def testBLJ(self):
import pele.defaults as defaults
X1 = np.copy(self.X1)
X2 = np.random.uniform(-1,1,[self.natoms*3])*(float(self.natoms))**(1./3)/2
#run a quench so the structure is not crazy
ret = defaults.quenchRoutine(X2, self.pot.getEnergyGradient)
X2 = ret[0]
self.runtest(X1, X2, findBestPermutation)
def testLJ_hungarian(self):
"""test findBestPermutationListOPTIM"""
import pele.defaults as defaults
X1 = np.copy(self.X1)
X2 = np.random.uniform(-1,1,[self.natoms*3])*(float(self.natoms))**(1./3)/2
#run a quench so the structure is not crazy
ret = defaults.quenchRoutine(X2, self.pot.getEnergyGradient)
X2 = ret[0]
self.runtest(X1, X2, findBestPermutationListHungarian, atomlist=self.permlist[0])
def setUp(self):
from pele.potentials.ljpshiftfast import LJpshift as BLJ
from pele import defaults
self.natoms = 15
self.ntypeA = int(self.natoms * .8)
self.pot = BLJ(self.natoms, self.ntypeA)
self.permlist = [range(self.ntypeA), range(self.ntypeA, self.natoms)]
self.X1 = np.random.uniform(-1,1,[self.natoms*3])*(float(self.natoms))**(1./3)/2
ret = defaults.quenchRoutine(self.X1, self.pot.getEnergyGradient, tol=.1)
self.X1 = ret[0]
def setUp(self):
from pele.potentials import LJ
from pele import defaults
from pele.mindist import CoMToOrigin
self.natoms = 15
self.pot = LJ(self.natoms)
self.permlist = [range(self.natoms)]
self.X1 = np.random.uniform(-1,1,[self.natoms*3])*(float(self.natoms))**(1./3)/2
ret = defaults.quenchRoutine(self.X1, self.pot.getEnergyGradient, tol=.1)
self.X1 = ret[0]
self.X1 = CoMToOrigin(self.X1)
def setUp(self):
from pele.potentials.ljpshiftfast import LJpshift as BLJ
from pele import defaults
self.natoms = 25
self.ntypeA = int(self.natoms * .8)
self.pot = BLJ(self.natoms, self.ntypeA)
self.permlist = [range(self.ntypeA), range(self.ntypeA, self.natoms)]
self.X1 = np.random.uniform(-1,1,[self.natoms*3])*(float(self.natoms))**(1./3)/2
#run a quench so the structure is not crazy
ret = defaults.quenchRoutine(self.X1, self.pot.getEnergyGradient, **defaults.quenchParams)
self.X1 = ret[0]
def _optimizePermRot(X1, X2, niter, permlist, verbose=False, use_quench=True):
if use_quench:
pot = MinPermDistPotential(X1, X2.copy(), permlist=permlist)
distbest = getDistxyz(X1, X2)
mxbest = np.identity(3)
X20 = X2.copy()
for i in range(niter):
#get and apply a random rotation
aa = random_aa()
if not use_quench:
mx = aa2mx(aa)
mxtot = mx
#print "X2.shape", X2.shape
else:
#optimize the rotation using a permutationally invariand distance metric
ret = defaults.quenchRoutine(aa, pot.getEnergyGradient, tol=0.01)
aa1 = ret[0]
mx1 = aa2mx(aa1)
mxtot = mx1
X2 = applyRotation(mxtot, X20)
#optimize the permutations
dist, X1, X2 = findBestPermutation(X1, X2, permlist)
if verbose:
print "dist", dist, "distbest", distbest
#print "X2.shape", X2.shape
#optimize the rotation
dist, Q2 = getAlignRotation(X1, X2)
# print "dist", dist, "Q2", Q2
mx2 = q2mx(Q2)
mxtot = np.dot(mx2, mxtot)
if dist < distbest:
distbest = dist
mxbest = mxtot
return distbest, mxbest
