Adds Jan25 presentation draft slides

docs/presentation_2018_01_25/beamerthemebjeldbak.sty

@@ -0,0 +1,63 @@
+% Insipired by Cameron Bracken's theme originally posted
+% here: http://cameron.bracken.bz/beamer-template
+% January 2009
+
+% Modifications done by Martin Bjeldbak Madsen
+% June 2014
+\mode<presentation>
+
+\RequirePackage{tgpagella}
+
+% \DeclareOptionBeamer{titlepage}{\PassOptionsToPackage{titlepage=#1}{beamertheme-Inner}}
+% \ProcessOptionsBeamer
+
+\useoutertheme[subsection=false,shadow]{miniframes}
+\useinnertheme{default}
+\usefonttheme{serif}
+
+\setbeamertemplate{footline} % show slide number on all slides but the first
+{%
+  \ifnum\c@framenumber=1
+  \else
+    \begin{beamercolorbox}[wd=0.95\paperwidth,right,dp=2ex]{page number}
+      \insertframenumber/\inserttotalframenumber%
+    \end{beamercolorbox}
+  \fi%
+  \begin{beamercolorbox}[colsep=1.25pt]{lower separation line foot}
+  \end{beamercolorbox}
+}
+
+% \ifnum\c@framenumber=1
+\usebackgroundtemplate{\includegraphics[width=1.3\paperwidth]{16x9_seal03.jpg}}
+% \else
+% \fi%
+
+% Display a slide before the current section with overview inf
+% \AtBeginSection[]
+% {%
+%    \begin{frame}
+%        \frametitle{Overview}
+%        \tableofcontents[currentsection,hideothersubsections]
+%    \end{frame}
+% }
+
+\setbeamerfont{title like}{shape=\scshape}
+\setbeamerfont{frametitle}{shape=\scshape}
+\setbeamerfont{section in head/foot}{shape=\scshape,size=\tiny}
+
+\setbeamertemplate{navigation symbols}{} % hide bottom nav buttons
+\setbeamercovered{transparent} % don't hide strip-teased bullet points
+
+% \definecolor{barcolor}{HTML}{77C4D3} % teal
+\definecolor{barcolor}{HTML}{DD0000} % teal
+\setbeamercolor{lower separation line head}{bg=barcolor}
+\setbeamercolor{lower separation line foot}{bg=barcolor}
+\setbeamercolor{normal text}{fg=black,bg=white}
+\setbeamercolor{alerted text}{fg=red}
+\setbeamercolor{example text}{fg=black}
+\setbeamercolor{structure}{fg=black}
+
+\setbeamercolor{palette tertiary}{fg=black,bg=black!10}
+\setbeamercolor{palette quaternary}{fg=black,bg=black!10}
+
+\mode<all>

docs/presentation_2018_01_25/main.tex

@@ -0,0 +1,129 @@
+\documentclass[english,aspectratio=43,8pt]{beamer}
+\usepackage{graphicx}
+\usepackage{amssymb}
+\usepackage{booktabs}
+\usepackage{siunitx}
+\usepackage{subcaption}
+\usepackage{marvosym}
+\usepackage{verbatim}
+\usepackage[normalem]{ulem}  % Needed for /sout
+
+\newcommand{\pb}{\si{\pico\barn}}%
+\newcommand{\fb}{\si{\femto\barn}}%
+\newcommand{\invfb}{\si{\per\femto\barn}}
+\newcommand{\GeV}{\si{\giga\electronvolt}}
+
+\hypersetup{colorlinks=true,urlcolor=blue}
+
+\usetheme[]{bjeldbak}
+
+\begin{document}
+
+    \title[]{Adding Tau Leptons to TTTT X-Section Measurement}
+    \author[C. Fangmeier]{\textbf{Caleb Fangmeier}}
+    \institute[UNL]{University of Nebraska \-- Lincoln}
+    \date{TTTT Working Meeting | January 25, 2018}
+
+    \titlegraphic{%
+    \begin{figure}
+        \includegraphics[width=1in]{CMSlogo.png}\hspace{0.75in}\includegraphics[width=1in]{nebraska-n.png}
+    \end{figure}
+    }
+
+    \begin{frame}[plain]
+        \titlepage
+        %
+    \end{frame}
+
+    \begin{frame}{Introduction}
+        \begin{itemize}
+            \item \textbf{Goal:} Investigate how the TTTT cross-section measurement could be improved by adding taus to the lepton selection.
+            \item To avoid the work of modifying the existing analysis looper to include taus, a simple ``mock''\footnote{\url{https://github.com/cfangmeier/FTAnalysis/tree/tau_studies/studies/tau}} analysis was implemented from scratch which attempts to replicate the main features of the real analysis.
+            \item Runs on same \texttt{CMS4} nTuples as existing analysis.
+            \item some text
+        \end{itemize}
+    \end{frame}
+
+    \begin{frame}{Mock Analysis 2016 Yields}
+        \begin{columns}
+            \begin{column}{0.4\textwidth}
+                \begin{itemize}
+                    \item Attempt to replicate AN yields with mock analysis using only electrons and muons
+                    \item Generally overestimate yield (wrt analysis note)by 30-50\%
+                    \item But shapes tend to match, so go ahead and add taus to make a comparison.
+                \end{itemize}
+            \end{column}
+            \begin{column}{0.59\textwidth}
+                \includegraphics[width=\textwidth]{figures/Yield_Ignoring_Taus.png}
+            \end{column}
+        \end{columns}
+    \end{frame}
+
+    \begin{frame}{Tau Selection}
+        \begin{itemize}
+            \item Taus to be considered in the SS pair must pass the following requirements
+            \begin{itemize}
+                \item Pass the ID: \texttt{byTightIsolationMVArun2v1DBdR03oldDMwLT}
+                \item Have $P_{T}>20$GeV
+                \item Isolated ($dR>0.4$) from any electrons or muons that pass their respective selection criteria
+            \end{itemize}
+            \item Generator-Level taus used in truth-matching must:
+            \begin{itemize}
+                \item Be flagged as prompt (ie. \texttt{tas::genps\_fromHardProcessDecayed()==1})
+                \item Decay hadronically ($\tau^\pm \rightarrow\pi^\pm \{\pi_0\}$, $\tau^\pm \rightarrow\pi^\pm\pi^\pm\pi^\mp\{\pi_0\}$)
+            \end{itemize}
+            \item A reconstructed tau is considered truth-matched if it is within $dR<0.3$ of a generator-level tau
+            \item Otherwise taus are treated exactly the same as electrons and muons in the SS-pair construction/Z-Veto/Jet-Cleaning.
+        \end{itemize}
+    \end{frame}
+
+
+    \begin{frame}{Yields/Truth Matching}
+        \begin{columns}
+            \begin{column}{0.5\textwidth}
+                \includegraphics[width=\textwidth]{figures/Yield_For_events_with_0_Tau.png}
+            \end{column}
+            \begin{column}{0.5\textwidth}
+                \includegraphics[width=\textwidth]{figures/Yield_For_events_with_1_Tau.png}
+            \end{column}
+        \end{columns}
+        \begin{itemize}
+            \item Above figures show yields when when there are zero selected taus(left) or one selected tau(right).
+            \item Orange Lines show yields when the tau is required to be truth-matched.
+        \end{itemize}
+    \end{frame}
+
+    \begin{frame}{Cross-Checking Truth Matching}
+        \begin{columns}
+            \begin{column}{0.4\textwidth}
+                \begin{itemize}
+                    \item Previous slide indicates $\approx30-40$\% of taus are fakes.
+                    \item Figure shows the yields for events in any SR broken down by \# of generator-level taus and \# of selected taus.
+                    \item The figure on the right corroborates the previous slide; In TTTT, for example, only about 40\% of events with 1 selected tau have 1 or more real taus.
+                \end{itemize}
+            \end{column}
+            \begin{column}{0.6\textwidth}
+                \includegraphics[width=\textwidth]{figures/Generated_tau_vs_Selected_tau.png}
+            \end{column}
+        \end{columns}
+    \end{frame}
+
+    \begin{frame}{Results with Expanded Binning}
+        \begin{columns}
+            \begin{column}{0.5\textwidth}
+                \includegraphics[width=\textwidth]{figures/Yield_For_events_with_0_Tau_NewSR.png}
+            \end{column}
+            \begin{column}{0.5\textwidth}
+                \includegraphics[width=\textwidth]{figures/Yield_For_events_with_1_Tau_NewSR.png}
+            \end{column}
+        \end{columns}
+        \begin{itemize}
+            \item Ongoing investigation of more fine binning\footnote{\url{https://github.com/cfangmeier/FTAnalysis/blob/tau\_studies/studies/tau/Yield.C#L261}}.
+        \end{itemize}
+    \end{frame}
+
+    \begin{frame}{Conclusions}
+        some text
+    \end{frame}
+
+\end{document}

filval-python

@@ -1 +1 @@
-Subproject commit 933573447dedebfcf1d8b02217b5a9d46a7f2307
+Subproject commit 46d3f728cea11d244fad940b4d5c1760906d58c6

publish.py

@@ -0,0 +1,16 @@
+#!/usr/bin/env python
+from datetime import datetime as dt
+from subprocess import run
+
+dir_name = f'yields_{dt.strftime(dt.now(), "%Y_%m_%d_%H")}'
+remote = 'caleb@fangmeier.tech:/var/www/tttt/tau/'
+run(('rm', '-rf', dir_name))
+run(('mkdir', '-p', dir_name+'/output'))
+run(('cp', '-r', 'data', dir_name))
+run(('cp', '-r', 'output', dir_name))
+
+run(('scp', '-r', dir_name, remote))
+print(f'Data available at https://tttt.fangmeier.tech/tau/{dir_name}/')
+
+
+

yields.py

@@ -3,9 +3,9 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 from filval.result_set import ResultSet
-from filval.histogram_utils import hist, hist_add, hist_normalize, hist_scale
-from filval.plotter import (decl_plot, render_plots, hist_plot, hist_plot_stack,
-                            Plot, generate_dashboard, hists_to_table)
+from filval.histogram import hist, hist2d, hist_add, hist_norm, hist_scale, hist2d_norm
+from filval.plotting import (decl_plot, render_plots, hist_plot, hist_plot_stack, hist2d_plot,
+                             Plot, generate_dashboard, hists_to_table)
 
 an_tttt = ([0.47, 0.33, 0.18, 0.78, 0.49, 0.52, 0.33, 0.49],
            [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])
@@ -60,7 +60,7 @@ def plot_yield_grid(rss, tau_category=-1):
 
     plt.sca(ax_tttt)
     hist_plot(tttt, title='TTTT', stats=False, label='Mock', include_errors=True)
-    if tau_category == -1:
+    if tau_category == -1 and len(tttt[0]) == len(an_tttt[0]):
         hist_plot(an_tttt, title='TTTT', stats=False, label='AN')
     elif tau_category >= 0:
         hist_plot(tm_tttt, title='TTTT', stats=False, label='Truth-Matched Taus', include_errors=True)
@@ -68,7 +68,7 @@ def plot_yield_grid(rss, tau_category=-1):
 
     plt.sca(ax_ttw)
     hist_plot(ttw, title='TTW', stats=False, label='Mock', include_errors=True)
-    if tau_category == -1:
+    if tau_category == -1 and len(tttt[0]) == len(an_tttt[0]):
         hist_plot(an_ttw, title='TTW', stats=False, label='AN')
     elif tau_category >= 0:
         hist_plot(tm_ttw, title='TTW', stats=False, label='Truth-Matched Taus', include_errors=True)
@@ -77,7 +77,7 @@ def plot_yield_grid(rss, tau_category=-1):
 
     plt.sca(ax_ttz)
     hist_plot(ttz, title='TTZ', stats=False, label='Mock', include_errors=True)
-    if tau_category == -1:
+    if tau_category == -1 and len(tttt[0]) == len(an_tttt[0]):
         hist_plot(an_ttz, title='TTZ', stats=False, label='AN')
     elif tau_category >= 0:
         hist_plot(tm_ttz, title='TTZ', stats=False, label='Truth-Matched Taus', include_errors=True)
@@ -86,7 +86,7 @@ def plot_yield_grid(rss, tau_category=-1):
 
     plt.sca(ax_tth)
     hist_plot(tth, title='TTH', stats=False, label='Mock', include_errors=True)
-    if tau_category == -1:
+    if tau_category == -1 and len(tttt[0]) == len(an_tttt[0]):
         hist_plot(an_tth, title='TTH', stats=False, label='AN')
     elif tau_category >= 0:
         hist_plot(tm_tth, title='TTH', stats=False, label='Truth-Matched Taus', include_errors=True)
@@ -95,7 +95,7 @@ def plot_yield_grid(rss, tau_category=-1):
 
     def to_table(hists):
         return hists_to_table(hists, row_labels=['TTTT', 'TTW', 'TTZ', 'TTH'],
-                              column_labels=[f'SR{n}' for n in range(1, 9)])
+                              column_labels=[f'SR{n}' for n in range(1, len(tttt[0])+1)])
 
     tables = '<h2>Mock</h2>'
     tables += to_table([tttt, ttw, ttz, tth])
@@ -108,6 +108,71 @@ def plot_yield_grid(rss, tau_category=-1):
     return tables
 
 
+@decl_plot
+def plot_yield_v_gen(rss, tau_category=-1):
+    r"""## Event Yield Vs. # of Generated Taus
+
+    """
+    def get_sr(rs):
+        h = None
+        if tau_category == 0:
+            h = rs.SRs_0tmtau_diff_nGenTau
+        if tau_category == 1:
+            h = rs.SRs_1tmtau_diff_nGenTau
+        elif tau_category == 2:
+            h = rs.SRs_2tmtau_diff_nGenTau
+        return hist2d_norm(hist2d(h), norm=1, axis=0)
+
+    _, ((ax_tttt, ax_ttw), (ax_ttz, ax_tth)) = plt.subplots(2, 2)
+    tttt, ttw, ttz, tth = [get_sr(rs) for rs in rss]
+
+    def do_plot(h, title):
+        hist2d_plot(h, title=title, txt_format='{:.2f}')
+
+    plt.sca(ax_tttt)
+    do_plot(tttt, 'TTTT')
+    plt.ylabel("\# Gen Taus")
+
+    plt.sca(ax_ttw)
+    do_plot(ttw, 'TTW')
+    plt.legend()
+
+    plt.sca(ax_ttz)
+    do_plot(ttz, 'TTZ')
+    plt.xlabel('Signal Region')
+    plt.ylabel("\# Gen Taus")
+
+    plt.sca(ax_tth)
+    do_plot(tth, 'TTH')
+    plt.xlabel('Signal Region')
+
+
+@decl_plot
+def plot_nGen_v_nSel(rss):
+    _, ((ax_tttt, ax_ttw), (ax_ttz, ax_tth)) = plt.subplots(2, 2)
+    tttt, ttw, ttz, tth = [hist2d(rs.nGen_v_RecoTaus_in_SR) for rs in rss]
+
+    def do_plot(h, title):
+        hist2d_plot(h, title=title, txt_format='{:.2f}')
+
+    plt.sca(ax_tttt)
+    do_plot(tttt, 'TTTT')
+    plt.ylabel("\# Selected Taus")
+
+    plt.sca(ax_ttw)
+    do_plot(ttw, 'TTW')
+    plt.legend()
+
+    plt.sca(ax_ttz)
+    do_plot(ttz, 'TTZ')
+    plt.xlabel('\# Gen Taus')
+    plt.ylabel("\# Selected Taus")
+
+    plt.sca(ax_tth)
+    do_plot(tth, 'TTH')
+    plt.xlabel('\# Gen Taus')
+
+
 @decl_plot
 def plot_yield_stack(rss):
     r"""## Event Yield - Stacked
@@ -116,10 +181,10 @@ def plot_yield_stack(rss):
     to the Moriond 2018 integrated luminosity ($35.9\textrm{fb}^{-1}$). Code for the histogram generation is
     here: <https://github.com/cfangmeier/FTAnalysis/blob/master/studies/tau/Yield.C>
     """
-    ft, ttw, ttz, tth = map(lambda rs: hist(rs.SRs), rss)
+    tttt, ttw, ttz, tth = map(lambda rs: hist(rs.SRs), rss)
     hist_plot_stack([ttw, ttz, tth], labels=['TTW', 'TTZ', 'TTH'])
-    ft = ft[0]*10, ft[1], ft[2]
-    hist_plot(ft, label='TTTT (x10)', stats=False, color='k')
+    tttt = tttt[0]*10, tttt[1], tttt[2]
+    hist_plot(tttt, label='TTTT (x10)', stats=False, color='k')
 
     plt.ylim((0, 60))
     plt.xlabel('Signal Region')
@@ -129,14 +194,14 @@ def plot_yield_stack(rss):
 @decl_plot
 def plot_lep_multi(rss, dataset):
     _, (ax_els, ax_mus, ax_taus) = plt.subplots(3, 1)
-    els = list(map(lambda rs: hist_normalize(hist(rs.nEls)), rss))
-    mus = list(map(lambda rs: hist_normalize(hist(rs.nMus)), rss))
-    taus = list(map(lambda rs: hist_normalize(hist(rs.nTaus)), rss))
+    els = list(map(lambda rs: hist_norm(hist(rs.nEls)), rss))
+    mus = list(map(lambda rs: hist_norm(hist(rs.nMus)), rss))
+    taus = list(map(lambda rs: hist_norm(hist(rs.nTaus)), rss))
 
     def _plot(ax, procs):
         plt.sca(ax)
-        ft, ttw, ttz, tth = procs
-        h = {'TTTT': ft,
+        tttt, ttw, ttz, tth = procs
+        h = {'TTTT': tttt,
              'TTW': ttw,
              'TTZ': ttz,
              'TTH': tth}[dataset]
@@ -158,10 +223,10 @@ def plot_sig_strength(rss):
     $\frac{S}{\sqrt{S+B}}$
 
     """
-    ft, ttw, ttz, tth = map(lambda rs: hist(rs.SRs), rss)
+    tttt, ttw, ttz, tth = map(lambda rs: hist(rs.SRs), rss)
     bg = hist_add(ttw, ttz, tth)
-    strength = ft[0] / np.sqrt(ft[0] + bg[0])
-    hist_plot((strength, ft[1], ft[2]), stats=False)
+    strength = tttt[0] / np.sqrt(tttt[0] + bg[0])
+    hist_plot((strength, tttt[1], tttt[2]), stats=False)
 
 
 @decl_plot
@@ -171,9 +236,9 @@ def plot_event_obs(rss, dataset, in_signal_region=True):
 
     """
     _, ((ax_njet, ax_nbjet), (ax_ht, ax_met)) = plt.subplots(2, 2)
-    # ft, ttw, ttz, tth = map(lambda rs: hist(rs.SRs), rss)
-    ft, ttw, ttz, tth = rss
-    rs = {'TTTT': ft,
+    # tttt, ttw, ttz, tth = map(lambda rs: hist(rs.SRs), rss)
+    tttt, ttw, ttz, tth = rss
+    rs = {'TTTT': tttt,
           'TTW': ttw,
           'TTZ': ttz,
           'TTH': tth}[dataset]
@@ -217,9 +282,9 @@ def plot_event_obs_stack(rss, in_signal_region=True):
                     'HT': 'ht',
                     'NJET': 'njet',
                     'NBJET': 'nbjet'}[obs]
-        ft, ttw, ttz, tth = map(lambda rs: hist(getattr(rs, attr)), rss)
+        tttt, ttw, ttz, tth = map(lambda rs: hist(getattr(rs, attr)), rss)
         hist_plot_stack([ttw, ttz, tth], labels=["TTW", "TTZ", "TTH"])
-        hist_plot(hist_scale(ft, 5), label="TTTT (x5)", color='k')
+        hist_plot(hist_scale(tttt, 5), label="TTTT (x5)", color='k')
         plt.xlabel(obs)
 
     _plot(ax_njet, 'NJET')
@@ -228,15 +293,31 @@ def plot_event_obs_stack(rss, in_signal_region=True):
     _plot(ax_ht, 'HT')
     _plot(ax_met, 'MET')
 
+# @decl_plot
+# def plot_s_over_b(rss):
+#     def get_sr(rs):
+#         h = None
+#         if tau_category == 0:
+#             h = rs.SRs_0tmtau_diff_nGenTau
+#         if tau_category == 1:
+#             h = rs.SRs_1tmtau_diff_nGenTau
+#         elif tau_category == 2:
+#             h = rs.SRs_2tmtau_diff_nGenTau
+#         return hist2d_norm(hist2d(h), norm=1, axis=0)
+#
+#     _, ((ax_tttt, ax_ttw), (ax_ttz, ax_tth)) = plt.subplots(2, 2)
+#     tttt =
+#     ttw, ttz, tth = [get_sr(rs) for rs in rss]
+#     pass
 
 @decl_plot
 def plot_tau_purity(rss):
-    _, ((ax_ft, ax_ttw), (ax_ttz, ax_tth)) = plt.subplots(2, 2)
-    ft, ttw, ttz, tth = list(map(lambda rs: hist(rs.tau_purity_v_pt), rss))
+    _, ((ax_tttt, ax_ttw), (ax_ttz, ax_tth)) = plt.subplots(2, 2)
+    tttt, ttw, ttz, tth = list(map(lambda rs: hist(rs.tau_purity_v_pt), rss))
 
     def _plot(ax, dataset):
         plt.sca(ax)
-        h = {'TTTT': ft,
+        h = {'TTTT': tttt,
              'TTW': ttw,
              'TTZ': ttz,
              'TTH': tth}[dataset]
@@ -244,19 +325,21 @@ def plot_tau_purity(rss):
         plt.text(200, 0.05, dataset)
         plt.xlabel(r"$P_T$(GeV)")
 
-    _plot(ax_ft, 'TTTT')
+    _plot(ax_tttt, 'TTTT')
     _plot(ax_ttw, 'TTW')
     _plot(ax_ttz, 'TTZ')
     _plot(ax_tth, 'TTH')
 
 
 if __name__ == '__main__':
+    data_path = 'data/output_new_sr_new_id_binning/'
+    save_plots = True
     # First create a ResultSet object which loads all of the objects from root file
     # into memory and makes them available as attributes
-    rss = (ResultSet("ft", 'data/yield_ft.root'),
-           ResultSet("ttw", 'data/yield_ttw.root'),
-           ResultSet("ttz", 'data/yield_ttz.root'),
-           ResultSet("tth", 'data/yield_tth.root'))
+    rss = (ResultSet("tttt", data_path+'yield_tttt.root'),
+           ResultSet("ttw", data_path+'yield_ttw.root'),
+           ResultSet("ttz", data_path+'yield_ttz.root'),
+           ResultSet("tth", data_path+'yield_tth.root'))
 
     # Next, declare all of the (sub)plots that will be assembled into full
     # figures later
@@ -265,22 +348,26 @@ if __name__ == '__main__':
     yield_tau_1tau = plot_yield_grid, (rss, 1)
     yield_tau_2tau = plot_yield_grid, (rss, 2)
 
-    ft_event_obs_in_sr = plot_event_obs, (rss, 'TTTT'), {'in_signal_region': True}
+    tttt_event_obs_in_sr = plot_event_obs, (rss, 'TTTT'), {'in_signal_region': True}
     ttw_event_obs_in_sr = plot_event_obs, (rss, 'TTW'), {'in_signal_region': True}
     ttz_event_obs_in_sr = plot_event_obs, (rss, 'TTZ'), {'in_signal_region': True}
     tth_event_obs_in_sr = plot_event_obs, (rss, 'TTH'), {'in_signal_region': True}
 
-    ft_event_obs = plot_event_obs, (rss, 'TTTT'), {'in_signal_region': False}
+    tttt_event_obs = plot_event_obs, (rss, 'TTTT'), {'in_signal_region': False}
     ttw_event_obs = plot_event_obs, (rss, 'TTW'), {'in_signal_region': False}
     ttz_event_obs = plot_event_obs, (rss, 'TTZ'), {'in_signal_region': False}
     tth_event_obs = plot_event_obs, (rss, 'TTH'), {'in_signal_region': False}
 
-    ft_lep_multi = plot_lep_multi, (rss, 'TTTT')
+    tttt_lep_multi = plot_lep_multi, (rss, 'TTTT')
     ttw_lep_multi = plot_lep_multi, (rss, 'TTW')
     ttz_lep_multi = plot_lep_multi, (rss, 'TTZ')
     tth_lep_multi = plot_lep_multi, (rss, 'TTH')
 
+    yield_v_gen_0tm = plot_yield_v_gen, (rss, 0)
+    yield_v_gen_1tm = plot_yield_v_gen, (rss, 1)
+    yield_v_gen_2tm = plot_yield_v_gen, (rss, 2)
     # tau_purity = plot_tau_purity, (rss)
+    nGen_v_nSel = plot_nGen_v_nSel, (rss,)
 
     # Now assemble the plots into figures.
     plots = [
@@ -293,7 +380,16 @@ if __name__ == '__main__':
         Plot([[yield_tau_2tau]],
              'Yield For events with 2 or more Tau'),
 
-        Plot([[ft_lep_multi]],
+        # Plot([[yield_v_gen_0tm]],
+        #      'Yield For events with 0 TM Tau Vs Gen Tau'),
+        # Plot([[yield_v_gen_1tm]],
+        #      'Yield For events with 1 TM Tau Vs Gen Tau'),
+        # Plot([[yield_v_gen_2tm]],
+        #      'Yield For events with 2 TM Tau Vs Gen Tau'),
+        Plot([[nGen_v_nSel]],
+             r'#Generated tau vs. #Selected tau'),
+
+        Plot([[tttt_lep_multi]],
              'Lepton Multiplicity - TTTT'),
         Plot([[ttw_lep_multi]],
              'Lepton Multiplicity - TTW'),
@@ -301,7 +397,7 @@ if __name__ == '__main__':
              'Lepton Multiplicity - TTZ'),
         Plot([[tth_lep_multi]],
              'Lepton Multiplicity - TTH'),
-        Plot([[ft_event_obs_in_sr]],
+        Plot([[tttt_event_obs_in_sr]],
              'TTTT - Event Observables (In SR)'),
         Plot([[ttw_event_obs_in_sr]],
              'TTW - Event Observables (In SR)'),
@@ -309,14 +405,14 @@ if __name__ == '__main__':
              'TTZ - Event Observables (In SR)'),
         Plot([[tth_event_obs_in_sr]],
              'TTH - Event Observables (In SR)'),
-        Plot([[ft_event_obs]],
-             'TTTT - Event Observables (All Events)'),
-        Plot([[ttw_event_obs]],
-             'TTW - Event Observables (All Events)'),
-        Plot([[ttz_event_obs]],
-             'TTZ - Event Observables (All Events)'),
-        Plot([[tth_event_obs]],
-             'TTH - Event Observables (All Events)'),
+        # Plot([[tttt_event_obs]],
+        #      'TTTT - Event Observables (All Events)'),
+        # Plot([[ttw_event_obs]],
+        #      'TTW - Event Observables (All Events)'),
+        # Plot([[ttz_event_obs]],
+        #      'TTZ - Event Observables (All Events)'),
+        # Plot([[tth_event_obs]],
+        #      'TTH - Event Observables (All Events)'),
 
         # Plot([[yield_notau]],
         #      'Yield Without Tau'),
@@ -338,6 +434,12 @@ if __name__ == '__main__':
 
     # Finally, render and save the plots and generate the html+bootstrap
     # dashboard to view them
-    render_plots(plots, to_disk=False)
-    generate_dashboard(plots, 'TTTT Yields', output='yield_breakout_4.html', source_file=__file__,
-                       ana_source="https://github.com/cfangmeier/FTAnalysis/commit/46fc1afab42f6b04c9e21ba519e6a30524983550")
+    render_plots(plots, to_disk=save_plots)
+    if not save_plots:
+        generate_dashboard(plots, 'TTTT Yields',
+                           output='yields.html',
+                           source=__file__,
+                           ana_source=("https://github.com/cfangmeier/FTAnalysis/commit/"
+                                       "0cbdac4509391fffb9fff87d0521b7dd0a30a55c"),
+                           config=data_path+'config.yaml'
+                           )