GeoMojo is a geospatial toolkit for the Mojo programming language, a (soon-to-be) strict superset of the Python language. Rather than re-invent the wheel (pun intended), GeoMojo focuses on accelerating heavy computational operations or workloads. That is because Mojo can import and interoperate with arbitrary Python libraries such as GeoPandas, PyProj, Shapely, Xarray, Dask, et cetera.
Hello, world!
Join us in our effort to accelerate geospatial computation, including artificial intelligence (AI), remote sensing (RS), geographic information systems (GIS), and simulation modeling.
Computer architectures are getting messy. Mojo is simple and fast. Sometimes really fast.
Mojo is not just another language in the Language Wars, but an entirely new approach to language design focusing on modern heterogeneous systems. That includes things like chiplets, accelerators, and unified memory, glued together with new interfaces such as Universal Chiplet Interconnect Express (UCIe). Mojo is the culmination of predecessor projects including LLVM, Swift, and MLIR. What makes Mojo special is its interpreter and compiler. That's right, Mojo can be interpreted dynamically or compiled into a static binary. It supports dynamic and/or static data and functions, as well as immutability. It's like a Rustified, XLAified native Python. It's the ultimate have-your-cake-and-eat-it solution to the two-language problem. It gives the Julia language a serious run for its money. Mojo looks like vanilla Python and runs like hell on a wide variety of systems through zero-cost abstractions.
The compiler, rather than the programmer, does all of the heavy lifting.
Apache 2.0