revert muon_plots.py

benchmarks/insert_muon/analysis/muon_plots.py

@@ -1,83 +1,83 @@
-import numpy as np, pandas as pd, matplotlib.pyplot as plt, matplotlib as mpl, awkward as ak, sys
-import mplhep as hep
-hep.style.use("CMS")
-
-plt.rcParams['figure.facecolor']='white'
-plt.rcParams['savefig.facecolor']='white'
-plt.rcParams['savefig.bbox']='tight'
-
-plt.rcParams["figure.figsize"] = (7, 7)
-
-config=sys.argv[1].split("/")[1]  #results/{config}/{benchmark_name}
-outdir=sys.argv[1]+"/"
-try:
-    import os
-    os.mkdir(outdir[:-1])
-except:
-    pass
-
-def Landau(x, normalization,location,stdev):
-    #print(type(x))
-    u=(x-location)*3.591/stdev/2.355
-    renormalization = 1.64872*normalization
-    return renormalization * np.exp(-u/2 - np.exp(-u)/2)
-
-import uproot as ur
-arrays_sim={}
-momenta=50,
-for p in momenta:
-    arrays_sim[p] = ur.concatenate({
-        f'sim_output/insert_muon/{config}_sim_mu-_{p}GeV_{index}.edm4hep.root': 'events'
-        for index in range(5)
-    })
-
-for array in arrays_sim.values():
-    tilt=-0.025
-    px=array['MCParticles.momentum.x'][:,2]
-    py=array['MCParticles.momentum.y'][:,2]
-    pz=array['MCParticles.momentum.z'][:,2]
-    p=np.sqrt(px**2+py**2+pz**2)
-
-    pxp=px*np.cos(tilt)-pz*np.sin(tilt)
-    pyp=py
-    pzp=pz*np.cos(tilt)+px*np.sin(tilt)
-
-    array['eta_truth']=1/2*np.log((p+pzp)/(p-pzp))
-    array['phi_truth']=np.arctan2(pyp,pxp)
-
-for p in 50,:
-    E=arrays_sim[p]["HcalEndcapPInsertHits.energy"]
-    y, x,_=plt.hist(1e3*ak.flatten(E),bins=100, range=(0, 1.2), histtype='step')
-    bc=(x[1:]+x[:-1])/2
-    from scipy.optimize import curve_fit
-    slc=abs(bc-.48)<.15
-    fnc=Landau
-    p0=[100, .5, .05]
-    #print(list(y), list(x))
-    coeff, var_matrix = curve_fit(fnc, list(bc[slc]), list(y[slc]), p0=p0,
-                                 sigma=list(np.sqrt(y[slc])+(y[slc]==0)), maxfev=10000)
-    print(coeff)
-    xx=np.linspace(0,.7, 100)
-    MIP=coeff[1]/1000
-    plt.plot(xx, fnc(xx,*coeff), label=f'Landau fit:\nMIP={coeff[1]*1e3:.0f}$\\pm${1e3*np.sqrt(var_matrix[1][1]):.0f} keV')
-    plt.xlabel("hit energy [MeV]")
-    plt.ylabel("hits")
-    plt.title(f"$E_{{\\mu^-}}=${p} GeV")
-    plt.legend(fontsize=20)
-    plt.savefig(outdir+"/MIP.pdf")
-
-    plt.figure(figsize=(10,7))
-    array=arrays_sim[p]
-    bins=30; r=((-np.pi, np.pi),(2.8, 4.2))
-    selection=np.sum(array["HcalEndcapPInsertHits.energy"]>0.5*MIP,axis=-1)>0
-    h1, xedges, yedges = np.histogram2d(list(array[selection]['phi_truth']),list(array[selection]['eta_truth']), bins=bins, range=r)
-    h2, xedges, yedges = np.histogram2d(list(array['phi_truth']),list(array['eta_truth']), bins=bins, range=r)
-
-    h = h1 / h2
-    pc=plt.pcolor(xedges, yedges, h.T,linewidth=0)
-    plt.xlabel("$\\phi^*$ [rad]")
-    plt.ylabel("$\\eta^*$")
-    cb = plt.colorbar(pc)
-    cb.set_label("acceptance")
-    plt.title(f"$E_{{\\mu^-}}=${p} GeV")
-    plt.savefig(outdir+"/acceptance.pdf")
+import numpy as np, pandas as pd, matplotlib.pyplot as plt, matplotlib as mpl, awkward as ak, sys
+import mplhep as hep
+hep.style.use("CMS")
+
+plt.rcParams['figure.facecolor']='white'
+plt.rcParams['savefig.facecolor']='white'
+plt.rcParams['savefig.bbox']='tight'
+
+plt.rcParams["figure.figsize"] = (7, 7)
+
+config=sys.argv[1].split("/")[1]  #results/{config}/{benchmark_name}
+outdir=sys.argv[1]+"/"
+try:
+    import os
+    os.mkdir(outdir[:-1])
+except:
+    pass
+
+def Landau(x, normalization,location,stdev):
+    #print(type(x))
+    u=(x-location)*3.591/stdev/2.355
+    renormalization = 1.64872*normalization
+    return renormalization * np.exp(-u/2 - np.exp(-u)/2)
+
+import uproot as ur
+arrays_sim={}
+momenta=50,
+for p in momenta:
+    arrays_sim[p] = ur.concatenate({
+        f'sim_output/insert_muon/{config}_sim_mu-_{p}GeV_{index}.edm4hep.root': 'events'
+        for index in range(5)
+    })
+
+for array in arrays_sim.values():
+    tilt=-0.025
+    px=array['MCParticles.momentum.x'][:,2]
+    py=array['MCParticles.momentum.y'][:,2]
+    pz=array['MCParticles.momentum.z'][:,2]
+    p=np.sqrt(px**2+py**2+pz**2)
+
+    pxp=px*np.cos(tilt)-pz*np.sin(tilt)
+    pyp=py
+    pzp=pz*np.cos(tilt)+px*np.sin(tilt)
+
+    array['eta_truth']=1/2*np.log((p+pzp)/(p-pzp))
+    array['phi_truth']=np.arctan2(pyp,pxp)
+
+for p in 50,:
+    E=arrays_sim[p]["HcalEndcapPInsertHits.energy"]
+    y, x,_=plt.hist(1e3*ak.flatten(E),bins=100, range=(0, 1.2), histtype='step')
+    bc=(x[1:]+x[:-1])/2
+    from scipy.optimize import curve_fit
+    slc=abs(bc-.48)<.15
+    fnc=Landau
+    p0=[100, .5, .05]
+    #print(list(y), list(x))
+    coeff, var_matrix = curve_fit(fnc, list(bc[slc]), list(y[slc]), p0=p0,
+                                 sigma=list(np.sqrt(y[slc])+(y[slc]==0)), maxfev=10000)
+    print(coeff)
+    xx=np.linspace(0,.7, 100)
+    MIP=coeff[1]/1000
+    plt.plot(xx, fnc(xx,*coeff), label=f'Landau fit:\nMIP={coeff[1]*1e3:.0f}$\\pm${1e3*np.sqrt(var_matrix[1][1]):.0f} keV')
+    plt.xlabel("hit energy [MeV]")
+    plt.ylabel("hits")
+    plt.title(f"$E_{{\\mu^-}}=${p} GeV")
+    plt.legend(fontsize=20)
+    plt.savefig(outdir+"/MIP.pdf")
+
+    plt.figure(figsize=(10,7))
+    array=arrays_sim[p]
+    bins=30; r=((-np.pi, np.pi),(2.8, 4.2))
+    selection=np.sum(array["HcalEndcapPInsertHits.energy"]>0.5*MIP,axis=-1)>0
+    h1, xedges, yedges = np.histogram2d(list(array[selection]['phi_truth']),list(array[selection]['eta_truth']), bins=bins, range=r)
+    h2, xedges, yedges = np.histogram2d(list(array['phi_truth']),list(array['eta_truth']), bins=bins, range=r)
+
+    h = h1 / h2
+    pc=plt.pcolor(xedges, yedges, h.T,linewidth=0)
+    plt.xlabel("$\\phi^*$ [rad]")
+    plt.ylabel("$\\eta^*$")
+    cb = plt.colorbar(pc)
+    cb.set_label("acceptance")
+    plt.title(f"$E_{{\\mu^-}}=${p} GeV")
+    plt.savefig(outdir+"/acceptance.pdf")