Map values from one grid element to another.
map_link_head_node_to_link(grid, var_name[, out]) |
Map values from a link head nodes to links. |
map_link_tail_node_to_link(grid, var_name[, out]) |
Map values from a link tail nodes to links. |
map_min_of_link_nodes_to_link(grid, var_name) |
Map the minimum of a link’s nodes to the link. |
map_max_of_link_nodes_to_link(grid, var_name) |
Map the maximum of a link’s nodes to the link. |
map_mean_of_link_nodes_to_link(grid, var_name) |
Map the mean of a link’s nodes to the link. |
map_value_at_min_node_to_link(grid, ...[, out]) |
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_max_node_to_link(grid, ...[, out]) |
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_node_to_cell(grid, var_name[, out]) |
Map values for nodes to cells. |
map_min_of_node_links_to_node(grid, var_name) |
Map the minimum value of a nodes’ links to the node. |
map_max_of_node_links_to_node(grid, var_name) |
Map the maximum value of a nodes’ links to the node. |
map_upwind_node_link_max_to_node(grid, var_name) |
Map the largest magnitude of the links bringing flux into the node to the node. |
map_downwind_node_link_max_to_node(grid, ...) |
Map the largest magnitude of the links carrying flux from the node to the node. |
map_upwind_node_link_mean_to_node(grid, var_name) |
Map the mean magnitude of the links bringing flux into the node to the node. |
map_downwind_node_link_mean_to_node(grid, ...) |
Map the mean magnitude of the links carrying flux out of the node to the node. |
map_value_at_upwind_node_link_max_to_node(...) |
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_value_at_downwind_node_link_max_to_node(...) |
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. |
dummy_func_to_demonstrate_docstring_modification(...) |
A dummy function to demonstrate automated docstring changes. |
Each link has a tail and head node. The tail nodes are located at the start of a link, while the head nodes are located at end of a link.
Below, the numbering scheme for links in RasterModelGrid is illustrated with an example of a four-row by five column grid (4x5). In this example, each * (or X) is a node, the lines represent links, and the ^ and > symbols indicate the direction and head of each link. Link heads in the RasterModelGrid always point in the cardinal directions North (N) or East (E).:
*--27-->*--28-->*--29-->*--30-->*
^ ^ ^ ^ ^
22 23 24 25 26
| | | | |
*--18-->*--19-->*--20-->*--21-->*
^ ^ ^ ^ ^
13 14 15 16 17
| | | | |
*---9-->*--10-->X--11-->*--12-->*
^ ^ ^ ^ ^
4 5 6 7 8
| | | | |
*--0--->*---1-->*--2--->*---3-->*
For example, node ‘X’ has four link-neighbors. From south and going clockwise, these neighbors are [6, 10, 15, 11]. Both link 6 and link 10 have node ‘X’ as their ‘head’ node, while links 15 and 11 have node ‘X’ as their tail node.
dummy_func_to_demonstrate_docstring_modification(grid, some_arg)[source]¶A dummy function to demonstrate automated docstring changes.
Construction:
dummy_func_to_demonstrate_docstring_modification(grid, some_arg)
| Parameters: | grid : ModelGrid
some_arg : whatever
|
|---|
Examples
...
LLCATS: DEPR MAP
map_downwind_node_link_max_to_node(grid, var_name, out=None)[source]¶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.
Construction:
map_downwind_node_link_max_to_node(grid, var_name, out=None)
| Parameters: | grid : ModelGrid
var_name : array or field name
out : ndarray, optional
|
|---|---|
| Returns: | 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'] = np.array([-1., -2., -1.,
... 0., 0., 0., 0.,
... -1., -2., -1.,
... 0., 0., 0., 0.,
... -1., -2., -1.])
>>> 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('node', 'z')
>>> 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
LLCATS: NINF LINF MAP
map_downwind_node_link_mean_to_node(grid, var_name, out=None)[source]¶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.
Construction:
map_downwind_node_link_mean_to_node(grid, var_name, out=None)
| Parameters: | grid : ModelGrid
var_name : array or field name
out : ndarray, optional
|
|---|---|
| Returns: | 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'] = np.array([-1., -2., -1.,
... -2., -3., -4., -5.,
... -1., -2., -1.,
... -1., -2., -3., -4.,
... -1., -2., -1.])
>>> 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('node', 'z')
>>> 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
LLCATS: NINF LINF MAP
map_link_head_node_to_link(grid, var_name, out=None)[source]¶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.
Construction:
map_link_head_node_to_link(grid, var_name, out=None)
| Parameters: | grid : ModelGrid
var_name : array or field name
out : ndarray, optional
|
|---|---|
| Returns: | 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
LLCATS: NINF LINF MAP
map_link_tail_node_to_link(grid, var_name, out=None)[source]¶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.
Construction:
map_link_tail_node_to_link(grid, var_name, out=None)
| Parameters: | grid : ModelGrid
var_name : array or field name
out : ndarray, optional
|
|---|---|
| Returns: | 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
LLCATS: NINF LINF MAP
map_link_vector_sum_to_patch(grid, var_name, ignore_inactive_links=True, out=None)[source]¶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.
Construction:
map_link_vector_sum_to_patch(grid, var_name, ignore_inctive_links=True,
out=None)
| Parameters: | grid : ModelGrid
var_name : array or field name
ignore_inactive_links : bool
out : len-2 list of npatches-long arrays, optional
|
|---|---|
| Returns: | 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
>>> from landlab import CLOSED_BOUNDARY, CORE_NODE, INACTIVE_LINK
>>> mg = HexModelGrid(4, 3)
>>> interior_nodes = mg.status_at_node == CORE_NODE
>>> exterior_nodes = mg.status_at_node != CORE_NODE
Add a ring of closed nodes at the edge:
>>> mg.status_at_node[exterior_nodes] = CLOSED_BOUNDARY
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('link', 'vals')
>>> A.fill(9.) # any old values on the inactive links
>>> A[mg.active_links] = np.array([ 1., -1., 1., -1., -1., -1., -1.])
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')
>>> np.allclose(xcomp[[6, 9, 10]], [2., 0., -1])
True
>>> np.allclose(ycomp[[6, 9, 10]]/np.sqrt(3.), [0., 0., -1.])
True
These are the patches with INACTIVE_LINKs 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.)
True
>>> np.allclose(ycomp[absent_patches], 0.)
True
Now demonstrate the remaining functionality:
>>> A = mg.at_link['vals'].copy()
>>> A.fill(1.)
>>> _ = map_link_vector_sum_to_patch(mg, A, ignore_inactive_links=False,
... out=[xcomp, ycomp])
>>> np.allclose(xcomp[absent_patches], 0.)
False
>>> np.allclose(ycomp[absent_patches], 0.)
False
LLCATS: PINF LINF MAP
map_max_of_link_nodes_to_link(grid, var_name, out=None)[source]¶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.
Construction:
map_max_of_link_nodes_to_link(grid, var_name, out=None)
| Parameters: | grid : ModelGrid
var_name : array or field name
out : ndarray, optional
|
|---|---|
| Returns: | 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('node', 'z',
... [[0, 1, 2, 3],
... [7, 6, 5, 4],
... [8, 9, 10, 11]])
>>> 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
LLCATS: NINF LINF MAP
map_max_of_node_links_to_node(grid, var_name, out=None)[source]¶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.
Construction:
map_max_of_node_links_to_node(grid, var_name, out=None)
| Parameters: | grid : ModelGrid
var_name : array or field name
out : ndarray, optional
|
|---|---|
| Returns: | 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('node', 'z')
>>> 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
LLCATS: NINF LINF MAP
map_max_of_patch_nodes_to_patch(grid, var_name, ignore_closed_nodes=True, out=None)[source]¶Map the maximum value of nodes around a patch to the patch.
Construction:
map_max_of_patch_nodes_to_patch(grid, var_name,
ignore_closed_nodes=True, out=None)
| Parameters: | grid : ModelGrid
var_name : array or field name
ignore_closed_nodes : bool
out : ndarray, optional
|
|---|---|
| Returns: | ndarray
|
Examples
>>> import numpy as np
>>> from landlab.grid.mappers import map_max_of_patch_nodes_to_patch
>>> from landlab import RasterModelGrid, CLOSED_BOUNDARY
>>> rmg = RasterModelGrid((3, 4))
>>> rmg.at_node['vals'] = np.array([5., 4., 3., 2.,
... 3., 4., 3., 2.,
... 3., 2., 1., 0.])
>>> map_max_of_patch_nodes_to_patch(rmg, 'vals')
array([ 5., 4., 3.,
4., 4., 3.])
>>> rmg.at_node['vals'] = np.array([5., 4., 3., 2.,
... 3., 4., 3., 2.,
... 3., 2., 1., 0.])
>>> rmg.status_at_node[rmg.node_x > 1.5] = CLOSED_BOUNDARY
>>> 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.])
LLCATS: PINF NINF MAP
map_mean_of_link_nodes_to_link(grid, var_name, out=None)[source]¶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.
Construction:
map_mean_of_link_nodes_to_link(grid, var_name, out=None)
| Parameters: | grid : ModelGrid
var_name : array or field name
out : ndarray, optional
|
|---|---|
| Returns: | 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
LLCATS: NINF LINF MAP
map_mean_of_patch_nodes_to_patch(grid, var_name, ignore_closed_nodes=True, out=None)[source]¶Map the mean value of nodes around a patch to the patch.
Construction:
map_mean_of_patch_nodes_to_patch(grid, var_name,
ignore_closed_nodes=True, out=None)
| Parameters: | grid : ModelGrid
var_name : array or field name
ignore_closed_nodes : bool
out : ndarray, optional
|
|---|---|
| Returns: | ndarray
|
Examples
>>> import numpy as np
>>> from landlab.grid.mappers import map_mean_of_patch_nodes_to_patch
>>> from landlab import RasterModelGrid, CLOSED_BOUNDARY
>>> rmg = RasterModelGrid((3, 4))
>>> rmg.at_node['vals'] = np.array([5., 4., 3., 2.,
... 5., 4., 3., 2.,
... 3., 2., 1., 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'] = np.array([5., 4., 3., 2.,
... 5., 4., 3., 2.,
... 3., 2., 1., 0.])
>>> rmg.status_at_node[rmg.node_x > 1.5] = CLOSED_BOUNDARY
>>> 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. ])
LLCATS: PINF NINF MAP
map_min_of_link_nodes_to_link(grid, var_name, out=None)[source]¶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.
Construction:
map_min_of_link_nodes_to_link(grid, var_name, out=None)
| Parameters: | grid : ModelGrid
var_name : array or field name
out : ndarray, optional
|
|---|---|
| Returns: | 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('node', 'z',
... [[ 0, 1, 2, 3],
... [ 7, 6, 5, 4],
... [ 8, 9, 10, 11]])
>>> 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
LLCATS: NINF LINF MAP
map_min_of_node_links_to_node(grid, var_name, out=None)[source]¶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.
Construction:
map_min_of_node_links_to_node(grid, var_name, out=None)
| Parameters: | grid : ModelGrid
var_name : array or field name
out : ndarray, optional
|
|---|---|
| Returns: | 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('node', 'z')
>>> 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
LLCATS: NINF LINF MAP
map_min_of_patch_nodes_to_patch(grid, var_name, ignore_closed_nodes=True, out=None)[source]¶Map the minimum value of nodes around a patch to the patch.
Construction:
map_min_of_patch_nodes_to_patch(grid, var_name,
ignore_closed_nodes=True, out=None)
| Parameters: | grid : ModelGrid
var_name : array or field name
ignore_closed_nodes : bool
out : ndarray, optional
|
|---|---|
| Returns: | ndarray
|
Examples
>>> import numpy as np
>>> from landlab.grid.mappers import map_min_of_patch_nodes_to_patch
>>> from landlab import RasterModelGrid, CLOSED_BOUNDARY
>>> rmg = RasterModelGrid((3, 4))
>>> rmg.at_node['vals'] = np.array([5., 4., 3., 2.,
... 5., 4., 3., 2.,
... 3., 2., 1., 0.])
>>> map_min_of_patch_nodes_to_patch(rmg, 'vals')
array([ 4., 3., 2.,
2., 1., 0.])
>>> rmg.at_node['vals'] = np.array([5., 4., 3., 2.,
... 5., 4., 3., 2.,
... 3., 2., 1., 0.])
>>> rmg.status_at_node[rmg.node_x > 1.5] = CLOSED_BOUNDARY
>>> 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.])
LLCATS: PINF NINF MAP
map_node_to_cell(grid, var_name, out=None)[source]¶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.
Construction:
map_node_to_cell(grid, var_name, out=None)
| Parameters: | grid : ModelGrid
var_name : array or field name
out : ndarray, optional
|
|---|---|
| Returns: | 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('node', 'z', np.arange(12.))
>>> 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
LLCATS: CINF NINF MAP
map_upwind_node_link_max_to_node(grid, var_name, out=None)[source]¶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.
Construction:
map_upwind_node_link_max_to_node(grid, var_name, out=None)
| Parameters: | grid : ModelGrid
var_name : array or field name
out : ndarray, optional
|
|---|---|
| Returns: | 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'] = np.array([-1., -2., -1.,
... 0., 0., 0., 0.,
... -1., -2., -1.,
... 0., 0., 0., 0.,
... -1., -2., -1.])
>>> map_upwind_node_link_max_to_node(rmg, 'grad')
array([ 0., 1., 2., 1.,
0., 1., 2., 1.,
0., 1., 2., 1.])
>>> values_at_nodes = rmg.add_empty('node', 'z')
>>> rtn = map_upwind_node_link_max_to_node(rmg, 'grad',
... out=values_at_nodes)
>>> values_at_nodes
array([ 0., 1., 2., 1.,
0., 1., 2., 1.,
0., 1., 2., 1.])
>>> rtn is values_at_nodes
True
LLCATS: NINF LINF MAP
map_upwind_node_link_mean_to_node(grid, var_name, out=None)[source]¶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.
Construction:
map_upwind_node_link_mean_to_node(grid, var_name, out=None)
| Parameters: | grid : ModelGrid
var_name : array or field name
out : ndarray, optional
|
|---|---|
| Returns: | 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'] = np.array([-1., -2., -1.,
... -2., -3., -4., -5.,
... -1., -2., -1.,
... -1., -2., -3., -4.,
... -1., -2., -1.])
>>> 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('node', 'z')
>>> 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
LLCATS: NINF LINF MAP
map_value_at_downwind_node_link_max_to_node(grid, control_name, value_name, out=None)[source]¶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.
Construction:
map_value_at_downwind_node_link_max_to_node(grid, control_name,
value_name, out=None)
| Parameters: | grid : ModelGrid
control_name : array or field name
value_name : array or field name
out : ndarray, optional
|
|---|---|
| Returns: | 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'] = np.array([-1., -2., -1.,
... 0., 0., 0., 0.,
... -1., -2., -1.,
... 0., 0., 0., 0.,
... -1., -2., -1.])
>>> 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('node', 'z')
>>> 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
LLCATS: NINF LINF MAP
map_value_at_max_node_to_link(grid, control_name, value_name, out=None)[source]¶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.
Construction:
map_value_at_max_node_to_link(grid, control_name, value_name,
out=None)
| Parameters: | grid : ModelGrid
control_name : array or field name
value_name : array or field name
out : ndarray, optional
|
|---|---|
| Returns: | 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('node', 'z',
... [[0, 1, 2, 3],
... [7, 6, 5, 4],
... [8, 9, 10, 11]])
>>> _ = rmg.add_field('node', 'vals_to_map',
... [[0, 10, 20, 30],
... [70, 60, 50, 40],
... [80, 90, 100, 110]])
>>> 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.])
LLCATS: NINF LINF MAP
map_value_at_min_node_to_link(grid, control_name, value_name, out=None)[source]¶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.
Construction:
map_value_at_min_node_to_link(grid, control_name, value_name,
out=None)
| Parameters: | grid : ModelGrid
control_name : array or field name
value_name : array or field name
out : ndarray, optional
|
|---|---|
| Returns: | 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('node', 'z',
... [[0, 1, 2, 3],
... [7, 6, 5, 4],
... [8, 9, 10, 11]])
>>> _ = rmg.add_field('node', 'vals_to_map',
... [[0, 10, 20, 30],
... [70, 60, 50, 40],
... [80, 90, 100, 110]])
>>> 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.])
LLCATS: NINF LINF MAP
map_value_at_upwind_node_link_max_to_node(grid, control_name, value_name, out=None)[source]¶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.
Construction:
map_value_at_upwind_node_link_max_to_node(grid, control_name,
value_name, out=None)
| Parameters: | grid : ModelGrid
control_name : array or field name
value_name : array or field name
out : ndarray, optional
|
|---|---|
| Returns: | 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'] = np.array([-1., -2., -1.,
... 0., 0., 0., 0.,
... -1., -2., -1.,
... 0., 0., 0., 0.,
... -1., -2., -1.])
>>> 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('node', 'z')
>>> 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
LLCATS: NINF LINF MAP