API reference¶

The public surface is everything exported from the top-level geohalo package. Each entry point comes in a convenience form (builds the operator for you) and a precomputed form (you pass a cached object).

Convenience	Precomputed	Builds
`reduce`	`reduce_with_stencil` / `reduce_with_operator`	`Stencil` / `ReduceOperator`
`resample_grid`	`resample_grid_with_matrix`	`Resampler`
`aggregate_bias`	`aggregate_bias_with_tree`	`BiasTree`

Reduction¶

geohalo.reduce ¶

reduce(
    grid: T,
    geoms: GeoSeries,
    *,
    target_resolution: float | None = None,
    resample_iterations: int = 1,
    spherical_correction: bool = True,
    lat_dim: str = "latitude",
    lon_dim: str = "longitude",
    geom_dim: str = "geom",
    weight_key: str | None = None,
    how: Literal["mean", "sum"] = "mean",
) -> T

geohalo.reduce_with_stencil ¶

reduce_with_stencil(
    grid: T,
    stencil: Stencil,
    *,
    resample_iterations: int = 1,
    lat_dim: str = "latitude",
    lon_dim: str = "longitude",
    geom_dim: str = "geom",
    weight_key: str | None = None,
    how: Literal["mean", "sum"] = "mean",
) -> T

geohalo.reduce_with_operator ¶

reduce_with_operator(
    grid: T,
    operator: ReduceOperator,
    *,
    how: Literal["mean", "sum"] = "mean",
    lat_dim: str = "latitude",
    lon_dim: str = "longitude",
    geom_dim: str = "geom",
) -> T

Reduce grid to per-polygon values with one fused matmul on the source grid.

Assumes clean (non-NaN, unweighted) data — the fused operator cannot renormalise per cell. For NaN or per-cell weighting, use reduce_with_stencil.

Resampling¶

geohalo.resample_grid ¶

resample_grid(
    source: T,
    target_resolution: float,
    *,
    lat_dim: str = "latitude",
    lon_dim: str = "longitude",
    iterations: int = 1,
) -> T

geohalo.resample_grid_with_matrix ¶

resample_grid_with_matrix(
    source: T,
    resampler: Resampler,
    *,
    lat_dim: str = "latitude",
    lon_dim: str = "longitude",
) -> T

Hierarchical rollups¶

geohalo.aggregate_bias ¶

aggregate_bias(
    reduced: T,
    edges: DataFrame,
    *,
    geom_dim: str = "geom",
    parent_col: str = "parent",
    weight_col: str | None = None,
    how: Literal["mean", "sum"] = "mean",
) -> T

geohalo.aggregate_bias_with_tree ¶

aggregate_bias_with_tree(
    reduced: T, tree: BiasTree, *, geom_dim: str = "geom"
) -> T

Precomputed operators¶

geohalo.Stencil `dataclass` ¶

geohalo.ReduceOperator `dataclass` ¶

geohalo.Resampler `dataclass` ¶

geohalo.BiasTree `dataclass` ¶

Caching¶

geohalo.LocalCache ¶

Bases: _Cache

Pickle files under path/<namespace>/<key>.pkl.

geohalo.RedisCache ¶