def run(params):
if os.path.isdir(params.workdir) and os.listdir(params.workdir):
print "Directory already exists and not empty:", params.workdir
return
if params.reference_file is not None and params.program != "xscale":
print "WARNING - reference file is not used unless program=xscale."
if not os.path.isdir(params.workdir):
os.makedirs(params.workdir)
if params.batch.engine == "sge":
batchjobs = batchjob.SGE(pe_name=params.batch.sge_pe_name)
elif params.batch.engine == "sh":
batchjobs = batchjob.ExecLocal(max_parallel=params.batch.sh_max_jobs)
else:
raise "Unknown batch engine: %s" % params.batch.engine
out = multi_out()
out.register("log", open(os.path.join(params.workdir, "multi_merge.log"), "w"), atexit_send_to=None)
out.register("stdout", sys.stdout)
print >>out, "Paramters:"
libtbx.phil.parse(master_params_str).format(params).show(out=out, prefix=" ")
print >>out, ""
# XXX Not works when clustering is used..
html_report = multi_merging.html_report.HtmlReportMulti(os.path.abspath(params.workdir))
try: html_report.add_params(params, master_params_str)
except: print >>out, traceback.format_exc()
xds_ascii_files = map(lambda x: x[:(x.index("#") if "#" in x else None)].strip(), open(params.lstin))
xds_ascii_files = filter(lambda x: x!="" and os.path.isfile(x), xds_ascii_files)
xds_ascii_files = map(lambda x: os.path.abspath(x), xds_ascii_files)
cells = collections.OrderedDict()
laues = {} # for check
for xac in xds_ascii_files:
try:
symm = XDS_ASCII(xac, read_data=False).symm
except:
try:
symm = any_reflection_file(xac).as_miller_arrays()[0].crystal_symmetry()
except:
print >>out, "Error in reading %s" % xac
print >>out, traceback.format_exc()
return
cells[xac] = symm.unit_cell().parameters()
laue = symm.space_group().build_derived_reflection_intensity_group(False).info()
laues.setdefault(str(laue),{}).setdefault(symm.space_group_info().type().number(), []).append(xac)
if len(laues) > 1:
print >>out, "ERROR! more than one space group included."
for laue in laues:
print "Laue symmetry", laue
for sg in laues[laue]:
print >>out, " SPACE_GROUP_NUMBER= %d (%d data)" % (sg, len(laues[laue][sg]))
for f in laues[laue][sg]: print >>out, " %s" % f
print >>out, ""
return
space_group = None
if params.space_group is not None:
space_group = sgtbx.space_group_info(params.space_group).group()
laue_given = str(space_group.build_derived_reflection_intensity_group(False).info())
if laue_given != laues.keys()[0]:
print >>out, "ERROR! user-specified space group (space_group=%s) is not compatible with input files (%s)" % (params.space_group, laues.keys()[0])
return
else:
tmp = sgtbx.space_group_info(laues.values()[0].keys()[0]).group().build_derived_reflection_intensity_group(True)
print >>out, "Space group for merging:", tmp.info()
try: html_report.add_cells_and_files(cells, laues.keys()[0])
except: print >>out, traceback.format_exc()
data_for_merge = []
if params.clustering == "blend":
if params.blend.use_old_result is None:
blend_wdir = os.path.join(params.workdir, "blend")
os.mkdir(blend_wdir)
blend.run_blend0R(blend_wdir, xds_ascii_files)
print >>out, "\nRunning BLEND with analysis mode"
else:
blend_wdir = params.blend.use_old_result
print >>out, "\nUsing precalculated BLEND result in %s" % params.blend.use_old_result
blend_clusters = blend.BlendClusters(workdir=blend_wdir, d_min=params.d_min)
summary_out = os.path.join(blend_wdir, "blend_cluster_summary.dat")
clusters = blend_clusters.show_cluster_summary(out=open(summary_out, "w"))
print >>out, "Clusters found by BLEND were summarized in %s" % summary_out
if params.blend.min_cmpl is not None:
clusters = filter(lambda x: x[3] >= params.blend.min_cmpl, clusters)
if params.blend.min_acmpl is not None:
clusters = filter(lambda x: x[5] >= params.blend.min_acmpl, clusters)
if params.blend.min_redun is not None:
clusters = filter(lambda x: x[4] >= params.blend.min_redun, clusters)
if params.blend.min_aredun is not None:
clusters = filter(lambda x: x[6] >= params.blend.min_aredun, clusters)
if params.blend.max_LCV is not None:
clusters = filter(lambda x: x[7] <= params.blend.max_LCV, clusters)
if params.blend.max_aLCV is not None:
clusters = filter(lambda x: x[8] <= params.blend.max_aLCV, clusters)
if params.max_clusters is not None and len(clusters) > params.max_clusters:
print >>out, "Only first %d (/%d) clusters will be merged (as specified by max_clusters=)" % (params.max_clusters, len(clusters))
clusters = clusters[:params.max_clusters]
print >>out, "With specified conditions, following %d clusters will be merged:" % len(clusters)
for clno, IDs, clh, cmpl, redun, acmpl, aredun, LCV, aLCV in clusters: # process largest first
print >>out, " Cluster_%.4d NumDS= %4d CLh= %5.1f Cmpl= %6.2f Redun= %4.1f ACmpl=%6.2f ARedun=%4.1f LCV= %5.1f aLCV=%5.1f" % (clno, len(IDs), clh, cmpl, redun, acmpl, aredun, LCV, aLCV)
data_for_merge.append((os.path.join(params.workdir, "cluster_%.4d"%clno),
map(lambda x: blend_clusters.files[x-1], IDs),
LCV, aLCV,clh))
print >>out
try: html_report.add_clutering_result(clusters, "blend")
except: print >>out, traceback.format_exc()
elif params.clustering == "cc":
ccc_wdir = os.path.join(params.workdir, "cc_clustering")
os.mkdir(ccc_wdir)
cc_clusters = cc_clustering.CCClustering(ccc_wdir, xds_ascii_files,
d_min=params.cc_clustering.d_min if params.cc_clustering.d_min is not None else params.d_min,
min_ios=params.cc_clustering.min_ios)
print >>out, "\nRunning CC-based clustering"
cc_clusters.do_clustering(nproc=params.cc_clustering.nproc,
b_scale=params.cc_clustering.b_scale,
use_normalized=params.cc_clustering.use_normalized,
html_maker=html_report)
summary_out = os.path.join(ccc_wdir, "cc_cluster_summary.dat")
clusters = cc_clusters.show_cluster_summary(d_min=params.d_min, out=open(summary_out, "w"))
print >>out, "Clusters were summarized in %s" % summary_out
if params.cc_clustering.min_cmpl is not None:
clusters = filter(lambda x: x[3] >= params.cc_clustering.min_cmpl, clusters)
if params.cc_clustering.min_acmpl is not None:
clusters = filter(lambda x: x[5] >= params.cc_clustering.min_acmpl, clusters)
if params.cc_clustering.min_redun is not None:
clusters = filter(lambda x: x[4] >= params.cc_clustering.min_redun, clusters)
if params.cc_clustering.min_aredun is not None:
clusters = filter(lambda x: x[6] >= params.cc_clustering.min_aredun, clusters)
if params.cc_clustering.max_clheight is not None:
clusters = filter(lambda x: x[2] <= params.cc_clustering.max_clheight, clusters)
if params.max_clusters is not None and len(clusters) > params.max_clusters:
print >>out, "Only first %d (/%d) clusters will be merged (as specified by max_clusters=)" % (params.max_clusters, len(clusters))
clusters = clusters[:params.max_clusters]
print >>out, "With specified conditions, following %d clusters will be merged:" % len(clusters)
for clno, IDs, clh, cmpl, redun, acmpl, aredun in clusters: # process largest first
print >>out, " Cluster_%.4d NumDS= %4d CLh= %5.1f Cmpl= %6.2f Redun= %4.1f ACmpl=%6.2f ARedun=%4.1f" % (clno, len(IDs), clh, cmpl, redun, acmpl, aredun)
data_for_merge.append((os.path.join(params.workdir, "cluster_%.4d"%clno),
map(lambda x: xds_ascii_files[x-1], IDs),
float("nan"),float("nan"),clh))
print >>out
try: html_report.add_clutering_result(clusters, "cc_clustering")
except: print >>out, traceback.format_exc()
else:
data_for_merge.append((os.path.join(params.workdir, "all_data"),
xds_ascii_files, float("nan"), float("nan"), 0))
ofs_summary = open(os.path.join(params.workdir, "cluster_summary.dat"), "w")
ofs_summary.write("# d_min= %.3f A\n" % (params.d_min if params.d_min is not None else float("nan")))
ofs_summary.write("# LCV and aLCV are values of all data\n")
ofs_summary.write(" cluster ClH LCV aLCV run ds.all ds.used Cmpl Redun I/sigI Rmeas CC1/2 Cmpl.ou Red.ou I/sig.ou Rmeas.ou CC1/2.ou Cmpl.in Red.in I/sig.in Rmeas.in CC1/2.in SigAno.in CCano.in WilsonB Aniso \n")
out.flush()
def write_ofs_summary(workdir, cycle, clh, LCV, aLCV, xds_files, num_files, stats):
tmps = "%12s %5.2f %4.1f %4.1f %3d %6d %7d %5.1f %5.1f %6.2f %5.1f %5.1f %7.1f %6.1f % 8.2f % 8.1f %8.1f %7.1f %6.1f % 8.2f % 8.1f %8.1f %9.1f %8.1f %7.2f %7.1e\n"
ofs_summary.write(tmps % (os.path.relpath(workdir, params.workdir), clh, LCV, aLCV, cycle,
len(xds_files), num_files,
stats["cmpl"][0],
stats["redundancy"][0],
stats["i_over_sigma"][0],
stats["r_meas"][0],
stats["cc_half"][0],
stats["cmpl"][2],
stats["redundancy"][2],
stats["i_over_sigma"][2],
stats["r_meas"][2],
stats["cc_half"][2],
stats["cmpl"][1],
stats["redundancy"][1],
stats["i_over_sigma"][1],
stats["r_meas"][1],
stats["cc_half"][1],
stats["sig_ano"][1],
stats["cc_ano"][1],
stats["xtriage_log"].wilson_b,
stats["xtriage_log"].anisotropy,
))
ofs_summary.flush()
# write_ofs_summary()
if "merging" in params.batch.par_run:
params.nproc = params.batch.nproc_each
jobs = []
for workdir, xds_files, LCV, aLCV, clh in data_for_merge:
if not os.path.exists(workdir): os.makedirs(workdir)
shname = "merge_%s.sh" % os.path.relpath(workdir, params.workdir)
pickle.dump((params, os.path.abspath(workdir), xds_files, cells, space_group, batchjobs), open(os.path.join(workdir, "args.pkl"), "w"), -1)
job = batchjob.Job(workdir, shname, nproc=params.batch.nproc_each)
job.write_script("""\
cd "%s" || exit 1
"%s" -c '\
import pickle; \
from yamtbx.dataproc.auto.command_line.multi_merge import merge_datasets; \
args = pickle.load(open("args.pkl")); \
ofs = open("result.pkl","w"); \
ret = merge_datasets(*args); \
pickle.dump(ret, ofs); \
'
""" % (os.path.abspath(workdir), sys.executable))
batchjobs.submit(job)
jobs.append(job)
batchjobs.wait_all(jobs)
for workdir, xds_files, LCV, aLCV, clh in data_for_merge:
try:
results = pickle.load(open(os.path.join(workdir, "result.pkl")))
except:
print >>out, "Error in unpickling result in %s" % workdir
print >>out, traceback.format_exc()
results = []
if len(results) == 0:
ofs_summary.write("#%s failed\n" % os.path.relpath(workdir, params.workdir))
lcv, alcv = float("nan"), float("nan")
for cycle, wd, num_files, stats in results:
lcv, alcv = stats.get("lcv", LCV), stats.get("alcv", aLCV)
write_ofs_summary(workdir, cycle, clh, lcv, alcv, xds_files, num_files, stats)
# Last lcv & alcv
try: html_report.add_merge_result(workdir, clh, lcv, alcv, xds_files, results[-1][2], results[-1][3])
except: print >>out, traceback.format_exc()
else:
for workdir, xds_files, LCV, aLCV, clh in data_for_merge:
print >>out, "Merging %s..." % os.path.relpath(workdir, params.workdir)
out.flush()
results = merge_datasets(params, workdir, xds_files, cells, space_group, batchjobs)
if len(results) == 0:
ofs_summary.write("#%s failed\n" % os.path.relpath(workdir, params.workdir))
for cycle, wd, num_files, stats in results:
lcv, alcv = stats.get("lcv", LCV), stats.get("alcv", aLCV)
write_ofs_summary(workdir, cycle, clh, lcv, alcv, xds_files, num_files, stats)
try: html_report.add_merge_result(workdir, clh, lcv, alcv, xds_files, results[-1][2], results[-1][3])
except: print >>out, traceback.format_exc()
try: html_report.write_html()
except: print >>out, traceback.format_exc()
print "firefox %s" % os.path.join(html_report.root, "report.html")
return
def run(params):
if os.path.isdir(params.workdir) and os.listdir(params.workdir):
print "Directory already exists and not empty:", params.workdir
return
# Check parameters
if params.program == "xscale":
if (params.xscale.frames_per_batch,
params.xscale.degrees_per_batch).count(None) == 0:
print "ERROR! You can't specify both of xscale.frames_per_batch and xscale.degrees_per_batch"
return
if params.reference_file is not None and params.program != "xscale":
print "WARNING - reference file is not used unless program=xscale."
if not os.path.isdir(params.workdir):
os.makedirs(params.workdir)
if params.batch.engine == "sge":
batchjobs = batchjob.SGE(pe_name=params.batch.sge_pe_name)
elif params.batch.engine == "sh":
batchjobs = batchjob.ExecLocal(max_parallel=params.batch.sh_max_jobs)
else:
raise "Unknown batch engine: %s" % params.batch.engine
out = multi_out()
out.register("log",
open(os.path.join(params.workdir, "multi_merge.log"), "w"),
atexit_send_to=None)
out.register("stdout", sys.stdout)
out.write("kamo.multi_merge started at %s\n\n" %
time.strftime("%Y-%m-%d %H:%M:%S"))
time_started = time.time()
print >> out, "Paramters:"
libtbx.phil.parse(master_params_str).format(params).show(out=out,
prefix=" ")
print >> out, ""
# XXX Not works when clustering is used..
html_report = multi_merging.html_report.HtmlReportMulti(
os.path.abspath(params.workdir))
try:
html_report.add_params(params, master_params_str)
except:
print >> out, traceback.format_exc()
xds_ascii_files = util.read_path_list(params.lstin,
only_exists=True,
as_abspath=True,
err_out=out)
if not xds_ascii_files:
print >> out, "ERROR! Cannot find (existing) files in %s." % params.lstin
return
if len(xds_ascii_files) < 2:
print >> out, "ERROR! Only one file in %s." % params.lstin
print >> out, " Give at least two files for merging."
return
cells = collections.OrderedDict()
laues = {} # for check
for xac in xds_ascii_files:
try:
symm = XDS_ASCII(xac, read_data=False).symm
except:
print >> out, "Error in reading %s" % xac
print >> out, traceback.format_exc()
return
cells[xac] = symm.unit_cell().parameters()
laue = symm.space_group().build_derived_reflection_intensity_group(
False).info()
laues.setdefault(str(laue), {}).setdefault(
symm.space_group_info().type().number(), []).append(xac)
if len(laues) > 1:
print >> out, "ERROR! more than one space group included."
for laue in laues:
print "Laue symmetry", laue
for sg in laues[laue]:
print >> out, " SPACE_GROUP_NUMBER= %d (%d data)" % (
sg, len(laues[laue][sg]))
for f in laues[laue][sg]:
print >> out, " %s" % f
print >> out, ""
return
space_group = None
if params.space_group is not None:
space_group = sgtbx.space_group_info(params.space_group).group()
laue_given = str(
space_group.build_derived_reflection_intensity_group(False).info())
if laue_given != laues.keys()[0]:
print >> out, "ERROR! user-specified space group (space_group=%s) is not compatible with input files (%s)" % (
params.space_group, laues.keys()[0])
return
sg_refset = space_group.info().as_reference_setting().group()
if space_group != sg_refset:
print >> out, "Sorry! currently space group in non-reference setting is not supported."
print >> out, "(You requested %s, which is different from reference setting: %s)" % (
space_group.info(), sg_refset.info())
return
else:
tmp = sgtbx.space_group_info(
laues.values()[0].keys()
[0]).group().build_derived_reflection_intensity_group(True)
print >> out, "Space group for merging:", tmp.info()
test_flag_will_be_transferred = False
if params.reference.data is not None:
params.reference.data = os.path.abspath(params.reference.data)
print >> out, "Reading reference data file: %s" % params.reference.data
tmp = iotbx.file_reader.any_file(params.reference.data,
force_type="hkl",
raise_sorry_if_errors=True)
if params.reference.copy_test_flag:
from yamtbx.dataproc.command_line import copy_free_R_flag
if None in copy_free_R_flag.get_flag_array(
tmp.file_server.miller_arrays, log_out=out):
print >> out, " Warning: no test flag found in reference file (%s)" % params.reference.data
else:
test_flag_will_be_transferred = True
print >> out, " test flag will be transferred"
if space_group is not None:
if space_group != tmp.file_server.miller_arrays[0].space_group():
print >> out, " ERROR! space_group=(%s) and that of reference.data (%s) do not match." % (
space_group.info(),
tmp.file_server.miller_arrays[0].space_group_info())
return
else:
space_group = tmp.file_server.miller_arrays[0].space_group()
print >> out, " space group for merging: %s" % space_group.info()
if params.add_test_flag:
if test_flag_will_be_transferred:
print >> out, "Warning: add_test_flag=True was set, but the flag will be transferred from the reference file given."
else:
from cctbx import r_free_utils
med_cell = numpy.median(cells.values(), axis=0)
d_min = max(
params.d_min - 0.2, 1.0
) if params.d_min is not None else 1.5 # to prevent infinite set
sg = space_group
if not sg:
sg = sgtbx.space_group_info(
laues.values()[0].keys()
[0]).group().build_derived_reflection_intensity_group(True)
tmp = miller.build_set(crystal.symmetry(tuple(med_cell),
space_group=sg),
False,
d_min=d_min,
d_max=None)
print >> out, "Generating test set using the reference symmetry:"
crystal.symmetry.show_summary(tmp, out, " ")
tmp = tmp.generate_r_free_flags(fraction=0.05,
max_free=None,
lattice_symmetry_max_delta=5.0,
use_lattice_symmetry=True,
n_shells=20)
tmp.show_r_free_flags_info(out=out, prefix=" ")
tmp = tmp.customized_copy(
data=r_free_utils.export_r_free_flags_for_ccp4(
flags=tmp.data(), test_flag_value=True))
mtz_object = tmp.as_mtz_dataset(
column_root_label="FreeR_flag").mtz_object()
test_flag_mtz = os.path.abspath(
os.path.join(params.workdir, "test_flag.mtz"))
mtz_object.write(file_name=test_flag_mtz)
# Override the parameters
params.reference.copy_test_flag = True
params.reference.data = test_flag_mtz
try:
html_report.add_cells_and_files(cells, laues.keys()[0])
except:
print >> out, traceback.format_exc()
data_for_merge = []
if params.clustering == "blend":
if params.blend.use_old_result is None:
blend_wdir = os.path.join(params.workdir, "blend")
os.mkdir(blend_wdir)
blend.run_blend0R(blend_wdir, xds_ascii_files)
print >> out, "\nRunning BLEND with analysis mode"
else:
blend_wdir = params.blend.use_old_result
print >> out, "\nUsing precalculated BLEND result in %s" % params.blend.use_old_result
blend_clusters = blend.BlendClusters(workdir=blend_wdir,
d_min=params.d_min)
summary_out = os.path.join(blend_wdir, "blend_cluster_summary.dat")
clusters = blend_clusters.show_cluster_summary(
out=open(summary_out, "w"))
print >> out, "Clusters found by BLEND were summarized in %s" % summary_out
if params.blend.min_cmpl is not None:
clusters = filter(lambda x: x[3] >= params.blend.min_cmpl,
clusters)
if params.blend.min_acmpl is not None:
clusters = filter(lambda x: x[5] >= params.blend.min_acmpl,
clusters)
if params.blend.min_redun is not None:
clusters = filter(lambda x: x[4] >= params.blend.min_redun,
clusters)
if params.blend.min_aredun is not None:
clusters = filter(lambda x: x[6] >= params.blend.min_aredun,
clusters)
if params.blend.max_LCV is not None:
clusters = filter(lambda x: x[7] <= params.blend.max_LCV, clusters)
if params.blend.max_aLCV is not None:
clusters = filter(lambda x: x[8] <= params.blend.max_aLCV,
clusters)
if params.max_clusters is not None and len(
clusters) > params.max_clusters:
print >> out, "Only first %d (/%d) clusters will be merged (as specified by max_clusters=)" % (
params.max_clusters, len(clusters))
clusters = clusters[:params.max_clusters]
if clusters:
print >> out, "With specified conditions, following %d clusters will be merged:" % len(
clusters)
else:
print >> out, "\nERROR: No clusters satisfied the specified conditions for merging!"
print >> out, "Please change criteria of completeness or redundancy"
print >> out, "Here is the table of completeness and redundancy for each cluster:\n"
print >> out, open(summary_out).read()
for clno, IDs, clh, cmpl, redun, acmpl, aredun, LCV, aLCV in clusters: # process largest first
print >> out, " Cluster_%.4d NumDS= %4d CLh= %5.1f Cmpl= %6.2f Redun= %4.1f ACmpl=%6.2f ARedun=%4.1f LCV= %5.1f aLCV=%5.1f" % (
clno, len(IDs), clh, cmpl, redun, acmpl, aredun, LCV, aLCV)
data_for_merge.append((os.path.join(params.workdir,
"cluster_%.4d" % clno),
map(lambda x: blend_clusters.files[x - 1],
IDs), LCV, aLCV, clh))
print >> out
try:
html_report.add_clutering_result(clusters, "blend")
except:
print >> out, traceback.format_exc()
elif params.clustering == "cc":
ccc_wdir = os.path.join(params.workdir, "cc_clustering")
os.mkdir(ccc_wdir)
cc_clusters = cc_clustering.CCClustering(
ccc_wdir,
xds_ascii_files,
d_min=params.cc_clustering.d_min
if params.cc_clustering.d_min is not None else params.d_min,
min_ios=params.cc_clustering.min_ios)
print >> out, "\nRunning CC-based clustering"
cc_clusters.do_clustering(
nproc=params.cc_clustering.nproc,
b_scale=params.cc_clustering.b_scale,
use_normalized=params.cc_clustering.use_normalized,
cluster_method=params.cc_clustering.method,
distance_eqn=params.cc_clustering.cc_to_distance,
min_common_refs=params.cc_clustering.min_common_refs,
html_maker=html_report)
summary_out = os.path.join(ccc_wdir, "cc_cluster_summary.dat")
clusters = cc_clusters.show_cluster_summary(d_min=params.d_min,
out=open(summary_out, "w"))
print >> out, "Clusters were summarized in %s" % summary_out
if params.cc_clustering.min_cmpl is not None:
clusters = filter(lambda x: x[3] >= params.cc_clustering.min_cmpl,
clusters)
if params.cc_clustering.min_acmpl is not None:
clusters = filter(lambda x: x[5] >= params.cc_clustering.min_acmpl,
clusters)
if params.cc_clustering.min_redun is not None:
clusters = filter(lambda x: x[4] >= params.cc_clustering.min_redun,
clusters)
if params.cc_clustering.min_aredun is not None:
clusters = filter(
lambda x: x[6] >= params.cc_clustering.min_aredun, clusters)
if params.cc_clustering.max_clheight is not None:
clusters = filter(
lambda x: x[2] <= params.cc_clustering.max_clheight, clusters)
if params.max_clusters is not None and len(
clusters) > params.max_clusters:
print >> out, "Only first %d (/%d) clusters will be merged (as specified by max_clusters=)" % (
params.max_clusters, len(clusters))
clusters = clusters[:params.max_clusters]
if clusters:
print >> out, "With specified conditions, following %d clusters will be merged:" % len(
clusters)
else:
print >> out, "\nERROR: No clusters satisfied the specified conditions for merging!"
print >> out, "Please change criteria of completeness or redundancy"
print >> out, "Here is the table of completeness and redundancy for each cluster:\n"
print >> out, open(summary_out).read()
for clno, IDs, clh, cmpl, redun, acmpl, aredun, ccmean, ccmin in clusters: # process largest first
print >> out, " Cluster_%.4d NumDS= %4d CLh= %5.1f Cmpl= %6.2f Redun= %4.1f ACmpl=%6.2f ARedun=%4.1f CCmean=% .4f CCmin=% .4f" % (
clno, len(IDs), clh, cmpl, redun, acmpl, aredun, ccmean, ccmin)
data_for_merge.append((os.path.join(params.workdir,
"cluster_%.4d" % clno),
map(lambda x: xds_ascii_files[x - 1],
IDs), float("nan"), float("nan"), clh))
print >> out
try:
html_report.add_clutering_result(clusters, "cc_clustering")
except:
print >> out, traceback.format_exc()
else:
data_for_merge.append((os.path.join(params.workdir,
"all_data"), xds_ascii_files,
float("nan"), float("nan"), 0))
ofs_summary = open(os.path.join(params.workdir, "cluster_summary.dat"),
"w")
ofs_summary.write(
"# d_min= %.3f A\n" %
(params.d_min if params.d_min is not None else float("nan")))
ofs_summary.write("# LCV and aLCV are values of all data\n")
ofs_summary.write(
" cluster ClH LCV aLCV run ds.all ds.used Cmpl Redun I/sigI Rmeas CC1/2 Cmpl.ou Red.ou I/sig.ou Rmeas.ou CC1/2.ou Cmpl.in Red.in I/sig.in Rmeas.in CC1/2.in SigAno.in CCano.in WilsonB Aniso.bst Aniso.wst dmin.est\n"
)
out.flush()
def write_ofs_summary(workdir, cycle, clh, LCV, aLCV, xds_files, num_files,
stats):
tmps = "%12s %6.2f %4.1f %4.1f %3d %6d %7d %5.1f %5.1f %6.2f %5.1f %5.1f %7.1f %6.1f % 8.2f % 8.1f %8.1f %7.1f %6.1f % 8.2f % 8.1f %8.1f %9.1f %8.1f %7.2f %9.2f %9.2f %.2f\n"
ofs_summary.write(tmps % (
os.path.relpath(workdir, params.workdir),
clh,
LCV,
aLCV,
cycle,
len(xds_files),
num_files,
stats["cmpl"][0],
stats["redundancy"][0],
stats["i_over_sigma"][0],
stats["r_meas"][0],
stats["cc_half"][0],
stats["cmpl"][2],
stats["redundancy"][2],
stats["i_over_sigma"][2],
stats["r_meas"][2],
stats["cc_half"][2],
stats["cmpl"][1],
stats["redundancy"][1],
stats["i_over_sigma"][1],
stats["r_meas"][1],
stats["cc_half"][1],
stats["sig_ano"][1],
stats["cc_ano"][1],
stats["xtriage_log"].wilson_b,
#stats["xtriage_log"].anisotropy,
stats["aniso"]["d_min_best"],
stats["aniso"]["d_min_worst"],
stats["dmin_est"],
))
ofs_summary.flush()
# write_ofs_summary()
if "merging" in params.batch.par_run:
params.nproc = params.batch.nproc_each
jobs = []
for workdir, xds_files, LCV, aLCV, clh in data_for_merge:
if not os.path.exists(workdir): os.makedirs(workdir)
shname = "merge_%s.sh" % os.path.relpath(workdir, params.workdir)
pickle.dump((params, os.path.abspath(workdir), xds_files, cells,
space_group),
open(os.path.join(workdir, "args.pkl"), "w"), -1)
job = batchjob.Job(workdir, shname, nproc=params.batch.nproc_each)
job.write_script("""\
cd "%s" || exit 1
"%s" -c '\
import pickle; \
from yamtbx.dataproc.auto.command_line.multi_merge import merge_datasets; \
args = pickle.load(open("args.pkl")); \
ret = merge_datasets(*args); \
pickle.dump(ret, open("result.pkl","w")); \
'
""" % (os.path.abspath(workdir), sys.executable))
batchjobs.submit(job)
jobs.append(job)
batchjobs.wait_all(jobs)
for workdir, xds_files, LCV, aLCV, clh in data_for_merge:
try:
results = pickle.load(open(os.path.join(workdir,
"result.pkl")))
except:
print >> out, "Error in unpickling result in %s" % workdir
print >> out, traceback.format_exc()
results = []
if len(results) == 0:
ofs_summary.write("#%s failed\n" %
os.path.relpath(workdir, params.workdir))
lcv, alcv = float("nan"), float("nan")
for cycle, wd, num_files, stats in results:
lcv, alcv = stats.get("lcv", LCV), stats.get("alcv", aLCV)
write_ofs_summary(workdir, cycle, clh, lcv, alcv, xds_files,
num_files, stats)
# Last lcv & alcv
try:
html_report.add_merge_result(workdir, clh, lcv, alcv,
xds_files, results[-1][2],
results[-1][3])
except:
print >> out, traceback.format_exc()
else:
for workdir, xds_files, LCV, aLCV, clh in data_for_merge:
print >> out, "Merging %s..." % os.path.relpath(
workdir, params.workdir)
out.flush()
results = merge_datasets(params, workdir, xds_files, cells,
space_group)
if len(results) == 0:
ofs_summary.write("#%s failed\n" %
os.path.relpath(workdir, params.workdir))
for cycle, wd, num_files, stats in results:
lcv, alcv = stats.get("lcv", LCV), stats.get("alcv", aLCV)
write_ofs_summary(workdir, cycle, clh, lcv, alcv, xds_files,
num_files, stats)
try:
html_report.add_merge_result(workdir, clh, lcv, alcv,
xds_files, results[-1][2],
results[-1][3])
except:
print >> out, traceback.format_exc()
try:
html_report.write_html()
except:
print >> out, traceback.format_exc()
print "firefox %s" % os.path.join(html_report.root, "report.html")
out.write("\nNormal exit at %s\n" % time.strftime("%Y-%m-%d %H:%M:%S"))
out.write("Total wall-clock time: %.2f sec.\n" %
(time.time() - time_started))
return
def run(params):
if os.path.isdir(params.workdir) and os.listdir(params.workdir):
print "Directory already exists and not empty:", params.workdir
return
if params.reference_file is not None and params.program != "xscale":
print "WARNING - reference file is not used unless program=xscale."
if not os.path.isdir(params.workdir):
os.makedirs(params.workdir)
if params.batch.engine == "sge":
batchjobs = batchjob.SGE(pe_name=params.batch.sge_pe_name)
elif params.batch.engine == "sh":
batchjobs = batchjob.ExecLocal()
else:
raise "Unknown batch engine: %s" % params.batch.engine
out = multi_out()
out.register("log", open(os.path.join(params.workdir, "multi_merge.log"), "w"), atexit_send_to=None)
out.register("stdout", sys.stdout)
print >>out, "Paramters:"
libtbx.phil.parse(master_params_str).format(params).show(out=out, prefix=" ")
print >>out, ""
# XXX Not works when clustering is used..
html_report = multi_merging.html_report.HtmlReportMulti(os.path.abspath(params.workdir))
try: html_report.add_params(params, master_params_str)
except: print >>out, traceback.format_exc()
xds_ascii_files = map(lambda x: x[:(x.index("#") if "#" in x else None)].strip(), open(params.lstin))
xds_ascii_files = filter(lambda x: x!="" and os.path.isfile(x), xds_ascii_files)
xds_ascii_files = map(lambda x: os.path.abspath(x), xds_ascii_files)
cells = collections.OrderedDict()
laues = {} # for check
for xac in xds_ascii_files:
try:
symm = XDS_ASCII(xac, read_data=False).symm
except:
print >>out, "Error in reading %s" % xac
print >>out, traceback.format_exc()
return
cells[xac] = symm.unit_cell().parameters()
laue = symm.space_group().build_derived_reflection_intensity_group(False).info()
laues.setdefault(str(laue),{}).setdefault(symm.space_group_info().type().number(), []).append(xac)
if len(laues) > 1:
print >>out, "ERROR! more than one space group included."
for laue in laues:
print "Laue symmetry", laue
for sg in laues[laue]:
print >>out, " SPACE_GROUP_NUMBER= %d (%d data)" % (sg, len(laues[laue][sg]))
for f in laues[laue][sg]: print >>out, " %s" % f
print >>out, ""
return
try: html_report.add_cells_and_files(cells, laues.keys()[0])
except: print >>out, traceback.format_exc()
data_for_merge = []
if params.clustering == "blend":
if params.blend.use_old_result is None:
blend_wdir = os.path.join(params.workdir, "blend")
os.mkdir(blend_wdir)
blend.run_blend0R(blend_wdir, xds_ascii_files)
print >>out, "\nRunning BLEND with analysis mode"
else:
blend_wdir = params.blend.use_old_result
print >>out, "\nUsing precalculated BLEND result in %s" % params.blend.use_old_result
blend_clusters = blend.BlendClusters(workdir=blend_wdir, d_min=params.d_min)
summary_out = os.path.join(blend_wdir, "blend_cluster_summary.dat")
clusters = blend_clusters.show_cluster_summary(out=open(summary_out, "w"))
print >>out, "Clusters found by BLEND were summarized in %s" % summary_out
if params.blend.min_cmpl is not None:
clusters = filter(lambda x: x[3] >= params.blend.min_cmpl, clusters)
if params.blend.min_acmpl is not None:
clusters = filter(lambda x: x[5] >= params.blend.min_acmpl, clusters)
if params.blend.min_redun is not None:
clusters = filter(lambda x: x[4] >= params.blend.min_redun, clusters)
if params.blend.min_aredun is not None:
clusters = filter(lambda x: x[6] >= params.blend.min_aredun, clusters)
if params.blend.max_LCV is not None:
clusters = filter(lambda x: x[7] <= params.blend.max_LCV, clusters)
if params.blend.max_aLCV is not None:
clusters = filter(lambda x: x[8] <= params.blend.max_aLCV, clusters)
print >>out, "With specified conditions, following %d clusters will be merged:" % len(clusters)
for clno, IDs, clh, cmpl, redun, acmpl, aredun, LCV, aLCV in clusters: # process largest first
print >>out, " Cluster_%.4d NumDS= %4d CLh= %5.1f Cmpl= %6.2f Redun= %4.1f ACmpl=%6.2f ARedun=%4.1f LCV= %5.1f aLCV=%5.1f" % (clno, len(IDs), clh, cmpl, redun, acmpl, aredun, LCV, aLCV)
data_for_merge.append((os.path.join(params.workdir, "cluster_%.4d"%clno),
map(lambda x: blend_clusters.files[x-1], IDs),
LCV, aLCV,clh))
print >>out
try: html_report.add_clutering_result(clusters, "blend")
except: print >>out, traceback.format_exc()
elif params.clustering == "cc":
ccc_wdir = os.path.join(params.workdir, "cc_clustering")
os.mkdir(ccc_wdir)
cc_clusters = cc_clustering.CCClustering(ccc_wdir, xds_ascii_files,
d_min=params.cc_clustering.d_min,
min_ios=params.cc_clustering.min_ios)
print >>out, "\nRunning CC-based clustering"
cc_clusters.do_clustering(nproc=params.cc_clustering.nproc,
b_scale=params.cc_clustering.b_scale,
use_normalized=params.cc_clustering.use_normalized,
html_maker=html_report)
summary_out = os.path.join(ccc_wdir, "cc_cluster_summary.dat")
clusters = cc_clusters.show_cluster_summary(d_min=params.d_min, out=open(summary_out, "w"))
print >>out, "Clusters were summarized in %s" % summary_out
if params.cc_clustering.min_cmpl is not None:
clusters = filter(lambda x: x[3] >= params.cc_clustering.min_cmpl, clusters)
if params.cc_clustering.min_acmpl is not None:
clusters = filter(lambda x: x[5] >= params.cc_clustering.min_acmpl, clusters)
if params.cc_clustering.min_redun is not None:
clusters = filter(lambda x: x[4] >= params.cc_clustering.min_redun, clusters)
if params.cc_clustering.min_aredun is not None:
clusters = filter(lambda x: x[6] >= params.cc_clustering.min_aredun, clusters)
if params.cc_clustering.max_clheight is not None:
clusters = filter(lambda x: x[2] <= params.cc_clustering.max_clheight, clusters)
print >>out, "With specified conditions, following %d clusters will be merged:" % len(clusters)
for clno, IDs, clh, cmpl, redun, acmpl, aredun in clusters: # process largest first
print >>out, " Cluster_%.4d NumDS= %4d CLh= %5.1f Cmpl= %6.2f Redun= %4.1f ACmpl=%6.2f ARedun=%4.1f" % (clno, len(IDs), clh, cmpl, redun, acmpl, aredun)
data_for_merge.append((os.path.join(params.workdir, "cluster_%.4d"%clno),
map(lambda x: xds_ascii_files[x-1], IDs),
float("nan"),float("nan"),clh))
print >>out
try: html_report.add_clutering_result(clusters, "cc_clustering")
except: print >>out, traceback.format_exc()
else:
data_for_merge.append((os.path.join(params.workdir, "all_data"),
xds_ascii_files, float("nan"), float("nan"), 0))
ofs_summary = open(os.path.join(params.workdir, "cluster_summary.dat"), "w")
ofs_summary.write("# d_min= %.3f A\n" % (params.d_min if params.d_min is not None else float("nan")))
ofs_summary.write("# LCV and aLCV are values of all data\n")
ofs_summary.write(" cluster ClH LCV aLCV run ds.all ds.used Cmpl Redun I/sigI Rmeas CC1/2 Cmpl.ou Red.ou I/sig.ou Rmeas.ou CC1/2.ou Cmpl.in Red.in I/sig.in Rmeas.in CC1/2.in SigAno.in CCano.in WilsonB Aniso \n")
out.flush()
def write_ofs_summary(workdir, cycle, clh, LCV, aLCV, xds_files, num_files, stats):
tmps = "%12s %5.2f %4.1f %4.1f %3d %6d %7d %5.1f %5.1f %6.2f %5.1f %5.1f %7.1f %6.1f % 8.2f % 8.1f %8.1f %7.1f %6.1f % 8.2f % 8.1f %8.1f %9.1f %8.1f %7.2f %7.1e\n"
ofs_summary.write(tmps % (os.path.relpath(workdir, params.workdir), clh, LCV, aLCV, cycle,
len(xds_files), num_files,
stats["cmpl"][0],
stats["redundancy"][0],
stats["i_over_sigma"][0],
stats["r_meas"][0],
stats["cc_half"][0],
stats["cmpl"][2],
stats["redundancy"][2],
stats["i_over_sigma"][2],
stats["r_meas"][2],
stats["cc_half"][2],
stats["cmpl"][1],
stats["redundancy"][1],
stats["i_over_sigma"][1],
stats["r_meas"][1],
stats["cc_half"][1],
stats["sig_ano"][1],
stats["cc_ano"][1],
stats["xtriage_log"].wilson_b,
stats["xtriage_log"].anisotropy,
))
ofs_summary.flush()
# write_ofs_summary()
if "merging" in params.batch.par_run:
params.nproc = params.batch.nproc_each
jobs = []
for workdir, xds_files, LCV, aLCV, clh in data_for_merge:
if not os.path.exists(workdir): os.makedirs(workdir)
shname = "merge_%s.sh" % os.path.relpath(workdir, params.workdir)
pickle.dump((params, os.path.abspath(workdir), xds_files, cells, batchjobs), open(os.path.join(workdir, "args.pkl"), "w"), -1)
job = batchjob.Job(workdir, shname, nproc=params.batch.nproc_each)
job.write_script("""\
"%s" -c '\
import pickle; \
from yamtbx.dataproc.auto.command_line.multi_merge import merge_datasets; \
args = pickle.load(open("args.pkl")); \
ret = merge_datasets(*args); \
pickle.dump(ret, open("result.pkl","w")); \
'
""" % sys.executable)
batchjobs.submit(job)
jobs.append(job)
batchjobs.wait_all(jobs)
for workdir, xds_files, LCV, aLCV, clh in data_for_merge:
try:
results = pickle.load(open(os.path.join(workdir, "result.pkl")))
except:
print >>out, "Error in unpickling result in %s" % workdir
print >>out, traceback.format_exc()
results = []
if len(results) == 0:
ofs_summary.write("#%s failed\n" % os.path.relpath(workdir, params.workdir))
for cycle, wd, num_files, stats in results:
write_ofs_summary(workdir, cycle, clh, LCV, aLCV, xds_files, num_files, stats)
try: html_report.add_merge_result(workdir, clh, LCV, aLCV, xds_files, results[-1][2], results[-1][3])
except: print >>out, traceback.format_exc()
else:
for workdir, xds_files, LCV, aLCV, clh in data_for_merge:
print >>out, "Merging %s..." % os.path.relpath(workdir, params.workdir)
out.flush()
results = merge_datasets(params, workdir, xds_files, cells, batchjobs)
if len(results) == 0:
ofs_summary.write("#%s failed\n" % os.path.relpath(workdir, params.workdir))
for cycle, wd, num_files, stats in results:
write_ofs_summary(workdir, cycle, clh, LCV, aLCV, xds_files, num_files, stats)
try: html_report.add_merge_result(workdir, clh, LCV, aLCV, xds_files, results[-1][2], results[-1][3])
except: print >>out, traceback.format_exc()
try: html_report.write_html()
except: print >>out, traceback.format_exc()
print "firefox %s" % os.path.join(html_report.root, "report.html")
return
