def test_fix_edge(self):
"""Test the 'edge' bias correction calculations."""
# With no gap, gain and loss should balance out
# 1. Wide target, no secondary corrections triggered
insert_size = 250
gap_size = np.zeros(1) # Adjacent
target_size = np.asarray([600])
loss = fix.edge_losses(target_size, insert_size)
gain = fix.edge_gains(target_size, gap_size, insert_size)
gain *= 2 # Same on the other side
self.assertAlmostEqual(loss, gain)
# 2. Trigger 'loss' correction (target_size < 2 * insert_size)
target_size = np.asarray([450])
self.assertAlmostEqual(fix.edge_losses(target_size, insert_size),
2 * fix.edge_gains(target_size, gap_size, insert_size))
# 3. Trigger 'gain' correction (target_size + gap_size < insert_size)
target_size = np.asarray([300])
self.assertAlmostEqual(fix.edge_losses(target_size, insert_size),
2 * fix.edge_gains(target_size, gap_size, insert_size))
def test_fix_edge(self):
"""Test the 'edge' bias correction calculations."""
# With no gap, gain and loss should balance out
# 1. Wide target, no secondary corrections triggered
insert_size = 250
gap_size = np.zeros(1) # Adjacent
target_size = np.asarray([600])
loss = fix.edge_losses(target_size, insert_size)
gain = fix.edge_gains(target_size, gap_size, insert_size)
gain *= 2 # Same on the other side
self.assertAlmostEqual(loss, gain)
# 2. Trigger 'loss' correction (target_size < 2 * insert_size)
target_size = np.asarray([450])
self.assertAlmostEqual(fix.edge_losses(target_size, insert_size),
2 * fix.edge_gains(target_size, gap_size, insert_size))
# 3. Trigger 'gain' correction (target_size + gap_size < insert_size)
target_size = np.asarray([300])
self.assertAlmostEqual(fix.edge_losses(target_size, insert_size),
2 * fix.edge_gains(target_size, gap_size, insert_size))