def get_method(name, type="deterministic"):
    """
    Return a callable function for the method corresponding to the given
    verification score.

    Parameters
    ----------
    name : str
        Name of the verification method. The available options are:\n\

        type: deterministic

        .. tabularcolumns:: |p{2cm}|L|

        +------------+--------------------------------------------------------+
        | Name       | Description                                            |
        +============+========================================================+
        |  ACC       | accuracy (proportion correct)                          |
        +------------+--------------------------------------------------------+
        |  BIAS      | frequency bias                                         |
        +------------+--------------------------------------------------------+
        |  CSI       | critical success index (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                    |
        +------------+--------------------------------------------------------+
        |  beta1     | linear regression slope (type 1 conditional bias)      |
        +------------+--------------------------------------------------------+
        |  beta2     | linear regression slope (type 2 conditional bias)      |
        +------------+--------------------------------------------------------+
        |  corr_p    | pearson's correleation coefficien (linear correlation) |
        +------------+--------------------------------------------------------+
        |  corr_s*   | spearman's correlation coefficient (rank correlation)  |
        +------------+--------------------------------------------------------+
        |  DRMSE     | debiased root mean squared error                       |
        +------------+--------------------------------------------------------+
        |  MAE       | mean absolute error of residuals                       |
        +------------+--------------------------------------------------------+
        |  ME        | mean error or bias of residuals                        |
        +------------+--------------------------------------------------------+
        |  MSE       | mean squared error                                     |
        +------------+--------------------------------------------------------+
        |  NMSE      | normalized mean squared error                          |
        +------------+--------------------------------------------------------+
        |  RMSE      | root mean squared error                                |
        +------------+--------------------------------------------------------+
        |  RV        | reduction of variance                                  |
        |            | (Brier Score, Nash-Sutcliffe Efficiency)               |
        +------------+--------------------------------------------------------+
        |  scatter*  | half the distance between the 16% and 84% percentiles  |
        |            | of the weighted cumulative error distribution,         |
        |            | where error = dB(pred/obs),                            |
        |            | as in Germann et al. (2006)                            |
        +------------+--------------------------------------------------------+
        |  binary_mse| binary MSE                                             |
        +------------+--------------------------------------------------------+
        |  FSS       | fractions skill score                                  |
        +------------+--------------------------------------------------------+
        |  SAL       | Structure-Amplitude-Location score                     |
        +------------+--------------------------------------------------------+

        type: ensemble

        .. tabularcolumns:: |p{2cm}|L|

        +------------+--------------------------------------------------------+
        | Name       | Description                                            |
        +============+========================================================+
        | ens_skill  | mean ensemble skill                                    |
        +------------+--------------------------------------------------------+
        | ens_spread | mean ensemble spread                                   |
        +------------+--------------------------------------------------------+
        | rankhist   | rank histogram                                         |
        +------------+--------------------------------------------------------+

        type: probabilistic

        .. tabularcolumns:: |p{2cm}|L|

        +------------+--------------------------------------------------------+
        | Name       | Description                                            |
        +============+========================================================+
        |  CRPS      | continuous ranked probability score                    |
        +------------+--------------------------------------------------------+
        |  reldiag   | reliability diagram                                    |
        +------------+--------------------------------------------------------+
        |  ROC       | ROC curve                                              |
        +------------+--------------------------------------------------------+

    type : {'deterministic', 'ensemble', 'probabilistic'}, optional
        Type of the verification method.

    Notes
    -----
    Multiplicative scores can be computed by passing log-tranformed values.
    Note that "scatter" is the only score that will be computed in dB units of
    the multiplicative error, i.e.: 10*log10(pred/obs).

    beta1 measures the degree of conditional bias of the observations given the
    forecasts (type 1).

    beta2 measures the degree of conditional bias of the forecasts given the
    observations (type 2).

    The normalized MSE is computed as
    NMSE = E[(pred - obs)^2]/E[(pred + obs)^2].

    The debiased RMSE is computed as DRMSE = sqrt(RMSE - ME^2).

    The reduction of variance score is computed as RV = 1 - MSE/Var(obs).

    Score names denoted by * can only be computed offline, meaning that the
    these cannot be computed using _init, _accum and _compute methods of this
    module.

    References
    ----------
    Germann, U. , Galli, G. , Boscacci, M. and Bolliger, M. (2006), Radar
    precipitation measurement in a mountainous region. Q.J.R. Meteorol. Soc.,
    132: 1669-1692. doi:10.1256/qj.05.190

    Potts, J. (2012), Chapter 2 - Basic concepts. Forecast verification: a
    practitioner’s guide in atmospheric sciences, I. T. Jolliffe, and D. B.
    Stephenson, Eds., Wiley-Blackwell, 11–29.
    """

    if name is None:
        name = "none"
    if type is None:
        type = "none"

    name = name.lower()
    type = type.lower()

    if type == "deterministic":
        from .detcatscores import det_cat_fct
        from .detcontscores import det_cont_fct
        from .spatialscores import binary_mse, fss
        from .salscores import sal

        # categorical
        if name in [
            "acc",
            "bias",
            "csi",
            "f1",
            "fa",
            "far",
            "gss",
            "hk",
            "hss",
            "mcc",
            "pod",
            "sedi",
        ]:

            def f(fct, obs, **kwargs):
                return det_cat_fct(fct, obs, kwargs.pop("thr"), [name])

            return f

        # continuous
        elif name in [
            "beta",
            "beta1",
            "beta2",
            "corr_p",
            "corr_s",
            "drmse",
            "mae",
            "mse",
            "me",
            "nmse",
            "rmse",
            "rv",
            "scatter",
        ]:

            def f(fct, obs, **kwargs):
                return det_cont_fct(fct, obs, [name], **kwargs)

            return f

        # spatial
        elif name == "binary_mse":
            return binary_mse
        elif name == "fss":
            return fss
        elif name == "sal":
            return sal

        else:
            raise ValueError("unknown deterministic method %s" % name)

    elif type == "ensemble":
        from .ensscores import ensemble_skill, ensemble_spread, rankhist

        if name == "ens_skill":
            return ensemble_skill
        elif name == "ens_spread":
            return ensemble_spread
        elif name == "rankhist":
            return rankhist
        else:
            raise ValueError("unknown ensemble method %s" % name)

    elif type == "probabilistic":
        from .probscores import CRPS, reldiag, ROC_curve

        if name == "crps":
            return CRPS
        elif name == "reldiag":
            return reldiag
        elif name == "roc":
            return ROC_curve
        else:
            raise ValueError("unknown probabilistic method %s" % name)

    else:
        raise ValueError("unknown type %s" % name)
Return a callable function for the method corresponding to the given
verification score.

Parameters
----------
name : str
Name of the verification method. The available options are:\n\

type: deterministic

.. tabularcolumns:: |p{2cm}|L|

+------------+--------------------------------------------------------+
| Name       | Description                                            |
+============+========================================================+
|  ACC       | accuracy (proportion correct)                          |
+------------+--------------------------------------------------------+
|  BIAS      | frequency bias                                         |
+------------+--------------------------------------------------------+
|  CSI       | critical success index (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                    |
+------------+--------------------------------------------------------+
|  beta1     | linear regression slope (type 1 conditional bias)      |
+------------+--------------------------------------------------------+
|  beta2     | linear regression slope (type 2 conditional bias)      |
+------------+--------------------------------------------------------+
|  corr_p    | pearson's correleation coefficien (linear correlation) |
+------------+--------------------------------------------------------+
|  corr_s*   | spearman's correlation coefficient (rank correlation)  |
+------------+--------------------------------------------------------+
|  DRMSE     | debiased root mean squared error                       |
+------------+--------------------------------------------------------+
|  MAE       | mean absolute error of residuals                       |
+------------+--------------------------------------------------------+
|  ME        | mean error or bias of residuals                        |
+------------+--------------------------------------------------------+
|  MSE       | mean squared error                                     |
+------------+--------------------------------------------------------+
|  NMSE      | normalized mean squared error                          |
+------------+--------------------------------------------------------+
|  RMSE      | root mean squared error                                |
+------------+--------------------------------------------------------+
|  RV        | reduction of variance                                  |
|            | (Brier Score, Nash-Sutcliffe Efficiency)               |
+------------+--------------------------------------------------------+
|  scatter*  | half the distance between the 16% and 84% percentiles  |
|            | of the weighted cumulative error distribution,         |
|            | where error = dB(pred/obs),                            |
|            | as in Germann et al. (2006)                            |
+------------+--------------------------------------------------------+
|  binary_mse| binary MSE                                             |
+------------+--------------------------------------------------------+
|  FSS       | fractions skill score                                  |
+------------+--------------------------------------------------------+
|  SAL       | Structure-Amplitude-Location score                     |
+------------+--------------------------------------------------------+

type: ensemble

.. tabularcolumns:: |p{2cm}|L|

+------------+--------------------------------------------------------+
| Name       | Description                                            |
+============+========================================================+
| ens_skill  | mean ensemble skill                                    |
+------------+--------------------------------------------------------+
| ens_spread | mean ensemble spread                                   |
+------------+--------------------------------------------------------+
| rankhist   | rank histogram                                         |
+------------+--------------------------------------------------------+

type: probabilistic

.. tabularcolumns:: |p{2cm}|L|

+------------+--------------------------------------------------------+
| Name       | Description                                            |
+============+========================================================+
|  CRPS      | continuous ranked probability score                    |
+------------+--------------------------------------------------------+
|  reldiag   | reliability diagram                                    |
+------------+--------------------------------------------------------+
|  ROC       | ROC curve                                              |
+------------+--------------------------------------------------------+

type : {'deterministic', 'ensemble', 'probabilistic'}, optional
Type of the verification method.

Notes
-----
Multiplicative scores can be computed by passing log-tranformed values.
Note that "scatter" is the only score that will be computed in dB units of
the multiplicative error, i.e.: 10*log10(pred/obs).

beta1 measures the degree of conditional bias of the observations given the
forecasts (type 1).

beta2 measures the degree of conditional bias of the forecasts given the
observations (type 2).

The normalized MSE is computed as
NMSE = E[(pred - obs)^2]/E[(pred + obs)^2].

The debiased RMSE is computed as DRMSE = sqrt(RMSE - ME^2).

The reduction of variance score is computed as RV = 1 - MSE/Var(obs).

Score names denoted by * can only be computed offline, meaning that the
these cannot be computed using _init, _accum and _compute methods of this
module.

References
----------
Germann, U. , Galli, G. , Boscacci, M. and Bolliger, M. (2006), Radar
precipitation measurement in a mountainous region. Q.J.R. Meteorol. Soc.,
132: 1669-1692. doi:10.1256/qj.05.190

Potts, J. (2012), Chapter 2 - Basic concepts. Forecast verification: a
practitioner’s guide in atmospheric sciences, I. T. Jolliffe, and D. B.
Stephenson, Eds., Wiley-Blackwell, 11–29.
pysteps/verification/interface.py