def test_raise(self):
# if func is not support pmap
self.assertRaises(ValueError, lambda: pt.pmap(pt.bfactors, self.traj))
# run time: openmp vs pmap
if 'OPENMP' in pt.compiled_info():
self.assertRaises(RuntimeError,
lambda: pt.pmap(pt.watershell, self.traj))
# if traj is not TrajectoryIterator
self.assertRaises(ValueError, lambda: pt.pmap(pt.radgyr, self.traj[:]))
# raise if a given method does not support pmap
def need_to_raise(traj=self.traj):
pt.pmap(2, pt.bfactors, traj)
self.assertRaises(ValueError, lambda: need_to_raise())
# raise if a traj is not TrajectoryIterator
def need_to_raise_2(traj=self.traj):
pt.pmap(pt.bfactors, traj[:], n_cores=2)
# raise if turn off pmap by setting _is_parallelizable to False
pt.radgyr._is_parallelizable = False
self.assertRaises(ValueError, lambda: pt.pmap(pt.radgyr, self.traj))
pt.radgyr._is_parallelizable = True
def test_raise(self):
# if func is not support pmap
self.assertRaises(ValueError, lambda: pt.pmap(pt.bfactors, self.traj))
# run time: openmp vs pmap
if 'OPENMP' in pt.compiled_info():
self.assertRaises(RuntimeError,
lambda: pt.pmap(pt.watershell, self.traj))
# if traj is not TrajectoryIterator
self.assertRaises(ValueError, lambda: pt.pmap(pt.radgyr, self.traj[:]))
# raise if a given method does not support pmap
def need_to_raise(traj=self.traj):
pt.pmap(2, pt.bfactors, traj)
self.assertRaises(ValueError, lambda: need_to_raise())
# raise if a traj is not TrajectoryIterator
def need_to_raise_2(traj=self.traj):
pt.pmap(pt.bfactors, traj[:], n_cores=2)
# raise if turn off pmap by setting _is_parallelizable to False
pt.radgyr._is_parallelizable = False
self.assertRaises(ValueError, lambda: pt.pmap(pt.radgyr, self.traj))
pt.radgyr._is_parallelizable = True
class TestNHOrderParamters(unittest.TestCase):
@unittest.skipIf('DNO_MATHLIB' in pt.compiled_info(), 'there is no LAPACK')
def test_nh_paramters(self):
parmfile = cpptraj_test_dir + '/Test_IRED/1IEE_A_prot.prmtop'
trajfile = cpptraj_test_dir + '/Test_IRED/1IEE_A_test.mdcrd'
traj = pt.iterload(trajfile, parmfile)
h_indices = pt.select_atoms('@H', traj.top)
n_indices = h_indices - 1
nh_indices = list(zip(n_indices, h_indices))
# single core
orders = pt.NH_order_parameters(traj, nh_indices, tcorr=8000.)
saved_S2 = np.loadtxt(cpptraj_test_dir +
'/Test_IRED/orderparam.save').T[-1]
aa_eq(orders, saved_S2)
# multiple core
# default 2
orders = pt.pmap(pt.NH_order_parameters, traj, nh_indices, tcorr=8000.)
aa_eq(orders, saved_S2)
for n_cores in [1, 2, 3, 4, -1]:
orders = pt.NH_order_parameters(traj,
nh_indices,
tcorr=8000.,
n_cores=n_cores)
aa_eq(orders, saved_S2)
orders = pt.pmap(pt.NH_order_parameters,
traj,
nh_indices,
tcorr=8000.,
n_cores=n_cores)
aa_eq(orders, saved_S2)
class TestIred(unittest.TestCase):
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')
@unittest.skipIf('DNO_MATHLIB' in pt.compiled_info(), 'there is no LAPACK')
# TODO: how can I get order paramters?
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)
@unittest.skip('do not test now, get nan in some runs')
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)
average crdset AVG
run
# Step two. RMS-Fit to average structure. Calculate covariance matrix.
# Save the fit coordinates.
rms ref AVG !@H=
matrix covar name MyMatrix !@H=
createcrd CRD1
run
# Step three. Diagonalize matrix.
runanalysis diagmatrix MyMatrix vecs 2 name MyEvecs
# Step four. Project saved fit coordinates along eigenvectors 1 and 2
crdaction CRD1 projection evecs MyEvecs !@H= out project.dat beg 1 end 2
'''
@unittest.skipIf('DNO_MATHLIB' in pt.compiled_info(), 'there is no LAPACK')
class TestCpptrajDatasetWithMathLib(unittest.TestCase):
def setUp(self):
self.state = pt.datafiles.load_cpptraj_state(txt)
self.state.run()
self.traj = pt.iterload('data/tz2.nc', 'data/tz2.parm7')
def test_call_values(self):
for d in self.state.data:
d.values
def test_dataset_coords_ref(self):
traj = pt.iterload('data/tz2.nc', 'data/tz2.parm7')
avg_frame = pt.mean_structure(traj(rmsfit=(0, '!@H=')))
state = self.state
average crdset AVG
run
# Step two. RMS-Fit to average structure. Calculate covariance matrix.
# Save the fit coordinates.
rms ref AVG !@H=
matrix covar name MyMatrix !@H=
createcrd CRD1
run
# Step three. Diagonalize matrix.
runanalysis diagmatrix MyMatrix vecs 2 name MyEvecs
# Step four. Project saved fit coordinates along eigenvectors 1 and 2
crdaction CRD1 projection evecs MyEvecs !@H= out project.dat beg 1 end 2
'''
@unittest.skipIf('DNO_MATHLIB' in pt.compiled_info(), 'there is no LAPACK')
class TestCpptrajDatasetWithMathLib(unittest.TestCase):
def setUp(self):
self.state = pt.datafiles.load_cpptraj_state(txt)
self.state.run()
self.traj = pt.iterload('data/tz2.nc', 'data/tz2.parm7')
def test_call_values(self):
for d in self.state.data:
d.values
def test_dataset_coords_ref(self):
traj = pt.iterload('data/tz2.nc', 'data/tz2.parm7')
avg_frame = pt.mean_structure(traj(rmsfit=(0, '!@H=')))
state = self.state
class TestDiagMatrix(unittest.TestCase):
@unittest.skipIf('DNO_MATHLIB' in pt.compiled_info(), 'there is no LAPACK')
def test_diagmatrix(self):
traj = pt.iterload("./data/tz2.nc", "./data/tz2.parm7")
state = pt.load_batch(traj, '''
#matrix covar @CA name mymat
matrix dist @CA name mymat
diagmatrix mymat vecs 6''')
state.run()
mat = state.data['mymat']
cpp_evals, cpp_evecs = state.data[-1].eigenvalues, state.data[
-1].eigenvectors
# test triu_indices
mat2 = mat.__class__()
indices = np.triu_indices(mat.n_cols)
mat2._set_data_half_matrix(mat._to_cpptraj_sparse_matrix(), mat.size,
mat.n_cols)
# OK
data = matrix.diagonalize(mat.values, n_vecs=6, dtype='dataset')[-1]
data2 = matrix.diagonalize(mat, n_vecs=6, dtype='dataset')[0]
aa_eq(data.eigenvalues, cpp_evals, decimal=10)
aa_eq(data2.eigenvalues, cpp_evals, decimal=10)
# try numpy
np_vals, np_vecs = np.linalg.eigh(mat2.values, UPLO='U')
np_vals = np_vals[::-1][:6]
np_vecs = np_vecs[:, ::-1].T[:6]
# at least the absolute values are equal
aa_eq(np.abs(data.eigenvectors), np.abs(cpp_evecs))
aa_eq(np.abs(np_vecs), np.abs(cpp_evecs))
# test raise if not having supported dtype
self.assertRaises(ValueError, lambda: pt.matrix.diagonalize(mat, 3, dtype='ndarray'))
# plot
def plot_():
try:
from pytraj.plot import plot_matrix
from matplotlib import pyplot as plt
fig, ax, bar = plot_matrix(np.abs(data.eigenvectors) - np.abs(
cpp_evecs))
fig.colorbar(bar)
plt.title('data vs cpp_evecs')
fig, ax, bar = plot_matrix(np.abs(np_vecs) - np.abs(cpp_evecs))
fig.colorbar(bar)
plt.title('np vs cpp_evecs')
fig, ax, bar = plot_matrix(np.abs(np_vecs) - np.abs(
data.eigenvectors))
fig.colorbar(bar)
plt.title('np vs data')
pt.show()
except ImportError:
pass
