Concepts Overview
This section answers: How does the cube grammar represent space, time, and operations? Use it to align on terminology before picking verbs, datasets, or recipes.
In this section you'll find: - How the cube grammar keeps dimensions explicit across a pipeline. - The relationship between cubes, pipes, verbs, and VirtualCubes. - Pointers to glossary terms and comparisons to other libraries.
Key links: - What is a cube? - Pipe and verbs - VirtualCubes - Glossary - Why not xarray? - Orientation aids: Documentation Overview and Reading Paths
Cube grammar pipeline
CubeDynamics expresses analysis as a sequence of verbs connected by pipes, operating on explicit cube dimensions. This grammar keeps space, time, and scale visible throughout a workflow and ensures that intermediate steps remain inspectable.
Dimensions stay explicit
At its core, CubeDynamics works with xarray-backed DataArrays but applies strong semantics: - named spatial and temporal dimensions - metadata about resolution, alignment, and scale - consistency enforced across operations
The goal is to avoid hidden assumptions about where operations run. Dimensions are visible and intentional, making workflows easier to reason about and share.
Streaming via VirtualCubes
Environmental datasets are often too large to load into memory. VirtualCubes represent a cube without materializing it and stream chunks through analysis pipelines. Code stays the same whether you stream or work in memory, so scale becomes a configuration choice.
Pipes and verbs
Instead of chaining methods, you compose verbs with pipe(...) to build declarative workflows:
from cubedynamics import pipe, verbs as v
pipe(cube) | v.mean() | v.rolling() | v.plot()
Pipelines remain inspectable objects, making it straightforward to debug, document, or extend analyses.