def _getSimlist(self):
logger.info('Postprocessing new data')
sims = simlist(glob(path.join(self.datapath, '*', '')), glob(path.join(self.inputpath, '*', '')),
glob(path.join(self.inputpath, '*', '')))
if self.filter:
sims = simfilter(sims, self.filteredpath, filtersel=self.filtersel)
return sims
def setUpClass(self):
from htmd.simlist import simlist, simfilter
from glob import glob
from htmd.projections.metric import Metric
from moleculekit.projections.metricdistance import MetricDistance
from moleculekit.projections.metricdihedral import MetricDihedral
from moleculekit.util import tempname
from htmd.home import home
from os.path import join
sims = simlist(
glob(join(home(dataDir="adaptive"), "data", "*", "")),
glob(join(home(dataDir="adaptive"), "input", "*")),
)
fsims = simfilter(sims, tempname(), "not water")
metr = Metric(fsims)
metr.set(
MetricDistance(
"protein and resid 10 and name CA",
"resname BEN and noh",
periodic="selections",
metric="contacts",
groupsel1="residue",
threshold=4,
)
)
self.data1 = metr.project()
metr.set(MetricDihedral())
self.data2 = metr.project()
def setUpClass(self):
from htmd.simlist import simlist, simfilter
from glob import glob
from htmd.projections.metric import Metric
from moleculekit.projections.metricdistance import MetricDistance
from moleculekit.projections.metricdihedral import MetricDihedral
from moleculekit.util import tempname
from htmd.home import home
from os.path import join
sims = simlist(glob(join(home(dataDir='adaptive'), 'data', '*', '')),
glob(join(home(dataDir='adaptive'), 'input', '*')))
fsims = simfilter(sims, tempname(), 'not water')
metr = Metric(fsims)
metr.set(
MetricDistance('protein and resid 10 and name CA',
'resname BEN and noh',
metric='contacts',
groupsel1='residue',
threshold=4))
self.data1 = metr.project()
metr.set(MetricDihedral())
self.data2 = metr.project()
def _algorithm(self):
logger.info('Postprocessing new data')
sims = simlist(glob(path.join(self.datapath, '*', '')),
glob(path.join(self.inputpath, '*', 'structure.pdb')),
glob(path.join(self.inputpath, '*', '')))
if self.filter:
sims = simfilter(sims, self.filteredpath, filtersel=self.filtersel)
metr = Metric(sims, skip=self.skip)
metr.set(self.projection)
#if self.contactsym is not None:
# contactSymmetry(data, self.contactsym)
if self.ticadim > 0:
# tica = TICA(metr, int(max(2, np.ceil(self.ticalag)))) # gianni: without project it was tooooo slow
tica = TICA(metr.project(), int(max(2, np.ceil(self.ticalag))))
datadr = tica.project(self.ticadim)
else:
datadr = metr.project()
datadr.dropTraj(
) # Preferably we should do this before any projections. Corrupted sims can affect TICA
datadr.cluster(
self.clustmethod(n_clusters=self._numClusters(datadr.numFrames)))
self._model = Model(datadr)
self._model.markovModel(self.lag, self._numMacrostates(datadr))
if self.save:
self._model.save('adapt_model_e' + str(self._getEpoch()) + '.dat')
relFrames = self._getSpawnFrames(self._model, datadr)
self._writeInputs(datadr.rel2sim(np.concatenate(relFrames)))
def _getSimlist(self):
logger.info('Postprocessing new data')
sims = simlist(glob(path.join(self.datapath, '*', '')), glob(path.join(self.inputpath, '*', '')),
glob(path.join(self.inputpath, '*', '')))
if self.filter:
sims = simfilter(sims, self.filteredpath, filtersel=self.filtersel)
return sims
def _getSimlist(self):
logger.info("Postprocessing new data")
sims = simlist(
glob(path.join(self.datapath, "*", "")),
glob(path.join(self.inputpath, "*", "")),
glob(path.join(self.inputpath, "*", "")),
)
if self.filter:
sims = simfilter(sims, self.filteredpath, filtersel=self.filtersel)
return sims
def _algorithm(self):
logger.info('Postprocessing new data')
sims = simlist(glob(path.join(self.datapath, '*', '')),
glob(path.join(self.inputpath, '*', 'structure.pdb')),
glob(path.join(self.inputpath, '*', '')))
if self.filter:
sims = simfilter(sims, self.filteredpath, filtersel=self.filtersel)
metr = Metric(sims, skip=self.skip)
metr.set(self.projection)
# if self.contactsym is not None:
# contactSymmetry(data, self.contactsym)
if self.ticadim > 0:
# tica = TICA(metr, int(max(2, np.ceil(self.ticalag)))) # gianni: without project it was tooooo slow
tica = TICA(metr.project(), int(max(2, np.ceil(self.ticalag))))
datadr = tica.project(self.ticadim)
else:
datadr = metr.project()
datadr.dropTraj(
) # Preferably we should do this before any projections. Corrupted sims can affect TICA
datadr.cluster(
self.clustmethod(n_clusters=self._numClusters(datadr.numFrames)))
model = Model(datadr)
self._model = model
self._model.markovModel(self.lag, self._numMacrostates(datadr))
if self.save:
self._model.save('adapt_model_e' + str(self._getEpoch()) + '.dat')
# Undirected component
uc = -model.data.N # Lower counts should give higher score hence the -
if self.statetype == 'micro':
uc = uc[model.cluster_ofmicro]
if self.statetype == 'macro':
uc = macroAccumulate(model, uc[model.cluster_ofmicro])
# Calculating the directed component
dc = self._calculateDirectedComponent(sims, model.data.St,
model.data.N)
if self.statetype == 'micro':
dc = dc[model.cluster_ofmicro]
if self.statetype == 'macro':
dc = macroAccumulate(model, dc[model.cluster_ofmicro])
uc = self._featScale(uc)
dc = self._featScale(dc)
reward = dc + self.ucscale * uc
relFrames = self._getSpawnFrames(reward, self._model, datadr)
self._writeInputs(datadr.rel2sim(np.concatenate(relFrames)))
def _algorithm(self):
logger.info('Postprocessing new data')
datalist = simlist(glob(path.join(self.datapath, '*', '')), glob(path.join(self.inputpath, '*', 'structure.pdb')),
glob(path.join(self.inputpath, '*', '')))
filtlist = simfilter(datalist, self.filteredpath, filtersel=self.filtersel)
if hasattr(self, 'metricsel2') and self.metricsel2 is not None:
proj = MetricDistance(self.metricsel1, self.metricsel2, metric=self.metrictype)
else:
proj = MetricSelfDistance(self.metricsel1, metric=self.metrictype)
metr = Metric(filtlist, skip=self.skip)
metr.projection(proj)
data = metr.project()
#if self.contactsym is not None:
# contactSymmetry(data, self.contactsym)
data.dropTraj()
if self.ticadim > 0:
tica = TICA(data, int(max(2, np.ceil(20/self.skip))))
datadr = tica.project(self.ticadim)
else:
datadr = data
K = int(max(np.round(0.6 * np.log10(datadr.numFrames/1000)*1000+50), 100)) # heuristic
if K > datadr.numFrames / 3: # Freaking ugly patches ...
K = int(datadr.numFrames / 3)
datadr.cluster(self.clustmethod(n_clusters=K), mergesmall=5)
replacement = False
if datadr.K < 10:
datadr.cluster(self.clustmethod(n_clusters=K))
replacement = True
model = Model(datadr)
macronum = self.macronum
if datadr.K < macronum:
macronum = np.ceil(datadr.K / 2)
logger.warning('Using less macrostates than requested due to lack of microstates. macronum = ' + str(macronum))
from pyemma.msm import timescales_msm
timesc = timescales_msm(datadr.St.tolist(), lags=self.lag, nits=macronum).get_timescales()
macronum = min(self.macronum, max(np.sum(timesc > self.lag), 2))
model.markovModel(self.lag, macronum)
p_i = self._criteria(model, self.method)
(spawncounts, prob) = self._spawn(p_i, self.nmax-self.running)
logger.debug('spawncounts {}'.format(spawncounts))
stateIdx = np.where(spawncounts > 0)[0]
_, relFrames = model.sampleStates(stateIdx, spawncounts[stateIdx], statetype='micro', replacement=replacement)
logger.debug('relFrames {}'.format(relFrames))
self._writeInputs(datadr.rel2sim(np.concatenate(relFrames)))
if __name__ == '__main__':
from htmd.simlist import simlist, simfilter
from glob import glob
from htmd.projections.metric import Metric
from htmd.projections.metricdistance import MetricDistance
from htmd.projections.metricdihedral import MetricDihedral
from htmd.util import tempname
from htmd.home import home
from os.path import join
testfolder = home(dataDir='adaptive')
sims = simlist(glob(join(testfolder, 'data', '*', '')),
glob(join(testfolder, 'input', '*', 'structure.pdb')))
fsims = simfilter(sims, tempname(), 'not water')
metr = Metric(fsims)
metr.set(
MetricDistance('protein and resid 10 and name CA',
'resname BEN and noh',
metric='contacts',
groupsel1='residue',
threshold=4))
data1 = metr.project()
metr.set(MetricDihedral())
data2 = metr.project()
# Testing combining of metrics
data1.combine(data2)
# Testing dimensions
def _algorithm(self):
logger.info('Postprocessing new data')
datalist = simlist(
glob(path.join(self.datapath, '*', '')),
glob(path.join(self.inputpath, '*', 'structure.pdb')),
glob(path.join(self.inputpath, '*', '')))
filtlist = simfilter(datalist,
self.filteredpath,
filtersel=self.filtersel)
if hasattr(self, 'metricsel2') and self.metricsel2 is not None:
proj = MetricDistance(self.metricsel1,
self.metricsel2,
metric=self.metrictype)
else:
proj = MetricSelfDistance(self.metricsel1, metric=self.metrictype)
metr = Metric(filtlist, skip=self.skip)
metr.projection(proj)
data = metr.project()
#if self.contactsym is not None:
# contactSymmetry(data, self.contactsym)
data.dropTraj()
if self.ticadim > 0:
tica = TICA(data, int(max(2, np.ceil(20 / self.skip))))
datadr = tica.project(self.ticadim)
else:
datadr = data
K = int(
max(np.round(0.6 * np.log10(datadr.numFrames / 1000) * 1000 + 50),
100)) # heuristic
if K > datadr.numFrames / 3: # Freaking ugly patches ...
K = int(datadr.numFrames / 3)
datadr.cluster(self.clustmethod(n_clusters=K), mergesmall=5)
replacement = False
if datadr.K < 10:
datadr.cluster(self.clustmethod(n_clusters=K))
replacement = True
model = Model(datadr)
macronum = self.macronum
if datadr.K < macronum:
macronum = np.ceil(datadr.K / 2)
logger.warning(
'Using less macrostates than requested due to lack of microstates. macronum = '
+ str(macronum))
from pyemma.msm import timescales_msm
timesc = timescales_msm(datadr.St.tolist(),
lags=self.lag,
nits=macronum).get_timescales()
macronum = min(self.macronum, max(np.sum(timesc > self.lag), 2))
model.markovModel(self.lag, macronum)
p_i = self._criteria(model, self.method)
(spawncounts, prob) = self._spawn(p_i, self.nmax - self.running)
logger.debug('spawncounts {}'.format(spawncounts))
stateIdx = np.where(spawncounts > 0)[0]
_, relFrames = model.sampleStates(stateIdx,
spawncounts[stateIdx],
statetype='micro',
replacement=replacement)
logger.debug('relFrames {}'.format(relFrames))
self._writeInputs(datadr.rel2sim(np.concatenate(relFrames)))
if __name__ == '__main__':
from htmd.simlist import simlist, simfilter
from glob import glob
from htmd.projections.metric import Metric
from htmd.projections.metricdistance import MetricDistance
from htmd.projections.metricdihedral import MetricDihedral
from htmd.util import tempname
from htmd.home import home
from os.path import join
testfolder = home(dataDir='adaptive')
sims = simlist(glob(join(testfolder, 'data', '*', '')), glob(join(testfolder, 'input', '*', 'structure.pdb')))
fsims = simfilter(sims, tempname(), 'not water')
metr = Metric(fsims)
metr.set(MetricDistance('protein and resid 10 and name CA', 'resname BEN and noh', metric='contacts',
groupsel1='residue', threshold=4))
data1 = metr.project()
metr.set(MetricDihedral())
data2 = metr.project()
# Testing combining of metrics
data1.combine(data2)
# Testing dimensions
assert np.array_equal(data1.description.shape, (897, 3)), 'combine not working correct'
assert np.array_equal(data1.trajectories[0].projection.shape, (6, 897)), 'combine not working correct'
assert np.array_equal(np.where(data1.description.type == 'contact')[0], [0, 1, 2, 3, 4, 5, 6, 7, 8]), 'combine not working correct'
