API for landlab.grid.icosphere#

IcosphereGlobalGrid class

Greg Tucker, CU Boulder, November 2023

class IcosphereGlobalGrid(radius=1.0, mesh_densification_level=0)[source]#

Bases: DualIcosphereGraph, ModelGrid

Icosphere-based quasi-spherical (“global”) Landlab grid.

The default configuration is a spherical grid of unit radius that forms the spherical version an icosahedron (20 triangular patches, 12 nodes), with the dual complement representing a dodecahedron (12 hexagonal cells, 20 corners). The mesh_densification_level parameter allows you to densify this initial shape by subdividing each triangular patch into four triangles, with corresponding addition of nodes (as the triangle vertices), together with corresponding cells and corners.

If you specify mesh_densification_level=1, you get a soccer ball: a combination of pentagons and hexagons as cells. Further densification produces more hexagons (as cells; the patches are always triangles).

Because there are no boundaries, there is a 1:1 relationship between nodes and cells (every node has a cell, and the ID of every cell is the same as the ID of its node), and similarly for corners and patches.

Link length is calculated as the arc-length of the sphere between the link’s two nodes. Patch area is calculated as the spherical (not flat) triangle area. Cell area is calculated by summing spherical triangles (pentagonal cell area is the sum of 5 triangles within the pentagon, while hexagonal cell area is the sum of 6 triangles).

Topography relative to the sphere can be created by adding a field that represents height relative to the sphere’s surface. Topography could, if desired, be configured to represent an ellipsoid or geoid surface, but the patch/cell areas and link/face lengths are computed as if on a sphere (i.e., the present version of the component does not include algorithms to calculate distances or areas on an ellipsoid).

The grid-generation and refinement algorithms are implemented in the superclass DualIcosphereGraph.

Parameters:
  • radius (float, optional) – Radius of the icosphere (default 1)

  • mesh_densification_level (int, optional) – Number of times to densify the initial icosahedron (default 0)

Examples

>>> ico = IcosphereGlobalGrid()
>>> ico.number_of_nodes
12
>>> ico.number_of_patches
20
>>> ico.number_of_corners
20
>>> ico.number_of_cells
12

Initialize the IcosphereGlobalGrid

BAD_INDEX = -1#

Indicates a node is bad index.

Indicates a link is active, and can carry flux

Indicates a link has a fixed gradient value, and behaves as a boundary

Indicates a link is inactive, and cannot carry flux

BC_NODE_IS_CLOSED = 4#

Indicates a boundary node is closed

BC_NODE_IS_CORE = 0#

Indicates a node is core.

BC_NODE_IS_FIXED_GRADIENT = 2#

Indicates a boundary node has a fixed gradient.

BC_NODE_IS_FIXED_VALUE = 1#

Indicates a boundary node has a fixed value.

BC_NODE_IS_LOOPED = 3#

Indicates a boundary node is wrap-around.

VALID_LOCATIONS = ('node', 'link', 'patch', 'corner', 'face', 'cell', 'grid')#

Grid elements on which fields can be placed.

__getitem__(name)#

Get the collection of fields from the named group.

__init__(radius=1.0, mesh_densification_level=0)[source]#

Initialize the IcosphereGlobalGrid

property active_adjacent_nodes_at_node#

Adjacent nodes for each grid node.

For each grid node, get the adjacent nodes ordered counterclockwise starting from the positive x axis.

Examples

>>> from landlab import RasterModelGrid, HexModelGrid
>>> grid = RasterModelGrid((4, 5))
>>> grid.active_adjacent_nodes_at_node[(-1, 6, 2),]
array([[-1, -1, -1, -1],
       [ 7, 11,  5,  1],
       [-1,  7, -1, -1]])

Setting a node to closed causes all links touching it to be inactive.

>>> grid.status_at_node[6] = grid.BC_NODE_IS_CLOSED
>>> grid.active_adjacent_nodes_at_node[(-1, 6, 2),]
array([[-1, -1, -1, -1],
       [-1, -1, -1, -1],
       [-1,  7, -1, -1]])
>>> grid.active_adjacent_nodes_at_node[7]
array([ 8, 12, -1,  2])
>>> grid.active_adjacent_nodes_at_node[2]
array([-1,  7, -1, -1])
>>> grid = HexModelGrid((3, 2))
>>> grid.status_at_node[0] = grid.BC_NODE_IS_CLOSED
>>> grid.active_adjacent_nodes_at_node
array([[-1, -1, -1, -1, -1, -1],
       [-1,  3, -1, -1, -1, -1],
       [ 3, -1, -1, -1, -1, -1],
       [ 4,  6,  5,  2, -1,  1],
       [-1,  3, -1, -1, -1, -1],
       [-1, -1,  3, -1, -1, -1],
       [-1,  3, -1, -1, -1, -1]])
property active_faces#

Get array of active faces.

Examples

>>> from landlab import RasterModelGrid
>>> grid = RasterModelGrid((3, 4))
>>> grid.active_faces
array([0, 1, 2, 3, 4, 5, 6])
>>> grid.status_at_node[6] = grid.BC_NODE_IS_CLOSED
>>> grid.active_faces
array([0, 2, 5])

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. Note that inactive links have a value of 0, but active and fixed links are both reported normally.

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 import RasterModelGrid
>>> grid = RasterModelGrid((3, 4))
.--->.--->.--->.
^    ^    ^    ^
|    |    |    |
.--->5--->.--->.
^    ^    ^    ^
|    |    |    |
.--->.--->.--->.
>>> grid.active_link_dirs_at_node[5]
array([-1, -1,  1,  1], dtype=int8)

If we set the nodes along the left edge to be closed, the links attached to those nodes become inactive and so their directions are reported as 0.

>>> grid.status_at_node[grid.nodes_at_left_edge] = grid.BC_NODE_IS_CLOSED
>>> grid.active_link_dirs_at_node
array([[ 0,  0,  0,  0], [ 0, -1,  0,  0], [ 0, -1,  0,  0], [ 0,  0,  0,  0],
       [ 0,  0,  0,  0], [-1, -1,  0,  1], [-1, -1,  1,  1], [ 0,  0,  1,  0],
       [ 0,  0,  0,  0], [ 0,  0,  0,  1], [ 0,  0,  0,  1], [ 0,  0,  0,  0]],
       dtype=int8)

Get array of active links.

Examples

>>> from landlab import RasterModelGrid
>>> grid = RasterModelGrid((3, 4))
>>> grid.active_links
array([ 4,  5,  7,  8,  9, 11, 12])
add_empty(name, at='node', units='-', clobber=False)#

Create and add an uninitialized array of values to the field.

Create a new array of the data field size, without initializing entries, and add it to the field as name. The units keyword gives the units of the new fields as a string. Remaining keyword arguments are the same as that for the equivalent numpy function.

This method is not valid for the group grid.

Parameters:
  • name (str) – Name of the new field to add.

  • at (str, optional) – Grid location to store values. If not given, values are assumed to be on node.

  • units (str, optional) – Optionally specify the units of the field.

  • clobber (bool, optional) – Raise an exception if adding to an already existing field.

Returns:

A reference to the newly-created array.

Return type:

numpy.ndarray

See also

numpy.empty

See for a description of optional keywords.

empty

Equivalent method that does not initialize the new array.

zeros

Equivalent method that initializes the data to 0.

add_field(name, value_array, at='node', units='-', copy=False, clobber=False)#

Add an array of values to the field.

Add an array of data values to a collection of fields and associate it with the key, name. Use the copy keyword to, optionally, add a copy of the provided array.

In the case of adding to the collection grid, the added field is a numpy scalar rather than a numpy array.

Parameters:
  • name (str) – Name of the new field to add.

  • value_array (numpy.array) – Array of values to add to the field.

  • at (str, optional) – Grid location to store values. If not given, values are assumed to be on node.

  • units (str, optional) – Optionally specify the units of the field.

  • copy (bool, optional) – If True, add a copy of the array to the field. Otherwise save add a reference to the array.

  • clobber (bool, optional) – Raise an exception if adding to an already existing field.

Returns:

The data array added to the field. Depending on the copy keyword, this could be a copy of value_array or value_array itself.

Return type:

numpy.ndarray

Raises:

ValueError – If value_array has a size different from the field.

Examples

>>> import numpy as np
>>> from landlab.field import GraphFields
>>> field = GraphFields()
>>> field.new_field_location("node", 4)
>>> values = np.ones(4, dtype=int)
>>> field.add_field("topographic__elevation", values, at="node")
array([1, 1, 1, 1])

A new field is added to the collection of fields. The saved value array is the same as the one initially created.

>>> field.at_node["topographic__elevation"] is values
True

If you want to save a copy of the array, use the copy keyword. In addition, adding values to an existing field will remove the reference to the previously saved array. The clobber=False keyword changes this behavior to raise an exception in such a case.

>>> field.add_field(
...     "topographic__elevation", values, at="node", copy=True, clobber=True
... )
array([1, 1, 1, 1])
>>> field.at_node["topographic__elevation"] is values
False
>>> field.add_field("topographic__elevation", values, at="node", clobber=False)
Traceback (most recent call last):
...
FieldError: topographic__elevation
add_full(*args, **kwds)#

Create and add an array of values, fill with fill_value.

Parameters:
  • name (str) – Name of the new field to add.

  • fill_value (scalar) – Fill value.

  • at (str, optional) – Grid location to store values. If not given, values are assumed to be on node.

  • units (str, optional) – Optionally specify the units of the field.

  • copy (bool, optional) – If True, add a copy of the array to the field. Otherwise save add a reference to the array.

  • clobber (bool, optional) – Raise an exception if adding to an already existing field.

Returns:

A reference to the newly-created array.

Return type:

numpy.ndarray

add_ones(name, at='node', units='-', clobber=False)#

Create and add an array of values, initialized to 1, to the field.

Create a new array of the data field size, filled with ones, and add it to the field as name. The units keyword gives the units of the new fields as a string. Remaining keyword arguments are the same as that for the equivalent numpy function.

This method is not valid for the group grid.

Parameters:
  • name (str) – Name of the new field to add.

  • at (str, optional) – Grid location to store values. If not given, values are assumed to be on node.

  • units (str, optional) – Optionally specify the units of the field.

  • clobber (bool, optional) – Raise an exception if adding to an already existing field.

Returns:

A reference to the newly-created array.

Return type:

numpy.ndarray

See also

numpy.ones

See for a description of optional keywords.

add_empty

Equivalent method that does not initialize the new array.

add_zeros

Equivalent method that initializes the data to 0.

Examples

Add a new, named field to a collection of fields.

>>> from landlab.field import GraphFields
>>> field = GraphFields()
>>> field.new_field_location("node", 4)
>>> field.add_ones("topographic__elevation", at="node")
array([1., 1., 1., 1.])
>>> list(field.keys("node"))
['topographic__elevation']
>>> field["node"]["topographic__elevation"]
array([1., 1., 1., 1.])
>>> field.at_node["topographic__elevation"]
array([1., 1., 1., 1.])
add_zeros(name, at='node', units='-', clobber=False)#

Create and add an array of values, initialized to 0, to the field.

Create a new array of the data field size, filled with zeros, and add it to the field as name. The units keyword gives the units of the new fields as a string. Remaining keyword arguments are the same as that for the equivalent numpy function.

Parameters:
  • name (str) – Name of the new field to add.

  • at (str, optional) – Grid location to store values. If not given, values are assumed to be on node.

  • units (str, optional) – Optionally specify the units of the field.

  • clobber (bool, optional) – Raise an exception if adding to an already existing field.

Returns:

A reference to the newly-created array.

Return type:

array

See also

numpy.zeros

See for a description of optional keywords.

add_empty

Equivalent method that does not initialize the new array.

add_ones

Equivalent method that initializes the data to 1.

property all_node_azimuths_map#

Get azimuths from every node to every other node.

Examples

>>> import numpy as np
>>> from landlab import RasterModelGrid
>>> grid = RasterModelGrid((3, 4))
>>> angles = grid.all_node_azimuths_map

The shape of the array is number_of_nodes by number_of_nodes and azimuth from a node to itself is zero.

>>> angles.shape == (grid.number_of_nodes, grid.number_of_nodes)
True
>>> angles.diagonal()
array([0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])

Angles are measured in radians and increase clockwise starting at north.

>>> angles *= 180.0 / np.pi
>>> angles[0, :4]
array([ 0.,  90.,  90.,  90.])
>>> angles[0, ::4]
array([0.,  0.,  0.])
>>> angles[0, ::5]
array([ 0.,  45.,  45.])
property all_node_distances_map#

Get distances from every node to every other node.

Examples

>>> from landlab import RasterModelGrid
>>> grid = RasterModelGrid((3, 4))
>>> distances = grid.all_node_distances_map

The shape of the array is number_of_nodes by number_of_nodes and distance from a node to itself is zero.

>>> distances.shape == (grid.number_of_nodes, grid.number_of_nodes)
True
>>> distances.diagonal()
array([0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])

The distances from the first node to all nodes in its row and all the nodes in its column.

>>> distances[0, :4]
array([0.,  1.,  2.,  3.])
>>> distances[0, ::4]
array([0.,  1.,  2.])

Find and return the angle of a link about the node at the link head.

Because links have direction, their angle can be specified as an angle about either the node at the link head, or the node at the link tail. The default behaviour of angle_of_link is to return the angle about the link tail, but this method gives the angle about the link head.

Examples

>>> from landlab import HexModelGrid
>>> import numpy as np
>>> grid = HexModelGrid((3, 2), node_layout="hex")
>>> np.round(grid.angle_of_link[:3] / np.pi * 3.0)
array([0., 2.,  1.])
>>> np.round(grid.angle_of_link_about_head[:3] / np.pi * 3.0)  # 60 deg segments
array([3.,  5.,  4.])
property area_of_patch#
as_dataarray(name, at=None, time=None)#

Create an xarray DataArray representation of a grid field.

Parameters:
  • name (str) – Name of a field. This can either be a canonical field name (of the form “at_<element>:<field_name>”, or just the field name. In the latter case, use the at keyword to specify where the field is defined.

  • at (str, optional) – The grid elements on which the field is defined. Use this only if name is not a canonical field name that already contains the grid element.

Returns:

The field represented as a newly-created xarray DataArray.

Return type:

DataArray

Examples

>>> from landlab import RasterModelGrid
>>> grid = RasterModelGrid((3, 4))
>>> _ = grid.add_full("elevation", 3.0, at="node")
>>> grid.as_dataarray("at_node:elevation")
<xarray.DataArray 'at_node:elevation' (node: 12)>
array([3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.])
Dimensions without coordinates: node
>>> all(
...     grid.as_dataarray("at_node:elevation")
...     == grid.as_dataarray("elevation", at="node")
... )
True
as_dataset(include='*', exclude=None, time=None)#

Create an xarray Dataset representation of a grid.

This method creates a new xarray Dataset object that contains the grid’s data fields. A particular grid type (e.g. a RasterModelGrid) should define its own as_dataset method to represent that particular grid type as a Dataset and then call this method to add the data fields.

Parameters:
  • include (str or iterable or str) – Glob-style patterns of fields to include in the dataset.

  • exclude (str or iterable or str) – Glob-style patterns of fields to exclude from the dataset.

Returns:

An xarray Dataset representation of a ModelGrid.

Return type:

Dataset

Examples

>>> from landlab import RasterModelGrid
>>> grid = RasterModelGrid((3, 4))

Add some fields to the grid. Notice that we have defined a field named “elevation” at both nodes and links.

>>> _ = grid.add_full("elevation", 3.0, at="node")
>>> _ = grid.add_full("elevation", 4.0, at="link")
>>> _ = grid.add_full("temperature", 5.0, at="node")
>>> ds = grid.as_dataset()
>>> sorted(ds.sizes.items())
[('dim', 2), ('link', 17), ('node', 12)]
>>> sorted([var for var in ds.data_vars if var.startswith("at_")])
['at_link:elevation', 'at_node:elevation', 'at_node:temperature']
>>> grid.event_layers.add(1.0, rho=0.5)
>>> ds = grid.as_dataset()
>>> sorted(ds.sizes.items())
[('cell', 2), ('dim', 2), ('layer', 1), ('link', 17), ('node', 12)]
>>> sorted([var for var in ds.data_vars if var.startswith("at_")])
['at_layer_cell:rho', 'at_layer_cell:thickness', 'at_link:elevation',
 'at_node:elevation', 'at_node:temperature']
at_cell = {}#
at_corner = {}#
at_face = {}#
at_grid = {}#
property at_layer#

EventLayers for each cell.

at_node = {}#
at_patch = {}#
property axis_name#

Get the name of each coordinate axis.

Returns:

The names of each axis.

Return type:

tuple of str

Examples

>>> from landlab import RasterModelGrid
>>> grid = RasterModelGrid((4, 5))
>>> grid.axis_name
('x', 'y')
>>> grid.axis_name = ("lon", "lat")
>>> grid.axis_name
('lon', 'lat')
property axis_units#

Get units for each axis.

Returns:

The units (as a string) for each of a grid’s coordinates.

Return type:

tuple of str

Examples

>>> from landlab import RasterModelGrid
>>> mg = RasterModelGrid((4, 5), xy_spacing=(3.0, 2.0))
>>> mg.axis_units
('-', '-')
>>> mg.axis_units = ("degrees_north", "degrees_east")
>>> mg.axis_units
('degrees_north', 'degrees_east')
>>> mg.axis_units = "m"
>>> mg.axis_units
('m', 'm')
property boundary_nodes#

Get array of boundary nodes.

Examples

>>> from landlab import RasterModelGrid
>>> mg = RasterModelGrid((4, 5))
>>> mg.boundary_nodes
array([ 0,  1,  2,  3,  4,  5,  9, 10, 14, 15, 16, 17, 18, 19])
calc_aspect_at_node(slope_component_tuple=None, elevs='topographic__elevation', unit='degrees', ignore_closed_nodes=True)#

Get array of aspect of a surface.

Calculates at returns the aspect of a surface. Aspect is returned as radians clockwise of north, unless input parameter units is set to ‘degrees’.

If slope_component_tuple is provided, i.e., (slope_x, slope_y), the aspect will be calculated from these data.

If it is not, it will be derived from elevation data at the nodes, which can either be a string referring to a grid field (default: ‘topographic__elevation’), or an nnodes-long numpy array of the values themselves.

If ignore_closed_nodes is False, all proximal elevation values will be used in the calculation. If True, only unclosed nodes are used.

Parameters:
  • slope_component_tuple ((slope_x_array, slope_y_array) (optional)) – Tuple of components of slope in the x and y directions, defined on nodes, if already known. If not, provide elevs.

  • elevs (str or array (optional)) – Node field name or node array of elevations. If slope_component_tuple is not provided, must be set, but unused otherwise.

  • unit ({'degrees', 'radians'}) – Controls the unit that the aspect is returned as.

  • ignore_closed_nodes (bool) – If True, do not incorporate values at closed nodes into the calc.

Examples

>>> from landlab import RasterModelGrid
>>> mg = RasterModelGrid((4, 4))
>>> z = mg.node_x**2 + mg.node_y**2
>>> mg.calc_aspect_at_node(elevs=z)
array([225.        ,  240.16585039,  255.2796318 ,  258.69006753,
       209.83414961,  225.        ,  243.54632481,  248.77808974,
       194.7203682 ,  206.45367519,  225.        ,  231.94498651,
       191.30993247,  201.22191026,  218.05501349,  225.        ])
>>> z = z.max() - z
>>> mg.calc_aspect_at_node(elevs=z)
array([45.        ,  60.16585039,  75.2796318 ,  78.69006753,
       29.83414961,  45.        ,  63.54632481,  68.77808974,
       14.7203682 ,  26.45367519,  45.        ,  51.94498651,
       11.30993247,  21.22191026,  38.05501349,  45.        ])
>>> mg = RasterModelGrid((4, 4), xy_spacing=(3.0, 2.0))
>>> z = mg.node_x**2 + mg.node_y**2
>>> mg.calc_aspect_at_node(elevs=z)
array([236.30993247,  247.52001262,  259.97326008,  262.40535663,
       220.75264634,  234.41577266,  251.13402374,  255.29210302,
       201.54258265,  215.47930877,  235.73541937,  242.24162456,
       196.69924423,  209.43534223,  229.19345757,  236.30993247])

Note that a small amount of asymmetry arises at the grid edges due to the “missing” nodes beyond the edge of the grid.

Calculate differences of node values over links.

Calculates the difference in quantity node_values at each link in the grid.

Parameters:
  • node_values (ndarray or field name) – Values at grid nodes.

  • out (ndarray, optional) – Buffer to hold the result.

Returns:

Differences across links.

Return type:

ndarray

Examples

>>> import numpy as np
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 3))
>>> z = np.zeros(9)
>>> z[4] = 1.0
>>> rmg.calc_diff_at_link(z)
array([ 0.,  0.,  0.,  1.,  0.,  1., -1.,  0., -1.,  0.,  0.,  0.])
calc_distances_of_nodes_to_point(coord, get_az=None, node_subset=None, out_distance=None, out_azimuth=None)#

Get distances for nodes to a given point.

Returns an array of distances for each node to a provided point. If “get_az” is set to ‘angles’, returns both the distance array and an array of azimuths from up/north. If it is set to ‘displacements’, it returns the azimuths as a 2xnnodes array of x and y displacements. If it is not set, returns just the distance array.

If “node_subset” is set as an ID, or list/array/etc of IDs method returns just the distance (and optionally azimuth) for that node. Point is provided as a tuple (x,y).

If out_distance (& out_azimuth) are provided, these arrays are used to store the outputs. This is recommended for memory management reasons if you are working with node subsets.

Note

Angles are returned in radians but measured clockwise from north.

Parameters:
  • coord (tuple of float) – Coodinates of point as (x, y).

  • get_az ({None, 'angles', 'displacements'}, optional) – Optionally calculate azimuths as either angles or displacements. The calculated values will be returned along with the distances as the second item of a tuple.

  • node_subset (array_like, optional) – Calculate distances on a subset of grid nodes. The default is to calculate distances from the provided points to all nodes.

  • out_distance (array_like, optional) – If provided, put the calculated distances here. Otherwise, create a new array.

  • out_azimuth (array_like, optional) – If provided, put the calculated distances here. Otherwise, create a new array.

Returns:

If get_az is None return the array of distances. Otherwise, return a tuple of distances and azimuths.

Return type:

ndarray or tuple of ndarray

Notes

Once you start working with node subsets in Landlab, which can change size between loops, it’s quite possible for Python’s internal memory management to crap out after large numbers of loops (~>10k). This is to do with the way it block allocates memory for arrays of differing lengths, then cannot free this memory effectively. The solution - as implemented here - is to pre-allocate all arrays as nnodes long, then only work with the first [len_subset] entries by slicing (in a pseudo-C-style). Care has to be taken not to “accidentally” allow Python to allocate a new array you don’t have control over. Then, to maintain efficient memory allocation, we create some “dummy” nnode-long arrays to store intermediate parts of the solution in.

Examples

>>> from landlab import RasterModelGrid
>>> grid = RasterModelGrid((4, 5))

Calculate distances from point at (2., 1.) to a subset of nodes on the grid.

>>> grid.calc_distances_of_nodes_to_point((2, 1), node_subset=(2, 6, 7, 8, 12))
array([1.,  1.,  0.,  1.,  1.])

Calculate distances from a point to all nodes on the grid.

>>> dist = grid.calc_distances_of_nodes_to_point((2, 1))
>>> dist.shape == (grid.number_of_nodes,)
True
>>> dist.take((2, 6, 7, 8, 12))
array([1.,  1.,  0.,  1.,  1.])

Put the distances into a buffer.

>>> out = np.empty(grid.number_of_nodes, dtype=float)
>>> dist = grid.calc_distances_of_nodes_to_point((2, 1), out_distance=out)
>>> out is dist
True
>>> out.take((2, 6, 7, 8, 12))
array([1.,  1.,  0.,  1.,  1.])

Calculate azimuths along with distances. The azimuths are calculated in radians but measured clockwise from north.

>>> (_, azim) = grid.calc_distances_of_nodes_to_point((2, 1), get_az="angles")
>>> azim.take((2, 6, 7, 8, 12)) * 180.0 / np.pi
array([180.,  270.,    0.,   90.,    0.])
>>> (_, azim) = grid.calc_distances_of_nodes_to_point(
...     (2, 1), get_az="angles", node_subset=(1, 3, 11, 13)
... )
>>> azim * 180.0 / np.pi
array([225.,  135.,  315.,   45.])

When calculating displacements, the first row contains displacements in x and the second displacements in y.

>>> (_, azim) = grid.calc_distances_of_nodes_to_point(
...     (2, 1), get_az="displacements", node_subset=(2, 6, 7, 8, 12)
... )
>>> azim
array([[ 0., -1.,  0.,  1.,  0.],
       [-1.,  0.,  0.,  0.,  1.]])
calc_flux_div_at_cell(unit_flux, out=None)#

Calculate divergence of link-based fluxes at cells.

This function is very similar to the function calc_flux_div_at_node.

Given a flux per unit width across each cell face in the grid, calculate the net outflux (or influx, if negative) divided by cell area, at each cell.

Parameters:

unit_flux_at_links_across_faces (ndarray or field name) – Flux per unit width along links at faces (x number of faces) or link field.

Returns:

Flux divergence at cells.

Return type:

ndarray (x number of cells)

Examples

>>> from landlab import RasterModelGrid
>>> from landlab.grid.divergence import calc_flux_div_at_cell
>>> rg = RasterModelGrid((3, 4), xy_spacing=10.0)
>>> import numpy as np
>>> z = rg.add_zeros("topographic__elevation", at="node")
>>> z[5] = 50.0
>>> z[6] = 36.0
>>> lg = rg.calc_grad_at_link(z)  # there are 17 links
>>> lg
array([ 0. ,  0. ,  0. ,  0. ,  5. ,  3.6,  0. ,  5. , -1.4, -3.6,  0. ,
       -5. , -3.6,  0. ,  0. ,  0. ,  0. ])
>>> fg = lg[rg.link_at_face]  # there are 7 faces
>>> fg
array([ 5. ,  3.6,  5. , -1.4, -3.6, -5. , -3.6])
>>> calc_flux_div_at_cell(rg, -fg)
array([1.64,  0.94])
>>> rg.set_status_at_node_on_edges(right=rg.BC_NODE_IS_CLOSED)
>>> rg.set_status_at_node_on_edges(top=rg.BC_NODE_IS_CLOSED)
>>> unit_flux_at_faces = np.zeros(rg.number_of_faces)
>>> unit_flux_at_faces[rg.active_faces] = -fg[rg.active_faces]
>>> calc_flux_div_at_cell(rg, unit_flux_at_faces)
array([1.14,  0.22])
>>> _ = rg.add_field("neg_grad_at_link", -lg, at="link")
>>> calc_flux_div_at_cell(rg, "neg_grad_at_link")
array([1.64,  0.94])

Notes

Performs a numerical flux divergence operation at cells.

calc_flux_div_at_node(unit_flux, out=None)#

Calculate divergence of link-based fluxes at nodes.

Given a flux per unit width across each face in the grid, calculate the net outflux (or influx, if negative) divided by cell area, at each node (zero or “out” value for nodes without cells).

Parameters:

unit_flux (ndarray or field name) – Flux per unit width along links (x number of links).

Returns:

Flux divergence at nodes.

Return type:

ndarray (x number of nodes)

Examples

>>> from landlab import RasterModelGrid
>>> from landlab.grid.divergence import calc_flux_div_at_node
>>> rg = RasterModelGrid((3, 4), xy_spacing=10.0)
>>> z = rg.add_zeros("topographic__elevation", at="node")
>>> z[5] = 50.0
>>> z[6] = 36.0
>>> lg = rg.calc_grad_at_link(z)  # there are 17 links
>>> lg
array([ 0. ,  0. ,  0. ,  0. ,  5. ,  3.6,  0. ,  5. , -1.4, -3.6,  0. ,
       -5. , -3.6,  0. ,  0. ,  0. ,  0. ])
>>> calc_flux_div_at_node(rg, -lg)
array([0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  1.64,  0.94,  0.  ,  0.  ,
       0.  ,  0.  ,  0.  ])
>>> rg.set_status_at_node_on_edges(right=rg.BC_NODE_IS_CLOSED)
>>> rg.set_status_at_node_on_edges(top=rg.BC_NODE_IS_CLOSED)
>>> unit_flux_at_links = np.zeros(rg.number_of_links)
>>> unit_flux_at_links[rg.active_links] = -lg[rg.active_links]
>>> calc_flux_div_at_node(rg, unit_flux_at_links)
array([0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  1.14,  0.22,  0.  ,  0.  ,
       0.  ,  0.  ,  0.  ])
>>> _ = rg.add_field("neg_grad_at_link", -lg, at="link")
>>> calc_flux_div_at_node(rg, "neg_grad_at_link")
array([0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  1.64,  0.94,  0.  ,  0.  ,
       0.  ,  0.  ,  0.  ])

Notes

Performs a numerical flux divergence operation on nodes.

Calculate gradients of node values at links.

Calculates the gradient in node_values at each link in the grid, returning an array of length number_of_links.

Parameters:
  • node_values (ndarray or field name (x number of nodes)) – Values at grid nodes.

  • out (ndarray, optional (x number of links)) – Buffer to hold the result.

Returns:

Gradients across active links.

Return type:

ndarray

Examples

>>> from landlab import RasterModelGrid
>>> rg = RasterModelGrid((3, 4), xy_spacing=10.0)
>>> z = rg.add_zeros("topographic__elevation", at="node")
>>> z[5] = 50.0
>>> z[6] = 36.0
>>> calc_grad_at_link(rg, z)  # there are 17 links
array([ 0. ,  0. ,  0. ,  0. ,  5. ,  3.6,  0. ,  5. , -1.4, -3.6,  0. ,
       -5. , -3.6,  0. ,  0. ,  0. ,  0. ])
>>> from landlab import HexModelGrid
>>> hg = HexModelGrid((3, 3), spacing=10.0)
>>> z = hg.add_zeros("topographic__elevation", at="node", clobber=True)
>>> z[4] = 50.0
>>> z[5] = 36.0
>>> calc_grad_at_link(hg, z)  # there are 11 faces
array([ 0. ,  0. ,  0. ,  5. ,  5. ,  3.6,  3.6,  0. ,  5. , -1.4, -3.6,
        0. , -5. , -5. , -3.6, -3.6,  0. ,  0. ,  0. ])
calc_grad_at_patch(elevs='topographic__elevation', ignore_closed_nodes=True, unit_normal=None, slope_magnitude=None)#

Calculate the components of the gradient at each patch.

If ignore_closed_nodes is True, closed nodes do not affect gradient calculations. If a closed node is present in a patch, the patch gradient is set to zero in both x and y directions.

Parameters:
  • elevs (str or ndarray, optional) – Field name or array of node values.

  • ignore_closed_nodes (bool) – If True, do not incorporate values at closed nodes into the calc.

  • unit_normal (array with shape (num_patches, 3) (optional)) – The unit normal vector to each patch, if already known.

  • slope_magnitude (array with size num_patches (optional)) – The slope of each patch, if already known.

Returns:

gradient_tuple – Len-2 tuple of arrays giving components of gradient in the x and y directions, in the units of units.

Return type:

(x_component_at_patch, y_component_at_patch)

Examples

>>> import numpy as np
>>> from landlab import RasterModelGrid
>>> mg = RasterModelGrid((4, 5))
>>> z = mg.node_y
>>> (x_grad, y_grad) = mg.calc_grad_at_patch(elevs=z)
>>> np.allclose(y_grad, np.pi / 4.0)
True
>>> np.allclose(x_grad, 0.0)
True
calc_hillshade_at_node(alt=45.0, az=315.0, slp=None, asp=None, unit='degrees', elevs='topographic__elevation')#

Get array of hillshade.

Code author: Katy Barnhart <katherine.barnhart@colorado.edu>

Parameters:
  • alt (float) – Sun altitude (from horizon) - defaults to 45 degrees

  • az (float) – Sun azimuth (CW from north) - defaults to 315 degrees

  • slp (float) – slope of cells at surface - optional

  • asp (float) – aspect of cells at surface (from north) - optional (with slp)

  • unit (string) –

    ‘degrees’ (default) or ‘radians’ - only needed if slp and asp

    are not provided

  • specified (If slp and asp are both not) –

  • as ('elevs' must be provided) –

  • an (a grid field name (defaults to 'topographic__elevation') or) –

  • case (nnodes-long array of elevation values. In this) –

  • will (the method) –

  • hillshade (calculate local slopes and aspects internally as part of the) –

  • production.

Returns:

Hillshade at each cell.

Return type:

ndarray of float

Notes

code taken from GeospatialPython.com example from December 14th, 2014 DEJH found what looked like minor sign problems, and adjusted to follow the ArcGIS algorithm <http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html#/How_Hillshade_works/009z000000z2000000/>.

Remember when plotting that bright areas have high values. cmap=’Greys’ will give an apparently inverted color scheme. cmap=’gray’ has white associated with the high values, so is recommended for plotting.

Examples

>>> import numpy as np
>>> from landlab import RasterModelGrid
>>> mg = RasterModelGrid((5, 5), xy_spacing=1.0)
>>> z = mg.x_of_node * np.tan(60.0 * np.pi / 180.0)
>>> mg.calc_hillshade_at_node(elevs=z, alt=30.0, az=210.0)
array([0.625,  0.625,  0.625,  0.625,  0.625,  0.625,  0.625,  0.625,
       0.625,  0.625,  0.625,  0.625,  0.625,  0.625,  0.625,  0.625,
       0.625,  0.625,  0.625,  0.625,  0.625,  0.625,  0.625,  0.625,
       0.625])
calc_net_flux_at_node(unit_flux_at_links, out=None)#

Calculate net link fluxes at nodes.

Given a flux per unit width along each link in the grid, calculate the net outflux (or influx, if negative) at each node. Fluxes are treated as zero for links that have no faces, and net fluxes are treated as zero for nodes that have no cell.

Parameters:
  • unit_flux_at_links (ndarray or field name) – Flux per unit width associated with links.

  • out (ndarray, optional) – Buffer to hold the result.

Returns:

Net flux at nodes.

Return type:

ndarray (x number of cells)

Examples

>>> from landlab import RasterModelGrid
>>> rg = RasterModelGrid((3, 4), xy_spacing=10.0)
>>> z = rg.add_zeros("topographic__elevation", at="node")
>>> z[5] = 50.0
>>> z[6] = 36.0
>>> lg = rg.calc_grad_at_link(z)  # there are 17 links
>>> lg
array([ 0. ,  0. ,  0. ,  0. ,  5. ,  3.6,  0. ,  5. , -1.4, -3.6,  0. ,
       -5. , -3.6,  0. ,  0. ,  0. ,  0. ])
>>> calc_net_flux_at_node(rg, -lg)
array([   0.,    0.,    0.,    0.,    0.,  164.,   94.,    0.,    0.,
          0.,    0.,    0.])
>>> rg.set_status_at_node_on_edges(right=rg.BC_NODE_IS_CLOSED)
>>> rg.set_status_at_node_on_edges(top=rg.BC_NODE_IS_CLOSED)
>>> unit_flux_at_links = np.zeros(rg.number_of_links)
>>> unit_flux_at_links[rg.active_links] = -lg[rg.active_links]
>>> nlfn = calc_net_flux_at_node(rg, unit_flux_at_links)
>>> np.round(nlfn)
array([   0.,    0.,    0.,    0.,    0.,  114.,   22.,    0.,    0.,
          0.,    0.,    0.])
>>> from landlab import HexModelGrid
>>> hg = HexModelGrid((3, 3), spacing=10.0)
>>> z = hg.add_zeros("topographic__elevation", at="node", clobber=True)
>>> z[4] = 50.0
>>> z[5] = 36.0
>>> lg = hg.calc_grad_at_link(z)  # there are ? links
>>> lg
array([ 0. ,  0. ,  0. ,  5. ,  5. ,  3.6,  3.6,  0. ,  5. , -1.4, -3.6,
        0. , -5. , -5. , -3.6, -3.6,  0. ,  0. ,  0. ])
>>> nlfn = calc_net_flux_at_node(hg, -lg)
>>> np.round(nlfn)
array([   0.,    0.,    0.,    0.,  152.,   96.,    0.,    0.,    0.,    0.])

Notes

This is essentially a line integral for the fluxes along the boundaries of each cell. Hence, the resulting output has dimensions of total flux (so, if the unit flux happens to be mass per time per face width, the output will be in mass per unit time). Because a line integral is undefined where there are no cells (i.e., perimeter nodes), the result is given as zeros for these nodes. The current algorithm uses fancy indexing (calling _calc_net_face_flux_at_cells) and could probably be made faster.

calc_slope_at_node(elevs='topographic__elevation', method='patch_mean', ignore_closed_nodes=True, return_components=False, **kwds)#

Array of slopes at nodes, averaged over neighboring patches.

Produces a value for node slope (i.e., mean gradient magnitude) at each node in a manner analogous to a GIS-style slope map. It averages the gradient on each of the patches surrounding the node, creating a value for node slope that better incorporates nonlocal elevation information. Directional information can still be returned through use of the return_components keyword.

Note that under these definitions, it is not always true that:

mag, cmp = mg.calc_slope_at_node(z)
mag**2 == cmp[0] ** 2 + cmp[1] ** 2  # not always true

If ignore_closed_nodes is False, all proximal elevation values will be used in the calculation. If True, only unclosed nodes are used.

Parameters:
  • elevs (str or ndarray, optional) – Field name or array of node values.

  • method ({'patch_mean', 'Horn'}) – By equivalence to the raster version, 'patch_mean' returns a scalar mean on the patches; 'Horn' returns a vector mean on the patches.

  • ignore_closed_nodes (bool) – If True, do not incorporate values at closed nodes into the calculation.

  • return_components (bool) – If True, return a tuple, (array_of_magnitude, (array_of_slope_x_radians, array_of_slope_y_radians)). If False, return an array of floats of the slope magnitude.

Returns:

If return_components, returns (array_of_magnitude, (array_of_slope_x_radians, array_of_slope_y_radians)). If not return_components, returns an array of slope magnitudes.

Return type:

float array or length-2 tuple of float arrays

Examples

>>> import numpy as np
>>> from landlab import RadialModelGrid, RasterModelGrid
>>> mg = RasterModelGrid((4, 5))
>>> z = mg.node_x
>>> slopes = mg.calc_slope_at_node(elevs=z)
>>> np.allclose(slopes, 45.0 / 180.0 * np.pi)
True
>>> mg = RasterModelGrid((4, 5))
>>> z = -mg.node_y
>>> slope_mag, cmp = mg.calc_slope_at_node(elevs=z, return_components=True)
>>> np.allclose(slope_mag, np.pi / 4.0)
True
>>> np.allclose(cmp[0], 0.0)
True
>>> np.allclose(cmp[1], -np.pi / 4.0)
True
>>> mg = RadialModelGrid(n_rings=3)
>>> z = mg.radius_at_node
>>> slope_at_node = np.round(mg.calc_slope_at_node(elevs=z), decimals=5)
>>> nodes_at_ring = [
...     np.where(np.isclose(mg.radius_at_node, radius)) for radius in range(3)
... ]
>>> slope_at_node[nodes_at_ring[0]]
array([0.85707])
>>> slope_at_node[nodes_at_ring[1]]
array([0.79417,  0.79417,  0.79417,  0.79417,  0.79417,  0.79417])
>>> slope_at_node[nodes_at_ring[2]]
array([0.77542,  0.78453,  0.78453,  0.77542,  0.77542,  0.78453,
       0.78453,  0.77542,  0.77542,  0.78453,  0.78453,  0.77542])
calc_slope_at_patch(elevs='topographic__elevation', ignore_closed_nodes=True, unit_normal=None)#

Calculate the slope (positive magnitude of gradient) at patches.

If ignore_closed_nodes is True, closed nodes do not affect slope calculations. If a closed node is present in a patch, the patch slope is set to zero.

Parameters:
  • elevs (str or ndarray, optional) – Field name or array of node values.

  • ignore_closed_nodes (bool) – If True, do not incorporate values at closed nodes into the calc.

  • unit_normal (array with shape (num_patches, 3) (optional)) – The unit normal vector to each patch, if already known.

Returns:

slopes_at_patch – The slope (positive gradient magnitude) of each patch.

Return type:

n_patches-long array

Examples

>>> import numpy as np
>>> from landlab import RasterModelGrid
>>> mg = RasterModelGrid((4, 5))
>>> z = mg.node_x
>>> S = mg.calc_slope_at_patch(elevs=z)
>>> S.size == mg.number_of_patches
True
>>> np.allclose(S, np.pi / 4.0)
True
calc_unit_normal_at_patch(elevs='topographic__elevation')#

Calculate and return the unit normal vector <a, b, c> to a patch.

Parameters:

elevs (str or ndarray, optional) – Field name or array of node values.

Returns:

nhat – The unit normal vector <a, b, c> to each patch.

Return type:

num-patches x length-3 array

Examples

>>> from landlab import HexModelGrid
>>> mg = HexModelGrid((3, 3))
>>> z = mg.node_x * 3.0 / 4.0
>>> mg.calc_unit_normal_at_patch(z)
array([[-0.6,  0. ,  0.8],
       [-0.6,  0. ,  0.8],
       [-0.6,  0. ,  0.8],
       [-0.6,  0. ,  0.8],
       [-0.6,  0. ,  0.8],
       [-0.6,  0. ,  0.8],
       [-0.6,  0. ,  0.8],
       [-0.6,  0. ,  0.8],
       [-0.6,  0. ,  0.8],
       [-0.6,  0. ,  0.8]])
property cell_area_at_node#

Cell areas in a nnodes-long array.

Zeros are entered at all perimeter nodes, which lack cells.

Returns:

Cell areas as an n_nodes-long array.

Return type:

ndarray

Examples

>>> from landlab import RasterModelGrid
>>> grid = RasterModelGrid((4, 5), xy_spacing=(3, 4))
>>> grid.status_at_node[7] = grid.BC_NODE_IS_CLOSED
>>> grid.cell_area_at_node
array([  0.,   0.,   0.,   0.,   0.,
         0.,  12.,  12.,  12.,   0.,
         0.,  12.,  12.,  12.,   0.,
         0.,   0.,   0.,   0.,   0.])
property cell_at_node#
property closed_boundary_nodes#

Get array of closed boundary nodes.

Examples

>>> from landlab import RasterModelGrid
>>> mg = RasterModelGrid((4, 5))
>>> mg.status_at_node[mg.nodes_at_top_edge] = mg.BC_NODE_IS_CLOSED
>>> mg.closed_boundary_nodes
array([15, 16, 17, 18, 19])
property core_cells#

Get array of core cells.

Examples

>>> from landlab import RasterModelGrid
>>> grid = RasterModelGrid((4, 5))

Initially all of the cells are “core”.

>>> grid.core_cells
array([0, 1, 2,
       3, 4, 5])

Setting a node to closed causes its cell to no longer be core.

>>> grid.status_at_node[8] = grid.BC_NODE_IS_CLOSED
>>> grid.core_cells
array([0, 1, 3, 4, 5])
property core_nodes#

Get array of core nodes.

Examples

>>> from landlab import RasterModelGrid
>>> mg = RasterModelGrid((4, 5))
>>> mg.core_nodes
array([ 6,  7,  8, 11, 12, 13])
property corners_at_cell#
property default_group#

Return the name of the group into which fields are put by default.

delete_field(loc, name)#

Erases an existing field.

Parameters:
  • loc (str) – Name of the group.

  • name (str) – Name of the field.

Raises:

KeyError – If the named field does not exist.

Return an (nnodes, X) shape array of link IDs of which links are downwind of each node, according to values (array or field).

X is the maximum downwind links at any node. Nodes with fewer downwind links than this have additional slots filled with bad_index. Links are ordered anticlockwise from east.

Parameters:
  • values (str or array) – Name of variable field defined at links, or array of values at links.

  • bad_index (int) – Index to place in array indicating no link.

Returns:

Array of upwind link IDs

Return type:

ndarray

Examples

>>> import numpy as np
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> rmg.at_link["grad"] = [
...     -1.0,
...     -2.0,
...     -1.0,
...     -2.0,
...     -3.0,
...     -4.0,
...     -5.0,
...     -1.0,
...     -2.0,
...     -1.0,
...     -1.0,
...     -2.0,
...     -3.0,
...     -4.0,
...     -1.0,
...     -2.0,
...     -1.0,
... ]
>>> rmg.downwind_links_at_node("grad", bad_index=rmg.BAD_INDEX)
array([[ 0,  3],
       [ 1,  4],
       [ 2,  5],
       [ 6, -1],
       [ 7, 10],
       [ 8, 11],
       [ 9, 12],
       [13, -1],
       [14, -1],
       [15, -1],
       [16, -1],
       [-1, -1]])
empty(*args, **kwds)#

Uninitialized array whose size is that of the field.

Return a new array of the data field size, without initializing entries. Keyword arguments are the same as that for the equivalent numpy function.

Parameters:

group (str) – Name of the group.

See also

numpy.empty

See for a description of optional keywords.

ones

Equivalent method that initializes the data to 1.

zeros

Equivalent method that initializes the data to 0.

Examples

>>> from landlab.field import GraphFields
>>> field = GraphFields()
>>> field.new_field_location("node", 4)
>>> field.empty("node")  
array([  2.31584178e+077,  -2.68156175e+154,   9.88131292e-324,
... 2.78134232e-309]) # Uninitialized memory

Note that a new field is not added to the collection of fields.

>>> list(field.keys("node"))
[]
property event_layers#

EventLayers for each cell.

property faces_at_cell#

Face-cell and node-link numbering are the same!

field_units(field, at=None)#

Get units for a field.

Returns the unit string associated with the data array in group and field.

Parameters:
  • field (str) – Name of the field withing group.

  • at (str, optional) – Name of the group.

Returns:

The units of the field.

Return type:

str

Raises:

KeyError – If either field or group does not exist.

field_values(field, at=None)#

Return the values of a field.

Given a group and a field, return a reference to the associated data array.

Parameters:
  • field (str) – Name of the field within group.

  • at (str, optional) – Name of the group.

Returns:

The values of the field.

Return type:

array

Raises:
  • landlab.field.errors.GroupError – If group does not exist

  • landlab.field.errors.FieldError – If field does not exist

Examples

Create a group of fields called node.

>>> from landlab.field import GraphFields
>>> fields = GraphFields()
>>> fields.new_field_location("node", 4)

Add a field, initialized to ones, called topographic__elevation to the node group. The field_values method returns a reference to the field’s data.

>>> _ = fields.add_ones("topographic__elevation", at="node")
>>> fields.field_values("topographic__elevation", at="node")
array([1., 1., 1., 1.])

Raise FieldError if field does not exist in group.

>>> fields.field_values("planet_surface__temperature", at="node")
Traceback (most recent call last):
...
FieldError: planet_surface__temperature

If group does not exists, raise GroupError.

>>> fields.field_values("topographic__elevation", at="cell")
Traceback (most recent call last):
...
GroupError: cell
fields(include='*', exclude=None)#

List of fields held by the grid.

The returned field names are returned as their canonical names. That is, as a string of the for "at_<location>:<name>". This allows for fields with the same name to be defined at different grid locations. You could have, for example, a variable “elevation” defined at both nodes and links.

Both the include and exclude patterns are glob-style expressions, not regular expressions. If either include or exclude are lists, then the patterns are matched using an “or”.

The include filters are applied before the exclude filters.

Parameters:
  • include (str, or iterable of str, optional) – Glob-style pattern for field names to include.

  • exclude (str, or iterable of str, optional) – Glob-style pattern for field names to exclude.

Returns:

Filtered set of canonical field names held by the grid

Return type:

set

Examples

>>> from landlab import RasterModelGrid
>>> grid = RasterModelGrid((3, 4))

Add some fields to the grid. Notice that we have defined a field named “elevation” at both nodes and links.

>>> _ = grid.add_full("elevation", 3.0, at="node")
>>> _ = grid.add_full("elevation", 4.0, at="link")
>>> _ = grid.add_full("temperature", 5.0, at="node")
>>> sorted(grid.fields())
['at_link:elevation', 'at_node:elevation', 'at_node:temperature']
>>> sorted(grid.fields(include="at_node*"))
['at_node:elevation', 'at_node:temperature']
>>> sorted(grid.fields(include="at_node*", exclude="*temp*"))
['at_node:elevation']

Fields can also be defined at layers. In the following example we’ve filtered the results to just return the layer fields.

>>> grid.event_layers.add(1.0, rho=0.5)
>>> sorted(grid.fields(include="at_layer*"))
['at_layer:rho']

If a list, the fields are matched with an “or”.

>>> sorted(grid.fields(include=["at_node*", "*elevation*"]))
['at_link:elevation', 'at_node:elevation', 'at_node:temperature']
property fixed_gradient_boundary_node_anchor_node#

Returns the node at the other end of the fixed link for a fixed gradient boundary node.

Degenerate NodeStatus.FIXED_GRADIENT nodes (e.g., corners) are handled as in fixed_gradient_boundary_node_fixed_link, by pointing to a neighboring NodeStatus.FIXED_GRADIENT node.

Examples

>>> from landlab import RasterModelGrid
>>> grid = RasterModelGrid((3, 4))
>>> leftedge = grid.nodes_at_left_edge
>>> grid.status_at_node[leftedge] = grid.BC_NODE_IS_FIXED_GRADIENT
>>> grid.fixed_gradient_boundary_nodes
array([0, 4, 8])
>>> grid.fixed_gradient_boundary_node_fixed_link
array([ 3,  7, 10])
>>> grid.fixed_gradient_boundary_node_anchor_node
array([4, 5, 4])

An array of the fixed_links connected to fixed gradient boundary nodes.

Note that on a raster, some nodes (notably the corners) can be NodeStatus.FIXED_GRADIENT, but not have a true LinkStatus.FIXED neighboring link. In such cases, the link returned will be a closed link joining the corner node to a neighboring NodeStatus.FIXED_GRADIENT node (see example).

An AssertionError will be raised if for some reason a NodeStatus.FIXED_GRADIENT node exists which has neither a NodeStatus.FIXED_GRADIENT neighbor, or a LinkStatus.FIXED.

Examples

>>> from landlab import RasterModelGrid
>>> grid = RasterModelGrid((3, 4))
>>> leftedge = grid.nodes_at_left_edge
>>> grid.status_at_node[leftedge] = grid.BC_NODE_IS_FIXED_GRADIENT
>>> grid.fixed_gradient_boundary_nodes
array([0, 4, 8])
>>> grid.fixed_gradient_boundary_node_fixed_link
array([ 3,  7, 10])
property fixed_gradient_boundary_nodes#

Get array of fixed gradient boundary nodes.

Examples

>>> from landlab import RasterModelGrid
>>> mg = RasterModelGrid((4, 5))
>>> mg.status_at_node[mg.nodes_at_top_edge] = mg.BC_NODE_IS_FIXED_GRADIENT
>>> mg.fixed_gradient_boundary_nodes
array([15, 16, 17, 18, 19])

Get array of fixed links.

Examples

>>> from landlab import NodeStatus, RasterModelGrid
>>> grid = RasterModelGrid((3, 4))
>>> grid.status_at_node.reshape(grid.shape)
array([[1, 1, 1, 1],
       [1, 0, 0, 1],
       [1, 1, 1, 1]], dtype=uint8)
>>> grid.fixed_links.size
0
>>> grid.status_at_node[:4] = NodeStatus.FIXED_GRADIENT
>>> grid.status_at_node.reshape(grid.shape)
array([[2, 2, 2, 2],
       [1, 0, 0, 1],
       [1, 1, 1, 1]], dtype=uint8)
>>> grid.fixed_links
array([4, 5])
property fixed_value_boundary_nodes#

Get array of fixed value boundary nodes.

Examples

>>> from landlab import RasterModelGrid
>>> grid = RasterModelGrid((4, 5))

Initially all the perimeter nodes are fixed value boundary.

>>> grid.fixed_value_boundary_nodes
array([ 0,  1,  2,  3,  4, 5,  9, 10, 14, 15, 16, 17, 18, 19])

Set left, right, and bottom edges to closed.

>>> for edge in (
...     grid.nodes_at_left_edge,
...     grid.nodes_at_right_edge,
...     grid.nodes_at_bottom_edge,
... ):
...     grid.status_at_node[edge] = grid.BC_NODE_IS_CLOSED

Now nodes on just the top edge are fixed.

>>> grid.fixed_value_boundary_nodes
array([16, 17, 18])
classmethod from_dict(params)#

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.])
classmethod from_file(file_like)#

Create grid from a file-like object.

File to load either as a file-like object, path to an existing file, or the contents of a file as a string.

Parameters:

file_like – File-like object, filepath, or string.

Examples

>>> from io import StringIO
>>> from landlab import RasterModelGrid
>>> filelike = StringIO(
...     '''
... shape:
...     - 3
...     - 4
... xy_spacing: 2
... '''
... )
>>> grid = RasterModelGrid.from_file(filelike)
>>> 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 groups#

List of group names.

Returns:

Names of field groupings.

Return type:

set

has_field(field, at=None)#

Check if a field is in a group.

Parameters:
  • field (str) – Name of the field.

  • at (str, optional) – Name of the group.

Returns:

True if the group contains the field, otherwise False.

Return type:

bool

Examples

Check if the field named topographic__elevation is contained in a group.

>>> from landlab.field import GraphFields
>>> fields = GraphFields()
>>> fields.new_field_location("node", 12)
>>> _ = fields.add_ones("topographic__elevation", at="node")
>>> fields.has_field("topographic__elevation", at="node")
True
>>> fields.has_field("topographic__elevation", at="cell")
False
>>> fields = GraphFields()
>>> fields.new_field_location("node", 12)
>>> _ = fields.add_ones("topographic__elevation", at="node")
>>> fields.has_field("node", "topographic__elevation")
True
>>> fields.has_field("cell", "topographic__elevation")
False
has_group(name)#

Check if a group exists.

Parameters:

name (str) – Name of the group.

Returns:

True if the field contains group, otherwise False.

Return type:

bool

Examples

Check if the field has the groups named node or cell.

>>> from landlab.field import GraphFields
>>> fields = GraphFields()
>>> fields.new_field_location("node", 12)
>>> fields.has_group("node")
True
>>> fields.has_group("cell")
False
imshow(*args, **kwds)#

Plot a data field.

This is a wrapper for plot.imshow_grid, and can take the same keywords. See that function for full documentation.

Parameters:

values (str, or array-like) – Name of a field or an array of values to plot.

See also

landlab.plot.imshow_grid

LLCATS

GINF

keys(group)#

Return the field names in a group.

Parameters:

group (str) – Group name.

Returns:

Names of fields held in the given group.

Return type:

list

Examples

>>> from landlab.field import GraphFields
>>> fields = GraphFields()
>>> fields.new_field_location("node", 4)
>>> list(fields.keys("node"))
[]
>>> _ = fields.add_empty("topographic__elevation", at="node")
>>> list(fields.keys("node"))
['topographic__elevation']

Return a boolean the same shape as links_at_node which flags links which are downwind of the node as True.

link_at_node_is_downwind iterates across the grid and identifies the link values at each link connected to a node. It then uses the link_dirs_at_node data structure to identify links carrying flux out of the node. It then return a boolean array the same shape as links_at_node flagging these links. e.g., for a raster, the returned array will be shape (nnodes, 4).

Parameters:
  • values (str or array) – Name of variable field defined at links, or array of values at links.

  • out (ndarray, optional) – Buffer to place mapped values into or None to create a new array. Must be correct shape and boolean dtype.

Returns:

Boolean of which links are downwind at nodes.

Return type:

ndarray

Examples

>>> import numpy as np
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> rmg.at_link["grad"] = [
...     -1.0,
...     -2.0,
...     -1.0,
...     -2.0,
...     -3.0,
...     -4.0,
...     -5.0,
...     -1.0,
...     -2.0,
...     -1.0,
...     -1.0,
...     -2.0,
...     -3.0,
...     -4.0,
...     -1.0,
...     -2.0,
...     -1.0,
... ]
>>> rmg.link_at_node_is_downwind("grad")
array([[ True,  True, False, False],
       [ True,  True, False, False],
       [ True,  True, False, False],
       [False,  True, False, False],
       [ True,  True, False, False],
       [ True,  True, False, False],
       [ True,  True, False, False],
       [False,  True, False, False],
       [ True, False, False, False],
       [ True, False, False, False],
       [ True, False, False, False],
       [False, False, False, False]])

Return a boolean the same shape as links_at_node which flags links which are upwind of the node as True.

link_at_node_is_upwind iterates across the grid and identifies the link values at each link connected to a node. It then uses the link_dirs_at_node data structure to identify links bringing flux into the node. It then return a boolean array the same shape as links_at_node flagging these links. e.g., for a raster, the returned array will be shape (nnodes, 4).

Parameters:
  • values (str or array) – Name of variable field defined at links, or array of values at links.

  • out (ndarray, optional) – Buffer to place mapped values into or None to create a new array. Must be correct shape and boolean dtype.

Returns:

Boolean of which links are upwind at nodes.

Return type:

ndarray

Examples

>>> import numpy as np
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> rmg.at_link["grad"] = [
...     -1.0,
...     -2.0,
...     -1.0,
...     -2.0,
...     -3.0,
...     -4.0,
...     -5.0,
...     -1.0,
...     -2.0,
...     -1.0,
...     -1.0,
...     -2.0,
...     -3.0,
...     -4.0,
...     -1.0,
...     -2.0,
...     -1.0,
... ]
>>> rmg.link_at_node_is_upwind("grad")
array([[False, False, False, False],
       [False, False,  True, False],
       [False, False,  True, False],
       [False, False,  True, False],
       [False, False, False,  True],
       [False, False,  True,  True],
       [False, False,  True,  True],
       [False, False,  True,  True],
       [False, False, False,  True],
       [False, False,  True,  True],
       [False, False,  True,  True],
       [False, False,  True,  True]])

Return array of IDs of links with given angle.

Examples

>>> from landlab import HexModelGrid
>>> grid = HexModelGrid((3, 3))
>>> grid.link_with_angle(0.0)
array([  0,  1,  8,  9, 10, 17, 18])
>>> grid.link_with_angle(60.0, in_degrees=True)
array([  3,  5,  7, 11, 13, 15])
>>> grid.link_with_angle(2.0944)  # 120 degrees
array([  2,  4,  6, 12, 14, 16])
>>> len(grid.link_with_angle(0.5236))  # no links at 30 deg
0
>>> grid = HexModelGrid((3, 3), orientation="vertical")
>>> grid.link_with_angle(30.0, in_degrees=True)
array([  1,  3,  8, 10, 15, 17])
>>> grid.link_with_angle(1.5708)  # 90 degrees
array([ 2,  5,  6,  9, 12, 13, 16])
>>> grid.link_with_angle(330.0, in_degrees=True)
array([ 0,  4,  7, 11, 14, 18])
>>> len(grid.link_with_angle(60.0, in_degrees=True))  # none at 60 deg
0

Links with a given node status.

Parameters:
  • status_at_tail (NodeStatus, optional) – Status of the link tail node.

  • status_at_head (NodeStatus, optional) – Status of the link head node.

Returns:

Links with the given tail and head node statuses.

Return type:

array of int

Examples

>>> from landlab import RasterModelGrid, NodeStatus
>>> grid = RasterModelGrid((4, 5))
>>> grid.status_at_node[13] = NodeStatus.FIXED_VALUE
>>> grid.status_at_node[2] = NodeStatus.CLOSED
>>> grid.link_with_node_status(
...     status_at_tail=NodeStatus.CORE, status_at_head=NodeStatus.CORE
... )
array([10, 11, 14, 15, 19])
>>> grid.link_with_node_status(
...     status_at_tail=NodeStatus.CORE, status_at_head=NodeStatus.FIXED_VALUE
... )
array([12, 16, 20, 23, 24])
>>> grid.link_with_node_status(
...     status_at_tail=NodeStatus.FIXED_VALUE, status_at_head=NodeStatus.CORE
... )
array([ 5,  7,  9, 18])
>>> grid.link_with_node_status(status_at_head=NodeStatus.CORE)
array([ 5,  6,  7,  9, 10, 11, 14, 15, 18, 19])
>>> grid.link_with_node_status(status_at_tail=NodeStatus.CORE)
array([10, 11, 12, 14, 15, 16, 19, 20, 23, 24])
>>> grid.link_with_node_status()
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30])

Map the largest magnitude of the links carrying flux from the node to the node.

map_downwind_node_link_max_to_node iterates across the grid and identifies the link values at each link connected to a node. It then uses the link_dirs_at_node data structure to identify links carrying flux out of the node, then maps the maximum magnitude of ‘var_name’ found on these links onto the node. If no downwind link is found, the value will be recorded as zero.

Parameters:
  • var_name (array or field name) – Values defined at links.

  • out (ndarray, optional) – Buffer to place mapped values into or None to create a new array.

Returns:

Mapped values at nodes.

Return type:

ndarray

Examples

>>> import numpy as np
>>> from landlab.grid.mappers import map_downwind_node_link_max_to_node
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> rmg.at_link["grad"] = [
...     -1.0,
...     -2.0,
...     -1.0,
...     0.0,
...     0.0,
...     0.0,
...     0.0,
...     -1.0,
...     -2.0,
...     -1.0,
...     0.0,
...     0.0,
...     0.0,
...     0.0,
...     -1.0,
...     -2.0,
...     -1.0,
... ]
>>> map_downwind_node_link_max_to_node(rmg, "grad")
array([1.,  2.,  1.,  0.,
       1.,  2.,  1.,  0.,
       1.,  2.,  1.,  0.])
>>> values_at_nodes = rmg.add_empty("z", at="node")
>>> rtn = map_downwind_node_link_max_to_node(rmg, "grad", out=values_at_nodes)
>>> values_at_nodes
array([1.,  2.,  1.,  0.,
       1.,  2.,  1.,  0.,
       1.,  2.,  1.,  0.])
>>> rtn is values_at_nodes
True

Map the mean magnitude of the links carrying flux out of the node to the node.

map_downwind_node_link_mean_to_node iterates across the grid and identifies the link values at each link connected to a node. It then uses the link_dirs_at_node data structure to identify links carrying flux out of the node, then maps the mean magnitude of ‘var_name’ found on these links onto the node. Links with zero values are not included in the means, and zeros are returned if no upwind links are found.

Parameters:
  • var_name (array or field name) – Values defined at links.

  • out (ndarray, optional) – Buffer to place mapped values into or None to create a new array.

Returns:

Mapped values at nodes.

Return type:

ndarray

Examples

>>> import numpy as np
>>> from landlab.grid.mappers import map_downwind_node_link_mean_to_node
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> rmg.at_link["grad"] = [
...     -1.0,
...     -2.0,
...     -1.0,
...     -2.0,
...     -3.0,
...     -4.0,
...     -5.0,
...     -1.0,
...     -2.0,
...     -1.0,
...     -1.0,
...     -2.0,
...     -3.0,
...     -4.0,
...     -1.0,
...     -2.0,
...     -1.0,
... ]
>>> map_downwind_node_link_mean_to_node(rmg, "grad")
array([1.5,  2.5,  2.5,  5. ,
       1. ,  2. ,  2. ,  4. ,
       1. ,  2. ,  1. ,  0. ])
>>> values_at_nodes = rmg.add_empty("z", at="node")
>>> rtn = map_downwind_node_link_mean_to_node(rmg, "grad", out=values_at_nodes)
>>> values_at_nodes
array([1.5,  2.5,  2.5,  5. ,
       1. ,  2. ,  2. ,  4. ,
       1. ,  2. ,  1. ,  0. ])
>>> rtn is values_at_nodes
True

Map values from a link head nodes to links.

Iterate over a grid and identify the node at the head. For each link, the value of var_name at the head node is mapped to the corresponding link.

In a RasterModelGrid, each one node has two adjacent “link heads”. This means each node value is mapped to two corresponding links.

Parameters:
  • var_name (array or field name) – Values defined at nodes.

  • out (ndarray, optional) – Buffer to place mapped values into or None to create a new array.

Returns:

Mapped values at links.

Return type:

ndarray

Examples

>>> import numpy as np
>>> from landlab.grid.mappers import map_link_head_node_to_link
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> rmg.at_node["z"] = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
>>> map_link_head_node_to_link(rmg, "z")
array([  1.,   2.,   3.,   4.,   5.,   6.,   7.,   5.,   6.,   7.,   8.,
      9.,  10.,  11.,   9.,  10.,  11.])
>>> values_at_links = rmg.empty(at="link")
>>> rtn = map_link_head_node_to_link(rmg, "z", out=values_at_links)
>>> values_at_links
array([  1.,   2.,   3.,   4.,   5.,   6.,   7.,   5.,   6.,   7.,   8.,
      9.,  10.,  11.,   9.,  10.,  11.])
>>> rtn is values_at_links
True

Map values from a link tail nodes to links.

map_link_tail_node_to_link iterates across the grid and identifies the node at the “tail”, or the “from” node for each link. For each link, the value of ‘var_name’ at the “from” node is mapped to the corresponding link.

In a RasterModelGrid, each one node has two adjacent “link tails”. This means each node value is mapped to two corresponding links.

Parameters:
  • var_name (array or field name) – Values defined at nodes.

  • out (ndarray, optional) – Buffer to place mapped values into or None to create a new array.

Returns:

Mapped values at links.

Return type:

ndarray

Examples

>>> import numpy as np
>>> from landlab.grid.mappers import map_link_tail_node_to_link
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> rmg.at_node["z"] = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
>>> map_link_tail_node_to_link(rmg, "z")
array([  0.,   1.,   2.,   0.,   1.,   2.,   3.,   4.,   5.,   6.,   4.,
         5.,   6.,   7.,   8.,   9.,  10.])
>>> values_at_links = rmg.empty(at="link")
>>> rtn = map_link_tail_node_to_link(rmg, "z", out=values_at_links)
>>> values_at_links
array([  0.,   1.,   2.,   0.,   1.,   2.,   3.,   4.,   5.,   6.,   4.,
         5.,   6.,   7.,   8.,   9.,  10.])
>>> rtn is values_at_links
True

Map (x,y) components of link data data_at_link onto nodes.

Examples

>>> import numpy as np
>>> from landlab import RasterModelGrid, HexModelGrid
>>> grid = RasterModelGrid((3, 4))
>>> link_data = np.arange(grid.number_of_links)
>>> vx, vy = grid.map_link_vector_components_to_node(link_data)
>>> vx[5:7]
array([7.5, 8.5])
>>> grid = HexModelGrid((3, 3))
>>> link_data = np.zeros(grid.number_of_links) + 0.5 * 3.0**0.5
>>> link_data[np.isclose(grid.angle_of_link, 0.0)] = 0.0
>>> vx, vy = grid.map_link_vector_components_to_node(link_data)
>>> vy
array([0.,  0.,  0.,  0.,  1.,  1.,  0.,  0.,  0.,  0.])

Map the vector sum of links around a patch to the patch.

The resulting vector is returned as a length-2 list, with the two items being arrays of the x component and the y component of the resolved vectors at the patches, respectively.

Parameters:
  • var_name (array or field name) – Values defined at links.

  • ignore_inactive_links (bool) – If True, do not incorporate inactive links into calc. If all links are inactive at a patch, record zero if out is None or leave the existing value if out.

  • out (len-2 list of npatches-long arrays, optional) – Buffer to place mapped values into or None to create a new array.

Returns:

[x_component_of_link_vals_at_patch, y_component_of_link_vals_at_patch].

Return type:

len-2 list of arrays

Examples

>>> import numpy as np
>>> from landlab.grid.mappers import map_link_vector_sum_to_patch
>>> from landlab import HexModelGrid
>>> mg = HexModelGrid((4, 3))
>>> interior_nodes = mg.status_at_node == mg.BC_NODE_IS_CORE
>>> exterior_nodes = mg.status_at_node != mg.BC_NODE_IS_CORE

Add a ring of closed nodes at the edge:

>>> mg.status_at_node[exterior_nodes] = mg.BC_NODE_IS_CLOSED

This gives us 5 core nodes, 7 active links, and 3 present patches

>>> (mg.number_of_core_nodes == 5 and mg.number_of_active_links == 7)
True
>>> A = mg.add_ones("vals", at="link")
>>> A.fill(9.0)  # any old values on the inactive links
>>> A[mg.active_links] = np.array([1.0, -1.0, 1.0, -1.0, -1.0, -1.0, -1.0])

This setup should give present patch 0 pure east, patch 1 zero (vorticity), and patch 2 westwards and downwards components.

>>> xcomp, ycomp = map_link_vector_sum_to_patch(mg, "vals")
>>> xcomp, ycomp = np.round(xcomp, decimals=5), np.round(ycomp, decimals=5)
>>> np.allclose(xcomp[(6, 9, 10),], [2.0, 0.0, -1.0])
True
>>> np.allclose(ycomp[(6, 9, 10),] / np.sqrt(3.0), [0.0, 0.0, -1.0])
True

These are the patches with LinksStatus.INACTIVE on all three sides:

>>> absent_patches = np.array([0, 1, 2, 4, 8, 11, 12, 15, 16, 17, 18])
>>> np.allclose(xcomp[absent_patches], 0.0)
True
>>> np.allclose(ycomp[absent_patches], 0.0)
True

Now demonstrate the remaining functionality:

>>> A = mg.at_link["vals"].copy()
>>> A.fill(1.0)
>>> _ = map_link_vector_sum_to_patch(
...     mg, A, ignore_inactive_links=False, out=[xcomp, ycomp]
... )
>>> np.allclose(xcomp[absent_patches], 0.0)
False
>>> np.allclose(ycomp[absent_patches], 0.0)
False

Map the maximum of a link’s nodes to the link.

map_max_of_link_nodes_to_link iterates across the grid and identifies the node values at both the “head” and “tail” of a given link. This function evaluates the value of ‘var_name’ at both the “to” and “from” node. The maximum value of the two node values is then mapped to the link.

Parameters:
  • var_name (array or field name) – Values defined at nodes.

  • out (ndarray, optional) – Buffer to place mapped values into or None to create a new array.

Returns:

Mapped values at links.

Return type:

ndarray

Examples

>>> import numpy as np
>>> from landlab.grid.mappers import map_max_of_link_nodes_to_link
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> _ = rmg.add_field(
...     "z",
...     [
...         [0, 1, 2, 3],
...         [7, 6, 5, 4],
...         [8, 9, 10, 11],
...     ],
...     at="node",
... )
>>> map_max_of_link_nodes_to_link(rmg, "z")
array([  1.,   2.,   3.,   7.,   6.,   5.,   4.,   7.,   6.,   5.,   8.,
         9.,  10.,  11.,   9.,  10.,  11.])
>>> values_at_links = rmg.empty(at="link")
>>> rtn = map_max_of_link_nodes_to_link(rmg, "z", out=values_at_links)
>>> values_at_links
array([  1.,   2.,   3.,   7.,   6.,   5.,   4.,   7.,   6.,   5.,   8.,
         9.,  10.,  11.,   9.,  10.,  11.])
>>> rtn is values_at_links
True

Map the maximum value of a nodes’ links to the node.

map_max_of_node_links_to_node iterates across the grid and identifies the link values at each link connected to a node. This function finds the maximum value of ‘var_name’ of each set of links, and then maps this value to the node. Note no attempt is made to honor the directionality of the links.

Parameters:
  • var_name (array or field name) – Values defined at links.

  • out (ndarray, optional) – Buffer to place mapped values into or None to create a new array.

Returns:

Mapped values at nodes.

Return type:

ndarray

Examples

>>> import numpy as np
>>> from landlab.grid.mappers import map_max_of_node_links_to_node
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> rmg.at_link["grad"] = np.arange(rmg.number_of_links)
>>> map_max_of_node_links_to_node(rmg, "grad")
array([  3.,   4.,   5.,   6.,
        10.,  11.,  12.,  13.,
        14.,  15.,  16.,  16.])
>>> values_at_nodes = rmg.add_empty("z", at="node")
>>> rtn = map_max_of_node_links_to_node(rmg, "grad", out=values_at_nodes)
>>> values_at_nodes
array([  3.,   4.,   5.,   6.,
        10.,  11.,  12.,  13.,
        14.,  15.,  16.,  16.])
>>> rtn is values_at_nodes
True
map_max_of_patch_nodes_to_patch(var_name, ignore_closed_nodes=True, out=None)#

Map the maximum value of nodes around a patch to the patch.

Parameters:
  • var_name (array or field name) – Values defined at nodes.

  • ignore_closed_nodes (bool) – If True, do not incorporate closed nodes into calc. If all nodes are masked at a patch, record zero if out is None or leave the existing value if out.

  • out (ndarray, optional) – Buffer to place mapped values into or None to create a new array.

Returns:

Mapped values at patches.

Return type:

ndarray

Examples

>>> import numpy as np
>>> from landlab.grid.mappers import map_max_of_patch_nodes_to_patch
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> rmg.at_node["vals"] = [
...     [5.0, 4.0, 3.0, 2.0],
...     [3.0, 4.0, 3.0, 2.0],
...     [3.0, 2.0, 1.0, 0.0],
... ]
>>> map_max_of_patch_nodes_to_patch(rmg, "vals")
array([5., 4., 3.,
       4., 4., 3.])
>>> rmg.at_node["vals"] = [
...     [5.0, 4.0, 3.0, 2.0],
...     [3.0, 4.0, 3.0, 2.0],
...     [3.0, 2.0, 1.0, 0.0],
... ]
>>> rmg.status_at_node[rmg.node_x > 1.5] = rmg.BC_NODE_IS_CLOSED
>>> ans = np.zeros(6, dtype=float)
>>> _ = map_max_of_patch_nodes_to_patch(rmg, "vals", out=ans)
>>> ans
array([5., 4., 0.,
       4., 4., 0.])

Map the mean of a link’s nodes to the link.

map_mean_of_link_nodes_to_link iterates across the grid and identifies the node values at both the “head” and “tail” of a given link. This function takes the sum of the two values of ‘var_name’ at both the “to” and “from” node. The average value of the two node values of ‘var_name’ is then mapped to the link.

Parameters:
  • var_name (array or field name) – Values defined at nodes.

  • out (ndarray, optional) – Buffer to place mapped values into or None to create a new array.

Returns:

Mapped values at links.

Return type:

ndarray

Examples

>>> import numpy as np
>>> from landlab.grid.mappers import map_mean_of_link_nodes_to_link
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> rmg.at_node["z"] = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
>>> map_mean_of_link_nodes_to_link(rmg, "z")
array([  0.5,   1.5,   2.5,   2. ,   3. ,   4. ,   5. ,   4.5,   5.5,
         6.5,   6. ,   7. ,   8. ,   9. ,   8.5,   9.5,  10.5])
>>> values_at_links = rmg.empty(at="link")
>>> rtn = map_mean_of_link_nodes_to_link(rmg, "z", out=values_at_links)
>>> values_at_links
array([  0.5,   1.5,   2.5,   2. ,   3. ,   4. ,   5. ,   4.5,   5.5,
         6.5,   6. ,   7. ,   8. ,   9. ,   8.5,   9.5,  10.5])
>>> rtn is values_at_links
True
map_mean_of_patch_nodes_to_patch(var_name, ignore_closed_nodes=True, out=None)#

Map the mean value of nodes around a patch to the patch.

Parameters:
  • var_name (array or field name) – Values defined at nodes.

  • ignore_closed_nodes (bool) – If True, do not incorporate closed nodes into calc. If all nodes are masked at a patch, record zero if out is None or leave the existing value if out.

  • out (ndarray, optional) – Buffer to place mapped values into or None to create a new array.

Returns:

Mapped values at patches.

Return type:

ndarray

Examples

>>> import numpy as np
>>> from landlab.grid.mappers import map_mean_of_patch_nodes_to_patch
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> rmg.at_node["vals"] = [
...     [5.0, 4.0, 3.0, 2.0],
...     [5.0, 4.0, 3.0, 2.0],
...     [3.0, 2.0, 1.0, 0.0],
... ]
>>> map_mean_of_patch_nodes_to_patch(rmg, "vals")
array([4.5, 3.5, 2.5,
       3.5, 2.5, 1.5])
>>> rmg.at_node["vals"] = [
...     [5.0, 4.0, 3.0, 2.0],
...     [5.0, 4.0, 3.0, 2.0],
...     [3.0, 2.0, 1.0, 0.0],
... ]
>>> rmg.status_at_node[rmg.node_x > 1.5] = rmg.BC_NODE_IS_CLOSED
>>> ans = np.zeros(6, dtype=float)
>>> _ = map_mean_of_patch_nodes_to_patch(rmg, "vals", out=ans)
>>> ans
array([4.5, 4. , 0. ,
       3.5, 3. , 0. ])

Map the minimum of a link’s nodes to the link.

map_min_of_link_nodes_to_link iterates across the grid and identifies the node values at both the “head” and “tail” of a given link. This function evaluates the value of ‘var_name’ at both the “to” and “from” node. The minimum value of the two node values is then mapped to the link.

Parameters:
  • var_name (array or field name) – Values defined at nodes.

  • out (ndarray, optional) – Buffer to place mapped values into or None to create a new array.

Returns:

Mapped values at links.

Return type:

ndarray

Examples

>>> import numpy as np
>>> from landlab.grid.mappers import map_min_of_link_nodes_to_link
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> _ = rmg.add_field(
...     "z",
...     [
...         [0, 1, 2, 3],
...         [7, 6, 5, 4],
...         [8, 9, 10, 11],
...     ],
...     at="node",
... )
>>> map_min_of_link_nodes_to_link(rmg, "z")
array([  0.,   1.,   2.,   0.,   1.,   2.,   3.,   6.,   5.,   4.,   7.,
         6.,   5.,   4.,   8.,   9.,  10.])
>>> values_at_links = rmg.empty(at="link")
>>> rtn = map_min_of_link_nodes_to_link(rmg, "z", out=values_at_links)
>>> values_at_links
array([  0.,   1.,   2.,   0.,   1.,   2.,   3.,   6.,   5.,   4.,   7.,
         6.,   5.,   4.,   8.,   9.,  10.])
>>> rtn is values_at_links
True

Map the minimum value of a nodes’ links to the node.

map_min_of_node_links_to_node iterates across the grid and identifies the link values at each link connected to a node. This function finds the minimum value of ‘var_name’ of each set of links, and then maps this value to the node. Note no attempt is made to honor the directionality of the links.

Parameters:
  • var_name (array or field name) – Values defined at links.

  • out (ndarray, optional) – Buffer to place mapped values into or None to create a new array.

Returns:

Mapped values at nodes.

Return type:

ndarray

Examples

>>> import numpy as np
>>> from landlab.grid.mappers import map_min_of_node_links_to_node
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> rmg.at_link["grad"] = np.arange(rmg.number_of_links)
>>> map_min_of_node_links_to_node(rmg, "grad")
array([  0.,   0.,   1.,   2.,
         3.,   4.,   5.,   6.,
        10.,  11.,  12.,  13.])
>>> values_at_nodes = rmg.add_empty("z", at="node")
>>> rtn = map_min_of_node_links_to_node(rmg, "grad", out=values_at_nodes)
>>> values_at_nodes
array([  0.,   0.,   1.,   2.,
         3.,   4.,   5.,   6.,
        10.,  11.,  12.,  13.])
>>> rtn is values_at_nodes
True
map_min_of_patch_nodes_to_patch(var_name, ignore_closed_nodes=True, out=None)#

Map the minimum value of nodes around a patch to the patch.

Parameters:
  • var_name (array or field name) – Values defined at nodes.

  • ignore_closed_nodes (bool) – If True, do not incorporate closed nodes into calc. If all nodes are masked at a patch, record zero if out is None or leave the existing value if out.

  • out (ndarray, optional) – Buffer to place mapped values into or None to create a new array.

Returns:

Mapped values at patches.

Return type:

ndarray

Examples

>>> import numpy as np
>>> from landlab.grid.mappers import map_min_of_patch_nodes_to_patch
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> rmg.at_node["vals"] = [
...     [5.0, 4.0, 3.0, 2.0],
...     [5.0, 4.0, 3.0, 2.0],
...     [3.0, 2.0, 1.0, 0.0],
... ]
>>> map_min_of_patch_nodes_to_patch(rmg, "vals")
array([4., 3., 2.,
       2., 1., 0.])
>>> rmg.at_node["vals"] = [
...     [5.0, 4.0, 3.0, 2.0],
...     [5.0, 4.0, 3.0, 2.0],
...     [3.0, 2.0, 1.0, 0.0],
... ]
>>> rmg.status_at_node[rmg.node_x > 1.5] = rmg.BC_NODE_IS_CLOSED
>>> ans = np.zeros(6, dtype=float)
>>> _ = map_min_of_patch_nodes_to_patch(rmg, "vals", out=ans)
>>> ans
array([4., 4., 0.,
       2., 2., 0.])
map_node_to_cell(var_name, out=None)#

Map values for nodes to cells.

map_node_to_cell iterates across the grid and identifies the all node values of ‘var_name’.

This function takes node values of ‘var_name’ and mapes that value to the corresponding cell area for each node.

Parameters:
  • var_name (array or field name) – Values defined at nodes.

  • out (ndarray, optional) – Buffer to place mapped values into or None to create a new array.

Returns:

Mapped values at cells.

Return type:

ndarray

Examples

>>> import numpy as np
>>> from landlab.grid.mappers import map_node_to_cell
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> _ = rmg.add_field("z", np.arange(12.0), at="node")
>>> map_node_to_cell(rmg, "z")
array([5.,  6.])
>>> values_at_cells = rmg.empty(at="cell")
>>> rtn = map_node_to_cell(rmg, "z", out=values_at_cells)
>>> values_at_cells
array([5.,  6.])
>>> rtn is values_at_cells
True

Assign values to links using a weighted combination of node values.

Assign to each link a weighted combination of values v at nodes using the Lax-Wendroff method for upwind weighting.

c is a scalar or link vector that gives the link-parallel signed Courant number. Where c is positive, velocity is in the direction of the link; where negative, velocity is in the opposite direction.

As an example, consider 3x5 raster grid with the following values at the nodes in the central row:

0---1---2---3---4

Consider a uniform Courant value c = +0.2 at the horizontal links. The mapped link values should be:

.-0.4-.-1.4-.-2.4-.-3.4-.

Values at links when c = -0.2:

.-0.6-.-1.6-.-2.6-.-3.6-.
Parameters:
  • v ((n_nodes,) ndarray) – Values at grid nodes.

  • c (float or (n_links,) ndarray) – Courant number to use at links.

  • out ((n_links,) ndarray, optional) – If provided, place calculated values in this array. Otherwise, create a new array.

Examples

>>> import numpy as np
>>> from landlab import RasterModelGrid
>>> grid = RasterModelGrid((3, 5))
>>> grid.at_node["node_value"] = [
...     [0.0, 0.0, 0.0, 0.0, 0.0],
...     [0.0, 1.0, 2.0, 3.0, 4.0],
...     [0.0, 0.0, 0.0, 0.0, 0.0],
... ]
>>> v = grid.at_node["node_value"]
>>> c = grid.add_zeros("courant_number", at="link")
>>> c[grid.horizontal_links] = 0.2

Set values for the middle row of horizontal links.

>>> val_at_link = grid.map_node_to_link_lax_wendroff(v, c)
>>> val_at_link[9:13]
array([0.4,  1.4,  2.4,  3.4])
>>> val_at_link = grid.map_node_to_link_lax_wendroff(v, -c)
>>> val_at_link[9:13]
array([0.6,  1.6,  2.6,  3.6])

Assign values to links from upstream nodes.

Assign to each link the value v associated with whichever of its two nodes lies upstream, according to link value u.

Consider a link k with tail node t(k) and head node t(h). Nodes have value v(n). We want to assign a value to links, v’(k). The assignment is:

v'(k) = v(t(k)) where u(k) > 0,
v'(k) = v(h(k)) where u(k) <= 0

As an example, consider 3x5 raster grid with the following values at the nodes in the central row:

0---1---2---3---4

Consider a uniform velocity value u = 1 at the horizontal links. The mapped link values should be:

.-0-.-1-.-2-.-3-.

If u < 0, the link values should be:

.-1-.-2-.-3-.-4-.
Parameters:
  • v ((n_nodes,), ndarray) – Values at grid nodes.

  • u ((n_links,) ndarray) – Values at grid links.

  • out ((n_links,) ndarray, optional) – If provided, place calculated values in this array. Otherwise, create a new array.

Examples

>>> import numpy as np
>>> from landlab import RasterModelGrid
>>> grid = RasterModelGrid((3, 5))
>>> grid.at_node["node_value"] = [
...     [0.0, 0.0, 0.0, 0.0, 0.0],
...     [0.0, 1.0, 2.0, 3.0, 4.0],
...     [0.0, 0.0, 0.0, 0.0, 0.0],
... ]
>>> v = grid.at_node["node_value"]
>>> u = grid.add_zeros("advection_speed", at="link")
>>> u[grid.horizontal_links] = 1.0

Set values for the middle row of horizontal links.

>>> val_at_link = grid.map_node_to_link_linear_upwind(v, u)
>>> val_at_link[9:13]
array([0.,  1.,  2.,  3.])
>>> val_at_link = grid.map_node_to_link_linear_upwind(v, -u)
>>> val_at_link[9:13]
array([1.,  2.,  3.,  4.])

Map the largest magnitude of the links bringing flux into the node to the node.

map_upwind_node_link_max_to_node iterates across the grid and identifies the link values at each link connected to a node. It then uses the link_dirs_at_node data structure to identify links bringing flux into the node, then maps the maximum magnitude of ‘var_name’ found on these links onto the node. If no upwind link is found, the value will be recorded as zero.

Parameters:
  • var_name (array or field name) – Values defined at links.

  • out (ndarray, optional) – Buffer to place mapped values into or None to create a new array.

Returns:

Mapped values at nodes.

Return type:

ndarray

Examples

>>> import numpy as np
>>> from landlab.grid.mappers import map_upwind_node_link_max_to_node
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> rmg.at_link["grad"] = [
...     -1.1,
...     -1.2,
...     -1.3,
...     1.4,
...     1.5,
...     1.6,
...     -1.7,
...     -1.8,
...     -1.9,
...     2.0,
...     2.1,
...     2.2,
...     -2.3,
...     2.4,
...     2.5,
...     2.6,
...     -2.7,
... ]
>>> map_upwind_node_link_max_to_node(rmg, "grad").reshape((3, 4))
array([[1.4,  1.5,  1.6,  1.3],
       [2.1,  2.2,  2. ,  2.4],
       [2.5,  2.6,  2.3,  2.7]])
>>> values_at_nodes = rmg.add_empty("z", at="node")
>>> rtn = map_upwind_node_link_max_to_node(rmg, "grad", out=values_at_nodes)
>>> values_at_nodes.reshape((3, 4))
array([[1.4,  1.5,  1.6,  1.3],
       [2.1,  2.2,  2. ,  2.4],
       [2.5,  2.6,  2.3,  2.7]])
>>> rtn is values_at_nodes
True

Map the mean magnitude of the links bringing flux into the node to the node.

map_upwind_node_link_mean_to_node iterates across the grid and identifies the link values at each link connected to a node. It then uses the link_dirs_at_node data structure to identify links bringing flux into the node, then maps the mean magnitude of ‘var_name’ found on these links onto the node. Links with zero values are not included in the means, and zeros are returned if no upwind links are found.

Parameters:
  • var_name (array or field name) – Values defined at links.

  • out (ndarray, optional) – Buffer to place mapped values into or None to create a new array.

Returns:

Mapped values at nodes.

Return type:

ndarray

Examples

>>> import numpy as np
>>> from landlab.grid.mappers import map_upwind_node_link_mean_to_node
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> rmg.at_link["grad"] = [
...     -1.0,
...     -2.0,
...     -1.0,
...     -2.0,
...     -3.0,
...     -4.0,
...     -5.0,
...     -1.0,
...     -2.0,
...     -1.0,
...     -1.0,
...     -2.0,
...     -3.0,
...     -4.0,
...     -1.0,
...     -2.0,
...     -1.0,
... ]
>>> map_upwind_node_link_mean_to_node(rmg, "grad")
array([0. ,  1. ,  2. ,  1. ,
       2. ,  2. ,  3. ,  3. ,
       1. ,  1.5,  2.5,  2.5])
>>> values_at_nodes = rmg.add_empty("z", at="node")
>>> rtn = map_upwind_node_link_mean_to_node(rmg, "grad", out=values_at_nodes)
>>> values_at_nodes
array([0. ,  1. ,  2. ,  1. ,
       2. ,  2. ,  3. ,  3. ,
       1. ,  1.5,  2.5,  2.5])
>>> rtn is values_at_nodes
True

Map the the value found in one link array to a node, based on the largest magnitude value of links carrying fluxes out of the node, found in a second node array or field.

map_downwind_node_link_max_to_node iterates across the grid and identifies the link control_values at each link connected to a node. It then uses the link_dirs_at_node data structure to identify links carrying flux out of the node, then identifies the link with the maximum magnitude. The value of the second field ‘value_name’ at these links is then mapped onto the node. If no downwind link is found, the value will be recorded as zero.

Parameters:
  • control_name (array or field name) – Values defined at nodes that dictate which end of the link to draw values from.

  • value_name (array or field name) – Values defined at nodes from which values are drawn, based on control_name.

  • out (ndarray, optional) – Buffer to place mapped values into or None to create a new array.

Returns:

Mapped values at nodes.

Return type:

ndarray

Examples

>>> import numpy as np
>>> from landlab.grid.mappers import map_value_at_downwind_node_link_max_to_node
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> rmg.at_link["grad"] = [
...     -1.0,
...     -2.0,
...     -1.0,
...     0.0,
...     0.0,
...     0.0,
...     0.0,
...     -1.0,
...     -2.0,
...     -1.0,
...     0.0,
...     0.0,
...     0.0,
...     0.0,
...     -1.0,
...     -2.0,
...     -1.0,
... ]
>>> rmg.at_link["vals"] = np.arange(rmg.number_of_links, dtype=float)
>>> map_value_at_downwind_node_link_max_to_node(rmg, "grad", "vals")
array([  0.,   1.,   2.,   0.,
         7.,   8.,   9.,   0.,
        14.,  15.,  16.,   0.])
>>> values_at_nodes = rmg.add_empty("z", at="node")
>>> rtn = map_value_at_downwind_node_link_max_to_node(
...     rmg, "grad", "vals", out=values_at_nodes
... )
>>> values_at_nodes
array([  0.,   1.,   2.,   0.,
         7.,   8.,   9.,   0.,
        14.,  15.,  16.,   0.])
>>> rtn is values_at_nodes
True

Map the the value found in one node array to a link, based on the maximum value found in a second node field or array.

map_value_at_max_node_to_link iterates across the grid and identifies the node values at both the “head” and “tail” of a given link. This function evaluates the value of ‘control_name’ at both the “to” and “from” node. The value of ‘value_name’ at the node with the maximum value of the two values of ‘control_name’ is then mapped to the link.

Parameters:
  • control_name (array or field name) – Name of field defined at nodes or a node array that dictates which end of the link to draw values from.

  • value_name (array or field name) – Name of field defined at nodes or node array from which values are drawn, based on control_name.

  • out (ndarray, optional) – Buffer to place mapped values into or None to create a new array.

Returns:

Mapped values at links.

Return type:

ndarray

Examples

>>> import numpy as np
>>> from landlab.grid.mappers import map_value_at_max_node_to_link
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> _ = rmg.add_field(
...     "z",
...     [
...         [0, 1, 2, 3],
...         [7, 6, 5, 4],
...         [8, 9, 10, 11],
...     ],
...     at="node",
... )
>>> _ = rmg.add_field(
...     "vals_to_map",
...     [
...         [0, 10, 20, 30],
...         [70, 60, 50, 40],
...         [80, 90, 100, 110],
...     ],
...     at="node",
... )
>>> map_value_at_max_node_to_link(rmg, "z", "vals_to_map")
array([  10.,   20.,   30.,   70.,   60.,   50.,   40.,   70.,   60.,
         50.,   80.,   90.,  100.,  110.,   90.,  100.,  110.])

Map the the value found in one node array to a link, based on the minimum value found in a second node field or array.

map_value_at_min_node_to_link iterates across the grid and identifies the node values at both the “head” and “tail” of a given link. This function evaluates the value of ‘control_name’ at both the “to” and “from” node. The value of ‘value_name’ at the node with the minimum value of the two values of ‘control_name’ is then mapped to the link.

Parameters:
  • control_name (array or field name) – Name of field defined at nodes or a node array that dictates which end of the link to draw values from.

  • value_name (array or field name) – Name of field defined at nodes or node array from which values are drawn, based on control_name.

  • out (ndarray, optional) – Buffer to place mapped values into or None to create a new array.

Returns:

Mapped values at links.

Return type:

ndarray

Examples

>>> import numpy as np
>>> from landlab.grid.mappers import map_value_at_min_node_to_link
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> _ = rmg.add_field(
...     "z",
...     [
...         [0, 1, 2, 3],
...         [7, 6, 5, 4],
...         [8, 9, 10, 11],
...     ],
...     at="node",
... )
>>> _ = rmg.add_field(
...     "vals_to_map",
...     [
...         [0, 10, 20, 30],
...         [70, 60, 50, 40],
...         [80, 90, 100, 110],
...     ],
...     at="node",
... )
>>> map_value_at_min_node_to_link(rmg, "z", "vals_to_map")
array([   0.,   10.,   20.,    0.,   10.,   20.,   30.,   60.,   50.,
         40.,   70.,   60.,   50.,   40.,   80.,   90.,  100.])

Map the the value found in one link array to a node, based on the largest magnitude value of links bringing fluxes into the node, found in a second node array or field.

map_upwind_node_link_max_to_node iterates across the grid and identifies the link control_values at each link connected to a node. It then uses the link_dirs_at_node data structure to identify links bringing flux into the node, then identifies the link with the maximum magnitude. The value of the second field ‘value_name’ at these links is then mapped onto the node. If no upwind link is found, the value will be recorded as zero.

Parameters:
  • control_name (array or field name) – Values defined at nodes that dictate which end of the link to draw values from.

  • value_name (array or field name) – Values defined at nodes from which values are drawn, based on control_name.

  • out (ndarray, optional) – Buffer to place mapped values into or None to create a new array.

Returns:

Mapped values at nodes.

Return type:

ndarray

Examples

>>> import numpy as np
>>> from landlab.grid.mappers import map_value_at_upwind_node_link_max_to_node
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> rmg.at_link["grad"] = [
...     -1.0,
...     -2.0,
...     -1.0,
...     0.0,
...     0.0,
...     0.0,
...     0.0,
...     -1.0,
...     -2.0,
...     -1.0,
...     0.0,
...     0.0,
...     0.0,
...     0.0,
...     -1.0,
...     -2.0,
...     -1.0,
... ]
>>> rmg.at_link["vals"] = np.arange(rmg.number_of_links, dtype=float)
>>> map_value_at_upwind_node_link_max_to_node(rmg, "grad", "vals")
array([  0.,   0.,   1.,   2.,
         0.,   7.,   8.,   9.,
         0.,  14.,  15.,  16.])
>>> values_at_nodes = rmg.add_empty("z", at="node")
>>> rtn = map_value_at_upwind_node_link_max_to_node(
...     rmg, "grad", "vals", out=values_at_nodes
... )
>>> values_at_nodes
array([  0.,   0.,   1.,   2.,
         0.,   7.,   8.,   9.,
         0.,  14.,  15.,  16.])
>>> rtn is values_at_nodes
True

Map magnitude and sign of vectors with components (ux, uy) onto grid links.

Examples

>>> from landlab import HexModelGrid
>>> import numpy
>>> hmg = HexModelGrid((3, 2))
>>> (numpy.round(10 * map_vectors_to_links(hmg, 1.0, 0.0))).astype(int)
array([10, -5,  5, -5,  5, 10, 10,  5, -5,  5, -5, 10])
property material_layers#

MaterialLayers for each cell.

property ndim#

Number of spatial dimensions of the grid.

new_field_location(loc, size=None)#

Add a new quantity to a field.

Create an empty group into which new fields can be added. The new group is created but no memory allocated yet. The dictionary of the new group can be through a new at_ attribute of the class instance.

Parameters:
  • loc (str) – Name of the new group to add to the field.

  • size (int, optional) – Number of elements in the new quantity. If not provided, the size is set to be the size of the first field added to the group.

Raises:

ValueError – If the field already contains the group.

Examples

Create a collection of fields and add two groups, node and cell, to it.

>>> from landlab.field import GraphFields
>>> fields = GraphFields()
>>> fields.new_field_location("node", 12)
>>> fields.new_field_location("cell", 2)

The group names in the collection are retrieved with the groups attribute as a set.

>>> names = list(fields.groups)
>>> names.sort()
>>> names
['cell', 'node']

Access the new (empty) groups with the at_ attributes.

>>> fields.at_cell
FieldDataset('cell', size=2, fixed_size=True)
>>> fields.at_node
FieldDataset('node', size=12, fixed_size=True)
>>> fields.new_field_location("core_node")
>>> fields.at_core_node.size is None
True
>>> fields.at_core_node["air__temperature"] = [0, 1]
>>> fields.at_core_node.size
2
property node_at_cell#
property node_at_core_cell#

Get array of nodes associated with core cells.

Examples

>>> from landlab import RasterModelGrid
>>> grid = RasterModelGrid((4, 5))

Initially each cell’s node is core.

>>> grid.node_at_core_cell
array([ 6,  7,  8,
       11, 12, 13])

Setting a node to closed causes means its cell is also “closed”.

>>> grid.status_at_node[8] = grid.BC_NODE_IS_CLOSED
>>> grid.node_at_core_cell
array([ 6,  7, 11, 12, 13])
node_axis_coordinates(axis=0)#

Get the coordinates of nodes along a particular axis.

Return node coordinates from a given axis (defaulting to 0). Axis numbering is the same as that for numpy arrays. That is, the zeroth axis is along the rows, and the first along the columns.

Parameters:

axis (int, optional) – Coordinate axis.

Returns:

Coordinates of nodes for a given axis.

Return type:

ndarray

Examples

>>> from landlab import RasterModelGrid
>>> grid = RasterModelGrid((4, 5))
>>> grid.node_axis_coordinates(0).reshape(grid.shape)
array([[0.,  0.,  0.,  0.,  0.],
       [1.,  1.,  1.,  1.,  1.],
       [2.,  2.,  2.,  2.,  2.],
       [3.,  3.,  3.,  3.,  3.]])
>>> grid.node_axis_coordinates(1).reshape(grid.shape)
array([[0.,  1.,  2.,  3.,  4.],
       [0.,  1.,  2.,  3.,  4.],
       [0.,  1.,  2.,  3.,  4.],
       [0.,  1.,  2.,  3.,  4.]])
node_has_boundary_neighbor()#

Check if ModelGrid nodes have neighbors that are boundary nodes.

Checks to see if one of the eight neighbor nodes of node(s) with id has a boundary node. Returns True if a node has a boundary node, False if all neighbors are interior.

Parameters:

ids (int, or iterable of int) – ID of node to test.

Returns:

True if node has a neighbor with a boundary ID, False otherwise.

Return type:

boolean

Examples

    0,  1,  2,  3,
  4,  5,  6,  7,  8,
9, 10,  11, 12, 13, 14,
  15, 16, 17, 18, 19,
    20, 21, 22, 23
>>> from landlab import HexModelGrid
>>> grid = HexModelGrid((5, 4))
>>> grid.node_has_boundary_neighbor()
array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True, False, False,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True])
>>> grid.node_has_boundary_neighbor()[6]
True
>>> grid.node_has_boundary_neighbor()[12]
False
>>> grid.node_has_boundary_neighbor()[((12, 0),)]
array([False,  True])
node_is_boundary(ids, boundary_flag=None)#

Check if nodes are boundary nodes.

Check if nodes at given ids are boundary nodes. Use the boundary_flag to specify a particular boundary type status flag.

Parameters:
  • ids (ndarray) – Node IDs to check.

  • boundary_flag (int, optional) – A boundary type to check for.

Returns:

Array of booleans indicating if nodes are boundary nodes.

Return type:

ndarray

Examples

>>> from landlab import RasterModelGrid
>>> mg = RasterModelGrid((4, 5))
>>> mg.node_is_boundary([0, 6])
array([ True, False])
>>> mg.node_is_boundary([0, 6], boundary_flag=mg.BC_NODE_IS_CLOSED)
array([False, False])
property number_of_active_faces#

Total number of active faces.

Returns:

Total number of active faces in the grid.

Return type:

int

Examples

>>> from landlab import RasterModelGrid
>>> grid = RasterModelGrid((3, 4))
>>> grid.number_of_active_faces
7

The number of active faces is updated when a node status changes.

>>> grid.status_at_node[6] = grid.BC_NODE_IS_CLOSED
>>> grid.number_of_active_faces
3

Number of active links.

Examples

>>> from landlab import RasterModelGrid
>>> mg = RasterModelGrid((4, 5))
>>> mg.number_of_active_links
17
>>> for edge in (
...     mg.nodes_at_left_edge,
...     mg.nodes_at_right_edge,
...     mg.nodes_at_bottom_edge,
... ):
...     mg.status_at_node[edge] = mg.BC_NODE_IS_CLOSED
>>> mg.number_of_active_links
10
property number_of_core_cells#

Number of core cells.

A core cell excludes all boundary cells.

Examples

>>> from landlab import RasterModelGrid
>>> grid = RasterModelGrid((4, 5))
>>> grid.number_of_core_cells
6
>>> grid.status_at_node[7] = grid.BC_NODE_IS_CLOSED
>>> grid.number_of_core_cells
5
property number_of_core_nodes#

Number of core nodes.

The number of core nodes on the grid (i.e., excluding all boundary nodes).

Examples

>>> from landlab import RasterModelGrid
>>> grid = RasterModelGrid((4, 5))
>>> grid.number_of_core_nodes
6
>>> grid.status_at_node[7] = grid.BC_NODE_IS_CLOSED
>>> grid.number_of_core_nodes
5
number_of_elements(name)#

Number of instances of an element.

Get the number of instances of a grid element in a grid.

Parameters:

name ({'node', 'cell', 'link', 'face', 'core_node', 'core_cell',) – ‘active_link’, ‘active_face’} Name of the grid element.

Returns:

Number of elements in the grid.

Return type:

int

Examples

>>> from landlab import RasterModelGrid
>>> mg = RasterModelGrid((4, 5))
>>> mg.number_of_elements("node")
20
>>> mg.number_of_elements("core_cell")
6
>>> mg.number_of_elements("link")
31
>>> mg.number_of_elements("active_link")
17
>>> mg.status_at_node[8] = mg.BC_NODE_IS_CLOSED
>>> mg.number_of_elements("link")
31
>>> mg.number_of_elements("active_link")
13

Number of fixed links.

Examples

>>> from landlab import NodeStatus, RasterModelGrid
>>> mg = RasterModelGrid((4, 5))
>>> mg.number_of_fixed_links
0
>>> mg.status_at_node[mg.nodes_at_top_edge] = NodeStatus.FIXED_GRADIENT
>>> mg.number_of_fixed_links
3

Return the number of patches at a link without a closed node.

Examples

>>> from landlab import RasterModelGrid
>>> mg = RasterModelGrid((3, 3))
>>> mg.status_at_node[mg.nodes_at_top_edge] = mg.BC_NODE_IS_CLOSED
>>> mg.patches_present_at_link
array([[False,  True],
       [False,  True],
       [ True, False],
       [ True,  True],
       [False,  True],
       [ True, False],
       [ True, False],
       [False, False],
       [False, False],
       [False, False],
       [False, False],
       [False, False]])
>>> mg.number_of_patches_present_at_link
array([1, 1, 1, 2, 1, 1, 1, 0, 0, 0, 0, 0])
property number_of_patches_present_at_node#

Return the number of patches at a node without a closed node.

Examples

>>> from landlab import RasterModelGrid
>>> mg = RasterModelGrid((3, 3))
>>> mg.status_at_node[mg.nodes_at_top_edge] = mg.BC_NODE_IS_CLOSED
>>> mg.patches_present_at_node
array([[ True, False, False, False],
       [ True,  True, False, False],
       [False,  True, False, False],
       [False, False, False,  True],
       [False, False,  True,  True],
       [False, False,  True, False],
       [False, False, False, False],
       [False, False, False, False],
       [False, False, False, False]])
>>> mg.number_of_patches_present_at_node
array([1, 2, 1, 1, 2, 1, 0, 0, 0])
ones(*args, **kwds)#

Array, initialized to 1, whose size is that of the field.

Return a new array of the data field size, filled with ones. Keyword arguments are the same as that for the equivalent numpy function.

Parameters:

group (str) – Name of the group.

See also

numpy.ones

See for a description of optional keywords.

empty

Equivalent method that does not initialize the new array.

zeros

Equivalent method that initializes the data to 0.

Examples

>>> from landlab.field import GraphFields
>>> field = GraphFields()
>>> field.new_field_location("node", 4)
>>> field.ones("node")
array([1., 1., 1., 1.])
>>> field.ones("node", dtype=int)
array([1, 1, 1, 1])

Note that a new field is not added to the collection of fields.

>>> list(field.keys("node"))
[]
property open_boundary_nodes#

Get array of open boundary nodes.

Examples

>>> from landlab import RasterModelGrid
>>> mg = RasterModelGrid((4, 5))
>>> for edge in (
...     mg.nodes_at_left_edge,
...     mg.nodes_at_right_edge,
...     mg.nodes_at_bottom_edge,
... ):
...     mg.status_at_node[edge] = mg.BC_NODE_IS_CLOSED
>>> mg.open_boundary_nodes
array([16, 17, 18])
property patches_at_node#

A boolean array, False where a patch has a closed node or is missing.

The array is the same shape as patches_at_link, and is designed to mask it.

Examples

>>> from landlab import RasterModelGrid
>>> mg = RasterModelGrid((3, 3))
>>> mg.status_at_node[mg.nodes_at_top_edge] = mg.BC_NODE_IS_CLOSED
>>> mg.patches_at_link
array([[-1,  0],
       [-1,  1],
       [ 0, -1],
       [ 1,  0],
       [-1,  1],
       [ 0,  2],
       [ 1,  3],
       [ 2, -1],
       [ 3,  2],
       [-1,  3],
       [ 2, -1],
       [ 3, -1]])
>>> mg.patches_present_at_link
array([[False,  True],
       [False,  True],
       [ True, False],
       [ True,  True],
       [False,  True],
       [ True, False],
       [ True, False],
       [False, False],
       [False, False],
       [False, False],
       [False, False],
       [False, False]])
>>> 1 in mg.patches_at_link * mg.patches_present_at_link
True
>>> 2 in mg.patches_at_link * mg.patches_present_at_link
False
property patches_present_at_node#

A boolean array, False where a patch has a closed node or is missing.

The array is the same shape as patches_at_node, and is designed to mask it.

Note that in cases where patches may have more than 3 nodes (e.g., rasters), a patch is considered still present as long as at least 3 open nodes are present.

Examples

>>> from landlab import RasterModelGrid
>>> mg = RasterModelGrid((3, 3))
>>> mg.status_at_node[mg.nodes_at_top_edge] = mg.BC_NODE_IS_CLOSED
>>> mg.patches_at_node
array([[ 0, -1, -1, -1],
       [ 1,  0, -1, -1],
       [-1,  1, -1, -1],
       [ 2, -1, -1,  0],
       [ 3,  2,  0,  1],
       [-1,  3,  1, -1],
       [-1, -1, -1,  2],
       [-1, -1,  2,  3],
       [-1, -1,  3, -1]])
>>> mg.patches_present_at_node
array([[ True, False, False, False],
       [ True,  True, False, False],
       [False,  True, False, False],
       [False, False, False,  True],
       [False, False,  True,  True],
       [False, False,  True, False],
       [False, False, False, False],
       [False, False, False, False],
       [False, False, False, False]])
>>> 1 in mg.patches_at_node * mg.patches_present_at_node
True
>>> 2 in mg.patches_at_node * mg.patches_present_at_node
False
property phi_of_node#
property r_of_node#
reset_status_at_node()#

Resolve the xy-components of links.

Resolves values provided defined on links into the x and y directions. Returns values_along_x, values_along_y

return_array_or_field_values(field, at=None)#

Return field given a field name, or array of with the correct shape.

Given a group and a field, return a reference to the associated data array. field is either a string that is a field in the group or an array of the correct size.

This function is meant to serve like the use_field_name_or_array decorator for bound functions.

Parameters:
  • field (str or array) – Name of the field withing group.

  • at (str, optional) – Name of the group.

Returns:

The values of the field.

Return type:

numpy.ndarray

Raises:
  • landlab.field.errors.GroupError – If group does not exist

  • landlab.field.errors.FieldError – If field does not exist

Examples

Create a group of fields called node.

>>> import numpy as np
>>> from landlab.field import GraphFields
>>> fields = GraphFields()
>>> fields.new_field_location("node", 4)

Add a field, initialized to ones, called topographic__elevation to the node group. The field_values method returns a reference to the field’s data.

>>> _ = fields.add_ones("topographic__elevation", at="node")
>>> fields.field_values("topographic__elevation", at="node")
array([1., 1., 1., 1.])

Alternatively, if the second argument is an array, its size is checked and returned if correct.

>>> vals = np.array([4.0, 5.0, 7.0, 3.0])
>>> fields.return_array_or_field_values(vals, at="node")
array([4., 5., 7., 3.])

Raise FieldError if field does not exist in group.

>>> fields.return_array_or_field_values("surface__temperature", at="node")
Traceback (most recent call last):
...
FieldError: surface__temperature

If group does not exists, Raise GroupError.

>>> fields.return_array_or_field_values("topographic__elevation", at="cell")
Traceback (most recent call last):
...
GroupError: cell

And if the array of values provided is incorrect, raise a ValueError.

>>> vals = np.array([3.0, 2.0, 1.0])
>>> fields.return_array_or_field_values(vals, at="node")
Traceback (most recent call last):
...
ValueError: Array has incorrect size.
set_nodata_nodes_to_closed(node_data, nodata_value)#

Make no-data nodes closed boundaries.

Sets node status to BC_NODE_IS_CLOSED for all nodes whose value of node_data is equal to the nodata_value.

Any links connected to BC_NODE_IS_CLOSED nodes are automatically set to LinkStatus.INACTIVE boundary.

Parameters:
  • node_data (ndarray) – Data values.

  • nodata_value (float) – Value that indicates an invalid value.

Examples

The following example uses the following grid:

*--I--->o------>o------>o
^       ^       ^       ^
I       I       |       |
|       |       |       |
*--I--->*--I--->o------>o
^       ^       ^       ^
I       I       I       I
|       |       |       |
*--I--->*--I--->*--I--->*

Note

Links set to LinkStatus.ACTIVE are not shown in this diagram.

* indicates the nodes that are set to NodeStatus.CLOSED

o indicates the nodes that are set to NodeStatus.CORE

I indicates the links that are set to LinkStatus.INACTIVE

>>> import numpy as np
>>> import landlab as ll
>>> mg = ll.RasterModelGrid((3, 4))
>>> mg.status_at_node.reshape(mg.shape)
array([[1, 1, 1, 1],
       [1, 0, 0, 1],
       [1, 1, 1, 1]], dtype=uint8)
>>> h = np.array(
...     [
...         [-9999, -9999, -9999, -9999],
...         [-9999, -9999, 12345.0, 0.0],
...         [-9999, 0.0, 0.0, 0.0],
...     ]
... ).flatten()
>>> mg.set_nodata_nodes_to_closed(h, -9999)
>>> mg.status_at_node.reshape(mg.shape)
array([[4, 4, 4, 4],
       [4, 4, 0, 1],
       [4, 1, 1, 1]], dtype=uint8)
set_nodata_nodes_to_fixed_gradient(node_data, nodata_value)#

Make no-data nodes fixed gradient boundaries.

Set node status to BC_NODE_IS_FIXED_VALUE for all nodes whose value of node_data is equal to nodata_value.

Any links between BC_NODE_IS_FIXED_GRADIENT nodes and BC_NODE_IS_CORE are automatically set to LinkStatus.FIXED boundary status.

Parameters:
  • node_data (ndarray) – Data values.

  • nodata_value (float) – Value that indicates an invalid value.

Examples

The following examples use this grid:

*--I--->*--I--->*--I--->*--I--->*--I--->*--I--->*--I--->*--I--->*
^       ^       ^       ^       ^       ^       ^       ^       ^
I       I       I       X       X       X       X       X       I
|       |       |       |       |       |       |       |       |
*--I--->*--I--->*--X--->o       o       o       o       o--X--->*
^       ^       ^       ^       ^       ^       ^       ^       ^
I       I       I       |       |       |       |       |       I
|       |       |       |       |       |       |       |       |
*--I--->*--I--->*--X--->o       o       o       o       o--X--->*
^       ^       ^       ^       ^       ^       ^       ^       ^
I       I       I       X       X       X       X       X       I
|       |       |       |       |       |       |       |       |
*--I--->*--I--->*--I--->*--I--->*--I--->*--I--->*--I--->*--I--->*

Note

Links set to LinkStatus.ACTIVE are not shown in this diagram.

X indicates the links that are set to LinkStatus.FIXED

I indicates the links that are set to LinkStatus.INACTIVE

o indicates the nodes that are set to NodeStatus.CORE

* indicates the nodes that are set to

BC_NODE_IS_FIXED_GRADIENT

>>> import numpy as np
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((4, 9))
>>> rmg.status_at_node.reshape(rmg.shape)
array([[1, 1, 1, 1, 1, 1, 1, 1, 1],
       [1, 0, 0, 0, 0, 0, 0, 0, 1],
       [1, 0, 0, 0, 0, 0, 0, 0, 1],
       [1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=uint8)
>>> z = np.array(
...     [
...         [-99.0, -99.0, -99.0, -99.0, -99.0, -99.0, -99.0, -99.0, -99.0],
...         [-99.0, -99.0, -99.0, 0.0, 0.0, 0.0, 0.0, 0.0, -99.0],
...         [-99.0, -99.0, -99.0, 0.0, 0.0, 0.0, 0.0, 0.0, -99.0],
...         [-99.0, -99.0, -99.0, -99.0, -99.0, -99.0, -99.0, -99.0, -99.0],
...     ]
... ).flatten()
>>> rmg.set_nodata_nodes_to_fixed_gradient(z, -99)
>>> rmg.status_at_node.reshape(rmg.shape)
array([[2, 2, 2, 2, 2, 2, 2, 2, 2],
       [2, 2, 2, 0, 0, 0, 0, 0, 2],
       [2, 2, 2, 0, 0, 0, 0, 0, 2],
       [2, 2, 2, 2, 2, 2, 2, 2, 2]], dtype=uint8)
>>> rmg.status_at_link[rmg.horizontal_links].reshape((4, 8))
array([[4, 4, 4, 4, 4, 4, 4, 4],
       [4, 4, 2, 0, 0, 0, 0, 2],
       [4, 4, 2, 0, 0, 0, 0, 2],
       [4, 4, 4, 4, 4, 4, 4, 4]], dtype=uint8)
>>> rmg.status_at_link[rmg.vertical_links].reshape((3, 9))
array([[4, 4, 4, 2, 2, 2, 2, 2, 4],
       [4, 4, 4, 0, 0, 0, 0, 0, 4],
       [4, 4, 4, 2, 2, 2, 2, 2, 4]], dtype=uint8)
size(group)#

Return the size of the arrays stored in a group.

Parameters:

group (str) – Group name.

Returns:

Array size.

Return type:

int

Examples

>>> from landlab.field import GraphFields
>>> fields = GraphFields()
>>> fields.new_field_location("node", 4)
>>> fields.size("node")
4

Get array of the status of all links.

Examples

>>> from landlab import RasterModelGrid
>>> mg = RasterModelGrid((4, 5))
>>> mg.status_at_node[mg.nodes_at_left_edge] = mg.BC_NODE_IS_CLOSED
>>> mg.status_at_node[mg.nodes_at_right_edge] = mg.BC_NODE_IS_FIXED_GRADIENT
>>> mg.status_at_link
array([4, 4, 4, 4, 4, 0, 0, 0, 4, 4, 0, 0, 2, 4, 0, 0, 0, 4, 4, 0, 0,
       2, 4, 0, 0, 0, 4, 4, 4, 4, 4], dtype=uint8)
property status_at_node#

Get array of the boundary status for each node.

Examples

>>> import numpy as np
>>> from landlab import LinkStatus, NodeStatus, RasterModelGrid
>>> mg = RasterModelGrid((4, 5))
>>> mg.status_at_node.reshape((4, 5))
array([[1, 1, 1, 1, 1],
       [1, 0, 0, 0, 1],
       [1, 0, 0, 0, 1],
       [1, 1, 1, 1, 1]], dtype=uint8)
>>> np.any(mg.status_at_link == LinkStatus.FIXED)
False
>>> mg.status_at_node[mg.nodes_at_left_edge] = NodeStatus.FIXED_GRADIENT
>>> mg.status_at_node.reshape((4, 5))
array([[2, 1, 1, 1, 1],
       [2, 0, 0, 0, 1],
       [2, 0, 0, 0, 1],
       [2, 1, 1, 1, 1]], dtype=uint8)
>>> np.any(mg.status_at_link == LinkStatus.FIXED)  # links auto-update
True
property theta_of_node#
property unit_vector_sum_xcomponent_at_node#

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

Examples

>>> from landlab import RasterModelGrid
>>> grid = RasterModelGrid((3, 3))
>>> len(grid.unit_vector_sum_xcomponent_at_node) == grid.number_of_nodes
True
>>> grid.unit_vector_sum_xcomponent_at_node
array([1.,  2.,  1.,  1.,  2.,  1.,  1.,  2.,  1.])
property unit_vector_sum_ycomponent_at_node#

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

Examples

>>> from landlab import RasterModelGrid
>>> grid = RasterModelGrid((3, 3))
>>> len(grid.unit_vector_sum_ycomponent_at_node) == grid.number_of_nodes
True
>>> grid.unit_vector_sum_ycomponent_at_node
array([1.,  1.,  1.,  2.,  2.,  2.,  1.,  1.,  1.])

Return an (nnodes, X) shape array of link IDs of which links are upwind of each node, according to values (field or array).

X is the maximum upwind links at any node. Nodes with fewer upwind links than this have additional slots filled with bad_index. Links are ordered anticlockwise from east.

Parameters:
  • values (str or array) – Name of variable field defined at links, or array of values at links.

  • bad_index (int) – Index to place in array indicating no link.

Returns:

Array of upwind link IDs

Return type:

ndarray

Examples

>>> import numpy as np
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> rmg.at_link["grad"] = [
...     -1.0,
...     -2.0,
...     -1.0,
...     -2.0,
...     -3.0,
...     -4.0,
...     -5.0,
...     -1.0,
...     -2.0,
...     -1.0,
...     -1.0,
...     -2.0,
...     -3.0,
...     -4.0,
...     -1.0,
...     -2.0,
...     -1.0,
... ]
>>> rmg.upwind_links_at_node("grad", bad_index=-1)
array([[-1, -1],
       [ 0, -1],
       [ 1, -1],
       [ 2, -1],
       [ 3, -1],
       [ 7,  4],
       [ 8,  5],
       [ 9,  6],
       [10, -1],
       [14, 11],
       [15, 12],
       [16, 13]])
property xy_of_reference#

Return the coordinates (x, y) of the reference point.

For RasterModelGrid and HexModelGrid the reference point is the minimum of x_of_node and of y_of_node. By default it is (0, 0). For VoronoiDelaunayGrid the reference point is (0, 0). For RadialModelGrid it is the (x, y) of the center point.

The intention of these coordinates is to provide a method to store the large float values of projected coordinates.

Example

>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((4, 5), xy_of_reference=(12345, 678910))
>>> rmg.xy_of_reference
(12345, 678910)
>>> rmg.xy_of_reference = (98765, 43210)
>>> rmg.xy_of_reference
(98765, 43210)
zeros(*args, **kwds)#

Array, initialized to 0, whose size is that of the field.

Parameters:
  • group (str) – Name of the group.

  • size (Return a new array of the data field) –

  • Keyword (filled with zeros.) –

  • function. (arguments are the same as that for the equivalent numpy) –

  • grid. (This method is not valid for the group) –

See also

numpy.zeros

See for a description of optional keywords.

empty

Equivalent method that does not initialize the new array.

ones

Equivalent method that initializes the data to 1.

Examples

>>> from landlab.field import GraphFields
>>> field = GraphFields()
>>> field.new_field_location("node", 4)
>>> field.zeros("node")
array([0., 0., 0., 0.])

Note that a new field is not added to the collection of fields.

>>> list(field.keys("node"))
[]