def test_timecorr():
with tempfolder():
# center of mass
trajin = """
parm {}
trajin {}
vector center v0
timecorr vec1 v0
""".format(tc5b_top, tc5b_trajin)
state = pt.load_cpptraj_state(trajin)
state.run()
cpptraj_output = state.data
traj = pt.iterload(tc5b_trajin, tc5b_top)
dslist0 = pt.center_of_mass(traj)
data = pt.timecorr(dslist0, dslist0)
aa_eq(data, cpptraj_output[-1].values)
# 2 vectors
cm = """
parm {}
trajin {}
vector v0 :2 :5
vector v1 :3 :7
timecorr vec1 v0 vec2 v1
""".format(tc5b_top, tc5b_trajin)
state = pt.load_cpptraj_state(cm)
state.run()
cpptraj_output = state.data
dslist0 = pt.vector.vector(traj, [':2 :5', ':3 :7'])
data = pt.timecorr(dslist0[0], dslist0[1])
aa_eq(data, cpptraj_output[-1].values)
# corrplane
cm = """
parm {}
trajin {}
vector v0 @2,@5,@9 corrplane
vector v1 @3,@7,@20 corrplane
timecorr vec1 v0 vec2 v1
""".format(tc5b_top, tc5b_trajin)
state = pt.load_cpptraj_state(cm)
state.run()
cpptraj_output = state.data
dslist0 = pt.vector.vector(traj,
['@2,@5,@9 corrplane', '@3,@7,@20 corrplane'])
dslist1 = pt.vector.corrplane(traj, ['@2,@5,@9', '@3,@7,@20'])
data0 = pt.timecorr(dslist0[0], dslist0[1])
data1 = pt.timecorr(dslist1[0], dslist1[1])
aa_eq(data0, cpptraj_output[-1].values)
aa_eq(data1, cpptraj_output[-1].values)
def test_superpose_different_mask_with_mass(self):
traj_on_disk = pt.iterload(fn('Tc5b.x'), fn('Tc5b.top'))
traj_on_mem = pt.load(fn('Tc5b.x'), fn('Tc5b.top'))
ref = pt.load(fn('tz2.nc'), fn('tz2.parm7'))[:1]
mask = ':3-12@CA'
ref_mask = ':1-10@CA'
traj_on_disk.superpose(mask=mask, ref=ref, ref_mask=ref_mask)
traj_on_mem.superpose(mask=mask, ref=ref, ref_mask=ref_mask)
aa_eq(traj_on_disk.xyz, traj_on_mem.xyz)
# cpptraj
cm = """
parm {parm_tc5b} [Tc5b]
trajin {tc5b} parm [Tc5b]
createcrd mycrd0
parm {parm_tz2} [tz2]
reference {tz2} 1 1 parm [tz2]
align reference {mask} {refmask}
createcrd mycrd
""".format(parm_tc5b=tc5b_top,
parm_tz2=fn('tz2.parm7'),
tz2=fn('tz2.nc'),
tc5b=tc5b_trajin,
mask=mask,
refmask=ref_mask)
state = pt.load_cpptraj_state(cm)
state.run()
cm2 = """
parm {parm_tc5b} [Tc5b]
trajin {tc5b} parm [Tc5b]
createcrd mycrd0
parm {parm_tz2} [tz2]
reference {tz2} 1 1 parm [tz2]
rms reference {mask} {refmask}
createcrd mycrd
""".format(parm_tc5b=tc5b_top,
parm_tz2=fn('tz2.parm7'),
tz2=fn('tz2.nc'),
tc5b=tc5b_trajin,
mask=mask,
refmask=ref_mask)
state2 = pt.load_cpptraj_state(cm2)
state2.run()
aa_eq(state.data['mycrd'].xyz, state2.data['mycrd'].xyz)
aa_eq(state.data['mycrd'].xyz, traj_on_disk.xyz)
aa_eq(state.data['mycrd'].xyz, traj_on_mem.xyz)
def test_volmap(self):
traj = pt.iterload("./data/tz2.ortho.nc", "./data/tz2.ortho.parm7")[:1]
size = ''
center = ''
state = pt.load_cpptraj_state(txt.format(cm, size, center))
state.run()
cpp_data = state.data[-1].values
traj = traj.superpose(mask=':1-13').center(':1-13 mass origin')
ds = pt.volmap(traj,
mask=':WAT@O',
grid_spacing=(0.5, 0.5, 0.5),
buffer=2.0,
centermask='!:1-13',
radscale=1.36)
aa_eq(cpp_data, ds)
# assert
self.assertRaises(AssertionError, lambda: pt.volmap(traj, mask=':WAT@O', grid_spacing='0.5 0.5 0.5'))
self.assertRaises(AssertionError, lambda: pt.volmap(traj, mask=':WAT@O', grid_spacing=(0.5, 0.5)))
self.assertRaises(ValueError, lambda: pt.volmap(traj, mask=':WAT@O', grid_spacing=(0.5, 0.5, 0.5), size='20 20 20'))
# test size
cm_no_buffer = cm.replace('buffer 2.0', '')
state = pt.load_cpptraj_state(txt.format(cm_no_buffer, 'size 20,20,20', ''))
state.run()
cpp_data = state.data[-1].values
ds = volmap(traj,
mask=':WAT@O',
grid_spacing=(0.5, 0.5, 0.5),
size=(20, 20, 20),
buffer=2.0,
centermask='!:1-13',
radscale=1.36)
aa_eq(cpp_data, ds)
# test center
state = pt.load_cpptraj_state(txt.format(cm_no_buffer, 'size 20,20,20', 'center 0.5,0.5,0.5'))
state.run()
cpp_data = state.data[-1].values
ds = volmap(traj,
mask=':WAT@O',
grid_spacing=(0.5, 0.5, 0.5),
size=(20, 20, 20),
center=(0.5, 0.5, 0.5),
buffer=2.0,
centermask='!:1-13',
radscale=1.36)
aa_eq(cpp_data, ds)
# raise RuntimeError
dry_traj = pt.iterload('data/tz2.nc', 'data/tz2.parm7')
self.assertRaises(RuntimeError, lambda: pt.volmap(dry_traj, mask=':WAT@O',
grid_spacing=(0.5, 0.5, 0.5)))
def test_0(self):
# center of mass
trajin = """
parm data/Tc5b.top
trajin data/Tc5b.x
vector center v0
timecorr vec1 v0
"""
state = pt.load_cpptraj_state(trajin)
state.run()
cpptraj_output = state.data
traj = pt.iterload("./data/Tc5b.x", "./data/Tc5b.top")
dslist0 = pt.center_of_mass(traj)
data = pt.timecorr(dslist0, dslist0)
aa_eq(data, cpptraj_output[-1].values)
# 2 vectors
cm = """
parm data/Tc5b.top
trajin data/Tc5b.x
vector v0 :2 :5
vector v1 :3 :7
timecorr vec1 v0 vec2 v1
"""
state = pt.load_cpptraj_state(cm)
state.run()
cpptraj_output = state.data
dslist0 = pt.calc_vector(traj, [':2 :5', ':3 :7'])
data = pt.timecorr(dslist0[0], dslist0[1])
aa_eq(data, cpptraj_output[-1].values)
# corrplane
cm = """
parm data/Tc5b.top
trajin data/Tc5b.x
vector v0 @2,@5,@9 corrplane
vector v1 @3,@7,@20 corrplane
timecorr vec1 v0 vec2 v1
"""
state = pt.load_cpptraj_state(cm)
state.run()
cpptraj_output = state.data
dslist0 = pt.calc_vector(traj,
['@2,@5,@9 corrplane', '@3,@7,@20 corrplane'])
dslist1 = pt.vector.corrplane(traj, ['@2,@5,@9', '@3,@7,@20'])
data0 = pt.timecorr(dslist0[0], dslist0[1])
data1 = pt.timecorr(dslist1[0], dslist1[1])
aa_eq(data0, cpptraj_output[-1].values)
aa_eq(data1, cpptraj_output[-1].values)
def test_0(self):
# center of mass
trajin = """
parm data/Tc5b.top
trajin data/Tc5b.x
vector center v0
timecorr vec1 v0
"""
state = pt.load_cpptraj_state(trajin)
state.run()
cpptraj_output = state.data
traj = pt.iterload("./data/Tc5b.x", "./data/Tc5b.top")
dslist0 = pt.center_of_mass(traj)
data = pt.timecorr(dslist0, dslist0)
aa_eq(data, cpptraj_output[-1].values)
# 2 vectors
cm = """
parm data/Tc5b.top
trajin data/Tc5b.x
vector v0 :2 :5
vector v1 :3 :7
timecorr vec1 v0 vec2 v1
"""
state = pt.load_cpptraj_state(cm)
state.run()
cpptraj_output = state.data
dslist0 = pt.calc_vector(traj, [':2 :5', ':3 :7'])
data = pt.timecorr(dslist0[0], dslist0[1])
aa_eq(data, cpptraj_output[-1].values)
# corrplane
cm = """
parm data/Tc5b.top
trajin data/Tc5b.x
vector v0 @2,@5,@9 corrplane
vector v1 @3,@7,@20 corrplane
timecorr vec1 v0 vec2 v1
"""
state = pt.load_cpptraj_state(cm)
state.run()
cpptraj_output = state.data
dslist0 = pt.calc_vector(traj,
['@2,@5,@9 corrplane', '@3,@7,@20 corrplane'])
dslist1 = pt.vector.corrplane(traj, ['@2,@5,@9', '@3,@7,@20'])
data0 = pt.timecorr(dslist0[0], dslist0[1])
data1 = pt.timecorr(dslist1[0], dslist1[1])
aa_eq(data0, cpptraj_output[-1].values)
aa_eq(data1, cpptraj_output[-1].values)
def test_pairdist():
traj = pt.iterload(fn("tz2.crd"), fn("tz2.parm7"))
for (mask, delta) in [('*', 0.1), ('@CA', '0.2')]:
data = pt.pairdist(traj, delta=delta, mask=mask, dtype='ndarray')
data0 = data[0].T
data1 = data[1].T
txt = '''
parm {0}
trajin {1}
pairdist out test.out mask {2} delta {3}
'''.format(fn('tz2.parm7'), fn('tz2.crd'), mask, delta)
# take data, skip DatasetTopology
state = pt.load_cpptraj_state(txt)
with tempfolder():
state.run()
cpp_data = state.data[1:].values
cpp_distance, cpp_Pr = cpp_data[0].T
_, cpp_std = cpp_data[1].T
aa_eq(data0[0], cpp_distance) # distance
aa_eq(data1[0], cpp_distance) # distance
aa_eq(data0[1], cpp_Pr, decimal=2) # Pr
aa_eq(data1[1], cpp_std, decimal=2) # std
def test_analyze_modes():
for mask in ['@CA', '@N,CA', '@CA,H']:
command = '''
matrix mwcovar name tz2 {}
diagmatrix tz2 name my_modes vecs 20
modes fluct name my_modes
'''.format(mask)
traj = pt.iterload(fn('tz2.nc'), fn('tz2.parm7'))
# cpptraj
state = pt.load_cpptraj_state(command, traj)
# pt._verbose()
state.run()
pt._verbose(False)
state.run()
cpp_dict = state.data[1:].to_dict()
c_dslist = state.data[1:]
cpp_modes = c_dslist[1]
# pytraj
mat = pt.matrix.mwcovar(traj, mask)
indices = traj.top.select(mask)
dslist = pt.matrix.diagonalize(mat,
n_vecs=20,
scalar_type='mwcovar',
mass=traj.top.mass[indices],
dtype='dataset')
evecs, evals = dslist[0].eigenvectors, dslist[0].eigenvalues
aa_eq(cpp_dict['tz2'], mat)
aa_eq(evals, cpp_modes.eigenvalues)
aa_eq(evecs, cpp_modes.eigenvectors)
with pytest.raises(AssertionError):
# wrong mass
dslist = pt.matrix.diagonalize(
mat,
n_vecs=20,
scalar_type='mwcovar',
mass=traj.top.mass[indices].tolist() + [1],
dtype='dataset')
with pytest.raises(AssertionError):
# mass is None
dslist = pt.matrix.diagonalize(mat,
n_vecs=20,
scalar_type='mwcovar',
mass=None,
dtype='dataset')
# pt._verbose()
fluct = pt.analyze_modes('fluct',
evecs,
evals,
scalar_type='mwcovar',
dtype='dataset')
# pt._verbose(False)
p_rms = fluct['FLUCT_00001[rms]'].values
c_rms = c_dslist['FLUCT_00003[rms]'].values
aa_eq(p_rms, c_rms)
def test_gist():
traj = pt.iterload(tz2_ortho_trajin, tz2_ortho_top, frame_slice=(0, 10))
traj.autoimage('origin')
command = "doorder doeij refdens 0.033422885325 gridcntr 1.44 0.67 0.29 \
griddim 10 12 10 gridspacn 2.0"
data = pt.all_actions.gist(traj,
do_order=True,
do_eij=True,
reference_density=0.033422885325,
grid_center=(1.44, 0.67, 0.29),
grid_dim=(10, 12, 10),
grid_spacing=2.0,
dtype='cpptraj_dataset')
state_command = """
parm {}
trajin {}
autoimage origin
gist {}
""".format(tz2_ortho_top, tz2_ortho_trajin, command)
state = pt.load_cpptraj_state(state_command)
with capture_stdout() as (out, _):
state.run()
data_dict = data.to_dict()
data_state_dict = state.data[1:].to_dict()
for key, state_key in zip(sorted(data_dict.keys()),
sorted(data_state_dict.keys())):
aa_eq(data_dict[key], data_state_dict[state_key])
def test_state():
print('cpptraj')
state = pt.load_cpptraj_state('''
parm GAAC.parm7
trajin GAAC3.1000frames.nc 1 1000
{0}'''.format(cpp_mask))
state.run()
def test_pca_with_ref_with_different_mask_from_matrix(self):
'''test_pca_with_ref_with_different_mask_from_matrix
has reference. Use !@H= for ref_mask and use * for covariance matrix and projection
from drroe: "You should be able to supply separate masks for fitting and creating the covariance matrix
It is common enough for example to only perform rms-fitting on heavy atoms while still wanting all atoms in eigenvectors."
pytraj: pt.pca(traj, mask=mask_matrix, n_vecs=2, ref=ref, ref_mask=mask_ref)
'''
command_ref_provided = '''
parm {}
trajin {} 1 3
reference {}
# only perform fitting on heavy atoms
rms reference !@H=
# all atoms
matrix covar name MyMatrix *
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
# all atoms
crdaction CRD1 projection evecs MyEvecs * out project.dat beg 1 end 2
'''.format(tz2_top, tz2_trajin, fn('tz2.rst7'))
traj = pt.load(fn('tz2.nc'),
fn('tz2.parm7'),
frame_indices=range(0, 3))
traj_on_disk = pt.iterload(fn('tz2.nc'),
fn('tz2.parm7'),
frame_slice=[(0, 3)])
ref = pt.load(fn('tz2.rst7'), traj.top)
state = pt.load_cpptraj_state(command_ref_provided)
state.run()
mask_ref = '!@H='
mask_matrix = '*'
data = pt.pca(traj,
mask=mask_matrix,
n_vecs=2,
ref=ref,
ref_mask=mask_ref)
data2 = pt.pca(traj_on_disk,
mask=mask_matrix,
n_vecs=2,
ref=ref,
ref_mask=mask_ref)
cpp_data = state.data[-2:].values
# use absolute values
aa_eq(np.abs(data[0]), np.abs(cpp_data), decimal=3)
aa_eq(np.abs(data2[0]), np.abs(cpp_data), decimal=3)
def test_state():
print('cpptraj')
state = pt.load_cpptraj_state('''
parm GAAC.parm7
trajin GAAC3.1000frames.nc 1 1000
{0}'''.format(cpp_mask))
state.run()
def test_symmrmsd(self):
tyr_trajin = cpptraj_test_dir + '/Test_SymmRmsd/TYR.nc'
tn = cpptraj_test_dir + '/Test_SymmRmsd/TYR.parm7'
saved_traj = pt.iterload(
cpptraj_test_dir + '/Test_SymmRmsd/TYR.remap.crd.save', tn)
traj_on_disk = pt.iterload(tyr_trajin, tn)
traj_on_mem = pt.load(tyr_trajin, tn)
aa_eq(traj_on_disk.xyz, traj_on_mem.xyz)
data = pt.symmrmsd(traj_on_mem, remap=True)
cm = """
parm {}
trajin {}
symmrmsd first remap myrmsd
createcrd mycrd
""".format(tn, tyr_trajin)
state = pt.load_cpptraj_state(cm)
with tempfolder():
state.run()
# rmsd
aa_eq(state.data['myrmsd'].values, data)
# coordinates
aa_eq(state.data['mycrd'].xyz, saved_traj.xyz, decimal=3)
aa_eq(state.data['mycrd'].xyz, traj_on_mem.xyz, decimal=3)
def test_cluster_kmeans(tmpdir):
kmeans = cluster.kmeans
traj = pt.iterload(tz2_trajin, tz2_top)
cm = 'cluster kmeans clusters 10 @CA rms randompoint kseed 2 {0} noinfo'
for sieve in [2, 3, 4]:
sieve_str = "sieve {0} sieveseed 12".format(sieve)
command = cm.format(
sieve_str) + ' cpopvtime cpptraj_cpopvtime.agr normframe'
state = pt.load_cpptraj_state(command, traj)
state.run()
data = kmeans(traj,
n_clusters=10,
kseed=2,
random_point=True,
metric='rms',
mask='@CA',
options=sieve_str +
' cpopvtime pytraj_cpopvtime.agr normframe')
assert data.n_frames == traj.n_frames
assert os.path.exists('cpptraj_cpopvtime.agr')
# Make sure pytraj could write data file.
assert os.path.exists('pytraj_cpopvtime.agr')
def test_volmap(self):
traj = pt.iterload(fn('tz2.ortho.nc'), fn('tz2.ortho.parm7'))[:1]
size = ''
center = ''
state = pt.load_cpptraj_state(
txt.format(fn('tz2.ortho.parm7'), fn('tz2.ortho.nc'), cm, size,
center))
state.run()
cpp_data = state.data[-2].values # last one is totalvolume
traj = traj.superpose(mask=':1-13').center(':1-13 mass origin')
ds = pt.volmap(traj,
mask=':WAT@O',
grid_spacing=(0.5, 0.5, 0.5),
buffer=2.0,
centermask='!:1-13',
radscale=1.36)
aa_eq(cpp_data, ds)
# assert
self.assertRaises(
AssertionError,
lambda: pt.volmap(traj, mask=':WAT@O', grid_spacing='0.5 0.5 0.5'))
self.assertRaises(
AssertionError,
lambda: pt.volmap(traj, mask=':WAT@O', grid_spacing=(0.5, 0.5)))
self.assertRaises(
ValueError, lambda: pt.volmap(traj,
mask=':WAT@O',
grid_spacing=(0.5, 0.5, 0.5),
size='20 20 20'))
def testTwoTrajTypes(self):
'''test different metrics with different traj objects
'''
funclist = [pt.iterload, pt.load]
txt = '''
parm {}
trajin {}
rms2d @CA metric_holder rmsout tmp.out
'''.format(tc5b_top, tc5b_trajin)
for func in funclist:
traj = func(tc5b_trajin, tc5b_top)
for metric in ['rms', 'nofit', 'dme']:
d0 = pt.pairwise_rmsd(traj(mask='@CA'), metric=metric)
d1 = pt.pairwise_rmsd(traj, mask='@CA', metric=metric)
d2 = pt.pairwise_rmsd(traj(), mask='@CA', metric=metric)
txt0 = txt.replace('metric_holder', metric)
state = pt.load_cpptraj_state(txt0)
with tempfolder():
state.run()
d3 = state.data[-1].values
aa_eq(d0, d1)
aa_eq(d0, d2)
aa_eq(d0, d3)
def test_closest_compared_to_cpptraj(self):
trajin = fn('tz2.ortho.nc')
parm = fn('tz2.ortho.parm7')
traj = pt.iterload(trajin, parm, frame_slice=[
(0, 2),
])
state = pt.load_cpptraj_state('''
parm {}
trajin {} 1 2
autoimage
closest 100 :1-13
createcrd mycrd'''.format(parm, trajin))
state.run()
fi, top = pt.closest(traj(autoimage=True),
mask=':1-13',
n_solvents=100)
xyz = pt.get_coordinates(fi)
t0 = pt.Trajectory(xyz=xyz, top=top)
aa_eq(state.data['mycrd'].xyz, t0.xyz)
# dtype = 'trajectory'
t1 = pt.closest(traj(autoimage=True),
mask=':1-13',
n_solvents=100,
dtype='trajectory')
aa_eq(state.data['mycrd'].xyz, t1.xyz)
def test_projection_with_None_average_coord(self):
cm = '''
matrix name correlmat {scalar_type} @CA
diagmatrix correlmat name evecs vecs 5
run
projection P1 modes evecs beg 1 end 2 @CA
'''
scalar_type = 'covar'
cm2 = cm.format(scalar_type=scalar_type)
traj = pt.iterload(fn('tz2.nc'), fn('tz2.parm7'))
state = pt.load_cpptraj_state(cm2, traj)
state.run()
mat = pt.matrix.covar(traj, '@CA')
eigenvalues, eigenvectors = pt.matrix.diagonalize(mat,
2,
dtype='tuple')
data = pt.all_actions.projection(traj,
'@CA',
eigenvalues,
eigenvectors,
scalar_type=scalar_type)
aa_eq(np.abs(state.data['Mode1'].values), np.abs(data[0]))
aa_eq(np.abs(state.data['Mode2'].values), np.abs(data[1]))
assert data.shape == (2, traj.n_frames)
def test_hbonds_from_pdb(self):
traj = pt.load(fn('1L2Y.pdb'))
hb = pt.search_hbonds(traj)
state = pt.load_cpptraj_state('''
parm {pdb}
trajin {pdb}
hbond series
'''.format(pdb=fn('1L2Y.pdb')))
state.run()
for data_p, data_cpp in zip(hb.data, state.data[1:]):
assert len(
data_p) == traj.n_frames == 38, 'size of dataset must be 38'
aa_eq(data_p, data_cpp.values)
# make sure distances are smaller than cutoff
distance_cutoff = 2.5
angle_cutoff = 135.
hb = pt.search_hbonds(traj)
distances = pt.distance(traj, hb.get_amber_mask()[0])
angles = pt.angles(traj, hb.get_amber_mask()[1])
dist_indices = np.where(distances > distance_cutoff)
angle_indices = np.where(angles < angle_cutoff)
saved_donor_acceptors = [
'ASP9_OD2-ARG16_NH1-HH12', 'ASP9_OD2-ARG16_NH2-HH22',
'ASP9_OD2-ARG16_NE-HE', 'ASP9_OD2-ARG16_NH2-HH21',
'ASP9_OD2-ARG16_NH1-HH11'
]
donor_acceptors = pt.search_hbonds(traj, ':9,16').donor_acceptor
assert saved_donor_acceptors == donor_acceptors, 'saved_donor_acceptors'
aa_eq(hb.total_solute_hbonds(), hb.data['total_solute_hbonds'])
def test_distance_rmsd():
traj = pt.iterload(tz2_trajin, tz2_top)
txt = '''
parm {}
trajin {}
drmsd drms_nofit out drmsd.dat
rms rms_nofit out drmsd.dat nofit
rms rms_fit out drmsd.dat
drmsd drms_fit out drmsd.dat
'''.format(tz2_top, tz2_trajin)
state = pt.load_cpptraj_state(txt)
with tempfolder():
state.run()
cpp_data = state.data[1:]
# distance_rmsd
data_drmsd = pt.distance_rmsd(traj)
aa_eq(data_drmsd, cpp_data[0])
aa_eq(pt.drmsd(traj), cpp_data[0])
# rms_nofit
aa_eq(cpp_data[1], pt.rmsd(traj, nofit=True))
# rms_fit
aa_eq(cpp_data[2], pt.rmsd(traj, nofit=False))
# drmsd with rmsfit
aa_eq(cpp_data[3], pt.distance_rmsd(traj(rmsfit=0), ref=traj[0]))
def test_makestructure_with_reference(self):
tz2_parm7 = cpptraj_test_dir + '/tz2.parm7'
ref_rst7 = cpptraj_test_dir + '/tz2.rst7'
trajin_rst7 = cpptraj_test_dir + '/Test_MakeStructure/pp2.rst7.save'
# cpptraj
command = """
parm {}
reference {}
trajin {}
makestructure "ref:1-13:tz2.rst7"
rmsd reference
createcrd mycrd
""".format(tz2_parm7, ref_rst7, trajin_rst7)
state = pt.load_cpptraj_state(command)
state.run()
cpp_traj = state.data['mycrd']
# pytraj
traj = pt.load(trajin_rst7, top=tz2_parm7)
ref = pt.load(ref_rst7, top=tz2_parm7)
pt.make_structure(traj, "ref:1-13:1-13", ref=ref)
pt.rmsd(traj, ref=ref)
aa_eq(traj.xyz, cpp_traj.xyz)
def test_iterload_and_load_remd():
# iterload_remd
traj = pt.iterload_remd(fn("Test_RemdTraj/rem.nc.000"),
fn("Test_RemdTraj/ala2.99sb.mbondi2.parm7"),
T=300.0)
for frame in traj:
assert frame.temperature == 300.0, 'frame temperature must be 300.0 K'
dist = pt.distance(traj, '@10 @20')
trajin_text = '''
parm {}
trajin {} remdtraj remdtrajtemp 300.
distance @10 @20
'''.format(fn('Test_RemdTraj/ala2.99sb.mbondi2.parm7'),
fn('Test_RemdTraj/rem.nc.000 '))
state = pt.load_cpptraj_state(trajin_text)
state.run()
aa_eq(dist, state.data[-1].values)
# load_remd
traj2 = pt.load_remd(fn("Test_RemdTraj/rem.nc.000"),
fn("Test_RemdTraj/ala2.99sb.mbondi2.parm7"),
T=300.0)
aa_eq(traj.xyz, traj2.xyz)
# with Topology
traj2 = pt.iterload_remd(fn("Test_RemdTraj/rem.nc.000"),
top=traj.top,
T=300.0)
aa_eq(traj.xyz, traj2.xyz)
def test_distance_to_point_or_reference(self):
tz2_pdb = os.path.join(cpptraj_test_dir, 'tz2.pdb')
traj = pt.iterload(fn('tz2.nc'), fn('tz2.parm7'))
ref_traj = pt.load(tz2_pdb)
ref = ref_traj[0]
ref.top = ref_traj.top
cmd = """
parm {parm}
reference {reference} name myref
trajin {trajin}
distance EndToEnd :1 :13
distance ToRef @1 @1 reference
distance Point :1 point 0.0 0.0 0.0
""".format(parm=fn("tz2.parm7"),
reference=tz2_pdb,
trajin=fn("tz2.nc"))
state = pt.load_cpptraj_state(cmd)
state.run()
# ensure same reference
aa_eq(ref.xyz, state.data.to_dict()['myref:1'], decimal=3)
dist_point = pt.distance_to_point(traj, ':1', point=[0., 0., 0.])
aa_eq(dist_point, state.data['Point'])
dist_ref = pt.distance_to_reference(traj, '@1 @1', ref=ref)
aa_eq(dist_ref, state.data['ToRef'].values)
def test_crdframes(self):
'''test crdframes in cpptraj
'''
max_frames = 50
traj = pt.iterload('data/tz2.nc',
'data/tz2.parm7',
frame_slice=(0, max_frames, 2))
state = pt.load_cpptraj_state('''
parm {0}
trajin {1} 1 {2} 2
trajin {1} 1 {2} 2
loadtraj name traj
crdaction traj rms crdframes 1,10
crdaction traj rms crdframes 1,30,2
'''.format(traj.top.filename, traj.filename, max_frames))
state.run()
rmsd_0 = pt.rmsd(traj, ref=0, frame_indices=range(10))
rmsd_crdframes = state.data[2].values
aa_eq(rmsd_0, rmsd_crdframes)
traj2 = traj.copy()
assert traj2.n_frames == traj.n_frames, 'must have the same n_frames'
traj2._load(traj.filename, frame_slice=(0, max_frames, 2))
assert traj2.n_frames == 2 * traj.n_frames, 'n_frames must be doubled after reload'
rmsd_1 = pt.rmsd(traj2, ref=0, frame_indices=range(0, 30, 2))
rmsd_crdframes = state.data[3].values
aa_eq(rmsd_1, rmsd_crdframes)
def test_crdframes(self):
'''test crdframes in cpptraj
'''
max_frames = 50
traj = pt.iterload(fn('tz2.nc'),
fn('tz2.parm7'),
frame_slice=(0, max_frames, 2))
state = pt.load_cpptraj_state('''
parm {0}
trajin {1} 1 {2} 2
trajin {1} 1 {2} 2
loadtraj name traj
crdaction traj rms crdframes 1,10
crdaction traj rms crdframes 1,30,2
'''.format(traj.top.filename, traj.filename, max_frames))
state.run()
rmsd_0 = pt.rmsd(traj, ref=0, frame_indices=range(10))
rmsd_crdframes = state.data[2].values
aa_eq(rmsd_0, rmsd_crdframes)
traj2 = traj.copy()
assert traj2.n_frames == traj.n_frames, 'must have the same n_frames'
traj2._load(traj.filename, frame_slice=(0, max_frames, 2))
assert traj2.n_frames == 2 * traj.n_frames, 'n_frames must be doubled after reload'
rmsd_1 = pt.rmsd(traj2, ref=0, frame_indices=range(0, 30, 2))
rmsd_crdframes = state.data[3].values
aa_eq(rmsd_1, rmsd_crdframes)
def test_projection_for_pca(self):
traj = pt.load("./data/tz2.nc", "./data/tz2.parm7")
state = pt.load_cpptraj_state(command)
state.run()
cpp_modes = state.data['MyEvecs']
cpp_arr_crd = np.array(cpp_modes._get_avg_crd())
cpp_arr_crd = cpp_arr_crd.reshape(117, 3)
mask = '!@H='
pt.superpose(traj, mask=mask)
avg = pt.mean_structure(traj)
atom_indices = traj.top(mask).indices
strip_avg_coords = avg.xyz[atom_indices]
pt.superpose(traj, mask=mask, ref=avg)
avg2 = pt.mean_structure(traj, mask=mask)
mat = pt.matrix.covar(traj, mask)
modes = pt.matrix.diagonalize(mat, n_vecs=2, dtype='dataset')[0]
aa_eq(cpp_arr_crd, avg2.xyz)
aa_eq(np.abs(modes.eigenvalues), np.abs(state.data['MyEvecs'].eigenvalues))
aa_eq(np.abs(modes.eigenvectors), np.abs(state.data['MyEvecs'].eigenvectors))
projection_data = pt.all_actions.projection(traj, mask=mask, average_coords=avg2.xyz,
eigenvalues=modes.eigenvalues,
eigenvectors=modes.eigenvectors,
scalar_type='covar')
aa_eq(np.abs(projection_data), np.abs(state.data[-2:].values), decimal=3)
def test_drmsd(self):
traj = pt.iterload("./data/tz2.nc", "./data/tz2.parm7")
txt = '''
parm data/tz2.parm7
trajin data/tz2.nc
drmsd drms_nofit out drmsd.dat
rms rms_nofit out drmsd.dat nofit
rms rms_fit out drmsd.dat
drmsd drms_fit out drmsd.dat
'''
state = pt.load_cpptraj_state(txt)
state.run()
cpp_data = state.data[1:]
# distance_rmsd
data_drmsd = pt.distance_rmsd(traj)
aa_eq(data_drmsd, cpp_data[0])
aa_eq(pt.drmsd(traj), cpp_data[0])
# rms_nofit
aa_eq(cpp_data[1], pt.rmsd(traj, nofit=True))
# rms_fit
aa_eq(cpp_data[2], pt.rmsd(traj, nofit=False))
# drmsd with rmsfit
aa_eq(cpp_data[3], pt.distance_rmsd(traj(rmsfit=0), ref=traj[0]))
def test_volume(self):
traj = pt.iterload(fn('tz2.ortho.nc'), fn('tz2.ortho.parm7'))
state = pt.load_cpptraj_state('''
volume''', traj)
state.run()
vol = pt.volume(traj)
aa_eq(state.data[-1], vol)
def test_radgyr_default():
traj = pt.iterload(fn("tz2.truncoct.nc"), fn("tz2.truncoct.parm7"))
state = pt.load_cpptraj_state("""
radgyr :1-13
""", traj)
state.run()
np.testing.assert_almost_equal(
pt.radgyr(traj, ':1-13'), state.data['RoG_00001'].values)
def test_crank(self):
traj = pt.iterload("data/tz2.nc", "data/tz2.parm7")
dihedrals = pt.dihedral(traj, [':1@C :2@N :2@CA :2@C', ':2@C :3@N :3@CA :3@C'])
state = pt.load_cpptraj_state(cm)
state.run()
# TODO: assert please
# cpptraj does not dump data to Dataset
pt.crank(dihedrals[0], dihedrals[1], mode='angle')
def test_volume(self):
traj = pt.iterload("data/tz2.ortho.nc", "data/tz2.ortho.parm7")
state = pt.load_cpptraj_state('''
volume''', traj)
state.run()
vol = pt.volume(traj)
aa_eq(state.data[-1], vol)
def rms2d_():
state = pt.load_cpptraj_state('''
parm {0}
loadcrd md.trj 0 {1} crd_
#crdaction crd_ rms2d
rms2d crdset crd_ out test.dat
'''.format(traj.top.filename, str(n_frames)))
state.run()
return state
def test_crank(self):
traj = pt.iterload("data/tz2.nc", "data/tz2.parm7")
dihedrals = pt.dihedral(
traj, [':1@C :2@N :2@CA :2@C', ':2@C :3@N :3@CA :3@C'])
state = pt.load_cpptraj_state(cm)
state.run()
# TODO: assert please
# cpptraj does not dump data to Dataset
pt.crank(dihedrals[0], dihedrals[1], mode='angle')
def rms2d_():
state = pt.load_cpptraj_state('''
parm {0}
loadcrd md.trj 0 {1} crd_
#crdaction crd_ rms2d
rms2d crdset crd_ out test.dat
'''.format(traj.top.filename, str(n_frames)))
state.run()
return state
def test_crank(self):
traj = pt.iterload(fn('tz2.nc'), fn('tz2.parm7'))
dihedrals = pt.dihedral(
traj, [':1@C :2@N :2@CA :2@C', ':2@C :3@N :3@CA :3@C'])
state = pt.load_cpptraj_state(cm)
with capture_stdout() as (out, _):
state.run()
data = pt.crank(dihedrals[0], dihedrals[1], mode='angle')
assert out.read() == data
def test_density(self):
rst7 = fn('DOPC.rst7')
tn = fn('DOPC.parm7')
traj = pt.load(fn('DOPC.rst7'), fn('DOPC.parm7'))
delta = '0.25'
masks = [":PC@P31", ":PC@N31", ":PC@C2", ":PC | :OL | :OL2"]
keys_no_space = [''.join(m.split()) for m in masks]
mask_str = ' '.join(['"' + m + '"' for m in masks])
density_types = ['number', 'mass', 'charge', 'electron']
state_data_dict = dict()
for density_type in density_types:
command = """
parm {parm}
trajin {trajin}
center ":PC | :OL | :OL2" origin
density {density_type} delta {delta} {mask}
""".format(
parm=tn,
trajin=rst7,
delta=delta,
mask=mask_str,
density_type=density_type)
state = pt.load_cpptraj_state(command)
state.run()
state_data_dict[density_type] = state.data[1:].to_dict()
pt.center(traj, '":PC | :OL | :OL2" origin')
density_dict = {}
for density_type in density_types:
density_data = pt.density(
traj, mask=masks, delta=delta, density_type=density_type)
density_dict[density_type] = density_data
# compate to cpptraj
for density_type in density_types:
for key in keys_no_space:
aa_eq(state_data_dict[density_type][key],
density_dict[density_type][key])
# assert raise: wrong density_type
def func():
pt.density(traj, mask=':WAT', density_type='hello')
with pytest.raises(AssertionError):
func()
# test 'z' value
saved_z_values = np.linspace(-24.1250, 23.8750, 193)
aa_eq(density_data['z'], saved_z_values)
def test_matrix(self):
import numpy as np
from pytraj import ArgList
from pytraj import matrix as ma
traj = pt.iterload(crd_file, top_file)
with tempfolder():
state = pt.load_cpptraj_state(all_commands)
state.run()
state_byres = pt.load_cpptraj_state(byres_cm)
state_byres.run()
byres_matlist = []
# no byres keyword
for idx, line in enumerate(command_list):
arg = ArgList(line)
# get function
act_key = arg.get_string_key("matrix")
slist = arg.get_string_key('out').split(".")
arg.get_next_mask()
".".join((slist[0], slist[-1], slist[1]))
# get correct name
func = ma.__dict__[act_key]
# get command
command = line.split(act_key)[1]
matout = func(traj, command, dtype='ndarray')
# cpptraj output has only 3 digits after decimal
if 'byres' not in command:
aa_eq(matout, state.data[idx + 1].values)
else:
# save data for asserting later
byres_matlist.append(matout)
byres_arr = np.array(byres_matlist, dtype='f8')
# only take byres datasets
saved_matbyres = state_byres.data[[1, 3]].values
aa_eq(byres_arr, saved_matbyres)
def test_rotdif(self):
traj = pt.load(trajin, parm)
ref = pt.load(reference, parm)
mat = pt.rotation_matrix(traj, ref=ref, mask='@CA,C,N,O')
data = pt.all_actions.rotdif(mat, short_cm)
state = pt.load_cpptraj_state(cm)
with capture_stdout() as (out, _):
state.run()
assert data == out.read()
def test_check_structure():
traj = pt.iterload(fn('DPDP.nc'), fn('DPDP.parm7'))
out_dict = pt.check_chirality(traj)
state = pt.load_cpptraj_state(cm)
state.run()
cpp_out_dict = state.data[1:].to_dict()
for ((key, value), (cpp_key, cpp_value)) in zip(out_dict.items(),
cpp_out_dict.items()):
aa_eq(value, cpp_value)
def test_vs_cpptraj(self):
traj = pt.iterload("./data/tz2.ortho.nc", "./data/tz2.ortho.parm7")
txt = '''
parm ./data/tz2.ortho.parm7
trajin ./data/tz2.ortho.nc
replicatecell name test all
'''
t0 = pt.replicate_cell(traj, direction='all')
state = pt.load_cpptraj_state(txt)
state.run()
saved_t0 = state.data[1]
aa_eq(saved_t0.xyz, t0.xyz)
def test_combine_nofit_mass_nomod(self):
cm = '''
parm data/tz2.parm7
trajin data/tz2.nc
rms @CA nofit mass nomod
'''
state = pt.load_cpptraj_state(cm)
state.run()
unmut_traj = pt.iterload('data/tz2.nc', 'data/tz2.parm7')
mut_traj = unmut_traj[:]
data = pt.rmsd(mut_traj, mask='@CA', mass=True, nofit=True, update_coordinate=False)
aa_eq(data, state.data[-1])
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_closest_compared_to_cpptraj(self):
traj = pt.iterload("./data/tz2.ortho.nc", "./data/tz2.ortho.parm7")
state = pt.load_cpptraj_state('''
autoimage
closest 100 :1-13
createcrd mycrd''', traj)
state.run()
fi, top = pt.closest(traj(autoimage=True), mask=':1-13', n_solvents=100)
xyz = pt.get_coordinates(fi)
t0 = pt.Trajectory(xyz=xyz, top=top)
aa_eq(state.data['mycrd'].xyz, t0.xyz)
# dtype = 'trajectory'
t1 = pt.closest(traj(autoimage=True), mask=':1-13', n_solvents=100, dtype='trajectory')
aa_eq(state.data['mycrd'].xyz, t1.xyz)
def test_reference(self):
traj = pt.iterload("./data/tz2.nc", "./data/tz2.parm7")
text = '''
parm {0}
trajin {1}
reference {1} 3 3 [ref0]
reference {1} 2 2 [ref1]
rms ref [ref0] @CA
rms ref [ref1] @CA
'''.format(traj.top.filename, traj.filename)
state = pt.load_cpptraj_state(text)
state.run()
rmsd_0 = pt.rmsd(traj, ref=2, mask='@CA')
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 test_calc_atomicfluct_with_unitcell(self):
# use iterload for load_batch
traj = pt.iterload("./data/tz2.ortho.nc", "./data/tz2.ortho.parm7")
state = pt.load_cpptraj_state('''
distance :3 :7
atomicfluct @CA out output/test.agr
distance :3 :7''', traj)
state.run()
# use `load` method
t0 = pt.load("./data/tz2.ortho.nc", "./data/tz2.ortho.parm7")
data = pt.atomicfluct(traj, '@CA')
aa_eq(data, state.data[-2].values)
# make sure that traj's coordinates were not altered
# https://github.com/Amber-MD/pytraj/issues/1166
aa_eq(pt.distance(t0, ':3 :7'), state.data[-1])
aa_eq(traj.xyz, t0.xyz)
def test_pca_with_ref_with_different_mask_from_matrix(self):
'''has reference. Use !@H= for ref_mask and use * for covariance matrix and projection
from drroe: "You should be able to supply separate masks for fitting and creating the covariance matrix
It is common enough for example to only perform rms-fitting on heavy atoms while still wanting all atoms in eigenvectors."
pytraj: pt.pca(traj, mask=mask_matrix, n_vecs=2, ref=ref, ref_mask=mask_ref)
'''
command_ref_provided = '''
parm data/tz2.parm7
trajin data/tz2.nc
reference data/tz2.rst7
# only perform fitting on heavy atoms
rms reference !@H=
# all atoms
matrix covar name MyMatrix *
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
# all atoms
crdaction CRD1 projection evecs MyEvecs * out project.dat beg 1 end 2
'''
traj = pt.load("data/tz2.nc", "data/tz2.parm7")
ref = pt.load('data/tz2.rst7', traj.top)
state = pt.load_cpptraj_state(command_ref_provided)
state.run()
mask_ref = '!@H='
mask_matrix = '*'
data = pt.pca(traj, mask=mask_matrix, n_vecs=2, ref=ref, ref_mask=mask_ref)
cpp_data = state.data[-2:].values
# use absolute values
aa_eq(np.abs(data[0]), np.abs(cpp_data), decimal=3)
def test_reference_with_different_topology_basic(self):
traj1 = pt.iterload(filename="./data/Tc5b.x",
top="./data/Tc5b.top")
traj2 = pt.iterload('data/tz2.nc', 'data/tz2.parm7')
# re-establish ActionList
dslist = CpptrajDatasetList()
dslist.add('reference', name='myref')
dslist[0].top = traj2.top
dslist[0].add_frame(traj2[0])
actlist = pt.ActionList(['rmsd @1-11 @CB ref myref'], top=traj1.top,
dslist=dslist)
for frame in traj1:
actlist.compute(frame)
# raise if ref_mask is given but not mask
self.assertRaises(ValueError, lambda:
pt.rmsd(traj1, ref=3, ref_mask='@CB'))
self.assertRaises(ValueError, lambda:
pt.rmsd(traj1, ref=traj2[:1], ref_mask='@CB'))
# assert to cpptraj
tc5b_traj = traj1[:]
tz2_traj = traj2[:1]
cm = '''
parm data/Tc5b.top [tc5b]
trajin data/Tc5b.x [tc5b]
parm data/tz2.parm7 [tz2]
reference data/tz2.nc parm [tz2] 1 [myref]
rms myrmsd ref [myref] @1-10 @11-20
'''
state = pt.load_cpptraj_state(cm)
state.run()
expected_rmsd = state.data[-1].values
rmsd_data = pt.rmsd(tc5b_traj, mask='@1-10',
ref=tz2_traj,
ref_mask='@11-20')
aa_eq(expected_rmsd, rmsd_data)
def test_pca_noref(self):
'''no reference
pytraj: pt.pca(traj, mask, n_vecs=2)
'''
command = '''
# Step one. Generate average structure.
# RMS-Fit to first frame to remove global translation/rotation.
parm data/tz2.parm7
trajin data/tz2.nc
rms first !@H=
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
'''
traj = pt.load("data/tz2.nc", "data/tz2.parm7")
# no reference
state = pt.load_cpptraj_state(command)
state.run()
mask = '!@H='
data = pt.pca(traj, mask, n_vecs=2)
cpp_data = state.data[-2:].values
# use absolute values
aa_eq(np.abs(data[0]), np.abs(cpp_data), decimal=3)
def test_dihedral(self):
import numpy as np
traj = mdio.iterload("./data/Tc5b.x", "./data/Tc5b.top")
fa = traj[:]
mask = ':2@CA :14@CA :15@CA :16@CA'
txt = '''
parm ./data/Tc5b.top
trajin ./data/Tc5b.x
dihedral %s
''' % mask
d0 = pt.dihedral(traj, mask, dtype='dataset').to_ndarray()
d1 = pt.dihedral(traj, mask)
d2 = pt.calc_dihedral(fa, mask)
state = pt.load_cpptraj_state(txt)
state.run()
dcpp = state.data[1:].values
aa_eq(d0, d1)
aa_eq(d0, d2)
aa_eq(d0, dcpp)
Nsize = 10
np.random.seed(1)
arr = np.random.randint(0, 300, size=Nsize * 4).reshape(Nsize, 4)
d3 = pt.calc_dihedral(fa, arr)
d4 = pt.dihedral(traj, arr)
d5 = pt.dihedral(traj, arr)
d6 = pt.dihedral(fa, arr)
d7 = pt.dihedral([fa, traj], arr, n_frames=2 * fa.n_frames)
aa_eq(d3, d4)
aa_eq(d3, d5)
aa_eq(d3, d6)
aa_eq(d3.T, d7.T[:fa.n_frames])
aa_eq(d3.T, d7.T[fa.n_frames:])
d8 = pt.dihedral(traj, mask, dtype='dataset')
d9 = pt.tools.dict_to_ndarray(pt.dihedral(traj, mask, dtype='dict'))
aa_eq(d0, d8.values)
aa_eq(d0, d9)
# raise
self.assertRaises(ValueError, lambda: pt.dihedrals(traj, [[0, 3, 2]]))
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_nupars(self):
fn = "./data/Test_NAstruct/adh026.3.pdb"
traj = pt.iterload(fn, fn)
data = pt.nastruct(traj)
# default
text = '''
parm "./data/Test_NAstruct/adh026.3.pdb"
trajin "./data/Test_NAstruct/adh026.3.pdb"
nastruct groovecalc 3dna
'''
state = pt.load_cpptraj_state(text)
state.run()
for key in ['major', 'minor', 'twist']:
cpp_data = np.array([x.values for x in state.data if x.aspect ==
key])
# need to transpose to get shape=(n_frames, n_pairs)
cpp_data = cpp_data.T
aa_eq(data[key][1], cpp_data)
# TODO: assert
data._summary(np.mean, indices=None)
data._summary(np.mean, indices=[1, ])
data._summary(np.mean, keys=['major', 'twist'], indices=[1, ])
data._summary(np.mean, keys='major', indices=[1, ])
data._summary(np.std, indices=[1, ])
data._summary([np.std, np.mean], indices=[1, ])
data._explain()
dir(data)
# pickle
pt.to_pickle(data, 'data/na.pk')
na2 = pt.read_pickle('data/na.pk')
for key in data.keys():
aa_eq(data[key][1], na2[key][1])
# raise
self.assertRaises(ValueError, lambda: pt.nastruct(traj, dtype='ndarray'))
def test_pca_with_ref(self):
'''has reference
from drroe: "If the user provides their own reference structure, do not create an average structure"
pytraj: pt.pca(traj, mask, n_vecs=2, ref=ref)
'''
command_ref_provided = '''
parm data/tz2.parm7
trajin data/tz2.nc
reference data/tz2.rst7
rms reference !@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
'''
traj = pt.load("data/tz2.nc", "data/tz2.parm7")
ref = pt.load('data/tz2.rst7', traj.top)
state = pt.load_cpptraj_state(command_ref_provided)
state.run()
mask = '!@H='
data = pt.pca(traj, mask, n_vecs=2, ref=ref)
cpp_data = state.data[-2:].values
# use absolute values
aa_eq(np.abs(data[0]), np.abs(cpp_data), decimal=3)
def test_pca_noref_nofit(self):
'''no reference and do not do fitting
from drroe: " Also, not fitting at all should be considered a legitimate option -
you may want to include global rotational and translational motion in your eigenvectors."
pytraj: pt.pca(traj, mask, n_vecs=2, fit=False)
'''
command = '''
parm data/tz2.parm7
trajin data/tz2.nc
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
'''
traj = pt.load("data/tz2.nc", "data/tz2.parm7")
# no reference
state = pt.load_cpptraj_state(command)
state.run()
mask = '!@H='
data = pt.pca(traj, mask, n_vecs=2, fit=False)
data_ref = pt.pca(traj, mask, n_vecs=2, fit=False, ref=3, ref_mask='@CA')
cpp_data = state.data[-2:].values
# use absolute values
aa_eq(np.abs(data[0]), np.abs(cpp_data), decimal=3)
# if fit=True, ref will be ignored
aa_eq(np.abs(data_ref[0]), np.abs(cpp_data), decimal=3)
def test_dihcovar(self):
traj = pt.iterload("./data/tz2.nc", "./data/tz2.parm7")
txt = '''
parm data/tz2.parm7
trajin data/tz2.nc
# Generation of phi/psi dihedral data
multidihedral BB phi psi resrange 2
run
# Calculate dihedral covariance matrix and obtain eigenvectors
matrix dihcovar dihedrals BB[*] name DIH
diagmatrix DIH vecs 4 name DIHMODES
run
# Project along eigenvectors
projection evecs DIHMODES beg 1 end 4 dihedrals BB[*]
'''
state = pt.load_cpptraj_state(txt)
state.run()
modes = state.data['DIHMODES']
def test_projection_with_None_average_coord(self):
cm = '''
matrix name correlmat {scalar_type} @CA
diagmatrix correlmat name evecs vecs 5
run
projection P1 modes evecs beg 1 end 2 @CA
'''
scalar_type = 'covar'
cm2 = cm.format(scalar_type=scalar_type)
traj = pt.iterload("data/tz2.nc", "data/tz2.parm7")
state = pt.load_cpptraj_state(cm2, traj)
state.run()
mat = pt.matrix.covar(traj, '@CA')
eigenvalues, eigenvectors = pt.matrix.diagonalize(mat, 2, dtype='tuple')
data = pt.all_actions.projection(traj, '@CA', eigenvalues, eigenvectors,
scalar_type=scalar_type)
aa_eq(np.abs(state.data['Mode1'].values), np.abs(data[0]))
aa_eq(np.abs(state.data['Mode2'].values), np.abs(data[1]))
assert data.shape == (2, traj.n_frames)
def test_hbonds_from_pdb(self):
traj = pt.load('data/1L2Y.pdb')
hb = pt.search_hbonds(traj)
state = pt.load_cpptraj_state('''
parm data/1L2Y.pdb
trajin data/1L2Y.pdb
hbond series
''')
state.run()
for data_p, data_cpp in zip(hb.data, state.data[1:]):
assert len(
data_p) == traj.n_frames == 38, 'size of dataset must be 38'
aa_eq(data_p, data_cpp)
# make sure distances are smaller than cutoff
distance_cutoff = 2.5
angle_cutoff = 135.
hb = pt.search_hbonds(traj)
distances = pt.distance(traj, hb.get_amber_mask()[0])
angles = pt.angles(traj, hb.get_amber_mask()[1])
dist_indices = np.where(distances > distance_cutoff)
angle_indices = np.where(angles < angle_cutoff)
print('FILL ME', dist_indices, angle_indices)
saved_donor_acceptors = ['ASP9_OD2-ARG16_NH1-HH12',
'ASP9_OD2-ARG16_NH2-HH22',
'ASP9_OD2-ARG16_NE-HE',
'ASP9_OD2-ARG16_NH2-HH21',
'ASP9_OD2-ARG16_NH1-HH11']
donor_acceptors = pt.search_hbonds(traj, ':9,16').donor_acceptor
assert saved_donor_acceptors == donor_acceptors, 'saved_donor_acceptors'
aa_eq(hb.total_solute_hbonds(), hb.data['total_solute_hbonds'])
def test_iterload_and_load_remd(self):
# iterload_remd
traj = pt.iterload_remd("data/Test_RemdTraj/rem.nc.000",
"data/Test_RemdTraj/ala2.99sb.mbondi2.parm7",
T=300.0)
for frame in traj:
assert frame.temperature == 300.0, 'frame temperature must be 300.0 K'
dist = pt.distance(traj, '@10 @20')
state = pt.load_cpptraj_state(self.trajin_text)
state.run()
aa_eq(dist, state.data[-1])
# load_remd
traj2 = pt.load_remd("data/Test_RemdTraj/rem.nc.000",
"data/Test_RemdTraj/ala2.99sb.mbondi2.parm7",
T=300.0)
aa_eq(traj.xyz, traj2.xyz)
# with Topology
traj2 = pt.iterload_remd("data/Test_RemdTraj/rem.nc.000",
top=traj.top,
T=300.0)
aa_eq(traj.xyz, traj2.xyz)
def test_load_cpptraj_state_from_text(self):
traj = pt.iterload("./data/tz2.nc", "./data/tz2.parm7")
text = '''
parm data/Test_RemdTraj/ala2.99sb.mbondi2.parm7
trajin data/Test_RemdTraj/rem.nc.000 remdtraj remdtrajtemp 300.
distance @10 @20
'''
input_file = 'data/Test_RemdTraj/traj.in'
state_from_file = pt.load_cpptraj_state(input_file)
state_from_file.run()
# remove DatasetTopology
data = state_from_file.data
data.remove_set(data[0])
data_0 = state_from_file.data.values
state_from_text = pt.datafiles.load_cpptraj_state(text)
state_from_text.run()
data = state_from_text.data
data.remove_set(data[0])
data_1 = state_from_text.data.values
aa_eq(data_0, data_1)
