def setUp(self):
self.traj = pt.iterload(fn("tz2.nc"), fn("tz2.parm7"))
cm_avg = '''
average avg.pdb
run
'''
pt.load_batch(self.traj, cm_avg).run()
def test_loading(self):
for frame_slice in [(0, -1, 1), (0, 8, 2), (3, 9, 3)]:
traj = pt.iterload('data/tz2.nc',
'./data/tz2.parm7',
frame_slice=frame_slice)
# load from text
text = '''
rms @CA
radgyr @CA nomax
'''
s = load_batch(traj, text)
s.run()
rmsd0 = pt.rmsd(traj, '@CA', ref=0)
r0 = pt.radgyr(traj, '@CA')
s.data._pop(0)
aa_eq(rmsd0, s.data[0])
aa_eq(r0, s.data[1])
assert len(s.data[0]) == traj.n_frames
# load from list
lines = ['rms @CA', ' radgyr @CA nomax']
s = load_batch(traj, lines)
s.run()
s.data._pop(0)
rmsd0 = pt.rmsd(traj, '@CA', ref=0)
r0 = pt.radgyr(traj, '@CA')
aa_eq(rmsd0, s.data[0])
aa_eq(r0, s.data[1])
assert len(s.data[0]) == traj.n_frames
def test_loading(self):
for frame_slice in [(0, -1, 1), (0, 8, 2), (3, 9, 3)]:
traj = pt.iterload(fn('tz2.nc'),
fn('tz2.parm7'),
frame_slice=frame_slice)
# load from text
text = '''
rms @CA
radgyr @CA nomax
'''
s = load_batch(traj, text)
s.run()
rmsd0 = pt.rmsd(traj, '@CA', ref=0)
r0 = pt.radgyr(traj, '@CA')
s.data._pop(0)
aa_eq(rmsd0, s.data[0])
aa_eq(r0, s.data[1])
assert len(s.data[0]) == traj.n_frames
# load from list
lines = ['rms @CA', ' radgyr @CA nomax']
s = load_batch(traj, lines)
s.run()
s.data._pop(0)
rmsd0 = pt.rmsd(traj, '@CA', ref=0)
r0 = pt.radgyr(traj, '@CA')
aa_eq(rmsd0, s.data[0])
aa_eq(r0, s.data[1])
assert len(s.data[0]) == traj.n_frames
def test_nomass(self):
traj = self.traj
# cpptraj output
cm = '''
reference avg.pdb
rms R0 reference @CA,C,N,O savematrices
'''
state = pt.load_batch(traj, cm)
state.run()
saved_mat = state.data[-1].values
# pytraj output
avg = pt.mean_structure(traj)
mat = pt.calc_rotation_matrix(traj, ref=avg, mask='@CA,C,N,O')
assert mat.shape == (traj.n_frames, 3, 3), 'mat shape'
aa_eq(mat.flatten(), saved_mat.flatten())
# not specify reference
cm = '''
rms R0 @CA,C,N,O savematrices
'''
state = pt.load_batch(traj, cm)
state.run()
saved_mat = state.data[-1].values
mat = pt.calc_rotation_matrix(traj, mask='@CA,C,N,O')
aa_eq(mat.flatten(), saved_mat.flatten())
def setUp(self):
self.traj = pt.iterload("./data/tz2.nc", "./data/tz2.parm7")
cm_avg = '''
average avg.pdb
run
'''
pt.load_batch(self.traj, cm_avg).run()
def test_raise_if_not_trajiter(self):
traj = pt.iterload(fn('tz2.nc'), fn('tz2.parm7'))
t0 = traj[:]
text = '''
rms @CA
radgyr @CA nomax
'''
with pytest.raises(ValueError):
load_batch(t0, text)
def test_frame_indices(self):
traj = pt.iterload("data/tz2.truncoct.nc", "data/tz2.truncoct.parm7")
traj2 = pt.iterload("data/tz2.truncoct.nc",
"data/tz2.truncoct.parm7",
frame_slice=(2, 8))
txt = '''
reference ./data/tz2.truncoct.nc 2 2
rmsd :2-11 refindex 0 perres perresout center.agr range 1 perrescenter
'''
state = pt.load_batch(traj2, txt)
state.run()
frame_indices = range(2, 8)
rmsd0 = pt.rmsd(traj, ref=1, mask=':2-11', frame_indices=frame_indices)
rmsdperres = pt.rmsd_perres(traj,
ref=1,
mask=':2-11',
perres_mask='*',
resrange='1',
perres_center=True,
frame_indices=frame_indices)
aa_eq(rmsd0, state.data[2])
aa_eq(rmsdperres[1], state.data[3].values)
def normal_(traj=traj):
print('serial')
state = pt.load_batch(traj, '''
matrix dist @P
''')
state.run()
return state.data[1:].values
def normal_(traj=traj):
print('serial')
state = pt.load_batch(traj, '''
rms !:WAT
''')
state.run()
return state.data[1:].values
def test_multivector(self):
traj = pt.iterload("./data/tz2.nc", "./data/tz2.parm7")
state = pt.load_batch(traj, '''
multivector resrange 3-7 name1 C name2 N
''')
state.run()
cpp_data = state.data[1:].values
aa_eq(
pt.multivector(traj,
resrange='3-7',
names=('C', 'N'),
dtype='ndarray'),
cpp_data)
aa_eq(
pt.multivector(traj,
resrange='3-7',
names='C N',
dtype='ndarray'),
cpp_data)
aa_eq(
pt.multivector(traj,
resrange='3-7',
names='name1 C name2 N',
dtype='ndarray'),
cpp_data)
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
def test_mass(self):
traj = self.traj
# cpptraj output
cm = '''
reference avg.pdb
rms R0 reference @CA,C,N,O savematrices mass
'''
state = pt.load_batch(traj, cm)
state.run()
saved_mat = state.data[-1].values
# pytraj output
avg = pt.mean_structure(traj)
mat = pt.calc_rotation_matrix(traj,
ref=avg,
mask='@CA,C,N,O',
mass=True)
assert mat.shape == (traj.n_frames, 3, 3), 'mat shape'
aa_eq(mat.flatten(), saved_mat.flatten())
# with rmsd
avg = pt.mean_structure(traj)
mat2, rmsd_ = pt.calc_rotation_matrix(traj,
ref=avg,
mask='@CA,C,N,O',
mass=True,
with_rmsd=True)
aa_eq(mat2.flatten(), saved_mat.flatten())
assert pt.tools.rmsd(rmsd_, state.data['R0']) < 1E-3
def test_frame_indices(self):
traj = pt.iterload("data/tz2.truncoct.nc", "data/tz2.truncoct.parm7")
traj2 = pt.iterload("data/tz2.truncoct.nc",
"data/tz2.truncoct.parm7",
frame_slice=(2, 8))
txt = '''
reference ./data/tz2.truncoct.nc 2 2
rmsd :2-11 refindex 0 perres perresout center.agr range 1 perrescenter
'''
state = pt.load_batch(traj2, txt)
state.run()
frame_indices = range(2, 8)
rmsd0 = pt.rmsd(traj, ref=1, mask=':2-11', frame_indices=frame_indices)
rmsdperres = pt.rmsd_perres(traj,
ref=1,
mask=':2-11',
perres_mask='*',
resrange='1',
perres_center=True,
frame_indices=frame_indices)
aa_eq(rmsd0, state.data[2])
aa_eq(rmsdperres[1], state.data[3].values)
def test_frame_indices(self):
traj = pt.iterload(fn("tz2.truncoct.nc"), fn("tz2.truncoct.parm7"))
traj2 = pt.iterload(fn("tz2.truncoct.nc"),
fn("tz2.truncoct.parm7"),
frame_slice=(2, 8))
txt = '''
reference {} 2 2
rmsd :2-11 refindex 0 perres perresout center.agr range 1 perrescenter
'''.format(fn('tz2.truncoct.nc'))
state = pt.load_batch(traj2, txt)
with tempfolder():
state.run()
frame_indices = range(2, 8)
rmsd0 = pt.rmsd(traj, ref=1, mask=':2-11', frame_indices=frame_indices)
rmsdperres = pt.rmsd_perres(traj,
ref=1,
mask=':2-11',
perres_mask='*',
resrange='1',
perres_center=True,
frame_indices=frame_indices)
aa_eq(rmsd0, state.data[2])
aa_eq(rmsdperres[1], state.data[3].values)
def test_multivector(self):
traj = pt.iterload(fn('tz2.nc'), fn('tz2.parm7'))
state = pt.load_batch(traj, '''
multivector resrange 3-7 name1 C name2 N
''')
state.run()
cpp_data = state.data[1:].values
aa_eq(
pt.multivector(traj,
resrange='3-7',
names=('C', 'N'),
dtype='ndarray'),
cpp_data)
aa_eq(
pt.multivector(traj,
resrange='3-7',
names='C N',
dtype='ndarray'),
cpp_data)
aa_eq(
pt.multivector(traj,
resrange='3-7',
names='name1 C name2 N',
dtype='ndarray'),
cpp_data)
def test_atomiccorr(self):
traj = pt.iterload("./data/tz2.nc", "./data/tz2.parm7")
state = pt.load_batch(traj, '''
atomiccorr out test.dat :1-13 byres
''')
state.run()
data = pt.atomiccorr(traj, ':1-13', byres=True)
aa_eq(data, state.data[1].values)
def normal_(traj=traj):
print('serial')
state = pt.load_batch(
traj, '''
distance :3 :2
molsurf !:WAT
''')
state.run()
return state.data[1:].values
def test_raise_if_not_trajiter(self):
traj = pt.iterload(fn('tz2.nc'), fn('tz2.parm7'))
t0 = traj[:]
text = '''
rms @CA
radgyr @CA nomax
'''
self.assertRaises(ValueError, lambda: load_batch(t0, text))
def test_atomiccorr(self):
traj = pt.iterload(fn('tz2.nc'), fn('tz2.parm7'))
state = pt.load_batch(
traj, '''
atomiccorr out test.dat :1-13 byres
''')
state.run()
data = pt.atomiccorr(traj, ':1-13', byres=True)
aa_eq(data, state.data[1].values)
def test_raise_if_not_trajiter(self):
traj = pt.iterload('data/tz2.nc', './data/tz2.parm7')
t0 = traj[:]
text = '''
rms @CA
radgyr @CA nomax
'''
self.assertRaises(ValueError, lambda: load_batch(t0, text))
def test_RMSF_with_options(self):
traj = pt.iterload(fn('tz2.nc'), fn('tz2.parm7'))
state = pt.load_batch(
traj, '''
atomicfluct @CA byres out fluct.agr''')
state.run()
data = pt.rmsf(traj, '@CA', options='byres')
aa_eq(data, state.data[-1].values)
def test_cpptraj(traj=traj):
state = pt.load_batch(
traj, '''
autoimage
distance :1 :3
distance :5 :18
molsurf @CA
multidihedral
''')
state.run()
return state
def test_watershell(self):
traj = pt.iterload("data/tz2.truncoct.nc", "data/tz2.truncoct.parm7")
state = pt.load_batch(traj, '''
watershell :1-7
''')
d0 = pt.watershell(traj, solute_mask=':1-7')
state.run()
aa_eq(d0.values, state.data[[1, 2]].values)
# need to provide solute_mask
self.assertRaises(ValueError, lambda: pt.watershell(traj))
def test_watershell(self):
traj = pt.iterload(fn('tz2.truncoct.nc'), fn('tz2.truncoct.parm7'))
state = pt.load_batch(traj, '''
watershell :1-7
''')
d0 = pt.watershell(traj, solute_mask=':1-7')
state.run()
aa_eq(d0.values, state.data[[1, 2]].values)
# need to provide solute_mask
self.assertRaises(ValueError, lambda: pt.watershell(traj))
def test_autoimage_rms_strip(self):
traj = pt.iterload("./data/tz2.ortho.nc", "./data/tz2.ortho.parm7")
state = pt.load_batch(traj, '''
autoimage
rms
strip !@CA
createcrd''')
state.run()
# get last datset (DatasetCoordsCRD)
crd = state.data[-1]
t0 = traj[:]
new_traj = t0.autoimage().superpose()['@CA']
aa_eq(new_traj.xyz, crd.xyz)
def test_autoimage_rms_strip(self):
traj = pt.iterload("./data/tz2.ortho.nc", "./data/tz2.ortho.parm7")
state = pt.load_batch(
traj, '''
autoimage
rms
strip !@CA
createcrd''')
state.run()
# get last datset (DatasetCoordsCRD)
crd = state.data[-1]
t0 = traj[:]
new_traj = t0.autoimage().superpose()['@CA']
aa_eq(new_traj.xyz, crd.xyz)
def test_RMSF(self):
traj = pt.iterload(fn('tz2.nc'), fn('tz2.parm7'))
state = pt.load_batch(traj, '''
rms first
average crdset MyAvg
run
rms ref MyAvg
atomicfluct out fluct.agr''')
state.run()
t0 = traj[:]
pt.superpose(t0, ref=0)
avg = pt.mean_structure(t0)
pt.superpose(t0, ref=avg)
data = pt.rmsf(t0)
aa_eq(data, state.data[-1].values)
def test_RMSF(self):
traj = pt.iterload("./data/tz2.nc", "./data/tz2.parm7")
state = pt.load_batch(traj, '''
rms first
average crdset MyAvg
run
rms ref MyAvg
atomicfluct out fluct.agr''')
state.run()
t0 = traj[:]
pt.superpose(t0, ref=0)
avg = pt.mean_structure(t0)
pt.superpose(t0, ref=avg)
data = pt.rmsf(t0)
aa_eq(data, state.data[-1].values)
def test_diagmatrix_mwcovar(self):
traj = pt.iterload(fn('tz2.nc'), fn('tz2.parm7'))
state = pt.load_batch(traj, '''
matrix mwcovar @CA name mymat
diagmatrix mymat vecs 6 name mydiag''')
state.run()
mat = pt.matrix.mwcovar(traj, '@CA')
ca_indices = traj.top.select('@CA')
eigenvectors, eigenvalues = pt.matrix.diagonalize(
mat,
n_vecs=6,
scalar_type='mwcovar',
mass=traj.top.mass[ca_indices])
aa_eq(np.abs(state.data['mydiag'].eigenvalues), np.abs(eigenvalues))
aa_eq(np.abs(state.data['mydiag'].eigenvectors), np.abs(eigenvectors))
def test_matrix(self):
import numpy as np
from pytraj import ArgList
from pytraj import matrix as ma
from pytraj.externals.six import iteritems
matrix_test_dir = cpptraj_test_dir + "/Test_Matrix/"
top_file = matrix_test_dir + "/1rrb_vac.prmtop"
crd_file = matrix_test_dir + "/1rrb_vac.mdcrd"
traj = pt.iterload(crd_file, top_file)
with tempfolder():
state = pt.load_cpptraj_state(all_commands, traj)
state.run()
state_byres = pt.load_batch(traj, 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(".")
mask = arg.get_next_mask()
fname = ".".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.flatten(), 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_reference(self):
traj = pt.iterload("./data/tz2.truncoct.nc", "data/tz2.truncoct.parm7")
txt = '''
reference data/tz2.truncoct.nc 2 2
rmsd :2-11 refindex 0 perres perresout center.agr range 1 perrescenter
'''
state = pt.load_batch(traj, txt).run()
# state.data has 3 datasets: ref, rmsd, rmsd perres
# cpptraj use 2nd reference
rmsd0 = pt.rmsd(traj, ref=1, mask=':2-11')
rmsdperres = pt.rmsd_perres(traj,
ref=1,
mask=':2-11',
perres_mask='*',
resrange='1',
perres_center=True)
aa_eq(rmsd0, state.data[2])
aa_eq(rmsdperres[1], state.data[3].values)
def test_distance_with_dry_traj_and_PBC_topology(self):
'''Situation: there is dry traj (no box) but Topology has box info
'''
traj = pt.iterload(
fn('dry_traj_with_PBC_top/strip.nc'),
fn('dry_traj_with_PBC_top/strip.prmtop'))
assert traj.top.has_box(), 'Topology must have box for testing'
correct_distance_with_image_True = pt.distance(
traj, ':8@OP2 :5@N1', image=True)
correct_distance_with_image_False = pt.distance(
traj, ':8@OP2 :5@N1', image=False)
state = pt.load_batch(traj, '''
distance :8@OP2 :5@N1
''')
state.run()
expected_distance = [3.08030475, 2.68452183]
aa_eq(correct_distance_with_image_False, expected_distance)
aa_eq(correct_distance_with_image_True, [0., 0.])
def test_reference(self):
traj = pt.iterload("./data/tz2.truncoct.nc", "data/tz2.truncoct.parm7")
txt = '''
reference data/tz2.truncoct.nc 2 2
rmsd :2-11 refindex 0 perres perresout center.agr range 1 perrescenter
'''
state = pt.load_batch(traj, txt).run()
# state.data has 3 datasets: ref, rmsd, rmsd perres
# cpptraj use 2nd reference
rmsd0 = pt.rmsd(traj, ref=1, mask=':2-11')
rmsdperres = pt.rmsd_perres(traj,
ref=1,
mask=':2-11',
perres_mask='*',
resrange='1',
perres_center=True)
aa_eq(rmsd0, state.data[2])
aa_eq(rmsdperres[1], state.data[3].values)
def test_mpi_load_batch():
# create ``comm`` so you can have the info about n_cpus, cpu id
comm = MPI.COMM_WORLD
# you are free to update anything below here
# _load_batch_pmap is temp method name, will be changed in future
traj_name = fn("tz2.nc")
parm_name = fn("tz2.parm7")
# load to TrajectoryIterator
traj = pt.iterload(traj_name, parm_name, frame_slice=(0, 4000))
# make a list of things you want to
lines = ['autoimage', 'distance :3 :10', 'molsurf @CA']
# gather the data to 1st core (rank=0)
#
n_cores = comm.size
data = _load_batch_pmap(n_cores,
lines=lines,
traj=traj,
mode='mpi',
dtype='dict')
if comm.rank != 0:
assert data is None
if comm.rank == 0:
# each core return a tuple (core_id, dict)
# so you need to concat the dict
# use `from pytraj.tools import concat_dict
data_0 = concat_dict(x[0] for x in data)
# assert to serial version (do not need to copy below to your script)
state = pt.load_batch(traj, lines)
state.run()
data = state.data[1:].to_dict()
for key_0, key in zip(sorted(data_0.keys()), sorted(data.keys())):
aa_eq(data_0[key_0], data[key])
def test_reference(self):
traj = pt.iterload(fn("tz2.truncoct.nc"), fn("tz2.truncoct.parm7"))
txt = '''
reference {} 2 2
rmsd :2-11 refindex 0 perres perresout center.agr range 1 perrescenter
'''.format(fn('tz2.truncoct.nc'))
with tempfolder():
state = pt.load_batch(traj, txt).run()
# state.data has 3 datasets: ref, rmsd, rmsd perres
# cpptraj use 2nd reference
rmsd0 = pt.rmsd(traj, ref=1, mask=':2-11')
rmsdperres = pt.rmsd_perres(traj,
ref=1,
mask=':2-11',
perres_mask='*',
resrange='1',
perres_center=True)
aa_eq(rmsd0, state.data[2])
aa_eq(rmsdperres[1], state.data[3].values)
def test_diagmatrix(self):
traj = pt.iterload(fn('tz2.nc'), fn('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__()
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'))
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'
#!/usr/bin/env python
import pytraj as pt
root_dir = "../../tests/data/"
traj_name = root_dir + "tz2.nc"
parm_name = root_dir + "tz2.parm7"
# load to TrajectoryIterator
traj = pt.iterload(traj_name, parm_name, frame_slice=(0, 4000))
state = pt.load_batch(traj, '''
autoimage
distance :3 :10
molsurf @CA
''')
state.run()
print([d for d in state.data])
# make a list of things you want to
lines = ['autoimage', 'distance :3 :10', 'molsurf @CA']
# gather the data to 1st core (rank=0)
#
n_cores = comm.size
data = _load_batch_pmap(n_cores,
lines=lines,
traj=traj,
mode='mpi',
dtype='dict')
if comm.rank != 0:
assert data is None
if comm.rank == 0:
from pytraj.tools import concat_dict
# each core return a tuple (core_id, dict)
# so you need to concat the dict
# use `from pytraj.tools import concat_dict
data_0 = concat_dict(x[1] for x in data)
# assert to serial version (do not need to copy below to your script)
state = pt.load_batch(traj, lines)
state.run()
data = state.data[1:].to_dict()
for key_0, key in zip(sorted(data_0.keys()), sorted(data.keys())):
aa_eq(data_0[key_0], data[key])
traj_name = root_dir + "tz2.nc"
parm_name = root_dir + "tz2.parm7"
# load to TrajectoryIterator
traj = pt.iterload(traj_name, parm_name, frame_slice=(0, 4000))
# make a list of things you want to
lines = ['autoimage', 'distance :3 :10', 'molsurf @CA']
# gather the data to 1st core (rank=0)
#
n_cores = comm.size
data = _load_batch_pmap(n_cores, lines=lines, traj=traj, mode='mpi', dtype='dict')
if comm.rank != 0:
assert data is None
if comm.rank == 0:
# each core return a tuple (core_id, dict)
# so you need to concat the dict
# use `from pytraj.tools import concat_dict
data_0 = concat_dict(x[0] for x in data)
# assert to serial version (do not need to copy below to your script)
state = pt.load_batch(traj, lines)
state.run()
data = state.data[1:].to_dict()
for key_0, key in zip(sorted(data_0.keys()), sorted(data.keys())):
aa_eq(data_0[key_0], data[key])
def test_rdf(self):
traj = pt.iterload("./data/tz2.truncoct.nc",
"./data/tz2.truncoct.parm7",
frame_slice=(0, 10))
command = '''
radial output/Radial.agr 0.5 10.0 :5@CD :WAT@O
radial output/cRadial.agr 0.5 10.0 :5 :WAT@O center1
radial output/cRadial.agr 0.5 10.0 :5 :WAT@O center2
radial output/cRadial.agr 0.5 20.0 :3 :WAT@O
radial output/cRadial.agr 0.5 20.0 :3 :WAT@O noimage
radial output/radial.dat 0.5 10.0 :5@CD :WAT@O
radial output/radial2.dat 0.25 10.0 :5@CD :WAT@O
'''
# get data directly from cpptraj
state = pt.load_batch(traj, command)
state.run()
# get data from pytraj
data0 = pt.rdf(traj,
solvent_mask=':WAT@O',
bin_spacing=0.5,
maximum=10.0,
solute_mask=':5@CD')
data01 = pt.rdf(traj,
solvent_mask=':WAT@O',
bin_spacing=0.5,
maximum=10.0,
solute_mask=':5@CD')
data1 = pt.rdf(traj,
solvent_mask=':WAT@O',
bin_spacing=0.5,
maximum=10.0,
center_solvent=True,
solute_mask=':5')
data2 = pt.rdf(traj,
solvent_mask=':WAT@O',
bin_spacing=0.5,
maximum=10.0,
center_solute=True,
solute_mask=':5')
data3 = pt.rdf(traj,
solvent_mask=':WAT@O',
bin_spacing=0.5,
maximum=20.0,
center_solute=False,
solute_mask=':3')
data4 = pt.rdf(traj,
solvent_mask=':WAT@O',
bin_spacing=0.5,
maximum=20.0,
center_solute=False,
image=False,
solute_mask=':3')
data5 = pt.rdf(traj,
solvent_mask=':WAT@O',
bin_spacing=0.25,
maximum=10.0,
solute_mask=':5@CD')
# solvent_mask is array
solvent_indices = pt.select(':WAT@O', traj.top)
data6 = pt.rdf(traj,
solvent_mask=solvent_indices,
bin_spacing=0.25,
maximum=10.0,
solute_mask=':5@CD')
# do assertion
aa_eq(data0[1], state.data[1], decimal=7)
aa_eq(data1[1], state.data[2], decimal=7)
aa_eq(data2[1], state.data[3], decimal=7)
aa_eq(data3[1], state.data[4], decimal=7)
aa_eq(data4[1], state.data[5], decimal=7)
# default solvent mask :WAT@O
aa_eq(data01[1], state.data[1], decimal=7)
steps = np.loadtxt('output/radial.dat').T[0]
aa_eq(data0[0], steps)
steps2 = np.loadtxt('output/radial2.dat').T[0]
aa_eq(data5[0], steps2)
aa_eq(data6[0], steps2)
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 test_rdf(self):
traj = pt.iterload("./data/tz2.truncoct.nc",
"./data/tz2.truncoct.parm7",
frame_slice=(0, 10))
command = '''
radial output/Radial.agr 0.5 10.0 :5@CD :WAT@O
radial output/cRadial.agr 0.5 10.0 :5 :WAT@O center1
radial output/cRadial.agr 0.5 10.0 :5 :WAT@O center2
radial output/cRadial.agr 0.5 20.0 :3 :WAT@O
radial output/cRadial.agr 0.5 20.0 :3 :WAT@O noimage
radial output/radial.dat 0.5 10.0 :5@CD :WAT@O
radial output/radial2.dat 0.25 10.0 :5@CD :WAT@O
'''
# get data directly from cpptraj
state = pt.load_batch(traj, command)
state.run()
# get data from pytraj
data0 = pt.rdf(traj,
solvent_mask=':WAT@O',
bin_spacing=0.5,
maximum=10.0,
solute_mask=':5@CD')
data01 = pt.rdf(traj,
solvent_mask=':WAT@O',
bin_spacing=0.5,
maximum=10.0,
solute_mask=':5@CD')
data1 = pt.rdf(traj,
solvent_mask=':WAT@O',
bin_spacing=0.5,
maximum=10.0,
center_solvent=True,
solute_mask=':5')
data2 = pt.rdf(traj,
solvent_mask=':WAT@O',
bin_spacing=0.5,
maximum=10.0,
center_solute=True,
solute_mask=':5')
data3 = pt.rdf(traj,
solvent_mask=':WAT@O',
bin_spacing=0.5,
maximum=20.0,
center_solute=False,
solute_mask=':3')
data4 = pt.rdf(traj,
solvent_mask=':WAT@O',
bin_spacing=0.5,
maximum=20.0,
center_solute=False,
image=False,
solute_mask=':3')
data5 = pt.rdf(traj,
solvent_mask=':WAT@O',
bin_spacing=0.25,
maximum=10.0,
solute_mask=':5@CD')
# solvent_mask is array
solvent_indices = pt.select(':WAT@O', traj.top)
data6 = pt.rdf(traj,
solvent_mask=solvent_indices,
bin_spacing=0.25,
maximum=10.0,
solute_mask=':5@CD')
# do assertion
aa_eq(data0[1], state.data[1], decimal=7)
aa_eq(data1[1], state.data[2], decimal=7)
aa_eq(data2[1], state.data[3], decimal=7)
aa_eq(data3[1], state.data[4], decimal=7)
aa_eq(data4[1], state.data[5], decimal=7)
# default solvent mask :WAT@O
aa_eq(data01[1], state.data[1], decimal=7)
steps = np.loadtxt('output/radial.dat').T[0]
aa_eq(data0[0], steps)
steps2 = np.loadtxt('output/radial2.dat').T[0]
aa_eq(data5[0], steps2)
aa_eq(data6[0], steps2)