Skip to content

Stats verbs

Statistic verbs summarize cubes along dimensions or compare axes. They live in cubedynamics.verbs and accept whatever xarray object flows through the pipe.

v.mean(dim="time", keep_dim=True)

Compute the mean along a dimension.

from cubedynamics import pipe, verbs as v

mean_cube = pipe(cube) | v.mean(dim="time", keep_dim=True)
  • Parameters: dim – dimension to summarize; keep_dim – retain the dimension as length 1 so the result stays (time, y, x) and Lexcube-ready.
  • Notes: When keep_dim=False, the reduced dimension is removed entirely.

v.variance(dim="time", keep_dim=True)

Compute the variance along a dimension.

from cubedynamics import pipe, verbs as v

var_cube = pipe(cube) | v.variance(dim="time", keep_dim=True)
  • Parameters: dim – dimension to collapse; keep_dim – retain the reduced axis (length 1) or drop it.
  • Returns: variance cube matching the input layout when keep_dim=True.

v.zscore(dim="time", std_eps=1e-4)

Standardizes each pixel/voxel along a dimension by subtracting the mean and dividing by the standard deviation. The verb always returns the same shape as the input cube so downstream visualization works without extra reshaping.

z = pipe(cube) | v.zscore(dim="time")
  • Parameters: dim, std_eps – same semantics as xarray reductions. std_eps prevents division by near-zero spread.
  • Notes: Keeps the original cube shape regardless of keep_dim.

v.correlation_cube(other, dim="time") (planned)

v.correlation_cube currently raises NotImplementedError and is reserved for a future streaming implementation. Use xr.corr or the rolling helpers under cubedynamics.stats today:

import xarray as xr

corr = xr.corr(ndvi_z, climate_anom, dim="time")

Rolling synchrony functions such as cubedynamics.rolling_corr_vs_center remain outside the verbs namespace.

Use these stats alongside transform verbs to build climate–vegetation synchrony analyses.

v.rolling_tail_dep_vs_center(window, dim="time", min_periods=5, tail_quantile=0.8)

Compute a rolling contrast between variability in the upper tail and variability across the full window. The result preserves the input cube shape (e.g., (time, y, x)).

from cubedynamics import pipe, verbs as v

td = (
    pipe(ndvi_z)
    | v.rolling_tail_dep_vs_center(
        window=90,
        dim="time",
        min_periods=5,
        tail_quantile=0.8,
    )
)

This verb rolls along dim and, for each window, compares the variance of values above tail_quantile to the variance of the entire window. Higher values indicate stronger variability in the tails relative to the center.