Comments (3)
There was an thread in groups in which people were having trouble fitting data in memory when looping over large arrays. Perhaps a better alternative would be to not scale by bitvolts until later in the analysis. Maybe we could implement an input option that would result in either scaling and double, or no scaling and int16.
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OK, after reading that thread, I see the benefit of using int16's in the type of operation that Ainhoa was attempting. Your suggestion of providing a choice sounds like a reasonable solution. I think that scaling and double should be the default (since it would cover most use cases, except those involving large data operations), does that sound good?
I can go ahead and make that change and submit a pull request.
from analysis-tools.
Sure, thanks!
from analysis-tools.
Related Issues (20)
- Splitting and Merging .dat Files HOT 3
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- Binary dat files to Plexon Offline sorter HOT 7
- Failing to read metadata.npy for TTL events in this specific setup HOT 4
- Converting continuous.dat into SpikeGLX flat binary HOT 4
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- Session? HOT 1
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- How to convert .continuous format to flat binary for using in Klusta HOT 1
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- load_open_ephys_data_faster.m memory error HOT 5
- Import continuous data recorded by neuropixels HOT 11
- Opening the binary data HOT 4
- issue running TT openephys data recorded in binary to Mclust
- Using analysis tools in Jupyter Notebook HOT 4
- Merging multiple OpenEphys binary (continuous.dat) files HOT 1
- Info Channel Header Location HOT 2
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- recovering timestamps HOT 1
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