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convert_Tree2Dask_EBcropsv2_single.py
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convert_Tree2Dask_EBcropsv2_single.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/SinglePi0_scan'
decays = ['SinglePi0Pt60_pythia8_2016_25ns_Moriond17MC_PoissonOOTPU_m800']
chunk_size_ = 250
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_)
if len(branches_) > 1:
X = np.array([np.concatenate(x).reshape(len(branches_),1) for x in X])
X = X.reshape((-1,len(branches_)))
else:
X = np.array([x[0] for x in X])
return X
for j,decay in enumerate(decays):
if j == 1:
pass
continue
#tfile_str = 'output.root'
tfile_str = '%s/%s_IMG.root'%(eosDir,decay)
tfile = ROOT.TFile(tfile_str)
tree = tfile.Get('fevt/RHTree')
nevts = tree.GetEntries()
neff = (nevts//1000)*1000
#neff = int(nevts)
neff = 96000
chunk_size = chunk_size_
#chunk_size = int(nevts)
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 = ["EB_energy"]
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
# SC0
readouts = [32,32]
branches = ["SC_energy0"]
X_crop0 = 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_crop0.shape
# SC0
readouts = [32,32]
branches = ["SC_energyT0", "SC_energyZ0"]
X_crop_stack0 = 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_crop_stack0.shape
# SC_mass0
branches = ["SC_mass0"]
y_mass0 = 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:", y_mass0.shape
# SC_pT0
branches = ["SC_pT0"]
y_pT0 = 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:", y_pT0.shape
# SC_DR0
branches = ["SC_DR0"]
y_DR0 = 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:", y_DR0.shape
# pho_pT0
branches = ["pho_pT0"]
pho_pT0 = 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:", pho_pT0.shape
# pho_E0
branches = ["pho_E0"]
pho_E0 = 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:", pho_E0.shape
# pho_eta0
branches = ["pho_eta0"]
pho_eta0 = 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:", pho_eta0.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
## Kinematics
#branches = ["pho_pT", "pho_E", "pho_eta", "pho_phi"]
#X_p4 = da.concatenate([\
# da.from_delayed(\
# load_single(tree,i,i+chunk_size, branches),\
# shape=(chunk_size,len(branches)),\
# dtype=np.float32)\
# for i in range(0,neff,chunk_size)])
#print " >> Expected shape:", X_p4.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_n%dk.hdf5"%(eosDir,decay,int(scale),neff//1000.)
#file_out_str = "test.hdf5"
print " >> Writing to:", file_out_str
#da.to_hdf5(file_out_str, {'/X': X, '/y': y, 'eventId': eventId, 'X_crop0': X_crop0, 'X_crop1': X_crop1}, compression='lzf')
da.to_hdf5(file_out_str, {
#'/X': X,
'/y': y,
#'eventId': eventId,
'X_crop0': X_crop0,
'X_crop_stack0': X_crop_stack0,
#'X_crop1': X_crop1
#'X_p4': X_p4
'y_mass': y_mass0,
'y_pT': y_pT0,
#'y_DR': y_DR0,
#'pho_pT0': pho_pT0,
#'pho_E0': pho_E0,
#'pho_eta0': pho_eta0
}, compression='lzf')
print " >> Done.\n"