def test_distpot():
#define two structures
natoms = 12
X1 = np.random.uniform(-1, 1, [natoms * 3]) * (float(natoms))**(1. / 3)
X2 = np.random.uniform(-1, 1, [natoms * 3]) * (float(natoms))**(1. / 3)
#make X2 a rotation of X1
print "testing with", natoms, "atoms, with X2 a rotated and permuted isomer of X1"
aa = rot.random_aa()
rot_mx = rot.aa2mx(aa)
for j in range(natoms):
i = 3 * j
X2[i:i + 3] = np.dot(rot_mx, X1[i:i + 3])
#import random, mindistutils
#perm = range(natoms)
#random.shuffle( perm )
#print perm
#X2 = mindistutils.permuteArray( X2, perm)
pot = MinPermDistPotential(X1, X2, L=.2)
aa = rot.random_aa()
e = pot.getEnergy(aa)
print "energy", e
de, dg = pot.getEnergyGradient(aa)
print "energy from gradient", de, "diff", e - de
den, dgn = pot.getEnergyGradientNumerical(aa)
maxgrad = np.max(np.abs(dg))
maxdiff = np.max(np.abs(dg - dgn))
print "maximum gradient", maxgrad
print "max difference in analytical vs numerical gradient", maxdiff
def test_distpot():
#define two structures
natoms = 12
X1 = np.random.uniform(-1,1,[natoms*3])*(float(natoms))**(1./3)
X2 = np.random.uniform(-1,1,[natoms*3])*(float(natoms))**(1./3)
#make X2 a rotation of X1
print "testing with", natoms, "atoms, with X2 a rotated and permuted isomer of X1"
aa = rot.random_aa()
rot_mx = rot.aa2mx( aa )
for j in range(natoms):
i = 3*j
X2[i:i+3] = np.dot( rot_mx, X1[i:i+3] )
#import random, mindistutils
#perm = range(natoms)
#random.shuffle( perm )
#print perm
#X2 = mindistutils.permuteArray( X2, perm)
pot = MinPermDistPotential(X1, X2, L = .2)
aa = rot.random_aa()
e = pot.getEnergy(aa)
print "energy", e
de, dg = pot.getEnergyGradient(aa)
print "energy from gradient", de, "diff", e-de
den, dgn = pot.getEnergyGradientNumerical(aa)
maxgrad= np.max( np.abs( dg ) )
maxdiff = np.max( np.abs( dg - dgn ) )
print "maximum gradient", maxgrad
print "max difference in analytical vs numerical gradient", maxdiff
def testBLJ_isomer(self):
"""
test with BLJ potential. We have two classes of permutable atoms
test case where X2 is an isomer of X1.
"""
import pygmin.utils.rotations as rot
X1i = np.copy(self.X1)
X1 = np.copy(self.X1)
X2 = np.copy(X1)
#rotate X2 randomly
aa = rot.random_aa()
rot_mx = rot.aa2mx( aa )
for j in range(self.natoms):
i = 3*j
X2[i:i+3] = np.dot( rot_mx, X1[i:i+3] )
#permute X2
import random, copy
from pygmin.mindist.permutational_alignment import permuteArray
for atomlist in self.permlist:
perm = copy.copy(atomlist)
random.shuffle( perm )
X2 = permuteArray( X2, perm)
X2i = np.copy(X2)
#distreturned, X1, X2 = self.runtest(X1, X2)
distreturned, X1, X2 = self.runtest(X1, X2, minPermDistStochastic)
#it's an isomer, so the distance should be zero
self.assertTrue( abs(distreturned) < 1e-14, "didn't find isomer: dist = %g" % (distreturned) )
def randomCoords(nmol):
coords = np.zeros(2*3*nmol, np.float64)
coords[0:3*nmol] = np.random.uniform(-1,1,[nmol*3]) * 1.3*(3*nmol)**(1./3)
for i in range(nmol):
k = 3*nmol + 3*i
coords[k : k + 3] = rot.random_aa()
return coords
def testBLJ_isomer(self):
"""
test with BLJ potential. We have two classes of permutable atoms
test case where X2 is an isomer of X1.
"""
import pygmin.utils.rotations as rot
X1i = np.copy(self.X1)
X1 = np.copy(self.X1)
X2 = np.copy(X1)
#rotate X2 randomly
aa = rot.random_aa()
rot_mx = rot.aa2mx(aa)
for j in range(self.natoms):
i = 3 * j
X2[i:i + 3] = np.dot(rot_mx, X1[i:i + 3])
#permute X2
import random, copy
from pygmin.mindist.permutational_alignment import permuteArray
for atomlist in self.permlist:
perm = copy.copy(atomlist)
random.shuffle(perm)
X2 = permuteArray(X2, perm)
X2i = np.copy(X2)
#distreturned, X1, X2 = self.runtest(X1, X2)
distreturned, X1, X2 = self.runtest(X1, X2, minPermDistStochastic)
#it's an isomer, so the distance should be zero
self.assertTrue(
abs(distreturned) < 1e-14,
"didn't find isomer: dist = %g" % (distreturned))
def get_random_coordinates(self):
coords = np.zeros([self.nmol*2, 3])
coords[:self.nmol,:] = np.random.uniform(-1,1,[self.nmol,3]) * (self.nmol*3)**(-1./3) * 1.5
from pygmin.utils.rotations import random_aa
for i in range(self.nmol, self.nmol*2):
coords[i,:] = random_aa()
return coords.flatten()
def test_binary_LJ(natoms=12, **kwargs):
import pygmin.defaults
import pygmin.utils.rotations as rot
quench = pygmin.defaults.quenchRoutine
printlist = []
ntypea = int(natoms * .8)
from pygmin.potentials.ljpshift import LJpshift
lj = LJpshift(natoms, ntypea)
permlist = [range(ntypea), range(ntypea, natoms)]
X1 = np.random.uniform(-1, 1, [natoms * 3]) * (float(natoms))**(1. / 3) / 2
printlist.append((X1.copy(), "very first"))
#quench X1
ret = quench(X1, lj.getEnergyGradient)
X1 = ret[0]
printlist.append((X1.copy(), "after quench"))
X2 = np.random.uniform(-1, 1, [natoms * 3]) * (float(natoms))**(1. / 3)
#make X2 a rotation of X1
print "testing with", natoms, "atoms,", ntypea, "type A atoms, with X2 a rotated and permuted isomer of X1"
aa = rot.random_aa()
rot_mx = rot.aa2mx(aa)
for j in range(natoms):
i = 3 * j
X2[i:i + 3] = np.dot(rot_mx, X1[i:i + 3])
printlist.append((X2.copy(), "x2 after rotation"))
import random, copy
from pygmin.mindist.permutational_alignment import permuteArray
for atomlist in permlist:
perm = copy.copy(atomlist)
random.shuffle(perm)
print perm
X2 = permuteArray(X2, perm)
printlist.append((X2.copy(), "x2 after permutation"))
#X1 = np.array( [ 0., 0., 0., 1., 0., 0., 0., 0., 1.,] )
#X2 = np.array( [ 0., 0., 0., 1., 0., 0., 0., 1., 0.,] )
X1i = copy.copy(X1)
X2i = copy.copy(X2)
atomtypes = ["N" for i in range(ntypea)]
for i in range(natoms - ntypea):
atomtypes.append("O")
print "******************************"
print "testing binary LJ ISOMER"
print "******************************"
test(X1, X2, lj, atomtypes=atomtypes, permlist=permlist, **kwargs)
print "******************************"
print "testing binary LJ non isomer"
print "******************************"
X2 = np.random.uniform(-1, 1, [natoms * 3]) * (float(natoms))**(1. / 3)
ret = quench(X2, lj.getEnergyGradient)
X2 = ret[0]
test(X1, X2, lj, atomtypes=atomtypes, permlist=permlist, **kwargs)
def random_coords(nmol, nsites = None):
if nsites == None: nsites = nmol
coords = np.zeros(2*3*nmol)
coords[0:nmol*3] = np.random.uniform(-1,1,[nmol*3]) * 1.3*(nsites)**(1./3)
for i in range(nmol):
k = nmol*3 + 3*i
coords[k : k + 3] = rot.random_aa()
return coords
def randomCoords(nmol):
coords = np.zeros(2 * 3 * nmol, np.float64)
coords[0:3 * nmol] = np.random.uniform(
-1, 1, [nmol * 3]) * 1.3 * (3 * nmol)**(1. / 3)
for i in range(nmol):
k = 3 * nmol + 3 * i
coords[k:k + 3] = rot.random_aa()
return coords
def get_random_coordinates(self):
coords = np.zeros([self.nmol * 2, 3])
coords[:self.nmol, :] = np.random.uniform(
-1, 1, [self.nmol, 3]) * (self.nmol * 3)**(-1. / 3) * 1.5
from pygmin.utils.rotations import random_aa
for i in range(self.nmol, self.nmol * 2):
coords[i, :] = random_aa()
return coords.flatten()
def randomCoordsAA(nmol):
import pygmin.utils.rotations as rot
coords = np.zeros(2 * 3 * nmol, np.float64)
coords[0 : 3 * nmol] = np.random.uniform(-1, 1, [nmol * 3]) * 1.3 * (3 * nmol) ** (1.0 / 3)
for i in range(nmol):
k = 3 * nmol + 3 * i
coords[k : k + 3] = rot.random_aa()
return coords
def random_coords(nmol, nsites=None):
if nsites == None: nsites = nmol
coords = np.zeros(2 * 3 * nmol)
coords[0:nmol *
3] = np.random.uniform(-1, 1, [nmol * 3]) * 1.3 * (nsites)**(1. / 3)
for i in range(nmol):
k = nmol * 3 + 3 * i
coords[k:k + 3] = rot.random_aa()
return coords
def __call__(self, coords1, coords2):
'''
Parameters
----------
coords1, coords2 : np.array
the structures to align. X2 will be aligned with X1, both
the center of masses will be shifted to the origin
Returns
-------
a tripple of (dist, coords1, coords2). coords1 are the unchanged coords1
and coords2 are brought in best alignment with coords2
'''
# we don't want to change the given coordinates
check_inversion = False
x1 = np.copy(coords1)
x2 = np.copy(coords2)
com1 = self.measure.get_com(x1)
self.transform.translate(x1, -com1)
com2 = self.measure.get_com(x2)
self.transform.translate(x2, -com2)
self.com_shift = com1
self.mxbest = np.identity(3)
self.distbest = self.measure.get_dist(x1, x2)
self.x2_best = x2.copy()
if self.distbest < self.tol:
return self.distbest, x1, x2
for rot, invert in self._standard_alignments(x1, x2):
self.check_match(x1, x2, rot, invert)
if self.distbest < self.tol:
dist, x2 = self.finalize_best_match(coords1)
return dist, coords1, x2
# if we didn't find a perfect match here, try random rotations to optimize the match
for i in range(self.niter):
rot = rotations.aa2mx(rotations.random_aa())
self.check_match(x1, x2, rot, False)
if (self.transform.can_invert()):
self.check_match(x1, x2, rot, True)
# self.transform.rotate(X2, mxbest)
# dist, perm = self.measure.find_permutation(X1, X2)
# X2 = self.transform.permute(X2, perm)
# tmp, mx = self.measure.find_rotation(X1.copy(), X2.copy())
# self.transform.rotate(X2, mx)
# TODO: should we do an additional sanity check for permutation / rotation?
dist, x2 = self.finalize_best_match(coords1)
return dist, coords1, x2
def takeStep(self, coords, **kwargs):
# easy access to coordinates
ca = CoordsAdapter(nrigid=coords.size/6, coords = coords)
# random displacement for positions
#ca.posRigid[:] = 2.*self.radius*(np.random.random(ca.posRigid.shape)-0.5)
# random rotation for angle-axis vectors
for rot in ca.rotRigid:
rot[:] = rotations.random_aa()
def takeStep(self, coords, **kwargs):
# easy access to coordinates
ca = CoordsAdapter(nrigid=coords.size / 6, coords=coords)
# random displacement for positions
#ca.posRigid[:] = 2.*self.radius*(np.random.random(ca.posRigid.shape)-0.5)
# random rotation for angle-axis vectors
for rot in ca.rotRigid:
rot[:] = rotations.random_aa()
def __call__(self, coords1, coords2):
'''
Parameters
----------
coords1, coords2 : np.array
the structures to align. X2 will be aligned with X1, both
the center of masses will be shifted to the origin
Returns
-------
a tripple of (dist, coords1, coords2). coords1 are the unchanged coords1
and coords2 are brought in best alignment with coords2
'''
# we don't want to change the given coordinates
check_inversion = False
x1 = np.copy(coords1)
x2 = np.copy(coords2)
com1 = self.measure.get_com(x1)
self.transform.translate(x1, -com1)
com2 = self.measure.get_com(x2)
self.transform.translate(x2, -com2)
self.com_shift = com1
self.mxbest = np.identity(3)
self.distbest = self.measure.get_dist(x1, x2)
self.x2_best = x2.copy()
if self.distbest < self.tol:
return self.distbest, x1, x2
for rot, invert in self._standard_alignments(x1, x2):
self.check_match(x1, x2, rot, invert)
if self.distbest < self.tol:
dist, x2 = self.finalize_best_match(coords1)
return dist, coords1, x2
# if we didn't find a perfect match here, try random rotations to optimize the match
for i in range(self.niter):
rot = rotations.aa2mx(rotations.random_aa())
self.check_match(x1, x2, rot, False)
if(self.transform.can_invert()):
self.check_match(x1, x2, rot, True)
# self.transform.rotate(X2, mxbest)
# dist, perm = self.measure.find_permutation(X1, X2)
# X2 = self.transform.permute(X2, perm)
# tmp, mx = self.measure.find_rotation(X1.copy(), X2.copy())
# self.transform.rotate(X2, mx)
# TODO: should we do an additional sanity check for permutation / rotation?
dist, x2 = self.finalize_best_match(coords1)
return dist, coords1, x2
def test_LJ(natoms = 12, **kwargs):
from pygmin.potentials.lj import LJ
from pygmin.optimize import mylbfgs
import pygmin.utils.rotations as rot
from pygmin.mindist.permutational_alignment import permuteArray
import random
quench = mylbfgs
lj = LJ()
X1 = np.random.uniform(-1,1,[natoms*3])*(float(natoms))**(1./3)
#quench X1
ret = quench( X1, lj)
X1 = ret.coords
X2 = np.random.uniform(-1,1,[natoms*3])*(float(natoms))**(1./3)
#make X2 a rotation of X1
print "testing with", natoms, "atoms, with X2 a rotated and permuted isomer of X1"
aa = rot.random_aa()
rot_mx = rot.aa2mx( aa )
for j in range(natoms):
i = 3*j
X2[i:i+3] = np.dot( rot_mx, X1[i:i+3] )
perm = range(natoms)
random.shuffle( perm )
print perm
X2 = permuteArray( X2, perm)
#X1 = np.array( [ 0., 0., 0., 1., 0., 0., 0., 0., 1.,] )
#X2 = np.array( [ 0., 0., 0., 1., 0., 0., 0., 1., 0.,] )
import copy
X1i = copy.copy(X1)
X2i = copy.copy(X2)
print "******************************"
print "testing normal LJ ISOMER"
print "******************************"
test(X1, X2, lj, **kwargs)
print "******************************"
print "testing normal LJ non isomer"
print "******************************"
X2 = np.random.uniform(-1,1,[natoms*3])*(float(natoms))**(1./3)
ret = quench( X2, lj)
X2 = ret.coords
Y = X1.reshape([-1,3])
Y+=np.random.random(3)
X1[:] = Y.flatten()
test(X1, X2, lj, **kwargs)
distinit = np.linalg.norm(X1-X2)
print "distinit", distinit
def test_LJ(natoms=12, **kwargs):
from pygmin.potentials.lj import LJ
from pygmin.optimize import mylbfgs
import pygmin.utils.rotations as rot
from pygmin.mindist.permutational_alignment import permuteArray
import random
quench = mylbfgs
lj = LJ()
X1 = np.random.uniform(-1, 1, [natoms * 3]) * (float(natoms))**(1. / 3)
#quench X1
ret = quench(X1, lj)
X1 = ret.coords
X2 = np.random.uniform(-1, 1, [natoms * 3]) * (float(natoms))**(1. / 3)
#make X2 a rotation of X1
print "testing with", natoms, "atoms, with X2 a rotated and permuted isomer of X1"
aa = rot.random_aa()
rot_mx = rot.aa2mx(aa)
for j in range(natoms):
i = 3 * j
X2[i:i + 3] = np.dot(rot_mx, X1[i:i + 3])
perm = range(natoms)
random.shuffle(perm)
print perm
X2 = permuteArray(X2, perm)
#X1 = np.array( [ 0., 0., 0., 1., 0., 0., 0., 0., 1.,] )
#X2 = np.array( [ 0., 0., 0., 1., 0., 0., 0., 1., 0.,] )
import copy
X1i = copy.copy(X1)
X2i = copy.copy(X2)
print "******************************"
print "testing normal LJ ISOMER"
print "******************************"
test(X1, X2, lj, **kwargs)
print "******************************"
print "testing normal LJ non isomer"
print "******************************"
X2 = np.random.uniform(-1, 1, [natoms * 3]) * (float(natoms))**(1. / 3)
ret = quench(X2, lj)
X2 = ret.coords
Y = X1.reshape([-1, 3])
Y += np.random.random(3)
X1[:] = Y.flatten()
test(X1, X2, lj, **kwargs)
distinit = np.linalg.norm(X1 - X2)
print "distinit", distinit
def test_LJ(natoms = 12, **kwargs):
from pygmin.potentials.lj import LJ
import pygmin.defaults
import pygmin.utils.rotations as rot
from pygmin.mindist.permutational_alignment import permuteArray
import random
quench = pygmin.defaults.quenchRoutine
lj = LJ()
X1 = np.random.uniform(-1,1,[natoms*3])*(float(natoms))**(1./3)
#quench X1
ret = quench( X1, lj.getEnergyGradient)
X1 = ret[0]
X2 = np.random.uniform(-1,1,[natoms*3])*(float(natoms))**(1./3)
#make X2 a rotation of X1
print "testing with", natoms, "atoms, with X2 a rotated and permuted isomer of X1"
aa = rot.random_aa()
rot_mx = rot.aa2mx( aa )
for j in range(natoms):
i = 3*j
X2[i:i+3] = np.dot( rot_mx, X1[i:i+3] )
perm = range(natoms)
random.shuffle( perm )
print perm
X2 = permuteArray( X2, perm)
#X1 = np.array( [ 0., 0., 0., 1., 0., 0., 0., 0., 1.,] )
#X2 = np.array( [ 0., 0., 0., 1., 0., 0., 0., 1., 0.,] )
import copy
X1i = copy.copy(X1)
X2i = copy.copy(X2)
print "******************************"
print "testing normal LJ ISOMER"
print "******************************"
test(X1, X2, lj, **kwargs)
print "******************************"
print "testing normal LJ non isomer"
print "******************************"
X2 = np.random.uniform(-1,1,[natoms*3])*(float(natoms))**(1./3)
ret = quench( X2, lj.getEnergyGradient)
X2 = ret[0]
test(X1, X2, lj, **kwargs)
distinit = np.linalg.norm(X1-X2)
print "distinit", distinit
def takeStep(self, coords, **kwargs):
# easy access to coordinates
ca = CoordsAdapter(nrigid=coords.size/6, coords = coords)
backbone = np.zeros(3)
# random rotation for angle-axis vectors
for pos, rot in zip(ca.posRigid, ca.rotRigid):
backbone = backbone + rotations.vec_random( )*0.7525
# choose a random rotation
rot[:] = rotations.random_aa()
# calcualte center of base from backgone
a1 = np.dot(rotations.aa2mx(rot), np.array([1., 0., 0.]))
pos[:] = backbone + 0.4 * a1
def test_LJ(natoms=12, **kwargs):
from pygmin.potentials.lj import LJ
import pygmin.defaults
import pygmin.utils.rotations as rot
from pygmin.mindist.permutational_alignment import permuteArray
import random
quench = pygmin.defaults.quenchRoutine
lj = LJ()
X1 = np.random.uniform(-1, 1, [natoms * 3]) * (float(natoms))**(1. / 3)
#quench X1
ret = quench(X1, lj.getEnergyGradient)
X1 = ret[0]
X2 = np.random.uniform(-1, 1, [natoms * 3]) * (float(natoms))**(1. / 3)
#make X2 a rotation of X1
print "testing with", natoms, "atoms, with X2 a rotated and permuted isomer of X1"
aa = rot.random_aa()
rot_mx = rot.aa2mx(aa)
for j in range(natoms):
i = 3 * j
X2[i:i + 3] = np.dot(rot_mx, X1[i:i + 3])
perm = range(natoms)
random.shuffle(perm)
print perm
X2 = permuteArray(X2, perm)
#X1 = np.array( [ 0., 0., 0., 1., 0., 0., 0., 0., 1.,] )
#X2 = np.array( [ 0., 0., 0., 1., 0., 0., 0., 1., 0.,] )
import copy
X1i = copy.copy(X1)
X2i = copy.copy(X2)
print "******************************"
print "testing normal LJ ISOMER"
print "******************************"
test(X1, X2, lj, **kwargs)
print "******************************"
print "testing normal LJ non isomer"
print "******************************"
X2 = np.random.uniform(-1, 1, [natoms * 3]) * (float(natoms))**(1. / 3)
ret = quench(X2, lj.getEnergyGradient)
X2 = ret[0]
test(X1, X2, lj, **kwargs)
distinit = np.linalg.norm(X1 - X2)
print "distinit", distinit
def translation_step(self, coords):
"""
take a random translational step.
note: this step is rotationally invariant, which is different than the normal take_step routine
note: this method also favors shorter steps than the normal take_step routine
"""
nmol = len(coords) / 2 / 3
stepsize = self.getTStep()
for j in range(nmol):
#get a random unit vector
#this is a dumb way to do it
v = rot.random_aa()
#make the step length random and uniform in [0,stepsize]
v *= self.RNG() * stepsize / np.linalg.norm(v)
#print "rand ", rand
coords[3 * j:3 * j + 3] += v
def translation_step(self, coords):
"""
take a random translational step.
note: this step is rotationally invariant, which is different than the normal take_step routine
note: this method also favors shorter steps than the normal take_step routine
"""
nmol = len(coords) / 2 / 3
stepsize = self.getTStep()
for j in range(nmol):
#get a random unit vector
#this is a dumb way to do it
v = rot.random_aa()
#make the step length random and uniform in [0,stepsize]
v *= self.RNG() * stepsize / np.linalg.norm(v)
#print "rand ", rand
coords[3*j : 3*j + 3] += v
def createBasinHopping(self):
import pygmin.basinhopping as bh
import pygmin.utils.rotations as rot
mysys = self.createSystem()
nsites = mysys.nsites
nmol = self.nmol
#set up initial coords
coords = np.zeros(2*3*nmol)
coords[0:nmol*3] = np.random.uniform(-1,1,[nmol*3]) * 1.0*(nsites)**(1./3)
for i in range(nmol):
k = nmol*3 + 3*i
coords[k : k + 3] = rot.random_aa()
#set up take step routine
from pygmin.potentials.rigid_bodies.take_step import RBTakeStep
step = RBTakeStep()
opt = bh.BasinHopping(coords,mysys,
temperature=1., takeStep=step, outstream=None)
return opt
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 test_takestep(nmol = 4):
#get an initial set of coordinates
import copy
nsites = nmol*3
comcoords = np.random.uniform(-1,1,[nmol*3]) * 1.3*(nsites)**(1./3)
aacoords = np.array( [copy.copy(rot.random_aa()) for i in range(nmol)] )
aacoords = aacoords.reshape(3*nmol)
coords = np.zeros(2*3*nmol, np.float64)
coords[0:3*nmol] = comcoords[:]
coords[3*nmol:2*3*nmol] = aacoords[:]
print "lencoords, len aacoords", len (coords), len(aacoords), len(comcoords)
takestep = RBTakeStep()
print coords
oldcoords = coords.copy()
takestep.translation_step(coords)
print coords
print coords - oldcoords
print ""
print "taking orientational step"
print ""
oldcoords = coords.copy()
takestep.orientational_step(coords)
print coords
print coords - oldcoords
print ""
print "taking one of each step"
print ""
oldcoords = coords.copy()
takestep.takeStep(coords)
takestep.takeStep(coords)
print coords
print coords - oldcoords
def test_takestep(nmol=4):
#get an initial set of coordinates
import copy
nsites = nmol * 3
comcoords = np.random.uniform(-1, 1, [nmol * 3]) * 1.3 * (nsites)**(1. / 3)
aacoords = np.array([copy.copy(rot.random_aa()) for i in range(nmol)])
aacoords = aacoords.reshape(3 * nmol)
coords = np.zeros(2 * 3 * nmol, np.float64)
coords[0:3 * nmol] = comcoords[:]
coords[3 * nmol:2 * 3 * nmol] = aacoords[:]
print "lencoords, len aacoords", len(coords), len(aacoords), len(comcoords)
takestep = RBTakeStep()
print coords
oldcoords = coords.copy()
takestep.translation_step(coords)
print coords
print coords - oldcoords
print ""
print "taking orientational step"
print ""
oldcoords = coords.copy()
takestep.orientational_step(coords)
print coords
print coords - oldcoords
print ""
print "taking one of each step"
print ""
oldcoords = coords.copy()
takestep.takeStep(coords)
takestep.takeStep(coords)
print coords
print coords - oldcoords
def takeStep(self, coords, **kwargs):
''' takeStep routine to generate random cell '''
ca = self.coordsadapter
ca.updateCoords(coords)
atomistic = np.zeros(3*GMIN.getNAtoms())
valid_configuration = False
for i in xrange(50):
volumeTarget = self.expand_current*lattice.volume(ca.lattice)
# random box
ca.lattice[[0,3,5]] = 1.0 + self.expand * np.random.random(3)
ca.lattice[[1,2,4]] = self.shear * np.random.random(3)
if(self.volume != None):
volumeTarget = self.volume[0] + (self.volume[1] - self.volume[0]) * np.random.random()
vol = lattice.volume(ca.lattice)
ca.lattice[:] = ca.lattice * (volumeTarget / vol)**(1.0/3.0)
GMIN.reduceCell(coords)
for i in xrange(50):# first choose random positions and rotations
for i in xrange(GMIN.getNRigidBody()):
ca.posRigid[i] = np.random.random()
ca.rotRigid[i] = rotations.random_aa()
if self.overlap is None:
return
GMIN.toAtomistic(atomistic, coords)
if not crystals.has_overlap(atomistic, self.overlap):
return
print "Could generate valid configuration for current box, choose new box"
raise Exception("GenRandomCrystal: failed to generate a non-overlapping configuration")
def random_rotation( aa):
aanew = rot.random_aa()
#print "aanew ", aanew
aa[:] = aanew[:]
def test_sandbox(nmol = 6):
import copy
from pygmin.potentials.lj import LJ
#define the molecule types.
#here use only one type, LWOTP
otp = molecule.setupLWOTP()
# define the interaction matrix for the system.
# for LWOTP there is only one atom type, so this is trivial
lj = LJ()
interaction_matrix = [[lj]]
#set up a list of molecules
mols = [otp for i in range(nmol)]
#set up the RBSandbox object
mysys = RBSandbox(mols, interaction_matrix)
nsites = mysys.nsites
#get an initial set of coordinates
comcoords = np.random.uniform(-1,1,[nmol*3]) * 1.3*(nsites)**(1./3)
aacoords = np.array( [copy.copy(rot.random_aa()) for i in range(nmol)] )
aacoords = aacoords.reshape(3*nmol)
coords = np.zeros(2*3*nmol, np.float64)
coords[0:3*nmol] = comcoords[:]
coords[3*nmol:2*3*nmol] = aacoords[:]
print "lencoords, len aacoords", len (coords), len(aacoords), len(comcoords)
#print "xyz coords", mysys.transformToXYZ(coords)
#save the initial set of coords
printlist = []
xyz = mysys.getxyz( coords )
printlist.append( (xyz.copy(), "initial"))
#calculate the initial energy
Einit = mysys.getEnergy(coords)
print "initial energy", Einit
#test the gradient
numericV = mysys.NumericalDerivative(coords, 1e-10)
numericV = numericV.copy()
print "numeric V", numericV
E, V = mysys.getEnergyGradient(coords)
print "energy from gradient", E, "difference", E - Einit
#print "analytic gradient", V
maxgrad_relative = np.max(np.abs(V-numericV)/np.abs(V))
maxgraddiff = np.max(np.abs(V - numericV))
print "max error in gradient", maxgraddiff, "max relative", maxgrad_relative
#do a quench to make sure everything is working well
from pygmin.optimize import lbfgs_scipy
coords, E, rms, funcalls = lbfgs_scipy(coords, mysys.getEnergyGradient, iprint=-1)
print "postquench E", E, "rms", rms, "funtion calls", funcalls
xyz = mysys.getxyz(coords )
printlist.append( (xyz.copy(), "post quench"))
#print the saved coords
fname = "otp.xyz"
print "saving xyz coords to", fname
from pygmin.printing.print_atoms_xyz import printAtomsXYZ as printxyz
with open(fname, "w") as fout:
for xyz, line2 in printlist:
printxyz( fout, xyz, line2=line2, atom_type=["N", "O", "O"])
test_symmetries(coords, mysys)
def test_sandbox(nmol=6):
import copy
from pygmin.potentials.lj import LJ
#define the molecule types.
#here use only one type, LWOTP
otp = molecule.setupLWOTP()
# define the interaction matrix for the system.
# for LWOTP there is only one atom type, so this is trivial
lj = LJ()
interaction_matrix = [[lj]]
#set up a list of molecules
mols = [otp for i in range(nmol)]
#set up the RBSandbox object
mysys = RBSandbox(mols, interaction_matrix)
nsites = mysys.nsites
#get an initial set of coordinates
comcoords = np.random.uniform(-1, 1, [nmol * 3]) * 1.3 * (nsites)**(1. / 3)
aacoords = np.array([copy.copy(rot.random_aa()) for i in range(nmol)])
aacoords = aacoords.reshape(3 * nmol)
coords = np.zeros(2 * 3 * nmol, np.float64)
coords[0:3 * nmol] = comcoords[:]
coords[3 * nmol:2 * 3 * nmol] = aacoords[:]
print "lencoords, len aacoords", len(coords), len(aacoords), len(comcoords)
#print "xyz coords", mysys.transformToXYZ(coords)
#save the initial set of coords
printlist = []
xyz = mysys.getxyz(coords)
printlist.append((xyz.copy(), "initial"))
#calculate the initial energy
Einit = mysys.getEnergy(coords)
print "initial energy", Einit
#test the gradient
numericV = mysys.NumericalDerivative(coords, 1e-10)
numericV = numericV.copy()
print "numeric V", numericV
E, V = mysys.getEnergyGradient(coords)
print "energy from gradient", E, "difference", E - Einit
#print "analytic gradient", V
maxgrad_relative = np.max(np.abs(V - numericV) / np.abs(V))
maxgraddiff = np.max(np.abs(V - numericV))
print "max error in gradient", maxgraddiff, "max relative", maxgrad_relative
#do a quench to make sure everything is working well
from pygmin.optimize import lbfgs_scipy
coords, E, rms, funcalls = lbfgs_scipy(coords,
mysys.getEnergyGradient,
iprint=-1)
print "postquench E", E, "rms", rms, "funtion calls", funcalls
xyz = mysys.getxyz(coords)
printlist.append((xyz.copy(), "post quench"))
#print the saved coords
fname = "otp.xyz"
print "saving xyz coords to", fname
from pygmin.printing.print_atoms_xyz import printAtomsXYZ as printxyz
with open(fname, "w") as fout:
for xyz, line2 in printlist:
printxyz(fout, xyz, line2=line2, atom_type=["N", "O", "O"])
test_symmetries(coords, mysys)
interaction_matrix = [[lj]]
#set up a list of molecules
mols = [otp for i in range(nmol)]
#set up the RBSandbox object
mysys = sandbox.RBSandbox(mols, interaction_matrix)
nsites = mysys.nsites
#get an initial set of coordinates
coords = np.zeros(2 * 3 * nmol, np.float64)
coords[0:nmol *
3] = np.random.uniform(-1, 1, [nmol * 3]) * 1.3 * (nsites)**(1. / 3)
for i in range(nmol):
k = nmol * 3 + 3 * i
coords[k:k + 3] = rot.random_aa()
#set up the takestep routine
from pygmin.potentials.rigid_bodies.take_step import RBTakeStep
takestep = RBTakeStep()
#set up the class to save lowest energy structures
from pygmin.storage.savenlowest import SaveN
saveit = SaveN(100)
#set up basinhopping
from pygmin.basinhopping import BasinHopping
bh = BasinHopping(coords, mysys, takestep, storage=saveit.insert)
#run basin hopping
bh.run(40)
def test_molecule():
otp = setupLWOTP()
xyz = otp.getxyz()
from pygmin.printing.print_atoms_xyz import printAtomsXYZ as printxyz
import sys
#with open("out.xyz", "w") as fout:
printxyz(sys.stdout, xyz)
aa = np.array([.2, .3, .4])
for xyz, aanew in otp.getSymmetries(np.zeros(3), aa):
printxyz(sys.stdout, xyz, line2="symmetry")
xyz = otp.getxyz(aa=aanew)
printxyz(sys.stdout, xyz, line2="symmetry from returned aa")
if __name__ == "__main__":
test_molecule()
aa1 = rot.random_aa()
aa2 = rot.random_aa()
rmat1 = rot.aa2mx(aa1)
rmat2 = rot.aa2mx(aa2)
rmat21 = np.dot(rmat2, rmat1)
aa21 = rot.rotate_aa(aa1, aa2)
rmat21aa = rot.aa2mx(aa21)
print rmat21
print rmat21aa
print abs(rmat21 - rmat21aa) < 1e-12
def get_random_configuration(self):
coords = 5.*np.random.random(6*self.nrigid)
ca = self.aasystem.coords_adapter(coords)
for p in ca.rotRigid:
p = rotations.random_aa()
return coords
from copy import deepcopy
water = RigidFragment()
rho = 0.9572
theta = 104.52/180.0*pi
water.add_atom("O", np.array([0., -1., 0.]), 1.)
water.add_atom("O", np.array([0., 1., 0.]), 1.)
#water.add_atom("H", rho*np.array([0.0, sin(0.5*theta), cos(0.5*theta)]), 1.)
#water.add_atom("H", rho*np.array([0.0, -sin(0.5*theta), cos(0.5*theta)]), 1.)
water.finalize_setup()
# define the whole water system
system = RBTopology()
nrigid = 1
system.add_sites([deepcopy(water) for i in xrange(nrigid)])
from pygmin.utils import rotations
rbcoords=np.zeros(6)
rbcoords[3:]= rotations.random_aa()
coords = system.to_atomistic(rbcoords)
print "rb coords\n", rbcoords
print "coords\n", coords
grad = (np.random.random(coords.shape) -0.5)
v = coords[1] - coords[0]
v/=np.linalg.norm(v)
grad[0] -= np.dot(grad[0], v)*v
grad[1] -= np.dot(grad[1], v)*v
grad -= np.average(grad, axis=0)
grad /= np.linalg.norm(grad)
interaction_matrix = [[lj]]
#set up a list of molecules
mols = [otp for i in range(nmol)]
#set up the RBSandbox object
mysys = sandbox.RBSandbox(mols, interaction_matrix)
nsites = mysys.nsites
#get an initial set of coordinates
coords = np.zeros(2*3*nmol, np.float64)
coords[0:nmol*3] = np.random.uniform(-1,1,[nmol*3]) * 1.3*(nsites)**(1./3)
for i in range(nmol):
k = nmol*3 + 3*i
coords[k : k + 3] = rot.random_aa()
#set up the takestep routine
from pygmin.potentials.rigid_bodies.take_step import RBTakeStep
takestep = RBTakeStep()
#set up the class to save lowest energy structures
from pygmin.storage.savenlowest import SaveN
saveit = SaveN(100)
#set up basinhopping
from pygmin.basinhopping import BasinHopping
bh = BasinHopping(coords, mysys, takestep, storage=saveit.insert )
#run basin hopping
x = np.random.random([natoms,3])*5
masses = [1., 1., 16.] #np.random.random(natoms)
print masses
x -= np.average(x, axis=0, weights=masses)
cog = np.average(x, axis=0)
S=np.zeros([3,3])
for i in xrange(3):
for j in xrange(3):
S[i][j]=np.sum(x[:,i]*x[:,j])
site = AASiteType(M=natoms, S=S, W=natoms, cog=cog)
X1=np.zeros(3)
p1=np.zeros(3)
X1 = 10.1*np.random.random(3)
X2 = 10.1*np.random.random(3)
p1 = rotations.random_aa()
p2 = rotations.random_aa()
R1 = rotations.aa2mx(p1)
R2 = rotations.aa2mx(p2)
x1 = np.dot(R1, x.transpose()).transpose() + X1
x2 = np.dot(R2, x.transpose()).transpose() + X2
import _aadist
print "site representation:", np.sum((x1-x2)**2)
print "distance function: ", site.distance_squared(X1, p1, X2, p2)
print "fortran function: ", _aadist.sitedist(X2 - X1, p1, p2, site.S, site.W, cog)
coords1 = np.random.random(120)
x = np.random.random([natoms,3])*5
masses = [1., 1., 16.] #np.random.random(natoms)
print masses
x -= np.average(x, axis=0, weights=masses)
cog = np.average(x, axis=0)
S=np.zeros([3,3])
for i in xrange(3):
for j in xrange(3):
S[i][j]=np.sum(x[:,i]*x[:,j])
site = AASiteType(M=natoms, S=S, W=natoms, cog=cog)
X1=np.zeros(3)
p1=np.zeros(3)
X1 = 10.1*np.random.random(3)
X2 = 10.1*np.random.random(3)
p1 = rotations.random_aa()
p2 = rotations.random_aa()
R1 = rotations.aa2mx(p1)
R2 = rotations.aa2mx(p2)
x1 = np.dot(R1, x.transpose()).transpose() + X1
x2 = np.dot(R2, x.transpose()).transpose() + X2
print "site representation:", np.sum((x1-x2)**2)
print "distance function: ", site.distance_squared(X1, p1, X2, p2)
print site.distance_squared_grad(X1, p1, X2, p2)
g_M = np.zeros(3)
g_P = np.zeros(3)
for i in xrange(3):
from copy import deepcopy
water = RigidFragment()
rho = 0.9572
theta = 104.52 / 180.0 * pi
water.add_atom("O", np.array([0., -1., 0.]), 1.)
water.add_atom("O", np.array([0., 1., 0.]), 1.)
#water.add_atom("H", rho*np.array([0.0, sin(0.5*theta), cos(0.5*theta)]), 1.)
#water.add_atom("H", rho*np.array([0.0, -sin(0.5*theta), cos(0.5*theta)]), 1.)
water.finalize_setup()
# define the whole water system
system = RBTopology()
nrigid = 1
system.add_sites([deepcopy(water) for i in xrange(nrigid)])
from pygmin.utils import rotations
rbcoords = np.zeros(6)
rbcoords[3:] = rotations.random_aa()
coords = system.to_atomistic(rbcoords)
print "rb coords\n", rbcoords
print "coords\n", coords
grad = (np.random.random(coords.shape) - 0.5)
v = coords[1] - coords[0]
v /= np.linalg.norm(v)
grad[0] -= np.dot(grad[0], v) * v
grad[1] -= np.dot(grad[1], v) * v
grad -= np.average(grad, axis=0)
grad /= np.linalg.norm(grad)
def random_rotation(aa):
aanew = rot.random_aa()
#print "aanew ", aanew
aa[:] = aanew[:]
xyz = otp.getxyz()
from pygmin.printing.print_atoms_xyz import printAtomsXYZ as printxyz
import sys
#with open("out.xyz", "w") as fout:
printxyz(sys.stdout, xyz)
aa = np.array([.2, .3, .4])
for xyz, aanew in otp.getSymmetries( np.zeros(3), aa):
printxyz(sys.stdout, xyz, line2="symmetry")
xyz = otp.getxyz(aa = aanew)
printxyz(sys.stdout, xyz, line2="symmetry from returned aa")
if __name__ == "__main__":
test_molecule()
aa1 = rot.random_aa()
aa2 = rot.random_aa()
rmat1 = rot.aa2mx(aa1)
rmat2 = rot.aa2mx(aa2)
rmat21 = np.dot(rmat2, rmat1)
aa21 = rot.rotate_aa(aa1, aa2)
rmat21aa = rot.aa2mx(aa21)
print rmat21
print rmat21aa
print abs(rmat21 - rmat21aa) < 1e-12
def get_random_configuration(self):
coords = 5.*np.random.random(6*self.nrigid)
ca = self.aasystem.coords_adapter(coords)
for p in ca.rotRigid:
p = rotations.random_aa()
return coords
def test_binary_LJ(natoms = 12, **kwargs):
import pygmin.defaults
import pygmin.utils.rotations as rot
quench = pygmin.defaults.quenchRoutine
printlist = []
ntypea = int(natoms*.8)
from pygmin.potentials.ljpshift import LJpshift
lj = LJpshift(natoms, ntypea)
permlist = [range(ntypea), range(ntypea, natoms)]
X1 = np.random.uniform(-1,1,[natoms*3])*(float(natoms))**(1./3)/2
printlist.append( (X1.copy(), "very first"))
#quench X1
ret = quench( X1, lj.getEnergyGradient)
X1 = ret[0]
printlist.append((X1.copy(), "after quench"))
X2 = np.random.uniform(-1,1,[natoms*3])*(float(natoms))**(1./3)
#make X2 a rotation of X1
print "testing with", natoms, "atoms,", ntypea, "type A atoms, with X2 a rotated and permuted isomer of X1"
aa = rot.random_aa()
rot_mx = rot.aa2mx( aa )
for j in range(natoms):
i = 3*j
X2[i:i+3] = np.dot( rot_mx, X1[i:i+3] )
printlist.append((X2.copy(), "x2 after rotation"))
import random, copy
from pygmin.mindist.permutational_alignment import permuteArray
for atomlist in permlist:
perm = copy.copy(atomlist)
random.shuffle( perm )
print perm
X2 = permuteArray( X2, perm)
printlist.append((X2.copy(), "x2 after permutation"))
#X1 = np.array( [ 0., 0., 0., 1., 0., 0., 0., 0., 1.,] )
#X2 = np.array( [ 0., 0., 0., 1., 0., 0., 0., 1., 0.,] )
X1i = copy.copy(X1)
X2i = copy.copy(X2)
atomtypes = ["N" for i in range(ntypea)]
for i in range(natoms-ntypea):
atomtypes.append("O")
print "******************************"
print "testing binary LJ ISOMER"
print "******************************"
test(X1, X2, lj, atomtypes=atomtypes, permlist = permlist, **kwargs)
print "******************************"
print "testing binary LJ non isomer"
print "******************************"
X2 = np.random.uniform(-1,1,[natoms*3])*(float(natoms))**(1./3)
ret = quench( X2, lj.getEnergyGradient)
X2 = ret[0]
test(X1, X2, lj, atomtypes=atomtypes, permlist=permlist, **kwargs)
