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catalystneuro / roiextractors

Python-based module for extracting from, converting between, and handling optical imaging data from several file formats. Inspired by SpikeInterface.
https://roiextractors.readthedocs.io/en/latest/index.html
BSD 3-Clause "New" or "Revised" License
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Feature request: Adding support for saving CNMFE results in Caiman #292

Open EricThomson opened 8 months ago

EricThomson commented 8 months ago

Hi this is related to #287 , which was about saving CNMF results in Caiman.

I started on CNMFE this morning, and everything from that issue carries over. Things all work great, we have mapping from:

As before, awesome it works great. The main concern is the naming as we already discussed, and it might be nice to have both denoised, and raw traces (C+YrA) instead of just denoised traces.

Also, with CNMFE, the model of background activity is different. In CNMF, it is just captured in low-rank spatial b (spatial) and f (temporal) components, and they are relatively simple (2p yippee). In CNMFE, we have the ring model, as discussed over at the other issue. Just to import from what I said there:

On the background model: things get more gnarly for the background model when we run CNMFE in Caiman, because then there is no b/f (they are None). Instead, we have the ring model, so the parameters are W and b0 (as I'm sure you know, in CNMFE there is a much more complicated background model, where each pixel has its own background fluctuations a certain distance in a ring from that pixel that are subtracted out -- this is captured in W that represents the ring model; b0 is a dc offset for each pixel).

So, if we wanted to save the output of the model, it would be good to save W and b0. The "convenient" thing is, checking for f and b is enough: if they are None, then you are running CNMFE and W/b0 will be defined (assuming you have run your estimator). So it isn't that much extra work. W is the same type as A (the spatial footprints) -- a compressed sparse column matrix.

The thing is, if we just ignored W and b0, handling #287 would be sufficient for handling CNMFE data -- the things that most people care about (calcium traces and such) are the same for CNMF/CNMFE. It tends to be the power users and developers that really want to see background (e.g., with neural model and background model you can calculate the residuals of the model, given the raw data).

Thanks for your time: these tools are really helpful! 🥇