def test_multiply_conformations():
traj = structure.load_coor(ref_file('goldBenchMark.coor'))
n_samples = 150
otraj = structure.multiply_conformations(traj, n_samples, 0.1)
# iterate over x,y,z and check if any of the bins are more than 3 STD from the mean
for i in [0,1,2]:
h = np.histogram(otraj.xyz[:,0,i])[0]
cutoff = h.std() * 3.0 # chosen arbitrarily
deviations = np.abs(h - h.mean())
print deviations / h.std()
if np.any( deviations > cutoff ):
raise RuntimeError('Highly unlikely centers of mass are randomly '
'distributed in space. Test is stochastic, though, so'
' try running again to make sure you didn\'t hit a '
'statistical anomaly')
def test_multiply_conformations():
traj = structure.load_coor(ref_file('goldBenchMark.coor'))
n_samples = 150
otraj = structure.multiply_conformations(traj, n_samples, 0.1)
# iterate over x,y,z and check if any of the bins are more than 3 STD from the mean
for i in [0,1,2]:
h = np.histogram(otraj.xyz[:,0,i])[0]
cutoff = h.std() * 3.0 # chosen arbitrarily
deviations = np.abs(h - h.mean())
print deviations / h.std()
if np.any( deviations > cutoff ):
raise RuntimeError('Highly unlikely centers of mass are randomly '
'distributed in space. Test is stochastic, though, so'
' try running again to make sure you didn\'t hit a '
'statistical anomaly')
def test_m_confs():
# smoke test
t = trajectory.load( ref_file('ala2.pdb') )
m = structure.multiply_conformations(t, 10, 1.0)
def test_m_confs():
# smoke test
t = trajectory.load( ref_file('ala2.pdb') )
m = structure.multiply_conformations(t, 10, 1.0)
