logger.info('Run info for #run=%d ' % 169064)
logger.info(80 * '*')
rinfo = run_info(169064)
for k in rinfo:
logger.info(" %30s : %-35s " % (k, rinfo[k]))
logger.info(80 * '*')
logger.info('Fill info for #fill=%d ' % 4691)
logger.info(80 * '*')
finfo = fill_info(4691)
for k in finfo:
logger.info(" %30s : %-35s " % (k, finfo[k]))
logger.info(80 * '*')
from Ostap.Utils import timing
runs = [0, 1, 169064, 5, 6, 98241980, 169064, 2334, 2334, 524387]
for run in runs:
with timing():
fill = fill_number(run)
logger.info('Run/Fill#:%12d/%-10d ' % (run, fill))
logger.info(80 * '*')
# =============================================================================
# The END
# =============================================================================
data7 = DataAndLumi('Y/Y', patterns[:2])
data8 = DataAndLumi('Y/Y', patterns[2:])
dataY = DataAndLumi('Y/Y', patterns)
logger.info('DATA@ 7TeV %s' % data7)
logger.info('DATA@ 8TeV %s' % data8)
logger.info('DATA@7+8TeV %s' % dataY)
logger.info('TChain %s' % data7.chain)
h1 = ROOT.TH1D('h1', '', 200, 8.5, 12.5)
h2 = h1.clone()
chain = dataY.chain
with timing('SEQUENTIAL(1M):'):
chain.project(h1, 'mass', 'minann_mu>0.5 && 8.5<=mass && mass<12.5', '',
1000000)
print h1.dump(40, 20)
import Ostap.Kisa
with timing('PARALLEL(%dM):' % int(len(chain) / 1.e+6)):
chain._project(h2, 'mass', 'minann_mu>0.5 && 8.5<=mass && mass<12.5')
print h2.dump(40, 20)
# =============================================================================
# The END
# =============================================================================
model_bw.signal.gamma.release()
result, frame = model_bw.fitTo(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
print result
else:
print 'Signal & Background are: ', result('S')[0], result('B')[0]
##print 'Mean & Gamma are: ', result ( 'mean_Gauss')[0] , result( 'gamma_BW' )[0]
models.append(model_bw)
#
## check that everything is serializable
#
logger.info('Saving all objects into DBASE')
import Ostap.ZipShelve as DBASE
from Ostap.Utils import timing
with timing(), DBASE.tmpdb() as db:
db['mass,vars'] = mass, varset0
db['dataset'] = dataset0
db['models'] = models
db['result'] = result
db['frame'] = frame
db.ls()
# =============================================================================
# The END
# =============================================================================
h2.Fill ( f2.GetRandom() )
h3.Fill ( f3.GetRandom() )
h4.Fill ( f4.GetRandom() )
h5.Fill ( f5.GetRandom() )
h6.Fill ( f6.GetRandom() )
# h1 - decreasing convex
# h2 - increasing convex
# h3 - increasing concave
# h4 - decreasing concave
# h5 - non-monothonic convex
# h6 - non-monothonic concave
with timing ( 'Bershtein' ) :
rB1 = h1.bernstein ( 3 )
rB2 = h2.bernstein ( 3 )
rB3 = h3.bernstein ( 3 )
rB4 = h4.bernstein ( 3 )
rB5 = h5.bernstein ( 3 )
rB6 = h6.bernstein ( 3 )
with timing ( 'Chebyshev' ) :
rC1 = h1.chebyshev ( 3 )
rC2 = h2.chebyshev ( 3 )
rC3 = h3.chebyshev ( 3 )
rC4 = h4.chebyshev ( 3 )
rC5 = h5.chebyshev ( 3 )
rC6 = h6.chebyshev ( 3 )
increasing = Models.Monothonic_pdf('PI', mass4, power=3, increasing=True)
decreasing = Models.Monothonic_pdf('PD', mass4, power=3, increasing=False)
inc_convex = Models.Convex_pdf('PIX',
mass4,
power=3,
increasing=True,
convex=True)
dec_convex = Models.Convex_pdf('PDX',
mass4,
power=3,
increasing=False,
convex=True)
convex = Models.ConvexOnly_pdf('PX', mass4, power=3, convex=True)
concave = Models.ConvexOnly_pdf('PX', mass4, power=3, convex=False)
with timing('Positive 5'), rooSilent():
r5, f = positive.fitTo(dataset5)
with timing('Increasing 5'), rooSilent():
i5, f = increasing.fitTo(dataset5)
with timing('Convex 5'), rooSilent():
x4, f = inc_convex.fitTo(dataset5)
with timing('ConvexOnly 5'), rooSilent():
c4, f = convex.fitTo(dataset5)
## logger.info ( 'Positive pars: %s' % positive .pdf.function().pars() )
## logger.info ( 'Increasing pars: %s' % increasing.pdf.function().pars() )
## logger.info ( 'Convex pars: %s' % inc_convex.pdf.function().pars() )
for i in (positive, increasing, inc_convex, convex):
pars = i.pdf.function().bernstein().pars()
pars = list(pars)
logger.info('ZipShelve keys: %s' % db_zip .keys() )
logger.info('RootShelve keys: %s' % db_root.keys() )
db_sql .close()
db_zip .close()
db_root.close()
logger.info('SQLiteShelve size: %d ' % os.path.getsize( db_sql_name ) )
logger.info('ZipShelve size: %d ' % os.path.getsize( db_zip_name ) )
logger.info('RootShelve size: %d ' % os.path.getsize( db_root_name ) )
db_sql = SQLiteShelve.open ( db_sql_name , 'r' )
db_zip = ZipShelve.open ( db_zip_name , 'r' )
db_root = RootShelve.open ( db_root_name , 'r' )
with timing ( 'h2-read/SQL' ) : h2_sql = db_sql [ 'histo-2D']
with timing ( 'h2_read/ZIP' ) : h2_zip = db_zip [ 'histo-2D']
with timing ( 'h2_read/ROOT' ) : h2_root = db_root [ 'histo-2D']
with timing ( 'tu-read/SQL' ) : tu_sql = db_sql [ 'both' ]
with timing ( 'tu_read/ZIP' ) : tu_zip = db_zip [ 'both' ]
with timing ( 'tu_read/ROOT' ) : tu_root = db_root [ 'both' ]
with timing ( 'h1-read/SQL' ) : h1_sql = db_sql [ 'histo-1D']
with timing ( 'h1-read/ZIP' ) : h1_zip = db_zip [ 'histo-1D']
with timing ( 'h1-read/ROOT' ) : h1_root = db_root [ 'histo-1D']
for i in h1_sql :
v = h1_sql [i] - h1_zip[i]
if not iszero ( v.value() ) :
logger.error('Large difference for 1D histogram(1)!')
v = h1_sql [i] - h1 [i]
if not iszero ( v.value() ) :
data8 = DataAndLumi('Y/Y', patterns[2:])
dataY = DataAndLumi('Y/Y', patterns)
logger.info('DATA@ 7TeV %s' % data7)
logger.info('DATA@ 8TeV %s' % data8)
logger.info('DATA@7+8TeV %s' % dataY)
logger.info('TChain %s' % data7.chain)
variables = {('mass', 'mass(mu+mu-)', 8.5, 11.5, lambda s: s.mass)}
import Ostap.Kisa as Kisa
ppservers = () ## 'lxplus051' , )
one_file = dataY.chain[:1]
with timing('One file %s' % len(one_file)):
ds0 = Kisa.fillDataSet(one_file,
variables,
'8.5<=mass && mass<11.5 && -0.1<c2dtf && c2dtf<5',
ppservers=ppservers)
logger.info('Dataset: %s' % ds0)
with timing('All files %s' % len(dataY.chain)):
dsa = Kisa.fillDataSet(dataY.chain,
variables,
'8.5<=mass && mass<11.5 && -0.1<c2dtf && c2dtf<5',
ppservers=ppservers)
logger.info('Dataset: %s' % dsa)
# =============================================================================