landlab.graph.graph¶
Define a graph of nodes-links-patches.
Nodes and links are required. If no patches are provided, no patches will be created.
Examples
>>> from landlab.graph import NetworkGraph, Graph
>>> node_x, node_y = [0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]
>>> graph = NetworkGraph((node_y, node_x), sort=True)
>>> graph.x_of_node
array([0., 1., 2., 0., 1., 2., 0., 1., 2.])
>>> graph.y_of_node
array([0., 0., 0., 1., 1., 1., 2., 2., 2.])
>>> graph.ndim
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.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]])
>>> graph.node_at_link_head
array([1, 2, 3, 4, 5, 4, 5, 6, 7, 8, 7, 8])
>>> graph.node_at_link_tail
array([0, 1, 0, 1, 2, 3, 4, 3, 4, 5, 6, 7])
>>> 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]])
>>> 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)
>>> patches = ((5, 3, 0, 2), (6, 4, 1, 3), (10, 8, 5, 7), (11, 9, 6, 8))
>>> 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],
[ 8, 10, 7, 5],
[ 9, 11, 8, 6]])
>>> graph.nodes_at_patch
array([[4, 3, 0, 1],
[5, 4, 1, 2],
[7, 6, 3, 4],
[8, 7, 4, 5]])
- class Graph[source]¶
Bases:
NetworkGraph
Define the connectivity of a graph of nodes, links, and patches.
Define a graph of connected nodes.
- Parameters:
mesh (Dataset) – xarray Dataset that defines the topology in ugrid format.
- __init__(node_y_and_x, links=None, patches=None, sort=False)[source]¶
Define a graph of connected nodes.
- Parameters:
mesh (Dataset) – xarray Dataset that defines the topology in ugrid format.
- __new__(**kwargs)¶
- property adjacent_links_at_link¶
- 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_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_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 ds¶
- freeze()¶
Freeze the graph by making arrays read-only.
- classmethod from_dict(meta)¶
- classmethod from_netcdf(fname)¶
- property frozen¶
- property 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.])
- property link_dirs_at_node¶
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)
- property links_at_node¶
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]])
- property links_at_patch¶
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)¶
- property 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_link_head¶
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])
- property node_at_link_tail¶
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_x¶
- property node_y¶
- property nodes¶
Get identifier for each 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.nodes array([0, 1, 2, 3, 4, 5])
- property nodes_at_link¶
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 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_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 patches_at_link¶
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_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])
- 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_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 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_link¶
- 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_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 NetworkGraph[source]¶
Bases:
object
Define the connectivity of a graph of nodes and links.
Unlike Graph, NetworkGraph does not have patches.
Define a graph of connected nodes.
- Parameters:
mesh (Dataset) – xarray Dataset that defines the topology in ugrid format.
- __init__(node_y_and_x, links=None, sort=False)[source]¶
Define a graph of connected nodes.
- Parameters:
mesh (Dataset) – xarray Dataset that defines the topology in ugrid format.
- __new__(**kwargs)¶
- property adjacent_links_at_link¶
- 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_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 ds¶
- property frozen¶
- property 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.])
- property link_dirs_at_node¶
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)
- property links_at_node¶
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]])
- property 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_link_head¶
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])
- property node_at_link_tail¶
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_x¶
- property node_y¶
- property nodes¶
Get identifier for each 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.nodes array([0, 1, 2, 3, 4, 5])
- property nodes_at_link¶
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 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_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 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])
- to_netcdf(*args, **kwds)[source]¶
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_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 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_link¶
- 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 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.])