Source code for rioxarray.raster_array

"""
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 typing import (
    Any,
    Dict,
    Hashable,
    Iterable,
    List,
    Literal,
    Mapping,
    Optional,
    Tuple,
    Union,
)

import numpy as np
import rasterio
import rasterio.mask
import rasterio.warp
import xarray
from affine import Affine
from packaging import version
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.crs import crs_from_user_input
from rioxarray.exceptions import (
    MissingCRS,
    NoDataInBounds,
    OneDimensionalRaster,
    RioXarrayError,
)
from rioxarray.raster_writer import (
    FILL_VALUE_NAMES,
    UNWANTED_RIO_ATTRS,
    RasterioWriter,
    _ensure_nodata_dtype,
)
from rioxarray.rioxarray import XRasterBase, _get_data_var_message, _make_coords

# DTYPE TO NODATA MAP
# Based on: https://github.com/OSGeo/gdal/blob/
# cde27dc7641964a872efdc6bbcf5e3d3f7ab9cfd/gdal/
# swig/python/gdal-utils/osgeo_utils/gdal_calc.py#L62
_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: -32768,  # GDT_CInt16
    9: -2147483647,  # GDT_CInt32
    10: 3.402823466e38,  # GDT_CFloat32
    11: 1.7976931348623158e308,  # GDT_CFloat64
}


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 np.isnan(xds.rio.encoded_nodata):
            out_image = out_image.astype(np.float64)
            out_image[out_image == xds.rio.encoded_nodata] = np.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(
                np.ma.masked_array(clip_mask_arr, ~clip_mask_arr)
            )
        )
    if xds.rio.nodata is not None and not np.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( dict(_FillValue=input_nodata), inplace=True ) else: data_obj.rio.update_attrs(dict(_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. .. 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) src_affine = None if "gcps" in kwargs 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(self._obj, dst_affine, dst_width, dst_height), 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[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) -> np.ndarray: extra_dim = self._check_dimensions() if extra_dim: dst_data = np.zeros( (self._obj[extra_dim].size, dst_height, dst_width), dtype=self._obj.dtype.type, ) else: dst_data = np.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: match_data_array[ match_data_array.rio.x_dim ].values.copy(), reprojected_data_array.rio.y_dim: match_data_array[ match_data_array.rio.y_dim ].values.copy(), } ) # 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 np.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, np.ndarray] = self._obj[self.y_dim] x_coord: Union[xarray.DataArray, np.ndarray] = self._obj[self.x_dim] if top - resolution_y < maxy: new_y_coord: np.ndarray = np.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 = np.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: np.ndarray = np.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 = np.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 = np.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 np.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, ) -> xarray.DataArray: """Clip the :obj:`xarray.DataArray` by a bounding box. 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. Returns ------- :obj:`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)}" ) # make sure that if the coordinates are # in reverse order that it still works resolution_x, resolution_y = self.resolution() if resolution_y < 0: top = maxy bottom = miny else: top = miny bottom = maxy if resolution_x < 0: left = maxx right = minx else: left = minx right = maxx # pull the data out window_error = None try: window = rasterio.windows.from_bounds( left=np.array(left).item(), bottom=np.array(bottom).item(), right=np.array(right).item(), top=np.array(top).item(), transform=self.transform(recalc=True), width=self.width, height=self.height, ) 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: 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 __geom_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: if version.parse(rasterio.__version__) >= version.parse("1.2"): geometries = rasterio.warp.transform_geom(crs, self.crs, geometries) else: geometries = [ rasterio.warp.transform_geom(crs, self.crs, geometry) for geometry in 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" ) -> np.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 = np.isnan(self.nodata) # type: ignore except TypeError: data_isnan = False if not data_isnan: data_bool = src_data_flat != self.nodata else: data_bool = ~np.isnan(src_data_flat) if not data_bool.any(): return src_data x_coords, y_coords = np.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 = np.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: str = None, dtype: Union[str, np.dtype] = None, tags: 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. 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 and version.parse(rasterio.__version__) < version.parse( "1.2" ): 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, )