For bug fixes or new features, please file an issue before submitting a pull request. If the change isn't trivial, it may be best to wait for feedback.
Make sure you clone the repository in recursive mode, so you also get the
wrapped code of FastANI which is exposed as a git submodule:
$ git clone --recursive https://github.com/althonos/pyfastaniCompiling requires the boost::math module from Boost.
Depending on your system, you may have to install them yourself.
To compile the extension, use the following command:
$ python -m pip install -v -e . --no-build-isolationTests are written as usual Python unit tests with the unittest module of
the standard library. Running them requires the extension to be built
locally:
$ python -m pip install -v -e . --no-build-isolation
$ python -m unittest pyfastani.tests -vvThe benches folder contains benchmarks for evaluating the performance of
the node connection scoring step, essentially to make sure that the
multi-threading makes it faster.
Start by building pyfastani locally, and making sure it is compiled
in release mode:
$ python -m pip install -v . --no-build-isolationThen make sure you have the required packages and data:
$ pip install --user -r benches/mapping/requirements.txt
$ python benches/data/download.pyFinally, run the benchmarks and plot the results:
$ python benches/mapping/bench.py -d benches/data/ -o times.json
$ python benches/mapping/plot.py -i times.json --showThis project targets Python 3.7 or later.
Python objects should be typed; since it is not supported by Cython,
you must manually declare types in type stubs (.pyi files). In Python
files, you can add type annotations to function signatures (supported in
Python 3.5) or in variable assignments (supported from Python 3.6
onward).
When interfacing with C, and in particular with pointers, use assertions everywhere you assume the pointer to be non-NULL.
When wrapping objects, use stack allocation where possible.