def test_trajiter_with_actionlist(self):
traj = pt.iterload(fn('tz2.ortho.nc'), fn('tz2.ortho.parm7'))
traj.autoimage().center('origin').superpose('@CA')
pk_fn = 'test.pk'
pt.to_pickle(traj, pk_fn)
traj2 = pt.read_pickle(pk_fn)
print(traj2._transform_commands)
aa_eq(traj.xyz, traj2.xyz)
def test_pickle_frame(self):
traj = pt.iterload(fn('tz2.nc'), fn('tz2.parm7'))
f0 = traj[0]
print(f0.mass)
fn = 'f.pk'
pt.to_pickle(f0, fn)
f1 = pt.read_pickle(fn)
print(f1.mass)
def test_pickle_frame(self):
traj = pt.iterload("./data/tz2.nc", "./data/tz2.parm7")
f0 = traj[0]
print(f0.mass)
fn = 'output/f.pk'
pt.to_pickle(f0, fn)
f1 = pt.read_pickle(fn)
print(f1.mass)
def test_pickle_frame(self):
traj = pt.iterload("./data/tz2.nc", "./data/tz2.parm7")
f0 = traj[0]
print(f0.mass)
fn = 'output/f.pk'
pt.to_pickle(f0, fn)
f1 = pt.read_pickle(fn)
print(f1.mass)
def test_nupars(self):
pdb_fn = fn('Test_NAstruct/adh026.3.pdb')
traj = pt.iterload(pdb_fn, pdb_fn)
data = pt.nastruct(traj)
# default
text = '''
parm {}
trajin {}
nastruct groovecalc 3dna
'''.format(fn('Test_NAstruct/adh026.3.pdb'),
fn('Test_NAstruct/adh026.3.pdb'))
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, 'na.pk')
na2 = pt.read_pickle('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_pickle_progress_bar():
traj = pt.datafiles.load_tz2()
with tempfolder():
t2 = ProgressBarTrajectory(traj)
pt.to_pickle(t2, 'test.pk')
t3 = pt.read_pickle('test.pk')
data0 = pt.rmsd(traj)
data1 = pt.rmsd(t2)
data2 = pt.rmsd(t3)
aa_eq(data0, data1)
aa_eq(data0, data2)
def test_frame(self):
traj = pt.iterload("data/Tc5b.x", "data/Tc5b.top")
frame = traj[0]
pt.to_pickle(frame, 'output/frame.pk')
frame0 = pt.read_pickle('output/frame.pk')
aa_eq(frame0.xyz, frame.xyz)
# test list of frames
fname = 'output/flist.pk'
pt.to_pickle([traj[0], traj[1]], fname)
f01 = pt.read_pickle(fname)
aa_eq(np.array([f.xyz for f in f01]), traj[[0, 1]].xyz)
def test_frame(self):
traj = pt.iterload(fn('Tc5b.x'), fn('Tc5b.top'))
frame = traj[0]
pt.to_pickle(frame, 'frame.pk')
frame0 = pt.read_pickle('frame.pk')
aa_eq(frame0.xyz, frame.xyz)
# test list of frames
fname = 'flist.pk'
pt.to_pickle([traj[0], traj[1]], fname)
f01 = pt.read_pickle(fname)
aa_eq(np.array([f.xyz for f in f01]), traj[[0, 1]].xyz)
def test_frame(self):
traj = pt.iterload("data/Tc5b.x", "data/Tc5b.top")
frame = traj[0]
pt.to_pickle(frame, 'output/frame.pk')
frame0 = pt.read_pickle('output/frame.pk')
aa_eq(frame0.xyz, frame.xyz)
# test list of frames
fname = 'output/flist.pk'
pt.to_pickle([traj[0], traj[1]], fname)
f01 = pt.read_pickle(fname)
aa_eq(np.array([f.xyz for f in f01]), traj[[0, 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'))
# load pytraj and mpi4py
import pytraj as pt
from mpi4py import MPI
# create mpi handler to get cpu rank
comm = MPI.COMM_WORLD
# load trajectory
# create filenames (could be a single filename or a list of filenames that cpptraj supported)
# (restart file, pdb, netcdf, mdcrd, dcd, ...)
# check more: http://amber-md.github.io/pytraj/latest/trajectory_exercise.html
filenames = ['fn1.pdb', 'fn2.pdb',]
topology_name = 'fn1.parm7'
traj = pt.iterload(filenames, top=topology_name)
# perform parallel calculation
data = pt.pmap_mpi(pt.energy_decomposition, traj, igb=8)
# data is a Python dict, it's up to you to save the data
# you can use pt.to_pickle to save the dict to disk and then use pt.read_pickle to reload the dict
# use rank == 0 since pytraj sends output to first cpu.
if comm.rank == 0:
#print(data)
pt.to_pickle(data, 'my_data.pk')
# reload for another analysis
# data = pt.read_pickle('my_data.pk')
def test_pickle_datasetlist(self):
traj = pt.iterload("data/Tc5b.x", "data/Tc5b.top")
dslist = pt.multidihedral(traj)
pt.to_pickle(dslist, 'output/ds.pk')
dslist2 = pt.read_pickle('output/ds.pk')
aa_eq(dslist.values, dslist2.values)
else:
pdb_dirs[index] = pdb
# get min filenames
_min_files = [
pdb_dir + '/no_restraint/' + 'min_NoH_' + x
for pdb_dir, x in zip(pdb_dirs, df[2])
]
end = '_0001'
min_files = []
for mf in _min_files:
if '0001_0001' in mf:
min_files.append(mf.replace('0001_0001', '0001') + '.rst7')
elif '0002_0001' in mf:
min_files.append(mf.replace('0002_0001', '0002') + '.rst7')
else:
min_files.append(mf)
epots = []
for pdb_dir, mf in zip(pdb_dirs, min_files):
if os.path.exists(mf):
parmfile = glob(pdb_dir + '/*.parm7')[0]
traj = pt.iterload(mf, parmfile)
epots.append(pt.energy_decomposition(traj, igb=8)['tot'][0])
else:
epots.append(None)
pt.to_pickle(epots, 'epots.pk')
def test_picle(self):
'''test_picle
'''
pt.to_pickle(self.traj.top, 'output/new_top.pk')
new_top = pt.read_pickle('output/new_top.pk')
assert_equal_topology(self.traj.top, new_top, self.traj)
def test_pickle_datasetlist(self):
traj = pt.iterload("data/Tc5b.x", "data/Tc5b.top")
dslist = pt.multidihedral(traj)
pt.to_pickle(dslist, 'output/ds.pk')
dslist2 = pt.read_pickle('output/ds.pk')
aa_eq(dslist.values, dslist2.values)
def pick_topology_(top):
pt.to_pickle(top, 'test.pk')
def test_pickle_datasetlist(self):
traj = pt.iterload(fn('Tc5b.x'), fn('Tc5b.top'))
dslist = pt.multidihedral(traj)
pt.to_pickle(dslist, 'ds.pk')
dslist2 = pt.read_pickle('ds.pk')
aa_eq(dslist.values, dslist2.values)
def test_picle(self):
'''test_picle
'''
pt.to_pickle(self.traj.top, 'new_top.pk')
new_top = pt.read_pickle('new_top.pk')
assert_equal_topology(self.traj.top, new_top, self.traj)
def main():
from MMPBSA_mods import API as MMPBSA_API
data = MMPBSA_API.load_mmpbsa_info('_MMPBSA_info')
decomp_data = data['decomp']['gb']['complex']['TDC']
pt.to_pickle(decomp_data, 'energy_per_residue_per_structure.pk')
def pick_topology_(top):
pt.to_pickle(top, 'test.pk')
elif 'decoys.set2' in pdb:
pdb_dirs[index] = pdb.replace('decoys.set2', 'decoys.set2.init')
else:
pdb_dirs[index] = pdb
# get min filenames
_min_files = [pdb_dir + '/no_restraint/' + 'min_NoH_' + x
for pdb_dir, x in zip(pdb_dirs, df[2])]
end = '_0001'
min_files = []
for mf in _min_files:
if '0001_0001' in mf:
min_files.append(mf.replace('0001_0001', '0001') + '.rst7')
elif '0002_0001' in mf:
min_files.append(mf.replace('0002_0001', '0002') + '.rst7')
else:
min_files.append(mf)
epots = []
for pdb_dir, mf in zip(pdb_dirs, min_files):
if os.path.exists(mf):
parmfile = glob(pdb_dir + '/*.parm7')[0]
traj = pt.iterload(mf, parmfile)
epots.append(pt.energy_decomposition(traj, igb=8)['tot'][0])
else:
epots.append(None)
pt.to_pickle(epots, 'epots.pk')
