def run_obs_signif(output_path, overwrite, log_path):
with utils.ROOTFile(output_path, "read") as rfile:
if not overwrite and rfile.Get("sig_obs"):
print(" Kept observed significance: {:8.3f}".format(
rfile.Get("sig_obs")[0]))
return
cwd = os.getcwd()
work_dir = tempfile.mkdtemp()
link_path = os.path.join(work_dir, os.path.basename(output_path))
os.symlink(output_path, link_path)
os.chdir(work_dir)
command = [
"combine", "-M", "ProfileLikelihood", "--significance", "--uncapped=1",
"--rMin=-10.", "-d", link_path
]
with open(log_path, "a") as dn:
subprocess.check_call(command, stdout=dn, stderr=dn)
os.chdir(cwd)
result_path = os.path.join(
work_dir, "higgsCombineTest.ProfileLikelihood.mH120.root")
signif = None
with utils.ROOTFile(result_path, "read") as result, utils.ROOTFile(
output_path, "update") as rfile:
tree = result.Get("limit")
tree.GetEntry(0)
signif = tree.limit
signif_vec = ROOT.TVectorD(1)
signif_vec[0] = signif
rfile.cd()
signif_vec.Write("sig_obs", ROOT.TObject.kWriteDelete)
shutil.rmtree(work_dir)
print("Saved observed significance: {:8.3f}".format(signif))
def run_fit(output_path, overwrite, log_path):
with utils.ROOTFile(output_path, "read") as rfile:
if not overwrite and rfile.Get("fit_b") and rfile.Get("fit_s"):
print(" Kept fit results")
return
cwd = os.getcwd()
work_dir = tempfile.mkdtemp()
link_path = os.path.join(work_dir, os.path.basename(output_path))
os.symlink(output_path, link_path)
os.chdir(work_dir)
command = [
"combine", "-M", "MaxLikelihoodFit", "--forceRecreateNLL",
"--saveWorkspace", "--saveWithUncertainties", "--minos=all", "-w", "w",
"--dataset", "data_obs", "-d", link_path
]
with open(log_path, "a") as dn:
subprocess.check_call(command, stdout=dn, stderr=dn)
os.chdir(cwd)
result_path = os.path.join(work_dir, "mlfit.root")
with utils.ROOTFile(result_path, "read") as result, utils.ROOTFile(
output_path, "update") as rfile:
fit_b = result.Get("fit_b")
rfile.cd()
fit_b.Write("fit_b", ROOT.TObject.kWriteDelete)
fit_s = result.Get("fit_s")
rfile.cd()
fit_s.Write("fit_s", ROOT.TObject.kWriteDelete)
shutil.rmtree(work_dir)
print("Saved fit results")
def goodness_of_fit(output_path, overwrite, ndof):
sat_nll = 0.
fit_nll = 0.
with utils.ROOTFile(output_path, "read") as rfile:
pval = rfile.Get("pval")
chi_sq = rfile.Get("chi2")
saved_ndof = rfile.Get("ndof")
if not overwrite and (pval or chi_sq or saved_ndof):
if pval:
print(" Kept p-value: {:8.3f}".format(pval[0]))
if chi_sq:
print(" Kept chi^2: {:8.3f}".format(chi_sq[0]))
if saved_ndof:
print(" Kept N.D.o.F.: {:4d}".format(
int(saved_ndof[0])))
return
if not rfile.Get("w_orig"):
print(
'Skipping goodness of fit. Run with "--full_fit --overwrite" first to enable.'
)
return
w = rfile.Get("w")
pdf = w.pdf("model_b")
fit_nll = -pdf.getLogVal()
sat_nll = 0.
variables = w.allVars()
iterator = variables.createIterator()
variable = iterator.Next()
while variable:
name = variable.GetName()
if "nobs_BLK_" in name or "nobsmc_BLK_" in name:
val = variable.getVal()
if val > 0:
sat_nll -= val * math.log(val) - val - math.lgamma(val +
1.)
variable = iterator.Next()
chi_sq = 2. * (fit_nll - sat_nll)
pval = scipy.stats.chi2.sf(chi_sq, ndof)
chi_sq_vec = ROOT.TVectorD(1)
ndof_vec = ROOT.TVectorD(1)
pval_vec = ROOT.TVectorD(1)
chi_sq_vec[0] = chi_sq
ndof_vec[0] = float(ndof)
pval_vec[0] = pval
with utils.ROOTFile(output_path, "update") as rfile:
rfile.cd()
chi_sq_vec.Write("chi2", ROOT.TObject.kWriteDelete)
ndof_vec.Write("ndof", ROOT.TObject.kWriteDelete)
pval_vec.Write("pval", ROOT.TObject.kWriteDelete)
print("Saved p-value: {:8.3f}".format(pval))
print("Saved chi^2: {:8.3f}".format(chi_sq))
print("Saved N.D.o.F.: {:4d}".format(int(ndof)))
def bin_significance(out_dir, input_path, ndof):
input_path = utils.full_path(input_path)
out_dir = utils.full_path(out_dir)
input_name = os.path.splitext(os.path.basename(input_path))[0]
with utils.ROOTFile(input_path, "read") as f:
w = f.Get("w")
fit_b = f.Get("fit_b")
fit_s = f.Get("fit_s")
bins = [[b, 0., 0., 0.] for b in get_bin_names(w)]
procs = get_process_names(w)
constraints = get_constraint_names(w)
r_sign = 0
r = fit_s.floatParsFinal().find("r")
if r.getVal() < 0.:
r_sign = -1
elif r.getVal() > 0.:
r_sign = 1
find_sat_nlls(bins, procs, w)
find_bkg_nlls(bins, procs, constraints, w, fit_b)
find_sig_nlls(bins, procs, constraints, w, fit_s)
print("{:>48s}: {:>12s} {:>12s} {:>12s}".format(
"Bin Name", "Bkg NLL", "Sig NLL", "Sat NLL"))
for b in bins:
print("{:>48s}: {:>12.3f} {:>12.3f} {:>12.3f}".format(
b[0], b[1], b[2], b[3]))
make_plot(out_dir, input_name, bins, ndof, r_sign, 1, 2)
make_plot(out_dir, input_name, bins, ndof, 0, 1, 3)
def fix_vars(output_path):
with utils.ROOTFile(output_path, "read") as rfile:
if not rfile.Get("fit_b"):
return
with utils.ROOTFile(output_path, "update") as rfile:
w = rfile.Get("w")
f = rfile.Get("fit_b")
if not rfile.Get("w_orig"):
w.Write("w_orig")
pars = f.floatParsFinal()
for i in range(pars.getSize()):
p = pars.at(i)
v = w.var(p.GetName())
v.setVal(p.getVal())
v.setError(p.getError())
w.Write("w", ROOT.TObject.kWriteDelete)
print("Set variables to background-only best fit values")
def run_limit(output_path, overwrite, log_path):
with utils.ROOTFile(output_path, "read") as rfile:
if not overwrite and rfile.Get("lim_obs") and rfile.Get(
"lim_exp16") and rfile.Get("lim_exp50") and rfile.Get(
"lim_exp84"):
print(" Kept observed limit: {:8.3f}".format(
rfile.Get("lim_obs")[0]))
print(" Kept expected limit (16%): {:8.3f}".format(
rfile.Get("lim_exp16")[0]))
print(" Kept expected limit (50%): {:8.3f}".format(
rfile.Get("lim_exp50")[0]))
print(" Kept expected limit (84%): {:8.3f}".format(
rfile.Get("lim_exp84")[0]))
return
cwd = os.getcwd()
work_dir = tempfile.mkdtemp()
link_path = os.path.join(work_dir, os.path.basename(output_path))
os.symlink(output_path, link_path)
os.chdir(work_dir)
command = ["combine", "-M", "Asymptotic", "-d", link_path]
with open(log_path, "a") as dn:
subprocess.check_call(command, stdout=dn, stderr=dn)
os.chdir(cwd)
result_path = os.path.join(work_dir,
"higgsCombineTest.Asymptotic.mH120.root")
obs = None
exp16 = None
exp50 = None
exp84 = None
with utils.ROOTFile(result_path, "read") as result, utils.ROOTFile(
output_path, "update") as rfile:
tree = result.Get("limit")
for entry in tree:
if entry.quantileExpected < 0.:
obs = entry.limit
elif entry.quantileExpected > 0.15 and entry.quantileExpected < 0.17:
exp16 = entry.limit
elif entry.quantileExpected > 0.49 and entry.quantileExpected < 0.51:
exp50 = entry.limit
elif entry.quantileExpected > 0.83 and entry.quantileExpected < 0.85:
exp84 = entry.limit
obs_vec = ROOT.TVectorD(1)
exp16_vec = ROOT.TVectorD(1)
exp50_vec = ROOT.TVectorD(1)
exp84_vec = ROOT.TVectorD(1)
obs_vec[0] = obs
exp16_vec[0] = exp16
exp50_vec[0] = exp50
exp84_vec[0] = exp84
rfile.cd()
obs_vec.Write("lim_obs", ROOT.TObject.kWriteDelete)
exp16_vec.Write("lim_exp16", ROOT.TObject.kWriteDelete)
exp50_vec.Write("lim_exp50", ROOT.TObject.kWriteDelete)
exp84_vec.Write("lim_exp84", ROOT.TObject.kWriteDelete)
shutil.rmtree(work_dir)
print("Saved observed limit: {:8.3f}".format(obs))
print("Saved expected limit (16%): {:8.3f}".format(exp16))
print("Saved expected limit (50%): {:8.3f}".format(exp50))
print("Saved expected limit (84%): {:8.3f}".format(exp84))