"""terrainbento **RandomPrecipitator**."""
import numpy as np
[docs]class RandomPrecipitator(object):
"""Generate random precipitation.
**RandomPrecipitator** populates the at-node field "rainfall__flux" with
random values drawn from a distribution. All distributions provided in the
`numpy.random submodule
<https://docs.scipy.org/doc/numpy-1.15.1/reference/routines.random.html#distributions>`_
are supported.
Examples
--------
>>> import numpy as np
>>> np.random.seed(42)
>>> from landlab import RasterModelGrid
>>> from terrainbento import RandomPrecipitator
>>> grid = RasterModelGrid((5,5))
>>> precipitator = RandomPrecipitator(grid)
>>> np.round(
... grid.at_node["rainfall__flux"].reshape(grid.shape),
... decimals=2)
array([[ 0.37, 0.95, 0.73, 0.6 , 0.16],
[ 0.16, 0.06, 0.87, 0.6 , 0.71],
[ 0.02, 0.97, 0.83, 0.21, 0.18],
[ 0.18, 0.3 , 0.52, 0.43, 0.29],
[ 0.61, 0.14, 0.29, 0.37, 0.46]])
>>> precipitator.run_one_step(10)
>>> np.round(
... grid.at_node["rainfall__flux"].reshape(grid.shape),
... decimals=2)
array([[ 0.79, 0.2 , 0.51, 0.59, 0.05],
[ 0.61, 0.17, 0.07, 0.95, 0.97],
[ 0.81, 0.3 , 0.1 , 0.68, 0.44],
[ 0.12, 0.5 , 0.03, 0.91, 0.26],
[ 0.66, 0.31, 0.52, 0.55, 0.18]])
"""
[docs] def __init__(self, grid, distribution="uniform", **kwargs):
"""
Parameters
----------
grid : model grid
distribution : str, optional
Name of the distribution provided by the np.random
submodule. Default is "uniform".
kwargs : dict
Keyword arguments to pass to the ``np.random`` distribution
function.
"""
self._grid = grid
if "rainfall__flux" not in grid.at_node:
grid.add_ones("node", "rainfall__flux")
self.function = np.random.__dict__[distribution]
self._kwargs = kwargs
self.run_one_step(0.0)
[docs] def run_one_step(self, step):
"""Run **RandomPrecipitator** forward by duration ``step``"""
values = self.function(size=self._grid.size("node"), **self._kwargs)
self._grid.at_node["rainfall__flux"][:] = values
