smash.signatures#

smash.signatures(model, sign=None, event_seg=None, domain='obs')[source]#

Compute simulated or observed hydrological signatures of Model.

Parameters:

modelModel

Primary data structure of the hydrological model smash.

signstr, list[str, …] or None, default None

Define signature(s) to compute. Should be one of

'Crc', 'Crchf', 'Crclf', 'Crch2r', 'Cfp2', 'Cfp10', 'Cfp50', 'Cfp90' (continuous signatures-based error metrics)
'Eff', 'Ebf', 'Erc', 'Erchf', 'Erclf', 'Erch2r', 'Elt', 'Epf' (flood event signatures-based error metrics)

Note

If not given, all of continuous and flood event signatures will be computed.

event_segdict[str, Any] or None, default None

A dictionary of event segmentation options when calculating flood event signatures. The keys are 'peak_quant', 'max_duration', and 'by'.

Note

If not given, default values will be set for all elements. If a specific element is not given in the dictionary, a default value will be set for that element. See hydrograph_segmentation for more.

domainstr, default ‘obs’

Compute observed ('obs') or simulated ('sim') signatures.

Returns:

signaturesSignatures: It returns an object containing the results of the signatures computation.

See also

Signatures: Represents signatures computation result.

Examples

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

Run the forward Model

>>> model.forward_run()

Compute simulated signatures

>>> sign = smash.signatures(model, domain="sim")
>>> sign
 cont: <class 'pandas.core.frame.DataFrame'>
event: <class 'pandas.core.frame.DataFrame'>

Access simulated flood event signatures as a pandas.DataFrame

>>> sign.event
       code  season               start  ...    Erch2r  Elt         Epf
0  V3524010  autumn 2014-11-03 03:00:00  ...  0.324836  3.0  100.448631
1  V3515010  autumn 2014-11-03 10:00:00  ...  0.339471  0.0   20.168104
2  V3517010  autumn 2014-11-03 08:00:00  ...  0.319605  1.0    5.356519
[3 rows x 12 columns]

Access simulated continuous signatures as a pandas.DataFrame

>>> sign.cont
       code       Crc     Crchf  ...         Cfp10     Cfp50      Cfp90
0  V3524010  0.208410  0.064490  ...  6.270661e-04  1.767450  27.806826
1  V3515010  0.139829  0.049087  ...  4.919724e-05  0.150414   5.161743
2  V3517010  0.139431  0.047900  ...  6.545733e-07  0.031947   1.355473
[3 rows x 9 columns]

Access specific simulated continuous signature for each gauge

>>> sign.cont["Crc"]
0    0.208410
1    0.139829
2    0.139431
Name: Crc, dtype: float64

Access all simulated continuous signatures for a single gauge

>>> sign.cont[sign.cont["code"] == "V3524010"]
       code      Crc    Crchf  ...     Cfp10    Cfp50      Cfp90
0  V3524010  0.20841  0.06449  ...  0.000627  1.76745  27.806826
[1 rows x 9 columns]

Access specific simulated continuous signatures for a single gauge

>>> sign.cont["Crc"][sign.cont["code"] == "V3524010"]
0    0.20841
Name: Crc, dtype: float64

Use the item method to return the underlying data as a Python scalar

>>> sign.cont["Crc"][sign.cont["code"] == "V3524010"].item()
0.20841039717197418