Installation#

Installation via pip or conda#

Stable versions of dynasor are provided via PyPI and as part of conda-forge. This implies that dynasor can be installed using pip:

pip install dynasor

or using conda:

conda install -c conda-forge dynasor

Installing the development version#

If you want to use the most recent (development) version you can install directly from the git repository:

pip install --user git+https://gitlab.com/materials-modeling/dynasor.git

Requirements#

dynasor requires Python 3.9+ and depends on the following libraries:

  • ASE (trajectory reading)

  • MDAnalysis (trajectory reading)

  • NumPy (numerical linear algebra)

  • Numba (computational efficiency)

  • SciPy (convenience functions for correlations)

  • pandas (data handling)

Numba and Intel SVML#

dynasor employs numba for the efficient calculation of correlation functions. Some Numba versions can use Intel’s Short Vector Math Library (SVML) to accelerate mathematical operations. Support depends on the installed Numba and llvmlite versions.

Run numba -s to check whether SVML is available in the active environment. When enabled, the output includes:

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

If SVML support is expected but any of these values are False, verify that intel-cmplr-lib-rt is installed in the active environment and that Numba can locate the SVML shared libraries when Python starts. Consult the current Numba performance tips for installation and troubleshooting instructions.