LoopStructural.interpolators.SurfeRBFInterpolator#

class LoopStructural.interpolators.SurfeRBFInterpolator(*args, **kwargs)#

Bases: GeologicalInterpolator

Dummy class to handle the case where Surfe is not installed. This will raise a warning when used.

Initialize the geological interpolator.

This method sets up the basic data structures and parameters required for geological interpolation.

Parameters:

  • data (dict, optional) – Dictionary containing constraint data arrays, by default {}

  • up_to_date (bool, optional) – Whether the interpolator is already built and up to date, by default False

Notes

This is an abstract method that must be implemented by subclasses. All subclasses should call this parent constructor to ensure proper initialization of the base data structures.

__init__(*args, **kwargs)#

Initialize the geological interpolator.

This method sets up the basic data structures and parameters required for geological interpolation.

Parameters:

  • data (dict, optional) – Dictionary containing constraint data arrays, by default {}

  • up_to_date (bool, optional) – Whether the interpolator is already built and up to date, by default False

Notes

This is an abstract method that must be implemented by subclasses. All subclasses should call this parent constructor to ensure proper initialization of the base data structures.

Methods

__init__(*args, **kwargs)

Initialize the geological interpolator.

add_gradient_constraints([w])

add_inequality_pairs_constraints([w, ...])

add_interface_constraints([w])

add_norm_constraints([w])

add_tangent_constraints([w])

add_value_constraints([w])

add_value_inequality_constraints([w])

check_array(array)

Validate and convert input to numpy array.

clean()

Removes all of the data from an interpolator

debug()

Helper function for debugging when the interpolator isn't working

evaluate_gradient(locations)

evaluate_value(locations)

get_data_locations()

Get the location of all data points

get_gradient_constraints()

get_inequality_pairs_constraints()

get_inequality_value_constraints()

get_interface_constraints()

Get the location of interface constraints

get_norm_constraints()

get_tangent_constraints()

get_value_constraints()

reset()

set_gradient_constraints(points)

Set gradient constraints for the interpolation.

set_inequality_pairs_constraints(points)

set_interface_constraints(points)

set_nelements(nelements)

Set the number of elements for the interpolation support.

set_normal_constraints(points)

set_region(**kwargs)

Set the interpolation region.

set_tangent_constraints(points)

set_value_constraints(points)

Set value constraints for the interpolation.

set_value_inequality_constraints(points)

setup(**kwargs)

Runs all of the required setting up stuff

setup_interpolator(**kwargs)

Runs all of the required setting up stuff

solve_system(solver[, solver_kwargs])

Solves the interpolation equations

to_dict()

to_json()

Return a JSON representation of the geological interpolator.

update()

Attributes

data

Get the constraint data dictionary.

n_elements

Get the number of elements in the interpolation support.
check_array(array: ndarray)#

Validate and convert input to numpy array.

Parameters:

array (array_like) – Input array to validate and convert

Returns:

np.ndarray – Validated numpy array

Raises:

LoopTypeError – If the array cannot be converted to a numpy array

clean()#

Removes all of the data from an interpolator

property data#

Get the constraint data dictionary.

Returns:

dict – Dictionary containing constraint data arrays

debug()#

Helper function for debugging when the interpolator isn’t working

get_data_locations()#

Get the location of all data points

Returns:

numpy array – Nx3 - X,Y,Z location of all data points

get_gradient_constraints()#
Returns:

numpy array

get_interface_constraints()#

Get the location of interface constraints

Returns:

numpy array – Nx4 - X,Y,Z,id location of all interface constraints

get_norm_constraints()#
Returns:

numpy array

get_tangent_constraints()#
Returns:

numpy array

get_value_constraints()#
Returns:

numpy array

abstract property n_elements: int#

Get the number of elements in the interpolation support.

Returns:

int – Number of elements

Notes

This is an abstract property that must be implemented by subclasses.

set_gradient_constraints(points: ndarray)#

Set gradient constraints for the interpolation.

Parameters:

points (np.ndarray) – Array containing gradient constraints with shape (n_points, 7-8). Columns should be [X, Y, Z, gx, gy, gz, weight]. If weight is not provided, a weight of 1.0 is assumed for all points.

Raises:

ValueError – If points array doesn’t have the minimum required columns

Notes

Gradient constraints specify the direction and magnitude of the scalar field gradient at specific locations. These are typically derived from structural measurements like bedding or foliation orientations.

abstractmethod set_nelements(nelements: int) → int#

Set the number of elements for the interpolation support.

Parameters:

nelements (int) – Target number of elements

Returns:

int – Actual number of elements set

Notes

This is an abstract method that must be implemented by subclasses. The actual number of elements may differ from the requested number depending on the interpolator’s constraints.

set_normal_constraints(points: ndarray)#
Parameters:

points (np.ndarray) – array containing the value constraints usually 7-8 columns. X,Y,Z,nx,ny,nz,(weight, default : 1 for each row)

Notes

If no weights are provided, w = 1 is assigned to each normal constraint.

abstractmethod set_region(**kwargs)#

Set the interpolation region.

Parameters:

**kwargs (dict) – Region parameters specific to the interpolator implementation

Notes

This is an abstract method that must be implemented by subclasses. The specific parameters depend on the interpolator type.

set_tangent_constraints(points: ndarray)#
Parameters:

points (np.ndarray) – array containing the value constraints usually 7-8 columns. X,Y,Z,nx,ny,nz,weight

set_value_constraints(points: ndarray)#

Set value constraints for the interpolation.

Parameters:

points (np.ndarray) – Array containing the value constraints with shape (n_points, 4-5). Columns should be [X, Y, Z, value, weight]. If weight is not provided, a weight of 1.0 is assumed for all points.

Raises:

ValueError – If points array doesn’t have the minimum required columns

Notes

Value constraints specify known scalar field values at specific locations. These are typically used for interface points or measured data values.

setup(**kwargs)#

Runs all of the required setting up stuff

abstractmethod setup_interpolator(**kwargs)#

Runs all of the required setting up stuff

abstractmethod solve_system(solver, solver_kwargs: dict = {}) → bool#

Solves the interpolation equations

to_json()#

Return a JSON representation of the geological interpolator.

Returns:

dict – Dictionary containing the interpolator’s state and configuration suitable for JSON serialization

Notes

This method packages the essential state of the interpolator including its type, constraints, data, and build status for serialization.