pysteps.verification.detcatscores.det_cat_fct

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pysteps.verification.detcatscores.det_cat_fct#

pysteps.verification.detcatscores.det_cat_fct(pred, obs, thr, scores='', axis=None)#

Calculate simple and skill scores for deterministic categorical (dichotomous) forecasts.

Parameters:
  • pred (array_like) – Array of predictions. NaNs are ignored.

  • obs (array_like) – Array of verifying observations. NaNs are ignored.

  • thr (float) – The threshold that is applied to predictions and observations in order to define events vs no events (yes/no).

  • scores ({string, list of strings}, optional) –

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

    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

  • axis (None or int or tuple of ints, optional) –

    Axis or axes along which a score is integrated. The default, axis=None, will integrate all of the elements of the input arrays.

    If axis is -1 (or any negative integer), the integration is not performed and scores are computed on all of the elements in the input arrays.

    If axis is a tuple of ints, the integration is performed on all of the axes specified in the tuple.

Returns:

result – Dictionary containing the verification results.

Return type:

dict