def __init__(self):
self.p4 = thep4
self.px = px
self.py = py
self.pz = pz
self.en = energy
self.pt = np.hypot(px, py)
self.phi = np.arctan2(py, px)
self.eta = np.arctanh(pz / np.sqrt(px * px + py * py + pz * pz))
self.mass = np.sqrt(
np.abs(energy * energy - (px * px + py * py + pz * pz)))
self.blah = energy * px
self.count = counts
def _fast_eta(p4):
""" quick eta calculation for caching """
px = p4.x
py = p4.y
pz = p4.z
pT = np.sqrt(px * px + py * py)
return np.arcsinh(pz / pT)
def _fast_mass(p4):
""" quick mass calculation for caching """
px = p4.x
py = p4.y
pz = p4.z
en = p4.t
p3mag2 = (px * px + py * py + pz * pz)
return np.sqrt(np.abs(en * en - p3mag2))
def dummy_four_momenta():
np.random.seed(12345)
nrows = 1000
counts = np.minimum(np.random.exponential(0.5, size=nrows).astype(int), 20)
px = np.random.normal(loc=20.0, scale=5.0, size=np.sum(counts))
py = np.random.normal(loc=20.0, scale=5.0, size=np.sum(counts))
pz = np.random.normal(loc=0, scale=55, size=np.sum(counts))
m_pi = np.full_like(px, fill_value=0.135)
energy = np.sqrt(px * px + py * py + pz * pz + m_pi * m_pi)
return (counts, px, py, pz, energy)
def convert_histo_root_file(file):
converted_file = {}
fin = uproot.open(file.strip())
for path, item in fin.iteritems(recursive=True):
nicepath = killcycle(path)
rootclass = item._classname
if rootclass in histTypes:
converted_file[(nicepath, 'dense_lookup')] = item.values, item.edges
if hasattr(item,'variances'):
converted_file[(nicepath+'_error'.encode(), 'dense_lookup')] = np.sqrt(item.variances), item.edges
elif rootclass in graphTypes:
# TODO: convert TGraph into interpolated lookup
continue
elif hasattr(item, '_fEX') and hasattr(item, '_fEY'):
# TODO what is this?
tempArrX = item._fEX
tempArrY = item._fEY
converted_file[(nicepath, 'dense_lookup')] = [tempArrX, tempArrY]
return converted_file
def calculateTopKFactor(top_pts, antitop_pts):
w1 = np.exp(0.0615 - 0.0005 * top_pts)
w2 = np.exp(0.0615 - 0.0005 * antitop_pts)
return np.sqrt(w1 * w2).content