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Want to Go Further with CubeDynamics?

CubeDynamics is an optional extension for teams working with labeled multidimensional arrays. It is not required for a successful Hackathon project, and teams should keep the scientific question ahead of the software.

Current installation status

As of September 2026, the official CubeDynamics repository describes version 0.1.0rc1 as alpha software preparing its first release candidate. There is no public PyPI package or GitHub Release installation. The supported event route is a mentor-maintained, checksum-verified wheel or environment supplied by facilitators.

Do not ask participants to guess an installation command, install an unverified artifact, or spend the short build window debugging a prerelease package. If the verified setup is unavailable, use xarray, the OLC notebooks, or another familiar tool and document the analytical logic.

When it may help

CubeDynamics may be useful when a team wants to make an operation over named dimensions explicit and inspectable—for example, averaging a time dimension in a gridded climate cube. The OLC Technical Extender notebooks remain the event-specific starting point.

Official example shape:

import matplotlib.pyplot as plt
import xarray as xr
from cubedynamics import pipe, verbs as v

with xr.open_dataset(
    "tests/fixtures/real_data/prism_boulder_january_2024.nc",
    engine="scipy",
) as observations:
    cube = observations["tmax"].load()

result = pipe(cube) | v.mean(over="time", keep_dim=False)
result.unwrap().plot()
plt.show()

This exact snippet depends on the repository’s example fixture and a verified CubeDynamics environment. It is an orientation example, not a participant installation test.

Useful entry points

If using CubeDynamics, record the exact artifact or environment supplied by facilitators, its checksum or version, the input data, the operation performed, and an equivalent plain-language description.