def run(project, atom_indices=None, traj_fn='all'):
n_atoms = project.load_conf()['XYZList'].shape[1]
if traj_fn.lower() == 'all':
SASA = np.ones(
(project.n_trajs, np.max(project.traj_lengths), n_atoms)) * -1
for traj_ind in xrange(project.n_trajs):
traj_asa = []
logger.info("Working on Trajectory %d", traj_ind)
traj_fn = project.traj_filename(traj_ind)
chunk_ind = 0
for traj_chunk in Trajectory.enum_chunks_from_lhdf(
traj_fn, AtomIndices=atom_indices):
#print chunk_ind
traj_asa.extend(
asa.calculate_asa(traj_chunk, n_sphere_points=24))
chunk_ind += 1
SASA[traj_ind, 0:project.traj_lengths[traj_ind]] = traj_asa
else:
traj_asa = []
for traj_chunk in Trajectory.enum_chunks_from_lhdf(
traj_fn, AtomIndices=atom_indices):
traj_asa.extend(asa.calculate_asa(traj_chunk))
SASA = np.array(traj_asa)
return SASA
def plot_gpu_cmd_correlation():
traj1 = Trajectory.load_trajectory_file(ww_1, Conf=ww_conf)
traj1_copy = Trajectory.load_trajectory_file(ww_1, Conf=ww_conf)
#traj2 = Trajectory.load_trajectory_file(ww_2, Conf=ww_conf)
#traj2_copy = Trajectory.load_trajectory_file(ww_2, Conf=ww_conf)
def gpudist(t):
gpurmsd = GPURMSD()
pt = gpurmsd.prepare_trajectory(t)
gpurmsd._gpurmsd.print_params()
return gpurmsd.one_to_all(pt, pt, 0)
def cpudist(t):
rmsd = RMSD()
pt = rmsd.prepare_trajectory(t)
return rmsd.one_to_all(pt, pt, 0)
g1 = gpudist(traj1) #, gpudist(traj2)
c1 = cpudist(traj1_copy) #, cpudist(traj2_copy)
pp.subplot(231)
pp.plot(c1)
pp.title('cpu rmsd drift along traj')
pp.xlabel('frame index')
pp.xlabel('cpurmsd($X_{0}$, $X_{frame_index}$)')
pp.subplot(232)
pp.scatter(g1, c1)
pp.xlabel('gpu rmsd')
pp.ylabel('cpu rmsd')
pp.subplot(233)
pp.plot(g1)
pp.title('gpu rmsd drift along traj')
pp.xlabel('frame index')
pp.xlabel('gpurmsd($X_{0}$, $X_{frame_index}$)')
#PLOT c2 and g2 in the lower portion of the graph
#pp.subplot(234)
#pp.plot(c2)
#pp.title('cpu rmsd drift along pre-aligned traj')
#pp.xlabel('frame index')
#pp.xlabel('cpurmsd($X_{0}$, $X_{frame_index}$)')
#pp.subplot(235)
#pp.scatter(g2, c2)
#pp.xlabel('gpu rmsd')
#pp.ylabel('cpu rmsd')
#pp.subplot(236)
#pp.plot(g2)
#pp.title('gpu rmsd drift along pre-aligned traj')
#pp.xlabel('frame index')
#pp.xlabel('gpurmsd($X_{0}$, $X_{frame_index}$)')
#pp.subplots_adjust(hspace=0.4)
#pp.savefig('gpucpu_correlation.png')
pp.show()
def run(project, atom_indices=None, traj_fn = 'all'):
n_atoms = project.load_conf()['XYZList'].shape[1]
if traj_fn.lower() == 'all':
SASA = np.ones((project.n_trajs, np.max(project.traj_lengths), n_atoms)) * -1
for traj_ind in xrange(project.n_trajs):
traj_asa = []
logger.info("Working on Trajectory %d", traj_ind)
traj_fn = project.traj_filename(traj_ind)
chunk_ind = 0
for traj_chunk in Trajectory.enum_chunks_from_lhdf( traj_fn, AtomIndices=atom_indices ):
#print chunk_ind
traj_asa.extend(asa.calculate_asa(traj_chunk, n_sphere_points = 24))
chunk_ind += 1
SASA[traj_ind, 0:project.traj_lengths[traj_ind]] = traj_asa
else:
traj_asa = []
for traj_chunk in Trajectory.enum_chunks_from_lhdf( traj_fn, AtomIndices=atom_indices ):
traj_asa.extend( asa.calculate_asa( traj_chunk ) )
SASA = np.array(traj_asa)
return SASA
def main(modeldir, genfile, type, write=False):
data=dict()
pops=numpy.loadtxt('%s/Populations.dat' % modeldir)
map=numpy.loadtxt('%s/Mapping.dat' % modeldir)
frames=numpy.where(map!=-1)[0]
unbound=numpy.loadtxt('%s/tpt-rmsd-%s/unbound_%s_states.txt' % (modeldir, type, type), dtype=int)
bound=numpy.loadtxt('%s/tpt-rmsd-%s/bound_%s_states.txt' % (modeldir, type, type), dtype=int)
dir=modeldir.split('Data')[0]
name=glob.glob('%s/fkbp*xtal*pdb' % dir)
pdb=Trajectory.load_from_pdb(name[0])
paths=io.loadh('%s/tpt-rmsd-%s/Paths.h5' % (modeldir, type))
committors=numpy.loadtxt('%s/commitor_states.txt' % modeldir, dtype=int)
colors=['red', 'orange', 'green', 'cyan', 'blue', 'purple']
colors=colors*40
if type=='strict':
ref=5
elif type=='super-strict':
ref=3
elif type=='medium':
ref=10
elif type=='loose':
ref=15
#for p in range(0, 3):
for p in range(0, 1):
path=paths['Paths'][p]
print "Bottleneck", paths['Bottlenecks'][p]
flux=paths['fluxes'][p]/paths['fluxes'][0]
if flux < 0.2:
break
print "flux %s" % flux
frames=numpy.where(path!=-1)[0]
path=numpy.array(path[frames], dtype=int)
print path
if write==True:
size=(paths['fluxes'][p]/paths['fluxes'][0])*1000
traj=Trajectory.load_from_xtc('%s/tpt-rmsd-%s/path%s_sample20.xtc' % (modeldir, type, p), Conf=pdb)
data=build_metric(dir, pdb, traj)
dir=modeldir.split('Data')[0]
for op in sorted(data.keys()):
#for op in residues:
pylab.figure()
pylab.scatter(data['rmsd'], data[op], c=colors[p], alpha=0.7) #, s=size)
for j in paths['Bottlenecks'][p]:
frame=numpy.where(paths['Paths'][p]==j)[0]
pylab.scatter(data['rmsd'][frame*20], data[op][frame*20], marker='x', c='k', alpha=0.7, s=50)
location=numpy.where(committors==paths['Paths'][p][frame])[0]
if location.size:
print "path %s state %s bottleneck in committors" % (p, j)
print data['rmsd'][frame*20], data[op][frame*20]
pylab.title('path %s' % p)
pylab.xlabel('P-L RMSD')
#pylab.xlabel('P-L COM')
pylab.ylabel(op)
pylab.xlim(0,max(data['rmsd'])+5)
#pylab.ylim(0,max(data[op])+5)
pylab.show()
def plot_gpu_cmd_correlation():
traj1 = Trajectory.load_trajectory_file(ww_1, Conf=ww_conf)
traj1_copy = Trajectory.load_trajectory_file(ww_1, Conf=ww_conf)
#traj2 = Trajectory.load_trajectory_file(ww_2, Conf=ww_conf)
#traj2_copy = Trajectory.load_trajectory_file(ww_2, Conf=ww_conf)
def gpudist(t):
gpurmsd = GPURMSD()
pt = gpurmsd.prepare_trajectory(t)
gpurmsd._gpurmsd.print_params()
return gpurmsd.one_to_all(pt, pt, 0)
def cpudist(t):
rmsd = RMSD()
pt = rmsd.prepare_trajectory(t)
return rmsd.one_to_all(pt, pt, 0)
g1 = gpudist(traj1) #, gpudist(traj2)
c1 = cpudist(traj1_copy) #, cpudist(traj2_copy)
pp.subplot(231)
pp.plot(c1)
pp.title('cpu rmsd drift along traj')
pp.xlabel('frame index')
pp.xlabel('cpurmsd($X_{0}$, $X_{frame_index}$)')
pp.subplot(232)
pp.scatter(g1, c1)
pp.xlabel('gpu rmsd')
pp.ylabel('cpu rmsd')
pp.subplot(233)
pp.plot(g1)
pp.title('gpu rmsd drift along traj')
pp.xlabel('frame index')
pp.xlabel('gpurmsd($X_{0}$, $X_{frame_index}$)')
#PLOT c2 and g2 in the lower portion of the graph
#pp.subplot(234)
#pp.plot(c2)
#pp.title('cpu rmsd drift along pre-aligned traj')
#pp.xlabel('frame index')
#pp.xlabel('cpurmsd($X_{0}$, $X_{frame_index}$)')
#pp.subplot(235)
#pp.scatter(g2, c2)
#pp.xlabel('gpu rmsd')
#pp.ylabel('cpu rmsd')
#pp.subplot(236)
#pp.plot(g2)
#pp.title('gpu rmsd drift along pre-aligned traj')
#pp.xlabel('frame index')
#pp.xlabel('gpurmsd($X_{0}$, $X_{frame_index}$)')
#pp.subplots_adjust(hspace=0.4)
#pp.savefig('gpucpu_correlation.png')
pp.show()
def test_xtc_dcd():
pdb_filename = get("native.pdb", just_filename=True)
xtc_filename = get('RUN00_frame0.xtc', just_filename=True)
dcd_filename = get('RUN00_frame0.dcd', just_filename=True)
r_xtc = Trajectory.load_from_xtc(xtc_filename, pdb_filename)
r_dcd = Trajectory.load_from_dcd(dcd_filename, pdb_filename)
x_xtc = r_xtc["XYZList"]
x_dcd = r_dcd["XYZList"]
eq(x_xtc, x_dcd, decimal=4)
def test_asa_3():
traj_ref = np.loadtxt( os.path.join(reference_dir(),'g_sas_ref.dat'))
Conf = Trajectory.load_from_pdb(os.path.join( fixtures_dir(), 'native.pdb'))
traj = Trajectory.load_trajectory_file( os.path.join(fixtures_dir(), 'trj0.xtc') , Conf=Conf)
traj_asa = calculate_asa(traj, probe_radius=0.14, n_sphere_points = 960)
# the algorithm used by gromacs' g_sas is slightly different than the one
# used here, so the results are not exactly the same -- see the comments
# in src/python/geomtry/asa.py or the readme file src/ext/asa/README.txt
# for details
npt.assert_array_almost_equal(traj_asa, traj_ref, decimal=2)
def load_frame(self, traj_index, frame_index):
"""Load one or more specified frames.
Example
-------
>>> project = Project.load_from('ProjectInfo.yaml')
>>> foo = project.load_frame(1,10)
>>> bar = Trajectory.read_frame(TrajFilename=project.traj_filename(1),
WhichFrame=10)
>>> np.all(foo['XYZList'] == bar)
True
Parameters
----------
traj_index : int, [int]
Index or indices of the trajectories to pull from
frame_index : int, [int]
Index or indices of the frames to pull from
Returns
-------
traj : msmbuilder.Trajectory
A trajectory object containing the requested frame(s).
"""
if np.isscalar(traj_index) and np.isscalar(frame_index):
xyz = Trajectory.read_frame(TrajFilename=self.traj_filename(traj_index),
WhichFrame=frame_index)
xyzlist = np.array([xyz])
else:
traj_index = np.array(traj_index)
frame_index = np.array(frame_index)
if not (traj_index.ndim == 1 and np.all(traj_index.shape == frame_index.shape)):
raise ValueError('traj_index and frame_index must be 1D and have the same length')
xyzlist = []
for i,j in zip(traj_index, frame_index):
if j >= self.traj_lengths[i]:
raise ValueError('traj %d too short (%d) to contain a frame %d' % (i, self.traj_lengths[i], j))
xyz = Trajectory.read_frame(TrajFilename=self.traj_filename(i),
WhichFrame=j)
xyzlist.append(xyz)
xyzlist = np.array(xyzlist)
conf = self.load_conf()
conf['XYZList'] = xyzlist
return conf
def test1():
"""
This test shows how to get the number of helix from a trajectory.
"""
traj = Trajectory.LoadFromLHDF('/Users/tud51931/projects/MSM/msm/ff03-hybridkcenter/sourcedata/Trajectories/trj34.lh5')
numhelix = compute_numhelix_trajectory(traj)
print len(numhelix)
def run(project, assignments, conformations_per_state, states, output_dir):
if states == "all":
states = np.arange(assignments.max() + 1)
inverse_assignments = defaultdict(lambda: [])
for i in xrange(assignments.shape[0]):
for j in xrange(assignments.shape[1]):
inverse_assignments[assignments[i, j]].append((i, j))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
empty_traj = project.empty_traj()
for s in states:
if len(inverse_assignments[s]) == 0:
raise ValueError('No assignments to state! %s' % s)
random.shuffle(inverse_assignments[s])
if len(inverse_assignments[s]) >= conformations_per_state:
confs = inverse_assignments[s][0:conformations_per_state]
else:
confs = inverse_assignments[s]
logger.warning('Not enough assignments in state %s', s)
for i, (traj_ind, frame) in enumerate(confs):
outfile = os.path.join(output_dir, 'State%d-%d.pdb' % (s, i))
if not os.path.exists(outfile):
logger.info('Saving state %d (traj %d, frame %d) as %s', s,
traj_ind, frame, outfile)
traj_filename = project.traj_filename(traj_ind)
xyz = Trajectory.read_frame(traj_filename, frame)
empty_traj['XYZList'] = np.array([xyz])
empty_traj.save_to_pdb(outfile)
else:
logger.warning('Skipping %s. Already exists', outfile)
def __init__(self, structure_or_filename, metric, max_distance):
"""Create an explosion validator
Checks the distance from every frame to a structure and
watches for things that are too far away
Parameters
----------
structure_or_filename : {msmbuilder.Trajectory, str}
The structure to measure distances to, either as a trajectory (the first
frame is the only one that counts) or a path to a trajectory
on disk that can be loaded
metric : msmbuilder distance metric
Metric by which you want to measure distance
max_distance : float
The threshold distance, above which a ValidationError
will be thrown
"""
if isinstance(structure_or_filename, Trajectory):
conf = structure_or_filename
elif isinstance(structure_or_filename, basestring):
conf = Trajectory.load_trajectory_file(structure_or_filename)
self.max_distance = max_distance
self.metric = metric
self._pconf = self.metric.prepare_trajectory(conf)
def __init__(self, structure_or_filename, metric, max_distance):
"""Create an explosion validator
Checks the distance from every frame to a structure and
watches for things that are too far away
Parameters
----------
structure_or_filename : {msmbuilder.Trajectory, str}
The structure to measure distances to, either as a trajectory (the first
frame is the only one that counts) or a path to a trajectory
on disk that can be loaded
metric : msmbuilder distance metric
Metric by which you want to measure distance
max_distance : float
The threshold distance, above which a ValidationError
will be thrown
"""
if isinstance(structure_or_filename, Trajectory):
conf = structure_or_filename
elif isinstance(structure_or_filename, basestring):
conf = Trajectory.load_trajectory_file(structure_or_filename)
self.max_distance = max_distance
self.metric = metric
self._pconf = self.metric.prepare_trajectory(conf)
def create_hcstrings_states(Assignments, outfile='HCstrings_states.txt'):
SA = hct.get_StatesAssignments(Assignments)
states = SA.keys()
HCstrings_states = {}
n = 0
for state in states:
n += 1
print "Get HC strings for state %d/%d" % (n, len(states))
TrajID = SA[state].keys()
numhelix_state = []
HCstrings_states[state] = []
for trajid in TrajID:
TrajFile = '/Users/tud51931/projects/MSM/msm/ff03-hybridkcenter/sourcedata/Trajectories/trj%s_hc.lh5' % trajid
Traj = Trajectory.LoadFromLHDF(TrajFile)
HCstrings_states[state] += [
Traj['HCs'][i] for i in SA[state][trajid]
]
fn = outfile
if os.path.exists(fn):
newfn = fn + '.bck'
os.system('mv %s %s' % (fn, newfn))
print "Write HCstings of states into %s" % fn
HCfile = open(fn, 'w')
pickle.dump(HCstrings_states, HCfile)
HCfile.close()
print "Done."
def test_traj_0():
aind = np.unique( np.random.randint( 22, size=4) )
stride = np.random.randint(1, 100 )
r_traj = get('Trajectories/trj0.lh5')
r_traj.restrict_atom_indices( aind )
r_traj['XYZList'] = r_traj['XYZList'][ ::stride ]
traj = Trajectory.load_from_lhdf(get('Trajectories/trj0.lh5', just_filename=True),
Stride=stride, AtomIndices=aind)
# make sure we loaded the right number of atoms
assert traj['XYZList'].shape[1] == len(aind)
for key in traj.keys():
if key in ['SerializerFilename'] :
continue
if key in ['IndexList']:
for row, r_row in zip( traj[key], r_traj[key] ):
eq(row, r_row)
elif key == 'XYZList':
eq(traj[key], r_traj[key])
else:
eq(traj[key], r_traj[key])
def test(self):
from msmbuilder.scripts.SaveStructures import save
project = get('ProjectInfo.yaml')
assignments = get('Assignments.h5')['arr_0']
which_states = [0, 1, 2]
list_of_trajs = project.get_random_confs_from_states(assignments,
which_states, num_confs=2, replacement=True,
random=np.random.RandomState(42))
assert isinstance(list_of_trajs, list)
assert isinstance(list_of_trajs[0], Trajectory)
eq(len(list_of_trajs), len(which_states))
for t in list_of_trajs:
eq(len(t), 2)
print list_of_trajs[0].keys()
# sep, tps, one
save(list_of_trajs, which_states, style='sep', format='lh5', outdir=self.td)
save(list_of_trajs, which_states, style='tps', format='lh5', outdir=self.td)
save(list_of_trajs, which_states, style='one', format='lh5', outdir=self.td)
names = ['State0-0.lh5', 'State0-1.lh5', 'State0.lh5', 'State1-0.lh5',
'State1-1.lh5', 'State1.lh5', 'State2-0.lh5', 'State2-1.lh5',
'State2.lh5']
for name in names:
t = Trajectory.load_trajectory_file(pjoin(self.td, name))
eq(t, get('save_structures/' + name))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-n', '--n_trajs', help='number of trajectories. Default=10', type=int, default=10)
parser.add_argument('-t', '--traj_length', help='trajectories length. Default=10000', type=int, default=10000)
args = parser.parse_args()
# these could be configured
kT = 15.0
dt = 0.1
mGamma = 1000.0
forcecalculator = muller.muller_force()
project = Project({'ConfFilename': os.path.join(mullermsm.__path__[0], 'conf.pdb'),
'NumTrajs': args.n_trajs,
'ProjectRootDir': '.',
'TrajFileBaseName': 'trj',
'TrajFilePath': 'Trajectories',
'TrajFileType': '.lh5',
'TrajLengths': [args.traj_length]*args.n_trajs})
if os.path.exists('ProjectInfo.h5'):
print >> sys.stderr, "The file ./ProjectInfo.h5 already exists. I don't want to overwrite anything, so i'm backing off"
sys.exit(1)
try:
os.mkdir('Trajectories')
except OSError:
print >> sys.stderr, "The directory ./Trajectores already exists. I don't want to overwrite anything, so i'm backing off"
sys.exit(1)
for i in range(args.n_trajs):
print 'simulating traj %s' % i
# select initial configs randomly from a 2D box
initial_x = [random.uniform(-1.5, 1.2), random.uniform(-0.2, 2)]
print 'starting conformation from randomly sampled points (%s, %s)' % (initial_x[0], initial_x[1])
print 'propagating for %s steps on the Muller potential with a Langevin integrator...' % args.traj_length
positions = muller.propagate(args.traj_length, initial_x, kT, dt, mGamma, forcecalculator)
# positions is N x 2, but we want to make it N x 1 x 3 where the additional
# column is just zeros. This way, being N x 1 x 3, it looks like a regular MD
# trajectory that would be N_frames x N_atoms x 3
positions3 = np.hstack((positions, np.zeros((len(positions),1)))).reshape((len(positions), 1, 3))
t = Trajectory.LoadTrajectoryFile(project['ConfFilename'])
t['XYZList'] = positions3
t.SaveToLHDF(project.GetTrajFilename(i))
print 'saving trajectory to %s' % project.GetTrajFilename(i)
project.SaveToHDF('ProjectInfo.h5')
print 'saved ProjectInfo.h5 file'
pickle.dump(metric.EuclideanMetric(), open('metric.pickl', 'w'))
print 'saved metric.pickl'
def save(self):
"Save the trajs as a n MSMBuilder project"
traj_dir = pjoin(self.project_dir, 'Trajectories')
if not os.path.exists(traj_dir):
os.makedirs(traj_dir)
t = Trajectory.load_trajectory_file(self.conf_filename)
traj_paths = []
for i, xyz in enumerate(self.trajectories):
t['IndexList'] = None # bug in msmbuilder
t['XYZList'] = xyz
traj_paths.append(pjoin(traj_dir, 'trj%d.lh5' % i))
t.save(traj_paths[-1])
p = Project({'conf_filename': os.path.abspath(self.conf_filename),
'traj_lengths': self.n_frames*np.ones(self.n_trajs),
'traj_paths': [os.path.abspath(e) for e in traj_paths],
'traj_converted_from': [[] for i in range(self.n_trajs)],
'traj_errors': [None for i in range(self.n_trajs)],
}, project_dir=self.project_dir, validate=True)
p.save(pjoin(self.project_dir,'Project.yaml'))
# just check again
p = Project.load_from(pjoin(self.project_dir,'Project.yaml'))
p._validate()
assert np.all((p.load_traj(0)['XYZList'] - self.trajectories[0])**2 < 1e-6)
def save(self):
"Save the trajs as a n MSMBuilder project"
traj_dir = pjoin(self.project_dir, 'Trajectories')
if not os.path.exists(traj_dir):
os.makedirs(traj_dir)
t = Trajectory.load_trajectory_file(self.conf_filename)
traj_paths = []
for i, xyz in enumerate(self.trajectories):
t['IndexList'] = None # bug in msmbuilder
t['XYZList'] = xyz
traj_paths.append(pjoin(traj_dir, 'trj%d.lh5' % i))
t.save(traj_paths[-1])
p = Project(
{
'conf_filename': os.path.abspath(self.conf_filename),
'traj_lengths': self.n_frames * np.ones(self.n_trajs),
'traj_paths': [os.path.abspath(e) for e in traj_paths],
'traj_converted_from': [[] for i in range(self.n_trajs)],
'traj_errors': [None for i in range(self.n_trajs)],
},
project_dir=self.project_dir,
validate=True)
p.save(pjoin(self.project_dir, 'Project.yaml'))
# just check again
p = Project.load_from(pjoin(self.project_dir, 'Project.yaml'))
p._validate()
assert np.all(
(p.load_traj(0)['XYZList'] - self.trajectories[0])**2 < 1e-6)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-g', '--generators', default='Data/Gens.lh5', help='Path to Gens.lh5')
parser.add_argument('-p', '--project', default='ProjectInfo.h5', help='Path to ProjectInfo.h5')
parser.add_argument('-s', '--stride', default=5, type=int, help='Stride to plot the data at')
args = parser.parse_args()
gens = Trajectory.LoadTrajectoryFile(args.generators)
gens_x = gens['XYZList'][:,0,0]
gens_y = gens['XYZList'][:,0,1]
points = np.array([gens_x, gens_y]).transpose()
tri = Delaunay(points)
PL = []
for p in points:
PL.append(Voronoi.Site(x=p[0],y=p[1]))
v,eqn,edges,wtf = Voronoi.computeVoronoiDiagram(PL)
edge_points=[]
for (l,x1,x2) in edges:
if x1>=0 and x2>=0:
edge_points.append((v[x1],v[x2]))
lines = LineCollection(edge_points, linewidths=0.5, color='k')
fig = pp.figure()
ax = fig.add_subplot(111)
fig.gca().add_collection(lines)
maxx, minx= np.max(gens_x), np.min(gens_x)
maxy, miny = np.max(gens_y), np.min(gens_y)
# plot the background
plot_v(minx=minx, maxx=maxx, miny=miny, maxy=maxy, ax=ax)
pp.xlim(minx, maxx)
pp.ylim(miny, maxy)
# plot a single trajectory
p = Project.LoadFromHDF(args.project)
t = p.LoadTraj(0)
x = t['XYZList'][:,0,0][::args.stride]
y = t['XYZList'][:,0,1][::args.stride]
cm = pp.get_cmap('spectral')
n_points = len(x)
ax.set_color_cycle([cm(1.*i/(n_points-1)) for i in range(n_points-1)])
for i in range(n_points-1):
ax.plot(x[i:i+2],y[i:i+2])
pp.title('Voronoi Microstate Decomposition, with first trajectory')
pp.show()
def test_asa_2():
t = Trajectory.load_trajectory_file(os.path.join(fixtures_dir(), 'trj0.lh5'))
val1 = np.sum(calculate_asa(t[0])) # calculate only frame 0
val2 = np.sum(calculate_asa(t)[0]) # calculate on all frames
true_frame_0_asa = 2.859646797180176
npt.assert_approx_equal(true_frame_0_asa, val1)
npt.assert_approx_equal(true_frame_0_asa, val2)
def load_gens(gens_fn, conf_fn, metric):
"""Setup a worker by adding pgens to its global namespace
This is necessary because pgens are not necessarily picklable, so we can't
just prepare them on the master and then push them to the remote workers --
instead we want to actually load the pgens from disk and prepare them on
the remote node
"""
from msmbuilder import Trajectory
global PGENS, CONF, METRIC, PREPARED
METRIC = metric
CONF = Trajectory.load_trajectory_file(conf_fn)
gens = Trajectory.load_trajectory_file(gens_fn)
PGENS = metric.prepare_trajectory(gens)
PREPARED = True
def test_c_Cluster(self):
# We need to be sure to skip the stochastic k-mediods
cmd = "Cluster.py -p {project} -s {stride} rmsd -a {atomindices} kcenters -d {rmsdcutoff}".format(project=ProjectFn, stride=Stride, atomindices="AtomIndices.dat", rmsdcutoff=RMSDCutoff)
print cmd
os.system(cmd)
try:
os.remove(os.path.join(WorkingDir, 'Data', 'Assignments.h5'))
os.remove(os.path.join(WorkingDir, 'Data', 'Assignments.h5.distances'))
except:
pass
G = Trajectory.load_trajectory_file(GensPath)
r_G = Trajectory.load_trajectory_file(ReferenceDir +'/'+ GensPath)
self.assert_trajectories_equal(G, r_G)
def _eval_traj_shapes(self):
lengths = np.zeros(self.n_trajs)
n_atoms = np.zeros(self.n_trajs)
conf = self.load_conf()
for i in xrange(self.n_trajs):
shape = Trajectory.load_trajectory_file(self.traj_filename(i), JustInspect=True, Conf=conf)
lengths[i] = shape[0]
n_atoms[i] = shape[1]
return lengths, n_atoms
def main(args, metric):
assignments_path = os.path.join(args.output_dir, "Assignments.h5")
distances_path = os.path.join(args.output_dir, "Assignments.h5.distances")
project = Project.load_from(args.project)
gens = Trajectory.load_trajectory_file(args.generators)
# this runs assignment and prints them to disk
assign_with_checkpoint(metric, project, gens, assignments_path, distances_path)
logger.info('All Done!')
def LoadTrajectory(trajectory):
if isinstance(trajectory, str):
try:
t = Trajectory.LoadFromLHDF(trajectory)
return t
except IOError:
raise IOError("Can not find %s" % trajectory)
elif isinstance(trajectory, Trajectory):
return trajectory
def test_g_GetRandomConfs(self):
P1 = Project.load_from(ProjectFn)
Assignments = io.loadh("Data/Assignments.Fixed.h5", 'arr_0')
# make a predictable stream of random numbers by seeding the RNG with 42
random_source = np.random.RandomState(42)
randomconfs = GetRandomConfs.run(P1, Assignments, NumRandomConformations, random_source)
reference = Trajectory.load_trajectory_file(os.path.join(ReferenceDir, "2RandomConfs.lh5"))
self.assert_trajectories_equal(reference, randomconfs)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-p', '--project', default='ProjectInfo.h5')
parser.add_argument(
'-t',
'--trajectories',
nargs='+',
help='''Supply either the path to a trajectory file (i.e. Data/Gens.lh5),
or an integer, which will be interepreted as a trajectory index
into the trajectories that accompany the project. default: plot all
of the trajectories''',
default=['-1'])
args = parser.parse_args()
p = Project.LoadFromHDF(args.project)
# record the bounding box of the points so that we know
# what to render for the background
maxx, minx, maxy, miny = 1.2, -1.5, 2, -0.2
# if -1 is included, add in ALL of the trajectories
if '-1' in args.trajectories:
args.trajectories.remove('-1')
args.trajectories.extend(range(p['NumTrajs']))
# remove duplicates
args.trajectories = set(args.trajectories)
for requested in args.trajectories:
if os.path.exists(str(requested)):
traj = Trajectory.LoadTrajectoryFile(str(requested))
print 'plotting %s' % requested
markersize = 50
else:
try:
i = int(requested)
traj = p.LoadTraj(i)
print 'plotting %s' % i
markersize = 5
except ValueError:
print >> sys.stderr, 'I couldnt figure out how to deal with the argument %s' % requested
continue
except IOError as e:
print >> sys.stderr, str(e)
continue
xyz = traj['XYZList']
x = xyz[:, 0, 0]
y = xyz[:, 0, 1]
maxx, maxy = max(np.max(x), maxx), max(np.max(y), maxy)
minx, miny = min(np.min(x), minx), min(np.min(y), miny)
pp.plot(x, y, '.', markersize=markersize, alpha=0.5)
plot_v(minx=minx, maxx=maxx, miny=miny, maxy=maxy)
pp.show()
def _load_traj(self, file_list):
"""
Load a set of xtc or dcd files as a single trajectory
Note that the ordering of `file_list` is relevant, as the trajectories
are catted together.
Returns
-------
traj : msmbuilder.Trajectory
"""
if self.input_traj_ext == '.xtc':
traj = Trajectory.load_from_xtc(file_list, PDBFilename=self.conf_filename,
discard_overlapping_frames=True)
elif self.input_traj_ext == '.dcd':
traj = Trajectory.load_from_xtc(file_list, PDBFilename=self.conf_filename)
else:
raise ValueError()
return traj
def test2():
"""
This test shows how to create new trj files with hc strings.
"""
path ="/Users/tud51931/projects/MSM/msm/ff03-hybridkcenter/sourcedata/Trajectories"
for i in range(0,100):
Trajfile = "%s/trj%d.lh5"%(path,i)
if os.path.exists(Trajfile):
T = Trajectory.LoadFromLHDF(Trajfile)
CreateTrajFileWithHCstrings(T)
print "Done."
def _eval_traj_shapes(self):
lengths = np.zeros(self.n_trajs)
n_atoms = np.zeros(self.n_trajs)
conf = self.load_conf()
for i in xrange(self.n_trajs):
shape = Trajectory.load_trajectory_file(self.traj_filename(i),
JustInspect=True,
Conf=conf)
lengths[i] = shape[0]
n_atoms[i] = shape[1]
return lengths, n_atoms
def run(project, assignments, num_confs_per_state, random_source=None):
"""
Pull random confs from each state in an MSM
Parameters
----------
project : msmbuilder.Project
Used to load up the trajectories, get topology
assignments : np.ndarray, dtype=int
State membership for each frame
num_confs_per_state : int
number of conformations to pull from each state
random_source : numpy.random.RandomState, optional
If supplied, random numbers will be pulled from this random source,
instead of the default, which is np.random. This argument is used
for testing, to ensure that the random number generator always
gives the same stream.
Notes
-----
A new random_source can be initialized by calling numpy.random.RandomState(seed)
with whatever seed you like. See http://stackoverflow.com/questions/5836335/consistenly-create-same-random-numpy-array
for some discussion.
"""
if random_source is None:
random_source = np.random
n_states = max(assignments.flatten()) + 1
logger.info("Pulling %s confs for each of %s confs", num_confs_per_state,
n_states)
inv = MSMLib.invert_assignments(assignments)
xyzlist = []
for s in xrange(n_states):
trj, frame = inv[s]
# trj and frame are a list of indices, such that
# project.load_traj(trj[i])[frame[i]] is a frame assigned to state s
for j in xrange(num_confs_per_state):
r = random_source.randint(len(trj))
xyz = Trajectory.read_frame(project.traj_filename(trj[r]),
frame[r])
xyzlist.append(xyz)
# xyzlist is now a list of (n_atoms, 3) arrays, and we're going
# to stack it along the third dimension
xyzlist = np.dstack(xyzlist)
# load up the conf to get the topology, put then pop in the new coordinates
output = project.load_conf()
output['XYZList'] = xyzlist
return output
def main(modeldir, start, type):
start=int(start)
data=dict()
project=Project.load_from('%s/ProjectInfo.yaml' % modeldir.split('Data')[0])
files=glob.glob('%s/fkbp*xtal.pdb' % modeldir.split('Data')[0])
pdb=files[0]
unbound=numpy.loadtxt('%s/tpt-%s/unbound_%s_states.txt' % (modeldir, type, type), dtype=int)
T=mmread('%s/tProb.mtx' % modeldir)
startstate=unbound[start]
ass=io.loadh('%s/Assignments.Fixed.h5' % modeldir)
steps=100000
print "on start state %s" % startstate
if os.path.exists('%s/tpt-%s/movie_state%s_1millisec.states.dat' % (modeldir, type, startstate)):
print "loading from states"
traj=numpy.loadtxt('%s/tpt-%s/movie_state%s_1millisec.states.dat' % (modeldir, type, startstate))
else:
traj=msm_analysis.sample(T, int(startstate),int(steps))
numpy.savetxt('%s/tpt-%s/movie_state%s_1millisec.states.dat' % (modeldir, type, startstate), traj)
print "checking for chkpt file"
checkfile=glob.glob('%s/tpt-%s/movie_state%s_*chkpt' % (modeldir, type, startstate))
if len(checkfile) > 0:
movie=Trajectory.load_from_xtc(checkfile[0], PDBFilename=pdb)
n=int(checkfile[0].split('xtc.state')[1].split('chkpt')[0])
os.system('mv %s %s.chkpt.cp' % (checkfile[0], checkfile[0].split('.xtc')[0]))
print "checkpointing at state index %s out of %s" % (n, len(traj))
checkfile=checkfile[0]
restart=True
else:
restart=False
n=0
movie=project.empty_traj()
while n < len(traj):
print "on state %s" % n
state=int(traj[n])
t=project.get_random_confs_from_states(ass['arr_0'], [int(state),], 10)
if n==0:
movie['XYZList']=t[0]['XYZList']
n+=1
continue
elif n % 100==0:
movie['XYZList']=numpy.vstack((movie['XYZList'], t[0]['XYZList']))
if restart==True:
os.system('mv %s %s.chkpt.cp' % (checkfile, checkfile.split('.xtc')[0]))
movie.save_to_xtc('%s/tpt-%s/movie_state%s_1millisec.xtc.state%schkpt' % (modeldir, type, startstate, n))
checkfile='%s/tpt-%s/movie_state%s_1millisec.xtc.state%schkpt' % (modeldir, type, startstate, n)
n+=1
continue
elif n!=0:
movie['XYZList']=numpy.vstack((movie['XYZList'], t[0]['XYZList']))
n+=1
continue
movie.save_to_xtc('%s/tpt-%s/movie_state%s_1millisec.xtc' % (modeldir, type, startstate))
def test_g_GetRandomConfs(self):
P1 = Project.load_from(ProjectFn)
Assignments = io.loadh("Data/Assignments.Fixed.h5", 'arr_0')
# make a predictable stream of random numbers by seeding the RNG with 42
random_source = np.random.RandomState(42)
randomconfs = GetRandomConfs.run(P1, Assignments,
NumRandomConformations, random_source)
reference = Trajectory.load_trajectory_file(
os.path.join(ReferenceDir, "2RandomConfs.lh5"))
self.assert_trajectories_equal(reference, randomconfs)
def test_c_Cluster(self):
# We need to be sure to skip the stochastic k-mediods
cmd = "Cluster.py -p {project} -s {stride} rmsd -a {atomindices} kcenters -d {rmsdcutoff}".format(
project=ProjectFn,
stride=Stride,
atomindices="AtomIndices.dat",
rmsdcutoff=RMSDCutoff)
print cmd
os.system(cmd)
try:
os.remove(os.path.join(WorkingDir, 'Data', 'Assignments.h5'))
os.remove(
os.path.join(WorkingDir, 'Data', 'Assignments.h5.distances'))
except:
pass
G = Trajectory.load_trajectory_file(GensPath)
r_G = Trajectory.load_trajectory_file(ReferenceDir + '/' + GensPath)
self.assert_trajectories_equal(G, r_G)
def get_project_object( traj_directory, conf_filename, out_filename=None ):
"""
This function constructs a msmbuilder.Project object
given a directory of trajectories saved as .lh5's.
Note that this is only really necessary when a script
like ConvertDataToLHDF.py converts the data but fails
to write out the ProjectInfo.yaml file.
This function can also be used to combine two projects
by copying and renaming the trajectories in a new
folder. Though, it's probably more efficient to just
do some bash stuff to cat the ProjectInfo.yaml's
together and rename the trajectories.
Inputs:
-------
1) traj_directory : directory to find the trajectories
2) conf_filename : file to find the conformation
3) out_filename [ None ] : if None, then this function
does not save the project file, but if given, the
function will save the project file and also
return the object
Outputs:
-------
project : msmbuilder.Project object corresponding to
your project.
"""
traj_paths = sorted( os.listdir( traj_directory ), key=keynat ) # relative to the traj_directory
traj_paths = [ os.path.join( traj_directory, filename ) for filename in traj_paths ] # relative to current directory
traj_lengths = []
for traj_filename in traj_paths: # Get the length of each trajectory
logger.info( traj_filename )
traj_lengths.append( Trajectory.load_from_lhdf( traj_filename, JustInspect=True )[0] )
# With JustInspect=True this just returns the shape of the XYZList
project = Project({'conf_filename': conf_filename,
'traj_lengths': traj_lengths,
'traj_paths': traj_paths,
'traj_errors': [None] * len(traj_paths),
'traj_converted_from': [ [None] ] * len(traj_paths) })
if out_filename is None:
return project
else:
project.save( out_filename )
logger.info('Saved project file to %s', out_filename)
return project
def calculatedistance(AtomName1, ResidueID1, AtomName2, ResidueID2, trajfile,
LongestTrajLength):
""" Calculate the distance between given two atoms in given trajectory"""
t = Trajectory.LoadFromLHDF(trajfile)
Atom1 = (t['AtomNames'] == AtomName1) * (t['ResidueID'] == ResidueID1)
Atom2 = (t['AtomNames'] == AtomName2) * (t['ResidueID'] == ResidueID2)
distance = []
for i in range(len(t['XYZList'])):
x = (t['XYZList'][i, Atom1, :] - t['XYZList'][i, Atom2, :])[0]
x = x.tolist()
distance.append(np.dot(x, x)**0.5)
distance += [-1] * (LongestTrajLength - len(t['XYZList']))
return distance
def FixGenFile(Mapping, GenFile, Outfile='./Gens.Fixed.lh5'):
"""
Use Mapping.dat file get a fixed(mapped) generator file.
New generator file will be Gens.Fixed.lh5
"""
gen = Trajectory.LoadFromLHDF(GenFile)
newgen = copy.deepcopy(gen)
mapping = loadtxt(Mapping)
GeneratorStateID = np.arange(len(gen['XYZList']))
newgen['StateID'] = GeneratorStateID[mapping >= 0]
newgen['XYZList'] = gen['XYZList'][mapping >= 0, :, :]
print "Save to %s" % Outfile
newgen.SaveToLHDF(Outfile)
def test_gpurmsd():
traj = Trajectory.load_trajectory_file(trj_path)
gpurmsd = GPURMSD()
ptraj = gpurmsd.prepare_trajectory(traj)
gpurmsd._gpurmsd.print_params()
gpu_distances = gpurmsd.one_to_all(ptraj, ptraj, 0)
cpurmsd = RMSD()
ptraj = cpurmsd.prepare_trajectory(traj)
cpu_distances = cpurmsd.one_to_all(ptraj, ptraj, 0)
npt.assert_array_almost_equal(cpu_distances, gpu_distances, decimal=4)
def run( project, output, num_procs=1, chunk_size=50000, traj_fn='all' ):
pool = mp.Pool( num_procs )
dssp_assignments = []
if traj_fn.lower() == 'all':
for i in xrange( project.n_trajs ):
traj_dssp_assignments = []
N = project.traj_lengths[i]
j = 0
for trj_chunk in Trajectory.enum_chunks_from_lhdf( project.traj_filename( i ), ChunkSize=chunk_size ):
result = pool.map_async( analyze_conf, trj_chunk['XYZList'] )
result.wait()
traj_dssp_assignments.extend( result.get() )
j+=len(trj_chunk)
print "Trajectory %d: %d / %d" % (i, j, N)
dssp_assignments.append( traj_dssp_assignments )
else:
traj_dssp_assignments = []
N = Trajectory.load_from_lhdf(traj_fn, JustInspect=True)[0]
j = 0
for trj_chunk in Trajectory.enum_chunks_from_lhdf(traj_fn, ChunkSize=chunk_size):
result = pool.map_async(analyze_conf, trj_chunk['XYZList'])
result.wait()
traj_dssp_assignments.extend(result.get())
j+=len(trj_chunk)
print "Trajectory %s: %d / %d" % (traj_fn, j, N)
dssp_assignments.append(traj_dssp_assignments)
dssp_assignments = np.array( dssp_assignments )
np.save( output, dssp_assignments )
DEVNULL.close()
def test_gpurmsd():
traj = Trajectory.load_trajectory_file(trj_path)
gpurmsd = GPURMSD()
ptraj = gpurmsd.prepare_trajectory(traj)
gpurmsd._gpurmsd.print_params()
gpu_distances = gpurmsd.one_to_all(ptraj, ptraj, 0)
cpurmsd = RMSD()
ptraj = cpurmsd.prepare_trajectory(traj)
cpu_distances = cpurmsd.one_to_all(ptraj, ptraj, 0)
npt.assert_array_almost_equal(cpu_distances, gpu_distances, decimal=4)
def run(project, assignments, num_confs_per_state, random_source=None):
"""
Pull random confs from each state in an MSM
Parameters
----------
project : msmbuilder.Project
Used to load up the trajectories, get topology
assignments : np.ndarray, dtype=int
State membership for each frame
num_confs_per_state : int
number of conformations to pull from each state
random_source : numpy.random.RandomState, optional
If supplied, random numbers will be pulled from this random source,
instead of the default, which is np.random. This argument is used
for testing, to ensure that the random number generator always
gives the same stream.
Notes
-----
A new random_source can be initialized by calling numpy.random.RandomState(seed)
with whatever seed you like. See http://stackoverflow.com/questions/5836335/consistenly-create-same-random-numpy-array
for some discussion.
"""
if random_source is None:
random_source = np.random
n_states = max(assignments.flatten()) + 1
logger.info("Pulling %s confs for each of %s confs", num_confs_per_state, n_states)
inv = MSMLib.invert_assignments(assignments)
xyzlist = []
for s in xrange(n_states):
trj, frame = inv[s]
# trj and frame are a list of indices, such that
# project.load_traj(trj[i])[frame[i]] is a frame assigned to state s
for j in xrange(num_confs_per_state):
r = random_source.randint(len(trj))
xyz = Trajectory.read_frame(project.traj_filename(trj[r]), frame[r])
xyzlist.append(xyz)
# xyzlist is now a list of (n_atoms, 3) arrays, and we're going
# to stack it along the third dimension
xyzlist = np.dstack(xyzlist)
# load up the conf to get the topology, put then pop in the new coordinates
output = project.load_conf()
output['XYZList'] = xyzlist
return output
def setUp(self):
test_dir = os.path.join( reference_dir(), 'cfep_reference/' )
self.generators = Trajectory.load_trajectory_file(test_dir + 'Gens.lh5')
N = len(self.generators)
self.counts = io.mmread(test_dir + 'tCounts.mtx')
self.lag_time = 1.0
self.pfolds = np.random.rand(N)
self.rescale = False
self.reactant = 0
self.product = N
def Reference_Rg(trajfile):
"""
Compute the Rg from single trajfile.
"""
t = Trajectory.LoadFromLHDF(trajfile)
Rg = []
for i in range(len(t['XYZList'])):
XYZ = t['XYZList'][i, :, :]
mu = XYZ.mean(0)
mu = mu.tolist()
XYZ2 = XYZ - np.tile(mu, (len(XYZ), 1))
Rg.append(((XYZList**2).sum() / n_atoms)**(0.5))
return Rg
def main(args, metric):
assignments_path = os.path.join(args.output_dir, "Assignments.h5")
distances_path = os.path.join(args.output_dir, "Assignments.h5.distances")
#arglib.die_if_path_exists(args.output_dir)
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
project = Project.load_from(args.project)
gens = Trajectory.load_trajectory_file(args.generators)
if isinstance(metric, metrics.RMSD):
# this is really bad design, and we're going to fix it soon in
# MSMBuilder3, but here's the deal. When Cluster.py loads up the
# trajectories (Cluster.py:load_trajectories()), it only loads the
# required indices for RMSD. This means that when it saves the Gens
# file, that file contains only a subset of the atoms. So when
# we run *this* script, we need to perform a restricted load of the
# the trajectories on disk, but we need to NOT perform a restricted
# load of the gens.lh5 file. (By restricted load, I mean loading
# only a subset of the data in the file)
if gens['XYZList'].shape[1] != len(metric.atomindices):
msg = ('Using RMSD clustering/assignment, this script expects '
'that the Cluster.py script saves a generators file that '
'only contains the indices of the atoms of interest, and '
'not any of the superfluous degrees of freedom that were '
'not used for clustering. But you supplied %d cluster '
'centers each containg %d atoms. Your atom indices file '
'on the other hand contains %d atoms') \
% (gens['XYZList'].shape[0], gens['XYZList'].shape[1],
len(metric.atomindices))
raise ValueError(msg)
# now that we're telling the assign function only to load up a
# subset of the atoms, an the generator is already only a subset,
# the actual RMSD object needs to, from ITS perspective, operate on
# every degree of freedom. So it shouldn't be aware of any special
# atom_indices
atom_indices = metric.atomindices
metric.atomindices = None
# this runs assignment and prints them to disk
assign_with_checkpoint(metric, project, gens, assignments_path,
distances_path, atom_indices_to_load=atom_indices)
else:
assign_with_checkpoint(metric, project, gens, assignments_path,
distances_path)
logger.info('All Done!')
def test_traj_0():
aind = np.unique( np.random.randint( 22, size=4) )
stride = np.random.randint(1, 100 )
r_traj = Trajectory.load_from_lhdf( os.path.join( fixtures_dir(), 'trj0.lh5' ), Stride=1 )
r_traj.restrict_atom_indices( aind )
r_traj['XYZList'] = r_traj['XYZList'][ ::stride ]
traj = Trajectory.load_from_lhdf( os.path.join( fixtures_dir(), 'trj0.lh5' ), Stride = stride, AtomIndices = aind )
for key in traj.keys():
if key in ['SerializerFilename'] :
continue
if key in ['IndexList']:
for row, r_row in zip( traj[key], r_traj[key] ):
npt.assert_array_equal( row, r_row )
elif key == 'XYZList':
npt.assert_array_almost_equal( traj[key], r_traj[key])
else:
npt.assert_array_equal( traj[key], r_traj[key] )
def setUp(self):
test_dir = os.path.join(reference_dir(), 'cfep_reference/')
self.generators = Trajectory.load_trajectory_file(test_dir +
'Gens.lh5')
N = len(self.generators)
self.counts = io.mmread(test_dir + 'tCounts.mtx')
self.lag_time = 1.0
self.pfolds = np.random.rand(N)
self.rescale = False
self.reactant = 0
self.product = N
def _load_traj(self, file_list):
"""
Load a set of xtc or dcd files as a single trajectory
Note that the ordering of `file_list` is relevant, as the trajectories
are catted together.
Returns
-------
traj : msmbuilder.Trajectory
"""
if self.input_traj_ext == '.xtc':
traj = Trajectory.load_from_xtc(file_list, Conf=self.conf,
discard_overlapping_frames=True)
elif self.input_traj_ext == '.dcd':
traj = Trajectory.load_from_dcd(file_list, Conf=self.conf,
discard_overlapping_frames=True)
else:
raise ValueError()
# return the number of files loaded, which in this case is all or
# nothing, since an error is raised if the Trajectory.load_from_<ext>
# doesn't work
return traj, len(file_list)
def _generate_equilibration_job():
"""Generate a single equilibration job from the first forcefield
No parameters -- reads from the database and from the Project file to get info.
Returns
-------
traj : models.Trajectory
An unsaved trajectory. Note that we "attach" the conformation that we want
to start from to the object as traj.init_pdb.
"""
logger.info('Constructing initial equilibration job')
conf = msmbuilder.Trajectory.load_from_pdb(Project().pdb_topology_file)
if Project().starting_confs_lh5 is None:
# start from pdb_topology_file
# copy to a new location so that the 'conf' can be deleted without
# looseing our topology file
logger.info('Using pdb topolgy to start equlibration run')
name = 'equilibration, starting from pdb toplogy'
else:
num_frames = msmbuilder.Trajectory.load_from_lhdf(Project().starting_confs_lh5, JustInspect=True)[0]
r = np.random.randint(num_frames)
xyz = msmbuilder.Trajectory.read_lhdf_frame(Project().starting_confs_lh5, r)
conf['XYZList'] = np.array([xyz])
logger.info('Using frame %s of starting_confs_lh5 (%s) to start equilibration run' % (r, Project().starting_confs_lh5))
name = 'equilibration, starting from frame %s of starting_confs_lh5 (%s)' % (r, Project().starting_confs_lh5)
forcefield = Session.query(Forcefield).first()
trj = Trajectory(forcefield=forcefield,
name=name, mode='Equilibration')
trj.init_pdb = conf
return trj
def run(project, pdb, traj_fn, atom_indices, alt_indices, permute_indices):
#project = Project.load_from_hdf(options.projectfn)
traj = Trajectory.load_trajectory_file(traj_fn, Conf=project.Conf)
# you could replace this with your own metric if you like
metric = LPRMSD(atom_indices, permute_indices, alt_indices)
ppdb = metric.prepare_trajectory(pdb)
ptraj = metric.prepare_trajectory(traj)
print ppdb['XYZList'].shape
print ptraj['XYZList'].shape
distances, xout = metric.one_to_all_aligned(ppdb, ptraj, 0)
print distances
return distances
def run(project, pdb, traj_fn, atom_indices, alt_indices, permute_indices):
#project = Project.load_from_hdf(options.projectfn)
traj = Trajectory.load_trajectory_file(traj_fn, Conf=project.Conf)
# you could replace this with your own metric if you like
metric = LPRMSD(atom_indices, permute_indices, alt_indices)
ppdb = metric.prepare_trajectory(pdb)
ptraj = metric.prepare_trajectory(traj)
print ppdb['XYZList'].shape
print ptraj['XYZList'].shape
distances, xout = metric.one_to_all_aligned(ppdb, ptraj, 0)
print distances
return distances
def test():
from msmbuilder import Trajectory
from scipy import io
print "Testing cfep code...."
test_dir = '/Users/TJ/Programs/msmbuilder.sandbox/tjlane/cfep/'
generators = Trajectory.load_trajectory_file(test_dir + 'Gens.lh5')
counts = io.mmread(test_dir + 'tCounts.mtx')
reactant = 0 # generator w/max RMSD
product = 10598 # generator w/min RMSD
pfolds = np.loadtxt(test_dir + 'FCommittors.dat')
# test the usual coordinate
#pfold_cfep = CutCoordinate(counts, generators, reactant, product)
#pfold_cfep.set_coordinate_values(pfolds)
#pfold_cfep.plot()
#pfold_cfep.set_coordinate_as_eigvector2()
#print pfold_cfep.reaction_coordinate_values
#pfold_cfep.plot()
#pfold_cfep.set_coordinate_as_committors()
#print pfold_cfep.reaction_coordinate_values
#pfold_cfep.plot()
# test the Variable Coordinate
initial_weights = np.ones( (1225,26104) )
contact_cfep = VariableCoordinate(contact_reaction_coordinate, initial_weights,
counts, generators, reactant, product)
contact_cfep.evaluate_partition_functions()
print contact_cfep.zh
print contact_cfep.zc
contact_cfep.optimize()
print "Finished optimization"
contact_cfep.plot()
return
def test():
from msmbuilder import Trajectory
from scipy import io
print "Testing cfep code...."
test_dir = '/Users/TJ/Programs/msmbuilder.sandbox/tjlane/cfep/'
generators = Trajectory.load_trajectory_file(test_dir + 'Gens.lh5')
counts = io.mmread(test_dir + 'tCounts.mtx')
reactant = 0 # generator w/max RMSD
product = 10598 # generator w/min RMSD
pfolds = np.loadtxt(test_dir + 'FCommittors.dat')
# test the usual coordinate
#pfold_cfep = CutCoordinate(counts, generators, reactant, product)
#pfold_cfep.set_coordinate_values(pfolds)
#pfold_cfep.plot()
#pfold_cfep.set_coordinate_as_eigvector2()
#print pfold_cfep.reaction_coordinate_values
#pfold_cfep.plot()
#pfold_cfep.set_coordinate_as_committors()
#print pfold_cfep.reaction_coordinate_values
#pfold_cfep.plot()
# test the Variable Coordinate
initial_weights = np.ones( (1225,26104) )
contact_cfep = VariableCoordinate(contact_reaction_coordinate, initial_weights,
counts, generators, reactant, product)
contact_cfep.evaluate_partition_functions()
print contact_cfep.zh
print contact_cfep.zc
contact_cfep.optimize()
print "Finished optimization"
contact_cfep.plot()
return
def main():
parser = arglib.ArgumentParser(
description="""
Assign data that were not originally used in the clustering (because of
striding) to the microstates. This is applicable to all medoid-based clustering
algorithms, which includes all those implemented by Cluster.py except the
hierarchical methods. (For assigning to a hierarchical clustering, use
AssignHierarchical.py)
Outputs:
-Assignments.h5
-Assignments.h5.distances
Assignments.h5 contains the assignment of each frame of each trajectory to a
microstate in a rectangular array of ints. Assignments.h5.distances is an
array of real numbers of the same dimension containing the distance (according
to whichever metric you choose) from each frame to to the medoid of the
microstate it is assigned to.""",
get_metric=True
) #, formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('project')
parser.add_argument(dest='generators',
help='''Output trajectory file containing
the structures of each of the cluster centers. Note that for hierarchical clustering
methods, this file will not be produced.''',
default='Data/Gens.lh5')
parser.add_argument('output_dir')
args, metric = parser.parse_args()
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
assignments_path = os.path.join(args.output_dir, "Assignments.h5")
distances_path = os.path.join(args.output_dir, "Assignments.h5.distances")
project = Project.load_from(args.project)
gens = Trajectory.load_trajectory_file(args.generators)
# this runs assignment and prints them to disk
assign_with_checkpoint(metric, project, gens, assignments_path,
distances_path)
logger.info('All Done!')
def run(project, pdb, metric, traj_fn=None):
ppdb = metric.prepare_trajectory(pdb)
if traj_fn == None:
distances = -1 * np.ones((project.n_trajs, np.max(project.traj_lengths)))
for i in xrange(project.n_trajs):
logger.info("Working on Trajectory %d", i)
ptraj = metric.prepare_trajectory(project.load_traj(i))
d = metric.one_to_all(ppdb, ptraj, 0)
distances[i, 0 : len(d)] = d
else:
traj = Trajectory.load_trajectory_file(traj_fn)
ptraj = metric.prepare_trajectory(traj)
distances = metric.one_to_all(ppdb, ptraj, 0)
return distances
def run(project, pdb, metric, traj_fn=None):
ppdb = metric.prepare_trajectory(pdb)
if traj_fn == None:
distances = -1 * np.ones(
(project.n_trajs, np.max(project.traj_lengths)))
for i in xrange(project.n_trajs):
logger.info("Working on Trajectory %d", i)
ptraj = metric.prepare_trajectory(project.load_traj(i))
d = metric.one_to_all(ppdb, ptraj, 0)
distances[i, 0:len(d)] = d
else:
traj = Trajectory.load_trajectory_file(traj_fn)
ptraj = metric.prepare_trajectory(traj)
distances = metric.one_to_all(ppdb, ptraj, 0)
return distances
