How to use xskillscore - 10 common examples

"""Computes the CRPS of verification data ``o`` relative to normally distributed
    forecasts with mean ``mu`` and standard deviation ``sig``.

    .. note::
        This is a helper function for CRPSS and cannot be called directly by a user.

    Args:
        forecast (xr.object): Forecast with ``member`` dim.
        mu (xr.object): The mean of the verification data.
        sig (xr.object): The standard deviation verification data.

    See also:
        * properscoring.crps_gaussian
        * xskillscore.crps_gaussian
    """
    return crps_gaussian(forecast, mu, sig)

Args:
        forecast (xr.object): Forecast with ``member`` dim.
        cdf_or_dist (callable or scipy.stats.distribution): Function which returns the
            cumulative density of the forecast distribution at value x.
        xmin (float): Lower bounds for integration.
        xmax (float): Upper bounds for integration.
        tol (float, optional): The desired accuracy of the CRPS. Larger values will
                               speed up integration. If ``tol`` is set to ``None``,
                               bounds errors or integration tolerance errors will be
                               ignored.

    See also:
        * properscoring.crps_quadrature
        * xskillscore.crps_quadrature
    """
    return crps_quadrature(forecast, cdf_or_dist, xmin, xmax, tol)

Example:
        >>> def pos(x): return x > 0
        >>> compute_perfect_model(ds, control, metric='brier_score', logical=pos)
    """
    if 'logical' in metric_kwargs:
        logical = metric_kwargs['logical']
        if not callable(logical):
            raise ValueError(f'`logical` must be `callable`, found {type(logical)}')
    else:
        raise ValueError(
            'Please provide a callable `logical` to be applied to comparison and \
             verification data to get values in  interval [0,1]; \
             see properscoring.brier_score.'
        )
    return brier_score(logical(verif), logical(forecast).mean('member'))

| **orientation** | negative  |
        +-----------------+-----------+

    Reference:
        * Matheson, James E., and Robert L. Winkler. “Scoring Rules for
          Continuous Probability Distributions.” Management Science 22, no. 10
          (June 1, 1976): 1087–96. https://doi.org/10/cwwt4g.
        * https://www.lokad.com/continuous-ranked-probability-score

    See also:
        * properscoring.crps_ensemble
        * xskillscore.crps_ensemble
    """
    weights = metric_kwargs.get('weights', None)
    # switch positions because xskillscore.crps_ensemble(verif, forecasts)
    return crps_ensemble(verif, forecast, weights=weights)

+-----------------+-----------------+
        | **minimum**     | 0.0             |
        +-----------------+-----------------+
        | **maximum**     | ∞               |
        +-----------------+-----------------+
        | **perfect**     | N/A             |
        +-----------------+-----------------+
        | **orientation** | positive        |
        +-----------------+-----------------+

    Reference:
        * Bretherton, Christopher S., et al. "The effective number of spatial degrees of
          freedom of a time-varying field." Journal of climate 12.7 (1999): 1990-2009.
    """
    skipna = metric_kwargs.get('skipna', False)
    return effective_sample_size(forecast, verif, dim=dim, skipna=skipna)

+-----------------+-----------+
        | **orientation** | negative  |
        +-----------------+-----------+

    See also:
        * xskillscore.mae

    Reference:
        * Ian T. Jolliffe and David B. Stephenson. Forecast Verification: A
          Practitioner’s Guide in Atmospheric Science. John Wiley & Sons, Ltd,
          Chichester, UK, December 2011. ISBN 978-1-119-96000-3 978-0-470-66071-3.
          URL: http://doi.wiley.com/10.1002/9781119960003.
    """
    weights = metric_kwargs.get('weights', None)
    skipna = metric_kwargs.get('skipna', False)
    return mae(forecast, verif, dim=dim, weights=weights, skipna=skipna)

+-----------------+-----------+
        | **minimum**     | 0.0       |
        +-----------------+-----------+
        | **maximum**     | ∞         |
        +-----------------+-----------+
        | **perfect**     | 0.0       |
        +-----------------+-----------+
        | **orientation** | negative  |
        +-----------------+-----------+

    See also:
        * xskillscore.mape
    """
    weights = metric_kwargs.get('weights', None)
    skipna = metric_kwargs.get('skipna', False)
    return mape(forecast, verif, dim=dim, weights=weights, skipna=skipna)

Details:
        +-----------------+-----------+
        | **minimum**     | 0.0       |
        +-----------------+-----------+
        | **maximum**     | ∞         |
        +-----------------+-----------+
        | **perfect**     | 0.0       |
        +-----------------+-----------+
        | **orientation** | negative  |
        +-----------------+-----------+

    See also:
        * xskillscore.median_absolute_error
    """
    skipna = metric_kwargs.get('skipna', False)
    return median_absolute_error(forecast, verif, dim=dim, skipna=skipna)

+-----------------+-----------+
        | **orientation** | negative  |
        +-----------------+-----------+

    See also:
        * xskillscore.mse

    Reference:
        * Ian T. Jolliffe and David B. Stephenson. Forecast Verification: A
          Practitioner’s Guide in Atmospheric Science. John Wiley & Sons, Ltd,
          Chichester, UK, December 2011. ISBN 978-1-119-96000-3 978-0-470-66071-3.
          URL: http://doi.wiley.com/10.1002/9781119960003.
    """
    weights = metric_kwargs.get('weights', None)
    skipna = metric_kwargs.get('skipna', False)
    return mse(forecast, verif, dim=dim, weights=weights, skipna=skipna)

| **maximum**     | 1.0       |
        +-----------------+-----------+
        | **perfect**     | 1.0       |
        +-----------------+-----------+
        | **orientation** | positive  |
        +-----------------+-----------+

    See also:
        * xskillscore.pearson_r
        * xskillscore.pearson_r_p_value
        * climpred.pearson_r_p_value
        * climpred.pearson_r_eff_p_value
    """
    weights = metric_kwargs.get('weights', None)
    skipna = metric_kwargs.get('skipna', False)
    return pearson_r(forecast, verif, dim=dim, weights=weights, skipna=skipna)