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_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 calcACFreff(self):
self.ACFreff = dict()
#self.test=dict()
for (r1, a1), (r2, a2) in zip(self.list1, self.list2):
label = str(r1) + a1 + ':' + str(r2) + a2
first_in_seq = list(self.traj.top.residues)[0].index
idx1 = pt.select_atoms(
searchAT[a1] + ' & :' + str(r1 - first_in_seq), self.traj.top)
idx2 = pt.select_atoms(
searchAT[a2] + ' & :' + str(r2 - first_in_seq), self.traj.top)
pairs = list(map(list, product(idx1, idx2)))
data_vec = va.vector_mask(self.traj, pairs, dtype='ndarray')
if len(data_vec.shape) < 3:
data_vec = [data_vec]
self.ACFreff[label] = [
pt.timecorr(vals,
vals,
order=2,
tstep=1,
tcorr=len(vals),
norm=False,
dtype='ndarray') for vals in data_vec
]
#for testing only: calculate the ACF without pytraj.timecorr
#self.test[label] = []
#for vals in data_vec:
#vals2 = np.array([unit_vector(v) for v in vals])
#x = vals2[:, 0]
#y = vals2[:, 1]
#z = vals2[:, 2]
#x2 = mycorrelate2(x**2, norm=False)
#y2 = mycorrelate2(y**2, norm=False)
#z2 = mycorrelate2(z**2, norm=False)
#xy = mycorrelate2(x*y, norm=False)
#yz = mycorrelate2(y*z, norm=False)
#xz = mycorrelate2(x*z, norm=False)
#tot = (x2+y2+z2+2*xy+2*xz+2*yz)
#tot /= tot[0]
#tot = 1.5*(tot)-.5
dump(self.ACFreff, open(get_today() + '-ACFreff.pkl', 'wb'))
def __init__(self,
top,
trajs,
rotfit=False,
mask='@CA',
stride=1,
n_blocks=1,
bframes=50000,
skip=25000):
#read sequence
if trajs[0][-3:] == 'dcd':
traj = pt.iterload(trajs[0], top)
else:
#filename, top_name = get_fn(trajs[0]), get_fn(top)
m_traj = md.load(trajs[0], top=top)
traj = pt.Trajectory(xyz=m_traj.xyz.astype('f8'), top=top)
self.sequence = {res.index + 1: res.name for res in traj.top.residues}
self.top = top
self.trajs = trajs
n_frames = len(traj)
self.n_indices = pt.select_atoms('@N & !(:1) & !(:PRO)', traj.top)
self.h_indices = self.n_indices + 1
self.nh_pairs = np.array(list(zip(self.n_indices, self.h_indices)))
res_list = sorted(
[k for k, val in self.sequence.items() if val != 'PRO' and k != 1])
self.acf = dict()
self.S2 = dict()
if n_blocks == 1:
for tt, t in enumerate(self.trajs):
if t[-3:] == 'dcd':
traj = pt.load(t, top, stride=stride)
else:
#filename, top_name = get_fn(t), get_fn(top)
m_traj = md.load(t, top=top)
traj = pt.Trajectory(xyz=m_traj.xyz.astype('f8'), top=top)
if rotfit == True:
#print('RMSD fitting on '+mask)
_ = traj.superpose(ref=-1, mask='@CA')
data_vec = va.vector_mask(traj, self.nh_pairs, dtype='ndarray')
self.acf[tt] = {
res_list[n]: pt.timecorr(vals,
vals,
order=2,
tstep=1,
tcorr=len(vals),
norm=False,
dtype='ndarray')
for n, vals in enumerate(data_vec)
}
self.S2[tt] = {
res_list[n]: S2(vals)
for n, vals in enumerate(data_vec)
}
else:
index = 0
bcount = 0
for tt, t in enumerate(self.trajs):
print(tt + 1, '/', len(self.trajs))
while bcount < n_blocks:
first_frame = bcount * skip
last_frame = first_frame + bframes
if t[-3:] == 'dcd':
traj = pt.load(t,
top,
frame_indices=slice(
first_frame, last_frame, stride))
else:
#filename, top_name = get_fn(t), get_fn(top)
m_traj = md.load(t, top=top)
m_traj = m_traj[first_frame:last_frame:stride]
traj = pt.Trajectory(xyz=m_traj.xyz.astype('f8'),
top=top)
if rotfit == True:
_ = traj.superpose(ref=-1, mask='@CA')
data_vec = va.vector_mask(traj,
self.nh_pairs,
dtype='ndarray')
self.acf[index] = {
res_list[n]: pt.timecorr(vals,
vals,
order=2,
tstep=1,
tcorr=len(vals),
norm=False,
dtype='ndarray')
for n, vals in enumerate(data_vec)
}
self.S2[index] = {
res_list[n]: S2(vals)
for n, vals in enumerate(data_vec)
}
bcount += 1
index += 1
bcount = 0