def mdstep(self,t,step,gamma=0.1,silent=False):
t0 = time.time()
xyzr = self.chain.get_particles()
o = IMP.atom.MolecularDynamics(self.model)
o.set_scoring_function(self.scoringFunction)
md = IMP.atom.VelocityScalingOptimizerState(self.model,xyzr,t)
#md = IMP.atom.LangevinThermostatOptimizerState(self.model,xyzr,t,gamma)
o.add_optimizer_state(md)
if self._record_step > 0:
self.cache_coordinates()
self.record.append(np.copy(self.xyz))
while step > self._record_step:
s = o.optimize(self._record_step)
self.cache_coordinates()
self.record.append(np.copy(self.xyz))
step -= self._record_step
s = o.optimize(step)
else:
s = o.optimize(step)
o.remove_optimizer_state(md)
if not silent:
self.logger.info('MD %d steps done @ %.1fs score = %f'%(step,utils.timespend(t0),s))
print 'MD %d steps done @ %.1fs score = %f'%(step,utils.timespend(t0),s)
return s
def SimulatedAnnealing_Scored(self,
hot,
cold,
nc=10,
nstep=500,
lowscore=10):
"""perform a cycle of simulated annealing but stop if reach low score"""
t0 = time.time()
dt = (hot - cold) / nc
for i in range(nc):
t = hot - dt * i
self.mdstep(t, nstep, silent=True)
score = self.cgstep(100, silent=True)
self.logger.info(u" Temp=%s Step=%s Time=%.1fs Score=%.8f" %
(t, nstep, alabutils.timespend(t0), score))
print " Temp=%s Step=%s Time=%.1fs Score=%.8f" % (
t, nstep, alabutils.timespend(t0), score)
if score < lowscore:
return t, score
break
self.mdstep(cold, 300, silent=True)
score = self.cgstep(100, silent=True)
self.logger.info(u" Temp=%s Step=%s Time=%.1fs Score=%.8f" %
(cold, nstep, alabutils.timespend(t0), score))
print " Temp=%s Step=%s Time=%.1fs Score=%.8f" % (
cold, nstep, alabutils.timespend(t0), score)
return t, score
def mdstep_withChromosomeTerritory(self, t, step):
"""
perform an mdstep with chromosome terriory restraint
Parameters
-----------
t : int
temperature
step : int
optimization steps
"""
t0 = time.time()
chrContainers = []
for chrom in self.genome.info['chrom']:
chromContainer1 = IMP.container.ListSingletonContainer(
self.model, 'Container %s s1' % chrom)
chromContainer2 = IMP.container.ListSingletonContainer(
self.model, 'Container %s s2' % chrom)
for j in np.flatnonzero(self.probmat.idx['chrom'] == chrom):
p = self.chain.get_particle(j)
chromContainer1.add(p)
p = self.chain.get_particle(j + self.nbead)
chromContainer2.add(p)
chrContainers.append(chromContainer1)
chrContainers.append(chromContainer2)
# set each chromosome to different containers
for st in range(step / 10):
ctRestraintSet = IMP.RestraintSet(self.model)
for i in range(len(self.genome.info['chrom'])):
comxyz = centerOfMass(chrContainers[2 * i])
comc = IMP.algebra.Vector3D(comxyz[0], comxyz[1], comxyz[2])
ub = IMP.core.HarmonicUpperBound(self.chromRadius[i], 0.2)
ss = IMP.core.DistanceToSingletonScore(ub, comc)
ct_rs = IMP.container.SingletonsRestraint(
ss, chrContainers[2 * i])
ctRestraintSet.add_restraint(ct_rs)
#another one
comxyz = centerOfMass(chrContainers[2 * i + 1])
comc = IMP.algebra.Vector3D(comxyz[0], comxyz[1], comxyz[2])
ub = IMP.core.HarmonicUpperBound(self.chromRadius[i], 0.2)
ss = IMP.core.DistanceToSingletonScore(ub, comc)
ct_rs = IMP.container.SingletonsRestraint(
ss, chrContainers[2 * i + 1])
ctRestraintSet.add_restraint(ct_rs)
self.updateScoringFunction([self.restraints, ctRestraintSet])
s = self.mdstep(t, 10, silent=True)
#---
#s = mdstep(model,chain,sf,t,1000)
self.updateScoringFunction()
self.logger.info(u'CT-MD %d steps done @ %.1fs score = %f' %
(step, alabutils.timespend(t0), s))
print 'CT-MD %d steps done @ %.1fs score = %f' % (
step, alabutils.timespend(t0), s)
return s
def SimulatedAnnealing(self,hot,cold,nc=10,nstep=500):
"""
perform a cycle of simulated annealing from hot to cold
"""
t0 = time.time()
dt = (hot-cold)/nc
for i in range(nc):
t = hot-dt*i
s = self.mdstep(t,nstep,silent=True)
self.logger.info(" Temp=%d Step=%d Time=%.1fs Score = %.8f"%(t,nstep,utils.timespend(t0),s))
print " Temp=%d Step=%d Time=%.1fs Score = %.8f"%(t,nstep,utils.timespend(t0),s)
s= self.mdstep(cold,nstep,silent=True)
self.logger.info(" Temp=%d Step=%d Time=%.1fs Score = %.8f"%(cold,nstep,utils.timespend(t0),s))
print " Temp=%d Step=%d Time=%.1fs Score = %.8f"%(cold,nstep,utils.timespend(t0),s)
self.cgstep(100,silent=True)
def mdstep_withChromosomeTerritory(self,t,step):
"""
perform an mdstep with chromosome terriory restraint
parameters:
-----------
t: temperature
step: optimization steps
"""
t0 = time.time()
chrContainers=[]
for chrom in self.genome.info['chrom']:
chromContainer1=IMP.container.ListSingletonContainer(self.model,'Container %s s1'%chrom)
chromContainer2=IMP.container.ListSingletonContainer(self.model,'Container %s s2'%chrom)
for j in np.flatnonzero(self.probmat.idx['chrom'] == chrom):
p=self.chain.get_particle(j)
chromContainer1.add(p)
p=self.chain.get_particle(j+self.nbead)
chromContainer2.add(p)
chrContainers.append(chromContainer1)
chrContainers.append(chromContainer2)
# set each chromosome to different containers
for st in range(step/10):
ctRestraintSet = IMP.RestraintSet(self.model)
for i in range(len(self.genome.info['chrom'])):
comxyz = centerOfMass(chrContainers[2*i])
comc = IMP.algebra.Vector3D(comxyz[0],comxyz[1],comxyz[2])
ub = IMP.core.HarmonicUpperBound(self.chromRadius[i],0.2)
ss = IMP.core.DistanceToSingletonScore(ub,comc)
ct_rs = IMP.container.SingletonsRestraint(ss,chrContainers[2*i])
ctRestraintSet.add_restraint(ct_rs)
#another one
comxyz = centerOfMass(chrContainers[2*i+1])
comc = IMP.algebra.Vector3D(comxyz[0],comxyz[1],comxyz[2])
ub = IMP.core.HarmonicUpperBound(self.chromRadius[i],0.2)
ss = IMP.core.DistanceToSingletonScore(ub,comc)
ct_rs = IMP.container.SingletonsRestraint(ss,chrContainers[2*i+1])
ctRestraintSet.add_restraint(ct_rs)
self.updateScoringFunction([self.restraints,ctRestraintSet])
s = self.mdstep(t,10,silent=True)
#---
#s = mdstep(model,chain,sf,t,1000)
self.updateScoringFunction()
self.logger.info('CT-MD %d steps done @ %.1fs score = %f'%(step,utils.timespend(t0),s))
print 'CT-MD %d steps done @ %.1fs score = %f'%(step,utils.timespend(t0),s)
return s
def mdstep(self, t, step, gamma=0.1, silent=False):
t0 = time.time()
#self.cache_coordinates()
xyzr = self.chain.get_particles()
o = IMP.atom.MolecularDynamics(self.model)
o.set_scoring_function(self.scoringFunction)
md = IMP.atom.VelocityScalingOptimizerState(self.model, xyzr, t)
#md = IMP.atom.LangevinThermostatOptimizerState(self.model,xyzr,t,gamma)
o.add_optimizer_state(md)
s = o.optimize(step)
o.remove_optimizer_state(md)
if not silent:
self.logger.info(u'MD %d steps done @ %.1fs score = %f' %
(step, alabutils.timespend(t0), s))
print 'MD %d steps done @ %.1fs score = %f' % (
step, alabutils.timespend(t0), s)
return s
def SimulatedAnnealing_Scored(self,hot,cold,nc=10,nstep=500,lowscore=10):
"""perform a cycle of simulated annealing but stop if reach low score"""
t0 = time.time()
dt = (hot-cold)/nc
for i in range(nc):
t = hot-dt*i
self.mdstep(t,nstep,silent=True)
score = self.cgstep(100,silent=True)
self.logger.info(" Temp=%s Step=%s Time=%.1fs Score=%.8f"%(t,nstep,utils.timespend(t0),score))
print " Temp=%s Step=%s Time=%.1fs Score=%.8f"%(t,nstep,utils.timespend(t0),score)
if score < lowscore:
return t,score
break
self.mdstep(cold,300,silent=True)
score = self.cgstep(100,silent=True)
self.logger.info(" Temp=%s Step=%s Time=%.1fs Score=%.8f"%(cold,nstep,utils.timespend(t0),score))
print " Temp=%s Step=%s Time=%.1fs Score=%.8f"%(cold,nstep,utils.timespend(t0),score)
return t,score
def SimulatedAnnealing(self, hot, cold, nc=10, nstep=500):
"""
perform a cycle of simulated annealing from hot to cold
"""
t0 = time.time()
dt = (hot - cold) / nc
for i in range(nc):
t = hot - dt * i
s = self.mdstep(t, nstep, silent=True)
self.logger.info(u" Temp=%d Step=%d Time=%.1fs Score = %.8f" %
(t, nstep, alabutils.timespend(t0), s))
print " Temp=%d Step=%d Time=%.1fs Score = %.8f" % (
t, nstep, alabutils.timespend(t0), s)
s = self.mdstep(cold, nstep, silent=True)
self.logger.info(u" Temp=%d Step=%d Time=%.1fs Score = %.8f" %
(cold, nstep, alabutils.timespend(t0), s))
print " Temp=%d Step=%d Time=%.1fs Score = %.8f" % (
cold, nstep, alabutils.timespend(t0), s)
self.cgstep(100, silent=True)
def cgstep(self,step,silent=False):
"""
perform conjugate gradient on model using scoring function sf
"""
t0 = time.time()
o = IMP.core.ConjugateGradients(self.model)
o.set_check_level(IMP.USAGE)
o.set_scoring_function(self.scoringFunction)
# For conjugate gradient we just record one snap shot
if self._record_step > 0:
self.cache_coordinates()
self.record.append(np.copy(self.xyz))
# start optimizing
try:
s = o.optimize(step)
except:
s = o.optimize(step)
if not silent:
self.logger.info('CG %d steps done @ %.1fs score = %f'%(step,utils.timespend(t0),s))
print 'CG %d steps done @ %.1fs score = %f'%(step,utils.timespend(t0),s)
return s
def cgstep(self,step,silent=False):
"""
perform conjugate gradient on model using scoring function sf
"""
t0 = time.time()
self.cache_coordinates()
o = IMP.core.ConjugateGradients(self.model)
o.set_check_level(IMP.USAGE)
o.set_scoring_function(self.scoringFunction)
try:
s = o.optimize(step)
except:
#tt = str(np.random.random_integers(1000))+'.txt'
#np.savetxt(tt,self.xyz,fmt='%.4f')
#print tt
#self.cache_coordinates()
#np.savetxt(tt+'.af',self.xyz,fmt='%.4f')
s = o.optimize(step)
if not silent:
self.logger.info(u'CG %d steps done @ %.1fs score = %f'%(step,alabutils.timespend(t0),s))
print 'CG %d steps done @ %.1fs score = %f'%(step,alabutils.timespend(t0),s)
return s
def cgstep(self, step, silent=False):
"""
perform conjugate gradient on model using scoring function sf
"""
t0 = time.time()
self.cache_coordinates()
o = IMP.core.ConjugateGradients(self.model)
o.set_check_level(IMP.USAGE)
o.set_scoring_function(self.scoringFunction)
try:
s = o.optimize(step)
except:
#tt = str(np.random.random_integers(1000))+'.txt'
#np.savetxt(tt,self.xyz,fmt='%.4f')
#print tt
#self.cache_coordinates()
#np.savetxt(tt+'.af',self.xyz,fmt='%.4f')
s = o.optimize(step)
if not silent:
self.logger.info(u'CG %d steps done @ %.1fs score = %f' %
(step, alabutils.timespend(t0), s))
print 'CG %d steps done @ %.1fs score = %f' % (
step, alabutils.timespend(t0), s)
return s