a İaz<@sddlZddlZddlZddlmZddlmZddlm Z ddl m Z ddl m Z dd lmZeZd ZGd d d e Zd dZdddZdS)N)indexing) DataArray) is_scalar) BackendArray)CachingFileManager)SerializableLockz~The kind of indexing operation you are trying to do is not valid on rasterio files. Try to load your data with ds.load()first.c@sJeZdZdZdddZeddZeddZd d Zd d Z d dZ dS)RasterioArrayWrapperz)A wrapper around rasterio dataset objectsNcCsddlm}||_||_|}|dur8||fi|}||_|j|j|jf|_ |j }t t ||dksvtdt |d|_dS)Nr WarpedVRTz$All bands should have the same dtype) rasterio.vrtr managerlockacquire vrt_paramscountheightwidth_shapedtypesnpallasarray ValueErrordtype_dtype)selfrrrr riodsrr8lib/python3.9/site-packages/xarray/backends/rasterio_.py__init__s zRasterioArrayWrapper.__init__cCs|jSN)rrrrr r,szRasterioArrayWrapper.dtypecCs|jSr")rr#rrr shape0szRasterioArrayWrapper.shapec Cst|dksJd|d}g}t|trN||jd\}}}t|||}t|d}t|t rx| tdg}g}t t |dd|jddD]\} \} } t| tr| | \}}}| tdd|nPt | r| d|  | }| d}n(t| t| d}}| | || ||fqt|dtjrxt|dtjrxtj|dd|dd<|t|t|t|fS)aGet indexer for rasterio array. Parameters ---------- key : tuple of int Returns ------- band_key: an indexer for the 1st dimension window: two tuples. Each consists of (start, stop). squeeze_axis: axes to be squeezed np_ind: indexer for loaded numpy array See Also -------- indexing.decompose_indexer z%rasterio datasets should always be 3DrrNr)len isinstancesliceindicesr$rarangertolistlistappend enumerateziprminmaxndarrayZix_tuple) rkeyband_keynp_indsstartstopstepwindow squeeze_axisiknrrr _get_indexer4s2  ,   $z!RasterioArrayWrapper._get_indexerc Csddlm}||\}}}}|r4tdd|Drbt|ftdd|D}tj||jd}n^|j H|j j dd} |j dur|| fi|j } | j ||d }Wdn1s0Y|rtj||d }||S) Nrr css|]\}}||kVqdSr"r.0r8r9rrr rz0RasterioArrayWrapper._getitem..css|]\}}||VqdSr"rrArrr rCtrD)rF)Z needs_lock)r;)Zaxis)r r r@anyr'r4rzerosrrrrrreadZsqueeze) rr5r r6r;r<r7r$outrrrr _getitemms  ,zRasterioArrayWrapper._getitemcCst||jtjj|jSr")rZexplicit_indexing_adapterr$ZIndexingSupportZOUTERrI)rr5rrr __getitem__sz RasterioArrayWrapper.__getitem__)N) __name__ __module__ __qualname____doc__r!propertyrr$r@rIrJrrrr r s   9r cs6dd}dd||dfdd|D}|S)aParse ENVI metadata into Python data structures. See the link for information on the ENVI header file format: http://www.harrisgeospatial.com/docs/enviheaderfiles.html Parameters ---------- meta : dict Dictionary of keys and str values to parse, as returned by the rasterio tags(ns='ENVI') call. Returns ------- parsed_meta : dict Dictionary containing the original keys and the parsed values cSstj|ddddS)N{}float,)rsep)rZ fromstringstripsrrr parsevecsz_parse_envi..parseveccSs |dS)NrP)rTrUrrr defaultsz_parse_envi..default)Z wavelengthZfwhmcs"i|]\}}|||qSr)get)rBr>vrXparserr rDz_parse_envi..)items)metarWZ parsed_metarr[r _parse_envis  r`cKsJtjdtddddl}ddlm}d}t||jjr>|j }nZt||j jr|} | j j }t | j | j| j| j| j| j| j| j| j| j| j| jd }|durt}t|j||d|d } | } |dur|| fi|} |dur|du}i} | jd krtd t !| j"| d <| jj#r|dur&d n|} | r| j| j}}| jt $|dt %|df\}}| jt %|dt $|df\}}|| d<|| d<n(|durdn|} | rtjdt&ddi}t'| jdd|d<t(| dr(| jr(z| j)|d<Wn"t*y&| j|d<Yn0t(| dr>| j+|d<t(| drZt ,| j-|d<t(| dr~t'dd| j.D|d<t(| dr| j/|d<t(| dr| j0|d<t(| drt1| j2r| j2|d<t(| drt1| j3r| j3|d<t4d d!d"}| j5}||vr|d#kr(||| 6}n||| j6|d$}|7D]H\}}t|t8t j9frt:|| jkrd t !|f| |<n|||<qDt;|}|r|durt;?|}t@|d%| |d&}|dur:dd'lAmB}ztCjDE|}WntFyd}Yn0||||}d(|}|jG|||d)}|H| jI|S)*aOpen a file with rasterio. .. deprecated:: 0.20.0 Deprecated in favor of rioxarray. For information about transitioning, see: https://corteva.github.io/rioxarray/stable/getting_started/getting_started.html 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). You can generate 2D coordinates from the file's attributes with:: >>> from affine import Affine >>> da = xr.open_rasterio( ... "https://github.com/mapbox/rasterio/raw/1.2.1/tests/data/RGB.byte.tif" ... ) >>> da [1703814 values with dtype=uint8] Coordinates: * band (band) int64 1 2 3 * y (y) float64 2.827e+06 2.826e+06 2.826e+06 ... 2.612e+06 2.612e+06 * x (x) float64 1.021e+05 1.024e+05 1.027e+05 ... 3.389e+05 3.392e+05 Attributes: transform: (300.0379266750948, 0.0, 101985.0, 0.0, -300.041782729805... crs: +init=epsg:32618 res: (300.0379266750948, 300.041782729805) is_tiled: 0 nodatavals: (0.0, 0.0, 0.0) scales: (1.0, 1.0, 1.0) offsets: (0.0, 0.0, 0.0) AREA_OR_POINT: Area >>> transform = Affine(*da.attrs["transform"]) >>> transform Affine(300.0379266750948, 0.0, 101985.0, 0.0, -300.041782729805, 2826915.0) >>> nx, ny = da.sizes["x"], da.sizes["y"] >>> x, y = transform * np.meshgrid(np.arange(nx) + 0.5, np.arange(ny) + 0.5) >>> x array([[102135.01896334, 102435.05689001, 102735.09481669, ..., 338564.90518331, 338864.94310999, 339164.98103666], [102135.01896334, 102435.05689001, 102735.09481669, ..., 338564.90518331, 338864.94310999, 339164.98103666], [102135.01896334, 102435.05689001, 102735.09481669, ..., 338564.90518331, 338864.94310999, 339164.98103666], ..., [102135.01896334, 102435.05689001, 102735.09481669, ..., 338564.90518331, 338864.94310999, 339164.98103666], [102135.01896334, 102435.05689001, 102735.09481669, ..., 338564.90518331, 338864.94310999, 339164.98103666], [102135.01896334, 102435.05689001, 102735.09481669, ..., 338564.90518331, 338864.94310999, 339164.98103666]]) 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. 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. Returns ------- data : DataArray The newly created DataArray. zopen_rasterio is Deprecated in favor of rioxarray. For information about transitioning, see: https://corteva.github.io/rioxarray/stable/getting_started/getting_started.htmlr) stacklevelrNr ) src_crscrs resampling tolerance src_nodatanodatarr src_transform transformr warp_extrasr)rmodekwargsrz Unknown dimsbandTg?yxFzThe file coordinates' transformation isn't rectilinear: xarray won't parse the coordinates in this case. 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