def find_max_cell(
reflections,
max_cell_multiplier=1.3,
step_size=45,
nearest_neighbor_percentile=None,
histogram_binning="linear",
nn_per_bin=5,
max_height_fraction=0.25,
filter_ice=True,
filter_overlaps=True,
overlaps_border=0,
):
logger.debug("Finding suitable max_cell based on %i reflections" %
len(reflections))
# Exclude potential ice-ring spots from nearest neighbour analysis if needed
if filter_ice:
ice_sel = ice_rings_selection(reflections)
reflections = reflections.select(~ice_sel)
logger.debug("Rejecting %i reflections at ice ring resolution" %
ice_sel.count(True))
# need bounding box in reflections to find overlaps; this is not there if
# spots are from XDS (for example)
if filter_overlaps and "bbox" in reflections:
overlap_sel = flex.bool(len(reflections), False)
overlaps = reflections.find_overlaps(border=overlaps_border)
for item in overlaps.edges():
i0 = overlaps.source(item)
i1 = overlaps.target(item)
overlap_sel[i0] = True
overlap_sel[i1] = True
logger.debug("Rejecting %i overlapping bounding boxes" %
overlap_sel.count(True))
reflections = reflections.select(~overlap_sel)
logger.debug("%i reflections remain for max_cell identification" %
len(reflections))
if not len(reflections):
raise DialsIndexError(
"Too few spots remaining for nearest neighbour analysis (%d)" %
len(reflections))
NN = NeighborAnalysis(
reflections,
step_size=step_size,
max_height_fraction=max_height_fraction,
tolerance=max_cell_multiplier,
percentile=nearest_neighbor_percentile,
histogram_binning=histogram_binning,
nn_per_bin=nn_per_bin,
)
return NN
def test_estimate_resolution(centroid_test_data, tmpdir):
experiments, reflections = centroid_test_data
ice_sel = per_image_analysis.ice_rings_selection(reflections, width=0.004)
assert ice_sel.count(True) == 76
plot_file = tmpdir.join("i_over_sigi_vs_resolution.png")
d_min = per_image_analysis.estimate_resolution_limit(
reflections=reflections, plot_filename=plot_file.strpath)
assert d_min == pytest.approx(1.446715534174674)
assert plot_file.check()
d_min = per_image_analysis.estimate_resolution_limit(
reflections=reflections, ice_sel=ice_sel)
assert d_min == pytest.approx(1.446715534174674)
def calculate_stats(self, verbose=False):
# Only accept "good" spots based on specific parameters, if selected
# 1. No ice
if self.cfg.getboolean("spf_ice_filter"):
ice_sel = per_image_analysis.ice_rings_selection(self.refl)
spots_no_ice = self.refl.select(~ice_sel)
else:
spots_no_ice = self.refl
# 2. Falls between 40 - 4.5A
if self.cfg.getboolean('spf_good_spots_only'):
res_lim_sel = self.filter_by_resolution(
refl=spots_no_ice,
d_min=self.cfg.getstr('spf_d_min'),
d_max=self.cfg.getstr('spf_d_max'))
good_spots = spots_no_ice.select(res_lim_sel)
else:
good_spots = spots_no_ice
# Saturation / distance are already filtered by the spotfinder
self.n_spots = good_spots.size()
# Estimate resolution by spots that aren't ice (susceptible to poor ice ring
# location)
if self.n_spots > 10:
self.hres = per_image_analysis.estimate_resolution_limit(spots_no_ice)
else:
self.hres = 99.9
if verbose:
print('SCORER: no. spots total = ', self.refl.size())
if self.cfg.getboolean('spf_ice_filter'):
print('SCORER: no. spots (no ice) = ', spots_no_ice.size())
no_ice = 'no ice, '
else:
no_ice = ''
if self.cfg.getboolean('spf_good_spots_only'):
print('SCORER: no. spots ({}w/in res limits) = '.format(no_ice),
good_spots.size())