def cns_xplor_text(positions, scores, dmif_format):
""" This function generates a list of strings for writing cns and xplor maps. """
density = ['\n', '{0:>8}\n'.format(1), 'REMARKS {} file generated by PyRod\n'.format(dmif_format)]
x_minimum, x_maximum, y_minimum, y_maximum, z_minimum, z_maximum, space = grid_characteristics(positions)
density.append('{0:>8}{1:>8}{2:>8}{3:>8}{4:>8}{5:>8}{6:>8}{7:>8}{8:>8}\n'.format(
int(((x_maximum - x_minimum) / space) + 1), int(x_minimum / space), int(x_maximum / space),
int(((y_maximum - y_minimum) / space) + 1), int(y_minimum / space), int(y_maximum / space),
int(((z_maximum - z_minimum) / space) + 1), int(z_minimum / space), int(z_maximum / space)))
density.append('{0:>12.5E}{1:>12.5E}{2:>12.5E}{3:>12.5E}{3:>12.5E}{3:>12.5E}\n'.format(
x_maximum - x_minimum, y_maximum - y_minimum, z_maximum - z_minimum, 90))
density.append('ZYX\n')
index_z = 0
index_xy = 0
xy = int(((x_maximum - x_minimum) / space) + 1) * int(((y_maximum - y_minimum) / space) + 1)
for score in scores:
if index_xy % xy == 0:
if index_xy % 6 == 0:
density.append('{0:>8}\n'.format(index_z))
else:
density.append('\n{0:>8}\n'.format(index_z))
index_z += 1
index_xy = 0
density.append('{0:>12.5E}'.format(score))
index_xy += 1
if index_xy % 6 == 0:
density.append('\n')
if dmif_format == 'cns':
if index_xy % 6 == 0:
density.append('{0:>8}\n'.format(-9999))
else:
density.append('\n{0:>8}\n'.format(-9999))
density.append(' {0:>12.5E}{1:>12.5E}\n'.format(mean(scores), standard_deviation(scores)))
density.append('\n')
return density
def get_center(positions, cutoff):
""" This function returns the approximate position with the most neighbors within the specified cutoff. If multiple
positions have the most neighbors, the position with the lowest standard deviation of the distances to its
neighbors is returned. """
x_minimum, x_maximum, y_minimum, y_maximum, z_minimum, z_maximum = grid_characteristics(
positions)[:-1]
x_center, y_center, z_center = [
round((x_minimum + x_maximum) / 2, 1),
round((y_minimum + y_maximum) / 2, 1),
round((z_minimum + z_maximum) / 2, 1)
]
x_length, y_length, z_length = [
round(x_maximum - x_minimum, 1),
round(y_maximum - y_minimum, 1),
round(z_maximum - z_minimum, 1)
]
grid = generate_grid([x_center, y_center, z_center],
[x_length, y_length, z_length], 0.1)
tree = cKDTree(positions)
length_positions = len(positions)
less_positions = [
positions[x]
for x in set(tree.query(grid, distance_upper_bound=0.1)[1])
if x != length_positions
]
small_tree = cKDTree(less_positions)
indices_lists = small_tree.query_ball_tree(tree, cutoff)
indices_maximal_neighbors = []
maximal_neighbours = max([len(x) for x in indices_lists])
for index, x in enumerate(indices_lists):
if len(x) == maximal_neighbours:
indices_maximal_neighbors.append(index)
if len(indices_maximal_neighbors) > 1:
minimal_stddev = None
index_minimal_stddev = None
for index in indices_maximal_neighbors:
stddev = standard_deviation([
distance(x, less_positions[index])
for x in [positions[x] for x in indices_lists[index]]
])
if minimal_stddev is None:
minimal_stddev = stddev
index_minimal_stddev = index
elif stddev < minimal_stddev:
minimal_stddev = stddev
index_minimal_stddev = index
return [
less_positions[index_minimal_stddev],
indices_lists[index_minimal_stddev]
]
else:
return [
less_positions[indices_maximal_neighbors[0]],
indices_lists[indices_maximal_neighbors[0]]
]
def test_population_standard_deviation(self):
""" This function tests the population_standard_deviation function. """
test_cases = [[
np.array([
-1.0, -2, -5, -1, -5, -5, -5, 5, 0, 5, -5, -4, -5, -1, -2, 3,
-2, -1, 0, 1, -3, 0, 3, -5, 2, -3, 5, 3, 1, -1
]), 3.20347034046239
],
[[
-1.0, -2, -5, -1, -5, -5, -5, 5, 0, 5, -5, -4, -5,
-1, -2, 3, -2, -1, 0, 1, -3, 0, 3, -5, 2, -3, 5, 3,
1, -1
], 3.20347034046239]]
for test_case in test_cases:
self.assertAlmostEqual(standard_deviation(test_case[0]),
test_case[1])