Awesome

rio-tiler-mvt

<img src="https://user-images.githubusercontent.com/10407788/57476379-72cf6000-7264-11e9-979d-bf9f486518c2.png" style="max-width: 800px;" alt="rio-tiler"></a> A rio-tiler plugin to translate tile array to MVT (using python-vtzero). <a href="https://github.com/cogeotiff/rio-tiler-mvt/actions?query=workflow%3ACI" target="_blank"> <img src="https://github.com/cogeotiff/rio-tiler-mvt/workflows/CI/badge.svg" alt="Test"> </a> <a href="https://codecov.io/gh/cogeotiff/rio-tiler-mvt" target="_blank"> <img src="https://codecov.io/gh/cogeotiff/rio-tiler-mvt/branch/master/graph/badge.svg" alt="Coverage"> </a> <a href="https://pypi.org/project/rio-tiler-mvt" target="_blank"> <img src="https://img.shields.io/pypi/v/rio-tiler-mvt?color=%2334D058&label=pypi%20package" alt="Package version"> </a> <a href="https://github.com/cogeotiff/rio-tiler-mvt/blob/master/LICENSE" target="_blank"> <img src="https://img.shields.io/github/license/cogeotiff/rio-tiler-mvt.svg" alt="Downloads"> </a>

Install

Requirements

rio-tiler-mvt use python-vtzero wrapper to encode point and polygons to MVT. Because VTZERO is a C++ library, python-vtzero is written in Cython, thus cython is required to compile this library.

$ pip install cython # see https://github.com/tilery/python-vtzero#requirements

$ pip install rio-tiler-mvt

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

Rio-tiler + Mapbox Vector tiles

API

pixel_encoder

pixels_encoder(
    data: numpy.ndarray,
    mask: numpy.ndarray,
    band_names: list = [],
    layer_name: str = "my_layer",
    feature_type: str = "point"
)

Inputs:

data: raster tile data to encode
mask: mask data
band_names: Raster band's names
layer_name: Layer name
feature_type: Feature type (point or polygon)

Returns:

mvt : Mapbox Vector Tile encoded data.

Examples:

from rio_tiler.io import COGReader
from rio_tiler_mvt import pixels_encoder

with COGReader("fixtures/test.tif") as cog
    img = cog.tile(72, 63, 7, resampling_method="nearest")
    mvt = pixels_encoder(img.data, img.mask, layer_name="test", feature_type="point")

shapes_encoder

shapes_encoder(
    data: numpy.ndarray,  # 1D array (height, width)
    mask: numpy.ndarray,
    layer_name: str = "my_layer",
    colormap: dict = {},
    class_names: dict = {}
)

Inputs:

data: raster tile data to encode
mask: mask data
layer_name: Layer name
colormap: GDAL colormap. If provided a color value will be added to the feature properties
class_names: Dictionary mapping pixel value with class names. If provided a name value will be added to the feature properties.

Returns:

mvt : Mapbox Vector Tile encoded data.

Contribution & Development

Issues and pull requests are more than welcome.

dev install

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

pre-commit

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