cc: Alex Malins

Hi! The `radioactivedecay` package seems interesting. And having the ability to use `pandas.Series` is always a good thing. But please provide more information on what would be the intended use of these two packages (`radioactivedecay`, and `nupaac`). It seems to me that this is "only" a educational scoping tool, which may or may not achieve a great accuracy. The stiffness problem (decay constants that vary for several decades) is a well known problem that to tackle it, people have devised many complex approaches (methods & computer codes). It would somewhat surprise me if this could be implemented in python and be fast enough for "real-life" applications. Has there been any verification of the accuracy of the calculations?

PS: I tested the two packages and `nupaac` seems to have non-compatible dependencies. If I am not mistaken, it requires Numpy version > 2. Can you suggest to me, how can this be remedied? And how to uninstall nupaac to revert to previous settings?

I recommend using a dedicated virtual environment for nupaac, this will avoid conflicts with packages on your host machine. Since `radioactivedecay` is a dependency of `nupaac`, you can use the virtual env to explore the capabilities of both packages.

There are plenty of different use-cases, have a look at the tutorial file that I linked in the initial post for `nupaac` (https://codeberg.org/Cs137/NuPaAc/src/branch/main/tutorial.md), and for `radioactivedecay` at its documentation: https://radioactivedecay.github.io/

The latter provides also information about the accuracy and methodology applied to perform decay calculations.