First, congrats on the paper acceptance! @apdealbao mentioned this in path meeting today, and I thought it might be interesting to try to make it easy for OpenPathSampling users to make use of the tools here.
In general, the idea would be that the core code stays here, but we add things to bridge to OPS. Assuming you're interested, there are a lot of options on how to do that, so details of implementation will depend on what you prefer.
The main thing we would need would be some way to get data from OPS into the data structures you use here. It might also be interesting to add a plug-in for the OPS CLI, which could make the analysis even easier to do.
This could all either be done in this repo, or in a separate repo (which might be more appealing if you don't want the OPS CLI plugin in here).
I'm happy to do a lot of the work on this -- mainly, I just need to know what the data structures need to look like. I can also help with setting up distribution here, making it so it can be installed via pip and conda.
First, congrats on the paper acceptance! @apdealbao mentioned this in path meeting today, and I thought it might be interesting to try to make it easy for OpenPathSampling users to make use of the tools here.
In general, the idea would be that the core code stays here, but we add things to bridge to OPS. Assuming you're interested, there are a lot of options on how to do that, so details of implementation will depend on what you prefer.
The main thing we would need would be some way to get data from OPS into the data structures you use here. It might also be interesting to add a plug-in for the OPS CLI, which could make the analysis even easier to do.
This could all either be done in this repo, or in a separate repo (which might be more appealing if you don't want the OPS CLI plugin in here).
I'm happy to do a lot of the work on this -- mainly, I just need to know what the data structures need to look like. I can also help with setting up distribution here, making it so it can be installed via
pip
andconda
.