rio_tiler.utils

rio_tiler.utils ¶

rio_tiler.utils: utility functions.

Timer ¶

Bases: object

Time a code block.

from_start `property` ¶

from_start

Return time elapsed from start.

enter ¶

__enter__()

Starts timer.

exit ¶

__exit__(ty, val, tb)

Stops timer.

CRS_to_uri ¶

CRS_to_uri(crs: CRS) -> str | None

Convert CRS to URI.

CRS_to_urn ¶

CRS_to_urn(crs: CRS) -> str | None

Convert CRS to URN.

_CRS_authority_info ¶

_CRS_authority_info(crs: CRS) -> tuple[str, str, str] | None

Convert CRS to URI.

Code adapted from github.com/developmentseed/morecantile/blob/1829fe12408e4a1feee7493308f3f02257ef4caf/morecantile/models.py#L148-L161

_array_gdal_name ¶

_array_gdal_name(data: ndarray) -> str

Return GDAL MEM dataset name.

_chunks ¶

_chunks(my_list: Sequence, chuck_size: int) -> Generator[Sequence, None, None]

Yield successive n-sized chunks from l.

_get_width_height ¶

_get_width_height(max_size, dataset_height, dataset_width) -> tuple[int, int]

Get Output Width/Height based on a max_size and dataset shape.

_missing_size ¶

_missing_size(w: int | None = None, h: int | None = None)

Check if one and only one size (width, height) is valid.

_requested_tile_aligned_with_internal_tile ¶

_requested_tile_aligned_with_internal_tile(src_dst: DatasetReader | DatasetWriter | WarpedVRT, bounds: BBox, bounds_crs: CRS = WEB_MERCATOR_CRS) -> bool

Check if tile is aligned with internal tiles.

_validate_shape_input ¶

_validate_shape_input(shape: dict) -> dict

Ensure input shape is valid and reduce features to geometry

cast_to_sequence ¶

cast_to_sequence(val: Any | None = None) -> Sequence

Cast input to sequence if not Tuple of List.

create_cutline ¶

create_cutline(src_dst: DatasetReader | DatasetWriter | WarpedVRT, geometry: dict, geometry_crs: CRS = None, op: Callable[[float], Any] | None = None) -> str

Create WKT Polygon Cutline for GDALWarpOptions.

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

Parameters:

src_dst (DatasetReader or DatasetWriter or WarpedVRT) –

Rasterio dataset.
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.
geometry_crs (CRS, default: None ) –

Input geometry Coordinate Reference System

Returns: str: WKT geometry in form of `POLYGON ((x y, x y, ...)))

get_array_statistics ¶

get_array_statistics(data: MaskedArray, categorical: bool = False, categories: list[float] | None = None, percentiles: list[int] | None = None, coverage: NDArray[floating] | None = None, **kwargs: Any) -> list[dict[Any, Any]]

Calculate per band array statistics.

Parameters:

data (MaskedArray) –

input masked array data to get the statistics from.
categorical (bool, default: False ) –

treat input data as categorical data. Defaults to False.
categories (list of numbers, default: None ) –

list of categories to return value for.
percentiles (list of numbers, default: None ) –

list of percentile values to calculate. Defaults to [2, 98].
coverage (array, default: None ) –

Data coverage fraction.
kwargs (optional, default: {} ) –

options to forward to numpy.histogram function (only applies for non-categorical data).

Returns:

list ( list[dict[Any, Any]] ) –

list of array statistics (dict)

Examples:

>>> data = numpy.ma.zeros((1, 256, 256))
>>> get_array_statistics(data)
[
    {
        'min': 0.0,
        'max': 0.0,
        'mean': 0.0,
        'count': 65536.0,
        'sum': 0.0,
        'std': 0.0,
        'median': 0.0,
        'majority': 0.0,
        'minority': 0.0,
        'unique': 1.0,
        'percentile_2': 0.0,
        'percentile_98': 0.0,
        'histogram': [
            [0, 0, 0, 0, 0, 65536, 0, 0, 0, 0],
            [-0.5, -0.4, -0.3, -0.19999999999999996, -0.09999999999999998, 0.0, 0.10000000000000009, 0.20000000000000007, 0.30000000000000004, 0.4, 0.5]
        ],
        'valid_pixels': 65536.0,
        'masked_pixels': 0.0,
        'valid_percent': 100.0
    }
]

get_overview_level ¶

get_overview_level(src_dst: DatasetReader | DatasetWriter | WarpedVRT, bounds: BBox, height: int, width: int, dst_crs: CRS = WEB_MERCATOR_CRS) -> 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:

src_dst (DatasetReader or DatasetWriter or WarpedVRT) –

Rasterio dataset.
bounds (tuple) –

Bounding box coordinates in target crs (dst_crs).
height (int) –

Desired output height of the array for the input bounds.
width (int) –

Desired output width of the array for the input bounds.
dst_crs (CRS, default: WEB_MERCATOR_CRS ) –

Target Coordinate Reference System. Defaults to epsg:3857.

Returns:

int ( int ) –

Overview level.

get_vrt_transform ¶

get_vrt_transform(src_dst: DatasetReader | DatasetWriter | WarpedVRT, bounds: BBox, height: int | None = None, width: int | None = None, dst_crs: CRS = WEB_MERCATOR_CRS, window_precision: int = 6, align_bounds_with_dataset: bool = False) -> tuple[Affine, int, int]

Calculate VRT transform.

Parameters:

src_dst (DatasetReader or DatasetWriter or WarpedVRT) –

Rasterio dataset.
bounds (tuple) –

Bounding box coordinates in target crs (dst_crs).
height (int, default: None ) –

Output height of the array for the input bounds.
width (int, default: None ) –

Output width of the array for the input bounds.
dst_crs (CRS, default: WEB_MERCATOR_CRS ) –

Target Coordinate Reference System. Defaults to epsg:3857.
align_bounds_with_dataset (bool, default: False ) –

Align input bounds with dataset transform. Defaults to False.

Returns:

tuple ( tuple[Affine, int, int] ) –

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

has_alpha_band ¶

has_alpha_band(src_dst: DatasetReader | DatasetWriter | WarpedVRT) -> bool

Check for alpha band or mask in source.

has_mask_band ¶

has_mask_band(src_dst: DatasetReader | DatasetWriter | WarpedVRT) -> bool

Check for mask band in source.

inherit_rasterio_env ¶

inherit_rasterio_env(f: Callable[_P, _R]) -> Callable[_P, _R]

Wrap a function to run in a rasterio environment like the current one.

This differs from the function rasterio.env.ensure_env_with_credentials in that this function copies the rasterio environment active at the time this function is invoked, rather than at the time the wrapped function is invoked. This enables replicating the environment from the thread active at the time this function is invoked to the thread active at the time the wrapped function is invoked.

Parameters:

f (Callable[_P, _R]) –

The function to wrap.

Returns:

Callable[_P, _R] –

Callable[P, R]: A function wrapping f that executes f within a rasterio environment configured identically to the rasterio environment active at the time this wrapper is created. Returns f unwrapped if no rasterio environment is active when this function is called.

linear_rescale ¶

linear_rescale(image: ndarray, in_range: IntervalTuple, out_range: IntervalTuple = (0, 255)) -> ndarray

Apply linear rescaling to a numpy array.

Parameters:

image (ndarray) –

array to rescale.
in_range (tuple) –

array min/max value to rescale from.
out_range (tuple, default: (0, 255) ) –

output min/max bounds to rescale to. Defaults to (0, 255).

Returns:

ndarray –

numpy.ndarray: linear rescaled array.

mapzen_elevation_rgb ¶

mapzen_elevation_rgb(data: ndarray) -> ndarray

Encode elevation value to RGB values compatible with Mapzen tangram.

Parameters:

data (ndarray) –

Image array to encode.

Returns numpy.ndarray: Elevation encoded in a RGB array.

non_alpha_indexes ¶

non_alpha_indexes(src_dst: DatasetReader | DatasetWriter | WarpedVRT) -> tuple

Return indexes of non-alpha bands.

normalize_bounds ¶

normalize_bounds(bounds: BBox) -> BBox

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

pansharpening_brovey ¶

pansharpening_brovey(rgb: ndarray, pan: ndarray, weight: float, pan_dtype: str) -> 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 ¶

render(data: ndarray, mask: ndarray | None = None, img_format: str = 'PNG', colormap: ColorMapType | None = None, **creation_options: Any) -> bytes

Translate numpy.ndarray to image bytes.

Parameters:

data (ndarray) –

Image array to encode.
mask (ndarray, default: None ) –

Mask array.
img_format (str, default: 'PNG' ) –

Image format. See: for the list of supported format by GDAL: www.gdal.org/formats_list.html. Defaults to PNG.
colormap (dict or sequence, default: None ) –

RGBA Color Table dictionary or sequence.
creation_options (optional, default: {} ) –

Image driver creation options to forward to GDAL.

Returns bytes: image body.

Examples:

>>> with Reader("my_tif.tif") as src:
    img = src.preview()
    with open('test.jpg', 'wb') as f:
        f.write(render(img.data, img.mask, img_format="jpeg"))

resize_array ¶

resize_array(data: ndarray, height: int, width: int, resampling_method: RIOResampling = 'nearest') -> ndarray

resize array to a given height and width.

rio_tiler.utils

rio_tiler.utils ¶

Timer ¶

from_start property ¶

__enter__ ¶

__exit__ ¶

CRS_to_uri ¶

CRS_to_urn ¶

_CRS_authority_info ¶

_array_gdal_name ¶

_chunks ¶

_get_width_height ¶

_missing_size ¶

_requested_tile_aligned_with_internal_tile ¶

_validate_shape_input ¶

cast_to_sequence ¶

create_cutline ¶

get_array_statistics ¶

get_overview_level ¶

get_vrt_transform ¶

has_alpha_band ¶

has_mask_band ¶

inherit_rasterio_env ¶

linear_rescale ¶

mapzen_elevation_rgb ¶

non_alpha_indexes ¶

normalize_bounds ¶

pansharpening_brovey ¶

render ¶

resize_array ¶

from_start `property` ¶

enter ¶

exit ¶