acc¶
Import path: earth2studio.statistics.acc
Documentation¶
Statistic for calculating the anomaly correlation coefficient of two tensors over a set of given dimensions, with respect to some optional climatology.
Parameters:
-
reduction_dimensions(list[str]) –A list of names corresponding to dimensions to perform the statistical reduction over. Example: ['lat', 'lon']
-
climatology(DataSource | None, default:None) –Optional (by default None) climatology to remove from tensors to create anomalies before computing the correlation coefficient.
-
weights(Tensor | None, default:None) –A tensor containing weights to assign to the reduction dimensions. Note that these weights must have the same number of dimensions as passed in reduction_dimensions. Example: if reduction_dimensions = ['lat', 'lon'] then assert weights.ndim == 2.
__call__ ¶
__call__(
x: Tensor,
x_coords: CoordSystem,
y: Tensor,
y_coords: CoordSystem,
) -> tuple[Tensor, CoordSystem]
Apply metric to data x and y, checking that their coordinates
are broadcastable.
Parameters:
-
x(Tensor) –Input tensor, typically the forecast or prediction tensor, but ACC is symmetric with respect to
xandy. -
x_coords(CoordSystem) –Ordered dict representing coordinate system that describes the
xtensor.reduction_dimensionsmust be in coords. "time" and "variable" must be in x_coords. -
y(Tensor) –Input tensor #2 intended to be used as validation data, but ACC is symmetric with respect to
xandy. -
y_coords(CoordSystem) –Ordered dict representing coordinate system that describes the
ytensor.reduction_dimensionsmust be in coords. "time" and "variable" must be in y_coords. If "lead_time" is in x_coords, then "lead_time" must also be in y_coords. The intention, in this case, is that users will usefetch_datato make it easier to match validation times.
Returns:
-
tuple[Tensor, CoordSystem]–Returns anomaly correlation coefficient tensor with appropriate reduced coordinates.
Note
For more information see the following references: