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submit_all.py
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submit_all.py
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import sys, os
import time
import itertools
import numpy
import json
from metis.Sample import DBSSample, DirectorySample, Sample
from metis.CondorTask import CondorTask
from metis.StatsParser import StatsParser
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--tag", help = "tag to identify this set of babies", type=str)
parser.add_argument("--filter", help = "only process mc/data with some requirement (e.g. 2016MC, 2017Data)", default="", type=str)
parser.add_argument("--dsfilter", help = "only process mc/data with some name pattern(e.g. DY***)", default="", type=str)
parser.add_argument("--soft_rerun", help = "don't remake tarball", action="store_true")
parser.add_argument("--skip_local", help = "don't submit jobs for local samples", action = "store_true")
parser.add_argument("--skip_central", help = "don't submit jobs for central samples", action = "store_true")
args = parser.parse_args()
# for central inputs
#dsdefs = []
## datasetname, filesPerOutput, filtername
from dsdefs_centralminiaod_UL import dsdefs
# for local inputs
local_sets = []
if not args.skip_local:
local_sets = [
# ("HHggtautau_Era2018_private", "/hadoop/cms/store/user/hmei/miniaod_runII/HHggtautau_2018_20201002_v1_STEP4_v1/", 10, "2018MC"),
# ("HHggtautau_Era2017_private", "/hadoop/cms/store/user/hmei/miniaod_runII/HHggtautau_2017_20201025_v1_STEP4_v1/", 10, "2017MC")
# ("HHggtautau_Era2016_private", "/hadoop/cms/store/user/hmei/miniaod_runII/HHggtautau_2016_20201124_v1_STEP4_v1/", 10, "2016MC")
# ("HHggZZ_Era2016_private", "/hadoop/cms/store/user/hmei/miniaod_runII/HHggZZ_2016_20210209_v1_STEP4_v1/", 10, "2016MC"),
# ("HHggZZ_Era2017_private", "/hadoop/cms/store/user/hmei/miniaod_runII/HHggZZ_2017_20210209_v1_STEP4_v1/", 10, "2017MC"),
# ("HHggZZ_Era2018_private", "/hadoop/cms/store/user/hmei/miniaod_runII/HHggZZ_2018_20210209_v1_STEP4_v1/", 10, "2018MC")
# vbf
("VBF_CV_0_5_C2V_1_C3_1_HHggtautau_Era2016_private", "/hadoop/cms/store/user/fsetti/nanoAOD_runII/VBF_CV_0_5_C2V_1_C3_1_HHggtautau_2016_20210425_v1_STEP4_v1/", 10, "2016MC"),
("VBF_CV_1_5_C2V_1_C3_1_HHggtautau_Era2016_private", "/hadoop/cms/store/user/fsetti/nanoAOD_runII/VBF_CV_1_5_C2V_1_C3_1_HHggtautau_2016_20210420_v1_STEP4_v1/", 10, "2016MC"),
("VBF_CV_1_C2V_1_C3_0_HHggtautau_Era2016_private", "/hadoop/cms/store/user/fsetti/nanoAOD_runII/VBF_CV_1_C2V_1_C3_0_HHggtautau_2016_20210425_v1_STEP4_v1/", 10, "2016MC"),
("VBF_CV_1_C2V_1_C3_2_HHggtautau_Era2016_private", "/hadoop/cms/store/user/fsetti/nanoAOD_runII/VBF_CV_1_C2V_1_C3_2_HHggtautau_2016_20210425_v1_STEP4_v1/", 10, "2016MC"),
("VBF_CV_1_C2V_1_C3_1_HHggtautau_Era2016_private", "/hadoop/cms/store/user/fsetti/nanoAOD_runII/VBF_CV_1_C2V_1_C3_1_HHggtautau_2016_20210420_v1_STEP4_v1/", 10, "2016MC"),
("VBF_CV_1_C2V_1_C3_1_HHggtautau_Era2016_private", "/hadoop/cms/store/user/fsetti/nanoAOD_runII/VBF_CV_1_C2V_0_C3_1_HHggtautau_2016_20210420_v1_STEP4_v1/", 10, "2016MC"),
("VBF_CV_1_C2V_1_C3_1_HHggtautau_Era2016_private", "/hadoop/cms/store/user/fsetti/nanoAOD_runII/VBF_CV_1_C2V_2_C3_1_HHggtautau_2016_20210420_v1_STEP4_v1/", 10, "2016MC"),
("VBF_CV_0_5_C2V_1_C3_1_HHggtautau_Era2017_private", "/hadoop/cms/store/user/fsetti/nanoAOD_runII/VBF_CV_0_5_C2V_1_C3_1_HHggtautau_2017_20210425_v1_STEP4_v1/", 10, "2017MC"),
("VBF_CV_1_5_C2V_1_C3_1_HHggtautau_Era2017_private", "/hadoop/cms/store/user/fsetti/nanoAOD_runII/VBF_CV_1_5_C2V_1_C3_1_HHggtautau_2017_20210420_v1_STEP4_v1/", 10, "2017MC"),
("VBF_CV_1_C2V_1_C3_0_HHggtautau_Era2017_private", "/hadoop/cms/store/user/fsetti/nanoAOD_runII/VBF_CV_1_C2V_1_C3_0_HHggtautau_2017_20210425_v1_STEP4_v1/", 10, "2017MC"),
("VBF_CV_1_C2V_1_C3_2_HHggtautau_Era2017_private", "/hadoop/cms/store/user/fsetti/nanoAOD_runII/VBF_CV_1_C2V_1_C3_2_HHggtautau_2017_20210425_v1_STEP4_v1/", 10, "2017MC"),
("VBF_CV_1_C2V_1_C3_1_HHggtautau_Era2017_private", "/hadoop/cms/store/user/fsetti/nanoAOD_runII/VBF_CV_1_C2V_1_C3_1_HHggtautau_2017_20210420_v1_STEP4_v1/", 10, "2017MC"),
("VBF_CV_1_C2V_1_C3_1_HHggtautau_Era2017_private", "/hadoop/cms/store/user/fsetti/nanoAOD_runII/VBF_CV_1_C2V_0_C3_1_HHggtautau_2017_20210420_v1_STEP4_v1/", 10, "2017MC"),
("VBF_CV_1_C2V_1_C3_1_HHggtautau_Era2017_private", "/hadoop/cms/store/user/fsetti/nanoAOD_runII/VBF_CV_1_C2V_2_C3_1_HHggtautau_2017_20210420_v1_STEP4_v1/", 10, "2017MC"),
("VBF_CV_0_5_C2V_1_C3_1_HHggtautau_Era2018_private", "/hadoop/cms/store/user/fsetti/nanoAOD_runII/VBF_CV_0_5_C2V_1_C3_1_HHggtautau_2018_20210425_v1_STEP4_v1/", 10, "2018MC"),
("VBF_CV_1_5_C2V_1_C3_1_HHggtautau_Era2018_private", "/hadoop/cms/store/user/fsetti/nanoAOD_runII/VBF_CV_1_5_C2V_1_C3_1_HHggtautau_2018_20210420_v1_STEP4_v1/", 10, "2018MC"),
("VBF_CV_1_C2V_1_C3_0_HHggtautau_Era2018_private", "/hadoop/cms/store/user/fsetti/nanoAOD_runII/VBF_CV_1_C2V_1_C3_0_HHggtautau_2018_20210425_v1_STEP4_v1/", 10, "2018MC"),
("VBF_CV_1_C2V_1_C3_2_HHggtautau_Era2018_private", "/hadoop/cms/store/user/fsetti/nanoAOD_runII/VBF_CV_1_C2V_1_C3_2_HHggtautau_2018_20210425_v1_STEP4_v1/", 10, "2018MC"),
("VBF_CV_1_C2V_1_C3_1_HHggtautau_Era2018_private", "/hadoop/cms/store/user/fsetti/nanoAOD_runII/VBF_CV_1_C2V_1_C3_1_HHggtautau_2018_20210420_v1_STEP4_v1/", 10, "2018MC"),
("VBF_CV_1_C2V_1_C3_1_HHggtautau_Era2018_private", "/hadoop/cms/store/user/fsetti/nanoAOD_runII/VBF_CV_1_C2V_2_C3_1_HHggtautau_2018_20210420_v1_STEP4_v1/", 10, "2018MC"),
("VBF_CV_1_C2V_1_C3_1_HHggtautau_Era2018_private", "/hadoop/cms/store/user/fsetti/nanoAOD_runII/VBF_CV_1_C2V_0_C3_1_HHggtautau_2018_20210420_v1_STEP4_v1/", 10, "2018MC"),
## resonant
("radionM300_HHggtautau_Era2017_private", "/hadoop/cms/store/user/hmei/nanoAOD_runII/res_Radion_M300_HHggtautau_2017_20210422_v1_STEP4_v1/", 10, "2017MC"),
("radionM400_HHggtautau_Era2017_private", "/hadoop/cms/store/user/hmei/nanoAOD_runII/res_Radion_M400_HHggtautau_2017_20210422_v1_STEP4_v1/", 10, "2017MC"),
("radionM500_HHggtautau_Era2017_private", "/hadoop/cms/store/user/hmei/nanoAOD_runII/res_Radion_M500_HHggtautau_2017_20210422_v1_STEP4_v1/", 10, "2017MC"),
("radionM800_HHggtautau_Era2017_private", "/hadoop/cms/store/user/hmei/nanoAOD_runII/res_Radion_M800_HHggtautau_2017_20210422_v1_STEP4_v1/", 10, "2017MC"),
("radionM1000_HHggtautau_Era2017_private", "/hadoop/cms/store/user/hmei/nanoAOD_runII/res_Radion_M1000_HHggtautau_2017_20210422_v1_STEP4_v1/", 10, "2017MC"),
("radionM300_HHggtautau_Era2018_private", "/hadoop/cms/store/user/hmei/nanoAOD_runII/res_Radion_M300_HHggtautau_2018_20210422_v1_STEP4_v1/", 10, "2018MC"),
("radionM400_HHggtautau_Era2018_private", "/hadoop/cms/store/user/hmei/nanoAOD_runII/res_Radion_M400_HHggtautau_2018_20210422_v1_STEP4_v1/", 10, "2018MC"),
("radionM500_HHggtautau_Era2018_private", "/hadoop/cms/store/user/hmei/nanoAOD_runII/res_Radion_M500_HHggtautau_2018_20210422_v1_STEP4_v1/", 10, "2018MC"),
("radionM800_HHggtautau_Era2018_private", "/hadoop/cms/store/user/hmei/nanoAOD_runII/res_Radion_M800_HHggtautau_2018_20210422_v1_STEP4_v1/", 10, "2018MC"),
("radionM1000_HHggtautau_Era2018_private", "/hadoop/cms/store/user/hmei/nanoAOD_runII/res_Radion_M1000_HHggtautau_2018_20210422_v1_STEP4_v1/", 10, "2018MC")
]
# some job configurations
job_dir = "nanoaod_runII/HHggtautau/"
job_tag = args.tag
job_filter = args.filter
ds_filter = args.dsfilter
skip_central = args.skip_central
hadoop_path = "{0}".format(job_dir)
#cmssw_ver = "CMSSW_10_2_22"
cmssw_ver = "CMSSW_10_6_20"
DOSKIM = False
#exec_path = "condor_exe_%s.sh" % args.tag
exec_path = "condor_exe.sh"
#tar_path = "nanoAOD_package_%s.tar.gz" % args.tag
if not args.soft_rerun:
# os.system("rm -rf tasks/*" + args.tag + "*")
os.system("rm package.tar.gz")
os.system("XZ_OPT='-3e -T24' tar -Jc --exclude='.git' --exclude='*.root' --exclude='*.tar*' --exclude='*.out' --exclude='*.err' --exclude='*.log' --exclude '*.nfs*' -f package.tar.gz %s" % cmssw_ver)
#os.system("cp package.tar.gz /hadoop/cms/store/user/smay/FCNC/tarballs/%s" % tar_path)
#os.system("hadoop fs -setrep -R 30 /cms/store/user/smay/FCNC/tarballs/%s" % tar_path)
total_summary = {}
while True:
allcomplete = True
# Loop through central samples
for ds,fpo,args in dsdefs[:]:
if skip_central: continue
if (job_filter != "") and (args not in job_filter) : continue
if (ds_filter != "") and (ds_filter not in ds) : continue
sample = DBSSample( dataset=ds )
print(ds, args)
task = CondorTask(
sample = sample,
open_dataset = False,
files_per_output = fpo,
output_name = "test_nanoaod.root",
tag = job_tag,
cmssw_version = cmssw_ver,
executable = exec_path,
tarfile = "./package.tar.gz",
condor_submit_params = {"sites" : "T2_US_UCSD",
#"classads": [["SingularityImage","/cvmfs/singularity.opensciencegrid.org/cmssw/cms:rhel6-m202006"]]},
"classads": [["SingularityImage","/cvmfs/singularity.opensciencegrid.org/cmssw/cms:rhel7-m202006"]]},
#"SingularityImage":"/cvmfs/singularity.opensciencegrid.org/cmssw/cms:rhel6-m202006"},
#condor_submit_params = {"sites" : "T2_US_UCSD,T2_US_CALTECH,T2_US_MIT,T2_US_WISCONSIN,T2_US_Nebraska,T2_US_Purdue,T2_US_Vanderbilt,T2_US_Florida"},
special_dir = hadoop_path,
arguments = args.replace(" ","|")
)
task.process()
allcomplete = allcomplete and task.complete()
# save some information for the dashboard
total_summary[ds] = task.get_task_summary()
with open("summary.json", "w") as f_out:
json.dump(total_summary, f_out, indent=4, sort_keys=True)
# Loop through local samples
for ds,loc,fpo,args in local_sets[:]:
sample = DirectorySample( dataset = ds, location = loc )
files = [f.name for f in sample.get_files()]
print "For sample %s in directory %s, there are %d input files" % (ds, loc, len(files))
#for file in files:
# print file
task = CondorTask(
sample = sample,
open_dataset = True,
files_per_output = fpo,
output_name = "test_nanoaod.root",
tag = job_tag,
cmssw_version = cmssw_ver,
executable = exec_path,
tarfile = "./package.tar.gz",
condor_submit_params = {"sites" : "T2_US_UCSD",
#"classads": [["SingularityImage","/cvmfs/singularity.opensciencegrid.org/cmssw/cms:rhel6-m202006"]]},
"classads": [["SingularityImage","/cvmfs/singularity.opensciencegrid.org/cmssw/cms:rhel7-m202006"]]},
special_dir = hadoop_path,
arguments = args.replace(" ","|")
)
task.process()
allcomplete = allcomplete and task.complete()
# save some information for the dashboard
total_summary[ds] = task.get_task_summary()
with open("summary.json", "w") as f_out:
json.dump(total_summary, f_out, indent=4, sort_keys=True)
# parse the total summary and write out the dashboard
StatsParser(data=total_summary, webdir="~/public_html/dump/metis_nanoaod_v8/").do()
os.system("chmod -R 755 ~/public_html/dump/metis_nanoaod_v8")
if allcomplete:
print ""
print "Job={} finished".format(job_tag)
print ""
break
print "Sleeping 1000 seconds ..."
time.sleep(1000)