landlab.graph.structured_quad.dual_structured_quad

class DualRectilinearGraph[source]

Bases: DualGraph, RectilinearGraph

Create a dual graph for a rectilinear grid.

Examples

>>> from landlab.graph import DualRectilinearGraph
>>> graph = DualRectilinearGraph(([0, 1, 3], [0, 5, 15, 30]))
>>> graph.x_of_corner.reshape((2, 3))
array([[  2.5,  10. ,  22.5],
       [  2.5,  10. ,  22.5]])
>>> graph.y_of_corner.reshape((2, 3))
array([[0.5, 0.5, 0.5],
       [2. , 2. , 2. ]])
>>> graph.number_of_cells == 2
True
>>> graph.faces_at_cell
array([[3, 5, 2, 0],
       [4, 6, 3, 1]])
__init__(node_y_and_x)[source]
__new__(**kwargs)
property adjacent_corners_at_corner

Get adjacent corners.

See also

adjacent_nodes_at_node

property adjacent_faces_at_face
property adjacent_nodes_at_node

Get adjacent nodes.

Examples

>>> from landlab.graph import Graph

First, a simple example with no diagonals.

>>> node_x, node_y = [0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> graph = Graph((node_y, node_x), links=links, sort=True)
>>> graph.adjacent_nodes_at_node
array([[ 1,  3, -1, -1],
       [ 2,  4,  0, -1],
       [ 5,  1, -1, -1],
       [ 4,  6,  0, -1],
       [ 5,  7,  3,  1],
       [ 8,  4,  2, -1],
       [ 7,  3, -1, -1],
       [ 8,  6,  4, -1],
       [ 7,  5, -1, -1]])

Next, we add the diagonal from node 0 to node 4.

>>> node_x, node_y = [0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
...     (0, 4),
... )
>>> graph = Graph((node_y, node_x), links=links, sort=True)
>>> graph.adjacent_nodes_at_node
array([[ 1,  4,  3, -1, -1],
       [ 2,  4,  0, -1, -1],
       [ 5,  1, -1, -1, -1],
       [ 4,  6,  0, -1, -1],
       [ 5,  7,  3,  0,  1],
       [ 8,  4,  2, -1, -1],
       [ 7,  3, -1, -1, -1],
       [ 8,  6,  4, -1, -1],
       [ 7,  5, -1, -1, -1]])
property angle_of_face

Get the angle of each face.

See also

angle_of_link

Get the angle of each link.

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])
>>> links = ((0, 1), (1, 2), (0, 3), (1, 4), (2, 5), (3, 4), (4, 5))
>>> graph = Graph((node_y, node_x), links=links)
>>> graph.angle_of_link * 180.0 / np.pi
array([ 0.,  0., 90., 90., 90.,  0.,  0.])
property area_of_cell

Get the area of each cell.

See also

area_of_patch

property area_of_patch

Get the area of each patch.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> patches = ((0, 3, 5, 2), (1, 4, 6, 3))
>>> graph = Graph((node_y, node_x), links=links, patches=patches)
>>> graph.area_of_patch
array([1.,  1.])
property cell_at_node
property cells_at_corner
property cells_at_face
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_at_patch
property corner_nodes
property corner_x
property corner_y
property corners

A shaped array of corner ids.

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_nodes_of_grid

Nodes at nodes of grid.

See also

nodes_at_corners_of_grid

property corners_at_right_edge
property corners_at_top_edge
property ds
property dual
property face_dirs_at_corner
property faces_at_cell

Get the faces that define a cell.

See also

links_at_patch

property faces_at_corner
freeze()

Freeze the graph by making arrays read-only.

classmethod from_dict(meta)
classmethod from_netcdf(fname)
property frozen
static get_corners(node_y_and_x)[source]
property horizontal_faces
property length_of_face

Get the length of faces.

See also

length_of_link

Get the length of links.

Examples

>>> import numpy as np
>>> from landlab.graph import UniformRectilinearGraph

>>> graph = UniformRectilinearGraph((2, 3), spacing=(1, 2))
>>> graph.length_of_link
array([2., 2., 1., 1., 1., 2., 2.])

Return link directions into each node.

A value of 1 indicates a link points toward a given node, while a value of -1 indicates a link points away from a node.

Returns:

Link directions relative to the nodes of a grid. The shape of the matrix will be number of nodes by the maximum number of links per node. A zero indicates no link at this position.

Return type:

(n_nodes, max_links_per_node) ndarray of int

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> graph = Graph((node_y, node_x), links=links)
>>> graph.link_dirs_at_node
array([[-1, -1,  0,  0], [-1, -1,  1,  0], [-1,  1,  0,  0],
       [-1, -1,  1,  0], [-1, -1,  1,  1], [-1,  1,  1,  0],
       [-1,  1,  0,  0], [-1,  1,  1,  0], [ 1,  1,  0,  0]],
      dtype=int8)

Get links touching a node.

Examples

>>> from landlab.graph import Graph
>>> node_x = [0, 1, 2, 0, 1, 2, 0, 1, 2]
>>> node_y = [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> graph = Graph((node_y, node_x), links=links)
>>> graph.links_at_node
array([[ 0,  2, -1, -1], [ 1,  3,  0, -1], [ 4,  1, -1, -1],
       [ 5,  7,  2, -1], [ 6,  8,  5,  3], [ 9,  6,  4, -1],
       [10,  7, -1, -1], [11, 10,  8, -1], [11,  9, -1, -1]])

Get the links that define a patch.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> patches = ((0, 3, 5, 2), (1, 4, 6, 3))
>>> graph = Graph((node_y, node_x), links=links, patches=patches, sort=True)
>>> graph.links_at_patch
array([[3, 5, 2, 0],
       [4, 6, 3, 1]])
classmethod load(source)
merge(dual, node_at_cell=None, nodes_at_face=None)
property midpoint_of_face

Get the middle of faces.

See also

midpoint_of_link

Get the middle of links.

Examples

>>> import numpy as np
>>> from landlab.graph import UniformRectilinearGraph

>>> graph = UniformRectilinearGraph((2, 3), spacing=(1, 2))
>>> graph.midpoint_of_link
array([[1. , 0. ], [3. , 0. ],
       [0. , 0.5], [2. , 0.5], [4. , 0.5],
       [1. , 1. ], [3. , 1. ]])
property ndim
property node_at_cell

Get nodes at link head.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> graph = Graph((node_y, node_x), links=links)
>>> graph.node_at_link_head
array([1, 2, 3, 4, 5, 4, 5, 6, 7, 8, 7, 8])

Get nodes at link tail.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> graph = Graph((node_y, node_x), links=links)
>>> graph.node_at_link_tail
array([0, 1, 0, 1, 2, 3, 4, 3, 4, 5, 6, 7])
property node_corners
property node_x
property node_y
property nodes

A shaped array of node ids.

Returns:

Node IDs in an array shaped as number_of_node_rows by number_of_node_columns.

Return type:

ndarray

property nodes_at_bottom_edge
property nodes_at_corners_of_grid

Nodes at corners of grid.

The nodes at at the corners of the grid. The nodes are returned counterclockwise starting with the upper-right.

Returns:

Nodes at the four corners.

Return type:

tuple of int

Examples

>>> from landlab.graph import UniformRectilinearGraph
>>> graph = UniformRectilinearGraph((4, 5))
>>> graph.nodes_at_corners_of_grid
(19, 15, 0, 4)
nodes_at_edge(edge)
property nodes_at_face
property nodes_at_left_edge

Get nodes at either end of links.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> graph = Graph((node_y, node_x), links=links)
>>> graph.nodes_at_link
array([[0, 1], [1, 2],
       [0, 3], [1, 4], [2, 5],
       [3, 4], [4, 5],
       [3, 6], [4, 7], [5, 8],
       [6, 7], [7, 8]])
property nodes_at_patch

Get the nodes that define a patch.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = ([0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2])
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> patches = ((0, 3, 5, 2), (1, 4, 6, 3))
>>> graph = Graph((node_y, node_x), links=links, patches=patches, sort=True)
>>> graph.nodes_at_patch
array([[4, 3, 0, 1],
       [5, 4, 1, 2]])
property nodes_at_right_edge
property nodes_at_top_edge
property number_of_cells

Get the number of cells.

See also

number_of_patches

property number_of_corner_columns
property number_of_corner_rows
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

Get nodes at link head.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> graph = Graph((node_y, node_x), links=links)
>>> graph.number_of_links == 12
True
property number_of_node_columns
property number_of_node_rows
property number_of_nodes

Get total number of nodes.

Examples

>>> 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))
>>> graph.number_of_nodes == 6
True
property number_of_patches

Get the number of patches.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> patches = ((0, 3, 5, 2), (1, 4, 6, 3))
>>> graph = Graph((node_y, node_x), links=links, patches=patches)
>>> graph.number_of_patches == 2
True
property orientation_of_face

Return array of face orientation codes (one value per face).

See also

orientation_of_link

Return array of link orientation codes (one value per link).

Orientation codes are defined by LinkOrientation; 1 = E, 2 = ENE, 4 = NNE, 8 = N, 16 = NNW, 32 = ESE (using powers of 2 allows for future applications that might want additive combinations).

property parallel_faces_at_face

Return similarly oriented faces connected to each face.

See also

parallel_links_at_link

Return similarly oriented links connected to each link.

Return IDs of links of the same orientation that are connected to each given link’s tail or head node.

The data structure is a numpy array of shape (n_links, 2) containing the IDs of the “tail-wise” (connected to tail node) and “head-wise” (connected to head node) links, or -1 if the link is inactive (e.g., on the perimeter) or it has no attached parallel neighbor in the given direction.

For instance, consider a 3x4 raster, in which link IDs are as shown:

.-14-.-15-.-16-.
|    |    |    |
10  11   12   13
|    |    |    |
.--7-.--8-.--9-.
|    |    |    |
3    4    5    6
|    |    |    |
.--0-.--1-.--2-.

Here’s a mapping of the tail-wise (shown at left or bottom of links) and head-wise (shown at right or top of links) links:

.----.----.----.
|    |    |    |
|    |    |    |
|    4    5    |
.---8.7--9.8---.
|   11   12    |
|    |    |    |
|    |    |    |
.----.----.----.

So the corresponding data structure would be mostly filled with -1, but for the 7 active links, it would look like:

4: [[-1, 11],
5:  [-1, 12],
7:  [-1,  8],
8:  [ 7,  9],
9:  [ 8, -1],
11: [ 4, -1],
12: [ 5, -1]]

Examples

>>> from landlab import RasterModelGrid
>>> grid = RasterModelGrid((3, 4))
>>> pll = grid.parallel_links_at_link
>>> pll[4:13, :]
array([[-1, 11],
       [-1, 12],
       [-1, 13],
       [-1,  8],
       [ 7,  9],
       [ 8, -1],
       [ 3, -1],
       [ 4, -1],
       [ 5, -1]])
property patch_at_corner

Get the patches on either side of each link.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = ([0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1])
>>> links = ((0, 1), (1, 2), (0, 3), (1, 4), (2, 5), (3, 4), (4, 5))
>>> patches = ((0, 3, 5, 2), (1, 4, 6, 3))
>>> graph = Graph((node_y, node_x), links=links, patches=patches)
>>> graph.patches_at_link
array([[ 0, -1], [ 1, -1],
       [ 0, -1], [ 0,  1], [ 1, -1],
       [ 0, -1], [ 1, -1]])
property patches_at_node

Get the patches that touch each node.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = ([0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1])
>>> links = ((0, 1), (1, 2), (0, 3), (1, 4), (2, 5), (3, 4), (4, 5))
>>> patches = ((0, 3, 5, 2), (1, 4, 6, 3))
>>> graph = Graph((node_y, node_x), links=links, patches=patches, sort=True)
>>> graph.patches_at_node
array([[ 0, -1], [ 1,  0], [ 1, -1],
       [ 0, -1], [ 0,  1], [ 1, -1]])
property perimeter_corners
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])
static setup_node_y_and_x(coords)
property shape
sort()

Sort graph elements.

thaw()

Thaw the graph by making arrays writable.

thawed()
to_dict()
to_json()
to_netcdf(*args, **kwds)

Write graph contents to a netCDF file.

See xarray.Dataset.to_netcdf for a complete list of parameters. Below are only the most common.

Parameters:

  • path (str, optional) – Path to which to save this graph.

  • mode ({'w', 'a'}, optional) – Write (‘w’) or append (‘a’) mode. If mode=’w’, any existing file at this location will be overwritten.

  • format ({'NETCDF4', 'NETCDF4_CLASSIC', 'NETCDF3_64BIT', 'NETCDF3_CLASSIC'}, optional) –

    File format for the resulting netCDF file:

    • NETCDF4: Data is stored in an HDF5 file, using netCDF4 API features.

    • NETCDF4_CLASSIC: Data is stored in an HDF5 file, using only netCDF 3 compatible API features.

    • NETCDF3_64BIT: 64-bit offset version of the netCDF 3 file format, which fully supports 2+ GB files, but is only compatible with clients linked against netCDF version 3.6.0 or later.

    • NETCDF3_CLASSIC: The classic netCDF 3 file format. It does not handle 2+ GB files very well.

    All formats are supported by the netCDF4-python library. scipy.io.netcdf only supports the last two formats.

    The default format is NETCDF4 if you are saving a file to disk and have the netCDF4-python library available. Otherwise, xarray falls back to using scipy to write netCDF files and defaults to the NETCDF3_64BIT format (scipy does not support netCDF4).

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

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

For each link, the x and y components of the link’s unit vector (that is, the link’s x and y dimensions if it were shrunk to unit length but retained its orientation).

Examples

The example below is a seven-node hexagonal grid, with six nodes around the perimeter and one node (#3) in the interior. There are four horizontal links with unit vector (1,0), and 8 diagonal links with unit vector (+/-0.5, +/-sqrt(3)/2) (note: sqrt(3)/2 ~ 0.866).

>>> from landlab.graph import TriGraph
>>> graph = TriGraph((3, 2), spacing=2.0, node_layout="hex", sort=True)

>>> np.round(graph.unit_vector_at_link[:, 0], decimals=5)
array([ 1. , -0.5,  0.5, -0.5,  0.5,  1. ,  1. ,  0.5, -0.5,  0.5, -0.5,
        1. ])
>>> np.round(graph.unit_vector_at_link[:, 1], decimals=5)
array([0.     , 0.86603, 0.86603, 0.86603, 0.86603, 0.     ,
       0.     , 0.86603, 0.86603, 0.86603, 0.86603, 0.     ])
property unit_vector_at_node

Get a unit vector for each node.

Examples

>>> from landlab.graph import UniformRectilinearGraph
>>> graph = UniformRectilinearGraph((3, 3))
>>> graph.unit_vector_at_node
array([[1., 1.],
       [2., 1.],
       [1., 1.],
       [1., 2.],
       [2., 2.],
       [1., 2.],
       [1., 1.],
       [2., 1.],
       [1., 1.]])

>>> from landlab.graph import TriGraph
>>> graph = TriGraph((3, 2), spacing=2.0, node_layout="hex", sort=True)

>>> unit_vector_at_node = np.round(graph.unit_vector_at_node, decimals=5)
>>> unit_vector_at_node[:, 0]
array([2., 2., 2., 4., 2., 2., 2.])
>>> unit_vector_at_node[:, 1]
array([1.73205, 1.73205, 1.73205, 3.4641 , 1.73205, 1.73205, 1.73205])
property vertical_faces
property x_of_corner

Get x-coordinate of corner.

See also

x_of_node

property x_of_node

Get x-coordinate of node.

Examples

>>> 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))
>>> graph.x_of_node
array([0., 1., 2., 0., 1., 2.])
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 xy_of_node

Get x and y-coordinates of node.

Examples

>>> 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))
>>> graph.xy_of_node[:, 0]
array([0., 1., 2., 0., 1., 2.])
>>> graph.xy_of_node[:, 1]
array([0., 0., 0., 1., 1., 1.])
property xy_of_patch

Get the centroid of each patch.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> patches = ((0, 3, 5, 2), (1, 4, 6, 3))
>>> graph = Graph((node_y, node_x), links=links, patches=patches)
>>> graph.xy_of_patch
array([[0.5, 0.5],
       [1.5, 0.5]])
property y_of_corner

Get y-coordinate of corner.

See also

y_of_node

property y_of_node

Get y-coordinate of node.

Examples

>>> 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))
>>> graph.y_of_node
array([0., 0., 0., 1., 1., 1.])
class DualStructuredQuadGraph[source]

Bases: DualGraph, StructuredQuadGraph

Dual graph of a structured grid of quadrilaterals.

Examples

>>> from landlab.graph import DualStructuredQuadGraph
>>> node_y = [-1, -2, -3, 0, 0, 0, 1, 2, 3]
>>> node_x = [0, 1, 2, 0, 2, 3, 0, 1, 2]
>>> graph = DualStructuredQuadGraph((node_y, node_x), shape=(3, 3), sort=True)
>>> graph.number_of_corners == 4
True
>>> graph.y_of_corner
array([-1.25, -0.75,  0.75,  1.25])
>>> graph.x_of_corner
array([2.  , 0.75, 0.75, 2.  ])
>>> graph.node_at_cell
array([4])
__init__(node_y_and_x, shape=None, sort=True)[source]
__new__(**kwargs)
property adjacent_corners_at_corner

Get adjacent corners.

See also

adjacent_nodes_at_node

property adjacent_faces_at_face
property adjacent_nodes_at_node

Get adjacent nodes.

Examples

>>> from landlab.graph import Graph

First, a simple example with no diagonals.

>>> node_x, node_y = [0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> graph = Graph((node_y, node_x), links=links, sort=True)
>>> graph.adjacent_nodes_at_node
array([[ 1,  3, -1, -1],
       [ 2,  4,  0, -1],
       [ 5,  1, -1, -1],
       [ 4,  6,  0, -1],
       [ 5,  7,  3,  1],
       [ 8,  4,  2, -1],
       [ 7,  3, -1, -1],
       [ 8,  6,  4, -1],
       [ 7,  5, -1, -1]])

Next, we add the diagonal from node 0 to node 4.

>>> node_x, node_y = [0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
...     (0, 4),
... )
>>> graph = Graph((node_y, node_x), links=links, sort=True)
>>> graph.adjacent_nodes_at_node
array([[ 1,  4,  3, -1, -1],
       [ 2,  4,  0, -1, -1],
       [ 5,  1, -1, -1, -1],
       [ 4,  6,  0, -1, -1],
       [ 5,  7,  3,  0,  1],
       [ 8,  4,  2, -1, -1],
       [ 7,  3, -1, -1, -1],
       [ 8,  6,  4, -1, -1],
       [ 7,  5, -1, -1, -1]])
property angle_of_face

Get the angle of each face.

See also

angle_of_link

Get the angle of each link.

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])
>>> links = ((0, 1), (1, 2), (0, 3), (1, 4), (2, 5), (3, 4), (4, 5))
>>> graph = Graph((node_y, node_x), links=links)
>>> graph.angle_of_link * 180.0 / np.pi
array([ 0.,  0., 90., 90., 90.,  0.,  0.])
property area_of_cell

Get the area of each cell.

See also

area_of_patch

property area_of_patch

Get the area of each patch.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> patches = ((0, 3, 5, 2), (1, 4, 6, 3))
>>> graph = Graph((node_y, node_x), links=links, patches=patches)
>>> graph.area_of_patch
array([1.,  1.])
property cell_at_node
property cells_at_corner
property cells_at_face
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_at_patch
property corner_nodes
property corner_x
property corner_y
property corners

A shaped array of corner ids.

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_nodes_of_grid

Nodes at nodes of grid.

See also

nodes_at_corners_of_grid

property corners_at_right_edge
property corners_at_top_edge
property ds
property dual
property face_dirs_at_corner
property faces_at_cell

Get the faces that define a cell.

See also

links_at_patch

property faces_at_corner
freeze()

Freeze the graph by making arrays read-only.

classmethod from_dict(meta)
classmethod from_netcdf(fname)
property frozen
static get_corners(node_y_and_x, shape)[source]
static get_node_at_cell(shape)[source]

Set up an array that gives the node at each cell.

Examples

>>> from landlab.graph.structured_quad import DualStructuredQuadGraph
>>> DualStructuredQuadGraph.get_node_at_cell((5, 6))
array([ 7,  8,  9, 10,
       13, 14, 15, 16,
       19, 20, 21, 22])
static get_nodes_at_face(shape)[source]

Set up an array that gives the nodes on either side of each face.

Examples

>>> from landlab.graph.structured_quad import DualStructuredQuadGraph
>>> DualStructuredQuadGraph.get_nodes_at_face((3, 4))
array([[ 1,  5], [ 2,  6],
       [ 4,  5], [ 5,  6], [ 6,  7],
       [ 5,  9], [ 6, 10]])
property horizontal_faces
property length_of_face

Get the length of faces.

See also

length_of_link

Get the length of links.

Examples

>>> import numpy as np
>>> from landlab.graph import UniformRectilinearGraph

>>> graph = UniformRectilinearGraph((2, 3), spacing=(1, 2))
>>> graph.length_of_link
array([2., 2., 1., 1., 1., 2., 2.])

Return link directions into each node.

A value of 1 indicates a link points toward a given node, while a value of -1 indicates a link points away from a node.

Returns:

Link directions relative to the nodes of a grid. The shape of the matrix will be number of nodes by the maximum number of links per node. A zero indicates no link at this position.

Return type:

(n_nodes, max_links_per_node) ndarray of int

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> graph = Graph((node_y, node_x), links=links)
>>> graph.link_dirs_at_node
array([[-1, -1,  0,  0], [-1, -1,  1,  0], [-1,  1,  0,  0],
       [-1, -1,  1,  0], [-1, -1,  1,  1], [-1,  1,  1,  0],
       [-1,  1,  0,  0], [-1,  1,  1,  0], [ 1,  1,  0,  0]],
      dtype=int8)

Get links touching a node.

Examples

>>> from landlab.graph import Graph
>>> node_x = [0, 1, 2, 0, 1, 2, 0, 1, 2]
>>> node_y = [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> graph = Graph((node_y, node_x), links=links)
>>> graph.links_at_node
array([[ 0,  2, -1, -1], [ 1,  3,  0, -1], [ 4,  1, -1, -1],
       [ 5,  7,  2, -1], [ 6,  8,  5,  3], [ 9,  6,  4, -1],
       [10,  7, -1, -1], [11, 10,  8, -1], [11,  9, -1, -1]])

Get the links that define a patch.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> patches = ((0, 3, 5, 2), (1, 4, 6, 3))
>>> graph = Graph((node_y, node_x), links=links, patches=patches, sort=True)
>>> graph.links_at_patch
array([[3, 5, 2, 0],
       [4, 6, 3, 1]])
classmethod load(source)
merge(dual, node_at_cell=None, nodes_at_face=None)
property midpoint_of_face

Get the middle of faces.

See also

midpoint_of_link

Get the middle of links.

Examples

>>> import numpy as np
>>> from landlab.graph import UniformRectilinearGraph

>>> graph = UniformRectilinearGraph((2, 3), spacing=(1, 2))
>>> graph.midpoint_of_link
array([[1. , 0. ], [3. , 0. ],
       [0. , 0.5], [2. , 0.5], [4. , 0.5],
       [1. , 1. ], [3. , 1. ]])
property ndim
property node_at_cell

Get nodes at link head.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> graph = Graph((node_y, node_x), links=links)
>>> graph.node_at_link_head
array([1, 2, 3, 4, 5, 4, 5, 6, 7, 8, 7, 8])

Get nodes at link tail.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> graph = Graph((node_y, node_x), links=links)
>>> graph.node_at_link_tail
array([0, 1, 0, 1, 2, 3, 4, 3, 4, 5, 6, 7])
property node_corners
property node_x
property node_y
property nodes

A shaped array of node ids.

Returns:

Node IDs in an array shaped as number_of_node_rows by number_of_node_columns.

Return type:

ndarray

property nodes_at_bottom_edge
property nodes_at_corners_of_grid

Nodes at corners of grid.

The nodes at at the corners of the grid. The nodes are returned counterclockwise starting with the upper-right.

Returns:

Nodes at the four corners.

Return type:

tuple of int

Examples

>>> from landlab.graph import UniformRectilinearGraph
>>> graph = UniformRectilinearGraph((4, 5))
>>> graph.nodes_at_corners_of_grid
(19, 15, 0, 4)
nodes_at_edge(edge)
property nodes_at_face
property nodes_at_left_edge

Get nodes at either end of links.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> graph = Graph((node_y, node_x), links=links)
>>> graph.nodes_at_link
array([[0, 1], [1, 2],
       [0, 3], [1, 4], [2, 5],
       [3, 4], [4, 5],
       [3, 6], [4, 7], [5, 8],
       [6, 7], [7, 8]])
property nodes_at_patch

Get the nodes that define a patch.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = ([0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2])
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> patches = ((0, 3, 5, 2), (1, 4, 6, 3))
>>> graph = Graph((node_y, node_x), links=links, patches=patches, sort=True)
>>> graph.nodes_at_patch
array([[4, 3, 0, 1],
       [5, 4, 1, 2]])
property nodes_at_right_edge
property nodes_at_top_edge
property number_of_cells

Get the number of cells.

See also

number_of_patches

property number_of_corner_columns
property number_of_corner_rows
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

Get nodes at link head.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> graph = Graph((node_y, node_x), links=links)
>>> graph.number_of_links == 12
True
property number_of_node_columns
property number_of_node_rows
property number_of_nodes

Get total number of nodes.

Examples

>>> 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))
>>> graph.number_of_nodes == 6
True
property number_of_patches

Get the number of patches.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> patches = ((0, 3, 5, 2), (1, 4, 6, 3))
>>> graph = Graph((node_y, node_x), links=links, patches=patches)
>>> graph.number_of_patches == 2
True
property orientation_of_face

Return array of face orientation codes (one value per face).

See also

orientation_of_link

Return array of link orientation codes (one value per link).

Orientation codes are defined by LinkOrientation; 1 = E, 2 = ENE, 4 = NNE, 8 = N, 16 = NNW, 32 = ESE (using powers of 2 allows for future applications that might want additive combinations).

property parallel_faces_at_face

Return similarly oriented faces connected to each face.

See also

parallel_links_at_link

Return similarly oriented links connected to each link.

Return IDs of links of the same orientation that are connected to each given link’s tail or head node.

The data structure is a numpy array of shape (n_links, 2) containing the IDs of the “tail-wise” (connected to tail node) and “head-wise” (connected to head node) links, or -1 if the link is inactive (e.g., on the perimeter) or it has no attached parallel neighbor in the given direction.

For instance, consider a 3x4 raster, in which link IDs are as shown:

.-14-.-15-.-16-.
|    |    |    |
10  11   12   13
|    |    |    |
.--7-.--8-.--9-.
|    |    |    |
3    4    5    6
|    |    |    |
.--0-.--1-.--2-.

Here’s a mapping of the tail-wise (shown at left or bottom of links) and head-wise (shown at right or top of links) links:

.----.----.----.
|    |    |    |
|    |    |    |
|    4    5    |
.---8.7--9.8---.
|   11   12    |
|    |    |    |
|    |    |    |
.----.----.----.

So the corresponding data structure would be mostly filled with -1, but for the 7 active links, it would look like:

4: [[-1, 11],
5:  [-1, 12],
7:  [-1,  8],
8:  [ 7,  9],
9:  [ 8, -1],
11: [ 4, -1],
12: [ 5, -1]]

Examples

>>> from landlab import RasterModelGrid
>>> grid = RasterModelGrid((3, 4))
>>> pll = grid.parallel_links_at_link
>>> pll[4:13, :]
array([[-1, 11],
       [-1, 12],
       [-1, 13],
       [-1,  8],
       [ 7,  9],
       [ 8, -1],
       [ 3, -1],
       [ 4, -1],
       [ 5, -1]])
property patch_at_corner

Get the patches on either side of each link.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = ([0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1])
>>> links = ((0, 1), (1, 2), (0, 3), (1, 4), (2, 5), (3, 4), (4, 5))
>>> patches = ((0, 3, 5, 2), (1, 4, 6, 3))
>>> graph = Graph((node_y, node_x), links=links, patches=patches)
>>> graph.patches_at_link
array([[ 0, -1], [ 1, -1],
       [ 0, -1], [ 0,  1], [ 1, -1],
       [ 0, -1], [ 1, -1]])
property patches_at_node

Get the patches that touch each node.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = ([0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1])
>>> links = ((0, 1), (1, 2), (0, 3), (1, 4), (2, 5), (3, 4), (4, 5))
>>> patches = ((0, 3, 5, 2), (1, 4, 6, 3))
>>> graph = Graph((node_y, node_x), links=links, patches=patches, sort=True)
>>> graph.patches_at_node
array([[ 0, -1], [ 1,  0], [ 1, -1],
       [ 0, -1], [ 0,  1], [ 1, -1]])
property perimeter_corners
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])
static setup_node_y_and_x(yx_at_node, shape=None)
property shape
sort()

Sort graph elements.

thaw()

Thaw the graph by making arrays writable.

thawed()
to_dict()
to_json()
to_netcdf(*args, **kwds)

Write graph contents to a netCDF file.

See xarray.Dataset.to_netcdf for a complete list of parameters. Below are only the most common.

Parameters:

  • path (str, optional) – Path to which to save this graph.

  • mode ({'w', 'a'}, optional) – Write (‘w’) or append (‘a’) mode. If mode=’w’, any existing file at this location will be overwritten.

  • format ({'NETCDF4', 'NETCDF4_CLASSIC', 'NETCDF3_64BIT', 'NETCDF3_CLASSIC'}, optional) –

    File format for the resulting netCDF file:

    • NETCDF4: Data is stored in an HDF5 file, using netCDF4 API features.

    • NETCDF4_CLASSIC: Data is stored in an HDF5 file, using only netCDF 3 compatible API features.

    • NETCDF3_64BIT: 64-bit offset version of the netCDF 3 file format, which fully supports 2+ GB files, but is only compatible with clients linked against netCDF version 3.6.0 or later.

    • NETCDF3_CLASSIC: The classic netCDF 3 file format. It does not handle 2+ GB files very well.

    All formats are supported by the netCDF4-python library. scipy.io.netcdf only supports the last two formats.

    The default format is NETCDF4 if you are saving a file to disk and have the netCDF4-python library available. Otherwise, xarray falls back to using scipy to write netCDF files and defaults to the NETCDF3_64BIT format (scipy does not support netCDF4).

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

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

For each link, the x and y components of the link’s unit vector (that is, the link’s x and y dimensions if it were shrunk to unit length but retained its orientation).

Examples

The example below is a seven-node hexagonal grid, with six nodes around the perimeter and one node (#3) in the interior. There are four horizontal links with unit vector (1,0), and 8 diagonal links with unit vector (+/-0.5, +/-sqrt(3)/2) (note: sqrt(3)/2 ~ 0.866).

>>> from landlab.graph import TriGraph
>>> graph = TriGraph((3, 2), spacing=2.0, node_layout="hex", sort=True)

>>> np.round(graph.unit_vector_at_link[:, 0], decimals=5)
array([ 1. , -0.5,  0.5, -0.5,  0.5,  1. ,  1. ,  0.5, -0.5,  0.5, -0.5,
        1. ])
>>> np.round(graph.unit_vector_at_link[:, 1], decimals=5)
array([0.     , 0.86603, 0.86603, 0.86603, 0.86603, 0.     ,
       0.     , 0.86603, 0.86603, 0.86603, 0.86603, 0.     ])
property unit_vector_at_node

Get a unit vector for each node.

Examples

>>> from landlab.graph import UniformRectilinearGraph
>>> graph = UniformRectilinearGraph((3, 3))
>>> graph.unit_vector_at_node
array([[1., 1.],
       [2., 1.],
       [1., 1.],
       [1., 2.],
       [2., 2.],
       [1., 2.],
       [1., 1.],
       [2., 1.],
       [1., 1.]])

>>> from landlab.graph import TriGraph
>>> graph = TriGraph((3, 2), spacing=2.0, node_layout="hex", sort=True)

>>> unit_vector_at_node = np.round(graph.unit_vector_at_node, decimals=5)
>>> unit_vector_at_node[:, 0]
array([2., 2., 2., 4., 2., 2., 2.])
>>> unit_vector_at_node[:, 1]
array([1.73205, 1.73205, 1.73205, 3.4641 , 1.73205, 1.73205, 1.73205])
property vertical_faces
property x_of_corner

Get x-coordinate of corner.

See also

x_of_node

property x_of_node

Get x-coordinate of node.

Examples

>>> 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))
>>> graph.x_of_node
array([0., 1., 2., 0., 1., 2.])
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 xy_of_node

Get x and y-coordinates of node.

Examples

>>> 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))
>>> graph.xy_of_node[:, 0]
array([0., 1., 2., 0., 1., 2.])
>>> graph.xy_of_node[:, 1]
array([0., 0., 0., 1., 1., 1.])
property xy_of_patch

Get the centroid of each patch.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> patches = ((0, 3, 5, 2), (1, 4, 6, 3))
>>> graph = Graph((node_y, node_x), links=links, patches=patches)
>>> graph.xy_of_patch
array([[0.5, 0.5],
       [1.5, 0.5]])
property y_of_corner

Get y-coordinate of corner.

See also

y_of_node

property y_of_node

Get y-coordinate of node.

Examples

>>> 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))
>>> graph.y_of_node
array([0., 0., 0., 1., 1., 1.])
class DualUniformRectilinearGraph[source]

Bases: DualGraph, UniformRectilinearGraph

Create a dual graph for a uniform rectilinear grid.

Examples

>>> from landlab.graph import DualUniformRectilinearGraph
>>> graph = DualUniformRectilinearGraph((4, 3))
>>> graph.x_of_corner.reshape((3, 2))
array([[0.5, 1.5],
       [0.5, 1.5],
       [0.5, 1.5]])
>>> graph.y_of_corner.reshape((3, 2))
array([[0.5, 0.5],
       [1.5, 1.5],
       [2.5, 2.5]])
>>> graph.number_of_cells == 2
True
>>> graph.faces_at_cell
array([[2, 3, 1, 0],
       [5, 6, 4, 3]])
__init__(shape, spacing=1.0, origin=(0.0, 0.0))[source]
__new__(**kwargs)
property adjacent_corners_at_corner

Get adjacent corners.

See also

adjacent_nodes_at_node

property adjacent_faces_at_face
property adjacent_nodes_at_node

Get adjacent nodes.

Examples

>>> from landlab.graph import Graph

First, a simple example with no diagonals.

>>> node_x, node_y = [0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> graph = Graph((node_y, node_x), links=links, sort=True)
>>> graph.adjacent_nodes_at_node
array([[ 1,  3, -1, -1],
       [ 2,  4,  0, -1],
       [ 5,  1, -1, -1],
       [ 4,  6,  0, -1],
       [ 5,  7,  3,  1],
       [ 8,  4,  2, -1],
       [ 7,  3, -1, -1],
       [ 8,  6,  4, -1],
       [ 7,  5, -1, -1]])

Next, we add the diagonal from node 0 to node 4.

>>> node_x, node_y = [0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
...     (0, 4),
... )
>>> graph = Graph((node_y, node_x), links=links, sort=True)
>>> graph.adjacent_nodes_at_node
array([[ 1,  4,  3, -1, -1],
       [ 2,  4,  0, -1, -1],
       [ 5,  1, -1, -1, -1],
       [ 4,  6,  0, -1, -1],
       [ 5,  7,  3,  0,  1],
       [ 8,  4,  2, -1, -1],
       [ 7,  3, -1, -1, -1],
       [ 8,  6,  4, -1, -1],
       [ 7,  5, -1, -1, -1]])
property angle_of_face

Get the angle of each face.

See also

angle_of_link

Get the angle of each link.

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])
>>> links = ((0, 1), (1, 2), (0, 3), (1, 4), (2, 5), (3, 4), (4, 5))
>>> graph = Graph((node_y, node_x), links=links)
>>> graph.angle_of_link * 180.0 / np.pi
array([ 0.,  0., 90., 90., 90.,  0.,  0.])
property area_of_cell

Get the area of each cell.

See also

area_of_patch

property area_of_patch

Get the area of each patch.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> patches = ((0, 3, 5, 2), (1, 4, 6, 3))
>>> graph = Graph((node_y, node_x), links=links, patches=patches)
>>> graph.area_of_patch
array([1.,  1.])
property cell_at_node
property cells_at_corner
property cells_at_face
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_at_patch
property corner_nodes
property corner_x
property corner_y
property corners

A shaped array of corner ids.

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_nodes_of_grid

Nodes at nodes of grid.

See also

nodes_at_corners_of_grid

property corners_at_right_edge
property corners_at_top_edge
property ds
property dual
property dx
property dy
property face_dirs_at_corner
property faces_at_cell

Get the faces that define a cell.

See also

links_at_patch

property faces_at_corner
freeze()

Freeze the graph by making arrays read-only.

classmethod from_dict(meta)
classmethod from_netcdf(fname)
property frozen
property horizontal_faces
property length_of_face

Get the length of faces.

See also

length_of_link

Get the length of links.

Examples

>>> import numpy as np
>>> from landlab.graph import UniformRectilinearGraph

>>> graph = UniformRectilinearGraph((2, 3), spacing=(1, 2))
>>> graph.length_of_link
array([2., 2., 1., 1., 1., 2., 2.])

Return link directions into each node.

A value of 1 indicates a link points toward a given node, while a value of -1 indicates a link points away from a node.

Returns:

Link directions relative to the nodes of a grid. The shape of the matrix will be number of nodes by the maximum number of links per node. A zero indicates no link at this position.

Return type:

(n_nodes, max_links_per_node) ndarray of int

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> graph = Graph((node_y, node_x), links=links)
>>> graph.link_dirs_at_node
array([[-1, -1,  0,  0], [-1, -1,  1,  0], [-1,  1,  0,  0],
       [-1, -1,  1,  0], [-1, -1,  1,  1], [-1,  1,  1,  0],
       [-1,  1,  0,  0], [-1,  1,  1,  0], [ 1,  1,  0,  0]],
      dtype=int8)

Get links touching a node.

Examples

>>> from landlab.graph import Graph
>>> node_x = [0, 1, 2, 0, 1, 2, 0, 1, 2]
>>> node_y = [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> graph = Graph((node_y, node_x), links=links)
>>> graph.links_at_node
array([[ 0,  2, -1, -1], [ 1,  3,  0, -1], [ 4,  1, -1, -1],
       [ 5,  7,  2, -1], [ 6,  8,  5,  3], [ 9,  6,  4, -1],
       [10,  7, -1, -1], [11, 10,  8, -1], [11,  9, -1, -1]])

Get the links that define a patch.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> patches = ((0, 3, 5, 2), (1, 4, 6, 3))
>>> graph = Graph((node_y, node_x), links=links, patches=patches, sort=True)
>>> graph.links_at_patch
array([[3, 5, 2, 0],
       [4, 6, 3, 1]])
classmethod load(source)
merge(dual, node_at_cell=None, nodes_at_face=None)
property midpoint_of_face

Get the middle of faces.

See also

midpoint_of_link

Get the middle of links.

Examples

>>> import numpy as np
>>> from landlab.graph import UniformRectilinearGraph

>>> graph = UniformRectilinearGraph((2, 3), spacing=(1, 2))
>>> graph.midpoint_of_link
array([[1. , 0. ], [3. , 0. ],
       [0. , 0.5], [2. , 0.5], [4. , 0.5],
       [1. , 1. ], [3. , 1. ]])
property ndim
property node_at_cell

Get nodes at link head.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> graph = Graph((node_y, node_x), links=links)
>>> graph.node_at_link_head
array([1, 2, 3, 4, 5, 4, 5, 6, 7, 8, 7, 8])

Get nodes at link tail.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> graph = Graph((node_y, node_x), links=links)
>>> graph.node_at_link_tail
array([0, 1, 0, 1, 2, 3, 4, 3, 4, 5, 6, 7])
property node_corners
property node_x
property node_y
property nodes

A shaped array of node ids.

Returns:

Node IDs in an array shaped as number_of_node_rows by number_of_node_columns.

Return type:

ndarray

property nodes_at_bottom_edge
property nodes_at_corners_of_grid

Nodes at corners of grid.

The nodes at at the corners of the grid. The nodes are returned counterclockwise starting with the upper-right.

Returns:

Nodes at the four corners.

Return type:

tuple of int

Examples

>>> from landlab.graph import UniformRectilinearGraph
>>> graph = UniformRectilinearGraph((4, 5))
>>> graph.nodes_at_corners_of_grid
(19, 15, 0, 4)
nodes_at_edge(edge)
property nodes_at_face
property nodes_at_left_edge

Get nodes at either end of links.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> graph = Graph((node_y, node_x), links=links)
>>> graph.nodes_at_link
array([[0, 1], [1, 2],
       [0, 3], [1, 4], [2, 5],
       [3, 4], [4, 5],
       [3, 6], [4, 7], [5, 8],
       [6, 7], [7, 8]])
property nodes_at_patch

Get the nodes that define a patch.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = ([0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2])
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> patches = ((0, 3, 5, 2), (1, 4, 6, 3))
>>> graph = Graph((node_y, node_x), links=links, patches=patches, sort=True)
>>> graph.nodes_at_patch
array([[4, 3, 0, 1],
       [5, 4, 1, 2]])
property nodes_at_right_edge
property nodes_at_top_edge
property number_of_cells

Get the number of cells.

See also

number_of_patches

property number_of_corner_columns
property number_of_corner_rows
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

Get nodes at link head.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> graph = Graph((node_y, node_x), links=links)
>>> graph.number_of_links == 12
True
property number_of_node_columns
property number_of_node_rows
property number_of_nodes

Get total number of nodes.

Examples

>>> 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))
>>> graph.number_of_nodes == 6
True
property number_of_patches

Get the number of patches.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> patches = ((0, 3, 5, 2), (1, 4, 6, 3))
>>> graph = Graph((node_y, node_x), links=links, patches=patches)
>>> graph.number_of_patches == 2
True
property orientation_of_face

Return array of face orientation codes (one value per face).

See also

orientation_of_link

Return array of link orientation codes (one value per link).

Orientation codes are defined by LinkOrientation; 1 = E, 2 = ENE, 4 = NNE, 8 = N, 16 = NNW, 32 = ESE (using powers of 2 allows for future applications that might want additive combinations).

property origin
property parallel_faces_at_face

Return similarly oriented faces connected to each face.

See also

parallel_links_at_link

Return similarly oriented links connected to each link.

Return IDs of links of the same orientation that are connected to each given link’s tail or head node.

The data structure is a numpy array of shape (n_links, 2) containing the IDs of the “tail-wise” (connected to tail node) and “head-wise” (connected to head node) links, or -1 if the link is inactive (e.g., on the perimeter) or it has no attached parallel neighbor in the given direction.

For instance, consider a 3x4 raster, in which link IDs are as shown:

.-14-.-15-.-16-.
|    |    |    |
10  11   12   13
|    |    |    |
.--7-.--8-.--9-.
|    |    |    |
3    4    5    6
|    |    |    |
.--0-.--1-.--2-.

Here’s a mapping of the tail-wise (shown at left or bottom of links) and head-wise (shown at right or top of links) links:

.----.----.----.
|    |    |    |
|    |    |    |
|    4    5    |
.---8.7--9.8---.
|   11   12    |
|    |    |    |
|    |    |    |
.----.----.----.

So the corresponding data structure would be mostly filled with -1, but for the 7 active links, it would look like:

4: [[-1, 11],
5:  [-1, 12],
7:  [-1,  8],
8:  [ 7,  9],
9:  [ 8, -1],
11: [ 4, -1],
12: [ 5, -1]]

Examples

>>> from landlab import RasterModelGrid
>>> grid = RasterModelGrid((3, 4))
>>> pll = grid.parallel_links_at_link
>>> pll[4:13, :]
array([[-1, 11],
       [-1, 12],
       [-1, 13],
       [-1,  8],
       [ 7,  9],
       [ 8, -1],
       [ 3, -1],
       [ 4, -1],
       [ 5, -1]])
property patch_at_corner

Get the patches on either side of each link.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = ([0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1])
>>> links = ((0, 1), (1, 2), (0, 3), (1, 4), (2, 5), (3, 4), (4, 5))
>>> patches = ((0, 3, 5, 2), (1, 4, 6, 3))
>>> graph = Graph((node_y, node_x), links=links, patches=patches)
>>> graph.patches_at_link
array([[ 0, -1], [ 1, -1],
       [ 0, -1], [ 0,  1], [ 1, -1],
       [ 0, -1], [ 1, -1]])
property patches_at_node

Get the patches that touch each node.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = ([0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1])
>>> links = ((0, 1), (1, 2), (0, 3), (1, 4), (2, 5), (3, 4), (4, 5))
>>> patches = ((0, 3, 5, 2), (1, 4, 6, 3))
>>> graph = Graph((node_y, node_x), links=links, patches=patches, sort=True)
>>> graph.patches_at_node
array([[ 0, -1], [ 1,  0], [ 1, -1],
       [ 0, -1], [ 0,  1], [ 1, -1]])
property perimeter_corners
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
sort()

Sort graph elements.

property spacing
thaw()

Thaw the graph by making arrays writable.

thawed()
to_dict()
to_json()
to_netcdf(*args, **kwds)

Write graph contents to a netCDF file.

See xarray.Dataset.to_netcdf for a complete list of parameters. Below are only the most common.

Parameters:

  • path (str, optional) – Path to which to save this graph.

  • mode ({'w', 'a'}, optional) – Write (‘w’) or append (‘a’) mode. If mode=’w’, any existing file at this location will be overwritten.

  • format ({'NETCDF4', 'NETCDF4_CLASSIC', 'NETCDF3_64BIT', 'NETCDF3_CLASSIC'}, optional) –

    File format for the resulting netCDF file:

    • NETCDF4: Data is stored in an HDF5 file, using netCDF4 API features.

    • NETCDF4_CLASSIC: Data is stored in an HDF5 file, using only netCDF 3 compatible API features.

    • NETCDF3_64BIT: 64-bit offset version of the netCDF 3 file format, which fully supports 2+ GB files, but is only compatible with clients linked against netCDF version 3.6.0 or later.

    • NETCDF3_CLASSIC: The classic netCDF 3 file format. It does not handle 2+ GB files very well.

    All formats are supported by the netCDF4-python library. scipy.io.netcdf only supports the last two formats.

    The default format is NETCDF4 if you are saving a file to disk and have the netCDF4-python library available. Otherwise, xarray falls back to using scipy to write netCDF files and defaults to the NETCDF3_64BIT format (scipy does not support netCDF4).

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

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

For each link, the x and y components of the link’s unit vector (that is, the link’s x and y dimensions if it were shrunk to unit length but retained its orientation).

Examples

The example below is a seven-node hexagonal grid, with six nodes around the perimeter and one node (#3) in the interior. There are four horizontal links with unit vector (1,0), and 8 diagonal links with unit vector (+/-0.5, +/-sqrt(3)/2) (note: sqrt(3)/2 ~ 0.866).

>>> from landlab.graph import TriGraph
>>> graph = TriGraph((3, 2), spacing=2.0, node_layout="hex", sort=True)

>>> np.round(graph.unit_vector_at_link[:, 0], decimals=5)
array([ 1. , -0.5,  0.5, -0.5,  0.5,  1. ,  1. ,  0.5, -0.5,  0.5, -0.5,
        1. ])
>>> np.round(graph.unit_vector_at_link[:, 1], decimals=5)
array([0.     , 0.86603, 0.86603, 0.86603, 0.86603, 0.     ,
       0.     , 0.86603, 0.86603, 0.86603, 0.86603, 0.     ])
property unit_vector_at_node

Get a unit vector for each node.

Examples

>>> from landlab.graph import UniformRectilinearGraph
>>> graph = UniformRectilinearGraph((3, 3))
>>> graph.unit_vector_at_node
array([[1., 1.],
       [2., 1.],
       [1., 1.],
       [1., 2.],
       [2., 2.],
       [1., 2.],
       [1., 1.],
       [2., 1.],
       [1., 1.]])

>>> from landlab.graph import TriGraph
>>> graph = TriGraph((3, 2), spacing=2.0, node_layout="hex", sort=True)

>>> unit_vector_at_node = np.round(graph.unit_vector_at_node, decimals=5)
>>> unit_vector_at_node[:, 0]
array([2., 2., 2., 4., 2., 2., 2.])
>>> unit_vector_at_node[:, 1]
array([1.73205, 1.73205, 1.73205, 3.4641 , 1.73205, 1.73205, 1.73205])
property vertical_faces
property x_of_corner

Get x-coordinate of corner.

See also

x_of_node

property x_of_node

Get x-coordinate of node.

Examples

>>> 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))
>>> graph.x_of_node
array([0., 1., 2., 0., 1., 2.])
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 xy_of_node

Get x and y-coordinates of node.

Examples

>>> 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))
>>> graph.xy_of_node[:, 0]
array([0., 1., 2., 0., 1., 2.])
>>> graph.xy_of_node[:, 1]
array([0., 0., 0., 1., 1., 1.])
property xy_of_patch

Get the centroid of each patch.

Examples

>>> from landlab.graph import Graph
>>> node_x, node_y = [0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2]
>>> links = (
...     (0, 1),
...     (1, 2),
...     (0, 3),
...     (1, 4),
...     (2, 5),
...     (3, 4),
...     (4, 5),
...     (3, 6),
...     (4, 7),
...     (5, 8),
...     (6, 7),
...     (7, 8),
... )
>>> patches = ((0, 3, 5, 2), (1, 4, 6, 3))
>>> graph = Graph((node_y, node_x), links=links, patches=patches)
>>> graph.xy_of_patch
array([[0.5, 0.5],
       [1.5, 0.5]])
property y_of_corner

Get y-coordinate of corner.

See also

y_of_node

property y_of_node

Get y-coordinate of node.

Examples

>>> 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))
>>> graph.y_of_node
array([0., 0., 0., 1., 1., 1.])