Exemplo n.º 1
0
def test(natoms=40, boxl=4.0):
    import pele.potentials.ljpshiftfast as ljpshift
    from pele.optimize import mylbfgs
    from pele.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) ** (1.0 / 3) / 2

    NLblj = ljpshift.LJpshift(natoms, ntypeA, rcut=rcut, boxl=boxl)
    blj = FreezePot(NLblj, freezelist, natoms)

    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
    pot.test_potential(coords)
    print "\n"

    ret1 = mylbfgs(coords, blj, iprint=-11)
    np.savetxt("out.coords", ret1.coords)
    print "energy from quench1", ret1.energy
    ret2 = mylbfgs(coords, pot, iprint=-1)
    print "energy from quench2", ret2.energy

    print "ret1 evaluated in both potentials", pot.getEnergy(ret1.coords), blj.getEnergy(ret1.coords)
    print "ret2 evaluated in both potentials", pot.getEnergy(ret2.coords), blj.getEnergy(ret2.coords)

    coords = ret1.coords
    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 pele.utils.pymolwrapper as pym

            pym.start()
            pym.draw_spheres(np.reshape(coords, [-1, 3]), "A", 1)
            pym.draw_spheres(np.reshape(ret1.coords, [-1, 3]), "A", 2)
            pym.draw_spheres(np.reshape(ret2.coords, [-1, 3]), "A", 3)
        except ImportError:
            print "Could not draw using pymol, skipping this step"
Exemplo n.º 2
0
def test(natoms=40, boxl=4.):  # pragma: no cover
    import pele.potentials.ljpshiftfast as ljpshift
    from pele.optimize import mylbfgs
    from pele.utils.neighbor_list import makeBLJNeighborListPot

    ntypeA = int(natoms * 0.8)
    ntypeB = natoms - ntypeA
    rcut = 2.5
    freezelist = list(range(old_div(ntypeA, 2))) + list(range(ntypeA, ntypeA + old_div(ntypeB, 2)))
    nfrozen = len(freezelist)
    print("nfrozen", nfrozen)
    coords = old_div(np.random.uniform(-1, 1, natoms * 3) * natoms ** (1. / 3), 2)

    NLblj = ljpshift.LJpshift(natoms, ntypeA, rcut=rcut, boxl=boxl)
    blj = FreezePot(NLblj, freezelist, natoms)

    pot = makeBLJNeighborListPotFreeze(natoms, freezelist, ntypeA=ntypeA, rcut=rcut, boxl=boxl)

    eblj = blj.getEnergy(coords)
    print("blj energy", eblj)

    epot = pot.getEnergy(coords)
    print("mcpot energy", epot)

    print("difference", old_div((epot - eblj), eblj))
    pot.test_potential(coords)
    print("\n")

    ret1 = mylbfgs(coords, blj, iprint=-11)
    np.savetxt("out.coords", ret1.coords)
    print("energy from quench1", ret1.energy)
    ret2 = mylbfgs(coords, pot, iprint=-1)
    print("energy from quench2", ret2.energy)

    print("ret1 evaluated in both potentials", pot.getEnergy(ret1.coords), blj.getEnergy(ret1.coords))
    print("ret2 evaluated in both potentials", pot.getEnergy(ret2.coords), blj.getEnergy(ret2.coords))

    coords = ret1.coords
    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", old_div(np.linalg.norm(g1), np.sqrt(len(g1))), old_div(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 pele.utils.pymolwrapper as pym

            pym.start()
            pym.draw_spheres(np.reshape(coords, [-1, 3]), "A", 1)
            pym.draw_spheres(np.reshape(ret1.coords, [-1, 3]), "A", 2)
            pym.draw_spheres(np.reshape(ret2.coords, [-1, 3]), "A", 3)
        except ImportError:
            print("Could not draw using pymol, skipping this step")
Exemplo n.º 3
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def minima_from_ts(pot, xt, n=None, quench=None, stepmin=0.01, **kwargs):
    """
    step off either side of a transition state and quench to find the minima
    
    Parameters
    ----------
    pot : potential object
    xt : array
        transition state coords
    n : array
        direction to step off
    quench : callable
        routine to use to do the quenching
    kwargs : dict
        parameters to pass to determine_pushoff
    """
    if n is None:
        # if no direction is given, choose random direction
        n = vec_random_ndim(xt.size)

    if quench is None:
        quench = lambda coords: mylbfgs(coords, pot)

    x1 = determine_pushoff(pot, xt, n, stepmin=stepmin, **kwargs)
    x2 = determine_pushoff(pot, xt, -n, stepmin=stepmin, **kwargs)
    minimum1 = quench(x1)
    minimum2 = quench(x2)
    return minimum1, minimum2
Exemplo n.º 4
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def test2():  # pragma: no cover
    import numpy as np
    from pele.potentials import LJ
    from pele.utils.frozen_atoms import FrozenPotWrapper
    from pele.optimize import mylbfgs

    natoms = 4
    pot = LJ()

    reference_coords = np.random.uniform(-1, 1, [3 * natoms])
    print reference_coords

    # freeze the first two atoms (6 degrees of freedom)
    frozen_dof = range(6)

    fpot = FrozenPotWrapper(pot, reference_coords, frozen_dof)

    reduced_coords = fpot.coords_converter.get_reduced_coords(reference_coords)

    print "the energy in the full representation:"
    print pot.getEnergy(reference_coords)
    print "is the same as the energy in the reduced representation:"
    print fpot.getEnergy(reduced_coords)

    ret = mylbfgs(reduced_coords, fpot)
    print "after a minimization the energy is ", ret.energy, "and the rms gradient is", ret.rms
    print "the coordinates of the frozen degrees of freedom are unchanged"
    print "starting coords:", reference_coords
    print "minimized coords:", fpot.coords_converter.get_full_coords(
        ret.coords)
Exemplo n.º 5
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def test2():
    import numpy as np
    from pele.potentials import LJ
    from pele.utils.frozen_atoms import FrozenPotWrapper
    from pele.optimize import mylbfgs

    natoms = 4
    pot = LJ()

    reference_coords = np.random.uniform(-1, 1, [3 * natoms])
    print reference_coords

    # freeze the first two atoms (6 degrees of freedom)
    frozen_dof = range(6)

    fpot = FrozenPotWrapper(pot, reference_coords, frozen_dof)

    reduced_coords = fpot.coords_converter.get_reduced_coords(reference_coords)

    print "the energy in the full representation:"
    print pot.getEnergy(reference_coords)
    print "is the same as the energy in the reduced representation:"
    print fpot.getEnergy(reduced_coords)

    ret = mylbfgs(reduced_coords, fpot)
    print "after a minimization the energy is ", ret.energy, "and the rms gradient is", ret.rms
    print "the coordinates of the frozen degrees of freedom are unchanged"
    print "starting coords:", reference_coords
    print "minimized coords:", fpot.coords_converter.get_full_coords(ret.coords)
Exemplo n.º 6
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def main():
    natoms = 4
    pot = LJ()

    reference_coords = np.random.uniform(-1, 1, [3 * natoms])
    print reference_coords

    # freeze the first two atoms (6 degrees of freedom)
    frozen_dof = range(6)

    fpot = FrozenPotWrapper(pot, reference_coords, frozen_dof)

    reduced_coords = fpot.coords_converter.get_reduced_coords(reference_coords)

    print "the energy in the full representation:"
    print pot.getEnergy(reference_coords)
    print "is the same as the energy in the reduced representation:"
    print fpot.getEnergy(reduced_coords)

    ret = mylbfgs(reduced_coords, fpot)
    print "after a minimization the energy is ", ret.energy, "and the rms gradient is", ret.rms
    print "the coordinates of the frozen degrees of freedom are unchanged"
    print "starting coords:", reference_coords
    print "minimized coords:", fpot.coords_converter.get_full_coords(
        ret.coords)
Exemplo n.º 7
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def minima_from_ts(pot, xt, n=None, quench=None, stepmin=0.01, **kwargs):
    """
    step off either side of a transition state and quench to find the minima
    
    Parameters
    ----------
    pot : potential object
    xt : array
        transition state coords
    n : array
        direction to step off
    quench : callable
        routine to use to do the quenching
    kwargs : dict
        parameters to pass to determine_pushoff
    """
    if n is None:
        # if no direction is given, choose random direction
        n = vec_random_ndim(xt.size)

    if quench:
        quenchRoutine = lambda coords: quench(coords, pot=pot, **kwargs)
    else:
        quenchRoutine = lambda coords: mylbfgs(coords, pot=pot, **kwargs)

    x1 = determine_pushoff(pot, xt, n, stepmin=stepmin, **kwargs)
    x2 = determine_pushoff(pot, xt, -n, stepmin=stepmin, **kwargs)
    minimum1 = quench(x1)
    minimum2 = quench(x2)
    return minimum1, minimum2
Exemplo n.º 8
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def test():
    pi = np.pi
    nspins = 6
    
    #phases = np.zeros(nspins)
    pot = XYModel(nspins, phi = np.pi/8.) #, phases=phases)
        
    angles = np.random.uniform(-pi/4, pi/4, nspins)
    print angles

    e = pot.getEnergy(angles)
    print e
    
    print "numerical gradient"
    ret = pot.getEnergyGradientNumerical(angles)
    print ret[1]
    print "analytical gradient"
    ret2 = pot.getEnergyGradient(angles)
    print ret2[1]
    print ret[0]
    print ret2[0]
    
    
    #try a quench
    from pele.optimize import mylbfgs
    ret = mylbfgs(angles, pot)
    
    print "quenched e = ", ret.energy
    print ret.coords
    
    test_basin_hopping(pot, angles)
Exemplo n.º 9
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    def setUp(self):
        self.natoms = 18
        self.pot = _lj.LJ()

        self.pot_comp = LJ()
        x = np.random.uniform(-1, 1, 3 * self.natoms)
        ret = mylbfgs(x, self.pot_comp, tol=10.)
        self.x = ret.coords
Exemplo n.º 10
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 def setUp(self):
     self.natoms = 18
     self.pot = _lj.LJ()
     
     self.pot_comp = LJ()
     x = np.random.uniform(-1,1, 3*self.natoms)
     ret = mylbfgs(x, self.pot_comp, tol=10.)
     self.x = ret.coords
Exemplo n.º 11
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 def setUp(self):
     from pele.optimize import mylbfgs
     self.natoms = 10
     self.coords = np.random.uniform(-1,1.,3*self.natoms) * self.natoms**(-1./3)
     self.pot = LJ()
     ret = mylbfgs(self.coords, self.pot, tol=2.)
     self.coords = ret.coords
     self.E = self.pot.getEnergy(self.coords)
     self.Egrad, self.grad = self.pot.getEnergyGradient(self.coords)
Exemplo n.º 12
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    def setUp(self):
        self.natoms = 18
        ntypeA = 5
        self.pot = _lj.BLJCut(self.natoms, ntypeA)

        self.pot_comp = LJpshift(self.natoms, ntypeA)
        x = np.random.uniform(-1, 1, 3 * self.natoms)
        ret = mylbfgs(x, self.pot_comp, tol=10.)
        self.x = ret.coords
Exemplo n.º 13
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    def testBLJ(self):
        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 = mylbfgs(X2, self.pot)
        X2 = ret.coords

        self.runtest(X1, X2, MinPermDistCluster(measure=MeasureAtomicCluster(permlist=self.permlist)))
Exemplo n.º 14
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    def setUp(self):
        self.natoms = 18
        ntypeA = 5
        self.pot = _lj.BLJCut(self.natoms, ntypeA)

        self.pot_comp = LJpshift(self.natoms, ntypeA)
        x = np.random.uniform(-1, 1, 3 * self.natoms)
        ret = mylbfgs(x, self.pot_comp, tol=10.0)
        self.x = ret.coords
Exemplo n.º 15
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 def setUp(self):
     self.natoms = 200
     rcut = 2.5
     self.pot = _lj.LJCut(rcut=rcut)
     
     self.pot_comp = LJpshift(self.natoms, self.natoms, rcut=rcut)
     x = np.random.uniform(-rcut*2,rcut*2, 3*self.natoms)
     ret = mylbfgs(x, self.pot_comp, tol=10.)
     self.x = ret.coords
Exemplo n.º 16
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    def setUp(self):
        self.natoms = 200
        rcut = 2.5
        self.pot = _lj.LJCut(rcut=rcut)

        self.pot_comp = LJpshift(self.natoms, self.natoms, rcut=rcut)
        x = np.random.uniform(-rcut * 2, rcut * 2, 3 * self.natoms)
        ret = mylbfgs(x, self.pot_comp, tol=10.)
        self.x = ret.coords
Exemplo n.º 17
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    def testBLJ(self):
        X1 = np.copy(self.X1)
        X2 = old_div(np.random.uniform(-1,1,[self.natoms*3])*(float(self.natoms))**(1./3),2)
        
        #run a quench so the structure is not crazy
        ret = mylbfgs(X2, self.pot)
        X2 = ret.coords

        self.runtest(X1, X2, MinPermDistCluster(measure=MeasureAtomicCluster(permlist=self.permlist)))
Exemplo n.º 18
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    def setUp(self):
        from pele.optimize import mylbfgs

        self.natoms = 10
        self.coords = np.random.uniform(-1, 1., 3 * self.natoms) * self.natoms ** (-1. / 3)
        self.pot = LJ()
        ret = mylbfgs(self.coords, self.pot, tol=2.)
        self.coords = ret.coords
        self.E = self.pot.getEnergy(self.coords)
        self.Egrad, self.grad = self.pot.getEnergyGradient(self.coords)
Exemplo n.º 19
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 def setUp(self):
     self.natoms = 200
     boxl = 7.
     self.boxvec = [boxl] * 3
     self.pot = _lj.LJ(boxvec=self.boxvec)
     
     self.pot_comp = LJ(boxl=boxl)
     x = np.random.uniform(-1,1, 3*self.natoms)
     ret = mylbfgs(x, self.pot_comp, tol=10.)
     self.x = ret.coords
Exemplo n.º 20
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    def setUp(self):
        self.natoms = 200
        boxl = 7.
        self.boxvec = [boxl] * 3
        self.pot = _lj.LJ(boxvec=self.boxvec)

        self.pot_comp = LJ(boxl=boxl)
        x = np.random.uniform(-1, 1, 3 * self.natoms)
        ret = mylbfgs(x, self.pot_comp, tol=10.)
        self.x = ret.coords
Exemplo n.º 21
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    def __init__(self,
                 coords,
                 potential,
                 takeStep,
                 storage=None,
                 event_after_step=None,
                 acceptTest=None,
                 temperature=1.0,
                 quench=None,
                 confCheck=None,
                 outstream=sys.stdout,
                 insert_rejected=False):
        #########################################################################
        # initialize MonteCarlo base class
        #########################################################################
        MonteCarlo.__init__(self,
                            coords,
                            potential,
                            takeStep,
                            storage=storage,
                            event_after_step=event_after_step,
                            acceptTest=acceptTest,
                            temperature=temperature,
                            confCheck=confCheck,
                            outstream=outstream,
                            store_initial=False)

        if quench is None:
            quench = lambda coords: mylbfgs(coords, self.potential)
        self.quench = quench

        #########################################################################
        # do initial quench
        #########################################################################
        self.markovE_old = self.markovE
        res = self.quench(self.coords)
        self.result.nfev += res.nfev

        self.coords = res.coords
        self.markovE = res.energy
        self.rms = res.rms
        self.funcalls = res.nfev

        self.insert_rejected = insert_rejected

        if self.storage:
            self.storage(self.markovE, self.coords)

        # print the initial quench
        self.acceptstep = True
        self.trial_energy = self.markovE
        self.printStep()

        self.result.energy = self.markovE
        self.result.coords = self.coords.copy()
Exemplo n.º 22
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    def setUp(self):
        self.natoms = 18
        self.ilist = np.array([(i,j) for i in xrange(self.natoms) for j in xrange(i+1,self.natoms)]).reshape(-1)
        assert self.ilist.size == self.natoms*(self.natoms-1)
#        print self.ilist
        self.pot = _lj.LJInteractionList(self.ilist) 
        
        self.pot_comp = LJ()
        x = np.random.uniform(-1,1, 3*self.natoms)
        ret = mylbfgs(x, self.pot_comp, tol=10.)
        self.x = ret.coords
Exemplo n.º 23
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 def setUp(self):
     self.natoms = 200
     rcut = 2.5
     boxl = 7.
     self.boxvec = [boxl] * 3
     self.pot = _lj.LJCut(rcut=rcut, boxvec=self.boxvec)
     
     self.pot_comp = LJpshift(self.natoms, self.natoms, rcut=rcut, boxl=boxl)
     x = np.random.uniform(-rcut*2,rcut*2, 3*self.natoms)
     ret = mylbfgs(x, self.pot_comp, tol=10.)
     self.x = ret.coords
Exemplo n.º 24
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    def setUp(self):
        self.natoms = 18
        self.ilist = np.array([(i,j) for i in range(self.natoms) for j in range(i+1,self.natoms)]).reshape(-1)
        assert self.ilist.size == self.natoms*(self.natoms-1)
#        print self.ilist
        self.pot = _lj.LJInteractionList(self.ilist) 
        
        self.pot_comp = LJ()
        x = np.random.uniform(-1,1, 3*self.natoms)
        ret = mylbfgs(x, self.pot_comp, tol=10.)
        self.x = ret.coords
Exemplo n.º 25
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    def setUp(self):
        self.natoms = 18
        frozen_atoms = np.array([1, 2, 5, 9], dtype=int)
        reference_coords = np.random.uniform(-1, 1, 3 * self.natoms)
        self.pot = _lj.LJFrozen(reference_coords, frozen_atoms)

        system = LJClusterFrozen(self.natoms, frozen_atoms, reference_coords)
        self.pot_comp = system.get_potential()
        x = system.get_random_configuration()
        ret = mylbfgs(x, self.pot_comp, tol=10.)
        self.x = ret.coords
Exemplo n.º 26
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 def setUp(self):
     self.natoms = 18
     frozen_atoms = np.array([1, 2, 5, 9], dtype=int)
     reference_coords = np.random.uniform(-1,1, 3*self.natoms)
     self.pot = _lj.LJFrozen(reference_coords, frozen_atoms)
     
     system = LJClusterFrozen(self.natoms, frozen_atoms, reference_coords)
     self.pot_comp = system.get_potential()
     x = system.get_random_configuration()
     ret = mylbfgs(x, self.pot_comp, tol=10.)
     self.x = ret.coords
Exemplo n.º 27
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def calculate_spointE(pot, db, nsteps=1000):
    min_energies = []
    ts_energies = []
    ts_id = []
    for m1 in db.minima():
        #pot = ase_ff(atoms.set_positions(m1.coords.reshape((7, -1))))
        res1 = mylbfgs(m1.coords, pot, nsteps=nsteps)
        E1 = res1.energy
        min_energies.append(E1)
        for m2 in db.minima():
            if m1 == m2 or m1.id() > m2.id():
                continue
            try:
                ts_coords = db.getTransitionState(m1, m2).coords
                res1 = mylbfgs(ts_coords, pot, nsteps=0)
                E1 = res1.energy
                ts_energies.append(E1)
                ts_id.append([m1.id(), m2.id()])
            except:
                pass
    return np.array(min_energies), np.array(ts_energies), np.array(ts_id)
Exemplo n.º 28
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 def setUp(self):
     from pele.potentials.ljpshiftfast import LJpshift as BLJ
     
     self.natoms = 25
     self.ntypeA = int(self.natoms * .8)
     self.pot = BLJ(self.natoms, self.ntypeA)
     self.permlist = [list(range(self.ntypeA)), list(range(self.ntypeA, self.natoms))]
     
     self.X1 = old_div(np.random.uniform(-1,1,[self.natoms*3])*(float(self.natoms))**(1./3),2)
     
     #run a quench so the structure is not crazy
     ret = mylbfgs(self.X1, self.pot)
     self.X1 = ret.coords
Exemplo n.º 29
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 def setUp(self):
     from pele.potentials.ljpshiftfast import LJpshift as BLJ
     
     self.natoms = 25
     self.ntypeA = int(self.natoms * .8)
     self.pot = BLJ(self.natoms, self.ntypeA)
     self.permlist = [list(range(self.ntypeA)), list(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 = mylbfgs(self.X1, self.pot)
     self.X1 = ret.coords
Exemplo n.º 30
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    def setUp(self):
        self.natoms = 200
        rcut = 2.5
        boxl = 7.
        self.boxvec = [boxl] * 3
        self.pot = _lj.LJCut(rcut=rcut, boxvec=self.boxvec)

        self.pot_comp = LJpshift(self.natoms,
                                 self.natoms,
                                 rcut=rcut,
                                 boxl=boxl)
        x = np.random.uniform(-rcut * 2, rcut * 2, 3 * self.natoms)
        ret = mylbfgs(x, self.pot_comp, tol=10.)
        self.x = ret.coords
 def minimise(self, cluster):
     '''Minimise a cluster
     parameters:
     cluster- a Cluster object from bcga.cluster
     
     Using this method will overwrite the coordinates and energy of the
     supplied Cluster object.'''
     # Set up pele minimiser
     coords = cluster._coords.flatten()
     quench = lambda coords: mylbfgs(coords, self.potential)
     # Minimise
     res = quench(coords)
     # Get cluster properties back from pele
     cluster.energy = res.energy
     cluster._coords = np.reshape(res.coords, (-1, 3))
     cluster.quenched = True
     return cluster
Exemplo n.º 32
0
def minima_from_ts(pot, xt, n=None, quench=None, **kwargs):
    """
    step off either side of a transition state and quench to find the minima
    
    Parameters
    ----------
    pot : potential object
    xt : array
        transition state coords
    stepmin : float
        initial guess for size of pushoff when stepping off the transition state
    stepmax : float
        maximum size of pushoff when stepping off the transition state
    gdiff : float
        criterion for choosing a step size.  Try to choose a stepsize so that:: 
        
            (gradpar - gradpar_ts) / gradpar_ts >= gdiff
        
        where gradpar and gradpar_ts is the parallel component of the gradient
        after the step and at the top of the transition state
    quenchRoutine : callable
        routine to use to do the quenching
    quenchParams : dict
        parameters to pass to quenchRoutine
    verbose : bool
    """
    # if no direction is given, choose random direction
    if n==None:
        # TODO: replace by better algorithm with uniform sampling
        n = np.random.random(xt.shape)-0.5
    
    if quench is None:
        quench = lambda coords : mylbfgs(coords, pot)        
    
    #x1 = xt - displace*n
    x1 = determinePushoff(pot, xt, n, **kwargs)
    x2 = determinePushoff(pot, xt, -n, **kwargs)
    #x2 = xt + displace*n
    #e1,g1 = getEnergyGradient(x1)
    #e2,g2 = getEnergyGradient(x2)
    #print np.dot(g1,g2)
    minimum1 = quench(x1)
    minimum2 = quench(x2)
    return minimum1, minimum2
Exemplo n.º 33
0
    def __init__(self, coords, potential, takeStep, storage=None,
                 event_after_step=[], acceptTest=None, temperature=1.0,
                 quench=None, confCheck=[], outstream=sys.stdout,
                 insert_rejected=False
                 ):
        #########################################################################
        # initialize MonteCarlo base class
        #########################################################################
        MonteCarlo.__init__(self, coords, potential, takeStep, storage=storage,
                            event_after_step=event_after_step,
                            acceptTest=acceptTest, temperature=temperature,
                            confCheck=confCheck, outstream=outstream,
                            store_initial=False)

        if quench is None:
            quench = lambda coords : mylbfgs(coords, self.potential)
        self.quench = quench
                
        #########################################################################
        # do initial quench
        #########################################################################
        self.markovE_old = self.markovE
        res = self.quench(self.coords)
        
        self.coords = res.coords
        self.markovE = res.energy
        self.rms = res.rms
        self.funcalls = res.nfev

        self.insert_rejected = insert_rejected
        
        if(self.storage):
            self.storage(self.markovE, self.coords)
        
        # print the initial quench
        self.acceptstep = True
        self.trial_energy = self.markovE
        self.printStep()
        
        self.result.energy = self.markovE
        self.result.coords = self.coords.copy()
Exemplo n.º 34
0
def main():
    natoms = 4
    pot = LJ()

    reference_coords = np.random.uniform(-1, 1, [3 * natoms])
    print reference_coords

    # freeze the first two atoms (6 degrees of freedom)
    frozen_dof = range(6)

    fpot = FrozenPotentialWrapper(pot, reference_coords, frozen_dof)

    reduced_coords = fpot.get_reduced_coords(reference_coords)

    print "the energy in the full representation:"
    print pot.getEnergy(reference_coords)
    print "is the same as the energy in the reduced representation:"
    print fpot.getEnergy(reduced_coords)

    ret = mylbfgs(reduced_coords, fpot)
    print "after a minimization the energy is ", ret.energy, "and the rms gradient is", ret.rms
    print "the coordinates of the frozen degrees of freedom are unchanged"
    print "starting coords:", reference_coords
    print "minimized coords:", fpot.get_full_coords(ret.coords)
Exemplo n.º 35
0
pot = XYModel( dim = [L,L], phi = np.pi)


angles = np.random.uniform(-pi, pi, nspins)
print angles

e = pot.getEnergy(angles)
print "energy ", e



# try a quench
if False:
    from pele.optimize import mylbfgs
    ret = mylbfgs(angles, pot)
    
    print ret


# set up and run basin hopping

from pele.basinhopping import BasinHopping
from pele.takestep.displace import RandomDisplacement
from pele.takestep.adaptive import AdaptiveStepsize
from pele.storage import savenlowest

# should probably use a different take step routine  which takes into account
# the cyclical periodicity of angles
takestep = RandomDisplacement(stepsize = np.pi/4)
takestepa = AdaptiveStepsize(takestep, frequency = 20)
Exemplo n.º 36
0
coords = np.zeros([nspins, 2])
for i in range(nspins):
    vec = rotations.vec_random()
    coords[i, :] = make2dVector(vec)
coords = np.reshape(coords, [nspins * 2])
coordsinit = np.copy(coords)

print coords

e = pot.getEnergy(coords)
print "energy ", e

print "try a quench"
from pele.optimize import mylbfgs

ret = mylbfgs(coords, pot)

print "quenched e = ", ret.energy, "funcalls", ret.nfev
print ret.coords

m = getm(ret[0])
print "magnetization after quench", m

# do basin hopping
from pele.basinhopping import BasinHopping
from pele.takestep.displace import RandomDisplacement
from pele.takestep.adaptive import AdaptiveStepsize
from pele.storage import savenlowest

takestep = RandomDisplacement(stepsize=np.pi / 4)
takestepa = AdaptiveStepsize(takestep, frequency=10)
Exemplo n.º 37
0
coords = np.zeros([nspins, 2])
for i in range(nspins):
    vec = rotations.vec_random()
    coords[i, :] = make2dVector(vec)
coords = np.reshape(coords, [nspins * 2])
coordsinit = np.copy(coords)

print coords

e = pot.getEnergy(coords)
print "energy ", e

print "try a quench"
from pele.optimize import mylbfgs

ret = mylbfgs(coords, pot)

print "quenched e = ", ret.energy, "funcalls", ret.nfev
print ret.coords

m = getm(ret[0])
print "magnetization after quench", m


# do basin hopping
from pele.basinhopping import BasinHopping
from pele.takestep.displace import RandomDisplacement
from pele.takestep.adaptive import AdaptiveStepsize
from pele.storage import savenlowest

takestep = RandomDisplacement(stepsize=np.pi / 4)
Exemplo n.º 38
0
    ntypeA = int(natoms*0.8)
    rcut = 2.5
    print "natoms", natoms, "ntypea", ntypeA
    
    bljslow = ljpshift.LJpshift(natoms, ntypeA, rcut=rcut, boxl=boxl, sigBB=1.7)
    blj =              LJpshift(natoms, ntypeA, rcut=rcut, boxl=boxl, sigBB=1.7)
    
    ebljslow = bljslow.getEnergy(coords)
    print "blj energy slow", ebljslow
    eblj = blj.getEnergy(coords)
    print "blj energy     ", eblj
    blj.test_potential(coords)
    print ""
    
    print "partially quenching coords"
    ret1 = mylbfgs(coords, blj, iprint=-11, tol=1.)
    coords = ret1.coords
    ebljslow = bljslow.getEnergy(coords)
    print "blj energy slow", ebljslow
    eblj = blj.getEnergy(coords)
    print "blj energy     ", eblj
    blj.test_potential(coords, eps=1e-6)
    print ""


    
    print "quenching coords"
    ret1 = mylbfgs(coords, blj, iprint=-11)
    coords = ret1.coords
    ebljslow = bljslow.getEnergy(coords)
    print "blj energy slow", ebljslow
Exemplo n.º 39
0
natoms = int(sys.argv[1])

print "benchmarking lennard jones potential, %d atoms, %d calls" % (natoms, N)
pot_old = LJ()
pot = _lj.LJ()

t0 = time.time()
for i in xrange(N):
    x = 1. * (np.random.random(3 * natoms) - 0.5)
    clbfgs = _lbfgs.LBFGS_CPP(pot, x, tol=1e-4)
    ret = clbfgs.run()

t1 = time.time()
for i in xrange(N):
    x = 1. * (np.random.random(3 * natoms) - 0.5)
    ret = mylbfgs(x, pot_old, tol=1e-4)

t2 = time.time()

for i in xrange(N):
    x = 1. * (np.random.random(3 * natoms) - 0.5)
    clbfgs = _lbfgs.LBFGS_CPP(_lj_cython.LJ_cython(), x, tol=1e-4)
    ret = clbfgs.run()

t3 = time.time()

for i in xrange(N):
    x = 1. * (np.random.random(3 * natoms) - 0.5)
    clbfgs = _lbfgs.LBFGS_CPP(pot_old, x, tol=1e-4)
    ret = clbfgs.run()
Exemplo n.º 40
0
L = 24
nspins = L**2

pot = XYModel(dim=[L, L], phi=np.pi)

angles = np.random.uniform(-pi, pi, nspins)
print(angles)

e = pot.getEnergy(angles)
print("energy ", e)

# try a quench
if False:
    from pele.optimize import mylbfgs

    ret = mylbfgs(angles, pot)

    print(ret)

# set up and run basin hopping

from pele.basinhopping import BasinHopping
from pele.takestep.displace import RandomDisplacement
from pele.takestep.adaptive import AdaptiveStepsize
from pele.storage import savenlowest

# should probably use a different take step routine  which takes into account
# the cyclical periodicity of angles
takestep = RandomDisplacement(stepsize=old_div(np.pi, 4))
takestepa = AdaptiveStepsize(takestep, frequency=20)
storage = savenlowest.SaveN(500)
Exemplo n.º 41
0
import sys
import _lj
import _lbfgs
from pele.optimize import mylbfgs
N=100
natoms=[10, 13, 20, 30, 31, 38, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150]

print "benchmarking lennard jones potential, %d atoms, %d calls", natoms, N
pot_old = LJ()
pot = _lj.LJ()

clbfgs = _lbfgs.LBFGS(pot)

res = open("results.txt", "w")

for na in natoms:
    t0 = time.time()
    for i in xrange(N):
        x = np.random.random(3*na) - 0.5
        ret = clbfgs.run(x)

    t1 = time.time()
    for i in xrange(N):
        x = np.random.random(3*na) - 0.5
        ret = mylbfgs(x, pot_old, tol=1e-5)
    res.write("%d %f %f\n"%(na, t1-t0, time.time()-t1))
    print "%d %f %f\n"%(na, t1-t0, time.time()-t1)

res.close()

Exemplo n.º 42
0
def test():
    pi = np.pi
    L = 8
    nspins = L**2
    
    #phases = np.zeros(nspins)
    pot = HeisenbergModel( dim = [L,L], field_disorder = 1.) #, phases=phases)
    
    coords = np.zeros([nspins, 2])
    for i in range(nspins): 
        vec = rotations.vec_random()
        coords[i,:] = make2dVector(vec)
    coords = np.reshape(coords, [nspins*2])
    if False:
        normfields = np.copy(pot.fields)
        for i in range(nspins): normfields[i,:] /= np.linalg.norm(normfields[i,:])
        coords = coords3ToCoords2( np.reshape(normfields, [nspins*3] ) )
        coords  = np.reshape(coords, nspins*2)
    #print np.shape(coords)
    coordsinit = np.copy(coords)
    
    #print "fields", pot.fields
    print coords
    
    if False:
        coords3 = coords2ToCoords3(coords)
        coords2 = coords3ToCoords2(coords3)
        print np.reshape(coords, [nspins,2])
        print coords2
        coords3new = coords2ToCoords3(coords2)
        print coords3
        print coords3new

    e = pot.getEnergy(coords)
    print "energy ", e
    if False:
        print "numerical gradient"
        ret = pot.getEnergyGradientNumerical(coords)
        print ret[1]
        if True:
            print "analytical gradient"
            ret2 = pot.getEnergyGradient(coords)
            print ret2[1]
            print ret[0]
            print ret2[0]
            print "ratio"
            print ret2[1] / ret[1]
            print "inverse sin"
            print 1./sin(coords)
            print cos(coords)

    
    print "try a quench"
    from pele.optimize import mylbfgs
    ret = mylbfgs(coords, pot, iprint=1)
    
    print "quenched e = ", ret.energy, "funcalls", ret.nfev
    print ret.coords
    with open("out.spins", "w") as fout:
        s = coords2ToCoords3( ret.coords )
        h = pot.fields
        c = coords2ToCoords3( coordsinit )
        for node in pot.G.nodes():
            i = pot.indices[node]
            fout.write( "%g %g %g %g %g %g %g %g %g %g %g\n" % (node[0], node[1], \
                s[i,0], s[i,1], s[i,2], h[i,0], h[i,1], h[i,2], c[i,0], c[i,1], c[i,2] ) )
    
    coords3 = coords2ToCoords3( ret.coords )
    m = np.linalg.norm( coords3.sum(0) ) / nspins
    print "magnetization after quench", m
    
    test_basin_hopping(pot, coords)
Exemplo n.º 43
0
def test2():
    import pele.potentials.ljpshiftfast as ljpshiftfast
    import pele.potentials.ljpshift as ljpshift
    from pele.optimize import mylbfgs

    fname = "/scratch/scratch2/js850/library/cluster/spherical/1620/PTMC/q4/oneatom/cavity200-8/ts/coords1.quench"
    fname = "/scratch/scratch2/js850/library/cluster/spherical/1620/PTMC/q4/oneatom/cavity200-8/ts/test.coords"
    # fname = "out.coords"
    if False:
        coords = np.array(np.loadtxt(fname))
        coords = coords.reshape(-1)
        boxl = 11.05209
    else:
        natoms = 200
        coords = np.random.uniform(-1, 1, natoms * 3) * (natoms) ** (1.0 / 3) / 2
        print "max, min coords", coords.max(), coords.min()
        boxl = 5

    natoms = len(coords) / 3
    ntypeA = int(natoms * 0.8)
    rcut = 2.5
    print "natoms", natoms, "ntypea", ntypeA

    blj = ljpshift.LJpshift(natoms, ntypeA, rcut=rcut, boxl=boxl)
    bljfast = ljpshiftfast.LJpshift(natoms, ntypeA, rcut=rcut, boxl=boxl)

    pot = makeBLJNeighborListPot(natoms, ntypeA=ntypeA, rcut=rcut, boxl=boxl)

    eblj = blj.getEnergy(coords)
    print "blj energy", eblj

    epot = pot.getEnergy(coords)
    print "mcpot energy", epot

    print "energy difference", (epot - eblj)

    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 False:
        print "quenching"
        ret1 = mylbfgs(coords, blj, iprint=-11)
        np.savetxt("out.coords", ret1.coords)
        print "energy from quench1", ret1.energy
        ret2 = mylbfgs(coords, pot, iprint=-1)
        print "energy from quench2", ret2.energy
        print "max, min quenched coords", coords.max(), coords.min()

        print "ret1 evaluated in both potentials", pot.getEnergy(ret1.coords), blj.getEnergy(ret1.coords)
        print "ret2 evaluated in both potentials", pot.getEnergy(ret2.coords), blj.getEnergy(ret2.coords)
    elif True:
        print "quenching"
        ret2 = mylbfgs(coords, pot, iprint=-1)
        print "energy from quench2", ret2.energy
        print "max, min quenched coords", ret2.coords.max(), ret2.coords.min()

        print "ret2 evaluated in both potentials", pot.getEnergy(ret2.coords), blj.getEnergy(ret2.coords)
        print "and in blj fast                  ", bljfast.getEnergy(ret2.coords)