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added benchmarks for pi0 and photon as well
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,59 @@ | ||
| rule femc_photon_generate: | ||
| input: | ||
| script="benchmarks/femc_photon/analysis/gen_particles.cxx", | ||
| params: | ||
| NEVENTS_GEN=1000, | ||
| th_max=28, | ||
| th_min=2.0 | ||
| output: | ||
| GEN_FILE="sim_output/femc_photon/photon_{P}GeV.hepmc" | ||
| shell: | ||
| """ | ||
| mkdir -p sim_output/femc_photon | ||
| root -l -b -q '{input.script}({params.NEVENTS_GEN},"{output.GEN_FILE}", "gamma", {params.th_min}, {params.th_max}, 0., 360., {wildcards.P})' | ||
| """ | ||
|
|
||
| rule femc_photon_simulate: | ||
| input: | ||
| GEN_FILE="sim_output/femc_photon/photon_{P}GeV.hepmc" | ||
| params: | ||
| PHYSICS_LIST="FTFP_BERT" | ||
| output: | ||
| SIM_FILE="sim_output/femc_photon/{DETECTOR_CONFIG}_sim_photon_{P}GeV.edm4hep.root" | ||
| shell: | ||
| """ | ||
| NEVENTS_SIM=1000 | ||
| # Running simulation | ||
| npsim \ | ||
| --compactFile $DETECTOR_PATH/{wildcards.DETECTOR_CONFIG}.xml \ | ||
| --numberOfEvents $NEVENTS_SIM \ | ||
| --physicsList {params.PHYSICS_LIST} \ | ||
| --inputFiles {input.GEN_FILE} \ | ||
| --outputFile {output.SIM_FILE} | ||
| """ | ||
|
|
||
| rule femc_photon_recon: | ||
| input: | ||
| SIM_FILE="sim_output/femc_photon/{DETECTOR_CONFIG}_sim_photon_{P}GeV.edm4hep.root" | ||
| output: | ||
| REC_FILE="sim_output/femc_photon/{DETECTOR_CONFIG}_rec_photon_{P}GeV.edm4hep.root" | ||
| shell: | ||
| """ | ||
| NEVENTS_REC=1000 | ||
| eicrecon {input.SIM_FILE} -Ppodio:output_file={output.REC_FILE} -Pdd4hep:xml_files=$DETECTOR_PATH/{wildcards.DETECTOR_CONFIG}.xml -Ppodio:output_collections=MCParticles,HcalEndcapPInsertRecHits,HcalEndcapPInsertClusters,HcalEndcapPInsertSubcellHits,EcalEndcapPInsertRecHits,EcalEndcapPInsertClusters,EcalEndcapPInsertRecHits,EcalEndcapPClusters -Pjana:nevents=$NEVENTS_REC | ||
| """ | ||
|
|
||
| rule femc_photon_analysis: | ||
| input: | ||
| expand("sim_output/femc_photon/{DETECTOR_CONFIG}_rec_photon_{P}GeV.edm4hep.root", | ||
| P=[20, 30, 40, 50, 60, 70, 80], | ||
| DETECTOR_CONFIG=["{DETECTOR_CONFIG}"], | ||
| particle=["{particle}"]), | ||
| script="benchmarks/femc_photon/analysis/femc_photon_plots.py", | ||
| output: | ||
| results_dir=directory("results/{DETECTOR_CONFIG}/femc_photon"), | ||
| shell: | ||
| """ | ||
| mkdir -p {output.results_dir} | ||
| python {input.script} {output.results_dir} | ||
| """ |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,171 @@ | ||
| import numpy as np, pandas as pd, matplotlib.pyplot as plt, matplotlib as mpl, awkward as ak, sys, uproot as ur | ||
| 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 | ||
|
|
||
| import uproot as ur | ||
| arrays_sim={p:ur.open(f'sim_output/femc_photon/{config}_rec_e-_{p}GeV.edm4hep.root:events').arrays() for p in (20, 30, 40, 50, 60,70,80)} | ||
|
|
||
| for p in arrays_sim: | ||
| array=arrays_sim[p] | ||
| tilt=-.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['nclust_endcap']=[len(array['EcalEndcapPClusters.energy'][i]) for i in range(len(array))] | ||
|
|
||
| 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) | ||
|
|
||
| #number of clusters | ||
| plt.figure() | ||
| for eta_min, eta_max, field in (1.5, 2.8, 'nclust_endcap'),: | ||
| for p in arrays_sim: | ||
| array=arrays_sim[p] | ||
| plt.hist(array[field][(array['eta_truth']>eta_min)&(array['eta_truth']<eta_max)], | ||
| bins=np.linspace(0,10,11), histtype='step', label=f'{p} GeV', density=True) | ||
| plt.ylabel("events") | ||
| plt.xlabel("# of Ecal clusters") | ||
| plt.legend() | ||
| plt.savefig(outdir+f"/{field}.pdf") | ||
|
|
||
| fig, axs=plt.subplots(1,2, figsize=(16,8)) | ||
| avgs=[] | ||
| stds=[] | ||
| pvals=[] | ||
|
|
||
| #number of hits per cluster | ||
| fig, axs=plt.subplots(1,2, figsize=(16,8)) | ||
| avgs=[] | ||
| stds=[] | ||
| pvals=[] | ||
|
|
||
| for p in arrays_sim: | ||
|
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||
| a=arrays_sim[p] | ||
| n=[] | ||
| nn=-a['EcalEndcapPClusters.hits_begin']+a['EcalEndcapPClusters.hits_end'] | ||
| E=a['EcalEndcapPClusters.energy'] | ||
| for evt in range(len(array)): | ||
| maxE=np.max(E[evt]) | ||
| found=False | ||
| for i in range(len(E[evt])): | ||
| if E[evt][i]==maxE: | ||
| n.append(nn[evt][i]) | ||
| found=True | ||
| break | ||
| #if not found: | ||
| # n.append(0) | ||
|
|
||
| if p ==50: | ||
| plt.sca(axs[0]) | ||
| y,x,_=plt.hist(n, range=(0,100), bins=100, histtype='step', label=f"E={p} GeV") | ||
| plt.ylabel("events") | ||
| plt.xlabel("# hits in cluster") | ||
| plt.title(f"e-, E={p} GeV") | ||
| pvals.append(p) | ||
| avgs.append(np.mean(n)) | ||
| stds.append(np.std(n)) | ||
|
|
||
| plt.sca(axs[1]) | ||
| plt.errorbar(pvals, avgs, stds, marker='o',ls='') | ||
| plt.xlabel("E [GeV]") | ||
| plt.ylabel("# hits in cluster [mean$\\pm$std]") | ||
| plt.ylim(0) | ||
| plt.savefig(outdir+"/nhits_per_cluster.pdf") | ||
|
|
||
|
|
||
| #energy resolution | ||
| def gauss(x, A,mu, sigma): | ||
| return A * np.exp(-(x-mu)**2/(2*sigma**2)) | ||
| from scipy.optimize import curve_fit | ||
|
|
||
| fig, axs=plt.subplots(1,3, figsize=(24,8)) | ||
| pvals=[] | ||
| res=[] | ||
| dres=[] | ||
| scale=[] | ||
| dscale=[] | ||
| for p in arrays_sim: | ||
| bins=np.linspace(15*p/20,22*p/20, 50) | ||
| if p==50: | ||
| plt.sca(axs[0]) | ||
| plt.title(f"E={p} GeV") | ||
| y,x,_=plt.hist(ak.flatten(arrays_sim[p]['EcalEndcapPClusters.energy']), bins=bins, histtype='step') | ||
| plt.ylabel("events") | ||
| plt.xlabel("$E^{rec}_e$ [GeV]") | ||
| else: | ||
| y,x=np.histogram(ak.flatten(arrays_sim[p]['EcalEndcapPClusters.energy']), bins=bins) | ||
| bcs=(x[1:]+x[:-1])/2 | ||
|
|
||
| fnc=gauss | ||
| slc=abs(bcs-p)<3 | ||
| sigma=np.sqrt(y[slc])+0.5*(y[slc]==0) | ||
| p0=(100, p, 3) | ||
|
|
||
| coeff, var_matrix = curve_fit(fnc, list(bcs[slc]), list(y[slc]), p0=p0,sigma=list(sigma)) | ||
| #res=np.abs(coeff[2]/coeff[1]) | ||
| if p==50: | ||
| xx=np.linspace(15*p/20,22*p/20, 100) | ||
|
|
||
| plt.plot(xx, fnc(xx,*coeff), label=f"$\\sigma_E/E={abs(coeff[2])/coeff[1]*100:.1f}\%$") | ||
| plt.axvline(p, color='g', ls='--', alpha=0.7) | ||
| plt.legend() | ||
| #plt.xlim(0,60) | ||
| #plt.show() | ||
| pvals.append(p) | ||
| res.append(abs(coeff[2])/coeff[1]) | ||
| dres.append(np.sqrt(var_matrix[2][2])/coeff[1]) | ||
| scale.append(abs(coeff[1])/p) | ||
| dscale.append(np.sqrt(var_matrix[1][1])/p) | ||
| plt.sca(axs[1]) | ||
| plt.errorbar(pvals, 100*np.array(res), 100*np.array(dres), ls='', marker='o') | ||
| fnc = lambda E, a, b: np.hypot(a,b/np.sqrt(E)) | ||
| p0=(.05, .12) | ||
| coeff, var_matrix = curve_fit(fnc, pvals, res, p0=p0,sigma=dres) | ||
| xx=np.linspace(15, 85, 100) | ||
| plt.plot(xx, 100*fnc(xx,*coeff), label=f'fit:{100*coeff[0]:.0f}%$\\oplus\\frac{{{100*coeff[1]:.1f}\\%}}{{\\sqrt{{E}}}}$') | ||
| plt.legend() | ||
| plt.ylim(0) | ||
| plt.ylabel("E resolution [%]") | ||
| plt.xlabel("E truth [GeV]") | ||
| plt.sca(axs[2]) | ||
|
|
||
| plt.errorbar(pvals, 100*np.array(scale), 100*np.array(dscale), ls='', marker='o') | ||
| plt.ylabel("energy scale [%]") | ||
| plt.xlabel("E truth [GeV]") | ||
| plt.axhline(100, color='0.5', alpha=0.5, ls='--') | ||
| plt.ylim(0, 110) | ||
| plt.tight_layout() | ||
| plt.savefig(outdir+"/energy_res.pdf") |
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