Minimalistic Root file Operation Example: Reading NanoAod and save a histograms in png format

NanoAod_histogramming.py

#Spark Context declaration                                                                                                                                                                          
from pyspark.sql import SparkSession
import pyspark.sql.functions
from histogrammar import *
import histogrammar.sparksql

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

spark = SparkSession.builder \
    .master(
        "spark://10.64.22.66:7077" #specify your master node here
    ) \
    .appName(                                                                                                                                         
             "NanoAod_histogramming" # Name of your application in the dashboard/UI
            ) \
    .config(# Tell Spark to load some extra libraries from Maven (the Java repository)                                                                                                              
            'spark.jars.packages',
            'org.diana-hep:spark-root_2.11:0.1.16,org.diana-hep:histogrammar-sparksql_2.11:1.0.4',
            ) \
    .config('spark.cores.max',3
            ) \
    .getOrCreate()
                                                                                                                                                                               
#read NanoAod from hdfs and define a dataframe                                                                                                                                                      
df = spark.read.format("org.dianahep.sparkroot").load("hdfs:///user/shoh/SingleMuonRun2016H-03Feb2017_ver2-v1_NANO.root")
#trim the dataframe into interesting variable                                                                                                                                                       
Event1 = df.select("RawMET_pt","Electron_pt","nElectron")
#Event selection, MET>=150 GeV; nElectron=1; ElectronPt>=30 GeV                                                                                                                                                                                    
Event1.filter(Event1.RawMET_pt>=150).filter(Event1.nElectron==1).filter(Event1.Electron_pt[0]>=30)
#declaration of histogram                                                                                                                                                                           
histogrammar.sparksql.addMethods(Event1)
histogrammar.sparksql.addMethods(df)
hist = df.Bin(100, 0, 100, Event1['RawMET_pt'])                                                                                                                                                                         
hist.plot.matplotlib(name="test")                                                                                                                                                                                   
plt.show()
plt.savefig('nanoRawMET_pt.png')                              

Execute the script with

python NanoAod_histogramming.py

NanoAod was processed by following the recipe (80X) here (https://twiki.cern.ch/twiki/bin/view/CMSPublic/WorkBookNanoAOD)

The NanoAod data structure is documented here (https://test-cms-nanoaod-integration.web.cern.ch/integration/master/mc80X_doc.html)

Bump up spark-root from 0.1.13 to 0.1.16. Tested