def __init__(self, interpolator, name='Feature', region=None, **kwargs):
"""
Constructor for a GeologicalFeatureInterpolator
Parameters
----------
interpolator : GeologicalInterpolator
An empty GeologicalInterpolator
region : lambda function
defining whether the location (xyz) should be included in the
kwargs - name of the feature, region to interpolate the feature
"""
self.interpolator = interpolator
self.name = name
self.interpolator.set_property_name(self.name)
# everywhere region is just a lambda that returns true for all locations
if region is None:
self.region = lambda pos: np.ones(pos.shape[0], dtype=bool)
else:
self.region = region
header = xyz_names()+val_name()+gradient_vec_names()+\
normal_vec_names()+tangent_vec_names()+weight_name()
self.data = pd.DataFrame(columns=header)
self.faults = []
self.data_added = False
self.interpolator.set_region(region=self.region)
def get_gradient_constraints(self):
"""
Get the gradient direction constraints
Returns
-------
numpy array
"""
mask = np.all(
~np.isnan(self.data.loc[:, gradient_vec_names()].to_numpy()),
axis=1)
if mask.shape[0] > 0:
return self.data.loc[mask,
xyz_names() + gradient_vec_names() +
weight_name()].to_numpy()
else:
return np.zeros((0, 7))
def __init__(
self,
interpolator: GeologicalInterpolator,
name="Feature",
region=None,
**kwargs,
):
"""
Constructor for a GeologicalFeatureBuilder
Parameters
----------
interpolator : GeologicalInterpolator
An empty GeologicalInterpolator
region : lambda function
defining whether the location (xyz) should be included in the
kwargs - name of the feature, region to interpolate the feature
"""
if issubclass(type(interpolator), GeologicalInterpolator) == False:
raise TypeError(
"interpolator is {} and must be a GeologicalInterpolator".
format(type(interpolator)))
self._interpolator = interpolator
self._name = name
self._interpolator.set_property_name(self._name)
# everywhere region is just a lambda that returns true for all locations
if region is None:
self.region = RegionEverywhere()
else:
self.region = region
header = (xyz_names() + val_name() + gradient_vec_names() +
normal_vec_names() + tangent_vec_names() + weight_name())
self.data = pd.DataFrame(columns=header)
self.faults = []
self.data_added = False
self._interpolator.set_region(region=self.region)
self._feature = None
self._up_to_date = False
self._build_arguments = {}
self._feature = GeologicalFeature(
self._name,
self._interpolator,
builder=self,
regions=[self.region],
faults=self.faults,
)
self._orthogonal_features = {}
self._equality_constraints = {}
def add_data_to_interpolator(self,
constrained=False,
force_constrained=False,
**kwargs):
"""
Iterates through the list of data and applies any faults active on the
data in the order they are added
Parameters
-----------
constrained : boolean
force_constrained : boolean
Returns
-------
"""
# first move the data for the fault
logger.info("Adding %i faults to %s" % (len(self.faults), self.name))
data = self.data.copy()
# convert data locations to numpy array and then update
for f in self.faults:
data.loc[:, xyz_names()] = f.apply_to_points(
self.get_data_locations())
# Now check whether there are enough constraints for the
# interpolator to be able to solve
# we need at least 2 different value points or a single norm
# constraint. If there are not enough
# try converting grad to norms, if still not enough send user an error
if constrained:
# Change normals to gradients
mask = np.all(~np.isnan(data.loc[:, normal_vec_names()]), axis=1)
if mask.shape[0] > 0:
data.loc[mask, gradient_vec_names()] = data.loc[
mask, normal_vec_names()].to_numpy()
data.loc[mask, normal_vec_names()] = np.nan
if self.get_norm_constraints().shape[0] > 0:
constrained = True
if np.unique(self.get_value_constraints()[:, 3]).shape[0] > 1:
constrained = True
if not constrained or force_constrained:
# change gradient constraints to normal vector constraints
mask = np.all(~np.isnan(data.loc[:, gradient_vec_names()]), axis=1)
if mask.shape[0] > 0:
data.loc[mask, normal_vec_names()] = data.loc[
mask, gradient_vec_names()].to_numpy()
data.loc[mask, gradient_vec_names()] = np.nan
logger.info("Setting gradient points to norm constraints")
constrained = True
mask = np.all(
~np.isnan(data.loc[:, normal_vec_names()].to_numpy()),
axis=1)
if not constrained:
logger.error("Not enough constraints for scalar field add more")
# self.interpolator.reset()
mask = ~np.isnan(data.loc[:, val_name()].to_numpy())
# add value constraints
if mask.shape[0] > 0:
value_data = data.loc[mask[:, 0],
xyz_names() + val_name() +
weight_name()].to_numpy()
self.interpolator.set_value_constraints(value_data)
# add gradient constraints
mask = np.all(~np.isnan(data.loc[:, gradient_vec_names()].to_numpy()),
axis=1)
if mask.shape[0] > 0:
gradient_data = data.loc[mask,
xyz_names() + gradient_vec_names() +
weight_name()].to_numpy()
self.interpolator.set_gradient_constraints(gradient_data)
# add normal vector data
mask = np.all(~np.isnan(data.loc[:, normal_vec_names()].to_numpy()),
axis=1)
if mask.shape[0] > 0:
normal_data = data.loc[mask,
xyz_names() + normal_vec_names() +
weight_name()].to_numpy()
self.interpolator.set_normal_constraints(normal_data)
# add tangent data
mask = np.all(~np.isnan(data.loc[:, tangent_vec_names()].to_numpy()),
axis=1)
if mask.shape[0] > 0:
tangent_data = data.loc[mask,
xyz_names() + tangent_vec_names() +
weight_name()].to_numpy()
self.interpolator.set_tangent_constraints(tangent_data)
# add interface constraints
mask = np.all(~np.isnan(data.loc[:, interface_name()].to_numpy()),
axis=1)
if mask.shape[0] > 0:
interface_data = data.loc[mask,
xyz_names() + interface_name() +
weight_name()].to_numpy()
self.interpolator.set_interface_constraints(interface_data)
self.data_added = True