DelaunayVoronoiDual

class openpnm.network.DelaunayVoronoiDual(*args, **kwargs)[source]

Combined and interconnected Voronoi and Delaunay tessellations

A Delaunay tessellation is performed on a set of base points then the corresponding Voronoi diagram is generated. Finally, each Delaunay node is connected to it’s neighboring Voronoi vertices to create interaction between the two networks.

All pores and throats are labelled according to their network (i.e. ‘pore.delaunay’), so they can be each assigned to a different Geometry.

The dual-nature of this network is meant for modeling transport in the void and solid space simultaneously by treating one network (i.e. Delaunay) as voids and the other (i.e. Voronoi) as solid. Interaction such as heat transfer between the solid and void can be accomplished via the interconnections between the Delaunay and Voronoi nodes.

Parameters
  • points (array_like or int) – Can either be an N-by-3 array of point coordinates which will be used, or a scalar value indicating the number of points to generate

  • shape (array_like) – The size and shape of the domain used for generating and trimming excess points. The coordinates are treated as the outer corner of a rectangle [x, y, z] whose opposite corner lies at [0, 0, 0]. By default, a domain size of [1, 1, 1] is used. To create a 2D network set the Z-dimension to 0.

  • name (str) – An optional name for the object to help identify it. If not given, one will be generated.

Examples

Points will be automatically generated if none are given:

>>> import openpnm as op
>>> net = op.network.DelaunayVoronoiDual(points=50, shape=[1, 1, 0])

The resulting network can be quickly visualized using opnepnm.topotools.plot_connections.

Attributes
Np

A shortcut to query the total number of pores on the object

Nt

A shortcut to query the total number of throats on the object

Ps

A shortcut to get a list of all pores on the object

Ts

A shortcut to get a list of all throats on the object

am

Adjacency matrix in the specified sparse format, with throat IDs indicating the non-zero values.

conns

Returns the connectivity matrix of the network.

coords

Returns the list of pore coordinates of the network.

im

Incidence matrix in the specified sparse format, with pore IDs indicating the non-zero values.

models

List of available models on the objects

name

String representing the name of the object

network

A shortcut to get a handle to the associated network.

project

A shortcut to get a handle to the associated project.

settings

Dictionary containing object settings.

tri

A shortcut to get a handle to the Delanuay subnetwork

vor

A shortcut to get a handle to the Voronoi subnetwork

Methods

add_boundary_pores([labels, offset])

Add boundary pores to the specified faces of the network.

add_model(propname, model[, regen_mode])

Adds a new model to the models dictionary.

check_network_health()

This method check the network topological health.

create_adjacency_matrix([weights, fmt, ...])

Generates a weighted adjacency matrix in the desired sparse format

create_incidence_matrix([weights, fmt, ...])

Creates a weighted incidence matrix in the desired sparse format

filter_by_label([pores, throats, labels, mode])

Returns which of the supplied pores (or throats) has the specified label(s)

find_connected_pores([throats, flatten, mode])

Return a list of pores connected to the given list of throats

find_connecting_throat(P1, P2)

Return the throat index connecting pairs of pores.

find_nearby_pores(pores, r[, flatten, ...])

Find all pores within a given radial distance of the input pore(s) regardless of whether or not they are toplogically connected.

find_neighbor_pores(pores[, mode, flatten, ...])

Returns a list of pores that are direct neighbors to the given pore(s)

find_neighbor_throats(pores[, mode, flatten])

Returns a list of throats neighboring the given pore(s)

find_pore_hulls([pores])

Finds the indices of the Voronoi nodes that define the convex hull around the given Delaunay nodes.

find_throat_facets([throats])

Finds the indicies of the Voronoi nodes that define the facet or ridge between the Delaunay nodes connected by the given throat.

get_adjacency_matrix([fmt])

Adjacency matrix in the specified sparse format, with throat IDs indicating the non-zero values.

get_conduit_data(poreprop[, throatprop, mode])

Combines requested data into a single 3-column array.

get_incidence_matrix([fmt])

Incidence matrix in the specified sparse format, with pore IDs indicating the non-zero values.

interleave_data(prop)

Retrieves requested property from associated objects, to produce a full Np or Nt length array.

interpolate_data(propname[, mode])

Determines a pore (or throat) property as the average of it's neighboring throats (or pores)

labels([pores, throats, element, mode])

Returns a list of labels present on the object

num_neighbors(pores[, mode, flatten])

Returns the number of neigbhoring pores for each given input pore

num_pores([labels, mode])

Returns the number of pores of the specified labels

num_throats([labels, mode])

Return the number of throats of the specified labels

params()

Return parameter names and values in a dictionary

pores([labels, mode, asmask, to_global])

Returns pore indicies where given labels exist, according to the logic specified by the mode argument.

props([element, mode, deep])

Returns a list containing the names of all defined pore or throat properties.

regenerate_models([propnames, exclude, deep])

Re-runs the specified model or models.

remove_model([propname, mode])

Removes model and data from object.

set_label(label[, pores, throats, mode])

Creates or updates a label array

show_hist([props, bins, fontsize])

Shows a quick plot of key property distributions.

throats([labels, mode, asmask, to_global])

Returns throat locations where given labels exist, according to the logic specified by the mode argument.

to_indices(mask)

Converts a boolean mask to a list of pore or throat indices.

to_mask([pores, throats])

Convert a list of pore or throat indices into a boolean mask of the correct length.