def benchmark_number_of_minima():
import time, sys
import numpy as np
db = Database("test.large.db")
if True:
istart = np.random.randint(0, sys.maxint)
for i in xrange(istart, istart + 10000):
e = float(i)
db.addMinimum(e, [e], commit=False)
db.session.commit()
else:
i = 1
t1 = time.clock()
print db.number_of_minima()
print "time", t1 - time.clock()
t1 = time.clock()
print db.number_of_minima()
print "time", t1 - time.clock()
t1 = time.clock()
print db.number_of_minima()
print "time", t1 - time.clock()
t1 = time.clock()
e = float(i + 1)
db.addMinimum(e, [e], commit=False)
t1 = time.clock()
print db.number_of_minima()
print "time", t1 - time.clock()
t1 = time.clock()
print len(db.minima())
print "time", t1 - time.clock()
t1 = time.clock()
def benchmark_number_of_minima():
import time, sys
import numpy as np
db = Database("test.large.db")
if True:
istart = np.random.randint(0, sys.maxint)
for i in xrange(istart,istart+10000):
e = float(i)
db.addMinimum(e, [e], commit=False)
db.session.commit()
else:
i=1
t1 = time.clock()
print db.number_of_minima()
print "time", t1 - time.clock(); t1 = time.clock()
print db.number_of_minima()
print "time", t1 - time.clock(); t1 = time.clock()
print db.number_of_minima()
print "time", t1 - time.clock(); t1 = time.clock()
e = float(i+1)
db.addMinimum(e, [e], commit=False)
t1 = time.clock()
print db.number_of_minima()
print "time", t1 - time.clock(); t1 = time.clock()
print len(db.minima())
print "time", t1 - time.clock(); t1 = time.clock()
def create_random_database(nmin=20, nts=None, natoms=2):
"""
create a database for test purposes
"""
from pygmin.storage import Database
import numpy as np
if nts is None:
nts = nmin
db = Database()
#generate random structures
minlist = []
for i in range(nmin):
coords = np.random.uniform(-1,1,natoms*3)
e = float(i) #make up a fake energy
minlist.append( db.addMinimum(e, coords) )
#add random transition states
for i in range(nts):
j1, j2 = 1, 1
while j1 == j2:
j1, j2 = np.random.randint(0, nmin, 2)
m1, m2 = minlist[j1], minlist[j2]
coords = np.random.uniform(-1,1,natoms*3)
e = float(j1 + j2)
db.addTransitionState(e, coords, m1, m2)
return db
def create_random_database(nmin=20, nts=None, natoms=2):
"""
create a database for test purposes
"""
from pygmin.storage import Database
import numpy as np
if nts is None:
nts = nmin
db = Database()
#generate random structures
minlist = []
for i in range(nmin):
coords = np.random.uniform(-1,1,natoms*3)
e = float(i) #make up a fake energy
minlist.append( db.addMinimum(e, coords) )
#add random transition states
for i in range(nts):
j1, j2 = 1, 1
while j1 == j2:
j1, j2 = np.random.randint(0, nmin, 2)
m1, m2 = minlist[j1], minlist[j2]
coords = np.random.uniform(-1,1,natoms*3)
e = float(j1 + j2)
db.addTransitionState(e, coords, m1, m2)
return db
def test():
from pygmin.storage import Database
coords1, coords2, pot, mindist, E1, E2 = getPairLJ()
db = Database()
min1 = db.addMinimum(E1, coords1)
min2 = db.addMinimum(E2, coords2)
local_connect = LocalConnect(pot, mindist)
local_connect.connect(min1, min2)
def test():
from pygmin.storage import Database
coords1, coords2, pot, mindist, E1, E2 = getPairLJ()
db = Database()
min1 = db.addMinimum(E1, coords1)
min2 = db.addMinimum(E2, coords2)
local_connect = LocalConnect(pot, mindist)
local_connect.connect(min1, min2)
def create_neb(self, coords1, coords2):
"""setup the NEB object"""
system = self.system
throwaway_db = Database()
min1 = throwaway_db.addMinimum(0., coords1)
min2 = throwaway_db.addMinimum(1., coords2)
#use the functions in DoubleEndedConnect to set up the NEB in the proper way
double_ended = system.get_double_ended_connect(min1,
min2,
throwaway_db,
fresh_connect=True)
local_connect = double_ended._getLocalConnectObject()
self.local_connect = local_connect
return local_connect.create_neb(system.get_potential(), coords1,
coords2, **local_connect.NEBparams)
def create_neb(self, coords1, coords2):
"""setup the NEB object"""
system = self.system
throwaway_db = Database()
min1 = throwaway_db.addMinimum(0., coords1)
min2 = throwaway_db.addMinimum(1., coords2)
#use the functions in DoubleEndedConnect to set up the NEB in the proper way
double_ended = system.get_double_ended_connect(min1, min2,
throwaway_db,
fresh_connect=True)
local_connect = double_ended._getLocalConnectObject()
self.local_connect = local_connect
return local_connect.create_neb(system.get_potential(),
coords1, coords2,
**local_connect.NEBparams)
def getNEB(coords1, coords2, system):
"""setup the NEB object"""
throwaway_db = Database()
min1 = throwaway_db.addMinimum(0., coords1)
min2 = throwaway_db.addMinimum(1., coords2)
#use the functions in DoubleEndedConnect to set up the NEB in the proper way
double_ended = system.get_double_ended_connect(min1, min2,
throwaway_db,
fresh_connect=True)
local_connect = double_ended._getLocalConnectObject()
neb = local_connect._getNEB(system.get_potential(),
coords1, coords2,
verbose=True,
**local_connect.NEBparams)
return neb
#import the starting and ending points and quench them,
coords1 = np.genfromtxt("coords.A")
coords2 = np.genfromtxt("coords.B")
res1 = lbfgs_py(coords1.reshape(-1), pot)
res2 = lbfgs_py(coords2.reshape(-1), pot)
coords1 = res1.coords
coords2 = res2.coords
E1 = res1.energy
E2 = res2.energy
natoms = len(coords1)/3
#add the minima to a database
dbfile = "database.sqlite"
database = Database(dbfile)
database.addMinimum(E1, coords1)
database.addMinimum(E2, coords2)
min1 = database.minima()[0]
min2 = database.minima()[1]
#set up the structural alignment routine.
#we have to deal with global translational, global rotational,
#and permutational symmetry.
permlist = [range(natoms)]
mindist = MinPermDistAtomicCluster(permlist=permlist, niter=10)
#The transition state search needs to know what the eigenvector corresponding
#to the lowest nonzero eigenvector is. For this we need to know what the
class TestDB(unittest.TestCase):
def setUp(self):
self.db = Database()
self.nminima = 10
for i in range(self.nminima):
e = float(i)
self.db.addMinimum(e, [e])
self.nts = 3
self.db.addTransitionState(0., [0.],
self.db.minima()[0],
self.db.minima()[1],
eigenval=0.,
eigenvec=[0.])
self.db.addTransitionState(0., [0.],
self.db.minima()[1],
self.db.minima()[2],
eigenval=0.,
eigenvec=[0.])
self.db.addTransitionState(0., [0.],
self.db.minima()[0],
self.db.minima()[2],
eigenval=0.,
eigenvec=[0.])
def test_size(self):
self.assertEqual(len(self.db.minima()), self.nminima)
def test_energy(self):
m = self.db.minima()[0]
self.assertEqual(m.energy, 0.)
def test_coords(self):
m = self.db.minima()[0]
self.assertEqual(m.coords, [0.])
def test_sizets(self):
self.assertEqual(len(self.db.transition_states()), self.nts)
def test_energyts(self):
ts = self.db.transition_states()[0]
self.assertEqual(ts.energy, 0.)
def test_coordsts(self):
ts = self.db.transition_states()[0]
self.assertEqual(ts.coords, [0.])
def test_remove_minimum(self):
m = self.db.minima()[0]
self.db.removeMinimum(m)
self.assertEqual(len(self.db.minima()), self.nminima - 1)
self.assertNotIn(m, self.db.minima())
# m should have 2 minima. both of those should be gone
self.assertEqual(len(self.db.transition_states()), self.nts - 2)
def test_remove_ts(self):
ts = self.db.transition_states()[0]
self.db.remove_transition_state(ts)
self.assertEqual(self.db.number_of_transition_states(), self.nts - 1)
self.assertNotIn(ts, self.db.transition_states())
# m should have 2 minima. both of those should be gone
self.assertEqual(self.db.number_of_minima(), self.nminima)
def test_getTransitionState(self):
m1 = self.db.minima()[0]
m2 = self.db.minima()[1]
m3 = self.db.minima()[-1]
self.assertIsNotNone(self.db.getTransitionState(m1, m2))
self.assertIsNone(self.db.getTransitionState(m1, m3))
def test_getMinimum(self):
m = self.db.minima()[0]
self.assertEqual(m, self.db.getMinimum(m._id))
def test_minimum_adder(self):
ma = self.db.minimum_adder()
ma(101., [101.])
self.assertEqual(len(self.db.minima()), self.nminima + 1)
def test_merge_minima(self):
m1 = self.db.minima()[0]
m2 = self.db.minima()[1]
self.db.mergeMinima(m1, m2)
self.assertEqual(len(self.db.minima()), self.nminima - 1)
# transition states shouldn't be deleted
self.assertEqual(len(self.db.transition_states()), self.nts)
def test_number_of_minima(self):
self.assertEqual(self.nminima, self.db.number_of_minima())
def test_number_of_transition_states(self):
self.assertEqual(self.nts, self.db.number_of_transition_states())
def test_highest_energy_minimum(self):
m1 = self.db._highest_energy_minimum()
m2 = self.db.minima()[-1]
self.assertEqual(m1, m2)
def test_maximum_number_of_minima(self):
m = self.db.addMinimum(-1., [-1.], max_n_minima=self.nminima)
self.assertEqual(self.nminima, self.db.number_of_minima())
self.assertIn(m, self.db.minima())
def test_maximum_number_of_minima_largestE(self):
e = float(self.nminima + 1)
m = self.db.addMinimum(e, [e], max_n_minima=self.nminima)
self.assertEqual(self.nminima, self.db.number_of_minima())
self.assertIsNone(m)
#ensure the highest energy minimum is still in the database
mmax = self.db._highest_energy_minimum()
self.assertEqual(mmax.energy, float(self.nminima - 1))
def test_maximum_number_of_minima_minima_adder(self):
ma = self.db.minimum_adder(max_n_minima=self.nminima)
m = ma(-1., [-1.])
self.assertEqual(self.nminima, self.db.number_of_minima())
self.assertIn(m, self.db.minima())
def process_events():
app.processEvents()
#setup system
natoms = 13
system = LJCluster(natoms)
system.params.double_ended_connect.local_connect_params.NEBparams.iter_density = 10.
system.params.double_ended_connect.local_connect_params.NEBparams.image_density = 3.
# system.params.double_ended_connect.local_connect_params.NEBparams.adaptive_nimages = 5.
system.params.double_ended_connect.local_connect_params.NEBparams.reinterpolate = 400
system.params.double_ended_connect.local_connect_params.NEBparams.max_images = 40
x1, e1 = system.get_random_minimized_configuration()[:2]
x2, e2 = system.get_random_minimized_configuration()[:2]
db = Database()
min1 = db.addMinimum(e1, x1)
min2 = db.addMinimum(e2, x2)
#setup neb dialog
wnd = ConnectExplorerDialog(system, app)
wnd.show()
glutInit()
#initilize the NEB and run it.
#we have to do it through QTimer because the gui has to
#be intitialized first... I don't really understand it
from PyQt4.QtCore import QTimer
QTimer.singleShot(10, start)
sys.exit(app.exec_())
from pygmin.systems import LJCluster
from pygmin.storage import Database
import pylab as pl
app = QApplication(sys.argv)
def process_events():
app.processEvents()
#setup system
natoms = 13
system = LJCluster(natoms)
system.params.double_ended_connect.local_connect_params.NEBparams.iter_density = 5.
x1, e1 = system.get_random_minimized_configuration()[:2]
x2, e2 = system.get_random_minimized_configuration()[:2]
db = Database()
min1 = db.addMinimum(e1, x1)
min2 = db.addMinimum(e2, x2)
#setup neb dialog
pl.ion()
# pl.show()
dlg = NEBDialog()
wnd = dlg.nebwgt
dlg.show()
wnd.process_events.connect(process_events)
#initilize the NEB and run it.
#we have to do it through QTimer because the gui has to
#be intitialized first... I don't really understand it
from PyQt4.QtCore import QTimer
QTimer.singleShot(10, start)
class TestDB(unittest.TestCase):
def setUp(self):
self.db = Database()
self.nminima = 10
for i in range(self.nminima):
e = float(i)
self.db.addMinimum(e, [e])
self.nts = 3
self.db.addTransitionState(0., [0.], self.db.minima()[0], self.db.minima()[1], eigenval=0., eigenvec=[0.])
self.db.addTransitionState(0., [0.], self.db.minima()[1], self.db.minima()[2], eigenval=0., eigenvec=[0.])
self.db.addTransitionState(0., [0.], self.db.minima()[0], self.db.minima()[2], eigenval=0., eigenvec=[0.])
def test_size(self):
self.assertEqual(len(self.db.minima()), self.nminima)
def test_energy(self):
m = self.db.minima()[0]
self.assertEqual(m.energy, 0.)
def test_coords(self):
m = self.db.minima()[0]
self.assertEqual(m.coords, [0.])
def test_sizets(self):
self.assertEqual(len(self.db.transition_states()), self.nts)
def test_energyts(self):
ts = self.db.transition_states()[0]
self.assertEqual(ts.energy, 0.)
def test_coordsts(self):
ts = self.db.transition_states()[0]
self.assertEqual(ts.coords, [0.])
def test_remove_minimum(self):
m = self.db.minima()[0]
self.db.removeMinimum(m)
self.assertEqual(len(self.db.minima()), self.nminima-1)
self.assertNotIn(m, self.db.minima())
# m should have 2 minima. both of those should be gone
self.assertEqual(len(self.db.transition_states()), self.nts-2)
def test_remove_ts(self):
ts = self.db.transition_states()[0]
self.db.remove_transition_state(ts)
self.assertEqual(self.db.number_of_transition_states(), self.nts-1)
self.assertNotIn(ts, self.db.transition_states())
# m should have 2 minima. both of those should be gone
self.assertEqual(self.db.number_of_minima(), self.nminima)
def test_getTransitionState(self):
m1 = self.db.minima()[0]
m2 = self.db.minima()[1]
m3 = self.db.minima()[-1]
self.assertIsNotNone(self.db.getTransitionState(m1, m2))
self.assertIsNone(self.db.getTransitionState(m1, m3))
def test_getMinimum(self):
m = self.db.minima()[0]
self.assertEqual(m, self.db.getMinimum(m._id))
def test_minimum_adder(self):
ma = self.db.minimum_adder()
ma(101., [101.])
self.assertEqual(len(self.db.minima()), self.nminima+1)
def test_merge_minima(self):
m1 = self.db.minima()[0]
m2 = self.db.minima()[1]
self.db.mergeMinima(m1, m2)
self.assertEqual(len(self.db.minima()), self.nminima-1)
# transition states shouldn't be deleted
self.assertEqual(len(self.db.transition_states()), self.nts)
def test_number_of_minima(self):
self.assertEqual(self.nminima, self.db.number_of_minima())
def test_number_of_transition_states(self):
self.assertEqual(self.nts, self.db.number_of_transition_states())
def test_highest_energy_minimum(self):
m1 = self.db._highest_energy_minimum()
m2 = self.db.minima()[-1]
self.assertEqual(m1, m2)
def test_maximum_number_of_minima(self):
m = self.db.addMinimum(-1., [-1.], max_n_minima=self.nminima)
self.assertEqual(self.nminima, self.db.number_of_minima())
self.assertIn(m, self.db.minima())
def test_maximum_number_of_minima_largestE(self):
e = float(self.nminima + 1)
m = self.db.addMinimum(e, [e], max_n_minima=self.nminima)
self.assertEqual(self.nminima, self.db.number_of_minima())
self.assertIsNone(m)
#ensure the highest energy minimum is still in the database
mmax = self.db._highest_energy_minimum()
self.assertEqual(mmax.energy, float(self.nminima-1))
def test_maximum_number_of_minima_minima_adder(self):
ma = self.db.minimum_adder(max_n_minima=self.nminima)
m = ma(-1., [-1.])
self.assertEqual(self.nminima, self.db.number_of_minima())
self.assertIn(m, self.db.minima())
