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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.

Legacy Technical Reference (kept for context)

Operations – transform verbs

In plain English:
Transform verbs reshape or filter a cube before you calculate statistics. They are small, chainable steps that keep the cube structure intact.

You will learn:
- What each transform verb does in plain language - How to combine transforms in a short pipe - Where to find the original technical reference

What this is

These functions live in cubedynamics.ops.transforms and are re-exported as cubedynamics.verbs. Each one returns a callable so you configure it once and feed it a cube through the | operator.

Why it matters

Transforming cubes in-place keeps your workflow compact and reproducible. You can center, filter, or subset data without losing metadata, which makes it easy to hand analyses to collaborators.

How to use it

anomaly(dim)

Calculates how far each value is from the mean along the chosen dimension.

from cubedynamics import pipe, verbs as v

daily_anom = pipe(cube) | v.anomaly(dim="time")

This mean-centers the cube along time so values show departures from the average.

month_filter(months)

Keeps only the month numbers you list, such as [6, 7, 8] for June–August.

summer = pipe(cube) | v.month_filter([6, 7, 8])

This is a quick way to create seasonal subsets before running statistics.

You can combine transforms freely:

pipe(cube) | v.anomaly(dim="time") | v.month_filter([6, 7, 8])

This first mean-centers the cube and then keeps only summer timesteps.

Original Reference (kept for context)

Operations Reference – Transforms

Transform verbs reshape or filter cubes before you compute downstream statistics. All functions live under cubedynamics.ops.transforms and are re-exported via cubedynamics.verbs. Examples assume from cubedynamics import pipe, verbs as v and a cube variable that is an xarray object.

anomaly(dim)

Computes anomalies by subtracting the mean along a dimension.

result = pipe(cube) | v.anomaly(dim="time")
  • Parameters
  • dim: dimension name (e.g., "time").
  • Returns: xarray.DataArray/Dataset with mean-centered values.

month_filter(months)

Filters a cube to the specified month numbers (1–12). Useful for seasonal composites before running statistics.

result = pipe(cube) | v.month_filter([6, 7, 8]) | v.anomaly(dim="time")
  • Parameters
  • months: iterable of integers representing desired months.
  • Behavior: keeps the original metadata while dropping other timesteps.

Use these verbs as building blocks ahead of stats like variance or correlation.