Standardize Transform

standardize_step(
    name: str,
    **kwargs: Any,
) -> MCStep

standardize_step applies per-column z-scores to a selected input array:

(x - x.mean(axis=0)) / x.std(axis=0, ddof=ddof)

Columns with zero standard deviation are returned as zeros.

Key Parameters:

Name	Default	Description
source	required	Input source channel.
filter_key	`"filter"`	Filter result key when reading filter output.
payload_key	`None`	Producer payload key when `source="payload"`.
columns	`None`	Optional column subset.
burn_in	`0`	Rows dropped before transformation.
drop_initial	`False`	Drop the initial state row for state inputs.
ddof	`0`	Standard-deviation degrees-of-freedom correction.

The output is stored in the step payload and can be consumed downstream with source="payload".