metrics: change contamination name

brainbox/metrics/metrics.py

@@ -603,7 +603,7 @@ def ptp_over_noise(ephys_file, ts, ch, t=2.0, sr=30000, n_ch_probe=385, dtype='i
     return ptp_sigma
 
 
-def contamination(ts, rp=0.002):
+def contamination_ks2(ts, rp=0.002):
     """
     An estimate of the contamination of the unit (i.e. a pseudo false positive measure) based on
     the number of spikes, number of isi violations, and time between the first and last spike.
@@ -643,7 +643,7 @@ def contamination(ts, rp=0.002):
     return ce
 
 
-def contamination_alt(ts, min_time, max_time, rp=0.002, min_isi=0.0001):
+def contamination(ts, min_time, max_time, rp=0.002, min_isi=0.0001):
     """
     An estimate of the contamination of the unit (i.e. a pseudo false positive measure) based on
     the number of spikes, number of isi violations, and time between the first and last spike.
@@ -683,7 +683,7 @@ def contamination_alt(ts, min_time, max_time, rp=0.002, min_isi=0.0001):
     1) Compute contamination estimate for unit 1, with a minimum isi for counting duplicate
     spikes of 0.1 ms.
         >>> ts = units_b['times']['1']
-        >>> ce = bb.metrics.contamination_alt(ts, min_isi=0.0001)
+        >>> ce = bb.metrics.contamination_ks2(ts, min_isi=0.0001)
     """
 
     duplicate_spikes = np.where(np.diff(ts) <= min_isi)[0]
@@ -936,16 +936,16 @@ def quick_unit_metrics(spike_clusters, spike_times, spike_amps, spike_depths,
         'amp_median',
         'amp_std_dB',
         'contamination',
-        'contamination_alt',
+        'contamination_ks2',
         'drift',
         'frac_isi_viol',
         'missed_spikes_est',
         'noise_cutoff',
         'presence_ratio',
         'presence_ratio_std',
         'slidingRP_viol',
-        'spike_count',
-        ]
+        'spike_count'
+    ]
 
     r = Bunch({k: np.full((nclust,), np.nan) for k in metrics_list})
     r['cluster_id'] = cluster_ids
@@ -983,8 +983,8 @@ def quick_unit_metrics(spike_clusters, spike_times, spike_amps, spike_depths,
 
         # compute metrics
         r.frac_isi_viol[ic], _, _ = isi_viol(ts, rp=params['refractory_period'])
-        r.contamination[ic] = contamination(ts, rp=params['refractory_period'])
-        r.contamination_alt[ic], _ = contamination_alt(
+        r.contamination_ks2[ic] = contamination_ks2(ts, rp=params['refractory_period'])
+        r.contamination[ic], _ = contamination(
             ts, tmin, tmax, rp=params['refractory_period'], min_isi=params['min_isi'])
         r.slidingRP_viol[ic] = slidingRP_viol(ts,
                                               bin_size=params['bin_size'],

brainbox/tests/test_metrics.py

@@ -1,5 +1,4 @@
 import numpy as np
-import alf.io
 from brainbox.metrics import quick_unit_metrics
 
 REC_LEN_SECS = 1000
@@ -16,7 +15,7 @@ def multiple_spike_trains(firing_rates=None, rec_len_secs=1000, cluster_ids=None
     if firing_rates is None:
         firing_rates = np.random.randint(150, 600, 10)
     if cluster_ids is None:
-        clusters_ids = np.arange(firing_rates.size)
+        cluster_ids = np.arange(firing_rates.size)
     st = np.empty(0)
     sa = np.empty(0)
     sc = np.empty(0)
@@ -38,7 +37,7 @@ def single_spike_train(firing_rate=200, rec_len_secs=1000):
     Basic spike train generator following a poisson process for spike-times and
     :param firing_rate:
     :param rec_len_secs:
-    :return:
+    :return: spike_times (secs) , spike_amplitudes (V)
     """
 
     # spike times: exponential decay prob
@@ -47,21 +46,22 @@ def single_spike_train(firing_rate=200, rec_len_secs=1000):
 
     # spike amplitudes: log normal (estimated from an IBL session)
     nspi = np.size(st)
-    sa = np.exp(np.random.normal(5.5, 0.5, nspi)) / 1e6
+    sa = np.exp(np.random.normal(5.5, 0.5, nspi)) / 1e6  # output is in V
 
     return st, sa
 
 
 def test_clusters_metrics():
-    t, a, c = multiple_spike_trains(firing_rates=[3, 200, 259, 567], rec_len_secs=1000,
-                                    cluster_ids=[0, 1, 3, 4])
+    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)
 
-    # probe_path = "/datadisk/FlatIron/mainenlab/Subjects/ZFM-01577/2020-11-04/001/alf/probe00"
+    np.allclose(dfm['firing_rate'], frs)
+    # probe_path = "/datadisk/FlatIron/m1ainenlab/Subjects/ZFM-01577/2020-11-04/001/alf/probe00"
     # spikes = alf.io.load_object(probe_path, 'spikes')
     # quick_unit_metrics(spikes['clusters'], spikes['times'], spikes['amps'], spikes['depths'])

ibllib/ephys/ephysqc.py

@@ -241,7 +241,6 @@ def unit_metrics_ks2(ks2_path=None, m=None, save=True):
 
     # compute labels based on metrics
     df_labels = pd.DataFrame(unit_labels(m.spike_clusters, m.spike_times, m.amplitudes))
-    # add labels to metrics dataframe
     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