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 missing clusters
dfm = quick_unit_metrics(c,
t,
a,
d,
cluster_ids=np.arange(5),
tbounds=[100, 900])
idf, _ = ismember(np.arange(5), cid)
_assertions(dfm, idf, np.arange(5))
def run_metrics(probe_path):
"""
Launch phy given an eid and probe name.
TODO calculate metrics and save as .tsvs to include in GUI when launching?
"""
# This is a first draft, no error handling and a draft dataset list.
probe_path = Path(probe_path)
if probe_path.name == 'pykilosort':
ses_path = probe_path.parent.parent.parent
probe_name = probe_path.parent.name
else:
ses_path = probe_path.parent.parent
probe_name = probe_path.name
ephys_file_dir = ses_path.joinpath('raw_ephys_data', probe_name)
raw_files = glob.glob(os.path.join(ephys_file_dir, '*ap.*bin'))
raw_file = [raw_files[0]] if raw_files else None
cluster_metrics_path = probe_path.joinpath('clusters_metrics.ibl.pqt')
if not cluster_metrics_path.exists():
print('computing metrics this may take a bit of time')
spikes = one.alf.io.load_object(probe_path, 'spikes',
attribute=['depths', 'times', 'amps', 'clusters'])
clusters = one.alf.io.load_object(probe_path, 'clusters', attribute=['channels',''])
r = quick_unit_metrics(spikes.clusters, spikes.times, spikes.amps, spikes.depths)
r = pd.DataFrame(r)
r.to_parquet(cluster_metrics_path)