supermorecado
Extend the functionality of morecantile with additional commands.
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has no known vulnerabilities, installs with low friction, and fills a clear gap for developers working with multiple TileMatrixSet grids. The MIT license is permissive. Install if you need to work with tiled geospatial data beyond Web Mercator or want a command-line tool for tile stream processing.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction installation as a pure Python wheel.
- Actively maintained with recent releases and a small but engaged user base.
- Depends on morecantile and rasterio, both stable geospatial libraries.
License · maintenance · safety
permissive license (permissive) — MIT License permits unrestricted use, modification, and distribution with minimal restrictions—suitable for both open and proprietary projects.
last release 2026-04-03 (133 days) · last repo commit 2026-06-29 · 17 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 117,272 downloads/mo, #12,172 on PyPI
Alternatives
Verify before relying
pip install supermorecado
# Command-line: burn GeoJSON to tiles at zoom 9
cat features.geojson | supermorecado burn 9
# Python API: burn tiles with custom TMS
import morecantile
from supermorecado import burnTiles
tms = morecantile.tms.get("WebMercatorQuad")
burner = burnTiles(tms=tms)
tiles = burner.burn(features)- Whether rasterio's compiled dependencies (GDAL) are pre-installed or require system setup on target platforms
- Performance characteristics when processing large GeoJSON streams or high-zoom tile sets
What it is and what it does
Supermorecado is a command-line and Python library that extends morecantile to perform geospatial tile operations across multiple TileMatrixSet (TMS) grids. It takes GeoJSON geometries or tile coordinate streams and produces tile intersections, edge tiles, unified footprints, or density heatmaps—all configurable by TMS identifier rather than locked to a single grid like its predecessor supermercado.
The package is designed for geospatial workflows where you need to map vector features onto tiled grids, analyze tile coverage, or visualize tile density. It exposes both a CLI for shell pipelines and a Python API for programmatic use, making it suitable for batch processing, data preparation, and interactive analysis in GIS and mapping applications.
Use it for
- Convert GeoJSON features to Web Mercator or other TMS tile coordinates for tile-based data ingestion pipelines
- Identify boundary tiles of a coverage area to optimize tile downloads or rendering
- Compute the unified footprint of a tile set as a single GeoJSON polygon for visualization or metadata
- Generate tile density heatmaps to identify coverage gaps or over-represented regions in tile inventories
- Migrate tile processing workflows from supermercado to support non-Web-Mercator grids like WGS84
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has no known vulnerabilities, installs with low friction, and fills a clear gap for developers working with multiple TileMatrixSet grids. The MIT license is permissive. Install if you need to work with tiled geospatial data beyond Web Mercator or want a command-line tool for tile stream processing.
Install
supermorecado on PyPI
Before you install
Low friction installation as a pure Python wheel. Actively maintained with recent releases and a small but engaged user base. Depends on morecantile and rasterio, both stable geospatial libraries.
License in practice
MIT License permits unrestricted use, modification, and distribution with minimal restrictions—suitable for both open and proprietary projects.
Quickstart
pip install supermorecado
# Command-line: burn GeoJSON to tiles at zoom 9
cat features.geojson | supermorecado burn 9
# Python API: burn tiles with custom TMS
import morecantile
from supermorecado import burnTiles
tms = morecantile.tms.get("WebMercatorQuad")
burner = burnTiles(tms=tms)
tiles = burner.burn(features)Verify before relying
- Whether rasterio's compiled dependencies (GDAL) are pre-installed or require system setup on target platforms
- Performance characteristics when processing large GeoJSON streams or high-zoom tile sets
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesmorecantilerasterio |
| Maintenance | Actively maintained 133 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 117,272 / month, #12,172 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: Information TechnologyIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseProgramming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering :: GIS |
Evidence: supermorecado-0.2.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “tile matrix set operations”
- supermorecadoExtends morecantile with command-line and Python tools to burn…
- cogeo-mosaicCreates and manages mosaics of Cloud Optimized GeoTIFF files…
- morecantileConstruct and work with map tile grids following the OGC…
Give your agent the search over MCP, or paste the wish link into any chat.
More GIS packages
Shapely provides Python tools for creating, manipulating, and analyzing 2D geometric objects (points, lines, polygons) using the GEOS library, with both scalar and vectorized NumPy-based operations.
pyproj provides a Python interface to PROJ, enabling cartographic projections and coordinate system transformations for geospatial applications.
GeoPandas extends pandas DataFrames to handle geographic data, combining pandas operations with shapely geometry and spatial analysis capabilities that would otherwise require a spatial database.
Install it if you work with geographic data in Python and want to avoid setting up a spatial database or learning a separate GIS tool.
geopy is a Python client for geocoding and distance calculation that converts addresses to coordinates and vice versa using multiple web-based geocoding services, and computes geodesic and great-circle distances between geographic points.
Install it if you need geocoding or distance calculations in your application.
Pyogrio provides fast, bulk-oriented read and write access to vector spatial data formats (Shapefile, GeoPackage, GeoJSON, etc.) via GDAL/OGR bindings, typically for use with GeoPandas GeoDataFrames.
h3 provides Python bindings to Uber's H3 geospatial indexing library, converting geographic coordinates into hierarchical hexagonal grid cells and performing spatial operations on them.
See also cogeo-mosaic · morecantile · tippecanoe · rio-tiler · mercantile · pmtiles · contextily · rio-cogeo · mapbox-vector-tile · titiler-application