A tool for empirical Arrhenius equation fitting for thermally-induced physicochemical processes.
sierras is a tool for empirical Arrhenius equation fitting for thermally-induced physicochemical processes.
You need Python 3.8+ to run sierras.
You can install the most recent stable release of sierras with pip
python -m pip install -U pip python -m pip install -U sierras
A simple example of use:
from sierras import ArrheniusRegressor # default constant is Boltzmann in eV/K areg = ArrheniusRegressor() # temperatures and target_process arrays-like as usually used in scikit-learn areg.fit(Temperatures, target_process) # print the activation energy ([eV] in the default case) and the extrapolated # process at room temperatures values (in the same units as target_process is) print(areg.activation_energy_, areg.extrapolated_process_) # plot the fitting fig, ax = plt.subplots() areg.plot(ax=ax)
You can contact me at firstname.lastname@example.org
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