Source code for umami.calculations.metric.mask_aggregation
import numpy as np
from .aggregate import _aggregate
[docs]def mask_aggregation(grid, field, mask, method, **kwds):
"""Aggregate a field value masked by a boolean field.
``mask_aggregation`` calculates aggregate values on a field masked by a
second, boolean field. 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.
mask : str
An at-node Landlab grid field of boolean type. Aggregation is done
where the masked value is True.
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 ``mask_aggregation`` function.
>>> from landlab import RasterModelGrid
>>> from landlab.components import FlowAccumulator
>>> from umami.calculations import mask_aggregation
>>> grid = RasterModelGrid((10, 10))
>>> z = grid.add_zeros("node", "topographic__elevation")
>>> z += grid.x_of_node + grid.y_of_node
Create a boolean mask and add it to the grid.
>>> mask = grid.add_field("mask", z > 11, at="node")
``mask_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
>>> mask_aggregation(grid, "topographic__elevation", "mask", "mean")
14.0
>>> mask_aggregation(
... grid,
... "topographic__elevation",
... "mask",
... "percentile",
... q=10)
12.0
Next, the same calculations are shown as part of an umami ``Metric``.
>>> from io import StringIO
>>> from umami import Metric
>>> file_like=StringIO('''
... mask_mean:
... _func: mask_aggregation
... mask: mask
... method: mean
... field: topographic__elevation
... mask_10thptile:
... _func: mask_aggregation
... mask: mask
... method: percentile
... field: topographic__elevation
... q: 10
... ''')
>>> metric = Metric(grid)
>>> metric.add_from_file(file_like)
>>> metric.names
['mask_mean', 'mask_10thptile']
>>> metric.calculate()
>>> metric.values
[14.0, 12.0]
"""
masked = grid.at_node[mask]
vals = grid.at_node[field][masked]
return _aggregate(vals, method, **kwds)