def plot_pTs(pT_lower_cut=100, pT_upper_cut=10000):
keywords = ['prescale', 'uncor_hardest_pT', 'cor_hardest_pT']
properties = parse_file(input_analysis_file,
pT_lower_cut=pT_lower_cut,
pT_upper_cut=pT_upper_cut,
keywords_to_populate=keywords)
uncorrected_pTs = properties['uncor_hardest_pT']
corrected_pTs = properties['cor_hardest_pT']
prescales = properties['prescale']
corrected_pt_hist = Hist(100,
5,
1005,
title='Jet Energy Corrected',
markersize=3.0,
color='black')
bin_width_corrected = (
corrected_pt_hist.upperbound() -
corrected_pt_hist.lowerbound()) / corrected_pt_hist.nbins()
uncorrected_pt_hist = Hist(100,
5,
1005,
title='Jet Energy Uncorrected',
markersize=3.0,
color='orange')
bin_width_uncorrected = (
uncorrected_pt_hist.upperbound() -
uncorrected_pt_hist.lowerbound()) / uncorrected_pt_hist.nbins()
map(uncorrected_pt_hist.Fill, uncorrected_pTs, prescales)
map(corrected_pt_hist.Fill, corrected_pTs, prescales)
corrected_pt_hist.Scale(
1.0 / (corrected_pt_hist.GetSumOfWeights() * bin_width_corrected))
uncorrected_pt_hist.Scale(
1.0 / (uncorrected_pt_hist.GetSumOfWeights() * bin_width_uncorrected))
gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1])
ax0 = plt.subplot(gs[0])
ax1 = plt.subplot(gs[1])
data_plot = rplt.errorbar(corrected_pt_hist,
axes=ax0,
emptybins=False,
marker='o',
markersize=10,
pickradius=8,
capthick=5,
capsize=8,
elinewidth=5)
uncorrected_data_plot = rplt.errorbar(uncorrected_pt_hist,
axes=ax0,
emptybins=False,
marker='o',
markersize=10,
pickradius=8,
capthick=5,
capsize=8,
elinewidth=5)
data_x_errors, data_y_errors = [], []
for x_segment in data_plot[2][0].get_segments():
data_x_errors.append((x_segment[1][0] - x_segment[0][0]) / 2.)
for y_segment in data_plot[2][1].get_segments():
data_y_errors.append((y_segment[1][1] - y_segment[0][1]) / 2.)
data_points_x = data_plot[0].get_xdata()
data_points_y = data_plot[0].get_ydata()
data_plot_points_x = []
data_plot_points_y = []
for i in range(0, len(data_points_x)):
data_plot_points_x.append(data_points_x[i])
data_plot_points_y.append(data_points_y[i])
uncorrected_data_x_errors, uncorrected_data_y_errors = [], []
for x_segment in uncorrected_data_plot[2][0].get_segments():
uncorrected_data_x_errors.append(
(x_segment[1][0] - x_segment[0][0]) / 2.)
for y_segment in uncorrected_data_plot[2][1].get_segments():
uncorrected_data_y_errors.append(
(y_segment[1][1] - y_segment[0][1]) / 2.)
uncorrected_data_points_x = uncorrected_data_plot[0].get_xdata()
uncorrected_data_points_y = uncorrected_data_plot[0].get_ydata()
uncorrected_data_plot_points_x = []
uncorrected_data_plot_points_y = []
for i in range(0, len(uncorrected_data_points_x)):
uncorrected_data_plot_points_x.append(uncorrected_data_points_x[i])
uncorrected_data_plot_points_y.append(uncorrected_data_points_y[i])
data_to_data_y_err = [(b / m)
for b, m in zip(data_y_errors, data_plot_points_y)]
data_to_data_x_err = [
(b / m) for b, m in zip(data_x_errors, [1] * len(data_plot_points_y))
]
uncorrected_to_corrected_y_err = [
(b / m) for b, m in zip(uncorrected_data_y_errors, data_plot_points_y)
]
uncorrected_to_corrected_x_err = [
(b / m) for b, m in zip(uncorrected_data_x_errors, [1] *
len(data_plot_points_y))
]
# Legends Begin.
legend = ax0.legend(loc=1,
frameon=0,
fontsize=60,
bbox_to_anchor=[1.0, 1.0])
ax0.add_artist(legend)
extra = Rectangle((0, 0),
1,
1,
fc="w",
fill=False,
edgecolor='none',
linewidth=0)
if pT_upper_cut != 10000:
labels = [
r"$ \textrm{Anti--}k_{t}\textrm{:}~R = 0.5$",
r"$p_{T} \in [" + str(pT_lower_cut) + ", " + str(pT_upper_cut) +
"]~\mathrm{GeV};\eta<2.4$"
]
else:
labels = [
r"$ \textrm{Anti--}k_{t}\textrm{:}~R = 0.5$",
r"$p_{T} > " + str(pT_lower_cut) + "~\mathrm{GeV};\eta<2.4$"
]
ax0.legend([extra, extra],
labels,
loc=7,
frameon=0,
borderpad=0.1,
fontsize=60,
bbox_to_anchor=[0.92, 0.70])
# Legends End.
ax0.set_xlabel('$p_T~\mathrm{(GeV)}$', fontsize=75, labelpad=45)
ax1.set_xlabel('$p_T~\mathrm{(GeV)}$', fontsize=75, labelpad=45)
ax0.set_ylabel('$\mathrm{A.U.}$', fontsize=75, rotation=0, labelpad=75.)
ax1.set_ylabel("Ratio \nto \n" + "Corrected" +
" ",
fontsize=55,
rotation=0,
labelpad=115,
y=0.31)
ab = AnnotationBbox(OffsetImage(read_png(
get_sample_data("/home/aashish/root/macros/MODAnalyzer/mod_logo.png",
asfileobj=False)),
zoom=0.15,
resample=1,
dpi_cor=1), (0.26, 0.93),
xycoords='figure fraction',
frameon=0)
plt.gca().add_artist(ab)
preliminary_text = "Prelim. (20\%)"
plt.gcf().text(0.32,
0.9215,
preliminary_text,
fontsize=50,
weight='bold',
color='#444444',
multialignment='center')
# Ratio Plot.
uncorrected_pt_hist.Divide(corrected_pt_hist)
corrected_pt_hist.Divide(corrected_pt_hist)
rplt.errorbar(corrected_pt_hist,
xerr=data_to_data_x_err,
yerr=data_to_data_y_err,
axes=ax1,
emptybins=False,
marker='o',
markersize=10,
pickradius=8,
capthick=5,
capsize=8,
elinewidth=5)
rplt.errorbar(uncorrected_pt_hist,
xerr=uncorrected_to_corrected_x_err,
yerr=uncorrected_to_corrected_y_err,
axes=ax1,
emptybins=False,
marker='o',
markersize=10,
pickradius=8,
capthick=5,
capsize=8,
elinewidth=5)
ax0.set_yscale('log')
ax0.autoscale(True)
ax1.autoscale(True)
ax0.set_ylim(10e-8, 0.5 * 10e-1)
ax1.set_ylim(0., 2.)
ax0.set_xlim(0, 1000)
ax1.set_xlim(0, 1000)
plt.gcf().set_size_inches(30, 30, forward=1)
plt.sca(ax0)
plt.gca().xaxis.set_minor_locator(MultipleLocator(25))
plt.tick_params(which='major', width=5, length=25, labelsize=70)
plt.tick_params(which='minor', width=3, length=15)
plt.sca(ax1)
plt.gca().xaxis.set_minor_locator(MultipleLocator(25))
# plt.gca().yaxis.set_minor_locator(MultipleLocator(50))
plt.tick_params(which='major', width=5, length=25, labelsize=70)
plt.tick_params(which='minor', width=3, length=15)
plt.tight_layout(pad=1.08, h_pad=1.08, w_pad=1.08)
print "Printing pT spectrum with pT > " + str(
pT_lower_cut) + " and pT < " + str(pT_upper_cut)
plt.savefig("plots/Version 5/pT/data_pT_data_lower_" + str(pT_lower_cut) +
"_pT_upper_" + str(pT_upper_cut) + ".pdf")
# plt.show()
plt.clf()
def plot_trigger_efficiency_curves(trigger_1, trigger_2, pT_upper_limit=800):
properties = parse_file_turn_on('/home/aashish/turn_on.dat')
# properties = parse_file_turn_on('./trigger_proper_turn_on.dat')
event_numbers = properties['event_number']
pTs = properties['corrected_hardest_pts']
trigger_names = properties['trigger_names']
prescales = properties['prescale']
colors = ['magenta', 'blue', 'orange', 'green', 'black', 'red']
expected_trigger_names = [
"HLT\_Jet180U", "HLT\_Jet140U", "HLT\_Jet100U", "HLT\_Jet70U",
"HLT\_Jet50U", "HLT\_Jet30U"
]
color = colors[expected_trigger_names.index(trigger_1.replace("_", "\_"))]
pt_hist_trigger_1 = Hist(100,
0,
pT_upper_limit,
title=trigger_1[4:],
color=color,
markersize=1.0,
linewidth=5)
pt_hist_trigger_2 = Hist(100,
0,
pT_upper_limit,
title=trigger_2[4:],
color=color,
markersize=1.0,
linewidth=5)
for i in range(0, len(pTs)):
if trigger_1 in trigger_names[i]:
pt_hist_trigger_1.Fill(pTs[i], prescales[i])
# The len thingy is to make sure trigger names like HLT_Jet15U_HcalNoiseFiltered_v3 are excluded.
# if trigger_2 in trigger_names[i] and len(trigger_names[i]) > (len(trigger_2) + 3):
if trigger_2 in trigger_names[i]:
pt_hist_trigger_2.Fill(pTs[i], prescales[i])
pt_hist_trigger_1.Divide(pt_hist_trigger_2)
rplt.errorbar(pt_hist_trigger_1,
color=color,
markersize=10,
pickradius=8,
capthick=5,
capsize=8,
elinewidth=5)
data_plot = rplt.errorbar(pt_hist_trigger_1,
color=color,
markersize=10,
pickradius=8,
capthick=5,
capsize=8,
elinewidth=5)
data_points_x = data_plot[0].get_xdata()
data_points_y = np.log(data_plot[0].get_ydata())
# print data_points_y[0:5]
fitted_poly_coeffs = np.polyfit(data_points_x, data_points_y, 1)
print fitted_poly_coeffs
fitted_poly = np.polynomial.Polynomial(fitted_poly_coeffs)
x = np.arange(120, 200, 5)
plt.plot(x, fitted_poly(x), lw=5, color="red")
plt.gca().xaxis.set_tick_params(width=5, length=20, labelsize=70)
plt.gca().yaxis.set_tick_params(width=5, length=20, labelsize=70)
ab = AnnotationBbox(OffsetImage(read_png(
get_sample_data(
"/home/aashish/root-6.04.06/macros/MODAnalyzer/mod_logo.png",
asfileobj=False)),
zoom=0.15,
resample=1,
dpi_cor=1), (0.23, 0.895),
xycoords='figure fraction',
frameon=0)
plt.gca().add_artist(ab)
plt.gcf().text(0.29,
0.885,
"Prelim. (20\%)",
fontsize=50,
weight='bold',
color='#444444',
multialignment='center')
plt.gcf().set_snap(1)
# Horizontal Line.
plt.plot(list(pt_hist_trigger_1.x()),
[1] * len(list(pt_hist_trigger_1.x())),
color="black",
linewidth=5,
linestyle="dashed")
if trigger_1 == "HLT_L1Jet6U":
lower_pT = 37
elif trigger_1 == "HLT_Jet15U":
lower_pT = 56
elif trigger_1 == "HLT_Jet30U":
lower_pT = 84
elif trigger_1 == "HLT_Jet50U":
lower_pT = 114
elif trigger_1 == "HLT_Jet70U":
lower_pT = 153
elif trigger_1 == "HLT_Jet100U":
lower_pT = 196
else:
lower_pT = 0
if lower_pT != 0:
# CMS Vertical line.
plt.plot([lower_pT, lower_pT], [plt.gca().get_ylim()[0], 1.],
color=color,
linewidth=3,
linestyle="dashed")
# # MOD Vertical line.
# efficient_pt_x = 0.0
# efficient_pt_y = 0.0
# efficient_pt_x_s = []
# efficient_pt_y_s = []
# distance_to_previous_point_close_to_one = 0
# for i in range(0, len(list(pt_hist_trigger_1.x()))):
# if abs(list(pt_hist_trigger_1.y())[i] - 1.00) < 0.1:
# if distance_to_previous_point_close_to_one > 25:
# # Last point close to one too far away.
# # Empty the lists.
# efficient_pt_x_s, efficient_pt_y_s = [], []
# efficient_pt_x_s.append(list(pt_hist_trigger_1.x())[i])
# efficient_pt_y_s.append(list(pt_hist_trigger_1.y())[i])
# distance_to_previous_point_close_to_one = 0
# else:
# distance_to_previous_point_close_to_one += 1
# if len(efficient_pt_x_s) > 75:
# efficient_pt_x = efficient_pt_x_s[0]
# efficient_pt_y = efficient_pt_y_s[0]
# break
# mod_efficient_pTs = [325, 260, 196, 153, 114, 84, 50, 32]
# mod_efficient_pT = mod_efficient_pTs[expected_trigger_names.index(trigger_1.replace("_", "\_"))]
# plt.plot([mod_efficient_pT, mod_efficient_pT], [plt.gca().get_ylim()[0], 1.], color="purple", linewidth=3, linestyle="dashed")
# if lower_pT != 0:
# plt.gca().annotate("CMS\n" + str(lower_pT) + " GeV", xy=(lower_pT, 1.), xycoords='data', xytext=(-100, 250), textcoords='offset points', color=color, size=40, va="center", ha="center", arrowprops=dict(arrowstyle="simple", facecolor=color, zorder=9999, connectionstyle="angle3,angleA=0,angleB=90") )
# plt.gca().annotate("MOD\n" + str(int(mod_efficient_pT)) + " GeV", xy=(mod_efficient_pT, 1.), xycoords='data', xytext=(250, 200), textcoords='offset points', color="purple", size=40, va="center", ha="center", arrowprops=dict(arrowstyle="simple", facecolor="purple", zorder=9999, connectionstyle="angle3,angleA=45,angleB=-90") )
plt.yscale('log')
plt.gca().set_ylim(plt.gca().get_ylim()[0], 100)
plt.legend(frameon=0)
plt.xlabel('$p_T~\mathrm{(GeV)}$', fontsize=55, rotation=0)
plt.ylabel('$\mathrm{A.U.}$', fontsize=75, rotation=0, labelpad=50.)
plt.gcf().set_size_inches(30, 21.4285714, forward=1)
plt.gcf().set_snap(True)
plt.tight_layout(pad=1.08, h_pad=1.08, w_pad=1.08)
plt.savefig("plots/Version 4/trigger_efficiency_fit/efficiency_curves_" +
trigger_1 + ".pdf")
# plt.show()
plt.clf()
def makeComparisionPage(histodicts, fileNames, fileDescr, separateFiles):
"""
Prepares a canvas comparing multiple histograms: plotting all in one pad and their ratios in the second
"""
import rootpy
from rootpy.plotting import Hist, Canvas, Legend
import ROOT
from ROOT import gPad
log = logging.getLogger('pyroplot')
cans = {}
colors = [
ROOT.kBlue, ROOT.kRed + 1, ROOT.kViolet - 1, ROOT.kOrange + 7,
ROOT.kGreen - 7, ROOT.kOrange - 6, ROOT.kPink - 9, ROOT.kTeal - 6,
ROOT.kBlue + 4, ROOT.kAzure + 2
]
log.info("Drawing histograms ..")
# prepare set of histograms to compare to the reference on (the first)
# loop over the reference set of histos (sorted by key):
for hidx, refname in enumerate(sorted(histodicts[0].keys())):
# prepare canvas
if separateFiles:
log.debug("Creating new canvas with index %d." % (hidx))
c = Canvas(600, 270)
cans[refname] = c
c.Divide(3, 1)
c.cd(1)
if not separateFiles and (hidx) % 4 == 0:
log.debug("Creating new canvas with index %d." % (hidx / 3))
# start a new canvas
c = Canvas(600, 800)
cans[refname] = c
c.Divide(3, 4)
# prepare histograms for drawing
log.debug("Drawing histogram #" + str(hidx + 1) + " (" + refname +
") on canvas #" + str(len(cans)))
hists = []
ratiohists = []
hiter = iter(histodicts)
# special treatment for tprofile: prepare the reference projection for the ratio
if histodicts[0][refname].__class__.__name__ == "Profile":
refProj = histodicts[0][refname].ProjectionX()
refProj.SetName("reference_proj")
for idx, h in enumerate(hiter):
# make sure we have this histogram loaded:
if not refname in h:
continue
# access the corresponding histogram of the other files at the same hidx as used for ref
h[refname].color = colors[idx]
h[refname].linestyle = idx
hists.append(h[refname])
# prepare ratio is this is not the first (i.e. reference) histogram
if idx:
# special treatment for TProfile:
if h[refname].__class__.__name__ == "Profile":
myratio = Hist(h[refname].nbins(), h[refname].lowerbound(),
h[refname].upperbound()) #dummy hist
myratioproj = h[refname].ProjectionX()
myratioproj.SetName("cmp_hist_proj" + str(idx))
try:
myratio.divide(myratioproj, refProj)
except rootpy.ROOTError, e:
log.error(
"Calculation of ratio for histogram %s caused ROOTError exception ('%s')"
% (h[refname].GetName(), e.msg))
break
myratio.color = colors[idx]
else:
myratio = h[refname].clone(
) # make sure that the ratio has the right type
try:
myratio.Divide(
h[refname],
histodicts[0][refname]) # divide by reference hist
except rootpy.ROOTError, e:
log.error(
"Calculation of ratio for histogram %s caused ROOTError exception ('%s')"
% (h[refname].GetName(), e.msg))
break
myratio.yaxis.SetTitle("(h_{cmp} - h_{ref})/h_{ref}")
myratio.SetTitle("ratio to reference")
myratio.SetMaximum(2)
myratio.SetMinimum(0)
myratio.SetStats(0)
ratiohists.append(myratio)
