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rio-tiler

User friendly Rasterio plugin to read raster datasets.

Documentation: https://cogeotiff.github.io/rio-tiler/

Source Code: https://github.com/cogeotiff/rio-tiler

Description

rio-tiler was initially designed to create slippy map tiles from large raster data sources and render these tiles dynamically on a web map. Since rio-tiler v2.0, we added many more helper methods to read data and metadata from any raster source supported by Rasterio/GDAL. This includes local and remote files via HTTP, AWS S3, Google Cloud Storage, etc.

At the low level, rio-tiler is just a wrapper around the rasterio and GDAL libraries.

Features

Read any dataset supported by GDAL/Rasterio

from rio_tiler.io import Reader

with Reader("my.tif") as image:
    print(image.dataset)  # rasterio opened dataset
    img = image.read()    # similar to rasterio.open("my.tif").read() but returns a rio_tiler.models.ImageData object

User friendly tile, part, feature, point reading methods

from rio_tiler.io import Reader

with Reader("my.tif") as image:
    img = image.tile(x, y, z)            # read mercator tile z-x-y
    img = image.part(bbox)               # read the data intersecting a bounding box
    img = image.feature(geojson_feature) # read the data intersecting a geojson feature
    img = image.point(lon,lat)           # get pixel values for a lon/lat coordinates

Enable property assignment (e.g nodata) on data reading

from rio_tiler.io import Reader

with Reader("my.tif") as image:
    img = image.tile(x, y, z, nodata=-9999) # read mercator tile z-x-y

STAC support

from rio_tiler.io import STACReader

with STACReader("item.json") as stac:
    print(stac.assets)  # available asset
    img = stac.tile(  # read tile for asset1 and indexes 1,2,3
        x,
        y,
        z,
        assets="asset1",
        indexes=(1, 2, 3),  # same as asset_indexes={"asset1": (1, 2, 3)},
    )

    # Merging data from different assets
    img = stac.tile(  # create an image from assets 1,2,3 using their first band
        x,
        y,
        z,
        assets=("asset1", "asset2", "asset3",),
        asset_indexes={"asset1": 1, "asset2": 1, "asset3": 1},
    )

Xarray support (>=4.0)

import xarray
from rio_tiler.io import XarrayReader

ds = xarray.open_dataset(
    "https://pangeo.blob.core.windows.net/pangeo-public/daymet-rio-tiler/na-wgs84.zarr/",
    engine="zarr",
    decode_coords="all",
    consolidated=True,
)
da = ds["tmax"]
with XarrayReader(da) as dst:
    print(dst.info())
    img = dst.tile(1, 1, 2)

Note: The XarrayReader needs optional dependencies to be installed pip install rio-tiler["xarray"].

Non-Geo Image support (>=4.0)

from rio_tiler.io import ImageReader

with ImageReader("image.jpeg") as src:
    im = src.tile(0, 0, src.maxzoom)  # read top-left `tile`
    im = src.part((0, 100, 100, 0))  # read top-left 100x100 pixels
    pt = src.point(0, 0)  # read pixel value

Note: ImageReader is also compatible with proper geo-referenced raster datasets.

Mosaic (merging or stacking)

from rio_tiler.io import Reader
from rio_tiler.mosaic import mosaic_reader

def reader(file, x, y, z, **kwargs):
    with Reader(file) as image:
        return image.tile(x, y, z, **kwargs)

img, assets = mosaic_reader(["image1.tif", "image2.tif"], reader, x, y, z)

Native support for multiple TileMatrixSet via morecantile

import morecantile
from rio_tiler.io import Reader

# Use EPSG:4326 (WGS84) grid
wgs84_grid = morecantile.tms.get("WorldCRS84Quad")
with Reader("my.tif", tms=wgs84_grid) as src:
    img = src.tile(1, 1, 1)

Install

You can install rio-tiler using pip

$ pip install -U pip
$ pip install -U rio-tiler

or install from source:

$ git clone https://github.com/cogeotiff/rio-tiler.git
$ cd rio-tiler
$ pip install -U pip
$ pip install -e .

Plugins

rio-tiler-pds

rio-tiler v1 included several helpers for reading popular public datasets (e.g. Sentinel 2, Sentinel 1, Landsat 8, CBERS) from cloud providers. This functionality is now in a separate plugin, enabling easier access to more public datasets.

rio-tiler-mvt

Create Mapbox Vector Tiles from raster sources

Implementations

titiler: A lightweight Cloud Optimized GeoTIFF dynamic tile server.

cogeo-mosaic: Create mosaics of Cloud Optimized GeoTIFF based on the mosaicJSON specification.

Contribution & Development

See CONTRIBUTING.md

Authors

The rio-tiler project was begun at Mapbox and was transferred to the cogeotiff Github organization in January 2019.

See AUTHORS.txt for a listing of individual contributors.

Changes

See CHANGES.md.

License

See LICENSE

	vrt_params = dict(
	add_alpha=True, crs="epsg:3857", resampling=Resampling[resampling_method]
	)

	vrt_transform, vrt_width, vrt_height = get_vrt_transform(src_dst, bounds)
	vrt_params.update(dict(transform=vrt_transform, width=vrt_width, height=vrt_height))

	indexes = indexes if indexes is not None else src_dst.indexes
	out_shape = (len(indexes), tilesize, tilesize)

	nodata = nodata if nodata is not None else src_dst.nodata
	if nodata is not None:
	vrt_params.update(dict(nodata=nodata, add_alpha=False, src_nodata=nodata))

	if has_alpha_band(src_dst):
	vrt_params.update(dict(add_alpha=False))

	with WarpedVRT(src_dst, **vrt_params) as vrt:
	data = vrt.read(
	out_shape=out_shape,
	indexes=indexes,
	resampling=Resampling[resampling_method],
	)

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