Module rio_tiler.utils

rio_tiler.utils: utility functions.

Variables

WEB_MERCATOR_CRS

Functions

aws_get_object

def aws_get_object(
    bucket: str,
    key: str,
    request_pays: bool = False,
    client: <function Session.client at 0x7ff2a2831ee0> = None
) -> bytes

AWS s3 get object content.

create_cutline

def create_cutline(
    src_dst: Union[rasterio.io.DatasetReader, rasterio.io.DatasetWriter, rasterio.vrt.WarpedVRT],
    geometry: Dict,
    geometry_crs: rasterio.crs.CRS = None
) -> str

Create WKT Polygon Cutline for GDALWarpOptions.

Ref: gdal.org/api/gdalwarp_cpp.html?highlight=vrt#_CPPv415GDALWarpOptions

Parameters:

Name	Type	Description	Default
src_dst	rasterio.io.DatasetReader or rasterio.io.DatasetWriter or rasterio.vrt.WarpedVRT	Rasterio dataset.	None
geometry	dict	GeoJSON feature or GeoJSON geometry. By default the coordinates are considered to be in the dataset CRS. Use geometry_crs to set a specific CRS.	None
geometry_crs	rasterio.crs.CRS	Input geometry Coordinate Reference System	None

Returns:

Type	Description
str	WKT geometry in form of `POLYGON ((x y, x y, ...)))

get_array_statistics

def get_array_statistics(
    data: numpy.ma.core.MaskedArray,
    categorical: bool = False,
    categories: Union[List[float], NoneType] = None,
    percentiles: Union[List[int], NoneType] = None,
    **kwargs: Any
) -> List[Dict[Any, Any]]

Calculate per band array statistics.

Parameters:

Name	Type	Description	Default
data	numpy.ma.MaskedArray	input masked array data to get the statistics from.	None
categorical	bool	treat input data as categorical data. Defaults to False.	False
categories	list of numbers	list of categories to return value for.	None
percentiles	list of numbers	list of percentile values to calculate. Defaults to [2, 98].	[2, 98]
kwargs	optional	options to forward to numpy.histogram function (only applies for non-categorical data).	None

Returns:

Type	Description
list	list of array statistics (dict)

get_overview_level

def get_overview_level(
    src_dst: Union[rasterio.io.DatasetReader, rasterio.io.DatasetWriter, rasterio.vrt.WarpedVRT],
    bounds: Tuple[float, float, float, float],
    height: int,
    width: int,
    dst_crs: rasterio.crs.CRS = CRS.from_epsg(3857)
) -> int

Return the overview level corresponding to the tile resolution.

Freely adapted from github.com/OSGeo/gdal/blob/41993f127e6e1669fbd9e944744b7c9b2bd6c400/gdal/apps/gdalwarp_lib.cpp#L2293-L2362

Parameters:

Name	Type	Description	Default
src_dst	rasterio.io.DatasetReader or rasterio.io.DatasetWriter or rasterio.vrt.WarpedVRT	Rasterio dataset.	None
bounds	tuple	Bounding box coordinates in target crs (dst_crs).	None
height	int	Desired output height of the array for the input bounds.	None
width	int	Desired output width of the array for the input bounds.	None
dst_crs	rasterio.crs.CRS	Target Coordinate Reference System. Defaults to epsg:3857.	epsg:3857

Returns:

Type	Description
int	Overview level.

get_vrt_transform

def get_vrt_transform(
    src_dst: Union[rasterio.io.DatasetReader, rasterio.io.DatasetWriter, rasterio.vrt.WarpedVRT],
    bounds: Tuple[float, float, float, float],
    height: Union[int, NoneType] = None,
    width: Union[int, NoneType] = None,
    dst_crs: rasterio.crs.CRS = CRS.from_epsg(3857),
    window_precision: int = 6
) -> Tuple[affine.Affine, int, int]

Calculate VRT transform.

Parameters:

Name	Type	Description	Default
src_dst	rasterio.io.DatasetReader or rasterio.io.DatasetWriter or rasterio.vrt.WarpedVRT	Rasterio dataset.	None
bounds	tuple	Bounding box coordinates in target crs (dst_crs).	None
height	int	Desired output height of the array for the input bounds.	None
width	int	Desired output width of the array for the input bounds.	None
dst_crs	rasterio.crs.CRS	Target Coordinate Reference System. Defaults to epsg:3857.	epsg:3857

Returns:

Type	Description
tuple	VRT transform (affine.Affine), width (int) and height (int)

has_alpha_band

def has_alpha_band(
    src_dst: Union[rasterio.io.DatasetReader, rasterio.io.DatasetWriter, rasterio.vrt.WarpedVRT]
) -> bool

Check for alpha band or mask in source.

has_mask_band

def has_mask_band(
    src_dst: Union[rasterio.io.DatasetReader, rasterio.io.DatasetWriter, rasterio.vrt.WarpedVRT]
) -> bool

Check for mask band in source.

linear_rescale

def linear_rescale(
    image: numpy.ndarray,
    in_range: Tuple[Union[float, int], Union[float, int]],
    out_range: Tuple[Union[float, int], Union[float, int]] = (0, 255)
) -> numpy.ndarray

Apply linear rescaling to a numpy array.

Parameters:

Name	Type	Description	Default
image	numpy.ndarray	array to rescale.	None
in_range	tuple	array min/max value to rescale from.	None
out_range	tuple	output min/max bounds to rescale to. Defaults to (0, 255).	(0, 255)

Returns:

Type	Description
numpy.ndarray	linear rescaled array.

mapzen_elevation_rgb

def mapzen_elevation_rgb(
    data: numpy.ndarray
) -> numpy.ndarray

Encode elevation value to RGB values compatible with Mapzen tangram.

Parameters:

Name	Type	Description	Default
data	numpy.ndarray	Image array to encode.	None

non_alpha_indexes

def non_alpha_indexes(
    src_dst: Union[rasterio.io.DatasetReader, rasterio.io.DatasetWriter, rasterio.vrt.WarpedVRT]
) -> Tuple

Return indexes of non-alpha bands.

normalize_bounds

def normalize_bounds(
    bounds: Tuple[float, float, float, float]
) -> Tuple[float, float, float, float]

Return BBox in correct minx, miny, maxx, maxy order.

pansharpening_brovey

def pansharpening_brovey(
    rgb: numpy.ndarray,
    pan: numpy.ndarray,
    weight: float,
    pan_dtype: str
) -> numpy.ndarray

Apply Brovey pansharpening method.

Brovey Method: Each resampled, multispectral pixel is multiplied by the ratio of the corresponding panchromatic pixel intensity to the sum of all the multispectral intensities.

Original code from mapbox/rio-pansharpen

render

def render(
    data: numpy.ndarray,
    mask: Union[numpy.ndarray, NoneType] = None,
    img_format: str = 'PNG',
    colormap: Union[Dict[int, Tuple[int, int, int, int]], Sequence[Tuple[Tuple[Union[float, int], Union[float, int]], Tuple[int, int, int, int]]], NoneType] = None,
    **creation_options: Any
) -> bytes

Translate numpy.ndarray to image bytes.

Parameters:

Name	Type	Description	Default
data	numpy.ndarray	Image array to encode.	None
mask	numpy.ndarray	Mask array.	None
img_format	str	Image format. See: for the list of supported format by GDAL: www.gdal.org/formats_list.html. Defaults to PNG.	PNG
colormap	dict or sequence	RGBA Color Table dictionary or sequence.	None
creation_options	optional	Image driver creation options to forward to GDAL.	None

resize_array

def resize_array(
    data: numpy.ndarray,
    height: int,
    width: int,
    resampling_method: rasterio.enums.Resampling = 'nearest'
) -> numpy.ndarray

resize array to a given height and width.