mapchete_eo.array package

Submodules

mapchete_eo.array.buffer module

mapchete_eo.array.buffer.buffer_array(array: ndarray, buffer: int = 0, out_array_dtype: DTypeLike | None = None) → ndarray

mapchete_eo.array.color module

mapchete_eo.array.color.color_array(shape: tuple, hex_color: str)

mapchete_eo.array.color.hex_to_rgb(hex_color)

Convert hex color to tuple of RGB(A) colors.

e.g. “#FFFFFF” –> (255, 255, 255) or “#00FF00FF” –> (0, 255, 0, 255)

mapchete_eo.array.color.outlier_pixels(arr: ndarray, axis: int = 0, range_threshold: int = 100) → ndarray

Detect outlier pixels containing extreme colors.

mapchete_eo.array.convert module

mapchete_eo.array.convert.to_bands_mask(arr: ndarray, bands: int = 1) → ndarray

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

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)

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: DTypeLike | None = None) → MaskedArray

Convert xr.DataArray to ma.MaskedArray.

Module contents