Collection of tools to handle Neuropixel 1.0 and 2.0 data
Python
20
284 commits
updated Oct 10, 2026
Collection of tools to handle Neuropixel 1.0 and 2.0 data (documentation coming soon...)
Minimum Python version supported is 3.10
uv pip install ibl-neuropixel
mtscompThe mtscomp util implements fast chunked compression for neurophysiology data in a single shard. Package repository is here.
from pathlib import Path
import spikeglx
file_spikeglx = Path('/datadisk/neuropixel/file.imec0.ap.bin')
sr = spikeglx.Reader(file_spikeglx)
sr.compress_file()
# note: you can use sr.compress_file(keep_original=False) to also remove the orginal bin file
The mtscomp util implements fast chunked compression for neurophysiology data in a single shard. Package repository is here.
from pathlib import Path
import spikeglx
import ibldsp.voltage
file_spikeglx = Path('/datadisk/Data/neuropixel/human/Pt01.imec0.ap.bin')
sr = spikeglx.Reader(file_spikeglx)
# reads in 300ms of data
raw = sr[10_300_000:10_310_000, :sr.nc - sr.nsync].T
destripe = ibldsp.voltage.destripe(raw, fs=sr.fs, neuropixel_version=1)
# display with matplotlib backend
import ibldsp.plots
ibldsp.plots.voltageshow(raw, fs=sr.fs, title='raw')
ibldsp.plots.voltageshow(destripe, fs=sr.fs, title='destripe')
# display with QT backend
from viewephys.gui import viewephys
eqc = {}
eqc['raw'] = viewephys(raw, fs=sr.fs, title='raw')
eqc['destripe'] = viewephys(destripe, fs=sr.fs, title='destripe')
This relies on a fast fourier transform external library: pip install pyfftw.
Minimal working example to destripe a neuropixel binary file.
from pathlib import Path
from ibldsp.voltage import decompress_destripe_cbin
sr_file = Path('/datadisk/Data/spike_sorting/pykilosort_tests/imec_385_100s.ap.bin')
out_file = Path('/datadisk/scratch/imec_385_100s.ap.bin')
decompress_destripe_cbin(sr_file=sr_file, output_file=out_file, nprocesses=8)
The best way to look at the results is to use viewephys, open an ephys viewer on the raw data.
![]()
You can then move within the raw data file.
The following describes the methods implemented in this repository. https://doi.org/10.6084/m9.figshare.19705522
Please see our contribution guidelines for details on how to contribute to this project.
Collection of tools to handle Neuropixel 1.0 and 2.0 data
Python
20
284 commits
updated Oct 10, 2026
Collection of tools to handle Neuropixel 1.0 and 2.0 data (documentation coming soon...)
Minimum Python version supported is 3.10
uv pip install ibl-neuropixel
mtscompThe mtscomp util implements fast chunked compression for neurophysiology data in a single shard. Package repository is here.
from pathlib import Path
import spikeglx
file_spikeglx = Path('/datadisk/neuropixel/file.imec0.ap.bin')
sr = spikeglx.Reader(file_spikeglx)
sr.compress_file()
# note: you can use sr.compress_file(keep_original=False) to also remove the orginal bin file
The mtscomp util implements fast chunked compression for neurophysiology data in a single shard. Package repository is here.
from pathlib import Path
import spikeglx
import ibldsp.voltage
file_spikeglx = Path('/datadisk/Data/neuropixel/human/Pt01.imec0.ap.bin')
sr = spikeglx.Reader(file_spikeglx)
# reads in 300ms of data
raw = sr[10_300_000:10_310_000, :sr.nc - sr.nsync].T
destripe = ibldsp.voltage.destripe(raw, fs=sr.fs, neuropixel_version=1)
# display with matplotlib backend
import ibldsp.plots
ibldsp.plots.voltageshow(raw, fs=sr.fs, title='raw')
ibldsp.plots.voltageshow(destripe, fs=sr.fs, title='destripe')
# display with QT backend
from viewephys.gui import viewephys
eqc = {}
eqc['raw'] = viewephys(raw, fs=sr.fs, title='raw')
eqc['destripe'] = viewephys(destripe, fs=sr.fs, title='destripe')
This relies on a fast fourier transform external library: pip install pyfftw.
Minimal working example to destripe a neuropixel binary file.
from pathlib import Path
from ibldsp.voltage import decompress_destripe_cbin
sr_file = Path('/datadisk/Data/spike_sorting/pykilosort_tests/imec_385_100s.ap.bin')
out_file = Path('/datadisk/scratch/imec_385_100s.ap.bin')
decompress_destripe_cbin(sr_file=sr_file, output_file=out_file, nprocesses=8)
The best way to look at the results is to use viewephys, open an ephys viewer on the raw data.
![]()
You can then move within the raw data file.
The following describes the methods implemented in this repository. https://doi.org/10.6084/m9.figshare.19705522
Please see our contribution guidelines for details on how to contribute to this project.