def get_top_solutions(self, ai, input_directions, size, cutoff_divisor):
# size was 30 in the Rossmann DPS paper
kval_cutoff = ai.getXyzSize() / cutoff_divisor
hemisphere_solutions = flex.Direction()
hemisphere_solutions.reserve(size)
for i in range(len(input_directions)):
sampled_direction = ai.fft_result(input_directions[i])
if sampled_direction.kval > kval_cutoff:
hemisphere_solutions.append(sampled_direction)
if (hemisphere_solutions.size() < 3):
return hemisphere_solutions
kvals = flex.double([
hemisphere_solutions[x].kval
for x in range(len(hemisphere_solutions))
])
perm = flex.sort_permutation(kvals, True)
# conventional algorithm; just take the top scoring hits.
# use this for quick_grid, when it is known ahead of time
# that many (hundreds) of directions will be retained
hemisphere_solutions_sort = flex.Direction(
[hemisphere_solutions[p] for p in perm[0:min(size, len(perm))]])
return hemisphere_solutions_sort
def get_top_solutions(self, ai, input_directions, size, cutoff_divisor,
grid):
kval_cutoff = ai.getXyzSize() / cutoff_divisor
hemisphere_solutions = flex.Direction()
hemisphere_solutions.reserve(size)
#print "# of input directions", len(input_directions)
for i in range(len(input_directions)):
D = sampled_direction = ai.fft_result(input_directions[i])
#hardcoded parameter in for silicon example. Not sure at this point how
#robust the algorithm is when this value is relaxed.
if D.real < self.max_cell and sampled_direction.kval > kval_cutoff:
if diagnostic: directional_show(D, "%5d" % i)
hemisphere_solutions.append(sampled_direction)
if (hemisphere_solutions.size() < 3):
return hemisphere_solutions
kvals = flex.double([
hemisphere_solutions[x].kval
for x in range(len(hemisphere_solutions))
])
perm = flex.sort_permutation(kvals, True)
# need to be more clever than just taking the top 30.
# one huge cluster around a strong basis direction could dominate the
# whole hemisphere map, preventing the discovery of three basis vectors
perm_idx = 0
unique_clusters = 0
hemisphere_solutions_sort = flex.Direction()
while perm_idx < len(perm) and \
unique_clusters < size:
test_item = hemisphere_solutions[perm[perm_idx]]
direction_ok = True
for list_item in hemisphere_solutions_sort:
distance = math.sqrt(
math.pow(list_item.dvec[0] - test_item.dvec[0], 2) +
math.pow(list_item.dvec[1] - test_item.dvec[1], 2) +
math.pow(list_item.dvec[2] - test_item.dvec[2], 2))
if distance < 0.087: #i.e., 5 degrees radius for clustering analysis
direction_ok = False
break
if direction_ok:
unique_clusters += 1
hemisphere_solutions_sort.append(test_item)
perm_idx += 1
return hemisphere_solutions_sort
def get_top_solutions(self, ai, input_directions, size, cutoff_divisor,
grid):
# size was 30 in the Rossmann DPS paper
kval_cutoff = ai.getXyzSize() / cutoff_divisor
hemisphere_solutions = flex.Direction()
hemisphere_solutions.reserve(size)
for i in range(len(input_directions)):
D = sampled_direction = ai.fft_result(input_directions[i])
if D.real < self.max_cell_input and sampled_direction.kval > kval_cutoff:
#directional_show(D, "ddd %5d"%i)
hemisphere_solutions.append(sampled_direction)
if (hemisphere_solutions.size() < 3):
return hemisphere_solutions
kvals = flex.double([
hemisphere_solutions[x].kval
for x in range(len(hemisphere_solutions))
])
perm = flex.sort_permutation(kvals, True)
# need to be more clever than just taking the top 30.
# one huge cluster around a strong basis direction could dominate the
# whole hemisphere map, preventing the discovery of three basis vectors
perm_idx = 0
unique_clusters = 0
hemisphere_solutions_sort = flex.Direction()
while perm_idx < len(perm) and \
unique_clusters < size:
test_item = hemisphere_solutions[perm[perm_idx]]
direction_ok = True
for list_item in hemisphere_solutions_sort:
distance = math.sqrt(
math.pow(list_item.dvec[0] - test_item.dvec[0], 2) +
math.pow(list_item.dvec[1] - test_item.dvec[1], 2) +
math.pow(list_item.dvec[2] - test_item.dvec[2], 2))
if distance < 0.087: #i.e., 5 degrees
direction_ok = False
break
if direction_ok:
unique_clusters += 1
hemisphere_solutions_sort.append(test_item)
perm_idx += 1
return hemisphere_solutions_sort
def refine_top_solutions_by_grid_search(self, old_solutions, ai):
new_solutions = flex.Direction()
final_target_grid = self.characteristic_grid / 100.
for item in old_solutions:
new_solutions.append(
ai.refine_direction(candidate=item,
current_grid=self.incr,
target_grid=final_target_grid))
return new_solutions
def restrict_target_angles_to_quick_winners(self, quick_sampling):
# find which target_grid directions are near neighbors to
# the quick_sampling winners from the first round of testing.
# in the second round, restrict the calculation of FFTs to those directions
self.restricted = flex.Direction()
target = flex.double()
for iz in self.angles:
# fairly inefficient in Python; expect big improvement in C++
v = iz.dvec
target.append(v[0])
target.append(v[1])
target.append(v[2])
kn = int(2 * self.second_round_sampling)
#construct k-d tree for the reference set
A = AnnAdaptor(data=target, dim=3, k=kn)
query = flex.double()
for j in quick_sampling:
v = j.dvec
query.append(v[0])
query.append(v[1])
query.append(v[2])
if abs((math.pi / 2.) -
j.psi) < 0.0001: #take care of equatorial boundary
query.append(-v[0])
query.append(-v[1])
query.append(-v[2])
A.query(query) #find nearest neighbors of query points
neighbors = flex.sqrt(A.distances)
neighborid = A.nn
accept_flag = flex.bool(len(self.angles))
for idx in range(len(neighbors)):
# use small angle approximation to test if target is within desired radius
if neighbors[idx] < self.quick_grid:
accept_flag[neighborid[idx]] = True
#go through all of the original target angles
for iz in range(len(self.angles)):
if accept_flag[iz]:
self.restricted.append(self.angles[iz])
self.angles = self.restricted
