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@@ -4,47 +4,108 @@ import matplotlib.pyplot as plt
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from filval.result_set import ResultSet
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from filval.histogram_utils import hist, hist_add, hist_normalize, hist_scale
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-from filval.plotter import (decl_plot, render_plots, hist_plot, hist_plot_stack, Plot, generate_dashboard)
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+from filval.plotter import (decl_plot, render_plots, hist_plot, hist_plot_stack,
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+ Plot, generate_dashboard, hists_to_table)
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+an_tttt = ([0.47, 0.33, 0.18, 0.78, 0.49, 0.52, 0.33, 0.49],
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+ [0, 0, 0, 0, 0, 0, 0, 0], [0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5])
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+an_ttw = ([2.29663, 0.508494, 0.161166, 1.03811, 0.256401, 0.127582, 0.181522, 0.141659],
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+ [0, 0, 0, 0, 0, 0, 0, 0], [0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5])
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+an_ttz = ([0.974751, 0.269195, 1e-06, 0.395831, 0.0264703, 0.06816, 0.8804, 0.274265],
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+ [0, 0, 0, 0, 0, 0, 0, 0], [0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5])
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+an_tth = ([1.13826, 0.361824, 0.162123, 0.683917, 0.137608, 0.0632719, 0.554491, 0.197864],
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+ [0, 0, 0, 0, 0, 0, 0, 0], [0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5])
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@decl_plot
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-def plot_yield_grid(rss):
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+def plot_yield_grid(rss, tau_category=-1):
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r"""## Event Yield
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The event yield for the eight signal regions defined in AN-17-115. Data is normalized
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- to the Moriond 2018 integrated luminosity ($35.9\textrm{fb}^{-1}$). Code for the histogram generation is
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- here: <https://github.com/cfangmeier/FTAnalysis/blob/master/studies/tau/Yield.C>
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+ to the integrated luminosity of $35.9\textrm{fb}^{-1}$.
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+
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+ Ignoring taus means both that there is no requirement on number of good taus *and*
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+ the taus, if present, are not considered for the SS pair.
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+
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+ If taus are not ignored, any good tau with $p_T$>20Gev is considered in constructing the SS lepton pair. The yields
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+ are then further broken down by the number of good taus in the event.
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+
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+ A "good" tau in the above means any tau candidate passing the `byTightIsolationMVArun2v1DBoldDMwLT` ID w/ pt>20GeV.
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+ It is also required to pass tau-lepton cross cleaning where it must not match any electron or muon within
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+ $\delta R < 0.4$.
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+
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+ Truth-matched taus are those that match within $\delta R < 0.3$ with gen-level taus that pass the flag `fromHardProcessDecayed`.
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"""
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+
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+ def get_sr(rs, tm=False):
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+ if tm:
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+ if tau_category == 0:
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+ return hist(rs.SRs_0tmtau)
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+ if tau_category == 1:
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+ return hist(rs.SRs_1tmtau)
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+ elif tau_category == 2:
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+ return hist(rs.SRs_2tmtau)
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+ else:
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+ if tau_category == -1:
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+ return hist(rs.ignore_tau_SRs)
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+ elif tau_category == 0:
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+ return hist(rs.SRs_0tau)
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+ elif tau_category == 1:
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+ return hist(rs.SRs_1tau)
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+ elif tau_category == 2:
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+ return hist(rs.SRs_2tau)
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+
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_, ((ax_tttt, ax_ttw), (ax_ttz, ax_tth)) = plt.subplots(2, 2)
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- ft, ttw, ttz, tth = map(lambda rs: hist(rs.SRs), rss)
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- # ft, ttw = map(lambda rs: hist(rs.SRs), rss[:2])
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+ tttt, ttw, ttz, tth = [get_sr(rs) for rs in rss]
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+ tm_tttt, tm_ttw, tm_ttz, tm_tth = [get_sr(rs, True) for rs in rss]
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+
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plt.sca(ax_tttt)
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- an = ((0.47, 0.33, 0.18, 0.78, 0.49, 0.52, 0.33, 0.49),
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- (0, 0, 0, 0, 0, 0, 0, 0), [0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5])
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- hist_plot(ft, title='TTTT', stats=False, label='Mock')
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- hist_plot(an, title='TTTT', stats=False, label='AN')
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+ hist_plot(tttt, title='TTTT', stats=False, label='Mock', include_errors=True)
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+ if tau_category == -1:
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+ hist_plot(an_tttt, title='TTTT', stats=False, label='AN')
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+ elif tau_category >= 0:
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+ hist_plot(tm_tttt, title='TTTT', stats=False, label='Truth-Matched Taus', include_errors=True)
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+ plt.ylim((0, None))
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plt.sca(ax_ttw)
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- an = ([2.29663, 0.508494, 0.161166, 1.03811, 0.256401, 0.127582, 0.181522, 0.141659],
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- [0, 0, 0, 0, 0, 0, 0, 0], [0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5])
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- hist_plot(ttw, title='TTW', stats=False, label='Mock')
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- hist_plot(an, title='TTW', stats=False, label='AN')
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+ hist_plot(ttw, title='TTW', stats=False, label='Mock', include_errors=True)
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+ if tau_category == -1:
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+ hist_plot(an_ttw, title='TTW', stats=False, label='AN')
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+ elif tau_category >= 0:
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+ hist_plot(tm_ttw, title='TTW', stats=False, label='Truth-Matched Taus', include_errors=True)
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+ plt.ylim((0, None))
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plt.legend()
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plt.sca(ax_ttz)
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- an = ([0.974751, 0.269195, 1e-06, 0.395831, 0.0264703, 0.06816, 0.8804, 0.274265],
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- [0, 0, 0, 0, 0, 0, 0, 0], [0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5])
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- hist_plot(ttz, title='TTZ', stats=False, label='Mock')
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- hist_plot(an, title='TTZ', stats=False, label='AN')
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+ hist_plot(ttz, title='TTZ', stats=False, label='Mock', include_errors=True)
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+ if tau_category == -1:
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+ hist_plot(an_ttz, title='TTZ', stats=False, label='AN')
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+ elif tau_category >= 0:
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+ hist_plot(tm_ttz, title='TTZ', stats=False, label='Truth-Matched Taus', include_errors=True)
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+ plt.ylim((0, None))
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plt.xlabel('Signal Region')
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plt.sca(ax_tth)
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- an = ([1.13826, 0.361824, 0.162123, 0.683917, 0.137608, 0.0632719, 0.554491, 0.197864],
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- [0, 0, 0, 0, 0, 0, 0, 0], [0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5])
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- hist_plot(tth, title='TTH', stats=False, label='Mock')
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- hist_plot(an, title='TTH', stats=False, label='AN')
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+ hist_plot(tth, title='TTH', stats=False, label='Mock', include_errors=True)
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+ if tau_category == -1:
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+ hist_plot(an_tth, title='TTH', stats=False, label='AN')
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+ elif tau_category >= 0:
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+ hist_plot(tm_tth, title='TTH', stats=False, label='Truth-Matched Taus', include_errors=True)
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+ plt.ylim((0, None))
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plt.xlabel('Signal Region')
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+ def to_table(hists):
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+ return hists_to_table(hists, row_labels=['TTTT', 'TTW', 'TTZ', 'TTH'],
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+ column_labels=[f'SR{n}' for n in range(1, 9)])
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+
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+ tables = '<h2>Mock</h2>'
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+ tables += to_table([tttt, ttw, ttz, tth])
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+ if tau_category == -1:
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+ tables += '<h2>AN</h2>'
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+ tables += to_table([an_tttt, an_ttw, an_ttz, an_tth])
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+ elif tau_category >= 0:
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+ tables += '<h2>TM Taus</h2>'
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+ tables += to_table([tm_tttt, tm_ttw, tm_ttz, tm_tth])
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+ return tables
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@decl_plot
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@@ -89,6 +150,7 @@ def plot_lep_multi(rss, dataset):
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_plot(ax_taus, taus)
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plt.xlabel('\\# Good Taus')
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+
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@decl_plot
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def plot_sig_strength(rss):
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r""" The signal strength of the TTTT signal defined as
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@@ -101,9 +163,11 @@ def plot_sig_strength(rss):
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strength = ft[0] / np.sqrt(ft[0] + bg[0])
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hist_plot((strength, ft[1], ft[2]), stats=False)
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+
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@decl_plot
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-def plot_event_obs(rss, dataset):
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- r"""
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+def plot_event_obs(rss, dataset, in_signal_region=True):
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+ r"""The distribution of $N_{jet}$, $N_{Bjet}$, MET, and $H_T$ in either all events
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+ or only signal region (igoring taus) events.
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"""
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_, ((ax_njet, ax_nbjet), (ax_ht, ax_met)) = plt.subplots(2, 2)
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@@ -116,10 +180,16 @@ def plot_event_obs(rss, dataset):
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def _plot(ax, obs):
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plt.sca(ax)
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- h = {'MET': rs.met_in_SR,
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- 'HT': rs.ht_in_SR,
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- 'NJET': rs.njet_in_SR,
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- 'NBJET': rs.nbjet_in_SR}[obs]
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+ if in_signal_region:
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+ h = {'MET': rs.met_in_SR,
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+ 'HT': rs.ht_in_SR,
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+ 'NJET': rs.njet_in_SR,
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+ 'NBJET': rs.nbjet_in_SR}[obs]
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+ else:
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+ h = {'MET': rs.met,
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+ 'HT': rs.ht,
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+ 'NJET': rs.njet,
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+ 'NBJET': rs.nbjet}[obs]
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hist_plot(hist(h), stats=False, label=dataset, xlabel=obs)
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_plot(ax_njet, 'NJET')
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@@ -127,8 +197,9 @@ def plot_event_obs(rss, dataset):
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_plot(ax_ht, 'HT')
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_plot(ax_met, 'MET')
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+
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@decl_plot
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-def plot_event_obs_stack(rss):
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+def plot_event_obs_stack(rss, in_signal_region=True):
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r"""
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"""
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@@ -136,10 +207,16 @@ def plot_event_obs_stack(rss):
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def _plot(ax, obs):
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plt.sca(ax)
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- attr = {'MET': 'met_in_SR',
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- 'HT': 'ht_in_SR',
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- 'NJET': 'njet_in_SR',
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- 'NBJET': 'nbjet_in_SR'}[obs]
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+ if in_signal_region:
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+ attr = {'MET': 'met_in_SR',
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+ 'HT': 'ht_in_SR',
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+ 'NJET': 'njet_in_SR',
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+ 'NBJET': 'nbjet_in_SR'}[obs]
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+ else:
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+ attr = {'MET': 'met',
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+ 'HT': 'ht',
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+ 'NJET': 'njet',
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+ 'NBJET': 'nbjet'}[obs]
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ft, ttw, ttz, tth = map(lambda rs: hist(getattr(rs, attr)), rss)
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hist_plot_stack([ttw, ttz, tth], labels=["TTW", "TTZ", "TTH"])
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hist_plot(hist_scale(ft, 5), label="TTTT (x5)", color='k')
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@@ -151,6 +228,7 @@ def plot_event_obs_stack(rss):
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_plot(ax_ht, 'HT')
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_plot(ax_met, 'MET')
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+
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@decl_plot
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def plot_tau_purity(rss):
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_, ((ax_ft, ax_ttw), (ax_ttz, ax_tth)) = plt.subplots(2, 2)
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@@ -173,46 +251,73 @@ def plot_tau_purity(rss):
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if __name__ == '__main__':
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- # First create a ResultSet object which loads all of the objects from output.root
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+ # First create a ResultSet object which loads all of the objects from root file
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# into memory and makes them available as attributes
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rss = (ResultSet("ft", 'data/yield_ft.root'),
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ResultSet("ttw", 'data/yield_ttw.root'),
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ResultSet("ttz", 'data/yield_ttz.root'),
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ResultSet("tth", 'data/yield_tth.root'))
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- rss_notau = (ResultSet("ft_notau", 'data/yield_ft_notau.root'),
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- ResultSet("ttw_notau", 'data/yield_ttw_notau.root'),
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- ResultSet("ttz_notau", 'data/yield_ttz_notau.root'),
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- ResultSet("tth_notau", 'data/yield_tth_notau.root'))
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# Next, declare all of the (sub)plots that will be assembled into full
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# figures later
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- yield_tau = (plot_yield_grid, (rss,), {})
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- yield_notau = (plot_yield_grid, (rss_notau,), {})
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+ yield_tau_ignore_tau = plot_yield_grid, (rss, -1)
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+ yield_tau_0tau = plot_yield_grid, (rss, 0)
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+ yield_tau_1tau = plot_yield_grid, (rss, 1)
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+ yield_tau_2tau = plot_yield_grid, (rss, 2)
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- yield_tau_stack = (plot_yield_stack, (rss,), {})
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- yield_notau_stack = (plot_yield_stack, (rss_notau,), {})
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+ ft_event_obs_in_sr = plot_event_obs, (rss, 'TTTT'), {'in_signal_region': True}
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+ ttw_event_obs_in_sr = plot_event_obs, (rss, 'TTW'), {'in_signal_region': True}
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+ ttz_event_obs_in_sr = plot_event_obs, (rss, 'TTZ'), {'in_signal_region': True}
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+ tth_event_obs_in_sr = plot_event_obs, (rss, 'TTH'), {'in_signal_region': True}
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- sig_strength_tau = (plot_sig_strength, (rss,), {})
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- sig_strength_notau = (plot_sig_strength, (rss_notau,), {})
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+ ft_event_obs = plot_event_obs, (rss, 'TTTT'), {'in_signal_region': False}
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+ ttw_event_obs = plot_event_obs, (rss, 'TTW'), {'in_signal_region': False}
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+ ttz_event_obs = plot_event_obs, (rss, 'TTZ'), {'in_signal_region': False}
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+ tth_event_obs = plot_event_obs, (rss, 'TTH'), {'in_signal_region': False}
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- ft_lep_multi = (plot_lep_multi, (rss, 'TTTT'), {})
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- ttw_lep_multi = (plot_lep_multi, (rss, 'TTW'), {})
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- ttz_lep_multi = (plot_lep_multi, (rss, 'TTZ'), {})
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- tth_lep_multi = (plot_lep_multi, (rss, 'TTH'), {})
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+ ft_lep_multi = plot_lep_multi, (rss, 'TTTT')
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+ ttw_lep_multi = plot_lep_multi, (rss, 'TTW')
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+ ttz_lep_multi = plot_lep_multi, (rss, 'TTZ')
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+ tth_lep_multi = plot_lep_multi, (rss, 'TTH')
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- ft_event_obs = (plot_event_obs, (rss_notau, 'TTTT'), {})
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- ttw_event_obs = (plot_event_obs, (rss_notau, 'TTW'), {})
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- ttz_event_obs = (plot_event_obs, (rss_notau, 'TTZ'), {})
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- tth_event_obs = (plot_event_obs, (rss_notau, 'TTH'), {})
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-
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- event_obs_stack = (plot_event_obs_stack, (rss_notau,), {})
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-
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- tau_purity = (plot_tau_purity, (rss,), {})
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+ # tau_purity = plot_tau_purity, (rss)
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# Now assemble the plots into figures.
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plots = [
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- # Plot([[yield_tau]],
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- # 'Yield With Tau'),
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+ Plot([[yield_tau_ignore_tau]],
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+ 'Yield Ignoring Taus'),
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+ Plot([[yield_tau_0tau]],
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+ 'Yield For events with 0 Tau'),
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+ Plot([[yield_tau_1tau]],
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+ 'Yield For events with 1 Tau'),
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+ Plot([[yield_tau_2tau]],
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+ 'Yield For events with 2 or more Tau'),
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+
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+ Plot([[ft_lep_multi]],
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+ 'Lepton Multiplicity - TTTT'),
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+ Plot([[ttw_lep_multi]],
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+ 'Lepton Multiplicity - TTW'),
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+ Plot([[ttz_lep_multi]],
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+ 'Lepton Multiplicity - TTZ'),
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+ Plot([[tth_lep_multi]],
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+ 'Lepton Multiplicity - TTH'),
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+ Plot([[ft_event_obs_in_sr]],
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+ 'TTTT - Event Observables (In SR)'),
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+ Plot([[ttw_event_obs_in_sr]],
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+ 'TTW - Event Observables (In SR)'),
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+ Plot([[ttz_event_obs_in_sr]],
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+ 'TTZ - Event Observables (In SR)'),
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+ Plot([[tth_event_obs_in_sr]],
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+ 'TTH - Event Observables (In SR)'),
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+ Plot([[ft_event_obs]],
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+ 'TTTT - Event Observables (All Events)'),
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+ Plot([[ttw_event_obs]],
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+ 'TTW - Event Observables (All Events)'),
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+ Plot([[ttz_event_obs]],
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+ 'TTZ - Event Observables (All Events)'),
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+ Plot([[tth_event_obs]],
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+ 'TTH - Event Observables (All Events)'),
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+
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# Plot([[yield_notau]],
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# 'Yield Without Tau'),
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# Plot([[yield_tau_stack]],
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|
|
@@ -225,29 +330,14 @@ if __name__ == '__main__':
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# Plot([[sig_strength_tau],
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# [sig_strength_notau]],
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# 'Signal Strength'),
|
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- # Plot([[ft_lep_multi]],
|
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- # 'Lepton Multiplicity - TTTT'),
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- # Plot([[ttw_lep_multi]],
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- # 'Lepton Multiplicity - TTW'),
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- # Plot([[ttz_lep_multi]],
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- # 'Lepton Multiplicity - TTZ'),
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|
- # Plot([[tth_lep_multi]],
|
|
|
- # 'Lepton Multiplicity - TTH'),
|
|
|
- # Plot([[ft_event_obs]],
|
|
|
- # 'TTTT - Event Observables'),
|
|
|
- # Plot([[ttw_event_obs]],
|
|
|
- # 'TTW - Event Observables'),
|
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|
- # Plot([[ttz_event_obs]],
|
|
|
- # 'TTZ - Event Observables'),
|
|
|
- # Plot([[tth_event_obs]],
|
|
|
- # 'TTH - Event Observables'),
|
|
|
# Plot([[event_obs_stack]],
|
|
|
# 'Event Observables'),
|
|
|
- Plot([[tau_purity]],
|
|
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- 'Tau Purity'),
|
|
|
+ # Plot([[tau_purity]],
|
|
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+ # 'Tau Purity'),
|
|
|
]
|
|
|
|
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# Finally, render and save the plots and generate the html+bootstrap
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# dashboard to view them
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render_plots(plots, to_disk=False)
|
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|
- generate_dashboard(plots, 'TTTT Yields', output='yield2.html', source_file=__file__)
|
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|
+ generate_dashboard(plots, 'TTTT Yields', output='yield_breakout_4.html', source_file=__file__,
|
|
|
+ ana_source="https://github.com/cfangmeier/FTAnalysis/commit/46fc1afab42f6b04c9e21ba519e6a30524983550")
|
