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_vector_and_matrix_pmap(self):
traj = pt.iterload(fn('tz2.nc'), fn('tz2.parm7'))
h = traj.top.select('@H')
n = h - 1
nh = list(zip(n, h))
exptected_vecs, exptected_mat = pt.ired_vector_and_matrix(traj, nh)
for n_cores in [2, 3]:
vecs, mat = pt.pmap(
pt.ired_vector_and_matrix, traj, nh, n_cores=n_cores)
aa_eq(exptected_vecs, vecs, decimal=7)
aa_eq(exptected_mat, mat, decimal=7)
def test_simple_for_coverage(self):
'''
'''
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_vector_and_matrix_pmap(self):
traj = pt.iterload("data/tz2.nc", "data/tz2.parm7")
h = traj.top.select('@H')
n = h - 1
nh = list(zip(n, h))
exptected_vecs, exptected_mat = pt.ired_vector_and_matrix(traj, nh)
for n_cores in [2, 3]:
vecs, mat = pt.pmap(pt.ired_vector_and_matrix,
traj,
nh,
n_cores=n_cores)
aa_eq(exptected_vecs, vecs, decimal=7)
aa_eq(exptected_mat, mat, decimal=7)
def test_ired_need_lapack_cpptraj(self):
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_eigenvalues, cpp_eigenvectors))
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)
def NH_order_paramters(traj, vector_pairs, order=2, tstep=1., tcorr=10000.):
'''the results seem ok.
'''
# compute N-H vectors and ired matrix
vecs_and_mat = pt.ired_vector_and_matrix(traj, vector_pairs, order=order)
state_vecs = vecs_and_mat[:-1].values
mat_ired = vecs_and_mat[-1]
mat_ired /= mat_ired[0, 0]
# get eigenvalues and eigenvectors
modes = pt.matrix.diagonalize(mat_ired, n_vecs=len(state_vecs))[0]
evals, evecs = modes.eigenvalues, modes.eigenvectors
data = pt._ired(state_vecs, modes=(evals, evecs), tcorr=tcorr, tstep=tstep)
order = [d.values.copy() for d in data if 'S2' in d.key][0]
return (order)
def test_ired_lapack_in_numpy(self):
parmfile = parm_dir
trajfile = traj_dir
# load to TrajectoryIterator
traj = pt.iterload(trajfile, parmfile)
# create N-H vectors
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))
# compute N-H vectors and ired matrix
vecs_and_mat = pt.ired_vector_and_matrix(traj,
mask=nh_indices,
order=2)
state_vecs = vecs_and_mat[:-1].values
mat_ired = vecs_and_mat[-1]
mat_ired /= mat_ired[0, 0]
# cpptraj
data_cpp = pt.matrix.diagonalize(mat_ired, n_vecs=len(state_vecs))[0]
print(data_cpp.eigenvectors)
# numpy
data_np = pt.matrix._diag_np(mat_ired, n_vecs=len(state_vecs))
def order_(modes):
data = _ired(state_vecs, modes=modes)
order_s2_v0 = data['IRED_00127[S2]']
# make sure the S2 values is 1st array
# 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_v0.values, cpp_order_s2, decimal=4)
order_(data_cpp.values)
def plot_(x, y):
import seaborn as sb
sb.heatmap(x - y)
pt.show()
print((data_cpp.values[1] - data_np[1]).shape)
def test_ired_vector(self):
'''test mask as a list of strings or as a 2D array of integers
'''
parm_dir = os.path.join(cpptraj_test_dir, 'Test_IRED',
'1IEE_A_prot.prmtop')
trajin_dir = os.path.join(cpptraj_test_dir, 'Test_IRED',
'1IEE_A_test.mdcrd')
traj = pt.iterload(trajin_dir, parm_dir)
# get a list of mask from cpptraj input
maskes = []
lines = None
n_indices_cpp = []
with open('data/ired.in', 'r') as fh:
lines = fh.readlines()
for line in lines:
if 'vector' in line and 'ired' in line:
# example: vector v100 @1541 ired @1542
sline = line.split()
mask = ' '.join((sline[2], sline[4]))
n_indices_cpp.append(int(sline[2][1:]) - 1)
maskes.append(mask)
h_indices_cpp = [i + 1 for i in n_indices_cpp]
# calcuate vector from a list of strings
data_vec = va.vector_mask(traj, maskes)
# calcuate vector from a 2d array of integers
nh_indices = np.array(list(zip(n_indices_cpp, h_indices_cpp)))
data_vec_2 = va.vector_mask(traj, nh_indices)
# re-create cpptraj input to run cpptraj
txt = ''.join(lines)
# add parm and trajin lines
txt = 'parm ' + parm_dir + '\n' + \
'trajin ' + trajin_dir + '\n' + \
txt
state = pt.datafiles.load_cpptraj_output(txt, dtype='state')
state.run()
cpp_data = state.datasetlist
cpp_vectors = cpp_data.grep('vector', mode='dtype').values
cpp_matired = cpp_data.grep('matrix', mode='dtype')['matired']
# assert between pytraj's data_vec and cpptraj's cpp_vectors
aa_eq(data_vec, cpp_vectors)
# from a 2D array of integers
aa_eq(data_vec_2, cpp_vectors)
# test ired vector with ired matrix
# open file
with open('data/ired_reduced.in', 'r') as fh:
text = ''.join(fh.readlines())
state2 = pt.load_batch(traj, text)
state2.run()
data = pt.ired_vector_and_matrix(traj, nh_indices, order=2)
data_vec_3 = data[0]
assert len(data_vec_3) == 126, 'must have 126 vectors'
matired = data[1]
# TODO: know why??
matired /= matired[0, 0]
aa_eq(data_vec_3, cpp_vectors)
assert pt.tools.rmsd(matired.flatten(),
cpp_matired.values) < 1E-6, 'matired'
def normal_(traj=traj, nh=nh):
print('serial')
return pt.ired_vector_and_matrix(traj, nh)
def test_ired_vector(self):
'''test mask as a list of strings or as a 2D array of integers
'''
parm_dir = os.path.join(cpptraj_test_dir, 'Test_IRED',
'1IEE_A_prot.prmtop')
trajin_dir = os.path.join(cpptraj_test_dir, 'Test_IRED',
'1IEE_A_test.mdcrd')
traj = pt.iterload(trajin_dir, parm_dir)
# get a list of mask from cpptraj input
maskes = []
lines = None
n_indices_cpp = []
with open('data/ired.in', 'r') as fh:
lines = fh.readlines()
for line in lines:
if 'vector' in line and 'ired' in line:
# example: vector v100 @1541 ired @1542
sline = line.split()
mask = ' '.join((sline[2], sline[4]))
n_indices_cpp.append(int(sline[2][1:]) - 1)
maskes.append(mask)
h_indices_cpp = [i + 1 for i in n_indices_cpp]
# calcuate vector from a list of strings
data_vec = va.vector_mask(traj, maskes)
# calcuate vector from a 2d array of integers
nh_indices = np.array(list(zip(n_indices_cpp, h_indices_cpp)))
data_vec_2 = va.vector_mask(traj, nh_indices)
# re-create cpptraj input to run cpptraj
txt = ''.join(lines)
# add parm and trajin lines
txt = 'parm ' + parm_dir + '\n' + \
'trajin ' + trajin_dir + '\n' + \
txt
state = pt.datafiles.load_cpptraj_output(txt, dtype='state')
state.run()
cpp_data = state.datasetlist
cpp_vectors = cpp_data.grep('vector', mode='dtype').values
cpp_matired = cpp_data.grep('matrix', mode='dtype')['matired']
# assert between pytraj's data_vec and cpptraj's cpp_vectors
aa_eq(data_vec, cpp_vectors)
# from a 2D array of integers
aa_eq(data_vec_2, cpp_vectors)
# test ired vector with ired matrix
# open file
with open('data/ired_reduced.in', 'r') as fh:
text = ''.join(fh.readlines())
state2 = pt.load_batch(traj, text)
state2.run()
data = pt.ired_vector_and_matrix(traj, nh_indices, order=2)
data_vec_3 = data[0]
assert len(data_vec_3) == 126, 'must have 126 vectors'
matired = data[1]
# TODO: know why??
matired /= matired[0, 0]
aa_eq(data_vec_3, cpp_vectors)
assert pt.tools.rmsd(matired.flatten(),
cpp_matired.values) < 1E-6, 'matired'
