# add information needed for MC corrections
parameters["pu_corrections_target"] = load_puhist_target(parameters["pu_corrections_file"])
ext = extractor()
for corr in parameters["corrections"]:
ext.add_weight_sets([corr])
ext.finalize()
evaluator = ext.make_evaluator()
if ibatch == 0:
print(dataset.printout())
# in case of DNN evaluation: load model
model = None
if args.DNN:
model = load_model(args.path_to_model, custom_objects=dict(itertools=itertools, mse0=mse0, mae0=mae0, r2_score0=r2_score0))
print(args.categories)
#### this is where the magic happens: run the main analysis
results += dataset.analyze(analyze_data, NUMPY_LIB=NUMPY_LIB, parameters=parameters, is_mc = is_mc, lumimask=lumimask, cat=args.categories, sample=args.sample, samples_info=samples_info, boosted=args.boosted, DNN=args.DNN, DNN_model=model)
print(results)
#Save the results
if not os.path.isdir(args.outdir):
os.makedirs(args.outdir)
results.save_json(os.path.join(outdir,"out_{}.json".format(args.sample)))
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(
loaded_model_json,
custom_objects=dict(itertools=itertools))
model.load_weights(args.path_to_model + "model.hdf5")
print(args.categories)
#### this is where the magic happens: run the main analysis
results += dataset.analyze(
analyze_data,
NUMPY_LIB=NUMPY_LIB,
parameters=parameters,
is_mc=is_mc,
lumimask=lumimask,
cat=args.categories,
sample=args.sample,
samples_info=samples_info,
DNN=args.DNN,
DNN_model=model,
jets_met_corrected=args.jets_met_corrected)
print(results)
#Save the results
if not os.path.isdir(args.outdir):
os.makedirs(args.outdir)
results.save_json(
os.path.join(outdir, "out_{0}{1}.json".format(args.sample,
args.outtag)))
import numpy as np
from hepaccelerate.utils import Histogram, Results
from glob import glob
import json,os,argparse
from pdb import set_trace
flist = glob('results/201*/v12/met20_btagDDBvL086/nominal/btagEfficiencyMaps/out_btagEfficiencyMaps_*json')
def divide(h1,h2):
contents = h1.contents/h2.contents
contents_w2 = h1.contents_w2/h2.contents_w2
edges = h1.edges
return Histogram(contents, contents_w2, edges)
for fn in flist:
with open(fn) as f:
data = json.load(f)
for h in data:
data[h] = Histogram( *data[h].values() )
for flav in ['b','l','lc']:
for var in ['central','updown']:
data[f'eff_flav{flav}_{var}'] = divide( data[f'btags_flav{flav}_{var}'], data[f'total_flav{flav}_{var}'] )
ret = Results(data)
ret.save_json(fn)
