def add_merge_result(self, workdir, clh, LCV, aLCV, xds_files, num_files, stats):
axis_opts = "cls ClH LCV aLCV 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".split()
cls = os.path.relpath(workdir, self.params.workdir)
tmps = "%12s %5.2f %4.1f %4.1f %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 %.1e"
tmps = tmps % (cls, clh, LCV, aLCV,
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,
)
tmptmp = tmps.replace("nan",'"nan"').split()
tmptmp[0] = '"%s"' % tmptmp[0]
self.html_merge_plot_data.append("{%s}"%",".join(map(lambda x: '"%s":%s'%tuple(x), zip(axis_opts, tmptmp))))
tmps = "".join(map(lambda x: "<td>%s</td>"%x, tmps.split()))
idno = len(self.html_merge_results)
if self.params.program == "xscale":
table_snip = xscalelp.snip_symm_and_cell(stats["lp"]) + "\n"
table_snip += xscalelp.snip_stats_table(stats["lp"])
else:
table_snip = ""
tmps2 = """ <tr><td onClick="toggle_show2(this, 'merge-td-%d');" id="merge-td-mark-%d"">▼</td>%s</tr>\n""" %(idno,idno,tmps)
tmps2 += """ <tr><td style="padding: 0px;"><td colspan="25" style="display:none;padding:0px;" id="merge-td-%d"><pre style="font-size: 1.1em;">%s</pre></td></tr>""" % (idno, table_snip)
self.html_merge_results.append(tmps2)
def add_merge_result(self, workdir, clh, LCV, aLCV, xds_files, num_files, stats):
axis_opts = "cls ClH LCV aLCV 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".split()
cls = os.path.relpath(workdir, self.params.workdir)
tmps = "%12s %5.2f %4.1f %4.1f %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 %.1e"
tmps = tmps % (cls, clh, LCV, aLCV,
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,
)
tmptmp = tmps.replace("nan",'"nan"').split()
tmptmp[0] = '"%s"' % tmptmp[0]
self.html_merge_plot_data.append("{%s}"%",".join(map(lambda x: '"%s":%s'%tuple(x), zip(axis_opts, tmptmp))))
tmps = "".join(map(lambda x: "<td>%s</td>"%x, tmps.split()))
idno = len(self.html_merge_results)
if self.params.program == "xscale":
table_snip = xscalelp.snip_symm_and_cell(stats["lp"]) + "\n"
table_snip += xscalelp.snip_stats_table(stats["lp"])
else:
table_snip = ""
tmps2 = """ <tr><td onClick="toggle_show2(this, 'merge-td-%d');" id="merge-td-mark-%d"">▼</td>%s</tr>\n""" %(idno,idno,tmps)
tmps2 += """ <tr><td style="padding: 0px;"><td colspan="25" style="display:none;padding:0px;" id="merge-td-%d"><pre style="font-size: 1.1em;">%s</pre></td></tr>""" % (idno, table_snip)
self.html_merge_results.append(tmps2)
def merge_datasets(params, workdir, xds_files, cells, space_group):
if not os.path.exists(workdir): os.makedirs(workdir)
out = open(os.path.join(workdir, "merge.log"), "w")
if params.program == "xscale":
cycles = multi_merging.xscale.XscaleCycles(
workdir,
anomalous_flag=params.anomalous,
d_min=params.d_min,
d_max=params.d_max,
reject_method=params.reject_method,
reject_params=params.rejection,
xscale_params=params.xscale,
res_params=params.resolution,
reference_file=params.reference_file,
space_group=space_group,
ref_mtz=params.reference.data
if params.reference.copy_test_flag else None,
out=out,
nproc=params.nproc,
batch_params=params.batch)
unused_files, reasons = cycles.run_cycles(xds_files)
used_files = set(xds_files).difference(set(unused_files))
print >> out
print >> out, " SUMMARY "
print >> out, "========================"
for i, files in enumerate((used_files, unused_files)):
print >> out, "\n%6s %4d files:\n" % (
("Used", "Unused")[i], len(files))
if len(files) == 0:
continue
maxlen_f = max(
map(lambda f: len(os.path.relpath(f, params.workdir)), files))
for f in files:
cell = cells[f]
merge_log = os.path.join(os.path.dirname(f),
"merging_stats.log")
try:
lines = open(merge_log).readlines()
resn = float(
filter(lambda x: x.startswith("Resolution:"),
lines)[0].split()[-1])
cmpl = float(
filter(lambda x: x.startswith("Completeness:"),
lines)[0].split()[-1].replace("%", ""))
except:
resn = float("nan")
cmpl = float("nan")
if i == 1: # print reason
print >> out, "%-15s" % reasons.get(f, "unknown"),
print >> out, ("%-" + str(maxlen_f) + "s") % os.path.relpath(
f, params.workdir), cell,
#print >>out, "ISa=%5.1f" % correctlp.get_ISa(os.path.join(os.path.dirname(f), "CORRECT.LP")),
print >> out, "Cmpl=%3.0f%%, Resn= %.1f" % (cmpl, resn)
ret = []
tkvals = lambda x: (x[-1], x[0], x[-2]) # overall, inner, outer
for i in xrange(1, cycles.get_last_cycle_number() + 1):
wd = os.path.join(workdir, "run_%.2d" % i)
xscale_lp = os.path.join(wd, "XSCALE.LP")
table = xscalelp.read_stats_table(xscale_lp)
num_files = len(xscalelp.get_read_data(xscale_lp))
xtriage_logfile = os.path.join(wd, "ccp4", "logfile.log")
aniso = xds_aniso_analysis.parse_logfile(
os.path.join(wd, "aniso.log"))
cellinfo = cycles.cell_info_at_cycles[i]
ret.append([
i, wd, num_files,
dict(cmpl=tkvals(table["cmpl"]),
redundancy=tkvals(table["redundancy"]),
i_over_sigma=tkvals(table["i_over_sigma"]),
r_meas=tkvals(table["r_meas"]),
cc_half=tkvals(table["cc_half"]),
sig_ano=tkvals(table["sig_ano"]),
cc_ano=tkvals(table["cc_ano"]),
drange=tkvals(table["d_range"]),
lp=xscale_lp,
xtriage_log=xtriage.XtriageLogfile(xtriage_logfile),
aniso=aniso,
lcv=cellinfo[1],
alcv=cellinfo[2],
dmin_est=cycles.dmin_est_at_cycles.get(i, float("nan")))
])
xscale_lp = os.path.join(cycles.current_working_dir(), "XSCALE.LP")
print >> out, "\nFinal statistics:\n"
print >> out, xscalelp.snip_stats_table(xscale_lp)
return ret
elif params.program == "aimless":
worker = Pointless()
print >> out, "\nRunning pointless"
runinfo = worker.run_copy(hklout="pointless.mtz",
wdir=workdir,
xdsin=xds_files,
logout=os.path.join(workdir,
"pointless.log"),
tolerance=30)
# Table of file name -> Batch range
assert len(xds_files) == len(runinfo)
batch_info = collections.OrderedDict(
map(lambda x: (x[0], (x[1][1:3])), zip(xds_files, runinfo)))
cycles = multi_merging.aimless.AimlessCycles(
workdir,
anomalous_flag=params.anomalous,
d_min=params.d_min,
d_max=params.d_max,
reject_method=params.reject_method,
cc_cutoff=params.rejection.lpstats.pwcc.abs_cutoff,
delta_cchalf_bin=params.rejection.delta_cchalf.bin,
mtzin=os.path.join(workdir, "pointless.mtz"),
batch_info=batch_info,
out=out,
nproc=params.nproc,
nproc_each=params.batch.nproc_each,
batchjobs=None) # FIXME batchjobs
unused_files, reasons = cycles.run_cycles(xds_files)
used_files = set(xds_files).difference(set(unused_files))
print >> out
print >> out, " SUMMARY "
print >> out, "========================"
for i, files in enumerate((used_files, unused_files)):
print >> out, "\n%6s %4d files:\n" % (
("Used", "Unused")[i], len(files))
if len(files) == 0:
continue
maxlen_f = max(
map(lambda f: len(os.path.relpath(f, params.workdir)), files))
for f in files:
cell = cells[f]
merge_log = os.path.join(os.path.dirname(f),
"merging_stats.log")
try:
lines = open(merge_log).readlines()
resn = float(
filter(lambda x: x.startswith("Resolution:"),
lines)[0].split()[-1])
cmpl = float(
filter(lambda x: x.startswith("Completeness:"),
lines)[0].split()[-1].replace("%", ""))
except:
resn = float("nan")
cmpl = float("nan")
if i == 1: # print reason
print >> out, "%-15s" % reasons.get(f, "unknown"),
print >> out, ("%-" + str(maxlen_f) + "s") % os.path.relpath(
f, params.workdir), cell,
print >> out, "ISa=%5.1f" % correctlp.get_ISa(
os.path.join(os.path.dirname(f), "CORRECT.LP")),
print >> out, "Cmpl=%3.0f%%, Resn= %.1f" % (cmpl, resn)
aimless_log = os.path.join(cycles.current_working_dir(), "aimless.log")
print >> out, "\nFinal statistics:\n"
print >> out, aimless.snip_summary(aimless_log)
# Write summary
table = aimless.read_summary(aimless_log)
tkvals = lambda x: (x[0], x[1], x[2]) # overall, inner, outer
return [
[
cycles.get_last_cycle_number(),
cycles.current_working_dir(),
len(used_files),
dict(cmpl=tkvals(table["cmpl"]),
redundancy=tkvals(table["redundancy"]),
i_over_sigma=tkvals(table["i_over_sigma"]),
r_meas=tkvals(table["r_meas"]),
cc_half=tkvals(table["cc_half"]),
sig_ano=(float("nan"), ) * 3,
cc_ano=tkvals(table["cc_ano"]))
],
]
#print >>out, "\nRunning aimless"
#aimless.run_aimless(mtzin="pointless.mtz",
# wdir=workdir,
# anomalous=params.anomalous, d_min=params.d_min, prefix=None)
else:
print >> out, "Unknown program:", params.program
return []
def merge_datasets(params, workdir, xds_files, cells, batchjobs):
if not os.path.exists(workdir): os.makedirs(workdir)
out = open(os.path.join(workdir, "merge.log"), "w")
if params.program == "xscale":
cycles = multi_merging.xscale.XscaleCycles(workdir,
anomalous_flag=params.anomalous,
d_min=params.d_min, d_max=params.d_max,
reject_method=params.reject_method,
reject_params=params.rejection,
xscale_params=params.xscale,
reference_file=params.reference_file,
out=out, nproc=params.nproc,
nproc_each=params.batch.nproc_each,
batchjobs=batchjobs if "deltacchalf" in params.batch.par_run else None)
unused_files, reasons = cycles.run_cycles(xds_files)
used_files = set(xds_files).difference(set(unused_files))
print >>out
print >>out, " SUMMARY "
print >>out, "========================"
for i, files in enumerate((used_files, unused_files)):
print >>out, "\n%6s %4d files:\n" % (("Used", "Unused")[i], len(files))
if len(files) == 0:
continue
maxlen_f = max(map(lambda f: len(os.path.relpath(f, params.workdir)), files))
for f in files:
cell = cells[f]
merge_log = os.path.join(os.path.dirname(f), "merging_stats.log")
try:
lines = open(merge_log).readlines()
resn = float(filter(lambda x:x.startswith("Resolution:"), lines)[0].split()[-1])
cmpl = float(filter(lambda x:x.startswith("Completeness:"), lines)[0].split()[-1].replace("%",""))
except:
resn = float("nan")
cmpl = float("nan")
if i == 1: # print reason
print >>out, "%-15s"%reasons.get(f, "unknown"),
print >>out, ("%-"+str(maxlen_f)+"s")%os.path.relpath(f, params.workdir), cell,
#print >>out, "ISa=%5.1f" % correctlp.get_ISa(os.path.join(os.path.dirname(f), "CORRECT.LP")),
print >>out, "Cmpl=%3.0f%%, Resn= %.1f" % (cmpl, resn)
ret = []
tkvals = lambda x: (x[-1], x[0], x[-2]) # overall, inner, outer
for i in xrange(1, cycles.get_last_cycle_number()+1):
wd = os.path.join(workdir, "run_%.2d"%i)
xscale_lp = os.path.join(wd, "XSCALE.LP")
table = xscalelp.read_stats_table(xscale_lp)
num_files = len(xscalelp.get_read_data(xscale_lp))
xtriage_logfile = os.path.join(wd, "ccp4", "logfile.log")
ret.append([i, wd, num_files,
dict(cmpl=tkvals(table["cmpl"]),
redundancy=tkvals(table["redundancy"]),
i_over_sigma=tkvals(table["i_over_sigma"]),
r_meas=tkvals(table["r_meas"]),
cc_half=tkvals(table["cc_half"]),
sig_ano=tkvals(table["sig_ano"]),
cc_ano=tkvals(table["cc_ano"]),
drange=tkvals(table["d_range"]),
lp=xscale_lp,
xtriage_log=xtriage.XtriageLogfile(xtriage_logfile))
])
xscale_lp = os.path.join(cycles.current_working_dir(), "XSCALE.LP")
print >>out, "\nFinal statistics:\n"
print >>out, xscalelp.snip_stats_table(xscale_lp)
return ret
elif params.program == "aimless":
worker = Pointless()
print >>out, "\nRunning pointless"
runinfo = worker.run_copy(hklout="pointless.mtz", wdir=workdir,
xdsin=xds_files,
logout=os.path.join(workdir, "pointless.log"),
tolerance=30)
# Table of file name -> Batch range
assert len(xds_files) == len(runinfo)
batch_info = collections.OrderedDict(map(lambda x: (x[0], (x[1][1:3])), zip(xds_files, runinfo)))
cycles = multi_merging.aimless.AimlessCycles(workdir,
anomalous_flag=params.anomalous,
d_min=params.d_min, d_max=params.d_max,
reject_method=params.reject_method,
cc_cutoff=params.rejection.lpstats.pwcc.abs_cutoff,
delta_cchalf_bin=params.rejection.delta_cchalf.bin,
mtzin=os.path.join(workdir, "pointless.mtz"),
batch_info=batch_info,
out=out, nproc=params.nproc,
nproc_each=params.batch.nproc_each,
batchjobs=batchjobs if "deltacchalf" in params.batch.par_run else None)
unused_files, reasons = cycles.run_cycles(xds_files)
used_files = set(xds_files).difference(set(unused_files))
print >>out
print >>out, " SUMMARY "
print >>out, "========================"
for i, files in enumerate((used_files, unused_files)):
print >>out, "\n%6s %4d files:\n" % (("Used", "Unused")[i], len(files))
if len(files) == 0:
continue
maxlen_f = max(map(lambda f: len(os.path.relpath(f, params.workdir)), files))
for f in files:
cell = cells[f]
merge_log = os.path.join(os.path.dirname(f), "merging_stats.log")
try:
lines = open(merge_log).readlines()
resn = float(filter(lambda x:x.startswith("Resolution:"), lines)[0].split()[-1])
cmpl = float(filter(lambda x:x.startswith("Completeness:"), lines)[0].split()[-1].replace("%",""))
except:
resn = float("nan")
cmpl = float("nan")
if i == 1: # print reason
print >>out, "%-15s"%reasons.get(f, "unknown"),
print >>out, ("%-"+str(maxlen_f)+"s")%os.path.relpath(f, params.workdir), cell,
print >>out, "ISa=%5.1f" % correctlp.get_ISa(os.path.join(os.path.dirname(f), "CORRECT.LP")),
print >>out, "Cmpl=%3.0f%%, Resn= %.1f" % (cmpl, resn)
aimless_log = os.path.join(cycles.current_working_dir(), "aimless.log")
print >>out, "\nFinal statistics:\n"
print >>out, aimless.snip_summary(aimless_log)
# Write summary
table = aimless.read_summary(aimless_log)
tkvals = lambda x: (x[0], x[1], x[2]) # overall, inner, outer
return [[cycles.get_last_cycle_number(), cycles.current_working_dir(), len(used_files),
dict(cmpl=tkvals(table["cmpl"]),
redundancy=tkvals(table["redundancy"]),
i_over_sigma=tkvals(table["i_over_sigma"]),
r_meas=tkvals(table["r_meas"]),
cc_half=tkvals(table["cc_half"]),
sig_ano=(float("nan"),)*3,
cc_ano=tkvals(table["cc_ano"]))], ]
#print >>out, "\nRunning aimless"
#aimless.run_aimless(mtzin="pointless.mtz",
# wdir=workdir,
# anomalous=params.anomalous, d_min=params.d_min, prefix=None)
else:
print >>out, "Unknown program:", params.program
return []
def run(csvin, prefix, rootdir, datadir=None):
html_head = """\
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8">
<script>
var toggle_show = function(caller, obj_id) {
var trg = document.getElementById(obj_id);
if (trg.style.display === 'block' || trg.style.display === '') {
trg.style.display = 'none';
trg.style.padding = '0px';
caller.innerHTML= '▼';
} else {
trg.style.display = '';
trg.style.padding = '7px';
caller.innerHTML= '▲';
}
}
</script>
<style>
pre {
font-family: Consolas, 'Courier New', Courier, Monaco, monospace;
}
.cells td, .dataset_table th,
.merge td {
font-size: 1em;
#border: 1px solid #98bf21;
padding: 4px 7px 4px 7px;
}
.cells th,
.merge th {
font-size: 1.1em;
text-align: center;
padding: 4px;
background-color: #A7C942;
color: #ffffff;
}
/*
.cells tr.alt td {
color: #000000;
background-color: #EAF2D3;
}
*/
.merge tr:nth-child(4n+3),
.merge tr:nth-child(4n),
.cells tr:nth-child(odd) {
color: #000000;
background-color: #EAF2D3;
}
.merge tr:nth-child(4n+1),
.merge tr:nth-child(4n+2),
.cells tr:nth-child(even) {
#color: #f8fbf1;
background-color: #f8fbf1;
}
</style>
</head>
<body>
<h1>KAMO.AUTO_MULTI_MERGE report</h1>
<div align="right">
workdir: %(wd)s<br />
created on %(cdate)s
</div>
<hr>
<table class="merge">
<tr>
<th colspan="2">Sample</th>
<th>#Collected</th>
<th>#Processed</th>
<th>#Mergeable</th>
<th>Symmetry</th>
<th>Unit cell</th>
<th>Cmpl(all)</th>
<th>Mult(all)</th>
<th><i>d</i><sub>min</sub></th>
</tr>
""" % dict(wd=rootdir, cdate=time.strftime("%Y-%m-%d %H:%M:%S"))
ofs = open(os.path.join(rootdir, "report.html"), "w")
ofs.write(html_head)
samples = auto_multi_merge.read_sample_info(csvin, datadir)
for name in samples:
workdir_rel = "%s%s" % (prefix, replace_forbidden_chars(name).replace(
" ", "_"))
workdir = os.path.join(rootdir, workdir_rel)
print workdir
assert os.path.isdir(workdir)
topdirs = samples[name][0]
ncol, dcol, npro, dpro, nmrg, dmrg = 0, 0, 0, 0, 0, 0
symm, cell = "?", "?"
rephtml = "#"
cmpl, redun, dmin = 0, 0, float("nan")
symm_group_info = "n/a" # now useless
deg_dict = {}
for topdir in topdirs:
for root, dirnames, filenames in os.walk(topdir):
if "XDS.INP" not in filenames: continue
print "Checking", name, root
deg = total_deg_from_xds_inp(os.path.join(root, "XDS.INP"))
deg_dict[root] = deg
ncol += 1
dcol += deg
if "XDS_ASCII.HKL_noscale" in filenames:
npro += 1
dpro += deg
mrg_lst = os.path.join(workdir, "formerge.lst")
if os.path.isfile(mrg_lst):
mrg_dirs = map(lambda x: os.path.dirname(x.strip()),
open(mrg_lst).readlines())
nmrg = len(mrg_dirs)
dmrg = sum(map(lambda x: deg_dict[x], mrg_dirs))
beam_plot_png = os.path.join(workdir, "beam_plot.png")
#if not os.path.isfile(beam_plot_png):
# beam_direction_plot.run_from_args([mrg_lst, 'plot_out="%s"'%beam_plot_png])
mrg_log = os.path.join(workdir, "multi_merge.log")
if os.path.isfile(mrg_log):
flag_read = False
flag_first = True
symm_group_info = ""
for l in open(mrg_log):
if flag_first and "members:" in l:
flag_read = True
flag_first = False
elif flag_read and ("members:" in l or l.strip() == ""):
flag_read = False
elif flag_read and "Members=" not in l:
symm_group_info += l
if "group_choice=" in l:
symm, cell = re.search("symmetry= ([^\(]+) \((.+)\)",
l).groups()
mrg_dirs = glob.glob(os.path.join(workdir, "*final/"))
best_result_loc, best_table_snip = "N/A", ""
if mrg_dirs:
mrg_dir = mrg_dirs[0]
dmin = float(re.search("_([0-9\.]+)A_final/", mrg_dir).group(1))
cls_dat = glob.glob(
os.path.join(mrg_dir, "*", "*_cluster_summary.dat"))[0]
tmp = open(cls_dat).readlines()[3].split()
cmpl, redun = float(tmp[3]), float(tmp[4])
rephtml = os.path.relpath(os.path.join(mrg_dir, "report.html"),
rootdir)
summary_dat = os.path.join(mrg_dir, "cluster_summary.dat")
best_result = auto_multi_merge.choose_best_result(
summary_dat, null_out())
if best_result:
best_result_loc = os.path.dirname(best_result)
lp = os.path.join(best_result_loc, "XSCALE.LP")
best_table_snip = xscalelp.snip_symm_and_cell(
lp) + "\n" + xscalelp.snip_stats_table(lp)
best_result_loc = os.path.relpath(best_result_loc,
rootdir) # to show in html
html_tr = """\
<tr>
<td onClick="toggle_show(this, 'sample-td-%(name)s');" id="sample-td-mark-%(name)s"">▼</td>
<td><a href="%(rephtml)s">%(name)s</a></td>
<td>%(ncol)d (%(dcol).0f°)</td>
<td>%(npro)d (%(dpro).0f°)</td>
<td>%(nmrg)d (%(dmrg).0f°)</td>
<td>%(symm)s</td>
<td>%(cell)s</td>
<td>%(cmpl).1f</td>
<td>%(redun).1f</td>
<td>%(dmin).1f</td>
</tr>
<tr>
<td style="padding: 0px;"></td>
<td colspan="9" style="display:none;padding:0px;" id="sample-td-%(name)s">
BEST RESULT: <a href="%(best_result_loc)s">%(best_result_loc)s</a> <br><br>
<pre>
%(best_table_snip)s
</pre>
</td>
</tr>
""" % locals()
ofs.write(html_tr)
ofs.flush()
#break
ofs.write("\n</table>\n")
ofs.write("\n</body></html>\n")
ofs.close()
print "Done!"
print "firefox", os.path.join(rootdir, "report.html")
def add_merge_result(self, workdir, clh, LCV, aLCV, xds_files, num_files,
stats):
axis_opts = "cls ClH LCV aLCV 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.best aniso.worst dmin.est".split(
)
cls = os.path.relpath(workdir, self.params.workdir)
tmps = "%12s %5.2f %4.1f %4.1f %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 %.2f %.2f %.2f"
tmps = tmps % (
cls,
clh,
LCV,
aLCV,
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"],
)
tmptmp = tmps.replace("nan", '"nan"').split()
tmptmp[0] = '"%s"' % tmptmp[0]
self.html_merge_plot_data.append("{%s}" % ",".join(
map(lambda x: '"%s":%s' % tuple(x), zip(axis_opts, tmptmp))))
tmps = "".join(map(lambda x: "<td>%s</td>" % x, tmps.split()))
idno = len(self.html_merge_results)
if self.params.program == "xscale":
table_snip = xscalelp.snip_symm_and_cell(stats["lp"]) + "\n"
table_snip += xscalelp.snip_stats_table(stats["lp"])
if stats["aniso"]:
table_snip += "\nAnisotropy:\n"
if stats["aniso"]["has_anisotropy"]:
if stats["aniso"]["aniso_cutoffs"]:
lab_maxlen = max(
len("direction"),
max(
map(lambda x: len(x[1]),
stats["aniso"]["aniso_cutoffs"])))
table_snip += ("%" + str(lab_maxlen) +
"s B_eigen Resol(CC1/2=0.5)\n"
) % "direction" # XXX if not 0.5?
for _, lab, reso, eig in stats["aniso"][
"aniso_cutoffs"]:
table_snip += ("%" + str(lab_maxlen) +
"s %7.2f %.2f\n") % (lab, eig, reso)
else:
table_snip += " Anisotropy analysis failed. Check the logfile.\n"
else:
table_snip += " No anisotropy in this symmetry.\n"
else:
table_snip = ""
tmps2 = """ <tr><td onClick="toggle_show2(this, 'merge-td-%d');" id="merge-td-mark-%d"">▼</td>%s</tr>\n""" % (
idno, idno, tmps)
tmps2 += """ <tr><td style="padding: 0px;"><td colspan="27" style="display:none;padding:0px;" id="merge-td-%d"><pre>%s</pre></td></tr>""" % (
idno, table_snip)
self.html_merge_results.append(tmps2)
