def test_clusters_metrics():
np.random.seed(54)
rec_length = 1000
frs = np.array([3, 100, 80, 40]) # firing rates
cid = [0, 1, 3, 4] # here we make sure one of the clusters has no spike
t, a, c = multiple_spike_trains(firing_rates=frs,
rec_len_secs=rec_length,
cluster_ids=cid)
d = np.sin(2 * np.pi * c / rec_length *
t) * 100 # sinusoidal shift where cluster id drives f
def _assertions(dfm, idf, target_cid):
# dfm: qc dataframe, idf: indices of existing clusters in dfm, cid: cluster ids
assert np.allclose(dfm['amp_median'][idf] / np.exp(5.5) * 1e6,
1,
rtol=1.1)
assert np.allclose(dfm['amp_std_dB'][idf] / 20 * np.log10(np.exp(0.5)),
1,
rtol=1.1)
assert np.allclose(dfm['drift'][idf],
np.array(cid) * 100 * 4 * 3.6,
rtol=1.1)
assert np.allclose(dfm['firing_rate'][idf], frs, rtol=1.1)
assert np.allclose(dfm['cluster_id'], target_cid)
# # check with straight indexing
# dfm = quick_unit_metrics(c, t, a, d)
# _assertions(dfm, np.arange(4), cid)
# check with missing clusters
dfm = quick_unit_metrics(c, t, a, d, cluster_ids=np.arange(5))
idf, _ = ismember(np.arange(5), cid)
_assertions(dfm, idf, np.arange(5))
def unit_metrics_ks2(ks2_path=None, m=None, save=True):
"""
Given a path containing kilosort 2 output, compute quality metrics and optionally save them
to a clusters_metric.csv file
:param ks2_path:
:param save
:return:
"""
# ensure that either a ks2_path or a phylib `TemplateModel` object with unit info is given
assert not(ks2_path is None and m is None), 'Must either specify a path to a ks2 output ' \
'directory, or a phylib `TemplateModel` object'
# create phylib `TemplateModel` if not given
m = phy_model_from_ks2_path(ks2_path) if None else m
# compute metrics and convert to `DataFrame`
r = pd.DataFrame(
quick_unit_metrics(m.spike_clusters, m.spike_times, m.amplitudes,
m.depths))
# TODO compute drift as a function of time here
# TODO compute metrics using sample waveforms here
# compute labels based on metrics
df_labels = pd.DataFrame(
unit_labels(m.spike_clusters, m.spike_times, m.amplitudes))
r = r.set_index('cluster_id',
drop=False).join(df_labels.set_index('cluster_id'))
# include the ks2 cluster contamination if `cluster_ContamPct` file exists
file_contamination = ks2_path.joinpath('cluster_ContamPct.tsv')
if file_contamination.exists():
contam = pd.read_csv(file_contamination, sep='\t')
contam.rename(columns={'ContamPct': 'ks2_contamination_pct'},
inplace=True)
r = r.set_index('cluster_id',
drop=False).join(contam.set_index('cluster_id'))
# include the ks2 cluster labels if `cluster_KSLabel` file exists
file_labels = ks2_path.joinpath('cluster_KSLabel.tsv')
if file_labels.exists():
ks2_labels = pd.read_csv(file_labels, sep='\t')
ks2_labels.rename(columns={'KSLabel': 'ks2_label'}, inplace=True)
r = r.set_index('cluster_id',
drop=False).join(ks2_labels.set_index('cluster_id'))
if save:
# the file name contains the label of the probe (directory name in this case)
r.to_csv(ks2_path.joinpath('cluster_metrics.csv'))
return r
def test_clusters_metrics():
frs = [3, 200, 259, 567] # firing rates
t, a, c = multiple_spike_trains(firing_rates=frs,
rec_len_secs=1000,
cluster_ids=[0, 1, 3, 4])
d = np.sin(2 * np.pi * c / 1000 *
t) * 100 # sinusoidal shift where cluster id drives period
dfm = quick_unit_metrics(c, t, a, d)
assert np.allclose(dfm['amp_median'] / np.exp(5.5) * 1e6, 1, rtol=1.1)
assert np.allclose(dfm['amp_std_dB'] / 20 * np.log10(np.exp(0.5)),
1,
rtol=1.1)
assert np.allclose(dfm['drift'],
np.array([0, 1, 3, 4]) * 100 * 4 * 3.6,
rtol=1.1)
np.allclose(dfm['firing_rate'], frs)