nonrad may be installed through pip from PyPI,

pip install nonrad

or directly through github,

pip install git+

Going faster

This code utilizes numba to speed up calculations, and there are various ways to improve the performance of numba.


On Intel processors, the short vector math library (SVML) can be enabled to speed up certain operations. The runtime libraries from Intel are required for this. On a conda installation, they should already be installed in the package icc_rt. The icc_rt package is also available through pip

pip install icc_rt

However, you will likely need to add your virtual environment to the library path:

export LD_LIBRARY_PATH=/path/to/.virtualenvs/env_name/lib/:$LD_LIBRARY_PATH

This can be added to your activate script in your virtual environment (/path/to/.virtualenvs/env_name/bin/activate) to make the change persistent. To check if the installation worked, run numba -s; the output should include


__SVML Information__
 SVML State, config.USING_SVML                 : True
 SVML Library Loaded                           : True
 llvmlite Using SVML Patched LLVM              : True
 SVML Operational                              : True


Numba Enviornment Variables

There are several environment variables for numba that can be enabled and may improve performance. If your machine has AVX instructions, we recommend enabling it with:


The full list of numba environment variables is available here.