %f" %
(geological_feature.name, geological_feature.min(),
geological_feature.max()))
vmin = kwargs.get('vmin', np.nanmin(val))
vmax = kwargs.get('vmax', np.nanmax(val))
surf.colourmap(cmap, range=(vmin, vmax))
def _write_cubeface_evtk(model,
file_name,
data_label,
nsteps,
real_coords=True):
"""
Writes out the model as a cuboid with six rectangular surfaces in VTK unstructured grid format
Parameters
----------
model : GeologicalModel object
Geological model to export
file_name : string
Name of file that model is exported to, including path, but without the file extension
data_label : string
A data label to insert into export file
nsteps : np.array([num-x-steps, num-y-steps, num-z-steps])
3d array dimensions
Returns
-------
True if successful
"""
# Evaluate model at points
points, tri = create_box(model.bounding_box, nsteps)
val = model.evaluate_model(points, scale=False)
if real_coords:
model.rescale(points)
# Define vertices
x = np.zeros(points.shape[0])
y = np.zeros(points.shape[0])
z = np.zeros(points.shape[0])
for i in range(points.shape[0]):
x[i], y[i], z[i] = points[i][0], points[i][1], points[i][2]
# Define connectivity or vertices that belongs to each element
conn = np.zeros(tri.shape[0] * 3)
for i in range(tri.shape[0]):
conn[i * 3], conn[i * 3 + 1], conn[i * 3 +
2] = tri[i][0], tri[i][1], tri[i][2]
# Define offset of last vertex of each element
offset = np.zeros(tri.shape[0])
for i in range(tri.shape[0]):
offset[i] = (i + 1) * 3
# Define cell types
ctype = np.full(tri.shape[0], VtkTriangle.tid)
try:
unstructuredGridToVTK(
file_name,
x,
y,
z,
connectivity=conn,
offsets=offset,
cell_types=ctype,
cellData=None,
pointData={data_label: val},
)
except Exception as e:
logger.warning(
f"Cannot export cuboid surface to VTK file {file_name}: {e}")
return False
return True