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)[source]#
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
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
Iffloat
, 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
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)[source]#
Create the graph.
- Parameters
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
Iffloat
, 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
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
Graph.adjacent_nodes_at_node
- property adjacent_faces_at_face#
- property angle_of_face#
Get the angle of each face.
See also
Graph.angle_of_link
- property area_of_cell#
Get the area of each cell.
See also
Graph.area_of_patch
- property cells_at_corner#
Get the cells that touch each corner.
See also
Graph.patches_at_node
- property cells_at_face#
Get the cells on either side of each face.
See also
Graph.patches_at_link
- property corner_at_face_head#
Get corners at face head.
See also
Graph.node_at_link_head
- property corner_at_face_tail#
Get corners at face tail.
See also
Graph.node_at_link_tail
- property corner_layout#
- property corner_x#
- property corner_y#
- property corners#
Get identifier for each corner.
See also
Graph.nodes
- property corners_at_bottom_edge#
- property corners_at_cell#
Get the corners that define a cell.
See also
Graph.nodes_at_patch
- property corners_at_face#
Get corners at either end of faces.
See also
Graph.nodes_at_link
- property corners_at_left_edge#
- property corners_at_right_edge#
- property corners_at_top_edge#
- property face_dirs_at_corner#
Get directions of faces touching a corner.
See also
Graph.link_dirs_at_node
- property faces_at_cell#
Get the faces that define a cell.
See also
Graph.links_at_patch
- property faces_at_corner#
Get faces touching a corner.
See also
Graph.links_at_node
- property length_of_face#
Get the length of faces.
See also
Graph.length_of_link
- property midpoint_of_face#
Get the middle of faces.
See also
Graph.midpoint_of_link
- property number_of_cells#
Get the number of cells.
See also
Graph.number_of_patches
- property number_of_corners#
Get total number of corners.
See also
Graph.number_of_nodes
- property number_of_faces#
Get corners at face head.
See also
Graph.number_of_links
- property perimeter_corners#
Get corners on the convex hull of a Graph.
See also
Graph.perimeter_nodes
- property unit_vector_at_corner#
Get a unit vector for each corner.
See also
Graph.unit_vector_at_node
- property unit_vector_at_face#
Make arrays to store the unit vectors associated with each face.
See also
Graph.unit_vector_at_link
- property x_of_corner#
Get x-coordinate of corner.
See also
Graph.x_of_node
- property xy_of_cell#
Get the centroid of each cell.
See also
Graph.xy_of_patch
- property xy_of_corner#
Get x and y-coordinates of corner.
See also
Graph.xy_of_node
- property xy_of_face#
- property y_of_corner#
Get y-coordinate of corner.
See also
Graph.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)[source]#
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
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
Iffloat
, 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
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)[source]#
Create the graph.
- Parameters
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
Iffloat
, 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
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[source]#
Bases:
object
The horizontal rectangular frame for the FramedVoronoiGraph.
- static corner_nodes(shape)[source]#
- Parameters
- 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)[source]#
- Parameters
- 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)[source]#
- Parameters
- Returns
Number of nodes
- Return type
Examples
>>> from landlab.graph.framed_voronoi.framed_voronoi import HorizontalRectVoronoiGraph >>> HorizontalRectVoronoiGraph.number_of_nodes((3, 2)) 6
- static number_of_perimeter_nodes(shape)[source]#
- Parameters
- Returns
Number of perimeter nodes
- Return type
Examples
>>> from landlab.graph.framed_voronoi.framed_voronoi import HorizontalRectVoronoiGraph >>> HorizontalRectVoronoiGraph.number_of_perimeter_nodes((3, 4)) 10
- static perimeter_nodes(shape)[source]#
- Parameters
- 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)[source]#
The x and y coordinates of the graph’s nodes.
Calculation of the x-y coordinates is done following these steps:
Generate a rectangular, regular meshgrid.
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.
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
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
. Iffloat
, 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)[source]#
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
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
Iffloat
, 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
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)[source]#
Create the graph.
- Parameters
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
Iffloat
, 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
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
Graph.adjacent_nodes_at_node
- property adjacent_faces_at_face#
- property angle_of_face#
Get the angle of each face.
See also
Graph.angle_of_link
- property area_of_cell#
Get the area of each cell.
See also
Graph.area_of_patch
- property cells_at_corner#
Get the cells that touch each corner.
See also
Graph.patches_at_node
- property cells_at_face#
Get the cells on either side of each face.
See also
Graph.patches_at_link
- property corner_at_face_head#
Get corners at face head.
See also
Graph.node_at_link_head
- property corner_at_face_tail#
Get corners at face tail.
See also
Graph.node_at_link_tail
- property corner_layout#
- property corner_x#
- property corner_y#
- property corners#
Get identifier for each corner.
See also
Graph.nodes
- property corners_at_bottom_edge#
- property corners_at_cell#
Get the corners that define a cell.
See also
Graph.nodes_at_patch
- property corners_at_face#
Get corners at either end of faces.
See also
Graph.nodes_at_link
- property corners_at_left_edge#
- property corners_at_right_edge#
- property corners_at_top_edge#
- property face_dirs_at_corner#
Get directions of faces touching a corner.
See also
Graph.link_dirs_at_node
- property faces_at_cell#
Get the faces that define a cell.
See also
Graph.links_at_patch
- property faces_at_corner#
Get faces touching a corner.
See also
Graph.links_at_node
- property length_of_face#
Get the length of faces.
See also
Graph.length_of_link
- property midpoint_of_face#
Get the middle of faces.
See also
Graph.midpoint_of_link
- property number_of_cells#
Get the number of cells.
See also
Graph.number_of_patches
- property number_of_corners#
Get total number of corners.
See also
Graph.number_of_nodes
- property number_of_faces#
Get corners at face head.
See also
Graph.number_of_links
- property perimeter_corners#
Get corners on the convex hull of a Graph.
See also
Graph.perimeter_nodes
- property unit_vector_at_corner#
Get a unit vector for each corner.
See also
Graph.unit_vector_at_node
- property unit_vector_at_face#
Make arrays to store the unit vectors associated with each face.
See also
Graph.unit_vector_at_link
- property x_of_corner#
Get x-coordinate of corner.
See also
Graph.x_of_node
- property xy_of_cell#
Get the centroid of each cell.
See also
Graph.xy_of_patch
- property xy_of_corner#
Get x and y-coordinates of corner.
See also
Graph.xy_of_node
- property xy_of_face#
- property y_of_corner#
Get y-coordinate of corner.
See also
Graph.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)[source]#
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
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
Iffloat
, 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
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)[source]#
Create the graph.
- Parameters
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
Iffloat
, 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
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#