smash.multiset_estimate#

smash.multiset_estimate(model, multiset, alpha=None, common_options=None, return_options=None)[source]#

Model assimilation using Bayesian-like estimation on multiple sets of solutions.

Parameters:

modelModel

Primary data structure of the hydrological model smash.

multisetMultipleForwardRun

The returned object created by multiple_forward_run method containing information about multiple sets of rainfall-runoff parameters or initial states.

alphafloat, list[float, …], or None, default None

A regularization parameter that controls the decay rate of the likelihood function. If alpha is a list, the L-curve approach will be used to find an optimal value for the regularization parameter.

Note

If not given, a default numeric range will be set for optimization through the L-curve process.

common_optionsdict[str, Any] or None, default None

Dictionary containing common options with two elements:

ncpuint, default 1: Number of CPU(s) to perform a parallel computation.

Warning

Parallel computation is not supported on Windows.
verbosebool, default False: Whether to display information about the running method.

return_optionsdict[str, Any] or None, default None

Dictionary containing return options to save additional simulation results. The elements are:

time_stepstr, pandas.Timestamp, pandas.DatetimeIndex or list[str, …], default ‘all’

Returned time steps. There are five ways to specify it:

An alias among 'all' (return all time steps).
A date as string which respect pandas.Timestamp format
A pandas.Timestamp.
A pandas.DatetimeIndex.
A sequence of dates as strings.

>>> return_options = {
    "time_step": "all",
}
>>> return_options = {
    "time_step": "1997-12-21",
}
>>> return_options = {
    "time_step": pd.Timestamp("19971221"),
}
>>> return_options = {
    "time_step": pd.date_range(
        start="1997-12-21",
        end="1998-12-21",
        freq="1D"
    ),
}
>>> return_options = {
    "time_step": ["1998-05-23", "1998-05-24", "1998-05-25"],
}

Note

It only applies to the following variables: 'rr_states' and 'q_domain'

rr_statesbool, default False

Whether to return rainfall-runoff states for specific time steps.

q_domainbool, default False

Whether to return simulated discharge on the whole domain for specific time steps.

internal_fluxesbool, default False

Whether to return internal fluxes depending on the model structure on the whole domain for specific time steps.

costbool, default False

Whether to return cost value.

jobsbool, default False

Whether to return jobs (observation component of cost) value.

lcurve_multisetbool, default False

Whether to return the multiset estimate L-curve.

Returns:

modelModel: It returns an updated copy of the initial Model object.
multiset_estimateMultisetEstimate or None, default None: It returns an object containing additional simulation results with the keys defined in return_options. If no keys are defined, it returns None.

See also

MultisetEstimate: Represents multiset estimate optional results.
MultipleForwardRun: Represents multiple forward run computation result.

Examples

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

Define sampling problem and generate samples

>>> problem = {
...    'num_vars': 4,
...    'names': ['cp', 'ct', 'kexc', 'llr'],
...    'bounds': [[1, 2000], [1, 1000], [-20, 5], [1, 1000]]
... }
>>> sr = generate_samples(problem, n=100, random_state=11)

Run Model with multiple sets of parameters

>>> mfr = smash.multiple_forward_run(model, samples=sr)
</> Multiple Forward Run
    Forward Run 100/100 (100%)

Estimate Model on multiple sets of solutions

>>> model_estim = smash.multiset_estimate(model, multiset=mfr)
</> Multiple Set Estimate
    L-curve Computing: 100%|████████████████████████████████████████| 50/50 [00:02<00:00, 17.75it/s]

Get the simulated discharges

>>> model_estim.response.q
array([[9.4571486e-05, 9.3920688e-05, 9.3143637e-05, ..., 1.7423288e+01,
        1.7193638e+01, 1.6963835e+01],
       [2.5758292e-05, 2.4744744e-05, 2.3561088e-05, ..., 3.6616585e+00,
        3.6165960e+00, 3.5724759e+00],
       [5.9654208e-06, 5.0872231e-06, 4.2139386e-06, ..., 9.0600485e-01,
        8.9525825e-01, 8.8473159e-01]], dtype=float32)

smash.multiset_estimate#

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