Adds additional yield plots

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filval

@@ -1 +1 @@
-Subproject commit f7ef387b4ef4fdd81c7789f2fa35b72a9465ebfb
+Subproject commit 7206f9c742acf25fa94e351679781b5d67117fa1

yields.py

@@ -1,9 +1,10 @@
 #!/usr/bin/env python
+import numpy as np
 import matplotlib.pyplot as plt
 
 from filval.result_set import ResultSet
-from filval.histogram_utils import hist
-from filval.plotter import (decl_plot, render_plots, hist_plot, Plot)
+from filval.histogram_utils import hist, hist_add
+from filval.plotter import (decl_plot, render_plots, hist_plot, hist_plot_stack, Plot)
 
 
 def generate_dashboard(plots, output='dashboard.htm'):
@@ -21,7 +22,7 @@ def generate_dashboard(plots, output='dashboard.htm'):
                         })
 
     def render_to_file(template_name, **kwargs):
-        with open(join('output', template_name), 'w') as tempout:
+        with open(join('output', output), 'w') as tempout:
             template = env.get_template(template_name)
             tempout.write(template.render(**kwargs))
 
@@ -31,33 +32,61 @@ def generate_dashboard(plots, output='dashboard.htm'):
             yield objs[:n]
             objs = objs[n:]
 
-    render_to_file(output, plots=get_by_n(plots, 3),
+    render_to_file('dashboard.htm', plots=get_by_n(plots, 3),
                    outdir="figures/")
 
 
 @decl_plot
-def plot_yield(rss):
-    r''' '''
-    from filval.plotter import StackHist
+def plot_yield_grid(rss):
+    r"""## Event Yield
+
+    The event yield for the eight signal regions defined in AN-17-115. Data is normalized
+    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>
+    """
+    _, ((ax_tttt, ax_ttw), (ax_ttz, ax_tth)) = plt.subplots(2, 2)
+    ft, ttw, ttz, tth = map(lambda rs: hist(rs.SRs), rss)
+    plt.sca(ax_tttt)
+    hist_plot(ft, title='TTTT', stats=False)
+    plt.sca(ax_ttw)
+    hist_plot(ttw, title='TTW', stats=False)
+    plt.sca(ax_ttz)
+    hist_plot(ttz, title='TTZ', stats=False)
+    plt.sca(ax_tth)
+    hist_plot(tth, title='TTH', stats=False)
+
+    plt.xlabel('Signal Region')
+
+
+@decl_plot
+def plot_yield_stack(rss):
+    r"""## Event Yield - Stacked
+
+    The event yield for the eight signal regions defined in AN-17-115. Data is normalized
+    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)
-    # ft10 = ft[0]*10, ft[1]*10, ft[2]
-    sh = StackHist()
-    sh.add_mc_background(rss[1].SRs, 'TTW')
-    sh.set_mc_signal(rss[0].SRs, 'TTTT')
-    sh.draw(plt.gca())
-    # hist_plot(ft10, include_errors=False, stats=False,
-    #           color='k', label='TTTT (x10)')
-    
-    # bg = 
-    # plt.hist(
-    # hist_plot(ttw, include_errors=False, stats=False,
-    #           color='g', label='TTW')
-    # hist_plot(ttz, include_errors=False, stats=False,
-    #           color='r', label='TTZ')
-    # hist_plot(tth, include_errors=False, stats=False,
-    #           color='b', label='TTH')
-    # plt.xlabel('Signal Region')
-    # plt.legend()
+    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')
+
+    plt.ylim((0, 160))
+    plt.xlabel('Signal Region')
+    plt.legend()
+
+
+@decl_plot
+def plot_sig_strength(rss):
+    r""" The signal strength of the TTTT signal defined as
+
+    $\frac{S}{\sqrt{S+B}}$
+
+    """
+    ft, 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)
 
 
 if __name__ == '__main__':
@@ -65,29 +94,44 @@ if __name__ == '__main__':
     # into memory and makes them available as attributes
     # if len(sys.argv) != 2:
     #     raise ValueError("please supply root file")
-    rss = (ResultSet("ft", 'yield_ft.root'),
-           ResultSet("ttw", 'yield_ttw.root'),
-           ResultSet("ttz", 'yield_ttz.root'),
-           ResultSet("tth", 'yield_tth.root'))
-    rss_notau = (ResultSet("ft_notau", 'yield_ft_notau.root'),
-                 ResultSet("ttw_notau", 'yield_ttw_notau.root'),
-                 ResultSet("ttz_notau", 'yield_ttz_notau.root'),
-                 ResultSet("tth_notau", 'yield_tth_notau.root'))
+    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_notau = (ResultSet("ft_notau", 'data/yield_ft_notau.root'),
+                 ResultSet("ttw_notau", 'data/yield_ttw_notau.root'),
+                 ResultSet("ttz_notau", 'data/yield_ttz_notau.root'),
+                 ResultSet("tth_notau", 'data/yield_tth_notau.root'))
 
     # Next, declare all of the (sub)plots that will be assembled into full
     # figures later
-    yield_tau = (plot_yield, (rss,), {})
-    yield_notau = (plot_yield, (rss_notau,), {})
+    yield_tau = (plot_yield_grid, (rss,), {})
+    yield_notau = (plot_yield_grid, (rss_notau,), {})
+
+    yield_tau_stack = (plot_yield_stack, (rss,), {})
+    yield_notau_stack = (plot_yield_stack, (rss_notau,), {})
 
+    sig_strength_tau = (plot_sig_strength, (rss,), {})
+    sig_strength_notau = (plot_sig_strength, (rss_notau,), {})
     # Now assemble the plots into figures.
     plots = [
         Plot([[yield_tau]],
              'Yield With Tau'),
         Plot([[yield_notau]],
              'Yield Without Tau'),
+        # Plot([[yield_tau_stack]],
+        #      'Yield With Tau Stacked'),
+        # Plot([[yield_notau_stack]],
+        #      'Yield Without Tau Stacked'),
+        Plot([[yield_tau_stack],
+              [yield_notau_stack]],
+             'Event Yield, top: with tau, bottom, no tau'),
+        Plot([[sig_strength_tau],
+              [sig_strength_notau]],
+             'Signal Strength')
     ]
 
     # Finally, render and save the plots and generate the html+bootstrap
     # dashboard to view them
     render_plots(plots, to_disk=False)
-    generate_dashboard(plots)
+    generate_dashboard(plots, output='yield.html')