def _ratioFigure(self):
self.axR = plt.subplot(self.gs[1])
for i, ratio in enumerate(self.ratios):
if self.plotType[i] == 'Data':
rplt.errorbar(ratio,
label=self.label[i],
markerfacecolor=self.colour[i],
markersize=8,
xerr=False,
yerr=False,
elinewidth=0,
markeredgewidth=0,
markeredgecolor=self.colour[i],
emptybins=False,
zorder=self.zorder[i],
axes=self.axR)
elif self.plotType[i] == 'CMSSW':
rplt.step(ratio,
label=self.label[i],
color=self.colour[i],
zorder=self.zorder[i],
linewidth=2,
axes=self.axR)
elif self.plotType[i] == 'Simulation':
plt.axhline(y=1, color='black', linewidth=2, axes=self.axR)
else:
pass
return None
def _plotFigure(self):
self.ax = plt.subplot(self.gs[0])
for i, hist in enumerate(self.hists):
if self.plotType[i] == 'Data':
rplt.errorbar(hist,
label=self.label[i],
markerfacecolor=self.colour[i],
markersize=8,
xerr=False,
yerr=False,
elinewidth=0,
markeredgewidth=0,
markeredgecolor=self.colour[i],
emptybins=False,
zorder=self.zorder[i],
axes=self.ax)
elif self.plotType[i] == 'Simulation':
rplt.hist(hist,
label=self.label[i],
color=self.colour[i],
alpha=self.alpha[i],
fill=self.fill[i],
zorder=self.zorder[i],
axes=self.ax)
elif self.plotType[i] == 'CMSSW':
rplt.step(hist,
label=self.label[i],
color=self.colour[i],
zorder=self.zorder[i],
linewidth=2,
axes=self.ax)
else:
pass
return None
def make_data_mc_comparison_plot(
histograms=[],
histogram_lables=[],
histogram_colors=[],
histogram_properties=Histogram_properties(),
data_index=0,
save_folder='plots/',
save_as=['pdf'],
normalise=False,
show_ratio=False,
show_stat_errors_on_mc=False,
draw_vertical_line=0,
systematics_for_ratio=None,
systematics_for_plot=None,
histograms_to_compare=None,
):
'''
systematics_for_plot takes the same input as systematics_for_ratio. There may be some repition with reagrds to
mc_error and mc_relative_errors, but these only deal with a flat error up and down.
'''
save_folder = check_save_folder(save_folder)
# make copies in order not to mess with existing histograms
histograms_ = deepcopy(histograms)
stack = HistStack()
add_mc = stack.Add
for index, histogram in enumerate(histograms_):
label = histogram_lables[index]
color = histogram_colors[index]
histogram.SetTitle(label)
if normalise:
histogram.Sumw2()
if not index == data_index:
histogram.fillstyle = 'solid'
histogram.fillcolor = color
histogram.legendstyle = 'F'
add_mc(histogram)
data = histograms_[data_index]
data.SetMarkerSize(CMS.data_marker_size)
if normalise:
n_events_data = data.Integral()
n_events_mc = stack.Integral()
data.Scale(1 / n_events_data)
stack.Scale(1 / n_events_mc)
# plot with matplotlib
plt.figure(figsize=CMS.figsize, dpi=CMS.dpi, facecolor=CMS.facecolor)
axes = None
if show_ratio:
ratio = data.Clone('ratio')
sumHists = sum(stack.GetHists())
for bin_i in range(1, sumHists.GetNbinsX()):
sumHists.SetBinError(bin_i, 0)
ratio.Divide(sum(stack.GetHists()))
ratio.SetMarkerSize(3)
gs = gridspec.GridSpec(2, 1, height_ratios=[5, 1])
axes = plt.subplot(gs[0])
else:
axes = plt.axes()
if histogram_properties.set_log_y:
axes.set_yscale('log', nonposy="clip")
axes.set_ylim(ymin=1e-2)
if systematics_for_plot != None:
plusErrors = [x + 1 for x in systematics_for_plot]
minusErrors = [1 - x for x in systematics_for_plot]
stack_lower = sum(stack.GetHists())
stack_upper = stack_lower.Clone('upper')
for bin_i in range(1, stack_lower.GetNbinsX() + 1):
central_value = stack_lower.GetBinContent(bin_i)
error_upper_bound = plusErrors[bin_i - 1] * central_value
error_lower_bound = minusErrors[bin_i - 1] * central_value
stack_upper.SetBinContent(bin_i, error_upper_bound)
stack_lower.SetBinContent(bin_i, error_lower_bound)
rplt.fill_between(
stack_lower,
stack_upper,
axes,
hatch='//',
# facecolor = 'Black',
facecolor='None',
edgecolor='Grey',
alpha=1.,
linewidth=0.,
zorder=len(histograms_) + 1)
mc_error = histogram_properties.mc_error
mc_relative_errors = histogram_properties.mc_relative_errors
if mc_relative_errors:
stack_lower = sum(stack.GetHists())
stack_upper = stack_lower.Clone('upper')
for bin_i in range(1, stack_lower.GetNbinsX()):
central_value = stack_lower.GetBinContent(bin_i)
relative_error = mc_relative_errors[bin_i - 1]
error_upper_bound = central_value * (1 + relative_error)
error_lower_bound = central_value * (1 - relative_error)
stack_lower.SetBinContent(bin_i, error_upper_bound)
stack_upper.SetBinContent(bin_i, error_lower_bound)
rplt.fill_between(stack_upper,
stack_lower,
axes,
facecolor='0.75',
alpha=0.5,
hatch='/',
zorder=len(histograms_) + 1)
else:
if mc_error > 0:
stack_lower = sum(stack.GetHists())
stack_upper = stack_lower.Clone('upper')
stack_lower.Scale(1 - mc_error)
stack_upper.Scale(1 + mc_error)
rplt.fill_between(stack_upper,
stack_lower,
axes,
facecolor='0.75',
alpha=0.5,
hatch='/',
zorder=len(histograms_) + 1)
if not mc_error > 0 and show_stat_errors_on_mc:
stack_lower = sum(stack.GetHists())
mc_errors = list(stack_lower.yerravg())
stack_upper = stack_lower.Clone('upper')
for bin_i in range(1, stack_lower.GetNbinsX()):
central_value = stack_lower.GetBinContent(bin_i)
error = mc_errors[bin_i - 1]
error_upper_bound = central_value + error
error_lower_bound = central_value - error
stack_lower.SetBinContent(bin_i, error_lower_bound)
stack_upper.SetBinContent(bin_i, error_upper_bound)
rplt.fill_between(stack_upper,
stack_lower,
axes,
facecolor='0.75',
alpha=0.5,
hatch='/',
zorder=len(histograms_) + 1)
# a comment on zorder: the MC stack should be always at the very back (z = 1),
# then the MC error (z = len(histograms_) + 1) and finally the data
# (z = len(histograms_) + 2)
rplt.hist(stack, stacked=True, axes=axes, zorder=1)
rplt.errorbar(data,
emptybins=histogram_properties.emptybins,
axes=axes,
xerr=histogram_properties.xerr,
elinewidth=2,
capsize=10,
capthick=2,
zorder=len(histograms_) + 2)
if histograms_to_compare:
h_compare = {}
for h, l, c in zip(histograms_to_compare['hists'],
histograms_to_compare['labels'],
histograms_to_compare['colours']):
for histogram in histograms_:
if histogram.GetTitle() not in [
histograms_to_compare['to_replace'], 'data'
]:
h += histogram
h_compare[l] = [h, c]
rplt.step(
h,
axes=axes,
label=l,
color=c,
linewidth=4,
)
# put legend into the correct order (data is always first!)
handles, labels = axes.get_legend_handles_labels()
data_label_index = labels.index('data')
data_handle = handles[data_label_index]
labels.remove('data')
handles.remove(data_handle)
labels.insert(0, 'data')
handles.insert(0, data_handle)
if mc_error > 0 or (not mc_error > 0 and show_stat_errors_on_mc):
p1 = Rectangle((0, 0), 1, 1, fc="0.75", alpha=0.5, hatch='/')
handles.append(p1)
labels.append(histogram_properties.mc_errors_label)
l1 = axes.legend(handles,
labels,
numpoints=1,
frameon=histogram_properties.legend_color,
bbox_to_anchor=histogram_properties.legend_location,
bbox_transform=plt.gcf().transFigure,
prop=CMS.legend_properties,
ncol=histogram_properties.legend_columns)
l1.set_zorder(102)
set_labels(plt,
histogram_properties,
show_x_label=not show_ratio,
axes=axes)
x_limits = histogram_properties.x_limits
y_limits = histogram_properties.y_limits
if len(x_limits) >= 2:
axes.set_xlim(xmin=x_limits[0], xmax=x_limits[-1])
if len(y_limits) >= 2:
axes.set_ylim(ymin=y_limits[0], ymax=y_limits[-1])
else:
y_max = get_best_max_y(
histograms_, x_limits=x_limits) * histogram_properties.y_max_scale
print("Chosen limits : ", 0, y_max)
axes.set_ylim(ymin=0, ymax=y_max)
if histogram_properties.set_log_y:
if not len(y_limits) == 2: # if not user set y-limits, set default
axes.set_ylim(ymin=1e-1)
#draw a red vertical line if needed:
if draw_vertical_line != 0:
plt.axvline(x=draw_vertical_line, color='red', linewidth=3)
if show_ratio:
plt.setp(axes.get_xticklabels(), visible=False)
ax1 = plt.subplot(gs[1])
ax1.minorticks_on()
ax1.grid(True, 'major', linewidth=1)
ax1.axhline(y=1, linewidth=1)
set_labels(plt,
histogram_properties,
show_x_label=True,
show_title=False)
plt.ylabel(r'$\frac{\mathrm{data}}{\mathrm{pred.}}$', CMS.y_axis_title)
ax1.yaxis.set_label_coords(-0.115, 0.8)
rplt.errorbar(ratio,
emptybins=histogram_properties.emptybins,
axes=ax1,
xerr=histogram_properties.xerr,
elinewidth=1.5,
capsize=5,
capthick=1.5)
if histograms_to_compare:
for l, h in h_compare.iteritems():
r = data.Clone(l).Divide(h[0])
rplt.step(
r,
axes=ax1,
label='',
colour=h[1],
linewidth=2,
)
if len(x_limits) >= 2:
ax1.set_xlim(xmin=x_limits[0], xmax=x_limits[-1])
if len(histogram_properties.ratio_y_limits) >= 2:
ax1.set_ylim(ymin=histogram_properties.ratio_y_limits[0],
ymax=histogram_properties.ratio_y_limits[-1])
# dynamic tick placement
adjust_ratio_ticks(ax1.yaxis,
n_ticks=3,
y_limits=histogram_properties.ratio_y_limits)
if histogram_properties.integerXVariable:
ax1.tick_params(axis='x', which='minor', bottom='off', top='off')
if systematics_for_ratio != None:
plusErrors = [x + 1 for x in systematics_for_ratio]
minusErrors = [1 - x for x in systematics_for_ratio]
ratioPlusError = ratio.Clone('plus')
ratioMinusError = ratio.Clone('minus')
for bin_i in range(1, ratioPlusError.GetNbinsX() + 1):
ratioPlusError.SetBinContent(bin_i, plusErrors[bin_i - 1])
ratioMinusError.SetBinContent(bin_i, minusErrors[bin_i - 1])
rplt.fill_between(
ratioPlusError,
ratioMinusError,
axes,
hatch='//',
# facecolor = 'Black',
facecolor='None',
edgecolor='Grey',
alpha=1.,
linewidth=0.,
# zorder = len(histograms_) + 1
zorder=0)
if CMS.tight_layout:
plt.tight_layout()
for save in save_as:
if save == 'root':
saveHistogramsToROOTFile(
data, stack,
save_folder + histogram_properties.name + '.' + save)
else:
plt.savefig(save_folder + histogram_properties.name + '.' + save)
plt.close()
h1b, h1c, h1u = get_hists(
dd2, (dd2["is_training"] == 0) & (abs(dd2["Jet_eta"]) >= etalow) &
(abs(dd2["Jet_eta"]) < etahigh), "cls_p0", (200, 0, 1))
h2b, h2c, h2u = get_hists(
dd2, (dd2["is_training"] == 1) & (abs(dd2["Jet_eta"]) >= etalow) &
(abs(dd2["Jet_eta"]) < etahigh), "cls_p0", (200, 0, 1))
for h in [h1b, h2b, h1c, h2c, h1u, h2u]:
h.Scale(1.0 / h.Integral())
plt.figure(figsize=(9, 3))
plt.title("Discriminator plots, " + str(etalow) + " < $|\eta|$ < " +
str(etahigh))
plt.subplot(1, 3, 1)
rplt.step(h1b, color="red", label="testing")
rplt.step(h2b, color="blue", label="training")
plt.xlabel("discriminator")
plt.ylabel("fraction of jets")
plt.title("b")
plt.yscale("log")
plt.legend(loc="best")
plt.subplot(1, 3, 2)
rplt.step(h1c, color="red")
rplt.step(h2c, color="blue")
plt.xlabel("discriminator")
plt.ylabel("fraction of jets")
plt.title("charm")
plt.yscale("log")
def plot_cmssw_charge_occupancy_variations(REGION, useFullDist=True):
'''
Data highOcc vs SCD vs SCD Po(N)
'''
make_folder_if_not_exists('plots/InputChargePoissonMatching/')
OCC = REGION_DETAILS[REGION]['Occ_Data']
hs_to_plot = []
rs_to_plot = []
occupancies_to_test = []
colors = ['blue', 'red', 'darkgreen', 'magenta']
if useFullDist:
f_hist = 'input/landau_scd_290118_orig.root'
occupancies_to_test = [OCC * 10, OCC * 20, OCC * 50, OCC * 100]
else:
f_hist = 'input/landau_scd_290118_cut.root'
occupancies_to_test = [OCC * 5, OCC * 10, OCC * 20, OCC * 50]
with root_open(f_hist) as f:
h = asrootpy(f.Get(REGION).Clone())
h.SetDirectory(0)
l_edges = list(h.xedges())
# with root_open('input/landau_lowPUTracks_290118_orig.root') as f:
# h_data_PU0 = asrootpy(f.Get(REGION).Clone())
# h_data_PU0.SetDirectory(0)
# h_data_PU0.Scale(1 /h_data_PU0.integral(xbin1=21,xbin2=200))
# hs_to_plot.append({
# 'hist' : h_data_PU0,
# 'label' : 'lowPU Strips from Tracks',
# # 'color' : '0.5',
# 'color' : 'grey',
# 'type' : 'Data'
# })
# with root_open('input/landau_lowPUClusters_290118_orig.root') as f:
# h_data_lowOcc = asrootpy(f.Get(REGION).Clone())
# h_data_lowOcc.SetDirectory(0)
# h_data_lowOcc.Scale(1 /h_data_lowOcc.integral(xbin1=21,xbin2=200))
# hs_to_plot.append({
# 'hist' : h_data_lowOcc,
# 'label' : 'lowOcc Strips from Clusters',
# # 'color' : '0',
# 'color' : 'brown',
# 'type' : 'Data'
# })
with root_open(
'input/landau_clusterData_010218_VFPFix_True_orig.root') as f:
h_data_highOcc = asrootpy(f.Get(REGION).Clone())
h_data_highOcc.SetDirectory(0)
h_data_highOcc.Scale(1 / h_data_highOcc.integral(xbin1=21, xbin2=200))
hs_to_plot.append({
'hist': h_data_highOcc,
'label': 'highOcc Strips from Clusters',
# 'color' : '0',
'color': 'black',
'type': 'Data'
})
hist_scd = Hist(l_edges)
h_tests = []
for occ, col in zip(occupancies_to_test, colors):
h_tmp = {
'occ': occ,
'hist': Hist(l_edges),
'color': col,
}
h_tests.append(h_tmp)
i = 0
while i < 250000:
v = return_strip_charge_from_Poisson(h,
OCC,
add_noise=False,
add_truncation=True)
hist_scd.Fill(v)
for h_test in h_tests:
v_occ = return_strip_charge_from_Poisson(h,
h_test['occ'],
add_noise=False,
add_truncation=True,
cut_charge=True)
if not v_occ < 10000: h_test['hist'].Fill(v_occ)
i = i + 1
# Normalising between 10000-100000
hist_scd.Scale(1 / hist_scd.integral(xbin1=21, xbin2=200))
for h_test in h_tests:
h_test['hist'].Scale(1 / h_test['hist'].integral(xbin1=21, xbin2=200))
scd = ''
if useFullDist:
scd = 'SCD'
else:
scd = 'Cut SCD'
r_scd = h_data_highOcc.Clone()
r_scd.SetDirectory(0)
r_scd.Divide(hist_scd)
hs_to_plot.append({
'hist':
hist_scd,
'label':
'{SCD} sampled Po({OCC})'.format(SCD=scd, OCC=OCC),
'color':
'black',
'type':
'SCD'
})
rs_to_plot.append({
'hist': r_scd,
'label': '',
'color': 'black',
'type': 'Ratio'
})
for h_test in h_tests:
r = h_data_highOcc.Clone()
r.SetDirectory(0)
r.Divide(h_test['hist'])
hs_to_plot.append({
'hist':
h_test['hist'],
'label':
'{SCD} sampled Po({OCC})'.format(SCD=scd, OCC=h_test['occ']),
'color':
h_test['color'],
'line':
'solid',
'type':
'CMSSW'
})
rs_to_plot.append({
'hist': r,
'label': '',
'color': h_test['color'],
'type': 'Ratio'
})
fig = plt.figure()
fig.suptitle(
"Charge Deposition from Simulation using Sampling from {SCD} distribution"
.format(SCD=scd),
fontsize=14,
fontweight='bold')
gs = gridspec.GridSpec(2,
1,
height_ratios=[5, 1],
wspace=0.025,
hspace=0.025)
ax = plt.subplot(gs[0])
plt.title(REGION.replace("_", " ") + " normalised 10,000-70,000",
loc='right')
for h_info in hs_to_plot:
if 'Data' in h_info['type']:
rplt.hist(
h_info['hist'],
label=h_info['label'],
color=h_info['color'],
alpha=0.35,
fill=True,
zorder=0,
)
if 'SCD' in h_info['type']:
rplt.errorbar(
h_info['hist'],
label=h_info['label'],
markerfacecolor=h_info['color'],
markersize=3,
xerr=False,
yerr=False,
elinewidth=0,
emptybins=False,
zorder=len(hs_to_plot),
)
if 'CMSSW' in h_info['type']:
rplt.step(h_info['hist'],
label=h_info['label'],
color=h_info['color'],
linestyle=h_info['line'])
ax.set_ylim([0.0001, 1.])
# ax.set_xlim([10000,70000])
ax.set_xlim([0.1, 70000])
ax.set_yscale("log", nonposy='clip')
ax.set_ylabel('N')
# ax.set_xlabel('Charge (e)')
plt.setp(ax.get_xticklabels(), visible=False)
leg = ax.legend(loc='upper right', numpoints=1, prop={'size': 10}, ncol=2)
leg.draw_frame(False)
ax_ratio = plt.subplot(gs[1])
ax_ratio.axhline(1, color='black')
for r_info in rs_to_plot:
rplt.errorbar(
r_info['hist'],
label=r_info['label'],
markerfacecolor=r_info['color'],
markersize=6,
markeredgewidth=0,
xerr=False,
yerr=False,
elinewidth=0,
emptybins=False,
axes=ax_ratio,
)
ax_ratio.set_ylim([0, 2])
ax_ratio.set_xlim([0.1, 70000])
# ax_ratio.set_xlim([10000,70000])
ax_ratio.set_xlabel('Charge deposited on APV in a bx(e) ')
ax_ratio.set_ylabel(
r'$\frac{\mathrm{Data\ High\ Occ}}{\mathrm{SCD\ Sampling}}$')
gs.tight_layout(fig, rect=[0, 0.03, 1, 0.95])
filename = 'CMSSWSimChargeFrom{SCD}_'.format(
SCD=scd.replace(" ", "")) + REGION
fig.savefig('plots/InputChargePoissonMatching/' + filename + '.pdf',
bbox_inches='tight')
fig.clf()
plt.close()
gc.collect()
def plot_cmssw_data_charge_comparisons(REGION):
'''
SCD vs CMSSWSimPostAPV lowOcc vs Data lowOcc vs Data highOcc
'''
make_folder_if_not_exists('plots/InputChargeComparisons/')
hs_to_plot = []
rs_to_plot = []
with root_open('input/180207/landau_Sim_scd_070218.root') as f:
h_scd = asrootpy(f.Get('demo/SCD/' + REGION).Clone())
h_scd = apply_max_cutoff(h_scd)
h_scd.Scale(1 / h_scd.integral(xbin1=21, xbin2=200))
h_scd.SetDirectory(0)
hs_to_plot.append({
'hist': h_scd,
'label': 'SCD',
'color': 'black',
'type': 'SCD'
})
h_clus = asrootpy(f.Get('demo/Clusters/' + REGION).Clone())
h_clus.Scale(1 / h_clus.integral(xbin1=21, xbin2=200))
h_clus.SetDirectory(0)
hs_to_plot.append({
'hist': h_clus,
'label': 'Cluster Strip Charge',
'color': 'red',
'line': 'solid',
'type': 'CMSSW'
})
with root_open(
'input/180129_lowPU/landau_lowPUClusters_290118_orig.root') as f:
h_data_lowOcc = asrootpy(f.Get(REGION).Clone())
h_data_lowOcc.Scale(1 / h_data_lowOcc.integral(xbin1=21, xbin2=200))
h_data_lowOcc.SetDirectory(0)
hs_to_plot.append({
'hist': h_data_lowOcc,
'label': 'Data lowOcc Strips from Clusters',
'color': 'black',
'type': 'Data'
})
r = h_data_lowOcc.Clone()
r.SetDirectory(0)
r.Divide(h_scd)
r2 = h_data_lowOcc.Clone()
r2.SetDirectory(0)
r2.Divide(h_clus)
rs_to_plot.append({
'hist': r,
'label': '',
'color': 'black',
'type': 'Ratio'
})
rs_to_plot.append({
'hist': r2,
'label': '',
'color': 'red',
'type': 'Ratio'
})
fig = plt.figure()
fig.suptitle(
"Low PU: SCD vs CMSSW Cluster Strip Charge vs Data Cluster Strip Charge",
fontsize=14,
fontweight='bold')
gs = gridspec.GridSpec(2,
1,
height_ratios=[5, 1],
wspace=0.025,
hspace=0.025)
ax = plt.subplot(gs[0])
plt.title(REGION.replace("_", " ") + " normalised 10,000-70,000",
loc='right')
for h_info in hs_to_plot:
if 'Data' in h_info['type']:
rplt.hist(
h_info['hist'],
label=h_info['label'],
color=h_info['color'],
alpha=0.35,
fill=True,
zorder=0,
)
if 'SCD' in h_info['type']:
rplt.errorbar(
h_info['hist'],
label=h_info['label'],
markerfacecolor=h_info['color'],
markersize=3,
xerr=False,
yerr=False,
elinewidth=0,
emptybins=False,
zorder=len(hs_to_plot),
)
if 'CMSSW' in h_info['type']:
rplt.step(h_info['hist'],
label=h_info['label'],
color=h_info['color'],
linestyle=h_info['line'])
ax.set_ylim([0.0001, 1.])
ax.set_xlim([0.1, 70000])
ax.set_yscale("log", nonposy='clip')
ax.set_ylabel('N')
plt.setp(ax.get_xticklabels(), visible=False)
leg = ax.legend(loc='upper right', numpoints=1, prop={'size': 10}, ncol=1)
leg.draw_frame(False)
ax_ratio = plt.subplot(gs[1])
ax_ratio.axhline(1, color='black')
for r_info in rs_to_plot:
rplt.errorbar(
r_info['hist'],
label=r_info['label'],
markerfacecolor=r_info['color'],
markersize=3,
markeredgewidth=0,
xerr=False,
yerr=False,
elinewidth=0,
emptybins=False,
axes=ax_ratio,
)
ax_ratio.set_ylim([0, 2])
ax_ratio.set_xlim([0.1, 70000])
ax_ratio.set_xlabel('Charge deposited on APV in a bx(e) ')
ax_ratio.set_ylabel(r'$\frac{\mathrm{Data}}{\mathrm{Sim}}$')
gs.tight_layout(fig, rect=[0, 0.03, 1, 0.95])
filename = 'CMSSWSimChargeDepositionVsData' + REGION
fig.savefig('plots/InputChargeComparisons/' + filename + '.png',
bbox_inches='tight')
fig.clf()
plt.close()
gc.collect()
def plot_cmssw_charge_distributions(REGION):
'''
CMSSWSim stages. SCD vs SCD+Noise+ZS vs SCD+Noise+ZS+ClusterStripsOnly
'''
make_folder_if_not_exists('plots/InputChargeDistributionsCMSSW/')
hs_to_plot = []
with root_open('input/180207/landau_Sim_scd_070218.root') as f:
h_scd = asrootpy(f.Get('demo/SCD/' + REGION).Clone())
h_scd = apply_max_cutoff(h_scd)
h_scd.Scale(1 / h_scd.integral(xbin1=21, xbin2=200))
h_scd.SetDirectory(0)
hs_to_plot.append({
'hist': h_scd,
'label': 'SCD',
'color': 'black',
'type': 'CMSSW'
})
h_zs = asrootpy(f.Get('demo/ZS/' + REGION).Clone())
h_zs = apply_max_cutoff(h_zs)
h_zs.Scale(1 / h_zs.integral(xbin1=21, xbin2=200))
h_zs.SetDirectory(0)
hs_to_plot.append({
'hist': h_zs,
'label': 'SCD + Noise + ZS',
'color': 'blue',
'type': 'CMSSW'
})
h_clus = asrootpy(f.Get('demo/Clusters/' + REGION).Clone())
h_clus.Scale(1 / h_clus.integral(xbin1=21, xbin2=200))
h_clus.SetDirectory(0)
hs_to_plot.append({
'hist': h_clus,
'label': 'Cluster Strip Charge',
'color': 'red',
'type': 'CMSSW'
})
fig = plt.figure()
fig.suptitle("Evolution of the SCD in CMSSW",
fontsize=14,
fontweight='bold')
gs = gridspec.GridSpec(1, 1, wspace=0.025, hspace=0.025)
ax = plt.subplot(gs[0])
plt.title(REGION.replace("_", " ") + " normalised between 10,000-70,000",
loc='right')
for h_info in hs_to_plot:
if 'CMSSW' in h_info['type']:
rplt.step(
h_info['hist'],
label=h_info['label'],
color=h_info['color'],
)
ax.set_ylim([0.0001, 10.])
ax.set_xlim([0, 70000])
ax.set_yscale("log", nonposy='clip')
ax.set_ylabel('N')
ax.set_xlabel('Charge (e)')
leg = ax.legend(loc='best', numpoints=1, prop={'size': 10})
leg.draw_frame(False)
gs.tight_layout(fig, rect=[0, 0.03, 1, 0.95])
filename = 'CMSSWSimChargeDepositionOrig' + REGION
fig.savefig('plots/InputChargeDistributionsCMSSW/' + filename + '.png',
bbox_inches='tight')
fig.clf()
plt.close()
gc.collect()