Skip to content
Snippets Groups Projects

Minimum Q2 values

Merged Minimum Q2 values
MinimumQ2-Values into master
Merged Tooba Ali requested to merge MinimumQ2-Values into master
Compare
and
4 files
+ 33
− 33
Compare changes
  • Side-by-side
  • Inline
Files
4
benchmarks/dis/analysis/kinematics_correlations.py
+ 24
− 24
@@ -35,12 +35,12 @@ x_bins = [4.09385E-05,6.47862E-05,0.000102535,0.000162619,0.000258118,0.00040911
#function to construct Q2 correlation plots
def Q2correlation(minq2,method): #minq2 can be 1,10,100, or 1000; method can be 'E','DA', or 'JB'
def Q2correlation(minq2,method): #minq2 can be 1,10,100, or 1000; method can be 'e','DA', or 'JB'
Q2values_Y = method_Q2values_dict['{}'.format(method)] #Q2 values of the given method, that are mapped onto the y axis
Q2_List_T = Q2values_T['{}'.format(minq2)] #Truth Q2 values for given minq2, mapped along x axis
Q2_List_Y = Q2values_Y['{}'.format(minq2)] #method (E/DA/JB) Q2 values for given minq2, mapped along y axis
Q2_List_T = Q2values_T #Truth Q2 values, mapped along x axis
Q2_List_Y = Q2values_Y #method (E/DA/JB) Q2 values, mapped along y axis
T_len = ak.count(Q2_List_T,axis=0) #total number of events in Truth
Y_len = ak.count(Q2_List_Y,axis=0) #total number of events in method
@@ -55,7 +55,7 @@ def Q2correlation(minq2,method): #minq2 can be 1,10,100, or 1000; method can be
Q2_List_Y_F = Q2_List_Y[T_boolean] #filtered method Q2 values
T_Q2s = np.array(ak.flatten(Q2_List_T_F)) #Truth Q2 values, mapped along x axis
Y_Q2s = np.array(ak.flatten(Q2_List_Y_F)) #methos Q2 values, mapped along y axis
Y_Q2s = np.array(ak.flatten(Q2_List_Y_F)) #method Q2 values, mapped along y axis
#2-dimensional histogram, h
h, xedges, yedges = np.histogram2d(x=T_Q2s,y=Y_Q2s, bins=[Q2_bins,Q2_bins])
@@ -77,7 +77,7 @@ def Q2correlation(minq2,method): #minq2 can be 1,10,100, or 1000; method can be
norm_h_text.append(norm_c_text)
fig = plt.figure()
mplhep.hist2dplot(H=norm_h,norm=mpl.colors.LogNorm(),labels=norm_h_text,xbins=Q2_bins,ybins=Q2_bins,cmax=1,cmin=1e-5)
mplhep.hist2dplot(H=norm_h,norm=mpl.colors.LogNorm(vmin= 1e-4, vmax= 1),labels=norm_h_text,xbins=Q2_bins,ybins=Q2_bins)
plt.yscale('log')
plt.xscale('log')
fig.set_figwidth(11)
@@ -86,17 +86,17 @@ def Q2correlation(minq2,method): #minq2 can be 1,10,100, or 1000; method can be
plt.ylabel('$Q^2$ [$GeV^2$] {}'.format(method_dict['{}'.format(method)]))
plt.title('{} $Q^2$ correlation {}x{} $minQ^2=${}$GeV^2$'.format(method_dict['{}'.format(method)],k,p,minq2))
plt.show()
plt.savefig(os.path.join(args.outdir, '%gon%g/Q2_correlation_%s_%gx%g_minQ2=%g.png' %(k,p,method,k,p,minq2)))
plt.savefig(os.path.join(args.outdir, '%gon%g/minQ2=%g/Q2_correlation_%s_%gx%g_minQ2=%g.png' %(k,p,minq2,method,k,p,minq2)))
#function to construct Bjorken-x correlation plots
def Xcorrelation(minq2,method): #minq2 can be 1,10,100, or 1000; method can be 'E','DA', or 'JB'
def Xcorrelation(minq2,method): #minq2 can be 1,10,100, or 1000; method can be 'e','DA', or 'JB'
Xvalues_Y = method_Xvalues_dict['{}'.format(method)] #x values of the given method, that are mapped onto the y axis
X_List_T = Xvalues_T['{}'.format(minq2)] #Truth x values for given minq2, mapped along x axis
X_List_Y = Xvalues_Y['{}'.format(minq2)] #method (E/DA/JB) x values for given minq2, mapped along y axis
X_List_T = Xvalues_T #Truth x values, mapped along x axis
X_List_Y = Xvalues_Y #method (E/DA/JB) x values, mapped along y axis
T_len = ak.count(X_List_T,axis=0) #total number of events in Truth
Y_len = ak.count(X_List_Y,axis=0) #total number of events in method
@@ -134,7 +134,7 @@ def Xcorrelation(minq2,method): #minq2 can be 1,10,100, or 1000; method can be '
norm_h_text.append(norm_c_text)
fig = plt.figure()
mplhep.hist2dplot(H=norm_h,norm=mpl.colors.LogNorm(),labels=norm_h_text,xbins=x_bins,ybins=x_bins,cmax=1,cmin=1e-5)
mplhep.hist2dplot(H=norm_h,norm=mpl.colors.LogNorm(vmin= 1e-4, vmax= 1),labels=norm_h_text,xbins=x_bins,ybins=x_bins)
plt.yscale('log')
plt.xscale('log')
fig.set_figwidth(11)
@@ -143,27 +143,27 @@ def Xcorrelation(minq2,method): #minq2 can be 1,10,100, or 1000; method can be '
plt.ylabel('$x$ {}'.format(method_dict['{}'.format(method)]))
plt.title('{} $x$ correlation {}x{} $minQ^2=${}$GeV^2$'.format(method_dict['{}'.format(method)],k,p,minq2))
plt.show()
plt.savefig(os.path.join(args.outdir, '%gon%g/x_correlation_%s_%gx%g_minQ2=%g.png' %(k,p,method,k,p,minq2)))
plt.savefig(os.path.join(args.outdir, '%gon%g/minQ2=%g/x_correlation_%s_%gx%g_minQ2=%g.png' %(k,p,minq2,method,k,p,minq2)))
keys = ur.concatenate(rec_file + ':events/' + 'InclusiveKinematicsTruth')
minq2_1_T = [keys['InclusiveKinematicsTruth.Q2'],keys['InclusiveKinematicsTruth.x']]
Truth = [keys['InclusiveKinematicsTruth.Q2'],keys['InclusiveKinematicsTruth.x']]
keys = ur.concatenate(rec_file + ':events/' + 'InclusiveKinematicsElectron')
minq2_1_E = [keys['InclusiveKinematicsElectron.Q2'], keys['InclusiveKinematicsElectron.x']]
Electron = [keys['InclusiveKinematicsElectron.Q2'], keys['InclusiveKinematicsElectron.x']]
keys = ur.concatenate(rec_file + ':events/' + 'InclusiveKinematicsDA')
minq2_1_DA = [keys['InclusiveKinematicsDA.Q2'], keys['InclusiveKinematicsDA.x']]
DoubleAngle = [keys['InclusiveKinematicsDA.Q2'], keys['InclusiveKinematicsDA.x']]
keys = ur.concatenate(rec_file + ':events/' + 'InclusiveKinematicsJB')
minq2_1_JB = [keys['InclusiveKinematicsJB.Q2'], keys['InclusiveKinematicsJB.x']]
Q2values_T = {'1':minq2_1_T[0]}
Q2values_E = {'1':minq2_1_E[0]}
Q2values_DA = {'1':minq2_1_DA[0]}
Q2values_JB = {'1':minq2_1_JB[0]}
Xvalues_T = {'1':minq2_1_T[1]}
Xvalues_E = {'1':minq2_1_E[1]}
Xvalues_DA = {'1':minq2_1_DA[1]}
Xvalues_JB = {'1':minq2_1_JB[1]}
JacquetBlondel = [keys['InclusiveKinematicsJB.Q2'], keys['InclusiveKinematicsJB.x']]
Q2values_T = Truth[0]
Q2values_E = Electron[0]
Q2values_DA = DoubleAngle[0]
Q2values_JB = JacquetBlondel[0]
Xvalues_T = Truth[1]
Xvalues_E = Electron[1]
Xvalues_DA = DoubleAngle[1]
Xvalues_JB = JacquetBlondel[1]
method_dict = {'e':'Electron','DA':'Double-Angle','JB':'Jacquet-Blondel'}
method_Q2values_dict = {'e':Q2values_E,'DA':Q2values_DA,'JB':Q2values_JB}