def __init__(
self,
map_data_obs,
xrs,
d_min,
vector_map,
box_dimension=30):
adopt_init_args(self, locals())
self.crystal_gridding = maptbx.crystal_gridding(
unit_cell = self.xrs.unit_cell(),
space_group_info = self.xrs.space_group_info(),
pre_determined_n_real = self.map_data_obs.all())
self.n_real = self.crystal_gridding.n_real()
crystal_gridding = maptbx.crystal_gridding(
unit_cell = self.xrs.unit_cell(),
space_group_info = self.xrs.space_group_info(),
pre_determined_n_real = self.map_data_obs.all())
mc = xrs.structure_factors(d_min=d_min).f_calc()
fft_map = miller.fft_map(
crystal_gridding = crystal_gridding,
fourier_coefficients = mc)
fft_map.apply_sigma_scaling()
self.map_data_calc = fft_map.real_map_unpadded()
scale = scale_k1(x=self.map_data_obs, y=self.map_data_calc)
self.map_data_calc = self.map_data_calc * scale
#
# result map
self.map_result = flex.double(flex.grid(self.map_data_obs.all()))
# iterate over boxes
self.box_iterator()
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 run(args, log=sys.stdout):
print >> log, "-" * 79
print >> log, legend
print >> log, "-" * 79
inputs = mmtbx.utils.process_command_line_args(
args=args, master_params=master_params())
file_names = inputs.pdb_file_names
if (len(file_names) != 1): raise Sorry("A PDB file is expected.")
xrs = iotbx.pdb.input(file_name=file_names[0]).xray_structure_simple()
params = inputs.params.extract()
#
crystal_gridding = maptbx.crystal_gridding(
unit_cell=xrs.unit_cell(),
d_min=params.resolution,
resolution_factor=params.resolution_factor,
symmetry_flags=maptbx.use_space_group_symmetry,
space_group_info=xrs.space_group().info())
mmtbx_masks_asu_mask_obj = mmtbx.masks.asu_mask(
xray_structure=xrs.expand_to_p1(sites_mod_positive=True),
n_real=crystal_gridding.n_real())
bulk_solvent_mask = mmtbx_masks_asu_mask_obj.mask_data_whole_uc()
#
fem.ccp4_map(cg=crystal_gridding,
file_name="mask.ccp4",
map_data=bulk_solvent_mask)
def prepare_reference_map_3(self, xrs, pdb_h):
""" with ramachandran outliers """
print >> self.log, "Preparing reference map, method 3"
outlier_selection_txt = mmtbx.building.loop_closure.utils. \
rama_outliers_selection(pdb_h, self.rama_manager, 1)
asc = pdb_h.atom_selection_cache()
print >> self.log, "rama outlier selection:", outlier_selection_txt
rama_out_sel = asc.selection(outlier_selection_txt)
xrs=xrs.set_b_iso(value=50)
# side_chain_no_cb_selection = ~ xrs.main_chain_selection()
side_chain_no_cb_selection = ~ xrs.backbone_selection()
xrs = xrs.set_b_iso(value=200, selection=side_chain_no_cb_selection)
xrs = xrs.set_b_iso(value=150, selection=rama_out_sel)
# xrs = xrs.set_occupancies(value=0.3, selection=rama_out_sel)
crystal_gridding = maptbx.crystal_gridding(
unit_cell = xrs.unit_cell(),
space_group_info = xrs.space_group_info(),
symmetry_flags = maptbx.use_space_group_symmetry,
d_min = self.params.reference_map_resolution)
fc = xrs.structure_factors(d_min = self.params.reference_map_resolution, algorithm = "direct").f_calc()
fft_map = miller.fft_map(
crystal_gridding=crystal_gridding,
fourier_coefficients=fc)
fft_map.apply_sigma_scaling()
self.reference_map = fft_map.real_map_unpadded(in_place=False)
fft_map.as_xplor_map(file_name="%s_3.map" % self.params.output_prefix)
def __init__(
self,
map_data,
pdb_hierarchy, # XXX redundant inputs
xray_structure, # XXX redundant inputs
d_min,
use_mask=False,
masking_atom_radius=5,
max_iterations=50,
macro_cycles=1,
prefix="",
log=None):
adopt_init_args(self, locals())
self.cc_best = None
self.sites_cart_best = None
if(self.log is None): self.log = sys.stdout
self.sites_cart_start = self.xray_structure.sites_cart()
assert approx_equal(self.pdb_hierarchy.atoms().extract_xyz(),
self.sites_cart_start, 1.e-3)
self.crystal_gridding = maptbx.crystal_gridding(
unit_cell = self.xray_structure.unit_cell(),
space_group_info = self.xray_structure.space_group_info(),
pre_determined_n_real = self.map_data.all())
self.complete_set = miller.build_set(
crystal_symmetry = self.xray_structure.crystal_symmetry(),
anomalous_flag = False,
d_min = self.d_min)
self._show_and_track()
self.d_mins = self._get_mz_resolution_limits()
for macro_cycle in xrange(self.macro_cycles):
self._refine()
self.xray_structure.set_sites_cart(self.sites_cart_best)
self.pdb_hierarchy.adopt_xray_structure(self.xray_structure)
def exercise_translational_phase_shift(n_sites=100,
d_min=1.5,
resolution_factor=0.3):
sgi = space_group_info("P1")
xrs = random_structure.xray_structure(
space_group_info=sgi,
elements=(("O", "N", "C") * (n_sites // 3 + 1))[:n_sites],
volume_per_atom=50,
min_distance=1.5)
f_calc = xrs.structure_factors(d_min=d_min).f_calc()
print f_calc.unit_cell()
from scitbx.matrix import col
shift_frac = col((.23984120, .902341127, .51219021))
# Shift phases directly
phase_shifted = f_calc.translational_shift(shift_frac=shift_frac)
# Check that map from phase_shifted FC matches map calculated from
# translated xrs
# Map from phase-shifted FC
shifted_fft_map = phase_shifted.fft_map(
resolution_factor=resolution_factor)
shifted_fft_map.apply_sigma_scaling()
shifted_map_data = shifted_fft_map.real_map_unpadded()
cs = xrs.crystal_symmetry()
from cctbx.maptbx import crystal_gridding
cg = crystal_gridding(unit_cell=cs.unit_cell(),
space_group_info=cs.space_group_info(),
pre_determined_n_real=shifted_map_data.all())
# Map from translated xrs
sites_shifted = xrs.sites_frac() + shift_frac
xrs.set_sites_frac(sites_shifted)
f_calc_from_shifted_xrs = xrs.structure_factors(d_min=d_min).f_calc()
fft_map_from_shifted_xrs = f_calc_from_shifted_xrs.fft_map(
resolution_factor=resolution_factor, crystal_gridding=cg)
map_data_from_shifted_xrs = fft_map_from_shifted_xrs.real_map_unpadded()
# shifted_map_data (map from phase shifted f_calc),
# map_data_from_shifted_xrs (recalculated with shifted xrs)
assert shifted_map_data.all() == map_data_from_shifted_xrs.all()
from cctbx import maptbx
sel = maptbx.grid_indices_around_sites(unit_cell=xrs.unit_cell(),
fft_n_real=shifted_map_data.focus(),
fft_m_real=shifted_map_data.all(),
sites_cart=xrs.sites_cart(),
site_radii=flex.double(
xrs.scatterers().size(), 1.5))
shifted_map_data = shifted_map_data.select(sel)
map_data_from_shifted_xrs = map_data_from_shifted_xrs.select(sel)
cc_map_data_from_shifted_xrs_shifted_map_data = flex.linear_correlation(
x=map_data_from_shifted_xrs.as_1d(),
y=shifted_map_data.as_1d()).coefficient()
print "cc_map_data_from_shifted_xrs_shifted_map_data",\
cc_map_data_from_shifted_xrs_shifted_map_data
assert cc_map_data_from_shifted_xrs_shifted_map_data > 0.99
print "*" * 25
def check(tuple_calc, selection, prefix):
miller_arrays = reflection_file_reader.any_reflection_file(
file_name=prefix + "polder_map_coeffs.mtz").as_miller_arrays()
mc_polder = None
for ma in miller_arrays:
lbl = ma.info().label_string()
if (lbl == "mFo-DFc_polder,PHImFo-DFc_polder"):
mc_polder = ma.deep_copy()
assert (mc_polder is not None)
cg = maptbx.crystal_gridding(unit_cell=mc_polder.unit_cell(),
d_min=mc_polder.d_min(),
resolution_factor=0.25,
space_group_info=mc_polder.space_group_info())
map_polder = get_map(cg=cg, mc=mc_polder)
pdb_hierarchy = iotbx.pdb.input(source_info=None,
lines=pdb_str).construct_hierarchy()
sel = pdb_hierarchy.atom_selection_cache().selection(string=selection)
sites_cart_lig = pdb_hierarchy.atoms().extract_xyz().select(sel)
sites_frac_lig = mc_polder.unit_cell().fractionalize(sites_cart_lig)
mp = get_map_stats(map=map_polder, sites_frac=sites_frac_lig)
#
mmm_mp = mp.min_max_mean().as_tuple()
print("Polder map : %7.3f %7.3f %7.3f" % mmm_mp)
assert approx_equal(mmm_mp, tuple_calc, eps=1.0), "\
calculated is %s and expected is %s" % (mmm_mp, tuple_calc)
def run(args):
assert len(args) == 1
timer = time_log("pdb.input").start()
pdb_inp = iotbx.pdb.input(file_name=args[0])
print "number of pdb atoms:", pdb_inp.atoms().size()
print timer.log()
crystal_symmetry = pdb_inp.crystal_symmetry()
assert crystal_symmetry is not None
crystal_symmetry.show_summary()
assert crystal_symmetry.unit_cell() is not None
assert crystal_symmetry.space_group_info() is not None
sites_cart = pdb_inp.atoms().extract_xyz()
site_radii = flex.double(sites_cart.size(), 2.5)
crystal_gridding = maptbx.crystal_gridding(
unit_cell=crystal_symmetry.unit_cell(),
d_min=2,
resolution_factor=1/3)
fft = fftpack.real_to_complex_3d(crystal_gridding.n_real())
print "n_real:", fft.n_real()
print "m_real:", fft.m_real()
timer = time_log("grid_indices_around_sites").start()
grid_indices = maptbx.grid_indices_around_sites(
unit_cell=crystal_symmetry.unit_cell(),
fft_n_real=fft.n_real(),
fft_m_real=fft.m_real(),
sites_cart=sites_cart,
site_radii=site_radii)
print "grid_indices.size():", grid_indices.size()
print timer.log()
print "grid fraction:", \
grid_indices.size() / matrix.col(fft.n_real()).product()
def run(args):
assert len(args) == 1
timer = time_log("pdb.input").start()
pdb_inp = iotbx.pdb.input(file_name=args[0])
print("number of pdb atoms:", pdb_inp.atoms().size())
print(timer.log())
crystal_symmetry = pdb_inp.crystal_symmetry()
assert crystal_symmetry is not None
crystal_symmetry.show_summary()
assert crystal_symmetry.unit_cell() is not None
assert crystal_symmetry.space_group_info() is not None
sites_cart = pdb_inp.atoms().extract_xyz()
site_radii = flex.double(sites_cart.size(), 2.5)
crystal_gridding = maptbx.crystal_gridding(
unit_cell=crystal_symmetry.unit_cell(),
d_min=2,
resolution_factor=1 / 3)
fft = fftpack.real_to_complex_3d(crystal_gridding.n_real())
print("n_real:", fft.n_real())
print("m_real:", fft.m_real())
timer = time_log("grid_indices_around_sites").start()
grid_indices = maptbx.grid_indices_around_sites(
unit_cell=crystal_symmetry.unit_cell(),
fft_n_real=fft.n_real(),
fft_m_real=fft.m_real(),
sites_cart=sites_cart,
site_radii=site_radii)
print("grid_indices.size():", grid_indices.size())
print(timer.log())
print("grid fraction:", \
grid_indices.size() / matrix.col(fft.n_real()).product())
def get_f_masks(xrs, miller_array):
crystal_gridding = maptbx.crystal_gridding(
unit_cell=xrs.unit_cell(),
d_min=miller_array.d_min(),
resolution_factor=1. / 4,
symmetry_flags=maptbx.use_space_group_symmetry,
space_group_info=xrs.space_group_info())
mp = mmtbx.masks.mask_master_params.extract()
mask_data = mmtbx.masks.mask_from_xray_structure(
xray_structure=xrs,
p1=True,
solvent_radius=mp.solvent_radius,
shrink_truncation_radius=mp.shrink_truncation_radius,
for_structure_factors=True,
n_real=crystal_gridding.n_real()).mask_data
n = mask_data.all()
mask_data1 = flex.double(flex.grid(n), 0)
mask_data2 = flex.double(flex.grid(n), 0)
I, J, K = range(n[0]), range(n[1]), range(n[2])
for i in I:
for j in J:
for k in K:
if (i < n[0] // 2 and j < n[1] // 2 and k < n[2] // 2):
mask_data1[i, j, k] = mask_data[i, j, k]
else:
mask_data2[i, j, k] = mask_data[i, j, k]
f_mask1 = miller_array.structure_factors_from_map(map=mask_data1,
use_scale=True,
anomalous_flag=False,
use_sg=False)
f_mask2 = miller_array.structure_factors_from_map(map=mask_data2,
use_scale=True,
anomalous_flag=False,
use_sg=False)
return [f_mask1.data(), f_mask2.data()]
def get_map_from_map_coeffs(map_coeffs=None,
crystal_symmetry=None,
n_real=None,
resolution_factor=None,
apply_sigma_scaling=True):
if resolution_factor is None:
resolution_factor = 0.25
from cctbx import maptbx
from cctbx.maptbx import crystal_gridding
if not crystal_symmetry:
crystal_symmetry = map_coeffs.crystal_symmetry()
if map_coeffs.crystal_symmetry().space_group_info()!= \
crystal_symmetry.space_group_info():
assert str(map_coeffs.crystal_symmetry().space_group_info()).replace(
" ", "").lower() == 'p1'
# use map_coeffs.crystal_symmetry
crystal_symmetry = map_coeffs.crystal_symmetry()
if n_real:
cg = crystal_gridding(
unit_cell=crystal_symmetry.unit_cell(),
space_group_info=crystal_symmetry.space_group_info(),
pre_determined_n_real=n_real)
else:
cg = None
fft_map = map_coeffs.fft_map(
resolution_factor=resolution_factor,
crystal_gridding=cg,
symmetry_flags=maptbx.use_space_group_symmetry)
if apply_sigma_scaling:
fft_map.apply_sigma_scaling()
else:
fft_map.apply_volume_scaling()
map_data = fft_map.real_map_unpadded()
return map_data
def __init__(
self,
map_data,
pdb_hierarchy, # XXX redundant inputs
xray_structure, # XXX redundant inputs
d_min,
use_mask=False,
masking_atom_radius=5,
max_iterations=50,
macro_cycles=1,
prefix="",
log=None):
adopt_init_args(self, locals())
self.cc_best = None
self.sites_cart_best = None
if(self.log is None): self.log = sys.stdout
self.sites_cart_start = self.xray_structure.sites_cart()
assert approx_equal(self.pdb_hierarchy.atoms().extract_xyz(),
self.sites_cart_start, 1.e-3)
self.crystal_gridding = maptbx.crystal_gridding(
unit_cell = self.xray_structure.unit_cell(),
space_group_info = self.xray_structure.space_group_info(),
pre_determined_n_real = self.map_data.all())
self.complete_set = miller.build_set(
crystal_symmetry = self.xray_structure.crystal_symmetry(),
anomalous_flag = False,
d_min = self.d_min)
self._show_and_track()
self.d_mins = self._get_mz_resolution_limits()
for macro_cycle in xrange(self.macro_cycles):
self._refine()
self.xray_structure.set_sites_cart(self.sites_cart_best)
self.pdb_hierarchy.adopt_xray_structure(self.xray_structure)
def get_f_masks(xrs, miller_array):
crystal_gridding = maptbx.crystal_gridding(
unit_cell = xrs.unit_cell(),
d_min = miller_array.d_min(),
resolution_factor = 1./4,
symmetry_flags = maptbx.use_space_group_symmetry,
space_group_info = xrs.space_group_info())
mp = mmtbx.masks.mask_master_params.extract()
mask_data = mmtbx.masks.mask_from_xray_structure(
xray_structure = xrs,
p1 = True,
solvent_radius = mp.solvent_radius,
shrink_truncation_radius = mp.shrink_truncation_radius,
for_structure_factors = True,
n_real = crystal_gridding.n_real()).mask_data
n = mask_data.all()
mask_data1 = flex.double(flex.grid(n), 0)
mask_data2 = flex.double(flex.grid(n), 0)
I,J,K = xrange(n[0]), xrange(n[1]), xrange(n[2])
for i in I:
for j in J:
for k in K:
if(i < n[0]//2 and j < n[1]//2 and k < n[2]//2):
mask_data1[i,j,k]=mask_data[i,j,k]
else:
mask_data2[i,j,k]=mask_data[i,j,k]
f_mask1 = miller_array.structure_factors_from_map(map=mask_data1,
use_scale = True, anomalous_flag = False, use_sg = False)
f_mask2 = miller_array.structure_factors_from_map(map=mask_data2,
use_scale = True, anomalous_flag = False, use_sg = False)
return [f_mask1.data(), f_mask2.data()]
def _find_peaks(self, min_peak_peak_distance=1.5):
cg = maptbx.crystal_gridding(
space_group_info=self.model.crystal_symmetry().space_group_info(),
symmetry_flags=maptbx.use_space_group_symmetry,
unit_cell=self.unit_cell,
pre_determined_n_real=self.map_data.accessor().all())
cgt = maptbx.crystal_gridding_tags(gridding=cg)
peak_search_parameters = maptbx.peak_search_parameters(
peak_search_level=3,
max_peaks=0,
peak_cutoff=self.cutoff,
interpolate=True,
min_distance_sym_equiv=0,
general_positions_only=False, # ???????XXXXXXXXXXXXXX
min_cross_distance=min_peak_peak_distance,
min_cubicle_edge=5)
psr = cgt.peak_search(parameters=peak_search_parameters,
map=self.map_data).all(max_clusters=99999999)
# Convert peaks into water xray.structure
mean_b = flex.mean(self.model.get_hierarchy().atoms().extract_b())
sp = crystal.special_position_settings(self.model.crystal_symmetry())
scatterers = flex.xray_scatterer()
for site_frac in psr.sites():
sc = xray.scatterer(label="O",
site=site_frac,
u=adptbx.b_as_u(mean_b),
occupancy=1.0)
scatterers.append(sc)
self.xrs_water = xray.structure(sp, scatterers)
#
self.ma.add(" total peaks found: %d" %
self.xrs_water.scatterers().size())
self.ma.add(" B factors set to : %8.3f" % mean_b)
if (self.debug): self._write_pdb_file(file_name_prefix="hoh_all_peaks")
def __init__(self,
xray_structure=None,
fmodel=None,
map_data=None,
d_min=None,
resolution_factor=0.25,
map_type="2mFo-DFc"):
"""
Utility to calculate correlation between two maps:
CC(xray_structure, map_data), xray_structure are map_data inputs
or
CC(2mFo-DFc, Fc), 2mFo-DFc and Fc are from input fmodel .
"""
assert [fmodel, map_data].count(None) == 1
assert [xray_structure, map_data].count(None) in [0, 2]
assert [fmodel, xray_structure].count(None) == 1
assert [d_min, fmodel].count(None) == 1
adopt_init_args(self, locals())
if (fmodel is not None): self.xray_structure = fmodel.xray_structure
# get map_data defined
if (self.fmodel is not None):
e_map_obj = fmodel.electron_density_map()
isotropize = True
if (fmodel.is_twin_fmodel_manager()): isotropize = False
mc = e_map_obj.map_coefficients(map_type=map_type,
fill_missing=False,
isotropize=isotropize)
crystal_gridding = self.fmodel.f_obs().crystal_gridding(
d_min=self.fmodel.f_obs().d_min(),
resolution_factor=resolution_factor)
fft_map = miller.fft_map(crystal_gridding=crystal_gridding,
fourier_coefficients=mc)
self.map_data = fft_map.real_map_unpadded()
# get model map
if (self.fmodel is not None):
if (fmodel.is_twin_fmodel_manager()):
f_model = self.fmodel.f_model()
else:
f_model = self.fmodel.f_model_scaled_with_k1()
fft_map = miller.fft_map(crystal_gridding=crystal_gridding,
fourier_coefficients=f_model)
fft_map.apply_sigma_scaling()
self.map_model = fft_map.real_map_unpadded()
else:
crystal_gridding = maptbx.crystal_gridding(
unit_cell=self.xray_structure.unit_cell(),
space_group_info=self.xray_structure.space_group_info(),
pre_determined_n_real=self.map_data.accessor().all())
f_model = self.xray_structure.structure_factors(
d_min=self.d_min).f_calc()
fft_map = miller.fft_map(crystal_gridding=crystal_gridding,
fourier_coefficients=f_model)
fft_map.apply_sigma_scaling()
self.map_model = fft_map.real_map_unpadded()
if (self.fmodel is not None):
self.sites_cart = self.fmodel.xray_structure.sites_cart()
self.sites_frac = self.fmodel.xray_structure.sites_frac()
else:
self.sites_cart = self.xray_structure.sites_cart()
self.sites_frac = self.xray_structure.sites_frac()
def exercise_01(d_min=1.0):
"""
Exercise maptbx.target_and_gradients_diffmap in action: minimization.
"""
xrs = get_xrs()
map_target, f_calc = get_map(xrs=xrs)
assert approx_equal(xrs.sites_cart(), [[0,0,0]])
for sx in [-1,0,1]:
for sy in [-1,0,1]:
for sz in [-1,0,1]:
xrs_cp = xrs.deep_copy_scatterers()
xrs_cp = xrs_cp.translate(x=0.3*sx, y=0.5*sy, z=0.7*sz)
assert approx_equal(xrs_cp.sites_cart(), [[0.3*sx,0.5*sy,0.7*sz]],1.e-6)
crystal_gridding = maptbx.crystal_gridding(
unit_cell = xrs_cp.unit_cell(),
space_group_info = xrs_cp.space_group_info(),
pre_determined_n_real = map_target.accessor().all())
o = minimization.run(
xray_structure = xrs_cp,
miller_array = f_calc,
crystal_gridding = crystal_gridding,
map_target = map_target,
step = d_min/4,
target_type = "diffmap")
assert approx_equal(xrs.sites_cart(), [[0,0,0]])
def check(tuple_calc, selection, prefix):
miller_arrays = reflection_file_reader.any_reflection_file(file_name =
prefix+"polder_map_coeffs.mtz").as_miller_arrays()
mc_polder = None
for ma in miller_arrays:
lbl = ma.info().label_string()
if(lbl == "mFo-DFc_polder,PHImFo-DFc_polder"):
mc_polder = ma.deep_copy()
assert (mc_polder is not None)
cg = maptbx.crystal_gridding(
unit_cell = mc_polder.unit_cell(),
d_min = mc_polder.d_min(),
resolution_factor = 0.25,
space_group_info = mc_polder.space_group_info())
map_polder = get_map(cg=cg, mc=mc_polder)
pdb_hierarchy = iotbx.pdb.input(
source_info=None, lines=pdb_str).construct_hierarchy()
sel = pdb_hierarchy.atom_selection_cache().selection(string = selection)
sites_cart_lig = pdb_hierarchy.atoms().extract_xyz().select(sel)
sites_frac_lig = mc_polder.unit_cell().fractionalize(sites_cart_lig)
mp = get_map_stats(
map = map_polder,
sites_frac = sites_frac_lig)
#
mmm_mp = mp.min_max_mean().as_tuple()
print "Polder map : %7.3f %7.3f %7.3f" % mmm_mp
assert approx_equal(mmm_mp, tuple_calc, eps=1.0), "\
calculated is %s and expected is %s" % (mmm_mp, tuple_calc)
def run(args, out=sys.stdout):
cmdline = iotbx.phil.process_command_line_with_files(
args=args,
master_phil_string=master_phil_str,
pdb_file_def="model",
map_file_def="map",
usage_string="""\
em_rscc.py model.pdb map.ccp4
%s""" % __doc__)
params = cmdline.work.extract()
assert (not None in [params.model, params.map])
pdb_in = cmdline.get_file(params.model).file_object
m = cmdline.get_file(params.map).file_object
print >> out, "Input electron density map:"
print >> out, "m.all() :", m.data.all()
print >> out, "m.focus() :", m.data.focus()
print >> out, "m.origin():", m.data.origin()
print >> out, "m.nd() :", m.data.nd()
print >> out, "m.size() :", m.data.size()
print >> out, "m.focus_size_1d():", m.data.focus_size_1d()
print >> out, "m.is_0_based() :", m.data.is_0_based()
print >> out, "map: min/max/mean:", flex.min(m.data), flex.max(
m.data), flex.mean(m.data)
print >> out, "unit cell:", m.unit_cell_parameters
symm = crystal.symmetry(space_group_symbol="P1",
unit_cell=m.unit_cell_parameters)
xrs = pdb_in.input.xray_structure_simple(crystal_symmetry=symm)
print >> out, "Setting up electron scattering table (d_min=%g)" % params.d_min
xrs.scattering_type_registry(d_min=params.d_min, table="electron")
fc = xrs.structure_factors(d_min=params.d_min).f_calc()
cg = maptbx.crystal_gridding(unit_cell=symm.unit_cell(),
space_group_info=symm.space_group_info(),
pre_determined_n_real=m.data.all())
fc_map = fc.fft_map(
crystal_gridding=cg).apply_sigma_scaling().real_map_unpadded()
assert (fc_map.all() == fc_map.focus() == m.data.all())
em_data = m.data.as_double()
unit_cell_for_interpolation = m.grid_unit_cell()
frac_matrix = unit_cell_for_interpolation.fractionalization_matrix()
sites_cart = xrs.sites_cart()
sites_frac = xrs.sites_frac()
print >> out, "PER-RESIDUE CORRELATION:"
for chain in pdb_in.hierarchy.only_model().chains():
for residue_group in chain.residue_groups():
i_seqs = residue_group.atoms().extract_i_seq()
values_em = flex.double()
values_fc = flex.double()
for i_seq in i_seqs:
rho_em = maptbx.non_crystallographic_eight_point_interpolation(
map=em_data,
gridding_matrix=frac_matrix,
site_cart=sites_cart[i_seq])
rho_fc = fc_map.eight_point_interpolation(sites_frac[i_seq])
values_em.append(rho_em)
values_fc.append(rho_fc)
cc = flex.linear_correlation(x=values_em,
y=values_fc).coefficient()
print >> out, residue_group.id_str(), cc
def exercise_2():
symmetry = crystal.symmetry(
unit_cell=(5.67, 10.37, 10.37, 90, 135.49, 90),
space_group_symbol="C2")
structure = xray.structure(crystal_symmetry=symmetry)
atmrad = flex.double()
xyzf = flex.vec3_double()
for k in xrange(100):
scatterer = xray.scatterer(
site = ((1.+k*abs(math.sin(k)))/1000.0,
(1.+k*abs(math.cos(k)))/1000.0,
(1.+ k)/1000.0),
scattering_type = "C")
structure.add_scatterer(scatterer)
atmrad.append(van_der_waals_radii.vdw.table[scatterer.element_symbol()])
xyzf.append(scatterer.site)
miller_set = miller.build_set(
crystal_symmetry=structure,
d_min=1.0,
anomalous_flag=False)
step = 0.5
crystal_gridding = maptbx.crystal_gridding(
unit_cell=structure.unit_cell(),
step=step)
nxyz = crystal_gridding.n_real()
shrink_truncation_radius = 1.0
solvent_radius = 1.0
m1 = around_atoms(
structure.unit_cell(),
structure.space_group().order_z(),
structure.sites_frac(),
atmrad,
nxyz,
solvent_radius,
shrink_truncation_radius)
assert m1.solvent_radius == 1
assert m1.shrink_truncation_radius == 1
assert flex.max(m1.data) == 1
assert flex.min(m1.data) == 0
assert m1.data.size() == m1.data.count(1) + m1.data.count(0)
m2 = mmtbx.masks.bulk_solvent(
xray_structure=structure,
gridding_n_real=nxyz,
ignore_zero_occupancy_atoms = False,
solvent_radius=solvent_radius,
shrink_truncation_radius=shrink_truncation_radius)
assert m2.data.all_eq(m1.data)
m3 = mmtbx.masks.bulk_solvent(
xray_structure=structure,
grid_step=step,
ignore_zero_occupancy_atoms = False,
solvent_radius=solvent_radius,
shrink_truncation_radius=shrink_truncation_radius)
assert m3.data.all_eq(m1.data)
f_mask2 = m2.structure_factors(miller_set=miller_set)
f_mask3 = m3.structure_factors(miller_set=miller_set)
assert approx_equal(f_mask2.data(), f_mask3.data())
assert approx_equal(flex.sum(flex.abs(f_mask3.data())), 1095.17999134)
def exercise_sampled_model_density_1():
import iotbx.pdb
pdb_str1 = """
CRYST1 10.000 10.000 10.000 90.00 90.00 90.00 P 1
ATOM 1 CB PHE A 1 5.000 5.000 5.000 1.00 15.00 C
ANISOU 1 CB PHE A 1 900 2900 100 0 0 0 C
TER
END
"""
pdb_str2 = """
CRYST1 10.000 10.000 10.000 90.00 90.00 90.00 P 1
ATOM 1 CB PHE A 1 5.000 5.000 5.000 1.00 15.00 C
TER
END
"""
#
for pdb_str in [pdb_str1, pdb_str2]:
print
pdb_inp = iotbx.pdb.input(source_info=None, lines=pdb_str)
xrs = pdb_inp.xray_structure_simple()
#
crystal_gridding = maptbx.crystal_gridding(
unit_cell=xrs.unit_cell(),
space_group_info=xrs.space_group_info(),
symmetry_flags=maptbx.use_space_group_symmetry,
step=0.1)
m = mmtbx.real_space.sampled_model_density(
xray_structure=xrs, n_real=crystal_gridding.n_real()).data()
#
max_index = [(i - 1) // 2 for i in crystal_gridding.n_real()]
complete_set = miller.build_set(
crystal_symmetry=xrs.crystal_symmetry(),
anomalous_flag=False,
max_index=max_index)
indices = complete_set.indices()
indices.append((0, 0, 0))
#
complete_set = complete_set.customized_copy(indices=indices)
f_obs_cmpl = complete_set.structure_factors_from_map(
map=m, use_scale=True, anomalous_flag=False, use_sg=False)
fc = complete_set.structure_factors_from_scatterers(
xray_structure=xrs).f_calc()
#
f1 = abs(fc).data()
f2 = abs(f_obs_cmpl).data()
r = 200 * flex.sum(flex.abs(f1 - f2)) / flex.sum(f1 + f2)
assert r < 0.5
print r
#
fft_map = miller.fft_map(crystal_gridding=crystal_gridding,
fourier_coefficients=f_obs_cmpl)
fft_map.apply_volume_scaling()
m_ = fft_map.real_map_unpadded()
print m.as_1d().min_max_mean().as_tuple()
print m_.as_1d().min_max_mean().as_tuple()
assert approx_equal(m.as_1d().min_max_mean().as_tuple(),
m_.as_1d().min_max_mean().as_tuple(),
1.e-3) # Must be smaller!?
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 get_map_data(xrs, d_min):
cg = maptbx.crystal_gridding(unit_cell=xrs.unit_cell(),
space_group_info=xrs.space_group_info(),
step=0.1)
fc = xrs.structure_factors(d_min=d_min, algorithm="direct").f_calc()
fft_map = miller.fft_map(crystal_gridding=cg,
fourier_coefficients=fc,
f_000=xrs.f_000())
fft_map.apply_volume_scaling()
return fft_map.real_map_unpadded()
def __init__(self,
map,
xray_structure,
d_min,
box=None,
compute_cc_box=False,
compute_cc_image=False,
compute_cc_mask=True,
compute_cc_volume=True,
compute_cc_peaks=True):
adopt_init_args(self, locals())
if (box is None and xray_structure.crystal_symmetry().space_group().
type().number() == 1):
box = True
else:
box = False
#
self.cc_box = None
self.cc_image = None
self.cc_mask = None
self.cc_volume = None
self.cc_peaks = None
#
self.crystal_gridding = maptbx.crystal_gridding(
unit_cell=xray_structure.unit_cell(),
space_group_info=xray_structure.space_group_info(),
pre_determined_n_real=map.accessor().all(),
symmetry_flags=maptbx.use_space_group_symmetry)
self.atom_radius = None
bs_mask = masks.mask_from_xray_structure(
xray_structure=self.xray_structure,
p1=True,
for_structure_factors=False,
n_real=self.map.accessor().all()).mask_data
maptbx.unpad_in_place(map=bs_mask)
self.sel_inside = (bs_mask == 0.).iselection()
self.n_nodes_inside = self.sel_inside.size()
del bs_mask
#
map_calc = self.get_map_calc()
#
if (compute_cc_mask):
self.cc_mask = from_map_map_selection(map_1=self.map,
map_2=map_calc,
selection=self.sel_inside)
del self.sel_inside
if (compute_cc_box):
self.cc_box = from_map_map(map_1=self.map, map_2=map_calc)
if (compute_cc_image):
self.atom_radius = self._atom_radius()
self.cc_image = self._cc_image(map_calc=map_calc)
if (compute_cc_volume):
self.cc_volume = self._cc_volume(map_calc=map_calc)
if (compute_cc_peaks):
self.cc_peaks = self._cc_peaks(map_calc=map_calc)
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 crystal_gridding(self, assert_shannon_sampling=True):
if (self._crystal_gridding is None):
self._crystal_gridding = maptbx.crystal_gridding(
unit_cell=self.unit_cell(),
d_min=self.d_min(),
resolution_factor=self.grid_resolution_factor(),
symmetry_flags=self.symmetry_flags(),
space_group_info=self.space_group_info(),
mandatory_factors=self.mandatory_grid_factors(),
max_prime=self.max_prime(),
assert_shannon_sampling=assert_shannon_sampling)
return self._crystal_gridding
def prepare_reference_map_3(self):
""" with ramachandran outliers """
xrs = self.model.get_xray_structure().deep_copy_scatterers()
pdb_h = self.model.get_hierarchy()
print("Preparing reference map, method 3", file=self.log)
outlier_selection_txt = mmtbx.building.loop_closure.utils. \
rama_score_selection(pdb_h, self.model.get_ramachandran_manager(), "outlier",1)
asc = self.model.get_atom_selection_cache()
# print >> self.log, "rama outlier selection:", outlier_selection_txt
rama_out_sel = asc.selection(outlier_selection_txt)
xrs=xrs.set_b_iso(value=50)
# side_chain_no_cb_selection = ~ xrs.main_chain_selection()
side_chain_no_cb_selection = ~ xrs.backbone_selection()
xrs = xrs.set_b_iso(value=200, selection=side_chain_no_cb_selection)
xrs = xrs.set_b_iso(value=150, selection=rama_out_sel)
# xrs = xrs.set_occupancies(value=0.3, selection=rama_out_sel)
crystal_gridding = maptbx.crystal_gridding(
unit_cell = xrs.unit_cell(),
space_group_info = xrs.space_group_info(),
symmetry_flags = maptbx.use_space_group_symmetry,
d_min = self.params.reference_map_resolution)
fc = xrs.structure_factors(
d_min = self.params.reference_map_resolution,
algorithm = "direct").f_calc()
fft_map = miller.fft_map(
crystal_gridding=crystal_gridding,
fourier_coefficients=fc)
fft_map.apply_sigma_scaling()
self.reference_map = fft_map.real_map_unpadded(in_place=False)
if self.params.debug:
fft_map.as_xplor_map(file_name="%s_3.map" % self.params.output_prefix)
self.master_map = self.reference_map.deep_copy()
if self.model.ncs_constraints_present():
# here we are negating non-master part of the model
# self.master_sel=master_sel
# self.master_map = self.reference_map.deep_copy()
mask = maptbx.mask(
xray_structure=xrs.select(self.model.get_master_selection()),
n_real=self.master_map.focus(),
mask_value_inside_molecule=1,
mask_value_outside_molecule=-1,
solvent_radius=0,
atom_radius=1.)
self.master_map = self.reference_map * mask
if self.params.debug:
iotbx.mrcfile.write_ccp4_map(
file_name="%s_3_master.map" % self.params.output_prefix,
unit_cell=xrs.unit_cell(),
space_group=xrs.space_group(),
map_data=self.master_map,
labels=flex.std_string([""]))
def exercise_2():
symmetry = crystal.symmetry(unit_cell=(5.67, 10.37, 10.37, 90, 135.49, 90),
space_group_symbol="C2")
structure = xray.structure(crystal_symmetry=symmetry)
atmrad = flex.double()
xyzf = flex.vec3_double()
for k in xrange(100):
scatterer = xray.scatterer(site=((1. + k * abs(math.sin(k))) / 1000.0,
(1. + k * abs(math.cos(k))) / 1000.0,
(1. + k) / 1000.0),
scattering_type="C")
structure.add_scatterer(scatterer)
atmrad.append(
van_der_waals_radii.vdw.table[scatterer.element_symbol()])
xyzf.append(scatterer.site)
miller_set = miller.build_set(crystal_symmetry=structure,
d_min=1.0,
anomalous_flag=False)
step = 0.5
crystal_gridding = maptbx.crystal_gridding(unit_cell=structure.unit_cell(),
step=step)
nxyz = crystal_gridding.n_real()
shrink_truncation_radius = 1.0
solvent_radius = 1.0
m1 = around_atoms(structure.unit_cell(),
structure.space_group().order_z(),
structure.sites_frac(), atmrad, nxyz, solvent_radius,
shrink_truncation_radius)
assert m1.solvent_radius == 1
assert m1.shrink_truncation_radius == 1
assert flex.max(m1.data) == 1
assert flex.min(m1.data) == 0
assert m1.data.size() == m1.data.count(1) + m1.data.count(0)
m2 = mmtbx.masks.bulk_solvent(
xray_structure=structure,
gridding_n_real=nxyz,
ignore_zero_occupancy_atoms=False,
solvent_radius=solvent_radius,
shrink_truncation_radius=shrink_truncation_radius)
assert m2.data.all_eq(m1.data)
m3 = mmtbx.masks.bulk_solvent(
xray_structure=structure,
grid_step=step,
ignore_zero_occupancy_atoms=False,
solvent_radius=solvent_radius,
shrink_truncation_radius=shrink_truncation_radius)
assert m3.data.all_eq(m1.data)
f_mask2 = m2.structure_factors(miller_set=miller_set)
f_mask3 = m3.structure_factors(miller_set=miller_set)
assert approx_equal(f_mask2.data(), f_mask3.data())
assert approx_equal(flex.sum(flex.abs(f_mask3.data())), 1095.17999134)
def start(self, f_obs, phases, f_000=0):
self.f_obs = f_obs
self.crystal_gridding = maptbx.crystal_gridding(
unit_cell=self.f_obs.unit_cell(),
space_group_info=sgtbx.space_group_info('P1'),
d_min=self.f_obs.d_min(),
resolution_factor=1 / 2,
symmetry_flags=maptbx.use_space_group_symmetry)
self.fft_scale = (self.f_obs.crystal_symmetry().unit_cell().volume() /
self.crystal_gridding.n_grid_points())
self.f_calc = self.f_obs.phase_transfer(phases)
self.f_000 = f_000
self.compute_electron_density_map()
def start(self, f_obs, phases, f_000=0):
self.f_obs = f_obs
self.crystal_gridding = maptbx.crystal_gridding(
unit_cell=self.f_obs.unit_cell(),
space_group_info=sgtbx.space_group_info('P1'),
d_min=self.f_obs.d_min(),
resolution_factor=1/2,
symmetry_flags=maptbx.use_space_group_symmetry)
self.fft_scale = (self.f_obs.crystal_symmetry().unit_cell().volume()
/ self.crystal_gridding.n_grid_points())
self.f_calc = self.f_obs.phase_transfer(phases)
self.f_000 = f_000
self.compute_electron_density_map()
def _filter_by_map_model_cc(self):
self.ma.add(" start: %d" % self.xrs_water.scatterers().size())
# Compute model Fourier map
m_all = self.model.deep_copy()
m_all.add_solvent(solvent_xray_structure=self.xrs_water,
atom_name="O",
residue_name="HOH",
chain_id="S",
refine_adp="isotropic")
m_all.setup_scattering_dictionaries(
scattering_table=self.scattering_table)
xrs_all = m_all.get_xray_structure()
f_calc = xrs_all.structure_factors(d_min=self.resolution).f_calc()
crystal_gridding = maptbx.crystal_gridding(
unit_cell=xrs_all.unit_cell(),
space_group_info=xrs_all.space_group_info(),
pre_determined_n_real=self.map_data.accessor().all())
fft_map = miller.fft_map(crystal_gridding=crystal_gridding,
fourier_coefficients=f_calc)
map_calc = fft_map.real_map_unpadded()
sites_cart = xrs_all.sites_cart()
# Remove water by CC
wsel = m_all.selection(string="water")
for i, s in enumerate(wsel):
if not s: continue # XXX
cc = mmtbx.maps.correlation.from_map_map_atom(
map_1=self.map_data_resampled,
map_2=map_calc,
site_cart=sites_cart[i],
unit_cell=xrs_all.unit_cell(),
radius=self.atom_radius)
if cc < self.cc_mask_filter_threshold: wsel[i] = False
self.xrs_water = m_all.select(wsel).get_xray_structure()
# Exclude poor macro-molecule atoms from interaction analysis
sites_cart = self.model.get_sites_cart()
for i, s in enumerate(self.interaction_selection):
if not s: continue # XXX
cc = mmtbx.maps.correlation.from_map_map_atom(
map_1=self.map_data_resampled,
map_2=map_calc,
site_cart=sites_cart[i],
unit_cell=xrs_all.unit_cell(),
radius=self.atom_radius)
if cc < self.cc_mask_threshold_interacting_atoms:
self.interaction_selection[i] = False
self.sites_frac_interaction = self.model.get_sites_frac().select(
self.interaction_selection)
#
self.ma.add(" final: %d" % self.xrs_water.scatterers().size())
def exercise_mask_data_3(space_group_info,
n_sites=100,
d_min=2.0,
resolution_factor=1. / 4):
from cctbx import maptbx
xrs = random_structure.xray_structure(
space_group_info=space_group_info,
elements=(("O", "N", "C") * (n_sites // 3 + 1))[:n_sites],
volume_per_atom=50,
min_distance=1.5)
crystal_gridding = maptbx.crystal_gridding(
unit_cell=xrs.unit_cell(),
space_group_info=xrs.space_group_info(),
symmetry_flags=maptbx.use_space_group_symmetry,
resolution_factor=resolution_factor,
d_min=d_min)
n_real = crystal_gridding.n_real()
dummy_set = xrs.structure_factors(d_min=d_min).f_calc()
xrs.shake_sites_in_place(mean_distance=10)
xrs_p1 = xrs.expand_to_p1(sites_mod_positive=True)
mo1 = mmtbx.masks.mask_from_xray_structure(xray_structure=xrs,
p1=False,
solvent_radius=1,
shrink_truncation_radius=1,
for_structure_factors=True,
n_real=n_real)
asu_mask, mask_data1 = mo1.asu_mask, mo1.mask_data
assert mask_data1.focus() == n_real
# get Fmask option 1
f_mask_1 = dummy_set.set().array(
data=asu_mask.structure_factors(dummy_set.indices()))
# get Fmask option 2
f_mask_2 = dummy_set.structure_factors_from_map(map=mask_data1,
use_scale=True,
anomalous_flag=False,
use_sg=True)
# get Fmask option 3
mo3 = mmtbx.masks.mask_from_xray_structure(xray_structure=xrs,
p1=True,
solvent_radius=1,
shrink_truncation_radius=1,
for_structure_factors=True,
n_real=n_real)
f_mask_3 = dummy_set.structure_factors_from_map(
map=mo3.mask_data, use_scale=True, anomalous_flag=False,
use_sg=False) # Note use_sg = False !
#
assert approx_equal(f_mask_1.data(), f_mask_2.data())
assert approx_equal(f_mask_1.data(), f_mask_3.data())
def get_mask_data(xrs, d_min):
crystal_gridding = maptbx.crystal_gridding(
unit_cell=xrs.unit_cell(),
d_min=d_min,
resolution_factor=1. / 4,
symmetry_flags=maptbx.use_space_group_symmetry,
space_group_info=xrs.space_group_info())
mp = mmtbx.masks.mask_master_params.extract()
return mmtbx.masks.mask_from_xray_structure(
xray_structure=xrs,
p1=True,
solvent_radius=mp.solvent_radius,
shrink_truncation_radius=mp.shrink_truncation_radius,
for_structure_factors=True,
n_real=crystal_gridding.n_real()).mask_data
def exercise_mask_data_1(space_group_info, n_sites=100):
from cctbx import maptbx
from cctbx.masks import vdw_radii_from_xray_structure
for d_min in [1, 1.5, 2.1]:
for resolution_factor in [1. / 2, 1. / 3, 1. / 4, 1. / 5]:
xrs = random_structure.xray_structure(
space_group_info=space_group_info,
elements=(("O", "N", "C") * (n_sites // 3 + 1))[:n_sites],
volume_per_atom=30,
min_distance=1)
atom_radii = vdw_radii_from_xray_structure(xray_structure=xrs)
asu_mask = masks.atom_mask(unit_cell=xrs.unit_cell(),
group=xrs.space_group(),
resolution=d_min,
grid_step_factor=resolution_factor,
solvent_radius=1.0,
shrink_truncation_radius=1.0)
asu_mask.compute(xrs.sites_frac(), atom_radii)
mask_data = asu_mask.mask_data_whole_uc()
assert flex.min(mask_data) == 0.0
# It's not just 0 and 1 ...
assert flex.max(mask_data) == xrs.space_group().order_z()
# In fact, it is a mixture ...
if 0: # XXX this will rightfully crash
mask_data_ = mask_data / xrs.space_group().order_z()
s0 = mask_data_ < 0.5
s1 = mask_data_ > 0.5
if (mask_data_.size() != s0.count(True) + s1.count(True)):
for d in mask_data_:
if (d != 0 and d != 1):
print(d, xrs.space_group().order_z())
assert mask_data_.size(
) == s0.count(True) + s1.count(True), [
mask_data_.size() - (s0.count(True) + s1.count(True))
]
if (
0
): # XXX This would crash with the message: "... The grid is not ..."
cr_gr = maptbx.crystal_gridding(
unit_cell=xrs.unit_cell(),
d_min=d_min,
resolution_factor=resolution_factor)
asu_mask = masks.atom_mask(unit_cell=xrs.unit_cell(),
space_group=xrs.space_group(),
gridding_n_real=cr_gr.n_real(),
solvent_radius=1.0,
shrink_truncation_radius=1.0)
asu_mask.compute(xrs.sites_frac(), atom_radii)
def __init__(self, map_data, xray_structure, d_min, atom_radius):
self.d_min = d_min
self.map_data = map_data
self.atom_radius = atom_radius
self.f_map_diff = None # XXX rudimentary left-over, remove later
self.crystal_gridding = maptbx.crystal_gridding( #XXX Likewise, remove later
unit_cell = xray_structure.unit_cell(),
pre_determined_n_real = map_data.all(),
space_group_info = xray_structure.space_group_info())
self.complete_set = miller.build_set(
crystal_symmetry = xray_structure.crystal_symmetry(),
anomalous_flag = False,
d_min = d_min)
self.miller_array = self.map_to_sf(map_data = self.map_data)
self.miller_array_masked = self.update_miller_array_masked(
xray_structure = xray_structure)
def get_fcalc_map(model, symmetry, d_min, emmap, scatterer="electron"):
"""Fcalc map generation borrowed from EMRinger"""
xrs = model.input.xray_structure_simple(crystal_symmetry=symmetry)
if scatterer == "electron":
# until ions are added to the electron scattering tables
neutralize_scatterers(xrs)
xrs.scattering_type_registry(d_min=d_min, table=scatterer)
fc = xrs.structure_factors(d_min=d_min).f_calc()
cg = maptbx.crystal_gridding(unit_cell=symmetry.unit_cell(),
space_group_info=symmetry.space_group_info(),
pre_determined_n_real=emmap.data.all())
fc_map = fc.fft_map(
crystal_gridding=cg).apply_sigma_scaling().real_map_unpadded()
scale = len(fc_map) / flex.sum(flex.abs(fc_map))
fc_map_scaled = fc_map * scale
return fc_map_scaled
def exercise_crystal_gridding():
crystal_symmetry = crystal.symmetry(
unit_cell=(95.2939, 95.2939, 98.4232, 94.3158, 115.226, 118.822),
space_group_symbol="Hall: C 2y (x+y,-x+y+z,z)")
for mandatory_factors,n_real in ((None,(90,90,90)),
((20,20,20),(100,100,100))):
crystal_gridding = maptbx.crystal_gridding(
unit_cell=crystal_symmetry.unit_cell(),
d_min=3.5,
resolution_factor=1/3.,
symmetry_flags=maptbx.use_space_group_symmetry,
space_group_info=crystal_symmetry.space_group_info(),
mandatory_factors=mandatory_factors,
max_prime=5,
assert_shannon_sampling=True)
assert crystal_gridding.n_real() == n_real
def __init__(self, map_data, xray_structure, d_min, atom_radius):
self.d_min = d_min
self.map_data = map_data
self.atom_radius = atom_radius
self.f_map_diff = None # XXX rudimentary left-over, remove later
self.crystal_gridding = maptbx.crystal_gridding( #XXX Likewise, remove later
unit_cell=xray_structure.unit_cell(),
pre_determined_n_real=map_data.all(),
space_group_info=xray_structure.space_group_info())
self.complete_set = miller.build_set(
crystal_symmetry=xray_structure.crystal_symmetry(),
anomalous_flag=False,
d_min=d_min)
self.miller_array = self.map_to_sf(map_data=self.map_data)
self.miller_array_masked = self.update_miller_array_masked(
xray_structure=xray_structure)
def exercise_crystal_gridding():
crystal_symmetry = crystal.symmetry(
unit_cell=(95.2939, 95.2939, 98.4232, 94.3158, 115.226, 118.822),
space_group_symbol="Hall: C 2y (x+y,-x+y+z,z)")
for mandatory_factors, n_real in ((None, (90, 90, 90)), ((20, 20, 20),
(100, 100, 100))):
crystal_gridding = maptbx.crystal_gridding(
unit_cell=crystal_symmetry.unit_cell(),
d_min=3.5,
resolution_factor=1 / 3.,
symmetry_flags=maptbx.use_space_group_symmetry,
space_group_info=crystal_symmetry.space_group_info(),
mandatory_factors=mandatory_factors,
max_prime=5,
assert_shannon_sampling=True)
assert crystal_gridding.n_real() == n_real
def scale_two_real_maps_in_fourier_space(m1, m2, cs, d_min, vector_map):
f1 = maptbx.map_to_map_coefficients(m=m1, cs=cs, d_min=d_min)
f2 = maptbx.map_to_map_coefficients(m=m2, cs=cs, d_min=d_min)
if (vector_map):
f2 = f2.phase_transfer(phase_source=f1)
ss = 1. / flex.pow2(f1.d_spacings().data()) / 4.
bs = flex.double([i for i in xrange(0, 100)])
mc = mmtbx.bulk_solvent.complex_f_minus_f_kb_scaled(
f1.data(), f2.data(), bs, ss)
crystal_gridding = maptbx.crystal_gridding(
unit_cell=cs.unit_cell(),
space_group_info=cs.space_group_info(),
pre_determined_n_real=m1.all())
fft_map = miller.fft_map(crystal_gridding=crystal_gridding,
fourier_coefficients=f1.array(data=mc))
return fft_map.real_map_unpadded()
def exercise_d99():
pdb_inp = iotbx.pdb.input(source_info=None, lines=pdb_str)
ph = pdb_inp.construct_hierarchy()
xrs = ph.extract_xray_structure(
crystal_symmetry=pdb_inp.crystal_symmetry())
crystal_gridding = maptbx.crystal_gridding(
unit_cell=xrs.unit_cell(),
space_group_info=xrs.space_group_info(),
resolution_factor=0.25,
d_min=2.,
symmetry_flags=maptbx.use_space_group_symmetry)
fc = xrs.structure_factors(d_min=2.).f_calc()
fft_map = fc.fft_map(crystal_gridding=crystal_gridding)
map = fft_map.real_map_unpadded()
#
o = maptbx.d99(map=map, crystal_symmetry=xrs.crystal_symmetry())
assert approx_equal(o.result.d999, 2.0, 0.03)
def run(args, log=sys.stdout):
print >> log, "-" * 79
print >> log, legend
print >> log, "-" * 79
inputs = mmtbx.utils.process_command_line_args(
args=args, master_params=master_params())
inputs.params.show(prefix=" ", out=log)
print >> log
file_names = inputs.pdb_file_names
if (len(file_names) != 1): raise Sorry("A PDB file is expected.")
pdb_inp = iotbx.pdb.input(file_name=file_names[0])
awl = list(pdb_inp.atoms_with_labels())
xrs = pdb_inp.xray_structure_simple().expand_to_p1(sites_mod_positive=True)
# Check for B=0
bs = xrs.extract_u_iso_or_u_equiv()
sel_zero = bs < 1.e-3
n_zeros = sel_zero.count(True)
if (n_zeros > 0):
print "Atoms with B=0:"
for i_seq in sel_zero.iselection():
print awl[i_seq].format_atom_record()
raise Sorry("Input model contains %d atoms with B=0" % n_zeros)
#
params = inputs.params.extract()
mmtbx.utils.setup_scattering_dictionaries(
scattering_table=params.scattering_table,
xray_structure=xrs,
d_min=0.5)
#
crystal_gridding = maptbx.crystal_gridding(
unit_cell=xrs.unit_cell(),
space_group_info=xrs.space_group_info(),
symmetry_flags=maptbx.use_space_group_symmetry,
step=params.grid_step)
m = mmtbx.real_space.sampled_model_density(
xray_structure=xrs, n_real=crystal_gridding.n_real())
map_data = m.data()
#
prefix = "model_map"
if (params.output_file_name_prefix is not None):
prefix = params.output_file_name_prefix
#
m.write_as_xplor_map(file_name="%s.xplor" % prefix)
fem.ccp4_map(cg=crystal_gridding,
file_name="%s.ccp4" % prefix,
map_data=map_data)
def exercise_mask_data_3(space_group_info, n_sites=100, d_min=2.0,
resolution_factor=1./4):
from cctbx import maptbx
xrs = random_structure.xray_structure(
space_group_info=space_group_info,
elements=(("O","N","C")*(n_sites//3+1))[:n_sites],
volume_per_atom=50,
min_distance=1.5)
crystal_gridding = maptbx.crystal_gridding(
unit_cell = xrs.unit_cell(),
space_group_info = xrs.space_group_info(),
symmetry_flags = maptbx.use_space_group_symmetry,
resolution_factor = resolution_factor,
d_min = d_min)
n_real = crystal_gridding.n_real()
dummy_set = xrs.structure_factors(d_min = d_min).f_calc()
xrs.shake_sites_in_place(mean_distance=10)
xrs_p1 = xrs.expand_to_p1(sites_mod_positive=True)
mo1 = mmtbx.masks.mask_from_xray_structure(
xray_structure=xrs,
p1=False,
solvent_radius=1,
shrink_truncation_radius=1,
for_structure_factors=True,
n_real=n_real)
asu_mask, mask_data1 = mo1.asu_mask, mo1.mask_data
assert mask_data1.focus()==n_real
# get Fmask option 1
f_mask_1 = dummy_set.set().array(
data = asu_mask.structure_factors(dummy_set.indices()))
# get Fmask option 2
f_mask_2 = dummy_set.structure_factors_from_map(map=mask_data1,
use_scale = True, anomalous_flag = False, use_sg = True)
# get Fmask option 3
mo3 = mmtbx.masks.mask_from_xray_structure(
xray_structure=xrs,
p1=True,
solvent_radius=1,
shrink_truncation_radius=1,
for_structure_factors=True,
n_real=n_real)
f_mask_3 = dummy_set.structure_factors_from_map(map=mo3.mask_data,
use_scale = True, anomalous_flag = False, use_sg = False) # Note use_sg = False !
#
assert approx_equal(f_mask_1.data(), f_mask_2.data())
assert approx_equal(f_mask_1.data(), f_mask_3.data())
def run(args, log=sys.stdout):
print >> log, "-"*79
print >> log, legend
print >> log, "-"*79
inputs = mmtbx.utils.process_command_line_args(args = args,
master_params = master_params())
inputs.params.show(prefix=" ", out=log)
print >> log
file_names = inputs.pdb_file_names
if(len(file_names) != 1): raise Sorry("A PDB file is expected.")
pdb_inp = iotbx.pdb.input(file_name = file_names[0])
awl = list(pdb_inp.atoms_with_labels())
xrs = pdb_inp.xray_structure_simple().expand_to_p1(sites_mod_positive=True)
# Check for B=0
bs = xrs.extract_u_iso_or_u_equiv()
sel_zero = bs<1.e-3
n_zeros = sel_zero.count(True)
if(n_zeros>0):
print "Atoms with B=0:"
for i_seq in sel_zero.iselection():
print awl[i_seq].format_atom_record()
raise Sorry("Input model contains %d atoms with B=0"%n_zeros)
#
params = inputs.params.extract()
mmtbx.utils.setup_scattering_dictionaries(
scattering_table = params.scattering_table,
xray_structure = xrs,
d_min = 0.5)
#
crystal_gridding = maptbx.crystal_gridding(
unit_cell = xrs.unit_cell(),
space_group_info = xrs.space_group_info(),
symmetry_flags = maptbx.use_space_group_symmetry,
step = params.grid_step)
m = mmtbx.real_space.sampled_model_density(
xray_structure = xrs,
n_real = crystal_gridding.n_real())
map_data = m.data()
#
prefix = "model_map"
if(params.output_file_name_prefix is not None):
prefix = params.output_file_name_prefix
#
m.write_as_xplor_map(file_name = "%s.xplor"%prefix)
fem.ccp4_map(cg=crystal_gridding, file_name="%s.ccp4"%prefix,
map_data=map_data)
def exercise_mask_data_1(space_group_info, n_sites=100):
from cctbx import maptbx
from cctbx.masks import vdw_radii_from_xray_structure
for d_min in [1, 1.5, 2.1]:
for resolution_factor in [1./2, 1./3, 1./4, 1./5]:
xrs = random_structure.xray_structure(
space_group_info=space_group_info,
elements=(("O","N","C")*(n_sites//3+1))[:n_sites],
volume_per_atom=30,
min_distance=1)
atom_radii = vdw_radii_from_xray_structure(xray_structure = xrs)
asu_mask = masks.atom_mask(
unit_cell = xrs.unit_cell(),
group = xrs.space_group(),
resolution = d_min,
grid_step_factor = resolution_factor,
solvent_radius = 1.0,
shrink_truncation_radius = 1.0)
asu_mask.compute(xrs.sites_frac(), atom_radii)
mask_data = asu_mask.mask_data_whole_uc()
assert flex.min(mask_data) == 0.0
# It's not just 0 and 1 ...
assert flex.max(mask_data) == xrs.space_group().order_z()
# In fact, it is a mixture ...
if 0: # XXX this will rightfully crash
mask_data_ = mask_data / xrs.space_group().order_z()
s0 = mask_data_ < 0.5
s1 = mask_data_ > 0.5
if(mask_data_.size() != s0.count(True)+s1.count(True)):
for d in mask_data_:
if(d != 0 and d != 1): print d, xrs.space_group().order_z()
assert mask_data_.size() == s0.count(True)+s1.count(True), [
mask_data_.size()-(s0.count(True)+s1.count(True))]
if(0): # XXX This would crash with the message: "... The grid is not ..."
cr_gr = maptbx.crystal_gridding(
unit_cell = xrs.unit_cell(),
d_min = d_min,
resolution_factor = resolution_factor)
asu_mask = masks.atom_mask(
unit_cell = xrs.unit_cell(),
space_group = xrs.space_group(),
gridding_n_real = cr_gr.n_real(),
solvent_radius = 1.0,
shrink_truncation_radius = 1.0)
asu_mask.compute(xrs.sites_frac(), atom_radii)
def prepare_reference_map(self, xrs, pdb_h):
print >> self.log, "Preparing reference map"
# new_h = pdb_h.deep_copy()
# truncate_to_poly_gly(new_h)
# xrs = new_h.extract_xray_structure(crystal_symmetry=xrs.crystal_symmetry())
xrs=xrs.set_b_iso(value=50)
crystal_gridding = maptbx.crystal_gridding(
unit_cell = xrs.unit_cell(),
space_group_info = xrs.space_group_info(),
symmetry_flags = maptbx.use_space_group_symmetry,
d_min = self.params.reference_map_resolution)
fc = xrs.structure_factors(d_min = self.params.reference_map_resolution, algorithm = "fft").f_calc()
fft_map = miller.fft_map(
crystal_gridding=crystal_gridding,
fourier_coefficients=fc)
fft_map.apply_sigma_scaling()
self.reference_map = fft_map.real_map_unpadded(in_place=False)
fft_map.as_xplor_map(file_name="%s.map" % self.params.output_prefix)
def get_mask_1(fmodel, grid_step_factor):
grid_step = fmodel.f_obs().d_min()*(1./grid_step_factor)
if(grid_step < 0.15): grid_step = 0.15
grid_step = min(0.8, grid_step)
crystal_gridding = maptbx.crystal_gridding(
unit_cell = fmodel.xray_structure.unit_cell(),
space_group_info = fmodel.xray_structure.space_group_info(),
symmetry_flags = maptbx.use_space_group_symmetry,
step = grid_step)
n_real = crystal_gridding.n_real()
# Compute mask in P1
mask_data_p1 = mmtbx.masks.mask_from_xray_structure(
xray_structure = fmodel.xray_structure,
p1 = True,
for_structure_factors = True,
n_real = n_real,
in_asu = False).mask_data
maptbx.unpad_in_place(map=mask_data_p1)
return mask_data_p1, n_real, crystal_gridding
def __init__(self,
xray_structure,
ignore_zero_occupancy_atoms,
solvent_radius,
shrink_truncation_radius,
ignore_hydrogen_atoms=True,
gridding_n_real=None,
grid_step=None,
atom_radii=None):
global number_of_mask_calculations
number_of_mask_calculations += 1
assert [gridding_n_real, grid_step].count(None) == 1
self.xray_structure = xray_structure
if (gridding_n_real is None):
gridding_n_real = maptbx.crystal_gridding(
unit_cell=xray_structure.unit_cell(),
step=grid_step).n_real()
if(atom_radii is None):
atom_radii = vdw_radii_from_xray_structure(xray_structure =
self.xray_structure)
sites_frac = xray_structure.sites_frac()
self.n_atoms_excluded = 0
selection = flex.bool(xray_structure.scatterers().size(), True)
if(ignore_zero_occupancy_atoms):
selection &= xray_structure.scatterers().extract_occupancies() > 0
if(ignore_hydrogen_atoms):
selection &= (~xray_structure.hd_selection())
sites_frac = sites_frac.select(selection)
atom_radii = atom_radii.select(selection)
self.n_atoms_excluded = selection.count(False)
around_atoms.__init__(self,
unit_cell = xray_structure.unit_cell(),
space_group_order_z = xray_structure.space_group().order_z(),
sites_frac = sites_frac,
atom_radii = atom_radii,
gridding_n_real = gridding_n_real,
solvent_radius = solvent_radius,
shrink_truncation_radius = shrink_truncation_radius)
introspection.virtual_memory_info().update_max()
def get_mask_2(fmodel, grid_step_factor):
sgt = fmodel.xray_structure.space_group().type()
mask_params = mmtbx.masks.mask_master_params.extract()
mask_params.grid_step_factor = grid_step_factor
asu_mask_obj = mmtbx.masks.asu_mask(
xray_structure = fmodel.xray_structure,
d_min = fmodel.f_obs().d_min(),
mask_params = mask_params).asu_mask
mask_data_p1 = asu_mask_obj.mask_data_whole_uc()
maptbx.unpad_in_place(map=mask_data_p1)
n_real = mask_data_p1.all()
crystal_gridding = maptbx.crystal_gridding(
unit_cell = fmodel.xray_structure.unit_cell(),
space_group_info = fmodel.xray_structure.space_group_info(),
symmetry_flags = maptbx.use_space_group_symmetry,
pre_determined_n_real = n_real)
#n_real = mask_data_p1.all()
#mask_data_p1 = asu_map_ext.asymmetric_map(sgt, mask_data_p1, n_real).symmetry_expanded_map()
#maptbx.unpad_in_place(map=mask_data_p1)
return mask_data_p1, n_real, crystal_gridding
def exercise1():
pdb_str="""
CRYST1 10.000 10.000 10.000 90.00 90.00 90.00 P 1
HETATM 1 C C 1 2.000 2.000 2.000 1.00 20.00 C
END
"""
pdb_inp = iotbx.pdb.input(source_info=None, lines=pdb_str)
xrs = pdb_inp.xray_structure_simple()
cg = maptbx.crystal_gridding(unit_cell=xrs.unit_cell(),
pre_determined_n_real=(100,100,100),
space_group_info=xrs.space_group_info())
fc = xrs.structure_factors(d_min = 1., algorithm = "direct").f_calc()
fft_map = miller.fft_map(crystal_gridding=cg, fourier_coefficients=fc)
map_data = fft_map.real_map_unpadded()
# pass map and threshold value
co = maptbx.connectivity(map_data=map_data, threshold=100.)
# get 'map' of the same size with integers: 0 where below threshold,
# 1,2,3... - for connected regions
map_result = co.result()
# to find out the number of connected region for particular point:
assert map_result[0,0,0] == 0 # means under threshold
assert map_result[20,20,20] == 1 # blob 1
# get 1d array of integer volumes and transform it to list.
volumes = list(co.regions())
# find max volume (except volume of 0-region which will be probably max)
max_volume = max(volumes[1:])
# find number of the region with max volume
max_index = volumes.index(max_volume)
v=[0,0,0]
for i in range(3):
# !!! Do not do this because it's extremely slow! Used for test purposes.
v[i] = (map_result==i).count(True)
assert v[2] == 0
assert v[1] < 15000
assert v[0]+v[1]+v[2] == 1000000
assert volumes == v[:2]
def run(args, log=sys.stdout):
print >> log, "-"*79
print >> log, legend
print >> log, "-"*79
inputs = mmtbx.utils.process_command_line_args(args = args,
master_params = master_params())
file_names = inputs.pdb_file_names
if(len(file_names) != 1): raise Sorry("A PDB file is expected.")
xrs = iotbx.pdb.input(file_name = file_names[0]).xray_structure_simple()
params = inputs.params.extract()
#
crystal_gridding = maptbx.crystal_gridding(
unit_cell = xrs.unit_cell(),
d_min = params.resolution,
resolution_factor = params.resolution_factor,
symmetry_flags = maptbx.use_space_group_symmetry,
space_group_info = xrs.space_group().info())
mmtbx_masks_asu_mask_obj = mmtbx.masks.asu_mask(
xray_structure = xrs.expand_to_p1(sites_mod_positive=True),
n_real = crystal_gridding.n_real())
bulk_solvent_mask = mmtbx_masks_asu_mask_obj.mask_data_whole_uc()
#
fem.ccp4_map(cg=crystal_gridding, file_name="mask.ccp4",
map_data=bulk_solvent_mask)
def exercise3():
pdb_str="""
CRYST1 10.000 10.000 10.000 90.00 90.00 90.00 P 1
HETATM 1 C C 1 2.000 2.000 2.000 1.00 2.00 C
HETATM 1 C C 1 3.500 2.000 2.000 1.00 2.00 C
END
"""
pdb_inp = iotbx.pdb.input(source_info=None, lines=pdb_str)
xrs = pdb_inp.xray_structure_simple()
cg = maptbx.crystal_gridding(unit_cell=xrs.unit_cell(),
pre_determined_n_real=(100,100,100),
space_group_info=xrs.space_group_info())
fc = xrs.structure_factors(d_min = 1., algorithm = "direct").f_calc()
fft_map = miller.fft_map(crystal_gridding=cg, fourier_coefficients=fc)
fft_map.apply_sigma_scaling()
map_data = fft_map.real_map_unpadded()
#all filled
v, volumes = getvs(map_data, -100)
v2, volumes2 = getvs(map_data, -100, False)
assert v == v2 == [0, 1000000, 0]
assert v[:2] == v2[:2] == volumes == volumes2
# can see one blob
v, volumes = getvs(map_data, 5)
assert v[0]+v[1]+v[2] == 1000000
assert v[2] == 0
assert v[:2] == volumes
# can see separate, approx equal volume bloobs
v, volumes = getvs(map_data, 10)
assert v[0]+v[1]+v[2] == 1000000
assert abs(v[1] - v[2]) < 5
assert v == volumes
# nothing to see
v, volumes = getvs(map_data, 1000)
assert v == [1000000, 0, 0]
assert v[:1] == volumes
def run(args, log = sys.stdout, as_gui_program=False):
if(len(args)==0):
parsed = defaults(log=log)
parsed.show(prefix=" ", out=log)
return
command_line = (option_parser()
.enable_symmetry_comprehensive()
.option("-q", "--quiet",
action="store_true",
default=False,
help="suppress output")
.option("--output_plots",
action="store_true",
default=False)
).process(args=args)
parsed = defaults(log=log)
processed_args = mmtbx.utils.process_command_line_args(
args=command_line.args,
cmd_cs=command_line.symmetry,
master_params=parsed,
log=log,
suppress_symmetry_related_errors=True)
processed_args.params.show(out=log)
params = processed_args.params.extract().density_modification
output_plots = command_line.options.output_plots
crystal_symmetry = crystal.symmetry(
unit_cell=params.input.unit_cell,
space_group_info=params.input.space_group)
reflection_files = {}
for rfn in (params.input.reflection_data.file_name,
params.input.experimental_phases.file_name,
params.input.map_coefficients.file_name):
if os.path.isfile(str(rfn)) and rfn not in reflection_files:
reflection_files.setdefault(
rfn, iotbx.reflection_file_reader.any_reflection_file(
file_name=rfn, ensure_read_access=False))
server = iotbx.reflection_file_utils.reflection_file_server(
crystal_symmetry=crystal_symmetry,
reflection_files=reflection_files.values())
fo = mmtbx.utils.determine_data_and_flags(
server,
parameters=params.input.reflection_data,
extract_r_free_flags=False,log=log).f_obs
hl_coeffs = mmtbx.utils.determine_experimental_phases(
server,
params.input.experimental_phases,
log=log,
parameter_scope="",
working_point_group=None,
symmetry_safety_check=True,
ignore_all_zeros=True)
if params.input.map_coefficients.file_name is not None:
map_coeffs = server.get_phases_deg(
file_name=params.input.map_coefficients.file_name,
labels=params.input.map_coefficients.labels,
convert_to_phases_if_necessary=False,
original_phase_units=None,
parameter_scope="",
parameter_name="labels").map_to_asu()
else:
map_coeffs = None
ncs_object = None
if params.input.ncs_file_name is not None:
ncs_object = ncs.ncs()
ncs_object.read_ncs(params.input.ncs_file_name)
ncs_object.display_all(log=log)
fo = fo.map_to_asu()
hl_coeffs = hl_coeffs.map_to_asu()
fo = fo.eliminate_sys_absent().average_bijvoet_mates()
hl_coeffs = hl_coeffs.eliminate_sys_absent().average_bijvoet_mates()
model_map = None
model_map_coeffs = None
if len(processed_args.pdb_file_names):
pdb_file = mmtbx.utils.pdb_file(
pdb_file_names=processed_args.pdb_file_names)
xs = pdb_file.pdb_inp.xray_structure_simple()
fo_, hl_ = fo, hl_coeffs
if params.change_basis_to_niggli_cell:
change_of_basis_op = xs.change_of_basis_op_to_niggli_cell()
xs = xs.change_basis(change_of_basis_op)
fo_ = fo_.change_basis(change_of_basis_op).map_to_asu()
hl_ = hl_.change_basis(change_of_basis_op).map_to_asu()
#fo_, hl_ = fo_.common_sets(hl_)
fmodel_refined = mmtbx.utils.fmodel_simple(
f_obs=fo_,
scattering_table="wk1995",#XXX pva: 1) neutrons? 2) move up as a parameter.
xray_structures=[xs],
bulk_solvent_correction=True,
anisotropic_scaling=True,
r_free_flags=fo_.array(data=flex.bool(fo_.size(), False)))
fmodel_refined.update(abcd=hl_)
master_phil = mmtbx.maps.map_and_map_coeff_master_params()
map_params = master_phil.fetch(iotbx.phil.parse("""\
map_coefficients {
map_type = 2mFo-DFc
isotropize = True
}
""")).extract().map_coefficients[0]
model_map_coeffs = mmtbx.maps.map_coefficients_from_fmodel(
fmodel=fmodel_refined, params=map_params)
model_map = model_map_coeffs.fft_map(
resolution_factor=params.grid_resolution_factor).real_map_unpadded()
import time
t0 = time.time()
dm = density_modify(
params,
fo,
hl_coeffs,
ncs_object=ncs_object,
map_coeffs=map_coeffs,
model_map_coeffs=model_map_coeffs,
log=log,
as_gui_program=as_gui_program)
time_dm = time.time()-t0
print >> log, "Time taken for density modification: %.2fs" %time_dm
# run cns
if 0:
from cctbx.development import cns_density_modification
cns_result = cns_density_modification.run(params, fo, hl_coeffs)
print cns_result.modified_map.all()
print dm.map.all()
dm_map_coeffs = dm.map_coeffs_in_original_setting
from cctbx import maptbx, miller
crystal_gridding = maptbx.crystal_gridding(
dm_map_coeffs.unit_cell(),
space_group_info=dm_map_coeffs.space_group().info(),
pre_determined_n_real=cns_result.modified_map.all())
dm_map = miller.fft_map(crystal_gridding, dm_map_coeffs).apply_sigma_scaling()
corr = flex.linear_correlation(cns_result.modified_map.as_1d(), dm_map.real_map_unpadded().as_1d())
print "CNS dm/mmtbx dm correlation:"
corr.show_summary()
if dm.model_map_coeffs is not None:
model_map = miller.fft_map(
crystal_gridding,
dm.miller_array_in_original_setting(dm.model_map_coeffs)).apply_sigma_scaling()
corr = flex.linear_correlation(cns_result.modified_map.as_1d(), model_map.real_map_unpadded().as_1d())
print "CNS dm/model correlation:"
corr.show_summary()
if output_plots:
plots_to_make = (
"fom", "skewness",
"r1_factor", "r1_factor_fom", "mean_solvent_density", "mean_protein_density",
"f000_over_v", "k_flip", "rms_solvent_density", "rms_protein_density",
"standard_deviation_local_rms", "mean_delta_phi", "mean_delta_phi_initial",
)
from matplotlib.backends.backend_pdf import PdfPages
from libtbx import pyplot
stats = dm.get_stats()
pdf = PdfPages("density_modification.pdf")
if len(dm.correlation_coeffs) > 1:
if 0:
start_coeffs, model_coeffs = dm.map_coeffs_start.common_sets(model_map_coeffs)
model_phases = model_coeffs.phases(deg=True).data()
exptl_phases = nearest_phase(
model_phases, start_coeffs.phases(deg=True).data(), deg=True)
corr = flex.linear_correlation(exptl_phases, model_phases)
corr.show_summary()
fig = pyplot.figure()
ax = fig.add_subplot(1,1,1)
ax.set_title("phases start")
ax.set_xlabel("Experimental phases")
ax.set_ylabel("Phases from refined model")
ax.scatter(exptl_phases,
model_phases,
marker="x", s=10)
pdf.savefig(fig)
#
dm_coeffs, model_coeffs = dm.map_coeffs.common_sets(model_map_coeffs)
model_phases = model_coeffs.phases(deg=True).data()
dm_phases = nearest_phase(
model_phases, dm_coeffs.phases(deg=True).data(), deg=True)
corr = flex.linear_correlation(dm_phases, model_phases)
corr.show_summary()
fig = pyplot.figure()
ax = fig.add_subplot(1,1,1)
ax.set_title("phases dm")
ax.set_xlabel("Phases from density modification")
ax.set_ylabel("Phases from refined model")
ax.scatter(dm_phases,
model_phases,
marker="x", s=10)
pdf.savefig(fig)
#
data = dm.correlation_coeffs
fig = pyplot.figure()
ax = fig.add_subplot(1,1,1)
ax.set_title("correlation coefficient")
ax.plot(range(1, dm.i_cycle+2), data)
pdf.savefig(fig)
#
data = dm.mean_phase_errors
fig = pyplot.figure()
ax = fig.add_subplot(1,1,1)
ax.set_title("Mean effective phase errors")
ax.plot(range(1, dm.i_cycle+2), data)
pdf.savefig(fig)
for plot in plots_to_make:
data = [getattr(stats.get_cycle_stats(i), plot) for i in range(1, dm.i_cycle+2)]
fig = pyplot.figure()
ax = fig.add_subplot(1,1,1)
ax.set_title(plot.replace("_", " "))
ax.plot(range(1, dm.i_cycle+2), data)
pdf.savefig(fig)
data = [stats.get_cycle_stats(i).rms_solvent_density/
stats.get_cycle_stats(i).rms_protein_density
for i in range(1, dm.i_cycle+2)]
fig = pyplot.figure()
ax = fig.add_subplot(1,1,1)
ax.set_title("RMS solvent/protein density ratio")
ax.plot(range(1, dm.i_cycle+2), data)
pdf.savefig(fig)
pdf.close()
dm_map_coeffs = dm.map_coeffs_in_original_setting
dm_hl_coeffs = dm.hl_coeffs_in_original_setting
# output map if requested
map_params = params.output.map
if map_params.file_name is not None:
fft_map = dm_map_coeffs.fft_map(resolution_factor=params.grid_resolution_factor)
if map_params.scale == "sigma":
fft_map.apply_sigma_scaling()
else:
fft_map.apply_volume_scaling()
gridding_first = gridding_last = None
title_lines = []
if map_params.format == "xplor":
fft_map.as_xplor_map(
file_name = map_params.file_name,
title_lines = title_lines,
gridding_first = gridding_first,
gridding_last = gridding_last)
else :
fft_map.as_ccp4_map(
file_name = map_params.file_name,
gridding_first = gridding_first,
gridding_last = gridding_last,
labels=title_lines)
# output map coefficients if requested
mtz_params = params.output.mtz
# Decide if we are going to actually write the mtz
if mtz_params.file_name is not None:
orig_fom,final_fom=dm.start_and_end_fom()
if mtz_params.skip_output_if_worse and final_fom < orig_fom:
ok_to_write_mtz=False
print "Not writing out mtz. Final FOM (%7.3f) worse than start (%7.3f)" %(
final_fom,orig_fom)
else: # usual
ok_to_write_mtz=True
else:
ok_to_write_mtz=True
if mtz_params.file_name is not None and ok_to_write_mtz:
label_decorator=iotbx.mtz.ccp4_label_decorator()
fo = dm.miller_array_in_original_setting(dm.f_obs_complete).common_set(dm_map_coeffs)
mtz_dataset = fo.as_mtz_dataset(
column_root_label="F",
label_decorator=label_decorator)
mtz_dataset.add_miller_array(
dm_map_coeffs,
column_root_label="FWT",
label_decorator=label_decorator)
phase_source = dm.miller_array_in_original_setting(dm.phase_source).common_set(dm_map_coeffs)
mtz_dataset.add_miller_array(
phase_source.array(data=flex.abs(phase_source.data())),
column_root_label="FOM",
column_types='W',
label_decorator=label_decorator)
mtz_dataset.add_miller_array(
phase_source.array(data=phase_source.phases(deg=True).data()),
column_root_label="PHIB",
column_types='P',
label_decorator=None)
if mtz_params.output_hendrickson_lattman_coefficients:
mtz_dataset.add_miller_array(
dm_hl_coeffs,
column_root_label="HL",
label_decorator=label_decorator)
mtz_dataset.mtz_object().write(mtz_params.file_name)
return result(
map_file=map_params.file_name,
mtz_file=mtz_params.file_name,
stats=dm.get_stats())
def run (args, out=sys.stdout) :
cmdline = iotbx.phil.process_command_line_with_files(
args=args,
master_phil_string=master_phil_str,
pdb_file_def="model",
map_file_def="map",
usage_string="""\
em_rscc.py model.pdb map.ccp4
%s""" % __doc__)
params = cmdline.work.extract()
assert (not None in [params.model, params.map])
pdb_in = cmdline.get_file(params.model).file_object
m = cmdline.get_file(params.map).file_object
print >> out, "Input electron density map:"
print >> out, "m.all() :", m.data.all()
print >> out, "m.focus() :", m.data.focus()
print >> out, "m.origin():", m.data.origin()
print >> out, "m.nd() :", m.data.nd()
print >> out, "m.size() :", m.data.size()
print >> out, "m.focus_size_1d():", m.data.focus_size_1d()
print >> out, "m.is_0_based() :", m.data.is_0_based()
print >> out, "map: min/max/mean:", flex.min(m.data), flex.max(m.data), flex.mean(m.data)
print >> out, "unit cell:", m.unit_cell_parameters
symm = crystal.symmetry(
space_group_symbol="P1",
unit_cell=m.unit_cell_parameters)
xrs = pdb_in.input.xray_structure_simple(crystal_symmetry=symm)
print >> out, "Setting up electron scattering table (d_min=%g)" % params.d_min
xrs.scattering_type_registry(
d_min=params.d_min,
table="electron")
fc = xrs.structure_factors(d_min=params.d_min).f_calc()
cg = maptbx.crystal_gridding(
unit_cell=symm.unit_cell(),
space_group_info=symm.space_group_info(),
pre_determined_n_real=m.data.all())
fc_map = fc.fft_map(
crystal_gridding=cg).apply_sigma_scaling().real_map_unpadded()
assert (fc_map.all() == fc_map.focus() == m.data.all())
em_data = m.data.as_double()
unit_cell_for_interpolation = m.grid_unit_cell()
frac_matrix = unit_cell_for_interpolation.fractionalization_matrix()
sites_cart = xrs.sites_cart()
sites_frac = xrs.sites_frac()
print >> out, "PER-RESIDUE CORRELATION:"
for chain in pdb_in.hierarchy.only_model().chains() :
for residue_group in chain.residue_groups() :
i_seqs = residue_group.atoms().extract_i_seq()
values_em = flex.double()
values_fc = flex.double()
for i_seq in i_seqs :
rho_em = maptbx.non_crystallographic_eight_point_interpolation(
map=em_data,
gridding_matrix=frac_matrix,
site_cart=sites_cart[i_seq])
rho_fc = fc_map.eight_point_interpolation(sites_frac[i_seq])
values_em.append(rho_em)
values_fc.append(rho_fc)
cc = flex.linear_correlation(x=values_em, y=values_fc).coefficient()
print >> out, residue_group.id_str(), cc
def __init__ (self, pdb_hierarchy, xray_structure, fmodel,
distance_cutoff=4.0, collect_all=True,
molprobity_map_params=None) :
validation.__init__(self)
from mmtbx.real_space_correlation import extract_map_stats_for_single_atoms
from cctbx import adptbx
from scitbx.matrix import col
self.n_bad = 0
self.n_heavy = 0
pdb_atoms = pdb_hierarchy.atoms()
if(len(pdb_atoms)>1):
assert (not pdb_atoms.extract_i_seq().all_eq(0))
unit_cell = xray_structure.unit_cell()
pair_asu_table = xray_structure.pair_asu_table(
distance_cutoff = distance_cutoff)
asu_mappings = pair_asu_table.asu_mappings()
asu_table = pair_asu_table.table()
u_isos = xray_structure.extract_u_iso_or_u_equiv()
occupancies = xray_structure.scatterers().extract_occupancies()
sites_cart = xray_structure.sites_cart()
sites_frac = xray_structure.sites_frac()
sel_cache = pdb_hierarchy.atom_selection_cache()
water_sel = sel_cache.selection("resname HOH and name O")
if (molprobity_map_params is not None):
# assume parameters have been validated (symmetry of pdb and map matches)
two_fofc_map = None
fc_map = None
d_min = None
crystal_gridding = None
# read two_fofc_map
if (molprobity_map_params.map_file_name is not None):
f = any_file(molprobity_map_params.map_file_name)
two_fofc_map = f.file_object.map_data()
d_min = molprobity_map_params.d_min
crystal_gridding = maptbx.crystal_gridding(
f.file_object.unit_cell(),
space_group_info=space_group_info(f.file_object.space_group_number),
pre_determined_n_real=f.file_object.unit_cell_grid)
elif (molprobity_map_params.map_coefficients_file_name is not None):
f = any_file(molprobity_map_params.map_coefficients_file_name)
fourier_coefficients = f.file_server.get_miller_array(
molprobity_map_params.map_coefficients_label)
crystal_symmetry = fourier_coefficients.crystal_symmetry()
d_min = fourier_coefficients.d_min()
crystal_gridding = maptbx.crystal_gridding(
crystal_symmetry.unit_cell(), d_min, resolution_factor=0.25,
space_group_info=crystal_symmetry.space_group_info())
two_fofc_map = miller.fft_map(
crystal_gridding=crystal_gridding,
fourier_coefficients=fourier_coefficients).apply_sigma_scaling().\
real_map_unpadded()
# calculate fc_map
assert( (d_min is not None) and (crystal_gridding is not None) )
f_calc = xray_structure.structure_factors(d_min=d_min).f_calc()
fc_map = miller.fft_map(crystal_gridding=crystal_gridding,
fourier_coefficients=f_calc)
fc_map = fc_map.apply_sigma_scaling().real_map_unpadded()
map_stats = extract_map_stats_for_single_atoms(
pdb_atoms=pdb_atoms,
xray_structure=xray_structure,
fmodel=None,
selection=water_sel,
fc_map=fc_map,
two_fofc_map=two_fofc_map)
else:
map_stats = extract_map_stats_for_single_atoms(
pdb_atoms=pdb_atoms,
xray_structure=xray_structure,
fmodel=fmodel,
selection=water_sel)
waters = []
for i_seq, atom in enumerate(pdb_atoms) :
if (water_sel[i_seq]) :
rt_mx_i_inv = asu_mappings.get_rt_mx(i_seq, 0).inverse()
self.n_total += 1
asu_dict = asu_table[i_seq]
nearest_atom = nearest_contact = None
for j_seq, j_sym_groups in asu_dict.items() :
atom_j = pdb_atoms[j_seq]
site_j = sites_frac[j_seq]
# Filter out hydrogens
if atom_j.element.upper().strip() in ["H", "D"]:
continue
for j_sym_group in j_sym_groups:
rt_mx = rt_mx_i_inv.multiply(asu_mappings.get_rt_mx(j_seq,
j_sym_group[0]))
site_ji = rt_mx * site_j
site_ji_cart = xray_structure.unit_cell().orthogonalize(site_ji)
vec_i = col(atom.xyz)
vec_ji = col(site_ji_cart)
dxyz = abs(vec_i - vec_ji)
if (nearest_contact is None) or (dxyz < nearest_contact) :
nearest_contact = dxyz
nearest_atom = atom_info(pdb_atom=atom_j, symop=rt_mx)
w = water(
pdb_atom=atom,
b_iso=adptbx.u_as_b(u_isos[i_seq]),
occupancy=occupancies[i_seq],
nearest_contact=nearest_contact,
nearest_atom=nearest_atom,
score=map_stats.two_fofc_ccs[i_seq],
fmodel=map_stats.fmodel_values[i_seq],
two_fofc=map_stats.two_fofc_values[i_seq],
fofc=map_stats.fofc_values[i_seq],
anom=map_stats.anom_values[i_seq],
n_hbonds=None) # TODO
if (w.is_bad_water()) :
w.outlier = True
self.n_bad += 1
elif (w.is_heavy_atom()) :
w.outlier = True
self.n_heavy += 1
if (w.outlier) or (collect_all) :
self.results.append(w)
self.n_outliers = len(self.results)
def run(args, out=sys.stdout, validated=False):
show_citation(out=out)
if (len(args) == 0):
master_phil.show(out=out)
print >> out,\
'\nUsage: phenix.map_comparison <CCP4> <CCP4>\n',\
' phenix.map_comparison <CCP4> <MTZ> mtz_label_1=<label>\n',\
' phenix.map_comparison <MTZ 1> mtz_label_1=<label 1> <MTZ 2> mtz_label_2=<label 2>\n'
sys.exit()
# process arguments
params = None
input_attributes = ['map_1', 'mtz_1', 'map_2', 'mtz_2']
try: # automatic parsing
params = phil.process_command_line_with_files(
args=args, master_phil=master_phil).work.extract()
except Exception: # map_file_def only handles one map phil
from libtbx.phil.command_line import argument_interpreter
arg_int = argument_interpreter(master_phil=master_phil)
command_line_args = list()
map_files = list()
for arg in args:
if (os.path.isfile(arg)):
map_files.append(arg)
else:
command_line_args.append(arg_int.process(arg))
params = master_phil.fetch(sources=command_line_args).extract()
# check if more files are necessary
n_defined = 0
for attribute in input_attributes:
if (getattr(params.input, attribute) is not None):
n_defined += 1
# matches files to phil scope, stops once there is sufficient data
for map_file in map_files:
if (n_defined < 2):
current_map = file_reader.any_file(map_file)
if (current_map.file_type == 'ccp4_map'):
n_defined += 1
if (params.input.map_1 is None):
params.input.map_1 = map_file
elif (params.input.map_2 is None):
params.input.map_2 = map_file
elif (current_map.file_type == 'hkl'):
n_defined += 1
if (params.input.mtz_1 is None):
params.input.mtz_1 = map_file
elif (params.input.mtz_2 is None):
params.input.mtz_2 = map_file
else:
print >> out, 'WARNING: only the first two files are used'
break
# validate arguments (GUI sets validated to true, no need to run again)
assert (params is not None)
if (not validated):
validate_params(params)
# ---------------------------------------------------------------------------
# check if maps need to be generated from mtz
n_maps = 0
maps = list()
map_names = list()
for attribute in input_attributes:
filename = getattr(params.input, attribute)
if (filename is not None):
map_names.append(filename)
current_map = file_reader.any_file(filename)
maps.append(current_map)
if (current_map.file_type == 'ccp4_map'):
n_maps += 1
# construct maps, if necessary
crystal_gridding = None
m1 = None
m2 = None
# 1 map, 1 mtz file
if (n_maps == 1):
for current_map in maps:
if (current_map.file_type == 'ccp4_map'):
uc = current_map.file_object.unit_cell()
sg_info = space_group_info(current_map.file_object.space_group_number)
n_real = current_map.file_object.unit_cell_grid
crystal_gridding = maptbx.crystal_gridding(
uc, space_group_info=sg_info, pre_determined_n_real=n_real)
m1 = current_map.file_object.map_data()
if (crystal_gridding is not None):
label = None
for attribute in [('mtz_1', 'mtz_label_1'),
('mtz_2', 'mtz_label_2')]:
filename = getattr(params.input, attribute[0])
label = getattr(params.input, attribute[1])
if ( (filename is not None) and (label is not None) ):
break
# labels will match currently open mtz file
for current_map in maps:
if (current_map.file_type == 'hkl'):
m2 = miller.fft_map(
crystal_gridding=crystal_gridding,
fourier_coefficients=current_map.file_server.get_miller_array(
label)).apply_sigma_scaling().real_map_unpadded()
else:
raise Sorry('Gridding is not defined.')
# 2 mtz files
elif (n_maps == 0):
crystal_symmetry = get_crystal_symmetry(maps[0])
d_min = min(get_d_min(maps[0]), get_d_min(maps[1]))
crystal_gridding = maptbx.crystal_gridding(
crystal_symmetry.unit_cell(), d_min=d_min,
resolution_factor=params.options.resolution_factor,
space_group_info=crystal_symmetry.space_group_info())
m1 = miller.fft_map(
crystal_gridding=crystal_gridding,
fourier_coefficients=maps[0].file_server.get_miller_array(
params.input.mtz_label_1)).apply_sigma_scaling().real_map_unpadded()
m2 = miller.fft_map(
crystal_gridding=crystal_gridding,
fourier_coefficients=maps[1].file_server.get_miller_array(
params.input.mtz_label_2)).apply_sigma_scaling().real_map_unpadded()
# 2 maps
else:
m1 = maps[0].file_object.map_data()
m2 = maps[1].file_object.map_data()
# ---------------------------------------------------------------------------
# analyze maps
assert ( (m1 is not None) and (m2 is not None) )
# show general statistics
s1 = maptbx.more_statistics(m1)
s2 = maptbx.more_statistics(m2)
show_overall_statistics(out=out, s=s1, header="Map 1 (%s):"%map_names[0])
show_overall_statistics(out=out, s=s2, header="Map 2 (%s):"%map_names[1])
cc_input_maps = flex.linear_correlation(x = m1.as_1d(),
y = m2.as_1d()).coefficient()
print >> out, "CC, input maps: %6.4f" % cc_input_maps
# compute CCpeak
cc_peaks = list()
m1_he = maptbx.volume_scale(map = m1, n_bins = 10000).map_data()
m2_he = maptbx.volume_scale(map = m2, n_bins = 10000).map_data()
cc_quantile = flex.linear_correlation(x = m1_he.as_1d(),
y = m2_he.as_1d()).coefficient()
print >> out, "CC, quantile rank-scaled (histogram equalized) maps: %6.4f" % \
cc_quantile
print >> out, "Peak correlation:"
print >> out, " cutoff CCpeak"
cutoffs = [i/100. for i in range(1,90)]+ [i/1000 for i in range(900,1000)]
for cutoff in cutoffs:
cc_peak = maptbx.cc_peak(map_1=m1_he, map_2=m2_he, cutoff=cutoff)
print >> out, " %3.2f %7.4f" % (cutoff, cc_peak)
cc_peaks.append((cutoff, cc_peak))
# compute discrepancy function (D-function)
discrepancies = list()
cutoffs = flex.double(cutoffs)
df = maptbx.discrepancy_function(map_1=m1_he, map_2=m2_he, cutoffs=cutoffs)
print >> out, "Discrepancy function:"
print >> out, " cutoff D"
for c, d in zip(cutoffs, df):
print >> out, " %3.2f %7.4f" % (c,d)
discrepancies.append((c, d))
# compute and output histograms
h1 = maptbx.histogram(map=m1, n_bins=10000)
h2 = maptbx.histogram(map=m2, n_bins=10000)
print >> out, "Map histograms:"
print >> out, "Map 1 (%s) Map 2 (%s)"%\
(params.input.map_1,params.input.map_2)
print >> out, "(map_value,cdf,frequency) <> (map_value,cdf,frequency)"
for a1,c1,v1, a2,c2,v2 in zip(h1.arguments(), h1.c_values(), h1.values(),
h2.arguments(), h2.c_values(), h2.values()):
print >> out, "(%9.5f %9.5f %9.5f) <> (%9.5f %9.5f %9.5f)"%\
(a1,c1,v1, a2,c2,v2)
# store results
s1_dict = create_statistics_dict(s=s1)
s2_dict = create_statistics_dict(s=s2)
results = dict()
inputs = list()
for attribute in input_attributes:
filename = getattr(params.input,attribute)
if (filename is not None):
inputs.append(filename)
assert (len(inputs) == 2)
results['map_files'] = inputs
results['map_statistics'] = (s1_dict, s2_dict)
results['cc_input_maps'] = cc_input_maps
results['cc_quantile'] = cc_quantile
results['cc_peaks'] = cc_peaks
results['discrepancies'] = discrepancies
results['map_histograms'] = ( (h1.arguments(), h1.c_values(), h1.values()),
(h2.arguments(), h2.c_values(), h2.values()) )
return results
def exercise_sampled_model_density_1():
import iotbx.pdb
pdb_str1="""
CRYST1 10.000 10.000 10.000 90.00 90.00 90.00 P 1
ATOM 1 CB PHE A 1 5.000 5.000 5.000 1.00 15.00 C
ANISOU 1 CB PHE A 1 900 2900 100 0 0 0 C
TER
END
"""
pdb_str2="""
CRYST1 10.000 10.000 10.000 90.00 90.00 90.00 P 1
ATOM 1 CB PHE A 1 5.000 5.000 5.000 1.00 15.00 C
TER
END
"""
#
for pdb_str in [pdb_str1, pdb_str2]:
print
pdb_inp = iotbx.pdb.input(source_info=None, lines=pdb_str)
xrs = pdb_inp.xray_structure_simple()
#
crystal_gridding = maptbx.crystal_gridding(
unit_cell = xrs.unit_cell(),
space_group_info = xrs.space_group_info(),
symmetry_flags = maptbx.use_space_group_symmetry,
step = 0.1)
m = mmtbx.real_space.sampled_model_density(
xray_structure = xrs,
n_real = crystal_gridding.n_real()).data()
#
max_index = [(i-1)//2 for i in crystal_gridding.n_real()]
complete_set = miller.build_set(
crystal_symmetry = xrs.crystal_symmetry(),
anomalous_flag = False,
max_index = max_index)
indices = complete_set.indices()
indices.append((0,0,0))
#
complete_set = complete_set.customized_copy(indices = indices)
f_obs_cmpl = complete_set.structure_factors_from_map(
map = m,
use_scale = True,
anomalous_flag = False,
use_sg = False)
fc = complete_set.structure_factors_from_scatterers(xray_structure=xrs).f_calc()
#
f1 = abs(fc).data()
f2 = abs(f_obs_cmpl).data()
r = 200*flex.sum(flex.abs(f1-f2))/flex.sum(f1+f2)
assert r<0.5
print r
#
fft_map = miller.fft_map(
crystal_gridding = crystal_gridding,
fourier_coefficients = f_obs_cmpl)
fft_map.apply_volume_scaling()
m_ = fft_map.real_map_unpadded()
print m.as_1d().min_max_mean().as_tuple()
print m_.as_1d().min_max_mean().as_tuple()
assert approx_equal(
m .as_1d().min_max_mean().as_tuple(),
m_.as_1d().min_max_mean().as_tuple(), 1.e-3) # Must be smaller!?
def _f_ordered_solvent(self):
fo = self._fo
fc = fo.structure_factors_from_scatterers(xray_structure = self._structure
).f_calc()
self._fc = fc
if(self.n_real is None):
crystal_gridding = maptbx.crystal_gridding(unit_cell = self._structure.unit_cell(),
step = self._grid_step)
n_real = crystal_gridding.n_real()
else:
n_real = self.n_real
xyzf = flex.vec3_double()
atmrad = flex.double()
elements = []
for scatterer in self._structure.scatterers():
xyzf.append(list(scatterer.site))
atmrad.append(van_der_waals_radii.vdw.table[scatterer.element_symbol()])
elements.append( scatterer.element_symbol() )
assert xyzf.size() == atmrad.size()
sel_flag = flex.int(xyzf.size(),1)
# XXX removed 2011-02-14: set sel_flag to zero if resname is HOH
assert sel_flag.size() == atmrad.size()
self._distribution = wat_dist()
self._distribution.do_wat_dist(
shell = 0.0,
xyzf = xyzf,
atmrad = atmrad,
element_symbol = elements,
uc = self._structure.unit_cell(),
sg = self._structure.space_group(),
nxnynz = n_real,
sel_flag = sel_flag,
rad = self.rad,
nshells = self.nshells)
data = self._distribution.data()
###############################
#mask_data = mask(0.0,
# 1.0,
# 1.0,
# xyzf,
# atmrad,
# self._structure.unit_cell(),
# self._structure.space_group(),
# crystal_gridding.n_real()).data()
#
#data.set_selected(mask_data == 0.0, 0.0)
###############################
map_of_coeff = map_of_coeff_scaled(data,
self._structure,
n_real)
from_map = maptbx.structure_factors.from_map(
space_group=self._structure.space_group_info().group(),
anomalous_flag=False,
miller_indices=fo.indices(),
complex_map=map_of_coeff,
conjugate_flag=True)
self._f = miller.array(
miller_set = miller.set(
crystal_symmetry = crystal.symmetry(
unit_cell = self._structure.unit_cell(),
space_group_info = self._structure.space_group_info()),
indices = fo.indices(),
anomalous_flag = False),
data = from_map.data())
assert fc.indices().all_eq(self._f.indices()) == 1
assert fc.indices().all_eq(fo.indices()) == 1
assert flex.max(abs(self._f).data()) < 1.0
return self
def run(args, log=None, ccp4_map=None, return_as_miller_arrays=False, nohl=False,
out=sys.stdout):
if log is None: log=out
inputs = mmtbx.utils.process_command_line_args(args = args,
master_params = master_params())
got_map = False
if ccp4_map: got_map=True
broadcast(m="Parameters:", log=log)
inputs.params.show(prefix=" ",out=out)
params = inputs.params.extract()
if(ccp4_map is None and inputs.ccp4_map is not None):
broadcast(m="Processing input CCP4 map file: %s"%inputs.ccp4_map_file_name,
log=log)
ccp4_map = inputs.ccp4_map
ccp4_map.show_summary(prefix=" ",out=out)
got_map = True
if(not got_map):
raise Sorry("Map file is needed.")
#
m = ccp4_map
broadcast(m="Input map information:", log=log)
print >>out,"m.all() :", m.data.all()
print >>out,"m.focus() :", m.data.focus()
print >>out,"m.origin():", m.data.origin()
print >>out,"m.nd() :", m.data.nd()
print >>out,"m.size() :", m.data.size()
print >>out,"m.focus_size_1d():", m.data.focus_size_1d()
print >>out,"m.is_0_based() :", m.data.is_0_based()
print >>out,"map: min/max/mean:", flex.min(m.data), flex.max(m.data), flex.mean(m.data)
print >>out,"unit cell:", m.unit_cell_parameters
#
map_data=m.data
shift_needed = not \
(map_data.focus_size_1d() > 0 and map_data.nd() == 3 and
map_data.is_0_based())
if(shift_needed):
map_data = map_data.shift_origin()
# generate complete set of Miller indices up to given high resolution d_min
n_real = map_data.focus()
cs = crystal.symmetry(m.unit_cell_parameters, 1)
crystal_gridding = maptbx.crystal_gridding(
unit_cell = cs.unit_cell(),
space_group_info = cs.space_group_info(),
#symmetry_flags = maptbx.use_space_group_symmetry,
pre_determined_n_real = n_real)
#
d_min = params.d_min
if(d_min is None and not params.box):
d_min = maptbx.d_min_from_map(
map_data = map_data,
unit_cell = cs.unit_cell())
if(d_min is None):
# box of reflections in |h|<N1/2, |k|<N2/2, 0<=|l|<N3/2
max_index = [(i-1)//2 for i in n_real]
print >>out,"max_index:", max_index
complete_set = miller.build_set(
crystal_symmetry = cs,
anomalous_flag = False,
max_index = max_index)
indices = complete_set.indices()
indices.append((0,0,0))
complete_set = complete_set.customized_copy(indices = indices)
#if(not params.box):
# # XXX What is sphere resolution corresponding to given box?
# uc = complete_set.unit_cell()
# d1 = uc.d([0,0,max_index[2]])
# d2 = uc.d([0,max_index[1],0])
# d3 = uc.d([max_index[0],1,0])
# print >>out, d1,d2,d3
# complete_set_sp = miller.build_set(
# crystal_symmetry = cs,
# anomalous_flag = False,
# d_min = min(d1,d2,d3))
# complete_set = complete_set.common_set(complete_set_sp)
else:
complete_set = miller.build_set(
crystal_symmetry = cs,
anomalous_flag = False,
d_min = d_min)
broadcast(m="Complete set information:", log=log)
complete_set.show_comprehensive_summary(prefix=" ",f=out)
try:
f_obs_cmpl = complete_set.structure_factors_from_map(
map = map_data.as_double(),
use_scale = True,
anomalous_flag = False,
use_sg = False)
except Exception, e:
if(str(e) == "cctbx Error: Miller index not in structure factor map."):
msg = "Too high resolution requested. Try running with larger d_min."
raise Sorry(msg)
else:
raise Sorry(str(e))
def __init__(self,
xray_structure = None,
fmodel = None,
map_data = None,
d_min = None,
resolution_factor = 0.25,
map_type = "2mFo-DFc"):
"""
Utility to calculate correlation between two maps:
CC(xray_structure, map_data), xray_structure are map_data inputs
or
CC(2mFo-DFc, Fc), 2mFo-DFc and Fc are from input fmodel .
"""
assert [fmodel, map_data].count(None) == 1
assert [xray_structure, map_data].count(None) in [0, 2]
assert [fmodel, xray_structure].count(None) == 1
assert [d_min, fmodel].count(None) == 1
adopt_init_args(self, locals())
if(fmodel is not None): self.xray_structure = fmodel.xray_structure
# get map_data defined
if(self.fmodel is not None):
e_map_obj = fmodel.electron_density_map()
isotropize = True
if(fmodel.is_twin_fmodel_manager()): isotropize = False
mc = e_map_obj.map_coefficients(
map_type = map_type,
fill_missing = False,
isotropize = isotropize)
crystal_gridding = self.fmodel.f_obs().crystal_gridding(
d_min = self.fmodel.f_obs().d_min(),
resolution_factor = resolution_factor)
fft_map = miller.fft_map(
crystal_gridding = crystal_gridding,
fourier_coefficients = mc)
self.map_data = fft_map.real_map_unpadded()
# get model map
if(self.fmodel is not None):
if(fmodel.is_twin_fmodel_manager()):
f_model = self.fmodel.f_model()
else:
f_model = self.fmodel.f_model_scaled_with_k1()
fft_map = miller.fft_map(
crystal_gridding = crystal_gridding,
fourier_coefficients = f_model)
fft_map.apply_sigma_scaling()
self.map_model = fft_map.real_map_unpadded()
else:
crystal_gridding = maptbx.crystal_gridding(
unit_cell = self.xray_structure.unit_cell(),
space_group_info = self.xray_structure.space_group_info(),
pre_determined_n_real = self.map_data.accessor().all())
f_model = self.xray_structure.structure_factors(d_min=self.d_min).f_calc()
fft_map = miller.fft_map(
crystal_gridding = crystal_gridding,
fourier_coefficients = f_model)
fft_map.apply_sigma_scaling()
self.map_model = fft_map.real_map_unpadded()
if(self.fmodel is not None):
self.sites_cart = self.fmodel.xray_structure.sites_cart()
self.sites_frac = self.fmodel.xray_structure.sites_frac()
self.weights = self.fmodel.xray_structure.atomic_weights()
else:
self.sites_cart = self.xray_structure.sites_cart()
self.sites_frac = self.xray_structure.sites_frac()
self.weights = self.xray_structure.atomic_weights()