def _start_inline(self, directories):
bar = ProgressBar(len(directories), description="Running QM Calculations")
if self.code == Code.Gaussian:
cmd = self.gaussian_binary + ' < input.gjf > output.gau 2>&1'
elif self.code == Code.PSI4:
cmd = self.psi4_binary + " -i psi4.in -o psi4.out 2>&1"
elif self.code == Code.TeraChem:
cmd = self.terachem_binary + " -i terachem.in > terachem.out 2>&1"
for d in directories:
f = open(os.path.join(d, "run.sh"), "w")
print("#!/bin/sh\n%s\n" % (cmd), file=f)
f.close()
os.chmod(os.path.join(d, "run.sh"), 0o700)
for directory in directories:
cwd = os.getcwd()
try:
os.chdir(directory)
if self.code == Code.Gaussian:
if not os.path.exists("output.gau"):
subprocess.call(cmd, shell=True)
elif self.code == Code.PSI4:
if not os.path.exists("psi4.out"):
subprocess.call(cmd, shell=True)
elif self.code == Code.TeraChem:
if not os.path.exists("terachem.out"):
subprocess.call(cmd, shell=True)
except:
os.chdir(cwd)
raise
os.chdir(cwd)
bar.progress()
bar.stop()
def progressbar(seq, description=None, total=None):
p = ProgressBar(total, description=description)
while True:
try:
yield next(seq)
p.progress() # Had to put progress after yield because last call goes over the total and then I can't decrement in stop()
except StopIteration:
p.stop()
raise
def tileMembrane(memb, xmin, ymin, xmax, ymax, buffer=1.5):
""" Tile a membrane in the X and Y dimensions to reach a specific size.
Parameters
----------
memb
xmin
ymin
xmax
ymax
buffer
Returns
-------
megamemb :
A big membrane Molecule
"""
from htmd.progress.progress import ProgressBar
memb = memb.copy()
memb.resid = sequenceID(memb.resid)
minmemb = np.min(memb.get('coords', 'water'), axis=0).flatten()
size = np.max(memb.get('coords', 'water'), axis=0) - np.min(memb.get('coords', 'water'), axis=0)
size = size.flatten()
xreps = int(np.ceil((xmax - xmin) / size[0]))
yreps = int(np.ceil((ymax - ymin) / size[1]))
logger.info('Replicating Membrane {}x{}'.format(xreps, yreps))
from htmd.molecule.molecule import Molecule
megamemb = Molecule()
bar = ProgressBar(xreps * yreps, description='Replicating Membrane')
k = 0
for x in range(xreps):
for y in range(yreps):
tmpmemb = memb.copy()
xpos = xmin + x * (size[0] + buffer)
ypos = ymin + y * (size[1] + buffer)
tmpmemb.moveBy([-float(minmemb[0]) + xpos, -float(minmemb[1]) + ypos, 0])
tmpmemb.remove('same resid as (x > {} or y > {})'.format(xmax, ymax), _logger=False)
tmpmemb.set('segid', 'M{}'.format(k))
megamemb.append(tmpmemb)
k += 1
bar.progress()
bar.stop()
# Membranes don't tile perfectly. Need to remove waters that clash with lipids of other tiles
# Some clashes will still occur between periodic images however
megamemb.remove('same fragment as water and within 1.5 of not water', _logger=False)
return megamemb
def __init__(self, data, lag):
from pyemma.coordinates import tica
# data.dat.tolist() might be better?
self.data = data
if isinstance(data, Metric):
from pyemma.coordinates.transform.tica import TICA
self.tic = TICA(lag)
p = ProgressBar(len(data.simulations))
for i in range(len(data.simulations)):
# Fix for pyemma bug. Remove eventually:
d, _, _ = data._projectSingle(i)
if d is None or d.shape[0] < lag:
continue
self.tic.partial_fit(d)
p.progress()
p.stop()
else:
self.tic = tica(data.dat.tolist(), lag=lag)
def __call__(self, index):
if CallBack.bars[
self.
parallel] == 0: # Oh man, the race conditions possible here... kill me
CallBack.bars[self.parallel] = ProgressBar(
self.parallel.n_dispatched,
description='Projecting trajectories')
CallBack.bars[self.parallel].progress()
if self.parallel._original_iterable:
self.parallel.dispatch_next()
def _run_qm_jobs_inline(self, directory):
cmd = self._env['BIN_G09']
fni = []
fno = []
for root, dirs, files in os.walk(directory):
for f in files:
if (f.endswith(".gjf")):
op = f.replace(".gjf", ".out")
if not os.path.exists(os.path.join(root, op)):
fni.append(os.path.join(root, f))
fno.append(os.path.join(root, op))
if (len(fni)):
bar = ProgressBar(len(fni), description="Running QM Calculations")
for i in range(len(fni)):
subprocess.check_output([cmd, fni[i], fno[i]])
bar.progress()
bar.stop()
def __init__(self, data, lag, units='frames'):
from pyemma.coordinates import tica
# data.dat.tolist() might be better?
self.data = data
if isinstance(data, Metric):
if units != 'frames':
raise RuntimeError(
'Cannot use delayed projection TICA with units other than frames for now. Report this to HTMD issues.'
)
metr = data
from pyemma.coordinates.transform.tica import TICA
self.tic = TICA(lag)
p = ProgressBar(len(metr.simulations))
for proj in _projectionGenerator(metr, _getNcpus()):
for pro in proj:
self.tic.partial_fit(pro[0])
p.progress(len(proj))
p.stop()
else:
lag = unitconvert(units, 'frames', lag, data.fstep)
if lag == 0:
raise RuntimeError(
'Lag time conversion resulted in 0 frames. Please use a larger lag-time for TICA.'
)
self.tic = tica(data.dat.tolist(), lag=lag)
def _start(self, directories):
bar = ProgressBar(len(directories),
description="Running QM Calculations")
for directory in directories:
cwd = os.getcwd()
try:
if self.execution == Execution.Inline:
os.chdir(directory)
if self.code == Code.Gaussian:
if not os.path.exists("output.gau"):
subprocess.call('"' + self.gaussian_binary +
'" < input.gjf > output.gau 2>&1',
shell=True)
elif self.code == Code.PSI4:
if not os.path.exists("psi4.out"):
subprocess.call([
self.psi4_binary, "-i", "psi4.in", "-o",
"psi4.out"
])
except:
os.chdir(cwd)
raise
os.chdir(cwd)
bar.progress()
bar.stop()
def __init__(self, data, lag, units='frames', dimensions=None):
from pyemma.coordinates.transform.tica import TICA as TICApyemma
self.data = data
self.dimensions = dimensions
if isinstance(data, Metric): # Memory efficient TICA projecting trajectories on the fly
if units != 'frames':
raise RuntimeError('Cannot use delayed projection TICA with units other than frames for now. Report this to HTMD issues.')
self.tic = TICApyemma(lag)
metr = data
p = ProgressBar(len(metr.simulations))
for proj in _projectionGenerator(metr, _getNcpus()):
for pro in proj:
if pro is None:
continue
if self.dimensions is None:
self.tic.partial_fit(pro[0])
else: # Sub-select dimensions for fitting
self.tic.partial_fit(pro[0][:, self.dimensions])
p.progress(len(proj))
p.stop()
else: # In-memory TICA
lag = unitconvert(units, 'frames', lag, data.fstep)
if lag == 0:
raise RuntimeError('Lag time conversion resulted in 0 frames. Please use a larger lag-time for TICA.')
self.tic = TICApyemma(lag)
if self.dimensions is None:
datalist = data.dat.tolist()
else: # Sub-select dimensions for fitting
datalist = [x[:, self.dimensions].copy() for x in data.dat]
self.tic.fit(datalist)
def tileMembrane(memb, xmin, ymin, xmax, ymax):
""" Tile the membrane in the X and Y dimensions to reach a specific size.
Returns
-------
megamemb :
A big membrane Molecule
"""
from htmd.progress.progress import ProgressBar
memb = memb.copy()
memb.resid = sequenceID(memb.resid)
minmemb = np.min(memb.get('coords', 'water'), axis=0).flatten()
size = np.max(memb.get('coords', 'water'), axis=0) - np.min(memb.get('coords', 'water'), axis=0)
size = size.flatten()
xreps = int(np.ceil((xmax - xmin) / size[0]))
yreps = int(np.ceil((ymax - ymin) / size[1]))
logger.info('Replicating Membrane {}x{}'.format(xreps, yreps))
from htmd.molecule.molecule import Molecule
megamemb = Molecule()
bar = ProgressBar(xreps * yreps, description='Replicating Membrane')
k = 0
for x in range(xreps):
for y in range(yreps):
tmpmemb = memb.copy()
xpos = xmin + x * size[0]
ypos = ymin + y * size[1]
tmpmemb.moveBy([-float(minmemb[0]) + xpos, -float(minmemb[1]) + ypos, 0])
sel = 'same resid as (x > {} or y > {})'.format(xmax, ymax)
tmpmemb.remove(sel, _logger=False)
tmpmemb.set('segid', 'M{}'.format(k))
megamemb.append(tmpmemb)
k += 1
bar.progress()
bar.stop()
return megamemb
def project(self, ndim=None):
""" Projects the data object given to the constructor onto `ndim` dimensions
Parameters
----------
ndim : int
The number of dimensions we want to project the data on.
Returns
-------
dataTica : :class:`MetricData <htmd.metricdata.MetricData>` object
A new :class:`MetricData <htmd.metricdata.MetricData>` object containing the projected data
Example
-------
>>> gw = GWPCA(data)
>>> dataproj = gw.project(5)
"""
from sklearn.decomposition import IncrementalPCA
from htmd.progress.progress import ProgressBar
from htmd.metricdata import MetricData
pca = IncrementalPCA(n_components=ndim, batch_size=10000)
p = ProgressBar(len(self.data.dat))
for d in self.data.dat:
pca.partial_fit(d * self.weights)
p.progress()
p.stop()
projdata = self.data.copy()
p = ProgressBar(len(self.data.dat))
for i, d in enumerate(self.data.dat):
projdata.dat[i] = pca.transform(d * self.weights)
p.progress()
p.stop()
# projdataconc = pca.fit_transform(self.weighedconcat)
# projdata.dat = projdata.deconcatenate(projdataconc)
return projdata
def progressbar(seq, description=None, total=None):
p = ProgressBar(total, description=description)
while True:
try:
yield next(seq)
p.progress(
) # Had to put progress after yield because last call goes over the total and then I can't decrement in stop()
except StopIteration:
p.stop()
raise
def fit(self, data):
""" Compute the centroids of data.
Parameters
----------
data : np.ndarray
A 2D array of data. Columns are features and rows are data examples.
"""
if len(self.cluster_centers_) != 0:
logger.warning('Clustering already exists. Reclustering data!')
self.cluster_centers_ = []
self.centerFrames = []
self.clusterSize = []
# Initialization
# select random point and assign all points to cluster 0
numpoints = np.size(data, 0)
idxCenter = np.random.randint(numpoints)
self.cluster_centers_.append(data[idxCenter, :])
self.centerFrames.append(idxCenter)
self.labels_ = np.zeros(numpoints, dtype=int)
dist = self._dist(self.cluster_centers_, data)
countCluster = 1
p = ProgressBar(self.n_clusters)
while len(self.cluster_centers_) < self.n_clusters:
if np.max(dist) == 0:
break
# find point furthest away from all center
newCenterIdx = np.argmax(dist)
newCenter = data[newCenterIdx, :]
self.centerFrames.append(newCenterIdx)
self.cluster_centers_.append(newCenter)
# find all points closer to new center than old center
newdist = self._dist(newCenter, data)
switchIdx = dist > newdist
# assign them to new cluster
self.labels_[switchIdx] = countCluster
dist[switchIdx] = newdist[switchIdx]
countCluster += 1
p.progress()
p.stop()
# update clusterSize
self.clusterSize = np.bincount(self.labels_)
self.distance = dist
self.cluster_centers_ = np.array(self.cluster_centers_)
def tileMembrane(memb, xmin, ymin, xmax, ymax):
""" Tile the membrane in the X and Y dimensions to reach a specific size.
Returns
-------
megamemb :
A big membrane Molecule
"""
from htmd.progress.progress import ProgressBar
memb = memb.copy()
memb.resid = sequenceID(memb.resid)
minmemb = np.min(memb.get('coords', 'water'), axis=0).flatten()
size = np.max(memb.get('coords', 'water'), axis=0) - np.min(
memb.get('coords', 'water'), axis=0)
size = size.flatten()
xreps = int(np.ceil((xmax - xmin) / size[0]))
yreps = int(np.ceil((ymax - ymin) / size[1]))
logger.info('Replicating Membrane {}x{}'.format(xreps, yreps))
from htmd.molecule.molecule import Molecule
megamemb = Molecule()
bar = ProgressBar(xreps * yreps, description='Replicating Membrane')
k = 0
for x in range(xreps):
for y in range(yreps):
tmpmemb = memb.copy()
xpos = xmin + x * size[0]
ypos = ymin + y * size[1]
tmpmemb.moveBy(
[-float(minmemb[0]) + xpos, -float(minmemb[1]) + ypos, 0])
sel = 'same resid as (x > {} or y > {})'.format(xmax, ymax)
tmpmemb.remove(sel, _logger=False)
tmpmemb.set('segid', 'M{}'.format(k))
megamemb.append(tmpmemb)
k += 1
bar.progress()
bar.stop()
return megamemb
def _run_qm_jobs_inline(self, directory):
cmd = self._env['BIN_G09']
fni = []
fno = []
for root, dirs, files in os.walk(directory):
for f in files:
if f.endswith(".gjf"):
op = f.replace(".gjf", ".out")
if not os.path.exists(os.path.join(root, op)):
fni.append(os.path.join(root, f))
fno.append(os.path.join(root, op))
if len(fni):
bar = ProgressBar(len(fni), description="Running QM Calculations")
for i in range(len(fni)):
subprocess.check_output([cmd, fni[i], fno[i]])
bar.progress()
bar.stop()
def __init__(self, data, lag, units='frames'):
from pyemma.coordinates import tica
# data.dat.tolist() might be better?
self.data = data
if isinstance(data, Metric):
from pyemma.coordinates.transform.tica import TICA
lag = unitconvert(units, 'frames', lag, data.fstep)
self.tic = TICA(lag)
p = ProgressBar(len(data.simulations))
for i in range(len(data.simulations)):
# Fix for pyemma bug. Remove eventually:
d, _, _ = data._projectSingle(i)
if d is None or d.shape[0] < lag:
continue
self.tic.partial_fit(d)
p.progress()
p.stop()
else:
self.tic = tica(data.dat.tolist(), lag=lag)
def _start_inline(self, directories):
bar = ProgressBar(len(directories),
description="Running QM Calculations")
if self.code == Code.Gaussian:
cmd = self.gaussian_binary + ' < input.gjf > output.gau 2>&1'
elif self.code == Code.PSI4:
cmd = self.psi4_binary + " -i psi4.in -o psi4.out 2>&1"
elif self.code == Code.TeraChem:
cmd = self.terachem_binary + " -i terachem.in > terachem.out 2>&1"
for d in directories:
f = open(os.path.join(d, "run.sh"), "w")
print("#!/bin/sh\n%s\n" % (cmd), file=f)
f.close()
os.chmod(os.path.join(d, "run.sh"), 0o700)
for directory in directories:
cwd = os.getcwd()
try:
os.chdir(directory)
if self.code == Code.Gaussian:
if not os.path.exists("output.gau"):
subprocess.call(cmd, shell=True)
elif self.code == Code.PSI4:
if not os.path.exists("psi4.out"):
subprocess.call(cmd, shell=True)
elif self.code == Code.TeraChem:
if not os.path.exists("terachem.out"):
subprocess.call(cmd, shell=True)
except:
os.chdir(cwd)
raise
os.chdir(cwd)
bar.progress()
bar.stop()
def _run_qm_jobs_lsf(self, directory):
cmd = self._env['BIN_G09']
fni = []
fno = []
for root, dirs, files in os.walk(directory):
for f in files:
if (f.endswith(".gjf")):
op = f.replace(".gjf", ".out")
if not os.path.exists(op):
fni.append(os.path.join(root, f))
fno.append(os.path.join(root, op))
if (len(fni)):
# Make an LSF script
for i in range(len(fni)):
fpbs = fni[i] + ".lsf"
if not os.path.exists(fpbs):
f = open(fpbs, "w")
print("#BSUB -n %d" % (self._config.NCORES), file=f)
print("#BSUB -R \"span[ptile=%d]\"" % (self._config.NCORES), file=f)
print("#BSUB -W 24:00", file=f)
print("#BSUB -J gaussian", file=f)
print("#BSUB -app gaussian", file=f)
print("#BSUB -o /dev/null", file=f)
print("#BSUB -M %d000" % (self._config.MEMORY), file=f)
print("\nmodule load gaussian\n", file=f)
print("cd \"%s\"" % (directory), file=f)
print("\"%s\" %s %s" % (cmd, fni[i], fno[i]), file=f)
f.close()
# Look to see if there is already a job submitted
# If not, qsub it
fpbsstate = fni[i] + ".jobid"
if not os.path.exists(fpbsstate):
# Qsub, saving jobid to file
subprocess.check_output(
"\"" + self._env['BIN_BSUB'] + "\" < \"" + fpbs + "\" > \"" + fpbsstate + "\"", shell=True)
# Finally monitor progress. Continue until all jobs have produced an output
# NB TODO FIXME: should also poll qstat to see if job is still live
bar = ProgressBar(len(fni), description="Running QM Calculations")
complete = False
lastcount = 0
while not complete:
count = 0
complete = True
for i in fno:
# print(" Checking [" + i +"]" )
# print( os.path.exists(i) )
# print( os.access(i, os.R_OK) )
try:
os.stat(i) # Try to flush any cache (for NFS)
except:
pass
if os.access(i, os.R_OK):
# print("FOUND")
count = count + 1
else:
complete = False
# print( str(count) + " completed of " + str(len(fno)) )
while (lastcount < count):
bar.progress()
lastcount = lastcount + 1;
time.sleep(10);
bar.stop()
time.sleep(5) # A bit of time for any outputfile to complete writing
def project(self, ndim=None):
""" Projects the data object given to the constructor onto the top `ndim` TICA dimensions
Parameters
----------
ndim : int
The number of TICA dimensions we want to project the data on. If None is given it will use choose a number
of dimensions to cover 95% of the kinetic variance.
Returns
-------
dataTica : :class:`MetricData <htmd.metricdata.MetricData>` object
A new :class:`MetricData <htmd.metricdata.MetricData>` object containing the TICA projected data
Example
-------
>>> from htmd.projections.tica import TICA
>>> tica = TICA(data,20)
>>> dataTica = tica.project(5)
"""
if ndim is not None:
self.tic.set_params(dim=ndim)
keepdata = []
keepdim = None
keepdimdesc = None
if isinstance(self.data, Metric): # Memory efficient TICA projecting trajectories on the fly
proj = []
refs = []
fstep = None
metr = self.data
p = ProgressBar(len(metr.simulations))
k = -1
droppedsims = []
for projecteddata in _projectionGenerator(metr, _getNcpus()):
for pro in projecteddata:
k += 1
if pro is None:
droppedsims.append(k)
continue
if self.dimensions is not None:
numDimensions = pro[0].shape[1]
keepdim = np.setdiff1d(range(numDimensions), self.dimensions)
keepdata.append(pro[0][:, keepdim])
proj.append(self.tic.transform(pro[0][:, self.dimensions]).astype(np.float32)) # Sub-select dimensions for projecting
else:
proj.append(self.tic.transform(pro[0]).astype(np.float32))
refs.append(pro[1])
if fstep is None:
fstep = pro[2]
p.progress(len(projecteddata))
p.stop()
simlist = self.data.simulations
simlist = np.delete(simlist, droppedsims)
ref = np.array(refs, dtype=object)
parent = None
if self.dimensions is not None:
from htmd.projections.metric import _singleMolfile
from htmd.molecule.molecule import Molecule
(single, molfile) = _singleMolfile(metr.simulations)
if single:
keepdimdesc = metr.getMapping(Molecule(molfile))
keepdimdesc = keepdimdesc.iloc[keepdim]
else:
if ndim is not None and self.data.numDimensions < ndim:
raise RuntimeError('TICA cannot increase the dimensionality of your data. Your data has {} dimensions and you requested {} TICA dimensions'.format(self.data.numDimensions, ndim))
if self.dimensions is not None:
keepdim = np.setdiff1d(range(self.data.numDimensions), self.dimensions)
keepdata = [x[:, keepdim] for x in self.data.dat]
if self.data.description is not None:
keepdimdesc = self.data.description.iloc[keepdim]
proj = self.tic.get_output()
simlist = self.data.simlist
ref = self.data.ref
fstep = self.data.fstep
parent = self.data
# If TICA is done on a subset of dimensions, combine non-projected data with projected data
if self.dimensions is not None:
newproj = []
for k, t in zip(keepdata, proj):
newproj.append(np.hstack((k, t)))
proj = newproj
if ndim is None:
ndim = self.tic.dimension()
logger.info('Kept {} dimension(s) to cover 95% of kinetic variance.'.format(ndim))
from htmd.metricdata import MetricData
datatica = MetricData(dat=np.array(proj), simlist=simlist, ref=ref, fstep=fstep, parent=parent)
from pandas import DataFrame
# TODO: Make this messy pandas creation cleaner. I'm sure I can append rows to DataFrame
types = []
indexes = []
description = []
for i in range(ndim):
types += ['tica']
indexes += [-1]
description += ['TICA dimension {}'.format(i+1)]
datatica.description = DataFrame({'type': types, 'atomIndexes': indexes, 'description': description})
if self.dimensions is not None and keepdimdesc is not None: # If TICA is done on a subset of dims
datatica.description = keepdimdesc.append(datatica.description, ignore_index=True)
return datatica
def project(self, ndim=None):
""" Projects the data object given to the constructor onto the top `ndim` TICA dimensions
Parameters
----------
ndim : int
The number of TICA dimensions we want to project the data on. If None is given it will use choose a number
of dimensions to cover 95% of the kinetic variance.
Returns
-------
dataTica : :class:`MetricData <htmd.metricdata.MetricData>` object
A new :class:`MetricData <htmd.metricdata.MetricData>` object containing the TICA projected data
Example
-------
>>> from htmd.projections.tica import TICA
>>> tica = TICA(data,20)
>>> dataTica = tica.project(5)
"""
if ndim is not None:
# self.tic._dim = ndim # Old way of doing it. Deprecated since pyEMMA 2.1
self.tic.set_params(dim=ndim) # Change to this in 2.1 pyEMMA version
if isinstance(self.data, Metric): # Doesn't project on correct number of dimensions
proj = []
refs = []
fstep = None
'''from htmd.config import _config
from joblib import Parallel, delayed
results = Parallel(n_jobs=_config['ncpus'], verbose=11)(
delayed(_test)(self.data, self.tic, i) for i in range(len(self.data.simulations)))
for i in range(len(results)):
proj.append(results[i][0])
refs.append(results[i][1])
fstep.append(results[i][2])'''
droppedsims = []
p = ProgressBar(len(self.data.simulations))
for i in range(len(self.data.simulations)):
d, r, f = self.data._projectSingle(i)
if d is None:
droppedsims.append(i)
continue
if fstep is None:
fstep = f
refs.append(r)
proj.append(self.tic.transform(d))
p.progress()
p.stop()
simlist = self.data.simulations
simlist = np.delete(simlist, droppedsims)
ref = np.array(refs, dtype=object)
#fstep = 0
parent = None
else:
proj = self.tic.get_output()
simlist = self.data.simlist
ref = self.data.ref
fstep = self.data.fstep
parent = self.data
if ndim is None:
logger.info('Kept {} dimension(s) to cover 95% of kinetic variance.'.format(self.tic.dimension()))
#print(np.shape(proj))
from htmd.metricdata import MetricData
datatica = MetricData(dat=np.array(proj, dtype=object), simlist=simlist, ref=ref, fstep=fstep, parent=parent)
'''datatica = self.data.copy()
#datatica.dat = self.data.deconcatenate(np.squeeze(proj))
datatica.dat = np.array(proj, dtype=object)
datatica.parent = self.data
datatica.St = None
datatica.Centers = None
datatica.N = None
datatica.K = None
datatica._dataid = random.random()
datatica._clusterid = None'''
return datatica
def simlist(datafolders, molfiles, inputfolders=None):
"""Creates a list of simulations
Parameters
----------
datafolders : str list
A list of directories, each containing a single trajectory
molfiles : str list
A list of pdb files corresponding to the trajectories in dataFolders. Can also be a single string to a single
structure which corresponds to all trajectories.
inputfolders : optional, str list
A list of directories, each containing the input files used to produce the trajectories in dataFolders
Return
------
sims : np.ndarray of :class:`Sim <htmd.simlist.Sim>` objects
A list of simulations
Examples
--------
>>> simlist(glob('./test/data/*/'), glob('./test/input/*/*.pdb'), glob('./test/input/*/'))
"""
if not datafolders:
raise NameError('No data folders were given, check your arguments.')
if not molfiles:
raise NameError('No molecule files were given, check your arguments.')
if isinstance(molfiles, str):
molfiles = [molfiles]
if isinstance(datafolders, str):
datafolders = [datafolders]
if inputfolders and isinstance(inputfolders, str):
inputfolders = [inputfolders]
#Sim = namedtuple('Sim', ['id', 'parent', 'input', 'trajectory', 'molfile'])
# I need to match the simulation names inside the globs given. The
# reason is that there can be more input folders in the glob than in
# the data glob due to not having been retrieved. Hence I need to match
# the folder names.
# Create a hash map of data folder names
datanames = dict()
for folder in datafolders:
if _simName(folder) in datanames:
raise NameError('Duplicate simulation name detected. Cannot name-match directories.')
datanames[_simName(folder)] = folder
molnames = dict()
for mol in molfiles:
molnames[_simName(mol)] = mol
if inputfolders:
inputnames = dict()
for inputf in inputfolders:
inputnames[_simName(inputf)] = inputf
logger.info('Starting listing of simulations.')
sims = []
keys = natsort.natsorted(datanames.keys())
i = 0
bar = ProgressBar(len(keys), description='Creating simlist')
for k in keys:
trajectories = _listXTCs(datanames[k])
if not trajectories:
bar.progress()
continue
if len(molfiles) > 1:
if k not in molnames:
raise NameError('Did not find molfile with folder name ' + k + ' in the given glob')
molfile = molnames[k]
else:
molfile = molfiles[0]
inputf = []
if inputfolders:
if k not in inputnames:
raise NameError('Did not find input with folder name ' + k + ' in the given glob')
inputf = inputnames[k]
sims.append(Sim(simid=i, parent=None, input=inputf, trajectory=trajectories, molfile=molfile))
i += 1
bar.progress()
bar.stop()
logger.info('Finished listing of simulations.')
return np.array(sims, dtype=object)
def simlist(datafolders, topologies, inputfolders=None):
"""Creates a list of simulations
Parameters
----------
datafolders : str list
A list of directories, each containing a single trajectory
topologies : str list
A list of topology files or folders containing a topology file corresponding to the trajectories in dataFolders.
Can also be a single string to a single structure which corresponds to all trajectories.
inputfolders : optional, str list
A list of directories, each containing the input files used to produce the trajectories in dataFolders
Return
------
sims : np.ndarray of :class:`Sim <htmd.simlist.Sim>` objects
A list of simulations
Examples
--------
>>> simlist(glob('./test/data/*/'), glob('./test/input/*/'), glob('./test/input/*/'))
>>> simlist(glob('./test/data/*/'), glob('./test/input/*/*.pdb'), glob('./test/input/*/'))
"""
from htmd.util import ensurelist
import natsort
if not datafolders:
raise FileNotFoundError(
'No data folders were given, check your arguments.')
if not topologies:
raise FileNotFoundError(
'No molecule files were given, check your arguments.')
topologies = ensurelist(topologies)
datafolders = ensurelist(datafolders)
for folder in datafolders:
if not os.path.isdir(folder):
raise NotADirectoryError('{}'.format(folder))
if inputfolders:
inputfolders = ensurelist(inputfolders)
for folder in inputfolders:
if not os.path.isdir(folder):
raise NotADirectoryError('{}'.format(folder))
# I need to match the simulation names inside the globs given. The
# reason is that there can be more input folders in the glob than in
# the data glob due to not having been retrieved. Hence I need to match
# the folder names.
# Create a hash map of data folder names
datanames = dict()
for folder in datafolders:
if _simName(folder) in datanames:
raise RuntimeError(
'Duplicate simulation name detected. Cannot name-match directories.'
)
datanames[_simName(folder)] = folder
molnames = dict()
for mol in topologies:
if not os.path.exists(mol):
raise FileNotFoundError('File {} does not exist'.format(mol))
molnames[_simName(mol)] = mol
if inputfolders:
inputnames = dict()
for inputf in inputfolders:
inputnames[_simName(inputf)] = inputf
logger.debug('Starting listing of simulations.')
sims = []
keys = natsort.natsorted(datanames.keys())
i = 0
from htmd.progress.progress import ProgressBar
bar = ProgressBar(len(keys), description='Creating simlist')
for k in keys:
trajectories = _autoDetectTrajectories(datanames[k])
if not trajectories:
bar.progress()
continue
if len(topologies) > 1:
if k not in molnames:
raise FileNotFoundError(
'Did not find molfile with folder name ' + k +
' in the given glob')
molfile = molnames[k]
else:
molfile = topologies[0]
if os.path.isdir(molfile):
molfile = _autoDetectTopology(molfile)
inputf = []
if inputfolders:
if k not in inputnames:
raise FileNotFoundError(
'Did not find input with folder name ' + k +
' in the given glob')
inputf = inputnames[k]
numframes = [_readNumFrames(f) for f in trajectories]
sims.append(
Sim(simid=i,
parent=None,
input=inputf,
trajectory=trajectories,
molfile=molfile,
numframes=numframes))
i += 1
bar.progress()
bar.stop()
logger.debug('Finished listing of simulations.')
return np.array(sims, dtype=object)
def simlist(datafolders, molfiles, inputfolders=None):
"""Creates a list of simulations
Parameters
----------
datafolders : str list
A list of directories, each containing a single trajectory
molfiles : str list
A list of pdb files corresponding to the trajectories in dataFolders. Can also be a single string to a single
structure which corresponds to all trajectories.
inputfolders : optional, str list
A list of directories, each containing the input files used to produce the trajectories in dataFolders
Return
------
sims : np.ndarray of :class:`Sim <htmd.simlist.Sim>` objects
A list of simulations
Examples
--------
>>> simlist(glob('./test/data/*/'), glob('./test/input/*/*.pdb'), glob('./test/input/*/'))
"""
if not datafolders:
raise NameError('No data folders were given, check your arguments.')
if not molfiles:
raise NameError('No molecule files were given, check your arguments.')
if isinstance(molfiles, str):
molfiles = [molfiles]
if isinstance(datafolders, str):
datafolders = [datafolders]
if inputfolders and isinstance(inputfolders, str):
inputfolders = [inputfolders]
#Sim = namedtuple('Sim', ['id', 'parent', 'input', 'trajectory', 'molfile'])
# I need to match the simulation names inside the globs given. The
# reason is that there can be more input folders in the glob than in
# the data glob due to not having been retrieved. Hence I need to match
# the folder names.
# Create a hash map of data folder names
datanames = dict()
for folder in datafolders:
if _simName(folder) in datanames:
raise NameError(
'Duplicate simulation name detected. Cannot name-match directories.'
)
datanames[_simName(folder)] = folder
molnames = dict()
for mol in molfiles:
molnames[_simName(mol)] = mol
if inputfolders:
inputnames = dict()
for inputf in inputfolders:
inputnames[_simName(inputf)] = inputf
logger.info('Starting listing of simulations.')
sims = []
keys = natsort.natsorted(datanames.keys())
i = 0
bar = ProgressBar(len(keys), description='Creating simlist')
for k in keys:
trajectories = _listTrajectories(datanames[k])
if not trajectories:
bar.progress()
continue
if len(molfiles) > 1:
if k not in molnames:
raise NameError('Did not find molfile with folder name ' + k +
' in the given glob')
molfile = molnames[k]
else:
molfile = molfiles[0]
inputf = []
if inputfolders:
if k not in inputnames:
raise NameError('Did not find input with folder name ' + k +
' in the given glob')
inputf = inputnames[k]
sims.append(
Sim(simid=i,
parent=None,
input=inputf,
trajectory=trajectories,
molfile=molfile))
i += 1
bar.progress()
bar.stop()
logger.info('Finished listing of simulations.')
return np.array(sims, dtype=object)
def solvate(mol,
pad=None,
minmax=None,
negx=0,
posx=0,
negy=0,
posy=0,
negz=0,
posz=0,
buffer=2.4,
watsize=65.4195,
prefix='WT',
keysel='name OH2',
rotate=False,
rotsel='all',
rotinc=36,
spdb=None,
spsf=None,
stop=None):
""" Solvates the system in a water box
Parameters
----------
mol : :class:`Molecule <htmd.molecule.molecule.Molecule>` object
The molecule object we want to solvate
pad : float
The padding to add to the minmax in all dimensions. You can specify different padding in each dimension using
the negx, negy, negz, posx, posy, posz options. This option will override any values in the neg and pos options.
minmax : list
Min and max dimensions. Should be a 2D matrix of the form [[minx, miny, minz], [maxx, maxy, maxz]]. If none is
given, it is calculated from the minimum and maximum coordinates in the mol.
negx : float
The padding in the -x dimension
posx : float
The padding in the +x dimension
negy : float
The padding in the -y dimension
posy : float
The padding in the +y dimension
negz : float
The padding in the -z dimension
posz : float
The padding in the +z dimension
buffer : float
How much buffer space to leave empty between waters and other molecules
watsize : float
The size of the water box
prefix : str
The prefix used for water segments
keysel : str
The key selection for water atoms
rotate : bool
Enable automated rotation of molecule to fit best in box
rotsel : str
The selection of atoms to rotate
rotinc : float
The increment in degrees to rotate
spdb : str
The path to the water pdb file
spsf : str
The path to the water psf file
stop : str
The path to the water topology file
Returns
-------
mol : :class:`Molecule <htmd.molecule.molecule.Molecule>` object
A solvated molecule
Examples
--------
>>> smol = solvate(mol, pad=10)
>>> smol = solvate(mol, minmax=[[-20, -20, -20],[20, 20, 20]])
"""
mol = mol.copy()
if mol.numFrames > 1:
logger.warning(
'Multiple frames in Molecule. Solvate keeps only frame 0 and discards the rest.'
)
mol.coords = np.atleast_3d(mol.coords[:, :, 0])
if spdb is None:
spdb = os.path.join(os.path.dirname(os.path.realpath(__file__)),
'wat.pdb')
if os.path.isfile(spdb):
logger.info('Using water pdb file at: ' + spdb)
water = Molecule(spdb)
else:
raise NameError('No solvent pdb file found in ' + spdb)
if pad is not None:
negx = pad
posx = pad
negy = pad
posy = pad
negz = pad
posz = pad
if rotate:
raise NameError('Rotation not implemented yet')
# Calculate min max coordinates from molecule
if mol.numAtoms > 0:
minmol = np.min(mol.get('coords'), axis=0)
maxmol = np.max(mol.get('coords'), axis=0)
else:
minmol = [np.inf, np.inf, np.inf]
maxmol = [-np.inf, -np.inf, -np.inf]
if minmax is None:
minc = minmol
maxc = maxmol
else:
if isinstance(minmax, list):
minmax = np.array(minmax)
minc = minmax[0, :]
maxc = minmax[1, :]
xmin = float(minc[0] - negx)
xmax = float(maxc[0] + posx)
ymin = float(minc[1] - negy)
ymax = float(maxc[1] + posy)
zmin = float(minc[2] - negz)
zmax = float(maxc[2] + posz)
dx = xmax - xmin
dy = ymax - ymin
dz = zmax - zmin
nx = int(np.ceil((dx + 2 * buffer) / watsize))
ny = int(np.ceil((dy + 2 * buffer) / watsize))
nz = int(np.ceil((dz + 2 * buffer) / watsize))
# Calculate number of preexisting water segments with given prefix
if mol.numAtoms > 0:
preexist = len(
np.unique(mol.get('segid', sel='segid "{}.*"'.format(prefix))))
else:
preexist = 0
numsegs = nx * ny * nz
logger.info('Replicating ' + str(numsegs) + ' water segments, ' + str(nx) +
' by ' + str(ny) + ' by ' + str(nz))
# Check that we won't run out of segment name characters, and switch to
# using hexadecimal or alphanumeric naming schemes in cases where decimal
# numbered segnames won't fit into the field width.
testsegname = '{0}{1:d}'.format(prefix, numsegs + preexist)
testsegnamehex = '{0}{1:X}'.format(prefix, numsegs + preexist)
writemode = 'decimal'
if len(testsegname) > 4 and len(testsegnamehex) <= 4:
writemode = 'hex'
logger.warning(
'Warning: decimal naming would overrun segname field. Using hexadecimal segnames instead...'
)
elif len(testsegnamehex) > 4:
writemode = 'alphanum'
logger.warning(
'Warning: decimal or hex naming would overrun segname field. Using alphanumeric segnames instead...'
)
minx = minmol[0] - buffer
miny = minmol[1] - buffer
minz = minmol[2] - buffer
maxx = maxmol[0] + buffer
maxy = maxmol[1] + buffer
maxz = maxmol[2] + buffer
bar = ProgressBar(nx * ny * nz, description='Solvating')
waterboxes = np.empty(numsegs, dtype=object)
n = preexist
w = 0
for i in range(nx):
movex = xmin + i * watsize
movexmax = movex + watsize
xoverlap = True
if movex > maxx or movexmax < minx:
xoverlap = False
for j in range(ny):
movey = ymin + j * watsize
moveymax = movey + watsize
yoverlap = True
if movey > maxy or moveymax < miny:
yoverlap = False
for k in range(nz):
movez = zmin + k * watsize
movezmax = movez + watsize
zoverlap = True
if movez > maxz or movezmax < minz:
zoverlap = False
if writemode == 'decimal':
segname = '{0}{1:d}'.format(prefix, n)
elif writemode == 'hex':
segname = '{0}{1:x}'.format(prefix, n)
elif writemode == 'alphanum':
segname = '{0}{1:c}{2:c}{3:c}'.format(
prefix, int(np.floor(np.floor(n / 26) / 26) + 65),
int(np.mod(np.floor(n / 26), 26) + 65),
int(np.mod(n, 26) + 65))
waterboxes[w] = water.copy()
waterboxes[w].moveBy([movex, movey, movez])
waterboxes[w].set('segid', segname)
mol.append(waterboxes[w])
watsel = mol.segid == segname
selover = np.zeros(len(watsel), dtype=bool)
if xoverlap and yoverlap and zoverlap: # Remove water overlapping with other segids
selover = _overlapWithOther(mol, segname, buffer)
# Remove water outside the boundaries
selout = _outOfBoundaries(mol, segname, xmin, xmax, ymin, ymax,
zmin, zmax)
sel = selover | selout
#mol.write('temp.pdb')
mol.filter(mol.segid != segname, _logger=False)
waterboxes[w].filter(np.invert(sel[watsel]), _logger=False)
#waterboxes[w].write('wat' + str(w) + '.pdb')
n += 1
w += 1
bar.progress()
bar.stop()
waters = 0
for i in range(numsegs):
waters += waterboxes[i].numAtoms
if waterboxes[i].numAtoms != 0:
mol.append(waterboxes[i])
logger.info('{} water molecules were added to the system.'.format(
int(waters / 3)))
return mol
def fitSoftTorsion(self, angle, geomopt=True):
bkp_coords = self.coords.copy()
phi_to_fit = None
frozens = []
for d in self._soft_dihedrals:
if (d.atoms == angle).all():
phi_to_fit = d
frozens.append(d.atoms)
else:
if not geomopt:
frozens.append(d.atoms)
if not phi_to_fit:
raise ValueError("specified phi is not a recognised soft dihedral")
self._makeDihedralUnique(phi_to_fit)
atoms = phi_to_fit.atoms
equivs = phi_to_fit.equivalents
# Number of rotamers for each dihedral to compute
nrotamer = 36
# Create a copy of molecule with nrotamer frames
mol = self.copy()
for _ in range(nrotamer - 1):
mol.appendFrames(self)
assert mol.numFrames == nrotamer
# Set rotamer coordinates
angles = np.linspace(-np.pi, np.pi, num=nrotamer, endpoint=False)
for frame, angle in enumerate(angles):
mol.frame = frame
mol.setDihedral(atoms, angle, bonds=mol.bonds)
dirname = "dihedral-single-point"
if geomopt:
dirname = "dihedral-opt"
dih_name = "%s-%s-%s-%s" % (self.name[atoms[0]], self.name[atoms[1]],
self.name[atoms[2]], self.name[atoms[3]])
fitdir = os.path.join(self.outdir, dirname, dih_name,
self.output_directory_name())
try:
os.makedirs(fitdir, exist_ok=True)
except:
raise OSError(
'Directory {} could not be created. Check if you have permissions.'
.format(fitdir))
qmset = QMCalculation(mol,
charge=self.netcharge,
directory=fitdir,
frozen=frozens,
optimize=geomopt,
theory=self.theory,
solvent=self.solvent,
basis=self.basis,
execution=self.execution,
code=self.qmcode)
ret = self._makeDihedralFittingSetFromQMResults(atoms, qmset.results())
# Get the initial parameters of the dihedral we are going to fit
param = self._prm.dihedralParam(self._rtf.type_by_index[atoms[0]],
self._rtf.type_by_index[atoms[1]],
self._rtf.type_by_index[atoms[2]],
self._rtf.type_by_index[atoms[3]])
# Save these parameters as the best fit (fit to beat)
best_param = np.zeros((13))
for t in range(6):
best_param[t] = param[t].k0
best_param[t + 6] = param[t].phi0
best_param[12] = 0.
# Evalaute the mm potential with this dihedral zeroed out
# The objective function will try to fit to the delta between
# The QM potential and the this modified mm potential
for t in param:
t.k0 = t.phi0 = 0.
#t.e14 = 1. # Use whatever e14 has been inherited for the type
self._prm.updateDihedral(param)
ffeval = FFEvaluate(self)
# Now evaluate the ff without the dihedral being fitted
for t in range(ret.N):
mm_zeroed = ffeval.run(ret.coords[t][:, :, 0])['total']
ret.mm_delta.append(ret.qm[t] - mm_zeroed)
ret.mm_zeroed.append(mm_zeroed)
mmin1 = min(ret.mm_zeroed)
mmin2 = min(ret.mm_delta)
for t in range(ret.N):
ret.mm_zeroed[t] = ret.mm_zeroed[t] - mmin1
ret.mm_delta[t] = ret.mm_delta[t] - mmin2
self._fitDihedral_results = ret
self._fitDihedral_phi = param
# Now measure all of the soft dihedrals phis that are mapped to this dihedral
ret.phis = []
for iframe in range(ret.N):
ret.phis.append([ret.phi[iframe]])
for atoms in equivs:
angle = dihedralAngle(ret.coords[iframe][atoms, :, 0])
ret.phis[iframe].append(angle)
best_chisq = self._fitDihedral_objective(best_param)
bar = ProgressBar(64, description="Fitting")
for iframe in range(64):
(bounds, start) = self._fitDihedral_make_bounds(iframe)
xopt = optimize.minimize(self._fitDihedral_objective,
start,
method="L-BFGS-B",
bounds=bounds,
options={'disp': False})
chisq = self._fitDihedral_objective(xopt.x)
if (chisq < best_chisq):
best_chisq = chisq
best_param = xopt.x
bar.progress()
bar.stop()
# Update the target dihedral with the optimized parameters
for iframe in range(6):
param[iframe].k0 = best_param[0 + iframe]
param[iframe].phi0 = best_param[6 + iframe]
self._prm.updateDihedral(param)
param = self._prm.dihedralParam(self._rtf.type_by_index[atoms[0]],
self._rtf.type_by_index[atoms[1]],
self._rtf.type_by_index[atoms[2]],
self._rtf.type_by_index[atoms[3]])
# Finally evaluate the fitted potential
ffeval = FFEvaluate(self)
for t in range(ret.N):
ret.mm_fitted.append(ffeval.run(ret.coords[t][:, :, 0])['total'])
mmin = min(ret.mm_fitted)
chisq = 0.
for t in range(ret.N):
ret.mm_fitted[t] = ret.mm_fitted[t] - mmin
delta = ret.mm_fitted[t] - ret.qm[t]
chisq = chisq + (delta * delta)
ret.chisq = chisq
# TODO Score it
self.coords = bkp_coords
return ret
def simlist(datafolders, topologies, inputfolders=None):
"""Creates a list of simulations
Parameters
----------
datafolders : str list
A list of directories, each containing a single trajectory
topologies : str list
A list of topology files or folders containing a topology file corresponding to the trajectories in dataFolders.
Can also be a single string to a single structure which corresponds to all trajectories.
inputfolders : optional, str list
A list of directories, each containing the input files used to produce the trajectories in dataFolders
Return
------
sims : np.ndarray of :class:`Sim <htmd.simlist.Sim>` objects
A list of simulations
Examples
--------
>>> simlist(glob('./test/data/*/'), glob('./test/input/*/'), glob('./test/input/*/'))
>>> simlist(glob('./test/data/*/'), glob('./test/input/*/*.pdb'), glob('./test/input/*/'))
"""
from htmd.util import ensurelist
import natsort
if not datafolders:
raise FileNotFoundError('No data folders were given, check your arguments.')
if not topologies:
raise FileNotFoundError('No molecule files were given, check your arguments.')
topologies = ensurelist(topologies)
datafolders = ensurelist(datafolders)
for folder in datafolders:
if not os.path.isdir(folder):
raise NotADirectoryError('{}'.format(folder))
if inputfolders:
inputfolders = ensurelist(inputfolders)
for folder in inputfolders:
if not os.path.isdir(folder):
raise NotADirectoryError('{}'.format(folder))
# I need to match the simulation names inside the globs given. The
# reason is that there can be more input folders in the glob than in
# the data glob due to not having been retrieved. Hence I need to match
# the folder names.
# Create a hash map of data folder names
datanames = dict()
for folder in datafolders:
if _simName(folder) in datanames:
raise RuntimeError('Duplicate simulation name detected. Cannot name-match directories.')
datanames[_simName(folder)] = folder
molnames = dict()
for mol in topologies:
if not os.path.exists(mol):
raise FileNotFoundError('File {} does not exist'.format(mol))
molnames[_simName(mol)] = mol
if inputfolders:
inputnames = dict()
for inputf in inputfolders:
inputnames[_simName(inputf)] = inputf
logger.debug('Starting listing of simulations.')
sims = []
keys = natsort.natsorted(datanames.keys())
i = 0
from htmd.progress.progress import ProgressBar
bar = ProgressBar(len(keys), description='Creating simlist')
for k in keys:
trajectories = _autoDetectTrajectories(datanames[k])
if not trajectories:
bar.progress()
continue
if len(topologies) > 1:
if k not in molnames:
raise FileNotFoundError('Did not find molfile with folder name ' + k + ' in the given glob')
molfile = molnames[k]
else:
molfile = topologies[0]
if os.path.isdir(molfile):
molfile = _autoDetectTopology(molfile)
inputf = []
if inputfolders:
if k not in inputnames:
raise FileNotFoundError('Did not find input with folder name ' + k + ' in the given glob')
inputf = inputnames[k]
numframes = [_readNumFrames(f) for f in trajectories]
sims.append(Sim(simid=i, parent=None, input=inputf, trajectory=trajectories, molfile=molfile, numframes=numframes))
i += 1
bar.progress()
bar.stop()
logger.debug('Finished listing of simulations.')
return np.array(sims, dtype=object)
def solvate(mol, pad=None, minmax=None, negx=0, posx=0, negy=0, posy=0, negz=0, posz=0, buffer=2.4, watsize=65.4195,
prefix='WT', keysel='name OH2', rotate=False, rotsel='all', rotinc=36, spdb=None,
spsf=None, stop=None):
""" Solvates the system in a water box
Parameters
----------
mol : :class:`Molecule <htmd.molecule.molecule.Molecule>` object
The molecule object we want to solvate
pad : float
The padding to add to the minmax in all dimensions. You can specify different padding in each dimension using
the negx, negy, negz, posx, posy, posz options. This option will override any values in the neg and pos options.
minmax : list
Min and max dimensions. Should be a 2D matrix of the form [[minx, miny, minz], [maxx, maxy, maxz]]. If none is
given, it is calculated from the minimum and maximum coordinates in the mol.
negx : float
The padding in the -x dimension
posx : float
The padding in the +x dimension
negy : float
The padding in the -y dimension
posy : float
The padding in the +y dimension
negz : float
The padding in the -z dimension
posz : float
The padding in the +z dimension
buffer : float
How much buffer space to leave empty between waters and other molecules
watsize : float
The size of the water box
prefix : str
The prefix used for water segments
keysel : str
The key selection for water atoms
rotate : bool
Enable automated rotation of molecule to fit best in box
rotsel : str
The selection of atoms to rotate
rotinc : float
The increment in degrees to rotate
spdb : str
The path to the water pdb file
spsf : str
The path to the water psf file
stop : str
The path to the water topology file
Returns
-------
mol : :class:`Molecule <htmd.molecule.molecule.Molecule>` object
A solvated molecule
Examples
--------
>>> smol = solvate(mol, pad=10)
>>> smol = solvate(mol, minmax=[[-20, -20, -20],[20, 20, 20]])
"""
mol = mol.copy()
if mol.numFrames > 1:
logger.warning('Multiple frames in Molecule. Solvate keeps only frame 0 and discards the rest.')
mol.coords = np.atleast_3d(mol.coords[:, :, 0])
if spdb is None:
spdb = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'wat.pdb')
if os.path.isfile(spdb):
logger.info('Using water pdb file at: ' + spdb)
water = Molecule(spdb)
else:
raise NameError('No solvent pdb file found in ' + spdb)
if pad is not None:
negx = pad; posx = pad; negy = pad; posy = pad; negz = pad; posz = pad
if rotate:
raise NameError('Rotation not implemented yet')
# Calculate min max coordinates from molecule
if mol.numAtoms > 0:
minmol = np.min(mol.get('coords'), axis=0)
maxmol = np.max(mol.get('coords'), axis=0)
else:
minmol = [np.inf, np.inf, np.inf]
maxmol = [-np.inf, -np.inf, -np.inf]
if minmax is None:
minc = minmol
maxc = maxmol
else:
if isinstance(minmax, list):
minmax = np.array(minmax)
minc = minmax[0, :]
maxc = minmax[1, :]
xmin = float(minc[0] - negx)
xmax = float(maxc[0] + posx)
ymin = float(minc[1] - negy)
ymax = float(maxc[1] + posy)
zmin = float(minc[2] - negz)
zmax = float(maxc[2] + posz)
dx = xmax - xmin
dy = ymax - ymin
dz = zmax - zmin
nx = int(np.ceil((dx + 2 * buffer) / watsize))
ny = int(np.ceil((dy + 2 * buffer) / watsize))
nz = int(np.ceil((dz + 2 * buffer) / watsize))
# Calculate number of preexisting water segments with given prefix
if mol.numAtoms > 0:
preexist = len(np.unique(mol.get('segid', sel='segid "{}.*"'.format(prefix))))
else:
preexist = 0
numsegs = nx * ny * nz
logger.info('Replicating ' + str(numsegs) + ' water segments, ' + str(nx) + ' by ' + str(ny) + ' by ' + str(nz))
# Check that we won't run out of segment name characters, and switch to
# using hexadecimal or alphanumeric naming schemes in cases where decimal
# numbered segnames won't fit into the field width.
testsegname = '{0}{1:d}'.format(prefix, numsegs + preexist)
testsegnamehex = '{0}{1:X}'.format(prefix, numsegs + preexist)
writemode = 'decimal'
if len(testsegname) > 4 and len(testsegnamehex) <= 4:
writemode = 'hex'
logger.warning('Warning: decimal naming would overrun segname field. Using hexadecimal segnames instead...')
elif len(testsegnamehex) > 4:
writemode = 'alphanum'
logger.warning('Warning: decimal or hex naming would overrun segname field. Using alphanumeric segnames instead...')
minx = minmol[0] - buffer; miny = minmol[1] - buffer; minz = minmol[2] - buffer
maxx = maxmol[0] + buffer; maxy = maxmol[1] + buffer; maxz = maxmol[2] + buffer
bar = ProgressBar(nx*ny*nz, description='Solvating')
waterboxes = np.empty(numsegs, dtype=object)
n = preexist
w = 0
for i in range(nx):
movex = xmin + i * watsize
movexmax = movex + watsize
xoverlap = True
if movex > maxx or movexmax < minx:
xoverlap = False
for j in range(ny):
movey = ymin + j * watsize
moveymax = movey + watsize
yoverlap = True
if movey > maxy or moveymax < miny:
yoverlap = False
for k in range(nz):
movez = zmin + k * watsize
movezmax = movez + watsize
zoverlap = True
if movez > maxz or movezmax < minz:
zoverlap = False
if writemode == 'decimal':
segname = '{0}{1:d}'.format(prefix, n)
elif writemode == 'hex':
segname = '{0}{1:x}'.format(prefix, n)
elif writemode == 'alphanum':
segname = '{0}{1:c}{2:c}{3:c}'.format(prefix, int(np.floor(np.floor(n/26)/26) + 65), int(np.mod(np.floor(n/26), 26) + 65), int(np.mod(n, 26) + 65))
waterboxes[w] = water.copy()
waterboxes[w].moveBy([movex, movey, movez])
waterboxes[w].set('segid', segname)
mol.append(waterboxes[w])
watsel = mol.segid == segname
selover = np.zeros(len(watsel), dtype=bool)
if xoverlap and yoverlap and zoverlap: # Remove water overlapping with other segids
selover = _overlapWithOther(mol, segname, buffer)
# Remove water outside the boundaries
selout = _outOfBoundaries(mol, segname, xmin, xmax, ymin, ymax, zmin, zmax)
sel = selover | selout
#mol.write('temp.pdb')
mol.filter(mol.segid != segname, _logger=False)
waterboxes[w].filter(np.invert(sel[watsel]), _logger=False)
#waterboxes[w].write('wat' + str(w) + '.pdb')
n += 1
w += 1
bar.progress()
bar.stop()
waters = 0
for i in range(numsegs):
waters += waterboxes[i].numAtoms
if waterboxes[i].numAtoms != 0:
mol.append(waterboxes[i])
logger.info('{} water molecules were added to the system.'.format(int(waters/3)))
return mol
def fitSoftTorsion(self, phi, geomopt=True):
found = False
phi_to_fit = None
frozens = []
dih_index = 0
i = 0
bkp_coords = self.coords.copy()
for d in self._soft_dihedrals:
if (d.atoms == phi).all():
phi_to_fit = d
dih_index = i
frozens.append(d.atoms)
else:
if not geomopt:
frozens.append(d.atoms)
i += 1
if not phi_to_fit:
raise ValueError("specified phi is not a recognised soft dihedral")
self._makeDihedralUnique(phi_to_fit)
atoms = phi_to_fit.atoms
left = phi_to_fit.left
right = phi_to_fit.right
equivs = phi_to_fit.equivalents
step = 10 # degrees
nstep = int(360 / step)
cset = np.zeros((self.natoms, 3, nstep))
i = 0
for phi in range(-180, 180, step):
cset[:, :, i] = setPhi(self.coords[:, :, 0], atoms, left, right, phi)
i += 1
mol = self.copy()
mol.coords = cset
dirname = "dihedral-single-point"
if geomopt:
dirname = "dihedral-opt"
dih_name = "%s-%s-%s-%s" % (self.name[atoms[0]], self.name[atoms[1]], self.name[atoms[2]], self.name[atoms[3]])
fitdir = os.path.join(self.outdir, dirname, dih_name, self.output_directory_name())
try:
os.makedirs(fitdir, exist_ok=True)
except:
raise OSError('Directory {} could not be created. Check if you have permissions.'.format(fitdir))
qmset = QMCalculation(mol, charge=self.netcharge, directory=fitdir, frozen=frozens, optimize=geomopt, theory=self.theory, solvent=self.solvent,
basis=self.basis, execution=self.execution, code=self.qmcode)
ret = self._makeDihedralFittingSetFromQMResults(atoms, qmset.results())
# Get the initial parameters of the dihedral we are going to fit
param = self._prm.dihedralParam(self._rtf.type_by_index[atoms[0]],
self._rtf.type_by_index[atoms[1]],
self._rtf.type_by_index[atoms[2]],
self._rtf.type_by_index[atoms[3]])
# Save these parameters as the best fit (fit to beat)
best_param = np.zeros((13))
for t in range(6):
best_param[t] = param[t].k0
best_param[t + 6] = param[t].phi0
best_param[12] = 0.
# print(param)
# Evalaute the mm potential with this dihedral zeroed out
# The objective function will try to fit to the delta between
# The QM potential and the this modified mm potential
for t in param:
t.k0 = t.phi0 = 0.
t.e14 = 1. # Always fit with e14 scaling of 1. per CHARMM
self._prm.updateDihedral(param)
ffe = FFEvaluate(self)
# print(ffe.evaluate( ret.coords[0] ) )
# input
# Now evaluate the ff without the dihedral being fitted
for t in range(ret.N):
mm_zeroed = (ffe.evaluate(ret.coords[t])["total"])
ret.mm_delta.append(ret.qm[t] - mm_zeroed)
ret.mm_zeroed.append(mm_zeroed)
mmin1 = min(ret.mm_zeroed)
mmin2 = min(ret.mm_delta)
for t in range(ret.N):
ret.mm_zeroed[t] = ret.mm_zeroed[t] - mmin1
ret.mm_delta[t] = ret.mm_delta[t] - mmin2
self._fitDihedral_results = ret
self._fitDihedral_phi = param
# Now measure all of the soft dihedrals phis that are mapped to this dihedral
ret.phis = []
for i in range(ret.N):
ret.phis.append([ret.phi[i]])
for e in equivs:
ret.phis[i].append(getPhi(ret.coords[i], e))
# print ("EQUIVALENT DIHEDRALS FOR THIS DIHEDRAL" )
# print(equivs)
# print ("PHI VALUES TO FIT")
# print (ret.phis)
# Set up the NOLOPT fit
# There are 13 parameters, k,phi for n=1,2,3,4,5,6 and a shift
N = 13
# initial guess,
st = np.zeros(13)
# bounds
best_chisq = self._fitDihedral_objective(best_param)
# print("CHISQ of initial = %f" % ( best_chisq ) )
# Now zero out the terms of the dihedral we are going to fit
bar = ProgressBar(64, description="Fitting")
for i in range(64):
(bounds, start) = self._fitDihedral_make_bounds(i)
xopt = optimize.minimize(self._fitDihedral_objective, start, method="L-BFGS-B", bounds=bounds,
options={'disp': False})
chisq = self._fitDihedral_objective(xopt.x)
# print( "CHISQ of fit = %f " % (chisq) )
if (chisq < best_chisq):
best_chisq = chisq
best_param = xopt.x
bar.progress()
bar.stop()
# print("Best ChiSQ = %f" %(best_chisq) )
# Update the target dihedral with the optimized parameters
# print(param)
# print(best_param )
for i in range(6):
param[i].k0 = best_param[0 + i]
param[i].phi0 = best_param[6 + i]
self._prm.updateDihedral(param)
# print(param)
param = self._prm.dihedralParam(self._rtf.type_by_index[atoms[0]],
self._rtf.type_by_index[atoms[1]],
self._rtf.type_by_index[atoms[2]],
self._rtf.type_by_index[atoms[3]])
# print(param)
# Finally evaluate the fitted potential
ffe = FFEvaluate(self)
for t in range(ret.N):
ret.mm_fitted.append(ffe.evaluate(ret.coords[t])["total"])
mmin = min(ret.mm_fitted)
chisq = 0.
# print( "QM energies" )
# print( ret.qm )
for t in range(ret.N):
ret.mm_fitted[t] = ret.mm_fitted[t] - mmin
delta = ret.mm_fitted[t] - ret.qm[t]
chisq = chisq + (delta * delta)
ret.chisq = chisq
# TODO Score it
self.coords = bkp_coords
return ret
def project(self, ndim=None):
""" Projects the data object given to the constructor onto the top `ndim` TICA dimensions
Parameters
----------
ndim : int
The number of TICA dimensions we want to project the data on. If None is given it will use choose a number
of dimensions to cover 95% of the kinetic variance.
Returns
-------
dataTica : :class:`MetricData <htmd.metricdata.MetricData>` object
A new :class:`MetricData <htmd.metricdata.MetricData>` object containing the TICA projected data
Example
-------
>>> from htmd.projections.tica import TICA
>>> tica = TICA(data,20)
>>> dataTica = tica.project(5)
"""
if ndim is not None:
# self.tic._dim = ndim # Old way of doing it. Deprecated since pyEMMA 2.1
self.tic.set_params(
dim=ndim) # Change to this in 2.1 pyEMMA version
if isinstance(
self.data,
Metric): # Doesn't project on correct number of dimensions
proj = []
refs = []
fstep = None
'''from htmd.config import _config
from joblib import Parallel, delayed
results = Parallel(n_jobs=_config['ncpus'], verbose=11)(
delayed(_test)(self.data, self.tic, i) for i in range(len(self.data.simulations)))
for i in range(len(results)):
proj.append(results[i][0])
refs.append(results[i][1])
fstep.append(results[i][2])'''
droppedsims = []
p = ProgressBar(len(self.data.simulations))
for i in range(len(self.data.simulations)):
d, r, f = self.data._projectSingle(i)
if d is None:
droppedsims.append(i)
continue
if fstep is None:
fstep = f
refs.append(r)
proj.append(self.tic.transform(d))
p.progress()
p.stop()
simlist = self.data.simulations
simlist = np.delete(simlist, droppedsims)
ref = np.array(refs, dtype=object)
#fstep = 0
parent = None
else:
proj = self.tic.get_output()
simlist = self.data.simlist
ref = self.data.ref
fstep = self.data.fstep
parent = self.data
if ndim is None:
logger.info(
'Kept {} dimension(s) to cover 95% of kinetic variance.'.
format(self.tic.dimension()))
#print(np.shape(proj))
from htmd.metricdata import MetricData
datatica = MetricData(dat=np.array(proj, dtype=object),
simlist=simlist,
ref=ref,
fstep=fstep,
parent=parent)
'''datatica = self.data.copy()
#datatica.dat = self.data.deconcatenate(np.squeeze(proj))
datatica.dat = np.array(proj, dtype=object)
datatica.parent = self.data
datatica.St = None
datatica.Centers = None
datatica.N = None
datatica.K = None
datatica._dataid = random.random()
datatica._clusterid = None'''
return datatica
def project(self, ndim=None):
""" Projects the data object given to the constructor onto `ndim` dimensions
Parameters
----------
ndim : int
The number of dimensions we want to project the data on.
Returns
-------
dataTica : :class:`MetricData <htmd.metricdata.MetricData>` object
A new :class:`MetricData <htmd.metricdata.MetricData>` object containing the projected data
Example
-------
>>> gw = GWPCA(data)
>>> dataproj = gw.project(5)
"""
from sklearn.decomposition import IncrementalPCA
from htmd.progress.progress import ProgressBar
from htmd.metricdata import MetricData
pca = IncrementalPCA(n_components=ndim, batch_size=10000)
p = ProgressBar(len(self.data.dat))
for d in self.data.dat:
pca.partial_fit(d * self.weights)
p.progress()
p.stop()
projdata = self.data.copy()
p = ProgressBar(len(self.data.dat))
for i, d in enumerate(self.data.dat):
projdata.dat[i] = pca.transform(d * self.weights)
p.progress()
p.stop()
# projdataconc = pca.fit_transform(self.weighedconcat)
# projdata.dat = projdata.deconcatenate(projdataconc)
return projdata
def project(self, ndim=None):
""" Projects the data object given to the constructor onto the top `ndim` TICA dimensions
Parameters
----------
ndim : int
The number of TICA dimensions we want to project the data on. If None is given it will use choose a number
of dimensions to cover 95% of the kinetic variance.
Returns
-------
dataTica : :class:`MetricData <htmd.metricdata.MetricData>` object
A new :class:`MetricData <htmd.metricdata.MetricData>` object containing the TICA projected data
Example
-------
>>> from htmd.projections.tica import TICA
>>> tica = TICA(data,20)
>>> dataTica = tica.project(5)
"""
if ndim is not None:
# self.tic._dim = ndim # Old way of doing it. Deprecated since pyEMMA 2.1
self.tic.set_params(
dim=ndim) # Change to this in 2.1 pyEMMA version
if isinstance(
self.data,
Metric): # Doesn't project on correct number of dimensions
proj = []
refs = []
fstep = None
metr = self.data
p = ProgressBar(len(metr.simulations))
k = -1
droppedsims = []
for projecteddata in _projectionGenerator(metr, _getNcpus()):
for pro in projecteddata:
k += 1
if pro is None:
droppedsims.append(k)
continue
proj.append(self.tic.transform(pro[0]))
refs.append(pro[1])
if fstep is None:
fstep = pro[2]
p.progress(len(projecteddata))
p.stop()
simlist = self.data.simulations
simlist = np.delete(simlist, droppedsims)
ref = np.array(refs, dtype=object)
#fstep = 0
parent = None
else:
proj = self.tic.get_output()
simlist = self.data.simlist
ref = self.data.ref
fstep = self.data.fstep
parent = self.data
if ndim is None:
logger.info(
'Kept {} dimension(s) to cover 95% of kinetic variance.'.
format(self.tic.dimension()))
from htmd.metricdata import MetricData
datatica = MetricData(dat=np.array(proj, dtype=object),
simlist=simlist,
ref=ref,
fstep=fstep,
parent=parent)
from pandas import DataFrame
types = []
indexes = []
description = []
for i in range(ndim):
types += ['tica']
indexes += [-1]
description += ['TICA dimension {}'.format(i + 1)]
datatica.map = DataFrame({
'type': types,
'indexes': indexes,
'description': description
})
return datatica
def tileMembrane(memb, xmin, ymin, xmax, ymax, buffer=1.5):
""" Tile a membrane in the X and Y dimensions to reach a specific size.
Parameters
----------
memb : :class:`Molecule <htmd.molecule.molecule.Molecule>` object
The membrane to be tiled
xmin : float
Minimum x coordinate
ymin : float
Minimum y coordinate
xmax : float
Maximum x coordinate
ymax : float
Maximum y coordinate
buffer : float
Buffer distance between tiles
Returns
-------
megamemb :
A big membrane Molecule
"""
from htmd.progress.progress import ProgressBar
memb = memb.copy()
memb.resid = sequenceID(
(memb.resid, memb.insertion, memb.chain, memb.segid))
minmemb = np.min(memb.get('coords', 'water'), axis=0).flatten()
size = np.max(memb.get('coords', 'water'), axis=0) - np.min(
memb.get('coords', 'water'), axis=0)
size = size.flatten()
xreps = int(np.ceil((xmax - xmin) / size[0]))
yreps = int(np.ceil((ymax - ymin) / size[1]))
logger.info('Replicating Membrane {}x{}'.format(xreps, yreps))
from htmd.molecule.molecule import Molecule
megamemb = Molecule()
bar = ProgressBar(xreps * yreps, description='Replicating Membrane')
k = 0
for x in range(xreps):
for y in range(yreps):
tmpmemb = memb.copy()
xpos = xmin + x * (size[0] + buffer)
ypos = ymin + y * (size[1] + buffer)
tmpmemb.moveBy(
[-float(minmemb[0]) + xpos, -float(minmemb[1]) + ypos, 0])
tmpmemb.remove('same resid as (x > {} or y > {})'.format(
xmax, ymax),
_logger=False)
if tmpmemb.numAtoms == 0:
continue
tmpmemb.set('segid', 'M{}'.format(k), sel='not water')
tmpmemb.set('segid', 'MW{}'.format(k), sel='water')
megamemb.append(tmpmemb)
k += 1
bar.progress()
bar.stop()
# Membranes don't tile perfectly. Need to remove waters that clash with lipids of other tiles
# Some clashes will still occur between periodic images however
megamemb.remove('same resid as water and within 1.5 of not water',
_logger=False)
return megamemb
def fitSoftDihedral( self, phi, geomopt=True ):
found=False
phi_to_fit = None
frozens=[]
dih_index=0
i=0
bkp_coords = self.coords.copy()
for d in self._soft_dihedrals:
if (d.atoms == phi).all():
phi_to_fit = d
dih_index=i
frozens.append(d.atoms)
else:
if not geomopt:
frozens.append(d.atoms)
i=i+1
if not phi_to_fit: raise ValueError( "specified phi is not a recognised soft dihedral" )
self._makeDihedralUnique( phi_to_fit )
atoms = phi_to_fit.atoms
left = phi_to_fit.left
right = phi_to_fit.right
equivs= phi_to_fit.equivalents
step = 10 # degrees
nstep = (int)(360/step)
cset = np.zeros( ( self.natoms, 3, nstep ) )
i=0
for phi in range( -180, 180, step ):
cset[:,:,i] = setPhi( self.coords[:,:,0], atoms, left, right, phi )
i=i+1
mol = self.copy()
mol.coords = cset
dirname = "dihedral-single-point"
if geomopt: dirname="dihedral-opt"
try:
os.mkdir( dirname )
except:
pass
dih_name = "%s-%s-%s-%s" % ( self.name[atoms[0]], self.name[atoms[1]], self.name[atoms[2]], self.name[atoms[3]] )
qmset = QMCalculation( mol, charge=self.netcharge, directory= os.path.join( dirname, (dih_name)) , frozen=frozens, optimize=geomopt )
ret = self._makeDihedralFittingSetFromQMResults( atoms, qmset.results() )
# Get the initial parameters of the dihedral we are going to fit
param = self._prm.dihedralParam( self._rtf.type_by_index[ atoms[0] ],
self._rtf.type_by_index[ atoms[1] ],
self._rtf.type_by_index[ atoms[2] ],
self._rtf.type_by_index[ atoms[3] ] )
# Save these parameters as the best fit (fit to beat)
best_param=np.zeros((13))
for t in range(6):
best_param[t] = param[t].k0
best_param[t+6] = param[t].phi0
best_param[12] = 0.
# print(param)
# Evalaute the mm potential with this dihedral zeroed out
# The objective function will try to fit to the delta between
# The QM potential and the this modified mm potential
for t in param:
t.k0 = t.phi0 = 0.
t.e14 = 1. # Always fit with e14 scaling of 1. per CHARMM
self._prm.updateDihedral( param )
ffe = FFEvaluate( self )
# print(ffe.evaluate( ret.coords[0] ) )
# input
# Now evaluate the ff without the dihedral being fitted
for t in range(ret.N):
mm_zeroed = ( ffe.evaluate( ret.coords[t] )["total"])
ret.mm_delta.append( ret.qm[t] - mm_zeroed )
ret.mm_zeroed.append( mm_zeroed )
mmin1 = min( ret.mm_zeroed )
mmin2 = min( ret.mm_delta )
for t in range(ret.N):
ret.mm_zeroed[t] = ret.mm_zeroed[t] - mmin1
ret.mm_delta[t] = ret.mm_delta[t] - mmin2
self._fitDihedral_results = ret
self._fitDihedral_phi = param
# Now measure all of the soft dihedrals phis that are mapped to this dihedral
ret.phis= []
for i in range(ret.N):
ret.phis.append( [ ret.phi[i] ] )
for e in equivs:
ret.phis[i].append( getPhi( ret.coords[i], e ) )
# print ("EQUIVALENT DIHEDRALS FOR THIS DIHEDRAL" )
# print(equivs)
# print ("PHI VALUES TO FIT")
# print (ret.phis)
# Set up the NOLOPT fit
# There are 13 parameters, k,phi for n=1,2,3,4,5,6 and a shift
N = 13
# initial guess,
st= np.zeros(13)
# bounds
best_chisq = self._fitDihedral_objective( best_param )
# print("CHISQ of initial = %f" % ( best_chisq ) )
# Now zero out the terms of the dihedral we are going to fit
bar=ProgressBar(64, description="Fitting")
for i in range(64):
( bounds, start ) = self._fitDihedral_make_bounds( i )
xopt = optimize.minimize( self._fitDihedral_objective, start, method="L-BFGS-B", bounds = bounds , options={'disp': False } )
chisq = self._fitDihedral_objective( xopt.x )
# print( "CHISQ of fit = %f " % (chisq) )
if( chisq < best_chisq ):
best_chisq = chisq
best_param = xopt.x
bar.progress()
bar.stop()
# print("Best ChiSQ = %f" %(best_chisq) )
# Update the target dihedral with the optimized parameters
# print(param)
# print(best_param )
for i in range(6):
param[i].k0 = best_param[0+i]
param[i].phi0 = best_param[6+i]
self._prm.updateDihedral( param )
# print(param)
param = self._prm.dihedralParam( self._rtf.type_by_index[ atoms[0] ],
self._rtf.type_by_index[ atoms[1] ],
self._rtf.type_by_index[ atoms[2] ],
self._rtf.type_by_index[ atoms[3] ] )
# print(param)
# Finally evaluate the fitted potential
ffe = FFEvaluate( self )
for t in range(ret.N):
ret.mm_fitted.append( ffe.evaluate( ret.coords[t] )["total"] )
mmin = min(ret.mm_fitted )
chisq=0.
# print( "QM energies" )
# print( ret.qm )
for t in range(ret.N):
ret.mm_fitted[t] = ret.mm_fitted[t] - mmin
delta = ret.mm_fitted[t] - ret.qm[t]
chisq = chisq + (delta * delta )
ret.chisq = chisq
# TODO Score it
self.coords = bkp_coords
return ret
def _run_qm_jobs_lsf(self, directory):
cmd = self._env['BIN_G09']
fni = []
fno = []
for root, dirs, files in os.walk(directory):
for f in files:
if (f.endswith(".gjf")):
op = f.replace(".gjf", ".out")
if not os.path.exists(op):
fni.append(os.path.join(root, f))
fno.append(os.path.join(root, op))
if (len(fni)):
# Make an LSF script
for i in range(len(fni)):
fpbs = fni[i] + ".lsf"
if not os.path.exists(fpbs):
f = open(fpbs, "w")
print("#BSUB -n %d" % self._config.NCORES, file=f)
print("#BSUB -R \"span[ptile=%d]\"" % self._config.NCORES,
file=f)
print("#BSUB -W 24:00", file=f)
print("#BSUB -J gaussian", file=f)
print("#BSUB -app gaussian", file=f)
print("#BSUB -o /dev/null", file=f)
print("#BSUB -M %d000" % self._config.MEMORY, file=f)
print("\nmodule load gaussian\n", file=f)
print("cd \"%s\"" % directory, file=f)
print("\"%s\" %s %s" % (cmd, fni[i], fno[i]), file=f)
f.close()
# Look to see if there is already a job submitted
# If not, qsub it
fpbsstate = fni[i] + ".jobid"
if not os.path.exists(fpbsstate):
# Qsub, saving jobid to file
subprocess.check_output("\"" + self._env['BIN_BSUB'] +
"\" < \"" + fpbs + "\" > \"" +
fpbsstate + "\"",
shell=True)
# Finally monitor progress. Continue until all jobs have produced an output
# NB TODO FIXME: should also poll qstat to see if job is still live
bar = ProgressBar(len(fni), description="Running QM Calculations")
complete = False
lastcount = 0
while not complete:
count = 0
complete = True
for i in fno:
# print(" Checking [" + i +"]" )
# print( os.path.exists(i) )
# print( os.access(i, os.R_OK) )
try:
os.stat(i) # Try to flush any cache (for NFS)
except:
pass
if os.access(i, os.R_OK):
# print("FOUND")
count += 1
else:
complete = False
# print( str(count) + " completed of " + str(len(fno)) )
while lastcount < count:
bar.progress()
lastcount += 1
time.sleep(10)
bar.stop()
time.sleep(
5) # A bit of time for any outputfile to complete writing
