def get_group_symmetry_reference_matched(self, ref_cs):
ref_v6 = xtal.v6cell(ref_cs.niggli_cell().unit_cell())
ncdists = []
for i, keys in enumerate(self.groups):
v6 = xtal.v6cell(
uctbx.unit_cell(self._average_p1_cell(keys)).niggli_cell())
ncdists.append(NCDist(v6, ref_v6))
print "Group %d: NCDist to reference: %f" % (i + 1, ncdists[-1])
return ncdists.index(min(ncdists)) + 1
def run(params):
if params.datout is None: params.datout = os.path.basename(params.lstin)+".dat"
xac_files = read_path_list(params.lstin)
ofs_dat = open(params.datout, "w")
ref_v6cell = None
if params.ref_cell is not None:
ref_v6cell = v6cell(uctbx.unit_cell(params.ref_cell).niggli_cell())
ofs_dat.write("# ref_cell= %s\n" % params.ref_cell)
if params.n_residues is not None: ofs_dat.write("# n_residues= %d\n" % params.n_residues)
ofs_dat.write("file ioversigma resnatsnr1 wilsonb abdist a b c al be ga\n")
ret = easy_mp.pool_map(fixed_func=lambda x: calc_stats(x, params.stats, params.n_residues, ref_v6cell,
params.min_peak, params.min_peak_percentile,
params.correct_peak),
args=xac_files,
processes=params.nproc)
for stat in ret:
getornan = lambda x: stat.get(x, float("nan")) # get or nan
ofs_dat.write("%s %.3f %.3f %.3f %.3e"%(stat["filename"],
getornan("ioversigma"), getornan("resnatsnr1"),
getornan("wilsonb"), getornan("abdist")))
ofs_dat.write(" %.3f %.3f %.3f %.2f %.2f %.2f\n" % stat["cell"])
ofs_dat.close()
def run(params):
if params.datout is None:
params.datout = os.path.basename(params.lstin) + ".dat"
xac_files = read_path_list(params.lstin)
ofs_dat = open(params.datout, "w")
ref_v6cell = None
if params.ref_cell is not None:
ref_v6cell = v6cell(uctbx.unit_cell(params.ref_cell).niggli_cell())
ofs_dat.write("# ref_cell= %s\n" % params.ref_cell)
if params.n_residues is not None:
ofs_dat.write("# n_residues= %d\n" % params.n_residues)
ofs_dat.write("file ioversigma resnatsnr1 wilsonb abdist a b c al be ga\n")
ret = easy_mp.pool_map(fixed_func=lambda x: calc_stats(
x, params.stats, params.n_residues, ref_v6cell, params.min_peak, params
.min_peak_percentile, params.correct_peak),
args=xac_files,
processes=params.nproc)
for stat in ret:
getornan = lambda x: stat.get(x, float("nan")) # get or nan
ofs_dat.write(
"%s %.3f %.3f %.3f %.3e" %
(stat["filename"], getornan("ioversigma"), getornan("resnatsnr1"),
getornan("wilsonb"), getornan("abdist")))
ofs_dat.write(" %.3f %.3f %.3f %.2f %.2f %.2f\n" % stat["cell"])
ofs_dat.close()
def calc_stats(xac_file, stat_choice, n_residues=None, ref_v6cell=None,
min_peak=None, min_peak_percentile=None, correct_peak=None):
# Open XDS_ASCII
if xac_file.endswith(".pkl"): xac = pickle.load(open(xac_file))
else: xac = xds_ascii.XDS_ASCII(xac_file)
sel_remove = flex.bool(xac.iobs.size(), False)
if min_peak is not None:
sel = xac.peak < min_peak
sel_remove |= sel
elif min_peak_percentile is not None:
q = numpy.percentile(xac.peak, min_peak_percentile)
print "percentile %.2f %s" % (q, xac)
sel = xac.peak < q
sel_remove |= sel
if correct_peak: sel_remove |= (xac.peak < 1) # remove PEAK==0
xac.remove_selection(sel_remove)
if params.correct_peak:
xac.iobs *= xac.peak * .01
xac.sigma_iobs *= xac.peak * .01
iobs = xac.i_obs(anomalous_flag=False)
iobs = iobs.select(iobs.sigmas()>0).merge_equivalents(use_internal_variance=False).array()
stats = dict(filename=xac_file, cell=iobs.unit_cell().parameters())
if iobs.size() == 0:
return stats
if "ioversigma" in stat_choice or "resnatsnr1" in stat_choice:
binner = iobs.setup_binner(auto_binning=True)
if "ioversigma" in stat_choice: stats["ioversigma"] = flex.mean(iobs.data()/iobs.sigmas())
if "resnatsnr1" in stat_choice:
res = float("nan")
for i_bin in binner.range_used():
sel = binner.selection(i_bin)
tmp = iobs.select(sel)
if tmp.size() == 0: continue
sn = flex.mean(tmp.data()/tmp.sigmas())
if sn <= 1:
res = binner.bin_d_range(i_bin)[1]
break
stats["resnatsnr1"] = res
if "abdist" in stat_choice:
from cctbx.uctbx.determine_unit_cell import NCDist
G6a, G6b = ref_v6cell, v6cell(iobs.unit_cell().niggli_cell())
abdist = NCDist(G6a, G6b)
stats["abdist"] = abdist
if "wilsonb" in stat_choice:
iso_scale_and_b = ml_iso_absolute_scaling(iobs, n_residues, 0)
stats["wilsonb"] = iso_scale_and_b.b_wilson
print stats
return stats
def calc_stats(xac_file,
stat_choice,
n_residues=None,
ref_v6cell=None,
min_peak=None,
min_peak_percentile=None,
correct_peak=None):
# Open XDS_ASCII
if xac_file.endswith(".pkl"): xac = pickle.load(open(xac_file))
else: xac = xds_ascii.XDS_ASCII(xac_file)
sel_remove = flex.bool(xac.iobs.size(), False)
if min_peak is not None:
sel = xac.peak < min_peak
sel_remove |= sel
elif min_peak_percentile is not None:
q = numpy.percentile(xac.peak, min_peak_percentile)
print "percentile %.2f %s" % (q, xac)
sel = xac.peak < q
sel_remove |= sel
if correct_peak: sel_remove |= (xac.peak < 1) # remove PEAK==0
xac.remove_selection(sel_remove)
if params.correct_peak:
xac.iobs *= xac.peak * .01
xac.sigma_iobs *= xac.peak * .01
iobs = xac.i_obs(anomalous_flag=False)
iobs = iobs.select(iobs.sigmas() > 0).merge_equivalents(
use_internal_variance=False).array()
stats = dict(filename=xac_file, cell=iobs.unit_cell().parameters())
if iobs.size() == 0:
return stats
if "ioversigma" in stat_choice or "resnatsnr1" in stat_choice:
binner = iobs.setup_binner(auto_binning=True)
if "ioversigma" in stat_choice:
stats["ioversigma"] = flex.mean(iobs.data() / iobs.sigmas())
if "resnatsnr1" in stat_choice:
res = float("nan")
for i_bin in binner.range_used():
sel = binner.selection(i_bin)
tmp = iobs.select(sel)
if tmp.size() == 0: continue
sn = flex.mean(tmp.data() / tmp.sigmas())
if sn <= 1:
res = binner.bin_d_range(i_bin)[1]
break
stats["resnatsnr1"] = res
if "abdist" in stat_choice:
from cctbx.uctbx.determine_unit_cell import NCDist
G6a, G6b = ref_v6cell, v6cell(iobs.unit_cell().niggli_cell())
abdist = NCDist(G6a, G6b)
stats["abdist"] = abdist
if "wilsonb" in stat_choice:
iso_scale_and_b = ml_iso_absolute_scaling(iobs, n_residues, 0)
stats["wilsonb"] = iso_scale_and_b.b_wilson
print stats
return stats
