pysteps.verification.detcatscores.det_cat_fct_compute¶

pysteps.verification.detcatscores.det_cat_fct_compute(contab, scores='')¶

Compute simple and skill scores for deterministic categorical (dichotomous) forecasts from a contingency table object.

Parameters

contab: dict

A contingency table object initialized with pysteps.verification.detcatscores.det_cat_fct_init and populated with pysteps.verification.detcatscores.det_cat_fct_accum.

scores: {string, list of strings}, optional

The name(s) of the scores. The default, scores=””, will compute all available scores. The available score names a

Name	Description
ACC	accuracy (proportion correct)
BIAS	frequency bias
CSI	critical success index (threat score)
ETS	equitable threat score
F1	the harmonic mean of precision and sensitivity
FA	false alarm rate (prob. of false detection, fall-out, false positive rate)
FAR	false alarm ratio (false discovery rate)
GSS	Gilbert skill score (equitable threat score)
HK	Hanssen-Kuipers discriminant (Pierce skill score)
HSS	Heidke skill score
MCC	Matthews correlation coefficient
POD	probability of detection (hit rate, sensitivity, recall, true positive rate)
SEDI	symmetric extremal dependency index

Returns

result: dict: Dictionary containing the verification results.