def find_peaks(self):
grid_real_binary = self.grid_real.deep_copy()
rmsd = math.sqrt(
flex.mean(
flex.pow2(grid_real_binary.as_1d() -
flex.mean(grid_real_binary.as_1d()))))
grid_real_binary.set_selected(
grid_real_binary < (self.params.rmsd_cutoff) * rmsd, 0)
grid_real_binary.as_1d().set_selected(grid_real_binary.as_1d() > 0, 1)
grid_real_binary = grid_real_binary.iround()
from cctbx import masks
flood_fill = masks.flood_fill(grid_real_binary, self.fft_cell)
if flood_fill.n_voids() < 4:
# Require at least peak at origin and one peak for each basis vector
raise Sorry(
"Indexing failed: fft3d peak search failed to find sufficient number of peaks."
)
# the peak at the origin might have a significantly larger volume than the
# rest so exclude this peak from determining maximum volume
isel = (flood_fill.grid_points_per_void() > int(
self.params.fft3d.peak_volume_cutoff *
flex.max(flood_fill.grid_points_per_void()[1:]))).iselection()
if self.params.optimise_initial_basis_vectors:
self.volumes = flood_fill.grid_points_per_void().select(isel)
sites_cart = flood_fill.centres_of_mass_cart().select(isel)
sites_cart_optimised = optimise_basis_vectors(
self.reflections['rlp'].select(
self.reflections_used_for_indexing), sites_cart)
self.sites = self.fft_cell.fractionalize(sites_cart_optimised)
diffs = (sites_cart_optimised - sites_cart)
norms = diffs.norms()
flex.min_max_mean_double(norms).show()
perm = flex.sort_permutation(norms, reverse=True)
for p in perm[:10]:
logger.debug(sites_cart[p], sites_cart_optimised[p], norms[p])
# only use those vectors which haven't shifted too far from starting point
sel = norms < (5 * self.fft_cell.parameters()[0] /
self.gridding[0])
self.sites = self.sites.select(sel)
self.volumes = self.volumes.select(sel)
#diff = (self.sites - flood_fill.centres_of_mass_frac().select(isel))
#flex.min_max_mean_double(diff.norms()).show()
else:
self.sites = flood_fill.centres_of_mass_frac().select(isel)
self.volumes = flood_fill.grid_points_per_void().select(isel)
def extract_from_scatterers_in_place(self, scatterers, tolerance=1.e-4):
fps = flex.double()
fdps = flex.double()
fps.reserve(self.iselection.size())
fdps.reserve(fps.size())
for i_seq in self.iselection:
fps.append(scatterers[i_seq].fp)
fdps.append(scatterers[i_seq].fdp)
for values, label in [(fps, "f_prime"), (fdps, "f_double_prime")]:
stats = flex.min_max_mean_double(values)
if (stats.max - stats.min <= tolerance):
setattr(self, label, stats.mean)
else:
msg = [
"Anomalous scatterer group with significantly different %s:"
% label
]
if (self.selection_string is not None):
msg.append(" Selection: %s" %
show_string(self.selection_string))
msg.append(" Number of selected scatterers: %d" % stats.n)
s = StringIO()
stats.show(out=s, prefix=" %s " % label, show_n=False)
msg.extend(s.getvalue().splitlines())
msg.append(" tolerance: %.6g" % tolerance)
raise RuntimeError("\n".join(msg))
def find_peaks(self):
grid_real_binary = self.grid_real.deep_copy()
rmsd = math.sqrt(
flex.mean(flex.pow2(grid_real_binary.as_1d()-flex.mean(grid_real_binary.as_1d()))))
grid_real_binary.set_selected(grid_real_binary < (self.params.rmsd_cutoff)*rmsd, 0)
grid_real_binary.as_1d().set_selected(grid_real_binary.as_1d() > 0, 1)
grid_real_binary = grid_real_binary.iround()
from cctbx import masks
flood_fill = masks.flood_fill(grid_real_binary, self.fft_cell)
if flood_fill.n_voids() < 4:
# Require at least peak at origin and one peak for each basis vector
raise Sorry("Indexing failed: fft3d peak search failed to find sufficient number of peaks.")
# the peak at the origin might have a significantly larger volume than the
# rest so exclude this peak from determining maximum volume
isel = (flood_fill.grid_points_per_void() > int(
self.params.fft3d.peak_volume_cutoff * flex.max(
flood_fill.grid_points_per_void()[1:]))).iselection()
if self.params.optimise_initial_basis_vectors:
self.volumes = flood_fill.grid_points_per_void().select(isel)
sites_cart = flood_fill.centres_of_mass_cart().select(isel)
sites_cart_optimised = optimise_basis_vectors(
self.reflections['rlp'].select(self.reflections_used_for_indexing),
sites_cart)
self.sites = self.fft_cell.fractionalize(sites_cart_optimised)
diffs = (sites_cart_optimised - sites_cart)
norms = diffs.norms()
flex.min_max_mean_double(norms).show()
perm = flex.sort_permutation(norms, reverse=True)
for p in perm[:10]:
logger.debug(sites_cart[p], sites_cart_optimised[p], norms[p])
# only use those vectors which haven't shifted too far from starting point
sel = norms < (5 * self.fft_cell.parameters()[0]/self.gridding[0])
self.sites = self.sites.select(sel)
self.volumes = self.volumes.select(sel)
#diff = (self.sites - flood_fill.centres_of_mass_frac().select(isel))
#flex.min_max_mean_double(diff.norms()).show()
else:
self.sites = flood_fill.centres_of_mass_frac().select(isel)
self.volumes = flood_fill.grid_points_per_void().select(isel)
def extract_from_scatterers_in_place(self, scatterers, tolerance=1.0e-4):
fps = flex.double()
fdps = flex.double()
fps.reserve(self.iselection.size())
fdps.reserve(fps.size())
for i_seq in self.iselection:
fps.append(scatterers[i_seq].fp)
fdps.append(scatterers[i_seq].fdp)
for values, label in [(fps, "f_prime"), (fdps, "f_double_prime")]:
stats = flex.min_max_mean_double(values)
if stats.max - stats.min <= tolerance:
setattr(self, label, stats.mean)
else:
msg = ["Anomalous scatterer group with significantly different %s:" % label]
if self.selection_string is not None:
msg.append(" Selection: %s" % show_string(self.selection_string))
msg.append(" Number of selected scatterers: %d" % stats.n)
s = StringIO()
stats.show(out=s, prefix=" %s " % label, show_n=False)
msg.extend(s.getvalue().splitlines())
msg.append(" tolerance: %.6g" % tolerance)
raise RuntimeError("\n".join(msg))
