def plot_hists(bg_hists,
sig_hist,
param,
legend_labels,
extra_output_name="",
options=None):
if not options:
options = {
"yaxis_log": True,
"xaxis_range": [0, 500],
}
options["output_name"] = output_prefix + extra_output_name + param + ".pdf"
options["lumi_value"] = 137.2
options["legend_smart"] = False
options["legend_percentageinbox"] = False
options["cms_label"] = "Preliminary"
options["xaxis_label"] = param
ply.plot_hist(bgs=bg_hists,
sigs=sig_hist,
legend_labels=legend_labels,
sig_labels=["HH to #gamma#gamma#tau#tau signal"],
options=options)
def plot_occupancy(hist, prefix):
filename_prefix = prefix.replace(" ", "_")
filename_prefix = folder_prefix + filename_prefix
#Fancy way to find xaxis range
nonzero_flag = False
ymax = 0
xaxis_range = [0, 500]
for i in range(1, hist.GetNbinsX() - 1):
if hist.GetBinContent(i) != 0:
if ymax < hist.GetBinContent(i):
ymax = hist.GetBinContent(i)
#Fancy way to find xaxis range
nonzero_flag = False
ymax = 0
xaxis_range = [0, 100]
summary_upper_limit = -1
for i in range(1, hist.GetNbinsX() - 1):
if hist.GetBinContent(i) != 0:
if ymax < hist.GetBinContent(i):
ymax = hist.GetBinContent(i)
if i > 2 and nonzero_flag == False:
xaxis_range[0] = hist.GetBinLowEdge(i - 2)
nonzero_flag = True
if hist.GetBinContent(i) != 0 and hist.GetBinContent(
i + 1) == 0 and nonzero_flag == True:
xaxis_range[1] = hist.GetBinLowEdge(i + 2)
if nonzero_flag and hist.Integral(0, i) / hist.Integral(
0, -1) > 0.99 and summary_upper_limit < 0:
summary_upper_limit = hist.GetBinLowEdge(i + 1)
url = (filename_prefix + ".pdf").replace(
"/home/users/bsathian/public_html/",
"http://uaf-10.t2.ucsd.edu/~bsathian/")
if nonzero_flag == False:
mdf.write("|{}|{}|{}|[plot]({})|\n".format(prefix, 0, 0, url))
else:
mdf.write("|{}|{}|{}|[plot]({})|\n".format(prefix, xaxis_range[0],
summary_upper_limit, url))
ply.plot_hist(bgs=[hist],
legend_labels=[prefix],
options={
"output_name": filename_prefix + ".pdf",
"xaxis_range": xaxis_range,
"xaxis_label": prefix,
"title": prefix,
"yaxis_log": True,
"yaxis_range": [0.1, ymax],
"legend_percentageinbox": False,
})
h2.Scale(1. / h2.Integral())
for i in xrange(10000, 100001, 10000):
model = load_model("gen_%d.weights" % i)
model.summary()
noise = np.random.normal(0, 1, (50000, 8))
#print(noise)
gen_imgs = model.predict(noise)
img = gen_imgs
masses = M(img[:, 0], img[:, 1], img[:, 2], img[:, 3], img[:, 4],
img[:, 5], img[:, 6], img[:, 7])
print(masses)
print("mean: %s, std: %s " % (masses.mean(), masses.std()))
h1 = r.TH1F("h1", "masses", 60, 60, 120)
plu.fast_fill(h1, masses)
#for val in masses: h1.Fill(val)
h1.Scale(1. / h1.Integral())
ply.plot_hist(
bgs=[h1, h2],
legend_labels=["pred", "truth"],
options={
"do_stack": False,
"yaxis_log": False,
#"ratio_numden_indices": [1,0],
"output_name": "masses_epoch_%d.pdf" % i,
})
def plot_hist_1d(hdata=None,
hbkgs=[],
hsigs=[],
hsyst=None,
options={},
colors=[],
sig_labels=[],
legend_labels=[]):
"""
Wrapper function to call Plottery.
"""
# Sanity check. If no histograms exit
if not hdata and len(hbkgs) == 0 and len(hsigs) == 0:
print "[plottery_wrapper] >>> Nothing to do!"
return
# If a blind option is set, blind the data histogram to None
# The later step will set the histogram of data to all zero
if "blind" in options:
if options["blind"]:
hdata = None
del options["blind"]
# If hdata is none clone one hist and fill with 0
if not hdata:
if len(hbkgs) != 0:
hdata = hbkgs[0].Clone("Data")
hdata.Reset()
elif len(hsigs) != 0:
hdata = hsigs[0].Clone("Data")
hdata.Reset()
# Compute some arguments that are missing (viz. colors, sig_labels, legend_labels)
hsig_labels = []
if len(sig_labels) == 0:
for hsig in hsigs:
hsig_labels.append(hsig.GetName())
hcolors = colors
if len(colors) == 0:
for index, hbg in enumerate(hbkgs):
hcolors.append(2001 + index)
hlegend_labels = []
if len(legend_labels) == 0:
for hbg in hbkgs:
hlegend_labels.append(hbg.GetName())
# Set maximum of the plot
totalbkg = None
if len(hbkgs) != 0:
totalbkg = get_total_hist(hbkgs)
yaxismax = get_max_yaxis_range([hdata, totalbkg]) * 1.8
# Once maximum is computed, set the y-axis label location
if yaxismax < 0.01:
options["yaxis_title_offset"] = 1.8
elif yaxismax < 0.1:
options["yaxis_title_offset"] = 1.6
elif yaxismax < 1.:
options["yaxis_title_offset"] = 1.5
elif yaxismax < 100:
options["yaxis_title_offset"] = 1.2
elif yaxismax < 1000:
options["yaxis_title_offset"] = 1.45
elif yaxismax < 10000:
options["yaxis_title_offset"] = 1.6
else:
options["yaxis_title_offset"] = 1.8
# Print histogram content for debugging
#totalbkg.Print("all")
#if len(hsigs) > 0:
# hsigs[0].Print("all")
#for hbg in hbkgs:
# hbg.Print("all")
# Print yield table if the option is turned on
if "print_yield" in options:
if options["print_yield"]:
print_yield_table(hdata, hbkgs, hsigs, hsyst, options)
del options["print_yield"]
# Inject signal option
if "inject_signal" in options:
if options["inject_signal"]:
if len(hsigs) > 0:
hdata = hsigs[0].Clone("test")
hdata.Reset()
for hsig in hsigs:
hdata.Add(hsig)
for hbkg in hbkgs:
hdata.Add(hbkg)
for i in xrange(1, hdata.GetNbinsX() + 1):
hdata.SetBinError(i, 0)
options["legend_datalabel"] = "Sig+Bkg"
del options["inject_signal"]
# If hsyst is not provided, compute one yourself from the bkg histograms
if not hsyst:
hsyst = get_total_err_hist(hbkgs)
# The uncertainties are all accounted in the hsyst so remove all errors from bkgs
remove_errors(hbkgs)
# Here are my default options for plottery
if not "canvas_width" in options: options["canvas_width"] = 604
if not "canvas_height" in options: options["canvas_height"] = 728
if not "yaxis_range" in options: options["yaxis_range"] = [0., yaxismax]
if not "legend_ncolumns" in options: options["legend_ncolumns"] = 2
if not "legend_alignment" in options:
options["legend_alignment"] = "topleft"
if not "legend_smart" in options: options["legend_smart"] = True
if not "legend_scalex" in options: options["legend_scalex"] = 1.2
if not "legend_scaley" in options: options["legend_scaley"] = 1.2
if not "legend_border" in options: options["legend_border"] = False
if not "legend_percentageinbox" in options:
options["legend_percentageinbox"] = False
if not "hist_line_none" in options: options["hist_line_none"] = True
if not "show_bkg_errors" in options: options["show_bkg_errors"] = False
if not "ratio_range" in options: options["ratio_range"] = [0.7, 1.3]
if not "ratio_name_size" in options: options["ratio_name_size"] = 0.13
if not "ratio_name_offset" in options: options["ratio_name_offset"] = 0.6
if not "ratio_xaxis_label_offset" in options:
options["ratio_xaxis_label_offset"] = 0.06
if not "ratio_yaxis_label_offset" in options:
options["ratio_yaxis_label_offset"] = 0.03
if not "ratio_xaxis_title_offset" in options:
options["ratio_xaxis_title_offset"] = 1.40
if not "ratio_xaxis_title_size" in options:
options["ratio_xaxis_title_size"] = 0.13
if not "ratio_label_size" in options: options["ratio_label_size"] = 0.13
if not "canvas_tick_one_side" in options:
options["canvas_tick_one_side"] = True
if not "canvas_main_y1" in options: options["canvas_main_y1"] = 0.2
if not "canvas_main_topmargin" in options:
options["canvas_main_topmargin"] = 0.2 / 0.7 - 0.2
if not "canvas_main_rightmargin" in options:
options["canvas_main_rightmargin"] = 50. / 600.
if not "canvas_main_bottommargin" in options:
options["canvas_main_bottommargin"] = 0.2
if not "canvas_main_leftmargin" in options:
options["canvas_main_leftmargin"] = 130. / 600.
if not "canvas_ratio_y2" in options: options["canvas_ratio_y2"] = 0.342
if not "canvas_ratio_topmargin" in options:
options["canvas_ratio_topmargin"] = 0.05
if not "canvas_ratio_rightmargin" in options:
options["canvas_ratio_rightmargin"] = 50. / 600.
if not "canvas_ratio_bottommargin" in options:
options["canvas_ratio_bottommargin"] = 0.4
if not "canvas_ratio_leftmargin" in options:
options["canvas_ratio_leftmargin"] = 130. / 600.
if not "xaxis_title_size" in options: options["xaxis_title_size"] = 0.06
if not "yaxis_title_size" in options: options["yaxis_title_size"] = 0.06
if not "xaxis_label_size_scale" in options:
options["xaxis_label_size_scale"] = 1.4
if not "yaxis_label_size_scale" in options:
options["yaxis_label_size_scale"] = 1.4
if not "xaxis_label_offset_scale" in options:
options["xaxis_label_offset_scale"] = 4.0
if not "yaxis_label_offset_scale" in options:
options["yaxis_label_offset_scale"] = 4.0
if not "xaxis_tick_length_scale" in options:
options["xaxis_tick_length_scale"] = -0.8
if not "yaxis_tick_length_scale" in options:
options["yaxis_tick_length_scale"] = -0.8
if not "ratio_tick_length_scale" in options:
options["ratio_tick_length_scale"] = -1.0
if not "output_name" in options: options["output_name"] = "plot.png"
if not "cms_label" in options: options["cms_label"] = "Preliminary"
if not "lumi_value" in options: options["lumi_value"] = "35.9"
if not "bkg_err_fill_style" in options:
options["bkg_err_fill_style"] = 3245
if not "bkg_err_fill_color" in options: options["bkg_err_fill_color"] = 1
# Call Plottery! I hope you win the Lottery!
p.plot_hist(data=hdata,
bgs=hbkgs,
sigs=hsigs,
syst=hsyst,
sig_labels=hsig_labels,
colors=hcolors,
legend_labels=hlegend_labels,
options=options)
# Set permission
os.system("chmod 644 {}".format(options["output_name"]))
# Call nice plots
copy_nice_plot_index_php(options)
#!/bin/env python
from plottery import plottery as ply
import ROOT as r
tfile = r.TFile("output.root")
h_mmumu = tfile.Get("Mmumu")
import os
if not os.path.exists("plots"):
os.makedirs("plots")
ply.plot_hist(bgs=[h_mmumu], options = { "output_name": "plots/Mmumu.pdf", "output_ic": True, })
truth = quantities_real[key]
hpred = r.TH1F("hpred", "hpred", 100,
truth.mean() - 3. * truth.std(),
truth.mean() + 3. * truth.std())
htruth = r.TH1F("htruth", "htruth", 100,
truth.mean() - 3. * truth.std(),
truth.mean() + 3. * truth.std())
plu.fill_fast(hpred, pred)
plu.fill_fast(htruth, truth)
hpred.Scale(1. / hpred.Integral())
htruth.Scale(1. / htruth.Integral())
ply.plot_hist(bgs=[htruth, hpred],
colors=[r.kAzure + 2, r.kRed - 2],
legend_labels=["Real", "Fake"],
options={
"bkg_sort_method": "unsorted",
"do_stack": False,
"legend_percentageinbox": False,
"legend_alignment": "bottom right",
"legend_scalex": 0.7,
"legend_scaley": 0.7,
"legend_border": False,
"xaxis_label": label_map[key],
"yaxis_label": "Entries",
"output_name": "plots/compare_{}.pdf".format(key),
"ratio_numden_indices": [1, 0],
"output_ic": True,
})