How to use the climpred.checks.is_in_list function in climpred

Returns:
        metric (Metric): metric class
        comparison (Comparison): comparison class.
        dim (list of str or str): corrected dimension to apply metric to.
    """
    # check kind allowed
    is_in_list(kind, ['hindcast', 'PM'], 'kind')
    # set default dim
    if dim is None:
        dim = 'init' if kind == 'hindcast' else ['init', 'member']
    # check allowed dims
    if kind == 'hindcast':
        is_in_list(dim, ['member', 'init'], 'dim')
    elif kind == 'PM':
        is_in_list(dim, ['member', 'init', ['init', 'member']], 'dim')

    # get metric and comparison strings incorporating alias
    metric = METRIC_ALIASES.get(metric, metric)
    comparison = COMPARISON_ALIASES.get(comparison, comparison)

    METRICS = HINDCAST_METRICS if kind == 'hindcast' else PM_METRICS
    COMPARISONS = HINDCAST_COMPARISONS if kind == 'hindcast' else PM_COMPARISONS
    metric = get_metric_class(metric, METRICS)
    comparison = get_comparison_class(comparison, COMPARISONS)

    # check whether combination of metric and comparison works
    PROBABILISTIC_COMPARISONS = (
        PROBABILISTIC_HINDCAST_COMPARISONS
        if kind == 'hindcast'
        else PROBABILISTIC_PM_COMPARISONS
    )

def _get_metric_comparison_dim(metric, comparison, dim, kind):
    """Returns `metric`, `comparison` and `dim` for compute functions.

    Args:
        metric (str): metric or alias string
        comparison (str): Description of parameter `comparison`.
        dim (list of str or str): dimension to apply metric to.
        kind (str): experiment type from ['hindcast', 'PM'].

    Returns:
        metric (Metric): metric class
        comparison (Comparison): comparison class.
        dim (list of str or str): corrected dimension to apply metric to.
    """
    # check kind allowed
    is_in_list(kind, ['hindcast', 'PM'], 'kind')
    # set default dim
    if dim is None:
        dim = 'init' if kind == 'hindcast' else ['init', 'member']
    # check allowed dims
    if kind == 'hindcast':
        is_in_list(dim, ['member', 'init'], 'dim')
    elif kind == 'PM':
        is_in_list(dim, ['member', 'init', ['init', 'member']], 'dim')

    # get metric and comparison strings incorporating alias
    metric = METRIC_ALIASES.get(metric, metric)
    comparison = COMPARISON_ALIASES.get(comparison, comparison)

    METRICS = HINDCAST_METRICS if kind == 'hindcast' else PM_METRICS
    COMPARISONS = HINDCAST_COMPARISONS if kind == 'hindcast' else PM_COMPARISONS
    metric = get_metric_class(metric, METRICS)

* compute_persistence()
    * compute_perfect_model()
    * compute_hindcast()

    Args:
        metric (str): name of metric.
        list_ (list): check whether metric in list

    Returns:
        metric (Metric): class object of the metric.
    """
    if isinstance(metric, metrics.Metric):
        return metric
    elif isinstance(metric, str):
        # check if metric allowed
        is_in_list(metric, list_, 'metric')
        metric = METRIC_ALIASES.get(metric, metric)
        return getattr(metrics, '__' + metric)
    else:
        raise ValueError(
            f'Please provide metric as str or Metric class, found {type(metric)}'
        )

dim (list of str or str): dimension to apply metric to.
        kind (str): experiment type from ['hindcast', 'PM'].

    Returns:
        metric (Metric): metric class
        comparison (Comparison): comparison class.
        dim (list of str or str): corrected dimension to apply metric to.
    """
    # check kind allowed
    is_in_list(kind, ['hindcast', 'PM'], 'kind')
    # set default dim
    if dim is None:
        dim = 'init' if kind == 'hindcast' else ['init', 'member']
    # check allowed dims
    if kind == 'hindcast':
        is_in_list(dim, ['member', 'init'], 'dim')
    elif kind == 'PM':
        is_in_list(dim, ['member', 'init', ['init', 'member']], 'dim')

    # get metric and comparison strings incorporating alias
    metric = METRIC_ALIASES.get(metric, metric)
    comparison = COMPARISON_ALIASES.get(comparison, comparison)

    METRICS = HINDCAST_METRICS if kind == 'hindcast' else PM_METRICS
    COMPARISONS = HINDCAST_COMPARISONS if kind == 'hindcast' else PM_COMPARISONS
    metric = get_metric_class(metric, METRICS)
    comparison = get_comparison_class(comparison, COMPARISONS)

    # check whether combination of metric and comparison works
    PROBABILISTIC_COMPARISONS = (
        PROBABILISTIC_HINDCAST_COMPARISONS
        if kind == 'hindcast'

* 'maximize': Use all available initializations at each lead that verify
               against the observations provided. This changes both the set of
               initializations and the verification window used at each lead.

    Returns:
        inits (dict): Keys are the lead time integer, values are an ``xr.DataArray`` of
            initialization dates.
        verif_dates (dict): Keys are the lead time integer, values are an
            ``xr.CFTimeIndex`` of verification dates.
    """
    if isinstance(reference, str):
        reference = [reference]
    elif reference is None:
        reference = []

    is_in_list(alignment, VALID_ALIGNMENTS, 'alignment')
    units = forecast['lead'].attrs['units']
    leads = forecast['lead'].values

    # `init` renamed to `time` in compute functions.
    all_inits = forecast['time']
    all_verifs = verif['time']

    # If aligning historical reference, need to account for potential differences in its
    # temporal coverage. Note that the historical reference only aligns verification
    # dates and doesn't care about inits.
    if hist is not None:
        all_verifs = np.sort(list(set(all_verifs.data) & set(hist['time'].data)))
        all_verifs = xr.DataArray(all_verifs, dims=['time'], coords=[all_verifs])

    # Construct list of `n` offset over all leads.
    n, freq = get_multiple_lead_cftime_shift_args(units, leads)

* e2o: Compare the ensemble mean to the verification data.
        * m2o: Compare each ensemble member to the verification data.

    Args:
        comparison (str): name of comparison.

    Returns:
        comparison (Comparison): comparison class.

    """
    if isinstance(comparison, comparisons.Comparison):
        return comparison
    elif isinstance(comparison, str):
        # check if comparison allowed
        is_in_list(comparison, list_, 'comparison')
        comparison = COMPARISON_ALIASES.get(comparison, comparison)
        return getattr(comparisons, '__' + comparison)
    else:
        is_in_list(comparison, list_, 'comparison')
        return getattr(comparisons, '__' + comparison)