from root_numpy import root2array, tree2array
from root_numpy import testdata
filename = testdata.get_filepath('test.root')
# Convert a TTree in a ROOT file into a NumPy structured array
arr = root2array(filename, 'tree')
# The TTree name is always optional if there is only one TTree in the file
# Or first get the TTree from the ROOT file
import ROOT
rfile = ROOT.TFile(filename)
intree = rfile.Get('tree')
# and convert the TTree into an array
array = tree2array(intree)
def load(data):
if isinstance(data, list):
return [get_filepath(x) for x in data]
return get_filepath(data)
from root_numpy import root2array, tree2array, testdata, array2root, array2tree
import scipy as sp
import matplotlib.pyplot as plt
import numpy as np
from numpy import array
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import plot_model
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_curve, auc
import ROOT
from ROOT import gROOT
np.random.seed(7)
#extract information from ROOT TTrees
file = testdata.get_filepath('out_train_PM.root')
Xtrain = root2array(file,
'tree_train',
branches=[
'pointing_angle_var', 'decay_length_var',
'Drecon_dca_var', 'trx_dca_var', 'pk_dca_var1',
'pk_dca_var2', 'dca_angle_var', 'paths_area_var',
'pk_p_frac_var'
]) #
Ytrain = root2array(file, 'tree_train', branches='validation_var')
Xtrain = Xtrain.view(np.float32).reshape(Xtrain.shape + (-1, ))
Ytrain = Ytrain.view(np.int32).reshape(Ytrain.shape + (-1, ))
Y_train = [0]
Y_train = Y_train * len(Ytrain)
for i in range(len(Y_train)):
Y_train[i] = int(Ytrain[i][0])