Skip to main content

A rio-tiler plugin to create tile for arbitraty grid

Project description

rio-tiler-crs

A rio-tiler plugin to create tiles in different projection

Packaging status CircleCI codecov

Install

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

# Or using source

$ pip install git+http://github.com/cogeotiff/rio-tiler-crs

How To

rio-tiler-crs uses morecantile to define the custom tiling grid schema.

  1. Define grid system
import morecantile
from rasterio.crs import CRS

# Use default TMS
tms = morecantile.tms.get("WorldCRS84Quad")

# or create a custom TMS
crs = CRS.from_epsg(3031)  # Morecantile TileMatrixSet uses Rasterio CRS object
extent = [-948.75, -543592.47, 5817.41, -3333128.95]  # From https:///epsg.io/3031
tms = morecantile.TileMatrixSet.custom(extent, crs)
  1. read tile
from rio_tiler_crs import COGReader

# Read tile x=10, y=10, z=4
with COGReader("myfile.tif", tms=tms) as cog:
    tile, mask = cog.tile( 10, 10, 4)

API

class COGReader:
    """
    Cloud Optimized GeoTIFF Reader.

    Examples
    --------
    with CogeoReader(src_path) as cog:
        cog.tile(...)
    
    with rasterio.open(src_path) as src_dst:
        with WarpedVRT(src_dst, ...) as vrt_dst:
            with CogeoReader(None, dataset=vrt_dst) as cog:
                cog.tile(...)

    with rasterio.open(src_path) as src_dst:
        with CogeoReader(None, dataset=src_dst) as cog:
            cog.tile(...)

    Attributes
    ----------
    filepath: str
        Cloud Optimized GeoTIFF path.
    dataset: rasterio.DatasetReader, optional
        Rasterio dataset.
    tms: morecantile.TileMatrixSet, optional
        TileMatrixSet to use, default is WebMercatorQuad.

    Properties
    ----------
    minzoom: int
        COG minimum zoom level in TMS projection.
    maxzoom: int
        COG maximum zoom level in TMS projection.
    bounds: tuple[float]
        COG bounds in WGS84 crs.
    center: tuple[float, float, int]
        COG center + minzoom
    colormap: dict
        COG internal colormap.
    info: dict
        General information about the COG (datatype, indexes, ...)

    Methods
    -------
    tile(0, 0, 0, indexes=(1,2,3), expression="
B1/B2", tilesize=512, resampling_methods="nearest")
        Read a map tile from the COG.
    part((0,10,0,10), indexes=(1,2,3,), expression="
B1/B20", max_size=1024)
        Read part of the COG.
    preview(max_size=1024)
        Read preview of the COG.
    point((10, 10), indexes=1)
        Read a point value from the COG.
    stats(pmin=5, pmax=95)
        Get Raster statistics.
    meta(pmin=5, pmax=95)
        Get info + raster statistics

    """
  • COGReader.tile(): Read map tile from a raster
tms = morecantile.tms.get("WorldCRS84Quad")
with COGReader("myfile.tif", tms=tms) as cog:
    tile, mask = cog.tile(1, 2, 3, tilesize=256)

# With indexes
with COGReader("myfile.tif", tms=tms) as cog:
    tile, mask = cog.tile(1, 2, 3, tilesize=256, indexes=1)

# With expression
with COGReader("myfile.tif", tms=tms) as cog:
    tile, mask = cog.tile(1, 2, 3, tilesize=256, expression="B1/B2")
  • COGReader.part(): Read part of a raster

Note: tms has no effect on part read.

tms = morecantile.tms.get("WorldCRS84Quad")
with COGReader("myfile.tif", tms=tms) as cog:
    data, mask = cog.part((10, 10, 20, 20))

# Limit output size (default is set to 1024)
with COGReader("myfile.tif", tms=tms) as cog:
    data, mask = cog.part((10, 10, 20, 20), max_size=2000)

# Read high resolution
with COGReader("myfile.tif", tms=tms) as cog:
    data, mask = cog.part((10, 10, 20, 20), max_size=None)

# With indexes
with COGReader("myfile.tif", tms=tms) as cog:
     data, mask = cog.part((10, 10, 20, 20), indexes=1)

# With expression
with COGReader("myfile.tif", tms=tms) as cog:
    data, mask = cog.part((10, 10, 20, 20), expression="B1/B2")
  • COGReader.preview(): Read a preview of a raster

Note: tms has no effect on part read.

with COGReader("myfile.tif") as cog: 
    data, mask = cog.preview()

# With indexes
with COGReader("myfile.tif") as cog: 
    data, mask = cog.preview(indexes=1)

# With expression
with COGReader("myfile.tif") as cog: 
    data, mask = cog.preview(expression="B1+2,B1*4")
  • COGReader.point(): Read point value of a raster

Note: tms has no effect on part read.

with COGReader("myfile.tif") as cog: 
    print(cog.point(-100, 25))

# With indexes
with COGReader("myfile.tif") as cog: 
    print(cog.point(-100, 25, indexes=1)) 
[1]

# With expression
with COGReader("myfile.tif") as cog: 
    print(cog.point(-100, 25, expression="B1+2,B1*4"))
[3, 4]
  • COGReader.info: Return simple metadata about the raster
with COGReader("myfile.tif") as cog:
    print(cog.info)
{
    "bounds": [-119.05915661478785, 13.102845359730287, -84.91821332299578, 33.995073647795806],
    "center": [-101.98868496889182, 23.548959503763047, 3],
    "minzoom": 3,
    "maxzoom": 12,
    "band_metadata": [[1, {}]],
    "band_descriptions": [[1,"band1"]],
    "dtype": "int8",
    "colorinterp": ["palette"],
    "nodata_type": "Nodata",
    "colormap": {
        "0": [0, 0, 0, 0],
        "1": [0, 61, 0, 255],
        ...
    }
}
  • COGReader.stats(): Return image statistics (Min/Max/Stdev)
with COGReader("myfile.tif") as cog:
    print(cog.stats())
{
    "1": {
        "pc": [1, 16],
        "min": 1,
        "max": 18,
        "std": 4.069636227214257,
        "histogram": [
            [...],
            [...]
        ]
    }
}
  • COGReader.metadata(): Return COG info + statistics
with COGReader("myfile.tif") as cog:
    print(cog.metadata())
{
    "bounds": [-119.05915661478785, 13.102845359730287, -84.91821332299578, 33.995073647795806],
    "center": [-101.98868496889182, 23.548959503763047, 3],
    "minzoom": 3,
    "maxzoom": 12,
    "band_metadata": [[1, {}]],
    "band_descriptions": [[1,"band1"]],
    "dtype": "int8",
    "colorinterp": ["palette"],
    "nodata_type": "Nodata",
    "colormap": {
        "0": [0, 0, 0, 0],
        "1": [0, 61, 0, 255],
        ...
    }
    "statistics" : {
        1: {
            "pc": [1, 16],
            "min": 1,
            "max": 18,
            "std": 4.069636227214257,
            "histogram": [
                [...],
                [...]
            ]
        }
    }
}
   

Example

See /demo

Contribution & Development

Issues and pull requests are more than welcome.

dev install

$ git clone https://github.com/cogeotiff/rio-tiler-crs.git
$ cd rio-tiler-crs
$ pip install -e .[dev]

Python >=3.7 only

This repo is set to use pre-commit to run isort, flake8, pydocstring, black ("uncompromising Python code formatter") and mypy when committing new code.

$ pre-commit install

$ git add .

$ git commit -m'my change'
isort....................................................................Passed
black....................................................................Passed
Flake8...................................................................Passed
Verifying PEP257 Compliance..............................................Passed
mypy.....................................................................Passed

$ git push origin

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for rio-tiler-crs, version 2.0.2
Filename, size File type Python version Upload date Hashes
Filename, size rio-tiler-crs-2.0.2.tar.gz (8.2 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page