<jupyter_start><jupyter_text>In Sample Test Data<jupyter_code>td = OurTrainingData([os.getcwd()+'/toydata/ChPsmNP.h5'],
                     [os.getcwd()+'/toydata/ChPgphi50.h5',],
                     #os.getcwd()+'/ChPgphim45e-1NP.h5',],
                     process = 'W+Z', parameters =['Gphi[TeV**-2]'], 
                     SMNLimits=int(5e5), BSMNLimits=int(5e5))

td.Data = td.Data[:, :7]
td.CurateAngles([3, 5])

Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights
Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()


td.Data = td.Data[:, :7]
td.CurateAngles([3, 5])

Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights
Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()


NSM = td.SMWeights.mean()*3000
NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]

h = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_50_in.h5', 'w')

h.create_dataset('Data', data=Data)
h.create_dataset('Weights', data=Weights)
h.create_dataset('Labels', data=Labels)
h.create_dataset('NSM', data = NSM)
h.create_dataset('NBSMList', data=NBSMList)

h.close()
td = OurTrainingData([os.getcwd()+'/toydata/ChPsmNP.h5'],
                     [os.getcwd()+'/toydata/ChPgphi200.h5',],
                     #os.getcwd()+'/ChPgphim45e-1NP.h5',],
                     process = 'W+Z', parameters =['Gphi[TeV**-2]'], 
                     SMNLimits=int(5e5), BSMNLimits=int(5e5))

td.Data = td.Data[:, :7]
td.CurateAngles([3, 5])

Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights
Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()


td.Data = td.Data[:, :7]
td.CurateAngles([3, 5])

Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights
Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()


NSM = td.SMWeights.mean()*3000
NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]

h = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_200_in.h5', 'w')

h.create_dataset('Data', data=Data)
h.create_dataset('Weights', data=Weights)
h.create_dataset('Labels', data=Labels)
h.create_dataset('NSM', data = NSM)
h.create_dataset('NBSMList', data=NBSMList)

h.close()
td = OurTrainingData([os.getcwd()+'/toydata/ChPsmNP.h5'],
                     [os.getcwd()+'/toydata/ChPgphi500.h5',],
                     #os.getcwd()+'/ChPgphim45e-1NP.h5',],
                     process = 'W+Z', parameters =['Gphi[TeV**-2]'], 
                     SMNLimits=int(5e5), BSMNLimits=int(5e5))

td.Data = td.Data[:, :7]
td.CurateAngles([3, 5])

Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights
Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()


td.Data = td.Data[:, :7]
td.CurateAngles([3, 5])

Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights
Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()


NSM = td.SMWeights.mean()*3000
NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]

h = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_500_in.h5', 'w')

h.create_dataset('Data', data=Data)
h.create_dataset('Weights', data=Weights)
h.create_dataset('Labels', data=Labels)
h.create_dataset('NSM', data = NSM)
h.create_dataset('NBSMList', data=NBSMList)

h.close()<jupyter_output>Loading Data Files for Process: W+Z, with new physics Parameters: ['Gphi[TeV**-2]']

Reading file .../madminer/software/madminer/examples/tutorial_particle_physics/toydata/ChPgphi500.h5
##### File Info:
GphiIdeal = 0.5[TeV**-2] data, Ideal Events. 
Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}. Converted from /data3/ConvertMGevents/Ideal_Events/ChP300gphi500.dat
 File with charge = 1
#####

Reading file .../madminer/software/madminer/examples/tutorial_particle_physics/toydata/ChPsmNP.h5
##### File Info:
SMIdeal = 0.[TeV**-2] data, Ideal Events. 
Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}. Converted from /data3/ConvertMGevents/Ideal_Events/ChP300smNP.dat
 File with charge = 1
#####

Loaded SM Files:
  ['Gphi[TeV**-2]']    #Data    XS[pb](avg.w)
-------------------  -------  ---------------
                  0  3000000         0.741835

Loaded BSM Files:
  ['Gphi[TeV**-2]']    #Data    XS[pb](avg.w)
-------------------  -------  ---------------
                [...]<jupyter_text>Out Sample Test Data<jupyter_code>td = OurTrainingData([os.getcwd()+'/toydata/ChPsmNP.h5'],
                     [os.getcwd()+'/toydata/ChPgphi50new.h5',],
                     #os.getcwd()+'/ChPgphim45e-1NP.h5',],
                     process = 'W+Z', parameters =['Gphi[TeV**-2]'], 
                     SMNLimits=int(5e5), BSMNLimits=int(5e5))

td.Data = td.Data[:, :7]
td.CurateAngles([3, 5])

Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights
Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()


td.Data = td.Data[:, :7]
td.CurateAngles([3, 5])

Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights
Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()


NSM = td.SMWeights.mean()*3000
NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]

h = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_50_out.h5', 'w')

h.create_dataset('Data', data=Data)
h.create_dataset('Weights', data=Weights)
h.create_dataset('Labels', data=Labels)
h.create_dataset('NSM', data = NSM)
h.create_dataset('NBSMList', data=NBSMList)

h.close()
td = OurTrainingData([os.getcwd()+'/toydata/ChPsmNP.h5'],
                     [os.getcwd()+'/toydata/ChPgphi200new.h5',],
                     #os.getcwd()+'/ChPgphim45e-1NP.h5',],
                     process = 'W+Z', parameters =['Gphi[TeV**-2]'], 
                     SMNLimits=int(5e5), BSMNLimits=int(5e5))

td.Data = td.Data[:, :7]
td.CurateAngles([3, 5])

Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights
Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()


td.Data = td.Data[:, :7]
td.CurateAngles([3, 5])

Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights
Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()


NSM = td.SMWeights.mean()*3000
NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]

h = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_200_out.h5', 'w')

h.create_dataset('Data', data=Data)
h.create_dataset('Weights', data=Weights)
h.create_dataset('Labels', data=Labels)
h.create_dataset('NSM', data = NSM)
h.create_dataset('NBSMList', data=NBSMList)

h.close()
td = OurTrainingData([os.getcwd()+'/toydata/ChPsmNP.h5'],
                     [os.getcwd()+'/toydata/ChPgphi500new.h5',],
                     #os.getcwd()+'/ChPgphim45e-1NP.h5',],
                     process = 'W+Z', parameters =['Gphi[TeV**-2]'], 
                     SMNLimits=int(5e5), BSMNLimits=int(5e5))

td.Data = td.Data[:, :7]
td.CurateAngles([3, 5])

Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights
Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()


td.Data = td.Data[:, :7]
td.CurateAngles([3, 5])

Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights
Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()


NSM = td.SMWeights.mean()*3000
NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]

h = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_500_out.h5', 'w')

h.create_dataset('Data', data=Data)
h.create_dataset('Weights', data=Weights)
h.create_dataset('Labels', data=Labels)
h.create_dataset('NSM', data = NSM)
h.create_dataset('NBSMList', data=NBSMList)

h.close()
td = OurTrainingData([os.getcwd()+'/toydata/ChPsmNP.h5'],
                     [os.getcwd()+'/toydata/ChPgphi45.h5',],
                     #os.getcwd()+'/ChPgphim45e-1NP.h5',],
                     process = 'W+Z', parameters =['Gphi[TeV**-2]'], 
                     SMNLimits=int(5e5), BSMNLimits=int(5e5))

td.Data = td.Data[:, :7]
td.CurateAngles([3, 5])

Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights
Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()


td.Data = td.Data[:, :7]
td.CurateAngles([3, 5])

Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights
Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()


NSM = td.SMWeights.mean()*3000
NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]

h = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_45_out.h5', 'w')

h.create_dataset('Data', data=Data)
h.create_dataset('Weights', data=Weights)
h.create_dataset('Labels', data=Labels)
h.create_dataset('NSM', data = NSM)
h.create_dataset('NBSMList', data=NBSMList)

h.close()
for gphival in [40, 30, 20, 10]:
    td = OurTrainingData([os.getcwd()+'/toydata/ChPsmNP.h5'],
                         [os.getcwd()+'/toydata/ChPgphi%d.h5'%(gphival),],
                         #os.getcwd()+'/ChPgphim45e-1NP.h5',],
                         process = 'W+Z', parameters =['Gphi[TeV**-2]'], 
                         SMNLimits=int(5e5), BSMNLimits=int(5e5))

    td.Data = td.Data[:, :7]
    td.CurateAngles([3, 5])

    Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights
    Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()


    td.Data = td.Data[:, :7]
    td.CurateAngles([3, 5])

    Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights
    Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()


    NSM = td.SMWeights.mean()*3000
    NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]

    h = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'w')

    h.create_dataset('Data', data=Data)
    h.create_dataset('Weights', data=Weights)
    h.create_dataset('Labels', data=Labels)
    h.create_dataset('NSM', data = NSM)
    h.create_dataset('NBSMList', data=NBSMList)

    h.close()<jupyter_output>Loading Data Files for Process: W+Z, with new physics Parameters: ['Gphi[TeV**-2]']

Reading file .../madminer/software/madminer/examples/tutorial_particle_physics/toydata/ChPgphi40.h5
##### File Info:
GphiIdeal = 0.04[TeV**-2] data, Ideal Events. 
Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}. Converted from /data3/ConvertMGevents/Ideal_Events/ChP300gphi40.dat
 File with charge = 1
#####

Reading file .../madminer/software/madminer/examples/tutorial_particle_physics/toydata/ChPsmNP.h5
##### File Info:
SMIdeal = 0.[TeV**-2] data, Ideal Events. 
Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}. Converted from /data3/ConvertMGevents/Ideal_Events/ChP300smNP.dat
 File with charge = 1
#####

Loaded SM Files:
  ['Gphi[TeV**-2]']    #Data    XS[pb](avg.w)
-------------------  -------  ---------------
                  0  3000000         0.741835

Loaded BSM Files:
  ['Gphi[TeV**-2]']    #Data    XS[pb](avg.w)
-------------------  -------  ---------------
               0.[...]<jupyter_text>New 2D format Data<jupyter_code>from OurTrainingTools2D import *

td = OurTrainingData([os.getcwd()+'/toydata/ChPsmNP.h5'],
                     [os.getcwd()+'/toydata/ChP_pt300_gphi5e-3.h5'],
                     #os.getcwd()+'/ChPgphim45e-1NP.h5',],
                     process = 'W+Z', parameters =['Gphi[TeV**-2]'], 
                     SMNLimits=int(5e5), BSMNLimits=int(1e6))

td.Data = td.Data[:, :7]
td.CurateAngles([3, 5])

Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights
Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()


td.Data = td.Data[:, :7]
td.CurateAngles([3, 5])

Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights
Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()


NSM = td.SMWeights.mean()*3000
NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]

h = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_5_out.h5', 'w')

h.create_dataset('Data', data=Data)
h.create_dataset('Weights', data=Weights)
h.create_dataset('Labels', data=Labels)
h.create_dataset('NSM', data = NSM)
h.create_dataset('NBSMList', data=NBSMList)

h.close()
from OurTrainingTools2D import *

td = OurTrainingData([os.getcwd()+'/toydata/ChPsmNP.h5'],
                     [os.getcwd()+'/toydata/ChP_pt300_gphi1e-2.h5'],
                     #os.getcwd()+'/ChPgphim45e-1NP.h5',],
                     process = 'W+Z', parameters =['Gphi[TeV**-2]'], 
                     SMNLimits=int(5e5), BSMNLimits=int(1e6))

td.Data = td.Data[:, :7]
td.CurateAngles([3, 5])

Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights
Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()


td.Data = td.Data[:, :7]
td.CurateAngles([3, 5])

Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights
Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()


NSM = td.SMWeights.mean()*3000
NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]

h = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_10_out.h5', 'w')

h.create_dataset('Data', data=Data)
h.create_dataset('Weights', data=Weights)
h.create_dataset('Labels', data=Labels)
h.create_dataset('NSM', data = NSM)
h.create_dataset('NBSMList', data=NBSMList)

h.close()
from OurTrainingTools2D import *

td = OurTrainingData([os.getcwd()+'/toydata/ChPsmNP.h5'],
                     [os.getcwd()+'/toydata/ChP_pt300_gphi2e-2.h5'],
                     #os.getcwd()+'/ChPgphim45e-1NP.h5',],
                     process = 'W+Z', parameters =['Gphi[TeV**-2]'], 
                     SMNLimits=int(5e5), BSMNLimits=int(1e6))

td.Data = td.Data[:, :7]
td.CurateAngles([3, 5])

Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights
Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()


td.Data = td.Data[:, :7]
td.CurateAngles([3, 5])

Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights
Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()


NSM = td.SMWeights.mean()*3000
NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]

h = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_20_out.h5', 'w')

h.create_dataset('Data', data=Data)
h.create_dataset('Weights', data=Weights)
h.create_dataset('Labels', data=Labels)
h.create_dataset('NSM', data = NSM)
h.create_dataset('NBSMList', data=NBSMList)

h.close()
from OurTrainingTools2D import *

td = OurTrainingData([os.getcwd()+'/toydata/ChPsmNP.h5'],
                     [os.getcwd()+'/toydata/ChP_pt300_gphi3e-2.h5'],
                     #os.getcwd()+'/ChPgphim45e-1NP.h5',],
                     process = 'W+Z', parameters =['Gphi[TeV**-2]'], 
                     SMNLimits=int(5e5), BSMNLimits=int(1e6))

td.Data = td.Data[:, :7]
td.CurateAngles([3, 5])

Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights
Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()


td.Data = td.Data[:, :7]
td.CurateAngles([3, 5])

Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights
Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()


NSM = td.SMWeights.mean()*3000
NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]

h = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_30_out.h5', 'w')

h.create_dataset('Data', data=Data)
h.create_dataset('Weights', data=Weights)
h.create_dataset('Labels', data=Labels)
h.create_dataset('NSM', data = NSM)
h.create_dataset('NBSMList', data=NBSMList)

h.close()
from OurTrainingTools2D import *

td = OurTrainingData([os.getcwd()+'/toydata/ChPsmNP.h5'],
                     [os.getcwd()+'/toydata/ChP_pt300_gphi4e-2.h5'],
                     #os.getcwd()+'/ChPgphim45e-1NP.h5',],
                     process = 'W+Z', parameters =['Gphi[TeV**-2]'], 
                     SMNLimits=int(5e5), BSMNLimits=int(1e6))

td.Data = td.Data[:, :7]
td.CurateAngles([3, 5])

Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights
Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()


td.Data = td.Data[:, :7]
td.CurateAngles([3, 5])

Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights
Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()


NSM = td.SMWeights.mean()*3000
NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]

h = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_40_out.h5', 'w')

h.create_dataset('Data', data=Data)
h.create_dataset('Weights', data=Weights)
h.create_dataset('Labels', data=Labels)
h.create_dataset('NSM', data = NSM)
h.create_dataset('NBSMList', data=NBSMList)

h.close()
from OurTrainingTools2D import *

td = OurTrainingData([os.getcwd()+'/toydata/ChPsmNP.h5'],
                     [os.getcwd()+'/toydata/ChP_pt300_gphi5e-2.h5'],
                     #os.getcwd()+'/ChPgphim45e-1NP.h5',],
                     process = 'W+Z', parameters =['Gphi[TeV**-2]'], 
                     SMNLimits=int(5e5), BSMNLimits=int(1e6))

td.Data = td.Data[:, :7]
td.CurateAngles([3, 5])

Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights
Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()


td.Data = td.Data[:, :7]
td.CurateAngles([3, 5])

Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights
Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()


NSM = td.SMWeights.mean()*3000
NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]

h = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_50_out.h5', 'w')

h.create_dataset('Data', data=Data)
h.create_dataset('Weights', data=Weights)
h.create_dataset('Labels', data=Labels)
h.create_dataset('NSM', data = NSM)
h.create_dataset('NBSMList', data=NBSMList)

h.close()<jupyter_output>Loading Data Files for Process: W+Z, with new physics Parameters: ['Gphi[TeV**-2]']

Reading file .../madminer/software/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_gphi5e-2.h5
##### File Info:
{Gphi, GW} = {0.05, 0.}[TeV**-2] data, Ideal Events. 
Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.
Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_gphi5e-2.dat.gz
Charge = 1 --- Process = W+Z
#####

Reading file .../madminer/software/madminer/examples/tutorial_particle_physics/toydata/ChPsmNP.h5
##### File Info:
SMIdeal = 0.[TeV**-2] data, Ideal Events. 
Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}. Converted from /data3/ConvertMGevents/Ideal_Events/ChP300smNP.dat
 File with charge = 1
#####

Loaded SM Files:
  ['Gphi[TeV**-2]']    #Data    XS[pb](avg.w)
-------------------  -------  ---------------
                  0  3000000         0.741835

Loaded BSM Files:
['Gphi[TeV**-2]']                                  #Data    XS[pb](avg.w)
[...]
{'fraction_non_alphanumeric': 0.19753399634666124, 'fraction_numerical': 0.03024152628374264, 'mean_word_length': 3.621101992966002, 'pattern_counts': {'":': 0, '<': 10, '<?xml version=': 0, '>': 10, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '563885', 'license': 'MIT', 'n_tokens_mistral': 8425, 'n_tokens_neox': 7963, 'n_words': 1323, 'path': 'examples/tutorial_particle_physics/ReadingData.ipynb', 'repo_name': 'siyuchen95/madminer', 'size': '19708'}