<filename>xarray_sentinel/reformat.py<gh_stars>100-1000
import typing as T

import xarray as xr

from . import esa_safe


def to_group_zarr(
    product_path: esa_safe.PathType,
    output_store: T.Any,
    groups: T.Optional[T.Dict[str, str]] = None,
) -> None:
    root = xr.open_dataset(product_path, engine="sentinel-1")  # type: ignore
    root.to_zarr(output_store, mode="w")

    if groups is None:
        groups = {g: g for g in root.attrs["subgroups"]}

    for group_out, group_in in groups.items():
        group_ds = xr.open_dataset(product_path, engine="sentinel-1", group=group_in)  # type: ignore
        group_ds.to_zarr(output_store, mode="a", group=group_out)


# Apparently there is no way to save SLC images because "netcdf4" doesn't support complex data
# and "n5necdf" crashes on same name dimensions.
def to_group_netcdf(
    product_path: esa_safe.PathType,
    output_store: str,
    groups: T.Optional[T.Dict[str, str]] = None,
    engine: T.Optional[str] = None,
) -> None:
    root = xr.open_dataset(product_path, engine="sentinel-1")  # type: ignore
    root.to_netcdf(output_store, mode="w", engine=engine)

    if groups is None:
        groups = {g: g for g in root.attrs["subgroups"]}

    for group_out, group_in in groups.items():
        group_ds = xr.open_dataset(product_path, engine="sentinel-1", group=group_in)  # type: ignore
        group_ds.to_netcdf(output_store, mode="a", group=group_out, engine=engine)

<filename>xarray_sentinel/reformat.py<gh_stars>100-1000
import typing as T

import xarray as xr

from . import esa_safe


def to_group_zarr(
    product_path: esa_safe.PathType,
    output_store: T.Any,
    groups: T.Optional[T.Dict[str, str]] = None,
) -> None:
    root = xr.open_dataset(product_path, engine="sentinel-1")  # type: ignore
    root.to_zarr(output_store, mode="w")

    if groups is None:
        groups = {g: g for g in root.attrs["subgroups"]}

    for group_out, group_in in groups.items():
        group_ds = xr.open_dataset(product_path, engine="sentinel-1", group=group_in)  # type: ignore
        group_ds.to_zarr(output_store, mode="a", group=group_out)


# Apparently there is no way to save SLC images because "netcdf4" doesn't support complex data
# and "n5necdf" crashes on same name dimensions.
def to_group_netcdf(
    product_path: esa_safe.PathType,
    output_store: str,
    groups: T.Optional[T.Dict[str, str]] = None,
    engine: T.Optional[str] = None,
) -> None:
    root = xr.open_dataset(product_path, engine="sentinel-1")  # type: ignore
    root.to_netcdf(output_store, mode="w", engine=engine)

    if groups is None:
        groups = {g: g for g in root.attrs["subgroups"]}

    for group_out, group_in in groups.items():
        group_ds = xr.open_dataset(product_path, engine="sentinel-1", group=group_in)  # type: ignore
        group_ds.to_netcdf(output_store, mode="a", group=group_out, engine=engine)

# type: ignore # type: ignore # Apparently there is no way to save SLC images because "netcdf4" doesn't support complex data # and "n5necdf" crashes on same name dimensions. # type: ignore # type: ignore