# import alf.io as aio
# import brainbox as bb
from oneibl.one import ONE
import brainbox.io.one as bbone
one = ONE()
# --- Example session:
eid = 'aad23144-0e52-4eac-80c5-c4ee2decb198' # Example: repeated site
# Test with eid that does not have any probe planned/histology values in Alyx
# eid = 'da188f2c-553c-4e04-879b-c9ea2d1b9a93'
# ----- RECOMMENDED: Option 1 (default) ------
# 1. Get spikes, cluster (with brain regions assigned to clusters) and channels
spikes, clusters, channels = bbone.load_spike_sorting_with_channel(eid,
one=one)
del spikes, clusters, channels # Delete for the purpose of the example
# ---------------------------------------------
# 2. Get only spikes and clusters (without brain regions assigned to clusters)
# data separately from channels
# Use merger function to get channels information into clusters
# Adding feature x, y from default
spikes, clusters = bbone.load_spike_sorting(eid, one=one)
channels = bbone.load_channel_locations(eid, one=one)
keys = ['x', 'y']
clusters_brain = bbone.merge_clusters_channels(clusters,
channels,
keys_to_add_extra=keys)
del spikes, clusters, clusters_brain, channels # Delete for the purpose of the example
Downloads and loads in spikes, clusters and channels data for a given session. Data is returned
"""
from oneibl.one import ONE
import brainbox.io.one as bbone
one = ONE()
# Find eid of interest
eid = one.search(subject='CSH_ZAD_001', date='2020-01-14')[0]
##################################################################################################
# Example 1:
# Download spikes, clusters and channels data for all available probes for this session.
# The data for each probe is returned as a dict
spikes, clusters, channels = bbone.load_spike_sorting_with_channel(eid,
one=one)
print(spikes.keys())
print(spikes['probe00'].keys())
##################################################################################################
# Example 2:
# Download spikes, clusters and channels data for a single probe
spikes, clusters, channels = bbone.load_spike_sorting_with_channel(
eid, one=one, probe='probe00')
print(spikes.keys())
##################################################################################################
# Example 3:
# The default spikes and clusters datasets that are downloaded are '
# ['clusters.channels',
# 'clusters.depths',
from oneibl.one import ONE one = ONE() EID = '15f742e1-1043-45c9-9504-f1e8a53c1744' REGION = 'SNr' PROBE = 'probe01' PRE_TIME = 0.6 POST_TIME = -0.1 DECODER = 'bayes-multinomial' ITERATIONS = 1000 DATA_PATH, FIG_PATH, SAVE_PATH = paths() FIG_PATH = join(FIG_PATH, 'Decoding', 'Sessions', DECODER) # %% # Load in data spikes, clusters, channels = bbone.load_spike_sorting_with_channel(EID, aligned=True, one=one) ses_path = one.path_from_eid(EID) trials = alf.io.load_object(join(ses_path, 'alf'), 'trials') # Get trial vectors incl_trials = (trials.probabilityLeft == 0.8) | (trials.probabilityLeft == 0.2) trial_times = trials.stimOn_times[incl_trials] probability_left = trials.probabilityLeft[incl_trials] trial_blocks = (trials.probabilityLeft[incl_trials] == 0.2).astype(int) # Get clusters in this brain region region_clusters = combine_layers_cortex(clusters[PROBE]['acronym']) clusters_in_region = clusters[PROBE].metrics.cluster_id[region_clusters == REGION] # Select spikes and clusters spks_region = spikes[PROBE].times[np.isin(spikes[PROBE].clusters, clusters_in_region)]
import ibllib.atlas as atlas
from oneibl.one import ONE
import brainbox.io.one as bbone
import brainbox.plot as bbplot
# === Parameters section (edit) ===
ba = atlas.AllenAtlas(25)
one = ONE(base_url="https://alyx.internationalbrainlab.org")
eid = 'aad23144-0e52-4eac-80c5-c4ee2decb198'
prob_des = one.load(eid, dataset_types=['probes.description'])
n_probe = len(prob_des[0])
# Get information for the session
spikes, clusters, channels = bbone.load_spike_sorting_with_channel(
eid, one=one, dataset_types=['spikes.depths'])
# Loop over probes
for i_probe in range(0, n_probe):
# Get single probe trajectory
probe_label = prob_des[0][i_probe].get('label')
trajs = one.alyx.rest('trajectories',
'list',
session=eid,
provenance='Histology track',
probe=probe_label)
if len(trajs) == 0:
print(f"No histology recorded for probe {probe_label}")
continue
