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RunProducer.py
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RunProducer.py
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"""
18 August 2021
The purpose of this module is to run a coffea producer to process root files.
Example usage:
singularity shell -B ${PWD} -B /afs -B /eos /cvmfs/unpacked.cern.ch/registry.hub.docker.com/coffeateam/coffea-dask:latest
python3 RunProducer.py --inDir="/eos/cms/store/group/dpg_ecal/alca_ecalcalib/Trigger/DoubleWeights/Run_352912/ETTAnalyzer_CMSSW_12_3_0_DoubleWeights/oneFile/oneFile/" --treename="tuplizer/ETTAnalyzerTree" --outDir="/eos/user/a/atishelm/www/EcalL1Optimization/ETT_Coffea_singleFile/" --condor="0"
"""
import os
import re
import pickle
from coffea.processor import run_uproot_job, futures_executor
import uproot3 as uproot
import argparse
from matplotlib.colors import LogNorm
import time
parser = argparse.ArgumentParser("")
parser.add_argument('--jobNum', type=int, default=1, help="")
parser.add_argument('--dims', type=str, default="2", help="Comma separated list of types of plots to make. Can be '1', '2', '1,2'" )
parser.add_argument('--doSyst', type=int, default=1, help="")
parser.add_argument('--infile', type=str, default="", help="")
parser.add_argument('--dataset', type=str, default="X", help="")
parser.add_argument('--nevt', type=str, default=-1, help="")
parser.add_argument('--treename', type=str, default="Events", help="")
parser.add_argument('--inDir', type=str, default="", help="") ##-- Comma separated list of directories
parser.add_argument('--outDir', type=str, default="./", help="") ##-- Comma separated list of directories
parser.add_argument('--condor', type=int, default=1, help="")
options = parser.parse_args()
dims = options.dims.split(',')
print("dims:",dims)
if(options.condor):
##-- Condor
from Producer import *
from SumWeights import *
else:
##-- Locally
from python.Producer import *
from python.SumWeights import *
pre_selection = ""
if float(options.nevt) > 0:
print((" passing this cut and : ", options.nevt))
pre_selection += ' && (Entry$ < {})'.format(options.nevt)
modules = []
for dim in dims:
severities = ["all", "zero", "three", "four"]
modules.append( ETT_NTuple( dim=dim,
do_syst=1,
syst_var='',
severities=severities,
haddFileName="tree_%s.root" % str(options.jobNum)))
for i in modules:
print("modules : ", i)
if(options.infile != ""):
if(options.condor):
filesToRun = [options.infile.split('/')[-1]] ##-- For condor
else:
filesToRun = [options.infile]
else:
filesToRun = []
print("Finding files...")
for root, dirs, files in os.walk(options.inDir, topdown=False):
for name in files:
if(name.endswith(".root")):
# print("found file:",os.path.join(root, name))
filesToRun.append(os.path.join(root, name))
print("Number of input files:",len(filesToRun))
# Create output directory
ol = options.outDir
if(not os.path.isdir(ol)):
print("Output directory '%s' does not exist"%(ol))
print("Creating output directory '%s'..."%(ol))
os.system("mkdir -p %s"%(ol))
##-- Assuming index.php file exists in previous directory for website
print("Copying php index '%s/../index.php' to '%s' (for websites only)..."%(ol, ol))
os.system("cp %s/../index.php %s"%(ol, ol))
for instance in modules:
print("instance:",instance)
dim = int(instance.dim)
output = run_uproot_job(
{"test": filesToRun},
treename=options.treename,
processor_instance=instance,
executor=futures_executor,
executor_args={ 'workers' : 10, ##-- 4510?, 3?
#'retries' : 2,
# 'savemetrics' : True,
},
chunksize=250000
)
# 1d histograms
if(dim == 1):
f = uproot.recreate("ETT_histograms_%s.root" % str(options.jobNum))
for h, hist in output.items():
f[h] = export1d(hist) ##-- for 1d histogram exporting to output root file
print(f'wrote {h} to ETT_histograms_{options.jobNum}.root')
# 2d histograms
elif(dim == 2):
##-- https://coffeateam.github.io/coffea/notebooks/histograms.html
##-- http://github.com/CoffeaTeam/coffea/blob/515877fa55e914ef82f51f686d1a0eaa9f0f71d1/coffea/hist/hist_tools.py#L903
# print("savemetrics:")
# print("output[1]:",output[1])
##-- Binning for different 2d histogram types
twod_plot_labels = ["EnergyVsTimeOccupancy", "EBOcc", "realVsEmu", "emuOverRealvstwrADC", "oneMinusEmuOverRealvstwrADCCourseBinning"]
mapColorDict = {
"EnergyVsTimeOccupancy" : "jet",
"realVsEmu" : "Blues",
"emuOverRealvstwrADC" : "jet",
#"oneMinusEmuOverRealvstwrADC" : "jet",
"oneMinusEmuOverRealvstwrADCCourseBinning" : "jet"
}
binDict = {
# "EnergyVsTimeOccupancy" : [[-50, 50], [1, 256]],
"EnergyVsTimeOccupancy" : [[-50, 50], [0, 35]],
"EBOcc" : [[0, 80], [-18, 18]],
"realVsEmu" : [[0, 256], [0, 256]],
"emuOverRealvstwrADC" : [[1, 256], [0, 1.2]],
#"oneMinusEmuOverRealvstwrADC" : [[1, 256], [0, 1.2]],
"oneMinusEmuOverRealvstwrADCCourseBinning" : [[1, 40], [0, 1.2]]
# "oneMinusEmuOverRealvstwrADCCourseBinning" : [[1, 256], [0, 1.2]]
# "oneMinusEmuOverRealvstwrADCCourseBinning" : [[1, 256], [-1, 1.2]] # 88, 'lo': -1, 'hi': 1.2
# "oneMinusEmuOverRealvstwrADCCourseBinning" : [[1, 256], [-2, 1.2]] # 88, 'lo': -1, 'hi': 1.2
# "oneMinusEmuOverRealvstwrADCCourseBinning" : [[1, 256], [-10, 1.2]] # 88, 'lo': -1, 'hi': 1.2
}
# for h, hist in output[0].items(): ##-- output[0] if savemetrics is on
for h, hist in output.items(): ##-- output[0] if savemetrics is on
print("Saving histogram %s..."%(h))
##-- Get yields
# h_cut_low = hist.integrate("twrADC", slice(1, 32))
# h_cut_low_vals = h_cut_low.values(sumw2=False)[()]
# low_energy_yield = np.sum(h_cut_low_vals)
# h_cut_high = hist.integrate("twrADC", slice(32, 256))
# h_cut_high_vals = h_cut_high.values(sumw2=False)[()]
# high_energy_yield = np.sum(h_cut_high_vals)
# yields = [low_energy_yield, high_energy_yield]
# print("yields",yields)
##-- for emu / real plot, get average of each x slice
# if("emuOverRealvstwrADC" in h):
# totals = []
# sliceValues = []
# # averages = []
# emuOverReal_bins = range(0, 1200, 25)
# emuOverReal_bins = [val/1000. for val in emuOverReal_bins]
# for twrADC_energy in range(1, 256):
# h_slice = hist.integrate("twrADC", slice(twrADC_energy, twrADC_energy + 1))
# h_slice_vals = h_slice.values(sumw2=False)[()]
# # h_slice_sum = np.sum(h_slice_vals)
# print("twrADC:",twrADC_energy)
# print("h_slice_vals:",h_slice_vals)
# sliceValues.append(h_slice_vals)
# print("---> integral:",h_slice_sum)
# average_conts = np.multiply(emuOverReal_bins, h_slice_vals)
# if(np.sum(h_slice_vals) == 0):
# average = -1
# else:
# average = np.average(emuOverReal_bins, weights=h_slice_vals)
# totals.append(h_slice_sum)
# averages.append(average)
# print("totals:",totals)
# print("averages:",averages)
# print("emuOverReal_bins:",emuOverReal_bins)
# print("sliceValues:",sliceValues)
##-- 2d histogram processing
values = hist.values()[()]
xaxis = hist.axes()[0]
histName = "%s_2d"%(h)
#print("values:",values)
##-- Pickle yields and values for plots
##-- Condor
if(options.condor):
# pickle.dump( yields, open( '%s_yields.p'%(h), "wb" )) ##-- per severity, selection
pickle.dump( values, open( '%s_values.p'%(h), "wb" )) # on condor, this then gets transferred from the condor area to your output location
# if("emuOverRealvstwrADC" in h):
# pickle.dump( sliceValues, open( '%s_sliceValues.p'%(h), "wb" ))
##-- Locally
else:
# pickle.dump( yields, open( '%s/%s_yields.p'%(ol, histName), "wb" ))
pickle.dump( values, open( '%s/%s_values.p'%(ol, histName), "wb" ))
for twod_plot_label in twod_plot_labels:
if(twod_plot_label in h):
mapColor = mapColorDict[twod_plot_label]
ax = plot2d(
hist,
xaxis = xaxis,
#norm = LogNorm(vmin=1),
patch_opts = dict(
# cmap = 'jet',
cmap = mapColor,
# vmin = 0
# norm = LogNorm(vmin = 1)
#norm = LogNorm(vmin=0)
norm = LogNorm()
)
)
for twod_plot_label in twod_plot_labels:
if(twod_plot_label in h):
xLims = binDict[twod_plot_label][0]
yLims = binDict[twod_plot_label][1]
xmin, xmax = xLims[0], xLims[1]
ymin, ymax = yLims[0], yLims[1]
ax.set_ylim(ymin, ymax)
ax.set_xlim(xmin, xmax)
# ax.plot([0, 256], [0, 256], linestyle = '-', color = 'black', linewidth = 0.01) ##-- for real vs emu
# ax.hlines([32], xmin = xmin, xmax = xmax, color = 'black')
# ax.set_xscale('log')
fig = ax.figure
##-- Pickle the axis for post-processing changes
pickle.dump( ax, open( '%s/%s.p'%(ol, histName), "wb" ))
# if("emuOverRealvstwrADC" in h):
# pickle.dump( sliceValues, open( '%s/%s_sliceValues.p'%(ol, histName), "wb" ))
##-- Save output plots
fig.savefig('%s/%s.png'%(ol, histName))
fig.savefig('%s/%s.pdf'%(ol, histName))
print("Saved plot %s/%s.png"%(ol, histName))
print("Saved plot %s/%s.pdf"%(ol, histName))
# 'Accent', 'Accent_r', 'Blues', 'Blues_r', 'BrBG', 'BrBG_r', 'BuGn', 'BuGn_r', 'BuPu', 'BuPu_r', 'CMRmap', 'CMRmap_r', 'Dark2', 'Dark2_r', 'GnBu', 'GnBu_r', 'Greens', 'Greens_r', 'Greys', 'Greys_r', 'OrRd', 'OrRd_r', 'Oranges', 'Oranges_r', 'PRGn', 'PRGn_r', 'Paired', 'Paired_r', 'Pastel1', 'Pastel1_r', 'Pastel2', 'Pastel2_r', 'PiYG', 'PiYG_r', 'PuBu', 'PuBuGn', 'PuBuGn_r', 'PuBu_r', 'PuOr', 'PuOr_r', 'PuRd', 'PuRd_r', 'Purples', 'Purples_r', 'RdBu', 'RdBu_r', 'RdGy', 'RdGy_r', 'RdPu', 'RdPu_r', 'RdYlBu', 'RdYlBu_r', 'RdYlGn', 'RdYlGn_r', 'Reds', 'Reds_r', 'Set1', 'Set1_r', 'Set2', 'Set2_r', 'Set3', 'Set3_r', 'Spectral', 'Spectral_r', 'Wistia', 'Wistia_r', 'YlGn', 'YlGnBu', 'YlGnBu_r', 'YlGn_r', 'YlOrBr', 'YlOrBr_r', 'YlOrRd', 'YlOrRd_r', 'afmhot', 'afmhot_r', 'autumn', 'autumn_r', 'binary', 'binary_r', 'bone', 'bone_r', 'brg', 'brg_r', 'bwr', 'bwr_r', 'cividis', 'cividis_r', 'cool', 'cool_r', 'coolwarm', 'coolwarm_r', 'copper', 'copper_r', 'cubehelix', 'cubehelix_r', 'flag', 'flag_r', 'gist_earth', 'gist_earth_r', 'gist_gray', 'gist_gray_r', 'gist_heat', 'gist_heat_r', 'gist_ncar', 'gist_ncar_r', 'gist_rainbow', 'gist_rainbow_r', 'gist_stern', 'gist_stern_r', 'gist_yarg', 'gist_yarg_r', 'gnuplot', 'gnuplot2', 'gnuplot2_r', 'gnuplot_r', 'gray', 'gray_r', 'hot', 'hot_r', 'hsv', 'hsv_r', 'inferno', 'inferno_r', 'jet', 'jet_r', 'magma', 'magma_r', 'nipy_spectral', 'nipy_spectral_r', 'ocean', 'ocean_r', 'pink', 'pink_r', 'plasma', 'plasma_r', 'prism', 'prism_r', 'rainbow', 'rainbow_r', 'seismic', 'seismic_r', 'spring', 'spring_r', 'summer', 'summer_r', 'tab10', 'tab10_r', 'tab20', 'tab20_r', 'tab20b', 'tab20b_r', 'tab20c', 'tab20c_r', 'terrain', 'terrain_r', 'turbo', 'turbo_r', 'twilight', 'twilight_r', 'twilight_shifted', 'twilight_shifted_r', 'viridis', 'viridis_r', 'winter', 'winter_r'