def scatter_amp_depth_fr_plot(spike_amps,
spike_clusters,
spike_depths,
spike_times,
cmap='hot',
display=False):
"""
Prepare data for 2D scatter plot of cluster depth vs cluster amp with colour indicating cluster
firing rate
:param spike_amps:
:param spike_clusters:
:param spike_depths:
:param spike_times:
:param cmap:
:param display: generate figure
:return: ScatterPlot object, if display=True also returns matplotlib fig and ax objects
"""
cluster, cluster_depth, n_cluster = compute_cluster_average(
spike_clusters, spike_depths)
_, cluster_amp, _ = compute_cluster_average(spike_clusters, spike_amps)
cluster_amp = cluster_amp * 1e6
cluster_fr = n_cluster / np.max(spike_times)
data = ScatterPlot(x=cluster_amp, y=cluster_depth, c=cluster_fr, cmap=cmap)
data.set_xlim((0.9 * np.min(cluster_amp), 1.1 * np.max(cluster_amp)))
if display:
fig, ax = plot_scatter(data.convert2dict())
return data.convert2dict(), fig, ax
return data
def prepare_data(self, spikes, clusters, trials):
self.spikes = spikes
self.clusters = clusters
self.trials = trials
self.ids = np.unique(spikes.clusters)
self.ids_idx = np.arange(len(self.ids))
self.metrics = np.array(clusters.metrics.ks2_label[self.ids_idx])
self.colours = np.array(clusters.metrics.ks2_label[self.ids_idx])
self.colours[np.where(self.colours != 'good')[0]] = QtGui.QColor(
'#fdc086') # default for noise and nan
self.colours[np.where(
self.colours == 'good')[0]] = QtGui.QColor('#7fc97f')
self.locations = clusters.locations
_, self.depths, self.nspikes = compute_cluster_average(
spikes.clusters, spikes.depths)
self.depths[np.where(np.isnan(
self.depths))[0]] = self.clusters.depths[np.where(
np.isnan(self.depths))[0]] # use channel depth as default
_, self.amps, _ = compute_cluster_average(spikes.clusters, spikes.amps)
self.amps = self.amps * 1e6
self.sort_by_id = np.arange(len(self.ids))
self.sort_by_nspikes = np.argsort(self.nspikes)
self.sort_by_nspikes = self.sort_by_nspikes[::-1]
self.sort_by_good = np.append(
np.where(self.metrics == 'good')[0],
np.where(self.metrics == 'mua')[0])
self.n_trials = len(trials['contrastRight'])
def test_compute_cluster_averag(self):
# Create fake data for 3 clusters
clust1 = np.ones(40)
clust1_vals = np.ones(40) * 200
clust2 = 2 * np.ones(40)
clust2_vals = np.r_[np.ones(20) * 300, np.ones(20) * 500]
clust100 = 100 * np.ones(50)
clust100_vals = np.r_[np.ones(25) * 0.5, np.ones(25) * 1.0]
# Concatenate data for 3 clusters together
spike_clust = np.r_[clust1, clust2, clust100]
spike_val = np.r_[clust1_vals, clust2_vals, clust100_vals]
# Shuffle the data to make order random
ind = np.arange(len(spike_clust))
np.random.shuffle(ind)
spike_clust = spike_clust[ind]
spike_val = spike_val[ind]
# Make sure the data you have created is correct dimension
assert (len(spike_clust) == len(spike_val))
# Compute the average value across clusters
clust, avg_val, count = processing.compute_cluster_average(
spike_clust, spike_val)
# Check output is as expected
assert (np.all(clust == (1, 2, 100)))
assert (avg_val[0] == 200)
assert (avg_val[1] == 400)
assert (avg_val[2] == 0.75)
assert (np.all(count == (40, 40, 50)))
def scatter_amp_depth_fr_plot(spike_amps,
spike_clusters,
spike_depths,
spike_times,
cmap='hot',
display=False,
title=None,
**kwargs):
"""
Prepare data for 2D scatter plot of cluster depth vs cluster amp with colour indicating cluster
firing rate
:param spike_amps:
:param spike_clusters:
:param spike_depths:
:param spike_times:
:param cmap:
:param display: generate figure
:return: ScatterPlot object, if display=True also returns matplotlib fig and ax objects
"""
title = title or 'Cluster depth vs amp vs firing rate'
cluster, cluster_depth, n_cluster = compute_cluster_average(
spike_clusters, spike_depths)
_, cluster_amp, _ = compute_cluster_average(spike_clusters, spike_amps)
cluster_amp = cluster_amp * 1e6
cluster_fr = n_cluster / np.max(spike_times)
data = ScatterPlot(x=cluster_amp, y=cluster_depth, c=cluster_fr, cmap=cmap)
data.set_xlim((0.9 * np.min(cluster_amp), 1.1 * np.max(cluster_amp)))
data.set_labels(title=title,
xlabel='Cluster Amplitude (uV)',
ylabel='Distance from probe tip (um)',
clabel='Firing rate (Hz)')
if display:
ax, fig = plot_scatter(data.convert2dict(), **kwargs)
return data.convert2dict(), fig, ax
return data
def __init__(self, eid, probe, one=None, spike_collection=None):
one = one or ONE()
if spike_collection == '':
collection = f'alf/{probe}'
elif spike_collection:
collection = f'alf/{probe}/{spike_collection}'
else:
# Pykilosort is default, if not present look for normal kilosort
all_collections = one.list_collections(eid)
if f'alf/{probe}/pykilosort' in all_collections:
collection = f'alf/{probe}/pykilosort'
else:
collection = f'alf/{probe}'
try:
self.spikes = one.load_object(eid, obj='spikes', collection=collection,
attribute=['clusters', 'times', 'amps', 'depths'])
self.clusters = one.load_object(eid, obj='clusters', collection=collection,
attribute=['metrics', 'waveforms'])
except alf.exceptions.ALFObjectNotFound:
logger.error(f'Could not load spike sorting for session: {eid} and probe: {probe}, GUI'
f' will not work')
raise
# Get everything we need for the clusters
# need to get rid of nans in amps and depths
self.clusters.clust_ids, self.clusters.depths, n_spikes = \
compute_cluster_average(self.spikes.clusters[~np.isnan(self.spikes.depths)],
self.spikes.depths[~np.isnan(self.spikes.depths)])
_, self.clusters.amps, _ = compute_cluster_average(
self.spikes.clusters[~np.isnan(self.spikes.amps)],
self.spikes.amps[~np.isnan(self.spikes.amps)])
# If we don't have metrics file, we can't assign colours
if not any(self.clusters.get('metrics', [None])):
col = np.full((len(self.clusters.clust_ids)), colours['no metric'])
self.clusters.colours_ks = col
self.clusters.colours_ibl = col
self.clusters['KS good'] = np.arange(len(self.clusters.clust_ids))
self.clusters['IBL good'] = np.arange(len(self.clusters.clust_ids))
elif not any(self.clusters.metrics.get('ks2_label', [None])):
col = np.full((len(self.clusters.clust_ids)), colours['no metric'])
self.clusters.colours_ks = col
colours_ibl = np.array(self.clusters.metrics.ks2_label[self.clusters.clust_ids])
good_ibl = np.where(self.clusters.metrics.label[self.clusters.clust_ids] == 1)[0]
colours_ibl[good_ibl] = colours['IBL good']
bad_ibl = np.where(self.clusters.metrics.label[self.clusters.clust_ids] != 1)[0]
colours_ibl[bad_ibl] = colours['IBL bad']
self.clusters.colours_ibl = colours_ibl
self.clusters.metrics['amp_median'] *= 1e6
self.clusters['KS good'] = np.arange(len(self.clusters.clust_ids))
self.clusters['IBL good'] = np.r_[good_ibl, bad_ibl]
else:
# KS2 good mua colours
# Bug in the KS2 units, in some cases, the clusters that do not exist in
# spikes.clusters have not been filled with nans like the other features in metrics
# Signature of these is for last values in ks_label to be None
if len(np.where(self.clusters.metrics.ks2_label.values == None)[0]) > 0: # noqa
colours_ks = np.array(self.clusters.metrics.ks2_label
[:len(self.clusters.clust_ids)])
else:
colours_ks = np.array(self.clusters.metrics.ks2_label[self.clusters.clust_ids])
good_ks = np.where(colours_ks == 'good')[0]
colours_ks[good_ks] = colours['KS good']
mua_ks = np.where(colours_ks == 'mua')[0]
colours_ks[mua_ks] = colours['KS mua']
self.clusters.colours_ks = colours_ks
# IBL good bad colours
colours_ibl = np.array(self.clusters.metrics.ks2_label[self.clusters.clust_ids])
good_ibl = np.where(self.clusters.metrics.label[self.clusters.clust_ids] == 1)[0]
colours_ibl[good_ibl] = colours['IBL good']
bad_ibl = np.where(self.clusters.metrics.label[self.clusters.clust_ids] != 1)[0]
colours_ibl[bad_ibl] = colours['IBL bad']
self.clusters.colours_ibl = colours_ibl
self.clusters.metrics['amp_median'] *= 1e6
self.clusters['KS good'] = np.r_[good_ks, mua_ks]
self.clusters['IBL good'] = np.r_[good_ibl, bad_ibl]
# Some extra info for sorting clusters
self.clusters['ids'] = np.arange(len(self.clusters.clust_ids))
self.clusters['n spikes'] = np.argsort(n_spikes)[::-1]
# Get trial data
self.trials = one.load_object(eid, obj='trials', collection='alf')
self.n_trials = self.trials['probabilityLeft'].shape[0]
self.trial_events = [key for key in self.trials.keys() if 'time' in key]
# Get behaviour data
wheel = one.load_object(eid, obj='wheel', collection='alf')
dlc_left = one.load_object(eid, obj='leftCamera', collection='alf',
attribute=['times', 'dlc'])
dlc_right = one.load_object(eid, obj='rightCamera', collection='alf',
attribute=['times', 'dlc'])
(self.behav, self.behav_events,
self.dlc_aligned) = self.combine_behaviour_data(wheel, dlc_left, dlc_right)
self.sess_qc = self.get_qc_info(eid, one)
self.spikes_raster = Bunch()
self.spikes_raster_psth = Bunch()
self.behav_raster = Bunch()
self.behav_raster_psth = Bunch()