def test_ired_simple_for_coverage():
'''
'''
traj = pt.iterload(traj_dir, parm_dir)
h_indices = pt.select_atoms('@H', traj.top)
n_indices = pt.select_atoms('@H', traj.top) - 1
nh_indices = list(zip(n_indices, h_indices))
vecs_and_mat = pt.ired_vector_and_matrix(traj, nh_indices, dtype='tuple')
vecs_and_mat = pt.ired_vector_and_matrix(traj, nh_indices, dtype='tuple')
state_vecs = vecs_and_mat[0]
mat_ired = vecs_and_mat[1]
# get eigenvalues and eigenvectors
modes = pt.matrix.diagonalize(mat_ired, n_vecs=len(state_vecs))
evals, evecs = modes
data_0 = _ired(
state_vecs, modes=(evals, evecs), NHbond=True, tcorr=10000, tstep=1.)
data_1 = _ired(state_vecs, modes=modes, NHbond=True, tcorr=10000, tstep=1)
for d0, d1 in zip(data_0, data_1):
if d0.dtype not in [
'modes',
]:
aa_eq(d0.values, d1.values)
else:
# modes
# values: tuple
aa_eq(d0.values[0], d1.values[0])
aa_eq(d0.values[1], d1.values[1])
# try different dtype
out_try_new_dtype = pt.ired_vector_and_matrix(
traj, nh_indices, dtype='cpptraj_dataset')
def test_ired_need_lapack_cpptraj():
state = pt.load_cpptraj_state(txt)
state.run()
xyz = state.data['CRD1'].xyz
top = state.data['CRD1'].top
traj = pt.Trajectory(xyz=xyz, top=top)
state_vecs = state.data[1:-3].values
h_indices = pt.select_atoms('@H', traj.top)
n_indices = pt.select_atoms('@H', traj.top) - 1
nh_indices = list(zip(n_indices, h_indices))
mat_ired = pt.ired_vector_and_matrix(traj, mask=nh_indices, order=2)[-1]
mat_ired /= mat_ired[0, 0]
# matired: make sure to reproduce cpptraj output
aa_eq(mat_ired, state.data['matired'].values)
# get modes
modes = state.data[-2]
cpp_eigenvalues = modes.eigenvalues
cpp_eigenvectors = modes.eigenvectors
evals, evecs = np.linalg.eigh(mat_ired)
# need to sort a bit
evals = evals[::-1]
# cpptraj's eigvenvalues
aa_eq(evals, cpp_eigenvalues)
# cpptraj's eigvenvectors
# use absolute values to avoid flipped sign
# from Dan Roe
# In practice, the "sign" of an eigenvector depends on the math library used to calculate it.
# This is in fact why the modes command displacement test is disabled for cpptraj.
# I bet if you use a different math library (e.g. use your system BLAS/LAPACK instead of the one bundled with Amber
# or vice versa) you will get different signs.
# Bottom line is that eigenvector sign doesn't matter.
aa_eq(np.abs(evecs[:, ::-1].T), np.abs(cpp_eigenvectors), decimal=4)
data = _ired(state_vecs, modes=(cpp_eigenvectors, cpp_eigenvalues))
order_s2 = data['IRED_00127[S2]']
# load cpptraj's output and compare to pytraj' values for S2 order paramters
cpp_order_s2 = np.loadtxt(
os.path.join(cpptraj_test_dir, 'Test_IRED', 'orderparam.save')).T[-1]
aa_eq(order_s2, cpp_order_s2, decimal=5)