def _metric_dist(self):
if self.metric == 'periodic':
if not 'cell_lengths' in self.metric_args:
raise ValueError('periodic metric require cell_lengths arg')
periodic = self.metric_args['cell_lengths']
return lambda a, b, periodic=np.array(periodic): periodic_distance(np.array(a), np.array(b), periodic)
else:
raise ValueError('metric %s unsupported.' % self.metric)
def _metric_dist(self):
if self.metric == 'periodic':
if not 'cell_lengths' in self.metric_args:
raise ValueError('periodic metric require cell_lengths arg')
periodic = self.metric_args['cell_lengths']
return lambda a, b, periodic=np.array(periodic): periodic_distance(
np.array(a), np.array(b), periodic)
else:
raise ValueError('metric %s unsupported.' % self.metric)
def test_find():
K = 100
tree = CoverTree(metric='periodic',
metric_args={'cell_lengths': [10, 10, 10]})
positions = np.random.uniform(0, 10, (K, 3))
tree.insert_many(positions)
check_invariant(tree)
i = 0
for p in np.random.uniform(0, 10, (K * 10, 3)):
ct, dist = tree.find(p)
bf = periodic_distance(p, positions, np.array([10, 10, 10])).min()
dr = dist - bf
if dr >= 1e-10:
real = np.argmin(periodic_distance(p, positions, np.array([10, 10, 10])))
print 'CoverTree', dist, ct, 'BruteForce', bf, positions[real
], real
assert dr <= 1e-10
def test_find():
K = 100
tree = CoverTree(metric='periodic',
metric_args={'cell_lengths': [10, 10, 10]})
positions = np.random.uniform(0, 10, (K, 3))
tree.insert_many(positions)
check_invariant(tree)
i = 0
for p in np.random.uniform(0, 10, (K * 10, 3)):
ct, dist = tree.find(p)
bf = periodic_distance(p, positions, np.array([10, 10, 10])).min()
dr = dist - bf
if dr >= 1e-10:
real = np.argmin(
periodic_distance(p, positions, np.array([10, 10, 10])))
print 'CoverTree', dist, ct, 'BruteForce', bf, positions[
real], real
assert dr <= 1e-10
def test_random_box():
na = Molecule([Atom("Na", [0.0, 0.0, 0.0])])
cl = Molecule([Atom("Cl", [0.0, 0.0, 0.0])])
water = Molecule([Atom("O", [0.0, 0.0, 0.0]), Atom("H", [0.0, 0.1, 0.0]), Atom("H", [0.1, 0.0, 0.0])])
box = random_box([na, cl, water], total=200, proportions=[0.1, 0.1, 0.8], size=[3, 3, 3])
from chemlab.utils.pbc import periodic_distance
for a, b in [("O", "Na"), ("O", "Cl"), ("Na", "Cl")]:
asys = box.sub(atom_type=a)
bsys = box.sub(atom_type=b)
D = periodic_distance(asys.r_array[None, :], bsys.r_array[:, None], np.array([3, 3, 3]))
ok_(D.min() > vdw_radius(a) + vdw_radius(b))
def test_query_ball():
#tree = CoverTree(distance=lambda a, b: periodic_distance(np.array(a), np.array(b), np.array([10, 10, 10])))
tree = CoverTree(metric='periodic',
metric_args={'cell_lengths': [10, 10, 10]})
np.random.seed(42)
random.seed(42)
positions = np.random.uniform(0, 5, (5000, 3))
np.set_printoptions(precision=2)
for p in positions:
# print 'inserting', p
node = tree.insert(p)
ref = (periodic_distance([1.0, 1.0, 1.0], positions,
np.array([10, 10, 10])) < 3.0).nonzero()[0]
res, dist = tree.query_ball([1, 1, 1], 3.0)
#tree.check_invariant()
#print ref, res
eq_(len(ref), len(res))
def test_query_ball():
#tree = CoverTree(distance=lambda a, b: periodic_distance(np.array(a), np.array(b), np.array([10, 10, 10])))
tree = CoverTree(metric='periodic',
metric_args={'cell_lengths': [10, 10, 10]})
np.random.seed(42)
random.seed(42)
positions = np.random.uniform(0, 5, (5000, 3))
np.set_printoptions(precision=2)
for p in positions:
# print 'inserting', p
node = tree.insert(p)
ref = (periodic_distance([1.0, 1.0, 1.0], positions, np.array(
[10, 10, 10])) < 3.0).nonzero()[0]
res, dist = tree.query_ball([1, 1, 1], 3.0)
#tree.check_invariant()
#print ref, res
eq_(len(ref), len(res))
