climpred.metrics.Metric.__init__#
- Metric.__init__(name: str, function: Callable, positive: Optional[bool], probabilistic: bool, unit_power: float, long_name: Union[None, str] = None, aliases: Optional[List[str]] = None, minimum: Optional[float] = None, maximum: Optional[float] = None, perfect: Optional[float] = None, normalize: bool = False, allows_logical: bool = False, requires_member_dim: bool = False)[source]#
Metric initialization. See Metrics.
- Parameters:
name – name of metric.
function – metric function.
positive – Is metric positively oriented? If
True
, higher skill value means better skill. IfFalse
, lower metric value means better skill.None
if different differentiation.probabilistic – Is metric probabilistic?
False
means deterministic.unit_power – Power of the unit of skill based on unit of input, e.g. input unit [m]: skill unit [(m)**unit_power]
long_name – long name of metric. Defaults to
None
.aliases – Allowed aliases for this metric. Defaults to
None
.min – Minimum skill for metric. Defaults to
None
.max – Maxmimum skill for metric. Defaults to
None
.perfect – Perfect skill for metric. Defaults to
None
.normalize – Will the metric be normalized? Then metric function will require to get Comparison passed. Defaults to
False
.allows_logical – Does the metric allow a logical to be passed in metric_kwargs? Some probabilistic metrics allow this. Defaults to
False
.requires_member_dim – Does xskillscore.metric expect a member dimension? Defaults to
False
.