smash.Model.set_rr_parameters#

Model.set_rr_parameters(key, value)[source]#

Set the values of a Model rainfall-runoff parameter.

This method performs an in-place operation on the Model object.

Parameters:

keystr: The name of the rainfall-runoff parameter.
valuefloat or numpy.ndarray: The value(s) to set to the rainfall-runoff parameter. If the value is a numpy.ndarray, its shape must be broadcastable into the rainfall-runoff parameter shape.

See also

Model.get_rr_parameters: Get the values of a Model rainfall-runoff parameter.
Model.rr_parameters: Model rainfall-runoff parameters.

Examples

>>> from smash.factory import load_dataset
>>> setup, mesh = load_dataset("cance")
>>> model = smash.Model(setup, mesh)

Set a specific value to a rainfall-runoff parameter grid

>>> model.set_rr_parameters("cp", 150)

Access its value

>>> model.get_rr_parameters("cp")
array([[150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150,
        150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150,
        ...
        150, 150, 150, 150, 150, 150]], dtype=float32)

Set a grid with a shape equivalent to the rainfall-runoff parameter filled with random values between 10 and 500

Get the rainfall-runoff parameter shape

>>> shape = model.get_rr_parameters("cp").shape
>>> shape
(28, 28)

Generate the random grid

>>> np.random.seed(99)
>>> random_arr = np.random.randint(10, 500, shape)
>>> random_arr
array([[139,  45, 195, 178, 211, 242, 220,  78, 207, 446, 395, 417, 264,
        301,  65, 459, 186, 231,  69, 496, 373, 254, 225, 140, 202, 150,
        ...
        107, 386]])

Set to the rainfall-runoff parameter the random grid

>>> model.set_rr_parameters("cp", random_arr)
>>> model.get_rr_parameters("cp")
array([[139.,  45., 195., 178., 211., 242., 220.,  78., 207., 446., 395.,
        417., 264., 301.,  65., 459., 186., 231.,  69., 496., 373., 254.,
        ...
        243., 424., 301., 413., 107., 386.]], dtype=float32)

Note

This method is equivalent to directly slicing the rr_parameters.values array (as shown below) and change the values but is simpler and safer to use.

Access the rainfall-runoff parameter keys

>>> model.rr_parameters.keys
array(['ci', 'cp', 'ct', 'kexc', 'llr'], dtype='<U4')

Get the index of the rainfall-runoff parameter 'cp'

>>> ind = np.argwhere(model.rr_parameters.keys == "cp").item()
>>> ind
1

Slice the rr_parameters.values array on the last axis and change its values

>>> model.rr_parameters.values[..., ind] = 56
>>> model.rr_parameters.values[..., ind]
array([[56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56.,
        56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56., 56.,
        ...
        56., 56.]], dtype=float32)

Warning

In that case, there’s no problem to set the value 56 to the rainfall-runoff parameter 'cp', but each rainfall-runoff parameter has a feasibility domain, and that outside this domain, the model cannot run. For example, the feasibility domain of 'cp' is \(]0, +\inf[\).

Trying to set a negative value to the rainfall-runoff parameter 'cp' without the setter

>>> model.rr_parameters.values[..., ind] = -47
>>> model.rr_parameters.values[..., ind]
array([[-47., -47., -47., -47., -47., -47., -47., -47., -47., -47., -47.,
        -47., -47., -47., -47., -47., -47., -47., -47., -47., -47., -47.,
        ...
        -47., -47., -47., -47., -47., -47.]], dtype=float32)

No particular problem doing this but trying with the setter

>>> model.set_rr_parameters("cp", -47)
ValueError: Invalid value for model rr_parameter 'cp'. rr_parameter domain [-47, -47] is not included
in the feasible domain ]0, inf[

Finally, trying to run the Model with a negative value set to the rainfall-runoff parameter 'cp' leads to the same error.

>>> model.forward_run()
ValueError: Invalid value for model rr_parameter 'cp'. rr_parameter domain [-47, -47] is not included
in the feasible domain ]0, inf[