propagation_timing.py

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import ROOT
import csv
import matplotlib.pyplot as plt
import numpy as np

# Data preparation
dataDict = {}

# Open the output file
with open('output.log', mode='r') as csv_file:
    csv_reader = csv.reader(csv_file, delimiter=',')
    # read lines and go for it
    for csv_row in csv_reader : 
        if len(csv_row) > 1 :                
            # get the job id
            jobID = csv_row[0]
            # we need the exec time
            exectime = 0.
            with open ('timing_'+jobID+".tsv") as tsv_file:
                tsv_reader = csv.reader(tsv_file, delimiter='\t')
                for tsv_row in tsv_reader :
                    if str(tsv_row[0])=='Algorithm:PropagationAlgorithm' :
                        exectime=float(tsv_row[2])
            # stepper and pt
            stepper = int(csv_row[1])
            ptvalue = float(csv_row[2])
            # now open the ROOT file and extract the numbers
            rfile = ROOT.TFile('propagation_steps_'+str(jobID)+'.root')
            stree = rfile.Get("propagation_steps")
            stree.Draw('@g_x->size()>>h_steps')
            h_steps = ROOT.gDirectory.Get('h_steps')
            steps       = h_steps.GetMean()
            stepsSpread = h_steps.GetMeanError()
            
            # Make sure you have all the keys ready
            try :
                cdict =  dataDict[ptvalue] 
            except :
                dataDict[ptvalue] = {}
                cdict =  dataDict[ptvalue] 
            
            # Now fill the sub dictionary
            try :
                vdict = cdict[stepper]
            except:
                cdict[stepper] = []    
                vdict = cdict[stepper] 
            
            vdict += [ steps, stepsSpread, exectime, exectime/steps ]

# plot the dataDict
plt.figure(figsize=(16, 5))

ax = plt.subplot(131)
plt.loglog(dataDict.keys(),[i[0][0] for i in np.array(list(dataDict.values()))],'+', label='line')
plt.loglog(dataDict.keys(),[i[1][0] for i in np.array(list(dataDict.values()))],'*', label='eigen')
plt.loglog(dataDict.keys(),[i[2][0] for i in np.array(list(dataDict.values()))],'o', label='atlas')
ax.set_xlabel('$p_T$ [GeV]')
ax.set_ylabel('#steps')
ax.set_xlim((-10,150))
plt.legend(numpoints=1)

ax = plt.subplot(132)
plt.loglog(dataDict.keys(),[i[0][2] for i in np.array(list(dataDict.values()))],'+')
plt.loglog(dataDict.keys(),[i[1][2] for i in np.array(list(dataDict.values()))],'*')
plt.loglog(dataDict.keys(),[i[2][2] for i in np.array(list(dataDict.values()))],'o')
ax.set_xlabel('$p_T$ [GeV]')
ax.set_ylabel('time [a.u.]')
ax.set_xlim((-10,150))


ax = plt.subplot(133)
plt.loglog(dataDict.keys(),[i[0][3] for i in np.array(list(dataDict.values()))],'+')
plt.loglog(dataDict.keys(),[i[1][3] for i in np.array(list(dataDict.values()))],'*')
plt.loglog(dataDict.keys(),[i[2][3] for i in np.array(list(dataDict.values()))],'o')
ax.set_xlabel('$p_T$ [GeV]')
ax.set_ylabel('time/steps [a.u.]')
ax.set_xlim((-10,150))


plt.suptitle("Stepper comparison: Constant Field")

plt.show()