changesreshape

Changeshape is a concept in data processing and scientific computing describing a utility or pattern for tracking and managing reshaping operations on array-like data. It emphasizes provenance of shape changes, enabling auditing, debugging, and reversible transformations within data pipelines.

A changeshape system typically wraps or decorates an array object to record a history of shape mutations. Each reshape action is logged with details such as the original shape, the new shape, a timestamp, and the operation that produced the change. The approach supports multi-dimensional arrays and can be implemented as a wrapper, a library function, or an in-place attribute tracker. It aims to preserve or clearly document the semantics of reshaping, including whether the operation returns a view or a copy.

Typical features include a current_shape accessor, a history log, and methods to apply, revert, or rebase reshapes. Revert returns the data to a prior shape, while history enables reconstruction of the reshape sequence for debugging or auditing. Some implementations integrate with existing array libraries such as NumPy or tensor frameworks, and may optimize for minimal copying when possible. Performance considerations include memory overhead for the history and the potential cost of maintaining precise provenance in large pipelines.

Usage generally involves wrapping an array with a changeshape interface, performing reshape operations through that interface, and querying history to review the sequence of shape changes. This can aid reproducibility in data preprocessing, machine learning pipelines, and scientific workflows.

See also: reshape, array provenance, data lineage.