def model_plots(model, all_nominal_templates = False, shape_templates = False):
plotdir = os.path.join(config.workdir, 'plots')
observables = sorted(list(model.observables.keys()))
processes = sorted(list(model.processes))
#TODO: more / better colors
background_colors = ['#edd400', '#f57900', '#c17d11', '#73d216', '#3465a4', '#75507b', '#d3d7cf', '#555753']
signal_colors = ['#ef2929', '#cc0000', '#a40000']
if not os.path.exists(plotdir): os.mkdir(plotdir)
f = open(os.path.join(config.workdir, 'model_plots.thtml'), 'w')
print >> f, "<h2>Stackplots</h2>"
print >> f, "<p>Everything normalized to expectation, i.e., to the normalization in the template input file, possibly scaled via the python script file.</p>"
print >> f, "<p>Color Code:</p><ul>"
i_bkg_col = 0
i_signal_col = 0
for p in processes:
if p in model.signal_processes:
color = signal_colors[i_signal_col]
i_signal_col = (i_signal_col + 1) % len(signal_colors)
else:
color = background_colors[i_bkg_col]
i_bkg_col = (i_bkg_col + 1) % len(background_colors)
print >> f, '<li><span style="background: %s;"> </span> %s</li>' % (color, p)
print >>f, '</ul>'
for o in observables:
background_pds = []
signal_pds = []
i_bkg_col = 0
i_signal_col = 0
for p in processes:
hf = model.get_histogram_function(o, p)
if hf is None: continue
pd = plotutil.plotdata()
pd.histo_triple(hf.get_nominal_histo())
xmin, xmax, data = hf.get_nominal_histo()
binwidth = (xmax - xmin) / len(data)
if p in model.signal_processes:
pd.color = signal_colors[i_signal_col]
i_signal_col = (i_signal_col + 1) % len(signal_colors)
signal_pds.append(pd)
else:
pd.fill_color = background_colors[i_bkg_col]
pd.lw = 1
pd.color = '#000000'
i_bkg_col = (i_bkg_col + 1) % len(background_colors)
background_pds.append(pd)
data_histo = model.get_data_histogram(o)
data_pd = None
if data_histo is not None:
data_pd = plotutil.plotdata()
data_pd.color = '#000000'
data_pd.histo_triple(data_histo)
data_pd.yerrors = map(math.sqrt, data_pd.y)
data_pd.circle = 'o'
plotutil.make_stack(background_pds)
plots = background_pds + signal_pds
if data_pd is not None: plots.append(data_pd)
plotutil.plot(plots, o, '$N / %.4g$' % binwidth, os.path.join(plotdir, '%s_stack.png' % o), xmin=xmin, xmax=xmax)
print >> f, "<p>Observable '%s':<br /><img src=\"plots/%s_stack.png\" /></p>" % (o, o)
if all_nominal_templates:
print >> f, "<h2>All 'nominal' Templates</h2>"
print >> f, "<p>Everything normalized to expectation, i.e., to the normalization in the template input file, possibly scaled via the python script file.</p>"
for o in observables:
for p in processes:
hf = model.get_histogram_function(o,p)
if hf is None: continue
xmin, xmax, data = hf.get_nominal_histo()
binwidth = (xmax - xmin) / len(data)
pd = plotutil.plotdata()
pd.x = [xmin + i*binwidth for i in range(len(data))]
pd.y = data[:]
pd.color = signal_colors[0]
xlabel = o
plotutil.plot([pd], xlabel, '$N / %.4g$' % binwidth, os.path.join(plotdir, '%s_%s.png' % (o, p)), xmin=xmin, xmax=xmax)
print >> f, '<p>Observable "%s", Process "%s":<br/><img src="plots/%s_%s.png"/></p>' % (o, p, o, p)
# make also one plot with all signal processes, and normalization versus ordering:
pd_norm = plotutil.plotdata()
pd_norm.x = []
pd_norm.y = []
pd_norm.as_histo = False
pd_norm.color = '#000000'
plots = []
i_signal_col = 0
x_to_y = {}
for p in processes:
if p not in model.signal_processes: continue
hf = model.get_histogram_function(o,p)
if hf is None: continue
xmin, xmax, data = hf.get_nominal_histo()
x_to_y[utils.extract_number(p)] = sum(data)
binwidth = (xmax - xmin) / len(data)
pd = plotutil.plotdata()
pd.x = [xmin + i*binwidth for i in range(len(data))]
pd.y = data[:]
pd.color = signal_colors[i_signal_col]
plots.append(pd)
i_signal_col = (i_signal_col + 1) % len(signal_colors)
for x in sorted(x_to_y.keys()):
pd_norm.x.append(x)
pd_norm.y.append(x_to_y[x])
plotutil.plot(plots, o, '$N / %.4g$' % binwidth, os.path.join(plotdir, '%s_signals.png' % o), xmin=xmin, xmax=xmax)
plotutil.plot([pd_norm], 'signal process', '$N$', os.path.join(plotdir, '%s_norm_vs_signals.png' % o))
print >> f, '<p>Observable "%s", all signals: <br/><img src="plots/%s_signals.png"/></p>' % (o, o)
print >> f, '<p>Observable "%s", signal normalization: <br/><img src="plots/%s_norm_vs_signals.png"/></p>' % (o, o)
# (end if all_nominal_templates)
if not shape_templates:
f.close()
return
# shape comparison for morphed templates:
color_nominal, color_plus, color_minus = '#333333', '#aa3333', '#3333aa'
print >> f, "<h2>Shape Uncertainty Plots</h2>"
print >> f, "<p>Color Code:</p><ul>"
print >> f, "<li><span style=\"background: %s;\"> </span> nominal</li><li><span style=\"background: %s;\"> </span> plus</li><li><span style=\"background: %s;\"> </span> minus</li>" % (color_nominal, color_plus, color_minus)
print >> f, "</ul>"
print >> f, "<p>Processes not appearing in the tables do not have any shape uncertainty for this observable.</p>"
print >> f, "<p>Click on an image to enlarge. If you have javascript, the image will be displayed on this page and you can click through all shape uncertainties of that observable \
(instead of clicking, you can also use the left/right key on the keyboard).</p>"
for o in observables:
print >> f, '<h3>Observable \'%s\'</h3>' % o
# save the triples (o,p,u) for which there is a plot:
opus = []
for p in model.get_processes(o):
hf = model.get_histogram_function(o,p)
for u in hf.syst_histos:
xmin, xmax, data_nominal = hf.nominal_histo
xmin, xmax, data_plus = hf.syst_histos[u][0]
xmin, xmax, data_minus = hf.syst_histos[u][1]
binwidth = (xmax - xmin) / len(data_nominal)
pd = plotutil.plotdata(color = color_nominal, legend = 'nominal')
pd.x = [xmin + i*binwidth for i in range(len(data_nominal))]
pd.y = data_nominal
pd_plus = plotutil.plotdata(color = color_plus, legend = 'plus variation')
pd_plus.x = pd.x
pd_plus.y = data_plus
pd_plus.color = color_plus
pd_minus = plotutil.plotdata(color = color_minus, legend = 'minus variation')
pd_minus.x = pd.x
pd_minus.y = data_minus
name = '%s__%s__%s' % (o,p,u)
plotutil.plot((pd, pd_plus, pd_minus), o, '$N / %.4g$' % binwidth, os.path.join(plotdir, name + '.png'), xmin=xmin, xmax = xmax)
opus.append((o,p,u))
#make a table for this observable:
t = table()
t.add_column('process', 'process / uncertainty')
us = sorted(list(set([u for o,p,u in opus])))
ps = sorted(list(set([p for o,p,u in opus])))
for u in us: t.add_column(u)
for p in ps:
t.set_column('process', p)
for u in us:
if (o,p,u) in opus:
t.set_column(u, '<a href="plots/%s__%s__%s.png" rel="lightbox[%s]"><img src="plots/%s__%s__%s.png" width="200"/></a>' % (o,p,u,o,o,p,u))
else:
t.set_column(u, '---')
t.add_row()
print >>f, t.html()
if len(opus)==0: print >> f, '<p>no shape uncertainties for this observable</p>'
f.close()
def model_plots_at(model, par_values, signal_stacked = False):
plotdir = os.path.join(config.workdir, 'plots')
processes = sorted(list(model.processes))
#TODO: more / better colors
h = str(hash(str(par_values)))
background_colors = ['#edd400', '#f57900', '#c17d11', '#73d216', '#3465a4', '#75507b', '#d3d7cf', '#555753']
signal_colors = ['#ef2929', '#cc0000', '#a40000']
if not os.path.exists(plotdir): os.mkdir(plotdir)
text = '<p>Templates evaluated at:<p><ul>'
for p in par_values:
text+='<li>%s = %.2g</li>\n' % (p, par_values[p])
text += '</ul>'
text += "<p>Everything normalized to expectation, i.e., to the normalization in the template input file, possibly scaled via the python script file.</p>"
text += "<p>Color Code:</p><ul>"
i_bkg_col = 0
i_signal_col = 0
for p in processes:
if p in model.signal_processes:
color = signal_colors[i_signal_col]
i_signal_col = (i_signal_col + 1) % len(signal_colors)
else:
color = background_colors[i_bkg_col]
i_bkg_col = (i_bkg_col + 1) % len(background_colors)
text += '<li><span style="background: %s;"> </span> %s</li>' % (color, p)
text += '</ul>'
templates = get_shifted_templates(model, par_values)
for o in templates:
background_pds = []
signal_pds = []
i_bkg_col = 0
i_signal_col = 0
for p in templates[o]:
pd = plotutil.plotdata()
pd.histo_triple(templates[o][p])
xmin, xmax, data = templates[o][p]
binwidth = (xmax - xmin) / len(data)
if p in model.signal_processes:
pd.color = signal_colors[i_signal_col]
i_signal_col = (i_signal_col + 1) % len(signal_colors)
signal_pds.append(pd)
else:
pd.fill_color = background_colors[i_bkg_col]
pd.lw = 1
pd.color = '#000000'
i_bkg_col = (i_bkg_col + 1) % len(background_colors)
background_pds.append(pd)
data_histo = model.get_data_histogram(o)
data_pd = None
if data_histo is not None:
xmin, xmax, data = data_histo
data_pd = plotutil.plotdata()
data_pd.color = '#000000'
data_pd.histo_triple(data_histo)
data_pd.yerrors = map(math.sqrt, data_pd.y)
data_pd.circle = 'o'
plots = background_pds
if signal_stacked:
plots.extend(signal_pds)
plotutil.make_stack(background_pds)
else:
plotutil.make_stack(background_pds)
plots.extend(signal_pds)
if data_pd is not None: plots.append(data_pd)
plotutil.plot(plots, o, '$N / %.4g$' % binwidth, os.path.join(plotdir, '%s_stack%s.png' % (o, h)), xmin=xmin, xmax=xmax)
text += "<p>Observable '%s':<br /><img src=\"plots/%s_stack%s.png\" /></p>" % (o, o, h)
config.report.new_section('Model Plots at parameter values', text)
