Pathfinding and terrain analysis library for Starcraft 2 bot api in Rust
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
You will need Python 3.7 or newer.
You will need the nightly build of Rust:
https://www.rust-lang.org/tools/install
rustup toolchain install nightly
Now Rust Nightly is installed but not activated.
To switch to nightly
globally, change the default with rustup default nightly:
$ rustup default nightly
info: using existing install for 'nightly'
info: default toolchain set to 'nightly'
nightly unchanged: rustc 1.9.0-nightly (02310fd31 2016-03-19)
Now any time you run cargo or rustc you will be running the
nightly compiler.
Clone the sc2-pathlib repository and run cargo build --release in the sc2-pathlib directory. This should create a sc2pathlib.so(Linux) or a sc2pathlib.dll(Windows) file in sc2-pathlib\target\release. If on Windows, you need to rename the sc2pathlib.dll file to sc2pathlib.pyd.
Alternatively, you can run build.bat which automates the process above for Windows or linux_build.sh to automate on Linux.
Copy sc2pathlib.so/sc2pathlib.pyd to the directory where your Python program resides to be able to import it as a Python library.
>>> import sc2pathlib
>>> pf = sc2pathlib.PathFind([[1,1,1,1],[0,0,0,1],[1,1,0,1],[1,1,1,1]], (0, 0), (2, 0))
>>> pf.find_path([[1,1,1,1],[0,0,0,1],[1,1,0,1],[1,1,1,1]], (0, 0), (2, 0))
([(0, 0), (0, 1), (0, 2), (0, 3), (1, 3), (2, 3), (3, 3), (3, 2), (3, 1), (2, 0)], 94142)
>>>
grid: A two-dimensional array using 1 for pathable and 0 for obstacles.
Example:
[[1,1,1,1],[0,0,0,1],[1,1,0,1],[1,1,1,1]]
Uses A* pathfinding algorithm and returns a tuple containing the path as an array of tuples and the distance.
start: Tuple with the x and y value of the start position.
end: Tuple with the x and y value of the end position.
possible_heuristic: Optional parameter with value between 0-2. Lower value uses less accurate heuristic for distance calculation for improved performance.
Same function as above but uses influence to calculate path and return influenced distance.
The same as find_path.