def scale(self):
"""Actually perform the scaling - this is delegated to the
implementation."""
if self._scalr_integraters == {}:
raise RuntimeError(
"no Integrater implementations assigned for scaling")
xname = self._scalr_xcrystal.get_name()
while not self.get_scaler_finish_done():
while not self.get_scaler_done():
while not self.get_scaler_prepare_done():
logger.notice(banner("Preparing %s" % xname))
self._scalr_prepare_done = True
self._scale_prepare()
logger.notice(banner("Scaling %s" % xname))
self._scalr_done = True
self._scalr_result = self._scale()
self._scalr_finish_done = True
self._scale_finish()
return self._scalr_result
def _index_prepare(self):
logger.notice(banner("Spotfinding %s" % self.get_indexer_sweep_name()))
super(XDSIndexerII, self)._index_prepare()
reflections_file = spot_xds_to_reflection_file(
self._indxr_payload["SPOT.XDS"],
working_directory=self.get_working_directory(),
)
refl = flex.reflection_table.from_file(reflections_file)
logger.info(spot_counts_per_image_plot(refl))
def get_output(self):
result = "Crystal: %s\n" % self._name
if self._aa_sequence:
result += "Sequence: %s\n" % self._aa_sequence.get_sequence()
for wavelength in self._wavelengths.keys():
result += self._wavelengths[wavelength].get_output()
scaler = self._get_scaler()
if scaler.get_scaler_finish_done():
for wname, xwav in self._wavelengths.iteritems():
for xsweep in xwav.get_sweeps():
idxr = xsweep._get_indexer()
if PhilIndex.params.xia2.settings.show_template:
result += "%s\n" % banner(
"Autoindexing %s (%s)" %
(idxr.get_indexer_sweep_name(),
idxr.get_template()))
else:
result += "%s\n" % banner(
"Autoindexing %s" % idxr.get_indexer_sweep_name())
result += "%s\n" % idxr.show_indexer_solutions()
intgr = xsweep._get_integrater()
if PhilIndex.params.xia2.settings.show_template:
result += "%s\n" % banner(
"Integrating %s (%s)" %
(intgr.get_integrater_sweep_name(),
intgr.get_template()))
else:
result += "%s\n" % banner(
"Integrating %s" %
intgr.get_integrater_sweep_name())
result += "%s\n" % intgr.show_per_image_statistics()
result += "%s\n" % banner("Scaling %s" % self.get_name())
for (
(dname, sname),
(limit, suggested),
) in scaler.get_scaler_resolution_limits().iteritems():
if suggested is None or limit == suggested:
result += "Resolution limit for %s/%s: %5.2f\n" % (
dname,
sname,
limit,
)
else:
result += (
"Resolution limit for %s/%s: %5.2f (%5.2f suggested)\n"
% (dname, sname, limit, suggested))
# this is now deprecated - be explicit in what you are
# asking for...
reflections_all = self.get_scaled_merged_reflections()
statistics_all = self._get_scaler().get_scaler_statistics()
# print some of these statistics, perhaps?
for key in statistics_all.keys():
result += format_statistics(statistics_all[key],
caption="For %s/%s/%s" % key)
# then print out some "derived" information based on the
# scaling - this is presented through the Scaler interface
# explicitly...
cell = self._get_scaler().get_scaler_cell()
cell_esd = self._get_scaler().get_scaler_cell_esd()
spacegroups = self._get_scaler().get_scaler_likely_spacegroups()
spacegroup = spacegroups[0]
resolution = self._get_scaler().get_scaler_highest_resolution()
from cctbx import sgtbx
sg = sgtbx.space_group_type(str(spacegroup))
spacegroup = sg.lookup_symbol()
CIF.set_spacegroup(sg)
mmCIF.set_spacegroup(sg)
if len(self._wavelengths) == 1:
CIF.set_wavelengths(
[w.get_wavelength() for w in self._wavelengths.itervalues()])
mmCIF.set_wavelengths(
[w.get_wavelength() for w in self._wavelengths.itervalues()])
else:
for wavelength in self._wavelengths.keys():
full_wave_name = "%s_%s_%s" % (
self._project._name,
self._name,
wavelength,
)
CIF.get_block(full_wave_name)[
"_diffrn_radiation_wavelength"] = self._wavelengths[
wavelength].get_wavelength()
mmCIF.get_block(full_wave_name)[
"_diffrn_radiation_wavelength"] = self._wavelengths[
wavelength].get_wavelength()
CIF.set_wavelengths({
name: wave.get_wavelength()
for name, wave in self._wavelengths.iteritems()
})
mmCIF.set_wavelengths({
name: wave.get_wavelength()
for name, wave in self._wavelengths.iteritems()
})
result += "Assuming spacegroup: %s\n" % spacegroup
if len(spacegroups) > 1:
result += "Other likely alternatives are:\n"
for sg in spacegroups[1:]:
result += "%s\n" % sg
if cell_esd:
from libtbx.utils import format_float_with_standard_uncertainty
def match_formatting(dimA, dimB):
def conditional_split(s):
return ((s[:s.index(".")],
s[s.index("."):]) if "." in s else (s, ""))
A, B = conditional_split(dimA), conditional_split(dimB)
maxlen = (max(len(A[0]), len(B[0])), max(len(A[1]), len(B[1])))
return (
A[0].rjust(maxlen[0]) + A[1].ljust(maxlen[1]),
B[0].rjust(maxlen[0]) + B[1].ljust(maxlen[1]),
)
formatted_cell_esds = tuple(
format_float_with_standard_uncertainty(v, sd)
for v, sd in zip(cell, cell_esd))
formatted_rows = (formatted_cell_esds[0:3],
formatted_cell_esds[3:6])
formatted_rows = zip(*(match_formatting(l, a)
for l, a in zip(*formatted_rows)))
result += "Unit cell (with estimated std devs):\n"
result += "%s %s %s\n%s %s %s\n" % (formatted_rows[0] +
formatted_rows[1])
else:
result += "Unit cell:\n"
result += "%7.3f %7.3f %7.3f\n%7.3f %7.3f %7.3f\n" % tuple(cell)
# now, use this information and the sequence (if provided)
# and also matthews_coef (should I be using this directly, here?)
# to compute a likely number of molecules in the ASU and also
# the solvent content...
if self._aa_sequence:
residues = self._aa_sequence.get_sequence()
if residues:
nres = len(residues)
# first compute the number of molecules using the K&R
# method
nmol = compute_nmol(
cell[0],
cell[1],
cell[2],
cell[3],
cell[4],
cell[5],
spacegroup,
resolution,
nres,
)
# then compute the solvent fraction
solvent = compute_solvent(
cell[0],
cell[1],
cell[2],
cell[3],
cell[4],
cell[5],
spacegroup,
nmol,
nres,
)
result += "Likely number of molecules in ASU: %d\n" % nmol
result += "Giving solvent fraction: %4.2f\n" % solvent
self._nmol = nmol
if isinstance(reflections_all, type({})):
for format in reflections_all.keys():
result += "%s format:\n" % format
reflections = reflections_all[format]
if isinstance(reflections, type({})):
for wavelength in reflections.keys():
target = FileHandler.get_data_file(
reflections[wavelength])
result += "Scaled reflections (%s): %s\n" % (
wavelength, target)
else:
target = FileHandler.get_data_file(reflections)
result += "Scaled reflections: %s\n" % target
CIF.write_cif()
mmCIF.write_cif()
return result
def index(self):
if not self.get_indexer_finish_done():
f = inspect.currentframe().f_back.f_back
m = f.f_code.co_filename
l = f.f_lineno
logger.debug("Index in %s called from %s %d" %
(self.__class__.__name__, m, l))
while not self.get_indexer_finish_done():
while not self.get_indexer_done():
while not self.get_indexer_prepare_done():
# --------------
# call prepare()
# --------------
self.set_indexer_prepare_done(True)
self._index_prepare()
# --------------------------------------------
# then do the proper indexing - using the best
# solution already stored if available (c/f
# eliminate above)
# --------------------------------------------
self.set_indexer_done(True)
if self.get_indexer_sweeps():
xsweeps = [s.get_name() for s in self.get_indexer_sweeps()]
if len(xsweeps) > 1:
# find "SWEEPn, SWEEP(n+1), (..), SWEEPm" and aggregate to "SWEEPS n-m"
xsweeps = [(int(x[5:]),
int(x[5:])) if x.startswith("SWEEP") else x
for x in xsweeps]
xsweeps[0] = [xsweeps[0]]
def compress(seen, nxt):
if (isinstance(seen[-1], tuple)
and isinstance(nxt, tuple)
and (seen[-1][1] + 1 == nxt[0])):
seen[-1] = (seen[-1][0], nxt[1])
else:
seen.append(nxt)
return seen
xsweeps = reduce(compress, xsweeps)
xsweeps = [
("SWEEP%d" %
x[0] if x[0] == x[1] else "SWEEPS %d to %d" %
(x[0], x[1])) if isinstance(x, tuple) else x
for x in xsweeps
]
if len(xsweeps) > 1:
sweep_names = ", ".join(xsweeps[:-1])
sweep_names += " & " + xsweeps[-1]
else:
sweep_names = xsweeps[0]
if PhilIndex.params.xia2.settings.show_template:
template = self.get_indexer_sweep().get_template()
logger.notice(
banner("Autoindexing %s (%s)", sweep_names,
template))
else:
logger.notice(banner("Autoindexing %s" % sweep_names))
if not self._indxr_helper:
self._index()
if not self._indxr_done:
logger.debug("Looks like indexing failed - try again!")
continue
solutions = {
k: c["cell"]
for k, c in self._indxr_other_lattice_cell.items()
}
# create a helper for the indexer to manage solutions
self._indxr_helper = _IndexerHelper(solutions)
solution = self._indxr_helper.get()
# compare these against the final solution, if different
# reject solution and return - correct solution will
# be used next cycle
if (self._indxr_lattice != solution[0]
and not self._indxr_input_cell and
not PhilIndex.params.xia2.settings.integrate_p1):
logger.info(
"Rerunning indexing lattice %s to %s",
self._indxr_lattice,
solution[0],
)
self.set_indexer_done(False)
else:
# rerun autoindexing with the best known current solution
solution = self._indxr_helper.get()
self._indxr_input_lattice = solution[0]
self._indxr_input_cell = solution[1]
self._index()
# next finish up...
self.set_indexer_finish_done(True)
self._index_finish()
if self._indxr_print:
logger.info(self.show_indexer_solutions())
def _index_prepare(self):
Citations.cite("dials")
# all_images = self.get_matching_images()
# first = min(all_images)
# last = max(all_images)
spot_lists = []
experiments_filenames = []
for imageset, xsweep in zip(self._indxr_imagesets, self._indxr_sweeps):
logger.notice(banner("Spotfinding %s" % xsweep.get_name()))
first, last = imageset.get_scan().get_image_range()
# at this stage, break out to run the DIALS code: this sets itself up
# now cheat and pass in some information... save re-reading all of the
# image headers
# FIXME need to adjust this to allow (say) three chunks of images
from dxtbx.model.experiment_list import ExperimentListFactory
sweep_filename = os.path.join(self.get_working_directory(),
"%s_import.expt" % xsweep.get_name())
ExperimentListFactory.from_imageset_and_crystal(
imageset, None).as_file(sweep_filename)
genmask = self.GenerateMask()
genmask.set_input_experiments(sweep_filename)
genmask.set_output_experiments(
os.path.join(
self.get_working_directory(),
"%s_%s_masked.expt" %
(genmask.get_xpid(), xsweep.get_name()),
))
genmask.set_params(PhilIndex.params.dials.masking)
sweep_filename, mask_pickle = genmask.run()
logger.debug("Generated mask for %s: %s", xsweep.get_name(),
mask_pickle)
gain = PhilIndex.params.xia2.settings.input.gain
if gain is libtbx.Auto:
gain_estimater = self.EstimateGain()
gain_estimater.set_sweep_filename(sweep_filename)
gain_estimater.run()
gain = gain_estimater.get_gain()
logger.info("Estimated gain: %.2f", gain)
PhilIndex.params.xia2.settings.input.gain = gain
# FIXME this should really use the assigned spot finding regions
# offset = self.get_frame_offset()
dfs_params = PhilIndex.params.dials.find_spots
spotfinder = self.Spotfinder()
if last - first > 10:
spotfinder.set_write_hot_mask(True)
spotfinder.set_input_sweep_filename(sweep_filename)
spotfinder.set_output_sweep_filename(
"%s_%s_strong.expt" %
(spotfinder.get_xpid(), xsweep.get_name()))
spotfinder.set_input_spot_filename(
"%s_%s_strong.refl" %
(spotfinder.get_xpid(), xsweep.get_name()))
if PhilIndex.params.dials.fast_mode:
wedges = self._index_select_images_i(imageset)
spotfinder.set_scan_ranges(wedges)
else:
spotfinder.set_scan_ranges([(first, last)])
if dfs_params.phil_file is not None:
spotfinder.set_phil_file(dfs_params.phil_file)
if dfs_params.min_spot_size is libtbx.Auto:
if imageset.get_detector()[0].get_type() == "SENSOR_PAD":
dfs_params.min_spot_size = 3
else:
dfs_params.min_spot_size = None
if dfs_params.min_spot_size is not None:
spotfinder.set_min_spot_size(dfs_params.min_spot_size)
if dfs_params.min_local is not None:
spotfinder.set_min_local(dfs_params.min_local)
if dfs_params.sigma_strong:
spotfinder.set_sigma_strong(dfs_params.sigma_strong)
gain = PhilIndex.params.xia2.settings.input.gain
if gain:
spotfinder.set_gain(gain)
if dfs_params.filter_ice_rings:
spotfinder.set_filter_ice_rings(dfs_params.filter_ice_rings)
if dfs_params.kernel_size:
spotfinder.set_kernel_size(dfs_params.kernel_size)
if dfs_params.global_threshold is not None:
spotfinder.set_global_threshold(dfs_params.global_threshold)
if dfs_params.threshold.algorithm is not None:
spotfinder.set_threshold_algorithm(
dfs_params.threshold.algorithm)
spotfinder.run()
spot_filename = spotfinder.get_spot_filename()
if not os.path.exists(spot_filename):
raise RuntimeError("Spotfinding failed: %s does not exist." %
os.path.basename(spot_filename))
spot_lists.append(spot_filename)
experiments_filenames.append(
spotfinder.get_output_sweep_filename())
refl = flex.reflection_table.from_file(spot_filename)
if not len(refl):
raise RuntimeError("No spots found in sweep %s" %
xsweep.get_name())
logger.info(spot_counts_per_image_plot(refl))
if not PhilIndex.params.dials.fast_mode:
detectblanks = self.DetectBlanks()
detectblanks.set_sweep_filename(experiments_filenames[-1])
detectblanks.set_reflections_filename(spot_filename)
detectblanks.run()
json = detectblanks.get_results()
blank_regions = json["strong"]["blank_regions"]
if len(blank_regions):
blank_regions = [(int(s), int(e))
for s, e in blank_regions]
for blank_start, blank_end in blank_regions:
logger.info(
"WARNING: Potential blank images: %i -> %i",
blank_start + 1,
blank_end,
)
if PhilIndex.params.xia2.settings.remove_blanks:
non_blanks = []
start, end = imageset.get_array_range()
last_blank_end = start
for blank_start, blank_end in blank_regions:
if blank_start > start:
non_blanks.append(
(last_blank_end, blank_start))
last_blank_end = blank_end
if last_blank_end + 1 < end:
non_blanks.append((last_blank_end, end))
xsweep = self.get_indexer_sweep()
xwav = xsweep.get_wavelength()
xsample = xsweep.sample
sweep_name = xsweep.get_name()
for i, (nb_start, nb_end) in enumerate(non_blanks):
assert i < 26
if i == 0:
sub_imageset = imageset[nb_start -
start:nb_end - start]
xsweep._frames_to_process = (nb_start + 1,
nb_end + 1)
self.set_indexer_prepare_done(done=False)
self._indxr_imagesets[
self._indxr_imagesets.index(
imageset)] = sub_imageset
xsweep._integrater._setup_from_imageset(
sub_imageset)
else:
min_images = (PhilIndex.params.xia2.settings.
input.min_images)
if (nb_end - nb_start) < min_images:
continue
new_name = "_".join(
(sweep_name, string.ascii_lowercase[i]))
new_sweep = xwav.add_sweep(
new_name,
xsample,
directory=os.path.join(
os.path.basename(
xsweep.get_directory()),
new_name,
),
image=imageset.get_path(nb_start - start),
frames_to_process=(nb_start + 1, nb_end),
beam=xsweep.get_beam_centre(),
reversephi=xsweep.get_reversephi(),
distance=xsweep.get_distance(),
gain=xsweep.get_gain(),
dmin=xsweep.get_resolution_high(),
dmax=xsweep.get_resolution_low(),
polarization=xsweep.get_polarization(),
user_lattice=xsweep.get_user_lattice(),
user_cell=xsweep.get_user_cell(),
epoch=xsweep._epoch,
ice=xsweep._ice,
excluded_regions=xsweep._excluded_regions,
)
logger.info(
"Generating new sweep: %s (%s:%i:%i)",
new_sweep.get_name(),
new_sweep.get_image(),
new_sweep.get_frames_to_process()[0],
new_sweep.get_frames_to_process()[1],
)
return
if not PhilIndex.params.xia2.settings.trust_beam_centre:
discovery = self.SearchBeamPosition()
discovery.set_sweep_filename(experiments_filenames[-1])
discovery.set_spot_filename(spot_filename)
# set scan_range to correspond to not more than 180 degrees
# if we have > 20000 reflections
width = imageset.get_scan().get_oscillation()[1]
if (last - first) * width > 180.0 and len(refl) > 20000:
end = first + int(round(180.0 / width)) - 1
logger.debug("Using %d to %d for beam search", first, end)
discovery.set_image_range((first, end))
try:
discovery.run()
result = discovery.get_optimized_experiments_filename()
# overwrite indexed.expt in experiments list
experiments_filenames[-1] = result
except Exception as e:
logger.debug("DIALS beam centre search failed: %s",
str(e),
exc_info=True)
self.set_indexer_payload("spot_lists", spot_lists)
self.set_indexer_payload("experiments", experiments_filenames)
def integrate(self):
"""Actually perform integration until we think we are done..."""
while not self.get_integrater_finish_done():
while not self.get_integrater_done():
while not self.get_integrater_prepare_done():
logger.debug("Preparing to do some integration...")
self.set_integrater_prepare_done(True)
# if this raises an exception, perhaps the autoindexing
# solution has too high symmetry. if this the case, then
# perform a self._intgr_indexer.eliminate() - this should
# reset the indexing system
try:
self._integrate_prepare()
except BadLatticeError as e:
logger.info("Rejecting bad lattice %s", str(e))
self._intgr_refiner.eliminate()
self._integrater_reset()
# FIXME x1698 - may be the case that _integrate() returns the
# raw intensities, _integrate_finish() returns intensities
# which may have been adjusted or corrected. See #1698 below.
logger.debug("Doing some integration...")
self.set_integrater_done(True)
template = self.get_integrater_sweep().get_template()
if self._intgr_sweep_name:
if PhilIndex.params.xia2.settings.show_template:
logger.notice(
banner("Integrating %s (%s)" %
(self._intgr_sweep_name, template)))
else:
logger.notice(
banner("Integrating %s" % self._intgr_sweep_name))
try:
# 1698
self._intgr_hklout_raw = self._integrate()
except BadLatticeError as e:
logger.info("Rejecting bad lattice %s", str(e))
self._intgr_refiner.eliminate()
self._integrater_reset()
self.set_integrater_finish_done(True)
try:
# allow for the fact that postrefinement may be used
# to reject the lattice...
self._intgr_hklout = self._integrate_finish()
except BadLatticeError as e:
logger.info("Bad Lattice Error: %s", str(e))
self._intgr_refiner.eliminate()
self._integrater_reset()
return self._intgr_hklout
def get_output(self):
result = 'Crystal: %s\n' % self._name
if self._aa_sequence:
result += 'Sequence: %s\n' % self._aa_sequence.get_sequence()
for wavelength in self._wavelengths.keys():
result += self._wavelengths[wavelength].get_output()
scaler = self._get_scaler()
if scaler.get_scaler_finish_done():
for wname, xwav in self._wavelengths.iteritems():
for xsweep in xwav.get_sweeps():
idxr = xsweep._get_indexer()
if PhilIndex.params.xia2.settings.show_template:
result += '%s\n' %banner('Autoindexing %s (%s)' %(
idxr.get_indexer_sweep_name(), idxr.get_template()))
else:
result += '%s\n' %banner(
'Autoindexing %s' %idxr.get_indexer_sweep_name())
result += '%s\n' %idxr.show_indexer_solutions()
intgr = xsweep._get_integrater()
if PhilIndex.params.xia2.settings.show_template:
result += '%s\n' %banner('Integrating %s (%s)' %(
intgr.get_integrater_sweep_name(), intgr.get_template()))
else:
result += '%s\n' %banner(
'Integrating %s' %intgr.get_integrater_sweep_name())
result += '%s\n' % intgr.show_per_image_statistics()
result += '%s\n' %banner('Scaling %s' %self.get_name())
for (dname, sname), (limit, suggested) in scaler.get_scaler_resolution_limits().iteritems():
if suggested is None or limit == suggested:
result += 'Resolution limit for %s/%s: %5.2f\n' %(dname, sname, limit)
else:
result += 'Resolution limit for %s/%s: %5.2f (%5.2f suggested)\n' %(dname, sname, limit, suggested)
# this is now deprecated - be explicit in what you are
# asking for...
reflections_all = self.get_scaled_merged_reflections()
statistics_all = self._get_scaler().get_scaler_statistics()
# print some of these statistics, perhaps?
for key in statistics_all.keys():
result += format_statistics(statistics_all[key], caption='For %s/%s/%s' % key)
# then print out some "derived" information based on the
# scaling - this is presented through the Scaler interface
# explicitly...
cell = self._get_scaler().get_scaler_cell()
cell_esd = self._get_scaler().get_scaler_cell_esd()
spacegroups = self._get_scaler().get_scaler_likely_spacegroups()
spacegroup = spacegroups[0]
resolution = self._get_scaler().get_scaler_highest_resolution()
from cctbx import sgtbx
sg = sgtbx.space_group_type(str(spacegroup))
spacegroup = sg.lookup_symbol()
CIF.set_spacegroup(sg)
mmCIF.set_spacegroup(sg)
if len(self._wavelengths) == 1:
CIF.set_wavelengths([w.get_wavelength() for w in self._wavelengths.itervalues()])
mmCIF.set_wavelengths([w.get_wavelength() for w in self._wavelengths.itervalues()])
else:
for wavelength in self._wavelengths.keys():
full_wave_name = '%s_%s_%s' % (self._project._name, self._name, wavelength)
CIF.get_block(full_wave_name)['_diffrn_radiation_wavelength'] = \
self._wavelengths[wavelength].get_wavelength()
mmCIF.get_block(full_wave_name)['_diffrn_radiation_wavelength'] = \
self._wavelengths[wavelength].get_wavelength()
CIF.set_wavelengths({name: wave.get_wavelength() for name, wave in self._wavelengths.iteritems()})
mmCIF.set_wavelengths({name: wave.get_wavelength() for name, wave in self._wavelengths.iteritems()})
result += 'Assuming spacegroup: %s\n' % spacegroup
if len(spacegroups) > 1:
result += 'Other likely alternatives are:\n'
for sg in spacegroups[1:]:
result += '%s\n' % sg
if cell_esd:
from libtbx.utils import format_float_with_standard_uncertainty
def match_formatting(dimA, dimB):
def conditional_split(s):
return (s[:s.index('.')],s[s.index('.'):]) if '.' in s else (s, '')
A, B = conditional_split(dimA), conditional_split(dimB)
maxlen = (max(len(A[0]), len(B[0])), max(len(A[1]), len(B[1])))
return (
A[0].rjust(maxlen[0])+A[1].ljust(maxlen[1]),
B[0].rjust(maxlen[0])+B[1].ljust(maxlen[1])
)
formatted_cell_esds = tuple(format_float_with_standard_uncertainty(v, sd) for v, sd in zip(cell, cell_esd))
formatted_rows = (formatted_cell_esds[0:3], formatted_cell_esds[3:6])
formatted_rows = zip(*(match_formatting(l, a) for l, a in zip(*formatted_rows)))
result += 'Unit cell (with estimated std devs):\n'
result += '%s %s %s\n%s %s %s\n' % (formatted_rows[0] + formatted_rows[1])
else:
result += 'Unit cell:\n'
result += '%7.3f %7.3f %7.3f\n%7.3f %7.3f %7.3f\n' % tuple(cell)
# now, use this information and the sequence (if provided)
# and also matthews_coef (should I be using this directly, here?)
# to compute a likely number of molecules in the ASU and also
# the solvent content...
if self._aa_sequence:
residues = self._aa_sequence.get_sequence()
if residues:
nres = len(residues)
# first compute the number of molecules using the K&R
# method
nmol = compute_nmol(cell[0], cell[1], cell[2],
cell[3], cell[4], cell[5],
spacegroup, resolution, nres)
# then compute the solvent fraction
solvent = compute_solvent(cell[0], cell[1], cell[2],
cell[3], cell[4], cell[5],
spacegroup, nmol, nres)
result += 'Likely number of molecules in ASU: %d\n' % nmol
result += 'Giving solvent fraction: %4.2f\n' % solvent
self._nmol = nmol
if type(reflections_all) == type({}):
for format in reflections_all.keys():
result += '%s format:\n' % format
reflections = reflections_all[format]
if type(reflections) == type({}):
for wavelength in reflections.keys():
target = FileHandler.get_data_file(
reflections[wavelength])
result += 'Scaled reflections (%s): %s\n' % \
(wavelength, target)
else:
target = FileHandler.get_data_file(
reflections)
result += 'Scaled reflections: %s\n' % target
CIF.write_cif()
mmCIF.write_cif()
return result
