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 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 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())