def as_cif_block(self, cif_block=None):
if cif_block is None:
cif_block = iotbx.cif.model.block()
cif_block["_reflns.B_iso_Wilson_estimate"] = round_2_for_cif(self.wilson_b)
cif_block["_refine.B_iso_mean"] = round_2_for_cif(self.b_mean_a)
#_refine.aniso_B[1][1]
#_refine.aniso_B[2][2]
#_refine.aniso_B[3][3]
#_refine.aniso_B[1][2]
#_refine.aniso_B[1][3]
#_refine.aniso_B[2][3]
return cif_block
def as_cif_block(self, cif_block=None):
if cif_block is None:
cif_block = iotbx.cif.model.block()
if self.data_x is not None:
cif_block = self.data_x.as_cif_block(cif_block=cif_block)
# XXX Neutron data?
if self.geometry is not None:
pdbx_refine_id = ''
if self.data_x is not None:
pdbx_refine_id = 'X-ray'
if self.data_n is not None:
pdbx_refine_id = 'Neutron' if self.data_x is None else 'X-ray+Neutron'
cif_block = self.geometry.as_cif_block(
cif_block=cif_block, pdbx_refine_id=pdbx_refine_id)
if self.adp is not None:
cif_block = self.adp.as_cif_block(cif_block=cif_block)
cif_block["_reflns.B_iso_Wilson_estimate"] = round_2_for_cif(
self.wilson_b)
cif_block = self.model.tls_groups_as_cif_block(cif_block=cif_block)
# What about anomalous_scatterer_groups here?
# adding NCS information.
# It is not clear why we dump cartesian NCS first, and if it is absent,
# Torsion NCS next. What about NCS constraints?
if self.model.cartesian_NCS_present():
self.model.cartesian_NCS_as_cif_block(cif_block=cif_block)
elif self.model.torsion_NCS_present():
self.model.torsion_NCS_as_cif_block(cif_block=cif_block)
return cif_block
def as_cif_block(self, cif_block=None):
if cif_block is None:
cif_block = iotbx.cif.model.block()
pdbx_refine_id = ''
if self.data_x is not None:
pdbx_refine_id = 'X-RAY DIFFRACTION'
if self.data_n is not None:
# !!! Warning: "X-ray+Neutron" is not compliant with mmCIF dictionary:
# http://mmcif.wwpdb.org/dictionaries/mmcif_pdbx_v50.dic/Items/_exptl.method.html
pdbx_refine_id = 'NEUTRON DIFFRACTION' if self.data_x is None else 'X-ray+Neutron'
if self.data_x is not None:
cif_block = self.data_x.as_cif_block(cif_block=cif_block, scattering_type=pdbx_refine_id)
# XXX Neutron data?
if self.geometry is not None:
cif_block = self.geometry.as_cif_block(cif_block=cif_block, pdbx_refine_id=pdbx_refine_id)
if self.adp is not None:
cif_block = self.adp.as_cif_block(cif_block=cif_block)
cif_block["_reflns.B_iso_Wilson_estimate"] = round_2_for_cif(self.wilson_b)
cif_block = self.model.tls_groups_as_cif_block(cif_block=cif_block)
# What about anomalous_scatterer_groups here?
# adding NCS information.
# It is not clear why we dump cartesian NCS first, and if it is absent,
# Torsion NCS next. What about NCS constraints?
if self.model.cartesian_NCS_present():
self.model.cartesian_NCS_as_cif_block(cif_block=cif_block)
elif self.model.torsion_NCS_present():
self.model.torsion_NCS_as_cif_block(cif_block=cif_block)
return cif_block
def as_cif_block(self, cif_block=None):
if cif_block is None:
cif_block = iotbx.cif.model.block()
if self._result.overall is not None:
cif_block["_refine.B_iso_mean"] = round_2_for_cif(self._result.overall.mean)
else:
cif_block["_refine.B_iso_mean"] = '?'
return cif_block
def as_cif_block(self, cif_block=None):
if cif_block is None:
cif_block = iotbx.cif.model.block()
if self.data_x is not None:
cif_block = self.data_x.as_cif_block(cif_block=cif_block, scattering_type=self._pdbx_refine_id)
if self.wilson_b is not None:
cif_block["_reflns.B_iso_Wilson_estimate"] = round_2_for_cif(self.wilson_b)
# XXX Neutron data?
if self.geometry is not None:
cif_block = self.geometry.as_cif_block(cif_block=cif_block, pdbx_refine_id=self._pdbx_refine_id)
if self.adp is not None:
cif_block = self.adp.as_cif_block(cif_block=cif_block)
cif_block = self.model.tls_groups_as_cif_block(cif_block=cif_block)
# What about anomalous_scatterer_groups here?
return cif_block
def as_cif_block(self,
cif_block=None,
scattering_type="X-RAY DIFFRACTION"):
import iotbx.cif.model
if cif_block is None:
cif_block = iotbx.cif.model.block()
cif_block["_refine.pdbx_refine_id"] = scattering_type
cif_block["_refine.ls_d_res_low"] = round_2_for_cif(self.d_max)
cif_block["_refine.ls_d_res_high"] = round_2_for_cif(self.d_min)
cif_block["_refine.pdbx_ls_sigma_F"] = round_2_for_cif(
self.min_f_obs_over_sigma)
cif_block["_refine.ls_percent_reflns_obs"] = round_2_for_cif(
self.completeness_in_range * 100.0)
cif_block["_refine.ls_number_reflns_obs"] = self.number_of_reflections
#_refine.ls_number_reflns_all
cif_block["_refine.ls_number_reflns_R_work"] = (
self.number_of_reflections - self.number_of_test_reflections)
cif_block[
"_refine.ls_number_reflns_R_free"] = self.number_of_test_reflections
cif_block["_refine.ls_R_factor_obs"] = round_4_for_cif(self.r_all)
cif_block["_refine.ls_R_factor_R_work"] = round_4_for_cif(self.r_work)
cif_block["_refine.ls_R_factor_R_free"] = round_4_for_cif(self.r_free)
cif_block["_refine.ls_percent_reflns_R_free"] = round_2_for_cif(
self.number_of_test_reflections / self.number_of_reflections * 100)
loop = iotbx.cif.model.loop(header=(
"_refine_ls_shell.pdbx_refine_id",
#"_refine_ls_shell.pdbx_total_number_of_bins_used",
"_refine_ls_shell.d_res_high",
"_refine_ls_shell.d_res_low",
"_refine_ls_shell.number_reflns_R_work",
"_refine_ls_shell.R_factor_R_work",
"_refine_ls_shell.percent_reflns_obs",
"_refine_ls_shell.R_factor_R_free",
#"_refine_ls_shell.R_factor_R_free_error",
#"_refine_ls_shell.percent_reflns_R_free",
"_refine_ls_shell.number_reflns_R_free",
#"_refine_ls_shell.number_reflns_all",
#"_refine_ls_shell.R_factor_all",
#"_refine_ls_shell.redundancy_reflns_obs",
#"_refine_ls_shell.number_reflns_obs",
))
for bin in self.bins:
d_max, d_min = [float(d) for d in bin.d_range.split('-')]
loop.add_row((scattering_type, round_2_for_cif(d_min),
round_2_for_cif(d_max), bin.n_work,
round_4_for_cif(bin.r_work),
round_2_for_cif(bin.completeness * 100),
round_4_for_cif(bin.r_free), bin.n_free))
cif_block.add_loop(loop)
cif_block["_refine.solvent_model_details"] = "FLAT BULK SOLVENT MODEL"
#_refine.solvent_model_param_ksol
#_refine.solvent_model_param_bsol
cif_block["_refine.pdbx_solvent_vdw_probe_radii"] = round_4_for_cif(
self.mask_solvent_radius)
cif_block["_refine.pdbx_solvent_shrinkage_radii"] = round_4_for_cif(
self.mask_shrink_radius)
# twinning?
cif_block["_refine.overall_SU_ML"] = round_4_for_cif(
self.ml_coordinate_error)
cif_block["_refine.pdbx_overall_phase_error"] = round_4_for_cif(
self.ml_phase_error)
return cif_block
def as_cif_block(self, cif_block=None):
if cif_block is None:
cif_block = iotbx.cif.model.block()
cif_block["_refine.B_iso_mean"] = \
round_2_for_cif(self._result.overall.mean)
return cif_block
