class RandomPrecipitator

RandomPrecipitator

terrainbento RandomPrecipitator.

class RandomPrecipitator(grid, distribution='uniform', **kwargs)

Bases: 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 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]])

__init__(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.

run_one_step(step)

Run RandomPrecipitator forward by duration step