def get_one_event(config='w.config', random_state=1, gen_params=None, **kwargs):
gen_params = gen_params or dict()
gen_params.setdefault('verbosity', 0)
gen_input = get_generator_input('pythia', config,
random_state=random_state,
**gen_params)
return list(cluster(generate_events(gen_input, ignore_weights=True), 1, **kwargs))[0]
def get_one_event_reco(pythia_config='w.config', pythia_random_state=1, delphes_random_state=1,
gen_params=None, delphes_params=None, **kwargs):
gen_params = gen_params or dict()
gen_params.setdefault('verbosity', 0)
delphes_params = delphes_params or dict()
gen_input = get_generator_input('pythia', pythia_config,
random_state=pythia_random_state,
**gen_params)
return list(cluster(reconstruct(generate_events(gen_input, ignore_weights=True),
random_state=delphes_random_state, **delphes_params),
1, **kwargs))[0]
def test_hdf5_vs_direct_hepmc():
testfile = get_filepath('sherpa_wz.hepmc')
with tempfile.NamedTemporaryFile() as tmp:
with h5.File(tmp.name, 'w') as h5output:
create_event_datasets(h5output, 1)
h5output['events'][0] = list(generate_events(HepMCInput(testfile), 1))[0]
h5input = h5.File(tmp.name, 'r')
jets = list(cluster(reconstruct(h5input['events'], random_state=1)))[0]
jets_direct = list(cluster(reconstruct(HepMCInput(testfile), random_state=1)))[0]
assert_equal(jets.jets[0]['pT'], jets_direct.jets[0]['pT'])
def test_hdf5_vs_direct_pythia():
testfile = get_filepath('pythia_wz.config')
with tempfile.NamedTemporaryFile() as tmp:
with h5.File(tmp.name, 'w') as h5output:
create_event_datasets(h5output, 1)
h5output['events'][0] = list(
generate_events(
get_generator_input('pythia', testfile, verbosity=0,
random_state=1), 1, ignore_weights=True))[0]
h5input = h5.File(tmp.name, 'r')
jets = list(cluster(reconstruct(h5input['events'], random_state=1)))[0]
jets_direct = list(
cluster(
reconstruct(
generate_events(
get_generator_input('pythia', testfile, verbosity=0,
random_state=1), 1, ignore_weights=True), random_state=1)))[0]
assert_true(jets.jets[0]['pT'] > 0)
assert_equal(jets.jets[0]['pT'], jets_direct.jets[0]['pT'])
def generate_event(renorm_fac=1., factor_fac=1., random_state=1):
# generate one event
gen_input = get_generator_input('pythia',
'w.config',
random_state=random_state,
verbosity=0,
params_dict={
'PhaseSpace:pTHatMin': 250,
'PhaseSpace:pTHatMax': 300,
'SigmaProcess:renormMultFac':
renorm_fac,
'SigmaProcess:factorMultFac':
factor_fac
})
event = list(
cluster(reconstruct(generate_events(gen_input, ignore_weights=True),
random_state=1),
events=1,
jet_size=1.0,
subjet_size=0.3,
trimmed_pt_min=250,
trimmed_pt_max=300,
trimmed_mass_min=65,
trimmed_mass_max=95))[0]
edges = pixel_edges(jet_size=1.0, pixel_size=(0.1, 0.1), border_size=0)
image = preprocess(event.subjets,
event.trimmed_constit,
edges,
zoom=1.,
normalize=True,
out_width=25)
return image
def test_cluster_reconstruct_generate_length():
assert_equal(len(list(cluster(reconstruct(generate_events('w.config', ignore_weights=True, verbosity=0)), 1))), 1)
assert_equal(len(list(cluster(reconstruct(generate_events('w.config', ignore_weights=True, verbosity=0)), 10))), 10)
assert_equal(len(list(cluster(reconstruct(generate_events('w.config', ignore_weights=True, verbosity=0)), 100))), 100)
from deepjets.generate import generate_events
for event in generate_events('w_vincia.config', 1, write_to='vincia.hepmc', shower='vincia', random_state=1, verbosity=0):
pass
for event, weight in generate_events('w.config', 1, write_to='dire.hepmc', shower='dire', random_state=1, verbosity=0):
print weight
from deepjets.generate import generate_events, get_generator_input
from deepjets.clustering import cluster
from deepjets.detector import reconstruct
from deepjets.preprocessing import preprocess, pixel_edges
gen_input = get_generator_input('pythia',
'w.config',
random_state=1,
verbosity=0)
edges = pixel_edges(jet_size=1.0, pixel_size=(0.1, 0.1), border_size=0)
for particles in generate_events(gen_input, events=1, ignore_weights=True):
print particles # numpy record array
for particle_jets in cluster(generate_events(gen_input, ignore_weights=True),
events=1):
print particle_jets # jet struct
image = preprocess(particle_jets.subjets, particle_jets.trimmed_constit,
edges)
for towers in reconstruct(generate_events(gen_input, ignore_weights=True),
events=1,
random_state=1):
print towers # numpy record array
for tower_jets in cluster(reconstruct(generate_events(gen_input,
ignore_weights=True),
random_state=1),
events=1):
print tower_jets # jet struct
image = preprocess(tower_jets.subjets, tower_jets.trimmed_constit, edges)