def __init__(self,
xac_files,
d_min=3,
min_ios=3,
nproc=1,
max_delta=3,
log_out=null_out()):
self.xac_files = xac_files
self.log_out = log_out
self.nproc = nproc
self.arrays = []
self.max_delta = max_delta
self.best_operators = None
print >> self.log_out, "Reading"
for i, f in enumerate(self.xac_files):
print >> self.log_out, "%4d %s" % (i, f)
xac = XDS_ASCII(f, i_only=True)
xac.remove_rejected()
a = xac.i_obs().resolution_filter(d_min=d_min)
if min_ios is not None:
a = a.select(a.data() / a.sigmas() >= min_ios)
a = a.as_non_anomalous_array().merge_equivalents(
use_internal_variance=False).array()
self.arrays.append(a)
print >> self.log_out, ""
def read_xac_files(self, from_p1=False):
op_to_p1 = None
if from_p1:
"""
This option is currently for multi_determine_symmetry.
Do not use this for ambiguity resolution! op_to_p1 is not considered when writing new HKL files.
"""
self.log_out.write("\nAveraging symmetry of all inputs..\n")
cells = []
sgs = []
for f in self.xac_files:
xac = XDS_ASCII(f, read_data=False)
cells.append(xac.symm.unit_cell().parameters())
sgs.append(xac.symm.space_group())
assert len(set(sgs)) < 2
avg_symm = crystal.symmetry(list(numpy.median(cells, axis=0)), space_group=sgs[0])
op_to_p1 = avg_symm.change_of_basis_op_to_niggli_cell()
self.log_out.write(" Averaged symmetry: %s (%s)\n" % (format_unit_cell(avg_symm.unit_cell()), sgs[0].info()))
self.log_out.write(" Operator to Niggli cell: %s\n" % op_to_p1.as_hkl())
self.log_out.write(" Niggli cell: %s\n" % format_unit_cell(avg_symm.unit_cell().change_basis(op_to_p1)))
print >>self.log_out, "\nReading"
cells = []
bad_files, good_files = [], []
for i, f in enumerate(self.xac_files):
print >>self.log_out, "%4d %s" % (i, f)
xac = XDS_ASCII(f, i_only=True)
self.log_out.write(" d_range: %6.2f - %5.2f" % xac.i_obs().resolution_range())
self.log_out.write(" n_ref=%6d" % xac.i_obs().size())
xac.remove_rejected()
a = xac.i_obs().resolution_filter(d_min=self.d_min)
if self.min_ios is not None: a = a.select(a.data()/a.sigmas()>=self.min_ios)
self.log_out.write(" n_ref_filtered=%6d" % a.size())
if from_p1:
a = a.change_basis(op_to_p1).customized_copy(space_group_info=sgtbx.space_group_info("P1"))
a = a.as_non_anomalous_array().merge_equivalents(use_internal_variance=False).array()
self.log_out.write(" n_ref_merged=%6d\n" % a.size())
if a.size() < 2:
self.log_out.write(" !! WARNING !! number of reflections is dangerously small!!\n")
bad_files.append(f)
else:
self.arrays.append(a)
cells.append(a.unit_cell().parameters())
good_files.append(f)
if bad_files:
self.xac_files = good_files
self.bad_files = bad_files
assert len(self.xac_files) == len(self.arrays) == len(cells)
print >>self.log_out, ""
self._representative_xs = crystal.symmetry(list(numpy.median(cells, axis=0)),
space_group_info=self.arrays[0].space_group_info())
def run(hklin,
hklin_merged=None,
cone_angle=20.,
n_bins=10,
anomalous=None,
do_fit=True,
log_out=null_out()):
if 1:
xac = XDS_ASCII(hklin, i_only=True)
xac.remove_rejected()
i_obs = xac.i_obs()
#else:
# import iotbx.mtz
# i_obs = filter(lambda x: "SIGI" in x.info().label_string(), iotbx.mtz.object(hklin).as_miller_arrays(merge_equivalents=False))[0]
print >> log_out, "Unmerged intensity read from", hklin
i_obs.show_summary(log_out, prefix=" ")
print >> log_out, ""
if anomalous is not None and i_obs.anomalous_flag() != anomalous:
print >> log_out, "Changing anomalous flag based on user's input"
i_obs = i_obs.customized_copy(anomalous_flag=anomalous)
if hklin_merged is not None:
f = iotbx.file_reader.any_file(hklin)
array_merged = f.file_server.get_xray_data(file_name=None,
labels=None,
ignore_all_zeros=True,
parameter_scope="",
prefer_anomalous=False,
prefer_amplitudes=False)
print >> log_out, "Merged intensity read from", hklin_merged
array_merged.show_summary(log_out, prefix=" ")
else:
array_merged = i_obs.merge_equivalents(
use_internal_variance=False).array()
print >> log_out, "Merged intensity calculated"
print >> log_out, ""
bad_data = array_merged.select(
array_merged.data() <
-3 * array_merged.sigmas()) # FIXME What if already omitted..
i_obs = i_obs.delete_indices(other=bad_data)
array_merged = array_merged.select(array_merged.sigmas() > 0)
if anomalous is not None and not anomalous and array_merged.anomalous_flag(
):
print >> log_out, "Converting to non-anomalous data..\n"
array_merged = array_merged.average_bijvoet_mates()
return make_aniso_stats_table(i_obs, array_merged, cone_angle, n_bins,
do_fit, log_out)
def read_xac_files(xac_files, d_min=None, d_max=None, min_ios=None):
arrays = collections.OrderedDict()
for f in xac_files:
xac = XDS_ASCII(f, i_only=True)
xac.remove_rejected()
a = xac.i_obs().resolution_filter(d_min=d_min, d_max=d_max)
a = a.as_non_anomalous_array().merge_equivalents(use_internal_variance=False).array()
if min_ios is not None: a = a.select(a.data()/a.sigmas()>=min_ios)
arrays[f] = a
return arrays
def read_xac_files(xac_files, d_min=None, d_max=None, min_ios=None):
arrays = collections.OrderedDict()
for f in xac_files:
xac = XDS_ASCII(f, i_only=True)
xac.remove_rejected()
a = xac.i_obs().resolution_filter(d_min=d_min, d_max=d_max)
a = a.as_non_anomalous_array().merge_equivalents(
use_internal_variance=False).array()
if min_ios is not None: a = a.select(a.data() / a.sigmas() >= min_ios)
arrays[f] = a
return arrays
def __init__(self, xac_files, d_min=3, min_ios=3, nproc=1, max_delta=3, log_out=null_out()):
self.xac_files = xac_files
self.log_out = log_out
self.nproc = nproc
self.arrays = []
self.max_delta = max_delta
self.best_operators = None
print >>self.log_out, "Reading"
for i, f in enumerate(self.xac_files):
print >>self.log_out, "%4d %s" % (i, f)
xac = XDS_ASCII(f, i_only=True)
xac.remove_rejected()
a = xac.i_obs().resolution_filter(d_min=d_min)
if min_ios is not None: a = a.select(a.data()/a.sigmas()>=min_ios)
a = a.as_non_anomalous_array().merge_equivalents(use_internal_variance=False).array()
self.arrays.append(a)
print >>self.log_out, ""
print "altered:", idxes
ksb.assign_operators([debug_op, sgtbx.change_of_basis_op("h,k,l")])
print "right?:", [
i for i, x in enumerate(ksb.best_operators) if not x.is_identity_op()
]
#ksb.debug_write_mtz()
#ksb.modify_xds_ascii_files()
quit()
arrays = []
for f in xac_files:
print "Reading", f
xac = XDS_ASCII(f, i_only=True)
xac.remove_rejected()
a = xac.i_obs().resolution_filter(d_min=3)
a = a.merge_equivalents(use_internal_variance=False).array()
arrays.append(a)
symm = arrays[0].crystal_symmetry()
cosets = reindex.reindexing_operators(symm, symm)
reidx_ops = cosets.combined_cb_ops()
reidx_ops.sort(key=lambda x: not x.is_identity_op())
print " Possible reindex operators:", map(lambda x: str(x.as_hkl()),
reidx_ops)
determined = set([
0,
])
old_ops = map(lambda x: 0, xrange(len(arrays)))
def run(lstin, params):
xac_files = read_path_list(lstin)
common0 = len(os.path.commonprefix(xac_files))
arrays = []
for f in xac_files:
xac = XDS_ASCII(f, i_only=True)
xac.remove_rejected()
a = xac.i_obs().resolution_filter(d_min=params.d_min, d_max=params.d_max)
a = a.merge_equivalents(use_internal_variance=False).array()
a = a.select(a.data() / a.sigmas() >= params.min_ios)
arrays.append(a)
# Prep
args = []
for i in xrange(len(arrays) - 1):
for j in xrange(i + 1, len(arrays)):
args.append((i, j))
# Calc all CC
worker = lambda x: calc_cc(arrays[x[0]], arrays[x[1]])
results = easy_mp.pool_map(fixed_func=worker, args=args, processes=params.nproc)
# Make matrix
mat = numpy.zeros(shape=(len(arrays), len(arrays)))
for (i, j), (cc, nref) in zip(args, results):
print j, i, cc
mat[j, i] = cc
open("%s.names" % params.prefix, "w").write("\n".join(map(lambda x: os.path.dirname(x[common0:]), xac_files)))
open("%s.matrix" % params.prefix, "w").write(" ".join(map(lambda x: "%.4f" % x, mat.flatten())))
ofs = open("%s.dat" % params.prefix, "w")
ofs.write("i j cc nref\n")
for (i, j), (cc, nref) in zip(args, results):
ofs.write("%4d %4d %.4f %4d\n" % (i, j, cc, nref))
open("%s_ana.R" % params.prefix, "w").write(
"""\
treeToList2 <- function(htree)
{ # stolen from $CCP4/share/blend/R/blend0.R
groups <- list()
itree <- dim(htree$merge)[1]
for (i in 1:itree)
{
il <- htree$merge[i,1]
ir <- htree$merge[i,2]
if (il < 0) lab1 <- htree$labels[-il]
if (ir < 0) lab2 <- htree$labels[-ir]
if (il > 0) lab1 <- groups[[il]]
if (ir > 0) lab2 <- groups[[ir]]
lab <- c(lab1,lab2)
lab <- as.integer(lab)
groups <- c(groups,list(lab))
}
return(groups)
}
cc<-scan("%s.matrix")
md<-matrix(1-cc, ncol=%d, byrow=TRUE)
labs<-read.table("%s.names")
filenames<-read.table("%s")$V1
rownames(md)<-labs$V1
hc <- hclust(as.dist(md),method="ward")
pdf("tree.pdf")
plot(hc)
dev.off()
hc$labels <- 1:nrow(md)
groups <- treeToList2(hc)
cat("ClNumber Nds Clheight\\n",file="./CLUSTERS.txt")
for (i in 1:length(groups))
{
sorted_groups <- sort(groups[[i]])
linea <- paste(sprintf(" %%03d %%3d %%7.3f\\n",
i,length(groups[[i]]),hc$height[i]),sep="")
cat(linea, file="./CLUSTERS.txt", append=TRUE)
write.table(filenames[sorted_groups], sprintf("cluster%%.3d.lst",i), quote=FALSE, row.names=FALSE, col.names=FALSE)
}
q(save="yes")
"""
% (params.prefix, len(arrays), params.prefix, lstin)
)
print "R --vanilla < %s_ana.R" % params.prefix
ksb.arrays[i] = ksb.arrays[i].customized_copy(indices=debug_op.apply(ksb.arrays[i].indices()))
print "altered:", idxes
ksb.assign_operators([debug_op, sgtbx.change_of_basis_op("h,k,l")])
print "right?:", [i for i, x in enumerate(ksb.best_operators) if not x.is_identity_op()]
#ksb.debug_write_mtz()
#ksb.modify_xds_ascii_files()
quit()
arrays = []
for f in xac_files:
print "Reading", f
xac = XDS_ASCII(f, i_only=True)
xac.remove_rejected()
a = xac.i_obs().resolution_filter(d_min=3)
a = a.merge_equivalents(use_internal_variance=False).array()
arrays.append(a)
symm = arrays[0].crystal_symmetry()
cosets = reindex.reindexing_operators(symm, symm)
reidx_ops = cosets.combined_cb_ops()
reidx_ops.sort(key=lambda x: not x.is_identity_op())
print " Possible reindex operators:", map(lambda x: str(x.as_hkl()), reidx_ops)
determined = set([0,])
old_ops = map(lambda x:0, xrange(len(arrays)))
for ncycle in xrange(100): # max cycle
new_ops = map(lambda x:0, xrange(len(arrays)))
