def addtogroupsplot2(csfunc):
combinemap = {}
combinemap[0] = []
combinemap[1] = []
combinemap[2] = []
combinemap[3] = []
combinemap[4] = []
combinemap[5] = []
combinemap[6] = []
combinemap[7] = []
combinemap[8] = []
combinemap[9] = []
for r in testsample:
combinemap[0].append(csfunc(r))
combinemap[groupify(r)].append(csfunc(r))
groups = range(1, 10)
hadgroup = [0] * 9
hadgroup_err = [0] * 9
for g in groups:
hadgroup[g - 1], hadgroup_err[g - 1] = jt.wmean(combinemap[g])
center_of_groups = jt.wmean(combinemap[0])[0]
centers, errors = Numeric.array(
hadgroup) / center_of_groups, Numeric.array(
hadgroup_err) / center_of_groups
chi2 = 0.
for c, e in zip(centers, errors):
print(c - 1), e
chi2 += ((c - 1) / e)**2
print chi2
def addtogroupsplot(p, csfunc, offset, color, kind):
combinemap = {}
combinemap[0] = []
combinemap[1] = []
combinemap[2] = []
combinemap[3] = []
combinemap[4] = []
combinemap[5] = []
combinemap[6] = []
combinemap[7] = []
combinemap[8] = []
combinemap[9] = []
for r in testsample:
combinemap[0].append(csfunc(r))
combinemap[groupify(r)].append(csfunc(r))
groups = range(1, 10)
hadgroup = [0] * 9
hadgroup_err = [0] * 9
for g in groups:
hadgroup[g - 1], hadgroup_err[g - 1] = jt.wmean(combinemap[g])
center_of_groups = jt.wmean(combinemap[0])[0]
tmp = biggles.Points(Numeric.array(groups) + offset,
Numeric.array(hadgroup) / center_of_groups,
type=kind,
color=color)
p.add(tmp)
p.add(
biggles.SymmetricErrorBarsY(Numeric.array(groups) + offset,
Numeric.array(hadgroup) / center_of_groups,
Numeric.array(hadgroup_err) /
center_of_groups,
color=color))
p.add(biggles.LineY(1., type="longdashed"))
return tmp
def use_cross_section(csfunc, filename):
hads = []
hads_err = []
for r in testsample:
c, e = csfunc(r)
hads.append(c)
hads_err.append(e)
hads_center = jt.wmean(zip(hads, hads_err))[0]
p = biggles.FramedPlot()
p.add(
biggles.Points(range(len(testsample)),
Numeric.array(hads) / hads_center,
type="filled circle"))
p.add(
biggles.SymmetricErrorBarsY(range(len(testsample)),
Numeric.array(hads) / hads_center,
Numeric.array(hads_err) / hads_center))
p.x1.draw_ticklabels = 0
p.x1.label = "Runs by index"
p.y1.label = "Normalized hadronic cross-section"
p.add(biggles.LineY(1.))
p.add(biggles.LineX(41.5, type="dashed"))
l, r = 0.8, 1.2
p.yrange = l, r + 0.001
p.add(biggles.DataLabel(41.5 - 10, l + 0.15 * (r - l), "db16"))
p.add(biggles.DataLabel(41.5 + 10, l + 0.15 * (r - l), "db17"))
p.aspect_ratio = 8.5 / 11.
p.show()
p.write_eps(filename)
def group_correction_factor(gees):
combine_correction_factor = []
combine_correction_factor_err = []
for r, c, e in zip(testsample, correction_factor, correction_factor_err):
if groupify(r) in gees:
combine_correction_factor.append(c)
combine_correction_factor_err.append(e)
return jt.wmean(
zip(combine_correction_factor, combine_correction_factor_err))[0]
p1 = biggles.FramedPlot()
p1.add(biggles.Points(rnum, eff, symboltype="filled circle", symbolsize=0.7))
p1.add(biggles.SymmetricErrorBarsY(rnum, eff, efferr))
p1.add(biggles.LineY(1., linetype="dotted"))
p1.yrange = (0.987, 1.005)
p1.xrange = (min(rnum) - 100, max(rnum) + 100)
p1.x1.label = "Run number"
p1.y1.label = "BarrelBhabha trigger efficiency"
p1.add(
biggles.PlotLabel(0.5, 0.15,
"five outliers, all with suspicious notes in Elog"))
p1.aspect_ratio = 8.5 / 11. / 1.5
# p1.show()
p1.write_eps("/home/mccann/synthesis/lumieff/ptatalk/afterexclude.eps")
effmean, effmeanerr = jt.wmean(zip(eff, efferr))
effsigmas = map(lambda (e, err): (e - effmean) / (err + 1e-10),
zip(eff, efferr))
p1ahist = hist.h1(100, -5., 5.).fill(effsigmas)
p1a = p1ahist.plot()
p1a.xlabel = "Sigmas away from mean efficiency of " + ("%3.1f%s" %
(effmean * 100., "%"))
effmeanmean = jt.mean(filter(lambda x: math.fabs(x) < 5, effsigmas))
effstdev = jt.stdev(filter(lambda x: math.fabs(x) < 5, effsigmas))
efffailures = sorted(
zip(*filter(lambda (r, e): e < -5., zip(rnum, effsigmas)))[0])
addinfobox(p1a, [["stdev", effstdev], ["underflow", p1ahist.underflow]] +
efffailures,
numformat="%g")
p.x1.ticklabels = [r"$e^+e^-$", r"$\mu^+\mu^-$", r"$\gamma\gamma$"]
p.x1.range = 0.5, 3.5
p.y1.label = r"all off-$\Upsilon$ luminosity (nb)"
p.show()
p.write_eps("/home/mccann/tmp.ps")
print zip([
L1ee[0] + L2ee[0] + L3ee[0], L1mm[0] + L2mm[0] + L3mm[0],
L1gg[0] + L2gg[0] + L3gg[0]
], [
trueerror2(L1ee, L2ee, L3ee),
trueerror2(L1mm, L2mm, L3mm),
trueerror2(L1gg, L2gg, L3gg)
])
(745015.96999999997, 3800.4106992897518),
(761084.32999999996, 4027.7956696546462),
(754823.03553299501, 3890.5976385867111)
print jt.wmean(
zip([
L1ee[0] + L2ee[0] + L3ee[0], L1mm[0] + L2mm[0] + L3mm[0],
L1gg[0] + L2gg[0] + L3gg[0]
], [
trueerror2(L1ee, L2ee, L3ee),
trueerror2(L1mm, L2mm, L3mm),
trueerror2(L1gg, L2gg, L3gg)
]))
(753334.9594020159, 2253.3621610084865)
def use_cross_section(csfunc, filename):
hads = []
hads_err = []
for r in testsample:
c, e = csfunc(r)
hads.append(c)
hads_err.append(e)
hads_center = jt.wmean(zip(hads, hads_err))[0]
hads_sigs = []
for r, c, e in zip(testsample, hads, hads_err):
hads_sigs.append((c - hads_center) / e)
hads_hist = hist.h1(50, -5, 5).fill(hads_sigs).rootn()
def gauss(x, a, m, s):
return a * math.exp(-(x - m)**2 / 2. / s**2) / math.sqrt(
2. * math.pi) / s
chipoints = hads_hist.frame + (hads_hist.frame[1] -
hads_hist.frame[0]) / 2.
def chi2(a, m, s):
c = 0.
for i, x in zip(range(len(hads_hist.frame)), chipoints):
if hads_hist.errs[i] > 0:
c += (hads_hist.vals[i] -
gauss(x, a, m, s))**2 / hads_hist.errs[i]**2
return c
# def hist_look(a, m, s):
# p = hads_hist.plot()
# values = []
# for x in chipoints:
# values.append(gauss(x, a, m, s))
# p.add(biggles.Curve(chipoints, values))
# p.show()
# hist_look(len(testsample)/5., 0, 1)
m = Minuit(chi2, start=[len(testsample) / 5., 0, 1])
m.migrad()
hist_chi2, hist_params, hist_errors = m.fval, m.values, m.errors
ndf = len(filter(lambda x: x > 0, hads_hist.errs))
values = []
for x in chipoints:
values.append(gauss(x, *hist_params))
hist_info = [["$\chi^2/ndf$", hist_chi2, "/", ndf], \
["RMS", hist_params[2], "$\pm$", hist_errors[2]]]
p = biggles.Table(2, 1)
p[0, 0] = biggles.FramedPlot()
p[0, 0].add(
biggles.Points(Numeric.array(hads) / hads_center,
range(len(testsample)),
type="filled circle"))
p[0, 0].add(
biggles.SymmetricErrorBarsX(
Numeric.array(hads) / hads_center, range(len(testsample)),
Numeric.array(hads_err) / hads_center))
p[0, 0].y1.draw_ticklabels = 0
p[0, 0].x1.label = "$\propto$ hadronic cross-section"
p[0, 0].add(biggles.LineX(1.))
p[0, 0].add(biggles.LineY(41.5, type="dashed"))
l, r = 0.8, 1.2
p[0, 0].xrange = l, r
p[0, 0].add(biggles.DataLabel(l + 0.15 * (r - l), 41.5 - 7, "db16"))
p[0, 0].add(biggles.DataLabel(l + 0.15 * (r - l), 41.5 + 7, "db17"))
p[0, 1] = hads_hist.plot()
p[0, 1].add(biggles.Curve(chipoints, values))
hist.addinfobox(p[0, 1],
hist_info,
width=0.8,
colwidth=[0.2, 0.09, 0.23],
corner=1,
numformat="%.2g")
p[0, 1].x1.label = "sigmas away from weighted mean"
p[0, 1].yrange = 0, 17
p.aspect_ratio = 8.5 / 11. * 4
p.show()
p.write_eps(filename)
for s in scanu3s: m = 10358.131397318795 if s == "dec05": m = 10359.4784845 if s == "dec19": m = 10357.5457012 scanfit[s] = Minuit(lambda area, mass, wide, back: chi2(y3sfunc, s, area, mass, wide, back), start=[3.4897, m, 4.317, 0.3497]) scanfit[s].migrad() a, m, w, b = scanfit[s].values plotoverlay(y3sfunc, s, a, m, w, b).show() print "reduced chi2", scanfit[s].fval/(len(scanen[s])-4), "precision", scanfit[s].errors[0]/scanfit[s].values[0]*100, "%" areas = [] for s in scanu1s: areas.append((scanfit[s].values[0], scanfit[s].errors[0])) print jt.wmean(areas)[1]/jt.wmean(areas)[0]*100. areas = [] for s in scanu2s: areas.append((scanfit[s].values[0], scanfit[s].errors[0])) print jt.wmean(areas)[1]/jt.wmean(areas)[0]*100. areas = [] for s in scanu3s: areas.append((scanfit[s].values[0], scanfit[s].errors[0])) print jt.wmean(areas)[1]/jt.wmean(areas)[0]*100. p = biggles.Table(5,5) i = 0