def masked_bin_eval(dim1_indices, dimN_bins, dimN_vals):
dimN_indices = np.empty_like(dim1_indices)
for i in np.unique(dim1_indices):
idx = np.where(dim1_indices == i)
dimN_indices[idx] = np.clip(
np.searchsorted(dimN_bins[i], dimN_vals[idx], side='right') - 1, 0,
len(dimN_bins[i]) - 2)
return dimN_indices
def parseGeneratorHistory(gp_pdgId_in,gp_parent_in):
inChain = activePdgIds
#index manipulation
offsets = gp_pdgId_in.offsets
parents = gp_pdgId_in.parents
pstarts = offsets[parents].astype('i4')
gp_pdgId = gp_pdgId_in.content
gp_ancestor = gp_parent_in.content + pstarts
gp_ancestor_valid = (gp_parent_in.content >= 0)
#create parentage bitmaps
gp_pdgId_mapped = np.zeros(shape=gp_pdgId.shape, dtype='u4')
for pdgId, bit in inChain.items():
if abs(pdgId) == 24:
gp_pdgId_mapped[gp_pdgId==pdgId] = bit
else:
gp_pdgId_mapped[np.abs(gp_pdgId)==pdgId] = bit
gp_proc = np.zeros(shape=gp_pdgId.shape, dtype='u4')
pdg_tmp = np.empty_like(gp_pdgId_mapped)
parent_tmp = np.empty_like(gp_ancestor)
niter = 0
while np.any(gp_ancestor_valid) and niter < 50:
np.take(gp_pdgId_mapped, gp_ancestor, out=pdg_tmp, mode='clip')
np.take(gp_parent_in.content, gp_ancestor, out=parent_tmp, mode='clip')
np.bitwise_or(gp_proc, pdg_tmp, where=gp_ancestor_valid, out=gp_proc)
np.bitwise_and(gp_ancestor_valid, parent_tmp>=0, where=gp_ancestor_valid, out=gp_ancestor_valid)
np.add(parent_tmp, pstarts, out=gp_ancestor)
niter += 1
#print 'Parsed ancestor tree in %d iterations'%niter
if niter == 50 and np.any(gp_ancestor_valid):
raise Exception('reached 50 iterations, gen particles not trustable')
return awkward.JaggedArray.fromoffsets(offsets, gp_proc)
def poisson_interval(sumw, sumw2, coverage=_coverage1sd):
"""
sumw: sum of weights
sumw2: sum weights**2
coverage: coverage, default to 68%
The so-called 'Garwood' interval
c.f. https://www.ine.pt/revstat/pdf/rs120203.pdf
or http://ms.mcmaster.ca/peter/s743/poissonalpha.html
For weighted data, approximate the observed count by sumw**2/sumw2
This choice effectively scales the unweighted poisson interval by the average weight
Maybe not the best... see https://arxiv.org/pdf/1309.1287.pdf for a proper treatment
When a bin is zero, find the scale of the nearest nonzero bin
If all bins zero, raise warning and set interval to sumw
"""
scale = np.empty_like(sumw)
scale[sumw != 0] = sumw2[sumw != 0] / sumw[sumw != 0]
if np.sum(sumw == 0) > 0:
missing = np.where(sumw == 0)
available = np.nonzero(sumw)
if len(available[0]) == 0:
warnings.warn(
"All sumw are zero! Cannot compute meaningful error bars",
RuntimeWarning)
return np.vstack([sumw, sumw])
nearest = sum([
np.subtract.outer(d, d0)**2 for d, d0 in zip(available, missing)
]).argmin(axis=0)
argnearest = tuple(dim[nearest] for dim in available)
scale[missing] = scale[argnearest]
counts = sumw / scale
lo = scale * scipy.stats.chi2.ppf((1 - coverage) / 2, 2 * counts) / 2.
hi = scale * scipy.stats.chi2.ppf(
(1 + coverage) / 2, 2 * (counts + 1)) / 2.
interval = np.array([lo, hi])
interval[interval == np.nan] = 0. # chi2.ppf produces nan for counts=0
return interval