def save(self, dbasename, prefix='', **args):
"""
Save histograms to data base
"""
import KaliCalo.ZipShelve as ZipShelve
dbase = ZipShelve.open(dbasename, **args)
for key in self:
pattern = prefix + 'Lambdas '
dbase[pattern + str(key)] = (key, self[key])
dbase.close()
return len(self)
def save(self, dbasename, prefix='', **args):
"""
Save histograms to data base
"""
import KaliCalo.ZipShelve as ZipShelve
dbase = ZipShelve.open(dbasename, **args)
for key in self:
pattern = prefix + 'Histo '
dbase[pattern + str(key)] = self[key]
dbase.close()
##
return len(self), self.entries()
def read(self, dbasename, prefix='', **args):
"""
Read the histograms from zipped data base
"""
## reset all histos
self._lambdas = {}
##
import KaliCalo.ZipShelve as ZipShelve
dbase = ZipShelve.open(dbasename, 'r', **args)
for key in dbase:
pattern = prefix + 'Lambdas '
if 0 != key.find(pattern): continue
o = dbase[key]
self._lambdas[o[0]] = o[1]
dbase.close()
return len(self)
def updateFromDB(self, dbasenames, prefix='', **args):
"""
Update the histograms from (zipped) data base
"""
if issubclass(type(dbasenames), str):
dbasenames = [dbasenames]
import KaliCalo.ZipShelve as ZipShelve
for dbasename in dbasenames:
dbase = ZipShelve.open(dbasename, 'r', **args)
for key in dbase:
pattern = prefix + 'Histo '
if 0 != key.find(pattern): continue
o = dbase[key]
if not issubclass(type(o), HistosBase): continue
if self._histos.has_key(o.cellID()):
self._histos[o.cellID()] += o
else:
self._histos[o.cellID()] = o
dbase.close()
return len(self), self.entries()
nHistos=60 ## number of histograms per core/machine
)
## save the fitted histograms
print 'SAVE fit_zdb.gz'
if os.path.exists(FittedHistoPath % i):
os.remove(FittedHistoPath % i)
histos.save(FittedHistoPath % i)
## save the obtained coefficients
print 'SAVE lambdas_zdb.gz'
lambdas.save(LambdasPath % i)
lambdas.save(LambdasPath % i)
## Report problematic cells:
dbase = ZipShelve.open(ProblemsPath % i)
## Print out and save the problematic cells
print 'BAD-CELLS : #', len(bad)
dbase['BadCells'] = bad
for b in bad:
print 'BadCell : ', b, histos[b].entries()
key = 'BadCell ' + str(b)
dbase[key] = histos[b]
print 'LOW-CELLS : #', len(low)
dbase['LowCells'] = low
for b in low:
print 'LowCell : ', b, histos[b].entries()
key = 'LowCell ' + str(b)
dbase[key] = histos[b]
print ' Date : %s ' % __date__
print '*'*120
ecal = getCalo()
cp = ecal.cellParams()
cells = []
for p in cp :
cell = p.cellID()
if ecal.valid ( cell ) and not cell.isPin() :
cells.append ( cell )
cells.sort()
import KaliCalo.ZipShelve as ZipShelve
dbase = ZipShelve.open( 'ecal_db.gz' )
dbase ['AllEcalCells'] = cells
dbase.close()
# =============================================================================
# The END
# =============================================================================
def CollectLambdas():
## all the coefficients databases
ToCollect = pt.getlambdamaps()
PrevLams = []
for i in range(1 + MaxIt / PassIt):
if LambdasLocation % i in ToCollect:
ToCollect.remove(LambdasLocation % i)
PrevLams.append(LambdasLocation % i)
ToCollect.sort()
PrevLams.sort()
## total database for all of them
lmap_tot = LambdaMap.LambdaMap()
if PrevLams:
lmap_tot.read(PrevLams[-1])
print PrevLams[-1]
print PrevLams, len(lmap_tot)
for ldb in ToCollect:
print "Adding the coefficients from", ldb
lmap = LambdaMap.LambdaMap()
lmap.read(ldb)
for k in lmap:
if lmap_tot.has_key(k):
lmap_tot[k].append(lmap[k][-1] * lmap_tot[k][-1])
else:
lmap_tot[k].append(lmap[k][-1])
print "Removing", ldb
os.remove(ldb)
## find the last one saved and
## save current as the next one
ih = 1
Pass = 1
while True:
print ih, PassIt
if ih > PassIt / 2:
Pass += 1
ih = 1
if os.path.exists(StoredLambdaPath %
(Pass, ih)): ## look through all the existing files
print StoredLambdaPath % (Pass, ih), 'exists'
ih += 1
continue
else: ## write to the file next to the latest existing
print 'Saving coefficients to ', StoredLambdaPath % (Pass, ih)
lmap_tot.save(StoredLambdaPath % (Pass, ih))
os.system('chmod a+rwx ' + StoredLambdaPath % (Pass, ih))
break
lmap_tot.save(LambdasLocation % Pass)
os.system('chmod a+rwx ' + LambdasLocation % (Pass))
print "Saving coefficients to ", LambdasLocation % Pass
#lmap_tot.save(pt.lambdas_location_it())
## print out the statistics of the coefficients variation
large = lmap_tot.large(0.15)
print '# of >15% ', len(large)
large = lmap_tot.large(0.10)
print '# of >10% ', len(large)
large = lmap_tot.large(0.05)
print '# of > 5% ', len(large)
large = lmap_tot.large(0.02)
print '# of > 2% ', len(large)
large = lmap_tot.large(0.01)
print '# of > 1% ', len(large)
#whole_list = {}
#cf = FakeCells.SameCell()
#from KaliCalo.Cells import CellID
#for kk in RealPrsCoefs.keys():
# aa = CellID(kk[0],kk[1],kk[2],kk[3])
# ic = cf(aa)
# if not lmap_tot.has_key(ic): continue
# whole_list[aa] = lmap_tot.lambdas()[ic]
## create the database for the re-reconstruction
dbase = zs.open(LambdasDBPath % Pass)
dbase['ecal'] = lmap_tot.lambdas()
dbase.close()
os.system('chmod a+rwx ' + LambdasDBPath % Pass)
def CollectLambdas():
## all the coefficients databases
ToCollect = pt.getlambdamaps()
PrevLams = []
for i in range(1+MaxIt/PassIt):
if LambdasLocation%i in ToCollect:
ToCollect.remove(LambdasLocation%i)
PrevLams.append(LambdasLocation%i)
ToCollect.sort()
PrevLams.sort()
## total database for all of them
lmap_tot = LambdaMap.LambdaMap()
if PrevLams: lmap_tot.read(PrevLams[-1]); print PrevLams[-1]
print PrevLams, len(lmap_tot)
for ldb in ToCollect:
print "Adding the coefficients from", ldb
lmap = LambdaMap.LambdaMap()
lmap.read(ldb)
for k in lmap:
if lmap_tot.has_key(k):
lmap_tot[k].append(lmap[k][-1]*lmap_tot[k][-1])
else:
lmap_tot[k].append(lmap[k][-1])
print "Removing", ldb
os.remove(ldb)
## find the last one saved and
## save current as the next one
ih = 1
Pass = 1
while True:
print ih, PassIt
if ih > PassIt/2:
Pass += 1
ih = 1
if os.path.exists(StoredLambdaPath%(Pass,ih)): ## look through all the existing files
print StoredLambdaPath%(Pass,ih), 'exists'
ih += 1
continue
else: ## write to the file next to the latest existing
print 'Saving coefficients to ', StoredLambdaPath%(Pass,ih)
lmap_tot.save(StoredLambdaPath%(Pass,ih))
os.system('chmod a+rwx '+StoredLambdaPath%(Pass,ih))
break
lmap_tot.save(LambdasLocation%Pass)
print "Saving coefficients to ", LambdasLocation%Pass
#lmap_tot.save(pt.lambdas_location_it())
## Print statistics
#print 'BAD-CELLS : #', len ( bad )
#print 'LOW-CELLS : #', len ( low )
#print 'NFIT-CELLS: #', len ( nfit )
## print out the statistics of the coefficients variation
large = lmap_tot.large ( 0.15 )
print '# of >15% ' , len(large)
large = lmap_tot.large ( 0.10 )
print '# of >10% ' , len(large)
large = lmap_tot.large ( 0.05 )
print '# of > 5% ' , len(large)
large = lmap_tot.large ( 0.02 )
print '# of > 2% ' , len(large)
large = lmap_tot.large ( 0.01 )
print '# of > 1% ' , len(large)
## create the database for the re-reconstruction
dbase = zs.open(LambdasDBPath%Pass)
dbase['ecal'] = lmap_tot.lambdas()
dbase.close()
print ' S/B : %.20s %.20s %.20s ' % (
r0[0] / r0[3], r1[0] / r1[3], r2[0] / r2[3]), key
continue
print ' %-25s %9.4g ' % (key, lams[key]), [
' %.3f ' % l for l in lambdas[key]
]
dbase_name = 'Ecal_2k+9-Loose-details-zdb'
histos.save(dbase_name)
lambdas.save(dbase_name)
celldb = shelve.open('cells_db', 'r')
cells = celldb['AllCells']
lams = lambdas.lambdas()
map = buildTable(lams, cells, fakeCell)
prsdbase = ZipShelve.open('prs-2k+9_zdb')
prs = prsdbase['Prs-2k+9']
dbase_r_name = 'Ecal_2k+9-Loose-results-zdb'
dbase = ZipShelve.open(dbase_r_name)
dbase['Ecal'] = map
dbase['Prs'] = prs
dbase['BadCells'] = bad
dbase['LowCells'] = low
# =============================================================================
# The END
# =============================================================================