API for landlab.grid.framed_voronoi#

Python implementation of FramedVoronoiGrid, a grid class used to create and manage unstructured Voronoi-Delaunay grids for 2D numerical models, with a structured perimeter layout

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Code author: sebastien lenard

class FramedVoronoiGrid(shape, xy_spacing=(1.0, 1.0), xy_of_lower_left=(0.0, 0.0), xy_min_spacing=(0.5, 0.5), seed=200, xy_of_reference=(0.0, 0.0), xy_axis_name=('x', 'y'), xy_axis_units='-')[source]#

Bases: DualFramedVoronoiGraph, ModelGrid

A grid of Voronoi Delaunay cells with a structured perimeter layout.

This inherited class implements a irregular 2D grid with Voronoi Delaunay cells and irregular patches. It is a special type of VoronoiDelaunayGrid grid 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 a random distance from their initial positions, bounded by a user-supplied threshold.

Examples

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

>>> from landlab import FramedVoronoiGrid
>>> grid = FramedVoronoiGrid((3, 2), xy_spacing=1.0)
>>> grid.number_of_nodes
6
>>> grid = FramedVoronoiGrid((4, 3), xy_spacing=(10., 10.), xy_min_spacing=(5., 5.), seed=200)
>>> grid.status_at_node
array([1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1], dtype=uint8)
>>> grid.x_of_node[3]
0.0
>>> grid.x_of_node[5]
20.0
>>> grid.y_of_node[0::3]       
array([  0.   ,   7.499,  17.499,  30.   ])
>>> grid = FramedVoronoiGrid((3, 5), xy_spacing=(10., 10.), xy_min_spacing=5., seed=None)
>>> grid.boundary_nodes
array([ 0,  1,  2,  3,  4,  5,  9, 10, 11, 12, 13, 14])

Create a grid of voronoi cells with a structured perimeter.

Create an irregular 2D grid with voronoi cells and triangular patches. It is a special type of VoronoiDelaunayGrid in which the initial set of points is arranged in a regular lattice determined by the parameters shape, and xy_spacing. The coordinates of the core points are then randomly moved while the perimeter points remaining fixed, in a way determined by the parameters xy_min_spacing, and seed.

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

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

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

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

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

  • xy_of_reference (tuple, optional) – Coordinate value in projected space of the reference point, xy_of_lower_left.

  • xy_axis_name (tuple of str, optional) – x and y axis names.

  • xy_axis_units (str, optional) – x and y axis units.

Returns

A newly-created grid.

Return type

FramedVoronoiGrid

Examples

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

>>> from landlab import FramedVoronoiGrid
>>> grid = FramedVoronoiGrid((3, 2), xy_spacing=1.0)
>>> grid.number_of_nodes
6
__init__(shape, xy_spacing=(1.0, 1.0), xy_of_lower_left=(0.0, 0.0), xy_min_spacing=(0.5, 0.5), seed=200, xy_of_reference=(0.0, 0.0), xy_axis_name=('x', 'y'), xy_axis_units='-')[source]#

Create a grid of voronoi cells with a structured perimeter.

Create an irregular 2D grid with voronoi cells and triangular patches. It is a special type of VoronoiDelaunayGrid in which the initial set of points is arranged in a regular lattice determined by the parameters shape, and xy_spacing. The coordinates of the core points are then randomly moved while the perimeter points remaining fixed, in a way determined by the parameters xy_min_spacing, and seed.

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

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

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

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

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

  • xy_of_reference (tuple, optional) – Coordinate value in projected space of the reference point, xy_of_lower_left.

  • xy_axis_name (tuple of str, optional) – x and y axis names.

  • xy_axis_units (str, optional) – x and y axis units.

Returns

A newly-created grid.

Return type

FramedVoronoiGrid

Examples

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

>>> from landlab import FramedVoronoiGrid
>>> grid = FramedVoronoiGrid((3, 2), xy_spacing=1.0)
>>> grid.number_of_nodes
6
property active_adjacent_corners_at_corner#

Adjacent corners for each grid corner.

See also

Graph.active_adjacent_nodes_at_node

property active_face_dirs_at_corner#

1=incoming flux, -1=outgoing

See also

Graph.active_link_dirs_at_node

Type

Link flux directions at each corner

property all_corner_azimuths_map#

Get azimuths from every corner to every other corner.

See also

Graph.all_node_azimuths_map

property all_corner_distances_map#

Get distances from every corner to every other corner.

See also

Graph.all_node_distances_map

property angle_of_face_about_head#

Find and return the angle of a face about the corner at the face head.

See also

Graph.angle_of_link_about_head

property boundary_corners#

Get array of boundary corners.

See also

Graph.boundary_nodes

property cells_present_at_corner#

A boolean array, False where a cell has a closed corner or is

See also

Graph.patches_present_at_node

property cells_present_at_face#

A boolean array, False where a cell has a closed corner or is

See also

Graph.patches_present_at_link

property closed_boundary_corners#

Get array of closed boundary corners.

See also

Graph.closed_boundary_nodes

property core_corners#

Get array of core corners.

See also

Graph.core_nodes

property core_patches#

Get array of core patches.

See also

Graph.core_cells

property corner_at_core_patch#

Get array of corners associated with core patches.

See also

Graph.node_at_core_cell

property face_status_at_corner#
property fixed_faces#

Get array of fixed faces.

See also

Graph.fixed_links

property fixed_gradient_boundary_corner_anchor_corner#

Returns the corner at the other end of the fixed face for a fixed

See also

Graph.fixed_gradient_boundary_node_anchor_node

property fixed_gradient_boundary_corner_fixed_face#

An array of the fixed_faces connected to fixed gradient boundary

See also

Graph.fixed_gradient_boundary_node_fixed_link

property fixed_gradient_boundary_corners#

Get array of fixed gradient boundary corners.

See also

Graph.fixed_gradient_boundary_nodes

property fixed_value_boundary_corners#

Get array of fixed value boundary corners.

See also

Graph.fixed_value_boundary_nodes

classmethod from_dict(kwds)[source]#

Create grid from dictionary.

Parameters

params (dictionary) – Dictionary of required parameters to create a model grid.

Examples

>>> from landlab import RasterModelGrid
>>> params = {"shape": (3,4), "xy_spacing": 2}
>>> grid = RasterModelGrid.from_dict(params)
>>> grid.x_of_node
array([ 0.,  2.,  4.,  6.,  0.,  2.,  4.,  6.,  0.,  2.,  4.,  6.])
>>> grid.y_of_node
array([ 0.,  0.,  0.,  0.,  2.,  2.,  2.,  2.,  4.,  4.,  4.,  4.])
property number_of_cells_present_at_corner#

Return the number of cells at a corner without a closed corner.

See also

Graph.number_of_patches_present_at_node

property number_of_cells_present_at_face#

Return the number of cells at a face without a closed corner.

See also

Graph.number_of_patches_present_at_link

property number_of_core_corners#

Number of core corners.

See also

Graph.number_of_core_nodes

property number_of_core_patches#

Number of core patches.

See also

Graph.number_of_core_cells

property number_of_fixed_faces#

Number of fixed faces.

See also

Graph.number_of_fixed_links

property open_boundary_corners#

Get array of open boundary corners.

See also

Graph.open_boundary_nodes

property patch_area_at_corner#

Cell areas in a ncorners-long array.

See also

Graph.cell_area_at_node

property status_at_corner#

Get array of the boundary status for each corner.

See also

Graph.status_at_node

property status_at_face#

Get array of the status of all faces.

See also

Graph.status_at_link

property unit_vector_sum_xcomponent_at_corner#

Get array of x-component of unit vector sums at each corner.

See also

Graph.unit_vector_sum_xcomponent_at_node

property unit_vector_sum_ycomponent_at_corner#

Get array of y-component of unit vector sums at each corner.

See also

Graph.unit_vector_sum_ycomponent_at_node