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_links_to_node(mg, var_name, out=None):
"""Map the mean of links touching a node to the node.
map_average_all_links_to_node takes a field *at the links* and finds the
average of all ~existing~ link neighbor values for each node in the grid.
it returns a field at the nodes with the same var_name
as the link field.
.. note::
This considers all inactive links to have a value of 0.
Parameters
----------
mg : ModelGrid
A landlab ModelGrid.
var_name : str
Name of variable field defined at nodes.
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([ 4. , 6. , 7. , 6.5 ,
5. , 7.25 , 8.25 , 7.66666667,
9. , 11.33333333, 12.33333333, 11.5 ])
"""
if out is None:
out = mg.empty(centering='node')
values_at_links = mg.at_link[var_name]
values_at_links = np.append(values_at_links, 0)
north, east = links._node_out_link_ids(mg.shape)
north, east = north.reshape(north.size), east.reshape(east.size)
south, west = links._node_in_link_ids(mg.shape)
south, west = south.reshape(south.size), west.reshape(west.size)
number_of_links = links.number_of_links_per_node(mg.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_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.
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("z", np.arange(17.), at="link")
>>> 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 a field *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 a field at the nodes with the same var_name
as the link field.
.. note::
This considers all inactive links to have a value of 0.
Parameters
----------
grid : ModelGrid
A landlab ModelGrid.
var_name : str
Name of variable field defined at nodes.
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.])
"""
if out is None:
out = grid.empty(centering='node')
values_at_links = grid.at_link[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_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.
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_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.
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
