def test_route_to_multiple_error_raised_watershed_mask():
mg = RasterModelGrid((10, 10))
z = mg.add_zeros("topographic__elevation", at="node")
z += mg.x_of_node + mg.y_of_node
fa = FlowAccumulator(mg, flow_director="MFD")
fa.run_one_step()
with pytest.raises(NotImplementedError):
get_watershed_mask(mg, 10)
def test_route_to_multiple_error_raised_watershed_mask():
mg = RasterModelGrid((10, 10))
z = mg.add_zeros("node", "topographic__elevation")
z += mg.x_of_node + mg.y_of_node
fa = FlowAccumulator(mg, flow_director="MFD")
fa.run_one_step()
with pytest.raises(NotImplementedError):
get_watershed_mask(mg, 10)
def hypsometric_integral(grid, outlet_id):
"""Calculate the hypsometric integral for the model grid.
The hypsometric integral :math:`I` is defined as
.. math::
I = \\frac{\\frac{1}{N} \\sum_{i=0}^{N}
\\left( z - \\min \\left( z\\right) \\right)}
{\\max \\left(z\\right) - \\min \\left( z \\right)}
Where :math:`z` is the set of elevation values, and :math:`N` is the number
of elevation values.
Parameters
----------
grid : Landlab model grid
outlet_id : int
Outlet id of the watershed.
Returns
-------
I : float
The hypsometric integral.
Examples
--------
First an example that only uses the ``hypsometric_integral`` function.
>>> from landlab import RasterModelGrid
>>> from landlab.components import FlowAccumulator
>>> from umami.calculations import hypsometric_integral
>>> grid = RasterModelGrid((10, 10))
>>> z = grid.add_zeros("node", "topographic__elevation")
>>> z += grid.x_of_node + grid.y_of_node
>>> fa = FlowAccumulator(grid)
>>> fa.run_one_step()
>>> hypsometric_integral(grid, 1)
0.5
Next, the same calculations are shown as part of an umami ``Metric``.
>>> from io import StringIO
>>> from umami import Metric
>>> file_like=StringIO('''
... hi:
... _func: hypsometric_integral
... outlet_id: 1
... ''')
>>> metric = Metric(grid)
>>> metric.add_from_file(file_like)
>>> metric.names
['hi']
>>> metric.calculate()
>>> metric.values
[0.5]
"""
# Get just those elevation values that are within the watershed
mask = get_watershed_mask(grid, outlet_id)
vals = grid.at_node["topographic__elevation"][mask]
# Get min and max
min_val = np.amin(vals)
max_val = np.amax(vals)
# Calc and return the hypsometric_integral
return np.mean(vals - min_val) / (max_val - min_val)
def kstest_watershed(model_grid, data_grid, field, outlet_id):
"""Calculate an Kolmogorov-Smirnov test for a watershed.
``kstest_watershed`` calculates the Kolmogorov-Smirnov test for
goodness of fit using the function ``ks_2samp`` from ``scipy.stats``.
Given an *outlet_id* it identifes a watershed mask for the *data_grid*. It
then uses that mask on both the *data_grid* and the *model_grid*.
If the field is "flow__distance", then this performs a KS test of the width
function.
Parameters
----------
model_grid : Landlab model grid
data_grid : Landlab model grid
field : str
An at-node Landlab grid field that is present on the model grid.
outlet_id : int
Returns
-------
out : float
The KS test statistic
Examples
--------
First an example that only uses the ``kstest`` function.
>>> import numpy as np
>>> from landlab import RasterModelGrid
>>> from landlab.components import FlowAccumulator
>>> from umami.calculations import kstest_watershed
>>> np.random.seed(42)
>>> model = RasterModelGrid((10, 10))
>>> z_model = model.add_zeros("node", "topographic__elevation")
>>> z_model += model.x_of_node + model.y_of_node
>>> data = RasterModelGrid((10, 10))
>>> z_data = data.add_zeros("node", "topographic__elevation")
>>> z_data += data.x_of_node + data.y_of_node
>>> z_data[data.core_nodes] += np.random.random(data.core_nodes.shape)
>>> data_fa = FlowAccumulator(data)
>>> data_fa.run_one_step()
>>> model_fa = FlowAccumulator(model)
>>> model_fa.run_one_step()
>>> np.round(
... kstest_watershed(
... model,
... data,
... "topographic__elevation",
... outlet_id=1),
... decimals=3)
0.5
Next, the same calculations are shown as part of an umami ``Residual``.
>>> from io import StringIO
>>> from umami import Residual
>>> file_like=StringIO('''
... ksw:
... _func: kstest_watershed
... outlet_id: 1
... field: topographic__elevation
... ''')
>>> residual = Residual(model, data)
>>> residual.add_from_file(file_like)
>>> residual.names
['ksw']
>>> residual.calculate()
>>> np.round(residual.values, decimals=3)
array([ 0.5])
"""
mask = get_watershed_mask(data_grid, outlet_id)
model_vals = model_grid.at_node[field][mask]
data_vals = data_grid.at_node[field][mask]
d, _ = ks_2samp(model_vals, data_vals)
return d
def watershed_aggregation(grid, field, outlet_id, method, **kwds):
"""Aggregate a field value over a watershed.
``watershed_aggregation`` calculates aggregate values on the nodes in a
watershed that drain to *outlet_id*. It supports all methods in the
`numpy`_ namespace that reduce an array to a scalar.
.. _numpy: https://numpy.org
Parameters
----------
grid : Landlab model grid
field : str
An at-node Landlab grid field that is present on the model grid.
outlet_id : int
Outlet id of the watershed.
method : str
The name of a numpy namespace method.
**kwds
Any additional keyword arguments needed by the method.
Returns
-------
out : float
The aggregate value.
Examples
--------
First an example that only uses the ``watershed_aggregation`` function.
>>> from landlab import RasterModelGrid
>>> from landlab.components import FlowAccumulator
>>> from umami.calculations import watershed_aggregation
>>> grid = RasterModelGrid((10, 10))
>>> z = grid.add_zeros("node", "topographic__elevation")
>>> z += grid.x_of_node + grid.y_of_node
>>> fa = FlowAccumulator(grid)
>>> fa.run_one_step()
``watershed_aggregation`` supports all functions in the `numpy`_ namespace.
Here we show `mean`_ and `percentile`_. The latter of which takes an
additional argument, *q*.
.. _numpy: https://numpy.org
.. _mean: https://docs.scipy.org/doc/numpy/reference/generated/numpy.mean.html
.. _percentile: https://docs.scipy.org/doc/numpy/reference/generated/numpy.percentile.html
>>> watershed_aggregation(grid, "topographic__elevation", 1, "mean")
5.0
>>> watershed_aggregation(
... grid,
... "topographic__elevation",
... 1,
... "percentile",
... q=10)
1.8
Next, the same calculations are shown as part of an umami ``Metric``.
>>> from io import StringIO
>>> from umami import Metric
>>> file_like=StringIO('''
... oid1_mean:
... _func: watershed_aggregation
... outlet_id: 1
... method: mean
... field: topographic__elevation
... oid1_10thptile:
... _func: watershed_aggregation
... outlet_id: 1
... method: percentile
... field: topographic__elevation
... q: 10
... ''')
>>> metric = Metric(grid)
>>> metric.add_from_file(file_like)
>>> metric.names
['oid1_mean', 'oid1_10thptile']
>>> metric.calculate()
>>> metric.values
[5.0, 1.8]
"""
mask = get_watershed_mask(grid, outlet_id)
vals = grid.at_node[field][mask]
return _aggregate(vals, method, **kwds)