save

save_cog(data_save, path_tiff_save, profile=None, descriptions=None, tags=None, dir_tmpfiles='.', fs=None)

Save data GeoData object as cloud optimized GeoTIFF

Parameters:

Name	Type	Description	Default
data_save	GeoData	GeoData (C, H, W) format with geoinformation (crs and transform).	required
descriptions	Optional[List[str]]	name of the bands	None
path_tiff_save	str	path to save the COG GeoTIFF	required
profile	Optional[Dict[str, Any]]	profile dict to save the data. crs and transform will be updated from data_save.	None
tags	Optional[Dict[str, Any]]	Dict to save as tags of the image	None
dir_tmpfiles	str	dir to create tempfiles if needed	'.'
fs	Optional[Any]	fsspec filesystem to save the file	None

Examples:

img = np.random.randn(4,256,256) transform = rasterio.Affine(10, 0, 799980.0, 0, -10, 1900020.0) data = GeoTensor(img, crs="EPSG:32644", transform=transform) save_cog(data, "example.tif", descriptions=["band1", "band2", "band3", "band4"])

Source code in georeader/save.py

def save_cog(data_save:GeoData, path_tiff_save:str,
             profile:Optional[Dict[str, Any]]=None,
             descriptions:Optional[List[str]] = None, 
             tags:Optional[Dict[str, Any]]=None,
             dir_tmpfiles:str=".",
             fs:Optional[Any]=None) -> None:
    """
    Save data GeoData object as cloud optimized GeoTIFF

    Args:
        data_save: GeoData (C, H, W) format with geoinformation (crs and transform).
        descriptions: name of the bands
        path_tiff_save: path to save the COG GeoTIFF
        profile: profile dict to save the data. crs and transform will be updated from data_save.
        tags: Dict to save as tags of the image
        dir_tmpfiles: dir to create tempfiles if needed
        fs: fsspec filesystem to save the file


    Examples:
        >> img = np.random.randn(4,256,256)
        >> transform = rasterio.Affine(10, 0, 799980.0, 0, -10, 1900020.0)
        >> data = GeoTensor(img, crs="EPSG:32644", transform=transform)
        >> save_cog(data, "example.tif", descriptions=["band1", "band2", "band3", "band4"])

    """
    if profile is None:
        profile = {
            "compress": "lzw",
            "RESAMPLING": "CUBICSPLINE",  # for pyramids
        }
    if len(data_save.shape) == 3:
        np_data = np.asanyarray(data_save.values)
    elif len(data_save.shape) == 2:
        np_data = np.asanyarray(data_save.values[np.newaxis])
    else:
        raise NotImplementedError(f"Expected data with 2 or 3 dimensions found: {data_save.shape}")

    profile["crs"] = data_save.crs
    profile["transform"] = data_save.transform

    if "nodata" not in profile:
        profile["nodata"] = data_save.fill_value_default

    _save_cog(np_data,
              path_tiff_save, profile, descriptions=descriptions,
              tags=tags, dir_tmpfiles=dir_tmpfiles, fs=fs)

save_tiled_geotiff(data_save, path_tiff_save, profile_arg=None, descriptions=None, tags=None, dir_tmpfiles='.', blocksize=BLOCKSIZE_DEFAULT, fs=None)

Save data GeoData object as tiled GeoTIFF (see save_cog to save as a Cloud Optimized GeoTIFF)

Parameters:

Name	Type	Description	Default
data_save	GeoData	GeoData (C, H, W) format with geoinformation (crs and transform).	required
path_tiff_save	str	path to save the GeoTIFF	required
profile_arg	Optional[Dict[str, Any]]	profile dict to save the data. crs and transform will be updated from data_save.	None
descriptions	Optional[List[str]]	name of the bands	None
profile		profile dict to save the data. crs and transform will be updated from data_save.	required
tags	Optional[Dict[str, Any]]	Dict to save as tags of the image	None
dir_tmpfiles	str	dir to create tempfiles if needed	'.'
blocksize	int	blocksize of the GeoTIFF	BLOCKSIZE_DEFAULT
fs	Optional[Any]	fsspec filesystem to save the file	None

Source code in georeader/save.py

def save_tiled_geotiff(data_save:GeoData, path_tiff_save:str,
                       profile_arg:Optional[Dict[str, Any]]=None,
                       descriptions:Optional[List[str]] = None,
                       tags:Optional[Dict[str, Any]]=None,
                       dir_tmpfiles:str=".",
                       blocksize:int=BLOCKSIZE_DEFAULT,
                       fs:Optional[Any]=None) -> None:
    """
    Save data GeoData object as tiled GeoTIFF (see `save_cog` to save as a Cloud Optimized GeoTIFF)

    Args:
        data_save: GeoData (C, H, W) format with geoinformation (crs and transform).
        path_tiff_save: path to save the GeoTIFF
        profile_arg: profile dict to save the data. crs and transform will be updated from data_save.
        descriptions: name of the bands
        profile: profile dict to save the data. crs and transform will be updated from data_save.
        tags: Dict to save as tags of the image
        dir_tmpfiles: dir to create tempfiles if needed
        blocksize: blocksize of the GeoTIFF
        fs: fsspec filesystem to save the file

    """
    profile = PROFILE_TILED_GEOTIFF_DEFAULT.copy()
    profile.update({"blockxsize": blocksize, "blockysize": blocksize})
    if profile_arg is not None:
        profile.update(profile_arg)

    if len(data_save.shape) == 3:
        out_np = np.asanyarray(data_save.values)
    elif len(data_save.shape) == 2:
        out_np = np.asanyarray(data_save.values[np.newaxis])
    else:
        raise NotImplementedError(f"Expected data with 2 or 3 dimensions found: {data_save.shape}")

    profile["crs"] = data_save.crs
    profile["transform"] = data_save.transform

    if "nodata" not in profile:
        profile["nodata"] = data_save.fill_value_default

    if descriptions is not None:
        assert len(descriptions) == out_np.shape[0], f"Unexpected band descriptions {len(descriptions)} expected {out_np.shape[0]}"

    # Set count, height, width
    for idx, c in enumerate(["count", "height", "width"]):
        if c in profile:
            assert profile[c] == out_np.shape[idx], f"Unexpected shape: {profile[c]} {out_np.shape}"
        else:
            profile[c] = out_np.shape[idx]

    if "dtype" not in profile:
        profile["dtype"] = str(out_np.dtype)

    # check blocksize
    for idx, b in enumerate(["blockysize", "blockxsize"]):
        if b in profile:
            profile[b] = min(profile[b], out_np.shape[idx + 1])

    if (out_np.shape[1] > profile["blockysize"]) or (out_np.shape[2] > profile["blockxsize"]):
        profile["tiled"] = True

    profile["driver"] = "GTiff"
    is_remote_file = any((path_tiff_save.startswith(ext) for ext in REMOTE_FILE_EXTENSIONS))

    # Create a tempfile if is a remote file
    if is_remote_file:
        with tempfile.NamedTemporaryFile(dir=dir_tmpfiles, suffix=".tif", delete=True) as fileobj:
            name_save = fileobj.name
    else:
        name_save = path_tiff_save

    with rasterio.open(name_save, "w", **profile) as rst_out:
        if tags is not None:
            rst_out.update_tags(**tags)
        rst_out.write(out_np)
        if descriptions is not None:
            for i in range(1, out_np.shape[0] + 1):
                rst_out.set_band_description(i, descriptions[i - 1])

    if is_remote_file:
        if fs is None:
            import fsspec
            fs = fsspec.filesystem(path_tiff_save.split(":")[0])

        if not os.path.exists(name_save):
            raise FileNotFoundError(f"File {name_save} have not been created")

        fs.put_file(name_save, path_tiff_save, overwrite=True)
        if os.path.exists(name_save):
            os.remove(name_save)