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convert_Tree2Dask_ECAL+HCAL.py
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convert_Tree2Dask_ECAL+HCAL.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
from skimage.measure import block_reduce
eosDir='/eos/uscms/store/user/mba2012/IMGs'
#decays = ["H125GGgluonfusion_Pt25_Eta23_13TeV_TuneCUETP8M1_HighLumiPileUpv2", "PromptDiPhoton_MGG80toInf_Pt25_Eta23_13TeV_TuneCUETP8M1_HighLumiPileUp"]
#decays = ['H125GGgluonfusion_Pt25_Eta14_13TeV_TuneCUETP8M1_HighLumiPileUpv3','GJet_DoubleEMEnriched_PtHat20_MGG80toInf_Pt25_Eta14_13TeV_TuneCUETP8M1_HighLumiPileUp']
#decays = ['dummy','GJet_DoubleEMEnriched_PtHat20_MGG80toInf_Pt25_Eta23_13TeV_TuneCUETP8M1_HighLumiPileUp']
decays = ['h22gammaSM_1j_1M_noPU', 'h24gamma_1j_1M_100MeV_noPU']
#chunk_size_ = 250
chunk_size_ = 100
#scale = [100., 150.]
scale = [1., 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
@delayed
def load_X_upsampled(tree, start_, stop_, branches_, readouts, scale, upscale):
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]))
#print "unsampled.shape",X.shape
X = np.stack([tile_stacked_array(x, upscale) for x in X])
#print "upsampled.shape",X.shape
X = np.transpose(X, [0,2,3,1])
# Rescale
X /= scale
return X
from numpy.lib.stride_tricks import as_strided
def tile_stacked_array(X, upscale):
#print "un-tile_stacked.shape",X.shape
X = np.stack([tile_array(x, upscale, upscale) for x in X])
#print "tile_stacked.shape",X.shape
return X
def tile_array(x, b0, b1):
r, c = x.shape # number of rows/columns
rs, cs = x.strides # row/column strides
x = as_strided(x, (r, b0, c, b1), (rs, 0, cs, 0)) # view a as larger 4D array
return x.reshape(r*b0, c*b1) # create new 2D array
def block_resample_EE(X):
return np.array([resample_EE(x) for x in X])
def resample_EE(imgECAL, factor=2):
imgECAL = np.squeeze(imgECAL)
#print('imgECAL.shape:',imgECAL.shape)
# EE-
imgEEm = imgECAL[:140-85] # EE- in the first 55 rows
imgEEm = np.pad(imgEEm, ((1,0),(0,0)), 'constant', constant_values=0) # for even downsampling, zero pad 55 -> 56
imgEEm_dn = block_reduce(imgEEm, block_size=(factor, factor), func=np.sum) # downsample by summing over [factor, factor] window
imgEEm_dn_up = tile_array(imgEEm_dn, factor, factor)/(factor*factor) # upsample will use same values so need to correct scale by factor**2
imgECAL[:140-85] = imgEEm_dn_up[1:] ## replace the old EE- rows
# EE+
imgEEp = imgECAL[140+85:] # EE+ in the last 55 rows
imgEEp = np.pad(imgEEp, ((0,1),(0,0)), 'constant', constant_values=0) # for even downsampling, zero pad 55 -> 56
imgEEp_dn = block_reduce(imgEEp, block_size=(factor, factor), func=np.sum) # downsample by summing over [factor, factor] window
imgEEp_dn_up = tile_array(imgEEp_dn, factor, factor)/(factor*factor) # upsample will use same values so need to correct scale by factor*factor
imgECAL[140+85:] = imgEEp_dn_up[:-1] # replace the old EE+ rows
return np.expand_dims(imgECAL, -1)
for j,decay in enumerate(decays):
if j == 0:
pass
continue
#tfile_str = '%s/%s_IMG.root'%(eosDir,decay)
tfile_str = '%s/%s_FEVTDEBUG_IMG.root'%(eosDir,decay)
tfile = ROOT.TFile(tfile_str)
tree = tfile.Get('fevt/RHTree')
nevts = tree.GetEntries()
#neff = (nevts//1000)*1000
#neff = (nevts//100)*100
#neff = 29900
neff = 100
chunk_size = chunk_size_
if neff > nevts:
neff = int(nevts)
chunk_size = int(nevts)
print " >> Doing decay:", decay
print " >> Input file:", tfile_str
print " >> Total events:", nevts
print " >> Effective events:", neff
# 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 " >> %s: %s"%(branches[0],eventId.shape)
# ECAL
readouts = [280,360]
branches = ["ECAL_energy"]
X_ECAL = da.concatenate([\
da.from_delayed(\
load_X(tree,i,i+chunk_size, branches, readouts, scale[0]),\
shape=(chunk_size, readouts[0], readouts[1], len(branches)),\
dtype=np.float32)\
for i in range(0,neff,chunk_size)])
#print " >> %s: %s"%(branches[0],X_ECAL.shape)
# ECAL with resampled EE
X_ECAL_EEup = X_ECAL.map_blocks(lambda x: block_resample_EE(x), dtype=np.float32)
print " >> %s: %s"%('ECAL_EEup_energy',X_ECAL_EEup.shape)
# EB
readouts = [170,360]
branches = ["EB_energy"]
X_EB = da.concatenate([\
da.from_delayed(\
load_X(tree,i,i+chunk_size, branches, readouts, scale[0]),\
shape=(chunk_size, readouts[0], readouts[1], len(branches)),\
dtype=np.float32)\
for i in range(0,neff,chunk_size)])
print " >> %s: %s"%(branches[0],X_EB.shape)
# EE-
readouts = [100,100]
branches = ["EEm_energy"]
X_EEm = da.concatenate([\
da.from_delayed(\
load_X(tree,i,i+chunk_size, branches, readouts, scale[1]),\
shape=(chunk_size, readouts[0], readouts[1], len(branches)),\
dtype=np.float32)\
for i in range(0,neff,chunk_size)])
print " >> %s: %s"%(branches[0],X_EEm.shape)
# EE+
readouts = [100,100]
branches = ["EEp_energy"]
X_EEp = da.concatenate([\
da.from_delayed(\
load_X(tree,i,i+chunk_size, branches, readouts, scale[1]),\
shape=(chunk_size, readouts[0], readouts[1], len(branches)),\
dtype=np.float32)\
for i in range(0,neff,chunk_size)])
print " >> %s: %s"%(branches[0],X_EEp.shape)
# HBHE
readouts = [56,72]
branches = ["HBHE_energy"]
X_HBHE = da.concatenate([\
da.from_delayed(\
load_X(tree,i,i+chunk_size, branches, readouts, scale[1]),\
shape=(chunk_size, readouts[0], readouts[1], len(branches)),\
dtype=np.float32)\
for i in range(0,neff,chunk_size)])
print " >> %s: %s"%(branches[0],X_HBHE.shape)
# HBHE_EM
readouts = [56,72]
branches = ["HBHE_EMenergy"]
X_HBHE_EM = da.concatenate([\
da.from_delayed(\
load_X(tree,i,i+chunk_size, branches, readouts, scale[1]),\
shape=(chunk_size, readouts[0], readouts[1], len(branches)),\
dtype=np.float32)\
for i in range(0,neff,chunk_size)])
print " >> %s: %s"%(branches[0],X_HBHE_EM.shape)
# HB_EB upsample
readouts = [34,72]
branches = ["HBHE_energy_EB"]
upscale = 5
X_HBHE_up = da.concatenate([\
da.from_delayed(\
load_X_upsampled(tree,i,i+chunk_size, branches, readouts, scale[1], upscale),\
shape=(chunk_size, readouts[0]*upscale, readouts[1]*upscale, len(branches)),\
dtype=np.float32)\
for i in range(0,neff,chunk_size)])
print " >> %s(upsampled): %s"%(branches[0],X_HBHE_up.shape)
# Class label
label = j
#label = 1
print " >> Class label:",label
y = da.from_array(\
np.full(len(eventId), label, dtype=np.float32),\
chunks=(chunk_size,))
file_out_str = "test.hdf5"
#file_out_str = "%s/%s_IMG_EBEEHBup_RH%d_n%dk.hdf5"%(eosDir,decay,int(scale[0]),neff//1000.)
#file_out_str = "%s/%s_IMG_RH%d-%d_n%dk.hdf5"%(eosDir,decay,int(scale[0]),int(scale[1]),neff//1000.)
print " >> Writing to:", file_out_str
#da.to_hdf5(file_out_str, {'/X_EB': X_EB, 'X_EEm': X_EEm, 'X_EEp': X_EEp, 'X_HBHE': X_HBHE, '/y': y}, compression='lzf')
#da.to_hdf5(file_out_str, {'/X': X_EB, 'X_EEm': X_EEm, 'X_EEp': X_EEp, 'X_HBHE': X_HBHE, '/y': y}, compression='lzf')
da.to_hdf5(file_out_str, {'eventId': eventId,
#'X_ECAL': X_ECAL,
'X_ECAL_EEup': X_ECAL_EEup,
'X_EB': X_EB,
'X_EEm': X_EEm,
'X_EEp': X_EEp,
'X_HBHE': X_HBHE,
'X_HBHE_EM': X_HBHE_EM,
'X_HBHE_up': X_HBHE_up,
'/y': y}, compression='lzf')
print " >> Done.\n"