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 get_cc(mc1, mc2, xrs):
crystal_gridding = mc1.crystal_gridding(
d_min = mc1.d_min(),
symmetry_flags = maptbx.use_space_group_symmetry,
resolution_factor = 0.25)
fft_map = miller.fft_map(
crystal_gridding = crystal_gridding,
fourier_coefficients = mc1)
fft_map.apply_sigma_scaling()
m1 = fft_map.real_map_unpadded()
fft_map = miller.fft_map(
crystal_gridding = crystal_gridding,
fourier_coefficients = mc2)
fft_map.apply_sigma_scaling()
m2 = fft_map.real_map_unpadded()
assert m1.focus()==m2.focus()
assert m1.all()==m2.all()
sel = maptbx.grid_indices_around_sites(
unit_cell = mc1.unit_cell(),
fft_n_real = m1.focus(),
fft_m_real = m1.all(),
sites_cart = flex.vec3_double(xrs.sites_cart()),
site_radii = flex.double([1.5]*xrs.scatterers().size()))
cc = flex.linear_correlation(x=m1.select(sel), y=m2.select(sel)).coefficient()
def md(m, xrs):
r = flex.double()
for sf in xrs.sites_frac():
r.append(m.eight_point_interpolation(sf))
return flex.mean(r)
return cc, md(m=m1, xrs=xrs), md(m=m2, xrs=xrs)
def get_cc(mc1, mc2, xrs):
crystal_gridding = mc1.crystal_gridding(
d_min = mc1.d_min(), resolution_factor = 0.25)
fft_map = miller.fft_map(
crystal_gridding = crystal_gridding,
fourier_coefficients = mc1)
fft_map.apply_sigma_scaling()
m1 = fft_map.real_map_unpadded()
fft_map = miller.fft_map(
crystal_gridding = crystal_gridding,
fourier_coefficients = mc2)
fft_map.apply_sigma_scaling()
m2 = fft_map.real_map_unpadded()
assert m1.focus()==m2.focus()
assert m1.all()==m2.all()
ccs = flex.double()
for site_cart in xrs.sites_cart():
sel = maptbx.grid_indices_around_sites(
unit_cell = mc1.unit_cell(),
fft_n_real = m1.focus(),
fft_m_real = m1.all(),
sites_cart = flex.vec3_double([site_cart]),
site_radii = flex.double([1.5]))
cc = flex.linear_correlation(x=m1.select(sel), y=m2.select(sel)).coefficient()
ccs.append(cc)
return ccs
def cc_peak(cutoff,
map_1=None,
map_2=None,
map_coeffs_1=None,
map_coeffs_2=None):
"""
Compute CCpeak as described in
Acta Cryst. (2014). D70, 2593-2606
Metrics for comparison of crystallographic maps
A. Urzhumtsev, P. V. Afonine, V. Y. Lunin, T. C. Terwilliger and P. D. Adams
"""
from cctbx import miller
assert [map_1, map_2].count(None) in [0, 2]
assert [map_coeffs_1, map_coeffs_2].count(None) in [0, 2]
if ([map_1, map_2].count(None) == 0):
# Maps are assumed to be quantile rank scaled (HE).
return ext.cc_peak(map_1=map_1, map_2=map_2, cutoff=cutoff)
elif ([map_coeffs_1, map_coeffs_2].count(None) == 0):
d_min = min(map_coeffs_1.d_min(), map_coeffs_2.d_min())
crystal_gridding = map_coeffs_1.crystal_gridding(
d_min=d_min,
symmetry_flags=use_space_group_symmetry,
resolution_factor=0.25)
fft_map = miller.fft_map(crystal_gridding=crystal_gridding,
fourier_coefficients=map_coeffs_1)
map_1 = fft_map.real_map_unpadded()
fft_map = miller.fft_map(crystal_gridding=crystal_gridding,
fourier_coefficients=map_coeffs_2)
map_2 = fft_map.real_map_unpadded()
m1_he = volume_scale(map=map_1, n_bins=10000).map_data()
m2_he = volume_scale(map=map_2, n_bins=10000).map_data()
return ext.cc_peak(map_1=m1_he, map_2=m2_he, cutoff=cutoff)
else:
raise RuntimeError("Combination of inputs not supported.")
def good_atoms_selection(crystal_gridding, map_coeffs, xray_structure):
#XXX copy from model_missing_reflections map_tools.py, consolidate later
#XXX Also look for similar crap in f_model.py
fft_map = miller.fft_map(crystal_gridding=crystal_gridding,
fourier_coefficients=map_coeffs)
fft_map.apply_sigma_scaling()
map_data = fft_map.real_map_unpadded()
rho_atoms = flex.double()
for site_frac in xray_structure.sites_frac():
rho_atoms.append(map_data.eight_point_interpolation(site_frac))
#rho_mean = flex.mean_default(rho_atoms.select(rho_atoms>1.0), 1.0)
sel_exclude = rho_atoms < 1.0 # XXX ??? TRY 0.5!
sites_cart = xray_structure.sites_cart()
#
f_calc = map_coeffs.structure_factors_from_scatterers(
xray_structure=xray_structure).f_calc()
fft_map = miller.fft_map(crystal_gridding=crystal_gridding,
fourier_coefficients=f_calc)
fft_map.apply_sigma_scaling()
map_data2 = fft_map.real_map_unpadded()
#
hd_sel = xray_structure.hd_selection()
for i_seq, site_cart in enumerate(sites_cart):
selection = maptbx.grid_indices_around_sites(
unit_cell=map_coeffs.unit_cell(),
fft_n_real=map_data.focus(),
fft_m_real=map_data.all(),
sites_cart=flex.vec3_double([site_cart]),
site_radii=flex.double([1.5]))
cc = flex.linear_correlation(
x=map_data.select(selection),
y=map_data2.select(selection)).coefficient()
if (cc < 0.7 or hd_sel[i_seq]): sel_exclude[i_seq] = True
return ~sel_exclude
def get_cc(mc1, mc2, xrs):
crystal_gridding = mc1.crystal_gridding(d_min=mc1.d_min(),
resolution_factor=0.25)
fft_map = miller.fft_map(crystal_gridding=crystal_gridding,
fourier_coefficients=mc1)
fft_map.apply_sigma_scaling()
m1 = fft_map.real_map_unpadded()
fft_map = miller.fft_map(crystal_gridding=crystal_gridding,
fourier_coefficients=mc2)
fft_map.apply_sigma_scaling()
m2 = fft_map.real_map_unpadded()
assert m1.focus() == m2.focus()
assert m1.all() == m2.all()
ccs = flex.double()
for site_cart in xrs.sites_cart():
sel = maptbx.grid_indices_around_sites(unit_cell=mc1.unit_cell(),
fft_n_real=m1.focus(),
fft_m_real=m1.all(),
sites_cart=flex.vec3_double(
[site_cart]),
site_radii=flex.double([1.5]))
cc = flex.linear_correlation(x=m1.select(sel),
y=m2.select(sel)).coefficient()
ccs.append(cc)
return ccs
def cc_peak(cutoff, map_1=None,map_2=None, map_coeffs_1=None,map_coeffs_2=None):
"""
Compute CCpeak as described in
Acta Cryst. (2014). D70, 2593-2606
Metrics for comparison of crystallographic maps
A. Urzhumtsev, P. V. Afonine, V. Y. Lunin, T. C. Terwilliger and P. D. Adams
"""
from cctbx import miller
assert [map_1,map_2].count(None) in [0,2]
assert [map_coeffs_1,map_coeffs_2].count(None) in [0,2]
if([map_1,map_2].count(None)==0):
# Maps are assumed to be quantile rank scaled (HE).
return ext.cc_peak(map_1=map_1, map_2=map_2, cutoff=cutoff)
elif([map_coeffs_1,map_coeffs_2].count(None)==0):
d_min = min(map_coeffs_1.d_min(), map_coeffs_2.d_min())
crystal_gridding = map_coeffs_1.crystal_gridding(
d_min = d_min,
symmetry_flags = use_space_group_symmetry,
resolution_factor = 0.25)
fft_map = miller.fft_map(
crystal_gridding = crystal_gridding,
fourier_coefficients = map_coeffs_1)
map_1 = fft_map.real_map_unpadded()
fft_map = miller.fft_map(
crystal_gridding = crystal_gridding,
fourier_coefficients = map_coeffs_2)
map_2 = fft_map.real_map_unpadded()
m1_he = volume_scale(map = map_1, n_bins = 10000).map_data()
m2_he = volume_scale(map = map_2, n_bins = 10000).map_data()
return ext.cc_peak(map_1=m1_he, map_2=m2_he, cutoff=cutoff)
else:
raise RuntimeError("Combination of inputs not supported.")
def get_cc(mc1, mc2, xrs):
crystal_gridding = mc1.crystal_gridding(
d_min=mc1.d_min(),
symmetry_flags=maptbx.use_space_group_symmetry,
resolution_factor=0.25)
fft_map = miller.fft_map(crystal_gridding=crystal_gridding,
fourier_coefficients=mc1)
fft_map.apply_sigma_scaling()
m1 = fft_map.real_map_unpadded()
fft_map = miller.fft_map(crystal_gridding=crystal_gridding,
fourier_coefficients=mc2)
fft_map.apply_sigma_scaling()
m2 = fft_map.real_map_unpadded()
assert m1.focus() == m2.focus()
assert m1.all() == m2.all()
sel = maptbx.grid_indices_around_sites(
unit_cell=mc1.unit_cell(),
fft_n_real=m1.focus(),
fft_m_real=m1.all(),
sites_cart=flex.vec3_double(xrs.sites_cart()),
site_radii=flex.double([1.5] * xrs.scatterers().size()))
cc = flex.linear_correlation(x=m1.select(sel),
y=m2.select(sel)).coefficient()
def md(m, xrs):
r = flex.double()
for sf in xrs.sites_frac():
r.append(m.eight_point_interpolation(sf))
return flex.mean(r)
return cc, md(m=m1, xrs=xrs), md(m=m2, xrs=xrs)
def __init__(
self,
fmodel,
coeffs):
# XXX see f_model.py: duplication! Consolidate.
self.fmodel = fmodel
self.coeffs = coeffs
crystal_gridding = fmodel.f_obs().crystal_gridding(
d_min = self.fmodel.f_obs().d_min(),
resolution_factor = 1./3)
fft_map = miller.fft_map(
crystal_gridding = crystal_gridding,
fourier_coefficients = self.coeffs)
fft_map.apply_sigma_scaling()
map_data = fft_map.real_map_unpadded()
rho_atoms = flex.double()
for site_frac in self.fmodel.xray_structure.sites_frac():
rho_atoms.append(map_data.eight_point_interpolation(site_frac))
rho_mean = flex.mean_default(rho_atoms.select(rho_atoms>0.5), 0.5)
sel_exclude = rho_atoms > min(rho_mean/2., 1)
sites_cart = fmodel.xray_structure.sites_cart()
#
fft_map = miller.fft_map(
crystal_gridding = crystal_gridding,
fourier_coefficients = self.fmodel.f_model())
fft_map.apply_sigma_scaling()
map_data2 = fft_map.real_map_unpadded()
#
for i_seq, site_cart in enumerate(sites_cart):
selection = maptbx.grid_indices_around_sites(
unit_cell = self.coeffs.unit_cell(),
fft_n_real = map_data.focus(),
fft_m_real = map_data.all(),
sites_cart = flex.vec3_double([site_cart]),
site_radii = flex.double([1.5]))
cc = flex.linear_correlation(x=map_data.select(selection),
y=map_data2.select(selection)).coefficient()
if(cc<0.7): sel_exclude[i_seq] = False
#
del map_data, fft_map, rho_atoms
self.d_min = fmodel.f_obs().d_min()
cs = self.fmodel.f_obs().average_bijvoet_mates().complete_set(d_min=self.d_min)
self.complete_set = cs.array(data = flex.double(cs.indices().size(), 0))
self.xray_structure_cut = self.fmodel.xray_structure.select(sel_exclude)
self.missing_set = self.complete_set.common_set(self.coeffs)
#
self.f_calc_missing = self.complete_set.structure_factors_from_scatterers(
xray_structure = self.xray_structure_cut).f_calc()
self.ss_missing = 1./flex.pow2(self.f_calc_missing.d_spacings().data()) / 4.
mask_manager = mmtbx.masks.manager(
miller_array = self.f_calc_missing,
miller_array_twin = None,
mask_params = None)
self.f_mask_missing = mask_manager.shell_f_masks(
xray_structure = self.xray_structure_cut,
force_update = True)
self.zero_data = flex.complex_double(self.f_calc_missing.data().size(), 0)
def __init__(
self,
fmodel,
coeffs):
# XXX see f_model.py: duplication! Consolidate.
self.fmodel = fmodel
self.coeffs = coeffs
crystal_gridding = fmodel.f_obs().crystal_gridding(
d_min = self.fmodel.f_obs().d_min(),
resolution_factor = 1./3)
fft_map = miller.fft_map(
crystal_gridding = crystal_gridding,
fourier_coefficients = self.coeffs)
fft_map.apply_sigma_scaling()
map_data = fft_map.real_map_unpadded()
rho_atoms = flex.double()
for site_frac in self.fmodel.xray_structure.sites_frac():
rho_atoms.append(map_data.eight_point_interpolation(site_frac))
rho_mean = flex.mean_default(rho_atoms.select(rho_atoms>0.5), 0.5)
sel_exclude = rho_atoms > min(rho_mean/2., 1)
sites_cart = fmodel.xray_structure.sites_cart()
#
fft_map = miller.fft_map(
crystal_gridding = crystal_gridding,
fourier_coefficients = self.fmodel.f_model())
fft_map.apply_sigma_scaling()
map_data2 = fft_map.real_map_unpadded()
#
for i_seq, site_cart in enumerate(sites_cart):
selection = maptbx.grid_indices_around_sites(
unit_cell = self.coeffs.unit_cell(),
fft_n_real = map_data.focus(),
fft_m_real = map_data.all(),
sites_cart = flex.vec3_double([site_cart]),
site_radii = flex.double([1.5]))
cc = flex.linear_correlation(x=map_data.select(selection),
y=map_data2.select(selection)).coefficient()
if(cc<0.7): sel_exclude[i_seq] = False
#
del map_data, fft_map, rho_atoms
self.d_min = fmodel.f_obs().d_min()
cs = self.fmodel.f_obs().average_bijvoet_mates().complete_set(d_min=self.d_min)
self.complete_set = cs.array(data = flex.double(cs.indices().size(), 0))
self.xray_structure_cut = self.fmodel.xray_structure.select(sel_exclude)
self.missing_set = self.complete_set.common_set(self.coeffs)
#
self.f_calc_missing = self.complete_set.structure_factors_from_scatterers(
xray_structure = self.xray_structure_cut).f_calc()
self.ss_missing = 1./flex.pow2(self.f_calc_missing.d_spacings().data()) / 4.
mask_manager = mmtbx.masks.manager(
miller_array = self.f_calc_missing,
miller_array_twin = None,
mask_params = None)
self.f_mask_missing = mask_manager.shell_f_masks(
xray_structure = self.xray_structure_cut,
force_update = True)
self.zero_data = flex.complex_double(self.f_calc_missing.data().size(), 0)
def __init__(self, f, f_000, n_cycles=100, resolution_factor=0.25, d_min=None,
crystal_gridding=None, complete_set=None):
self.f = f
self.d_min = d_min
self.map = None
self.crystal_gridding = crystal_gridding
from cctbx import miller
if(self.d_min is None): self.d_min = self.f.d_min()
if(complete_set is None):
complete_set = self.f.complete_set(d_min = self.d_min)
if(self.crystal_gridding is None):
self.crystal_gridding = self.f.crystal_gridding(
d_min = d_min,
resolution_factor = resolution_factor,
grid_step = None,
symmetry_flags = None,
mandatory_factors = None,
max_prime = 5,
assert_shannon_sampling = True)
self.f_mod = self.f.deep_copy()
for i in xrange(n_cycles):
fft_map = miller.fft_map(
crystal_gridding = self.crystal_gridding,
fourier_coefficients = self.f_mod,
f_000 = f_000)
if(f_000 is not None): fft_map.apply_volume_scaling()
self.map = fft_map.real_map_unpadded()
convert_to_non_negative(self.map, 0)
self.f_mod = complete_set.structure_factors_from_map(
map = self.map,
use_scale = True,
anomalous_flag = False,
use_sg = False)
self.f_mod = self.f.complete_with(other = self.f_mod, scale=True,
replace_phases=True)
def _get_map(self):
f_calc = self.xray_structure.structure_factors(d_min=self.d_min).f_calc()
fft_map = miller.fft_map(
crystal_gridding = self.crystal_gridding,
fourier_coefficients = f_calc)
fft_map.apply_sigma_scaling() # XXX not really needed
return fft_map.real_map_unpadded()
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 _get_map(self):
f_calc = self.xray_structure.structure_factors(d_min=self.d_min).f_calc()
fft_map = miller.fft_map(
crystal_gridding = self.crystal_gridding,
fourier_coefficients = f_calc)
fft_map.apply_sigma_scaling() # XXX not really needed
return fft_map.real_map_unpadded()
def _get_map_at_d_min(self, d_min):
done = False
cntr = 0
while not done:
if (cntr > 5):
raise RuntimeError(
"Number of trial resolution increments exceeded.")
try:
f_obs_cmpl = self.complete_set.resolution_filter(
d_min=d_min).structure_factors_from_map(
map=self.map_data,
use_scale=True,
anomalous_flag=False,
use_sg=True)
done = True
except KeyboardInterrupt:
raise
except Exception as e:
if (str(
e
) == "cctbx Error: Miller index not in structure factor map."):
d_min += 0.1
cntr += 1
fft_map = miller.fft_map(crystal_gridding=self.crystal_gridding,
fourier_coefficients=f_obs_cmpl)
fft_map.apply_sigma_scaling()
return fft_map.real_map_unpadded()
def fft_map(self,
resolution_factor = 1/3.,
symmetry_flags = None,
map_coefficients = None,
other_fft_map = None,
map_type = None,
force_anomalous_flag_false = None,
acentrics_scale = 2.0,
centrics_pre_scale = 1.0,
use_all_data = True):
if(map_coefficients is None):
map_coefficients = self.map_coefficients(
map_type = map_type,
acentrics_scale = acentrics_scale,
centrics_pre_scale = centrics_pre_scale)
if(force_anomalous_flag_false):
map_coefficients = map_coefficients.average_bijvoet_mates()
if(force_anomalous_flag_false):
map_coefficients = map_coefficients.average_bijvoet_mates()
if(not use_all_data):
map_coefficients = map_coefficients.select(self.fmodel.arrays.work_sel)
if(other_fft_map is None):
return map_coefficients.fft_map(
resolution_factor = resolution_factor,
symmetry_flags = symmetry_flags)
else:
return miller.fft_map(
crystal_gridding = other_fft_map,
fourier_coefficients = map_coefficients)
def compute_electron_density_map(self):
""" Compute the electron density from the structure factors self.f_calc
and the 000 component self.f_000, scaling by the unit cell volume """
self.rho_map = miller.fft_map(self.crystal_gridding,
self.f_calc,
self.f_000)
self.rho_map.apply_volume_scaling()
def iterations(self):
self.cntr = 0
while self.cntr < self.max_iterations:
self.f_mem = self.f.structure_factors_from_map(
map = self.rho,
use_scale = False,
anomalous_flag = False,
use_sg = False)
self.f_mem = self.f_mem.customized_copy(data = self.f_mem.data()/self.N)
fft_map = miller.fft_map(
crystal_gridding = self.crystal_gridding,
fourier_coefficients = self.f_mem)
rho_mod = fft_map.real_map_unpadded()
rho_trial = self.rho.deep_copy()
self.meio_obj = maptbx.mem_iteration(
rho_mod,
self.rho_obs,
rho_trial,
self.lam*self.N,
self.n_real,
self.Agd,
self.beta,
self.use_scale)
if(self.detect_convergence and self.is_converged(rho_trial=rho_trial)):
break
else: self.rho = rho_trial
self.Z = self.meio_obj.z()
if(self.verbose): self.show()
if(self.cntr%25==0): self.Agd = self.Agd/self.Z
self.cntr += 1
if(self.lambda_increment_factor is not None):
self.lam *= self.lambda_increment_factor
self.update_metrics()
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 fft_map(self,
resolution_factor = 1/3.,
symmetry_flags = None,
map_coefficients = None,
other_fft_map = None,
map_type = None,
force_anomalous_flag_false = None,
acentrics_scale = 2.0,
centrics_pre_scale = 1.0,
use_all_data = True):
if(map_coefficients is None):
map_coefficients = self.map_coefficients(
map_type = map_type,
acentrics_scale = acentrics_scale,
centrics_pre_scale = centrics_pre_scale)
if(force_anomalous_flag_false):
map_coefficients = map_coefficients.average_bijvoet_mates()
if(force_anomalous_flag_false):
map_coefficients = map_coefficients.average_bijvoet_mates()
if(not use_all_data):
map_coefficients = map_coefficients.select(self.fmodel.arrays.work_sel)
if(other_fft_map is None):
return map_coefficients.fft_map(
resolution_factor = resolution_factor,
symmetry_flags = symmetry_flags)
else:
return miller.fft_map(
crystal_gridding = other_fft_map,
fourier_coefficients = map_coefficients)
def compute_map(target_map, xray_structure):
mc = target_map.miller_array.structure_factors_from_scatterers(
xray_structure=xray_structure).f_calc()
fft_map = miller.fft_map(crystal_gridding=target_map.crystal_gridding,
fourier_coefficients=mc)
fft_map.apply_sigma_scaling()
return fft_map.real_map_unpadded()
def compute_map(target_map, xray_structure):
mc = target_map.miller_array.structure_factors_from_scatterers(
xray_structure = xray_structure).f_calc()
fft_map = miller.fft_map(
crystal_gridding = target_map.crystal_gridding,
fourier_coefficients = mc)
fft_map.apply_sigma_scaling()
return fft_map.real_map_unpadded()
def compute_map(self):
map_coefficients = self.miller_array.structure_factors_from_scatterers(
xray_structure = self.xray_structure).f_calc()
fft_map = miller.fft_map(crystal_gridding = self.crystal_gridding,
fourier_coefficients = map_coefficients)
fft_map.apply_sigma_scaling()
result = fft_map.real_map_unpadded()
return result
def compute_map(self):
map_coefficients = self.miller_array.structure_factors_from_scatterers(
xray_structure = self.xray_structure).f_calc()
fft_map = miller.fft_map(crystal_gridding = self.crystal_gridding,
fourier_coefficients = map_coefficients)
fft_map.apply_sigma_scaling()
result = fft_map.real_map_unpadded()
return result
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 simple(fmodel, pdb_hierarchy, params=None, log=None, show_results=False):
if(params is None): params =master_params().extract()
if(log is None): log = sys.stdout
# compute map coefficients
e_map_obj = fmodel.electron_density_map()
coeffs_1 = e_map_obj.map_coefficients(
map_type = params.map_1.type,
fill_missing = params.map_1.fill_missing_reflections,
isotropize = params.map_1.isotropize)
coeffs_2 = e_map_obj.map_coefficients(
map_type = params.map_2.type,
fill_missing = params.map_2.fill_missing_reflections,
isotropize = params.map_2.isotropize)
# compute maps
fft_map_1 = coeffs_1.fft_map(resolution_factor = params.resolution_factor)
fft_map_1.apply_sigma_scaling()
map_1 = fft_map_1.real_map_unpadded()
fft_map_2 = miller.fft_map(
crystal_gridding = fft_map_1,
fourier_coefficients = coeffs_2)
fft_map_2.apply_sigma_scaling()
map_2 = fft_map_2.real_map_unpadded()
# compute cc
broadcast(m="Map correlation and map values", log=log)
overall_cc = flex.linear_correlation(x = map_1.as_1d(),
y = map_2.as_1d()).coefficient()
print >> log, " Overall map cc(%s,%s): %6.4f"%(params.map_1.type,
params.map_2.type, overall_cc)
detail, atom_radius = params.detail, params.atom_radius
detail, atom_radius = set_detail_level_and_radius(detail=detail,
atom_radius=atom_radius, d_min=fmodel.f_obs().d_min())
use_hydrogens = params.use_hydrogens
if(use_hydrogens is None):
if(params.scattering_table == "neutron" or fmodel.f_obs().d_min() <= 1.2):
use_hydrogens = True
else:
use_hydrogens = False
hydrogen_atom_radius = params.hydrogen_atom_radius
if(hydrogen_atom_radius is None):
if(params.scattering_table == "neutron"):
hydrogen_atom_radius = atom_radius
else:
hydrogen_atom_radius = 1
results = compute(
pdb_hierarchy = pdb_hierarchy,
unit_cell = fmodel.xray_structure.unit_cell(),
fft_n_real = map_1.focus(),
fft_m_real = map_1.all(),
map_1 = map_1,
map_2 = map_2,
detail = detail,
atom_radius = atom_radius,
use_hydrogens = use_hydrogens,
hydrogen_atom_radius = hydrogen_atom_radius)
if(show_results):
show(log=log, results=results, params=params, detail=detail)
return results
def compute_map(self, xray_structure):
self.assert_pdb_hierarchy_xray_structure_sync()
mc = self.target_map_object.miller_array.structure_factors_from_scatterers(
xray_structure = xray_structure).f_calc()
fft_map = miller.fft_map(
crystal_gridding = self.target_map_object.crystal_gridding,
fourier_coefficients = mc)
fft_map.apply_sigma_scaling()
return fft_map.real_map_unpadded()
def __init__(self,
crystal_gridding,
fmodel,
map_type,
max_boxes,
box_size_as_fraction=None):
sgt = fmodel.f_obs().space_group().type()
assert sgt.number() == 1
def get_map(fmodel, map_type, external_complete_set=None):
f_map = fmodel.electron_density_map().map_coefficients(
map_type=map_type, isotropize=True, fill_missing=False)
fft_map = miller.fft_map(crystal_gridding=crystal_gridding,
fourier_coefficients=f_map)
return fft_map.real_map_unpadded()
f_model = fmodel.f_model_scaled_with_k1()
fft_map = miller.fft_map(crystal_gridding=crystal_gridding,
fourier_coefficients=f_model)
f_model_map_data = fft_map.real_map_unpadded()
zero_complex_ma = f_model.customized_copy(
data=flex.complex_double(f_model.data().size(), 0))
b = maptbx.boxes(fraction=0.3,
n_real=f_model_map_data.focus(),
max_boxes=max_boxes,
log=sys.stdout)
self.map_result = flex.double(flex.grid(b.n_real))
self.r = flex.double()
for s, e in zip(b.starts, b.ends):
f_model_map_data_omit = maptbx.set_box_copy(
value=0, map_data_to=f_model_map_data, start=s, end=e)
f_model_omit = f_model.structure_factors_from_map(
map=f_model_map_data_omit,
use_scale=True,
anomalous_flag=False,
use_sg=False)
fmodel_ = mmtbx.f_model.manager(f_obs=fmodel.f_obs(),
r_free_flags=fmodel.r_free_flags(),
f_calc=f_model_omit,
f_mask=zero_complex_ma)
self.r.append(fmodel_.r_work())
f_map_data = get_map(fmodel=fmodel_, map_type=map_type)
etmp = [e[0] - 1, e[1] - 1,
e[2] - 1] # because .copy() includes right edge
box = maptbx.copy(f_map_data, s, etmp)
box.reshape(flex.grid(box.all()))
maptbx.set_box(map_data_from=box,
map_data_to=self.map_result,
start=s,
end=e)
sd = self.map_result.sample_standard_deviation()
self.map_result = self.map_result / sd
self.map_coefficients = fmodel.f_obs().structure_factors_from_map(
map=self.map_result,
use_scale=True,
anomalous_flag=False,
use_sg=False)
def compute_map(self, xray_structure):
self.assert_pdb_hierarchy_xray_structure_sync()
mc = self.target_map_object.miller_array.structure_factors_from_scatterers(
xray_structure=xray_structure).f_calc()
fft_map = miller.fft_map(
crystal_gridding=self.target_map_object.crystal_gridding,
fourier_coefficients=mc)
fft_map.apply_sigma_scaling()
return fft_map.real_map_unpadded()
def get_map(fmodel, map_type, external_complete_set=None):
f_map = fmodel.electron_density_map().map_coefficients(
map_type = map_type,
isotropize = True,
fill_missing = False)
fft_map = miller.fft_map(
crystal_gridding = crystal_gridding,
fourier_coefficients = f_map)
return fft_map.real_map_unpadded()
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 map_cc(map_coeffs_1, map_coeffs_2):
fft_map_1 = map_coeffs_1.fft_map(resolution_factor=0.25)
map_1 = fft_map_1.real_map_unpadded()
fft_map_2 = miller.fft_map(crystal_gridding=fft_map_1,
fourier_coefficients=map_coeffs_2)
map_2 = fft_map_2.real_map_unpadded()
assert map_1.size() == map_2.size()
m1 = map_1.as_1d()
m2 = map_2.as_1d()
return flex.linear_correlation(x=m1, y=m2).coefficient()
def _get_map_at_d_min(self, d_min):
f_obs_cmpl = self.complete_set.resolution_filter(
d_min=d_min).structure_factors_from_map(map=self.map_data,
use_scale=True,
anomalous_flag=False,
use_sg=True)
fft_map = miller.fft_map(crystal_gridding=self.crystal_gridding,
fourier_coefficients=f_obs_cmpl)
fft_map.apply_sigma_scaling()
return fft_map.real_map_unpadded()
def compute_map_from_model(high_resolution, low_resolution, xray_structure,
grid_resolution_factor, crystal_gridding = None):
f_calc = xray_structure.structure_factors(d_min = high_resolution).f_calc()
f_calc = f_calc.resolution_filter(d_max = low_resolution)
if(crystal_gridding is None):
return f_calc.fft_map(
resolution_factor = min(0.5,grid_resolution_factor),
symmetry_flags = None)
return miller.fft_map(
crystal_gridding = crystal_gridding,
fourier_coefficients = f_calc)
def map_cc(map_coeffs_1, map_coeffs_2):
fft_map_1 = map_coeffs_1.fft_map(resolution_factor=0.25)
map_1 = fft_map_1.real_map_unpadded()
fft_map_2 = miller.fft_map(
crystal_gridding = fft_map_1,
fourier_coefficients = map_coeffs_2)
map_2 = fft_map_2.real_map_unpadded()
assert map_1.size() == map_2.size()
m1 = map_1.as_1d()
m2 = map_2.as_1d()
return flex.linear_correlation(x = m1, y = m2).coefficient()
def _get_map_at_d_min(self, d_min):
f_obs_cmpl = self.complete_set.resolution_filter(
d_min=d_min).structure_factors_from_map(
map = self.map_data,
use_scale = True,
anomalous_flag = False,
use_sg = True)
fft_map = miller.fft_map(
crystal_gridding = self.crystal_gridding,
fourier_coefficients = f_obs_cmpl)
fft_map.apply_sigma_scaling()
return fft_map.real_map_unpadded()
def ft_dp(self, dp, u_extra):
multiplier = (self.unit_cell().volume() /
matrix.row(self.rfft().n_real()).product() *
self.space_group().order_z() /
dp.multiplicities().data().as_double())
coeff = dp.deep_copy()
xray.apply_u_extra(self.unit_cell(), u_extra, coeff.indices(),
coeff.data())
coeff_data = coeff.data()
coeff_data *= flex.polar(multiplier, 0)
return miller.fft_map(crystal_gridding=self.crystal_gridding(),
fourier_coefficients=coeff)
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 run():
target_structure = random_structure.xray_structure(
space_group_info=sgtbx.space_group_info("I432"),
elements=['C']*6+['O'],
use_u_iso=False,
use_u_aniso=True,
)
shift = tuple(flex.random_double(3))
print "shift to be found: (%.3f, %.3f, %.3f)" % shift
target_structure_in_p1 = target_structure.expand_to_p1().apply_shift(shift)
miller_indices = miller.build_set(
crystal_symmetry=target_structure,
anomalous_flag=True,
d_min=0.8)
f_obs = miller_indices.structure_factors_from_scatterers(
xray_structure=target_structure,
algorithm="direct").f_calc().amplitudes()
miller_indices_in_p1 = miller.build_set(
crystal_symmetry=target_structure_in_p1,
anomalous_flag=True,
d_min=0.8)
f_calc = miller_indices_in_p1.structure_factors_from_scatterers(
xray_structure=target_structure_in_p1,
algorithm="direct").f_calc()
crystal_gridding = f_calc.crystal_gridding(
symmetry_flags=translation_search.symmetry_flags(
is_isotropic_search_model=False,
have_f_part=False),
resolution_factor=1/2
)
omptbx.env.num_threads = 1
t_fast_tf = show_times()
fast_tf_map = translation_search.fast_nv1995(
gridding=crystal_gridding.n_real(),
space_group=f_obs.space_group(),
anomalous_flag=f_obs.anomalous_flag(),
miller_indices_f_obs=f_obs.indices(),
f_obs=f_obs.data(),
f_part=flex.complex_double(), ## no sub-structure is already fixed
miller_indices_p1_f_calc=f_calc.indices(),
p1_f_calc=f_calc.data()).target_map()
print
print "Fast translation function"
t_fast_tf()
t_cross_corr = show_times()
for op in target_structure.space_group():
f, op_times_f = f_calc.original_and_transformed(op)
cross_corr_map = miller.fft_map(crystal_gridding,
f * op_times_f.conjugate().data())
print
print "Traditional cross-correlation"
t_cross_corr()
def compute_map_from_model(high_resolution, low_resolution, xray_structure,
grid_resolution_factor=None,
crystal_gridding = None):
f_calc = xray_structure.structure_factors(d_min = high_resolution).f_calc()
if (low_resolution is not None):
f_calc = f_calc.resolution_filter(d_max = low_resolution)
if (crystal_gridding is None):
return f_calc.fft_map(
resolution_factor = min(0.5,grid_resolution_factor),
symmetry_flags = None)
return miller.fft_map(
crystal_gridding = crystal_gridding,
fourier_coefficients = f_calc)
def good_atoms_selection(
crystal_gridding,
map_coeffs,
xray_structure):
#XXX copy from model_missing_reflections map_tools.py, consolidate later
#XXX Also look for similar crap in f_model.py
fft_map = miller.fft_map(
crystal_gridding = crystal_gridding,
fourier_coefficients = map_coeffs)
fft_map.apply_sigma_scaling()
map_data = fft_map.real_map_unpadded()
rho_atoms = flex.double()
for site_frac in xray_structure.sites_frac():
rho_atoms.append(map_data.eight_point_interpolation(site_frac))
#rho_mean = flex.mean_default(rho_atoms.select(rho_atoms>1.0), 1.0)
sel_exclude = rho_atoms < 1.0 # XXX ??? TRY 0.5!
sites_cart = xray_structure.sites_cart()
#
f_calc = map_coeffs.structure_factors_from_scatterers(
xray_structure = xray_structure).f_calc()
fft_map = miller.fft_map(
crystal_gridding = crystal_gridding,
fourier_coefficients = f_calc)
fft_map.apply_sigma_scaling()
map_data2 = fft_map.real_map_unpadded()
#
hd_sel = xray_structure.hd_selection()
for i_seq, site_cart in enumerate(sites_cart):
selection = maptbx.grid_indices_around_sites(
unit_cell = map_coeffs.unit_cell(),
fft_n_real = map_data.focus(),
fft_m_real = map_data.all(),
sites_cart = flex.vec3_double([site_cart]),
site_radii = flex.double([1.5]))
cc = flex.linear_correlation(x=map_data.select(selection),
y=map_data2.select(selection)).coefficient()
if(cc<0.7 or hd_sel[i_seq]): sel_exclude[i_seq] = True
return ~sel_exclude
def get_map(fmodel):
map_coeffs = map_tools.electron_density_map(fmodel = fmodel).map_coefficients(
map_type = "2mFo-DFc",
isotropize = True,
fill_missing = False)
crystal_gridding = fmodel.f_obs().crystal_gridding(
d_min = fmodel.f_obs().d_min(),
symmetry_flags = maptbx.use_space_group_symmetry,
resolution_factor = 1./4)
fft_map = miller.fft_map(
crystal_gridding = crystal_gridding,
fourier_coefficients = map_coeffs)
fft_map.apply_sigma_scaling()
return fft_map.real_map_unpadded()
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 _get_map_calc(self):
if(self.box is True):
f_calc = miller.structure_factor_box_from_map(
crystal_symmetry = self.xray_structure.crystal_symmetry(),
n_real = self.map.focus()).structure_factors_from_scatterers(
xray_structure = self.xray_structure).f_calc()
d_spacings = f_calc.d_spacings().data()
sel = d_spacings > d_min
f_calc = f_calc.select(sel)
else:
f_calc = self.xray_structure.structure_factors(d_min=self.d_min).f_calc()
fft_map = miller.fft_map(
crystal_gridding = self.crystal_gridding,
fourier_coefficients = f_calc)
return fft_map.real_map_unpadded()
def run():
target_structure = random_structure.xray_structure(
space_group_info=sgtbx.space_group_info("I432"),
elements=['C'] * 6 + ['O'],
use_u_iso=False,
use_u_aniso=True,
)
shift = tuple(flex.random_double(3))
print "shift to be found: (%.3f, %.3f, %.3f)" % shift
target_structure_in_p1 = target_structure.expand_to_p1().apply_shift(shift)
miller_indices = miller.build_set(crystal_symmetry=target_structure,
anomalous_flag=True,
d_min=0.8)
f_obs = miller_indices.structure_factors_from_scatterers(
xray_structure=target_structure,
algorithm="direct").f_calc().amplitudes()
miller_indices_in_p1 = miller.build_set(
crystal_symmetry=target_structure_in_p1,
anomalous_flag=True,
d_min=0.8)
f_calc = miller_indices_in_p1.structure_factors_from_scatterers(
xray_structure=target_structure_in_p1, algorithm="direct").f_calc()
crystal_gridding = f_calc.crystal_gridding(
symmetry_flags=translation_search.symmetry_flags(
is_isotropic_search_model=False, have_f_part=False),
resolution_factor=1 / 2)
omptbx.env.num_threads = 1
t_fast_tf = show_times()
fast_tf_map = translation_search.fast_nv1995(
gridding=crystal_gridding.n_real(),
space_group=f_obs.space_group(),
anomalous_flag=f_obs.anomalous_flag(),
miller_indices_f_obs=f_obs.indices(),
f_obs=f_obs.data(),
f_part=flex.complex_double(), ## no sub-structure is already fixed
miller_indices_p1_f_calc=f_calc.indices(),
p1_f_calc=f_calc.data()).target_map()
print
print "Fast translation function"
t_fast_tf()
t_cross_corr = show_times()
for op in target_structure.space_group():
f, op_times_f = f_calc.original_and_transformed(op)
cross_corr_map = miller.fft_map(crystal_gridding,
f * op_times_f.conjugate().data())
print
print "Traditional cross-correlation"
t_cross_corr()
def ft_dp(self, dp, u_extra):
multiplier = ( self.unit_cell().volume()
/ matrix.row(self.rfft().n_real()).product()
* self.space_group().order_z()
/ dp.multiplicities().data().as_double())
coeff = dp.deep_copy()
xray.apply_u_extra(
self.unit_cell(),
u_extra,
coeff.indices(),
coeff.data())
coeff_data = coeff.data()
coeff_data *= flex.polar(multiplier, 0)
return miller.fft_map(
crystal_gridding=self.crystal_gridding(),
fourier_coefficients=coeff)
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 get_poler_diff_map(f_obs):
fmodel = mmtbx.f_model.manager(f_obs=f_obs,
r_free_flags=r_free_flags,
f_calc=f_calc,
f_mask=f_mask)
fmodel.update_all_scales(remove_outliers=False)
mc_diff = map_tools.electron_density_map(
fmodel=fmodel).map_coefficients(map_type="mFo-DFc",
isotropize=True,
fill_missing=False)
fft_map = miller.fft_map(crystal_gridding=cpm_obj.crystal_gridding,
fourier_coefficients=mc_diff)
fft_map.apply_sigma_scaling()
map_data = fft_map.real_map_unpadded()
return mmtbx.utils.extract_box_around_model_and_map(
xray_structure=xrs_selected, map_data=map_data, box_cushion=2.1)
def optimize(d_min_min, d_min_max, step):
d_min = d_min_min
d_min_result = None
cc_best = -1.e6
while d_min < d_min_max + 1.e-6:
f_calc = xrs.structure_factors(d_min=d_min).f_calc()
fft_map = miller.fft_map(crystal_gridding=cg,
fourier_coefficients=f_calc)
map_data_ = fft_map.real_map_unpadded()
cc = flex.linear_correlation(
x=map_data_selected_as_1d,
y=map_data_.select(sel).as_1d()).coefficient()
if (cc > cc_best):
cc_best = cc
d_min_result = d_min
d_min += step
return d_min_result
def optimize(d_min_min, d_min_max, step):
d_min = d_min_min
d_min_result = None
cc_best=-1.e6
while d_min < d_min_max+1.e-6:
f_calc = xrs.structure_factors(d_min=d_min).f_calc()
fft_map = miller.fft_map(
crystal_gridding = cg,
fourier_coefficients = f_calc)
map_data_ = fft_map.real_map_unpadded()
cc = flex.linear_correlation(
x=map_data_selected_as_1d,
y=map_data_.select(sel).as_1d()).coefficient()
if(cc > cc_best):
cc_best = cc
d_min_result = d_min
d_min += step
return d_min_result
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 run(pdb_str, d_min, b):
#
pdb_inp = iotbx.pdb.input(source_info=None, lines=pdb_str_box)
cs = pdb_inp.crystal_symmetry()
ph = pdb_inp.construct_hierarchy()
xrs = ph.extract_xray_structure(crystal_symmetry=cs)
xrs = xrs.set_b_iso(value=b)
fc = xrs.structure_factors(d_min=d_min).f_calc()
cg = maptbx.crystal_gridding(unit_cell=cs.unit_cell(),
space_group_info=cs.space_group_info(),
d_min=d_min)
fft_map = miller.fft_map(crystal_gridding=cg, fourier_coefficients=fc)
map_obs = fft_map.real_map_unpadded()
#
pdb_inp = iotbx.pdb.input(source_info=None, lines=pdb_str)
xrs = ph.extract_xray_structure(crystal_symmetry=cs)
o = maptbx.resolution_from_map_and_model.run(map_data=map_obs,
xray_structure=xrs)
return o.d_min, o.b_iso, o.d_fsc_model
def get_poler_diff_map(f_obs):
fmodel = mmtbx.f_model.manager(
f_obs = f_obs,
r_free_flags = r_free_flags,
f_calc = f_calc,
f_mask = f_mask)
fmodel.update_all_scales(remove_outliers=False)
mc_diff = map_tools.electron_density_map(
fmodel = fmodel).map_coefficients(
map_type = "mFo-DFc",
isotropize = True,
fill_missing = False)
fft_map = miller.fft_map(
crystal_gridding = cpm_obj.crystal_gridding,
fourier_coefficients = mc_diff)
fft_map.apply_sigma_scaling()
map_data = fft_map.real_map_unpadded()
return mmtbx.utils.extract_box_around_model_and_map(
xray_structure = xrs_selected,
map_data = map_data,
box_cushion = 2.1)
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 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 __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 get_map(map_coeffs, crystal_gridding):
fft_map = miller.fft_map(
crystal_gridding = crystal_gridding,
fourier_coefficients = map_coeffs)
fft_map.apply_sigma_scaling()
return fft_map.real_map_unpadded()
def __init__ (self,
f,
f_000 = None,
lam = None,
start_map = "lde",
resolution_factor = 0.25,
mean_density = 0.375,
max_iterations = 2000,
beta = 0.9,
use_modification = True,
xray_structure = None,
verbose = False,
lambda_increment_factor = None,
convergence_at_r_factor = 0,
convergence_r_threshold = 0.1,
detect_convergence = True,
crystal_gridding = None,
use_scale = True,
log = None):
if (log is None) : log = sys.stdout
self.log = log
self.start_map = start_map
assert start_map in ["flat", "lde", "min_shifted"]
self.lam = lam
self.max_iterations = max_iterations
self.f_000 = f_000
self.verbose = verbose
self.beta = beta
self.f = f
self.use_modification = use_modification
self.meio_obj = None
self.xray_structure = xray_structure
self.lambda_increment_factor = lambda_increment_factor
self.convergence_at_r_factor = convergence_at_r_factor
self.detect_convergence = detect_convergence
self.crystal_gridding = crystal_gridding
self.use_scale = use_scale
self.convergence_r_threshold = convergence_r_threshold
#
if(self.f.anomalous_flag()):
merged = self.f.as_non_anomalous_array().merge_equivalents()
self.f = merged.array().set_observation_type( self.f )
# current monitor and optimized functional values
self.cntr = None
self.r = None
self.h_n = None
self.h_w = None
self.Z = None
self.scale_kc = None
self.a_gd = None
self.q_x = None
self.q_tot = None
self.tp = None
self.f_mem = None
self.header_shown = False
self.cc = None
self.r_factors = flex.double()
self.cc_to_answer = flex.double()
#
if(self.crystal_gridding is None):
self.crystal_gridding = self.f.crystal_gridding(
d_min = self.f.d_min(),
resolution_factor = resolution_factor,
grid_step = None,
symmetry_flags = None,
mandatory_factors = None,
max_prime = 5,
assert_shannon_sampling = True)
self.n_real = self.crystal_gridding.n_real()
max_index = [int((i-1)/2.) for i in self.n_real]
self.N = self.n_real[0]*self.n_real[1]*self.n_real[2]
self.full_set = self.f.complete_set(max_index=max_index)
self.f_calc = None
if(self.xray_structure is not None):
self.f_calc = self.full_set.structure_factors_from_scatterers(
xray_structure = self.xray_structure).f_calc()
if(verbose):
print >> self.log, "Resolution factor: %-6.4f"%resolution_factor
print >> self.log, \
" N, n1,n2,n3:",self.N,self.n_real[0],self.n_real[1],self.n_real[2]
print >> self.log, "Box: "
print >> self.log, " resolution: %6.4f"%self.full_set.d_min()
print >> self.log, " max. index |h|,|k|,|l|<nreal/2:", max_index
print >> self.log, " n.refl.:", self.full_set.indices().size()
# STEP 1
if(self.f_000 is None): self.f_000=mean_density*self.f.unit_cell().volume()
Cobs = 1
Ca = 0.37
self.Agd = Ca/self.N
if(verbose):
print >> self.log, \
"Cobs, Ca, Agd, f_000:", Cobs, Ca, self.Agd, "%6.3f"%self.f_000
print >> self.log, "Cobs/(N*f_000): ",Cobs/(self.N*self.f_000)
print >> self.log, "memory factor (beta):", self.beta
self.f = self.f.customized_copy(data=self.f.data()*Cobs/(self.N*self.f_000))
fft_map = miller.fft_map(
crystal_gridding = self.crystal_gridding,
fourier_coefficients = self.f)
self.rho_obs = fft_map.real_map_unpadded()
if(verbose):
show_map_stat(m = self.rho_obs, prefix = "rho_obs (formula #13)",
out=self.log)
# STEP 2
self.rho = self.normalize_start_map()
if(verbose):
show_map_stat(m = self.rho, prefix = "rho_0 (initial approximation)",
out=self.log)
# STEP 3
self.iterations()
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())