ASAM | Modeling Attention and Memory for Auditory Selection | Speech library

Here is the sql request you want :

You already have mf4 file. Is this file converted from an mdf file? If it is as I thought, you should use the mdf file to convert it into a CSV file. Here it is anyway and hopefully you or someone else can come up with a better method.

Are you trying to reduce duplicate code (as in not having to type a new Meta class for each type), or are you trying to improve the type hints used by tools like mypy or your IDE ?

In general, the typing module is used for the latter. But I believe you are looking for the former.

In that case, I would try to use factory functions. Something like this:

First solution with sets and isdisjoint and filter by boolean indexing with inverted mask by ~:

You are already using the right methods. When the file is opened only the metadata is loaded, and the actual channel samples are only read as needed (when you call to_dataframe).

The bottleneck might be the fact that you do not provide the raster argument (see the documentation here https://asammdf.readthedocs.io/en/latest/api.html#asammdf.mdf.MDF.to_dataframe) and in this case the output dataframe will be build using the union of all time stamp of the selected channels and doing interpolation for all the columns. Play around with the raster argument