landlab.graph.framed_voronoi package

Subpackages

Submodules

landlab.graph.framed_voronoi.dual_framed_voronoi module

Implement the DualFramedVoronoiGraph

@author sebastien lenard @date 2022, Aug

class DualFramedVoronoiGraph(shape, xy_spacing=(1.0, 1.0), xy_of_lower_left=(0.0, 0.0), sort=False, xy_min_spacing=(0.5, 0.5), seed=200)

Bases: DualGraph, FramedVoronoiGraph

Graph of a unstructured grid of Voronoi Delaunay cells and irregular patches. It is a special type of VoronoiDelaunay graph in which the initial set of points is arranged in a fixed lattice (e.g. like a rectangular raster grid) named here “layout” and the core points are then moved aroung their initial position by a random distance, lower than a certain threshold.

Examples

>>> from landlab.graph import DualFramedVoronoiGraph

>>> graph = DualFramedVoronoiGraph((3, 3), seed=200)
>>> graph.number_of_nodes
9

>>> graph.x_of_node[2:4]    
array([ 2.,  0.])
>>> graph.y_of_node[2:4]    
array([ 0.   ,  0.749])
>>> graph.y_of_node[5]    
1.2509999999999999

Create the graph.

Parameters:

• shape (tuple of int) – Number of rows and columns of nodes.

• xy_spacing (float or tuple of float, optional) – Node spacing along x and y coordinates. If float, same spacing at x and y.

• xy_of_lower_left (tuple, optional) – Minimum x-of-node and y-of-node values. Depending on the grid, there may not be a node present at this location.

• sort (bool) – If True, nodes, links and patches are re-numbered according to their position.

• xy_min_spacing (float or tuple of float, optional) – Final minimal spacing between nodes. Random moves of the core nodes around their position cannot be above this threshold: (xy_spacing - xy_min_spacing) / 2 If float, same minimal spacing for x and y.

• seed (int, optional) – Seed used to generate the random x and y moves. When set, controls a pseudo-randomness of moves to ensure reproducibility. When None, seed is random and the moves of coordinates are completely random.

Returns:

A newly-created graph.

Return type:

DualFramedVoronoiGraph

Examples

Create a grid with 3 rows and 2 columns of nodes.

>>> from landlab.graph import DualFramedVoronoiGraph
>>> graph = DualFramedVoronoiGraph((3, 2), xy_spacing=1.0)
>>> graph.number_of_nodes
6

__init__(shape, xy_spacing=(1.0, 1.0), xy_of_lower_left=(0.0, 0.0), sort=False, xy_min_spacing=(0.5, 0.5), seed=200)

Create the graph.

Parameters:

• shape (tuple of int) – Number of rows and columns of nodes.

• xy_spacing (float or tuple of float, optional) – Node spacing along x and y coordinates. If float, same spacing at x and y.

• xy_of_lower_left (tuple, optional) – Minimum x-of-node and y-of-node values. Depending on the grid, there may not be a node present at this location.

• sort (bool) – If True, nodes, links and patches are re-numbered according to their position.

• xy_min_spacing (float or tuple of float, optional) – Final minimal spacing between nodes. Random moves of the core nodes around their position cannot be above this threshold: (xy_spacing - xy_min_spacing) / 2 If float, same minimal spacing for x and y.

• seed (int, optional) – Seed used to generate the random x and y moves. When set, controls a pseudo-randomness of moves to ensure reproducibility. When None, seed is random and the moves of coordinates are completely random.

Returns:

A newly-created graph.

Return type:

DualFramedVoronoiGraph

Examples

Create a grid with 3 rows and 2 columns of nodes.

>>> from landlab.graph import DualFramedVoronoiGraph
>>> graph = DualFramedVoronoiGraph((3, 2), xy_spacing=1.0)
>>> graph.number_of_nodes
6

property adjacent_corners_at_corner

Get adjacent corners.

See also

adjacent_nodes_at_node

property adjacent_faces_at_face

property angle_of_face

Get the angle of each face.

See also

angle_of_link

property area_of_cell

Get the area of each cell.

See also

area_of_patch

property cells_at_corner

Get the cells that touch each corner.

See also

patches_at_node

property cells_at_face

Get the cells on either side of each face.

See also

patches_at_link

property corner_at_face_head

Get corners at face head.

See also

node_at_link_head

property corner_at_face_tail

Get corners at face tail.

See also

node_at_link_tail

property corner_layout

property corner_x

property corner_y

property corners

Get identifier for each corner.

See also

nodes

property corners_at_bottom_edge

property corners_at_cell

Get the corners that define a cell.

See also

nodes_at_patch

property corners_at_face

Get corners at either end of faces.

See also

nodes_at_link

property corners_at_left_edge

property corners_at_right_edge

property corners_at_top_edge

property face_dirs_at_corner

Return face directions into each corner.

See also

link_dirs_at_node

property faces_at_cell

Get the faces that define a cell.

See also

links_at_patch

property faces_at_corner

Get faces touching a corner.

See also

links_at_node

property length_of_face

Get the length of faces.

See also

length_of_link

property midpoint_of_face

Get the middle of faces.

See also

midpoint_of_link

property number_of_cells

Get the number of cells.

See also

number_of_patches

property number_of_corners

Get total number of corners.

See also

number_of_nodes

property number_of_faces

Get corners at face head.

See also

number_of_links

property perimeter_corners

property unit_vector_at_corner

Get a unit vector for each corner.

See also

unit_vector_at_node

property unit_vector_at_face

Make arrays to store the unit vectors associated with each face.

See also

unit_vector_at_link

property x_of_corner

Get x-coordinate of corner.

See also

x_of_node

property xy_of_cell

Get the centroid of each cell.

See also

xy_of_patch

property xy_of_corner

Get x and y-coordinates of corner.

See also

xy_of_node

property xy_of_face

property y_of_corner

Get y-coordinate of corner.

See also

y_of_node

landlab.graph.framed_voronoi.framed_voronoi module

Implementation of the FramedVoronoiGraph and its static layout: HorizontalRectVoronoiGraph. This pattern is inspired from the developments of the HexModelGrid

Code author: sebastien lenard

class FramedVoronoiGraph(shape, xy_spacing=(1.0, 1.0), xy_of_lower_left=(0.0, 0.0), sort=False, xy_min_spacing=(0.5, 0.5), seed=200)

Bases: DelaunayGraph

VoronoiDelaunay graph based on a fixed lattice.

Graph of an unstructured grid of Voronoi Delaunay cells and irregular patches. It is a special type of :class`~.VoronoiDelaunayGraph` in which the initial set of points is arranged in a fixed lattice (e.g. like a RasterModelGrid) named here “layout” and the core points are then moved from their initial position by a random distance, lower than a certain threshold.

Examples

>>> from landlab.graph import FramedVoronoiGraph

>>> graph = FramedVoronoiGraph((3, 3), seed=200)
>>> graph.number_of_nodes
9

>>> graph.x_of_node[2:4]    
array([ 2.,  0.])
>>> graph.y_of_node[2:4]    
array([ 0.   ,  0.749])
>>> graph.y_of_node[5]    
1.2509999999999999

>>> graph.number_of_links
16
>>> graph.number_of_patches
8

Create the graph.

Parameters:

• shape (tuple of int) – Number of rows and columns of nodes.

• xy_spacing (float or tuple of float, optional) – Node spacing along x and y coordinates. If float, same spacing x and y spacing.

• xy_of_lower_left (tuple, optional) – Minimum x-of-node and y-of-node values. Depending on the grid, a node may not be present at this location.

• sort (bool) – If True, nodes, links and patches are re-numbered according certain their positions. Currently not used.

• xy_min_spacing (float or tuple of float, optional) – Final minimal spacing between nodes. Random moves of the core nodes around their position cannot be above this threshold: (xy_spacing - xy_min_spacing) / 2 If float, same minimal spacing for x and y.

• seed (int, optional) – Seed used to generate the random x and y moves. When set, controls a pseudo-randomness of moves to ensure reproducibility. When None, seed is random and the moves of coordinates are completely random.

Returns:

A newly-created graph.

Return type:

FramedVoronoiGraph

Examples

Create a grid with 3 rows and 2 columns of nodes.

>>> from landlab.graph import FramedVoronoiGraph
>>> graph = FramedVoronoiGraph((3, 2))
>>> graph.number_of_nodes
6

__init__(shape, xy_spacing=(1.0, 1.0), xy_of_lower_left=(0.0, 0.0), sort=False, xy_min_spacing=(0.5, 0.5), seed=200)

Create the graph.

Parameters:

• shape (tuple of int) – Number of rows and columns of nodes.

• xy_spacing (float or tuple of float, optional) – Node spacing along x and y coordinates. If float, same spacing x and y spacing.

• xy_of_lower_left (tuple, optional) – Minimum x-of-node and y-of-node values. Depending on the grid, a node may not be present at this location.

• sort (bool) – If True, nodes, links and patches are re-numbered according certain their positions. Currently not used.

• xy_min_spacing (float or tuple of float, optional) – Final minimal spacing between nodes. Random moves of the core nodes around their position cannot be above this threshold: (xy_spacing - xy_min_spacing) / 2 If float, same minimal spacing for x and y.

• seed (int, optional) – Seed used to generate the random x and y moves. When set, controls a pseudo-randomness of moves to ensure reproducibility. When None, seed is random and the moves of coordinates are completely random.

Returns:

A newly-created graph.

Return type:

FramedVoronoiGraph

Examples

Create a grid with 3 rows and 2 columns of nodes.

>>> from landlab.graph import FramedVoronoiGraph
>>> graph = FramedVoronoiGraph((3, 2))
>>> graph.number_of_nodes
6

property node_layout

property nodes_at_bottom_edge

property nodes_at_left_edge

property nodes_at_right_edge

property nodes_at_top_edge

property orientation

property perimeter_nodes

Get nodes on the convex hull of a Graph.

Examples

>>> import numpy as np
>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1]
>>> graph = Graph((node_y, node_x))
>>> np.sort(graph.perimeter_nodes)
array([0, 2, 3, 5])

property shape

property xy_spacing

class HorizontalRectVoronoiGraph

Bases: object

The horizontal rectangular frame for the FramedVoronoiGraph.

static corner_nodes(shape)

Parameters:

shape (tuple of int) – Number of rows and number of columns

Returns:

Ids of the corner nodes

Return type:

ndarray of int

Examples

>>> from landlab.graph.framed_voronoi.framed_voronoi import HorizontalRectVoronoiGraph
>>> HorizontalRectVoronoiGraph.corner_nodes((3, 4))
(11, 8, 0, 3)

static nodes_at_edge(shape)

Parameters:

shape (tuple of int) – Number of rows and number of columns

Returns:

right, top, left, bottom – For each edge give the ids of the nodes present at the edge

Return type:

ndarray of int

Examples

>>> from landlab.graph.framed_voronoi.framed_voronoi import HorizontalRectVoronoiGraph
>>> HorizontalRectVoronoiGraph.nodes_at_edge((3, 3))
(array([2, 5]), array([8, 7]), array([6, 3]), array([0, 1]))

static number_of_nodes(shape)

Parameters:

shape (tuple of int) – Number of rows and number of columns

Returns:

Number of nodes

Return type:

int

Examples

>>> from landlab.graph.framed_voronoi.framed_voronoi import HorizontalRectVoronoiGraph
>>> HorizontalRectVoronoiGraph.number_of_nodes((3, 2))
6

static number_of_perimeter_nodes(shape)

Parameters:

shape (tuple of int) – Number of rows and number of columns

Returns:

Number of perimeter nodes

Return type:

int

Examples

>>> from landlab.graph.framed_voronoi.framed_voronoi import HorizontalRectVoronoiGraph
>>> HorizontalRectVoronoiGraph.number_of_perimeter_nodes((3, 4))
10

static perimeter_nodes(shape)

Parameters:

shape (tuple of int) – Number of rows and number of columns

Returns:

Ids of the perimeter nodes

Return type:

ndarray of int

Examples

>>> from landlab.graph.framed_voronoi.framed_voronoi import HorizontalRectVoronoiGraph
>>> HorizontalRectVoronoiGraph.perimeter_nodes((3, 3))
array([2, 5, 8, 7, 6, 3, 0, 1])

static xy_of_node(shape, xy_spacing=(1.0, 1.0), xy_of_lower_left=(0.0, 0.0), xy_min_spacing=(0.5, 0.5), seed=200)

The x and y coordinates of the graph’s nodes.

Calculation of the x-y coordinates is done following these steps:

1. Generate a rectangular, regular meshgrid.

2. Move the coordinates of the core nodes over a random distance around their initial position, within a threshold calculated from xy_spacing and xy_min_spacing.

3. Rectify the y-coordinates of the nodes of the left and right to ensure that the leftmost node of a row has a lower y than the rightmost node. This ensures that the ids of these nodes are not modified by subsequent sorting operations on the graph and make it possible to get the perimeter nodes in simple way.

Parameters:

• shape (tuple of int) – Number of rows and columns of nodes.

• xy_spacing (float or tuple of float, optional) – Node spacing along x and y coordinates. If float, same spacing at x and y.

• xy_of_lower_left (tuple, optional) – Minimum x-of-node and y-of-node values. Depending on the grid, a node may not be present at this location.

• xy_min_spacing (float or tuple of float, optional) – Final minimal spacing between nodes. Random moves of the core nodes around their initial positions cannot be above this threshold: (xy_spacing - xy_min_spacing) / 2. If float, same minimal spacing for x and y.

• seed (int, optional) – Seed used to generate the random x and y moves. When set, controls a pseudo-randomness of moves to ensure reproducibility. When None, seed is random and the moves of coordinates are completely random.

Returns:

x_of_node, y_of_node – The arrays of x and y coordinates.

Return type:

ndarray of float

Examples

>>> from landlab.graph.framed_voronoi.framed_voronoi import HorizontalRectVoronoiGraph

>>> x_of_node, y_of_node = HorizontalRectVoronoiGraph.xy_of_node((3, 3), seed=200)

Coordinates of the lower left node,

>>> x_of_node[0], y_of_node[0]
(0.0, 0.0)

x coordinates of the left and right edges,

>>> x_of_node[3], x_of_node[5]
(0.0, 2.0)

y coordinate of the middle row of the left edge,

>>> y_of_node[3]
0.749

Module contents

class DualFramedVoronoiGraph(shape, xy_spacing=(1.0, 1.0), xy_of_lower_left=(0.0, 0.0), sort=False, xy_min_spacing=(0.5, 0.5), seed=200)

Bases: DualGraph, FramedVoronoiGraph

Graph of a unstructured grid of Voronoi Delaunay cells and irregular patches. It is a special type of VoronoiDelaunay graph in which the initial set of points is arranged in a fixed lattice (e.g. like a rectangular raster grid) named here “layout” and the core points are then moved aroung their initial position by a random distance, lower than a certain threshold.

Examples

>>> from landlab.graph import DualFramedVoronoiGraph

>>> graph = DualFramedVoronoiGraph((3, 3), seed=200)
>>> graph.number_of_nodes
9

>>> graph.x_of_node[2:4]    
array([ 2.,  0.])
>>> graph.y_of_node[2:4]    
array([ 0.   ,  0.749])
>>> graph.y_of_node[5]    
1.2509999999999999

Create the graph.

Parameters:

• shape (tuple of int) – Number of rows and columns of nodes.

• xy_spacing (float or tuple of float, optional) – Node spacing along x and y coordinates. If float, same spacing at x and y.

• xy_of_lower_left (tuple, optional) – Minimum x-of-node and y-of-node values. Depending on the grid, there may not be a node present at this location.

• sort (bool) – If True, nodes, links and patches are re-numbered according to their position.

• xy_min_spacing (float or tuple of float, optional) – Final minimal spacing between nodes. Random moves of the core nodes around their position cannot be above this threshold: (xy_spacing - xy_min_spacing) / 2 If float, same minimal spacing for x and y.

• seed (int, optional) – Seed used to generate the random x and y moves. When set, controls a pseudo-randomness of moves to ensure reproducibility. When None, seed is random and the moves of coordinates are completely random.

Returns:

A newly-created graph.

Return type:

DualFramedVoronoiGraph

Examples

Create a grid with 3 rows and 2 columns of nodes.

>>> from landlab.graph import DualFramedVoronoiGraph
>>> graph = DualFramedVoronoiGraph((3, 2), xy_spacing=1.0)
>>> graph.number_of_nodes
6

__init__(shape, xy_spacing=(1.0, 1.0), xy_of_lower_left=(0.0, 0.0), sort=False, xy_min_spacing=(0.5, 0.5), seed=200)

Create the graph.

Parameters:

• shape (tuple of int) – Number of rows and columns of nodes.

• xy_spacing (float or tuple of float, optional) – Node spacing along x and y coordinates. If float, same spacing at x and y.

• xy_of_lower_left (tuple, optional) – Minimum x-of-node and y-of-node values. Depending on the grid, there may not be a node present at this location.

• sort (bool) – If True, nodes, links and patches are re-numbered according to their position.

• xy_min_spacing (float or tuple of float, optional) – Final minimal spacing between nodes. Random moves of the core nodes around their position cannot be above this threshold: (xy_spacing - xy_min_spacing) / 2 If float, same minimal spacing for x and y.

• seed (int, optional) – Seed used to generate the random x and y moves. When set, controls a pseudo-randomness of moves to ensure reproducibility. When None, seed is random and the moves of coordinates are completely random.

Returns:

A newly-created graph.

Return type:

DualFramedVoronoiGraph

Examples

Create a grid with 3 rows and 2 columns of nodes.

>>> from landlab.graph import DualFramedVoronoiGraph
>>> graph = DualFramedVoronoiGraph((3, 2), xy_spacing=1.0)
>>> graph.number_of_nodes
6

property adjacent_corners_at_corner

Get adjacent corners.

See also

adjacent_nodes_at_node

property adjacent_faces_at_face

property angle_of_face

Get the angle of each face.

See also

angle_of_link

property area_of_cell

Get the area of each cell.

See also

area_of_patch

property cells_at_corner

Get the cells that touch each corner.

See also

patches_at_node

property cells_at_face

Get the cells on either side of each face.

See also

patches_at_link

property corner_at_face_head

Get corners at face head.

See also

node_at_link_head

property corner_at_face_tail

Get corners at face tail.

See also

node_at_link_tail

property corner_layout

property corner_x

property corner_y

property corners

Get identifier for each corner.

See also

nodes

property corners_at_bottom_edge

property corners_at_cell

Get the corners that define a cell.

See also

nodes_at_patch

property corners_at_face

Get corners at either end of faces.

See also

nodes_at_link

property corners_at_left_edge

property corners_at_right_edge

property corners_at_top_edge

property face_dirs_at_corner

Return face directions into each corner.

See also

link_dirs_at_node

property faces_at_cell

Get the faces that define a cell.

See also

links_at_patch

property faces_at_corner

Get faces touching a corner.

See also

links_at_node

property length_of_face

Get the length of faces.

See also

length_of_link

property midpoint_of_face

Get the middle of faces.

See also

midpoint_of_link

property number_of_cells

Get the number of cells.

See also

number_of_patches

property number_of_corners

Get total number of corners.

See also

number_of_nodes

property number_of_faces

Get corners at face head.

See also

number_of_links

property perimeter_corners

property unit_vector_at_corner

Get a unit vector for each corner.

See also

unit_vector_at_node

property unit_vector_at_face

Make arrays to store the unit vectors associated with each face.

See also

unit_vector_at_link

property x_of_corner

Get x-coordinate of corner.

See also

x_of_node

property xy_of_cell

Get the centroid of each cell.

See also

xy_of_patch

property xy_of_corner

Get x and y-coordinates of corner.

See also

xy_of_node

property xy_of_face

property y_of_corner

Get y-coordinate of corner.

See also

y_of_node

class FramedVoronoiGraph(shape, xy_spacing=(1.0, 1.0), xy_of_lower_left=(0.0, 0.0), sort=False, xy_min_spacing=(0.5, 0.5), seed=200)

Bases: DelaunayGraph

VoronoiDelaunay graph based on a fixed lattice.

Graph of an unstructured grid of Voronoi Delaunay cells and irregular patches. It is a special type of :class`~.VoronoiDelaunayGraph` in which the initial set of points is arranged in a fixed lattice (e.g. like a RasterModelGrid) named here “layout” and the core points are then moved from their initial position by a random distance, lower than a certain threshold.

Examples

>>> from landlab.graph import FramedVoronoiGraph

>>> graph = FramedVoronoiGraph((3, 3), seed=200)
>>> graph.number_of_nodes
9

>>> graph.x_of_node[2:4]    
array([ 2.,  0.])
>>> graph.y_of_node[2:4]    
array([ 0.   ,  0.749])
>>> graph.y_of_node[5]    
1.2509999999999999

>>> graph.number_of_links
16
>>> graph.number_of_patches
8

Create the graph.

Parameters:

• shape (tuple of int) – Number of rows and columns of nodes.

• xy_spacing (float or tuple of float, optional) – Node spacing along x and y coordinates. If float, same spacing x and y spacing.

• xy_of_lower_left (tuple, optional) – Minimum x-of-node and y-of-node values. Depending on the grid, a node may not be present at this location.

• sort (bool) – If True, nodes, links and patches are re-numbered according certain their positions. Currently not used.

• xy_min_spacing (float or tuple of float, optional) – Final minimal spacing between nodes. Random moves of the core nodes around their position cannot be above this threshold: (xy_spacing - xy_min_spacing) / 2 If float, same minimal spacing for x and y.

• seed (int, optional) – Seed used to generate the random x and y moves. When set, controls a pseudo-randomness of moves to ensure reproducibility. When None, seed is random and the moves of coordinates are completely random.

Returns:

A newly-created graph.

Return type:

FramedVoronoiGraph

Examples

Create a grid with 3 rows and 2 columns of nodes.

>>> from landlab.graph import FramedVoronoiGraph
>>> graph = FramedVoronoiGraph((3, 2))
>>> graph.number_of_nodes
6

__init__(shape, xy_spacing=(1.0, 1.0), xy_of_lower_left=(0.0, 0.0), sort=False, xy_min_spacing=(0.5, 0.5), seed=200)

Create the graph.

Parameters:

• shape (tuple of int) – Number of rows and columns of nodes.

• xy_spacing (float or tuple of float, optional) – Node spacing along x and y coordinates. If float, same spacing x and y spacing.

• xy_of_lower_left (tuple, optional) – Minimum x-of-node and y-of-node values. Depending on the grid, a node may not be present at this location.

• sort (bool) – If True, nodes, links and patches are re-numbered according certain their positions. Currently not used.

• xy_min_spacing (float or tuple of float, optional) – Final minimal spacing between nodes. Random moves of the core nodes around their position cannot be above this threshold: (xy_spacing - xy_min_spacing) / 2 If float, same minimal spacing for x and y.

• seed (int, optional) – Seed used to generate the random x and y moves. When set, controls a pseudo-randomness of moves to ensure reproducibility. When None, seed is random and the moves of coordinates are completely random.

Returns:

A newly-created graph.

Return type:

FramedVoronoiGraph

Examples

Create a grid with 3 rows and 2 columns of nodes.

>>> from landlab.graph import FramedVoronoiGraph
>>> graph = FramedVoronoiGraph((3, 2))
>>> graph.number_of_nodes
6

property node_layout

property nodes_at_bottom_edge

property nodes_at_left_edge

property nodes_at_right_edge

property nodes_at_top_edge

property orientation

property perimeter_nodes

Get nodes on the convex hull of a Graph.

Examples

>>> import numpy as np
>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1]
>>> graph = Graph((node_y, node_x))
>>> np.sort(graph.perimeter_nodes)
array([0, 2, 3, 5])

property shape

property xy_spacing