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NO values for weights for a histogram #264

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sarahwarad opened this issue Oct 9, 2024 · 5 comments
Open

NO values for weights for a histogram #264

sarahwarad opened this issue Oct 9, 2024 · 5 comments
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❓question Further information is requested

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@sarahwarad
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Hello
I hope you are doing well.

I use MA with MG. after generating a process and run MA, in the file bin/ANALYSIS_0/Output/Histos/MadAnalysis5job_0/selection_0.py, there is the code written in python language but all values in y1_M_0_weights are zeros. although the cross section is 3015 pb.

Can you help with this issue, please?

@sarahwarad sarahwarad added the ❓question Further information is requested label Oct 9, 2024
@BFuks
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BFuks commented Oct 9, 2024

Hi Sarah,

Could you please provide us information so that we could reproduce the problem?

Regards,

Benjamin

@sarahwarad
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thank you so much for your reply

can you clarify which information you need?

best regards
Sarah

@BFuks
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BFuks commented Oct 9, 2024

Hi Sarah,

As I wrote, I need to be able to reproduce your problem. I therefore need your version of the code, a small sample of events representative to what you analysed, the MA5 script used, etc.

Regards,

Benjamin

@sarahwarad
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OK
that is what I did:

I generated the process

Process p p > l+ l- /a [QCD] ; p p > l+ l- j /a [QCD] ; p p > l+ l- j /a [QCD]
Run at p-p collider (6500.0 + 6500.0 GeV)
Number of events generated: 10000
Total cross section: 5.603e+03 +- 1.9e+01 pb

Running MadAnalysis5 [arXiv:1206.1599]
INFO: Hadron input files considered:
INFO: --> /mainfs/scratch/swa1a19/MG5_aMC_v3_3_1/bin/dyjj/Events/run_01/events_PYTHIA8_0.hepmc.gz
INFO: MadAnalysis5 now running the reconstruction 'BasicReco'...
INFO: Follow Madanalysis5 run with the following command in a separate terminal:
INFO: tail -f /mainfs/scratch/swa1a19/MG5_aMC_v3_3_1/bin/dyjj/Events/run_01/tag_1_MA5__reco_BasicReco.log
INFO: MadAnalysis5 successfully completed the reconstruction 'BasicReco'. Links to the reconstructed event files are:
INFO: --> /mainfs/scratch/swa1a19/MG5_aMC_v3_3_1/bin/dyjj/Events/run_01/events_PYTHIA8_0_BasicReco.lhe.gz
INFO: MadAnalysis5 now running the 'analysis2_BasicReco' analysis...
INFO: Follow Madanalysis5 run with the following command in a separate terminal:
INFO: tail -f /mainfs/scratch/swa1a19/MG5_aMC_v3_3_1/bin/dyjj/Events/run_01/tag_1_MA5_analysis2_BasicReco.log
ERROR: MadAnalysis5 failed to create PDF output
INFO: MadAnalysis5 successfully completed the analysis 'analysis2_BasicReco'. Reported results are placed in:
INFO: --> /mainfs/scratch/swa1a19/MG5_aMC_v3_3_1/bin/dyjj/Events/run_01/tag_1_MA5_hadron_analysis_analysis2_BasicReco.pdf
INFO: Finished MA5 analyses.

In MA card

Uncomment the line below to skip this analysis altogether

@MG5aMC skip_analysis

@MG5aMC stdout_lvl=INFO

@MG5aMC inputs = *.hepmc, *.hep, *.stdhep, *.lhco, *.fifo

Reconstruction using FastJet

@MG5aMC reconstruction_name = BasicReco
@MG5aMC reco_output = lhe

Multiparticle definition

define invisible = 14 -12 -16 16 12 -14
set main.fastsim.package = fastjet
set main.fastsim.algorithm = antikt
set main.fastsim.radius = 0.4
set main.fastsim.ptmin = 5.0

b-tagging

set main.fastsim.bjet_id.matching_dr = 0.4
set main.fastsim.bjet_id.efficiency = 1.0
set main.fastsim.bjet_id.misid_cjet = 0.0
set main.fastsim.bjet_id.misid_ljet = 0.0

tau-tagging

set main.fastsim.tau_id.efficiency = 1.0
set main.fastsim.tau_id.misid_ljet = 0.0

Analysis using the fastjet reco

@MG5aMC analysis_name = analysis2

Uncomment the next line to bypass this analysis

@MG5aMC skip_analysis

@MG5aMC set_reconstructions = ['BasicReco']

plot tunning: dsigma/sigma is plotted.

set main.stacking_method = normalize2one

object definition

define e = e+ e-
define mu = mu+ mu-
select (j) PT > 20
select (b) PT > 20
select (e) PT > 10
select (mu) PT > 10
select (j) ABSETA < 2.5
select (b) ABSETA < 2.5
select (e) ABSETA < 2.5
select (mu) ABSETA < 2.5

Basic plots

plot MET 40 0 500
plot THT 40 0 500

basic properties of the non-b-tagged jets

plot PT(j[1]) 40 0 500 [logY]
plot ETA(j[1]) 40 -10 10 [logY]
plot MT_MET(j[1]) 40 0 500 [logY]
plot PT(j[2]) 40 0 500 [logY]
plot ETA(j[2]) 40 -10 10 [logY]
plot MT_MET(j[2]) 40 0 500 [logY]

basic properties of the leptons

plot PT(e[1]) 40 0 500 [logY]
plot PT(e[2]) 40 0 500 [logY]
plot ETA(e[2]) 40 -10 10 [logY]
plot MT_MET(e[2]) 40 0 500 [logY]
plot PT(mu[1]) 40 0 500 [logY]
plot ETA(mu[1]) 40 -10 10 [logY]
plot MT_MET(mu[1]) 40 0 500 [logY]
plot PT(mu[2]) 40 0 500 [logY]
plot ETA(mu[2]) 40 -10 10 [logY]
plot MT_MET(mu[2]) 40 0 500 [logY]

Invariant-mass distributions

plot M(e[1] e[2]) 40 0 500 [logY]
plot M(e[1] mu[1]) 40 0 500 [logY]
plot M(e[1] mu[2]) 40 0 500 [logY]
plot M(e[2] mu[1]) 40 0 500 [logY]
plot M(e[2] mu[2]) 40 0 500 [logY]
plot M(j[1] e[1]) 40 0 500 [logY]
plot M(j[1] e[2]) 40 0 500 [logY]
plot M(j[1] j[2]) 40 0 500 [logY]
plot M(j[1] mu[1]) 40 0 500 [logY]
plot M(j[1] mu[2]) 40 0 500 [logY]
plot M(j[2] e[1]) 40 0 500 [logY]
plot M(j[2] e[2]) 40 0 500 [logY]
plot M(j[2] mu[1]) 40 0 500 [logY]
plot M(j[2] mu[2]) 40 0 500 [logY]
plot M(mu[1] mu[2]) 40 0 500 [logY]

Angular distance distributions

plot DELTAR(e[1],e[2]) 40 0 10 [logY]
plot DELTAR(e[1],mu[1]) 40 0 10 [logY]
plot DELTAR(e[1],mu[2]) 40 0 10 [logY]
plot DELTAR(e[2],mu[1]) 40 0 10 [logY]
plot DELTAR(e[2],mu[2]) 40 0 10 [logY]
plot DELTAR(j[1],e[1]) 40 0 10 [logY]
plot DELTAR(j[1],e[2]) 40 0 10 [logY]
plot DELTAR(j[1],j[2]) 40 0 10 [logY]
</MA5Card_hadron>

muR FxFx merging scale
muF1 FxFx merging scale
muF2 FxFx merging scale
QES H_T/2 := sum_i mT(i)/2, i=final state


1 0.330000E+00
2 0.330000E+00
3 0.500000E+00
4 0.150000E+01
5 0.480000E+01
11 0.510999E-03
13 0.105658E+00
15 0.177682E+01
21 0.000000E+00

2212 2212 0.65000000E+04 0.65000000E+04 -1 -1 244800 244800 -4 1 0.56028641E+04 0.18525980E+02 0.97657642E+04 0 --------------------------------------- then from MA I did

import /mainfs/scratch/swa1a19/MG5_aMC_v3_3_1/bin/dyjj/Events/run_01/events_PYTHIA8_0.hepmc.gz
define e = e+ e-
define mu = mu+ mu-
select (j) PT > 20
select (mu) PT > 10
select (e) PT > 15
select (j) ABSETA < 2.4
select (e) ABSETA < 2.5
select (mu) ABSETA < 2.4
select (j) DELTAR(j) = 0.4
select (l) DELTAR(l) = 0.3
select (mu) DELTAR(l) = 0.3
define l+ = mu+ e+
define l- = mu- e-
define l= l+ l-
select (j) PT > 20
select (mu) PT > 10
select (l) DELTAR(l) = 0.3
plot M(j[1] j[2]) 100 0 1000
submit

then in the file bin/ANALYSIS_0/Output/Histos/MadAnalysis5job_0/selection_0.py

all values of Y axis are zeros

Library import

import numpy
import matplotlib
import matplotlib.pyplot   as plt
import matplotlib.gridspec as gridspec

# Library version
matplotlib_version = matplotlib.__version__
numpy_version      = numpy.__version__

# Histo binning
xBinning = numpy.linspace(0.0,1000.0,101,endpoint=True)

# Creating data sequence: middle of each bin
xData = numpy.array([5.0,15.0,25.0,35.0,45.0,55.0,65.0,75.0,85.0,95.0,105.0,115.0,125.0,135.0,145.0,155.0,165.0,175.0,185.0,195.0,205.0,215.0,225.0,235.0,245.0,255.0,265.0,275.0,285.0,295.0,305.0,315)

# Creating weights for histo: y1_M_0
y1_M_0_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0)

# Creating a new Canvas
fig   = plt.figure(figsize=(8.75,6.25),dpi=80)
frame = gridspec.GridSpec(1,1)
pad   = fig.add_subplot(frame[0])

# Creating a new Stack
pad.hist(x=xData, bins=xBinning, weights=y1_M_0_weights,\
         label="$defaultset$", rwidth=1.0,\
         color="#5954d8", edgecolor="#5954d8", linewidth=1, linestyle="solid",\
         bottom=None, cumulative=False, density=False, align="mid", orientation="vertical")


# Axis
plt.rc('text',usetex=False)
plt.xlabel(r"$M$ $[ j_{1} j_{2} ]$ $(GeV/c^{2})$ ",\
           fontsize=16,color="black")
plt.ylabel(r"$\mathrm{Events}$ $(\mathcal{L}_{\mathrm{int}} = 10\ \mathrm{fb}^{-1})$ ",\
           fontsize=16,color="black")

# Boundary of y-axis
ymax=(y1_M_0_weights).max()*1.1
ymin=0 # linear scale
#ymin=min([x for x in (y1_M_0_weights) if x])/100. # log scale
plt.gca().set_ylim(ymin,ymax)

# Log/Linear scale for X-axis
plt.gca().set_xscale("linear")
#plt.gca().set_xscale("log",nonpositive="clip")

# Log/Linear scale for Y-axis
plt.gca().set_yscale("linear")
#plt.gca().set_yscale("log",nonpositive="clip")

# Saving the image
plt.savefig('../../HTML/MadAnalysis5job_0/selection_0.png')
plt.savefig('../../PDF/MadAnalysis5job_0/selection_0.png')
plt.savefig('../../DVI/MadAnalysis5job_0/selection_0.eps')

@BFuks
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BFuks commented Oct 10, 2024

Hi Sarah,

Please, share an event file with O(10) events, and the MA5 script you use to analyse it as a file. Otherwise, this will be too much of a burden for me...

Cheers,

Benjamin

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