def load_channel_locations(eid, one=None, probe=None):
"""
From an eid, get brain locations from Alyx database
analysis.
:param eid: session eid or dictionary returned by one.alyx.rest('sessions', 'read', id=eid)
:param dataset_types: additional spikes/clusters objects to add to the standard list
:return: channels
"""
if isinstance(eid, dict):
ses = eid
eid = ses['url'][-36:]
else:
# need to query alyx. Make sure we have a one client before we hit the endpoint
if not one:
one = ONE()
ses = one.alyx.rest('sessions', 'read', id=eid)
if isinstance(probe, str):
probe = [probe]
labels = probe if probe else [pi['name'] for pi in ses['probe_insertion']]
channels = Bunch({})
for label in labels:
i = [
i for i, pi in enumerate(ses['probe_insertion'])
if pi['name'] == label
]
if len(i) == 0:
continue
trajs = ses['probe_insertion'][i[0]]['trajectory_estimate']
if not trajs:
continue
# the trajectories are ordered within the serializer: histology processed, histology,
# micro manipulator, planned so the first is always the desired one
traj = trajs[0]
channels[label] = Bunch({
'atlas_id':
np.array([ch['brain_region']['id'] for ch in traj['channels']]),
'acronym':
np.array(
[ch['brain_region']['acronym'] for ch in traj['channels']]),
'x':
np.array([ch['x'] for ch in traj['channels']]) / 1e6,
'y':
np.array([ch['y'] for ch in traj['channels']]) / 1e6,
'z':
np.array([ch['z'] for ch in traj['channels']]) / 1e6,
'axial_um':
np.array([ch['axial'] for ch in traj['channels']]),
'lateral_um':
np.array([ch['lateral'] for ch in traj['channels']])
})
# Check that channels mapping matches coordinate on Flatiron
channel_coord = one.load_dataset(
eid=eid, dataset_type='channels.localCoordinates')
if len(traj['channels']) == 0:
continue
assert np.all(np.c_[channels[label]['lateral_um'],
channels[label]['axial_um']] == channel_coord)
return channels
"""
import numpy as np
from brainbox.ephys_plots import scatter_raster_plot
from brainbox.plot_base import plot_scatter
from oneibl.one import ONE
import matplotlib.pyplot as plt
import matplotlib
one = ONE()
eid = '671c7ea7-6726-4fbe-adeb-f89c2c8e489b'
probe = 'probe00'
spikes = one.load_object(eid, obj='spikes', collection=f'alf/{probe}')
metrics = one.load_dataset(eid, dataset='clusters.metrics', collection=f'alf/{probe}')
# Find the clusters that have been labelled as good and their corresponding spike indices
good_clusters = np.where(metrics.label == 1)
spike_idx = np.where(np.isin(spikes['clusters'], good_clusters))[0]
# Also filter for nans in amplitude and depth
kp_idx = spike_idx[np.where(~np.isnan(spikes['depths'][spike_idx])
& ~np.isnan(spikes['amps'][spike_idx]))[0]]
# Get ScatterPlot object
data = scatter_raster_plot(spikes['amps'][kp_idx], spikes['depths'][kp_idx],
spikes['times'][kp_idx])
# Add v lines 10s after start and 10s before end or recording
x1 = np.min(spikes['times'][kp_idx] + 100)
