def _dump(a):
PInfo('PandaAnalysis.Flat.analysis', 'Summary of analysis %s:' % (a.name))
for k in dir(a):
if k[0] == '_':
continue
PInfo(
'PandaAnalysis.Flat.analysis',
' %20s = %s' % (k, 'True' if bool(getattr(a, k)) else 'False'))
def _dump(a):
PInfo('PandaAnalysis.Flat.analysis','Summary of analysis %s:'%(a.name))
print a.monojet
for k in dir(a):
if k[0] == '_':
continue
if type(getattr(a, k)) != int:
continue
PInfo('PandaAnalysis.Flat.analysis',' %20s = %s'%(k, 'True' if getattr(a, k) else 'False'))
def process_tree(t, fpath, n_to_print=0, do_truth=True):
N = t.GetEntriesFast()
imgpath = fpath + '_img%i.png'
gen_arrs = []
truth_arrs = []
dims = None
threshold = 0
for iE in xrange(N):
if float(iE) / N > threshold:
PInfo('convert_images.process_tree',
'%5.2f (%i/%i)' % (float(iE) / N, iE, N))
threshold += 0.1
t.GetEntry(iE)
gen = t.gen
truth = t.truth
if n_to_print > 0 and iE < n_to_print:
img(gen, imgpath % iE)
img(truth, (imgpath % iE).replace('.png', '_truth.png'))
if not dims:
dims = (gen.GetNbinsX(), gen.GetNbinsY())
gen_arrs.append(conv(gen, dims))
truth_arrs.append(conv(truth, dims, dtype=np.int16))
gen_arr = np.array(gen_arrs)
np.save(fpath + '_gen.npy', gen_arr)
truth_arr = np.array(truth_arrs)
np.save(fpath + '_truth.npy', truth_arr)
def run(self, f_out_path):
f_out = root.TFile.Open(f_out_path, 'RECREATE')
for proc in chain(self.__data_procs, self.__mc_procs):
proc.run(f_out)
f_out.Close()
PInfo('fitting_forest.RegionFactory.run',
'Created output in %s' % f_out_path)
def run(self, f_out):
PInfo('fitting_forest.Process.run', 'Running ' + self.name)
branches = sorted(self.all_branches.values())
try:
xarr = root_interface.read_tree(tree=self.tree,
branches=branches,
cut=self.cut)
fields = self.variables.keys() + ['nominal']
self.__write_out(f_out, xarr, fields, '')
for shift, weight in self.weights.iteritems():
fields = self.variables.keys() + [shift]
self.__write_out(f_out, xarr, fields, '_' + shift)
except ValueError as e:
PError('fitting_forest.Process.run', str(e))
return
last_lock = int(time())
if args.submit_only and (mc.missing + data.missing > 0):
submit(silent=(args.monitor is not None))
if args.monitor:
sleep(1)
else:
return
### MAIN ###
if args.kill:
kill()
if args.clean_output:
PInfo('task.py', 'Cleaning up %s and %s' % (lockdir, outdir))
sleep(2)
system('rm -rf %s/* %s/* &' % (lockdir, outdir))
if args.clean:
PInfo('task.py', 'Cleaning up %s and %s' % (logdir, workdir))
sleep(2)
system('rm -rf %s/* %s/*' % (logdir, workdir))
if args.check:
if args.monitor is not None:
curses.wrapper(check)
else:
check()
else:
PInfo('task.py', 'TASK = ' + submit_name)
if args.build_only and (not path.isfile(workdir + '/submission.pkl')
import argparse
import subprocess
from re import sub
from os import getenv
from PandaCore.Tools.Misc import PInfo
from PandaCore.Tools.job_management import DataSample,convert_catalog
workdir = getenv('SUBMIT_WORKDIR')
parser = argparse.ArgumentParser(description='convert configuration')
parser.add_argument('--infile',type=str,default=None)
parser.add_argument('--outfile',type=str,default=None)
parser.add_argument('--nfiles',type=int,default=None)
args = parser.parse_args()
fin = open(args.infile)
samples = convert_catalog(list(fin),as_dict=True)
fout = open(args.outfile,'w')
keys = sorted(samples)
counter=0
for k in keys:
sample = samples[k]
configs = sample.get_config(args.nfiles,suffix='_%i')
for c in configs:
fout.write(c%(counter,counter))
counter += 1
PInfo('buildConfig.py','Submission will have %i jobs'%(counter))
fout.close()
h_pt = f_pt.Get('h_%s' % (name.split('_')[0]))
f_pt_scaled = root.TFile.Open(datadir + 'flatten_scaled.root')
h_pt_scaled = f_pt_scaled.Get('h_%s' % (name.split('_')[0]))
data = {}
for fpath in fcfg.readlines():
d = np.load(fpath.strip())
mask = (d['nPartons'] == n_partons)
for k, v in d.iteritems():
if v.shape[0]:
if k not in data:
data[k] = []
data[k].append(v[mask])
if not len(data):
PInfo(me, 'This was an empty config!')
exit(0)
for k, v in data.iteritems():
data[k] = np.concatenate(v)
if not data['pt'].shape[0]:
PInfo(me, 'Nothing passed the mask')
exit(0)
if NORM:
deep_utils.normalize_arrays(data, 'pf')
deep_utils.normalize_arrays(data, 'sv')
def reweight(x_pt):
import argparse
import subprocess
from re import sub
from os import getenv
from PandaCore.Tools.Misc import PInfo
from PandaCore.Tools.job_management import DataSample, convert_catalog
workdir = getenv('SUBMIT_WORKDIR')
parser = argparse.ArgumentParser(description='convert configuration')
parser.add_argument('--infile', type=str, default=None)
parser.add_argument('--outfile', type=str, default=None)
parser.add_argument('--nfiles', type=int, default=None)
args = parser.parse_args()
fin = open(args.infile)
samples = convert_catalog(list(fin), as_dict=True)
fout = open(args.outfile, 'w')
keys = sorted(samples)
counter = 0
for k in keys:
sample = samples[k]
configs = sample.get_config(args.nfiles, suffix='_%i')
for c in configs:
fout.write(c % (counter, counter))
counter += 1
PInfo('configBuilder.py', 'Submission will have %i jobs' % (counter))
fout.close()
flist = glob(argv[2])
target_evt = np.int64(argv[1])
lumis = []
pts = []
msds = []
pfs = []
for f in flist:
arr = np.load(f)
evt = arr['eventNumber']
mask = (evt == target_evt)
if np.sum(mask):
idx = np.argmax(mask)
pfs.append(arr['pf'][idx])
pts.append(arr['pt'][idx])
msds.append(arr['msd'][idx])
lumis.append(arr['lumi'])
PInfo(argv[0], 'Found %i in %s' % (target_evt, f))
if lumis:
np.savez('sliced.npz',
pf=np.array(pfs),
msd=np.array(msds),
pt=np.array(pts),
lumi=np.array(lumis))
else:
PError(argv[0], 'Could not find %i in %s' % (target_evt, argv[2]))
exit(1)