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Migrate read_spikegadgets to the catalogue pattern #418

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Migrate read_spikegadgets to the catalogue pattern #418

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152 changes: 42 additions & 110 deletions src/probeinterface/io.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,6 +21,7 @@
from . import __version__
from .probe import Probe
from .probegroup import ProbeGroup
from .neuropixels_tools import build_neuropixels_probe
from .utils import import_safely


Expand Down Expand Up @@ -749,15 +750,6 @@ def read_spikegadgets(file: str | Path, raise_error: bool = True) -> ProbeGroup:
probe_group : ProbeGroup object

"""
# ------------------------- #
# Npix 1.0 constants #
# ------------------------- #
TOTAL_NPIX_ELECTRODES = 960
MAX_ACTIVE_CHANNELS = 384
CONTACT_WIDTH = 16 # um
CONTACT_HEIGHT = 20 # um
# ------------------------- #

# Read the header and get Configuration elements
header_txt = parse_spikegadgets_header(file)
root = ElementTree.fromstring(header_txt)
Expand All @@ -773,122 +765,62 @@ def read_spikegadgets(file: str | Path, raise_error: bool = True) -> ProbeGroup:
raise Exception("No Neuropixels 1.0 probes found")
return None

# Container to store Probe objects
probe_group = ProbeGroup()

for curr_probe in range(1, n_probes + 1):
probe_config = probe_configs[curr_probe - 1]

# Get number of active channels from probe Device element
# Get active electrode indices from the channelsOn bitmask (960 elements, 0-indexed)
active_channel_str = [option for option in probe_config if option.attrib["name"] == "channelsOn"][0].attrib[
"data"
]
active_channels = [int(ch) for ch in active_channel_str.split(" ") if ch]
n_active_channels = sum(active_channels)
assert len(active_channels) == TOTAL_NPIX_ELECTRODES
assert n_active_channels <= MAX_ACTIVE_CHANNELS

"""
Within the SpikeConfiguration header element (sconf), there is a SpikeNTrode element
for each electrophysiology channel that contains information relevant to scaling and
otherwise displaying the information from that channel, as well as the id of the electrode
from which it is recording ('id').

Nested within each SpikeNTrode element is a SpikeChannel element with information about
the electrode dynamically connected to that channel. This contains information relevant
for spike sorting, i.e., its spatial location along the probe shank and the hardware channel
to which it is connected.

Excerpt of a sample SpikeConfiguration element:

<SpikeConfiguration chanPerChip="1889715760" device="neuropixels1" categories="">
<SpikeNTrode viewLFPBand="0"
viewStimBand="0"
id="1384" # @USE: The first digit is the probe number; the last three digits are the electrode number
lfpScalingToUv="0.018311105685598315"
LFPChan="1"
notchFreq="60"
rawRefOn="0"
refChan="1"
viewSpikeBand="1"
rawScalingToUv="0.018311105685598315" # For Neuropixels 1.0, raw and spike scaling are identical
spikeScalingToUv="0.018311105685598315" # Extracted when reading the raw data
refNTrodeID="1"
notchBW="10"
color="#c83200"
refGroup="2"
filterOn="1"
LFPHighFilter="200"
moduleDataOn="0"
groupRefOn="0"
lowFilter="600"
refOn="0"
notchFilterOn="0"
lfpRefOn="0"
lfpFilterOn="0"
highFilter="6000"
>
<SpikeChannel thresh="60"
coord_dv="-480" # @USE: dorsal-ventral coordinate in um (in pairs for staggered probe)
spikeSortingGroup="1782505664"
triggerOn="1"
stimCapable="0"
coord_ml="3192" # @USE: medial-lateral coordinate in um
coord_ap="3700" # doesn't vary, assuming the shank's flat face is along the ML axis
maxDisp="400"
hwChan="735" # @USE: unique device channel that is reading from electrode
/>
</SpikeNTrode>
...
</SpikeConfiguration>
"""
# Find all channels/electrodes that belong to the current probe
contact_ids = []
device_channels = []
positions = np.zeros((n_active_channels, 2))

nt_i = 0 # Both probes are in sconf, so need an independent counter of probe electrodes while iterating through
channels_on = np.array([int(ch) for ch in active_channel_str.split(" ") if ch])
active_indices = np.nonzero(channels_on)[0]

# Build full catalogue probe and slice to active electrodes.
#
# The SpikeGadgets XML format does not include the probe part number, so we
# hardcode "NP1000" (standard Neuropixels 1.0). This is safe because the
# Bennu manual (Rev3, 2025) explicitly states support for "Neuropixels 1.0
# probes (every version except NHP) OR Neuropixels 2.0". The supported
# NP 1.0 variants (NP1000, NP1001, PRB_1_2_0480_2, PRB_1_4_0480_1,
# PRB_1_4_0480_1_C) share identical 2D geometry in the catalogue: same
# contact positions, pitch, stagger, shank width, tip length, shank length,
# contour, electrode count, and ADC/MUX tables. The only fields that differ
# are metadata (description, datasheet, is_commercial) and shank_thickness_um
# (Z-axis), none of which probeinterface uses.
#
# NP 2.0 support: the Bennu uses a different cone and firmware for 2.0
# probes, and the workspace creation step distinguishes "Neuropixels 1.0"
# from "Neuropixels 2.0". When .rec files from NP 2.0 recordings become
# available, this reader will need to detect the probe type (likely from
# the device name in the XML) and call build_neuropixels_probe with the
# appropriate part number.
full_probe = build_neuropixels_probe("NP1000")
probe = full_probe.get_slice(active_indices)

# Clear part-number-specific metadata since we don't know the actual part number.
probe.model_name = ""
probe.description = ""

# Parse SpikeNTrode elements to build the device channel mapping.
# Each SpikeNTrode has an id like "1384" where the first digit is the probe number
# and the remaining digits are the 1-based electrode number. The catalogue uses
# 0-based electrode indices, so catalogue_index = electrode_number - 1.
electrode_to_hwchan = {}
for ntrode in sconf:
electrode_id = ntrode.attrib["id"]
if int(electrode_id[0]) == curr_probe: # first digit of electrode id is probe number
contact_ids.append(electrode_id)
positions[nt_i, :] = (ntrode[0].attrib["coord_ml"], ntrode[0].attrib["coord_dv"])
device_channels.append(ntrode[0].attrib["hwChan"])
nt_i += 1
assert len(contact_ids) == n_active_channels

# Construct Probe object
probe = Probe(ndim=2, si_units="um", model_name="Neuropixels 1.0", manufacturer="IMEC")
probe.set_contacts(
contact_ids=contact_ids,
positions=positions,
shapes="square",
shank_ids=None,
shape_params={"width": CONTACT_WIDTH, "height": CONTACT_HEIGHT},
)
if int(electrode_id[0]) == curr_probe:
catalogue_index = int(electrode_id[1:]) - 1
hw_chan = int(ntrode[0].attrib["hwChan"])
electrode_to_hwchan[catalogue_index] = hw_chan

# Wire it (i.e., point contact/electrode ids to corresponding hardware/channel ids)
device_channels = np.array([electrode_to_hwchan[idx] for idx in active_indices])
probe.set_device_channel_indices(device_channels)

# Create a nice polygon background when plotting the probes
x_min = positions[:, 0].min()
x_max = positions[:, 0].max()
x_mid = 0.5 * (x_max + x_min)
y_min = positions[:, 1].min()
y_max = positions[:, 1].max()
polygon_default = [
(x_min - 20, y_min - CONTACT_HEIGHT / 2),
(x_mid, y_min - 100),
(x_max + 20, y_min - CONTACT_HEIGHT / 2),
(x_max + 20, y_max + 20),
(x_min - 20, y_max + 20),
]
probe.set_planar_contour(polygon_default)

# If there are multiple probes, they must be shifted such that they don't occupy the same coordinates.
# Shift multiple probes so they don't overlap when plotted
probe.move([250 * (curr_probe - 1), 0])

# Add the probe to the probe container
probe_group.add_probe(probe)

return probe_group
Expand Down
2 changes: 1 addition & 1 deletion tests/test_io/test_spikegadgets.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,7 @@ def test_neuropixels_1_reader():
for probe in probe_group.probes:
probe_dict = probe.to_dict(array_as_list=True)
validate_probe_dict(probe_dict)
assert "1.0" in probe.model_name
assert probe.model_name == ""
assert probe.get_shank_count() == 1
assert probe.get_contact_count() == 384
assert probe_group.get_contact_count() == 768
Expand Down
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