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.maxsize)
for i in range(istart, istart + 10000):
e = float(i)
db.addMinimum(e, [e], commit=False)
db.session.commit()
else:
i = 1
t1 = time.process_time()
print(db.number_of_minima())
print("time", t1 - time.process_time())
t1 = time.process_time()
print(db.number_of_minima())
print("time", t1 - time.process_time())
t1 = time.process_time()
print(db.number_of_minima())
print("time", t1 - time.process_time())
t1 = time.process_time()
e = float(i + 1)
db.addMinimum(e, [e], commit=False)
t1 = time.process_time()
print(db.number_of_minima())
print("time", t1 - time.process_time())
t1 = time.process_time()
print(len(db.minima()))
print("time", t1 - time.process_time())
t1 = time.process_time()
def test1(self):
current_dir = os.path.dirname(__file__)
db = Database()
converter = OptimDBConverter(
db,
mindata=os.path.join(current_dir, "min.data"),
tsdata=os.path.join(current_dir, "ts.data"),
pointsmin=os.path.join(current_dir, "points.min"),
pointsts=os.path.join(current_dir, "points.ts"),
endianness="<")
converter.convert()
self.assertEqual(db.number_of_minima(), 2)
self.assertEqual(db.number_of_transition_states(), 1)
ts = db.transition_states()[0]
self.assertAlmostEqual(ts.coords[0], 1.548324, 5)
self.assertAlmostEqual(ts.coords[2], 0.18178001, 5)
self.assertAlmostEqual(ts.coords[-1], -0.50953229, 5)
self.assertEqual((180, ), ts.coords.shape)
m = db.minima()[0]
print repr(m.coords)
self.assertAlmostEqual(m.coords[0], 1.53700142, 5)
self.assertAlmostEqual(m.coords[2], 0.87783657, 5)
self.assertAlmostEqual(m.coords[-1], -0.50953229, 5)
self.assertEqual((180, ), m.coords.shape)
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 test1(self):
current_dir = os.path.dirname(__file__)
db = Database()
converter = OptimDBConverter(db,
mindata=os.path.join(current_dir, "min.data"),
tsdata=os.path.join(current_dir, "ts.data"),
pointsmin=os.path.join(current_dir, "points.min"),
pointsts=os.path.join(current_dir, "points.ts"),
endianness="<")
converter.convert()
self.assertEqual(db.number_of_minima(), 2)
self.assertEqual(db.number_of_transition_states(), 1)
ts = db.transition_states()[0]
self.assertAlmostEqual(ts.coords[0], 1.548324, 5)
self.assertAlmostEqual(ts.coords[2], 0.18178001, 5)
self.assertAlmostEqual(ts.coords[-1], -0.50953229, 5)
self.assertEqual((180,), ts.coords.shape)
m = db.minima()[0]
print(repr(m.coords))
self.assertAlmostEqual(m.coords[0], 1.53700142, 5)
self.assertAlmostEqual(m.coords[2], 0.87783657, 5)
self.assertAlmostEqual(m.coords[-1], -0.50953229, 5)
self.assertEqual((180,), m.coords.shape)
def bh_no_system_class():
import numpy as np
from pele.potentials import LJ
natoms = 17
potential = LJ()
x0 = np.random.uniform(-1, 1, 3*natoms)
from pele.takestep import RandomDisplacement, AdaptiveStepsizeTemperature
displace = RandomDisplacement()
adaptive_displacement = AdaptiveStepsizeTemperature(displace)
from pele.storage import Database
database = Database("lj17.sqlite")
from pele.basinhopping import BasinHopping
bh = BasinHopping(x0, potential, adaptive_displacement, storage=database.minimum_adder)
bh.run(10)
for m in database.minima():
print m.energy
def test2(self):
from pele.storage import Database
from pele.systems import LJCluster
np.random.seed(0)
natoms = 13
system = LJCluster(natoms)
pot = system.get_potential()
mindist = system.get_mindist(niter=1)
db = Database()
db.addMinimum(pot.getEnergy(_x1), _x1)
db.addMinimum(pot.getEnergy(_x2), _x2)
m1, m2 = db.minima()
connect = DoubleEndedConnect(m1, m2, pot, mindist, db, verbosity=10)
connect.connect()
self.assertTrue(connect.success())
path = connect.returnPath()
def test2(self):
from pele.utils.tests.test_disconnectivity_graph import create_random_database
db = create_random_database(nmin=20, nts=15)
delete = False
mdata = tempfile.NamedTemporaryFile(delete=delete, suffix=".min.data")
tsdata = tempfile.NamedTemporaryFile(delete=delete, suffix=".ts.data")
pm = tempfile.NamedTemporaryFile(delete=delete)
pts = tempfile.NamedTemporaryFile(delete=delete)
print mdata.name, tsdata.name
writer = WritePathsampleDB(db,
mindata=mdata.name,
tsdata=tsdata.name,
pointsmin=pm.name,
pointsts=pts.name,
endianness="<")
writer.write_db()
newdb = Database()
reader = OptimDBConverter(newdb,
mindata=mdata.name,
tsdata=tsdata.name,
pointsmin=pm.name,
pointsts=pts.name,
endianness="<")
reader.convert()
for m1, m2 in izip(db.minima(), newdb.minima()):
self.assertAlmostEqual(m1.energy, m2.energy)
_base_test.assert_arrays_almost_equal(self, m1.coords, m2.coords)
for ts1, ts2 in izip(db.transition_states(),
newdb.transition_states()):
self.assertAlmostEqual(ts1.energy, ts2.energy)
_base_test.assert_arrays_almost_equal(self, ts1.coords, ts2.coords)
self.assertAlmostEqual(ts1.minimum1.energy, ts2.minimum1.energy)
self.assertAlmostEqual(ts1.minimum2.energy, ts2.minimum2.energy)
def test2(self):
from pele.utils.tests.test_disconnectivity_graph import create_random_database
db = create_random_database(nmin=20, nts=15)
delete=False
mdata = tempfile.NamedTemporaryFile(delete=delete, suffix=".min.data")
tsdata = tempfile.NamedTemporaryFile(delete=delete, suffix=".ts.data")
pm = tempfile.NamedTemporaryFile(delete=delete)
pts = tempfile.NamedTemporaryFile(delete=delete)
print(mdata.name, tsdata.name)
writer = WritePathsampleDB(db,
mindata=mdata.name,
tsdata=tsdata.name,
pointsmin=pm.name,
pointsts=pts.name,
endianness="<")
writer.write_db()
newdb = Database()
reader = OptimDBConverter(newdb,
mindata=mdata.name,
tsdata=tsdata.name,
pointsmin=pm.name,
pointsts=pts.name,
endianness="<")
reader.convert()
for m1, m2 in zip(db.minima(), newdb.minima()):
self.assertAlmostEqual(m1.energy, m2.energy)
_base_test.assert_arrays_almost_equal(self, m1.coords, m2.coords)
for ts1, ts2 in zip(db.transition_states(), newdb.transition_states()):
self.assertAlmostEqual(ts1.energy, ts2.energy)
_base_test.assert_arrays_almost_equal(self, ts1.coords, ts2.coords)
self.assertAlmostEqual(ts1.minimum1.energy, ts2.minimum1.energy)
self.assertAlmostEqual(ts1.minimum2.energy, ts2.minimum2.energy)
class TestConnectManager(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])
def connect_min(self, m1, m2):
e = np.random.uniform(0, 100)
self.db.addTransitionState(e, [e], m1, m2)
def test_gmin(self):
manager = ConnectManager(self.db, strategy="gmin")
m0 = self.db.minima()[0]
for i in range(5):
m1, m2 = manager.get_connect_job()
self.assertEqual(m1, m0)
self.connect_min(m1, m2)
def test_random(self):
manager = ConnectManager(self.db, strategy="random")
for i in range(5):
m1, m2 = manager.get_connect_job()
self.connect_min(m1, m2)
def test_combine(self):
minima = self.db.minima()
i = 5
for m1, m2 in zip(minima[:i - 1], minima[1:i]):
self.connect_min(m1, m2)
for m1, m2 in zip(minima[i:], minima[i + 1:]):
self.connect_min(m1, m2)
# at this point the minima should be in two disconnected groups
g = database2graph(self.db)
self.assertEqual(len(list(nx.connected_components(g))), 2)
manager = ConnectManager(self.db, strategy="combine")
m1, m2 = manager.get_connect_job()
self.connect_min(m1, m2)
# they should all be connected now
g = database2graph(self.db)
self.assertEqual(len(list(nx.connected_components(g))), 1)
def test_untrap(self):
# first connect them all randomly
manager = ConnectManager(self.db, strategy="random")
while True:
try:
m1, m2 = manager.get_connect_job()
except manager.NoMoreConnectionsError:
break
self.connect_min(m1, m2)
for i in range(5):
try:
m1, m2 = manager.get_connect_job(strategy="untrap")
except manager.NoMoreConnectionsError:
break
self.connect_min(m1, m2)
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
#eivenvector corresponding to the zero eigenvalues are. These are related
#to global symmetries. for this system we have 3 zero eigenvalues for translational
parser.add_argument("--Tmax", type=float, help="Minimum temperature for the calculation.", default=1.0)
parser.add_argument("--Tcount", type=int, help="Number of temperature points for the calculation.", default=300)
parser.add_argument("--OPTIM", action="store_true", help="read data from a min.data file instead."
"fname should be the filename of the min.data file")
args = parser.parse_args()
print args.fname
print args
k = args.k
# get the list of minima
if args.OPTIM:
# fname is a min.data file
db = Database()
converter = OptimDBConverter(db, mindata=args.fname)
converter.convert_no_coords()
minima = db.minima()
else:
dbfname = args.fname
db = Database(dbfname, createdb=False)
minima = [m for m in db.minima() if m.fvib is not None and m.pgorder is not None]
if len(minima) == 0:
print "There are not minima with the necessary thermodynamic information in the database. Have you computed the normal mode"\
" frequencies and point group order for all the minima? See pele.thermodynamics "\
" for more information"
exit(1)
print "computing heat capacity from", len(minima), "minima"
Tmin = args.Tmin
Tmax = args.Tmax
nT = args.Tcount
dT = (Tmax-Tmin) / nT
run_gui(system, db=dbname)
if __name__ == "__main__":
p = 5
N = 20
#run_gui(N, p)
#event_after_step = lambda energy, coords, acceptstep : normalize_spins(coords)
#event_after_step=[event_after_step]
if False:
system = MeanFieldPSpinSphericalSystem(N, p=p)
db = system.create_database("pspin_spherical_p{}_N{}.sqlite".format(p,N))
bh = system.get_basinhopping(database=db, outstream=None)
bh.run(100)
if True:
run_gui_db(dbname="pspin_spherical_p{}_N{}.sqlite".format(p,N))
if False:
compare_minima = lambda m1, m2 : compare_exact(m1.coords, m2.coords, rel_tol=1e-7, debug=False)
db = Database("pspin_spherical_p{}_N{}.sqlite".format(p,N))
minima = db.minima()
minima.sort(key=lambda m: m.energy)
#for m in minima:
# print m.energy, m.coords
print minima[0].energy, minima[0].coords
print minima[1].energy, minima[1].coords
print compare_minima(minima[0],minima[1])
#path = [ x for x in InterpolatedPath(path[0].copy(), path[-1].copy(), 34) ]
traj = open("traj.xyz", "w")
#for x in path:
# #export_xyz(traj, x)
# #ret = quench.mylbfgs(x, pot.getEnergyGradient)
# #print i,pot.getEnergy(x), ret[1]
#
# # export_xyz(traj, ret[0])
for x in path:
export_xyz(traj, x)
import pickle
pickle.dump(path, open("interpolate.pickle", "w"))
exit()
db=Database(db="oxdna.sqlite")
path[0]=db.minima()[19].coords
path[-1]=db.minima()[0].coords
e1 = []
e2 = []
e1.append(pot.getEnergy(path[0]))
e2.append(pot.getEnergy(path[0]))
#for i in xrange(1):
for i in xrange(len(path) - 1):
e1.append(pot.getEnergy(path[i + 1]))
c1 = CoordsAdapter(nrigid=13, coords=path[i])
c2 = CoordsAdapter(nrigid=13, coords=path[i + 1])
com1 = np.sum(c1.posRigid, axis=0) / float(13)
com2 = np.sum(c1.posRigid, axis=0) / float(13)
# default="otp.db")
parser.add_argument("k", type=int, help="number of degrees of vibrational freedom")
parser.add_argument("fname", type=str, help="database file name")
args = parser.parse_args()
print args.fname
k = args.k
dbfname = args.fname
db = Database(dbfname)
Tmin = .001
Tmax = .5
nT = 300
dT = (Tmax-Tmin) / nT
T = np.array([Tmin + dT*i for i in range(nT)])
Z, U, U2, Cv = compute_cv(db.minima(), T, k)
print Z, Cv
with open("cv_ha", "w") as fout:
fout.write("#T Cv <E> <E**2>\n")
for vals in zip(T, Cv, U, U2):
fout.write("%g %g %g %g\n" % vals)
import pylab as pl
pl.plot(T, Cv, 'o-')
pl.xlabel("T")
pl.ylabel("Cv")
pl.savefig("cv_ha.pdf")
if min_state_n > 0:
selected_procs = process_table[process_table['productID'] >= dir]
else:
selected_procs = process_table[process_table['productID'] >= 0]
for i in range(len(selected_procs['productID'])):
ps_ID = selected_procs['productID'].iloc[i]
#print ps_ID
if ps_ID >= max_state_n:
continue
try:
ps = db.addMinimum(states_e[ps_ID], [ps_ID], max_n_minima=-1)
ts = db.addTransitionState(selected_procs['saddle energy'].iloc[i],
[dir, ps_ID], rs, ps)
except:
continue
print "#of states:", len(db.minima())
#for mini in db.minima():
# print mini.coords
if Emax is None:
Emax = -1e20
for ts in db.transition_states():
if ts.energy > Emax:
Emax = ts.energy
print 'max ts:', Emax
#check the structures with energy larger than check_e
#print paras['check_structure']
if check_structure:
for ts in db.transition_states():
if ts.energy > check_e:
print ts.coords
for rs in db.minima():
parser.add_argument("k",
type=int,
help="number of degrees of vibrational freedom")
parser.add_argument("fname", type=str, help="database file name")
args = parser.parse_args()
print args.fname
k = args.k
dbfname = args.fname
db = Database(dbfname)
Tmin = .001
Tmax = .5
nT = 300
dT = (Tmax - Tmin) / nT
T = np.array([Tmin + dT * i for i in range(nT)])
Z, U, U2, Cv = compute_cv(db.minima(), T, k)
print Z, Cv
with open("cv_ha", "w") as fout:
fout.write("#T Cv <E> <E**2>\n")
for vals in zip(T, Cv, U, U2):
fout.write("%g %g %g %g\n" % vals)
import pylab as pl
pl.plot(T, Cv, 'o-')
pl.xlabel("T")
pl.ylabel("Cv")
pl.savefig("cv_ha.pdf")
parser.add_argument("--OPTIM",
action="store_true",
help="read data from a min.data file instead."
"fname should be the filename of the min.data file")
args = parser.parse_args()
print(args.fname)
print(args)
k = args.k
# get the list of minima
if args.OPTIM:
# fname is a min.data file
db = Database()
converter = OptimDBConverter(db, mindata=args.fname)
converter.convert_no_coords()
minima = db.minima()
else:
dbfname = args.fname
db = Database(dbfname, createdb=False)
minima = [
m for m in db.minima()
if m.fvib is not None and m.pgorder is not None
]
if len(minima) == 0:
print("There are not minima with the necessary thermodynamic information in the database. Have you computed the normal mode"\
" frequencies and point group order for all the minima? See pele.thermodynamics "\
" for more information")
exit(1)
print("computing heat capacity from", len(minima), "minima")
Tmin = args.Tmin
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 test_getTSfromID(self):
ts = self.db.transition_states()[0]
ts1 = self.db.getTransitionStateFromID(ts._id)
self.assertEqual(ts, ts1)
def test_property(self):
# add some system properties and ensure they were added correctly
self.db.add_property("natoms", 10)
p = self.db.get_property("natoms")
self.assertEqual(p.value(), 10)
self.db.add_property("eps", 2.1)
p = self.db.get_property("eps")
self.assertEqual(p.value(), 2.1)
self.db.add_property("author", "Jake")
p = self.db.get_property("author")
self.assertEqual(p.value(), "Jake")
self.db.add_property("data", [1, 2])
p = self.db.get_property("data")
self.assertEqual(p.value(), [1, 2])
# assert that the not set values are None
p = self.db.get_property("natoms")
self.assertIsNone(p.string_value)
self.assertIsNone(p.float_value)
self.assertIsNone(p.pickle_value)
p = self.db.get_property("noprop")
self.assertIsNone(p)
props = self.db.properties(as_dict=True)
self.assertIsInstance(props, dict)
self.assertDictContainsSubset(dict(natoms=10), props)
self.assertEqual(len(props.items()), 4)
props = self.db.properties(as_dict=False)
self.assertIn(("natoms", 10), [p.item() for p in props])
self.assertEqual(len(props), 4)
def test_property_dtype(self):
# add some system properties and ensure they were added correctly
self.db.add_property("natoms", 10, dtype="int")
p = self.db.get_property("natoms")
self.assertEqual(p.value(), 10)
self.db.add_property("eps", 2.1, dtype="float")
p = self.db.get_property("eps")
self.assertEqual(p.value(), 2.1)
self.db.add_property("author", "Jake", dtype="string")
p = self.db.get_property("author")
self.assertEqual(p.value(), "Jake")
self.db.add_property("data", [1, 2], dtype="pickle")
p = self.db.get_property("data")
self.assertEqual(p.value(), [1, 2])
def test_bad_property(self):
self.db.add_property("natoms", 10, dtype="string")
p = self.db.get_property("natoms")
self.assertEqual(p.value(), "10")
def test_property_overwrite_false(self):
self.db.add_property("natoms", 10)
# overwriting with the same value should not raise error
self.db.add_property("natoms", 10, overwrite=False)
# overwriting with a different value should raise error
with self.assertRaises(RuntimeError):
self.db.add_property("natoms", 11, overwrite=False)
def test_add_properties(self):
props = dict(natoms=10, author="jake")
self.db.add_properties(props)
for name, value in props.iteritems():
p = self.db.get_property(name)
self.assertEqual(p.value(), value)
def test_load_wrong_schema(self):
current_dir = os.path.dirname(__file__)
dbname = current_dir + "/lj6_schema1.sqlite"
with self.assertRaises(IOError):
db = Database(dbname, createdb=False)
def test_load_right_schema(self):
current_dir = os.path.dirname(__file__)
dbname = current_dir + "/lj6_schema2.sqlite"
db = Database(dbname, createdb=False)
def test_invalid(self):
m = self.db.minima()[0]
self.assertFalse(m.invalid)
m.invalid = True
self.db.session.commit()
self.assertTrue(m.invalid)
# now with a ts
m = self.db.transition_states()[0]
self.assertFalse(m.invalid)
m.invalid = True
self.db.session.commit()
self.assertTrue(m.invalid)
def test_user_data(self):
m = self.db.minima()[0]
m.user_data = dict(key="value")
self.db.session.commit()
v = m.user_data["key"]
# now with a transition state
m = self.db.transition_states()[0]
m.user_data = dict(key="value")
self.db.session.commit()
v = m.user_data["key"]
sysAmb = AMBERSystem('../aladipep/coords.prmtop', '../aladipep/coords.inpcrd')
# load existing database
from pele.storage import Database
dbcurr = Database(db="../aladipep/aladipep.db")
# ------ Test potential
print 'testing potential in ambSystem'
sysAmb.test_potential("../aladipep/coords.pdb")
# ------ BH
print 'testing BH'
nsteps = 1
sysAmb.test_BH(dbcurr, nsteps)
for minimum in dbcurr.minima():
print minimum._id, minimum.energy
# -- test connect
dbcurr = Database(db="aladipep.db")
sysAmb.test_connect(dbcurr)
#print "---------id, m1_id, m2_id, tsener"
#for ts in dbcurr.transition_states() :
# print ts._id, ts._minimum1_id, ts._minimum2_id, ts.energy
# connect to existing db
# sysOpenMM.create_database(db=dbcurr)
# calculation.
minima = db.session.query(Minimum).filter(Minimum.energy <= Emax)
g.add_nodes_from(minima)
# if we order by energy first and add the transition states with the largest
# the we will take the smallest energy transition state in the case of duplicates
ts = db.session.query(TransitionState).filter(TransitionState.energy <= Emax)\
.order_by(-TransitionState.energy)
for t in ts:
g.add_edge(t.minimum1, t.minimum2, ts=t)
return g
if __name__ == "__main__":
db = Database("lj31.db", createdb=False)
if len(db.minima()) < 2:
raise Exception("database has no minima")
if True:
from pele.systems import LJCluster
from pele.thermodynamics import get_thermodynamic_information
system = LJCluster(31)
get_thermodynamic_information(system, db, nproc=10)
app = QApplication(sys.argv)
md = DGraphDialog(db)
md.show()
md.rebuild_disconnectivity_graph()
sys.exit(app.exec_())
from __future__ import print_function
from pele.utils.disconnectivity_graph import DisconnectivityGraph
from pele.storage import Database
from pele.landscape import TSGraph
import pylab as pl
import numpy as np
kbT = 0.75
db = Database(db="tip4p_8.sqlite", createdb=False)
graph = TSGraph(db)
dg = DisconnectivityGraph(graph.graph, db.minima(), subgraph_size=20)
dg.calculate()
dg.plot()
for m in db.minima():
if m.pgorder != 2:
print(m.pgorder)
m.free_energy = m.energy + kbT * 0.5*m.fvib + kbT*np.log(m.pgorder)
for ts in db.transition_states():
# if ts.pgorder != 2:
print(ts.pgorder)
#assert ts.pgorder == 2
ts.free_energy = ts.energy + kbT * 0.5*ts.fvib + kbT*np.log(ts.pgorder) + kbT*np.log(kbT)
if ts.free_energy > ts.minimum1.free_energy or ts.free_energy > ts.minimum2.free_energy:
print("warning, free energy of transition state lower than minimum")
from pele.utils.disconnectivity_graph import DisconnectivityGraph
from pele.storage import Database
from pele.landscape import TSGraph
import pylab as pl
import numpy as np
kbT = 0.75
db = Database(db="tip4p_8.sqlite", createdb=False)
graph = TSGraph(db)
dg = DisconnectivityGraph(graph.graph, db.minima(), subgraph_size=20)
dg.calculate()
dg.plot()
for m in db.minima():
if m.pgorder != 2:
print m.pgorder
m.free_energy = m.energy + kbT * 0.5 * m.fvib + kbT * np.log(m.pgorder)
for ts in db.transition_states():
# if ts.pgorder != 2:
print ts.pgorder
#assert ts.pgorder == 2
ts.free_energy = ts.energy + kbT * 0.5 * ts.fvib + kbT * np.log(
ts.pgorder) + kbT * np.log(kbT)
if ts.free_energy > ts.minimum1.free_energy or ts.free_energy > ts.minimum2.free_energy:
print "warning, free energy of transition state lower than minimum"
print ts.free_energy, ts.minimum1.free_energy, ts.minimum2.free_energy
if __name__ == "__main__":
p = 5
N = 20
#run_gui(N, p)
#event_after_step = lambda energy, coords, acceptstep : normalize_spins(coords)
#event_after_step=[event_after_step]
if False:
system = MeanFieldPSpinSphericalSystem(N, p=p)
db = system.create_database("pspin_spherical_p{}_N{}.sqlite".format(
p, N))
bh = system.get_basinhopping(database=db, outstream=None)
bh.run(100)
if True:
run_gui_db(dbname="pspin_spherical_p{}_N{}.sqlite".format(p, N))
if False:
compare_minima = lambda m1, m2: compare_exact(
m1.coords, m2.coords, rel_tol=1e-7, debug=False)
db = Database("pspin_spherical_p{}_N{}.sqlite".format(p, N))
minima = db.minima()
minima.sort(key=lambda m: m.energy)
#for m in minima:
# print m.energy, m.coords
print(minima[0].energy, minima[0].coords)
print(minima[1].energy, minima[1].coords)
print(compare_minima(minima[0], minima[1]))
#path = [ x for x in InterpolatedPath(path[0].copy(), path[-1].copy(), 34) ]
traj = open("traj.xyz", "w")
#for x in path:
# #export_xyz(traj, x)
# #ret = quench.mylbfgs(x, pot.getEnergyGradient)
# #print i,pot.getEnergy(x), ret[1]
#
# # export_xyz(traj, ret[0])
for x in path:
export_xyz(traj, x)
import pickle
pickle.dump(path, open("interpolate.pickle", "w"))
exit()
db = Database(db="oxdna.sqlite")
path[0] = db.minima()[19].coords
path[-1] = db.minima()[0].coords
e1 = []
e2 = []
e1.append(pot.getEnergy(path[0]))
e2.append(pot.getEnergy(path[0]))
#for i in xrange(1):
for i in range(len(path) - 1):
e1.append(pot.getEnergy(path[i + 1]))
c1 = CoordsAdapter(nrigid=13, coords=path[i])
c2 = CoordsAdapter(nrigid=13, coords=path[i + 1])
com1 = np.sum(c1.posRigid, axis=0) / float(13)
com2 = np.sum(c1.posRigid, axis=0) / float(13)
class TestConnectManager(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])
def connect_min(self, m1, m2):
e = np.random.uniform(0,100)
self.db.addTransitionState(e, [e], m1, m2)
def test_gmin(self):
manager = ConnectManager(self.db, strategy="gmin")
m0 = self.db.minima()[0]
for i in range(5):
m1, m2 = manager.get_connect_job()
self.assertEqual(m1, m0)
self.connect_min(m1, m2)
def test_random(self):
manager = ConnectManager(self.db, strategy="random")
for i in range(5):
m1, m2 = manager.get_connect_job()
self.connect_min(m1, m2)
def test_combine(self):
minima = self.db.minima()
i = 5
for m1, m2 in zip(minima[:i-1], minima[1:i]):
self.connect_min(m1, m2)
for m1, m2 in zip(minima[i:], minima[i+1:]):
self.connect_min(m1, m2)
# at this point the minima should be in two disconnected groups
g = database2graph(self.db)
self.assertEqual(len(list(nx.connected_components(g))), 2)
manager = ConnectManager(self.db, strategy="combine")
m1, m2 = manager.get_connect_job()
self.connect_min(m1, m2)
# they should all be connected now
g = database2graph(self.db)
self.assertEqual(len(list(nx.connected_components(g))), 1)
def test_untrap(self):
# first connect them all randomly
manager = ConnectManager(self.db, strategy="random")
while True:
try:
m1, m2 = manager.get_connect_job()
except manager.NoMoreConnectionsError:
break
self.connect_min(m1, m2)
for i in range(5):
try:
m1, m2 = manager.get_connect_job(strategy="untrap")
except manager.NoMoreConnectionsError:
break
self.connect_min(m1, m2)
# calculation.
minima = db.session.query(Minimum).filter(Minimum.energy <= Emax)
g.add_nodes_from(minima)
# if we order by energy first and add the transition states with the largest
# the we will take the smallest energy transition state in the case of duplicates
ts = db.session.query(TransitionState).filter(TransitionState.energy <= Emax)\
.order_by(-TransitionState.energy)
for t in ts:
g.add_edge(t.minimum1, t.minimum2, ts=t)
return g
if __name__ == "__main__":
db = Database("lj31.db", createdb=False)
if len(db.minima()) < 2:
raise Exception("database has no minima")
if True:
from pele.systems import LJCluster
from pele.thermodynamics import get_thermodynamic_information
system = LJCluster(31)
get_thermodynamic_information(system, db, nproc=10)
app = QApplication(sys.argv)
md = DGraphDialog(db)
md.show()
md.rebuild_disconnectivity_graph()
sys.exit(app.exec_())
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
# eivenvector corresponding to the zero eigenvalues are. These are related
# to global symmetries. for this system we have 3 zero eigenvalues for translational
center_gmin=True,
order_by_value=orderByValue())
dg_lj.calculate()
fig = plt.figure(figsize=(9, 7))
fig.set_facecolor('white')
ax = fig.add_subplot(111, adjustable='box')
#ax = fig.add_subplot(111)
#ax.axvline(linewidth=4, color="g")
ax.spines['left'].set_linewidth(10.0)
dg_lj.plot(linewidth=2., axes=ax, ylinewidth=1.5, xmin_offset=0.15)
#ylabel='Potential Energy')
plt.subplots_adjust(left=0.2, right=0.95, top=0.95, bottom=0.1)
dg_lj.draw_minima(db_lj.minima(),
marker='o',
c='black',
s=50.0,
linewidths=2.0,
alpha=1)
dg_amp.calculate()
colors = []
ms = []
for m in db_amp.minima()[0:]:
colors.append('red')
ms.append([m])
print len(colors)
dg_amp.color_by_group(ms, colors=colors)
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_add_minimum(self):
# add a duplicate minimum and ensure the db doesn't change
self.db.addMinimum(0, [0])
self.assertEqual(self.db.number_of_minima(), self.nminima)
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_minimum_adder_Ecut(self):
ma = self.db.minimum_adder(Ecut=0)
n0 = self.db.number_of_minima()
ma(101., [101.])
self.assertEqual(n0, self.db.number_of_minima())
ma(-101., [-101.])
self.assertEqual(n0 + 1, self.db.number_of_minima())
def test_minimum_adder_commit_interval(self):
ma = self.db.minimum_adder(commit_interval=2)
# replace db.session.commit with a wrapper that keeps track of how
# many times it's been called
self.real_commit = self.db.session.commit
self.count = 0
def commit_wrapper():
self.count += 1
self.real_commit()
self.db.session.commit = commit_wrapper
# commit should be called for the first minimum
ma(101., [101.])
self.assertEqual(self.count, 1)
self.assertEqual(self.nminima + 1, self.db.number_of_minima())
# commit should not be called for the second minimum
ma(102., [102.])
self.assertEqual(self.count, 1)
self.assertEqual(self.nminima + 2, self.db.number_of_minima())
# yes for the third, no for the 4th
ma(103., [103.])
self.assertEqual(self.count, 2)
ma(104., [104.])
self.assertEqual(self.count, 2)
# commit should be called when minimum adder is deleted
del ma
self.assertEqual(self.count, 3)
self.assertEqual(self.nminima + 4, self.db.number_of_minima())
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 test_getTSfromID(self):
ts = self.db.transition_states()[0]
ts1 = self.db.getTransitionStateFromID(ts._id)
self.assertEqual(ts, ts1)
def test_property(self):
# add some system properties and ensure they were added correctly
self.db.add_property("natoms", 10)
p = self.db.get_property("natoms")
self.assertEqual(p.value(), 10)
self.db.add_property("eps", 2.1)
p = self.db.get_property("eps")
self.assertEqual(p.value(), 2.1)
self.db.add_property("author", "Jake")
p = self.db.get_property("author")
self.assertEqual(p.value(), "Jake")
self.db.add_property("data", [1, 2])
p = self.db.get_property("data")
self.assertEqual(p.value(), [1, 2])
# assert that the not set values are None
p = self.db.get_property("natoms")
self.assertIsNone(p.string_value)
self.assertIsNone(p.float_value)
self.assertIsNone(p.pickle_value)
p = self.db.get_property("noprop")
self.assertIsNone(p)
props = self.db.properties(as_dict=True)
self.assertIsInstance(props, dict)
self.assertDictContainsSubset(dict(natoms=10), props)
self.assertEqual(len(list(props.items())), 4)
props = self.db.properties(as_dict=False)
self.assertIn(("natoms", 10), [p.item() for p in props])
self.assertEqual(len(props), 4)
def test_property_dtype(self):
# add some system properties and ensure they were added correctly
self.db.add_property("natoms", 10, dtype="int")
p = self.db.get_property("natoms")
self.assertEqual(p.value(), 10)
self.db.add_property("eps", 2.1, dtype="float")
p = self.db.get_property("eps")
self.assertEqual(p.value(), 2.1)
self.db.add_property("author", "Jake", dtype="string")
p = self.db.get_property("author")
self.assertEqual(p.value(), "Jake")
self.db.add_property("data", [1, 2], dtype="pickle")
p = self.db.get_property("data")
self.assertEqual(p.value(), [1, 2])
def test_bad_property(self):
self.db.add_property("natoms", 10, dtype="string")
p = self.db.get_property("natoms")
self.assertEqual(p.value(), "10")
def test_property_overwrite_false(self):
self.db.add_property("natoms", 10)
# overwriting with the same value should not raise error
self.db.add_property("natoms", 10, overwrite=False)
# overwriting with a different value should raise error
with self.assertRaises(RuntimeError):
self.db.add_property("natoms", 11, overwrite=False)
def test_add_properties(self):
props = dict(natoms=10, author="jake")
self.db.add_properties(props)
for name, value in props.items():
p = self.db.get_property(name)
self.assertEqual(p.value(), value)
def test_load_wrong_schema(self):
current_dir = os.path.dirname(__file__)
dbname = current_dir + "/lj6_schema1.sqlite"
with self.assertRaises(IOError):
db = Database(dbname, createdb=False)
def test_load_right_schema(self):
current_dir = os.path.dirname(__file__)
dbname = current_dir + "/lj6_schema2.sqlite"
db = Database(dbname, createdb=False)
def test_invalid(self):
m = self.db.minima()[0]
self.assertFalse(m.invalid)
m.invalid = True
self.db.session.commit()
self.assertTrue(m.invalid)
# now with a ts
m = self.db.transition_states()[0]
self.assertFalse(m.invalid)
m.invalid = True
self.db.session.commit()
self.assertTrue(m.invalid)
def test_user_data(self):
m = self.db.minima()[0]
m.user_data = dict(key="value")
self.db.session.commit()
v = m.user_data["key"]
# now with a transition state
m = self.db.transition_states()[0]
m.user_data = dict(key="value")
self.db.session.commit()
v = m.user_data["key"]
from pele.utils.disconnectivity_graph import DisconnectivityGraph
from pele.storage import Database
from pele.landscape import TSGraph
import pylab as pl
import numpy as np
kbT = 0.75
db = Database(db="tip4p_8.sqlite", createdb=False)
graph = TSGraph(db)
dg = DisconnectivityGraph(graph.graph, db.minima(), subgraph_size=20)
dg.calculate()
dg.plot()
for m in db.minima():
if m.pgorder != 2:
print m.pgorder
m.free_energy = m.energy + kbT * 0.5*m.fvib + kbT*np.log(m.pgorder)
for ts in db.transition_states():
# if ts.pgorder != 2:
print ts.pgorder
#assert ts.pgorder == 2
ts.free_energy = ts.energy + kbT * 0.5*ts.fvib + kbT*np.log(ts.pgorder) + kbT*np.log(kbT)
if ts.free_energy > ts.minimum1.free_energy or ts.free_energy > ts.minimum2.free_energy:
print "warning, free energy of transition state lower than minimum"
print ts.free_energy, ts.minimum1.free_energy, ts.minimum2.free_energy