Source code for rioxarray._io



This file was adopted from: # noqa
Source file: # noqa

import contextlib
import os
import re
import threading
import warnings
from typing import Any, Dict, Hashable, List, Optional, Tuple, Union

import numpy as np
import rasterio
from packaging import version
from rasterio.errors import NotGeoreferencedWarning
from rasterio.vrt import WarpedVRT
from xarray import Dataset, IndexVariable
from xarray.backends.common import BackendArray
from xarray.backends.file_manager import CachingFileManager, FileManager
from xarray.backends.locks import SerializableLock
from xarray.coding import times, variables
from xarray.core import indexing
from xarray.core.dataarray import DataArray
from xarray.core.dtypes import maybe_promote
from xarray.core.utils import is_scalar
from xarray.core.variable import as_variable

from rioxarray.exceptions import RioXarrayError
from rioxarray.rioxarray import _generate_spatial_coords

# TODO: should this be GDAL_LOCK instead?
RASTERIO_LOCK = SerializableLock()
NO_LOCK = contextlib.nullcontext()

RasterioReader = Union[, rasterio.vrt.WarpedVRT]

class FileHandleLocal(threading.local):
    This contains the thread local ThreadURIManager

    def __init__(self):  # pylint: disable=super-init-not-called
        self.thread_manager = None  # Initialises in each thread

class ThreadURIManager:
    This handles opening & closing file handles in each thread.

    def __init__(
        self._opener = opener
        self._args = args
        self._mode = mode
        self._kwargs = {} if kwargs is None else dict(kwargs)
        self._file_handle = None

    def file_handle(self):
        File handle returned by the opener.
        if self._file_handle is not None:
            return self._file_handle
        self._file_handle = self._opener(*self._args, mode=self._mode, **self._kwargs)
        return self._file_handle

    def close(self):
        Close file handle.
        if self._file_handle is not None:
            self._file_handle = None

    def __del__(self):

    def __enter__(self):
        return self

    def __exit__(self, type_, value, traceback):

class URIManager(FileManager):
    The URI manager is used for lockless reading

    def __init__(
        self._opener = opener
        self._args = args
        self._mode = mode
        self._kwargs = {} if kwargs is None else dict(kwargs)
        self._local = FileHandleLocal()

    def acquire(self, needs_lock=True):
        if self._local.thread_manager is None:
            self._local.thread_manager = ThreadURIManager(
                self._opener, *self._args, mode=self._mode, kwargs=self._kwargs
        return self._local.thread_manager.file_handle

    def acquire_context(self, needs_lock=True):
            yield self.acquire(needs_lock=needs_lock)
        except Exception:

    def close(self, needs_lock=True):
        if self._local.thread_manager is not None:
            self._local.thread_manager = None

    def __del__(self):

    def __getstate__(self):
        """State for pickling."""
        return (self._opener, self._args, self._mode, self._kwargs)

    def __setstate__(self, state):
        """Restore from a pickle."""
        opener, args, mode, kwargs = state
        self.__init__(opener, *args, mode=mode, kwargs=kwargs)

class RasterioArrayWrapper(BackendArray):
    """A wrapper around rasterio dataset objects"""

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

    def __init__(
        self.manager = manager
        self.lock = lock
        self.masked = masked or mask_and_scale
        self.mask_and_scale = mask_and_scale

        # cannot save riods as an attribute: this would break pickleability
        riods = manager.acquire()
        if vrt_params is not None:
            riods = WarpedVRT(riods, **vrt_params)
        self.vrt_params = vrt_params
        self._shape = (riods.count, riods.height, riods.width)

        self._dtype = None
        dtypes = riods.dtypes
        if not np.all(np.asarray(dtypes) == dtypes[0]):
            raise ValueError("All bands should have the same dtype")

        dtype = _rasterio_to_numpy_dtype(dtypes)

        # handle unsigned case
        if mask_and_scale and unsigned and dtype.kind == "i":
            self._dtype = np.dtype(f"u{dtype.itemsize}")
        elif mask_and_scale and unsigned:
                f"variable {name!r} has _Unsigned attribute but is not "
                "of integer type. Ignoring attribute.",
        self._fill_value = riods.nodata
        if self._dtype is None:
            if self.masked:
                self._dtype, self._fill_value = maybe_promote(dtype)
                self._dtype = dtype

    def dtype(self):
        Data type of the array
        return self._dtype

    def fill_value(self):
        Fill value of the array
        return self._fill_value

    def shape(self):
        Shape of the array
        return self._shape

    def _get_indexer(self, key):
        """Get indexer for rasterio array.

        key: tuple of int

        band_key: an indexer for the 1st dimension
        window: two tuples. Each consists of (start, stop).
        squeeze_axis: axes to be squeezed
        np_ind: indexer for loaded numpy array

        See also
        if len(key) != 3:
            raise RioXarrayError("rasterio datasets should always be 3D")

        # bands cannot be windowed but they can be listed
        band_key = key[0]
        np_inds = []
        # bands (axis=0) cannot be windowed but they can be listed
        if isinstance(band_key, slice):
            start, stop, step = band_key.indices(self.shape[0])
            band_key = np.arange(start, stop, step)
        # be sure we give out a list
        band_key = (np.asarray(band_key) + 1).tolist()
        if isinstance(band_key, list):  # if band_key is not a scalar

        # but other dims can only be windowed
        window = []
        squeeze_axis = []
        for iii, (ikey, size) in enumerate(zip(key[1:], self.shape[1:])):
            if isinstance(ikey, slice):
                # step is always positive. see indexing.decompose_indexer
                start, stop, step = ikey.indices(size)
                np_inds.append(slice(None, None, step))
            elif is_scalar(ikey):
                # windowed operations will always return an array
                # we will have to squeeze it later
                squeeze_axis.append(-(2 - iii))
                start = ikey
                stop = ikey + 1
                start, stop = np.min(ikey), np.max(ikey) + 1
                np_inds.append(ikey - start)
            window.append((start, stop))

        if isinstance(key[1], np.ndarray) and isinstance(key[2], np.ndarray):
            # do outer-style indexing
            np_inds[-2:] = np.ix_(*np_inds[-2:])

        return band_key, tuple(window), tuple(squeeze_axis), tuple(np_inds)

    def _getitem(self, key):
        band_key, window, squeeze_axis, np_inds = self._get_indexer(key)

        if not band_key or any(start == stop for (start, stop) in window):
            # no need to do IO
            shape = (len(band_key),) + tuple(stop - start for (start, stop) in window)
            out = np.zeros(shape, dtype=self.dtype)
            with self.lock:
                riods = self.manager.acquire(needs_lock=False)
                if self.vrt_params is not None:
                    riods = WarpedVRT(riods, **self.vrt_params)
                out =, window=window, masked=self.masked)
                if self.masked:
                    out =, self.fill_value)
                if self.mask_and_scale:
                    for iii, band_iii in enumerate(np.atleast_1d(band_key) - 1):
                        out[iii] = (
                            out[iii] * riods.scales[band_iii] + riods.offsets[band_iii]

        if squeeze_axis:
            out = np.squeeze(out, axis=squeeze_axis)
        return out[np_inds]

    def __getitem__(self, key):
        return indexing.explicit_indexing_adapter(
            key, self.shape, indexing.IndexingSupport.OUTER, self._getitem

def _parse_envi(meta):
    """Parse ENVI metadata into Python data structures.

    See the link for information on the ENVI header file format:

    meta : dict
        Dictionary of keys and str values to parse, as returned by the rasterio
        tags(ns='ENVI') call.

    parsed_meta : dict
        Dictionary containing the original keys and the parsed values


    def parsevec(value):
        return np.fromstring(value.strip("{}"), dtype="float", sep=",")

    def default(value):
        return value.strip("{}")

    parse = {"wavelength": parsevec, "fwhm": parsevec}
    parsed_meta = {key: parse.get(key, default)(value) for key, value in meta.items()}
    return parsed_meta

def _rasterio_to_numpy_dtype(dtypes):
    """Numpy dtype from first entry of rasterio dataset.dtypes"""
    # rasterio has some special dtype names (complex_int16 -> np.complex64)
    if dtypes[0] == "complex_int16":
        dtype = np.dtype("complex64")
        dtype = np.dtype(dtypes[0])

    return dtype

def _to_numeric(value: Any) -> float:
    Convert the value to a number
        value = int(value)
    except (TypeError, ValueError):
            value = float(value)
        except (TypeError, ValueError):
    return value

def _parse_tag(key: str, value: Any) -> Tuple[str, Any]:
    # NC_GLOBAL is appended to tags with netcdf driver and is not really needed
    key = key.split("NC_GLOBAL#")[-1]
    if value.startswith("{") and value.endswith("}"):
            new_val = np.fromstring(value.strip("{}"), dtype="float", sep=",")
            # pylint: disable=len-as-condition
            value = new_val if len(new_val) else _to_numeric(value)
        except ValueError:
            value = _to_numeric(value)
        value = _to_numeric(value)
    return key, value

def _parse_tags(tags: Dict) -> Dict:
    parsed_tags = {}
    for key, value in tags.items():
        key, value = _parse_tag(key, value)
        parsed_tags[key] = value
    return parsed_tags

    0: object,  # NC_NAT
    1: np.byte,  # NC_BYTE
    2: np.char,  # NC_CHAR
    3: np.short,  # NC_SHORT
    4: np.int_,  # NC_INT, NC_LONG
    5: float,  # NC_FLOAT
    6: np.double,  # NC_DOUBLE
    7: np.ubyte,  # NC_UBYTE
    8: np.ushort,  # NC_USHORT
    9: np.uint,  # NC_UINT
    10: np.int64,  # NC_INT64
    11: np.uint64,  # NC_UINT64
    12: object,  # NC_STRING

def _load_netcdf_attrs(tags: Dict, data_array: DataArray) -> None:
    Loads the netCDF attributes into the data array

    Attributes stored in this format:
    - variable_name#attr_name: attr_value
    for key, value in tags.items():
        key, value = _parse_tag(key, value)
        key_split = key.split("#")
        if len(key_split) != 2:
        variable_name, attr_name = key_split
        if variable_name in data_array.coords:
            data_array.coords[variable_name].attrs.update({attr_name: value})

def _load_netcdf_1d_coords(tags: Dict) -> Dict:
    Dimension information:
        - NETCDF_DIM_EXTRA: '{time}' (comma separated list of dim names)
        - NETCDF_DIM_time_DEF: '{2,6}' (dim size, dim dtype)
        - NETCDF_DIM_time_VALUES: '{0,872712.659688}' (comma separated list of data)
    dim_names = tags.get("NETCDF_DIM_EXTRA")
    if not dim_names:
        return {}
    dim_names = dim_names.strip("{}").split(",")
    coords = {}
    for dim_name in dim_names:
        dim_def = tags.get(f"NETCDF_DIM_{dim_name}_DEF")
        if not dim_def:
        # pylint: disable=unused-variable
        dim_size, dim_dtype = dim_def.strip("{}").split(",")
        dim_dtype = NETCDF_DTYPE_MAP.get(int(dim_dtype), object)
        dim_values = tags[f"NETCDF_DIM_{dim_name}_VALUES"].strip("{}")
        coords[dim_name] = IndexVariable(
            dim_name, np.fromstring(dim_values, dtype=dim_dtype, sep=",")
    return coords

def build_subdataset_filter(
    group_names: Optional[Union[str, List[str], Tuple[str, ...]]],
    variable_names: Optional[Union[str, List[str], Tuple[str, ...]]],

    group_names: str or list or tuple
        Name or names of netCDF groups to filter by.

    variable_names: str or list or tuple
        Name or names of netCDF variables to filter by.

    re.SRE_Pattern: output of re.compile()
    variable_query = r"\w+"
    if variable_names is not None:
        if not isinstance(variable_names, (tuple, list)):
            variable_names = [variable_names]
        variable_names = [re.escape(variable_name) for variable_name in variable_names]
        variable_query = rf"(?:{'|'.join(variable_names)})"
    if group_names is not None:
        if not isinstance(group_names, (tuple, list)):
            group_names = [group_names]
        group_names = [re.escape(group_name) for group_name in group_names]
        group_query = rf"(?:{'|'.join(group_names)})"
        return re.compile(r"".join([r".*(?:\:/|\:)(/+)?", variable_query, r"$"]))
    return re.compile(
            [r".*(?:\:/|\:)(/+)?", group_query, r"[:/](/+)?", variable_query, r"$"]

def _get_rasterio_attrs(riods: RasterioReader):
    Get rasterio specific attributes
    # pylint: disable=too-many-branches
    # Add rasterio attributes
    attrs = _parse_tags(riods.tags(1))
    if riods.nodata is not None:
        # The nodata values for the raster bands
        attrs["_FillValue"] = riods.nodata
    # The scale values for the raster bands
    if len(set(riods.scales)) > 1:
        attrs["scales"] = riods.scales
            "Offsets differ across bands. The 'scale_factor' attribute will "
            "not be added. See the 'scales' attribute."
        attrs["scale_factor"] = riods.scales[0]
    # The offset values for the raster bands
    if len(set(riods.offsets)) > 1:
        attrs["offsets"] = riods.offsets
            "Offsets differ across bands. The 'add_offset' attribute will "
            "not be added. See the 'offsets' attribute."
        attrs["add_offset"] = riods.offsets[0]
    if any(riods.descriptions):
        if len(set(riods.descriptions)) == 1:
            attrs["long_name"] = riods.descriptions[0]
            # Descriptions for each dataset band
            attrs["long_name"] = riods.descriptions
    if any(riods.units):
        # A list of units string for each dataset band
        if len(riods.units) == 1:
            attrs["units"] = riods.units[0]
            attrs["units"] = riods.units

    return attrs

def _decode_datetime_cf(
    data_array: DataArray,
    decode_times: bool,
    decode_timedelta: Optional[bool],
) -> DataArray:
    Decide the datetime based on CF conventions
    if decode_timedelta is None:
        decode_timedelta = decode_times

    for coord in data_array.coords:
        time_var = None
        if decode_times and "since" in data_array[coord].attrs.get("units", ""):
            time_var = times.CFDatetimeCoder(use_cftime=True).decode(
                as_variable(data_array[coord]), name=coord
        elif (
            and data_array[coord].attrs.get("units") in times.TIME_UNITS
            time_var = times.CFTimedeltaCoder().decode(
                as_variable(data_array[coord]), name=coord
        if time_var is not None:
            dimensions, data, attributes, encoding = variables.unpack_for_decoding(
            data_array = data_array.assign_coords(
                    coord: IndexVariable(
    return data_array

def _parse_driver_tags(
    riods: RasterioReader,
    attrs: Dict,
    coords: Dict,
) -> None:
    # Parse extra metadata from tags, if supported
    parsers = {"ENVI": _parse_envi}

    driver = riods.driver
    if driver in parsers:
        meta = parsers[driver](riods.tags(ns=driver))

        for key, value in meta.items():
            # Add values as coordinates if they match the band count,
            # as attributes otherwise
            if isinstance(value, (list, np.ndarray)) and len(value) == riods.count:
                coords[key] = ("band", np.asarray(value))
                attrs[key] = value

def _load_subdatasets(
    riods: RasterioReader,
    group: Optional[Union[str, List[str], Tuple[str, ...]]],
    variable: Optional[Union[str, List[str], Tuple[str, ...]]],
    parse_coordinates: bool,
    chunks: Optional[Union[int, Tuple, Dict]],
    cache: Optional[bool],
    lock: Any,
    masked: bool,
    mask_and_scale: bool,
    decode_times: bool,
    decode_timedelta: Optional[bool],
) -> Union[Dataset, List[Dataset]]:
    Load in rasterio subdatasets
    base_tags = _parse_tags(riods.tags())
    dim_groups = {}
    subdataset_filter = None
    if any((group, variable)):
        subdataset_filter = build_subdataset_filter(group, variable)
    for subdataset in riods.subdatasets:
        if subdataset_filter is not None and not subdataset_filter.match(subdataset):
        with as rds:
            shape = rds.shape
        rioda: DataArray = open_rasterio(  # type: ignore
            parse_coordinates=shape not in dim_groups and parse_coordinates,
            default_name=subdataset.split(":")[-1].lstrip("/").replace("/", "_"),
        if shape not in dim_groups:
            dim_groups[shape] = { rioda}
            dim_groups[shape][] = rioda

    if len(dim_groups) > 1:
        dataset: Union[Dataset, List[Dataset]] = [
            Dataset(dim_group, attrs=base_tags) for dim_group in dim_groups.values()
    elif not dim_groups:
        dataset = Dataset(attrs=base_tags)
        dataset = Dataset(list(dim_groups.values())[0], attrs=base_tags)
    return dataset

def _prepare_dask(
    result: DataArray,
    riods: RasterioReader,
    filename: Union[str, os.PathLike],
    chunks: Union[int, Tuple, Dict],
) -> DataArray:
    Prepare the data for dask computations
    # pylint: disable=import-outside-toplevel
    from dask.base import tokenize

    # augment the token with the file modification time
        mtime = os.path.getmtime(filename)
    except OSError:
        # the filename is probably an s3 bucket rather than a regular file
        mtime = None

    if chunks in (True, "auto"):
        import dask
        from dask.array.core import normalize_chunks

        if version.parse(dask.__version__) < version.parse("0.18.0"):
            msg = (
                "Automatic chunking requires dask.__version__ >= 0.18.0 . "
                f"You currently have version {dask.__version__}"
            raise NotImplementedError(msg)
        block_shape = (1,) + riods.block_shapes[0]
        chunks = normalize_chunks(
            chunks=(1, "auto", "auto"),
            shape=(riods.count, riods.height, riods.width),
            previous_chunks=tuple((c,) for c in block_shape),
    token = tokenize(filename, mtime, chunks)
    name_prefix = f"open_rasterio-{token}"
    return result.chunk(chunks, name_prefix=name_prefix, token=token)

def _handle_encoding(
    result: DataArray, mask_and_scale: bool, masked: bool, da_name: Optional[Hashable]
) -> None:
    Make sure encoding handled properly
    if "grid_mapping" in result.attrs:
        variables.pop_to(result.attrs, result.encoding, "grid_mapping", name=da_name)
    if mask_and_scale:
        if "scale_factor" in result.attrs:
                result.attrs, result.encoding, "scale_factor", name=da_name
        if "add_offset" in result.attrs:
            variables.pop_to(result.attrs, result.encoding, "add_offset", name=da_name)
    if masked:
        if "_FillValue" in result.attrs:
            variables.pop_to(result.attrs, result.encoding, "_FillValue", name=da_name)
        if "missing_value" in result.attrs:
                result.attrs, result.encoding, "missing_value", name=da_name

[docs]def open_rasterio( filename: Union[ str, os.PathLike,, rasterio.vrt.WarpedVRT ], parse_coordinates: Optional[bool] = None, chunks: Optional[Union[int, Tuple, Dict]] = None, cache: Optional[bool] = None, lock: Any = None, masked: bool = False, mask_and_scale: bool = False, variable: Optional[Union[str, List[str], Tuple[str, ...]]] = None, group: Optional[Union[str, List[str], Tuple[str, ...]]] = None, default_name: Optional[str] = None, decode_times: bool = True, decode_timedelta: Optional[bool] = None, **open_kwargs, ) -> Union[Dataset, DataArray, List[Dataset]]: # pylint: disable=too-many-statements,too-many-locals,too-many-branches """Open a file with rasterio (experimental). This should work with any file that rasterio can open (most often: geoTIFF). The x and y coordinates are generated automatically from the file's geoinformation, shifted to the center of each pixel (see `"PixelIsArea" Raster Space <>`_ for more information). Parameters ---------- filename: str,, or rasterio.vrt.WarpedVRT Path to the file to open. Or already open rasterio dataset. parse_coordinates: bool, optional Whether to parse the x and y coordinates out of the file's ``transform`` attribute or not. The default is to automatically parse the coordinates only if they are rectilinear (1D). It can be useful to set ``parse_coordinates=False`` if your files are very large or if you don't need the coordinates. chunks: int, tuple or dict, optional Chunk sizes along each dimension, e.g., ``5``, ``(5, 5)`` or ``{'x': 5, 'y': 5}``. If chunks is provided, it used to load the new DataArray into a dask array. Chunks can also be set to ``True`` or ``"auto"`` to choose sensible chunk sizes according to ``dask.config.get("array.chunk-size")``. cache: bool, optional If True, cache data loaded from the underlying datastore in memory as NumPy arrays when accessed to avoid reading from the underlying data- store multiple times. Defaults to True unless you specify the `chunks` argument to use dask, in which case it defaults to False. lock: bool or dask.utils.SerializableLock, optional If chunks is provided, this argument is used to ensure that only one thread per process is reading from a rasterio file object at a time. By default and when a lock instance is provided, a :class:`xarray.backends.CachingFileManager` is used to cache File objects. Since rasterio also caches some data, this will make repeated reads from the same object fast. When ``lock=False``, no lock is used, allowing for completely parallel reads from multiple threads or processes. However, a new file handle is opened on each request. masked: bool, optional If True, read the mask and set values to NaN. Defaults to False. mask_and_scale: bool, default=False Lazily scale (using the `scales` and `offsets` from rasterio) and mask. If the _Unsigned attribute is present treat integer arrays as unsigned. variable: str or list or tuple, optional Variable name or names to use to filter loading. group: str or list or tuple, optional Group name or names to use to filter loading. default_name: str, optional The name of the data array if none exists. Default is None. decode_times: bool, default=True If True, decode times encoded in the standard NetCDF datetime format into datetime objects. Otherwise, leave them encoded as numbers. decode_timedelta: bool, optional If True, decode variables and coordinates with time units in {“days”, “hours”, “minutes”, “seconds”, “milliseconds”, “microseconds”} into timedelta objects. If False, leave them encoded as numbers. If None (default), assume the same value of decode_time. **open_kwargs: kwargs, optional Optional keyword arguments to pass into :func:``. Returns ------- :obj:`xarray.Dataset` | :obj:`xarray.DataArray` | List[:obj:`xarray.Dataset`]: The newly created dataset(s). """ parse_coordinates = True if parse_coordinates is None else parse_coordinates masked = masked or mask_and_scale vrt_params = None if isinstance(filename, filename = elif isinstance(filename, rasterio.vrt.WarpedVRT): vrt = filename filename = vrt_params = dict( src_crs=vrt.src_crs.to_string() if vrt.src_crs else None, if else None, resampling=vrt.resampling, tolerance=vrt.tolerance, src_nodata=vrt.src_nodata, nodata=vrt.nodata, width=vrt.width, height=vrt.height, src_transform=vrt.src_transform, transform=vrt.transform, dtype=vrt.working_dtype, warp_extras=vrt.warp_extras, ) if lock in (True, None): lock = RASTERIO_LOCK elif lock is False: lock = NO_LOCK # ensure default for sharing is False # ref open_kwargs["sharing"] = open_kwargs.get("sharing", False) with warnings.catch_warnings(record=True) as rio_warnings: if lock is not NO_LOCK: manager: FileManager = CachingFileManager(, filename, lock=lock, mode="r", kwargs=open_kwargs ) else: manager = URIManager(, filename, mode="r", kwargs=open_kwargs) riods = manager.acquire() captured_warnings = rio_warnings.copy() # raise the NotGeoreferencedWarning if applicable for rio_warning in captured_warnings: if not riods.subdatasets or not isinstance( rio_warning.message, NotGeoreferencedWarning ): warnings.warn(str(rio_warning.message), type(rio_warning.message)) # type: ignore # open the subdatasets if they exist if riods.subdatasets: return _load_subdatasets( riods=riods, group=group, variable=variable, parse_coordinates=parse_coordinates, chunks=chunks, cache=cache, lock=lock, masked=masked, mask_and_scale=mask_and_scale, decode_times=decode_times, decode_timedelta=decode_timedelta, **open_kwargs, ) if vrt_params is not None: riods = WarpedVRT(riods, **vrt_params) if cache is None: cache = chunks is None # Get bands if riods.count < 1: raise ValueError("Unknown dims") # parse tags & load alternate coords attrs = _get_rasterio_attrs(riods=riods) coords = _load_netcdf_1d_coords(riods.tags()) _parse_driver_tags(riods=riods, attrs=attrs, coords=coords) for coord in coords: if f"NETCDF_DIM_{coord}" in attrs: coord_name = coord attrs.pop(f"NETCDF_DIM_{coord}") break else: coord_name = "band" coords[coord_name] = np.asarray(riods.indexes) has_gcps = riods.gcps[0] if has_gcps: parse_coordinates = False # Get geospatial coordinates if parse_coordinates: coords.update( _generate_spatial_coords(riods.transform, riods.width, riods.height) ) unsigned = False encoding: Dict[Hashable, Any] = {} if mask_and_scale and "_Unsigned" in attrs: unsigned = variables.pop_to(attrs, encoding, "_Unsigned") == "true" if masked: encoding["dtype"] = str(_rasterio_to_numpy_dtype(riods.dtypes)) da_name = attrs.pop("NETCDF_VARNAME", default_name) data: Any = indexing.LazilyOuterIndexedArray( RasterioArrayWrapper( manager, lock, name=da_name, vrt_params=vrt_params, masked=masked, mask_and_scale=mask_and_scale, unsigned=unsigned, ) ) # this lets you write arrays loaded with rasterio data = indexing.CopyOnWriteArray(data) if cache and chunks is None: data = indexing.MemoryCachedArray(data) result = DataArray( data=data, dims=(coord_name, "y", "x"), coords=coords, attrs=attrs, name=da_name ) result.encoding = encoding # update attributes from NetCDF attributess _load_netcdf_attrs(riods.tags(), result) result = _decode_datetime_cf( result, decode_times=decode_times, decode_timedelta=decode_timedelta ) # make sure the _FillValue is correct dtype if "_FillValue" in attrs: attrs["_FillValue"] = result.dtype.type(attrs["_FillValue"]) # handle encoding _handle_encoding(result, mask_and_scale, masked, da_name) # Affine transformation matrix (always available) # This describes coefficients mapping pixel coordinates to CRS # For serialization store as tuple of 6 floats, the last row being # always (0, 0, 1) per definition (see #, inplace=True) rio_crs = or if rio_crs:, inplace=True) if has_gcps:*riods.gcps, inplace=True) if chunks is not None: result = _prepare_dask(result, riods, filename, chunks) # Make the file closeable result.set_close(manager.close) = manager # add file path to encoding result.encoding["source"] = result.encoding["rasterio_dtype"] = str(riods.dtypes[0]) return result