Adding HEKA loading module

[easy-electrophysiology]

We have recently released a python module for loading HEKA filetypes (https://github.com/easy-electrophysiology/load-heka-python). Currently HEKA loading is available in Neo through the Stimfit module, though a couple of benefits of this python module is that avoids any problems with compiling stimfit, and handles some more recent versions of the HEKA filetype. It also supports lazy loading so can be written up as a full rawio module (though still need to add memmap).

If this is something you would be interested in supporting I will open a pull request once some progress is made integrating it with rawio / io. Before starting, I wanted your advice as the HEKA file has 4 levels of data grouping - 'Group' > 'Series' (analogous to Neo block) > 'Sweep' (Neo Segment) > 'Record' (Neo Analogsignal). Would the best way to handle this be to have an extra arguments for the read_block() function to indicate the index of the Group and Series to load?

Best Wishes,
Joe

[samuelgarcia]

If you implement this at rawio level.
All the read_block() will automatic at neo.io level thanks to BaseFromraw
See https://github.com/NeuralEnsemble/python-neo/blob/master/neo/io/basefromrawio.py
For instance for spike2io here is the wrapper https://github.com/NeuralEnsemble/python-neo/blob/master/neo/io/spike2io.py.
Super simple.

In BaseFromrawread_block() you have the selection by block_index see here https://github.com/NeuralEnsemble/python-neo/blob/master/neo/io/basefromrawio.py#L76

[JuliaSprenger]

@easy-electrophysiology Tell us if you need more detailed instructions for the implementation than the references above.

[JuliaSprenger]

Hi @easy-electrophysiology Any news on this?

[easy-electrophysiology]

Hi @JuliaSprenger, apologies I would still be interested in this but struggling to find time, I'm not sure if I will be able to work on this over the summer, but after summer it would be nice to dedicate some time to this.