![]() ![]() IMPORTANT: the map must be loaded before getting or setting feature state. Is currently no support for promoteId like in MapLibre GL JS. NOTE: features must already have a unique, numeric ID set on each feature. getFeatureState ( "exampleSource", "exampleLayer", "0" ) # returns None removeFeatureState ( "exampleSource", "exampleLayer", "0", "a" ) map. ![]() type f -name "*.whl.ubuntu-22.04" -print0 -exec bash -c 'mv "$" # remove the state value for key "a" map. Something like this for Ubuntu 22.04: wget ubuntu-20.04 suffixes to the wheel names, which have toīe stripped off before you can install them. Unfortunately, Python wheel names are very restrictive, so we have added Wheels are available on the release page in Github. Successfully compile maplibre-gl-native, wheels are only available for To verify that it installed correctly, run the included test suite: python -m pip install pytest Pillow numpy pixelmatch python-dotenvĭue to the complexity of building manylinux wheels that include OpenGL and Wheels are available on PyPI: pip install pymgl Install Supported operating systems MacOS 10.15+ (x86_64 only) It does not provide higher-level functionality such as a web server or a CLI.įor a stand-alone service implmenting rendering functionality, see This package provides only the Python API for interacting with maplibre-gl-native Server-side rendering of maps for use in reports. This package is intended to provide a lightweight interface to maplibre-gl-nativeįor rendering Mapbox GL to PNG image data using Python. WARNING: this package is under active development and the API may change without notice. This package provides an interface to mapblibre-gl-native to render Mapbox GL One of the formats that makes use of all the mentioned optimizations is the tiled GeoTIFF format.PyMGL: Maplibre GL Native Static Renderer for Python 256x256 or 512x512 will significantly speed up the processing time.īoth things can be done using command-line tools of the GDAL library, such as gdal_translate. A block size that is similar to the tile output size e.g. MapTiler Engine is the most effective at reading 256x256 blocks of data.įor large raster datasets, a tile-based format (as opposed to scanline-based) will drastically speed up processing. In the vast majority of cases, the overhead of the network connection and protocols for reading the input data over the network introduces a bottleneck that severely impairs processing performance.Īpart from buying more CPUs, you can also reach the speed gain by optimizing your workflow, especially for a large amount of data. Hence, avoid using HDDs, so you allow MapTiler Engine to fully utilize the potential of your machine's CPU.Īlso, processing input data from network shares is discouraged. MapTiler Engine is mainly CPU-intensive, but the storage speed comes right after it. It's highly recommended to use a fast local SSD drive to store the input data. You can check the number of CPUs available in your subscription plan as well as the number of CPUs your machine has by going to Account -> License key. To learn more, please refer to the MapTiler Engine pricing page. continents or the whole world).įor custom needs, we also offer the Enterprise plan, that can be fitted to more complex deployment scenarios. With such power, it is possible to render extremely large datasets (e.g. The Pro version is able to render on 16 CPUs. The Free version is limited to 1 CPU, and MapTiler Engine Plus has a limit of 4 CPUs for rendering. This article provides some tips that might help to improve the performance of the map rendering process. This way MapTiler Engine can provide higher performance even on a dual-core computer. The modern CPUs have multiple cores and support Hyperthreading which provides multiple logical CPUs per core. ![]() MapTiler Engine is a multi-threaded program. ![]()
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