smash.SignSensResult#

class smash.SignSensResult[source]#

Represents signatures sensitivity computation result.

See also

Model.signatures_sensitivity

Compute the first- and total-order variance-based sensitivity (Sobol indices) of spatially uniform hydrological model parameters to the output signatures.

Notes

This class is essentially a subclass of dict with attribute accessors.

Attributes:
contdict

A dictionary with two keys

  • ‘total_si’ : representing the total-order Sobol indices of the hydrological model parameters to continuous signatures.

  • ‘first_si’ : representing the first-order Sobol indices of the hydrological model parameters to continuous signatures.

Each value of the dictionary is a sub-dictionary with the keys are the hydrological model parameters. Then each value of each sub-dictionary (associating to a model parameter) is a dataframe containing the sensitivities of the associated model parameter to all studied signatures. The column names of each dataframe consist of the catchment code and studied signature names.

eventdict

A dictionary with two keys

  • ‘total_si’ : representing the total-order Sobol indices of the hydrological model parameters to flood event signatures.

  • ‘first_si’ : representing the first-order Sobol indices of the hydrological model parameters to flood event signatures.

Each value of the dictionary is a sub-dictionary with the keys are the hydrological model parameters. Then each value of each sub-dictionary (associating to a model parameter) is a dataframe containing the sensitivities of the associated model parameter to all studied signatures. The column names of each dataframe consist of the catchment code, the season that event occurrs, the beginning/end of each event and studied signature names.

sample: pandas.DataFrame

A dataframe containing the generated samples used to compute sensitivity indices.

Methods

clear()

copy()

fromkeys(iterable[, value])

Create a new dictionary with keys from iterable and values set to value.

get(key[, default])

Return the value for key if key is in the dictionary, else default.

items()

keys()

pop(key[, default])

If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem(/)

Remove and return a (key, value) pair as a 2-tuple.

setdefault(key[, default])

Insert key with a value of default if key is not in the dictionary.

update([E, ]**F)

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values()