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convert_Tree2Dask_EBv5+FC.py
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convert_Tree2Dask_EBv5+FC.py
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import numpy as np
import ROOT
from root_numpy import tree2array
from dask.delayed import delayed
import dask.array as da
#eosDir='/eos/uscms/store/user/mba2012/IMGs/HighLumi_ROOTv2'
#eosDir='/eos/uscms/store/user/mba2012/IMGs/h24gamma_eta14'
eosDir='/eos/cms/store/user/mandrews/OPENDATA/IMGs/MGG90_Eta14v3'
#decays = ['h22gammaSM_1j_1M_noPU', 'h24gamma_1j_1M_1GeV_noPU']
#decays = ['SM2gamma_1j_1M_noPU', 'h24gamma_1j_1M_1GeV_noPU']
#decays = ['SM2gamma_1j_1M_noPU', 'h22gammaSM_1j_1M_noPU']
#decays = ['SM2gamma_1j_1M_noPU', 'h22gammaSM_1j_1M_noPU', 'h24gamma_1j_1M_1GeV_noPU']
decays = ['DiPhotonBorn_MGG90_Eta14v3', 'GluGluHToGG_MGG90_Eta14v3', 'GJet_MGG90_Eta14v3']
#chunk_size_ = 250
chunk_size_ = 200
#chunk_size_ = 273
scale = 1.
@delayed
def load_X(tree, start_, stop_, branches_, readouts, scale):
X = tree2array(tree, start=start_, stop=stop_, branches=branches_)
# Convert the object array X to a multidim array:
# 1: for each event x in X, concatenate the object columns (branches) into a flat array of shape (readouts*branches)
# 2: reshape the flat array into a stacked array: (branches, readouts)
# 3: embed each stacked array as a single row entry in a list via list comprehension
# 4: convert this list into an array with shape (events, branches, readouts)
X = np.array([np.concatenate(x).reshape(len(branches_),readouts[0]*readouts[1]) for x in X])
#print "X.shape:",X.shape
X = X.reshape((-1,len(branches_),readouts[0],readouts[1]))
X = np.transpose(X, [0,2,3,1])
# Rescale
X /= scale
return X
@delayed
def load_single(tree, start_, stop_, branches_):
X = tree2array(tree, start=start_, stop=stop_, branches=branches_)
X = np.array([x[0] for x in X])
return X
for j,decay in enumerate(decays):
#if j == 2 or j == 1:
if j != 2:
pass
#continue
tfile_str = '%s/%s_IMG.root'%(eosDir,decay)
#tfile_str = '%s/%s_FEVTDEBUG_IMG.root'%(eosDir,decay)
#tfile_str = '%s/%s_FEVTDEBUG_nXXX_IMG.root'%(eosDir,decay)
tfile = ROOT.TFile(tfile_str)
tree = tfile.Get('fevt/RHTree')
#tree = ROOT.TChain("fevt/RHTree")
#tree.Add('%s/DiPhotonBorn_MGG90All_Eta14_IMG.root'%eosDir)
#tree.Add('%s/DiPhotonBox_MGG90All_Eta14_IMG.root'%eosDir)
nevts = tree.GetEntries()
#neff = (nevts//1000)*1000
#neff = 200
#neff = 63000
neff = 57400
#neff = 84600
#neff = 110000
#neff = 81900
chunk_size = chunk_size_
if neff > nevts:
neff = int(nevts)
chunk_size = int(nevts)
#neff = 1000
#neff = 233000
print " >> Doing decay:", decay
print " >> Input file:", tfile_str
print " >> Total events:", nevts
print " >> Effective events:", neff
# EB
readouts = [170,360]
#branches = ["TracksPt_EB","EB_energy"]
branches = ["EB_energyT", "EB_energyZ"]
X = da.concatenate([\
da.from_delayed(\
load_X(tree,i,i+chunk_size, branches, readouts, scale),\
shape=(chunk_size, readouts[0], readouts[1], len(branches)),\
dtype=np.float32)\
for i in range(0,neff,chunk_size)])
print " >> Expected shape:", X.shape
## eventId
#branches = ["eventId"]
#eventId = da.concatenate([\
# da.from_delayed(\
# load_single(tree,i,i+chunk_size, branches),\
# shape=(chunk_size,),\
# dtype=np.int32)\
# for i in range(0,neff,chunk_size)])
#print " >> Expected shape:", eventId.shape
# runId
branches = ["runId"]
runId = da.concatenate([\
da.from_delayed(\
load_single(tree,i,i+chunk_size, branches),\
shape=(chunk_size,),\
dtype=np.int32)\
for i in range(0,neff,chunk_size)])
print " >> Expected shape:", runId.shape
# m0
branches = ["m0"]
m0 = da.concatenate([\
da.from_delayed(\
load_single(tree,i,i+chunk_size, branches),\
shape=(chunk_size,),\
dtype=np.float32)\
for i in range(0,neff,chunk_size)])
print " >> Expected shape:", m0.shape
# diPhoE
branches = ["diPhoE"]
diPhoE = da.concatenate([\
da.from_delayed(\
load_single(tree,i,i+chunk_size, branches),\
shape=(chunk_size,),\
dtype=np.float32)\
for i in range(0,neff,chunk_size)])
print " >> Expected shape:", diPhoE.shape
# diPhoPt
branches = ["diPhoPt"]
diPhoPt = da.concatenate([\
da.from_delayed(\
load_single(tree,i,i+chunk_size, branches),\
shape=(chunk_size,),\
dtype=np.float32)\
for i in range(0,neff,chunk_size)])
print " >> Expected shape:", diPhoPt.shape
# FC inputs
branches = ["FC_inputs"]
X_FC = da.concatenate([\
da.from_delayed(\
load_single(tree,i,i+chunk_size, branches),\
shape=(chunk_size,5),\
dtype=np.float32)\
for i in range(0,neff,chunk_size)])
print " >> Expected shape:", X_FC.shape
# Class label
label = j
#label = 1
print " >> Class label:",label
y = da.from_array(\
np.full(X.shape[0], label, dtype=np.float32),\
chunks=(chunk_size,))
file_out_str = "%s/%s_IMG_RH%d+FC_n%d_label%d.hdf5"%(eosDir,decay,int(scale),neff,label)
#file_out_str = "%s/DiPhotonAll_MGG90All_Eta14_IMG_RH%d_n%d_label%d.hdf5"%(eosDir,int(scale),neff,label)
#file_out_str = "%s/%s_IMG_RH%d_n%dk_label%d.hdf5"%(eosDir,decay,int(scale),neff//1000.,label)
#file_out_str = "test.hdf5"
print " >> Writing to:", file_out_str
#da.to_hdf5(file_out_str, {'/X': X, '/y': y, 'runId': runId, 'm0': m0, 'diPhoE': diPhoE, 'diPhoPt': diPhoPt}, compression='lzf')
da.to_hdf5(file_out_str, {
'/X': X,
'/X_FC': X_FC,
'/y': y,
'runId': runId,
'm0': m0,
'diPhoE': diPhoE,
'diPhoPt': diPhoPt}, compression='lzf')
print " >> Done.\n"