"""
This module is an extension for xarray to provide rasterio capabilities
to xarray dataarrays.
Credits: The `reproject` functionality was adopted from https://github.com/opendatacube/datacube-core # noqa: E501
Source file:
- https://github.com/opendatacube/datacube-core/blob/084c84d78cb6e1326c7fbbe79c5b5d0bef37c078/datacube/api/geo_xarray.py # noqa: E501
datacube is licensed under the Apache License, Version 2.0:
- https://github.com/opendatacube/datacube-core/blob/1d345f08a10a13c316f81100936b0ad8b1a374eb/LICENSE # noqa: E501
"""
import copy
import os
from collections.abc import Hashable, Iterable, Mapping
from pathlib import Path
from typing import Any, Literal, Optional, Union
import numpy
import rasterio
import rasterio.mask
import rasterio.warp
import xarray
from affine import Affine
from rasterio.dtypes import dtype_rev
from rasterio.enums import Resampling
from rasterio.features import geometry_mask
from xarray.backends.file_manager import FileManager
from xarray.core.dtypes import get_fill_value
from rioxarray._io import FILL_VALUE_NAMES, UNWANTED_RIO_ATTRS
from rioxarray.crs import crs_from_user_input
from rioxarray.exceptions import (
MissingCRS,
NoDataInBounds,
OneDimensionalRaster,
RioXarrayError,
)
from rioxarray.raster_writer import RasterioWriter, _ensure_nodata_dtype
from rioxarray.rioxarray import (
XRasterBase,
_get_data_var_message,
_make_coords,
_order_bounds,
)
# DTYPE TO NODATA MAP
# Based on: https://github.com/OSGeo/gdal/blob/
# dee861e7c91c2da7ef8ff849947713e4d9bd115c/
# swig/python/gdal-utils/osgeo_utils/gdal_calc.py#L61
_NODATA_DTYPE_MAP = {
1: 255, # GDT_Byte
2: 65535, # GDT_UInt16
3: -32768, # GDT_Int16
4: 4294967293, # GDT_UInt32
5: -2147483647, # GDT_Int32
6: 3.402823466e38, # GDT_Float32
7: 1.7976931348623158e308, # GDT_Float64
8: None, # GDT_CInt16
9: None, # GDT_CInt32
10: 3.402823466e38, # GDT_CFloat32
11: 1.7976931348623158e308, # GDT_CFloat64
12: None, # GDT_Int64
13: None, # GDT_UInt64
14: None, # GDT_Int8
}
def _generate_attrs(
src_data_array: xarray.DataArray, dst_nodata: Optional[float]
) -> dict[str, Any]:
# add original attributes
new_attrs = copy.deepcopy(src_data_array.attrs)
# remove all nodata information
for unwanted_attr in FILL_VALUE_NAMES + UNWANTED_RIO_ATTRS:
new_attrs.pop(unwanted_attr, None)
# add nodata information
fill_value = (
src_data_array.rio.nodata
if src_data_array.rio.nodata is not None
else dst_nodata
)
if src_data_array.rio.encoded_nodata is None and fill_value is not None:
new_attrs["_FillValue"] = fill_value
return new_attrs
def _add_attrs_proj(
new_data_array: xarray.DataArray, src_data_array: xarray.DataArray
) -> xarray.DataArray:
"""Make sure attributes and projection correct"""
# make sure dimension information is preserved
if new_data_array.rio._x_dim is None:
new_data_array.rio._x_dim = src_data_array.rio.x_dim
if new_data_array.rio._y_dim is None:
new_data_array.rio._y_dim = src_data_array.rio.y_dim
# make sure attributes preserved
new_attrs = _generate_attrs(src_data_array, None)
# remove fill value if it already exists in the encoding
# this is for data arrays pulling the encoding from a
# source data array instead of being generated anew.
if "_FillValue" in new_data_array.encoding:
new_attrs.pop("_FillValue", None)
new_data_array.rio.set_attrs(new_attrs, inplace=True)
# make sure projection added
new_data_array.rio.write_grid_mapping(src_data_array.rio.grid_mapping, inplace=True)
new_data_array.rio.write_crs(src_data_array.rio.crs, inplace=True)
new_data_array.rio.write_coordinate_system(inplace=True)
new_data_array.rio.write_transform(inplace=True)
# make sure encoding added
new_data_array.encoding = src_data_array.encoding.copy()
return new_data_array
def _make_dst_affine(
src_data_array: xarray.DataArray,
src_crs: rasterio.crs.CRS,
dst_crs: rasterio.crs.CRS,
dst_resolution: Optional[Union[float, tuple[float, float]]] = None,
dst_shape: Optional[tuple[float, float]] = None,
**kwargs,
):
"""Determine the affine of the new projected `xarray.DataArray`"""
src_bounds = () if "gcps" in kwargs else src_data_array.rio.bounds()
src_height, src_width = src_data_array.rio.shape
dst_height, dst_width = dst_shape if dst_shape is not None else (None, None)
# pylint: disable=isinstance-second-argument-not-valid-type
if isinstance(dst_resolution, Iterable):
dst_resolution = tuple(abs(res_val) for res_val in dst_resolution) # type: ignore
elif dst_resolution is not None:
dst_resolution = abs(dst_resolution) # type: ignore
for key, value in (
("resolution", dst_resolution),
("dst_height", dst_height),
("dst_width", dst_width),
):
if value is not None:
kwargs[key] = value
dst_affine, dst_width, dst_height = rasterio.warp.calculate_default_transform(
src_crs,
dst_crs,
src_width,
src_height,
*src_bounds,
**kwargs,
)
return dst_affine, dst_width, dst_height
def _clip_from_disk(
xds: xarray.DataArray,
geometries: Iterable,
all_touched: bool,
drop: bool,
invert: bool,
) -> Optional[xarray.DataArray]:
"""
clip from disk if the file object is available
"""
try:
out_image, out_transform = rasterio.mask.mask(
xds.rio._manager.acquire(),
geometries,
all_touched=all_touched,
invert=invert,
crop=drop,
)
if xds.rio.encoded_nodata is not None and not numpy.isnan(
xds.rio.encoded_nodata
):
out_image = out_image.astype(numpy.float64)
out_image[out_image == xds.rio.encoded_nodata] = numpy.nan
height, width = out_image.shape[-2:]
cropped_ds = xarray.DataArray(
name=xds.name,
data=out_image,
coords=_make_coords(xds, out_transform, width, height),
dims=xds.dims,
attrs=xds.attrs,
)
cropped_ds.encoding = xds.encoding
return cropped_ds
except AttributeError:
return None
def _clip_xarray(
xds: xarray.DataArray,
geometries: Iterable,
all_touched: bool,
drop: bool,
invert: bool,
) -> xarray.DataArray:
"""
clip the xarray DataArray
"""
clip_mask_arr = geometry_mask(
geometries=geometries,
out_shape=(int(xds.rio.height), int(xds.rio.width)),
transform=xds.rio.transform(recalc=True),
invert=not invert,
all_touched=all_touched,
)
clip_mask_xray = xarray.DataArray(
clip_mask_arr,
dims=(xds.rio.y_dim, xds.rio.x_dim),
)
cropped_ds = xds.where(clip_mask_xray)
if drop:
cropped_ds.rio.set_spatial_dims(
x_dim=xds.rio.x_dim, y_dim=xds.rio.y_dim, inplace=True
)
cropped_ds = cropped_ds.rio.isel_window(
rasterio.windows.get_data_window(
numpy.ma.masked_array(clip_mask_arr, ~clip_mask_arr)
)
)
if xds.rio.nodata is not None and not numpy.isnan(xds.rio.nodata):
cropped_ds = cropped_ds.fillna(xds.rio.nodata)
return cropped_ds.astype(xds.dtype)
[docs]
@xarray.register_dataarray_accessor("rio")
class RasterArray(XRasterBase):
"""This is the GIS extension for :obj:`xarray.DataArray`"""
def __init__(self, xarray_obj: xarray.DataArray):
super().__init__(xarray_obj)
self._obj: xarray.DataArray
# properties
self._nodata: Optional[float] = None
self._manager: Optional[
FileManager
] = None # https://github.com/corteva/rioxarray/issues/254
[docs]
def set_nodata(
self, input_nodata: Optional[float], inplace: bool = True
) -> xarray.DataArray:
"""
Set the nodata value for the DataArray without modifying
the data array.
Parameters
----------
input_nodata: Optional[float]
Valid nodata for dtype.
inplace: bool, optional
If True, it will write to the existing dataset. Default is True.
Returns
-------
:obj:`xarray.DataArray`:
Dataset with nodata attribute set.
"""
obj: xarray.DataArray = self._get_obj(inplace=inplace) # type: ignore
obj.rio._nodata = input_nodata
return obj
[docs]
def write_nodata(
self, input_nodata: Optional[float], encoded: bool = False, inplace=False
) -> xarray.DataArray:
"""
Write the nodata to the DataArray in a CF compliant manner.
Parameters
----------
input_nodata: Optional[float]
Nodata value for the DataArray.
If input_nodata is None, it will remove the _FillValue attribute.
encoded: bool, optional
If True, it will write the nodata value in the encoding and remove
the fill value from the attributes. This is useful for masking
with nodata. Default is False.
inplace: bool, optional
If True, it will write to the existing DataArray. Default is False.
Returns
-------
:obj:`xarray.DataArray`:
Modified DataArray with CF compliant nodata information.
Examples
--------
To write the nodata value if it is missing:
>>> raster.rio.write_nodata(-9999, inplace=True)
To write the nodata value on a copy:
>>> raster = raster.rio.write_nodata(-9999)
To mask with nodata:
>>> nodata = raster.rio.nodata
>>> raster = raster.where(raster != nodata)
>>> raster.rio.write_nodata(nodata, encoded=True, inplace=True)
"""
data_obj: xarray.DataArray = self._get_obj(inplace=inplace) # type: ignore
input_nodata = False if input_nodata is None else input_nodata
if input_nodata is not False:
input_nodata = _ensure_nodata_dtype(input_nodata, self._obj.dtype)
if encoded:
data_obj.rio.update_encoding({"_FillValue": input_nodata}, inplace=True)
else:
data_obj.rio.update_attrs({"_FillValue": input_nodata}, inplace=True)
if input_nodata is False or encoded:
new_attrs = dict(data_obj.attrs)
new_attrs.pop("_FillValue", None)
data_obj.rio.set_attrs(new_attrs, inplace=True)
if input_nodata is False and encoded:
new_encoding = dict(data_obj.encoding)
new_encoding.pop("_FillValue", None)
data_obj.rio.set_encoding(new_encoding, inplace=True)
if not encoded:
data_obj.rio.set_nodata(input_nodata, inplace=True)
return data_obj
@property
def encoded_nodata(self) -> Optional[float]:
"""Return the encoded nodata value for the dataset if encoded."""
encoded_nodata = self._obj.encoding.get("_FillValue")
if encoded_nodata is None:
return None
return _ensure_nodata_dtype(encoded_nodata, self._obj.dtype)
@property
def nodata(self) -> Optional[float]:
"""Get the nodata value for the dataset."""
if self._nodata is not None:
return None if self._nodata is False else self._nodata
if self.encoded_nodata is not None:
self._nodata = get_fill_value(self._obj.dtype)
else:
self._nodata = self._obj.attrs.get(
"_FillValue",
self._obj.attrs.get(
"missing_value",
self._obj.attrs.get("fill_value", self._obj.attrs.get("nodata")),
),
)
# look in places used by `xarray.open_rasterio`
if self._nodata is None:
try:
self._nodata = self._manager.acquire().nodata # type: ignore
except AttributeError:
try:
self._nodata = self._obj.attrs["nodatavals"][0]
except (KeyError, IndexError):
pass
if self._nodata is None:
self._nodata = False
return None
self._nodata = _ensure_nodata_dtype(self._nodata, self._obj.dtype)
return self._nodata
[docs]
def reproject(
self,
dst_crs: Any,
resolution: Optional[Union[float, tuple[float, float]]] = None,
shape: Optional[tuple[int, int]] = None,
transform: Optional[Affine] = None,
resampling: Resampling = Resampling.nearest,
nodata: Optional[float] = None,
**kwargs,
) -> xarray.DataArray:
"""
Reproject :obj:`xarray.DataArray` objects
Powered by :func:`rasterio.warp.reproject`
.. note:: Only 2D/3D arrays with dimensions 'x'/'y' are currently supported.
Requires either a grid mapping variable with 'spatial_ref' or
a 'crs' attribute to be set containing a valid CRS.
If using a WKT (e.g. from spatiareference.org), make sure it is an OGC WKT.
.. note:: To re-project with dask, see
`odc-geo <https://odc-geo.readthedocs.io/>`__ &
`pyresample <https://pyresample.readthedocs.io/>`__.
.. versionadded:: 0.0.27 shape
.. versionadded:: 0.0.28 transform
.. versionadded:: 0.5.0 nodata, kwargs
Parameters
----------
dst_crs: str
OGC WKT string or Proj.4 string.
resolution: float or tuple(float, float), optional
Size of a destination pixel in destination projection units
(e.g. degrees or metres).
shape: tuple(int, int), optional
Shape of the destination in pixels (dst_height, dst_width). Cannot be used
together with resolution.
transform: Affine, optional
The destination transform.
resampling: rasterio.enums.Resampling, optional
See :func:`rasterio.warp.reproject` for more details.
nodata: float, optional
The nodata value used to initialize the destination;
it will remain in all areas not covered by the reprojected source.
Defaults to the nodata value of the source image if none provided
and exists or attempts to find an appropriate value by dtype.
**kwargs: dict
Additional keyword arguments to pass into :func:`rasterio.warp.reproject`.
To override:
- src_transform: `rio.write_transform`
- src_crs: `rio.write_crs`
- src_nodata: `rio.write_nodata`
Returns
-------
:obj:`xarray.DataArray`:
The reprojected DataArray.
"""
if resolution is not None and (shape is not None or transform is not None):
raise RioXarrayError("resolution cannot be used with shape or transform.")
if self.crs is None:
raise MissingCRS(
"CRS not found. Please set the CRS with 'rio.write_crs()'."
f"{_get_data_var_message(self._obj)}"
)
gcps = self.get_gcps()
if gcps:
kwargs.setdefault("gcps", gcps)
gcps_or_rpcs = "gcps" in kwargs or "rpcs" in kwargs
src_affine = None if gcps_or_rpcs else self.transform(recalc=True)
if transform is None:
dst_affine, dst_width, dst_height = _make_dst_affine(
self._obj, self.crs, dst_crs, resolution, shape, **kwargs
)
else:
dst_affine = transform
if shape is not None:
dst_height, dst_width = shape
else:
dst_height, dst_width = self.shape
dst_data = self._create_dst_data(dst_height, dst_width)
dst_nodata = self._get_dst_nodata(nodata)
rasterio.warp.reproject(
source=self._obj.values,
destination=dst_data,
src_transform=src_affine,
src_crs=self.crs,
src_nodata=self.nodata,
dst_transform=dst_affine,
dst_crs=dst_crs,
dst_nodata=dst_nodata,
resampling=resampling,
**kwargs,
)
# add necessary attributes
new_attrs = _generate_attrs(self._obj, dst_nodata)
# make sure dimensions with coordinates renamed to x,y
dst_dims: list[Hashable] = []
for dim in self._obj.dims:
if dim == self.x_dim:
dst_dims.append("x")
elif dim == self.y_dim:
dst_dims.append("y")
else:
dst_dims.append(dim)
xda = xarray.DataArray(
name=self._obj.name,
data=dst_data,
coords=_make_coords(
src_data_array=self._obj,
dst_affine=dst_affine,
dst_width=dst_width,
dst_height=dst_height,
force_generate=gcps_or_rpcs,
),
dims=tuple(dst_dims),
attrs=new_attrs,
)
xda.encoding = self._obj.encoding
xda.rio.write_transform(dst_affine, inplace=True)
xda.rio.write_crs(dst_crs, inplace=True)
xda.rio.write_coordinate_system(inplace=True)
return xda
def _get_dst_nodata(self, nodata: Optional[float]) -> Optional[float]:
default_nodata = (
_NODATA_DTYPE_MAP.get(dtype_rev[self._obj.dtype.name])
if self.nodata is None
else self.nodata
)
dst_nodata = default_nodata if nodata is None else nodata
return dst_nodata
def _create_dst_data(self, dst_height: int, dst_width: int) -> numpy.ndarray:
extra_dim = self._check_dimensions()
if extra_dim:
dst_data = numpy.zeros(
(self._obj[extra_dim].size, dst_height, dst_width),
dtype=self._obj.dtype.type,
)
else:
dst_data = numpy.zeros((dst_height, dst_width), dtype=self._obj.dtype.type)
return dst_data
[docs]
def reproject_match(
self,
match_data_array: Union[xarray.DataArray, xarray.Dataset],
resampling: Resampling = Resampling.nearest,
**reproject_kwargs,
) -> xarray.DataArray:
"""
Reproject a DataArray object to match the resolution, projection,
and region of another DataArray.
Powered by :func:`rasterio.warp.reproject`
.. note:: Only 2D/3D arrays with dimensions 'x'/'y' are currently supported.
Requires either a grid mapping variable with 'spatial_ref' or
a 'crs' attribute to be set containing a valid CRS.
If using a WKT (e.g. from spatiareference.org), make sure it is an OGC WKT.
.. versionadded:: 0.9 reproject_kwargs
Parameters
----------
match_data_array: :obj:`xarray.DataArray` | :obj:`xarray.Dataset`
DataArray of the target resolution and projection.
resampling: rasterio.enums.Resampling, optional
See :func:`rasterio.warp.reproject` for more details.
**reproject_kwargs:
Other options to pass to :meth:`rioxarray.raster_array.RasterArray.reproject`
Returns
--------
:obj:`xarray.DataArray`:
Contains the data from the src_data_array, reprojected to match
match_data_array.
"""
reprojected_data_array = self.reproject(
match_data_array.rio.crs,
transform=match_data_array.rio.transform(recalc=True),
shape=match_data_array.rio.shape,
resampling=resampling,
**reproject_kwargs,
)
# hack to resolve: https://github.com/corteva/rioxarray/issues/298
# may be resolved in the future by flexible indexes:
# https://github.com/pydata/xarray/pull/4489#issuecomment-831809607
x_attrs = reprojected_data_array[reprojected_data_array.rio.x_dim].attrs.copy()
y_attrs = reprojected_data_array[reprojected_data_array.rio.y_dim].attrs.copy()
# ensure coords the same
reprojected_data_array = reprojected_data_array.assign_coords(
{
reprojected_data_array.rio.x_dim: copy.copy(
match_data_array[match_data_array.rio.x_dim].values
),
reprojected_data_array.rio.y_dim: copy.copy(
match_data_array[match_data_array.rio.y_dim].values
),
}
)
# ensure attributes copied
reprojected_data_array[reprojected_data_array.rio.x_dim].attrs = x_attrs
reprojected_data_array[reprojected_data_array.rio.y_dim].attrs = y_attrs
return reprojected_data_array
[docs]
def pad_xy(
self,
minx: float,
miny: float,
maxx: float,
maxy: float,
constant_values: Union[
float, tuple[int, int], Mapping[Any, tuple[int, int]], None
] = None,
) -> xarray.DataArray:
"""Pad the array to x,y bounds.
.. versionadded:: 0.0.29
Parameters
----------
minx: float
Minimum bound for x coordinate.
miny: float
Minimum bound for y coordinate.
maxx: float
Maximum bound for x coordinate.
maxy: float
Maximum bound for y coordinate.
constant_values: scalar, tuple or mapping of hashable to tuple
The value used for padding. If None, nodata will be used if it is
set, and numpy.nan otherwise.
Returns
-------
:obj:`xarray.DataArray`:
The padded object.
"""
# pylint: disable=too-many-locals
left, bottom, right, top = self._internal_bounds()
resolution_x, resolution_y = self.resolution()
y_before = y_after = 0
x_before = x_after = 0
y_coord: Union[xarray.DataArray, numpy.ndarray] = self._obj[self.y_dim]
x_coord: Union[xarray.DataArray, numpy.ndarray] = self._obj[self.x_dim]
if top - resolution_y < maxy:
new_y_coord: numpy.ndarray = numpy.arange(bottom, maxy, -resolution_y)[::-1]
y_before = len(new_y_coord) - len(y_coord)
y_coord = new_y_coord
top = y_coord[0]
if bottom + resolution_y > miny:
new_y_coord = numpy.arange(top, miny, resolution_y)
y_after = len(new_y_coord) - len(y_coord)
y_coord = new_y_coord
bottom = y_coord[-1]
if left - resolution_x > minx:
new_x_coord: numpy.ndarray = numpy.arange(right, minx, -resolution_x)[::-1]
x_before = len(new_x_coord) - len(x_coord)
x_coord = new_x_coord
left = x_coord[0]
if right + resolution_x < maxx:
new_x_coord = numpy.arange(left, maxx, resolution_x)
x_after = len(new_x_coord) - len(x_coord)
x_coord = new_x_coord
right = x_coord[-1]
if constant_values is None:
constant_values = numpy.nan if self.nodata is None else self.nodata
superset = self._obj.pad(
pad_width={
self.x_dim: (x_before, x_after),
self.y_dim: (y_before, y_after),
},
constant_values=constant_values, # type: ignore
).rio.set_spatial_dims(x_dim=self.x_dim, y_dim=self.y_dim, inplace=True)
superset[self.x_dim] = x_coord
superset[self.y_dim] = y_coord
superset.rio.write_transform(inplace=True)
return superset
[docs]
def pad_box(
self,
minx: float,
miny: float,
maxx: float,
maxy: float,
constant_values: Union[
float, tuple[int, int], Mapping[Any, tuple[int, int]], None
] = None,
) -> xarray.DataArray:
"""Pad the :obj:`xarray.DataArray` to a bounding box
.. versionadded:: 0.0.29
Parameters
----------
minx: float
Minimum bound for x coordinate.
miny: float
Minimum bound for y coordinate.
maxx: float
Maximum bound for x coordinate.
maxy: float
Maximum bound for y coordinate.
constant_values: scalar, tuple or mapping of hashable to tuple
The value used for padding. If None, nodata will be used if it is
set, and numpy.nan otherwise.
Returns
-------
:obj:`xarray.DataArray`:
The padded object.
"""
resolution_x, resolution_y = self.resolution()
pad_minx = minx - abs(resolution_x) / 2.0
pad_miny = miny - abs(resolution_y) / 2.0
pad_maxx = maxx + abs(resolution_x) / 2.0
pad_maxy = maxy + abs(resolution_y) / 2.0
pd_array = self.pad_xy(pad_minx, pad_miny, pad_maxx, pad_maxy, constant_values)
# make sure correct attributes preserved & projection added
_add_attrs_proj(pd_array, self._obj)
return pd_array
[docs]
def clip_box(
self,
minx: float,
miny: float,
maxx: float,
maxy: float,
auto_expand: Union[bool, int] = False,
auto_expand_limit: int = 3,
crs: Optional[Any] = None,
) -> xarray.DataArray:
"""Clip the :obj:`xarray.DataArray` by a bounding box.
.. versionadded:: 0.12 crs
Parameters
----------
minx: float
Minimum bound for x coordinate.
miny: float
Minimum bound for y coordinate.
maxx: float
Maximum bound for x coordinate.
maxy: float
Maximum bound for y coordinate.
auto_expand: Union[bool, int]
If True, it will expand clip search if only 1D raster found with clip.
auto_expand_limit: int
maximum number of times the clip will be retried before raising
an exception.
crs: :obj:`rasterio.crs.CRS`, optional
The CRS of the bounding box. Default is to assume it is the same
as the dataset.
Returns
-------
xarray.DataArray:
The clipped object.
"""
if self.width == 1 or self.height == 1:
raise OneDimensionalRaster(
"At least one of the raster x,y coordinates has only one point."
f"{_get_data_var_message(self._obj)}"
)
if crs is not None and self.crs is None:
raise MissingCRS(
"CRS not found. Please set the CRS with 'rio.write_crs()'."
f"{_get_data_var_message(self._obj)}"
)
crs = crs_from_user_input(crs) if crs is not None else self.crs
if self.crs != crs:
minx, miny, maxx, maxy = rasterio.warp.transform_bounds(
src_crs=crs,
dst_crs=self.crs,
left=minx,
bottom=miny,
right=maxx,
top=maxy,
)
if (
self.crs is not None
and self.crs.is_geographic # pylint: disable=no-member
and minx > maxx
):
raise RioXarrayError(
"Transformed bounds crossed the antimeridian. "
"Please transform your bounds manually using "
"rasterio.warp.transform_bounds and clip using "
"the bounding box(es) desired."
)
resolution_x, resolution_y = self.resolution()
# make sure that if the coordinates are
# in reverse order that it still works
left, bottom, right, top = _order_bounds(
minx=minx,
miny=miny,
maxx=maxx,
maxy=maxy,
resolution_x=resolution_x,
resolution_y=resolution_y,
)
# pull the data out
window_error = None
try:
window = rasterio.windows.from_bounds(
left=numpy.array(left).item(),
bottom=numpy.array(bottom).item(),
right=numpy.array(right).item(),
top=numpy.array(top).item(),
transform=self.transform(recalc=True),
)
cl_array: xarray.DataArray = self.isel_window(window) # type: ignore
except rasterio.errors.WindowError as err:
window_error = err
# check that the window has data in it
if window_error or cl_array.rio.width <= 1 or cl_array.rio.height <= 1:
if auto_expand and auto_expand < auto_expand_limit:
return self.clip_box(
minx=minx - abs(resolution_x) / 2.0,
miny=miny - abs(resolution_y) / 2.0,
maxx=maxx + abs(resolution_x) / 2.0,
maxy=maxy + abs(resolution_y) / 2.0,
auto_expand=int(auto_expand) + 1,
auto_expand_limit=auto_expand_limit,
)
if window_error:
raise window_error
if cl_array.rio.width < 1 or cl_array.rio.height < 1:
raise NoDataInBounds(
f"No data found in bounds.{_get_data_var_message(self._obj)}"
)
if cl_array.rio.width == 1 or cl_array.rio.height == 1:
raise OneDimensionalRaster(
"At least one of the clipped raster x,y coordinates"
" has only one point."
f"{_get_data_var_message(self._obj)}"
)
# make sure correct attributes preserved & projection added
_add_attrs_proj(cl_array, self._obj)
return cl_array
[docs]
def clip(
self,
geometries: Iterable,
crs: Optional[Any] = None,
all_touched: bool = False,
drop: bool = True,
invert: bool = False,
from_disk: bool = False,
) -> xarray.DataArray:
"""
Crops a :obj:`xarray.DataArray` by geojson like geometry dicts.
Powered by `rasterio.features.geometry_mask`.
Examples:
>>> geometry = ''' {"type": "Polygon",
... "coordinates": [
... [[-94.07955380199459, 41.69085871273774],
... [-94.06082436942204, 41.69103313774798],
... [-94.06063203899649, 41.67932439500822],
... [-94.07935807746362, 41.679150041277325],
... [-94.07955380199459, 41.69085871273774]]]}'''
>>> cropping_geometries = [geojson.loads(geometry)]
>>> xds = xarray.open_rasterio('cool_raster.tif')
>>> cropped = xds.rio.clip(geometries=cropping_geometries, crs=4326)
.. versionadded:: 0.2 from_disk
Parameters
----------
geometries: Iterable
A list of geojson geometry dicts or objects with __geo_interface__ with
if you have rasterio 1.2+.
crs: :obj:`rasterio.crs.CRS`, optional
The CRS of the input geometries. Default is to assume it is the same
as the dataset.
all_touched : bool, optional
If True, all pixels touched by geometries will be burned in. If
false, only pixels whose center is within the polygon or that
are selected by Bresenham's line algorithm will be burned in.
drop: bool, optional
If True, drop the data outside of the extent of the mask geoemtries
Otherwise, it will return the same raster with the data masked.
Default is True.
invert: boolean, optional
If False, pixels that do not overlap shapes will be set as nodata.
Otherwise, pixels that overlap the shapes will be set as nodata.
False by default.
from_disk: boolean, optional
If True, it will clip from disk using rasterio.mask.mask if possible.
This is beneficial when the size of the data is larger than memory.
Default is False.
Returns
-------
:obj:`xarray.DataArray`:
The clipped object.
"""
if self.crs is None:
raise MissingCRS(
"CRS not found. Please set the CRS with 'rio.write_crs()'."
f"{_get_data_var_message(self._obj)}"
)
crs = crs_from_user_input(crs) if crs is not None else self.crs
if self.crs != crs:
geometries = rasterio.warp.transform_geom(crs, self.crs, geometries)
cropped_ds = None
if from_disk:
cropped_ds = _clip_from_disk(
self._obj,
geometries=geometries,
all_touched=all_touched,
drop=drop,
invert=invert,
)
if cropped_ds is None:
cropped_ds = _clip_xarray(
self._obj,
geometries=geometries,
all_touched=all_touched,
drop=drop,
invert=invert,
)
if (
cropped_ds.coords[self.x_dim].size < 1
or cropped_ds.coords[self.y_dim].size < 1
):
raise NoDataInBounds(
f"No data found in bounds.{_get_data_var_message(self._obj)}"
)
# make sure correct attributes preserved & projection added
_add_attrs_proj(cropped_ds, self._obj)
return cropped_ds
def _interpolate_na(
self, src_data: Any, method: Literal["linear", "nearest", "cubic"] = "nearest"
) -> numpy.ndarray:
"""
This method uses scipy.interpolate.griddata to interpolate missing data.
Parameters
----------
src_data: Any
Input data array.
method: {'linear', 'nearest', 'cubic'}, optional
The method to use for interpolation in `scipy.interpolate.griddata`.
Returns
-------
:class:`numpy.ndarray`:
An interpolated :class:`numpy.ndarray`.
"""
try:
from scipy.interpolate import ( # pylint: disable=import-outside-toplevel,import-error
griddata,
)
except ModuleNotFoundError as err:
raise ModuleNotFoundError(
"scipy is not found. Use rioxarray[interp] to install."
) from err
src_data_flat = src_data.flatten()
try:
data_isnan = numpy.isnan(self.nodata) # type: ignore
except TypeError:
data_isnan = False
if not data_isnan:
data_bool = src_data_flat != self.nodata
else:
data_bool = ~numpy.isnan(src_data_flat)
if not data_bool.any():
return src_data
x_coords, y_coords = numpy.meshgrid(
self._obj.coords[self.x_dim].values, self._obj.coords[self.y_dim].values
)
return griddata(
points=(x_coords.flatten()[data_bool], y_coords.flatten()[data_bool]),
values=src_data_flat[data_bool],
xi=(x_coords, y_coords),
method=method,
fill_value=self.nodata,
)
[docs]
def interpolate_na(
self, method: Literal["linear", "nearest", "cubic"] = "nearest"
) -> xarray.DataArray:
"""
This method uses scipy.interpolate.griddata to interpolate missing data.
.. warning:: scipy is an optional dependency.
Parameters
----------
method: {'linear', 'nearest', 'cubic'}, optional
The method to use for interpolation in `scipy.interpolate.griddata`.
Returns
-------
:obj:`xarray.DataArray`:
An interpolated :obj:`xarray.DataArray` object.
"""
if self.nodata is None:
raise RioXarrayError(
"nodata not found. Please set the nodata with 'rio.write_nodata()'."
f"{_get_data_var_message(self._obj)}"
)
extra_dim = self._check_dimensions()
if extra_dim:
interp_data = []
for _, sub_xds in self._obj.groupby(extra_dim):
interp_data.append(
self._interpolate_na(sub_xds.load().data, method=method)
)
interp_data = numpy.array(interp_data) # type: ignore
else:
interp_data = self._interpolate_na(self._obj.load().data, method=method) # type: ignore
interp_array = xarray.DataArray(
name=self._obj.name,
data=interp_data,
coords=self._obj.coords,
dims=self._obj.dims,
attrs=self._obj.attrs,
)
interp_array.encoding = self._obj.encoding
# make sure correct attributes preserved & projection added
_add_attrs_proj(interp_array, self._obj)
return interp_array
[docs]
def to_raster(
self,
raster_path: Union[str, os.PathLike],
driver: Optional[str] = None,
dtype: Optional[Union[str, numpy.dtype]] = None,
tags: Optional[dict[str, str]] = None,
windowed: bool = False,
recalc_transform: bool = True,
lock: Optional[bool] = None,
compute: bool = True,
**profile_kwargs,
) -> None:
"""
Export the DataArray to a raster file.
..versionadded:: 0.2 lock
Parameters
----------
raster_path: Union[str, os.PathLike]
The path to output the raster to.
driver: str, optional
The name of the GDAL/rasterio driver to use to export the raster.
Default is "GTiff" if rasterio < 1.2 otherwise it will autodetect.
dtype: str, optional
The data type to write the raster to. Default is the datasets dtype.
tags: dict, optional
A dictionary of tags to write to the raster.
windowed: bool, optional
If True, it will write using the windows of the output raster.
This is useful for loading data in chunks when writing. Does not
do anything when writing with dask.
Default is False.
recalc_transform: bool, optional
If False, it will write the raster with the cached transform from
the dataarray rather than recalculating it.
Default is True.
lock: boolean or Lock, optional
Lock to use to write data using dask.
If not supplied, it will use a single process for writing.
compute: bool, optional
If True and data is a dask array, then compute and save
the data immediately. If False, return a dask Delayed object.
Call ".compute()" on the Delayed object to compute the result
later. Call ``dask.compute(delayed1, delayed2)`` to save
multiple delayed files at once. Default is True.
**profile_kwargs
Additional keyword arguments to pass into writing the raster. The
nodata, transform, crs, count, width, and height attributes
are ignored.
Returns
-------
:obj:`dask.Delayed`:
If the data array is a dask array and compute
is True. Otherwise None is returned.
"""
if driver is None:
extension = Path(raster_path).suffix
# https://github.com/rasterio/rasterio/pull/2008
if extension in (".tif", ".tiff"):
driver = "GTiff"
# get the output profile from the rasterio object
# if opened with xarray.open_rasterio()
try:
out_profile = self._manager.acquire().profile # type: ignore
except AttributeError:
out_profile = {}
out_profile.update(profile_kwargs)
# filter out the generated attributes
out_profile = {
key: value
for key, value in out_profile.items()
if key
not in (
"driver",
"height",
"width",
"crs",
"transform",
"nodata",
"count",
"dtype",
)
}
rio_nodata = (
self.encoded_nodata if self.encoded_nodata is not None else self.nodata
)
return RasterioWriter(raster_path=raster_path).to_raster(
xarray_dataarray=self._obj,
tags=tags,
driver=driver,
height=int(self.height),
width=int(self.width),
count=int(self.count),
dtype=dtype,
crs=self.crs,
transform=self.transform(recalc=recalc_transform),
gcps=self.get_gcps(),
nodata=rio_nodata,
windowed=windowed,
lock=lock,
compute=compute,
**out_profile,
)