from typing import Callable, Iterable, Tuple, Union
import numpy
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 rasterio.merge.merge function think that
the xarray.DataArray is a rasterio Dataset.
"""
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)
def read(self, window, out_shape, *args, **kwargs) -> numpy.ma.array:
"""
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
return numpy.ma.array(
data_window, mask=mask, fill_value=fill_value, dtype=self.dtypes[0]
)
[docs]def merge_arrays(
dataarrays: Iterable[DataArray],
bounds: Union[Tuple, None] = None,
res: Union[Tuple, None] = None,
nodata: Union[float, None] = None,
precision: Union[float, None] = None,
method: Union[str, Callable, None] = None,
parse_coordinates: bool = True,
) -> DataArray:
"""
Merge data arrays geospatially.
Uses rasterio.merge.merge:
https://rasterio.readthedocs.io/en/stable/api/rasterio.merge.html#rasterio.merge.merge
Parameters
----------
dataarrays: list
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.
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
)
merged_data, merged_transform = _rio_merge(
[RasterioDatasetDuck(dataarray) for dataarray in dataarrays],
**{key: val for key, val in input_kwargs.items() if val is not None},
)
merged_shape = merged_data.shape
representative_array = dataarrays[0]
if parse_coordinates:
coords = _make_coords(
representative_array, merged_transform, merged_shape[-1], merged_shape[-2]
)
else:
coords = _get_nonspatial_coords(representative_array)
out_attrs = representative_array.attrs
xda = DataArray(
name=dataarrays[0].name,
data=merged_data,
coords=coords,
dims=tuple(representative_array.dims),
attrs=out_attrs,
)
out_nodata = nodata if nodata is not None else representative_array.rio.nodata
xda.rio.write_nodata(out_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: Iterable[Dataset],
bounds: Union[Tuple, None] = None,
res: Union[Tuple, None] = None,
nodata: Union[float, None] = None,
precision: Union[float, None] = None,
method: Union[str, Callable, None] = None,
) -> DataArray:
"""
Merge datasets geospatially.
Uses rasterio.merge.merge:
https://rasterio.readthedocs.io/en/stable/api/rasterio.merge.html#rasterio.merge.merge
Parameters
----------
datasets: list
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.
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,
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],
),
attrs=representative_ds.attrs,
)
xds.rio.write_crs(representative_ds.rio.crs, inplace=True)
return xds