def test_filter_actual_energy(self):
generator = wallpaper.generate_wallpaper((1, 1),
place_max=5,
sample_count=100)
result = [None]
def callback(energies):
result[0] = energies
potentials = {
(0, 0): potential.LennardJonesType(lambda_=-1),
(1, 1): potential.LennardJonesType(lambda_=-1),
(0, 1): potential.LennardJonesType(lambda_=1)
}
distance = 4.0
filter = minimization.filter(generator,
potentials,
distance,
20,
histogram_callback=callback)
# Make sure true energies are actually less than cutoff
filter_results = list(filter) # Exhaust generator to trigger callback
energies = result[0]
for cell in filter_results:
self.assertLessEqual(
potential._evaluate_fast(cell[1], potentials, distance)[0],
min(energies[20:]))
def test_filter_callback(self):
generator = wallpaper.generate_wallpaper((1, 1),
place_max=5,
sample_count=100)
result = [None]
def callback(energies):
result[0] = energies
filter = minimization.filter(
generator, {
(0, 0): potential.LennardJonesType(lambda_=-1),
(1, 1): potential.LennardJonesType(lambda_=-1),
(0, 1): potential.LennardJonesType(lambda_=1)
},
4.0,
10,
histogram_callback=callback)
# Check for proper filtering behavior
self.assertEqual(len(list(filter)), 10)
# Get energies (callback should have been triggered)
energies = result[0]
sorted_energies = sorted(energies)
# Check partitioning
self.assertEqual(energies[10],
sorted_energies[10]) # Did partition get target?
self.assertLessEqual(max(energies[:10]), min(
energies[10:])) # Did partition move elements?
def test_filter_radii(self):
generator = wallpaper.generate_wallpaper((1, 1),
place_max=5,
sample_count=100)
filter = minimization.filter(
generator, {
(0, 0): potential.LennardJonesType(lambda_=-1),
(1, 1): potential.LennardJonesType(lambda_=-1),
(0, 1): potential.LennardJonesType(lambda_=1)
}, 4.0, 10, (0.5, 0.5))
self.assertEqual(len(list(filter)), 10)
def test_filter_generator(self):
count = 100
distance = 3.0
radii = (2.**(1. / 6.) / 2., 2.**(1. / 6.) / 2.)
potentials = {
(0, 0): potential.LennardJonesType(epsilon=0),
(1, 1): potential.LennardJonesType(epsilon=1),
(0, 1): potential.LennardJonesType(epsilon=0.5)
}
mm = similarity.Histogram(distance,
0.05,
0.99,
norm=similarity.Minimum())
# Generate an ensemble of candidates
generator = wallpaper.generate_wallpaper(
(1, 1),
place_max=4,
random_seed=0,
sample_count=count,
merge_sets=False,
sample_groups=[wallpaper.WallpaperGroup(name="p3")])
results = list(generator)
# All "count" structures should be provided
self.assertEqual(len(results), count)
# Scale cells to "contact"
scale_cell = lambda cell: crystal.CellTools.scale(
cell, cell.vectors / cell.scale_factor(radii))
cells = [scale_cell(c) for g, c in results]
energies = numpy.array([
potential._evaluate_fast(c, potentials, distance)[0] for c in cells
])
res = zip(cells, energies)
# Find "unique" ones
reduced_cells = similarity.reduce(res, mm)
manually_reduced = len(reduced_cells)
self.assertEqual(manually_reduced, 89)
# Do in an automated fashion to compare
generator2 = wallpaper.generate_wallpaper(
(1, 1),
place_max=4,
random_seed=0,
sample_count=count,
merge_sets=False,
sample_groups=[wallpaper.WallpaperGroup(name="p3")])
filter2 = minimization.filter(generator2,
potentials,
distance,
count,
radii,
similarity_metric=mm)
self.assertEqual(len(list(filter2)), manually_reduced)
# Confirm that cells from automatic reduction have not been rescaled
for idx, (g, c) in enumerate(list(filter2)):
self.assertTrue(c != reduced_cells[idx][0])
self.assertTrue(scale_cell(c) == reduced_cells[idx][0])
self.assertTrue(
potential._evaluate_fast(scale_cell(c), potentials, distance)
[0] == reduced_cells[idx][1])