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parallelize zdc and insert simulations
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,86 +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: | ||
| filename=f'sim_output/insert_muon/{config}_sim_mu-_{p}GeV.edm4hep.root' | ||
| print("opening file", filename) | ||
| events = ur.open(filename+':events') | ||
| arrays_sim[p] = events.arrays() | ||
| import gc | ||
| gc.collect() | ||
| print("read", filename) | ||
|
|
||
| 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))) | ||
| 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}_rec_mu-_{p}GeV_{index}.edm4eic.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))) | ||
| 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") |
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This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
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