def get_offspring(self, cluster_a, cluster_b):
'''Generate an offspring structure from two parent structures.'''
#Prepare clusters
for cluster in [cluster_a, cluster_b]:
cluster.centre()
cluster.rotate_random()
cluster.sort_z()
#Choose cutting plane
cut = np.random.randint(1, cluster_a.natoms)
#Make new cluster
coords = np.empty(shape=(cluster_a.natoms, 3))
atom_types = []
for i in range(0, cut):
coords[i] = cluster_a.get_coords(i)
atom_types.append(cluster_a.atom_types[i])
for i in range(cut, cluster_a.natoms):
coords[i] = cluster_b.get_coords(i)
atom_types.append(cluster_b.atom_types[i])
composition = get_composition(cluster_a.atom_types)
atom_types = fix_composition(composition, atom_types)
offspring = Cluster(cluster_a.natoms,
coords,
cluster_a.minimiser,
atom_types=atom_types,
labels=cluster_a.labels)
offspring.quenched = False
offspring.sort_type()
cluster_a.sort_type()
cluster_b.sort_type()
return offspring
def get_offspring(self,cluster_a,cluster_b):
'''Generate an offspring structure from two parent structures.'''
#Prepare clusters
for cluster in [cluster_a,cluster_b]:
cluster.centre()
cluster.rotate_random()
cluster.sort_z()
#Choose cutting plane
cut=np.random.randint(1,cluster_a.natoms)
#Make new cluster
coords=np.empty(shape=(cluster_a.natoms,3))
atom_types=[]
for i in range(0,cut):
coords[i]=cluster_a.get_coords(i)
atom_types.append(cluster_a.atom_types[i])
for i in range(cut,cluster_a.natoms):
coords[i]=cluster_b.get_coords(i)
atom_types.append(cluster_b.atom_types[i])
composition = get_composition(cluster_a.atom_types)
atom_types=fix_composition(composition,atom_types)
offspring=Cluster(cluster_a.natoms,
coords,
cluster_a.minimiser,
atom_types=atom_types,
labels=cluster_a.labels)
offspring.quenched=False
offspring.sort_type()
cluster_a.sort_type()
cluster_b.sort_type()
return offspring
def read_db(self,minimum):
'''Read structure from Pele minimum'''
cluster=Cluster(self.natoms,
np.reshape(minimum.coords,(-1,3)),
self.minimiser,
atom_types=get_atom_types(self.composition),
labels=self.labels)
cluster.energy=minimum.energy
cluster.quenched=True
return cluster
def get_random_cluster(self):
'''Return a cluster with random coordinates'''
coords=(np.random.rand(self.natoms,3) -0.5) * 1.4 * float(self.natoms)**(1./3.)
cluster = Cluster(self.natoms,
coords,
self.minimiser,
atom_types=get_atom_types(self.composition),
labels=self.labels)
cluster.quenched=False
return cluster
def read_db(self, minimum):
'''Read structure from Pele minimum'''
cluster = Cluster(self.natoms,
np.reshape(minimum.coords, (-1, 3)),
self.minimiser,
atom_types=get_atom_types(self.composition),
labels=self.labels)
cluster.energy = minimum.energy
cluster.quenched = True
return cluster
class ClusterTest(unittest.TestCase):
def setUp(self):
self.natoms=3
minimiser=PeleMinimiser(lj.LJ())
coords=np.array(((0.,0.,0.1),
(1.,1.,0.3),
(0.,2.,0.2)))
self.cluster = Cluster(self.natoms,coords,minimiser)
def test_z_sort(self):
self.cluster.sort_z()
for i in range(1,self.natoms):
self.assertGreater(self.cluster.get_coords(i)[2],
self.cluster.get_coords(i-1)[2])
def test_get_energy(self):
self.assertAlmostEquals(self.cluster.get_energy(),-3.)
def test_centre(self):
self.assertEquals(self.cluster.centre()[1],-1.)
def test_get_atom(self):
self.assertEquals(self.cluster.get_coords(0)[0],0.0)
self.assertEquals(self.cluster.get_coords(0)[1],0.0)
self.assertEquals(self.cluster.get_coords(0)[2],0.1)
def test_get_label(self):
self.assertEquals(self.cluster.get_label(2),"X")
class ClusterTest(unittest.TestCase):
def setUp(self):
self.natoms = 3
minimiser = PeleMinimiser(lj.LJ())
coords = np.array(((0., 0., 0.1), (1., 1., 0.3), (0., 2., 0.2)))
self.cluster = Cluster(self.natoms, coords, minimiser)
def test_z_sort(self):
self.cluster.sort_z()
for i in range(1, self.natoms):
self.assertGreater(
self.cluster.get_coords(i)[2],
self.cluster.get_coords(i - 1)[2])
def test_get_energy(self):
self.assertAlmostEquals(self.cluster.get_energy(), -3.)
def test_centre(self):
self.assertEquals(self.cluster.centre()[1], -1.)
def test_get_atom(self):
self.assertEquals(self.cluster.get_coords(0)[0], 0.0)
self.assertEquals(self.cluster.get_coords(0)[1], 0.0)
self.assertEquals(self.cluster.get_coords(0)[2], 0.1)
def test_get_label(self):
self.assertEquals(self.cluster.get_label(2), "X")
def setUp(self):
self.natoms = 3
minimiser = PeleMinimiser(BLJCut(3, 1))
coords = np.array(((0., 0., 0.1), (1., 1., 0.3), (0., 2., 0.2)))
types = [0, 1, 0]
labels = ["X", "Y"]
self.cluster = Cluster(self.natoms,
coords,
minimiser,
atom_types=types,
labels=labels)
def get_random_cluster(self):
'''Return a cluster with random coordinates'''
coords = (np.random.rand(self.natoms, 3) - 0.5) * 1.4 * float(
self.natoms)**(1. / 3.)
cluster = Cluster(self.natoms,
coords,
self.minimiser,
atom_types=get_atom_types(self.composition),
labels=self.labels)
cluster.quenched = False
return cluster
class OverlapTest(unittest.TestCase):
def setUp(self):
self.natoms = 3
minimiser = PeleMinimiser(lj.LJ())
coords = np.array(((0., 0., 0.1), (0.1, 0.1, 0.1), (0., 0.2, 0.2)))
self.cluster = Cluster(self.natoms, coords, minimiser)
def test_cutoff_default(self):
self.cluster.fix_overlaps()
self.assertGreaterEqual(min_dist(self.cluster._coords), 1.0)
def test_cutoff_2(self):
self.cluster.fix_overlaps(2.0)
self.assertGreaterEqual(min_dist(self.cluster._coords), 2.0)
def get_mutant(self, cluster):
cluster.sort_type()
atom_types = list(cluster.atom_types)
swap0 = np.random.randint(0, get_composition(atom_types)[0])
swap1 = np.random.randint(
get_composition(atom_types)[0], cluster.natoms)
atom_types[swap0] = 1
atom_types[swap1] = 0
mutant = Cluster(cluster.natoms,
np.array(cluster._coords),
cluster.minimiser,
atom_types=atom_types,
labels=cluster.labels)
mutant.sort_type()
return mutant
def read_xyz(self,xyz_file):
'''Read structure from xyz file.'''
natoms=int(xyz_file.readline())
coords=np.empty(shape=(natoms,3))
energy=float(xyz_file.readline().split()[1])
for i in range(0,natoms):
coords[i]=xyz_file.readline().split()[1:4]
cluster=Cluster(natoms,
coords,
self.minimiser,
atom_types=get_atom_types(self.composition),
labels=self.labels)
cluster.energy=energy
cluster.quenched=True
return cluster
def read_xyz(self, xyz_file):
'''Read structure from xyz file.'''
natoms = int(xyz_file.readline())
coords = np.empty(shape=(natoms, 3))
energy = float(xyz_file.readline().split()[1])
for i in range(0, natoms):
coords[i] = xyz_file.readline().split()[1:4]
cluster = Cluster(natoms,
coords,
self.minimiser,
atom_types=get_atom_types(self.composition),
labels=self.labels)
cluster.energy = energy
cluster.quenched = True
return cluster
def get_mutant(self,cluster):
cluster.sort_type()
atom_types=list(cluster.atom_types)
swap0=np.random.randint(0,
get_composition(atom_types)[0])
swap1=np.random.randint(get_composition(atom_types)[0],
cluster.natoms)
atom_types[swap0]=1
atom_types[swap1]=0
mutant=Cluster(cluster.natoms,
np.array(cluster._coords),
cluster.minimiser,
atom_types=atom_types,
labels=cluster.labels)
mutant.sort_type()
return mutant
class OverlapTest(unittest.TestCase):
def setUp(self):
self.natoms=3
minimiser=PeleMinimiser(lj.LJ())
coords=np.array(((0. ,0. ,0.1),
(0.1 ,0.1 ,0.1),
(0. ,0.2 ,0.2)))
self.cluster = Cluster(self.natoms,coords,minimiser)
def test_cutoff_default(self):
self.cluster.fix_overlaps()
self.assertGreaterEqual(min_dist(self.cluster._coords), 1.0)
def test_cutoff_2(self):
self.cluster.fix_overlaps(2.0)
self.assertGreaterEqual(min_dist(self.cluster._coords), 2.0)
def setUp(self):
self.natoms=3
minimiser=PeleMinimiser(lj.LJ())
coords=np.array(((0. ,0. ,0.1),
(0.1 ,0.1 ,0.1),
(0. ,0.2 ,0.2)))
self.cluster = Cluster(self.natoms,coords,minimiser)
def get_mutant(self, cluster):
coords = (np.random.rand(cluster.natoms, 3) - 0.5) * 1.4 * float(
cluster.natoms)**(1. / 3.)
mutant = Cluster(cluster.natoms,
coords,
cluster.minimiser,
atom_types=list(cluster.atom_types),
labels=cluster.labels)
return mutant
def setUp(self):
self.natoms=3
minimiser=PeleMinimiser(BLJCut(3,1))
coords=np.array(((0.,0.,0.1),
(1.,1.,0.3),
(0.,2.,0.2)))
types=[0,1,0]
labels=["X","Y"]
self.cluster = Cluster(self.natoms,coords,minimiser,
atom_types=types,labels=labels)
class BinaryClusterTest(unittest.TestCase):
def setUp(self):
self.natoms=3
minimiser=PeleMinimiser(BLJCut(3,1))
coords=np.array(((0.,0.,0.1),
(1.,1.,0.3),
(0.,2.,0.2)))
types=[0,1,0]
labels=["X","Y"]
self.cluster = Cluster(self.natoms,coords,minimiser,
atom_types=types,labels=labels)
def test_labels(self):
self.assertEquals(self.cluster.get_label(0),"X")
self.assertEquals(self.cluster.get_label(1),"Y")
self.assertEquals(self.cluster.get_label(2),"X")
def test_sort_labels(self):
self.cluster.sort_z()
self.assertEquals(self.cluster.get_label(0),"X")
self.assertEquals(self.cluster.get_label(1),"X")
self.assertEquals(self.cluster.get_label(2),"Y")
class BinaryClusterTest(unittest.TestCase):
def setUp(self):
self.natoms = 3
minimiser = PeleMinimiser(BLJCut(3, 1))
coords = np.array(((0., 0., 0.1), (1., 1., 0.3), (0., 2., 0.2)))
types = [0, 1, 0]
labels = ["X", "Y"]
self.cluster = Cluster(self.natoms,
coords,
minimiser,
atom_types=types,
labels=labels)
def test_labels(self):
self.assertEquals(self.cluster.get_label(0), "X")
self.assertEquals(self.cluster.get_label(1), "Y")
self.assertEquals(self.cluster.get_label(2), "X")
def test_sort_labels(self):
self.cluster.sort_z()
self.assertEquals(self.cluster.get_label(0), "X")
self.assertEquals(self.cluster.get_label(1), "X")
self.assertEquals(self.cluster.get_label(2), "Y")
def setUp(self):
self.natoms = 3
minimiser = PeleMinimiser(lj.LJ())
coords = np.array(((0., 0., 0.1), (0.1, 0.1, 0.1), (0., 0.2, 0.2)))
self.cluster = Cluster(self.natoms, coords, minimiser)
def setUp(self):
natoms = 3
self.minimiser = PeleMinimiser(lj.LJ())
coords = np.array(((0., 0., 0.1), (1., 1., 0.3), (0., 2., 0.2)))
self.cluster = Cluster(natoms, coords, self.minimiser)