xrspatial.classify.maximum_breaks#

xrspatial.classify.maximum_breaks(agg: DataArray, k: int = 5, num_sample: int | None = 20000, name: str | None = 'maximum_breaks') → DataArray[source]#

Classify data using the Maximum Breaks algorithm.

Finds the k-1 largest gaps between sorted unique values and uses midpoints of those gaps as bin edges.

Parameters:

  • agg (xr.DataArray or xr.Dataset) – 2D NumPy, CuPy, NumPy-backed Dask, or CuPy-backed Dask array of values to be classified.

  • k (int, default=5) – Number of classes to be produced.

  • num_sample (int or None, default=20000) – Number of sample data points used to fit the model. For dask-backed arrays the sample is drawn lazily to avoid materialising the entire array into RAM. None means use all data (safe for numpy/cupy, automatically capped for dask).

  • name (str, default='maximum_breaks') – Name of output aggregate array.

Returns:

max_breaks_agg – 2D aggregate array of maximum break classifications. All other input attributes are preserved. If agg is a Dataset, returns a Dataset with each variable classified independently.

Return type:

xr.DataArray or xr.Dataset

References