def load(self, filename):
""" Load a :class:`MetricData <htmd.metricdata.MetricData>` object from disk
Parameters
----------
filename : str
Path to the saved MetricData object
Examples
--------
>>> model = Model()
>>> model.load('./model.dat')
"""
import sys
import pickle
from htmd.metricdata import MetricData
try:
import pandas.indexes
except ImportError:
import pandas.core.indexes
sys.modules['pandas.indexes'] = pandas.core.indexes # Hacky fix for new pandas version
f = open(filename, 'rb')
z = pickle.load(f)
f.close()
for k in z:
if k == 'data':
m = MetricData()
m.load(z[k].__dict__)
self.__dict__[k] = m
else:
self.__dict__[k] = z[k]
def load(self, filename):
""" Load a :class:`MetricData <htmd.metricdata.MetricData>` object from disk
Parameters
----------
filename : str
Path to the saved MetricData object
Examples
--------
>>> model = Model()
>>> model.load('./model.dat')
"""
import sys
import pickle
from htmd.metricdata import MetricData
try:
import pandas.indexes
except ImportError:
import pandas.core.indexes
sys.modules[
'pandas.indexes'] = pandas.core.indexes # Hacky fix for new pandas version
f = open(filename, 'rb')
z = pickle.load(f)
f.close()
for k in z:
if k == 'data':
m = MetricData()
m.load(z[k].__dict__)
self.__dict__[k] = m
else:
self.__dict__[k] = z[k]
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
return MetricData(dat=self.data.deconcatenate(dist), ref=self.data.ref, simlist=self.data.simlist,
fstep=self.data.fstep, parent=self.data)
def newMetricData(self, datasource, trajectories=None, olddata=None):
"""Converts trajectory indexes to a new MetricData object"""
dat = []
ref = []
sim = []
for traj in trajectories:
d, r, s = self._collectTrajectory(datasource, traj)
dat.append(d)
ref.append(r)
sim.append(s)
newdata = MetricData(
dat=dat,
ref=ref,
simlist=sim,
description=datasource.description,
fstep=datasource.fstep,
)
if olddata is not None:
olddata.append(newdata) # Merge with old data
return olddata
else:
return newdata
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, 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, 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 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 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)
"""
from tqdm import tqdm
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
k = -1
droppedsims = []
pbar = tqdm(total=len(metr.simulations))
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]
pbar.update(len(projecteddata))
pbar.close()
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.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 _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 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 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 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 _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