Source code for rioxarray.merge

"""
This module allows you to merge xarray Datasets/DataArrays
geospatially with the `rasterio.merge` module.
"""

from typing import Callable, Optional, Sequence, Tuple, Union

import numpy
from rasterio.crs import CRS
from rasterio.merge import merge as _rio_merge
from xarray import DataArray, Dataset

from rioxarray.rioxarray import _get_nonspatial_coords, _make_coords


class RasterioDatasetDuck:
    """
    This class is to provide the attributes and methods necessary
    to make the :func:`rasterio.merge.merge` function think that
    the :obj:`xarray.DataArray` is a :obj:`rasterio.io.DatasetReader`.
    """

    # pylint: disable=too-many-instance-attributes

    def __init__(self, xds: DataArray):
        self._xds = xds
        self.bounds = xds.rio.bounds(recalc=True)
        self.count = int(xds.rio.count)
        self.dtypes = [xds.dtype]
        self.name = xds.name
        self.nodatavals = [xds.rio.nodata]
        res = xds.rio.resolution(recalc=True)
        self.res = (abs(res[0]), abs(res[1]))
        self.transform = xds.rio.transform(recalc=True)
        try:
            rio_file = xds.rio._manager.acquire()
            self.profile = rio_file.profile
        except AttributeError:
            self.profile = {}
        self.profile.update(
            dtype=xds.dtype,
            crs=xds.rio.crs,
            nodata=xds.rio.nodata,
        )

    def colormap(self, *args, **kwargs):
        """
        Lazy load colormap through _manager.acquire()
        for the scenario many file handles are opened

        See: https://github.com/corteva/rioxarray/issues/479
        """
        try:
            rio_file = self.xds.rio._manager.acquire()
            return rio_file.colormap(*args, **kwargs)
        except AttributeError:
            return None

    def read(self, window, out_shape, *args, **kwargs) -> numpy.ma.MaskedArray:
        # pylint: disable=unused-argument
        """
        This method is meant to be used by the rasterio.merge.merge function.
        """
        data_window = self._xds.rio.isel_window(window)
        if data_window.shape != out_shape:
            # in this section, the data is geographically the same
            # however it is not the same dimensions as requested
            # so need to resample to the requested shape
            if len(out_shape) == 3:
                _, out_height, out_width = out_shape
            else:
                out_height, out_width = out_shape
            data_window = self._xds.rio.reproject(
                self._xds.rio.crs,
                transform=self.transform,
                shape=(out_height, out_width),
            )

        nodata = self.nodatavals[0]
        mask = False
        fill_value = None
        if nodata is not None and numpy.isnan(nodata):
            mask = numpy.isnan(data_window)
        elif nodata is not None:
            mask = data_window == nodata
            fill_value = nodata

        # make sure the returned shape matches
        # the expected shape. This can be the case
        # when the xarray dataset was squeezed to 2D beforehand
        if len(out_shape) == 3 and len(data_window.shape) == 2:
            data_window = data_window.values.reshape((1, out_height, out_width))

        return numpy.ma.array(
            data_window, mask=mask, fill_value=fill_value, dtype=self.dtypes[0]
        )


[docs]def merge_arrays( dataarrays: Sequence[DataArray], bounds: Optional[Tuple] = None, res: Optional[Tuple] = None, nodata: Optional[float] = None, precision: Optional[float] = None, method: Union[str, Callable, None] = None, crs: Optional[CRS] = None, parse_coordinates: bool = True, ) -> DataArray: """ Merge data arrays geospatially. Uses :func:`rasterio.merge.merge` .. versionadded:: 0.2 crs Parameters ---------- dataarrays: List[xarray.DataArray] List of xarray.DataArray's with all geo attributes. The first one is assumed to have the same CRS, dtype, and dimensions as the others in the array. bounds: tuple, optional Bounds of the output image (left, bottom, right, top). If not set, bounds are determined from bounds of input DataArrays. res: tuple, optional Output resolution in units of coordinate reference system. If not set, the resolution of the first DataArray is used. If a single value is passed, output pixels will be square. nodata: float, optional nodata value to use in output file. If not set, uses the nodata value in the first input DataArray. precision: float, optional Number of decimal points of precision when computing inverse transform. method: str or callable, optional See rasterio docs. crs: rasterio.crs.CRS, optional Output CRS. If not set, the CRS of the first DataArray is used. parse_coordinates: bool, optional If False, it will disable loading spatial coordinates. Returns ------- :obj:`xarray.DataArray`: The geospatially merged data. """ input_kwargs = dict( bounds=bounds, res=res, nodata=nodata, precision=precision, method=method ) if crs is None: crs = dataarrays[0].rio.crs if res is None: res = tuple(abs(res_val) for res_val in dataarrays[0].rio.resolution()) # prepare the duck arrays rioduckarrays = [] for dataarray in dataarrays: da_res = tuple(abs(res_val) for res_val in dataarray.rio.resolution()) if da_res != res or dataarray.rio.crs != crs: rioduckarrays.append( RasterioDatasetDuck( dataarray.rio.reproject(dst_crs=crs, resolution=res) ) ) else: rioduckarrays.append(RasterioDatasetDuck(dataarray)) # use rasterio to merge merged_data, merged_transform = _rio_merge( rioduckarrays, **{key: val for key, val in input_kwargs.items() if val is not None}, ) # generate merged data array representative_array = rioduckarrays[0]._xds if parse_coordinates: coords = _make_coords( representative_array, merged_transform, merged_data.shape[-1], merged_data.shape[-2], ) else: coords = _get_nonspatial_coords(representative_array) # make sure the output merged data shape is 2D if the # original data was 2D. this can happen if the # xarray datasarray was squeezed. if len(merged_data.shape) == 3 and len(representative_array.shape) == 2: merged_data = merged_data.squeeze() xda = DataArray( name=representative_array.name, data=merged_data, coords=coords, dims=tuple(representative_array.dims), attrs=representative_array.attrs, ) xda.rio.write_nodata( nodata if nodata is not None else representative_array.rio.nodata, inplace=True ) xda.rio.write_crs(representative_array.rio.crs, inplace=True) xda.rio.write_transform(merged_transform, inplace=True) return xda
[docs]def merge_datasets( datasets: Sequence[Dataset], bounds: Optional[Tuple] = None, res: Optional[Tuple] = None, nodata: Optional[float] = None, precision: Optional[float] = None, method: Union[str, Callable, None] = None, crs: Optional[CRS] = None, ) -> Dataset: """ Merge datasets geospatially. Uses :func:`rasterio.merge.merge` .. versionadded:: 0.2 crs Parameters ---------- datasets: List[xarray.Dataset] List of xarray.Dataset's with all geo attributes. The first one is assumed to have the same CRS, dtype, dimensions, and data_vars as the others in the array. bounds: tuple, optional Bounds of the output image (left, bottom, right, top). If not set, bounds are determined from bounds of input Dataset. res: tuple, optional Output resolution in units of coordinate reference system. If not set, the resolution of the first Dataset is used. If a single value is passed, output pixels will be square. nodata: float, optional nodata value to use in output file. If not set, uses the nodata value in the first input Dataset. precision: float, optional Number of decimal points of precision when computing inverse transform. method: str or callable, optional See rasterio docs. crs: rasterio.crs.CRS, optional Output CRS. If not set, the CRS of the first DataArray is used. Returns ------- :obj:`xarray.Dataset`: The geospatially merged data. """ representative_ds = datasets[0] merged_data = {} for data_var in representative_ds.data_vars: merged_data[data_var] = merge_arrays( [dataset[data_var] for dataset in datasets], bounds=bounds, res=res, nodata=nodata, precision=precision, method=method, crs=crs, parse_coordinates=False, ) data_var = list(representative_ds.data_vars)[0] xds = Dataset( merged_data, coords=_make_coords( merged_data[data_var], merged_data[data_var].rio.transform(), merged_data[data_var].shape[-1], merged_data[data_var].shape[-2], force_generate=True, ), attrs=representative_ds.attrs, ) xds.rio.write_crs(merged_data[data_var].rio.crs, inplace=True) return xds