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plotter.py
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plotter.py
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# Jennet's plotter for coffea histograms
# March 26, 2021
import uproot3
import numpy as np
from coffea import hist
import matplotlib
import matplotlib.pyplot as plt
import mplhep as hep
plt.style.use([hep.style.CMS])
global color_dict
color_dict = {}
color_dict['QCD'] = 'black'
color_dict['Wjets'] = 'gray'
color_dict['Zjets'] = 'deepskyblue'
color_dict['Zjetsbb'] = 'blue'
color_dict['ttbar'] = 'purple'
color_dict['singlet'] = 'hotpink'
color_dict['VV'] = 'darkorange'
color_dict['ttH'] = 'purple'
color_dict['WH'] = 'blue'
color_dict['ZH'] = 'gold'
color_dict['VBF'] = 'green'
color_dict['ggF'] = 'red'
def plot_systs_sig_bybin(syst,df16,df17,df18):
fig = plt.figure()
plt.subplots_adjust(hspace=0)
plt.suptitle(syst+' [%]')
samples = ['ggF','VBF','WH','ZH','ttH']
colors = ['red','limegreen','orange','deepskyblue','purple']
thebins = ['ggf 1','ggf 2','ggf 3','ggf 4','ggf 5','ggf 6','vbf 1','vbf 2']
binnames = []
for i in range(len(samples)):
binnames += [thebins]
# PASS
dfs = {}
dfs['2016'] = df16[(df16["syst"]==syst) & (df16["region"]=="pass")].drop(columns=["syst","region"])
dfs['2017'] = df17[(df17["syst"]==syst) & (df17["region"]=="pass")].drop(columns=["syst","region"])
dfs['2018'] = df18[(df18["syst"]==syst) & (df18["region"]=="pass")].drop(columns=["syst","region"])
for i,y in enumerate(['2016','2017','2018']):
to_plot = []
for s in samples:
to_plot_samp = []
df3 = dfs[y][dfs[y]["samp"]==s].drop(columns=["samp"])
for b in thebins:
df4 = df3[df3["bin"]==b]
if len(df4) == 1:
to_plot_samp += list(df4["val"]*100)
else:
to_plot_samp += [0]
to_plot += [to_plot_samp]
ax = fig.add_subplot(3,2,2*i+1)
ax.hist(x=binnames,bins=range(len(thebins)+1),weights=to_plot,histtype='bar',color=colors,label=samples)
ax.set_ylabel(y)
ax.set_xticklabels([])
ax.set_xticklabels(thebins)
plt.xticks(rotation=45)
ax.set_xlabel('DDB Pass')
# FAIL
dfs = {}
dfs['2016'] = df16[(df16["syst"]==syst) & (df16["region"]=="fail")].drop(columns=["syst","region"])
dfs['2017'] = df17[(df17["syst"]==syst) & (df17["region"]=="fail")].drop(columns=["syst","region"])
dfs['2018'] = df18[(df18["syst"]==syst) & (df18["region"]=="fail")].drop(columns=["syst","region"])
for i,y in enumerate(['2016','2017','2018']):
to_plot = []
for s in samples:
to_plot_samp = []
df3 = dfs[y][dfs[y]["samp"]==s].drop(columns=["samp"])
for b in thebins:
df4 = df3[df3["bin"]==b]
if len(df4) == 1:
to_plot_samp += list(df4["val"]*100)
else:
to_plot_samp += [0]
to_plot += [to_plot_samp]
ax = fig.add_subplot(3,2,2*i+2)
ax.hist(x=binnames,bins=range(len(thebins)+1),weights=to_plot,histtype='bar',color=colors,label=samples)
#ax.set_ylabel(y)
ax.set_xticklabels([])
ax.set_xticklabels(thebins)
plt.xticks(rotation=45)
ax.set_xlabel('DDB Fail')
plt.legend(bbox_to_anchor=(1.05, 1))
plt.show()
name = 'plots/bybin_sig_'+syst
png_name = name+'.png'
fig.savefig(png_name,bbox_inches='tight')
pdf_name = name+'.pdf'
fig.savefig(pdf_name,bbox_inches='tight')
def plot_systs_bkg_bybin(syst,df16,df17,df18):
fig = plt.figure()
plt.subplots_adjust(hspace=0)
plt.suptitle(syst+' [%]')
samples = ['Wjets','Zjets','Zjetsbb','ttbar','singlet','VV']
colors=['gray','deepskyblue','blue','purple','hotpink','darkorange']
thebins = ['ggf 1','ggf 2','ggf 3','ggf 4','ggf 5','ggf 6','vbf 1','vbf 2']
binnames = []
for i in range(len(samples)):
binnames += [thebins]
# PASS
dfs = {}
dfs['2016'] = df16[(df16["syst"]==syst) & (df16["region"]=="pass")].drop(columns=["syst","region"])
dfs['2017'] = df17[(df17["syst"]==syst) & (df17["region"]=="pass")].drop(columns=["syst","region"])
dfs['2018'] = df18[(df18["syst"]==syst) & (df18["region"]=="pass")].drop(columns=["syst","region"])
for i,y in enumerate(['2016','2017','2018']):
to_plot = []
for s in samples:
to_plot_samp = []
df3 = dfs[y][dfs[y]["samp"]==s].drop(columns=["samp"])
for b in thebins:
df4 = df3[df3["bin"]==b]
if len(df4) == 1:
to_plot_samp += list(df4["val"]*100)
else:
to_plot_samp += [0]
to_plot += [to_plot_samp]
ax = fig.add_subplot(3,2,2*i+1)
ax.hist(x=binnames,bins=range(len(thebins)+1),weights=to_plot,histtype='bar',color=colors,label=samples)
ax.set_ylabel(y)
ax.set_xticklabels([])
ax.set_xticklabels(thebins)
plt.xticks(rotation=45)
ax.set_xlabel('DDB Pass')
# FAIL
dfs = {}
dfs['2016'] = df16[(df16["syst"]==syst) & (df16["region"]=="fail")].drop(columns=["syst","region"])
dfs['2017'] = df17[(df17["syst"]==syst) & (df17["region"]=="fail")].drop(columns=["syst","region"])
dfs['2018'] = df18[(df18["syst"]==syst) & (df18["region"]=="fail")].drop(columns=["syst","region"])
for i,y in enumerate(['2016','2017','2018']):
to_plot = []
for s in samples:
to_plot_samp = []
df3 = dfs[y][dfs[y]["samp"]==s].drop(columns=["samp"])
for b in thebins:
df4 = df3[df3["bin"]==b]
if len(df4) == 1:
to_plot_samp += list(df4["val"]*100)
else:
to_plot_samp += [0]
to_plot += [to_plot_samp]
ax = fig.add_subplot(3,2,2*i+2)
ax.hist(x=binnames,bins=range(len(thebins)+1),weights=to_plot,histtype='bar',color=colors,label=samples)
#ax.set_ylabel(y)
ax.set_xticklabels([])
ax.set_xticklabels(thebins)
plt.xticks(rotation=45)
ax.set_xlabel('DDB Fail')
plt.legend(bbox_to_anchor=(1.05, 1))
plt.show()
name = 'plots/bybin_bkg_'+syst
png_name = name+'.png'
fig.savefig(png_name,bbox_inches='tight')
pdf_name = name+'.pdf'
fig.savefig(pdf_name,bbox_inches='tight')
def plot_compare_years(h16, h17, h18, name, title, log=True):
hist.plot1d(h18)
hist.plot1d(h17)
hist.plot1d(h16)
plt.legend(['2018','2017','2016'],bbox_to_anchor=(1.05, 1), loc='upper left')
plt.title(title)
# save as name
png_name = name+'.png'
plt.savefig(png_name,bbox_inches='tight')
pdf_name = name+'.pdf'
plt.savefig(pdf_name,bbox_inches='tight')
def plot_datamc_ggfvbf(h, name, title, xlim=-1, log=True):
# make sure you sum over ddb1
h = h.sum('ddb1')
fig = plt.figure()
#plt.suptitle(title)
ax1 = fig.add_subplot(4,1,(1,3))
plt.subplots_adjust(hspace=0)
# https://matplotlib.org/stable/gallery/color/named_colors.html
labels = ['bkg H', 'VV','single t','tt','W+jets','Z+jets','QCD']
mc = ['QCD','Wjets','Zjets','ttbar','singlet','VV', ['ttH','WH','ZH']]
colors=['gray','deepskyblue','blue','purple','hotpink','darkorange','gold']
if log:
mc = [x for x in reversed(mc)]
colors = [x for x in reversed(colors)]
labels = [x for x in reversed(labels)]
# Plot stacked hist
hist.plot1d(h,order=mc,stack=True,fill_opts={'color':colors,'edgecolor':'black'})
sig1 = h.integrate('process','ggF')
sig2 = h.integrate('process','VBF')
hist.plot1d(sig1,stack=False,line_opts={'color':'red','linestyle':'dashed'})
hist.plot1d(sig2,stack=False,line_opts={'color':'green'})
hist.plot1d(h.integrate('process','data'),error_opts={'marker':'o','color':'k','markersize':5})
if 'eta1' in name or 'dphi' in name:
ax1.set_xlim(0,xlim)
if 'deta' in name:
ax1.set_xlim(xlim,7)
if 'mjj' in name:
ax1.set_xlim(xlim,10000)
ax1.get_xaxis().set_visible(False)
labels = ['Process'] + labels + ['ggF','VBF','Data']
plt.legend(labels,bbox_to_anchor=(1.05, 1), loc='upper left')
allweights = hist.export1d(h.integrate('process','data')).numpy()[0]
ax1.set_ylim(0,1.2*np.amax(allweights))
if log:
ax1.set_yscale('log')
ax1.set_ylim(0.01,10*np.amax(allweights))
ax2 = fig.add_subplot(4,1,(4,4))
hist.plotratio(num=h.integrate('process','data'),denom=h.integrate('process',mc),ax=ax2,unc='num',error_opts={'marker':'o','color':'k','markersize':5},guide_opts={})
ax2.set_ylabel('Ratio')
ax2.set_xlabel(title)
ax2.set_xlim(ax1.get_xlim())
ax2.set_ylim(0.6,1.4)
# save as name
png_name = name+'.png'
plt.savefig(png_name,bbox_inches='tight')
pdf_name = name+'.pdf'
plt.savefig(pdf_name,bbox_inches='tight')
def plot_datamc_muoncr(h, name, title, xlim=-1, log=True):
fig = plt.figure()
#plt.suptitle(title)
ax1 = fig.add_subplot(211)
plt.subplots_adjust(hspace=0)
# https://matplotlib.org/stable/gallery/color/named_colors.html
labels = ['Higgs','VV','W+jets','Z+jets','QCD','single t','tt']
mc = ['ttbar','singlet','QCD','Wjets','Zjets','VV',['ZH','WH','ttH','ggF']]
colors=['purple','hotpink','gray','deepskyblue','blue','darkorange','gold']
if log:
mc = [x for x in reversed(mc)]
colors = [x for x in reversed(colors)]
labels = [x for x in reversed(labels)]
if 'ddb1' in name:
h = h.rebin("ddb1", hist.Bin("ddb1new", "rebinned ddb1", 25,0,1))
# Plot stacked hist
hist.plot1d(h,order=mc,stack=True,fill_opts={'color':colors,'edgecolor':'black'})
# Overlay data
hist.plot1d(h.integrate('process','muondata'),error_opts={'marker':'o','color':'k','markersize':5})
labels = ['Process'] + labels + ['Data']
if 'eta1' in name or 'dphi' in name or 'mjj' in name:
ax1.set_xlim(0,xlim)
if 'deta' in name:
ax1.set_xlim(xlim,7)
ax1.get_xaxis().set_visible(False)
plt.legend(labels=labels,bbox_to_anchor=(1.05, 1), loc='upper left')
allweights = hist.export1d(h.integrate('process','muondata')).numpy()[0]
if log:
ax1.set_yscale('log')
ax1.set_ylim(0.01,5*np.amax(allweights))
# ratio
ax2 = fig.add_subplot(212)
hist.plotratio(num=h.integrate('process','muondata'),denom=h.integrate('process',mc),ax=ax2,unc='num',error_opts={'marker':'o','color':'k','markersize':5},guide_opts={})
ax2.set_ylabel('Ratio')
ax2.set_xlabel(title)
ax2.set_xlim(ax1.get_xlim())
png_name = name+'.png'
plt.savefig(png_name,bbox_inches='tight')
pdf_name = name+'.pdf'
plt.savefig(pdf_name,bbox_inches='tight')
def plot_datamc_muoncr_pf(h, name, title, xlim=-1, log=True):
fig, ((ax1,ax2),(ax3,ax4)) = plt.subplots(2, 2, gridspec_kw={'height_ratios': [3,1]},figsize=(12,8))
plt.subplots_adjust(hspace=0)
# https://matplotlib.org/stable/gallery/color/named_colors.html
labels = ['Higgs','VV','W+jets','Z+jets','QCD','single t','tt']
mc = ['ttbar','singlet','QCD','Wjets','Zjets','VV',['ZH','WH','ttH','ggF']]
colors=['purple','hotpink','gray','deepskyblue','blue','darkorange','gold']
if log:
mc = [x for x in reversed(mc)]
colors = [x for x in reversed(colors)]
labels = [x for x in reversed(labels)]
hpass = h.integrate('ddb1',int_range=slice(0.64,1))
hfail = h.integrate('ddb1',int_range=slice(0,0.64))
hist.plot1d(hfail,order=mc,stack=True,fill_opts={'color':colors,'edgecolor':'black'},ax=ax1)
ax1.get_legend().remove()
muondata = hfail.integrate('process','muondata')
x=muondata.axes()[0].centers()
bins=muondata.axes()[0].edges()
ax1.errorbar(x=x,y=muondata.values()[()],yerr=np.sqrt(muondata.values()[()]),marker='o',linestyle='',color='black')
if 'eta1' in name or 'dphi' in name or 'mjj' in name or 'etamu' in name:
ax1.set_xlim(0,xlim)
if 'deta' in name:
ax1.set_xlim(xlim,7)
ax1.get_xaxis().set_visible(False)
allweights = hist.export1d(hfail.integrate('process','muondata')).numpy()[0]
if log:
ax1.set_yscale('log')
ax1.set_ylim(0.01,5*np.amax(allweights))
ax1.title.set_text("DDB fail")
# ratio
hist.plotratio(num=hfail.integrate('process','muondata'),denom=hfail.integrate('process',mc),unc='num',error_opts={'marker':'o','color':'k','markersize':5},guide_opts={},ax=ax3)
ax3.set_ylabel("Ratio")
if ax3.get_ylim()[1] > 5:
ax3.set_ylim(ax3.get_ylim()[0],5)
ax3.set_xlabel(title)
ax3.set_xlim(ax1.get_xlim())
hist.plot1d(hpass,order=mc,stack=True,fill_opts={'color':colors,'edgecolor':'black'},ax=ax2,legend_opts={'labels':labels,'bbox_to_anchor':(1.05, 1),'loc':'upper left'})
muondata = hpass.integrate('process','muondata')
x=muondata.axes()[0].centers()
bins=muondata.axes()[0].edges()
ax2.errorbar(x=x,y=muondata.values()[()],yerr=np.sqrt(muondata.values()[()]),marker='o',linestyle='',color='black')
ax2.title.set_text("DDB pass")
ax2.set_xlim(ax1.get_xlim())
ax2.get_xaxis().set_visible(False)
ax2.set_ylabel("")
allweights = hist.export1d(hpass.integrate('process','muondata')).numpy()[0]
if log:
ax2.set_yscale('log')
ax2.set_ylim(0.01,5*np.amax(allweights))
# ratio
hist.plotratio(num=hpass.integrate('process','muondata'),denom=hpass.integrate('process',mc),unc='num',error_opts={'marker':'o','color':'k','markersize':5},guide_opts={},ax=ax4)
ax4.set_ylabel("")
if(ax4.get_ylim()[1] > 5):
ax4.set_ylim(ax4.get_ylim()[0],5)
ax4.set_xlabel(title)
ax4.set_xlim(ax1.get_xlim())
png_name = name+'_PF.png'
plt.savefig(png_name,bbox_inches='tight')
pdf_name = name+'_PF.pdf'
plt.savefig(pdf_name,bbox_inches='tight')
def plot_syst(h, syst, title, name, log=True):
fig = plt.figure()
plt.suptitle(title)
ax1 = fig.add_subplot(211)
plt.subplots_adjust(hspace=0)
nom = h.integrate('systematic','nominal')
up = h.integrate('systematic',syst+'Up')
do = h.integrate('systematic',syst+'Down')
hist.plot1d(nom,line_opts={'color':'black'},error_opts={'color':'black'})
hist.plot1d(up,line_opts={'color':'blue'},error_opts={'color':'blue'})
hist.plot1d(do,line_opts={'color':'red'},error_opts={'color':'red'})
#plt.legend(labels=['nominal', syst+' up', syst + ' down'], bbox_to_anchor=(1.05, 1), loc='upper left')
nom_array = hist.export1d(nom).numpy()[0]
up_array = hist.export1d(up).numpy()[0]
do_array = hist.export1d(do).numpy()[0]
msd = hist.export1d(nom).numpy()[1]
allweights = np.concatenate([nom_array,up_array,do_array])
ax1.set_ylim(0.9*np.amin(allweights),1.1*np.amax(allweights))
# ratio
up_ratio = np.array([up_array[i]/nom_array[i] for i in range(len(nom_array))])
do_ratio = np.array([do_array[i]/nom_array[i] for i in range(len(nom_array))])
np.nan_to_num(up_ratio,copy=False,nan=0,posinf=1)
np.nan_to_num(do_ratio,copy=False,nan=0,posinf=1)
ax2 = fig.add_subplot(212)
ax2.hist(msd[:-1],weights=np.ones(len(up_ratio)),bins=msd,histtype='step',color='gray',linestyle='--')
# hist.plotratio(num=up,denom=nom,unc='num',error_opts={'color':'blue'},guide_opts={},ax=ax2)
ax2.hist(x=msd[:-1],weights=up_ratio,bins=msd,histtype='step',color='blue',linewidth=2)
# hist.plotratio(num=do,denom=nom,unc='num',error_opts={'color':'red'},guide_opts={},ax=ax2)
ax2.hist(x=msd[:-1],weights=do_ratio,bins=msd,histtype='step',color='red',linewidth=2)
allweights_ratio = np.concatenate([up_ratio,do_ratio])
ax2.set_xlim(ax1.get_xlim())
ratmin = 0
ratmax = 1
if np.amin(allweights_ratio) > 0:
ratmin = 0.95*np.amin(allweights_ratio)
if np.amax(allweights_ratio) > 1:
ratmax = 1.05*np.amax(allweights_ratio)
ax2.set_ylim(ratmin,ratmax)
ax2.set_ylabel('Ratio')
png_name = name+'.png'
plt.savefig(png_name,bbox_inches='tight')
pdf_name = name+'.pdf'
plt.savefig(pdf_name,bbox_inches='tight')
def plot_syst_allbkg(h, g, syst, xtitle, title, name):
fig, ax = plt.subplots(6,2,sharex=True)
fig.suptitle(title)
axes = fig.axes
mc = ['ttbar','singlet','VV']
names = ['W(qq\')+jets','Z(qq)+jets','Z(bb)+jets','tt','t','VV']
ggf_hists = []
ggf_hists += [h.integrate('process','Wjets').integrate('genflavor',int_range=slice(1,3))]
ggf_hists += [h.integrate('process','Zjets').integrate('genflavor',int_range=slice(1,3))]
ggf_hists += [h.integrate('process','Zjets').integrate('genflavor',int_range=slice(3,4))]
ggf_hists += [h.sum('genflavor').integrate('process',p) for p in mc]
vbf_hists = []
vbf_hists += [g.integrate('process','Wjets').integrate('genflavor',int_range=slice(1,3))]
vbf_hists += [g.integrate('process','Zjets').integrate('genflavor',int_range=slice(1,3))]
vbf_hists += [g.integrate('process','Zjets').integrate('genflavor',int_range=slice(3,4))]
vbf_hists += [g.sum('genflavor').integrate('process',p) for p in mc]
for i in range(6):
p = names[i]
nom = ggf_hists[i].integrate('systematic','nominal')
up = ggf_hists[i].integrate('systematic',syst+'Up')
do = ggf_hists[i].integrate('systematic',syst+'Down')
nom_array = hist.export1d(nom).numpy()[0]
up_array = hist.export1d(up).numpy()[0]
do_array = hist.export1d(do).numpy()[0]
x = hist.export1d(nom).numpy()[1]
up_ratio = np.array([up_array[i]/nom_array[i] for i in range(len(nom_array))])
do_ratio = np.array([do_array[i]/nom_array[i] for i in range(len(nom_array))])
np.nan_to_num(up_ratio,copy=False,nan=1,posinf=1)
np.nan_to_num(do_ratio,copy=False,nan=1,posinf=1)
axes[2*i].set_xlim(x[0],x[-1])
axes[2*i].hist(x[:-1],weights=np.ones(len(up_ratio)),bins=x,histtype='step',color='gray',linestyle='--')
axes[2*i].hist(x=x[:-1],weights=up_ratio,bins=x,histtype='step',color='blue',linewidth=2)
axes[2*i].hist(x=x[:-1],weights=do_ratio,bins=x,histtype='step',color='red',linewidth=2)
allweights_ratio = np.concatenate([up_ratio,do_ratio])
ratmin = 0
ratmax = 1
if np.amin(allweights_ratio) > 0:
ratmin = 0.95*np.amin(allweights_ratio)
if np.amax(allweights_ratio) > 1:
ratmax = 1.05*np.amax(allweights_ratio)
axes[2*i].set_ylim(ratmin,ratmax)
axes[2*i].set_ylabel(names[i],rotation=45)
# VBF
nom = vbf_hists[i].integrate('systematic','nominal')
up = vbf_hists[i].integrate('systematic',syst+'Up')
do = vbf_hists[i].integrate('systematic',syst+'Down')
nom_array = hist.export1d(nom).numpy()[0]
up_array = hist.export1d(up).numpy()[0]
do_array = hist.export1d(do).numpy()[0]
x = hist.export1d(nom).numpy()[1]
up_ratio = np.array([up_array[i]/nom_array[i] for i in range(len(nom_array))])
do_ratio = np.array([do_array[i]/nom_array[i] for i in range(len(nom_array))])
np.nan_to_num(up_ratio,copy=False,nan=1,posinf=1)
np.nan_to_num(do_ratio,copy=False,nan=1,posinf=1)
axes[2*i+1].set_xlim(x[0],x[-1])
axes[2*i+1].hist(x[:-1],weights=np.ones(len(up_ratio)),bins=x,histtype='step',color='gray',linestyle='--')
axes[2*i+1].hist(x=x[:-1],weights=up_ratio,bins=x,histtype='step',color='blue',linewidth=2)
axes[2*i+1].hist(x=x[:-1],weights=do_ratio,bins=x,histtype='step',color='red',linewidth=2)
allweights_ratio = np.concatenate([up_ratio,do_ratio])
ratmin = 0
ratmax = 1
if np.amin(allweights_ratio) > 0:
ratmin = 0.95*np.amin(allweights_ratio)
if np.amax(allweights_ratio) > 1:
ratmax = 1.05*np.amax(allweights_ratio)
axes[2*i+1].set_ylim(ratmin,ratmax)
axes[0].set_title("ggF cat.")
axes[1].set_title("VBF cat.")
axes[10].set_xlabel(xtitle)
axes[11].set_xlabel(xtitle)
png_name = name+'.png'
plt.savefig(png_name,bbox_inches='tight')
pdf_name = name+'.pdf'
plt.savefig(pdf_name,bbox_inches='tight')
def plot_syst_allsig(h, g, syst, xtitle, title, name):
fig, ax = plt.subplots(5,2,sharex=True)
fig.suptitle(title)
axes = fig.axes
mc = ['ggF','VBF','WH','ZH','ttH']
names = ['ggF','VBF','WH','ZH','ttH']
ggf_hists = [h.integrate('process',p) for p in mc]
vbf_hists = [g.integrate('process',p) for p in mc]
for i in range(5):
p = names[i]
nom = ggf_hists[i].sum('genflavor').integrate('systematic','nominal')
up = ggf_hists[i].sum('genflavor').integrate('systematic',syst+'Up')
do = ggf_hists[i].sum('genflavor').integrate('systematic',syst+'Down')
nom_array = hist.export1d(nom).numpy()[0]
up_array = hist.export1d(up).numpy()[0]
do_array = hist.export1d(do).numpy()[0]
x = hist.export1d(nom).numpy()[1]
up_ratio = np.array([up_array[i]/nom_array[i] for i in range(len(nom_array))])
do_ratio = np.array([do_array[i]/nom_array[i] for i in range(len(nom_array))])
np.nan_to_num(up_ratio,copy=False,nan=1,posinf=1)
np.nan_to_num(do_ratio,copy=False,nan=1,posinf=1)
axes[2*i].set_xlim(x[0],x[-1])
axes[2*i].hist(x[:-1],weights=np.ones(len(up_ratio)),bins=x,histtype='step',color='gray',linestyle='--')
axes[2*i].hist(x=x[:-1],weights=up_ratio,bins=x,histtype='step',color='blue',linewidth=2)
axes[2*i].hist(x=x[:-1],weights=do_ratio,bins=x,histtype='step',color='red',linewidth=2)
allweights_ratio = np.concatenate([up_ratio,do_ratio])
ratmin = 0
ratmax = 1
if np.amin(allweights_ratio) > 0:
ratmin = 0.95*np.amin(allweights_ratio)
if np.amax(allweights_ratio) > 1:
ratmax = 1.05*np.amax(allweights_ratio)
axes[2*i].set_ylim(ratmin,ratmax)
axes[2*i].set_ylabel(names[i],rotation=45)
# VBF
nom = vbf_hists[i].sum('genflavor').integrate('systematic','nominal')
up = vbf_hists[i].sum('genflavor').integrate('systematic',syst+'Up')
do = vbf_hists[i].sum('genflavor').integrate('systematic',syst+'Down')
nom_array = hist.export1d(nom).numpy()[0]
up_array = hist.export1d(up).numpy()[0]
do_array = hist.export1d(do).numpy()[0]
x = hist.export1d(nom).numpy()[1]
up_ratio = np.array([up_array[i]/nom_array[i] for i in range(len(nom_array))])
do_ratio = np.array([do_array[i]/nom_array[i] for i in range(len(nom_array))])
np.nan_to_num(up_ratio,copy=False,nan=1,posinf=1)
np.nan_to_num(do_ratio,copy=False,nan=1,posinf=1)
axes[2*i+1].set_xlim(x[0],x[-1])
axes[2*i+1].hist(x[:-1],weights=np.ones(len(up_ratio)),bins=x,histtype='step',color='gray',linestyle='--')
axes[2*i+1].hist(x=x[:-1],weights=up_ratio,bins=x,histtype='step',color='blue',linewidth=2)
axes[2*i+1].hist(x=x[:-1],weights=do_ratio,bins=x,histtype='step',color='red',linewidth=2)
allweights_ratio = np.concatenate([up_ratio,do_ratio])
ratmin = 0
ratmax = 1
if np.amin(allweights_ratio) > 0:
ratmin = 0.95*np.amin(allweights_ratio)
if np.amax(allweights_ratio) > 1:
ratmax = 1.05*np.amax(allweights_ratio)
axes[2*i+1].set_ylim(ratmin,ratmax)
axes[0].set_title("ggF cat.")
axes[1].set_title("VBF cat.")
axes[8].set_xlabel(xtitle)
axes[9].set_xlabel(xtitle)
png_name = name+'.png'
plt.savefig(png_name,bbox_inches='tight')
pdf_name = name+'.pdf'
plt.savefig(pdf_name,bbox_inches='tight')
def plot_syst_mucr(h, syst, xtitle, title, name):
fig, ax = plt.subplots(6,1,sharex=True)
fig.suptitle(title)
axes = fig.axes
mc = ['ttbar','singlet','VV']
names = ['W(qq\')+jets','Z(qq)+jets','Z(bb)+jets','tt','t','VV']
hists = []
hists += [h.integrate('process','Wjets').integrate('genflavor',int_range=slice(1,3))]
hists += [h.integrate('process','Zjets').integrate('genflavor',int_range=slice(1,3))]
hists += [h.integrate('process','Zjets').integrate('genflavor',int_range=slice(3,4))]
hists += [h.sum('genflavor').integrate('process',p) for p in mc]
for i in range(6):
p = names[i]
nom = hists[i].integrate('systematic','nominal')
up = hists[i].integrate('systematic',syst+'Up')
do = hists[i].integrate('systematic',syst+'Down')
nom_array = hist.export1d(nom).numpy()[0]
up_array = hist.export1d(up).numpy()[0]
do_array = hist.export1d(do).numpy()[0]
x = hist.export1d(nom).numpy()[1]
up_ratio = np.array([up_array[i]/nom_array[i] for i in range(len(nom_array))])
do_ratio = np.array([do_array[i]/nom_array[i] for i in range(len(nom_array))])
np.nan_to_num(up_ratio,copy=False,nan=1,posinf=1)
np.nan_to_num(do_ratio,copy=False,nan=1,posinf=1)
axes[i].set_xlim(x[0],x[-1])
axes[i].hist(x[:-1],weights=np.ones(len(up_ratio)),bins=x,histtype='step',color='gray',linestyle='--')
axes[i].hist(x=x[:-1],weights=up_ratio,bins=x,histtype='step',color='blue',linewidth=2)
axes[i].hist(x=x[:-1],weights=do_ratio,bins=x,histtype='step',color='red',linewidth=2)
allweights_ratio = np.concatenate([up_ratio,do_ratio])
ratmin = 0
ratmax = 1
if np.amin(allweights_ratio) > 0:
ratmin = 0.995*np.amin(allweights_ratio)
if np.amax(allweights_ratio) > 1:
ratmax = 1.005*np.amax(allweights_ratio)
axes[i].set_ylim(ratmin,ratmax)
axes[i].set_ylabel(names[i],rotation=45)
axes[0].set_title(syst)
axes[5].set_xlabel(xtitle)
png_name = name+'.png'
plt.savefig(png_name,bbox_inches='tight')
pdf_name = name+'.pdf'
plt.savefig(pdf_name,bbox_inches='tight')
def plot_syst_scalevar(h, g, title, name):
fig, ax = plt.subplots(5,2,sharex=True)
fig.suptitle(title)
axes = fig.axes
mc = ['ggF','VBF','WH','ZH','ttH']
names = ['ggF','VBF','WH','ZH','ttH']
syst = ['scalevar_7pt','scalevar_3pt','scalevar_3pt','scalevar_3pt','scalevar_7pt']
ggf_hists = [h.sum('genflavor').integrate('process',p) for p in mc]
vbf_hists = [g.sum('genflavor').integrate('process',p) for p in mc]
for i in range(5):
p = names[i]
nom = ggf_hists[i].integrate('systematic','nominal')
up = ggf_hists[i].integrate('systematic',syst[i]+'Up')
do = ggf_hists[i].integrate('systematic',syst[i]+'Down')
nom_array = hist.export1d(nom).numpy()[0]
up_array = hist.export1d(up).numpy()[0]
do_array = hist.export1d(do).numpy()[0]
x = hist.export1d(nom).numpy()[1]
up_ratio = np.array([up_array[i]/nom_array[i] for i in range(len(nom_array))])
do_ratio = np.array([do_array[i]/nom_array[i] for i in range(len(nom_array))])
np.nan_to_num(up_ratio,copy=False,nan=1,posinf=1)
np.nan_to_num(do_ratio,copy=False,nan=1,posinf=1)
axes[2*i].set_xlim(x[0],x[-1])
axes[2*i].hist(x[:-1],weights=np.ones(len(up_ratio)),bins=x,histtype='step',color='gray',linestyle='--')
axes[2*i].hist(x=x[:-1],weights=up_ratio,bins=x,histtype='step',color='blue',linewidth=2)
axes[2*i].hist(x=x[:-1],weights=do_ratio,bins=x,histtype='step',color='red',linewidth=2)
allweights_ratio = np.concatenate([up_ratio,do_ratio])
ratmin = 0
ratmax = 1
if np.amin(allweights_ratio) > 0:
ratmin = 0.95*np.amin(allweights_ratio)
if np.amax(allweights_ratio) > 1:
ratmax = 1.05*np.amax(allweights_ratio)
axes[2*i].set_ylim(ratmin,ratmax)
axes[2*i].set_ylabel(names[i],rotation=45)
# VBF
nom = vbf_hists[i].integrate('systematic','nominal')
up = vbf_hists[i].integrate('systematic',syst[i]+'Up')
do = vbf_hists[i].integrate('systematic',syst[i]+'Down')
nom_array = hist.export1d(nom).numpy()[0]
up_array = hist.export1d(up).numpy()[0]
do_array = hist.export1d(do).numpy()[0]
x = hist.export1d(nom).numpy()[1]
up_ratio = np.array([up_array[i]/nom_array[i] for i in range(len(nom_array))])
do_ratio = np.array([do_array[i]/nom_array[i] for i in range(len(nom_array))])
np.nan_to_num(up_ratio,copy=False,nan=1,posinf=1)
np.nan_to_num(do_ratio,copy=False,nan=1,posinf=1)
axes[2*i+1].set_xlim(x[0],x[-1])
axes[2*i+1].hist(x[:-1],weights=np.ones(len(up_ratio)),bins=x,histtype='step',color='gray',linestyle='--')
axes[2*i+1].hist(x=x[:-1],weights=up_ratio,bins=x,histtype='step',color='blue',linewidth=2)
axes[2*i+1].hist(x=x[:-1],weights=do_ratio,bins=x,histtype='step',color='red',linewidth=2)
allweights_ratio = np.concatenate([up_ratio,do_ratio])
ratmin = 0
ratmax = 1
if np.amin(allweights_ratio) > 0:
ratmin = 0.95*np.amin(allweights_ratio)
if np.amax(allweights_ratio) > 1:
ratmax = 1.05*np.amax(allweights_ratio)
axes[2*i+1].set_ylim(ratmin,ratmax)
axes[8].set_xlabel("ggF cat. $m_{sd}$ [GeV]")
axes[9].set_xlabel("VBF cat. $m_{sd}$ [GeV]")
png_name = name+'.png'
plt.savefig(png_name,bbox_inches='tight')
pdf_name = name+'.pdf'
plt.savefig(pdf_name,bbox_inches='tight')
def plot_syst_Vjets(h, g, p, xtitle, title, name):
fig, ax = plt.subplots(6,2,sharex=True)
fig.suptitle(title)
axes = fig.axes
names = ['d1K_NLO','d2K_NLO','d3K_NLO','d1kappa_EW']
if p == 'Wjets':
ggf_hists = h.integrate('genflavor',int_range=slice(1,4)).integrate('process',p)
vbf_hists = g.integrate('genflavor',int_range=slice(1,4)).integrate('process',p)
names += ['W_d2kappa_EW','W_d3kappa_EW']
if p == 'Zjets':
ggf_hists = h.integrate('genflavor',int_range=slice(1,3)).integrate('process',p)
vbf_hists = g.integrate('genflavor',int_range=slice(1,3)).integrate('process',p)
names += ['Z_d2kappa_EW','Z_d3kappa_EW']
if p == 'Zjetsbb':
ggf_hists = h.integrate('genflavor',int_range=slice(3,4)).integrate('process','Zjets')
vbf_hists = g.integrate('genflavor',int_range=slice(3,4)).integrate('process','Zjets')
names += ['Z_d2kappa_EW','Z_d3kappa_EW']
syst = names
for i in range(6):
p = names[i]
nom = ggf_hists.integrate('systematic','nominal')
up = ggf_hists.integrate('systematic',syst[i]+'Up')
do = ggf_hists.integrate('systematic',syst[i]+'Down')
nom_array = hist.export1d(nom).numpy()[0]
up_array = hist.export1d(up).numpy()[0]
do_array = hist.export1d(do).numpy()[0]
x = hist.export1d(nom).numpy()[1]
up_ratio = np.array([up_array[i]/nom_array[i] for i in range(len(nom_array))])
do_ratio = np.array([do_array[i]/nom_array[i] for i in range(len(nom_array))])
np.nan_to_num(up_ratio,copy=False,nan=1,posinf=1)
np.nan_to_num(do_ratio,copy=False,nan=1,posinf=1)
axes[2*i].set_xlim(x[0],x[-1])
axes[2*i].hist(x[:-1],weights=np.ones(len(up_ratio)),bins=x,histtype='step',color='gray',linestyle='--')
axes[2*i].hist(x=x[:-1],weights=up_ratio,bins=x,histtype='step',color='blue',linewidth=2)
axes[2*i].hist(x=x[:-1],weights=do_ratio,bins=x,histtype='step',color='red',linewidth=2)
allweights_ratio = np.concatenate([up_ratio,do_ratio])
ratmin = 0
ratmax = 1
if np.amin(allweights_ratio) > 0:
ratmin = 0.95*np.amin(allweights_ratio)
if np.amax(allweights_ratio) > 1:
ratmax = 1.05*np.amax(allweights_ratio)
axes[2*i].set_ylim(ratmin,ratmax)
axes[2*i].set_ylabel(names[i],rotation=45)
# VBF
nom = vbf_hists.integrate('systematic','nominal')
up = vbf_hists.integrate('systematic',syst[i]+'Up')
do = vbf_hists.integrate('systematic',syst[i]+'Down')
nom_array = hist.export1d(nom).numpy()[0]
up_array = hist.export1d(up).numpy()[0]
do_array = hist.export1d(do).numpy()[0]
x = hist.export1d(nom).numpy()[1]
up_ratio = np.array([up_array[i]/nom_array[i] for i in range(len(nom_array))])
do_ratio = np.array([do_array[i]/nom_array[i] for i in range(len(nom_array))])
np.nan_to_num(up_ratio,copy=False,nan=1,posinf=1)
np.nan_to_num(do_ratio,copy=False,nan=1,posinf=1)
axes[2*i+1].set_xlim(x[0],x[-1])
axes[2*i+1].hist(x[:-1],weights=np.ones(len(up_ratio)),bins=x,histtype='step',color='gray',linestyle='--')
axes[2*i+1].hist(x=x[:-1],weights=up_ratio,bins=x,histtype='step',color='blue',linewidth=2)
axes[2*i+1].hist(x=x[:-1],weights=do_ratio,bins=x,histtype='step',color='red',linewidth=2)
allweights_ratio = np.concatenate([up_ratio,do_ratio])
ratmin = 0
ratmax = 1
if np.amin(allweights_ratio) > 0:
ratmin = 0.95*np.amin(allweights_ratio)
if np.amax(allweights_ratio) > 1:
ratmax = 1.05*np.amax(allweights_ratio)
axes[2*i+1].set_ylim(ratmin,ratmax)
axes[10].set_xlabel("ggF cat. "+xtitle)
axes[11].set_xlabel("VBF cat. "+xtitle)
png_name = name+'.png'
plt.savefig(png_name,bbox_inches='tight')
pdf_name = name+'.pdf'
plt.savefig(pdf_name,bbox_inches='tight')
def plot_syst_bins(h, g, p, s, xtitle, title, name):
ptbins = [450, 500, 550, 600, 675, 800, 1200]
mjjbins = [1000,2000,10000]
fig, ax = plt.subplots(6,2,sharex=True)
fig.suptitle(title)
axes = fig.axes
ggf_hists = h.integrate('genflavor',int_range=slice(1,4)).integrate('process',p)
vbf_hists = g.integrate('genflavor',int_range=slice(1,4)).integrate('process',p)
for i in range(6):
nom = ggf_hists.integrate('pt1',int_range=slice(ptbins[i],ptbins[i+1])).integrate('systematic','nominal')
up = ggf_hists.integrate('pt1',int_range=slice(ptbins[i],ptbins[i+1])).integrate('systematic',s+'Up')
do = ggf_hists.integrate('pt1',int_range=slice(ptbins[i],ptbins[i+1])).integrate('systematic',s+'Down')
nom_array = hist.export1d(nom).numpy()[0]
up_array = hist.export1d(up).numpy()[0]
do_array = hist.export1d(do).numpy()[0]
x = hist.export1d(nom).numpy()[1]
up_ratio = np.array([up_array[i]/nom_array[i] for i in range(len(nom_array))])
do_ratio = np.array([do_array[i]/nom_array[i] for i in range(len(nom_array))])
np.nan_to_num(up_ratio,copy=False,nan=1,posinf=1)
np.nan_to_num(do_ratio,copy=False,nan=1,posinf=1)
axes[2*i].set_xlim(x[0],x[-1])
axes[2*i].hist(x[:-1],weights=np.ones(len(up_ratio)),bins=x,histtype='step',color='gray',linestyle='--')
axes[2*i].hist(x=x[:-1],weights=up_ratio,bins=x,histtype='step',color='blue',linewidth=2)
axes[2*i].hist(x=x[:-1],weights=do_ratio,bins=x,histtype='step',color='red',linewidth=2)
allweights_ratio = np.concatenate([up_ratio,do_ratio])
ratmin = 0
ratmax = 1
if np.amin(allweights_ratio) > 0:
ratmin = 0.95*np.amin(allweights_ratio)
if np.amax(allweights_ratio) > 1:
ratmax = 1.05*np.amax(allweights_ratio)
axes[2*i].set_ylim(ratmin,ratmax)
axes[2*i].set_ylabel("Bin " + str(i+1),rotation=45)
# VBF
for i in range(2):
nom = vbf_hists.integrate('mjj',int_range=slice(mjjbins[i],mjjbins[i+1])).integrate('systematic','nominal')
up = vbf_hists.integrate('mjj',int_range=slice(mjjbins[i],mjjbins[i+1])).integrate('systematic',s+'Up')
do = vbf_hists.integrate('mjj',int_range=slice(mjjbins[i],mjjbins[i+1])).integrate('systematic',s+'Down')
nom_array = hist.export1d(nom).numpy()[0]
up_array = hist.export1d(up).numpy()[0]
do_array = hist.export1d(do).numpy()[0]
x = hist.export1d(nom).numpy()[1]
up_ratio = np.array([up_array[i]/nom_array[i] for i in range(len(nom_array))])
do_ratio = np.array([do_array[i]/nom_array[i] for i in range(len(nom_array))])
np.nan_to_num(up_ratio,copy=False,nan=1,posinf=1)
np.nan_to_num(do_ratio,copy=False,nan=1,posinf=1)
axes[2*i+1].set_xlim(x[0],x[-1])
axes[2*i+1].hist(x[:-1],weights=np.ones(len(up_ratio)),bins=x,histtype='step',color='gray',linestyle='--')
axes[2*i+1].hist(x=x[:-1],weights=up_ratio,bins=x,histtype='step',color='blue',linewidth=2)
axes[2*i+1].hist(x=x[:-1],weights=do_ratio,bins=x,histtype='step',color='red',linewidth=2)
allweights_ratio = np.concatenate([up_ratio,do_ratio])
ratmin = 0
ratmax = 1
if np.amin(allweights_ratio) > 0:
ratmin = 0.95*np.amin(allweights_ratio)
if np.amax(allweights_ratio) > 1:
ratmax = 1.05*np.amax(allweights_ratio)
axes[2*i+1].set_ylim(ratmin,ratmax)
axes[10].set_xlabel("ggF cat. "+xtitle)
axes[11].set_xlabel("VBF cat. "+xtitle)
png_name = name+'.png'
plt.savefig(png_name,bbox_inches='tight')
pdf_name = name+'.pdf'
plt.savefig(pdf_name,bbox_inches='tight')
def plot_syst_Vjets_mucr(h, p, xtitle, title, name):
fig, ax = plt.subplots(6,1,sharex=True)
fig.suptitle(title)
axes = fig.axes
names = ['d1K_NLO','d2K_NLO','d3K_NLO','d1kappa_EW']
if p == 'Wjets':
hists = h.integrate('genflavor',int_range=slice(1,4)).integrate('process',p)
names += ['W_d2kappa_EW','W_d3kappa_EW']
if p == 'Zjets':
hists = h.integrate('genflavor',int_range=slice(1,3)).integrate('process',p)
names += ['Z_d2kappa_EW','Z_d3kappa_EW']
if p == 'Zjetsbb':
hists = h.integrate('genflavor',int_range=slice(3,4)).integrate('process','Zjets')
names += ['Z_d2kappa_EW','Z_d3kappa_EW']
syst = names
for i in range(6):
p = names[i]
nom = hists.integrate('systematic','nominal')
up = hists.integrate('systematic',syst[i]+'Up')
do = hists.integrate('systematic',syst[i]+'Down')
nom_array = hist.export1d(nom).numpy()[0]
up_array = hist.export1d(up).numpy()[0]
do_array = hist.export1d(do).numpy()[0]
x = hist.export1d(nom).numpy()[1]
up_ratio = np.array([up_array[i]/nom_array[i] for i in range(len(nom_array))])
do_ratio = np.array([do_array[i]/nom_array[i] for i in range(len(nom_array))])
np.nan_to_num(up_ratio,copy=False,nan=1,posinf=1)
np.nan_to_num(do_ratio,copy=False,nan=1,posinf=1)
axes[i].set_xlim(x[0],x[-1])
axes[i].hist(x[:-1],weights=np.ones(len(up_ratio)),bins=x,histtype='step',color='gray',linestyle='--')
axes[i].hist(x=x[:-1],weights=up_ratio,bins=x,histtype='step',color='blue',linewidth=2)
axes[i].hist(x=x[:-1],weights=do_ratio,bins=x,histtype='step',color='red',linewidth=2)
allweights_ratio = np.concatenate([up_ratio,do_ratio])
ratmin = 0
ratmax = 1