def minimize_divide_clustering(self):
x = self.coords_reduced[:, :1].as_1d()
y = self.coords_reduced[:, 1:2].as_1d()
from cctbx.merging.brehm_diederichs import minimize_divide
selection = minimize_divide(x, y).plus_minus()
self.cluster_labels = flex.int(x.size(), 0)
self.cluster_labels.set_selected(selection, 1)
def _minimize_divide_clustering(self):
assert self.params.cluster.n_clusters in (2, Auto)
x = self.coords_reduced[:, :1].as_1d()
y = self.coords_reduced[:, 1:2].as_1d()
from cctbx.merging.brehm_diederichs import minimize_divide
selection = minimize_divide(x, y).plus_minus()
cluster_labels = flex.int(x.size(), 0)
cluster_labels.set_selected(selection, 1)
return cluster_labels
def alignment_by_embedding(reports, plot=False):
#from IPython import embed; embed()
"""reports is a list of tranch results, one list item per composite tranch
Each item is a list, over cosets, e.g. two elements for a merohedral twinning op
Each element is itself a list of uuid's assigned to that coset.
"""
n_tranches = len(reports)
reports = copy.deepcopy(reports)
# will now amend the reports-list so that it has a new section for each permutation of cosets.
for itranch in range(len(reports)):
cache_permutations = list(permutations(reports[itranch]))
reports.append(cache_permutations[1])
# will have to rewrite this code if there is more that one symmetry operator XXX FIXME
rij, wij = get_proposal_score(reports)
mt = flex.mersenne_twister(seed=0)
#from IPython import embed; embed()
NN = len(reports)
xcoord = mt.random_double(size=NN)
ycoord = mt.random_double(size=NN)
if plot:
from matplotlib import pyplot as plt
plt.plot(xcoord, ycoord, "g.")
plt.show()
from cctbx.merging.brehm_diederichs import minimize as mz, minimize_divide
M = mz(xcoord, ycoord, rij, wij, verbose=True)
coord_x = M.x[0:NN]
coord_y = M.x[NN:2 * NN]
P = minimize_divide(coord_x, coord_y)
selection = P.plus_minus()
if plot:
plt.plot(coord_x.select(selection),
coord_y.select(selection),
"r.",
markersize=2.)
plt.plot(coord_x.select(~selection),
coord_y.select(~selection),
"k.",
markersize=3.)
plt.show()
print(list(selection))
reformed_reports = [[] for i in range(n_tranches)
] # output should have as many reports as tranches
n_permutations = NN // n_tranches
for iflag, select_flag in enumerate(selection):
if select_flag:
itranch = iflag % n_tranches
#print( itranch, iflag, n_permutations)
reformed_reports[itranch] = reports[iflag]
assert [] not in reformed_reports # all tranches must have coset assignments
return reformed_reports