mlt_py
Python bindings for the MapLibre Tile (MLT) format via PyO3.
Decoding
import maplibre_tiles
data = open("tile.mlt", "rb").read()
# Structured decode with geo-referencing
layers = maplibre_tiles.decode_mlt(data, z=14, x=8297, y=10749, tms=True)
for layer in layers:
print(f"Layer: {layer.name}, extent: {layer.extent}")
for feat in layer.features[:3]:
print(f" id={feat.id}, type={feat.geometry_type}")
print(f" wkb={len(feat.wkb)} bytes, props={dict(feat.properties)}")
# Raw tile-local coordinates (no z/x/y needed)
layers = maplibre_tiles.decode_mlt(data)
# GeoJSON string output (tile-local coords)
geojson_str = maplibre_tiles.decode_mlt_to_geojson(data)
# Fast layer listing (no full decode)
names = maplibre_tiles.list_layers(data)
Decoded objects
decode_mlt(...) returns a list of MltLayer objects.
Each MltLayer represents one decoded MLT layer and exposes:
name: str- the layer name.extent: int- the layer extent.z_step: int | None- the power of ten of the z grid's step in metres, orNonewhen the vertices carry no z.features: list[MltFeature]- the decoded features in that layer.
Each MltFeature represents one decoded feature and exposes:
id: int | None- the feature id, if present.geometry_type: str- the decoded geometry type.wkb: bytes- the geometry as WKB. A layer with z writes ISO WKB Z, with grid values in tile coordinates and elevations in metres oncez/x/yproject.properties: dict- the feature properties.
import maplibre_tiles
layers = maplibre_tiles.decode_mlt(data)
for layer in layers:
print(layer.name, layer.extent)
for feature in layer.features[:2]:
print(feature.id, feature.geometry_type, dict(feature.properties))
Encoding
encode_geojson(geojson, name, extent=4096, *, tessellate=False, sort="auto", shared_dict=True, fsst=True, fastpfor=True, z_step=None) -> bytes encodes a single layer to an MLT blob.
geojsonis a GeoJSONFeatureCollection. Geometry is in tile-local coordinate space (no projection), matchingtilezen/mapbox-vector-tile's default. Coordinates must be integers, and 2D unlessz_stepis set. They must be JSON integers (2048), not floats: a float-typed value such as2048.0raisesValueError.nameandextentset the MLT layer metadata, since aFeatureCollectionhas no slot for them.extentdefaults to4096.tessellategenerates triangulation data for polygons and multi-polygons.sortcontrols which feature ordering(s) the encoder tries:all,auto,morton,hilbert,id, ornone.shared_dictenables grouping strings into shared dictionaries.fsstenables FSST string compression.fastpforenables FastPFOR integer compression.z_stepis the power of ten of the z grid's step in metres, from-3(1 mm) to4(10 km). Every position is then[x, y, z], wherez = 0sits at -10000 m, and the layer is written in the experimental v2 format.
encode_mvt(data, *, tessellate=False, sort="auto", shared_dict=True, fsst=True, fastpfor=True) -> bytes encodes an entire raw Mapbox Vector Tile (protobuf) to MLT using the same encoding options.
import maplibre_tiles
blob = maplibre_tiles.encode_geojson(
{
"type": "FeatureCollection",
"features": [
{
"type": "Feature",
"id": 1,
"geometry": {"type": "Point", "coordinates": },
"properties": {"name": "main", "lanes": 3},
},
],
},
name="roads",
extent=4096,
)
# Multi-layer tiles -> encode each layer and concatenate the bytes
tile = b"".join([
maplibre_tiles.encode_geojson(roads, name="roads"),
maplibre_tiles.encode_geojson(water, name="water"),
])
import maplibre_tiles
mvt = open("tile.mvt", "rb").read()
blob = maplibre_tiles.encode_mvt(mvt)
# With explicit encoding options
blob = maplibre_tiles.encode_mvt(
mvt,
tessellate=False,
sort="auto",
shared_dict=True,
fsst=True,
fastpfor=True,
)
Input is validated strictly.
A non-FeatureCollection input, a non-Feature member, float-typed or 3D coordinates, null or empty geometry, nested or non-scalar property values, a non-u64 id, and an empty collection all raise ValueError.
1 maintainer
Metadata
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Release files for maplibre-tiles 0.2.6
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