pyramids

Construct and write pyramid of lower-resolution levels.

build_pyramid(*, zarrurl, overwrite=False, num_levels=2, coarsening_xy=2, chunksize=None, aggregation_function=None)

Starting from on-disk highest-resolution data, build and write to disk a pyramid with (num_levels - 1) coarsened levels. This function works for 2D, 3D or 4D arrays.

PARAMETER	DESCRIPTION
zarrurl	Path of the image zarr group, not including the multiscale-level path (e.g. "some/path/plate.zarr/B/03/0"). TYPE: Union[str, Path]
overwrite	Whether to overwrite existing pyramid levels. TYPE: bool DEFAULT: False
num_levels	Total number of pyramid levels (including 0). TYPE: int DEFAULT: 2
coarsening_xy	Linear coarsening factor between subsequent levels. TYPE: int DEFAULT: 2
chunksize	Shape of a single chunk. TYPE: Optional[Sequence[int]] DEFAULT: None
aggregation_function	Function to be used when downsampling. TYPE: Optional[Callable] DEFAULT: None

Source code in fractal_tasks_core/pyramids.py

def build_pyramid(
    *,
    zarrurl: Union[str, pathlib.Path],
    overwrite: bool = False,
    num_levels: int = 2,
    coarsening_xy: int = 2,
    chunksize: Optional[Sequence[int]] = None,
    aggregation_function: Optional[Callable] = None,
) -> None:

    """
    Starting from on-disk highest-resolution data, build and write to disk a
    pyramid with `(num_levels - 1)` coarsened levels.
    This function works for 2D, 3D or 4D arrays.

    Args:
        zarrurl: Path of the image zarr group, not including the
            multiscale-level path (e.g. `"some/path/plate.zarr/B/03/0"`).
        overwrite: Whether to overwrite existing pyramid levels.
        num_levels: Total number of pyramid levels (including 0).
        coarsening_xy: Linear coarsening factor between subsequent levels.
        chunksize: Shape of a single chunk.
        aggregation_function: Function to be used when downsampling.
    """

    # Clean up zarrurl
    zarrurl = str(pathlib.Path(zarrurl))  # FIXME

    # Select full-resolution multiscale level
    zarrurl_highres = f"{zarrurl}/0"
    logger.info(f"[build_pyramid] High-resolution path: {zarrurl_highres}")

    # Lazily load highest-resolution data
    data_highres = da.from_zarr(zarrurl_highres)
    logger.info(f"[build_pyramid] High-resolution data: {str(data_highres)}")

    # Check the number of axes and identify YX dimensions
    ndims = len(data_highres.shape)
    if ndims not in [2, 3, 4]:
        raise ValueError(f"{data_highres.shape=}, ndims not in [2,3,4]")
    y_axis = ndims - 2
    x_axis = ndims - 1

    # Set aggregation_function
    if aggregation_function is None:
        aggregation_function = np.mean

    # Compute and write lower-resolution levels
    previous_level = data_highres
    for ind_level in range(1, num_levels):
        # Verify that coarsening is doable
        if min(previous_level.shape[-2:]) < coarsening_xy:
            raise ValueError(
                f"ERROR: at {ind_level}-th level, "
                f"coarsening_xy={coarsening_xy} "
                f"but previous level has shape {previous_level.shape}"
            )
        # Apply coarsening
        newlevel = da.coarsen(
            aggregation_function,
            previous_level,
            {y_axis: coarsening_xy, x_axis: coarsening_xy},
            trim_excess=True,
        ).astype(data_highres.dtype)

        # Apply rechunking
        if chunksize is None:
            newlevel_rechunked = newlevel
        else:
            newlevel_rechunked = newlevel.rechunk(chunksize)
        logger.info(
            f"[build_pyramid] Level {ind_level} data: "
            f"{str(newlevel_rechunked)}"
        )

        # Write zarr and store output (useful to construct next level)
        previous_level = newlevel_rechunked.to_zarr(
            zarrurl,
            component=f"{ind_level}",
            overwrite=overwrite,
            compute=True,
            return_stored=True,
            write_empty_chunks=False,
            dimension_separator="/",
        )