def load_coords_pymol(self,
coordslist,
oname,
index=1): # pragma: no cover
"""load the coords into pymol
the new object must be named oname so we can manipulate it later
Parameters
----------
coordslist : list of arrays
oname : str
the new pymol object must be named oname so it can be manipulated
later
index : int
we can have more than one molecule on the screen at one time. index tells
which one to draw. They are viewed at the same time, so should be
visually distinct, e.g. different colors. accepted values are 1 or 2
Notes
-----
the implementation here is a bit hacky. we create a temporary xyz file from coords
and load the molecule in pymol from this file.
"""
# pymol is imported here so you can do, e.g. basinhopping without installing pymol
import pymol
# create the temporary file
suffix = ".xyz"
f = tempfile.NamedTemporaryFile(mode="w", suffix=suffix)
fname = f.name
# write the coords into the xyz file
from pele.mindist import CoMToOrigin
for coords in coordslist:
coords = CoMToOrigin(coords.copy())
write_xyz(f, coords, title=oname, atomtypes=["LA"])
f.flush()
# load the molecule from the temporary file
pymol.cmd.load(fname)
# get name of the object just create and change it to oname
objects = pymol.cmd.get_object_list()
objectname = objects[-1]
pymol.cmd.set_name(objectname, oname)
# set the representation
pymol.cmd.hide("everything", oname)
pymol.cmd.show("spheres", oname)
# set the color according to index
if index == 1:
pymol.cmd.color("red", oname)
else:
pymol.cmd.color("gray", oname)
def load_coords_pymol(self, coordslist, oname, index=1): # pragma: no cover
"""load the coords into pymol
the new object must be named oname so we can manipulate it later
Parameters
----------
coordslist : list of arrays
oname : str
the new pymol object must be named oname so it can be manipulated
later
index : int
we can have more than one molecule on the screen at one time. index tells
which one to draw. They are viewed at the same time, so should be
visually distinct, e.g. different colors. accepted values are 1 or 2
Notes
-----
the implementation here is a bit hacky. we create a temporary xyz file from coords
and load the molecule in pymol from this file.
"""
# pymol is imported here so you can do, e.g. basinhopping without installing pymol
from . import pymol
# create the temporary file
suffix = ".xyz"
f = tempfile.NamedTemporaryFile(mode="w", suffix=suffix)
fname = f.name
# write the coords into the xyz file
from pele.mindist import CoMToOrigin
for coords in coordslist:
coords = CoMToOrigin(coords.copy())
write_xyz(f, coords, title=oname, atomtypes=["LA"])
f.flush()
# load the molecule from the temporary file
pymol.cmd.load(fname)
# get name of the object just create and change it to oname
objects = pymol.cmd.get_object_list()
objectname = objects[-1]
pymol.cmd.set_name(objectname, oname)
# set the representation
pymol.cmd.hide("everything", oname)
pymol.cmd.show("spheres", oname)
# set the color according to index
if index == 1:
pymol.cmd.color("red", oname)
else:
pymol.cmd.color("gray", oname)
def load_coords_pymol(self, coordslist, oname, index=1):
"""load the coords into pymol
the new object must be named oname so we can manipulate it later
Parameters
----------
coordslist : list of arrays
oname : str
the new pymol object must be named oname so it can be manipulated
later
index : int
we can have more than one molecule on the screen at one time. index tells
which one to draw. They are viewed at the same time, so they should be
visually distinct, e.g. different colors. accepted values are 1 or 2
Notes
-----
the implementation here is a bit hacky. we create a temporary xyz file from coords
and load the molecule in pymol from this file.
"""
# pymol is imported here so you can do, e.g. basinhopping without installing pymol
import pymol
# create the temporary file (.xyz or .pdb, or whatever else pymol can read)
# note: this is the part that will be really system dependent.
f = tempfile.NamedTemporaryFile(mode="w", suffix=".xyz")
fname = f.name
# write the coords into file
for coords in coordslist:
write_xyz(f, coords, title=oname)
f.flush()
# load the molecule from the temporary file
pymol.cmd.load(fname)
# get name of the object just create and change it to oname
objects = pymol.cmd.get_object_list()
objectname = objects[-1]
pymol.cmd.set_name(objectname, oname)
# here you might want to change the representation of the molecule, e.g.
# >>> pymol.cmd.hide("everything", oname)
# >>> pymol.cmd.show("spheres", oname)
# set the color according to index
if index == 1:
pymol.cmd.color("red", oname)
else:
pymol.cmd.color("gray", oname)
def load_coords_pymol(self, coordslist, oname, index=1):
"""load the coords into pymol
the new object must be named oname so we can manipulate it later
Parameters
----------
coordslist : list of arrays
oname : str
the new pymol object must be named oname so it can be manipulated
later
index : int
we can have more than one molecule on the screen at one time. index tells
which one to draw. They are viewed at the same time, so they should be
visually distinct, e.g. different colors. accepted values are 1 or 2
Notes
-----
the implementation here is a bit hacky. we create a temporary xyz file from coords
and load the molecule in pymol from this file.
"""
# pymol is imported here so you can do, e.g. basinhopping without installing pymol
import pymol
# create the temporary file (.xyz or .pdb, or whatever else pymol can read)
# note: this is the part that will be really system dependent.
f = tempfile.NamedTemporaryFile(mode="w", suffix=".xyz")
fname = f.name
# write the coords into file
for coords in coordslist:
write_xyz(f, coords, title=oname)
f.flush()
# load the molecule from the temporary file
pymol.cmd.load(fname)
# get name of the object just create and change it to oname
objects = pymol.cmd.get_object_list()
objectname = objects[-1]
pymol.cmd.set_name(objectname, oname)
# here you might want to change the representation of the molecule, e.g.
# >>> pymol.cmd.hide("everything", oname)
# >>> pymol.cmd.show("spheres", oname)
# set the color according to index
if index == 1:
pymol.cmd.color("red", oname)
else:
pymol.cmd.color("gray", oname)
def _compute_energy_in_SA(self, replica):
"""compute the energy of the coordinates in replica.coords in the harmonic superposition approximation
This is where most of the difficulty is in the algorithm.
This is also where all the system dependence is
"""
# quench to nearest minimum
qresult = self.minimizer(replica.x)
# check if that minimum is in the database. reject if not
m, transformation = self.minima_searcher.get_minima(
qresult.energy, qresult.coords)
if m is None:
return None
# put replica.coords into best alignment with the structure stored in m.coords
# this involves accounting for symmetries of the Hamiltonian like translational,
# rotational and permutations symmetries. You can use the coordinates in qresult.coords
# to help find the best permutation. Ultimately it must be aligned with the structure in m.coords
# The hessian eigenvectors were computed with a given permutation
# e.g. account for trivial translational and rotational degrees of freedom
x = replica.x.copy()
if transformation is not None:
# transformation is the set of transformations that put qresult.coords into exact
# alignment with m.coords. If we apply these transformations to replica.x then
# replica.x will be in good (although not perfect) alignment already with m.coords
x = self.compare_structures.apply_transformation(x, transformation)
# Do a final round of optimization to further improve the alignment
if self.mindist is not None:
dist, x0, x = self.mindist(m.coords.copy(), x)
if self.debug:
diff = np.linalg.norm(x0 - m.coords)
if diff > .01:
with open("error.xyz", "w") as fout:
from pele.utils.xyz import write_xyz
write_xyz(fout, x0)
write_xyz(fout, m.coords)
raise Exception(
"warning, mindist appears to have changed x0. the norm of the difference is %g"
% diff)
assert (x0 == m.coords).all()
energy = self.sa_sampler.compute_energy(x, m)
return energy
def water(xyzfile): # TIP4P-16.xyz"
ref = xyz.read_xyz(open(xyzfile))
xyz.write_xyz(open("test.xyz", "w"), coords=ref.coords)
# lookup table for atom masses
mass_lookup = {'O': 16., 'H': 1.}
rb_sites = []
for atomtype, x, i in zip(ref.atomtypes, ref.coords, xrange(len(ref.atomtypes))):
# every 3rd atom, define a new reigid molecule
if i % 3 == 0:
rb = rigidbody.RigidFragment()
rb_sites.append(rb)
rb.add_atom(atomtype, x, mass_lookup[atomtype])
# finalize the rigid body setup
for rb in rb_sites:
rb.finalize_setup()
return rb_sites, ref
def _compute_energy_in_SA(self, replica):
"""compute the energy of the coordinates in replica.coords in the harmonic superposition approximation
This is where most of the difficulty is in the algorithm.
This is also where all the system dependence is
"""
# quench to nearest minimum
qresult = self.minimizer(replica.x)
# check if that minimum is in the database. reject if not
m, transformation = self.minima_searcher.get_minima(qresult.energy, qresult.coords)
if m is None:
return None
# put replica.coords into best alignment with the structure stored in m.coords
# this involves accounting for symmetries of the Hamiltonian like translational,
# rotational and permutations symmetries. You can use the coordinates in qresult.coords
# to help find the best permutation. Ultimately it must be aligned with the structure in m.coords
# The hessian eigenvectors were computed with a given permutation
# e.g. account for trivial translational and rotational degrees of freedom
x = replica.x.copy()
if transformation is not None:
# transformation is the set of transformations that put qresult.coords into exact
# alignment with m.coords. If we apply these transformations to replica.x then
# replica.x will be in good (although not perfect) alignment already with m.coords
x = self.compare_structures.apply_transformation(x, transformation)
# Do a final round of optimization to further improve the alignment
if self.mindist is not None:
dist, x0, x = self.mindist(m.coords.copy(), x)
if self.debug:
diff = np.linalg.norm(x0 - m.coords)
if diff > .01:
with open("error.xyz", "w") as fout:
from pele.utils.xyz import write_xyz
write_xyz(fout, x0)
write_xyz(fout, m.coords)
raise Exception("warning, mindist appears to have changed x0. the norm of the difference is %g" % diff)
assert (x0 == m.coords).all()
energy = self.sa_sampler.compute_energy(x, m)
return energy
def main():
#test class
natoms = 3
coords = np.random.uniform(-1,1,natoms*3)*2
lj = ATLJ(Z=1.)
E = lj.getEnergy(coords)
print "E", E
E, V = lj.getEnergyGradient(coords)
print "E", E
print "V"
print V
print "try a quench"
from pele.optimize import mylbfgs as quench
ret = quench( coords, lj, iprint=-1 )
#quench( coords, lj.getEnergyGradientNumerical, iprint=1 )
print "energy ", ret.energy
print "rms gradient", ret.rms
print "number of function calls", ret.nfev
from pele.utils.xyz import write_xyz
coords = ret.coords
printlist = []
for i in range(100):
coords = np.random.uniform(-1,1,natoms*3)*2
#coords = np.array([0,0,1., 0,0,0, 0,0,2])
#coords[6:] += np.random.uniform(-1,1,3)*0.1
ret = quench( coords, lj.getEnergyGradient, iprint=-1 )
coords = ret.coords
X = np.reshape(coords, [natoms,3])
com = X.sum(0) / natoms
X[:,:] -= com[np.newaxis,:]
printlist.append(np.reshape(X, natoms*3))
with open("out.xyz", "w") as fout:
for coords in printlist:
write_xyz(fout, coords)
def test_soft_sphere(natoms = 9):
rho = 1.6
boxl = 1.
meandiam = boxl / (float(natoms)/rho)**(1./3)
print "mean diameter", meandiam
#set up potential
diams = np.array([meandiam for i in range(natoms)]) #make them all the same
pot = SoftSphere(diams = diams)
#initial coordinates
coords = np.random.uniform(-1,1,[natoms*3]) * (natoms)**(1./3)
print len(coords)
E = pot.getEnergy(coords)
print "initial energy", E
printlist = []
printlist.append((coords.copy(), "intial coords"))
#test a quench with default lbfgs
from pele.optimize import mylbfgs as quench
res = quench(coords, pot, iprint=-1)
printlist.append((res.coords.copy(), "intial coords"))
print "energy post quench", pot.getEnergy(coords)
fname = "out.xyz"
print "saving coordinates to", fname
from pele.utils.xyz import write_xyz
with open(fname, "w") as fout:
for xyz,line2 in printlist:
xyz = putInBox(xyz, boxl)
write_xyz(fout, xyz, title=line2)
from scipy.optimize import check_grad
res = check_grad(pot.getEnergy, pot.getGradient, coords)
print "testing gradient (should be small)", res
def testing(): # pragma: no cover
# test class
natoms = 3
coords = np.random.uniform(-1, 1, natoms * 3) * 2
lj = ATLJ(Z=1.)
E = lj.getEnergy(coords)
print("E", E)
E, V = lj.getEnergyGradient(coords)
print("E", E)
print("V")
print(V)
print("try a quench")
from pele.optimize import mylbfgs as quench
ret = quench(coords, lj, iprint=-1)
print("energy ", ret.energy)
print("rms gradient", ret.rms)
print("number of function calls", ret.nfev)
from pele.utils.xyz import write_xyz
printlist = []
for i in range(100):
coords = np.random.uniform(-1, 1, natoms * 3) * 2
ret = quench(coords, lj.getEnergyGradient, iprint=-1)
coords = ret.coords
X = np.reshape(coords, [natoms, 3])
com = X.sum(0) / natoms
X[:, :] -= com[np.newaxis, :]
printlist.append(np.reshape(X, natoms * 3))
with open("out.xyz", "w") as fout:
for coords in printlist:
write_xyz(fout, coords)
def test_soft_sphere(natoms=9):
rho = 1.6
boxl = 1.
meandiam = boxl / (float(natoms) / rho)**(1. / 3)
print "mean diameter", meandiam
#set up potential
diams = np.array([meandiam
for i in range(natoms)]) #make them all the same
pot = SoftSphere(diams=diams)
#initial coordinates
coords = np.random.uniform(-1, 1, [natoms * 3]) * (natoms)**(1. / 3)
print len(coords)
E = pot.getEnergy(coords)
print "initial energy", E
printlist = []
printlist.append((coords.copy(), "intial coords"))
#test a quench with default lbfgs
from pele.optimize import mylbfgs as quench
res = quench(coords, pot, iprint=-1)
printlist.append((res.coords.copy(), "intial coords"))
print "energy post quench", pot.getEnergy(coords)
fname = "out.xyz"
print "saving coordinates to", fname
from pele.utils.xyz import write_xyz
with open(fname, "w") as fout:
for xyz, line2 in printlist:
xyz = putInBox(xyz, boxl)
write_xyz(fout, xyz, title=line2)
from scipy.optimize import check_grad
res = check_grad(pot.getEnergy, pot.getGradient, coords)
print "testing gradient (should be small)", res
def testing(): # pragma: no cover
# test class
natoms = 3
coords = np.random.uniform(-1, 1, natoms * 3) * 2
lj = ATLJ(Z=1.)
E = lj.getEnergy(coords)
print("E", E)
E, V = lj.getEnergyGradient(coords)
print("E", E)
print("V")
print(V)
print("try a quench")
from pele.optimize import mylbfgs as quench
ret = quench(coords, lj, iprint=-1)
print("energy ", ret.energy)
print("rms gradient", ret.rms)
print("number of function calls", ret.nfev)
from pele.utils.xyz import write_xyz
printlist = []
for i in range(100):
coords = np.random.uniform(-1, 1, natoms * 3) * 2
ret = quench(coords, lj.getEnergyGradient, iprint=-1)
coords = ret.coords
X = np.reshape(coords, [natoms, 3])
com = X.sum(0) / natoms
X[:, :] -= com[np.newaxis, :]
printlist.append(np.reshape(X, natoms * 3))
with open("out.xyz", "w") as fout:
for coords in printlist:
write_xyz(fout, coords)
def printpath(fout, coordslist):
nimages = len(coordslist[:,0])
for i in range(nimages):
write_xyz(fout, coordslist[i,:])
def printwrapper(self, E, coords, accepted):
if self.count % self.printfrq == 0:
self.center(coords)
write_xyz(self.fout, coords, title=str(E))
self.count += 1
def printpath(fout, coordslist, atomtypes = ["LA"]):
nimages = len(coordslist[:,0])
for i in range(nimages):
write_xyz(fout, coordslist[i,:], atom_type=atomtypes)
def load_coords_pymol(self, coordslist, oname, index=1):
"""load the coords into pymol
the new object must be named oname so we can manipulate it later
Parameters
----------
coordslist : list of arrays
oname : str
the new pymol object must be named oname so it can be manipulated
later
index : int
we can have more than one molecule on the screen at one time. index tells
which one to draw. They are viewed at the same time, so should be
visually distinct, e.g. different colors. accepted values are 1 or 2
Notes
-----
the implementation here is a bit hacky. we create a temporary xyz file from coords
and load the molecule in pymol from this file.
"""
# pymol is imported here so you can do, e.g. basinhopping without installing pymol
import pymol
# create the temporary file
suffix = ".xyz"
f = tempfile.NamedTemporaryFile(mode="w", suffix=suffix)
fname = f.name
# write the atomistic coords into the xyz file
from pele.mindist import CoMToOrigin
for coords in coordslist:
if hasattr(self, "atom_types"):
atom_types = self.atom_types
else:
atom_types = ["O"]
atom_coords = self.aasystem.to_atomistic(coords)
write_xyz(f, atom_coords, title=oname, atomtypes=atom_types)
f.flush()
# load the molecule from the temporary file into pymol
pymol.cmd.load(fname)
# get name of the object just create and change it to oname
objects = pymol.cmd.get_object_list()
objectname = objects[-1]
pymol.cmd.set_name(objectname, oname)
# set the representation as spheres
pymol.cmd.hide("everything", oname)
pymol.cmd.show("spheres", oname)
# draw the bonds
if hasattr(self, "draw_bonds"):
pymol.cmd.unbond(oname, oname)
for i1, i2 in self.draw_bonds:
pymol.cmd.bond("id %d and %s" % (i1+1, oname),
"id %d and %s" % (i2+1, oname))
pymol.cmd.show("lines", oname)
# set the color of index 2 so they appear different
if index == 2:
pymol.cmd.color("gray", oname)
com = np.array([ 0.,0.,0., 1.,0.,0., 0.,1.,0. ])
aacoords = np.array([ 1.,0.,0., 0.,1.,0., 0.,0.,1.,] )
coords = np.zeros( 3*2*nmol, np.float64)
coords[0:3*nmol] = com[:]
coords[3*nmol:] = aacoords[:]
printlist = []
printlist.append( coords.copy() )
grad, E = sandbox.getenergygradient(coords, [2*nmol])
print E, grad
sandbox.takestep(coords, False, True, s, os, myas,[natoms])
printlist.append( coords.copy() )
sandbox.takestep(coords, True, False, s, os, myas,[natoms])
printlist.append( coords.copy() )
from pele.utils.xyz import write_xyz
with open("out.xyz", "w") as fout:
for x in printlist:
write_xyz(fout, x)
def testpot1():
import itertools
pot, coords, coords1, coords2 = guesstsLJ()
coordsinit = np.copy(coords)
natoms = len(coords)/3
c = np.reshape(coords, [-1,3])
for i, j in itertools.combinations(range(natoms), 2):
r = np.linalg.norm(c[i,:] - c[j,:])
print i, j, r
e, g = pot.getEnergyGradient(coords)
print "initial E", e
print "initial G", g, np.linalg.norm(g)
print ""
from pele.utils.xyz import write_xyz
#print ret
with open("out.xyz", "w") as fout:
e = pot.getEnergy(coords1)
print "energy of minima 1", e
write_xyz(fout, coords1, title=str(e))
e, grad = pot.getEnergyGradient(coordsinit)
print "energy of NEB guess for the transition state", e, "rms grad", \
np.linalg.norm(grad) / np.sqrt(float(len(coords))/3.)
write_xyz(fout, coordsinit, title=str(e))
e = pot.getEnergy(coords2)
print "energy of minima 2", e
write_xyz(fout, coords2, title=str(e))
#mess up coords a bit
coords += np.random.uniform(-1,1,len(coords))*0.05
e = pot.getEnergy(coords)
write_xyz(fout, coords, title=str(e))
printevent = PrintEvent(fout)
print ""
print "starting the transition state search"
ret = findTransitionState(coords, pot, iprint=-1)
print ret
#coords, eval, evec, e, grad, rms = ret
e = pot.getEnergy(ret.coords)
write_xyz(fout, coords2, title=str(e))
print "finished searching for transition state"
print "energy", e
print "rms grad", ret.rms
print "eigenvalue", ret.eigenval
if False:
print "now try the same search with the dimer method"
from pele.NEB.dimer import findTransitionState as dimerfindTS
coords = coordsinit.copy()
tau = np.random.uniform(-1,1,len(coords))
tau /= np.linalg.norm(tau)
ret = dimerfindTS(coords, pot, tau )
enew, grad = pot.getEnergyGradient(ret.coords)
print "energy", enew
print "rms grad", np.linalg.norm(grad) / np.sqrt(float(len(ret.coords))/3.)
coords[k : k + 3] = rot.random_aa()
#set up the takestep routine
from pele.potentials.rigid_bodies.take_step import RBTakeStep
takestep = RBTakeStep()
#set up the class to save lowest energy structures
from pele.storage.savenlowest import SaveN
saveit = SaveN(100)
#set up basinhopping
from pele.basinhopping import BasinHopping
bh = BasinHopping(coords, mysys, takestep, storage=saveit.insert )
#run basin hopping
bh.run(40)
#print the saved coords
fname = "otp.xyz"
print "saving xyz coords to", fname
from pele.utils.xyz import write_xyz
with open(fname, "w") as fout:
for minimum in saveit.data:
xyz = mysys.getxyz(minimum.coords)
write_xyz( fout, xyz, atomtypes=["N", "O", "O"])
Example 3: Saving all minima found to an xyz file
"""
from pele.systems import LJCluster
from pele.utils.xyz import write_xyz
natoms = 12
niter = 100
system = LJCluster(natoms)
db = system.create_database()
bh = system.get_basinhopping(database=db)
bh.run(niter)
with open("lowest", "w") as fout:
for minimum in db.minima():
title = "energy = ", str(minimum.energy)
write_xyz(fout, minimum.coords, title)
############################################################
# some visualization
############################################################
try:
import pele.utils.pymolwrapper as pym
pym.start()
frame=1
for minimum in db.minima():
pym.draw_spheres(minimum.coords.reshape(-1, 3), "A", frame)
frame=frame+1
except:
print "Could not draw using pymol, skipping this step"
mints, S, energies = myconnect.returnPath()
nmin = (len(mints)-1)/2 + 1
nts = nmin-1
print "the path has %d minima and %d transition states" % (nmin, nts)
eofs = "path.EofS"
print "saving energies to", eofs
with open(eofs, "w") as fout:
for i in range(len(S)):
fout.write("%f %f\n" % (S[i], energies[i]))
xyzfile = "path.xyz"
print "saving path in xyz format to", xyzfile
with open(xyzfile, "w") as fout:
for m in mints:
write_xyz(fout, m.coords, title=str(m.energy))
xyzfile = "path.smooth.xyz"
print "saving smoothed path in xyz format to", xyzfile
clist = [m.coords for m in mints]
smoothed = smoothPath(clist, mindist)
with open(xyzfile, "w") as fout:
for coords in smoothed:
write_xyz(fout, coords)
if False:
try:
import matplotlib.pyplot as plt
plt.plot(S, energies)
plt.show()
printlist = [] #list of coordinates saved for printing
printlist.append((coords.copy(), "intial coords"))
#test a quench with default lbfgs
#from optimize.quench import quench
from pele.optimize import lbfgs_ase as quench
coords, E, rms, funcalls = quench(coords, pot.getEnergyGradient, iprint=1)
printlist.append((coords.copy(), "intial coords"))
print "energy post quench", pot.getEnergy(coords)
from scipy.optimize import check_grad
res = check_grad(pot.getEnergy, pot.getGradient, coords)
print "testing gradient (should be small)", res
fname = "out.xyz"
print "saving coordinates to", fname
from pele.utils.xyz import write_xyz
with open(fname, "w") as fout:
for xyz,line2 in printlist:
#xyz = putInBox(xyz, boxl)
write_xyz(fout, xyz, title=line2)
import numpy as np
from copy import deepcopy
from pele.angleaxis import RBTopology, RBSystem
# For rigid fragment
import numpy as np
from pele.potentials import LJ
from pele.utils import xyz
from pele.angleaxis import rigidbody
from pele.optimize import lbfgs_py
# read in coordinates from xyz file
ref = xyz.read_xyz(open("TIP4P-16.xyz"))
xyz.write_xyz(open("test.xyz", "w"), coords=ref.coords)
# lookup table for atom masses
mass_lookup = {'O': 16., 'H': 1.}
def water(xyzfile): # TIP4P-16.xyz"
ref = xyz.read_xyz(open(xyzfile))
xyz.write_xyz(open("test.xyz", "w"), coords=ref.coords)
# lookup table for atom masses
mass_lookup = {'O': 16., 'H': 1.}
rb_sites = []
for atomtype, x, i in zip(ref.atomtypes, ref.coords, xrange(len(ref.atomtypes))):
# every 3rd atom, define a new reigid molecule
if i % 3 == 0:
rb = rigidbody.RigidFragment()
rb_sites.append(rb)
rb.add_atom(atomtype, x, mass_lookup[atomtype])
# finalize the rigid body setup
for rb in rb_sites:
import ambgmin_ as GMIN
import pele.potentials.gminpotential as gminpot
import numpy as np
import pele.basinhopping as bh
from pele.optimize import _quench as quench
from pele.takestep import displace
# export PYTHONPATH=/home/ss2029/svn/GMIN/bin:$PWD/../..
GMIN.initialize()
pot = gminpot.GMINPotental(GMIN)
coords = pot.getCoords()
step = displace.RandomDisplacement(stepsize=0.7)
opt = bh.BasinHopping(coords, pot, takeStep=step, quench=quench.lbfgs_py)
opt.quenchParameters['tol'] = 1e-4
opt.run(3)
# some visualization
try:
import pele.utils.pymolwrapper as pym
pym.start()
pym.draw_spheres(opt.coords, "A", 1)
except:
print "Could not draw using pymol, skipping this step"
from pele.utils.xyz import write_xyz
write_xyz(open("final.xyz", "w"), opt.coords)
def load_coords_pymol(self,
coordslist,
oname,
index=1): # pragma: no cover
"""load the coords into pymol
the new object must be named oname so we can manipulate it later
Parameters
----------
coordslist : list of arrays
oname : str
the new pymol object must be named oname so it can be manipulated
later
index : int
we can have more than one molecule on the screen at one time. index tells
which one to draw. They are viewed at the same time, so should be
visually distinct, e.g. different colors. accepted values are 1 or 2
Notes
-----
the implementation here is a bit hacky. we create a temporary xyz file from coords
and load the molecule in pymol from this file.
"""
# pymol is imported here so you can do, e.g. basinhopping without installing pymol
import pymol
# create the temporary file
suffix = ".xyz"
f = tempfile.NamedTemporaryFile(mode="w", suffix=suffix)
fname = f.name
# write the atomistic coords into the xyz file
for coords in coordslist:
if hasattr(self, "atom_types"):
atom_types = self.atom_types
else:
atom_types = ["O"]
atom_coords = self.aasystem.to_atomistic(coords)
write_xyz(f, atom_coords, title=oname, atomtypes=atom_types)
f.flush()
# load the molecule from the temporary file into pymol
pymol.cmd.load(fname)
# get name of the object just create and change it to oname
objects = pymol.cmd.get_object_list()
objectname = objects[-1]
pymol.cmd.set_name(objectname, oname)
# set the representation as spheres
pymol.cmd.hide("everything", oname)
pymol.cmd.show("spheres", oname)
# draw the bonds
if hasattr(self, "draw_bonds"):
pymol.cmd.unbond(oname, oname)
for i1, i2 in self.draw_bonds:
pymol.cmd.bond("id %d and %s" % (i1 + 1, oname),
"id %d and %s" % (i2 + 1, oname))
pymol.cmd.show("lines", oname)
# set the color of index 2 so they appear different
if index == 2:
pymol.cmd.color("gray", oname)
In the above we used three imports. We used the `numpy` library to construct a random
one dimensional array. We used the Lennard-Jones potential :class:`.LJ`, and we used the minimization
routine :func:`.lbfgs_py` which is just a wrapper for the class :class:`.LBFGS`.
The return value is an optimization result, which is just a container (:class:`.Result`) that stores
the final energy, final coordinates, the number of function calls, etc.
If we want to then save the minimized coordinates in an xyz file we can use the function :func:`.write_xyz`
::
from pele.utils.xyz import write_xyz
with open("out.xyz", "w") as fout:
title = "energy = " + str(result.energy)
write_xyz(fout, result.coords, title=title)
"""
import numpy as np
from pele.potentials import LJ
from pele.optimize import lbfgs_py
natoms = 5
x = np.random.uniform(-2, 2, natoms * 3)
pot = LJ()
result = lbfgs_py(x, pot)
print result
from pele.utils.xyz import write_xyz
with open("out.xyz", "w") as fout:
title = "energy = " + str(result.energy)
write_xyz(fout, result.coords, title=title)
print ""
mints, S, energies = myconnect.returnPath()
nmin = (len(mints) - 1) / 2 + 1
nts = nmin - 1
print "the path has %d minima and %d transition states" % (nmin, nts)
eofs = "path.EofS"
print "saving energies to", eofs
with open(eofs, "w") as fout:
for i in range(len(S)):
fout.write("%f %f\n" % (S[i], energies[i]))
xyzfile = "path.xyz"
print "saving path in xyz format to", xyzfile
with open(xyzfile, "w") as fout:
for m in mints:
write_xyz(fout, m.coords, title=str(m.energy))
xyzfile = "path.smooth.xyz"
print "saving smoothed path in xyz format to", xyzfile
clist = [m.coords for m in mints]
smoothed = smoothPath(clist, mindist)
with open(xyzfile, "w") as fout:
for coords in smoothed:
write_xyz(fout, coords)
if False:
try:
import matplotlib.pyplot as plt
plt.plot(S, energies)
plt.show()
import ambgmin_ as GMIN
import pele.potentials.gminpotential as gminpot
import numpy as np
import pele.basinhopping as bh
from pele.optimize import _quench as quench
from pele.takestep import displace
# export PYTHONPATH=/home/ss2029/svn/GMIN/bin:$PWD/../..
GMIN.initialize()
pot = gminpot.GMINPotental(GMIN)
coords = pot.getCoords()
step = displace.RandomDisplacement(stepsize=0.7)
opt = bh.BasinHopping(coords, pot, takeStep=step, quenchRoutine=quench.lbfgs_py)
opt.quenchParameters['tol'] = 1e-4
opt.run(3)
# some visualization
try:
import pele.utils.pymolwrapper as pym
pym.start()
pym.draw_spheres(opt.coords, "A", 1)
except:
print "Could not draw using pymol, skipping this step"
from pele.utils.xyz import write_xyz
write_xyz(open("final.xyz", "w"), opt.coords)
from __future__ import print_function
from builtins import str
from pele.systems import LJCluster
from pele.utils.xyz import write_xyz
natoms = 12
niter = 100
system = LJCluster(natoms)
db = system.create_database()
bh = system.get_basinhopping(database=db)
bh.run(niter)
with open("lowest", "w") as fout:
for minimum in db.minima():
title = "energy = ", str(minimum.energy)
write_xyz(fout, minimum.coords, title)
############################################################
# some visualization
############################################################
try:
import pele.utils.pymolwrapper as pym
pym.start()
frame = 1
for minimum in db.minima():
pym.draw_spheres(minimum.coords.reshape(-1, 3), "A", frame)
frame += 1
except:
print("Could not draw using pymol, skipping this step")
from __future__ import print_function
from builtins import zip
from builtins import range
import numpy as np
from pele.potentials import LJ
from pele.utils import xyz
from pele.angleaxis import rigidbody
from pele.optimize import lbfgs_py
# read in coordinates from xyz file
ref = xyz.read_xyz(open("water.xyz"))
xyz.write_xyz(open("test.xyz", "w"), coords=ref.coords)
# lookup table for atom masses
mass_lookup = {'O': 16., 'H': 1.}
#ref.coords[:] *= 3
# now define a new rigid body system
rb_sites = []
for atomtype, x, i in zip(ref.atomtypes, ref.coords,
range(len(ref.atomtypes))):
# every 3rd atom, define a new reigid molecule
if i % 3 == 0:
rb = rigidbody.RigidFragment()
rb_sites.append(rb)
rb.add_atom(atomtype, x, mass_lookup[atomtype])
# finalize the rigid body setup
for rb in rb_sites:
rb.finalize_setup()
In the above we used three imports. We used the `numpy` library to construct a random
one dimensional array. We used the Lennard-Jones potential :class:`.LJ`, and we used the minimization
routine :func:`.lbfgs_py` which is just a wrapper for the class :class:`.LBFGS`.
The return value is an optimization result, which is just a container (:class:`.Result`) that stores
the final energy, final coordinates, the number of function calls, etc.
If we want to then save the minimized coordinates in an xyz file we can use the function :func:`.write_xyz`
::
from pele.utils.xyz import write_xyz
with open("out.xyz", "w") as fout:
title = "energy = " + str(result.energy)
write_xyz(fout, result.coords, title=title)
"""
import numpy as np
from pele.potentials import LJ
from pele.optimize import lbfgs_py
natoms = 5
x = np.random.uniform(-2, 2, natoms*3)
pot = LJ()
result = lbfgs_py(x, pot)
print result
from pele.utils.xyz import write_xyz
with open("out.xyz", "w") as fout:
title = "energy = " + str(result.energy)
write_xyz(fout, result.coords, title=title)
import numpy as np
from src.runmc import mc_cython
from lj_run import LJClusterNew, MonteCarloCompiled
from nested_sampling import MonteCarloChain
from pele.takestep import RandomDisplacement
from pele.utils.xyz import write_xyz
system = LJClusterNew(31)
x = system.get_random_configuration()
with open("test.xyz", "w") as fout:
write_xyz(fout, x)
mciter = 100000
stepsize = .01
Emax = 1e20
radius = 2.5
mcc = MonteCarloCompiled(system, radius)
mcc(x.copy(), mciter, stepsize, Emax)
print mcc.naccept, mcc.nsteps, mcc.energy
takestep = RandomDisplacement(stepsize=stepsize)
mc = MonteCarloChain(system.get_potential(), x.copy(), takestep, Emax,
system.get_config_tests())
for i in xrange(mciter):
mc.step()
print mc.naccept, mc.nsteps, mc.energy
import numpy as np
from pele.potentials import LJ
from pele.utils import xyz
from pele.angleaxis import rigidbody
from pele.optimize import lbfgs_py
# read in coordinates from xyz file
ref = xyz.read_xyz(open("water.xyz"))
xyz.write_xyz(open("test.xyz", "w"), coords = ref.coords)
# lookup table for atom masses
mass_lookup = {'O': 16., 'H': 1.}
#ref.coords[:] *= 3
# now define a new rigid body system
rb_sites = []
for atomtype, x, i in zip(ref.atomtypes, ref.coords, xrange(len(ref.atomtypes))):
# every 3rd atom, define a new reigid molecule
if i%3 == 0:
rb = rigidbody.RigidFragment()
rb_sites.append(rb)
rb.add_atom(atomtype, x, mass_lookup[atomtype])
# finalize the rigid body setup
for rb in rb_sites:
rb.finalize_setup()
# define a new rigid body system
rbsystem = rigidbody.RBSystem()
rbsystem.add_sites(rb_sites)
def load_coords_pymol(self, coordslist, oname, index=1):
"""load the coords into pymol
the new object must be named oname so we can manipulate it later
Parameters
----------
coordslist : list of arrays
oname : str
the new pymol object must be named oname so it can be manipulated
later
index : int
we can have more than one molecule on the screen at one time. index tells
which one to draw. They are viewed at the same time, so should be
visually distinct, e.g. different colors. accepted values are 1 or 2
Notes
-----
the implementation here is a bit hacky. we create a temporary xyz file from coords
and load the molecule in pymol from this file.
"""
#pymol is imported here so you can do, e.g. basinhopping without installing pymol
import pymol
#create the temporary file
suffix = ".xyz"
f = tempfile.NamedTemporaryFile(mode="w", suffix=suffix)
fname = f.name
#write the coords into the xyz file
from pele.mindist import CoMToOrigin
labels = ["LA" for i in range(self.ntypeA)] + \
["LB" for i in range(self.natoms - self.ntypeA)]
for coords in coordslist:
coords = CoMToOrigin(coords.copy())
write_xyz(f, coords, title=oname, atomtypes=labels)
f.flush()
# self.f = f # so the file is not deleted
# print fname
#load the molecule from the temporary file
pymol.cmd.load(fname)
#get name of the object just create and change it to oname
objects = pymol.cmd.get_object_list()
objectname = objects[-1]
pymol.cmd.set_name(objectname, oname)
#set the representation
pymol.cmd.hide("everything", oname)
pymol.cmd.show("spheres", oname)
#make the B atoms smaller
seleA = "%s and name LA" % (oname)
seleB = "%s and name LB" % (oname)
pymol.cmd.set("sphere_scale", value=1.0, selection=seleA)
pymol.cmd.set("sphere_scale", value=0.8, selection=seleB)
#set the color according to index
if index == 1:
pymol.cmd.color("red", selection=seleA)
pymol.cmd.color("firebrick", selection=seleB)
else:
pymol.cmd.color("deepolive", selection=seleA)
pymol.cmd.color("smudge", selection=seleB)
def __call__(self, coords, **kwargs):
from pele.utils.xyz import write_xyz
write_xyz(self.fout, coords)
self.coordslist.append( coords.copy() )