Roberto

Temporary workaround: ``` import nbformat # Load your notebook notebook_path = '/home/roberto/Github/Transcriptomics-5-HT/Figure 1b.ipynb' # Replace with your notebook path with open(notebook_path, 'r', encoding='utf-8') as f: nb = nbformat.read(f, as_version=4) #...

Didn't have time to try this yet. But if u tested it we can close the issue.

the json exported from onix is ordered by "contac_ids" and not 'device_channel_indices' . 

oh! ok than maybe the problem is that I should keep the original order!?

is that possible to do easily?

The main problem is that when I extract the waveforms, thay are all flat. The code does work with other rec types though. 

no, I just do ``` raw_rec = read_binary(path_recording, sampling_frequency=fs_hz, dtype=np.uint16, num_channels=num_channels, gain_to_uV=gain_to_uV, offset_to_uV=offset_to_uV) probes = read_probeinterface(probe_json) raw_rec = raw_rec.set_probegroup(probes) raw_rec = spre.scale_to_uV(recording=raw_rec) ``` I see that the scaling happens automatically...

I just noticed that they are not totally flat, this is the biggest cluster by amplitude  they are just unusually small.

am I correct in thinking that it is enough to high pass at the line of the sorting analyzer? `create_sorting_analyzer(sorting, spre.highpass_filter(recording=sub_rec))` kilosort is already doing it by itself when sorting.

Ok this looks better now thanks!