Comments (4)
Have you tried using spikes.groupby('cl')? Then you can loop over the tuples (cluster_number, spikes within that cluster) and use all the pandas machinery to efficiently calculate the grouped means and amplitudes. In my experience that was much faster than iterating over the cluster labels.
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Indeed, wow, thank you Fernando! This is now much faster, and as a side effect, DBSCAN now works flawlessly.
from hs2.
Pandas is magical ;)
from hs2.
Ah, this is my fault, I don't understand Pandas very well. Thanks Fernando!
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Related Issues (20)
- Data lost at end of files HOT 1
- Save the version info in output files HOT 1
- `probe.py` path does not exist error when `RecordingExtractor` is called from SpikeToolkit
- Detection state is saving the wrong cutout_length
- Version in __version__ HOT 7
- clustering using DBSCAN HOT 1
- bug with HS 0.3.7 HOT 2
- Run `HS2` without probe file HOT 2
- version 0.3.99 fails at importing probe utils HOT 1
- Building wheel for herdingspikes (setup.py) ... error error: subprocess-exited-with-error HOT 3
- Version 0.3.100 fails at import HOT 3
- bug with the 0.3.7 version HOT 2
- Localization difference between BioCam and RecordingExtractor objects HOT 4
- create_probe_files leading to write OSError HOT 3
- Cant run herdingspikes HOT 3
- Trouble running HS on Spikeinterface HOT 5
- HS on Spike Interface HOT 1
- Kernel died when using herdingspikes in SpikeInterface HOT 2
- Build fails on Windows 10 HOT 3
- HerdingSpike through SpikeInterface wrongly scales waveform. HOT 1
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