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parameters.py
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parameters.py
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# Defines the parameters that users might need to change
# Must be included manually in each script
# /!\ Two types of files need to be filled with users parameters
# - parameters.py (mort important one)
# - sampleList.py (on what samples to run)
# (optionnaly NeuralNet.py for early_stopping etc)
import multiprocessing
from keras.losses import binary_crossentropy, mean_squared_error, logcosh, cosine_proximity
from keras.optimizers import RMSprop, Adam, Nadam, SGD
from keras.activations import relu, elu, selu, softmax, tanh, sigmoid
from keras.regularizers import l1,l2
################################## Path variables ####################################
main_path = '/home/ucl/cp3/fbury/MoMEMtaNeuralNet/'
path_out = '/nfs/scratch/fynu/fbury/MoMEMta_output/NNOutput/'
path_model = '/home/ucl/cp3/fbury/MoMEMtaNeuralNet/model'
############################## Datasets proportion #################################
# Total must be 1 #
training_ratio = 0.7 # Training set sent to keras
validation_ratio = 0.1 # Validation set sent to autom8
output_ratio = 0.2 # Output set for plotting later
# Will only be taken into account for the masks generation, ignored after
############################### Slurm parameters ######################################
# For GPU #
partition = 'cp3-gpu' # Def, cp3 or cp3-gpu
QOS = 'cp3-gpu' # cp3 or normal
time = '5-00:00:00' # days-hh:mm:ss
mem = '60000' # ram in MB
tasks = '20' # Number of threads(as a string)
# For CPU #
#partition = 'cp3' # Def, cp3 or cp3-gpu
#QOS = 'cp3' # cp3 or normal
#time = '0-02:00:00' # days-hh:mm:ss
#mem = '60000' # ram in MB
#tasks = '20' # Number of threads(as a string)
###################################### Names ########################################
# Model name (only for scans)
#model = 'NeuralNetModel'
#model = 'ClassificationModel'
#model = 'BinaryModel'
model = 'NeuralNetGeneratorModel'
# scaler and mask names #
#suffix = 'binary'
suffix = 'class'
#suffix = 'class_param_test'
#suffix = 'gen_ME'
#suffix = 'gen_ME_newvar4'
# scaler_name -> 'scaler_{suffix}.pkl' If does not exist will be created
# mask_name -> 'mask_{suffix}_{sample}.npy' If does not exist will be created
# Training resume #
resume_model = ''
# Generator #
#path_gen_training = '/home/ucl/cp3/fbury/scratch/MoMEMta_output/ME_TTBar_generator_MCTF/path3' # For training
#path_gen_validation = '/home/ucl/cp3/fbury/scratch/MoMEMta_output/ME_TTBar_generator_MCTF/path0' # For val_loss during training
#path_gen_evaluation = '/home/ucl/cp3/fbury/scratch/MoMEMta_output/ME_TTBar_generator_MCTF/path1' # for model evaluation
#path_gen_output = '/home/ucl/cp3/fbury/scratch/MoMEMta_output/ME_TTBar_generator_MCTF/path2' # for output
path_gen_training = '/nfs/scratch/fynu/fbury/MoMEMta_output/ME_TTBar_generator_GaussianTF/path_training/' # For training
path_gen_validation = '/nfs/scratch/fynu/fbury/MoMEMta_output/ME_TTBar_generator_GaussianTF/path_validation/' # For val_loss during training
path_gen_evaluation = '/nfs/scratch/fynu/fbury/MoMEMta_output/ME_TTBar_generator_GaussianTF/path_evaluation/' # for model evaluation
path_gen_output = '/nfs/scratch/fynu/fbury/MoMEMta_output/ME_TTBar_generator_GaussianTF/path_output/' # for output
workers = 20
# Output #
output_batch_size = 10000
############################## Evaluation criterion ################################
eval_criterion = "eval_error" # either val_loss or eval_error
################################# Scan dictionary ##################################
# /!\ Lists must always contain something (even if 0), otherwise 0 hyperparameters #
# Classification #
#p = {
# 'lr' : [0.001],
# 'first_neuron' : [500],
# 'activation' : [relu],
# 'dropout' : [0],
# 'hidden_layers' : [3], # does not take into account the first layer
# 'output_activation' : [selu],
# 'l2' : [0],
# 'optimizer' : [Adam],
# 'epochs' : [1],
# 'batch_size' : [50000],
# 'loss_function' : [mean_squared_error]
#}
#p = {
# 'lr' : [0.0001],
# 'first_neuron' : [300],
# 'activation' : [relu],
# 'dropout' : [0],
# 'hidden_layers' : [4], # does not take into account the first layer
# 'output_activation' : [sigmoid],
# 'l2' : [0],
# 'optimizer' : [Adam],
# 'epochs' : [5],
# 'batch_size' : [512],
# 'loss_function' : [binary_crossentropy]
#}
# Regression #
#p = {
# 'lr' : [0.001],
# 'first_neuron' : [100],
# 'activation' : [relu],
# 'dropout' : [0],
# 'hidden_layers' : [5], # does not take into account the first layer
# 'output_activation' : [elu],
# 'l2' : [0],
# 'optimizer' : [Adam],
# 'epochs' : [100],
# 'batch_size' : [50000],
# 'loss_function' : [mean_squared_error]
#}
p = {
'lr' : [0.001],
'first_neuron' : [200],
'activation' : [relu],
'dropout' : [0],
'hidden_layers' : [9], # does not take into account the first layer
'output_activation' : [elu],
'l2' : [0.],
'optimizer' : [Adam],
'epochs' : [100],
'batch_size' : [10000],
'loss_function' : [mean_squared_error],
}
repetition = 1 # How many times each hyperparameter has to be used
################################### Variables ######################################
cut = None #'abs(-log10(output_ME)+log10(MEPdf))>1'
#weights = 'total_weight'
weights = None
product2Momentas = '-{0}.Px()*{1}.Px()-{0}.Py()*{1}.Py()-{0}.Pz()*{1}.Pz()+{0}.E()*{1}.E()'
product2Plus1Momentas = '-({0}.Px()+{1}.Px())*{2}.Px()-({0}.Py()+{1}.Py())*{2}.Py()-({0}.Pz()+{1}.Pz())*{2}.Pz()+({0}.E()+{1}.E())*{2}.E()'
invMass2Momentas = 'sqrt(-pow({0}.Px()+{1}.Px(),2)-pow({0}.Py()+{1}.Py(),2)-pow({0}.Pz()+{1}.Pz(),2)+pow({0}.E()+{1}.E(),2))'
invMass3Momentas = 'sqrt(-pow({0}.Px()+{1}.Px()+{2}.Px(),2)-pow({0}.Py()+{1}.Py()+{2}.Py(),2)-pow({0}.Pz()+{1}.Pz()+{2}.Pz(),2)+pow({0}.E()+{1}.E()+{2}.E(),2))'
inputs = [
####### Regression ##########
#'lep1_p4.Pt()',
#'lep1_p4.Eta()',
#'lep2_p4.Pt()',
#'lep2_p4.Eta()',
#'lep2_p4.Phi()-lep1_p4.Phi()',
#'jet1_p4.Pt()',
#'jet1_p4.Eta()',
#'jet1_p4.Phi()-lep1_p4.Phi()',
#'jet2_p4.Pt()',
#'jet2_p4.Eta()',
#'jet2_p4.Phi()-lep1_p4.Phi()',
#'met_pt',
#'met_phi-lep1_p4.Phi()',
####### Classification ##########
'-log10(weight_TT)',
'-log10(weight_DY)',
'-log10(weight_HToZA_mH_200_mA_50)',
'-log10(weight_HToZA_mH_200_mA_100)',
'-log10(weight_HToZA_mH_250_mA_50)',
'-log10(weight_HToZA_mH_250_mA_100)',
'-log10(weight_HToZA_mH_300_mA_50)',
'-log10(weight_HToZA_mH_300_mA_100)',
'-log10(weight_HToZA_mH_300_mA_200)',
'-log10(weight_HToZA_mH_500_mA_50)',
'-log10(weight_HToZA_mH_500_mA_100)',
'-log10(weight_HToZA_mH_500_mA_200)',
'-log10(weight_HToZA_mH_500_mA_300)',
'-log10(weight_HToZA_mH_500_mA_400)',
'-log10(weight_HToZA_mH_650_mA_50)',
'-log10(weight_HToZA_mH_800_mA_50)',
'-log10(weight_HToZA_mH_800_mA_100)',
'-log10(weight_HToZA_mH_800_mA_200)',
'-log10(weight_HToZA_mH_800_mA_400)',
'-log10(weight_HToZA_mH_800_mA_700)',
'-log10(weight_HToZA_mH_1000_mA_50)',
'-log10(weight_HToZA_mH_1000_mA_200)',
'-log10(weight_HToZA_mH_1000_mA_500)',
'-log10(weight_HToZA_mH_2000_mA_1000)',
'-log10(weight_HToZA_mH_3000_mA_2000)',
# Reprocess #
#'lep1_p4_Pt',
#'lep1_p4_Eta',
#'lep2_p4_Pt',
#'lep2_p4_Eta',
#'lep2_p4_Phi _minus_ lep1_p4_Phi',
#'jet1_p4_Pt',
#'jet1_p4_Eta',
#'jet1_p4_Phi _minus_ lep1_p4_Phi',
#'jet2_p4_Pt',
#'jet2_p4_Eta',
#'jet2_p4_Phi _minus_ lep1_p4_Phi',
#'met_pt',
#'met_phi _minus_ lep1_p4_Phi',
# Reprocess #
#'lep1_p4_Pt',
#'lep1_p4_Eta',
#'lep2_p4_Pt',
#'lep2_p4_Eta',
#'lep2_p4_Phi-lep1_p4_Phi',
#'jet1_p4_Pt',
#'jet1_p4_Eta',
#'jet1_p4_Phi-lep1_p4_Phi',
#'jet2_p4_Pt',
#'jet2_p4_Eta',
#'jet2_p4_Phi-lep1_p4_Phi',
#'met_pt',
#'met_phi-lep1_p4_Phi',
# Matrix Element #
#'init1_p4.E()',
#'init2_p4.E()',
#'positron_p4.Px()',
#'positron_p4.Py()',
#'positron_p4.Pz()',
#'positron_p4.E()',
#'neutrino_p4.Px()',
#'neutrino_p4.Py()',
#'neutrino_p4.Pz()',
#'neutrino_p4.E()',
#'bjet_p4.Px()',
#'bjet_p4.Py()',
#'bjet_p4.Pz()',
#'bjet_p4.E()',
#'electron_p4.Px()',
#'electron_p4.Py()',
#'electron_p4.Pz()',
#'electron_p4.E()',
#'antineutrino_p4.Px()',
#'antineutrino_p4.Py()',
#'antineutrino_p4.Pz()',
#'antineutrino_p4.E()',
#'antibjet_p4.Px()',
#'antibjet_p4.Py()',
#'antibjet_p4.Pz()',
#'antibjet_p4.E()',
# Matrix Element newVar #
#'init1_p4.E()',
#'init2_p4.E()',
#'positron_p4.Pt()',
#'positron_p4.Eta()',
#'neutrino_p4.Pt()',
#'neutrino_p4.Eta()',
#'neutrino_p4.Phi()-positron_p4.Phi()',
#'bjet_p4.Pt()',
#'bjet_p4.Eta()',
#'bjet_p4.Phi()-positron_p4.Phi()',
#'electron_p4.Pt()',
#'electron_p4.Eta()',
#'electron_p4.Phi()-positron_p4.Phi()',
#'antineutrino_p4.Pt()',
#'antineutrino_p4.Eta()',
#'antineutrino_p4.Phi()-positron_p4.Phi()',
#'antibjet_p4.Pt()',
#'antibjet_p4.Eta()',
#'antibjet_p4.Phi()-positron_p4.Phi()',
# Matrix Element newVar2 #
#invMass2Momentas.format('init1_p4','init2_p4'), # s = (P1+p2)²
#invMass2Momentas.format('electron_p4','antineutrino_p4'),
#invMass2Momentas.format('positron_p4','neutrino_p4'),
#product2Momentas.format('electron_p4','neutrino_p4'),
#product2Momentas.format('electron_p4','antineutrino_p4'),
#product2Momentas.format('electron_p4','positron_p4'),
#product2Momentas.format('electron_p4','bjet_p4'),
#product2Momentas.format('electron_p4','antibjet_p4'),
#product2Momentas.format('neutrino_p4','antineutrino_p4'),
#product2Momentas.format('neutrino_p4','positron_p4'),
#product2Momentas.format('neutrino_p4','bjet_p4'),
#product2Momentas.format('neutrino_p4','antibjet_p4'),
#product2Momentas.format('bjet_p4','antineutrino_p4'),
#product2Momentas.format('bjet_p4','positron_p4'),
#product2Momentas.format('bjet_p4','antibjet_p4'),
#product2Momentas.format('positron_p4','antineutrino_p4'),
#product2Momentas.format('positron_p4','antibjet_p4'),
#product2Momentas.format('antineutrino_p4','antibjet_p4'),
#invMass3Momentas.format('electron_p4','antineutrino_p4','bjet_p4'),# Wrong : antibjet
#invMass3Momentas.format('positron_p4','neutrino_p4','antibjet_p4'),# Wrong : bjet
#'positron_p4.Pt()',
#'positron_p4.Eta()',
#'neutrino_p4.Pt()',
#'neutrino_p4.Eta()',
#'neutrino_p4.Phi()-positron_p4.Phi()',
#'bjet_p4.Pt()',
#'bjet_p4.Eta()',
#'bjet_p4.Phi()-positron_p4.Phi()',
#'electron_p4.Pt()',
#'electron_p4.Eta()',
#'electron_p4.Phi()-positron_p4.Phi()',
#'antineutrino_p4.Pt()',
#'antineutrino_p4.Eta()',
#'antineutrino_p4.Phi()-positron_p4.Phi()',
#'antibjet_p4.Pt()',
#'antibjet_p4.Eta()',
#'antibjet_p4.Phi()-positron_p4.Phi()',
# # Matrix Element newVar3 #
#invMass3Momentas.format('electron_p4','antineutrino_p4','bjet_p4'), # Wrong : antibjet
#invMass3Momentas.format('positron_p4','neutrino_p4','antibjet_p4'), # Wrong : bjet
#invMass2Momentas.format('init1_p4','init2_p4'), # s = (P1+p2)²
#invMass2Momentas.format('electron_p4','antineutrino_p4'),
#invMass2Momentas.format('positron_p4','neutrino_p4'),
#product2Momentas.format('electron_p4','neutrino_p4'),
#product2Momentas.format('electron_p4','antineutrino_p4'),
#product2Momentas.format('electron_p4','positron_p4'),
#product2Momentas.format('electron_p4','bjet_p4'),
#product2Momentas.format('electron_p4','antibjet_p4'),
#product2Momentas.format('neutrino_p4','antineutrino_p4'),
#product2Momentas.format('neutrino_p4','positron_p4'),
#product2Momentas.format('neutrino_p4','bjet_p4'),
#product2Momentas.format('neutrino_p4','antibjet_p4'),
#product2Momentas.format('bjet_p4','antineutrino_p4'),
#product2Momentas.format('bjet_p4','positron_p4'),
#product2Momentas.format('bjet_p4','antibjet_p4'),
#product2Momentas.format('positron_p4','antineutrino_p4'),
#product2Momentas.format('positron_p4','antibjet_p4'),
#product2Momentas.format('antineutrino_p4','antibjet_p4'),
#'init1_p4.E()',
#'init2_p4.E()',
#'positron_p4.Pt()',
#'neutrino_p4.Pt()',
#'bjet_p4.Pt()',
#'electron_p4.Pt()',
#'antineutrino_p4.Pt()',
#'antibjet_p4.Pt()',
# Matrix Element newVar4 #
#invMass2Momentas.format('init1_p4','init2_p4'), # s = (P1+p2)²
#invMass3Momentas.format('electron_p4','antineutrino_p4','antibjet_p4'),
#invMass3Momentas.format('positron_p4','neutrino_p4','bjet_p4'),
#invMass2Momentas.format('electron_p4','antineutrino_p4'),
#invMass2Momentas.format('positron_p4','neutrino_p4'),
#product2Momentas.format('electron_p4','neutrino_p4'),
#product2Momentas.format('electron_p4','antineutrino_p4'),
#product2Momentas.format('electron_p4','positron_p4'),
#product2Momentas.format('electron_p4','bjet_p4'),
#product2Momentas.format('electron_p4','antibjet_p4'),
#product2Momentas.format('neutrino_p4','antineutrino_p4'),
#product2Momentas.format('neutrino_p4','positron_p4'),
#product2Momentas.format('neutrino_p4','bjet_p4'),
#product2Momentas.format('neutrino_p4','antibjet_p4'),
#product2Momentas.format('bjet_p4','antineutrino_p4'),
#product2Momentas.format('bjet_p4','positron_p4'),
#product2Momentas.format('bjet_p4','antibjet_p4'),
#product2Momentas.format('positron_p4','antineutrino_p4'),
#product2Momentas.format('positron_p4','antibjet_p4'),
#product2Momentas.format('antineutrino_p4','antibjet_p4'),
#'init1_p4.E()',
#'init2_p4.E()',
#'positron_p4.Pt()',
#'positron_p4.Eta()',
#'neutrino_p4.Pt()',
#'neutrino_p4.Eta()',
#'neutrino_p4.Phi()-positron_p4.Phi()',
#'bjet_p4.Pt()',
#'bjet_p4.Eta()',
#'bjet_p4.Phi()-positron_p4.Phi()',
#'electron_p4.Pt()',
#'electron_p4.Eta()',
#'electron_p4.Phi()-positron_p4.Phi()',
#'antineutrino_p4.Pt()',
#'antineutrino_p4.Eta()',
#'antineutrino_p4.Phi()-positron_p4.Phi()',
#'antibjet_p4.Pt()',
#'antibjet_p4.Eta()',
#'antibjet_p4.Phi()-positron_p4.Phi()',
# Matrix Element reprocessing #
#'init1_p4_E',
#'init2_p4_E',
#'positron_p4_Px',
#'positron_p4_Py',
#'positron_p4_Pz',
#'positron_p4_E',
#'neutrino_p4_Px',
#'neutrino_p4_Py',
#'neutrino_p4_Pz',
#'neutrino_p4_E',
#'bjet_p4_Px',
#'bjet_p4_Py',
#'bjet_p4_Pz',
#'bjet_p4_E',
#'electron_p4_Px',
#'electron_p4_Py',
#'electron_p4_Pz',
#'electron_p4_E',
#'antineutrino_p4_Px',
#'antineutrino_p4_Py',
#'antineutrino_p4_Pz',
#'antineutrino_p4_E',
#'antibjet_p4_Px',
#'antibjet_p4_Py',
#'antibjet_p4_Pz',
#'antibjet_p4_E',
]
outputs = [
####### Regression ##########
#'weight_TT',
#'weight_DY',
#'weight_HToZA_mH_200_mA_50',
#'weight_HToZA_mH_200_mA_100',
#'weight_HToZA_mH_250_mA_50',
#'weight_HToZA_mH_250_mA_100',
#'weight_HToZA_mH_300_mA_50',
#'weight_HToZA_mH_300_mA_100',
#'weight_HToZA_mH_300_mA_200',
#'weight_HToZA_mH_500_mA_50',
#'weight_HToZA_mH_500_mA_100',
#'weight_HToZA_mH_500_mA_200',
#'weight_HToZA_mH_500_mA_300',
#'weight_HToZA_mH_500_mA_400',
#'weight_HToZA_mH_650_mA_50',
#'weight_HToZA_mH_800_mA_50',
#'weight_HToZA_mH_800_mA_100',
#'weight_HToZA_mH_800_mA_200',
#'weight_HToZA_mH_800_mA_400',
#'weight_HToZA_mH_800_mA_700',
#'weight_HToZA_mH_1000_mA_50',
#'weight_HToZA_mH_1000_mA_200',
#'weight_HToZA_mH_1000_mA_500',
#'weight_HToZA_mH_2000_mA_1000',
#'weight_HToZA_mH_3000_mA_2000',
#'weight_HToZA_mH_600_mA_250', # interpolation
####### Classification ##########
######## Matrix Element ##########
#'-log10(MEPdf)'
]
other_variables = [
#'MEPdf',
#'output_ME',
#'lep1_p4.Pt()',
#'lep1_p4.Eta()',
#'lep1_p4.Phi()',
#'lep2_p4.Pt()',
#'lep2_p4.Eta()',
#'lep2_p4.Phi()',
#'jet1_p4.Pt()',
#'jet1_p4.Eta()',
#'jet1_p4.Phi()',
#'jet2_p4.Pt()',
#'jet2_p4.Eta()',
#'jet2_p4.Phi()',
#'met_pt',
#'met_phi',
#'lep1_p4.Px()',
#'lep1_p4.Py()',
#'lep1_p4.Pz()',
#'lep1_p4.E()',
#'lep2_p4.Px()',
#'lep2_p4.Py()',
#'lep2_p4.Pz()',
#'lep2_p4.E()',
#'jet1_p4.Px()',
#'jet1_p4.Py()',
#'jet1_p4.Pz()',
#'jet1_p4.E()',
#'jet2_p4.Px()',
#'jet2_p4.Py()',
#'jet2_p4.Pz()',
#'jet2_p4.E()',
#'ll_M',
#'jj_M',
#'lljj_M',
'weight_TT',
'weight_DY',
'output_DY',
'output_TT',
'weight_HToZA_mH_200_mA_50',
'weight_HToZA_mH_200_mA_100',
'weight_HToZA_mH_250_mA_50',
'weight_HToZA_mH_250_mA_100',
'weight_HToZA_mH_300_mA_50',
'weight_HToZA_mH_300_mA_100',
'weight_HToZA_mH_300_mA_200',
'weight_HToZA_mH_500_mA_50',
'weight_HToZA_mH_500_mA_100',
'weight_HToZA_mH_500_mA_200',
'weight_HToZA_mH_500_mA_300',
'weight_HToZA_mH_500_mA_400',
'weight_HToZA_mH_650_mA_50',
'weight_HToZA_mH_800_mA_50',
'weight_HToZA_mH_800_mA_100',
'weight_HToZA_mH_800_mA_200',
'weight_HToZA_mH_800_mA_400',
'weight_HToZA_mH_800_mA_700',
'weight_HToZA_mH_1000_mA_50',
'weight_HToZA_mH_1000_mA_200',
'weight_HToZA_mH_1000_mA_500',
'weight_HToZA_mH_2000_mA_1000',
'weight_HToZA_mH_3000_mA_2000',
'output_HToZA_mH_200_mA_50',
'output_HToZA_mH_200_mA_100',
'output_HToZA_mH_250_mA_50',
'output_HToZA_mH_250_mA_100',
'output_HToZA_mH_300_mA_50',
'output_HToZA_mH_300_mA_100',
'output_HToZA_mH_300_mA_200',
'output_HToZA_mH_500_mA_50',
'output_HToZA_mH_500_mA_100',
'output_HToZA_mH_500_mA_200',
'output_HToZA_mH_500_mA_300',
'output_HToZA_mH_500_mA_400',
'output_HToZA_mH_650_mA_50',
'output_HToZA_mH_800_mA_50',
'output_HToZA_mH_800_mA_100',
'output_HToZA_mH_800_mA_200',
'output_HToZA_mH_800_mA_400',
'output_HToZA_mH_800_mA_700',
'output_HToZA_mH_1000_mA_50',
'output_HToZA_mH_1000_mA_200',
'output_HToZA_mH_1000_mA_500',
'output_HToZA_mH_2000_mA_1000',
'output_HToZA_mH_3000_mA_2000',
#'weight_DY_time',
#'weight_DY_err',
#'weight_TT_time',
#'weight_TT_err',
#'weight_HToZA_mH_200_mA_50_err',
#'weight_HToZA_mH_200_mA_100_err',
#'weight_HToZA_mH_250_mA_50_err',
#'weight_HToZA_mH_250_mA_100_err',
#'weight_HToZA_mH_300_mA_50_err',
#'weight_HToZA_mH_300_mA_100_err',
#'weight_HToZA_mH_300_mA_200_err',
#'weight_HToZA_mH_500_mA_50_err',
#'weight_HToZA_mH_500_mA_100_err',
#'weight_HToZA_mH_500_mA_200_err',
#'weight_HToZA_mH_500_mA_300_err',
#'weight_HToZA_mH_500_mA_400_err',
#'weight_HToZA_mH_650_mA_50_err',
#'weight_HToZA_mH_800_mA_50_err',
#'weight_HToZA_mH_800_mA_100_err',
#'weight_HToZA_mH_800_mA_200_err',
#'weight_HToZA_mH_800_mA_400_err',
#'weight_HToZA_mH_800_mA_700_err',
#'weight_HToZA_mH_1000_mA_50_err',
#'weight_HToZA_mH_1000_mA_200_err',
#'weight_HToZA_mH_1000_mA_500_err',
#'weight_HToZA_mH_2000_mA_1000_err',
#'weight_HToZA_mH_3000_mA_2000_err',
#'weight_HToZA_mH_200_mA_100_time',
#'weight_HToZA_mH_250_mA_50_time',
#'weight_HToZA_mH_250_mA_100_time',
#'weight_HToZA_mH_300_mA_50_time',
#'weight_HToZA_mH_300_mA_100_time',
#'weight_HToZA_mH_300_mA_200_time',
#'weight_HToZA_mH_500_mA_50_time',
#'weight_HToZA_mH_500_mA_100_time',
#'weight_HToZA_mH_500_mA_200_time',
#'weight_HToZA_mH_500_mA_300_time',
#'weight_HToZA_mH_500_mA_400_time',
#'weight_HToZA_mH_650_mA_50_time',
#'weight_HToZA_mH_800_mA_50_time',
#'weight_HToZA_mH_800_mA_100_time',
#'weight_HToZA_mH_800_mA_200_time',
#'weight_HToZA_mH_800_mA_400_time',
#'weight_HToZA_mH_800_mA_700_time',
#'weight_HToZA_mH_1000_mA_50_time',
#'weight_HToZA_mH_1000_mA_200_time',
#'weight_HToZA_mH_1000_mA_500_time',
#'weight_HToZA_mH_2000_mA_1000_time',
#'weight_HToZA_mH_3000_mA_2000_time',
#'weight_HToZA_mH_600_mA_250_err',
#'weight_HToZA_mH_600_mA_250_time',
#'is_JEC',
]
################################ dtype operation ##############################
def make_dtype(list_names): # root_numpy does not like . and ()
list_dtype = [(name.replace('.','_').replace('(','').replace(')','').replace('-','_minus_').replace('*','_times_')) for name in list_names]
return list_dtype