def exercise_groel_sampling(verbose):
crystal_symmetry = crystal.symmetry(
unit_cell="255.260 265.250 184.400 90.00 90.00",
space_group_symbol="P 21 21 2")
t00 = time.time()
n_sites = []
for use_space_group_symmetry in [True,False]:
for use_seminvariants in [True,False]:
for all_twelve_neighbors in [False,True]:
sampling_generator = close_packing.hexagonal_sampling(
crystal_symmetry=crystal_symmetry,
symmetry_flags=sgtbx.search_symmetry_flags(
use_space_group_symmetry=use_space_group_symmetry,
use_space_group_ltr=0,
use_seminvariants=use_seminvariants,
use_normalizer_k2l=False,
use_normalizer_l2n=False),
point_distance=2,
buffer_thickness=-1,
all_twelve_neighbors=all_twelve_neighbors)
t0 = time.time()
n_sites.append(sampling_generator.count_sites())
if (verbose):
print n_sites[-1], "%.2f" % (time.time() - t0)
assert n_sites == [41712, 45319, 304200, 315809, # depends an float_asu, ...
162240, 170797, 1195830, 1232774]
print "time groel_sampling: %.2f seconds" % (time.time() - t00)
def exercise_groel_sampling(verbose):
crystal_symmetry = crystal.symmetry(
unit_cell="255.260 265.250 184.400 90.00 90.00",
space_group_symbol="P 21 21 2")
t00 = time.time()
n_sites = []
for use_space_group_symmetry in [True, False]:
for use_seminvariants in [True, False]:
for all_twelve_neighbors in [False, True]:
sampling_generator = close_packing.hexagonal_sampling(
crystal_symmetry=crystal_symmetry,
symmetry_flags=sgtbx.search_symmetry_flags(
use_space_group_symmetry=use_space_group_symmetry,
use_space_group_ltr=0,
use_seminvariants=use_seminvariants,
use_normalizer_k2l=False,
use_normalizer_l2n=False),
point_distance=2,
buffer_thickness=-1,
all_twelve_neighbors=all_twelve_neighbors)
t0 = time.time()
n_sites.append(sampling_generator.count_sites())
if (verbose):
print(n_sites[-1], "%.2f" % (time.time() - t0))
assert n_sites == [
41712,
45319,
304200,
315809, # depends an float_asu, ...
162240,
170797,
1195830,
1232774
]
print("time groel_sampling: %.2f seconds" % (time.time() - t00))
def __init__(self,
xray_structure,
solvent_radius,
shrink_truncation_radius,
ignore_hydrogen_atoms=False,
crystal_gridding=None,
grid_step=None,
d_min=None,
resolution_factor=1 / 4,
atom_radii_table=None,
use_space_group_symmetry=False):
self.xray_structure = xray_structure
if crystal_gridding is None:
self.crystal_gridding = maptbx.crystal_gridding(
unit_cell=xray_structure.unit_cell(),
space_group_info=xray_structure.space_group_info(),
step=grid_step,
d_min=d_min,
resolution_factor=resolution_factor,
symmetry_flags=sgtbx.search_symmetry_flags(
use_space_group_symmetry=use_space_group_symmetry))
else:
self.crystal_gridding = crystal_gridding
if use_space_group_symmetry:
atom_radii = cctbx.masks.vdw_radii(
xray_structure, table=atom_radii_table).atom_radii
asu_mappings = xray_structure.asu_mappings(
buffer_thickness=flex.max(atom_radii) + solvent_radius)
scatterers_asu_plus_buffer = flex.xray_scatterer()
frac = xray_structure.unit_cell().fractionalize
for sc, mappings in zip(xray_structure.scatterers(),
asu_mappings.mappings()):
for mapping in mappings:
scatterers_asu_plus_buffer.append(
sc.customized_copy(site=frac(mapping.mapped_site())))
xs = xray.structure(crystal_symmetry=xray_structure,
scatterers=scatterers_asu_plus_buffer)
else:
xs = xray_structure.expand_to_p1()
self.vdw_radii = cctbx.masks.vdw_radii(xs, table=atom_radii_table)
self.mask = cctbx.masks.around_atoms(
unit_cell=xs.unit_cell(),
space_group_order_z=xs.space_group().order_z(),
sites_frac=xs.sites_frac(),
atom_radii=self.vdw_radii.atom_radii,
gridding_n_real=self.crystal_gridding.n_real(),
solvent_radius=solvent_radius,
shrink_truncation_radius=shrink_truncation_radius)
if use_space_group_symmetry:
tags = self.crystal_gridding.tags()
tags.tags().apply_symmetry_to_mask(self.mask.data)
self.flood_fill = cctbx.masks.flood_fill(self.mask.data,
xray_structure.unit_cell())
self.exclude_void_flags = [False] * self.flood_fill.n_voids()
self.solvent_accessible_volume = self.n_solvent_grid_points() \
/ self.mask.data.size() * xray_structure.unit_cell().volume()
def __init__(
self,
xray_structure,
solvent_radius,
shrink_truncation_radius,
ignore_hydrogen_atoms=False,
crystal_gridding=None,
grid_step=None,
d_min=None,
resolution_factor=1 / 4,
atom_radii_table=None,
use_space_group_symmetry=False,
):
self.xray_structure = xray_structure
if crystal_gridding is None:
self.crystal_gridding = maptbx.crystal_gridding(
unit_cell=xray_structure.unit_cell(),
space_group_info=xray_structure.space_group_info(),
step=grid_step,
d_min=d_min,
resolution_factor=resolution_factor,
symmetry_flags=sgtbx.search_symmetry_flags(use_space_group_symmetry=use_space_group_symmetry),
)
else:
self.crystal_gridding = crystal_gridding
if use_space_group_symmetry:
atom_radii = cctbx.masks.vdw_radii(xray_structure, table=atom_radii_table).atom_radii
asu_mappings = xray_structure.asu_mappings(buffer_thickness=flex.max(atom_radii) + solvent_radius)
scatterers_asu_plus_buffer = flex.xray_scatterer()
frac = xray_structure.unit_cell().fractionalize
for sc, mappings in zip(xray_structure.scatterers(), asu_mappings.mappings()):
for mapping in mappings:
scatterers_asu_plus_buffer.append(sc.customized_copy(site=frac(mapping.mapped_site())))
xs = xray.structure(crystal_symmetry=xray_structure, scatterers=scatterers_asu_plus_buffer)
else:
xs = xray_structure.expand_to_p1()
self.vdw_radii = cctbx.masks.vdw_radii(xs, table=atom_radii_table)
self.mask = cctbx.masks.around_atoms(
unit_cell=xs.unit_cell(),
space_group_order_z=xs.space_group().order_z(),
sites_frac=xs.sites_frac(),
atom_radii=self.vdw_radii.atom_radii,
gridding_n_real=self.crystal_gridding.n_real(),
solvent_radius=solvent_radius,
shrink_truncation_radius=shrink_truncation_radius,
)
if use_space_group_symmetry:
tags = self.crystal_gridding.tags()
tags.tags().apply_symmetry_to_mask(self.mask.data)
self.flood_fill = cctbx.masks.flood_fill(self.mask.data, xray_structure.unit_cell())
self.exclude_void_flags = [False] * self.flood_fill.n_voids()
self.solvent_accessible_volume = (
self.n_solvent_grid_points() / self.mask.data.size() * xray_structure.unit_cell().volume()
)
def exercise_gridding():
u = uctbx.unit_cell((4,6,7))
assert maptbx.ext.determine_gridding(u, 2, 1/3., (1,1,1), 5, True) \
== (8,9,12)
f = sgtbx.search_symmetry_flags(
use_space_group_symmetry=True,
use_space_group_ltr=0,
use_seminvariants=False,
use_normalizer_k2l=True,
use_normalizer_l2n=False)
t = sgtbx.space_group_info("F 2 2 2").primitive_setting().type()
assert maptbx.ext.determine_gridding(u, 2, 1/3., f, t, (1,1,1), 5, True) \
== (12, 12, 12)
def __init__(self, space_group_info, use_k2l, use_l2n):
adopt_init_args(self, locals())
search_symmetry = sgtbx.search_symmetry(
flags=sgtbx.search_symmetry_flags(use_space_group_symmetry=False,
use_space_group_ltr=-1,
use_seminvariants=True,
use_normalizer_k2l=use_k2l,
use_normalizer_l2n=use_l2n),
space_group_type=space_group_info.type(),
seminvariant=space_group_info.structure_seminvariants())
self.rt_mx = search_symmetry.subgroup()
self.continuous_shifts = search_symmetry.continuous_shifts()
assert search_symmetry.continuous_shifts_are_principal()
self.continuous_shift_flags = search_symmetry.continuous_shift_flags()
def exercise_all_twelve_neighbors():
sites_cart = hexagonal_close_packing_sampling(
crystal_symmetry=crystal.symmetry(
unit_cell=(14.4225, 14.4225, 14.4225, 90, 90, 90),
space_group_symbol="F m -3 m"),
symmetry_flags=sgtbx.search_symmetry_flags(
use_space_group_symmetry=True,
use_space_group_ltr=0,
use_seminvariants=True,
use_normalizer_k2l=False,
use_normalizer_l2n=False),
point_distance=2,
buffer_thickness=-1,
all_twelve_neighbors=True)
assert len(sites_cart) == 36 # depends an float_asu, which depends on cb_op,
def __init__(self, space_group_info, use_k2l, use_l2n):
adopt_init_args(self, locals())
search_symmetry = sgtbx.search_symmetry(
flags=sgtbx.search_symmetry_flags(
use_space_group_symmetry=False,
use_space_group_ltr=-1,
use_seminvariants=True,
use_normalizer_k2l=use_k2l,
use_normalizer_l2n=use_l2n),
space_group_type=space_group_info.type(),
seminvariant=space_group_info.structure_seminvariants())
self.rt_mx = search_symmetry.subgroup()
self.continuous_shifts = search_symmetry.continuous_shifts()
assert search_symmetry.continuous_shifts_are_principal()
self.continuous_shift_flags = search_symmetry.continuous_shift_flags()
def exercise_all_twelve_neighbors():
sites_cart = hexagonal_close_packing_sampling(
crystal_symmetry=crystal.symmetry(unit_cell=(14.4225, 14.4225, 14.4225,
90, 90, 90),
space_group_symbol="F m -3 m"),
symmetry_flags=sgtbx.search_symmetry_flags(
use_space_group_symmetry=True,
use_space_group_ltr=0,
use_seminvariants=True,
use_normalizer_k2l=False,
use_normalizer_l2n=False),
point_distance=2,
buffer_thickness=-1,
all_twelve_neighbors=True)
assert len(
sites_cart) == 36 # depends an float_asu, which depends on cb_op,
def run_call_back(flags, space_group_info):
crystal_symmetry = crystal.symmetry(
unit_cell=space_group_info.any_compatible_unit_cell(volume=1000),
space_group_info=space_group_info)
if (flags.Verbose):
print crystal_symmetry.unit_cell()
symmetry_flags=sgtbx.search_symmetry_flags(
use_space_group_symmetry=True,
use_space_group_ltr=0,
use_seminvariants=True,
use_normalizer_k2l=False,
use_normalizer_l2n=False)
point_distance = 2
buffer_thickness = -1
all_twelve_neighbors = False
if (flags.strictly_inside):
buffer_thickness = 0
if (flags.all_twelve_neighbors):
all_twelve_neighbors = True
if (flags.Verbose):
print "buffer_thickness:", buffer_thickness
print "all_twelve_neighbors:", all_twelve_neighbors
sites_cart = hexagonal_close_packing_sampling(
crystal_symmetry=crystal_symmetry,
symmetry_flags=symmetry_flags,
point_distance=point_distance,
buffer_thickness=buffer_thickness,
all_twelve_neighbors=all_twelve_neighbors)
if (1):
check_distances(
sites_cart=sites_cart,
point_distance=point_distance,
verbose=flags.Verbose)
if (1):
check_with_grid_tags(
inp_symmetry=crystal_symmetry,
symmetry_flags=symmetry_flags,
sites_cart=sites_cart,
point_distance=point_distance,
strictly_inside=flags.strictly_inside,
flag_write_pdb=flags.write_pdb,
verbose=flags.Verbose)
if (flags.write_pdb):
dump_pdb(
file_name="hex_sites.pdb",
crystal_symmetry=crystal_symmetry,
sites_cart=sites_cart)
def run_call_back(flags, space_group_info):
crystal_symmetry = crystal.symmetry(
unit_cell=space_group_info.any_compatible_unit_cell(volume=1000),
space_group_info=space_group_info)
if (flags.Verbose):
print(crystal_symmetry.unit_cell())
symmetry_flags = sgtbx.search_symmetry_flags(use_space_group_symmetry=True,
use_space_group_ltr=0,
use_seminvariants=True,
use_normalizer_k2l=False,
use_normalizer_l2n=False)
point_distance = 2
buffer_thickness = -1
all_twelve_neighbors = False
if (flags.strictly_inside):
buffer_thickness = 0
if (flags.all_twelve_neighbors):
all_twelve_neighbors = True
if (flags.Verbose):
print("buffer_thickness:", buffer_thickness)
print("all_twelve_neighbors:", all_twelve_neighbors)
sites_cart = hexagonal_close_packing_sampling(
crystal_symmetry=crystal_symmetry,
symmetry_flags=symmetry_flags,
point_distance=point_distance,
buffer_thickness=buffer_thickness,
all_twelve_neighbors=all_twelve_neighbors)
if (1):
check_distances(sites_cart=sites_cart,
point_distance=point_distance,
verbose=flags.Verbose)
if (1):
check_with_grid_tags(inp_symmetry=crystal_symmetry,
symmetry_flags=symmetry_flags,
sites_cart=sites_cart,
point_distance=point_distance,
strictly_inside=flags.strictly_inside,
flag_write_pdb=flags.write_pdb,
verbose=flags.Verbose)
if (flags.write_pdb):
dump_pdb(file_name="hex_sites.pdb",
crystal_symmetry=crystal_symmetry,
sites_cart=sites_cart)
def exercise_grid_tags():
t = maptbx.grid_tags((8,10,12))
assert not t.is_valid()
assert t.tag_array().all() == (8,10,12)
s = sgtbx.space_group_info("P 21")
for i_flags in xrange(8):
f = sgtbx.search_symmetry_flags(
use_space_group_symmetry=i_flags % 2 != 0,
use_space_group_ltr=0,
use_seminvariants=(i_flags//4) % 2 != 0,
use_normalizer_k2l=(i_flags//2) % 2 != 0,
use_normalizer_l2n=False)
t.build(s.type(), f)
assert t.is_valid()
assert t.space_group_type().group() == s.group()
assert t.symmetry_flags() == f
if (f.use_seminvariants()):
assert [(vm.v, vm.m) for vm in t.grid_ss_continuous()] \
== [((0, 1, 0), 10)]
assert t.n_grid_misses() == 0
assert t.n_independent() == (960, 480, 484, 242, 24, 14, 14, 14)[i_flags]
assert t.n_independent() + t.n_dependent() == t.tag_array().size()
for flex_type in flex_types():
d = flex_type(t.tag_array().accessor())
assert t.n_dependent() == 0 \
or approx_equal(t.dependent_correlation(d, 1.e-10).coefficient(),0)
assert t.verify(d, 0)
t.sum_sym_equiv_points(d)
if (i_flags == 0):
assert t.n_independent() == t.tag_array().size()
else:
assert t.n_independent() < t.tag_array().size()
for flex_type in flex_types():
d = flex_type([random.random() for x in xrange(t.tag_array().size())])
d.resize(t.tag_array().accessor())
assert not t.verify(d)
t.sum_sym_equiv_points(d)
assert t.verify(d)
def exercise(space_group_info, redundancy_counter=0):
n_real = (12,12,12)
miller_max = (2,2,2)
gt = maptbx.grid_tags(n_real)
uc = space_group_info.any_compatible_unit_cell(volume=1000)
fl = sgtbx.search_symmetry_flags(use_space_group_symmetry=True)
gt.build(space_group_info.type(), fl)
fft = fftpack.real_to_complex_3d(n_real)
map0 = flex.double(flex.grid(fft.m_real()).set_focus(fft.n_real()), 0)
weight_map = map0.deep_copy()
map = map0.deep_copy()
ta = gt.tag_array()
order_z = space_group_info.group().order_z()
problems_expected = (redundancy_counter != 0)
for ijk in flex.nested_loop(n_real):
t = ta[ijk]
if (t < 0):
xyz = [i/n for i,n in zip(ijk, n_real)]
ss = sgtbx.site_symmetry(
unit_cell=uc,
space_group=space_group_info.group(),
original_site=xyz,
min_distance_sym_equiv=1e-5)
m = space_group_info.group().multiplicity(
site=boost.rational.vector(ijk, n_real))
assert m == ss.multiplicity()
w = m / order_z
weight_map[ijk] = w
map[ijk] = w
elif (redundancy_counter != 0):
redundancy_counter -= 1
ijk_asu = n_dim_index_from_one_dim(i1d=t, sizes=n_real)
assert ta.accessor()(ijk_asu) == t
map[ijk] = map[ijk_asu]
sf_map = fft.forward(map)
del map
mi = miller.index_generator(
space_group_info.type(), False, miller_max).to_array()
assert mi.size() != 0
from_map = maptbx.structure_factors.from_map(
space_group=space_group_info.group(),
anomalous_flag=False,
miller_indices=mi,
complex_map=sf_map,
conjugate_flag=True)
sf = [iround(abs(f)) for f in from_map.data()]
if (sf != [0]*len(sf)):
assert problems_expected
return
else:
not problems_expected
#
map_p1 = map0.deep_copy()
map_sw = map0.deep_copy()
for ijk in flex.nested_loop(n_real):
t = ta[ijk]
if (t < 0):
v = random.random()*2-1
map_p1[ijk] = v
map_sw[ijk] = v * weight_map[ijk]
else:
ijk_asu = n_dim_index_from_one_dim(i1d=t, sizes=n_real)
assert ta.accessor()(ijk_asu) == t
assert map_p1[ijk_asu] != 0
map_p1[ijk] = map_p1[ijk_asu]
#
# fft followed by symmetry summation in reciprocal space
sf_map_sw = fft.forward(map_sw)
del map_sw
sf_sw = maptbx.structure_factors.from_map(
space_group=space_group_info.group(),
anomalous_flag=False,
miller_indices=mi,
complex_map=sf_map_sw,
conjugate_flag=True).data()
del sf_map_sw
#
# symmetry expansion in real space (done above already) followed fft
sf_map_p1 = fft.forward(map_p1)
del map_p1
sf_p1 = maptbx.structure_factors.from_map(
space_group=sgtbx.space_group(),
anomalous_flag=False,
miller_indices=mi,
complex_map=sf_map_p1,
conjugate_flag=True).data()
del sf_map_p1
#
corr = flex.linear_correlation(x=flex.abs(sf_sw), y=flex.abs(sf_p1))
assert corr.is_well_defined
assert approx_equal(corr.coefficient(), 1)
class statistics(ext.statistics):
def __init__(self, map):
ext.statistics.__init__(self, map)
class _(boost.python.injector, ext.statistics):
def show_summary(self, f=None, prefix=""):
if (f is None): f = sys.stdout
print >> f, prefix + "max %.6g" % (self.max())
print >> f, prefix + "min %.6g" % (self.min())
print >> f, prefix + "mean %.6g" % (self.mean())
print >> f, prefix + "sigma %.6g" % (self.sigma())
use_space_group_symmetry = sgtbx.search_symmetry_flags(
use_space_group_symmetry=True)
class _(boost.python.injector, ext.histogram):
"""
Injector for extending cctbx.maptbx.histogram
"""
# XXX make a method of scitbx
def get_percentile_cutoffs(self, map, vol_cutoff_plus_percent,
vol_cutoff_minus_percent):
"""
For the double-step filtration in cctbx.miller (used as part of the
procedure for replacing missing F-obs in maps), we need to calculate upper
and lower cutoffs for the data based on percentile values. This can be
done in just a few lines of code by using flex.sort_permutation over the
def exercise(space_group_info, redundancy_counter=0):
n_real = (12, 12, 12)
miller_max = (2, 2, 2)
gt = maptbx.grid_tags(n_real)
uc = space_group_info.any_compatible_unit_cell(volume=1000)
fl = sgtbx.search_symmetry_flags(use_space_group_symmetry=True)
gt.build(space_group_info.type(), fl)
fft = fftpack.real_to_complex_3d(n_real)
map0 = flex.double(flex.grid(fft.m_real()).set_focus(fft.n_real()), 0)
weight_map = map0.deep_copy()
map = map0.deep_copy()
ta = gt.tag_array()
order_z = space_group_info.group().order_z()
problems_expected = (redundancy_counter != 0)
for ijk in flex.nested_loop(n_real):
t = ta[ijk]
if (t < 0):
xyz = [i / n for i, n in zip(ijk, n_real)]
ss = sgtbx.site_symmetry(unit_cell=uc,
space_group=space_group_info.group(),
original_site=xyz,
min_distance_sym_equiv=1e-5)
m = space_group_info.group().multiplicity(
site=boost.rational.vector(ijk, n_real))
assert m == ss.multiplicity()
w = m / order_z
weight_map[ijk] = w
map[ijk] = w
elif (redundancy_counter != 0):
redundancy_counter -= 1
ijk_asu = n_dim_index_from_one_dim(i1d=t, sizes=n_real)
assert ta.accessor()(ijk_asu) == t
map[ijk] = map[ijk_asu]
sf_map = fft.forward(map)
del map
mi = miller.index_generator(space_group_info.type(), False,
miller_max).to_array()
assert mi.size() != 0
from_map = maptbx.structure_factors.from_map(
space_group=space_group_info.group(),
anomalous_flag=False,
miller_indices=mi,
complex_map=sf_map,
conjugate_flag=True)
sf = [iround(abs(f)) for f in from_map.data()]
if (sf != [0] * len(sf)):
assert problems_expected
return
else:
not problems_expected
#
map_p1 = map0.deep_copy()
map_sw = map0.deep_copy()
for ijk in flex.nested_loop(n_real):
t = ta[ijk]
if (t < 0):
v = random.random() * 2 - 1
map_p1[ijk] = v
map_sw[ijk] = v * weight_map[ijk]
else:
ijk_asu = n_dim_index_from_one_dim(i1d=t, sizes=n_real)
assert ta.accessor()(ijk_asu) == t
assert map_p1[ijk_asu] != 0
map_p1[ijk] = map_p1[ijk_asu]
#
# fft followed by symmetry summation in reciprocal space
sf_map_sw = fft.forward(map_sw)
del map_sw
sf_sw = maptbx.structure_factors.from_map(
space_group=space_group_info.group(),
anomalous_flag=False,
miller_indices=mi,
complex_map=sf_map_sw,
conjugate_flag=True).data()
del sf_map_sw
#
# symmetry expansion in real space (done above already) followed fft
sf_map_p1 = fft.forward(map_p1)
del map_p1
sf_p1 = maptbx.structure_factors.from_map(space_group=sgtbx.space_group(),
anomalous_flag=False,
miller_indices=mi,
complex_map=sf_map_p1,
conjugate_flag=True).data()
del sf_map_p1
#
corr = flex.linear_correlation(x=flex.abs(sf_sw), y=flex.abs(sf_p1))
assert corr.is_well_defined
assert approx_equal(corr.coefficient(), 1)
class statistics(ext.statistics):
def __init__(self, map):
ext.statistics.__init__(self, map)
class _(boost.python.injector, ext.statistics):
def show_summary(self, f=None, prefix=""):
if (f is None): f = sys.stdout
print >> f, prefix + "max %.6g" % (self.max())
print >> f, prefix + "min %.6g" % (self.min())
print >> f, prefix + "mean %.6g" % (self.mean())
print >> f, prefix + "sigma %.6g" % (self.sigma())
use_space_group_symmetry = sgtbx.search_symmetry_flags(
use_space_group_symmetry=True)
class _(boost.python.injector, ext.histogram) :
"""
Injector for extending cctbx.maptbx.histogram
"""
# XXX make a method of scitbx
def get_percentile_cutoffs (self, map, vol_cutoff_plus_percent,
vol_cutoff_minus_percent) :
"""
For the double-step filtration in cctbx.miller (used as part of the
procedure for replacing missing F-obs in maps), we need to calculate upper
and lower cutoffs for the data based on percentile values. This can be
done in just a few lines of code by using flex.sort_permutation over the
entire map, but this has a huge memory overhead (and possibly computational
overhead as well). Since we are only interested in subsets of values at
