def test(natoms = 40, boxl=4.):
import pygmin.potentials.ljpshiftfast as ljpshift
import pygmin.defaults as defaults
from pygmin.utils.neighbor_list import makeBLJNeighborListPot
ntypeA = int(natoms*0.8)
ntypeB = natoms - ntypeA
rcut = 2.5
freezelist = range(ntypeA/2) + range(ntypeA,ntypeA+ntypeB/2)
nfrozen = len(freezelist)
print "nfrozen", nfrozen
coords = np.random.uniform(-1,1,natoms*3)*(natoms)**(1./3)/2
NLblj = ljpshift.LJpshift(natoms, ntypeA, rcut=rcut, boxl=boxl)
blj = FreezePot(NLblj, freezelist)
pot = makeBLJNeighborListPotFreeze(natoms, freezelist, ntypeA=ntypeA, rcut=rcut, boxl=boxl)
#pot = FreezePot(NLpot, freezelist)
eblj = blj.getEnergy(coords)
print "blj energy", eblj
epot = pot.getEnergy(coords)
print "mcpot energy", epot
print "difference", (epot - eblj)/eblj
ret1 = defaults.quenchRoutine(coords, blj.getEnergyGradient, iprint=-11)
np.savetxt("out.coords", ret1[0])
print "energy from quench1", ret1[1]
ret2 = defaults.quenchRoutine(coords, pot.getEnergyGradient, iprint=-1)
print "energy from quench2", ret2[1]
print "ret1 evaluated in both potentials", pot.getEnergy(ret1[0]), blj.getEnergy(ret1[0])
print "ret2 evaluated in both potentials", pot.getEnergy(ret2[0]), blj.getEnergy(ret2[0])
coords = ret1[0]
e1, g1 = blj.getEnergyGradient(coords)
e2, g2 = pot.getEnergyGradient(coords)
print "energy difference from getEnergyGradient", (e2 - e1)
print "largest gradient difference", np.max(np.abs(g2-g1))
print "rms gradients", np.linalg.norm(g1)/np.sqrt(len(g1)), np.linalg.norm(g2)/np.sqrt(len(g1))
if True:
for subpot in pot.pot.potentials:
nl = subpot
print "number of times neighbor list was remade:", nl.buildcount, "out of", nl.count
if False:
try:
import pygmin.utils.pymolwrapper as pym
pym.start()
pym.draw_spheres(np.reshape(coords,[-1,3]), "A", 1)
pym.draw_spheres(np.reshape(ret1[0],[-1,3]), "A", 2)
pym.draw_spheres(np.reshape(ret2[0],[-1,3]), "A", 3)
except ImportError:
print "Could not draw using pymol, skipping this step"
def testBLJ(self):
import pygmin.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 pygmin.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 pygmin.potentials.ljpshiftfast import LJpshift as BLJ
from pygmin 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 pygmin.potentials import LJ
from pygmin import defaults
from pygmin.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 pygmin.potentials.ljpshiftfast import LJpshift as BLJ
from pygmin 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 setUp(self):
from pygmin.potentials.ljpshiftfast import LJpshift as BLJ
from pygmin 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
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
def getRandomCoords(pot, natoms):
import numpy as np
coords = np.random.uniform(-1,1,natoms*3)*natoms**(1./3)*1.5
ret = defaults.quenchRoutine(coords, pot.getEnergyGradient)
return ret
boxl = 4
natoms = len(coords) /3
ntypeA = int(natoms*0.8)
rcut = 2.5
print "natoms", natoms, "ntypea", ntypeA
bljslow = ljpshift.LJpshift(natoms, ntypeA, rcut=rcut, boxl=boxl)
blj = LJpshift(natoms, ntypeA, rcut=rcut, boxl=boxl)
ebljslow = bljslow.getEnergy(coords)
print "blj energy slow", ebljslow
eblj = blj.getEnergy(coords)
print "blj energy ", eblj
print "quenching coords"
ret1 = defaults.quenchRoutine(coords, blj.getEnergyGradient, iprint=-11)
coords = ret1[0]
ebljslow = bljslow.getEnergy(coords)
print "blj energy slow", ebljslow
eblj = blj.getEnergy(coords)
print "blj energy ", eblj
