def test_list_directories():
directories = rnp.list_directories(load('directories.root'))
assert_equal(set(directories), set(['Dir1', 'Dir2']))
def calculate_arrays(args):
input_file = args[0]
treename = args[1]
foldername = args[2]
output_file = args[3]
model_path = args[4]
full_output = args[5]
channel = args[6]
mode = args[7]
lock = args[8]
try:
arr = root2array(input_file,
foldername + "/" + treename,
branches=branches)
except:
# The input file has not been found or the tree size is 0
return (input_file, 0)
# pre-allocate the input vector to Keras
X = np.zeros([arr.shape[0], len(branches)])
L = np.zeros([arr.shape[0], 4])
# convert inputs to cartesian coordinates
for index, a in enumerate(arr):
tau_1 = FourMomentum(a[0], a[1], a[2], a[3], False)
tau_2 = FourMomentum(a[4], a[5], a[6], a[7], False)
met = FourMomentum(0, a[8], 0, a[9], False)
metcovxx = np.sqrt(a[10])
metcovyy = np.sqrt(a[11])
metcovxy = a[12]
X[index, :] = np.array([
tau_1.e, tau_1.px, tau_1.py, tau_1.pz, tau_2.e, tau_2.px, tau_2.py,
tau_2.pz, met.px, met.py, metcovxx, metcovyy, metcovxy
])
visible = tau_1 + tau_2
L[index, :] = visible.as_numpy_array()
Y = predict(model_path, X, channel)
# convert Y to usual hadron-collider coordinates
fullvector_hc, fullvector_cartesian = full_fourvector(
Y,
L,
cartesian_types=[("e_N", np.float64), ("px_N", np.float64),
("py_N", np.float64), ("pz_N", np.float64)],
hc_types=[("pt_N", np.float64), ("eta_N", np.float64),
("phi_N", np.float64), ("m_N", np.float64)])
#outputs = [fullvector_hc, fullvector_cartesian]
outputs = [fullvector_hc]
# if mode == 'copy':
# outputs.append(arr_all)
# if full_output:
# for i in range(2):
# neutrino_four_momenta = []
# for line in range(Y.shape[0]):
# neutrino_four_momenta.append(FourMomentum(Y[line,13+4*i], Y[line,14+4*i], Y[line,15+4*i], Y[line,16+4*i], cartesian=True))
# s = "_n" + str(i+1)
# neutrino_hc, neutrino_cartesian = transform_fourvector(neutrino_four_momenta,
# cartesian_types = [("e"+s,np.float64), ("px"+s, np.float64), ("py"+s, np.float64), ("pz"+s, np.float64)],
# hc_types = [("pt"+s,np.float64), ("eta"+s, np.float64), ("phi"+s, np.float64), ("m"+s, np.float64)])
# outputs.append(neutrino_hc)
# outputs.append(neutrino_cartesian)
lock.acquire()
#print os.getpid(), ": lock hold by process creating", output_file, " lock: ", lock
if not os.path.exists(os.path.dirname(output_file)):
os.makedirs(os.path.dirname(output_file))
if not os.path.exists(output_file):
if mode == 'copy':
copyfile(input_file, output_file)
f = TFile(output_file, "UPDATE")
if not foldername in list_directories(output_file):
f.mkdir(foldername)
if mode == 'friend':
tree = None
elif mode == 'copy':
tree = f.Get(foldername + "/" + treename)
getattr(f, foldername).cd()
for output in outputs:
tree = array2tree(output, name=treename, tree=tree)
f.Write("", r.TObject.kOverwrite)
f.Close()
lock.release()
#print os.getpid(), ": lock released by process "
return (input_file, X.shape[0])
def test_list_directories():
directories = rnp.list_directories(load('directories.root'))
assert_equal(set(directories), set(['Dir1', 'Dir2']))
15.9), np.percentile(outputs[nick], 84.1)
print nick, "CORRECTED", "median, down, up:", np.percentile(
diffcorr, 50.0), np.percentile(diffcorr, 15.9), np.percentile(
diffcorr,
84.1), "absolute median, down, up:", np.percentile(
outputs_corrected[nick], 50.0), np.percentile(
outputs_corrected[nick],
15.9), np.percentile(outputs_corrected[nick], 84.1)
print "-----------------------------------------"
outputs[nick] = np.array(outputs[nick],
dtype=[('m_DDT', np.float32)])
outputs_corrected[nick] = np.array(outputs_corrected[nick],
dtype=[('m_DDTcorr', np.float32)
])
if not foldername in list_directories(output_file):
F.mkdir(foldername)
tree = F.Get("/".join([foldername, treename]))
getattr(F, foldername).cd()
print "writing DNN outputs to tree"
tree = array2tree(outputs[nick], name=treename, tree=tree)
tree = array2tree(outputs_corrected[nick],
name=treename,
tree=tree)
print "Saving tree to file"
F.Write("", r.TObject.kOverwrite)
print "Closing file"
F.Close()
exit()