mapchete_eo.array package
Submodules
mapchete_eo.array.buffer module
mapchete_eo.array.color module
mapchete_eo.array.convert module
- mapchete_eo.array.convert.to_bands_mask(arr: ndarray, bands: int = 1) ndarray[source]
Expands a 2D mask to a full band mask.
- mapchete_eo.array.convert.to_dataarray(masked_arr: MaskedArray, nodataval: float | None = None, name: str | None = None, band_names: List[str] | None = None, band_axis_name: str = 'bands', x_axis_name: str = 'x', y_axis_name: str = 'y', attrs: dict | None = None) DataArray[source]
Convert ma.MaskedArray to xr.DataArray.
Depending on whether the array is 2D or 3D, the axes will be named accordingly.
A 2-dimensional array indicates that we only have a spatial x- and y-axis. A 3rd dimension will be interpreted as bands.
- mapchete_eo.array.convert.to_dataset(masked_arr: MaskedArray, nodataval: float | None = None, slice_names: List[str] | None = None, band_names: List[str] | None = None, slices_attrs: List[dict | None] | None = None, slice_axis_name: str = 'time', band_axis_name: str = 'bands', x_axis_name: str = 'x', y_axis_name: str = 'y', attrs: dict | None = None)[source]
Convert a 3D or 4D ma.MaskedArray to an xarray.Dataset.
- mapchete_eo.array.convert.to_masked_array(xarr: Dataset | DataArray, copy: bool = False, out_dtype: dtype[Any] | None | type[Any] | _SupportsDType[dtype[Any]] | str | tuple[Any, int] | tuple[Any, SupportsIndex | Sequence[SupportsIndex]] | list[Any] | _DTypeDict | tuple[Any, Any] = None) MaskedArray[source]
Convert xr.DataArray to ma.MaskedArray.