def test_do_rotation(self):
traj = pt.iterload("./data/Tc5b.x", "./data/Tc5b.top")
cm = '''
principal * dorotation mass name pout
createcrd myname
'''
state = pt.load_cpptraj_state(cm, traj)
state.run()
mut_traj_0 = traj[:]
mut_traj_1 = traj[:]
data = pt.principal_axes(mut_traj_0, mask='*', dorotation=True, mass=True)
pt.align_principal_axis(mut_traj_1, mask='*', mass=True)
aa_eq(data[0], state.data[1].values)
aa_eq(data[1], state.data[2].values)
aa_eq(state.data[-1].xyz, mut_traj_0.xyz)
aa_eq(state.data[-1].xyz, mut_traj_1.xyz)
def test_align_principal_axis(self):
traj = pt.iterload(fn('tz2.ortho.nc'), fn('tz2.ortho.parm7'))
t0 = traj[:]
t1 = traj[:]
aa_eq(t0.align_principal_axis().xyz, pt.align_principal_axis(t1).xyz)
# compare to cpptraj
cm = '''
principal dorotation
createcrd mycrd
'''
state = pt.load_cpptraj_state(cm, traj)
state.run()
aa_eq(state.data['mycrd'].xyz, t1.xyz)
def test_align_principal_axis(self):
traj = pt.iterload("./data/tz2.ortho.nc", "./data/tz2.ortho.parm7")
t0 = traj[:]
t1 = traj[:]
aa_eq(t0.align_principal_axis().xyz, pt.align_principal_axis(t1).xyz)
# compare to cpptraj
cm = '''
principal dorotation
createcrd mycrd
'''
state = pt.load_cpptraj_state(cm, traj)
state.run()
aa_eq(state.data['mycrd'].xyz, t1.xyz)
def test_frame_indices_for_function(self):
traj = self.traj
pdict = pt.__dict__
funclist = list(
set(pdict[key] for key in dir(pt)
if hasattr(pdict[key], '_issuper_dispatched')))
frame_indices = [0, 2]
# remove 'calc_jcoupling' since does not have kfile on travis
# remove energy_decomposition since does not have sander
# remove center, why?
# remove search_neighbors, why? (got messup with Frame memory owner)
excluded_fn = [
jcoupling,
volmap,
center,
search_neighbors,
atomiccorr,
autoimage,
closest,
volume,
superpose,
randomize_ions,
check_structure,
align_principal_axis,
]
func_nu = [
calc_epsilon,
calc_alpha,
calc_zeta,
calc_beta,
calc_nu1,
calc_nu2,
calc_delta,
calc_chin,
calc_gamma,
]
# default mask, default ref
for func in funclist:
if func not in excluded_fn:
if func is pt.calc_multivector:
data_0 = func(
traj,
resrange='1-6',
names='C N',
frame_indices=frame_indices)
data_1 = func(
traj[frame_indices], resrange='1-6', names='C N')
elif func is pt.volmap:
# use water
data_0 = func(
self.traj_ortho,
mask=':WAT@O',
grid_spacing=(0.2, 0.2, 0.2),
centermask='!:1-13',
frame_indices=frame_indices)
data_1 = func(
self.traj_ortho[frame_indices],
mask=':WAT@O',
centermask='!:1-13',
grid_spacing=(0.2, 0.2, 0.2))
elif func in func_nu:
data_0 = func(self.traj_nu, frame_indices=frame_indices)
data_1 = func(self.traj_nu[frame_indices])
else:
data_0 = func(traj, frame_indices=frame_indices)
data_1 = func(traj[frame_indices])
if isinstance(data_0, np.ndarray):
aa_eq(data_0, data_1)
elif isinstance(data_0, pt.DatasetList):
for arr0, arr1 in zip(data_0, data_1):
# do each element in case we can not convert DatasetList to
# ndarray
if not isinstance(arr0[0], string_types):
aa_eq(arr0.values, arr1.values)
elif isinstance(data_0, DatasetHBond):
aa_eq(data_0.data.values, data_1.data.values)
elif isinstance(data_0, (list, tuple)):
# dssp
aa_eq(data_0[-1].values, data_1[-1].values)
else:
raise RuntimeError(
'must return ndarray or DatasetList or DatasetHBond')
# test excluded fns
aa_eq(
pt.atomiccorr(traj[frame_indices], '@CA'),
pt.atomiccorr(traj, '@CA', frame_indices=frame_indices))
# align_principal_axis
indices = [0, 3, 7]
t0 = self.traj[:]
t1 = self.traj[indices]
pt.align_principal_axis(t0, frame_indices=indices)
pt.align_principal_axis(t1)
aa_eq(t0[indices].xyz, t1.xyz)
# make sure that other frames are not affected
other_indices = list(set(range(10)) - set(indices))
aa_eq(self.traj[other_indices].xyz, t0[other_indices].xyz)
def test_frame_indices_for_function(self):
traj = self.traj
pdict = pt.__dict__
funclist = list(set(pdict[key]
for key in dir(pt)
if hasattr(pdict[key], '_issuper_dispatched')))
frame_indices = [0, 2]
# remove 'calc_jcoupling' since does not have kfile on travis
# remove energy_decomposition since does not have sander
# remove center, why?
# remove search_neighbors, why? (got messup with Frame memory owner)
excluded_fn = [calc_jcoupling, calc_volmap,
energy_decomposition, center, search_neighbors,
calc_atomiccorr, autoimage, closest,
calc_volume, superpose, randomize_ions,
check_structure,
align_principal_axis, ]
func_nu = [
calc_epsilon, calc_alpha, calc_zeta, calc_beta, calc_nu1, calc_nu2,
calc_delta, calc_chin,
calc_gamma, ]
# default mask, default ref
for func in funclist:
if func not in excluded_fn:
if func is pt.calc_multivector:
data_0 = func(traj,
resrange='1-6',
names='C N',
frame_indices=frame_indices)
data_1 = func(traj[frame_indices],
resrange='1-6',
names='C N')
elif func is pt.volmap:
# use water
data_0 = func(self.traj_ortho,
mask=':WAT@O',
grid_spacing=(0.2, 0.2, 0.2),
centermask='!:1-13',
frame_indices=frame_indices)
data_1 = func(self.traj_ortho[frame_indices],
mask=':WAT@O',
centermask='!:1-13',
grid_spacing=(0.2, 0.2, 0.2))
elif func in func_nu:
data_0 = func(self.traj_nu, frame_indices=frame_indices)
data_1 = func(self.traj_nu[frame_indices])
else:
data_0 = func(traj, frame_indices=frame_indices)
data_1 = func(traj[frame_indices])
if isinstance(data_0, np.ndarray):
aa_eq(data_0, data_1)
elif isinstance(data_0, pt.DatasetList):
for arr0, arr1 in zip(data_0, data_1):
# do each element in case we can not convert DatasetList to
# ndarray
if not isinstance(arr0[0], string_types):
aa_eq(arr0.values, arr1.values)
elif isinstance(data_0, DatasetHBond):
aa_eq(data_0.data.values, data_1.data.values)
elif isinstance(data_0, (list, tuple)):
# dssp
aa_eq(data_0[-1].values, data_1[-1].values)
else:
raise RuntimeError(
'must return ndarray or DatasetList or DatasetHBond')
# test excluded fns
aa_eq(pt.atomiccorr(traj[frame_indices], '@CA'),
pt.atomiccorr(traj, '@CA', frame_indices=frame_indices))
# align_principal_axis
indices = [0, 3, 7]
t0 = self.traj[:]
t1 = self.traj[indices]
pt.align_principal_axis(t0, frame_indices=indices)
pt.align_principal_axis(t1)
aa_eq(t0[indices].xyz, t1.xyz)
# make sure that other frames are not affected
other_indices = list(set(range(10)) - set(indices))
aa_eq(self.traj[other_indices].xyz, t0[other_indices].xyz)