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Transform verbs

In plain English:
Transform verbs reshape or adjust cubes (filtering months, reprojecting, normalizing). They now run tile by tile when a VirtualCube streams large data, so you can apply the same transforms to decade-spanning cubes.

What this page helps you do:
- Learn how transforms behave on streaming cubes - See examples with VirtualCube inputs - Know when to materialize for specialized needs

Streaming-friendly transforms

Most transforms operate independently per tile, so the outputs combine cleanly.

from cubedynamics import pipe, verbs as v

# Stream a tile-aware anomaly
streamed = pipe(cube) | v.anomaly(dim="time") | v.month_filter([6, 7, 8])

Tips for large requests

  • Keep transforms that require context (e.g., global normalization) aware of tile size; adjust time_tile if needed.
  • Use .debug_tiles() on the source cube to see how many tiles will flow through the verbs.
  • Add v.peek() or logging after key transforms when diagnosing unexpected values.

Visualization after transforms

Plotting still streams:

pipe(cube) | v.variance(dim="time") | v.plot_timeseries()

Large AOIs update as each tile finishes; reduce date spans to speed up.

This material has been moved to the Legacy Reference page.