def test_segmetrics(self):
"""The 'segmetrics' command."""
cnarr = cnvlib.read("formats/amplicon.cnr")
segarr = cnvlib.read("formats/amplicon.cns")
for func in (
lambda x: metrics.confidence_interval_bootstrap(x, 0.05, 100),
lambda x: metrics.prediction_interval(x, 0.05),
):
lo, hi = commands._segmetric_interval(segarr, cnarr, func)
self.assertEqual(len(lo), len(segarr))
self.assertEqual(len(hi), len(segarr))
sensible_segs_mask = (np.asarray(segarr['probes']) > 3)
means = segarr[sensible_segs_mask, 'log2']
los = lo[sensible_segs_mask]
his = hi[sensible_segs_mask]
self.assertTrue((los < means).all())
self.assertTrue((means < his).all())
def test_segmetrics(self):
"""The 'segmetrics' command."""
cnarr = cnvlib.read("formats/amplicon.cnr")
segarr = cnvlib.read("formats/amplicon.cns")
for func in (
lambda x: metrics.confidence_interval_bootstrap(x, 0.05, 100),
lambda x: metrics.prediction_interval(x, 0.05),
):
lo, hi = commands._segmetric_interval(segarr, cnarr, func)
self.assertEqual(len(lo), len(segarr))
self.assertEqual(len(hi), len(segarr))
sensible_segs_mask = (np.asarray(segarr['probes']) > 3)
means = segarr[sensible_segs_mask, 'log2']
los = lo[sensible_segs_mask]
his = hi[sensible_segs_mask]
self.assertTrue((los < means).all())
self.assertTrue((means < his).all())