def open_rasterio(
filename,
parse_coordinates=None,
chunks=None,
cache=None,
lock=None,
masked=False,
**open_kwargs
):
"""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
<http://web.archive.org/web/20160326194152/http://remotesensing.org/geotiff/spec/geotiff2.5.html#2.5.2>`_
for more information).
You can generate 2D coordinates from the file's attributes with::
from affine import Affine
da = xr.open_rasterio('path_to_file.tif')
transform = Affine.from_gdal(*da.attrs['transform'])
nx, ny = da.sizes['x'], da.sizes['y']
x, y = np.meshgrid(np.arange(nx)+0.5, np.arange(ny)+0.5) * transform
Parameters
----------
filename : str, rasterio.DatasetReader, or rasterio.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 : False, True or threading.Lock, optional
If chunks is provided, this argument is passed on to
:py:func:`dask.array.from_array`. By default, a global lock is
used to avoid issues with concurrent access to the same file when using
dask's multithreaded backend.
masked : bool, optional
If True, read the mask and to set values to NaN. Defaults to False.
**open_kwargs: kwargs, optional
Optional keyword arguments to pass into rasterio.open().
Returns
-------
data : DataArray
The newly created DataArray.
"""
parse_coordinates = True if parse_coordinates is None else parse_coordinates
import rasterio
from rasterio.vrt import WarpedVRT
vrt_params = None
if isinstance(filename, rasterio.io.DatasetReader):
filename = filename.name
elif isinstance(filename, rasterio.vrt.WarpedVRT):
vrt = filename
filename = vrt.src_dataset.name
vrt_params = dict(
crs=vrt.crs.to_string(),
resampling=vrt.resampling,
src_nodata=vrt.src_nodata,
dst_nodata=vrt.dst_nodata,
tolerance=vrt.tolerance,
transform=vrt.transform,
width=vrt.width,
height=vrt.height,
warp_extras=vrt.warp_extras,
)
if lock is None:
lock = RASTERIO_LOCK
# ensure default for sharing is False
# ref https://github.com/mapbox/rasterio/issues/1504
open_kwargs["sharing"] = open_kwargs.get("sharing", False)
manager = CachingFileManager(
rasterio.open, filename, lock=lock, mode="r", kwargs=open_kwargs
)
riods = manager.acquire()
# open the subdatasets if they exist
if riods.subdatasets:
data_arrays = {}
for iii, subdataset in enumerate(riods.subdatasets):
rioda = open_rasterio(
subdataset,
parse_coordinates=iii == 0 and parse_coordinates,
chunks=chunks,
cache=cache,
lock=lock,
masked=masked,
)
data_arrays[rioda.name] = rioda
return Dataset(data_arrays)
if vrt_params is not None:
riods = WarpedVRT(riods, **vrt_params)
if cache is None:
cache = chunks is None
coords = OrderedDict()
# Get bands
if riods.count < 1:
raise ValueError("Unknown dims")
coords["band"] = np.asarray(riods.indexes)
# Get coordinates
if LooseVersion(rasterio.__version__) < LooseVersion("1.0"):
transform = riods.affine
else:
transform = riods.transform
if transform.is_rectilinear and parse_coordinates:
# 1d coordinates
coords.update(affine_to_coords(riods.transform, riods.width, riods.height))
elif parse_coordinates:
# 2d coordinates
warnings.warn(
"The file coordinates' transformation isn't "
"rectilinear: xarray won't parse the coordinates "
"in this case. Set `parse_coordinates=False` to "
"suppress this warning.",
RuntimeWarning,
stacklevel=3,
)
# Attributes
attrs = _parse_tags(riods.tags(1))
encoding = dict()
# 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
# https://github.com/sgillies/affine)
attrs["transform"] = tuple(transform)[:6]
if hasattr(riods, "nodata") and riods.nodata is not None:
# The nodata values for the raster bands
if masked:
encoding["_FillValue"] = riods.nodata
else:
attrs["_FillValue"] = riods.nodata
if hasattr(riods, "scales"):
# The scale values for the raster bands
attrs["scales"] = riods.scales
if hasattr(riods, "offsets"):
# The offset values for the raster bands
attrs["offsets"] = riods.offsets
if hasattr(riods, "descriptions") and any(riods.descriptions):
# Descriptions for each dataset band
attrs["descriptions"] = riods.descriptions
if hasattr(riods, "units") and any(riods.units):
# A list of units string for each dataset band
attrs["units"] = riods.units
# 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 k, v in meta.items():
# Add values as coordinates if they match the band count,
# as attributes otherwise
if isinstance(v, (list, np.ndarray)) and len(v) == riods.count:
coords[k] = ("band", np.asarray(v))
else:
attrs[k] = v
data = indexing.LazilyOuterIndexedArray(
RasterioArrayWrapper(manager, lock, vrt_params, masked=masked)
)
# this lets you write arrays loaded with rasterio
data = indexing.CopyOnWriteArray(data)
if cache and chunks is None:
data = indexing.MemoryCachedArray(data)
da_name = attrs.pop("NETCDF_VARNAME", None)
result = DataArray(
data=data, dims=("band", "y", "x"), coords=coords, attrs=attrs, name=da_name
)
result.encoding = encoding
if hasattr(riods, "crs") and riods.crs:
result.rio.write_crs(riods.crs, inplace=True)
if chunks is not None:
from dask.base import tokenize
# augment the token with the file modification time
try:
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"):
from dask.array.core import normalize_chunks
import dask
if LooseVersion(dask.__version__) < LooseVersion("0.18.0"):
msg = (
"Automatic chunking requires dask.__version__ >= 0.18.0 . "
"You currently have version %s" % 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),
dtype=riods.dtypes[0],
previous_chunks=tuple((c,) for c in block_shape),
)
token = tokenize(filename, mtime, chunks)
name_prefix = "open_rasterio-%s" % token
result = result.chunk(chunks, name_prefix=name_prefix, token=token)
# Make the file closeable
result._file_obj = manager
return result
def open_rasterio(
filename,
parse_coordinates=None,
chunks=None,
cache=None,
lock=None,
masked=False,
variable=None,
group=None,
default_name=None,
**open_kwargs,
):
"""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
<http://web.archive.org/web/20160326194152/http://remotesensing.org/geotiff/spec/geotiff2.5.html#2.5.2>`_
for more information).
You can generate 2D coordinates from the file's attributes with::
from affine import Affine
da = xr.open_rasterio('path_to_file.tif')
transform = Affine.from_gdal(*da.attrs['transform'])
nx, ny = da.sizes['x'], da.sizes['y']
x, y = np.meshgrid(np.arange(nx)+0.5, np.arange(ny)+0.5) * transform
Parameters
----------
filename: str, rasterio.DatasetReader, or rasterio.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: False, True or threading.Lock, optional
If chunks is provided, this argument is passed on to
:py:func:`dask.array.from_array`. By default, a global lock is
used to avoid issues with concurrent access to the same file when using
dask's multithreaded backend.
masked: bool, optional
If True, read the mask and to set values to NaN. Defaults to False.
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.
**open_kwargs: kwargs, optional
Optional keyword arguments to pass into rasterio.open().
Returns
-------
data : DataArray
The newly created DataArray.
"""
parse_coordinates = True if parse_coordinates is None else parse_coordinates
vrt_params = None
if isinstance(filename, rasterio.io.DatasetReader):
filename = filename.name
elif isinstance(filename, rasterio.vrt.WarpedVRT):
vrt = filename
filename = vrt.src_dataset.name
vrt_params = dict(
crs=vrt.crs.to_string(),
resampling=vrt.resampling,
src_nodata=vrt.src_nodata,
dst_nodata=vrt.dst_nodata,
tolerance=vrt.tolerance,
transform=vrt.transform,
width=vrt.width,
height=vrt.height,
warp_extras=vrt.warp_extras,
)
if lock is None:
lock = RASTERIO_LOCK
# ensure default for sharing is False
# ref https://github.com/mapbox/rasterio/issues/1504
open_kwargs["sharing"] = open_kwargs.get("sharing", False)
manager = CachingFileManager(
rasterio.open, filename, lock=lock, mode="r", kwargs=open_kwargs
)
riods = manager.acquire()
# 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,
)
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")
coords = OrderedDict()
coords["band"] = np.asarray(riods.indexes)
# parse tags
attrs, encoding = _get_rasterio_attrs(riods=riods, masked=masked)
_parse_driver_tags(riods=riods, attrs=attrs, coords=coords)
# Get geospatial coordinates
transform = _rio_transform(riods)
if parse_coordinates and transform.is_rectilinear:
# 1d coordinates
coords.update(affine_to_coords(riods.transform, riods.width, riods.height))
elif parse_coordinates:
# 2d coordinates
warnings.warn(
"The file coordinates' transformation isn't "
"rectilinear: xarray won't parse the coordinates "
"in this case. Set `parse_coordinates=False` to "
"suppress this warning.",
RuntimeWarning,
stacklevel=3,
)
data = indexing.LazilyOuterIndexedArray(
RasterioArrayWrapper(manager, lock, vrt_params, masked=masked)
)
# this lets you write arrays loaded with rasterio
data = indexing.CopyOnWriteArray(data)
if cache and chunks is None:
data = indexing.MemoryCachedArray(data)
# create the output data array
da_name = attrs.pop("NETCDF_VARNAME", default_name)
result = DataArray(
data=data, dims=("band", "y", "x"), coords=coords, attrs=attrs, name=da_name
)
result.encoding = encoding
if hasattr(riods, "crs") and riods.crs:
result.rio.write_crs(riods.crs, inplace=True)
if chunks is not None:
result = _prepare_dask(result, riods, filename, chunks)
# Make the file closeable
result._file_obj = manager
return result
