def spike_sorting_metrics(times,
clusters,
amps,
depths,
cluster_ids=None,
params=METRICS_PARAMS):
"""
Computes:
- cell level metrics (cf quick_unit_metrics)
- label the metrics according to quality thresholds
- estimates drift as a function of time
:param times: vector of spike times
:param clusters:
:param amplitudes:
:param depths:
:param cluster_ids (optional): set of clusters (if None the output datgrame will match
the unique set of clusters represented in spike clusters)
:param params: dict (optional) parameters for qc computation (
see constant at the top of the module for default values and keys)
:return: data_frame of metrics (cluster records, columns are qc attributes)|
:return: dictionary of recording qc (keys 'time_scale' and 'drift_um')
"""
# compute metrics and convert to `DataFrame`
df_units = quick_unit_metrics(clusters,
times,
amps,
depths,
cluster_ids=cluster_ids,
params=params)
df_units = pd.DataFrame(df_units)
# compute drift as a function of time and put in a dictionary
drift, ts = electrode_drift.estimate_drift(times, amps, depths)
rec_qc = {'time_scale': ts, 'drift_um': drift}
return df_units, rec_qc
def test_drift_estimate():
"""
From spike depths, xcorrelate drift maps to find a drift estimate
"""
np.random.seed(42)
ncells = 200
cells_depth = np.random.random(ncells) * 3800 + 50
frs = np.random.randn(ncells) * 50 + 200
t, a, c = multiple_spike_trains(firing_rates=frs, rec_len_secs=200)
# test negative times, no drift
drift, ts = electrode_drift.estimate_drift(t - 2, a, cells_depth[c])
assert(np.all(np.abs(drift) < 0.01))
# test drift recovery - sinusoid 40 um peak amplitude
dcor = np.sin(2 * np.pi * t / np.max(t) * 2) * 50
drift, ts = electrode_drift.estimate_drift(t, a, cells_depth[c] + dcor, display=False)
drift_ = np.sin(2 * np.pi * ts / np.max(t) * 2) * 50
# import matplotlib.pyplot as plt
# plt.plot(ts, drift_)
# plt.plot(ts, drift)
assert np.all(np.abs(drift - drift_)[2:] < 4)
one = ONE()
# Find sessions
dataset_types = ['spikes.times', 'spikes.amps', 'spikes.depths']
# eids = one.search(dataset_types=dataset_types,
# project='ibl_neuropixel_brainwide_01',
# task_protocol='_iblrig_tasks_ephysChoiceWorld')
#
# eid = eids[0] # Test with little drift: '7cdb71fb-928d-4eea-988f-0b655081f21c'
eid = '89f0d6ff-69f4-45bc-b89e-72868abb042a' # Test with huge drift
# Get dataset
spike_times, spike_amps, spike_depths = \
one.load(eid, dataset_types=dataset_types)
drift = estimate_drift(spike_times, spike_amps, spike_depths, display=False)
# PLOT
# Tight layout
fig3 = plt.figure(constrained_layout=True)
gs = fig3.add_gridspec(3, 3)
f3_ax0 = fig3.add_subplot(gs[0, :])
f3_ax0.plot(drift)
f3_ax1 = fig3.add_subplot(gs[1:, :])
bbplot.driftmap(spike_times, spike_depths, ax=f3_ax1, plot_style='bincount')
f3_ax0.set_xlim(f3_ax1.get_xlim())
"""
Download data and plot drift over the session
==============================================
Downloads LFP power spectrum for a given session and probe and plots a heatmap of power spectrum
on the channels along probe against frequency
"""
# import modules
from one.api import ONE
from brainbox.metrics import electrode_drift
# instantiate one
one = ONE(base_url='https://openalyx.internationalbrainlab.org', silent=True)
# Specify subject, date and probe we are interested in
subject = 'CSHL049'
date = '2020-01-08'
sess_no = 1
probe_label = 'probe00'
eid = one.search(subject=subject, date=date, number=sess_no)[0]
# Download and load the spikes data
spikes = one.load_object(eid, 'spikes', collection=f'alf/{probe_label}')
# Use brainbox function to compute drift over session
drift = electrode_drift.estimate_drift(spikes['times'],
spikes['amps'],
spikes['depths'],
display=True)
import matplotlib.pyplot as plt one = ONE() # Find sessions dsets = ['spikes.times.npy', 'spikes.amps.npy', 'spikes.depths.npy'] # eids = one.search(dataset=dsets, # project='ibl_neuropixel_brainwide_01', # task_protocol='_iblrig_tasks_ephysChoiceWorld') # # eid = eids[0] # Test with little drift: '7cdb71fb-928d-4eea-988f-0b655081f21c' eid = '89f0d6ff-69f4-45bc-b89e-72868abb042a' # Test with huge drift # Get dataset spikes = one.load_object(eid, 'spikes', collection='alf/probe00') drift = estimate_drift(spikes.times, spikes.amps, spikes.depths, display=False) # PLOT # Tight layout fig3 = plt.figure(constrained_layout=True) gs = fig3.add_gridspec(3, 3) f3_ax0 = fig3.add_subplot(gs[0, :]) f3_ax0.plot(drift) f3_ax1 = fig3.add_subplot(gs[1:, :]) bbplot.driftmap(spikes.times, spikes.depths, ax=f3_ax1, plot_style='bincount') f3_ax0.set_xlim(f3_ax1.get_xlim())
