#!/usr/bin/env python
from TauAnalysis.Configuration.recoSampleDefinitionsAHtoMuTau_7TeV_grid_cfi import recoSampleDefinitionsAHtoMuTau_7TeV
from TauAnalysis.Configuration.harvestAnalysisResults_grid import harvestAnalysisResults
import TauAnalysis.Configuration.userRegistry as reg
channel = 'AHtoMuTau'
#reg.overrideJobId(channel, 'Run33')
analysisFilePath = reg.getAnalysisFilePath(channel)
harvestingFilePath = reg.getHarvestingFilePath(channel)
jobId = reg.getJobId(channel)
tmpFilePath = reg.getTmpFilePath(channel)
harvestAnalysisResults(channel = channel, samples = recoSampleDefinitionsAHtoMuTau_7TeV,
inputFilePath = analysisFilePath, outputFilePath = harvestingFilePath, jobId = jobId,
tmpFilePath = tmpFilePath)
#!/usr/bin/env python
from TauAnalysis.TauIdEfficiency.recoSampleDefinitionsTauIdEfficiency_7TeV_grid_cfi import recoSampleDefinitionsTauIdEfficiency_7TeV
from TauAnalysis.Configuration.harvestAnalysisResults_grid import harvestAnalysisResults
from TauAnalysis.Configuration.userRegistry import getAnalysisFilePath, getHarvestingFilePath, getJobId, getTmpFilePath
import os
import subprocess
import TauAnalysis.Configuration.tools.castor as castor
from TauAnalysis.Configuration.tools.harvestingMakefile import buildMakefile
channel = 'ZtoMuTau_tauIdEff'
analysisFilePath = getAnalysisFilePath(channel)
harvestingFilePath = getHarvestingFilePath(channel)
jobId = getJobId(channel)
tmpFilePath = getTmpFilePath(channel)
SAMPLES_TO_ANALYZE = [
'data_SingleMu_Run2011A_May10ReReco_v1',
'data_SingleMu_Run2011A_PromptReco_v4',
'Ztautau_pythia',
'Zmumu_pythia',
'PPmuXptGt20Mu15',
'WplusJets_madgraph',
'TTplusJets_madgraph'
]
def harvestTauIdEffSamples(channel = None, samples = None, inputFilePath = None,
outputFilePath = None, jobId = None,
tmpFilePath = None):
'integrated'] = meName_distribution + "IntegratedShape_%s" % selectionName
#print("meNameMapping:")
#print(meNameMapping)
#--------------------------------------------------------------------------------
# load template histogram for Z --> tau+ tau- signal events,
# produced by MCEmbeddingTools from Z --> mu+ mu- events selected in (pseudo)data
#--------------------------------------------------------------------------------
process.loadTemplateHistZtoMuTau_Ztautau = cms.EDAnalyzer(
"DQMFileLoader",
Ztautau=cms.PSet(
inputFileNames=cms.vstring(
'file:/afs/crc.nd.edu/group/NDCMS/data02/jkolb/TauResults/' +
getJobId("ZtoMuTau_bgEstTemplate") + '/visMass_fromEmbedding_' +
getJobId("ZtoMuTau_bgEstTemplate") + '.root'),
scaleFactor=cms.double(1.),
dqmDirectory_store=cms.string(
'/analysis/harvested/ZtoMuTau_from_ZmumuEmbedding' + '/' +
dqmDirectories_control['ZmumuEmbedding']
#'/analysis/harvested/ZtoMuTau_from_ZmumuEmbedding'
)))
#--------------------------------------------------------------------------------
# load template histogram of for different types of background events,
# obtained from background enriched control samples
#--------------------------------------------------------------------------------
process.loadTemplateHistZtoMuTau = cms.EDAnalyzer(
"DQMFileLoader",
'/castor/cern.ch/')
print(" inputFilePath = %s" % inputFilePath)
mode = None
if inputFilePath.find('/castor/') == 0:
mode = 'castor'
if inputFilePath.find('/store/') == 0:
mode = 'eos'
else:
mode = 'local'
if jobId is None:
reg.overrideJobId(
channel, '2011Oct30'
) # CV: need to overwrite this in order to match Mauro's filenames
jobId = reg.getJobId(channel)
print(" jobId = %s" % jobId)
if mode == 'castor':
files = [file_info for file_info in castor.nslsl(inputFilePath)]
elif mode == 'eos':
files = [file_info for file_info in eos.lsl(inputFilePath)]
else:
commandLine = '%s %s' % (options['executable_ls'][mode], inputFilePath)
args = shlex.split(commandLine)
retval = subprocess.Popen(args, stdout=subprocess.PIPE)
#retval.wait()
files = retval.stdout.read().split('\n')
#print(" files = %s" % files)
def make_harvest_scripts(
plot_regex,
skim_regex,
channel="",
sampleToAnalyze="",
job_id=None,
# An iterable that gives the input files
input_source=None,
# Pass input_files_info explicitely in case input files
# do not yet exist an time when make_harvest_scripts is called
input_files_info=None,
# Allow to switch between 'genericHarvester.py' and 'hadd'
harvester_command=_HARVESTER_CMD,
# Abort harvesting/merging scripts in case input files fail to get copied
abort_on_rfcp_error=True,
# Where to put the output
castor_output_directory=None,
script_directory=None,
merge_script_name=None,
local_copy_mapper=None,
chunk_size=1e9, # 1 GB
max_input_files_per_chunk=50,
run_harvesting=True,
run_merging=True,
check_old_files=True,
max_bsub_concurrent_file_access=2000,
verbosity=1):
# Get the jobId from the user registry
if job_id is None:
job_id = reg.getJobId(channel)
if script_directory is None:
script_directory = reg.getHarvestScriptLocation()
# Create the directory where we store the scripts if it doesn't exist
if not os.path.exists(script_directory):
os.mkdir(script_directory)
if input_files_info is None:
# Get all files with nonzero size in the input castor directory
print "Getting files to harvest from input, input_source = %s" % input_source
input_files_info = [x for x in input_source if x['size']]
#print "input_files_info = %s" % input_files_info
print "Getting files from destination"
# Get all the tmp files (that are non-zero)
if castor_output_directory.find("/castor") != -1:
tmp_files_info = [
x for x in castor_source(castor_output_directory) if x['size']
]
elif castor_output_directory.find("/store") != -1:
tmp_files_info = [
x for x in eos_source(castor_output_directory) if x['size']
]
else:
local_output_directory = None
if castor_output_directory.find(":") != -1:
local_output_directory = castor_output_directory[
castor_output_directory.find(":") + 1:]
else:
local_output_directory = castor_output_directory
tmp_files_info = []
for x in os.listdir(local_output_directory):
file_info = {
'path': os.path.join(local_output_directory,
os.path.basename(x)),
'size': 1, # dummy
'time': time.localtime(),
'file': os.path.basename(x),
'permissions':
'mrw-r--r--' # "ordinary" file access permissions
}
tmp_files_info.append(file_info)
tmp_files = set(x['file'] for x in tmp_files_info)
#print "tmp_files_info = %s" % tmp_files_info
# Make a repository of info about our files
all_files_dict = {}
for file_info in input_files_info + tmp_files_info:
all_files_dict[file_info['path']] = file_info
#print "all_files_dict = %s" % all_files_dict
# Keep track of files that we put in tmp with these jobs and that we care
# about. We can stop caring about old files if after adding new files (i.e.
# crab jobs finishing), the merge block they were in contains a different
# set of files.
relevant_tmp_files = set([])
# Keep track of the final harvested output
final_harvest_files = []
harvest_script_name = "_".join(
['submit', channel, sampleToAnalyze, job_id, 'harvest']) + '.sh'
harvest_log = open(
'_'.join(
('harvest', channel, sampleToAnalyze, job_id, 'log')) + '.txt',
'a')
# Keep track of the names of lxbatch jobs
bsub_job_names = []
if run_harvesting:
# Select those that match our given regex for a sample
plot_file_map = defaultdict(list)
# Keep track of a hash of
plot_matcher = re.compile(plot_regex)
plot_source_hashes = defaultdict(hashlib.md5)
for file in input_files_info:
match = plot_matcher.match(file['file'])
if match:
full_file = file['path']
sample = None
if sampleToAnalyze != "":
sample = sampleToAnalyze
else:
sample = match.group('sample')
plot_file_map[sample].append(full_file)
plot_source_hashes[sample].update(full_file)
harvest_log.write('=========== %s ================' % time.asctime())
for sample in sorted(plot_source_hashes.keys()):
# Write down the hash of the input files
harvest_log.write(" ".join([
channel, sample, job_id,
'<harvest>'
'input[%s]' % plot_source_hashes[sample].hexdigest()
]) + '\n')
submit_file = open(harvest_script_name, 'w')
# Make the bsub scripts
submit_file.write("#!/bin/bash\n")
# Now build mergers for each of the samples
# Count how many bsubs we have created, so we can put pauses in to
# thwart the rate limit.
bsub_file_access_counter = 0
for sample in plot_file_map.keys():
#if sample.find('NoPU') == -1:
# continue
# Add helpful comments
write_comment_header(
submit_file,
"Harvesting channel %s, sample %s" % (channel, sample))
print " Building harvesting for channel %s, sample %s" % (channel,
sample)
print " -- Found %i files to harvest" % len(plot_file_map[sample])
# Build merge tree. We add the source has to the sample name.
split = 6
if 'PPmuX' in sample:
print "High yield sample %s detected, setting split to 4" % sample
split = 4
merge_jobs = jobtools.make_merge_dependency_tree(
"_".join([channel, sample, job_id]),
plot_file_map[sample],
local_output_directory,
split=split)
#print "merge_jobs = %s" % merge_jobs
# Only do work that hasn't been done before. We can check and see
# if the output of a given merge layer is already in the temp
# directory. As the filenames contain a suffix with the hash of the
# input file names, we can be sure that if a file is out of date we
# will notice.
merge_jobs_needed = []
files_to_build = set([])
print " --- Generated %i harvest layers:" % len(merge_jobs)
for i, layer in enumerate(merge_jobs):
# Figure out how many files we need to build
layer_jobs_needed = []
for layer_job in layer:
#print "layer_job = ", layer_job
# Check if we've already built this output file in the tmp
file_base_name = os.path.basename(layer_job[0])
needed = True
# Check if we are rebuilding a dependency
building_a_dependency = any(file in files_to_build
for file in layer_job[1])
if not building_a_dependency and file_base_name in tmp_files:
output_m_time = all_files_dict[layer_job[0]]['time']
out_of_date = False
for input_file in layer_job[1]:
#print "input_file = ", input_file
if not input_file in all_files_dict.keys(
) or all_files_dict[input_file][
'time'] > output_m_time:
print "File: %s is older than its dependency %s, rebuilding!" % (
file_base_name, input_file)
# Check if it's out of date
out_of_date = True
break
if not out_of_date:
needed = False
if needed:
layer_jobs_needed.append(layer_job)
# Keep track of the relevant files, so we can delete old
# cruft
relevant_tmp_files.add(file_base_name)
# Check if this is the final output layer
if len(layer) == 1:
final_harvest_files.append((sample, layer[0][0]))
print " ---- layer %i has %i jobs, of which %i not done" % (
i, len(layer), len(layer_jobs_needed))
merge_jobs_needed.append(layer_jobs_needed)
# Keep track of what jobId was used for a paticular output file
job_registry = {}
# If a file is not produced by a job (already exists in CASTOR/EOS),
# then the job ID returned is none.
get_job_name = lambda x: x in job_registry and job_registry[
x] or None
for ilayer, layer in enumerate(merge_jobs_needed):
write_comment_header(submit_file, "Layer %i" % ilayer)
submit_file.write(
"echo Submitting layer %i of channel %s, sample %s\n" %
(ilayer, channel, sample))
for ijob, (output_file, input_files) in enumerate(layer):
# Get the job name (if it exists) and file name for the
# input files.
input_files_and_jobs = [(get_job_name(file), file)
for file in input_files]
# Build a function that constructs our log file name given the
# job file hash.
# Build a function that constructs our log file name given the
# job file hash.
if not os.path.exists('lxbatch_log'):
os.makedirs('lxbatch_log')
def log_file_maker(job_hash):
log_fileName = os.path.join(
'lxbatch_log', "_".join(
['harvest', job_hash,
'layer_%i' % ilayer]) + '.log')
# CV: delete log-files from previous job submissions
os.system("rm -f %s" % log_fileName)
return log_fileName
# Build the script
job_name, script = jobtools.make_bsub_script(
output_file,
input_files_and_jobs,
log_file_maker,
harvester_command,
abort_on_rfcp_error=abort_on_rfcp_error,
label="_".join([
"harvest", channel, sample, "layer",
str(ilayer), "job",
str(ijob)
]))
#print "job_name = %s" % job_name
bsub_job_names.append(job_name)
# Register our job
#print "--> registering channel %s, sample %s, jobId %s" % (channel, sample, job_id)
#print " script_directory = %s" % script_directory
job_registry[output_file] = job_name
#print " job_registry[%s] = %s" % (output_file, job_registry[output_file])
script_file = os.path.join(
script_directory, "_".join([
"harvest", channel, sample, job_id, "layer",
str(ilayer), "job",
str(ijob)
]) + ".sh")
submit_file.write("bsub < %s\n" % script_file)
# Keep track of how many files we access
bsub_file_access_counter += split
if bsub_file_access_counter > max_bsub_concurrent_file_access:
bsub_file_access_counter = 0
submit_file.write("# thwart rate limit\n")
submit_file.write(
"echo Sleeping for 500 seconds, it is now:\n")
submit_file.write("date\n")
submit_file.write("sleep 500\n")
with open(script_file, 'w') as script_file:
script_file.write(script)
submit_file.close()
if run_merging:
# Now build skim merging file
# Select those that match our given regex for a sample
skim_file_map = defaultdict(list)
skim_fileinhash_map = defaultdict(hashlib.md5)
skim_fileouthash_map = defaultdict(hashlib.md5)
skim_matcher = re.compile(skim_regex)
for file in input_files_info:
match = skim_matcher.match(file['file'])
if match:
full_file = file['path']
# Parse the sample from the regex
sample = None
if sampleToAnalyze != "":
sample = sampleToAnalyze
else:
sample = match.group('sample')
# For the skims, keep track of the file size well, since we use it
# to group the jobs.
skim_file_map[sample].append(
(file['time'], file['size'], full_file))
# Keep track of the hash of all input files so we know what went
# into our output
skim_fileinhash_map[sample].update(full_file)
def make_skim_name(sample, chunk, hash):
" Generate a nice name for an output skim "
return "_".join(["skim", sample, "chunk",
str(chunk), hash]) + ".root"
if merge_script_name is None:
merge_script_name = "_".join(
['submit', channel, sampleToAnalyze, job_id, 'merge']) + '.sh'
with open(merge_script_name, 'w') as merge_script:
merge_jobs_counter = 0
bsub_file_access_counter = 0
for sample in skim_file_map.keys():
write_comment_header(merge_script, " Merging " + sample)
print "Merging channel %s, sample %s" % (channel, sample)
files = skim_file_map[sample]
num_files = len(files)
total_file_size = sum(map(lambda x: x[1], files)) / 1e6
# Divide the job up into chunks that are about 1 GB each in size
chunks = list(
jobtools.split(files, chunk_size,
max_input_files_per_chunk, lambda x: x[1]))
print " Total sample size: %i files, %i MB - splitting into %i chunks" % (
num_files, total_file_size, len(chunks)),
# Keep track of jobs we are actually running
skim_merge_jobs = []
for ichunk, input_files in enumerate(chunks):
# Figure out the name for our file. It contains a hash of its
# inputs. We don't add the time, as we never have any LXBatch
# job dependencies.
just_the_files = [x[2] for x in input_files]
output_file = make_skim_name(
"_".join([channel, sample, job_id]), ichunk,
jobtools.hash_files(just_the_files, add_time=False))
skim_fileouthash_map[sample].update(output_file)
relevant_tmp_files.add(output_file)
if output_file not in tmp_files:
output_file_full_path = os.path.join(
castor_output_directory, output_file)
# Add "None" as the job id of the input files to indicate we
# don't care about any dependencies. The index on x takes
# out only the filename, not the size or the time.
skim_merge_jobs.append((output_file_full_path,
map(lambda x: (None, x[2]),
input_files)))
print " -- %i chunks are already done, skipping" % (
len(chunks) - len(skim_merge_jobs))
for ijob, (output_file,
input_files) in enumerate(skim_merge_jobs):
def merge_log_file_maker(job_hash):
log_fileName = os.path.join(
'lxbatch_log',
"_".join(['merge', job_hash,
'job_%i' % ijob]) + '.log')
# CV: delete log-files from previous job submissions
os.system("rm -f %s" % log_fileName)
return log_fileName
# Generate script contents
job_name, script = jobtools.make_bsub_script(
output_file,
input_files,
merge_log_file_maker,
_MERGER_CMD,
abort_on_rfcp_error=abort_on_rfcp_error,
label="_".join(
["merge", channel, sample, "chunk",
str(ijob)]))
script_file = os.path.join(
script_directory, "_".join([
"merge", channel, sample, job_id, "chunk",
str(ijob)
]) + ".sh")
# Add our bsub command
merge_script.write("bsub < %s\n" % script_file)
merge_jobs_counter += 1
bsub_file_access_counter += len(input_files)
if bsub_file_access_counter > max_bsub_concurrent_file_access:
bsub_file_access_counter = 0
merge_script.write("# thwart rate limit\n")
merge_script.write(
"echo Sleeping for 500 seconds, it is now:\n")
merge_script.write('date\n')
merge_script.write("sleep 500\n")
with open(script_file, 'w') as script_file:
script_file.write(script)
print " Built %i merge jobs in %s" % (merge_jobs_counter,
merge_script_name)
for sample in sorted(skim_fileinhash_map.keys()):
# Write down the hash of the input files
harvest_log.write(" ".join([
job_id,
sample,
'<merge>'
'input[%s]' % skim_fileinhash_map[sample].hexdigest(),
'output[%s]' % skim_fileouthash_map[sample].hexdigest(),
]) + '\n')
if check_old_files:
# Compute all the extra tmp files
extra_crap = tmp_files - relevant_tmp_files
if extra_crap:
print " Found %i extra files from previous harvest jobs" % len(
extra_crap)
print " Writing these files to file garbage.txt. To delete, please run: "
print " cat garbage.txt | xargs -P 10 -n 1 rfrm "
with open('garbage.txt', 'w') as garbage_file:
for file in extra_crap:
full_path = os.path.join(castor_output_directory, file)
garbage_file.write(full_path + '\n')
local_copy_script = 'copy_harvest_local_%s.sh' % job_id
with open(local_copy_script, 'w') as copy_script:
for sample, file in final_harvest_files:
copy_script.write('rfcp %s %s &\n' %
(file, local_copy_mapper(sample)))
copy_script.write('wait\n')
if verbosity > 0:
if run_harvesting:
print "To harvest plots, run %s" % harvest_script_name
if run_merging:
print "To merge skims, run %s" % merge_script_name
print "After harvesting is done, run %s to copy files locally" % local_copy_script
# Make all our stuff executable
os.chmod(local_copy_script, 0755)
if run_harvesting:
os.chmod(harvest_script_name, 0755)
if run_merging:
os.chmod(merge_script_name, 0755)
# Return name of scripts
retVal = {}
retVal['harvest_script_name'] = harvest_script_name
retVal['bsub_job_names'] = bsub_job_names
retVal['final_harvest_files'] = final_harvest_files
retVal['merge_script_name'] = merge_script_name
retVal['local_copy_script'] = local_copy_script
return retVal
def submitAnalysisToLXBatch(configFile=None,
channel=None,
samples=None,
samplesToAnalyze=None,
samplesToSkip=None,
disableFactorization=False,
disableSysUncertainties=False,
disableZrecoilCorrections=False,
script_directory=None,
cfgdir='lxbatch',
inputFileMap=None,
outputFileMap=None,
outputDirectory=None,
queue='1nd',
enableEventDumps=False,
enableFakeRates=False,
processName=None,
changeTauId=None,
saveFinalEvents=False,
jobExtention=''):
"""
Submit analysis job (event selection, filling of histogram)
to local machine
"""
# check that configFile, channel, samples and jobId
# parameters are defined and non-empty
for param in ["configFile", "channel", "samples", "outputDirectory"]:
if locals()[param] is None:
raise ValueError("Undefined '%s' parameter!!" % param)
jobId = reg.getJobId(channel)
# If not specified take script directory from user preferences.
if script_directory is None:
script_directory = reg.getHarvestScriptLocation()
# Make sure our output file for the scripts is okay
if not os.path.exists(script_directory):
os.makedirs(script_directory)
# Get all the files in our output directory that have non-zero size
tmp_files = set(x['file'] for x in castor.nslsl(outputDirectory)
if x['size'])
# Keep track of the files we care about
relevant_files = set([])
submit_file_name = 'submit_lxbatch_analysis_' + jobId + '.sh'
with open(submit_file_name, 'w') as submit_file:
# Loop over the samples to be analyzed
for sample in samples['SAMPLES_TO_ANALYZE']:
write_comment_header(submit_file, " Sample: " + sample)
# Skip submitting crab job in case
# o list of samples for which crab jobs are to be submitted has been
# explicitely specified
# o sample has explicitely been requested to be skipped
if samplesToAnalyze:
if sample not in samplesToAnalyze:
print "Skipping", sample
continue
if samplesToSkip:
if sample in samplesToSkip:
print "Skipping", sample
continue
sample_info = samples['RECO_SAMPLES'][sample]
# Make job info
jobInfo = {'channel': channel, 'sample': sample, 'id': jobId}
# Now build the scripts to feed to bsub
# Find the input files
input_files = list(inputFileMap(channel, sample, jobId))
if len(input_files) > 0:
print("Submitting %s in %i part(s)" %
(sample, len(input_files)))
else:
print("No local input files for %s found !!" % sample)
for job, file in enumerate(input_files):
input_files = [file]
# The None in the tuple indicates this file has no dependencies in
# the batch job.
input_files_and_jobs = [(None, file) for file in input_files]
# Need to prepend file:, and strip off the directory since we
# always have bsub rfcp the input files to the working
# directory.
input_files_for_cfgOptions = [
'file:' + os.path.basename(file) for file in input_files
]
output_file = outputFileMap(channel, sample, jobId)
input_file_hash = jobtools.hash_files(input_files,
add_time=False)
# Add the hash of the input file so we know the provenance of all
# files
output_file = os.path.join(
outputDirectory,
output_file.replace(
'.root',
'_' + str(job) + '_' + input_file_hash + '.root'))
relevant_files.add(os.path.basename(output_file))
# Uncomment to skip rerunning of old jobs
#if os.path.basename(output_file) in tmp_files:
#print " done; skipping", output_file
#continue
# First, prepare the configuration file
newConfigFile = getNewConfigFileName(configFile,
cfgdir,
sample,
jobId,
index=job,
label="@lxbatch")
write_comment_header(submit_file, " cfg: " + newConfigFile)
#--------------------------------------------------------------------
# CV: temporary "hack" for producing (ED)Ntuples/skims for tau id. efficiency measurement
jobCustomizations = []
jobCustomizations.append(
"if hasattr(process, 'ntupleOutputModule'):")
jobCustomizations.append(
" process.ntupleOutputModule.fileName = '%s'" %
os.path.basename(output_file))
jobCustomizations.append(
"if hasattr(process, 'patTupleOutputModule'):")
jobCustomizations.append(
" process.patTupleOutputModule.fileName = '%s'" %
os.path.basename(output_file))
jobCustomizations.append(
"if hasattr(process, 'skimOutputModule'):")
jobCustomizations.append(
" process.skimOutputModule.fileName = '%s'" %
os.path.basename(output_file))
HLTprocessName = 'HLT'
if 'hlt' in samples['RECO_SAMPLES'][sample].keys():
HLTprocessName = samples['RECO_SAMPLES'][sample][
'hlt'].getProcessName()
jobCustomizations.append("if hasattr(process, 'hltMu'):")
jobCustomizations.append(
" process.hltMu.selector.src = cms.InputTag('TriggerResults::%s')"
% HLTprocessName)
jobCustomizations.append(
"if hasattr(process, 'patTrigger'):")
jobCustomizations.append(
" process.patTrigger.processName = '%s'" %
HLTprocessName)
jobCustomizations.append(
"if hasattr(process, 'patTriggerEvent'):")
jobCustomizations.append(
" process.patTriggerEvent.processName = '%s'" %
HLTprocessName)
if samples['RECO_SAMPLES'][sample]['type'] == 'Data':
jobCustomizations.append(
"if hasattr(process, 'prePatProductionSequence')" +
" and hasattr(process, 'prePatProductionSequenceGen'):"
)
jobCustomizations.append(
" process.prePatProductionSequence.remove(process.prePatProductionSequenceGen)"
)
jobCustomizations.append(
"if hasattr(process, 'ntupleProducer'):")
jobCustomizations.append(
" if hasattr(process.ntupleProducer.sources, 'tauGenJets'):"
)
jobCustomizations.append(
" delattr(process.ntupleProducer.sources, 'tauGenJets')"
)
jobCustomizations.append(
" if hasattr(process.ntupleProducer.sources, 'genJets'):"
)
jobCustomizations.append(
" delattr(process.ntupleProducer.sources, 'genJets')"
)
jobCustomizations.append(
" if hasattr(process.ntupleProducer.sources, 'genPhaseSpaceEventInfo'):"
)
jobCustomizations.append(
" delattr(process.ntupleProducer.sources, 'genPhaseSpaceEventInfo')"
)
jobCustomizations.append(
" if hasattr(process.ntupleProducer.sources, 'genPileUpEventInfo'):"
)
jobCustomizations.append(
" delattr(process.ntupleProducer.sources, 'genPileUpEventInfo')"
)
jobCustomizations.append(
"if hasattr(process, 'patTriggerEventSequence') and hasattr(process, 'patTriggerSequence'):"
)
jobCustomizations.append(
" process.patDefaultSequence.replace(process.patTriggerEventSequence,"
)
jobCustomizations.append(
" process.patTriggerSequence + process.patTriggerEventSequence)"
)
#jobCustomizations.append("print process.dumpPython()")
#--------------------------------------------------------------------
prepareConfigFile(
configFile=configFile,
jobInfo=jobInfo,
newConfigFile=newConfigFile,
sample_infos=samples,
disableFactorization=disableFactorization,
disableSysUncertainties=disableSysUncertainties,
disableZrecoilCorrections=disableZrecoilCorrections,
# We always copy the input files to the local directory
# before running cmsRun, so just take the basname
input_files=input_files_for_cfgOptions,
output_file=os.path.basename(output_file),
enableEventDumps=enableEventDumps,
enableFakeRates=enableFakeRates,
processName=processName,
saveFinalEvents=saveFinalEvents,
changeTauId=changeTauId,
customizations=jobCustomizations)
# Build a function that constructs our log file name given the
# job file hash.
if not os.path.exists('lxbatch_log'):
os.makedirs('lxbatch_log')
def log_file_maker(job_hash):
return os.path.join(
'lxbatch_log',
"_".join(['run', channel, sample, jobId, job_hash]) +
'.log')
# Build our batch job
jobname, script = jobtools.make_bsub_script(
output_file,
input_files_and_jobs,
log_file_maker,
"cmsRun %s" % newConfigFile,
pass_io_files=False)
bsub_script_file = os.path.join(
script_directory, "_".join([
'analyze' + jobExtention, sample, 'job',
str(job), input_file_hash
]) + '.sh')
with open(bsub_script_file, 'w') as bsub_script:
bsub_script.write(script)
# Add this bsub to our submission script
submit_file.write("bsub -q %s < %s\n" %
(queue, bsub_script_file))
print len(tmp_files)
garbage = tmp_files - relevant_files
print len(garbage)
if garbage:
print "Found %i files not generated by this job!!" % len(garbage)
print " You should really run:"
print " cat ana_garbage.txt | xargs -n 1 -P 10 rfrm"
with open('ana_garbage.txt', 'w') as garbage_script:
for file in garbage:
garbage_script.write('%s\n' %
os.path.join(outputDirectory, file))
print "Run ./%s to submit jobs" % submit_file_name
os.chmod(submit_file_name, 0755)
return submit_file_name
def submitAnalysisToLXBatch(configFile = None, channel = None, samples = None,
samplesToAnalyze = None, samplesToSkip = None,
disableFactorization = False,
disableSysUncertainties = False,
disableZrecoilCorrections = False,
script_directory=None,
cfgdir = 'lxbatch',
inputFileMap = None, outputFileMap = None,
outputDirectory = None,
queue = '1nd',
enableEventDumps = False,
enableFakeRates = False,
processName = None,
changeTauId = None,
saveFinalEvents = False,
jobExtention = ''):
"""
Submit analysis job (event selection, filling of histogram)
to local machine
"""
# check that configFile, channel, samples and jobId
# parameters are defined and non-empty
for param in ["configFile", "channel", "samples",
"outputDirectory"]:
if locals()[param] is None:
raise ValueError("Undefined '%s' parameter!!" % param)
jobId = reg.getJobId(channel)
# If not specified take script directory from user preferences.
if script_directory is None:
script_directory = reg.getHarvestScriptLocation()
# Make sure our output file for the scripts is okay
if not os.path.exists(script_directory):
os.makedirs(script_directory)
# Get all the files in our output directory that have non-zero size
tmp_files = set(x['file'] for x in castor.nslsl(outputDirectory)
if x['size'])
# Keep track of the files we care about
relevant_files = set([])
submit_file_name = 'submit_lxbatch_analysis_' + jobId + '.sh'
with open(submit_file_name, 'w') as submit_file:
# Loop over the samples to be analyzed
for sample in samples['SAMPLES_TO_ANALYZE']:
write_comment_header(submit_file, " Sample: " + sample)
# Skip submitting crab job in case
# o list of samples for which crab jobs are to be submitted has been
# explicitely specified
# o sample has explicitely been requested to be skipped
if samplesToAnalyze:
if sample not in samplesToAnalyze:
print "Skipping", sample
continue
if samplesToSkip:
if sample in samplesToSkip:
print "Skipping", sample
continue
sample_info = samples['RECO_SAMPLES'][sample]
# Make job info
jobInfo = {
'channel' : channel,
'sample' : sample,
'id' : jobId
}
# Now build the scripts to feed to bsub
# Find the input files
input_files = list(inputFileMap(channel, sample, jobId))
if len(input_files) > 0:
print("Submitting %s in %i part(s)" % (sample, len(input_files)))
else:
print("No local input files for %s found !!" % sample)
for job, file in enumerate(input_files):
input_files = [file]
# The None in the tuple indicates this file has no dependencies in
# the batch job.
input_files_and_jobs = [ (None, file) for file in input_files ]
# Need to prepend file:, and strip off the directory since we
# always have bsub rfcp the input files to the working
# directory.
input_files_for_cfgOptions = [
'file:' + os.path.basename(file) for file in input_files]
output_file = outputFileMap(channel, sample, jobId)
input_file_hash = jobtools.hash_files(
input_files, add_time=False)
# Add the hash of the input file so we know the provenance of all
# files
output_file = os.path.join(outputDirectory, output_file.replace(
'.root', '_' + str(job) + '_' + input_file_hash + '.root'))
relevant_files.add(os.path.basename(output_file))
# Uncomment to skip rerunning of old jobs
#if os.path.basename(output_file) in tmp_files:
#print " done; skipping", output_file
#continue
# First, prepare the configuration file
newConfigFile = getNewConfigFileName(
configFile, cfgdir, sample,
jobId, index = job, label = "@lxbatch")
write_comment_header(submit_file, " cfg: " + newConfigFile)
#--------------------------------------------------------------------
# CV: temporary "hack" for producing (ED)Ntuples/skims for tau id. efficiency measurement
jobCustomizations = []
jobCustomizations.append("if hasattr(process, 'ntupleOutputModule'):")
jobCustomizations.append(" process.ntupleOutputModule.fileName = '%s'" % os.path.basename(output_file))
jobCustomizations.append("if hasattr(process, 'patTupleOutputModule'):")
jobCustomizations.append(" process.patTupleOutputModule.fileName = '%s'" % os.path.basename(output_file))
jobCustomizations.append("if hasattr(process, 'skimOutputModule'):")
jobCustomizations.append(" process.skimOutputModule.fileName = '%s'" % os.path.basename(output_file))
HLTprocessName = 'HLT'
if 'hlt' in samples['RECO_SAMPLES'][sample].keys():
HLTprocessName = samples['RECO_SAMPLES'][sample]['hlt'].getProcessName()
jobCustomizations.append("if hasattr(process, 'hltMu'):")
jobCustomizations.append(" process.hltMu.selector.src = cms.InputTag('TriggerResults::%s')" % HLTprocessName)
jobCustomizations.append("if hasattr(process, 'patTrigger'):")
jobCustomizations.append(" process.patTrigger.processName = '%s'" % HLTprocessName)
jobCustomizations.append("if hasattr(process, 'patTriggerEvent'):")
jobCustomizations.append(" process.patTriggerEvent.processName = '%s'" % HLTprocessName)
if samples['RECO_SAMPLES'][sample]['type'] == 'Data':
jobCustomizations.append("if hasattr(process, 'prePatProductionSequence')"
+ " and hasattr(process, 'prePatProductionSequenceGen'):")
jobCustomizations.append(" process.prePatProductionSequence.remove(process.prePatProductionSequenceGen)")
jobCustomizations.append("if hasattr(process, 'ntupleProducer'):")
jobCustomizations.append(" if hasattr(process.ntupleProducer.sources, 'tauGenJets'):")
jobCustomizations.append(" delattr(process.ntupleProducer.sources, 'tauGenJets')")
jobCustomizations.append(" if hasattr(process.ntupleProducer.sources, 'genJets'):")
jobCustomizations.append(" delattr(process.ntupleProducer.sources, 'genJets')")
jobCustomizations.append(" if hasattr(process.ntupleProducer.sources, 'genPhaseSpaceEventInfo'):")
jobCustomizations.append(" delattr(process.ntupleProducer.sources, 'genPhaseSpaceEventInfo')")
jobCustomizations.append(" if hasattr(process.ntupleProducer.sources, 'genPileUpEventInfo'):")
jobCustomizations.append(" delattr(process.ntupleProducer.sources, 'genPileUpEventInfo')")
jobCustomizations.append("if hasattr(process, 'patTriggerEventSequence') and hasattr(process, 'patTriggerSequence'):")
jobCustomizations.append(" process.patDefaultSequence.replace(process.patTriggerEventSequence,")
jobCustomizations.append(" process.patTriggerSequence + process.patTriggerEventSequence)")
#jobCustomizations.append("print process.dumpPython()")
#--------------------------------------------------------------------
prepareConfigFile(
configFile = configFile, jobInfo = jobInfo,
newConfigFile = newConfigFile,
sample_infos = samples,
disableFactorization = disableFactorization,
disableSysUncertainties = disableSysUncertainties,
disableZrecoilCorrections = disableZrecoilCorrections,
# We always copy the input files to the local directory
# before running cmsRun, so just take the basname
input_files = input_files_for_cfgOptions,
output_file = os.path.basename(output_file),
enableEventDumps = enableEventDumps, enableFakeRates = enableFakeRates,
processName = processName,
saveFinalEvents = saveFinalEvents,
changeTauId = changeTauId,
customizations = jobCustomizations)
# Build a function that constructs our log file name given the
# job file hash.
if not os.path.exists('lxbatch_log'):
os.makedirs('lxbatch_log')
def log_file_maker(job_hash):
return os.path.join(
'lxbatch_log', "_".join(
['run', channel, sample, jobId, job_hash]) + '.log')
# Build our batch job
jobname, script = jobtools.make_bsub_script(
output_file, input_files_and_jobs, log_file_maker,
"cmsRun %s" % newConfigFile, pass_io_files = False)
bsub_script_file = os.path.join(
script_directory, "_".join([
'analyze'+jobExtention, sample, 'job',
str(job), input_file_hash]) + '.sh')
with open(bsub_script_file, 'w') as bsub_script:
bsub_script.write(script)
# Add this bsub to our submission script
submit_file.write("bsub -q %s < %s\n" % (queue, bsub_script_file))
print len(tmp_files)
garbage = tmp_files - relevant_files
print len(garbage)
if garbage:
print "Found %i files not generated by this job!!" % len(garbage)
print " You should really run:"
print " cat ana_garbage.txt | xargs -n 1 -P 10 rfrm"
with open('ana_garbage.txt', 'w') as garbage_script:
for file in garbage:
garbage_script.write(
'%s\n' % os.path.join(outputDirectory, file))
print "Run ./%s to submit jobs" % submit_file_name
os.chmod(submit_file_name, 0755)
return submit_file_name
meNameMapping_distName['rebinned'] = meName_distribution + "Rebinned_%s" % selectionName
meNameMapping_distName['template'] = meName_distribution + "Shape_%s" % selectionName
meNameMapping_distName['integrated'] = meName_distribution + "IntegratedShape_%s" % selectionName
#print("meNameMapping:")
#print(meNameMapping)
#--------------------------------------------------------------------------------
# load template histogram for Z --> tau+ tau- signal events,
# produced by MCEmbeddingTools from Z --> mu+ mu- events selected in (pseudo)data
#--------------------------------------------------------------------------------
process.loadTemplateHistZtoMuTau_Ztautau = cms.EDAnalyzer("DQMFileLoader",
Ztautau = cms.PSet(
inputFileNames = cms.vstring(
'file:/afs/crc.nd.edu/group/NDCMS/data02/jkolb/TauResults/' + getJobId("ZtoMuTau_bgEstTemplate") + '/visMass_fromEmbedding_' + getJobId("ZtoMuTau_bgEstTemplate") + '.root'
),
scaleFactor = cms.double(1.),
dqmDirectory_store = cms.string(
'/analysis/harvested/ZtoMuTau_from_ZmumuEmbedding' + '/' + dqmDirectories_control['ZmumuEmbedding']
#'/analysis/harvested/ZtoMuTau_from_ZmumuEmbedding'
)
)
)
#--------------------------------------------------------------------------------
# load template histogram of for different types of background events,
# obtained from background enriched control samples
#--------------------------------------------------------------------------------
process.loadTemplateHistZtoMuTau = cms.EDAnalyzer("DQMFileLoader",
def make_harvest_scripts(plot_regex, skim_regex,
channel = "",
sampleToAnalyze = "",
job_id = None,
# An iterable that gives the input files
input_source = None,
# Pass input_files_info explicitely in case input files
# do not yet exist an time when make_harvest_scripts is called
input_files_info = None,
# Allow to switch between 'genericHarvester.py' and 'hadd'
harvester_command = _HARVESTER_CMD,
# Abort harvesting/merging scripts in case input files fail to get copied
abort_on_rfcp_error = True,
# Where to put the output
castor_output_directory = None,
script_directory = None,
merge_script_name = None,
local_copy_mapper = None,
chunk_size = 1e9, # 1 GB
max_input_files_per_chunk = 50,
run_harvesting = True,
run_merging = True,
check_old_files = True,
max_bsub_concurrent_file_access = 2000,
verbosity = 1):
# Get the jobId from the user registry
if job_id is None:
job_id = reg.getJobId(channel)
if script_directory is None:
script_directory = reg.getHarvestScriptLocation()
# Create the directory where we store the scripts if it doesn't exist
if not os.path.exists(script_directory):
os.mkdir(script_directory)
if input_files_info is None:
# Get all files with nonzero size in the input castor directory
print "Getting files to harvest from input, input_source = %s" % input_source
input_files_info = [ x for x in input_source if x['size'] ]
#print "input_files_info = %s" % input_files_info
print "Getting files from destination"
# Get all the tmp files (that are non-zero)
if castor_output_directory.find("/castor") != -1:
tmp_files_info = [ x for x in castor_source(castor_output_directory) if x['size'] ]
elif castor_output_directory.find("/store") != -1:
tmp_files_info = [ x for x in eos_source(castor_output_directory) if x['size'] ]
else:
local_output_directory = None
if castor_output_directory.find(":") != -1:
local_output_directory = castor_output_directory[castor_output_directory.find(":") + 1:]
else:
local_output_directory = castor_output_directory
tmp_files_info = []
for x in os.listdir(local_output_directory):
file_info = {
'path' : os.path.join(local_output_directory, os.path.basename(x)),
'size' : 1, # dummy
'time' : time.localtime(),
'file' : os.path.basename(x),
'permissions' : 'mrw-r--r--' # "ordinary" file access permissions
}
tmp_files_info.append(file_info)
tmp_files = set(x['file'] for x in tmp_files_info)
#print "tmp_files_info = %s" % tmp_files_info
# Make a repository of info about our files
all_files_dict = {}
for file_info in input_files_info + tmp_files_info:
all_files_dict[file_info['path']] = file_info
#print "all_files_dict = %s" % all_files_dict
# Keep track of files that we put in tmp with these jobs and that we care
# about. We can stop caring about old files if after adding new files (i.e.
# crab jobs finishing), the merge block they were in contains a different
# set of files.
relevant_tmp_files = set([])
# Keep track of the final harvested output
final_harvest_files = []
harvest_script_name = "_".join(['submit', channel, sampleToAnalyze, job_id, 'harvest']) + '.sh'
harvest_log = open('_'.join(('harvest', channel, sampleToAnalyze, job_id, 'log')) + '.txt', 'a')
# Keep track of the names of lxbatch jobs
bsub_job_names = []
if run_harvesting:
# Select those that match our given regex for a sample
plot_file_map = defaultdict(list)
# Keep track of a hash of
plot_matcher = re.compile(plot_regex)
plot_source_hashes = defaultdict(hashlib.md5)
for file in input_files_info:
match = plot_matcher.match(file['file'])
if match:
full_file = file['path']
sample = None
if sampleToAnalyze != "":
sample = sampleToAnalyze
else:
sample = match.group('sample')
plot_file_map[sample].append(full_file)
plot_source_hashes[sample].update(full_file)
harvest_log.write('=========== %s ================' % time.asctime())
for sample in sorted(plot_source_hashes.keys()):
# Write down the hash of the input files
harvest_log.write(
" ".join([channel, sample, job_id, '<harvest>' 'input[%s]' %
plot_source_hashes[sample].hexdigest()])+'\n')
submit_file = open(harvest_script_name, 'w')
# Make the bsub scripts
submit_file.write("#!/bin/bash\n")
# Now build mergers for each of the samples
# Count how many bsubs we have created, so we can put pauses in to
# thwart the rate limit.
bsub_file_access_counter = 0
for sample in plot_file_map.keys():
#if sample.find('NoPU') == -1:
# continue
# Add helpful comments
write_comment_header(submit_file, "Harvesting channel %s, sample %s" % (channel, sample))
print " Building harvesting for channel %s, sample %s" % (channel, sample)
print " -- Found %i files to harvest" % len(plot_file_map[sample])
# Build merge tree. We add the source has to the sample name.
split = 6
if 'PPmuX' in sample:
print "High yield sample %s detected, setting split to 4" % sample
split = 4
merge_jobs = jobtools.make_merge_dependency_tree(
"_".join([channel, sample, job_id]), plot_file_map[sample],
local_output_directory, split = split)
#print "merge_jobs = %s" % merge_jobs
# Only do work that hasn't been done before. We can check and see
# if the output of a given merge layer is already in the temp
# directory. As the filenames contain a suffix with the hash of the
# input file names, we can be sure that if a file is out of date we
# will notice.
merge_jobs_needed = []
files_to_build = set([])
print " --- Generated %i harvest layers:" % len(merge_jobs)
for i, layer in enumerate(merge_jobs):
# Figure out how many files we need to build
layer_jobs_needed = []
for layer_job in layer:
#print "layer_job = ", layer_job
# Check if we've already built this output file in the tmp
file_base_name = os.path.basename(layer_job[0])
needed = True
# Check if we are rebuilding a dependency
building_a_dependency = any(
file in files_to_build for file in layer_job[1])
if not building_a_dependency and file_base_name in tmp_files:
output_m_time = all_files_dict[layer_job[0]]['time']
out_of_date = False
for input_file in layer_job[1]:
#print "input_file = ", input_file
if not input_file in all_files_dict.keys() or all_files_dict[input_file]['time'] > output_m_time:
print "File: %s is older than its dependency %s, rebuilding!" % (
file_base_name, input_file)
# Check if it's out of date
out_of_date = True
break
if not out_of_date:
needed = False
if needed:
layer_jobs_needed.append(layer_job)
# Keep track of the relevant files, so we can delete old
# cruft
relevant_tmp_files.add(file_base_name)
# Check if this is the final output layer
if len(layer) == 1:
final_harvest_files.append((sample, layer[0][0]))
print " ---- layer %i has %i jobs, of which %i not done" % (
i, len(layer), len(layer_jobs_needed))
merge_jobs_needed.append(layer_jobs_needed)
# Keep track of what jobId was used for a paticular output file
job_registry = {}
# If a file is not produced by a job (already exists in CASTOR/EOS),
# then the job ID returned is none.
get_job_name = lambda x : x in job_registry and job_registry[x] or None
for ilayer, layer in enumerate(merge_jobs_needed):
write_comment_header(submit_file, "Layer %i" % ilayer)
submit_file.write("echo Submitting layer %i of channel %s, sample %s\n"
% (ilayer, channel, sample))
for ijob, (output_file, input_files) in enumerate(layer):
# Get the job name (if it exists) and file name for the
# input files.
input_files_and_jobs = [
(get_job_name(file), file) for file in input_files]
# Build a function that constructs our log file name given the
# job file hash.
# Build a function that constructs our log file name given the
# job file hash.
if not os.path.exists('lxbatch_log'):
os.makedirs('lxbatch_log')
def log_file_maker(job_hash):
log_fileName = os.path.join(
'lxbatch_log', "_".join(
['harvest', job_hash, 'layer_%i' % ilayer]) + '.log')
# CV: delete log-files from previous job submissions
os.system("rm -f %s" % log_fileName)
return log_fileName
# Build the script
job_name, script = jobtools.make_bsub_script(
output_file, input_files_and_jobs,
log_file_maker,
harvester_command,
abort_on_rfcp_error = abort_on_rfcp_error,
label = "_".join([
"harvest",
channel, sample,
"layer", str(ilayer),
"job", str(ijob)]))
#print "job_name = %s" % job_name
bsub_job_names.append(job_name)
# Register our job
#print "--> registering channel %s, sample %s, jobId %s" % (channel, sample, job_id)
#print " script_directory = %s" % script_directory
job_registry[output_file] = job_name
#print " job_registry[%s] = %s" % (output_file, job_registry[output_file])
script_file = os.path.join(
script_directory, "_".join([
"harvest",
channel, sample, job_id,
"layer", str(ilayer),
"job", str(ijob)]
) + ".sh")
submit_file.write("bsub < %s\n" % script_file)
# Keep track of how many files we access
bsub_file_access_counter += split
if bsub_file_access_counter > max_bsub_concurrent_file_access:
bsub_file_access_counter = 0
submit_file.write("# thwart rate limit\n")
submit_file.write(
"echo Sleeping for 500 seconds, it is now:\n")
submit_file.write(
"date\n")
submit_file.write("sleep 500\n")
with open(script_file, 'w') as script_file:
script_file.write(script)
submit_file.close()
if run_merging:
# Now build skim merging file
# Select those that match our given regex for a sample
skim_file_map = defaultdict(list)
skim_fileinhash_map = defaultdict(hashlib.md5)
skim_fileouthash_map = defaultdict(hashlib.md5)
skim_matcher = re.compile(skim_regex)
for file in input_files_info:
match = skim_matcher.match(file['file'])
if match:
full_file = file['path']
# Parse the sample from the regex
sample = None
if sampleToAnalyze != "":
sample = sampleToAnalyze
else:
sample = match.group('sample')
# For the skims, keep track of the file size well, since we use it
# to group the jobs.
skim_file_map[sample].append(
(file['time'], file['size'], full_file))
# Keep track of the hash of all input files so we know what went
# into our output
skim_fileinhash_map[sample].update(full_file)
def make_skim_name(sample, chunk, hash):
" Generate a nice name for an output skim "
return "_".join(["skim", sample, "chunk", str(chunk), hash]) + ".root"
if merge_script_name is None:
merge_script_name = "_".join(['submit', channel, sampleToAnalyze, job_id, 'merge']) + '.sh'
with open(merge_script_name, 'w') as merge_script:
merge_jobs_counter = 0
bsub_file_access_counter = 0
for sample in skim_file_map.keys():
write_comment_header(merge_script, " Merging " + sample)
print "Merging channel %s, sample %s" % (channel, sample)
files = skim_file_map[sample]
num_files = len(files)
total_file_size = sum(map(lambda x: x[1], files))/1e6
# Divide the job up into chunks that are about 1 GB each in size
chunks = list(jobtools.split(files, chunk_size, max_input_files_per_chunk, lambda x: x[1]))
print " Total sample size: %i files, %i MB - splitting into %i chunks" % (
num_files, total_file_size, len(chunks)),
# Keep track of jobs we are actually running
skim_merge_jobs = []
for ichunk, input_files in enumerate(chunks):
# Figure out the name for our file. It contains a hash of its
# inputs. We don't add the time, as we never have any LXBatch
# job dependencies.
just_the_files = [x[2] for x in input_files]
output_file = make_skim_name(
"_".join([channel, sample, job_id]), ichunk, jobtools.hash_files(
just_the_files, add_time=False))
skim_fileouthash_map[sample].update(output_file)
relevant_tmp_files.add(output_file)
if output_file not in tmp_files:
output_file_full_path = os.path.join(
castor_output_directory, output_file)
# Add "None" as the job id of the input files to indicate we
# don't care about any dependencies. The index on x takes
# out only the filename, not the size or the time.
skim_merge_jobs.append(
(output_file_full_path,
map(lambda x: (None, x[2]), input_files))
)
print " -- %i chunks are already done, skipping" % (
len(chunks) - len(skim_merge_jobs))
for ijob, (output_file, input_files) in enumerate(skim_merge_jobs):
def merge_log_file_maker(job_hash):
log_fileName = os.path.join(
'lxbatch_log', "_".join(
['merge', job_hash, 'job_%i' % ijob]) + '.log')
# CV: delete log-files from previous job submissions
os.system("rm -f %s" % log_fileName)
return log_fileName
# Generate script contents
job_name, script = jobtools.make_bsub_script(
output_file,
input_files,
merge_log_file_maker,
_MERGER_CMD,
abort_on_rfcp_error = abort_on_rfcp_error,
label = "_".join([
"merge",
channel, sample,
"chunk", str(ijob)]))
script_file = os.path.join(
script_directory, "_".join([
"merge",
channel, sample, job_id,
"chunk", str(ijob)])
+ ".sh")
# Add our bsub command
merge_script.write("bsub < %s\n" % script_file)
merge_jobs_counter += 1
bsub_file_access_counter += len(input_files)
if bsub_file_access_counter > max_bsub_concurrent_file_access:
bsub_file_access_counter = 0
merge_script.write("# thwart rate limit\n")
merge_script.write(
"echo Sleeping for 500 seconds, it is now:\n")
merge_script.write('date\n')
merge_script.write("sleep 500\n")
with open(script_file, 'w') as script_file:
script_file.write(script)
print " Built %i merge jobs in %s" % (merge_jobs_counter,
merge_script_name)
for sample in sorted(skim_fileinhash_map.keys()):
# Write down the hash of the input files
harvest_log.write(
" ".join([job_id, sample, '<merge>'
'input[%s]' % skim_fileinhash_map[sample].hexdigest(),
'output[%s]' % skim_fileouthash_map[sample].hexdigest(),
])+'\n')
if check_old_files:
# Compute all the extra tmp files
extra_crap = tmp_files - relevant_tmp_files
if extra_crap:
print " Found %i extra files from previous harvest jobs"%len(extra_crap)
print " Writing these files to file garbage.txt. To delete, please run: "
print " cat garbage.txt | xargs -P 10 -n 1 rfrm "
with open('garbage.txt', 'w') as garbage_file:
for file in extra_crap:
full_path = os.path.join(castor_output_directory, file)
garbage_file.write(full_path + '\n')
local_copy_script = 'copy_harvest_local_%s.sh' % job_id
with open(local_copy_script, 'w') as copy_script:
for sample, file in final_harvest_files:
copy_script.write('rfcp %s %s &\n' % (
file, local_copy_mapper(sample)))
copy_script.write('wait\n')
if verbosity > 0:
if run_harvesting:
print "To harvest plots, run %s" % harvest_script_name
if run_merging:
print "To merge skims, run %s" % merge_script_name
print "After harvesting is done, run %s to copy files locally" % local_copy_script
# Make all our stuff executable
os.chmod(local_copy_script, 0755)
if run_harvesting:
os.chmod(harvest_script_name, 0755)
if run_merging:
os.chmod(merge_script_name, 0755)
# Return name of scripts
retVal = {}
retVal['harvest_script_name'] = harvest_script_name
retVal['bsub_job_names'] = bsub_job_names
retVal['final_harvest_files'] = final_harvest_files
retVal['merge_script_name'] = merge_script_name
retVal['local_copy_script'] = local_copy_script
return retVal