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_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_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_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.
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("z", np.arange(17.), at="link")
>>> 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(mg, var_name, out=None):
"""Map the max of links leaving a node to the node.
map_max_outlink_value_to_node takes a field *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 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_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([ 8., 9., 10., 3., 11., 12., 13., 7., 14., 15., 16.,
0.])
"""
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)
np.maximum(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.
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
