Comments (3)
I compared both t_lfp vector obtained with the load_open_ephys_faster and the previous version of load_open_ephys. They are different. The fast version is so FAST please could you check ??
from analysis-tools.
I've just checked. The faster version returns the raw timestamps instead of converting them to seconds, but it seems it only does that for continuous data. Simply dividing the timestamps by the sample rate gives identical results to the ones returned by the slower version. I'll add the division to the code later.
Also, the fast version also appends and empty block of 1024 samples to the end of the acquired data for some reason. It can be safely ignored.
from analysis-tools.
Thank you for the answer.
from analysis-tools.
Related Issues (20)
- Splitting and Merging .dat Files HOT 3
- Trouble loading events data in matlab when saving in flat binary format with the updated GUI: load_open_ephys_binary.m looks for channels.npy HOT 3
- 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
- data and timestamp length unequal using load_open_ephys_binary HOT 4
- Session? HOT 1
- Flat binary file has more/less samples than expected HOT 4
- How to convert .continuous format to flat binary for using in Klusta HOT 1
- How to display the specific time point for spikes ? HOT 1
- 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
- Missing Probeinterface file for Neuropixel 1.0 (Open Ephys)
- recovering timestamps HOT 1
- OEP v0.6.2 Python KeyError: 'source_processor_sub_idx HOT 3
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from analysis-tools.