topotools

Collection of functions for manipulating network topology

This module contains a selection of functions that deal specifically with network topology.

Functions

add_boundary_pores(network, pores[, offset, ...])	This method uses clone_pores to clone the input pores, then shifts them the specified amount and direction, then applies the given label.
add_reservoir_pore(cls, network, pores[, offset])	Adds a single pore connected to all pores to act as a reservoir
bond_percolation(network, occupied_bonds)	Assigns cluster numbers to sites and bonds acccording to a bond percolation process, given a list of occupied bonds.
clone_pores(network, pores[, labels, mode])	Clones the specified pores and adds them to the network
connect_pores(network, pores1, pores2[, labels])	Returns the possible connections between two groups of pores
conns_to_am(*args, **kwargs)	Converts a list of connections into a Scipy sparse adjacency matrix
dimensionality(network)	Determines whether a network is 1D, 2D or 3D, and in which dimensions
drop_sites(am, sites)	Update adjacency matrix after dropping nodes
extend(network[, coords, conns, labels])	Add pores or throats to the network from a list of coords or conns.
filter_pores_by_z(network, pores, z)	Filters a list of nodes to those with a given number of neighbors
find_clusters(network[, mask])	Identify connected clusters of pores and throats in the network.
find_connected_sites(bonds, **kwargs)	Finds which nodes are connected to a given set of edges
find_connecting_bonds(sites, **kwargs)	Finds the edge that connects each pair of given nodes
find_interface_throats(network, P1, P2)	Finds edges shared between two sets of nodes
find_isolated_clusters(network, mask, inlets)	Identifies pores and throats that are invaded but not connected to the inlets
find_neighbor_bonds(sites, **kwargs)	Finds all edges that are connected to the given input nodes
find_neighbor_sites(sites, **kwargs)	Finds all nodes that are directly connected to the input nodes
find_path(network, pore_pairs[, weights])	Find the shortest path between pairs of nodes
find_surface_pores(network[, markers, label])	Find the pores on the surface of the domain by performing a Delaunay triangulation between the network pores and some external markers.
generate_base_points(num_points, domain_size)	Generates a set of randomly distributed points in rectilinear coordinates for use in spatial tessellations
get_domain_area(network[, inlets, outlets])	Determine the cross sectional area relative to the inlets/outlets.
get_domain_length(network[, inlets, outlets])	Determine the domain length relative to the inlets/outlets.
get_shape(network)	Determine shape of a cubic network
get_spacing(network)	Determine spacing of a cubic network
hull_centroid(points)	Computes centroid of the convex hull enclosing the given coordinates.
is_fully_connected(network[, pores_BC])	Checks whether network is fully connected, i.e. not clustered.
iscoplanar(coords)	Determines if given pores are coplanar with each other
isoutside(**kwargs)	Identifies sites that lie outside the specified shape
ispercolating(network, inlets, outlets)	Determines if a percolating cluster exists in the network spanning the given inlet and outlet nodes
issymmetric(am)	A method to check if a square matrix is symmetric Returns True if the sparse adjacency matrix is symmetric
istriangular(am)	Returns True if the sparse adjacency matrix is either upper or lower triangular
istril(am)	Returns True if the sparse adjacency matrix is lower triangular
istriu(am)	Returns True if the sparse adjacency matrix is upper triangular
label_faces(network[, tol, label])	Finds pores on the surface of the network and labels them according to whether they are on the top, bottom, etc.
merge_networks(network[, donor])	Combine multiple networks into one without making any topological connections
merge_pores(network, pores[, labels, ...])	Combines a selection of pores into a new single pore located at the centroid of the selected pores (and optionally their neighbors) and connected to all of their neighbors.
reduce_coordination(network, z)	Deletes throats on network to match specified average coordination number
reflect_base_points(points, domain_size)	Relects a set of points about the faces of a given domain
rotate_coords(network, **kwargs)	Rotates coordinates a given amount about each axis
shear_coords(network, **kwargs)	Shears the coordinates a given amount about along axis
site_percolation(network, occupied_sites)	Assigns cluster numbers to sites and bonds acccording to a site percolation process, given a list of occupied sites.
stitch(network, donor, P_network, P_donor[, ...])	Stitches a second a network to the current network.
template_cylinder_annulus(z, r_outer[, r_inner])	This method generates an image array of a disc-ring.
template_sphere_shell(r_outer[, r_inner])	This method generates an image array of a sphere-shell.
tri_to_am(tri)	Given a Delaunay triangulation object from Scipy's spatial module, converts to a sparse adjacency matrix network representation.
trim(network[, pores, throats])	Remove pores or throats from the network
trim_disconnected_clusters(**kwargs)	Computes actual node and edge occupancy based on connectivity to the given inlets
vor_to_am(vor)	Given a Voronoi tessellation object from Scipy's spatial module, converts to a sparse adjacency matrix network representation in COO format.