#! /usr/bin/env python
"""Grid element mappers that are specific to raster grids.
Mapping functions unique to raster grids
+++++++++++++++++++++++
.. autosummary::
:toctree: generated/
~landlab.grid.raster_mappers.map_sum_of_inlinks_to_node
~landlab.grid.raster_mappers.map_mean_of_inlinks_to_node
~landlab.grid.raster_mappers.map_max_of_inlinks_to_node
~landlab.grid.raster_mappers.map_min_of_inlinks_to_node
~landlab.grid.raster_mappers.map_sum_of_outlinks_to_node
~landlab.grid.raster_mappers.map_mean_of_outlinks_to_node
~landlab.grid.raster_mappers.map_max_of_outlinks_to_node
~landlab.grid.raster_mappers.map_min_of_outlinks_to_node
~landlab.grid.raster_mappers.map_mean_of_links_to_node
~landlab.grid.raster_mappers.map_mean_of_horizontal_links_to_node
~landlab.grid.raster_mappers.map_mean_of_horizontal_active_links_to_node
~landlab.grid.raster_mappers.map_mean_of_vertical_links_to_node
~landlab.grid.raster_mappers.map_mean_of_vertical_active_links_to_node
"""
from __future__ import division
import numpy as np
from landlab.grid.structured_quad import links
def map_sum_of_inlinks_to_node(grid, var_name, out=None):
"""Map the sum of links entering a node to the node.
map_sum_of_inlinks_to_node takes an array *at the links* and finds the
inlink values for each node in the grid. it sums the inlinks and returns
values at the nodes.
.. note::
This considers all inactive links to have a value of 0.
Construction::
map_sum_of_inlinks_to_node(grid, var_name, out=None)
Parameters
----------
grid : ModelGrid
A landlab ModelGrid.
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
-------
ndarray
Mapped values at nodes.
Examples
--------
>>> import numpy as np
>>> from landlab.grid.raster_mappers import map_sum_of_inlinks_to_node
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> _ = rmg.add_field('link', 'z', np.arange(17.))
>>> map_sum_of_inlinks_to_node(rmg, 'z')
array([ 0., 0., 1., 2., 3., 11., 13., 15., 10., 25., 27.,
29.])
LLCATS: NINF LINF MAP
"""
if out is None:
out = grid.empty(centering='node')
if type(var_name) is str:
values_at_links = grid.at_link[var_name]
else:
values_at_links = var_name
values_at_links = np.append(values_at_links, 0)
south, west = links._node_in_link_ids(grid.shape)
south, west = south.reshape(south.size), west.reshape(west.size)
out[:] = values_at_links[south] + values_at_links[west]
return out
def map_mean_of_inlinks_to_node(grid, var_name, out=None):
"""Map the mean of links entering a node to the node.
map_mean_of_inlinks_to_node takes an array *at the links* and finds the
inlink values for each node in the grid. It finds the average of
the inlinks and returns values at the nodes.
This considers all inactive links to have a value of 0.
Construction::
map_mean_of_inlinks_to_node(grid, var_name, out=None)
Parameters
----------
grid : ModelGrid
A landlab ModelGrid.
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
-------
ndarray
Mapped values at nodes.
Examples
--------
>>> import numpy as np
>>> from landlab.grid.raster_mappers import map_mean_of_inlinks_to_node
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> _ = rmg.add_field('link', 'z', np.arange(17.))
>>> map_mean_of_inlinks_to_node(rmg, 'z')
array([ 0. , 0. , 0.5, 1. , 1.5, 5.5, 6.5, 7.5, 5. ,
12.5, 13.5, 14.5])
LLCATS: NINF LINF MAP
"""
if out is None:
out = grid.empty(centering='node')
if type(var_name) is str:
values_at_links = grid.at_link[var_name]
else:
values_at_links = var_name
values_at_links = np.append(values_at_links, 0)
south, west = links._node_in_link_ids(grid.shape)
south, west = south.reshape(south.size), west.reshape(west.size)
out[:] = 0.5 * (values_at_links[south] + values_at_links[west])
return out
def map_max_of_inlinks_to_node(grid, var_name, out=None):
"""Map the maximum of links entering a node to the node.
map_max_of_inlinks_to_node takes an array *at the links* and finds the
inlink values for each node in the grid. it finds the maximum value at the
the inlinks and returns values at the nodes.
.. note::
This considers all inactive links to have a value of 0.
Construction::
map_max_of_inlinks_to_node(grid, var_name, out=None)
Parameters
----------
grid : ModelGrid
A landlab ModelGrid.
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
-------
ndarray
Mapped values at nodes.
Examples
--------
>>> import numpy as np
>>> from landlab.grid.raster_mappers import map_max_of_inlinks_to_node
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> _ = rmg.add_field('link', 'z', np.arange(17.))
>>> map_max_of_inlinks_to_node(rmg, 'z')
array([ 0., 0., 1., 2.,
3., 7., 8., 9.,
10., 14., 15., 16.])
LLCATS: NINF LINF MAP
"""
if out is None:
out = grid.empty(centering='node')
if type(var_name) is str:
values_at_links = grid.at_link[var_name]
else:
values_at_links = var_name
values_at_links = np.append(values_at_links, 0)
south, west = links._node_in_link_ids(grid.shape)
south, west = south.reshape(south.size), west.reshape(west.size)
out[:] = np.maximum(values_at_links[south], values_at_links[west])
return out
def map_min_of_inlinks_to_node(grid, var_name, out=None):
"""Map the minimum of links entering a node to the node.
map_min_of_inlinks_to_node takes an array *at the links* and finds the
inlink values for each node in the grid. it finds the minimum value at the
the inlinks and returns values at the nodes.
.. note::
This considers all inactive links to have a value of 0.
Construction::
map_min_of_inlinks_to_node(grid, var_name, out=None)
Parameters
----------
grid : ModelGrid
A landlab ModelGrid.
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
-------
ndarray
Mapped values at nodes.
Examples
--------
>>> import numpy as np
>>> from landlab.grid.raster_mappers import map_min_of_inlinks_to_node
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> _ = rmg.add_field('link', 'z', np.arange(17.))
>>> map_min_of_inlinks_to_node(rmg, 'z')
array([ 0., 0., 0., 0., 0., 4., 5., 6., 0., 11., 12.,
13.])
LLCATS: NINF LINF MAP
"""
if out is None:
out = grid.empty(centering='node')
if type(var_name) is str:
values_at_links = grid.at_link[var_name]
else:
values_at_links = var_name
values_at_links = np.append(values_at_links, 0)
south, west = links._node_in_link_ids(grid.shape)
south, west = south.reshape(south.size), west.reshape(west.size)
out[:] = np.minimum(values_at_links[south], values_at_links[west])
return out
def map_sum_of_outlinks_to_node(grid, var_name, out=None):
"""Map the sum of links leaving a node to the node.
map_sum_of_outlinks_to_node takes an array *at the links* and finds the
outlink values for each node in the grid. it sums the outlinks and returns
values at the nodes.
.. note::
This considers all inactive links to have a value of 0.
Construction::
map_sum_of_outlinks_to_node(grid, var_name, out=None)
Parameters
----------
grid : ModelGrid
A landlab ModelGrid.
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
-------
ndarray
Mapped values at nodes.
Examples
--------
>>> import numpy as np
>>> from landlab.grid.raster_mappers import map_sum_of_outlinks_to_node
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> _ = rmg.add_field('link', 'z', np.arange(17.))
>>> map_sum_of_outlinks_to_node(rmg, 'z')
array([ 3., 5., 7., 6., 17., 19., 21., 13., 14., 15., 16.,
0.])
LLCATS: NINF LINF MAP
"""
if out is None:
out = grid.empty(centering='node')
if type(var_name) is str:
values_at_links = grid.at_link[var_name]
else:
values_at_links = var_name
values_at_links = np.append(values_at_links, 0)
north, east = links._node_out_link_ids(grid.shape)
north, east = north.reshape(north.size), east.reshape(east.size)
out[:] = values_at_links[north] + values_at_links[east]
return out
def map_mean_of_outlinks_to_node(grid, var_name, out=None):
"""Map the mean of links leaving a node to the node.
map_mean_of_outlinks_to_node takes an array *at the links* and finds the
outlink values for each node in the grid. it finds the average of
the outlinks and returns values at the nodes.
.. note::
This considers all inactive links to have a value of 0.
Construction::
map_mean_of_outlinks_to_node(grid, var_name, out=None)
Parameters
----------
grid : ModelGrid
A landlab ModelGrid.
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
-------
ndarray
Mapped values at nodes.
Examples
--------
>>> import numpy as np
>>> from landlab.grid.raster_mappers import map_mean_of_outlinks_to_node
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> _ = rmg.add_field('link', 'z', np.arange(17.))
>>> map_mean_of_outlinks_to_node(rmg, 'z')
array([ 1.5, 2.5, 3.5, 3. , 8.5, 9.5, 10.5, 6.5, 7. ,
7.5, 8. , 0. ])
LLCATS: NINF LINF MAP
"""
if out is None:
out = grid.empty(centering='node')
if type(var_name) is str:
values_at_links = grid.at_link[var_name]
else:
values_at_links = var_name
values_at_links = np.append(values_at_links, 0)
north, east = links._node_out_link_ids(grid.shape)
north, east = north.reshape(north.size), east.reshape(east.size)
out[:] = 0.5 * (values_at_links[north] + values_at_links[east])
return out
def map_max_of_outlinks_to_node(grid, var_name, out=None):
"""Map the max of links leaving a node to the node.
map_max_of_outlinks_to_node takes an array *at the links* and finds the
outlink values for each node in the grid. it finds the maximum value at the
the outlinks and returns values at the nodes.
.. note::
This considers all inactive links to have a value of 0.
Construction::
map_max_of_outlinks_to_node(grid, var_name, out=None)
Parameters
----------
grid : ModelGrid
A landlab ModelGrid.
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
-------
ndarray
Mapped values at nodes.
Examples
--------
>>> import numpy as np
>>> from landlab.grid.raster_mappers import map_max_of_outlinks_to_node
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> _ = rmg.add_field('link', 'z', np.arange(17.))
>>> map_max_of_outlinks_to_node(rmg, 'z')
array([ 3., 4., 5., 6., 10., 11., 12., 13., 14., 15., 16.,
0.])
LLCATS: NINF LINF MAP
"""
if out is None:
out = grid.empty(centering='node')
if type(var_name) is str:
values_at_links = grid.at_link[var_name]
else:
values_at_links = var_name
values_at_links = np.append(values_at_links, 0)
north, east = links._node_out_link_ids(grid.shape)
north, east = north.reshape(north.size), east.reshape(east.size)
np.maximum(values_at_links[north], values_at_links[east], out=out)
return out
def map_min_of_outlinks_to_node(grid, var_name, out=None):
"""Map the min of links leaving a node to the node.
map_min_of_outlinks_to_node takes an array *at the links* and finds the
outlink values for each node in the grid. It finds the minimum value at the
the outlinks and returns values at the nodes.
.. note::
This considers all inactive links to have a value of 0.
Construction::
map_min_of_outlinks_to_node(grid, var_name, out=None)
Parameters
----------
grid : ModelGrid
A landlab ModelGrid.
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
-------
ndarray
Mapped values at nodes.
Examples
--------
>>> import numpy as np
>>> from landlab.grid.raster_mappers import map_min_of_outlinks_to_node
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> _ = rmg.add_field('link', 'z', np.arange(17.))
>>> map_min_of_outlinks_to_node(rmg, 'z')
array([ 0., 1., 2., 0., 7., 8., 9., 0., 0., 0., 0., 0.])
LLCATS: NINF LINF MAP
"""
if out is None:
out = grid.empty(centering='node')
if type(var_name) is str:
values_at_links = grid.at_link[var_name]
else:
values_at_links = var_name
values_at_links = np.append(values_at_links, 0)
north, east = links._node_out_link_ids(grid.shape)
north, east = north.reshape(north.size), east.reshape(east.size)
np.minimum(values_at_links[north], values_at_links[east], out=out)
return out
def map_mean_of_links_to_node(grid, var_name, out=None):
"""Map the mean of links touching a node to the node.
map_mean_all_links_to_node takes an array *at the links* and finds the
average of all ~existing~ link neighbor values for each node in the grid.
it returns values at the nodes.
.. note::
This considers all inactive links to have a value of 0.
Construction::
map_mean_of_links_to_node(grid, var_name, out=None)
Parameters
----------
grid : ModelGrid
A landlab ModelGrid.
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
-------
ndarray
Mapped values at nodes.
Examples
--------
>>> import numpy as np
>>> from landlab.grid.raster_mappers import map_mean_of_links_to_node
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> _ = rmg.add_field('link', 'z', np.arange(17.))
>>> map_mean_of_links_to_node(rmg, 'z')
array([ 1.5 , 1.66666667, 2.66666667, 4. ,
6.66666667, 7.5 , 8.5 , 9.33333333,
12. , 13.33333333, 14.33333333, 14.5 ])
LLCATS: NINF LINF MAP
"""
if out is None:
out = grid.empty(centering='node')
if type(var_name) is str:
values_at_links = grid.at_link[var_name]
else:
values_at_links = var_name
values_at_links = np.append(values_at_links, 0)
north, east = links._node_out_link_ids(grid.shape)
north, east = north.reshape(north.size), east.reshape(east.size)
south, west = links._node_in_link_ids(grid.shape)
south, west = south.reshape(south.size), west.reshape(west.size)
number_of_links = links.number_of_links_per_node(grid.shape)
number_of_links = number_of_links.reshape(number_of_links.size)
number_of_links.astype(float, copy=False)
out[:] = (values_at_links[north] + values_at_links[east] +
values_at_links[south] + values_at_links[west]) / number_of_links
return out
def map_mean_of_horizontal_links_to_node(grid, var_name, out=None):
"""
Map the mean of links in the x direction touching a node to the node.
map_mean_of_horizontal_links_to_node takes an array *at the links* and
finds the average of all horizontal (x-direction) link neighbor values
for each node in the grid.
It returns an array at the nodes of the mean of these values. If a link
is absent, it is ignored.
Note that here a positive returned value means flux to the east, and
a negative to the west.
Parameters
----------
grid : ModelGrid
A landlab ModelGrid.
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
-------
ndarray
Mapped values at nodes.
Examples
--------
>>> import numpy as np
>>> from landlab.grid.raster_mappers import map_mean_of_horizontal_links_to_node
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> _ = rmg.add_field('link', 'z', np.arange(17.))
>>> map_mean_of_horizontal_links_to_node(rmg, 'z')
array([ 0. , 0.5, 1.5, 2. , 7. , 7.5, 8.5, 9. , 14. ,
14.5, 15.5, 16. ])
LLCATS: NINF LINF MAP
"""
if out is None:
out = grid.empty(centering='node')
if type(var_name) is str:
values_at_links = grid.at_link[var_name]
else:
values_at_links = var_name
hoz_links = grid.links_at_node[:, [0, 2]]
hoz_link_dirs = np.fabs(grid.link_dirs_at_node[:, [0, 2]])
# ^retain "true" directions of links
valid_links = values_at_links[hoz_links]*hoz_link_dirs # invalids = 0
num_valid_links = hoz_link_dirs.sum(axis=1)
np.divide(valid_links.sum(axis=1), num_valid_links, out=out)
return out
def map_mean_of_horizontal_active_links_to_node(grid, var_name, out=None):
"""
Map the mean of active links in the x direction touching node to the node.
map_mean_of_horizontal_active_links_to_node takes an array *at the links*
and finds the average of all horizontal (x-direction) link neighbor values
for each node in the grid.
It returns an array at the nodes of the mean of these values. If a link
is absent, it is ignored. If a node has no active links, it receives 0.
Note that here a positive returned value means flux to the east, and
a negative to the west.
Parameters
----------
grid : ModelGrid
A landlab ModelGrid.
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
-------
ndarray
Mapped values at nodes.
Examples
--------
>>> import numpy as np
>>> from landlab.grid.raster_mappers import map_mean_of_horizontal_active_links_to_node
>>> from landlab import RasterModelGrid, CLOSED_BOUNDARY
>>> rmg = RasterModelGrid((3, 4))
>>> _ = rmg.add_field('link', 'z', -np.arange(17, dtype=float))
>>> rmg.status_at_node[rmg.nodes_at_left_edge] = CLOSED_BOUNDARY
>>> map_mean_of_horizontal_active_links_to_node(rmg, 'z')
array([ 0. , 0. , 0. , 0. , 0. , -8. , -8.5, -9. , 0. , 0. , 0. ,
0. ])
LLCATS: NINF LINF MAP
"""
if out is None:
out = grid.zeros(centering='node', dtype=float)
else:
out.fill(0.)
if type(var_name) is str:
values_at_links = grid.at_link[var_name]
else:
values_at_links = var_name
hoz_links = grid.links_at_node[:, [0, 2]]
hoz_link_dirs = np.fabs(grid.active_link_dirs_at_node[:, [0, 2]])
# ^retain "true" directions of links; no inactives now
valid_links = values_at_links[hoz_links]*hoz_link_dirs # invalids = 0
num_valid_links = hoz_link_dirs.sum(axis=1)
good_nodes = num_valid_links != 0
out[good_nodes] = (valid_links.sum(axis=1)[good_nodes] /
num_valid_links[good_nodes])
return out
def map_mean_of_vertical_links_to_node(grid, var_name, out=None):
"""
Map the mean of links in the y direction touching a node to the node.
map_mean_of_vertical_links_to_node takes an array *at the links* and
finds the average of all vertical (y-direction) link neighbor values
for each node in the grid.
It returns an array at the nodes of the mean of these values. If a link
is absent, it is ignored.
Note that here a positive returned value means flux to the north, and
a negative to the south.
Parameters
----------
grid : ModelGrid
A landlab ModelGrid.
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
-------
ndarray
Mapped values at nodes.
Examples
--------
>>> import numpy as np
>>> from landlab.grid.raster_mappers import map_mean_of_vertical_links_to_node
>>> from landlab import RasterModelGrid
>>> rmg = RasterModelGrid((3, 4))
>>> _ = rmg.add_field('link', 'z', np.arange(17.))
>>> map_mean_of_vertical_links_to_node(rmg, 'z')
array([ 3. , 4. , 5. , 6. , 6.5, 7.5, 8.5, 9.5, 10. ,
11. , 12. , 13. ])
LLCATS: NINF LINF MAP
"""
if out is None:
out = grid.empty(centering='node')
if type(var_name) is str:
values_at_links = grid.at_link[var_name]
else:
values_at_links = var_name
vert_links = grid.links_at_node[:, [1, 3]]
vert_link_dirs = np.fabs(grid.link_dirs_at_node[:, [1, 3]])
# ^retain "true" directions of links
valid_links = values_at_links[vert_links]*vert_link_dirs # invalids = 0
num_valid_links = vert_link_dirs.sum(axis=1)
np.divide(valid_links.sum(axis=1), num_valid_links, out=out)
return out
def map_mean_of_vertical_active_links_to_node(grid, var_name, out=None):
"""
Map the mean of active links in the y direction touching node to the node.
map_mean_of_vertical_active_links_to_node takes an array *at the links*
and finds the average of all vertical (y-direction) link neighbor values
for each node in the grid.
It returns an array at the nodes of the mean of these values. If a link
is absent, it is ignored. If a node has no active links, it receives 0.
Note that here a positive returned value means flux to the north, and
a negative to the south.
Parameters
----------
grid : ModelGrid
A landlab ModelGrid.
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
-------
ndarray
Mapped values at nodes.
Examples
--------
>>> import numpy as np
>>> from landlab.grid.raster_mappers import map_mean_of_vertical_active_links_to_node
>>> from landlab import RasterModelGrid, CLOSED_BOUNDARY
>>> rmg = RasterModelGrid((3, 4))
>>> _ = rmg.add_field('link', 'z', -np.arange(17, dtype=float))
>>> rmg.status_at_node[rmg.nodes_at_bottom_edge] = CLOSED_BOUNDARY
>>> map_mean_of_vertical_active_links_to_node(rmg, 'z')
array([ 0., 0., 0., 0., 0., -11., -12., 0., 0., -11., -12.,
0.])
LLCATS: NINF LINF MAP
"""
if out is None:
out = grid.zeros(centering='node', dtype=float)
else:
out.fill(0.)
if type(var_name) is str:
values_at_links = grid.at_link[var_name]
else:
values_at_links = var_name
vert_links = grid.links_at_node[:, [1, 3]]
vert_link_dirs = np.fabs(grid.active_link_dirs_at_node[:, [1, 3]])
# ^retain "true" directions of links; no inactives now
valid_links = values_at_links[vert_links]*vert_link_dirs # invalids = 0
num_valid_links = vert_link_dirs.sum(axis=1)
good_nodes = num_valid_links != 0
out[good_nodes] = (valid_links.sum(axis=1)[good_nodes] /
num_valid_links[good_nodes])
return out
