def exercise_flood_fill():
uc = uctbx.unit_cell('10 10 10 90 90 90')
for uc in (uctbx.unit_cell('10 10 10 90 90 90'),
uctbx.unit_cell('9 10 11 87 91 95')):
gridding = maptbx.crystal_gridding(
unit_cell=uc,
pre_determined_n_real=(5,5,5))
corner_cube = (0,4,20,24,100,104,120,124) # cube across all 8 corners
channel = (12,37,38,39,42,43,62,63,67,68,87,112)
data = flex.int(flex.grid(gridding.n_real()))
for i in (corner_cube + channel): data[i] = 1
flood_fill = masks.flood_fill(data, uc)
assert data.count(0) == 105
for i in corner_cube: assert data[i] == 2
for i in channel: assert data[i] == 3
assert approx_equal(flood_fill.centres_of_mass(),
((-0.5, -0.5, -0.5), (-2.5, 7/3, 2.5)))
assert approx_equal(flood_fill.centres_of_mass_frac(),
((-0.1, -0.1, -0.1), (-0.5, 7/15, 0.5)))
assert approx_equal(flood_fill.centres_of_mass_cart(),
uc.orthogonalize(flood_fill.centres_of_mass_frac()))
assert flood_fill.n_voids() == 2
assert approx_equal(flood_fill.grid_points_per_void(), (8, 12))
if 0:
from crys3d import wx_map_viewer
wx_map_viewer.display(raw_map=data.as_double(), unit_cell=uc, wires=False)
#
gridding = maptbx.crystal_gridding(
unit_cell=uc,
pre_determined_n_real=(10,10,10))
data = flex.int(flex.grid(gridding.n_real()))
# parallelogram
points = [(2,4,5),(3,4,5),(4,4,5),(5,4,5),(6,4,5),
(3,5,5),(4,5,5),(5,5,5),(6,5,5),(7,5,5),
(4,6,5),(5,6,5),(6,6,5),(7,6,5),(8,6,5)]
points_frac = flex.vec3_double()
for p in points:
data[p] = 1
points_frac.append([p[i]/gridding.n_real()[i] for i in range(3)])
points_cart = uc.orthogonalize(points_frac)
flood_fill = masks.flood_fill(data, uc)
assert data.count(2) == 15
assert approx_equal(flood_fill.centres_of_mass_frac(), ((0.5,0.5,0.5),))
pai_cart = math.principal_axes_of_inertia(
points=points_cart, weights=flex.double(points_cart.size(),1.0))
F = matrix.sqr(uc.fractionalization_matrix())
O = matrix.sqr(uc.orthogonalization_matrix())
assert approx_equal(
pai_cart.center_of_mass(), flood_fill.centres_of_mass_cart()[0])
assert approx_equal(
flood_fill.covariance_matrices_cart()[0],
(F.transpose() * matrix.sym(
sym_mat3=flood_fill.covariance_matrices_frac()[0]) * F).as_sym_mat3())
assert approx_equal(
pai_cart.inertia_tensor(), flood_fill.inertia_tensors_cart()[0])
assert approx_equal(pai_cart.eigensystem().vectors(),
flood_fill.eigensystems_cart()[0].vectors())
assert approx_equal(pai_cart.eigensystem().values(),
flood_fill.eigensystems_cart()[0].values())
return
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 _find_peaks(self, grid_real, d_min):
grid_real_binary = 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
# real space FFT grid dimensions
cell_lengths = [self._n_points * d_min / 2 for i in range(3)]
self._fft_cell = uctbx.unit_cell(cell_lengths + [90] * 3)
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 indexing.DialsIndexError(
"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 any anomalously large peaks from determining minimum volume
from scitbx.math import five_number_summary
outliers = flex.bool(flood_fill.n_voids(), False)
grid_points_per_void = flood_fill.grid_points_per_void()
min_x, q1_x, med_x, q3_x, max_x = five_number_summary(
grid_points_per_void)
iqr_multiplier = 5
iqr_x = q3_x - q1_x
cut_x = iqr_multiplier * iqr_x
outliers.set_selected(
grid_points_per_void.as_double() > (q3_x + cut_x), True)
# print q3_x + cut_x, outliers.count(True)
isel = (grid_points_per_void > int(
self._params.peak_volume_cutoff *
flex.max(grid_points_per_void.select(~outliers)))).iselection()
sites = flood_fill.centres_of_mass_frac().select(isel)
volumes = flood_fill.grid_points_per_void().select(isel)
return sites, volumes
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 exercise_flood_fill():
uc = uctbx.unit_cell('10 10 10 90 90 90')
for uc in (uctbx.unit_cell('10 10 10 90 90 90'),
uctbx.unit_cell('9 10 11 87 91 95')):
gridding = maptbx.crystal_gridding(unit_cell=uc,
pre_determined_n_real=(5, 5, 5))
corner_cube = (0, 4, 20, 24, 100, 104, 120, 124
) # cube across all 8 corners
channel = (12, 37, 38, 39, 42, 43, 62, 63, 67, 68, 87, 112)
data = flex.int(flex.grid(gridding.n_real()))
for i in (corner_cube + channel):
data[i] = 1
flood_fill = masks.flood_fill(data, uc)
assert data.count(0) == 105
for i in corner_cube:
assert data[i] == 2
for i in channel:
assert data[i] == 3
assert approx_equal(flood_fill.centres_of_mass(),
((-0.5, -0.5, -0.5), (-2.5, 7 / 3, 2.5)))
assert approx_equal(flood_fill.centres_of_mass_frac(),
((-0.1, -0.1, -0.1), (-0.5, 7 / 15, 0.5)))
assert approx_equal(
flood_fill.centres_of_mass_cart(),
uc.orthogonalize(flood_fill.centres_of_mass_frac()))
assert flood_fill.n_voids() == 2
assert approx_equal(flood_fill.grid_points_per_void(), (8, 12))
if 0:
from crys3d import wx_map_viewer
wx_map_viewer.display(raw_map=data.as_double(),
unit_cell=uc,
wires=False)
#
gridding = maptbx.crystal_gridding(unit_cell=uc,
pre_determined_n_real=(10, 10, 10))
data = flex.int(flex.grid(gridding.n_real()))
# parallelogram
points = [(2, 4, 5), (3, 4, 5), (4, 4, 5), (5, 4, 5), (6, 4, 5),
(3, 5, 5), (4, 5, 5), (5, 5, 5), (6, 5, 5), (7, 5, 5),
(4, 6, 5), (5, 6, 5), (6, 6, 5), (7, 6, 5), (8, 6, 5)]
points_frac = flex.vec3_double()
for p in points:
data[p] = 1
points_frac.append([p[i] / gridding.n_real()[i] for i in range(3)])
points_cart = uc.orthogonalize(points_frac)
flood_fill = masks.flood_fill(data, uc)
assert data.count(2) == 15
assert approx_equal(flood_fill.centres_of_mass_frac(),
((0.5, 0.5, 0.5), ))
pai_cart = math.principal_axes_of_inertia(points=points_cart,
weights=flex.double(
points_cart.size(), 1.0))
F = matrix.sqr(uc.fractionalization_matrix())
O = matrix.sqr(uc.orthogonalization_matrix())
assert approx_equal(pai_cart.center_of_mass(),
flood_fill.centres_of_mass_cart()[0])
assert approx_equal(
flood_fill.covariance_matrices_cart()[0],
(F.transpose() *
matrix.sym(sym_mat3=flood_fill.covariance_matrices_frac()[0]) *
F).as_sym_mat3())
assert approx_equal(pai_cart.inertia_tensor(),
flood_fill.inertia_tensors_cart()[0])
assert approx_equal(pai_cart.eigensystem().vectors(),
flood_fill.eigensystems_cart()[0].vectors())
assert approx_equal(pai_cart.eigensystem().values(),
flood_fill.eigensystems_cart()[0].values())
return
def integrate(self):
from scitbx.array_family import flex
from scitbx import matrix
nslow, nfast = self.raw_data.focus()
binary_map = self.get_signal_mask().as_1d().as_int()
binary_map.reshape(flex.grid(1, nslow, nfast))
# find connected regions of spots - hacking code for density modification
# this is used to determine the integration masks for the reflections
from cctbx import masks
from cctbx import uctbx
uc = uctbx.unit_cell((1, nslow, nfast, 90, 90, 90))
flood_fill = masks.flood_fill(binary_map, uc)
binary_map = binary_map.as_1d()
coms = flood_fill.centres_of_mass()
# now iterate through these blobs, find the intensity and error, and
# find the Miller index
UB = matrix.sqr(self.crystal.get_A())
UBi = UB.inverse()
winv = 1 / self.beam.get_wavelength()
data = self.raw_data.as_double()
background = self.make_background()
from dials.array_family import flex
reflections = flex.reflection_table()
num_pixels_foreground = flex.int()
background_mean = flex.double()
background_sum_value = flex.double()
background_sum_variance = flex.double()
intensity_sum_value = flex.double()
intensity_sum_variance = flex.double()
miller_index = flex.miller_index()
xyzcal_px = flex.vec3_double()
bbox = flex.int6()
dq = flex.double()
fast = flex.int(self.raw_data.size(), -1)
fast.reshape(self.raw_data.accessor())
slow = flex.int(self.raw_data.size(), -1)
slow.reshape(self.raw_data.accessor())
nslow, nfast = fast.focus()
for j in range(nslow):
for i in range(nfast):
fast[(j, i)] = i
slow[(j, i)] = j
for j in range(flood_fill.n_voids()):
sel = binary_map == (j + 2)
pixels = data.select(sel)
if flex.min(pixels) < 0:
continue
bg_pixels = background.select(sel)
n = pixels.size()
d = flex.sum(pixels)
b = flex.sum(bg_pixels)
s = d - b
# FIXME is this the best centre of mass? if this spot is actually
# there, probably no, but if not there (i.e. no spot) then background
# subtracted centre of mass likely to be very noisy - keeping the
# background in likely to make this a little more stable
xy = coms[j][2], coms[j][1]
fs = fast.select(sel)
ss = slow.select(sel)
fd = fs.as_double()
sd = ss.as_double()
p = matrix.col(
self.panel.get_pixel_lab_coord(xy)).normalize() * winv
q = p - matrix.col(self.beam.get_s0())
hkl = UBi * q
ihkl = [int(round(h)) for h in hkl]
dq.append((q - UB * ihkl).length())
# puzzle out the bounding boxes - hack here, we have maps with the
# fast and slow positions in; select from these then find max, min of
# this selection
f_min = flex.min(fs)
f_max = flex.max(fs)
s_min = flex.min(ss)
s_max = flex.max(ss)
bbox.append((f_min, f_max + 1, s_min, s_max + 1, 0, 1))
num_pixels_foreground.append(n)
background_mean.append(b / n)
background_sum_value.append(b)
background_sum_variance.append(b)
intensity_sum_value.append(s)
intensity_sum_variance.append(d + b)
miller_index.append(ihkl)
xyzcal_px.append((xy[0], xy[1], 0.0))
reflections["num_pixels.foreground"] = num_pixels_foreground
reflections["background.mean"] = background_mean
reflections["background.sum.value"] = background_sum_value
reflections["background.sum.variance"] = background_sum_variance
reflections["intensity.sum.value"] = intensity_sum_value
reflections["intensity.sum.variance"] = intensity_sum_variance
reflections["miller_index"] = miller_index
reflections["xyzcal.px"] = xyzcal_px
reflections["id"] = flex.int(miller_index.size(), 0)
reflections["panel"] = flex.size_t(miller_index.size(), 0)
reflections["bbox"] = bbox
reflections["dq"] = dq
from dials.algorithms.shoebox import MaskCode
reflections["shoebox"] = flex.shoebox(reflections["panel"],
reflections["bbox"],
allocate=True)
reflections.extract_shoeboxes(self.imageset, verbose=True)
# now iterate through these (how?!) and assign mask values
fg = self.get_signal_mask()
for reflection in reflections:
s = reflection["shoebox"]
b = reflection["bbox"]
dz, dy, dx = s.mask.focus()
for j in range(dy):
for i in range(dx):
_i = b[0]
_j = b[2]
if fg[(j + _j, i + _i)]:
m = MaskCode.Valid | MaskCode.Foreground
else:
m = MaskCode.Valid | MaskCode.Background
s.mask[(0, j, i)] = m
return reflections
