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
-------
>>> tri = KMeansTri(data)
>>> datatri = tri.project(5)
"""
import scipy.spatial.distance as scidist
from sklearn.cluster import MiniBatchKMeans
from htmd.metricdata import MetricData
datconcat = np.concatenate(self.data.dat)
mb = MiniBatchKMeans(n_clusters=ndim)
mb.fit(datconcat)
# TODO: Could make it into a loop to waste less memory
dist = scidist.cdist(datconcat, mb.cluster_centers_)
dist = np.mean(dist, axis=1)[:, np.newaxis] - dist
dist[dist < 0] = 0
projdata = MetricData()
projdata.simlist = self.data.simlist
projdata.dat = self.data.deconcatenate(dist)
projdata.ref = self.data.ref
projdata.parent = self.data
projdata.fstep = self.data.fstep
return projdata
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
-------
>>> tri = KMeansTri(data)
>>> datatri = tri.project(5)
"""
import scipy.spatial.distance as scidist
from sklearn.cluster import MiniBatchKMeans
from htmd.metricdata import MetricData
datconcat = np.concatenate(self.data.dat)
mb = MiniBatchKMeans(n_clusters=ndim)
mb.fit(datconcat)
# TODO: Could make it into a loop to waste less memory
dist = scidist.cdist(datconcat, mb.cluster_centers_)
dist = np.mean(dist, axis=1)[:, np.newaxis] - dist
dist[dist < 0] = 0
projdata = MetricData()
projdata.simlist = self.data.simlist
projdata.dat = self.data.deconcatenate(dist)
projdata.ref = self.data.ref
projdata.parent = self.data
projdata.fstep = self.data.fstep
return projdata
def _metrify(self, sims, skip, verbose, update):
"""
Takes a set of trajectory folders and projects all trajectories within them onto the given space defined by the Metric* class.
Parameters
----------
simList : numpy list of structs
A list of structs produced by the simList function.
skip : int
Skips every x frames.
verbose : int
Verbosity toggle
update : MetricData object
Provide a previous MetricData object and only metrify new trajectories.
Returns
-------
data : MetricData object
Returns a MetricData object containing the projected data and the ref data.
"""
if isinstance(sims, Molecule):
return self.processTraj(sims)
# [updList, oldList] = checkUpdate(simList, update, verbose);
updList = sims
numSim = len(updList)
# Find out if there is a unique molfile. If there is, initialize a single Molecule to speed up calculations
uniqueMol = 0
uqMol = []
map = []
(single, molfile) = _singleMolfile(updList)
if single:
uniqueMol = 1
uqMol = Molecule(molfile)
# Calculating the mapping of metric columns to atom pair indeces
map = self._getMapping(uqMol)
logger.info('Metric: Starting projection of trajectories.')
metrics = np.empty(numSim, dtype=object)
ref = np.empty(numSim, dtype=object)
deleteSims = np.zeros(numSim, dtype=bool)
fstep = np.empty(numSim)
#global parpool
Parallel(n_jobs=6, backend="threading")(
delayed(_processSimPyemma)(self, i, updList, uniqueMol, uqMol,
skip, deleteSims, metrics, ref, fstep)
for i in range(numSim))
logger.info('Finished projecting the trajectories.')
# Removing empty trajectories
emptyM = [True if np.size(x) == 0 else False for x in metrics]
emptyR = [True if np.size(x) == 0 else False for x in ref]
#assert np.all(deleteSims == emptyM)# and np.all(emptyR == emptyM)
metrics = np.delete(metrics, np.where(emptyM))
ref = np.delete(ref, np.where(emptyM))
#updList = np.delete(updList, emptyM)
if len(metrics) == 0:
raise NameError('No trajectories were read')
# Constructing a MetricData object
if not update:
data = MetricData(dat=metrics, ref=ref, map=map, simlist=updList)
else:
data = update
data.dat.extend(metrics)
data.ref.extend(ref)
data.simList.extend(updList)
uqfsteps = np.unique(fstep)
data.fstep = stats.mode(fstep).mode
if len(uqfsteps) != 1:
logger.warning(
'Multiple framesteps were read from the simulations. Taking the statistical mode: '
+ str(data.fstep) + 'ns.')
logger.warning(
'If it looks wrong, you can modify it by manually setting the MetricData.fstep property.'
)
return data
def project(self):
"""
Applies all projections stored in Metric on all simulations.
Returns
-------
data : MetricData object
Returns a MetricData object containing the projected data.
"""
if len(self.projectionlist) == 0:
raise NameError(
'You need to provide projections using the Metric.projection method.'
)
if isinstance(self.simulations, Molecule):
data = []
for proj in self.projectionlist:
data.append(proj.project(self.simulations))
return data
numSim = len(self.simulations)
# Find out if there is a unique molfile. If there is, initialize a single Molecule to speed up calculations
uqMol = None
map = []
(single, molfile) = _singleMolfile(self.simulations)
if single:
uqMol = Molecule(molfile)
for proj in self.projectionlist:
proj._precalculate(uqMol)
#map.append(np.array(proj.getMapping(uqMol), dtype=object))
#map = np.hstack(map)
logger.info('Metric: Starting projection of trajectories.')
metrics = np.empty(numSim, dtype=object)
ref = np.empty(numSim, dtype=object)
deletesims = np.zeros(numSim, dtype=bool)
fstep = np.zeros(numSim)
from htmd.config import _config
results = Parallel(n_jobs=_config['ncpus'], verbose=11)(
delayed(_processSim)(self.simulations[i], self.projectionlist,
uqMol, self.skip) for i in range(numSim))
for i in range(len(results)):
metrics[i] = results[i][0]
ref[i] = results[i][1]
fstep[i] = results[i][2]
deletesims[i] = results[i][3]
logger.info('Finished projecting the trajectories.')
# Removing empty trajectories
emptyM = np.array([True if x is None else False for x in metrics],
dtype=bool)
emptyR = np.array([True if x is None else False for x in ref],
dtype=bool)
assert np.all(deletesims == emptyM) and np.all(emptyR == emptyM)
metrics = np.delete(metrics, np.where(emptyM)[0])
ref = np.delete(ref, np.where(emptyM)[0])
updlist = np.delete(self.simulations, np.where(emptyM)[0])
if len(metrics) == 0:
raise NameError('No trajectories were read')
# Constructing a MetricData object
data = MetricData(dat=metrics, ref=ref, map=map, simlist=updlist)
uqfsteps = np.unique(fstep)
data.fstep = float(stats.mode(fstep).mode)
if len(uqfsteps) != 1:
logger.warning(
'Multiple framesteps were read from the simulations. '
'Taking the statistical mode: ' + str(data.fstep) + 'ns. '
'If it looks wrong, you can modify it by manually setting the MetricData.fstep property.'
)
else:
logger.info(
'Frame step {}ns was read from the trajectories. If it looks wrong, redefine it by manually '
'setting the MetricData.fstep property.'.format(data.fstep))
return data
def _metrify(self, sims, skip, update):
"""
Takes a set of trajectory folders and projects all trajectories within them onto the given space defined by the Metric* class.
Parameters
----------
simList : numpy list of structs
A list of structs produced by the simList function.
skip : int
Skips every x frames.
update : MetricData object
Provide a previous MetricData object and only metrify new trajectories.
Returns
-------
data : MetricData object
Returns a MetricData object containing the projected data and the ref data.
"""
if isinstance(sims, Molecule):
return self._processTraj(sims)
# [updList, oldList] = checkUpdate(simList, update, verbose);
updList = sims
numSim = len(updList)
# Find out if there is a unique molfile. If there is, initialize a single Molecule to speed up calculations
uniqueMol = 0
uqMol = []
map = []
(single, molfile) = _singleMolfile(updList)
if single:
uniqueMol = 1
uqMol = Molecule(molfile)
# Calculating the mapping of metric columns to atom pair indeces
map = self._getMapping(uqMol)
logger.info('Metric: Starting projection of trajectories.')
metrics = np.empty(numSim, dtype=object)
ref = np.empty(numSim, dtype=object)
deletesims = np.zeros(numSim, dtype=bool)
fstep = np.zeros(numSim)
# Monkey-patching callback class
#oldcallback = joblib.parallel.CallBack
#joblib.parallel.CallBack = CallBack
#p = ProgressBar(numSim, description='Projecting trajectories')
from htmd.config import _config
results = Parallel(n_jobs=_config['ncpus'], verbose=11)(
delayed(_processSimOld)(self, i, updList, uniqueMol, uqMol, skip,
deletesims, metrics, ref, fstep)
for i in range(numSim))
#joblib.parallel.CallBack = oldcallback
for i in range(len(results)):
metrics[i] = results[i][0]
ref[i] = results[i][1]
fstep[i] = results[i][2]
deletesims[i] = results[i][3]
logger.info('Finished projecting the trajectories.')
# Removing empty trajectories
emptyM = np.array([True if x is None else False for x in metrics],
dtype=bool)
emptyR = np.array([True if x is None else False for x in ref],
dtype=bool)
assert np.all(deletesims == emptyM) and np.all(emptyR == emptyM)
metrics = np.delete(metrics, np.where(emptyM)[0])
ref = np.delete(ref, np.where(emptyM)[0])
updList = np.delete(updList, np.where(emptyM)[0])
if len(metrics) == 0:
raise NameError('No trajectories were read')
# Constructing a MetricData object
if not update:
data = MetricData(dat=metrics, ref=ref, map=map, simlist=updList)
else:
data = update
data.dat.extend(metrics)
data.ref.extend(ref)
data.simList.extend(
updList) # This is wrong but we don't use update anyways
uqfsteps = np.unique(fstep)
data.fstep = float(stats.mode(fstep).mode)
if len(uqfsteps) != 1:
logger.warning(
'Multiple framesteps were read from the simulations. Taking the statistical mode: '
+ str(data.fstep) +
'ns. If it looks wrong, you can modify it by manually setting the MetricData.fstep property.'
)
else:
logger.info(
'Frame step {}ns was read from the trajectories. If it looks wrong, redefine it by manually setting the MetricData.fstep property.'
.format(data.fstep))
return data
def project(self):
"""
Applies all projections stored in Metric on all simulations.
Returns
-------
data : MetricData object
Returns a MetricData object containing the projected data.
"""
if len(self.projectionlist) == 0:
raise RuntimeError('You need to provide projections using the Metric.set method.')
# Projecting single Molecules
if isinstance(self.simulations, Molecule):
data = []
mol = self.simulations
for proj in self.projectionlist:
data.append(_project(proj, mol))
return data
numSim = len(self.simulations)
# Find out if there is a unique molfile. If there is, initialize a single Molecule to speed up calculations
uqMol = None
(single, molfile) = _singleMolfile(self.simulations)
if single:
uqMol = Molecule(molfile)
for proj in self.projectionlist:
if isinstance(proj, Projection):
proj._precalculate(uqMol)
else:
logger.warning('Cannot calculate description of dimensions due to different topology files for each trajectory.')
mapping = self.getMapping(uqMol)
logger.debug('Metric: Starting projection of trajectories.')
from htmd.config import _config
aprun = ParallelExecutor(n_jobs=_config['ncpus'])
results = aprun(total=numSim, description='Projecting trajectories')(delayed(_processSim)(self.simulations[i], self.projectionlist, uqMol, self.skip) for i in range(numSim))
metrics = np.empty(numSim, dtype=object)
ref = np.empty(numSim, dtype=object)
deletesims = np.zeros(numSim, dtype=bool)
fstep = np.zeros(numSim)
for i in range(len(results)):
metrics[i] = results[i][0]
ref[i] = results[i][1]
fstep[i] = results[i][2]
deletesims[i] = results[i][3]
logger.debug('Finished projecting the trajectories.')
# Removing empty trajectories
metrics, ref, updlist, fstep = self._removeEmpty(metrics, ref, deletesims, fstep)
# Constructing a MetricData object
data = MetricData(dat=metrics, ref=ref, description=mapping, simlist=updlist)
uqfsteps = np.unique(fstep)
data.fstep = float(stats.mode(fstep).mode)
if len(uqfsteps) != 1:
logger.warning('Multiple framesteps [{}] ns were read from the simulations. '
'Taking the statistical mode: {}ns. '
'If it looks wrong, you can modify it by manually '
'setting the MetricData.fstep property.'.format(', '.join(map(str,uqfsteps)), data.fstep))
else:
logger.info('Frame step {}ns was read from the trajectories. If it looks wrong, redefine it by manually '
'setting the MetricData.fstep property.'.format(data.fstep))
return data
def project(self):
"""
Applies all projections stored in Metric on all simulations.
Returns
-------
data : MetricData object
Returns a MetricData object containing the projected data.
"""
if len(self.projectionlist) == 0:
raise RuntimeError(
'You need to provide projections using the Metric.set method.')
# Projecting single Molecules
if isinstance(self.simulations, Molecule):
data = []
mol = self.simulations
for proj in self.projectionlist:
data.append(_project(proj, mol))
return data
numSim = len(self.simulations)
# Find out if there is a unique molfile. If there is, initialize a single Molecule to speed up calculations
uqMol = None
(single, molfile) = _singleMolfile(self.simulations)
if single:
uqMol = Molecule(molfile)
for proj in self.projectionlist:
if isinstance(proj, Projection):
proj._precalculate(uqMol)
else:
logger.warning(
'Cannot calculate description of dimensions due to different topology files for each trajectory.'
)
mapping = self.getMapping(uqMol)
logger.debug('Metric: Starting projection of trajectories.')
from htmd.config import _config
aprun = ParallelExecutor(n_jobs=_config['ncpus'])
results = aprun(total=numSim, description='Projecting trajectories')(
delayed(_processSim)(self.simulations[i], self.projectionlist,
uqMol, self.skip) for i in range(numSim))
metrics = np.empty(numSim, dtype=object)
ref = np.empty(numSim, dtype=object)
deletesims = np.zeros(numSim, dtype=bool)
fstep = np.zeros(numSim)
for i in range(len(results)):
metrics[i] = results[i][0]
ref[i] = results[i][1]
fstep[i] = results[i][2]
deletesims[i] = results[i][3]
logger.debug('Finished projecting the trajectories.')
# Removing empty trajectories
metrics, ref, updlist, fstep = self._removeEmpty(
metrics, ref, deletesims, fstep)
# Constructing a MetricData object
data = MetricData(dat=metrics,
ref=ref,
description=mapping,
simlist=updlist)
uqfsteps = np.unique(fstep)
data.fstep = float(stats.mode(fstep).mode)
if len(uqfsteps) != 1:
logger.warning(
'Multiple framesteps [{}] ns were read from the simulations. '
'Taking the statistical mode: {}ns. '
'If it looks wrong, you can modify it by manually '
'setting the MetricData.fstep property.'.format(
', '.join(map(str, uqfsteps)), data.fstep))
else:
logger.info(
'Frame step {}ns was read from the trajectories. If it looks wrong, redefine it by manually '
'setting the MetricData.fstep property.'.format(data.fstep))
return data
def project(self, njobs=None):
"""
Applies all projections stored in Metric on all simulations.
Parameters
----------
njobs : int
Number of parallel jobs to spawn for projection of trajectories. Take care that this can use large amounts
of memory as multiple trajectories are loaded at once. If None it will use the default from htmd.config.
Returns
-------
data : MetricData object
Returns a MetricData object containing the projected data.
"""
if len(self.projectionlist) == 0:
raise RuntimeError(
"You need to provide projections using the Metric.set method.")
# Projecting single Molecules
if isinstance(self.simulations, Molecule):
data = []
mol = self.simulations
for proj in self.projectionlist:
data.append(_project(proj, mol))
return data
numSim = len(self.simulations)
# Find out if there is a unique molfile. If there is, initialize a single Molecule to speed up calculations
uqMol = None
(single, molfile) = _singleMolfile(self.simulations)
if single:
uqMol = Molecule(molfile)
for proj in self.projectionlist:
if isinstance(proj, Projection):
proj._setCache(uqMol)
else:
logger.warning(
"Cannot calculate description of dimensions due to different topology files for each trajectory."
)
mapping = self.getMapping(uqMol)
logger.debug("Metric: Starting projection of trajectories.")
from htmd.config import _config
aprun = ParallelExecutor(
n_jobs=njobs if njobs is not None else _config["njobs"])
results = aprun(total=numSim, desc="Projecting trajectories")(
delayed(_processSim)(self.simulations[i], self.projectionlist,
uqMol, self.skip) for i in range(numSim))
metrics = np.empty(numSim, dtype=object)
ref = np.empty(numSim, dtype=object)
deletesims = np.zeros(numSim, dtype=bool)
fstep = np.zeros(numSim)
for i in range(len(results)):
metrics[i] = results[i][0]
ref[i] = results[i][1]
fstep[i] = results[i][2]
deletesims[i] = results[i][3]
logger.debug("Finished projecting the trajectories.")
# Removing empty trajectories
metrics, ref, updlist, fstep = self._removeEmpty(
metrics, ref, deletesims, fstep)
# Constructing a MetricData object
data = MetricData(dat=metrics,
ref=ref,
description=mapping,
simlist=updlist)
uqfsteps = np.unique(fstep)
if np.all(np.isnan(uqfsteps)):
logger.warning(
"No framestep could be read from the trajectories. Please manually set the MetricData.fstep"
" property, otherwise calculations in Model and Kinetics classes can fail."
)
else:
data.fstep = float(stats.mode(fstep).mode)
if len(uqfsteps) != 1:
logger.warning(
"Multiple framesteps [{}] ns were read from the simulations. "
"Taking the statistical mode: {}ns. "
"If it looks wrong, you can modify it by manually "
"setting the MetricData.fstep property.".format(
", ".join(map(str, uqfsteps)), data.fstep))
else:
if data.fstep == 0:
logger.warning(
"A framestep of 0 was read from the trajectories. Please manually set the MetricData.fstep"
" property, otherwise calculations in Model and Kinetics classes can fail."
)
else:
logger.info(
"Frame step {}ns was read from the trajectories. If it looks wrong, redefine it by manually "
"setting the MetricData.fstep property.".format(
data.fstep))
return data
def project(self):
"""
Applies all projections stored in Metric on all simulations.
Returns
-------
data : MetricData object
Returns a MetricData object containing the projected data.
"""
if len(self.projectionlist) == 0:
raise NameError('You need to provide projections using the Metric.projection method.')
if isinstance(self.simulations, Molecule):
data = []
for proj in self.projectionlist:
data.append(proj.project(self.simulations))
return data
numSim = len(self.simulations)
# Find out if there is a unique molfile. If there is, initialize a single Molecule to speed up calculations
uqMol = None
map = []
(single, molfile) = _singleMolfile(self.simulations)
if single:
uqMol = Molecule(molfile)
for proj in self.projectionlist:
proj._precalculate(uqMol)
#map.append(np.array(proj.getMapping(uqMol), dtype=object))
#map = np.hstack(map)
logger.info('Metric: Starting projection of trajectories.')
metrics = np.empty(numSim, dtype=object)
ref = np.empty(numSim, dtype=object)
deletesims = np.zeros(numSim, dtype=bool)
fstep = np.zeros(numSim)
# # Monkey-patching callback class
# oldcallback = joblib.parallel.BatchCompletionCallBack
# joblib.parallel.BatchCompletionCallBack = BatchCompletionCallBack
# from htmd.config import _config
# results = Parallel(n_jobs=_config['ncpus'], verbose=11)(
# delayed(_processSim)(self.simulations[i], self.projectionlist, uqMol, self.skip) for i in range(numSim))
# joblib.parallel.BatchCompletionCallBack = oldcallback
from htmd.config import _config
results = Parallel(n_jobs=_config['ncpus'], verbose=11)(
delayed(_processSim)(self.simulations[i], self.projectionlist, uqMol, self.skip) for i in range(numSim))
for i in range(len(results)):
metrics[i] = results[i][0]
ref[i] = results[i][1]
fstep[i] = results[i][2]
deletesims[i] = results[i][3]
logger.info('Finished projecting the trajectories.')
# Removing empty trajectories
emptyM = np.array([True if x is None else False for x in metrics], dtype=bool)
emptyR = np.array([True if x is None else False for x in ref], dtype=bool)
assert np.all(deletesims == emptyM) and np.all(emptyR == emptyM)
metrics = np.delete(metrics, np.where(emptyM)[0])
ref = np.delete(ref, np.where(emptyM)[0])
updlist = np.delete(self.simulations, np.where(emptyM)[0])
if len(metrics) == 0:
raise NameError('No trajectories were read')
# Constructing a MetricData object
data = MetricData(dat=metrics, ref=ref, map=map, simlist=updlist)
uqfsteps = np.unique(fstep)
data.fstep = float(stats.mode(fstep).mode)
if len(uqfsteps) != 1:
logger.warning('Multiple framesteps were read from the simulations. '
'Taking the statistical mode: ' + str(data.fstep) + 'ns. '
'If it looks wrong, you can modify it by manually setting the MetricData.fstep property.')
else:
logger.info('Frame step {}ns was read from the trajectories. If it looks wrong, redefine it by manually '
'setting the MetricData.fstep property.'.format(data.fstep))
return data
def _metrify(self, sims, skip, verbose, update):
"""
Takes a set of trajectory folders and projects all trajectories within them onto the given space defined by the Metric* class.
Parameters
----------
simList : numpy list of structs
A list of structs produced by the simList function.
skip : int
Skips every x frames.
verbose : int
Verbosity toggle
update : MetricData object
Provide a previous MetricData object and only metrify new trajectories.
Returns
-------
data : MetricData object
Returns a MetricData object containing the projected data and the ref data.
"""
if isinstance(sims, Molecule):
return self.processTraj(sims)
# [updList, oldList] = checkUpdate(simList, update, verbose);
updList = sims
numSim = len(updList)
# Find out if there is a unique molfile. If there is, initialize a single Molecule to speed up calculations
uniqueMol = 0
uqMol = []
map = []
(single, molfile) = _singleMolfile(updList)
if single:
uniqueMol = 1
uqMol = Molecule(molfile)
# Calculating the mapping of metric columns to atom pair indeces
map = self._getMapping(uqMol)
logger.info('Metric: Starting projection of trajectories.')
metrics = np.empty(numSim, dtype=object)
ref = np.empty(numSim, dtype=object)
deleteSims = np.zeros(numSim, dtype=bool)
fstep = np.empty(numSim)
#global parpool
Parallel(n_jobs=6, backend="threading")(delayed(_processSimPyemma)(self, i, updList, uniqueMol, uqMol, skip, deleteSims, metrics, ref, fstep) for i in range(numSim))
logger.info('Finished projecting the trajectories.')
# Removing empty trajectories
emptyM = [True if np.size(x) == 0 else False for x in metrics]
emptyR = [True if np.size(x) == 0 else False for x in ref]
#assert np.all(deleteSims == emptyM)# and np.all(emptyR == emptyM)
metrics = np.delete(metrics, np.where(emptyM))
ref = np.delete(ref, np.where(emptyM))
#updList = np.delete(updList, emptyM)
if len(metrics) == 0:
raise NameError('No trajectories were read')
# Constructing a MetricData object
if not update:
data = MetricData(dat=metrics, ref=ref, map=map, simlist=updList)
else:
data = update
data.dat.extend(metrics)
data.ref.extend(ref)
data.simList.extend(updList)
uqfsteps = np.unique(fstep)
data.fstep = stats.mode(fstep).mode
if len(uqfsteps) != 1:
logger.warning('Multiple framesteps were read from the simulations. Taking the statistical mode: ' + str(data.fstep) + 'ns.')
logger.warning('If it looks wrong, you can modify it by manually setting the MetricData.fstep property.')
return data
def _metrify(self, sims, skip, update):
"""
Takes a set of trajectory folders and projects all trajectories within them onto the given space defined by the Metric* class.
Parameters
----------
simList : numpy list of structs
A list of structs produced by the simList function.
skip : int
Skips every x frames.
update : MetricData object
Provide a previous MetricData object and only metrify new trajectories.
Returns
-------
data : MetricData object
Returns a MetricData object containing the projected data and the ref data.
"""
if isinstance(sims, Molecule):
return self._processTraj(sims)
# [updList, oldList] = checkUpdate(simList, update, verbose);
updList = sims
numSim = len(updList)
# Find out if there is a unique molfile. If there is, initialize a single Molecule to speed up calculations
uniqueMol = 0
uqMol = []
map = []
(single, molfile) = _singleMolfile(updList)
if single:
uniqueMol = 1
uqMol = Molecule(molfile)
# Calculating the mapping of metric columns to atom pair indeces
map = self._getMapping(uqMol)
logger.info('Metric: Starting projection of trajectories.')
metrics = np.empty(numSim, dtype=object)
ref = np.empty(numSim, dtype=object)
deletesims = np.zeros(numSim, dtype=bool)
fstep = np.zeros(numSim)
# Monkey-patching callback class
#oldcallback = joblib.parallel.CallBack
#joblib.parallel.CallBack = CallBack
#p = ProgressBar(numSim, description='Projecting trajectories')
from htmd.config import _config
results = Parallel(n_jobs=_config['ncpus'], verbose=11)(delayed(_processSimOld)(self, i, updList, uniqueMol, uqMol, skip, deletesims, metrics, ref, fstep) for i in range(numSim))
#joblib.parallel.CallBack = oldcallback
for i in range(len(results)):
metrics[i] = results[i][0]
ref[i] = results[i][1]
fstep[i] = results[i][2]
deletesims[i] = results[i][3]
logger.info('Finished projecting the trajectories.')
# Removing empty trajectories
emptyM = np.array([True if x is None else False for x in metrics], dtype=bool)
emptyR = np.array([True if x is None else False for x in ref], dtype=bool)
assert np.all(deletesims == emptyM) and np.all(emptyR == emptyM)
metrics = np.delete(metrics, np.where(emptyM)[0])
ref = np.delete(ref, np.where(emptyM)[0])
updList = np.delete(updList, np.where(emptyM)[0])
if len(metrics) == 0:
raise NameError('No trajectories were read')
# Constructing a MetricData object
if not update:
data = MetricData(dat=metrics, ref=ref, map=map, simlist=updList)
else:
data = update
data.dat.extend(metrics)
data.ref.extend(ref)
data.simList.extend(updList) # This is wrong but we don't use update anyways
uqfsteps = np.unique(fstep)
data.fstep = float(stats.mode(fstep).mode)
if len(uqfsteps) != 1:
logger.warning('Multiple framesteps were read from the simulations. Taking the statistical mode: ' + str(data.fstep) + 'ns. If it looks wrong, you can modify it by manually setting the MetricData.fstep property.')
else:
logger.info('Frame step {}ns was read from the trajectories. If it looks wrong, redefine it by manually setting the MetricData.fstep property.'.format(data.fstep))
return data
