temp_i = 0
if args.signal:
for index, row in df_sig_masses.iterrows():
temp_i += 5
label = '$M_{\mathrm{Squark}}$ = ' + str(
row["M_sq"]) + ', $M_{\mathrm{LSP}}$ = ' + str(row["M_lsp"])
print(label, var)
df_temp = df_sig.loc[(df_sig['M_sq'] == row['M_sq'])
& (df_sig['M_lsp'] == row['M_lsp'])]
df_temp = df_temp.compute()
if args.NMinusOne:
df_temp = df_NMinusOne(df_temp, var, args.region)
else:
df_temp = df_temp[[var, 'weight']]
if df_temp[var].shape[0] > 0:
h = Hist(dict[var]['bin'], weight='weight')
h.fill(df_temp)
df = h.pandas(normalized=args.norm).reset_index()[1:-1]
df[var] = df[var].apply(lambda x: x.left)
plt.hist(df[var],
bins=df[var],
weights=df['count()'],
normed=args.norm,
label=label,
log=True,
histtype="step",
linewidth=linewidth,
zorder=35 - temp_i)
if args.MSSM:
label = 'MSSM-like: $M_{\mathrm{Squark}}$ = ' + str(
import time, socket
class Worker(object):
Columns = ["A.p", "n"]
def run(self, events, job, database):
job.fill(n = events.n)
job.fill(p = events.A.p)
"""
dataset = "Sample"
session = Session("striped_dev.yaml")
h_n = Hist(hbin("n", 20, 0, 20))
h_p = Hist(hbin("p", 20, 0, 20))
job = session.createJob(dataset,
user_params={"param": {
"hello": "world"
}},
worker_class_source=worker_class,
histograms=[h_n, h_p])
job.run()
runtime = job.TFinish - job.TStart
nevents = job.EventsProcessed
print "%s: %.6fM events, %.6fM events/second" % (
dataset, float(nevents) / 1000000, nevents / runtime / 1000000)
print h_n.pandas()
import json
from QWorker import DistributedStripedSession as Session
from QWorker import Histogram
from datasets import Datasets
from histbook import Hist, beside
from histbook import bin
#import vegascope; canvas = vegascope.LocalCanvas()
registry_url = "http://ifdb01.fnal.gov:9867"
data_server_url = "http://dbweb7.fnal.gov:9091/striped/app"
session = Session(data_server_url, registry_url)
muon_e = Hist(bin("sqrt(mu_px**2+mu_py**2+mu_pz**2)", 10, 10.0, 1000.0),
bin("sqrt(mu_e**2-mu_px**2-mu_py**2-mu_pz**2)", 15, 0.1, 0.15))
job = session.createJob(
"Summer16.SMS-T5qqqqZH-mGluino1700_TuneCUETP8M1_13TeV-madgraphMLM-pythia8",
worker_class_file="worker_new.py",
display=False)
job.addHistogram(muon_e, ["mu_e", "mu_px", "mu_py", "mu_pz"])
job.start()
job.waitDone()
print muon_e.pandas()
worker_class = """
import cloudpickle
class Worker(object):
Columns = ["NJets"]
def run(self, events, job):
job.message("%d events" % (len(events),))
x = 5/0
"""
session = Session(("ifdb01.fnal.gov", 8765))
h_by_dataset = Hist(hbin("NJets", 20, 0, 20), groupby("dataset"))
datasets = [
"Summer16.TTHH_TuneCUETP8M2T4_13TeV-madgraph-pythia8" # 100000 events
]
class Callback:
def on_message(self, wid, nevents, message):
print "Message received from worker %d after seeing %d events: <%s>" % (wid, nevents, message)
def on_exception(self, wid, info):
print "Worker %d failed with exception:\n%s" % (wid, info)
callback = Callback()
worker_class = """
class Worker(object):
Columns = ["nJet","nMuon","nElectron","Jet.pt", "Muon.pt"]
def run(self, events, job):
job.fill(nJet=events.nJet)
job.fill(nElectron=events.nElectron)
job.fill(nMuon=events.nMuon)
job.fill(JetPt = events.Jet.pt)
job.fill(MuonPt = events.Muon.pt)
"""
session = Session()
njets = Hist(hbin("nJet", 20, 0, 20))
nmuon = Hist(hbin("nMuon", 20, 0, 20))
nelectron = Hist(hbin("nElectron", 20, 0, 20))
muon_pt = Hist(hbin("MuonPt", 70, 0., 700.))
jet_pt = Hist(hbin("JetPt", 70, 0., 700.))
dataset = "QCD_HT200to300_PUMoriond17_05Feb2018_94X_mcRun2_asymptotic_v2-v1_NANOAODSIM"
#dataset = "JetHT_Run2016H_05Feb2018_ver2-v1_NANOAOD"
class Callback:
def on_exception(self, wid, info):
print "Exception:", info
job = session.createJob(dataset,
import numpy as np
class Worker(object):
Columns = ["NJets"]
def run(self, events, job, db):
data = np.frombuffer(db["calib200"], "<f4")
job.fill(x = data)
job.message("average=%f" % (np.mean(data),))
"""
job_server = ("ifdb02.fnal.gov", 8765)
session = Session(job_server)
h = Hist(hbin("x", 20, 0, 1))
dataset = "Summer16.TTHH_TuneCUETP8M2T4_13TeV-madgraph-pythia8"
job = session.createJob(dataset,
worker_class_source=worker_class,
histograms=[h])
job.run()
runtime = job.TFinish - job.TStart
nevents = job.EventsProcessed
print "%s: %.6fM events, %.6fM events/second" % (
dataset, float(nevents) / 1000000, nevents / runtime / 1000000)
data_frame = h.pandas()
print data_frame