def _formatElement(self, obj):
"""
Format data for a Element object.
:param obj: A Element object to be printed.
"""
elDic = {}
elDic["ID"] = obj.elID
elDic["Particles"] = str(obj.getParticles())
elDic["Masses (GeV)"] = [[m.asNumber(GeV) for m in br] for br in obj.getMasses()]
elDic["PIDs"] = obj.getPIDs()
elDic["Weights (fb)"] = {}
elDic["final states"] = obj.getFinalStates()
sqrts = [info.sqrts.asNumber(TeV) for info in obj.weight.getInfo()]
allsqrts = sorted(list(set(sqrts)))
for ssqrts in allsqrts:
sqrts = ssqrts * TeV
xsecs = [xsec.value.asNumber(fb) for xsec in obj.weight.getXsecsFor(sqrts)]
if len(xsecs) != 1:
logger.warning("Element cross sections contain multiple values for %s .\
Only the first cross section will be printed" %str(sqrts))
xsecs = xsecs[0]
sqrtsStr = 'xsec '+str(sqrts.asNumber(TeV))+' TeV'
elDic["Weights (fb)"][sqrtsStr] = xsecs
return elDic
def equalObjs(obj1,obj2,allowedDiff,ignore=[], where=None ):
"""
Compare two objects.
The numerical values are compared up to the precision defined by allowedDiff.
:param obj1: First python object to be compared
:param obj2: Second python object to be compared
:param allowedDiff: Allowed % difference between two numerical values
:param ignore: List of keys to be ignored
:param where: keep track of where we are, for easier debugging.
:return: True/False
"""
if type(obj1) in [ float, int ] and type ( obj2) in [ float, int ]:
obj1,obj2=float(obj1),float(obj2)
if type(obj1) != type(obj2):
logger.warning("Data types differ (%s,%s)" %(type(obj1),type(obj2)))
return False
if isinstance(obj1,unum.Unum):
if obj1 == obj2:
return True
diff = 2.*abs(obj1-obj2)/abs(obj1+obj2)
return diff.asNumber() < allowedDiff
elif isinstance(obj1,float):
if obj1 == obj2:
return True
diff = 2.*abs(obj1-obj2)/abs(obj1+obj2)
return diff < allowedDiff
elif isinstance(obj1,str):
return obj1 == obj2
elif isinstance(obj1,dict):
for key in obj1:
if key in ignore: continue
if not key in obj2:
logger.warning("Key %s missing" %key)
return False
if not equalObjs(obj1[key],obj2[key],allowedDiff, ignore=ignore, where=key ):
logger.warning('Objects differ in %s:\n %s\n and\n %s' %(where, str(obj1[key]),str(obj2[key])))
#s1,s2 = str(obj1[key]),str(obj2[key])
#if False: # len(s1) + len(s2) > 200:
# logger.warning ( "The values are too long to print." )
#else:
# logger.warning( 'The values are: >>%s<< (this run) versus >>%s<< (default)'%\
# ( s1[:20],s2[:20] ) )
return False
elif isinstance(obj1,list):
if len(obj1) != len(obj2):
logger.warning('Lists differ in length:\n %i (this run)\n and\n %i (default)' %\
(len(obj1),len(obj2)))
return False
for ival,val in enumerate(obj1):
if not equalObjs(val,obj2[ival],allowedDiff):
logger.warning('Lists differ:\n %s (this run)\n and\n %s (default)' %\
(str(val),str(obj2[ival])))
return False
else:
return obj1 == obj2
return True
def getEfficiencyFor(self,expid,dataset,txname,massarray):
"""
Get an efficiency for the given experimental id,
the dataset name, the txname, and the massarray.
Can only be used for EfficiencyMap-type experimental results.
Interpolation is done, if necessary.
:param expid: experimental id (string)
:param dataset: dataset name (string)
:param txname: txname (string).
:param massarray: list of masses with units, e.g.
[[ 400.*GeV, 100.*GeV],[400.*GeV, 100.*GeV]]
:return: efficiency
"""
#First select the experimental results matching the id and the result type:
expres = None
for expResult in self:
if expResult.getValuesFor('id')[0] != expid:
continue
else:
if 'efficiencyMap' in expResult.getValuesFor('dataType'):
expres = expResult
break
if not expres:
logger.warning( "Could not find efficiencyMap result %s."\
" getEfficiencyForr can only be\
used for efficiencyMap results." % (expid))
return None
return expres.getEfficiencyFor(txname=txname, mass=massarray, dataset=dataset)
def getULForSR(self,expid,datasetID):
"""
Get an upper limit for the given experimental id and dataset (signal region).
Can only be used for efficiency-map results.
:param expid: experimental id (string)
:param datasetID: string defining the dataset id, e.g. ANA5-CUT3.
:return: upper limit [fb]
"""
#First select the experimental results matching the id and the result type:
expres = None
for expResult in self:
if expResult.getValuesFor('id')[0] != expid:
continue
else:
if 'efficiencyMap' in expResult.getValuesFor('dataType'):
expres = expResult
break
if not expres:
logger.warning ("Could not find efficiency-map result %s . getULForSR can only be\
used for efficiency-map results." % (expid))
return None
for dataset in expres.datasets:
if dataset.getID() != datasetID:
continue
return dataset.getSRUpperLimit()
logger.warning ( "Could not find dataset %s ." % (datasetID))
return None
def _combineElements(elements, dataset, maxDist):
"""
Combine elements according to the data set type.
If expResult == upper limit type, first group elements with different TxNames
and then into mass clusters.
If expResult == efficiency map type, group all elements into a single cluster.
:parameter elements: list of elements (Element objects)
:parameter expResult: Data Set to be considered (DataSet object)
:returns: list of element clusters (ElementCluster objects)
"""
clusters = []
if dataset.getType() == 'efficiencyMap':
cluster = clusterTools.groupAll(elements)
clusters.append(cluster)
elif dataset.getType() == 'upperLimit':
txnames = list(set([el.txname for el in elements]))
for txname in txnames:
txnameEls = []
for element in elements:
if not element.txname == txname:
continue
else: txnameEls.append(element)
txnameClusters = clusterTools.clusterElements(txnameEls, maxDist)
clusters += txnameClusters
else:
logger.warning("Unkown data type: %s. Data will be ignored."
% dataset.getType())
return clusters
def __add__(self,other):
newList = self.copy()
if type(other) != type(self):
logger.warning("Trying to add a XSectionList and a "+str(type(other)))
return self
newList.combineWith(other)
return newList
def __init__(self, base=None, force_load = None, discard_zeroes = True,
progressbar = False, subpickle = True):
"""
:param base: path to the database, or pickle file (string), or http
address. If None, "official", or "official_fastlim",
use the official database (including fastlim results, if specified).
:param force_load: force loading the text database ("txt"),
or binary database ("pcl"), dont force anything if None
:param discard_zeroes: discard txnames with only zeroes as entries.
:param progressbar: show a progressbar when building pickle file
(needs the python-progressbar module)
:param subpickle: produce small pickle files per exp result.
Should only be used when working on the database.
"""
self.source=""
self.force_load = force_load
self.subpickle = subpickle
if base in [ None, "official" ]:
from smodels.installation import officialDatabase
base = officialDatabase()
if base in [ "official_fastlim" ]:
from smodels.installation import officialDatabase
base = officialDatabase( fastlim=True )
if base in [ "unittest" ]:
from smodels.installation import testDatabase
base = testDatabase()
base, pclfile = self.checkPathName(base, discard_zeroes )
self.pcl_meta = Meta( pclfile )
self.expResultList = []
self.txt_meta = self.pcl_meta
if not self.force_load == "pcl":
self.txt_meta = Meta ( base, discard_zeroes = discard_zeroes )
self.progressbar = None
if progressbar:
try:
import progressbar as P
self.progressbar = P.ProgressBar( widgets=
[ "Building Database ", P.Percentage(),
P.Bar( marker=P.RotatingMarker() ), P.ETA() ] )
except ImportError as e:
logger.warning ( "progressbar requested, but python-progressbar is not installed." )
if self.force_load=="txt":
self.loadTextDatabase()
self.txt_meta.printFastlimBanner()
return
if self.force_load=="pcl":
self.loadBinaryFile()
self.pcl_meta.printFastlimBanner()
return
if self.force_load in [ None, "none", "None" ]:
self.loadDatabase()
self.txt_meta.printFastlimBanner()
return
logger.error ( "when initialising database: force_load=%s is not " \
"recognized. Valid values are: pcl, txt, None." % force_load )
sys.exit()
def _getDictionariesFromSLHA(slhafile):
"""
Create mass and BR dictionaries from an SLHA file.
Ignore decay blocks with R-parity violating or unknown decays
"""
from smodels.particlesLoader import rEven, rOdd
res = pyslha.readSLHAFile(slhafile)
# Get mass and branching ratios for all particles
brDic = {}
writeIgnoreMessage(res.decays.keys(), rEven, rOdd)
for pid in res.decays.keys():
if not pid in rOdd:
continue
brs = []
for decay in res.decays[pid].decays:
nEven = nOdd = 0.
for pidd in decay.ids:
if pidd in rOdd: nOdd += 1
elif pidd in rEven: nEven += 1
else:
logger.warning("Particle %i not defined in particles.py,decay %i -> [%s] will be ignored" %(pidd,pid,decay.ids))
break
if nOdd + nEven == len(decay.ids) and nOdd == 1:
brs.append(decay)
else:
logger.info("Ignoring decay: %i -> [%s]",pid,decay.ids)
brsConj = copy.deepcopy(brs)
for br in brsConj:
br.ids = [-x for x in br.ids]
brDic[pid] = brs
brDic[-pid] = brsConj
# Get mass list for all particles
massDic = dict(res.blocks['MASS'].items())
for pid in list ( massDic.keys() )[:]:
massDic[pid] = round(abs(massDic[pid]),1)*GeV
if not -pid in massDic: massDic[-pid] = massDic[pid]
#Include proxy for displaced decays
if 0 in massDic or 0 in brDic:
logger.error("PDG = 0 is reserved for displaced decays and it can not be used for other particles. Please redefine the input model PDG assignments.")
raise SModelSError()
else:
dispPid = 0
massDic[dispPid] = 0. * GeV
dispDec = pyslha.Decay(br=1., ids=[], nda=0)
brDic[dispPid] = [dispDec]
return brDic, massDic
def hasOnlyZeroes ( self ):
ozs = self.txnameData.onlyZeroValues()
if self.txnameDataExp:
e_ozs = self.txnameDataExp.onlyZeroValues()
if ozs and e_ozs:
return True
if (ozs and not e_ozs) or (e_ozs and not ozs):
logger.warning ( "%s is weird. One of the (expected, observed) results is zeroes-only, the other one isnt." )
return False
return ozs
def writeIgnoreMessage(keys, rEven, rOdd):
msg = ""
for pid in keys:
if not pid in list(rEven) + list(rOdd):
logger.warning("Particle %i not defined in particles.py, its decays will be ignored" %(pid))
continue
if pid in rEven:
msg += "%s, " % rEven[pid]
continue
if len(msg)>0:
logger.debug ( "Ignoring %s decays" % msg[:-2] )
def __init__(self, output, filename):
self.name = "basic"
self.outputList = []
self.filename = filename
self.output = output
self.printingOrder = []
self.toPrint = []
if filename and os.path.isfile(filename):
logger.warning("Removing file %s" %filename)
os.remove(filename)
def loadBinaryFile ( self, lastm_only = False ):
"""
Load a binary database, returning last modified, file count, database.
:param lastm_only: if true, the database itself is not read.
:returns: database object, or None, if lastm_only == True.
"""
if lastm_only and self.pcl_meta.mtime:
## doesnt need to load database, and mtime is already
## loaded
return None
if not os.path.exists ( self.pcl_meta.pathname ):
return None
try:
with open ( self.pcl_meta.pathname, "rb" ) as f:
t0=time.time()
pclfilename = self.pcl_meta.pathname
self.pcl_meta = serializer.load ( f )
self.pcl_meta.pathname = pclfilename
if self.force_load == "pcl":
self.txt_meta = self.pcl_meta
if not lastm_only:
if not self.force_load == "pcl" and self.pcl_meta.needsUpdate ( self.txt_meta ):
logger.warning ( "Something changed in the environment."
"Regenerating." )
self.createBinaryFile()
return self
logger.info ( "loading binary db file %s format version %s" %
( self.pcl_meta.pathname, self.pcl_meta.format_version ) )
if sys.version[0]=="2":
self.expResultList = serializer.load ( f )
else:
self.expResultList = serializer.load ( f, encoding="latin1" )
t1=time.time()-t0
logger.info ( "Loaded database from %s in %.1f secs." % \
( self.pcl_meta.pathname, t1 ) )
except (EOFError,ValueError) as e:
os.unlink ( self.pcl_meta.pathname )
if lastm_only:
self.pcl_meta.format_version = -1
self.pcl_meta.mtime = 0
return self
logger.error ( "%s is not readable (%s)." % \
( self.pcl_meta.pathname, str(e) ) )
if self.source in [ "http", "ftp", "pcl" ]:
logger.error ( "source cannot be rebuilt. supply a different path to the database in your ini file." )
sys.exit()
self.createBinaryFile()
# self.txt_meta = self.pcl_meta
return self
def elementFromEvent(event, weight=None):
"""
Creates an element from a LHE event and the corresponding event weight.
:param event: LHE event
:param weight: event weight. Must be a XSectionList object (usually with a
single entry) or None if not specified.
:returns: element
"""
if not event.particles:
logger.error("Empty event")
return None
brDic, massDic = _getDictionariesFromEvent(event)
# Create branch list
finalBranchList = []
from smodels.particlesLoader import rOdd, rEven
for ip, particle in enumerate(event.particles):
keys = list ( rEven.keys() ) + \
list ( rOdd.keys() )
if not particle.pdg in keys:
logger.warning("Particle %i not defined in particles.py, events containing this particle will be ignored" %(particle.pdg))
return None
# Particle came from initial state (primary mother)
if 1 in particle.moms:
mombranch = branch.Branch()
mombranch.PIDs = [[particle.pdg]]
if weight:
mombranch.maxWeight = weight.getMaxXsec()
else:
mombranch.maxWeight = 0.*fb
# Get simple BR and Mass dictionaries for the corresponding branch
branchBR = brDic[ip]
branchMass = massDic[ip]
mombranch.masses = [branchMass[mombranch.PIDs[0][0]]]
# Generate final branches (after all R-odd particles have decayed)
finalBranchList += branch.decayBranches([mombranch], branchBR,
branchMass, sigcut=0. * fb )
if len(finalBranchList) != 2:
logger.error(str(len(finalBranchList)) + " branches found in event; "
"Possible R-parity violation")
raise SModelSError()
# Create element from event
newElement = element.Element(finalBranchList)
if weight:
newElement.weight = copy.deepcopy(weight)
return newElement
def setOutPutFile(self,filename,overwrite=True,silent=False):
"""
Set the basename for the text printer. The output filename will be
filename.xml.
:param filename: Base filename
:param overwrite: If True and the file already exists, it will be removed.
:param silent: dont comment removing old files
"""
self.filename = filename +'.xml'
if overwrite and os.path.isfile(self.filename):
if not silent:
logger.warning("Removing old output file " + self.filename)
os.remove(self.filename)
def getXsecFromSLHAFile(slhafile, useXSecs=None, xsecUnit = pb):
"""
Obtain cross sections for pair production of R-odd particles from input SLHA file.
The default unit for cross section is pb.
:parameter slhafile: SLHA input file with cross sections
:parameter useXSecs: if defined enables the user to select cross sections to
use. Must be a XSecInfoList object
:parameter xsecUnit: cross section unit in the input file (must be a Unum unit)
:returns: a XSectionList object
"""
# Store information about all cross sections in the SLHA file
xSecsInFile = XSectionList()
f=pyslha.readSLHAFile ( slhafile )
from smodels.particlesLoader import rOdd
for production in f.xsections:
rEvenParticles = list(set(production[2:]).difference(set(rOdd.keys())))
if rEvenParticles:
# ignore production of R-Even Particles
logger.warning("Particles %s not defined as R-odd, cross section for %s production will be ignored"
%(rEvenParticles,str(production)))
continue
process = f.xsections.get ( production )
for pxsec in process.xsecs:
csOrder = pxsec.qcd_order
wlabel = str( int ( pxsec.sqrts / 1000) ) + ' TeV'
if csOrder == 0:
wlabel += ' (LO)'
elif csOrder == 1:
wlabel += ' (NLO)'
elif csOrder == 2:
wlabel += ' (NLL)'
else:
logger.error ( "Unknown QCD order %d" % csOrder )
raise SModelSError()
xsec = XSection()
xsec.info.sqrts = pxsec.sqrts/1000. * TeV
xsec.info.order = csOrder
xsec.info.label = wlabel
xsec.value = pxsec.value * pb
xsec.pid = production[2:]
# Do not add xsecs which do not match the user required ones:
if (useXSecs and not xsec.info in useXSecs):
continue
else: xSecsInFile.add(xsec)
return xSecsInFile
def fetchFromServer(self, path, discard_zeroes):
import requests, time, json
logger.debug("need to fetch from server: %s" % path)
self.source = "http"
if "ftp://" in path:
self.source = "ftp"
store = "." + path.replace(":", "_").replace("/", "_").replace(
".", "_")
if not os.path.isfile(store):
## completely new! fetch the description and the db!
return self.fetchFromScratch(path, store, discard_zeroes)
with open(store, "r") as f:
jsn = json.load(f)
filename = "./" + jsn["url"].split("/")[-1]
class _: ## pseudo class for pseudo requests
def __init__(self):
self.status_code = -1
r = _()
try:
r = requests.get(path)
except Exception:
pass
if r.status_code != 200:
logger.warning ( "Error %d: could not fetch %s from server." % \
( r.status_code, path ) )
if not os.path.isfile(filename):
logger.error(
"Cant find a local copy of the pickle file. Exit.")
sys.exit()
logger.warning(
"I do however have a local copy of the file. I work with that."
)
self.force_load = "pcl"
# next step: check the timestamps
return ("./", filename)
if r.json()["lastchanged"] > jsn["lastchanged"]:
## has changed! redownload everything!
return self.fetchFromScratch(path, store, discard_zeroes)
if not os.path.isfile(filename):
return self.fetchFromScratch(path, store, discard_zeroes)
self.force_load = "pcl"
# next step: check the timestamps
return ("./", filename)
def _addDecay(self, br, massDictionary):
"""
Generate a new branch adding a 1-step cascade decay
This is described by the br object, with particle masses given by
massDictionary.
:parameter br: branching ratio object (see pyslha). Contains information about the decay.
:parameter massDictionary: dictionary containing the masses for all intermediate states.
:returns: extended branch (Branch object). False if there was an error.
"""
from smodels.particlesLoader import rEven
newBranch = self.copy()
newparticles = []
newmass = []
if len(self.PIDs) != 1:
logger.error("During decay the branch should \
not have multiple PID lists!")
return False
for partID in br.ids:
# Add R-even particles to final state
if partID in rEven:
newparticles.append(rEven[partID])
else:
# Add masses of non R-even particles to mass vector
newmass.append(massDictionary[partID])
newBranch.PIDs[0].append(partID)
if len(newmass) > 1:
logger.warning("Multiple R-odd particles in the final state: " +
str(br.ids))
return False
if newparticles:
newBranch.particles.append(sorted(newparticles))
if newmass:
newBranch.masses.append(newmass[0])
if not self.maxWeight is None:
newBranch.maxWeight = self.maxWeight * br.br
#If there are no daughters, assume branch is stable
if not br.ids:
newBranch.stable = True
return newBranch
def get_slha_data(slhaFile):
"""
Uses pyslha to read the SLHA file. Return a pyslha.Doc objec, if successful.
"""
if not os.path.isfile(slhaFile):
logger.warning("SLHA file %s not found. This point will be ignored" %
slhaFile)
return False
try:
slhaData = pyslha.readSLHAFile(slhaFile)
except:
logger.warning("Error reading SLHA file %s." % slhaFile)
return False
return slhaData
def _loadExpResults(self):
"""
Checks the database folder and generates a list of ExpResult objects for
each (globalInfo.txt,sms.py) pair.
:returns: list of ExpResult objects
"""
#Try to load particles from databaseParticles.py
self._setParticles(self._getParticles())
folders = []
#for root, _, files in os.walk(self.txt_meta.pathname):
# for root, _, files in cleanWalk(self._base):
for root, _, files in cleanWalk(self.txt_meta.pathname):
folders.append((root, files))
folders.sort()
roots = []
for root, files in folders:
if "/.git/" in root:
continue
if root[-11:] == "/validation":
continue
if root[-5:] == "/orig":
continue
if not 'globalInfo.txt' in files:
continue
else:
roots.append(root)
if self.progressbar:
self.progressbar.maxval = len(roots)
self.progressbar.start()
resultsList = []
for ctr, root in enumerate(roots):
if self.progressbar:
self.progressbar.update(ctr)
expres = self.createExpResult(root)
if expres:
resultsList.append(expres)
if not resultsList:
logger.warning("Zero results loaded.")
if self.progressbar:
self.progressbar.finish()
return resultsList
def _loadExpResults(self):
"""
Checks the database folder and generates a list of ExpResult objects for
each (globalInfo.txt,sms.py) pair.
:returns: list of ExpResult objects
"""
folders=[]
#for root, _, files in os.walk(self.txt_meta.pathname):
# for root, _, files in cleanWalk(self._base):
for root, _, files in cleanWalk(self.txt_meta.pathname):
folders.append ( (root, files) )
folders.sort()
roots = []
for root,files in folders:
if "/.git/" in root:
continue
if root[-11:] == "/validation":
continue
if root[-5:] == "/orig":
continue
if not 'globalInfo.txt' in files:
continue
else:
roots.append ( root )
if self.progressbar:
self.progressbar.maxval = len ( roots )
self.progressbar.start()
resultsList = []
for ctr,root in enumerate(roots):
if self.progressbar:
self.progressbar.update(ctr)
expres = self.createExpResult ( root )
if expres:
resultsList.append(expres)
if not resultsList:
logger.warning("Zero results loaded.")
if self.progressbar:
self.progressbar.finish()
return resultsList
def setPrinterOptions(self, parser):
"""
Define the printer types and their options.
:param parser: ConfigParser storing information from the parameters file
"""
#Define the printer types and the printer-specific options:
printerTypes = parser.get("printer", "outputType").split(",")
for prt in printerTypes:
prt = prt.strip() ## trailing spaces shouldnt matter
if prt == 'python':
newPrinter = PyPrinter(output='file')
elif prt == 'summary':
newPrinter = SummaryPrinter(output='file')
elif prt == 'stdout':
newPrinter = TxTPrinter(output='stdout')
elif prt == 'log':
newPrinter = TxTPrinter(output='file')
elif prt == 'xml':
newPrinter = XmlPrinter(output='file')
elif prt == 'slha':
newPrinter = SLHAPrinter(output='file')
if parser.getboolean("options",
"doCompress") or parser.getboolean(
"options", "doInvisible"):
newPrinter.docompress = 1
else:
logger.warning("Unknown printer format: %s" % str(prt))
continue
#Copy stdout options to log options:
if 'log' in printerTypes:
if parser.has_section(
'stdout-printer'
) and not parser.has_section('log-printer'):
parser.add_section('log-printer')
for option, val in parser.items('stdout-printer'):
parser.set('log-printer', option, val)
#Set printer-specific options:
if parser.has_section(prt + '-printer'):
newPrinter.setOptions(parser.items(prt + '-printer'))
self.Printers[prt] = newPrinter
def getValuesFor(self, attribute=None):
"""
Returns a list for the possible values appearing in the ExpResult
for the required attribute (sqrts,id,constraint,...).
If there is a single value, returns the value itself.
:param attribute: name of a field in the database (string). If not
defined it will return a dictionary with all fields
and their respective values
:return: list of values or value
"""
fieldDict = list(self.__dict__.items())
valuesDict = {}
while fieldDict:
for field, value in fieldDict:
if not '<smodels.experiment' in str(value):
if not field in valuesDict:
valuesDict[field] = [value]
else:
valuesDict[field].append(value)
else:
if isinstance(value, list):
for entry in value:
fieldDict += entry.__dict__.items()
else:
fieldDict += value.__dict__.items()
fieldDict.remove((field, value))
# Try to keep only the set of unique values
for key, val in valuesDict.items():
try:
valuesDict[key] = list(set(val))
except TypeError:
pass
if not attribute:
return valuesDict
elif not attribute in valuesDict.keys():
logger.warning("Could not find field %s in %s", attribute,
self.path)
return False
else:
return valuesDict[attribute]
def getValuesFor(self,attribute=None,expResult=None):
"""
Returns a list for the possible values appearing in the database
for the required attribute (sqrts,id,constraint,...).
:param attribute: name of a field in the database (string). If not defined
it will return a dictionary with all fields and their respective
values
:param expResult: if defined, restricts the list to the corresponding expResult.
Must be an ExpResult object.
:return: list of values
"""
fieldDict = []
if expResult and isinstance(expResult,ExpResult):
fieldDict = list(expResult.__dict__.items()) #Use only the entries for the expResult
else:
for expResult in self:
fieldDict += list(expResult.__dict__.items()) #Use all entries/expResults
valuesDict = {}
while fieldDict:
for field,value in fieldDict[:]:
if not '<smodels.experiment' in str(value):
if not field in valuesDict: valuesDict[field] = [value]
else: valuesDict[field].append(value)
else:
if isinstance(value,list):
for entry in value: fieldDict += list(entry.__dict__.items())
else: fieldDict += list(value.__dict__.items())
fieldDict.remove((field,value))
#Try to keep only the set of unique values
for key,val in valuesDict.items():
try: valuesDict[key] = list(set(val))
except TypeError: pass
if not attribute: return valuesDict
elif not attribute in valuesDict:
logger.warning("Could not find field %s in database" % attribute)
return False
else:
return valuesDict[attribute]
def getULFor(self, expid, txname, massarray, expected=False):
"""
Get an upper limit for the given experimental id, the txname,
and the massarray.
Can only be used for UL experimental results.
Interpolation is done, if necessary.
:param expid: experimental id (string)
:param txname: txname (string). ONLY required for upper limit results
:param massarray: list of masses with units, e.g.
[[ 400.*GeV, 100.*GeV],[400.*GeV, 100.*GeV]]
:param expected: If true, return expected upper limit, otherwise
return observed upper limit.
:return: upper limit [fb]
"""
#First select the experimental results matching the id and the result type:
expres = None
for expResult in self:
if expResult.globalInfo.id != expid:
continue
else:
if 'upperLimit' in [
ds.dataInfo.dataType for ds in expResult.datasets
]:
expres = expResult
break
if not expres:
logger.warning("Could not find UL result %s. getULFor can only be\
used for upper-limit results." % (expid))
return None
txnames = expres.getTxNames()
for tx in txnames:
if not tx.txName == txname:
continue
return tx.getULFor(massarray, expected)
logger.warning("Could not find TxName %s ." % (txname))
return None
def fetchFromServer ( self, path, discard_zeroes ):
import requests, time, json
logger.debug ( "need to fetch from server: %s" % path )
self.source = "http"
if "ftp://" in path:
self.source = "ftp"
store = "." + path.replace ( ":","_" ).replace( "/", "_" ).replace(".","_" )
if not os.path.isfile ( store ):
## completely new! fetch the description and the db!
return self.fetchFromScratch ( path, store, discard_zeroes )
with open(store,"r") as f:
jsn = json.load(f)
filename= "./" + jsn["url"].split("/")[-1]
class _: ## pseudo class for pseudo requests
def __init__ ( self ): self.status_code = -1
r=_()
try:
r = requests.get( path )
except Exception:
pass
if r.status_code != 200:
logger.warning ( "Error %d: could not fetch %s from server." % \
( r.status_code, path ) )
if not os.path.isfile ( filename ):
logger.error ( "Cant find a local copy of the pickle file. Exit." )
sys.exit()
logger.warning ( "I do however have a local copy of the file. I work with that." )
self.force_load = "pcl"
# next step: check the timestamps
return ( "./", filename )
if r.json()["lastchanged"] > jsn["lastchanged"]:
## has changed! redownload everything!
return self.fetchFromScratch ( path, store, discard_zeroes )
if not os.path.isfile ( filename ):
return self.fetchFromScratch ( path, store, discard_zeroes )
self.force_load = "pcl"
# next step: check the timestamps
return ( "./", filename )
def getValuesFor(self, attribute=None):
"""
Returns a list for the possible values appearing in the ExpResult
for the required attribute (sqrts,id,constraint,...).
If there is a single value, returns the value itself.
:param attribute: name of a field in the database (string). If not
defined it will return a dictionary with all fields
and their respective values
:return: list of values or value
"""
fieldDict = list ( self.__dict__.items() )
valuesDict = {}
while fieldDict:
for field, value in fieldDict:
if not '<smodels.experiment' in str(value):
if not field in valuesDict:
valuesDict[field] = [value]
else: valuesDict[field].append(value)
else:
if isinstance(value, list):
for entry in value:
fieldDict += entry.__dict__.items()
else: fieldDict += value.__dict__.items()
fieldDict.remove((field, value))
# Try to keep only the set of unique values
for key, val in valuesDict.items():
try:
valuesDict[key] = list(set(val))
except TypeError:
pass
if not attribute:
return valuesDict
elif not attribute in valuesDict.keys():
logger.warning("Could not find field %s in %s", attribute, self.path)
return False
else:
return valuesDict[attribute]
def setPrinterOptions(self,parser):
"""
Define the printer types and their options.
:param parser: ConfigParser storing information from the parameters file
"""
#Define the printer types and the printer-specific options:
printerTypes = parser.get("printer", "outputType").split(",")
for prt in printerTypes:
prt = prt.strip() ## trailing spaces shouldnt matter
if prt == 'python':
newPrinter = PyPrinter(output = 'file')
elif prt == 'summary':
newPrinter = SummaryPrinter(output = 'file')
elif prt == 'stdout':
newPrinter = TxTPrinter(output = 'stdout')
elif prt == 'log':
newPrinter = TxTPrinter(output = 'file')
elif prt == 'xml':
newPrinter = XmlPrinter(output = 'file')
elif prt == 'slha':
newPrinter = SLHAPrinter(output = 'file')
if parser.getboolean("options", "doCompress") or parser.getboolean("options", "doInvisible"):
newPrinter.docompress = 1
else:
logger.warning("Unknown printer format: %s" %str(prt))
continue
#Copy stdout options to log options:
if 'log' in printerTypes:
if parser.has_section('stdout-printer') and not parser.has_section('log-printer'):
parser.add_section('log-printer')
for option,val in parser.items('stdout-printer'):
parser.set('log-printer',option,val)
#Set printer-specific options:
if parser.has_section(prt+'-printer'):
newPrinter.setOptions(parser.items(prt+'-printer'))
self.Printers[prt] = newPrinter
def _loadExpResults(self):
"""
Checks the database folder and generates a list of ExpResult objects for
each (globalInfo.txt,sms.py) pair.
:returns: list of ExpResult objects
"""
folders = []
for root, _, files in os.walk(self._base):
folders.append((root, files))
folders.sort()
roots = []
for root, files in folders:
if "/.git/" in root:
continue
if root[-11:] == "/validation":
continue
if root[-5:] == "/orig":
continue
if not 'globalInfo.txt' in files:
# logger.debug("Missing globalInfo.txt in %s", root)
continue
else:
roots.append(root)
resultsList = []
for root in roots:
expres = ExpResult(root)
if expres:
resultsList.append(expres)
contact = expres.globalInfo.getInfo("contact")
if contact and "fastlim" in contact.lower():
self.hasFastLim = True
if not resultsList:
logger.warning("Zero results loaded.")
return resultsList
def visible(self, pid, decay=None):
"""
Check if pid is detectable.
If pid is not known, consider it as visible.
If pid not SM particle and decay = True, check if particle or decay products are visible.
"""
if pid in SMvisible: return True
if pid in SMinvisible: return False
qn = Qnumbers(pid)
if qn.pid == 0:
return True
if qn.charge3 != 0 or qn.cdim != 1:
return True
if decay:
if not pid in self.slha.decays:
logger.warning("Missing decay block for pid %s" % (str(pid)))
return None # Note: purposely distinguished from False so I can propagate the information to the output file
for decay in self.slha.decays[pid].decays:
for pids in decay.ids:
if self.visible(abs(pids), decay=True): return True
return False
def visible(self, pid, decay=None):
"""
Check if pid is detectable.
If pid is not known, consider it as visible.
If pid not SM particle and decay = True, check if particle or decay products are visible.
"""
if pid in SMvisible: return True
if pid in SMinvisible: return False
qn = Qnumbers(pid)
if qn.pid == 0:
return True
if qn.charge3 != 0 or qn.cdim != 1:
return True
if decay:
if not pid in self.slha.decays:
logger.warning("Missing decay block for pid %s" % (str(pid)))
return None # Note: purposely distinguished from False so I can propagate the information to the output file
for decay in self.slha.decays[pid].decays:
for pids in decay.ids:
if self.visible(abs(pids), decay=True): return True
return False
def addXSecToFile(self, xsecs, slhafile, comment=None, complain=True):
"""
Write cross sections to an SLHA file.
:param xsecs: a XSectionList object containing the cross sections
:param slhafile: target file for writing the cross sections in SLHA format
:param comment: optional comment to be added to each cross section block
:param complain: complain if there are already cross sections in file
"""
if not os.path.isfile(slhafile):
logger.error("SLHA file not found.")
raise SModelSError()
if len(xsecs) == 0:
logger.warning("No cross sections available.")
return False
# Check if file already contain cross section blocks
xSectionList = crossSection.getXsecFromSLHAFile(slhafile)
if xSectionList and complain:
logger.info("SLHA file already contains XSECTION blocks. Adding "
"only missing cross sections.")
# Write cross sections to file, if they do not overlap any cross section in
# the file
outfile = open(slhafile, 'a')
for xsec in xsecs:
writeXsec = True
for oldxsec in xSectionList:
if oldxsec.info == xsec.info and set(oldxsec.pid) == set(
xsec.pid):
writeXsec = False
break
if writeXsec:
outfile.write(
self.xsecToBlock(xsec, (2212, 2212), comment) + "\n")
outfile.close()
return True
def getValuesFor(self, attribute=None):
"""
Returns a list for the possible values appearing in the DataSet
for the required attribute.
:param attribute: name of a field in the database (string). If not defined
it will return a dictionary with all fields and
their respective values
:return: list of values
"""
fieldDict = self.__dict__.items()[:]
valuesDict = {}
while fieldDict:
for field, value in fieldDict[:]:
if not '<smodels.experiment' in str(value):
if not field in valuesDict: valuesDict[field] = [value]
else: valuesDict[field].append(value)
else:
if isinstance(value, list):
for entry in value:
fieldDict += entry.__dict__.items()[:]
else:
fieldDict += value.__dict__.items()[:]
fieldDict.remove((field, value))
#Try to keep only the set of unique values
for key, val in valuesDict.items():
try:
valuesDict[key] = list(set(val))
except TypeError as e:
pass
if not attribute: return valuesDict
elif not attribute in valuesDict:
logger.warning("Could not find field %s in database" % attribute)
return False
else:
return valuesDict[attribute]
def getULFor(self,expid,txname,massarray, expected=False ):
"""
Get an upper limit for the given experimental id, the txname,
and the massarray.
Can only be used for UL experimental results.
Interpolation is done, if necessary.
:param expid: experimental id (string)
:param txname: txname (string). ONLY required for upper limit results
:param massarray: list of masses with units, e.g.
[[ 400.*GeV, 100.*GeV],[400.*GeV, 100.*GeV]]
:param expected: If true, return expected upper limit, otherwise
return observed upper limit.
:return: upper limit [fb]
"""
#First select the experimental results matching the id and the result type:
expres = None
for expResult in self:
if expResult.getValuesFor('id')[0] != expid:
continue
else:
if 'upperLimit' in expResult.getValuesFor('dataType'):
expres = expResult
break
if not expres:
logger.warning( "Could not find UL result %s. getULFor can only be\
used for upper-limit results." % (expid))
return None
txnames = expres.getTxNames()
for tx in txnames:
if not tx.txName == txname:
continue
return tx.getValueFor(massarray,expected)
logger.warning( "Could not find TxName %s ." % (txname))
return None
def loadBinaryFile(self, lastm_only=False):
"""
Load a binary database, returning last modified, file count, database.
:param lastm_only: if true, the database itself is not read.
:returns: database object, or None, if lastm_only == True.
"""
if lastm_only and self.pcl_mtime[0]:
## doesnt need to load database, and mtime is already
## loaded
return None
if self.pcl_db:
return self.pcl_db
if not os.path.exists(self.binfile):
return None
try:
with open(self.binfile, "rb") as f:
t0 = time.time()
self.pcl_python = serializer.load(f)
self.pcl_format_version = serializer.load(f)
self.pcl_mtime = serializer.load(f)
self._databaseVersion = serializer.load(f)
if not lastm_only:
if self.pcl_python != sys.version:
logger.warning(
"binary file was written with a different "
"python version. Regenerating.")
self.createBinaryFile()
return self
if self.pcl_format_version != self.sw_format_version:
logger.warning(
"binary file format (%s) and format "
"supported by software (%s) disagree." %
(self.pcl_format_version, self.sw_format_version))
logger.warning("will recreate binary.")
self.createBinaryFile()
return self
logger.info("loading binary db file %s format version %s" %
(self.binfile, self.pcl_format_version))
self.hasFastLim = serializer.load(f)
self.expResultList = serializer.load(f)
t1 = time.time() - t0
logger.info ( "Loaded database from %s in %.1f secs." % \
( self.binfile, t1 ) )
except EOFError as e:
os.unlink(self.binfile)
if lastm_only:
self.pcl_format_version = -1
self.pcl_mtime = 0
return self
logger.error("%s is not a binary database file! recreate it!" %
self.binfile)
self.createBinaryFile()
return self
def import_python_output(smodelsFile):
"""
Imports the smodels output from each .py file.
"""
try:
with open(smodelsFile, 'rb') as fsmodels: ## imports smodels file
smodelsOut = imp.load_module("smodelsOutput", fsmodels,
smodelsFile,
('.py', 'rb', imp.PY_SOURCE))
smodelsOutput = smodelsOut.smodelsOutput
except:
logger.debug(
"Error loading smodels file %s. Does it contain a smodelsOutput dictionary?"
% smodelsFile)
return False
if not isinstance(smodelsOutput, dict):
logger.warning("smodelsOutput in file %s is not a dictionary." %
smodelsFile)
return False
return smodelsOutput
def getLifetime(self, pid, ctau=False):
"""
Compute lifetime from decay-width for a particle with pid.
:parameter pid: PID of particle
:parameter ctau: set True to multiply lifetime by c
:returns: lifetime
"""
widths = self.getDecayWidths()
try:
if widths[abs(pid)]: lt = (1.0 / widths[abs(pid)]) / 1.51926778e24
else:
# Particle is stable
return -1
if self.emptyDecay(pid): return -1 # if decay block is empty particle is also considered stable
if ctau:
return lt * 3E8
else:
return lt
except KeyError:
logger.warning("No decay block for %s, consider it as a stable particle" % str(pid) )
return -1
def checkInstallation(self, compile=True ):
"""
Checks if installation of tool is correct by looking for executable and
executing it. If check is False and compile is True, then try and compile it.
:returns: True, if everything is ok
"""
if not os.path.exists(self.executablePath):
if compile:
logger.warn("%s executable not found. Trying to compile it now. This may take a while." % self.name )
self.compile()
else:
logger.warn("%s exectuable not found." % self.name )
self.complain()
return False
if not os.path.exists(self.executablePath):
logger.error("Compilation of %s failed Is a according compiler installed?" % self.name )
self.complain()
if not os.access(self.executablePath, os.X_OK):
logger.warning("%s is not executable Trying to chmod" % self.executable)
self.chmod()
return True
def getLifetime(self, pid, ctau=False):
"""
Compute lifetime from decay-width for a particle with pid.
:parameter pid: PID of particle
:parameter ctau: set True to multiply lifetime by c
:returns: lifetime
"""
widths = self.getDecayWidths()
try:
if widths[abs(pid)]: lt = (1.0 / widths[abs(pid)]) / 1.51926778e24
else:
# Particle is stable
return -1
if self.emptyDecay(pid): return -1 # if decay block is empty particle is also considered stable
if ctau:
return lt * 3E8
else:
return lt
except KeyError:
logger.warning("No decay block for %s, consider it as a stable particle" % str(pid) )
return -1
def checkInstallation(self, compile=True ):
"""
Checks if installation of tool is correct by looking for executable and
executing it. If check is False and compile is True, then try and compile it.
:returns: True, if everything is ok
"""
if not os.path.exists(self.executablePath):
if compile:
logger.warn("%s executable not found. Trying to compile it now. This may take a while." % self.name )
self.compile()
else:
logger.warn("%s executable not found." % self.name )
self.complain()
return False
if not os.path.exists(self.executablePath):
if self.maycompile: ## should have worked
logger.error("Compilation of %s failed. Is a according compiler installed?" % self.name )
self.complain()
if not os.access(self.executablePath, os.X_OK):
logger.warning("%s is not executable Trying to chmod" % self.executable)
self.chmod()
return True
def addHigherOrders(self, sqrts, slhafile):
""" add higher order xsecs """
xsecs = copy.deepcopy(self.loXsecs)
wlabel = str(int(sqrts / TeV)) + ' TeV'
if self.maxOrder == LO:
wlabel += ' (LO)'
elif self.maxOrder == NLO:
wlabel += ' (NLO)'
elif self.maxOrder == NLL:
wlabel += ' (NLO+NLL)'
for ixsec, xsec in enumerate(xsecs):
xsecs[ixsec].info.label = wlabel
xsecs[ixsec].info.order = self.maxOrder
if self.maxOrder > 0:
pIDs = self.loXsecs.getPIDpairs()
nllfast = toolBox.ToolBox().get("nllfast%d" % sqrts.asNumber(TeV))
nllfast.maycompile = self.maycompile
for pID in pIDs:
k = 0.
kNLO, kNLL = nllfast.getKfactorsFor(pID, slhafile)
if self.maxOrder == NLO and kNLO:
k = kNLO
elif self.maxOrder == NLL and kNLL and kNLO:
k = kNLO * kNLL
elif self.maxOrder > 2 and kNLL and kNLO:
logger.warning(
"Unkown xsec order, using NLL+NLO k-factor, "
"if available")
k = kNLO * kNLL
k = float(k)
for i, xsec in enumerate(xsecs):
if set(xsec.pid) == set(pID):
# Apply k-factor
xsecs[i] = xsec * k
# Remove zero cross sections
while len(xsecs) > 0 and xsecs.getMinXsec() == 0. * pb:
for xsec in xsecs:
if xsec.value == 0. * pb:
xsecs.delete(xsec)
break
if self.maxOrder > 0 and len(xsecs) == 0:
self.countNoNLOXSecs += 1
if self.countNoNLOXSecs < 3:
logger.warning("No NLO or NLL cross sections available.")
if self.countNoNLOXSecs == 3:
logger.warning(
"No NLO or NLL cross sections available (will quench such warnings in future)."
)
#for i in xsecs:
# logger.error ( "xsec=%s (%s)" % (i,type(i)) )
return xsecs
def selectExpResultsWith(self, **restrDict):
"""
Loads the list of the experimental results (pair of InfoFile and DataFile)
satisfying the restrictions to the _selectedExpResults.
The restrictions specified as a dictionary.
:param restrDict: selection fields and their allowed values.
E.g. lumi = [19.4/fb, 20.3/fb], txName = 'T1',....}
The values can be single entries or a list of values.
For the fields not listed, all values are assumed to be allowed.
"""
#First check all the selected fields exist and build the corresponding
#restriction dictionary
rDict = {}
allfields = self.getAttributes()
for tag, value in restrDict.items():
if tag in allfields: rDict[tag] = value
else:
logger.warning(
"Field/attribute %s not found (will be ignored)." % tag)
results = self.database.expResultList[:]
for expRes in results[:]:
expAttributes = expRes.getAttributes()
for tag in restrDict:
#Check if the restriction tag appears in the experimental result
if not tag in expAttributes:
results.remove(expRes)
break
vals = expRes.getValuesFor(tag)
#If it does, check if any of the values in expResult match any of the values given
#as restrictions
if not isinstance(restrDict[tag], list):
rvals = [restrDict[tag]]
else:
rvals = restrDict[tag]
#If there is a type mismatch, also remove
try:
intersec = numpy.intersect1d(vals, rvals)
except unum.IncompatibleUnitsError:
logger.warning("Incompatible units, skipping result.")
results.remove(expRes)
break
if len(intersec) == 0:
results.remove(expRes)
break
self._selectedExpResults = results[:]
if not self._selectedExpResults: logger.warning("Zero results loaded.")
def selectExpResultsWith(self,**restrDict):
"""
Loads the list of the experimental results (pair of InfoFile and DataFile)
satisfying the restrictions to the _selectedExpResults.
The restrictions specified as a dictionary.
:param restrDict: selection fields and their allowed values.
E.g. lumi = [19.4/fb, 20.3/fb], txName = 'T1',....}
The values can be single entries or a list of values.
For the fields not listed, all values are assumed to be allowed.
"""
#First check all the selected fields exist and build the corresponding
#restriction dictionary
rDict = {}
allfields = self.getAttributes()
for tag,value in restrDict.items():
if tag in allfields: rDict[tag] = value
else: logger.warning("Field/attribute %s not found (will be ignored)." % tag)
results = self.database.expResultList[:]
for expRes in results[:]:
values = self.getValuesFor(attribute=None, expResult=expRes)
for tag in restrDict:
#Check if the restriction tag appears in the experimental result
if not tag in values:
results.remove(expRes)
break
vals = values[tag]
#If it does, check if any of the values in expResult match any of the values given
#as restrictions
if not isinstance(restrDict[tag],list): rvals = [restrDict[tag]]
else: rvals = restrDict[tag]
#If there is a type mismatch, also remove
try: intersec = numpy.intersect1d(vals,rvals)
except unum.IncompatibleUnitsError:
logger.warning("Incompatible units, skipping result.")
results.remove(expRes)
break
if len(intersec) == 0:
results.remove(expRes)
break
self._selectedExpResults = results[:]
if not self._selectedExpResults: logger.warning("Zero results loaded.")
def checkPyhfVersion ( self ):
""" check the pyhf version, currently we need 0.6.1+ """
if pyhfinfo["ver"] < pyhfinfo["required"]:
logger.warning ( f"pyhf version is {'.'.join(pyhfinfo['ver'])}. SModelS currently requires pyhf>={'.'.join(pyhfinfo['required'])}. You have been warned." )
def getCombinedUpperLimitFor(self, nsig, expected=False, deltas_rel=0.2):
"""
Get combined upper limit. If covariances are given in globalInfo then simplified likelihood is used, else if json files are given pyhf cimbination is performed.
:param nsig: list of signal events in each signal region/dataset. The list
should obey the ordering in globalInfo.datasetOrder.
:param expected: return expected, not observed value
:param deltas_rel: relative uncertainty in signal (float). Default value is 20%.
:returns: upper limit on sigma*eff
"""
if hasattr(self.globalInfo, "covariance" ):
cov = self.globalInfo.covariance
if type(cov) != list:
raise SModelSError( "covariance field has wrong type: %s" % type(cov))
if len(cov) < 1:
raise SModelSError( "covariance matrix has length %d." % len(cov))
computer = UpperLimitComputer(ntoys=10000)
nobs = [x.dataInfo.observedN for x in self._datasets]
bg = [x.dataInfo.expectedBG for x in self._datasets]
no = nobs
ret = computer.ulSigma(Data(observed=no, backgrounds=bg, covariance=cov,
third_moment=None, nsignal=nsig, deltas_rel=deltas_rel),
marginalize=self._marginalize,
expected=expected)
if ret != None:
#Convert limit on total number of signal events to a limit on sigma*eff
ret = ret/self.globalInfo.lumi
logger.debug("SL upper limit : {}".format(ret))
return ret
elif hasattr(self.globalInfo, "jsonFiles" ):
logger.debug("Using pyhf")
if all([s == 0 for s in nsig]):
logger.warning("All signals are empty")
return None
ulcomputer, combinations = self.getPyhfComputer( nsig )
if ulcomputer.nWS == 1:
ret = ulcomputer.ulSigma(expected=expected)
ret = ret/self.globalInfo.lumi
logger.debug("pyhf upper limit : {}".format(ret))
return ret
else:
# Looking for the best combination
logger.debug('self.bestCB : {}'.format(self.bestCB))
if self.bestCB == None:
logger.debug("Performing best expected combination")
ulMin = float('+inf')
for i_ws in range(ulcomputer.nWS):
ul = ulcomputer.ulSigma(expected=True, workspace_index=i_ws)
if ul == None:
continue
if ul < ulMin:
ulMin = ul
i_best = i_ws
self.bestCB = combinations[i_best] # Keeping the index of the best combination for later
logger.debug('Best combination : %s' % self.bestCB)
# Computing upper limit using best combination
if expected:
try:
ret = ulMin/self.globalInfo.lumi
except NameError:
ret = ulcomputer.ulSigma(expected=True, workspace_index=combinations.index(self.bestCB))
ret = ret/self.globalInfo.lumi
else:
ret = ulcomputer.ulSigma(expected=False, workspace_index=combinations.index(self.bestCB))
ret = ret/self.globalInfo.lumi
logger.debug("pyhf upper limit : {}".format(ret))
return ret
else:
logger.error ( "no covariance matrix or json file given in globalInfo.txt for %s" % self.globalInfo.id )
raise SModelSError( "no covariance matrix or json file given in globalInfo.txt for %s" % self.globalInfo.id )
def __init__(self, sec):
self.sec = sec
if type ( sec ) != int:
logger.warning ( "timeout set to a non-integral number of seconds."
" Will try to cast to integer." )
self.sec = int ( sec )
def _checkMaxOrder(self, maxOrder):
smaxorder = {"LO": 0, "NLO": 1, "NLL": 2}
if maxOrder in smaxorder.keys():
logger.warning("maxorder given as string, please supply integer.")
maxOrder = smaxorder[maxOrder]
return maxOrder
def getKfactorsFor( self, pIDs, slhafile, pdf='cteq' ):
"""
Read the NLLfast grid and returns a pair of k-factors (NLO and NLL) for
the PIDs pair.
:returns: k-factors = None, if NLLfast does not contain the process; uses
the slhafile to obtain the SUSY spectrum.
"""
if not os.path.isfile(slhafile):
logger.error("SLHA file %s not found", slhafile)
return False
energy = str(int(self.sqrts)) + 'TeV'
# Get process name (in NLLfast notation)
process = self._getProcessName(pIDs)
if not process:
# Return k-factors = None, if NLLfast does not have the process
return (None, None)
# Obtain relevant masses
readfile = pyslha.readSLHAFile(slhafile)
masses=readfile.blocks['MASS']
check_pids=squarks+gluinos+third
for check in check_pids:
if not check in masses.entries:
logger.error ( "cannot compute k factor for pdgid %d: " \
" no particle mass given. will set mass to inf." % check )
masses.entries[check]=1.e10
gluinomass = abs(masses.entries[1000021])
squarkmass = sum([abs(masses.entries[pid])
for pid in squarks]) / 8.
pid1, pid2 = sorted(pIDs)
if pid1 in antisquarks and pid2 in squarks:
squarkmass = (abs(masses.entries[abs(pid1)]) +
abs(masses.entries[pid2])) / 2.
elif pid1 in squarks and pid2 in squarks:
squarkmass = (abs(masses.entries[pid1]) + abs(masses.entries[pid2])) / 2.
elif abs(pid1) == pid2 and pid2 in third:
squarkmass = abs(masses.entries[abs(pid1)])
#if tool == None:
# logger.warning("No NLLfast data for sqrts = " + str(sqrts))
# return (None, None)
nllpath = self.installDirectory()
# self.pathOfExecutable()
self.checkInstallation()
nll_output = self._compute ( energy, pIDs, pdf, squarkmass, gluinomass )
# If run was successful, return k-factors:
if "K_NLO" in nll_output:
# NLLfast ran ok, try to get the k-factors
kFacs = self._getKfactorsFrom(nll_output)
if not kFacs or min(kFacs) <= 0.:
logger.warning("Error obtaining k-factors")
return (None, None)
else:
return kFacs
# If run was not successful, check for decoupling error messages:
elif not "too low/high" in nll_output.lower():
logger.warning("Error running NLLfast")
return (None, None)
# Check for decoupling cases with a decoupling grid (only for sb and gg)
doDecoupling = False
if "too low/high gluino" in nll_output.lower():
if gluinomass > 500. and process == 'sb':
doDecoupling = True
dcpl_mass = gluinomass
elif "too low/high squark" in nll_output.lower():
if squarkmass > 500. and process == 'gg':
doDecoupling = True
dcpl_mass = squarkmass
# If process do not have decoupled grids, return None:
if not doDecoupling:
logger.warning("Masses of (q,g)=(%s,%s) out of NLLfast grid for %s, %s" % ( squarkmass, gluinomass, process, energy ))
return (None, None)
# Obtain k-factors from the NLLfast decoupled grid
kfacs = self._getDecoupledKfactors(process,energy,pdf,min(gluinomass,squarkmass))
# Decoupling limit is satisfied, do not interpolate
if not kfacs:
logger.warning("Error obtaining k-factors from the NLLfast decoupled grid for " + process)
return (None, None)
elif dcpl_mass/min(gluinomass,squarkmass) > 10.:
return kfacs
# Interpolate between the non-decoupled and decoupled grids
else:
kFacsVector = [[10.*min(gluinomass,squarkmass),kfacs]] #First point for interpolation (decoupled grid)
kfacs = None
while not kfacs and dcpl_mass > 500.:
dcpl_mass -= 100. # Reduce decoupled mass, until NLLfast produces results
if process == 'sb': nllinput = (process, pdf, squarkmass, dcpl_mass)
else: nllinput = (process, pdf, dcpl_mass, gluinomass)
nll_output = self._runForDecoupled ( energy, nllinput )
kfacs = self._getKfactorsFrom(nll_output)
kFacsVector.append([dcpl_mass, kfacs]) #Second point for interpolation (non-decoupled grid)
if len(kFacsVector) < 2:
logger.warning("Not enough points for interpolation in the decoupling "
"limit")
return (None, None)
else:
# Interpolate k-factors
kFacs = self._interpolateKfactors(kFacsVector,
max(squarkmass, gluinomass))
return kFacs
def __init__(self, sec):
self.sec = sec
if type(sec) != int:
logger.warning("timeout set to a non-integral number of seconds."
" Will try to cast to integer.")
self.sec = int(sec)
def loadParameters(self):
"""
Reads the parameters from the plotting parameter file.
"""
logger.info("Reading parameters from %s ..." %(self.parameterFile))
parFile = self.parameterFile
import imp
try:
with open(self.parameterFile, 'rb') as fParameters: ## imports parameter file
parameters = imp.load_module("parameters",fParameters,self.parameterFile,('.py', 'rb', imp.PY_SOURCE))
except:
logger.error("Error loading parameters file %s" %self.parameterFile)
return False
if not hasattr(parameters, 'slha_hover_information'):
logger.debug("slha_hover_information dictionary was not found in %s. SLHA data will not be included in info box." %parFile)
self.slha_hover_information = {}
else:
self.slha_hover_information = parameters.slha_hover_information
if not hasattr(parameters, 'ctau_hover_information'):
logger.debug("ctau_hover_information dictionary was not found in %s. Lifetime data will not be included in info box." %parFile)
self.ctau_hover_information = {}
else:
self.ctau_hover_information = parameters.ctau_hover_information
if not hasattr(parameters, 'BR_hover_information'):
logger.debug("BR_hover_information dictionary was not found in %s. Branching ratio data will not be included in info box." %parFile)
self.BR_hover_information = {}
else:
self.BR_hover_information = parameters.BR_hover_information
if not hasattr(parameters, 'SModelS_hover_information'):
logger.debug("SModelS_hover_information dictionary was not found in %s. SModelS data will not be included in info box." %parFile)
self.SModelS_hover_information = {}
else:
self.SModelS_hover_information = list(set(parameters.SModelS_hover_information))
if not hasattr(parameters, 'plot_data'):
logger.debug("plot_data list was not found in %s. All points will be plotted" %parFile)
self.plot_data = ['all']
else:
self.plot_data = list(set(parameters.plot_data))
if not hasattr(parameters, 'variable_x'):
raise SModelSError("variable_x was not found in %s. Please define the variable to be plotted in the x-axis." %parFile)
else:
self.variable_x = parameters.variable_x
if not hasattr(parameters, 'variable_y'):
raise SModelSError("variable_y was not found in %s. Please define the variable to be plotted in the y-axis." %parFile)
else:
self.variable_y = parameters.variable_y
if not hasattr(parameters, 'plot_list'):
raise SModelSError("plot_list was not found in %s. Please define the list of plots to be plotted." %parFile)
else:
self.plot_list = list(set(parameters.plot_list))
if not hasattr(parameters,'BR_get_top'):
logger.debug("BR_get_top not found in %s. Will include all decay channels")
self.BR_get_top = 'all'
else:
self.BR_get_top = parameters.BR_get_top
if not hasattr(parameters,'plot_title'):
logger.warning("plot_title not defined in %s. Using default title" %parFile)
self.plot_title = 'interactive-plots'
else:
self.plot_title = parameters.plot_title
def _doCluster(elements, txdata, maxDist):
"""
Cluster algorithm to cluster elements.
:parameter elements: list of all elements to be clustered
:parameter txdata: TxNameData object to be used for computing distances in UL space
:parameter maxDist: maximum mass distance for clustering two elements
:returns: a list of ElementCluster objects containing the elements
belonging to the cluster
"""
# First build the element:mass, element:position in UL space
# and element:maxWeight (in fb) dictionaries
#(Combine elements with identical masses)
massMap = {}
posMap = {}
weightMap = {}
for iel, el in enumerate(elements):
if not el.getMasses() in massMap.values():
massMap[iel] = el.getMasses()
posMap[iel] = massPosition(massMap[iel], txdata)
weightMap[iel] = el.weight.getMaxXsec() / fb
else:
j = list(massMap.keys())[list(massMap.values()).index(el.getMasses())]
weightMap[j] += el.weight.getMaxXsec() / fb
# Start with maximal clusters
clusterList = []
for iel in posMap:
indices = [iel]
for jel in posMap:
if distance(posMap[iel], posMap[jel]) <= maxDist:
indices.append(jel)
indexCluster = IndexCluster(massMap, posMap, weightMap, set(indices),txdata)
#Ignore cluster which average mass falls oustide the grid:
if indexCluster.avgPosition:
clusterList.append(indexCluster)
#Split the maximal clusters until all elements inside each cluster are
#less than maxDist apart from each other and the cluster average position
#is less than maxDist apart from all elements
finalClusters = []
newClusters = True
while newClusters:
newClusters = []
for indexCluster in clusterList:
# cluster is good
if indexCluster._getMaxInternalDist() < maxDist:
if not indexCluster in finalClusters:
finalClusters.append(indexCluster)
continue
# Distance to cluster center (average)
distAvg = indexCluster._getDistanceTo(indexCluster.avgPosition)
#Loop over cluster elements and if element distance or cluster
#average distance falls outside the cluster, remove element
for iel in indexCluster:
dist = indexCluster._getDistanceTo(iel)
if max(dist, distAvg) > maxDist:
newcluster = indexCluster.copy()
newcluster.remove(iel)
if not newcluster in newClusters:
#Ignore cluster which average mass falls oustide the grid:
if newcluster.avgPosition:
newClusters.append(newcluster)
clusterList = newClusters
# Check for oversized list of indexCluster (too time consuming)
if len(clusterList) > 100:
logger.warning("ElementCluster failed, using unclustered masses")
finalClusters = []
clusterList = []
# finalClusters = finalClusters + clusterList
# Add clusters of individual masses (just to be safe)
for iel in massMap:
finalClusters.append(IndexCluster(massMap, posMap, weightMap,
set([iel])))
# Clean up clusters (remove redundant clusters)
for ic, clusterA in enumerate(finalClusters):
if clusterA is None:
continue
for jc, clusterB in enumerate(finalClusters):
if clusterB is None:
continue
if ic != jc and clusterB.indices.issubset(clusterA.indices):
finalClusters[jc] = None
while finalClusters.count(None) > 0:
finalClusters.remove(None)
# Transform index clusters to element clusters:
clusterList = []
for indexCluster in finalClusters:
cluster = ElementCluster()
masses = [massMap[iel] for iel in indexCluster]
for el in elements:
if el.getMasses() in masses:
cluster.elements.append(el)
clusterList.append(cluster)
return clusterList
def getUpperLimitFor(self,element=None,expected = False, txnames = None
,compute=False,alpha=0.05,deltas_rel=0.2):
"""
Returns the upper limit for a given element (or mass) and txname. If
the dataset hold an EM map result the upper limit is independent of
the input txname or mass.
For UL results if an Element object is given the corresponding upper limit
will be rescaled according to the lifetimes of the element intermediate particles.
On the other hand, if a mass is given, no rescaling will be applied.
:param txname: TxName object or txname string (only for UL-type results)
:param element: Element object or mass array with units (only for UL-type results)
:param alpha: Can be used to change the C.L. value. The default value is 0.05
(= 95% C.L.) (only for efficiency-map results)
:param deltas_rel: relative uncertainty in signal (float). Default value is 20%.
:param expected: Compute expected limit, i.e. Nobserved = NexpectedBG
(only for efficiency-map results)
:param compute: If True, the upper limit will be computed
from expected and observed number of events.
If False, the value listed in the database will be used
instead.
:return: upper limit (Unum object)
"""
if self.getType() == 'efficiencyMap':
upperLimit = self.getSRUpperLimit(expected=expected,alpha=alpha,compute=compute,
deltas_rel=deltas_rel)
if (upperLimit/fb).normalize()._unit:
logger.error("Upper limit defined with wrong units for %s and %s"
%(self.globalInfo.id,self.getID()))
return False
else:
return upperLimit
elif self.getType() == 'upperLimit':
if not txnames or not element:
logger.error("A TxName and mass array must be defined when \
computing ULs for upper-limit results.")
return False
elif isinstance(txnames,list):
if len(txnames) != 1:
logger.error("txnames must be a TxName object, a string or a list with a single Txname object")
return False
else:
txname = txnames[0]
else:
txname = txnames
if not isinstance(txname, txnameObj.TxName) and \
not isinstance(txname, str):
logger.error("txname must be a TxName object or a string")
return False
if not isinstance(element, list) and not isinstance(element,Element):
logger.error("Element must be an element object or a mass array")
return False
for tx in self.txnameList:
if tx == txname or tx.txName == txname:
upperLimit = tx.getULFor(element,expected)
return upperLimit
else:
logger.warning("Unkown data type: %s. Data will be ignored.",
self.getType())
return None
def getUpperLimitFor(self,mass=None,expected = False, txnames = None
,compute=False,alpha=0.05,deltas_rel=0.2):
"""
Returns the upper limit for a given mass and txname. If
the dataset hold an EM map result the upper limit is independent of
the input txname or mass.
:param txname: TxName object or txname string (only for UL-type results)
:param mass: Mass array with units (only for UL-type results)
:param alpha: Can be used to change the C.L. value. The default value is 0.05
(= 95% C.L.) (only for efficiency-map results)
:param deltas_rel: relative uncertainty in signal (float). Default value is 20%.
:param expected: Compute expected limit, i.e. Nobserved = NexpectedBG
(only for efficiency-map results)
:param compute: If True, the upper limit will be computed
from expected and observed number of events.
If False, the value listed in the database will be used
instead.
:return: upper limit (Unum object)
"""
if self.getType() == 'efficiencyMap':
upperLimit = self.getSRUpperLimit(expected=expected,alpha=alpha,compute=compute,
deltas_rel=deltas_rel)
if (upperLimit/fb).normalize()._unit:
logger.error("Upper limit defined with wrong units for %s and %s"
%(self.globalInfo.id,self.getID()))
return False
else:
return upperLimit
elif self.getType() == 'upperLimit':
if not txnames or not mass:
logger.error("A TxName and mass array must be defined when \
computing ULs for upper-limit results.")
return False
elif isinstance(txnames,list):
if len(txnames) != 1:
logger.error("txnames must be a TxName object, a string or a list with a single Txname object")
return False
else:
txname = txnames[0]
else:
txname = txnames
if not isinstance(txname, txnameObj.TxName) and \
not isinstance(txname, str):
logger.error("txname must be a TxName object or a string")
return False
if not isinstance(mass, list):
logger.error("mass must be a mass array")
return False
for tx in self.txnameList:
if tx == txname or tx.txName == txname:
if expected:
if not tx.txnameDataExp:
upperLimit = None
else:
upperLimit = tx.txnameDataExp.getValueFor(mass)
else:
upperLimit = tx.txnameData.getValueFor(mass)
return upperLimit
else:
logger.warning("Unkown data type: %s. Data will be ignored.",
self.getType())
return None
def loadParameters(self):
"""
Reads the parameters from the plotting parameter file.
"""
logger.info("Reading parameters from %s ..." % (self.parameterFile))
parFile = self.parameterFile
import imp
try:
with open(self.parameterFile,
'rb') as fParameters: ## imports parameter file
parameters = imp.load_module("parameters", fParameters,
self.parameterFile,
('.py', 'rb', imp.PY_SOURCE))
# except Exception as e:
except (IOError, ValueError, ImportError, SyntaxError) as e:
logger.error("Error loading parameters file %s: %s" %
(self.parameterFile, e))
raise SModelSError()
if not hasattr(parameters, 'slha_hover_information'):
logger.debug(
"slha_hover_information dictionary was not found in %s. SLHA data will not be included in info box."
% parFile)
self.slha_hover_information = {}
else:
self.slha_hover_information = parameters.slha_hover_information
if not hasattr(parameters, 'ctau_hover_information'):
logger.debug(
"ctau_hover_information dictionary was not found in %s. Lifetime data will not be included in info box."
% parFile)
self.ctau_hover_information = {}
else:
self.ctau_hover_information = parameters.ctau_hover_information
if not hasattr(parameters, 'BR_hover_information'):
logger.debug(
"BR_hover_information dictionary was not found in %s. Branching ratio data will not be included in info box."
% parFile)
self.BR_hover_information = {}
else:
self.BR_hover_information = parameters.BR_hover_information
if not hasattr(parameters, 'SModelS_hover_information'):
logger.debug(
"SModelS_hover_information dictionary was not found in %s. SModelS data will not be included in info box."
% parFile)
self.SModelS_hover_information = {}
else:
self.SModelS_hover_information = list(
set(parameters.SModelS_hover_information))
if not hasattr(parameters, 'plot_data'):
logger.debug(
"plot_data list was not found in %s. All points will be plotted"
% parFile)
self.plot_data = ['all']
else:
self.plot_data = list(set(parameters.plot_data))
if not hasattr(parameters, 'variable_x'):
raise SModelSError(
"variable_x was not found in %s. Please define the variable to be plotted in the x-axis."
% parFile)
else:
self.variable_x = parameters.variable_x
if not hasattr(parameters, 'variable_y'):
raise SModelSError(
"variable_y was not found in %s. Please define the variable to be plotted in the y-axis."
% parFile)
else:
self.variable_y = parameters.variable_y
if not hasattr(parameters, 'plot_list'):
raise SModelSError(
"plot_list was not found in %s. Please define the list of plots to be plotted."
% parFile)
else:
self.plot_list = list(set(parameters.plot_list))
if not hasattr(parameters, 'BR_get_top'):
logger.debug(
"BR_get_top not found in %s. Will include all decay channels")
self.BR_get_top = 'all'
else:
self.BR_get_top = parameters.BR_get_top
if not hasattr(parameters, 'plot_title'):
logger.warning(
"plot_title not defined in %s. Using default title" % parFile)
self.plot_title = 'interactive-plots'
else:
self.plot_title = parameters.plot_title
def _checkSqrts(self, sqrts):
if type(sqrts) == type(float) or type(sqrts) == type(int):
logger.warning("sqrt(s) given as scalar, will add TeV as unit.")
sqrts = float(sqrts) * TeV
return sqrts
def setDecays(self,decaysDict,promptWidth,stableWidth,erasePrompt):
allPDGs = list(set(self.getValuesFor('pdg')))
evenPDGs,oddPDGs = self.getEvenOddList()
for particle in self.BSMparticles:
if isinstance(particle,MultiParticle):
continue
if not hasattr(particle,'pdg') or not hasattr(particle,'Z2parity'):
raise SModelSError("PDG and/or Z2-parity for particle %s has not been defined" %particle.label)
pdg = particle.pdg
particle.decays = []
if pdg in decaysDict:
particleData = decaysDict[pdg]
chargeConj = 1
elif -pdg in decaysDict:
particleData = decaysDict[-pdg]
chargeConj = -1
else:
logger.error("Decay information for particle %i could not be found" %pdg)
raise SModelSError()
particle.totalwidth = abs(particleData.totalwidth)*GeV
if particle.totalwidth < stableWidth:
particle.totalwidth = 0.*GeV #Treat particle as stable
logger.debug("Particle %s has width below the threshold and will be assumed as stable" %particle.pdg)
continue
if particle.totalwidth > promptWidth:
particle.totalwidth = float('inf')*GeV #Treat particle as prompt
logger.debug("Particle %s has width above the threshold and will be assumed as prompt." %particle.pdg)
if erasePrompt and particle.Z2parity == -1:
logger.debug("Erasing quantum numbers of (prompt) particle %s." %particle.pdg)
for attr in erasePrompt:
delattr(particle,attr)
else:
particle.decays.append(None) #Include possibility for particle being long-lived (non-prompt)
for decay in particleData.decays:
pids = decay.ids
missingIDs = set(pids).difference(set(allPDGs))
if missingIDs:
logger.info("Particle(s) %s is not defined within model. Decay %s will be ignored" %(missingIDs,decay))
continue
oddPids = [pid for pid in decay.ids if abs(pid) in oddPDGs]
evenPids = [pid for pid in decay.ids if abs(pid) in evenPDGs]
if len(oddPids) != 1 or len(evenPids+oddPids) != len(decay.ids):
logger.debug("Decay %i -> %s is not of the form Z2-odd -> Z2-odd + [Z2-even particles] and will be ignored" %(pdg,pids))
continue
#Conjugated decays if needed
#(if pid*chargeConj is not in model, assume the particle is its own anti-particle)
decayIDs = [pid*chargeConj if pid*chargeConj in allPDGs else pid for pid in decay.ids]
newDecay = pyslha.Decay(br=decay.br,nda=decay.nda,parentid=decay.parentid,ids=decayIDs)
#Convert PDGs to particle objects:
daughters = []
for pdg in newDecay.ids:
daughter = self.getParticlesWith(pdg=pdg)
if not daughter:
raise SModelSError("Particle with PDG = %i was not found in model. Check the model definitions." %pdg)
elif len(daughter) > 1:
raise SModelSError("Multiple particles defined with PDG = %i. PDG ids must be unique." %pdg)
else:
daughter = daughter[0]
daughters.append(daughter)
oddParticles = [p for p in daughters if p.Z2parity == -1]
evenParticles = ParticleList([p for p in daughters if p.Z2parity == 1])
newDecay.oddParticles = oddParticles
newDecay.evenParticles = evenParticles
particle.decays.append(newDecay)
#Check if all unstable particles have decay channels defined:
for p in self.BSMparticles:
if p.totalwidth < stableWidth:
continue
ndecays = len([dec for dec in p.decays if dec is not None])
if ndecays == 0:
if p.Z2parity == -1:
logger.warning("No valid decay found for %s. It will be considered stable." %p)
p.totalwidth = 0.*GeV
def doCluster(elements, dataset, maxDist):
"""
Cluster algorithm to cluster elements.
:parameter elements: list of all elements to be clustered
:parameter dataset: Dataset object to be used when computing distances in upper limit space
:parameter maxDist: maximum distance for clustering two elements
:returns: a list of ElementCluster objects containing the elements
belonging to the cluster
"""
#Get average elements:
averageElements = groupElements(elements,dataset)
#Index average elements:
elementList = sorted(averageElements, key = lambda el: el._upperLimit)
for iel,el in enumerate(elementList):
el._index = iel
#Pre-compute all necessary distances:
distanceMatrix = np.zeros((len(elementList),len(elementList)))
for iel,elA in enumerate(elementList):
for jel,elB in enumerate(elementList):
if jel <= iel:
continue
distanceMatrix[iel,jel] = relativeDistance(elA, elB, dataset)
distanceMatrix = distanceMatrix + distanceMatrix.T
#Start building maximal clusters
clusterList = []
for el in elementList:
cluster = ElementCluster([],dataset,distanceMatrix)
for elB in elementList:
if distanceMatrix[el._index,elB._index] <= maxDist:
cluster.add(elB)
if not cluster.elements:
continue
if cluster.averageElement()._upperLimit is None:
continue
if not cluster in clusterList:
clusterList.append(cluster)
#Split the maximal clusters until all elements inside each cluster are
#less than maxDist apart from each other and the cluster average position
#is less than maxDist apart from all elements
finalClusters = []
while clusterList:
newClusters = []
for cluster in clusterList:
#Check if maximal internal distance is below maxDist
isConsistent = cluster.isConsistent(maxDist)
if isConsistent and cluster.maxInternalDist < maxDist:
if not cluster in finalClusters:
finalClusters.append(cluster)
#Cluster violates maxDist:
else:
#Loop over cluster elements and if element distance
#falls outside the cluster, remove element
for el in cluster:
if cluster.getDistanceTo(el) > maxDist or not isConsistent:
newcluster = cluster.copy()
newcluster.remove(el)
if newcluster.averageElement()._upperLimit is None:
continue
if newcluster in newClusters:
continue
newClusters.append(newcluster)
clusterList = newClusters
# Check for oversized list of indexCluster (too time consuming)
if len(clusterList) > 100:
logger.warning("ElementCluster failed, using unclustered masses")
finalClusters = []
clusterList = []
# finalClusters = finalClusters + clusterList
# Add clusters of individual masses (just to be safe)
for el in elementList:
finalClusters.append(ElementCluster([el],dataset,distanceMatrix))
# Clean up clusters (remove redundant clusters)
for ic, clusterA in enumerate(finalClusters):
if clusterA is None:
continue
for jc, clusterB in enumerate(finalClusters):
if clusterB is None:
continue
if ic != jc and set(clusterB.indices()).issubset(set(clusterA.indices())):
finalClusters[jc] = None
while finalClusters.count(None) > 0:
finalClusters.remove(None)
#Replace average elements by the original elements:
for cluster in finalClusters:
originalElements = []
for avgEl in cluster.elements[:]:
originalElements += avgEl.elements[:]
cluster.elements = originalElements[:]
return finalClusters
def equalObjs(obj1, obj2, allowedDiff, ignore=[], where=None):
"""
Compare two objects.
The numerical values are compared up to the precision defined by allowedDiff.
:param obj1: First python object to be compared
:param obj2: Second python object to be compared
:param allowedDiff: Allowed % difference between two numerical values
:param ignore: List of keys to be ignored
:param where: keep track of where we are, for easier debugging.
:return: True/False
"""
if type(obj1) in [float, int] and type(obj2) in [float, int]:
obj1, obj2 = float(obj1), float(obj2)
if type(obj1) != type(obj2):
logger.warning("Data types differ (%s,%s)" % (type(obj1), type(obj2)))
return False
if isinstance(obj1, unum.Unum):
if obj1 == obj2:
return True
diff = 2. * abs(obj1 - obj2) / abs(obj1 + obj2)
return diff.asNumber() < allowedDiff
elif isinstance(obj1, float):
if obj1 == obj2:
return True
diff = 2. * abs(obj1 - obj2) / abs(obj1 + obj2)
return diff < allowedDiff
elif isinstance(obj1, str):
return obj1 == obj2
elif isinstance(obj1, dict):
for key in obj1:
if key in ignore: continue
if not key in obj2:
logger.warning("Key %s missing" % key)
return False
if not equalObjs(
obj1[key], obj2[key], allowedDiff, ignore=ignore,
where=key):
logger.warning('Objects differ in %s:\n %s\n and\n %s' %
(where, str(obj1[key]), str(obj2[key])))
#s1,s2 = str(obj1[key]),str(obj2[key])
#if False: # len(s1) + len(s2) > 200:
# logger.warning ( "The values are too long to print." )
#else:
# logger.warning( 'The values are: >>%s<< (this run) versus >>%s<< (default)'%\
# ( s1[:20],s2[:20] ) )
return False
elif isinstance(obj1, list):
if len(obj1) != len(obj2):
logger.warning('Lists differ in length:\n %i (this run)\n and\n %i (default)' %\
(len(obj1),len(obj2)))
return False
for ival, val in enumerate(obj1):
if not equalObjs(val, obj2[ival], allowedDiff):
logger.warning('Lists differ:\n %s (this run)\n and\n %s (default)' %\
(str(val),str(obj2[ival])))
return False
else:
return obj1 == obj2
return True
def getKfactorsFor(pIDs, sqrts, slhafile, pdf='cteq'):
"""
Read the NLLfast grid and returns a pair of k-factors (NLO and NLL) for the
pair.
:returns: k-factors = None, if NLLfast does not contain the process; uses
the slhafile to obtain the SUSY spectrum.
"""
if not os.path.isfile(slhafile):
logger.error("SLHA file %s not found", slhafile)
return False
# Get process name (in NLLfast notation)
process = getProcessName(pIDs)
if not process:
# Return k-factors = None, if NLLfast does not have the process
return (None, None)
# Obtain relevant masses
readfile = pyslha.readSLHAFile(slhafile)
masses = readfile.blocks['MASS']
check_pids = squarks + gluinos + third
for check in check_pids:
if not check in masses.entries:
logger.error ( "cannot compute k factor for pdgid %d: " \
" no particle mass given. will set mass to inf." % check )
masses.entries[check] = 1.e10
gluinomass = abs(masses.entries[1000021])
squarkmass = sum([abs(masses.entries[pid]) for pid in squarks]) / 8.
pid1, pid2 = sorted(pIDs)
if pid1 in antisquarks and pid2 in squarks:
squarkmass = (abs(masses.entries[abs(pid1)]) +
abs(masses.entries[pid2])) / 2.
elif pid1 in squarks and pid2 in squarks:
squarkmass = (abs(masses.entries[pid1]) +
abs(masses.entries[pid2])) / 2.
elif abs(pid1) == pid2 and pid2 in third:
squarkmass = abs(masses.entries[abs(pid1)])
# Set up NLLfast run, the old way
sqrtS = float(sqrts / TeV)
energy = str(int(sqrtS)) + 'TeV'
toolname = "nllfast%d" % int(sqrtS)
box = toolBox.ToolBox()
tool = box.get(toolname)
if tool == None:
logger.warning("No NLLfast data for sqrts = " + str(sqrts))
return (None, None)
nllpath = tool.installDirectory()
tool.pathOfExecutable()
tool.checkInstallation()
if process == "st":
nll_run = "./nllfast_" + energy + " %s %s %s" % \
(process, pdf, squarkmass)
else:
nll_run = "./nllfast_" + energy + " %s %s %s %s" % \
(process, pdf, squarkmass, gluinomass)
# Run NLLfast
nll_output = runNLLfast(nll_run, nllpath)
# If run was successful, return k-factors:
if "K_NLO" in nll_output:
# NLLfast ran ok, try to get the k-factors
kFacs = getKfactorsFrom(nll_output)
if not kFacs or min(kFacs) <= 0.:
logger.warning("Error obtaining k-factors")
return (None, None)
else:
return kFacs
# If run was not successful, check for decoupling error messages:
elif not "too low/high" in nll_output.lower():
logger.warning("Error running NLLfast")
return (None, None)
# Check for decoupling cases with a decoupling grid (only for sb and gg)
doDecoupling = False
if "too low/high gluino" in nll_output.lower():
if gluinomass > 500. and process == 'sb':
doDecoupling = True
dcpl_mass = gluinomass
elif "too low/high squark" in nll_output.lower():
if squarkmass > 500. and process == 'gg':
doDecoupling = True
dcpl_mass = squarkmass
# If process do not have decoupled grids, return None:
if not doDecoupling:
logger.warning("Masses out of NLLfast grid for " + process)
return (None, None)
# Obtain k-factors from the NLLfast decoupled grid
kfacs = getDecoupledKfactors(nllpath, process, energy, pdf,
min(gluinomass, squarkmass))
# Decoupling limit is satisfied, do not interpolate
if not kfacs:
logger.warning(
"Error obtaining k-factors from the NLLfast decoupled grid for " +
process)
return (None, None)
elif dcpl_mass / min(gluinomass, squarkmass) > 10.:
return kfacs
# Interpolate between the non-decoupled and decoupled grids
else:
kFacsVector = [[10. * min(gluinomass, squarkmass), kfacs]
] #First point for interpolation (decoupled grid)
kfacs = None
while not kfacs and dcpl_mass > 500.:
dcpl_mass -= 100. # Reduce decoupled mass, until NLLfast produces results
if process == 'sb':
nllinput = (process, pdf, squarkmass, dcpl_mass)
else:
nllinput = (process, pdf, dcpl_mass, gluinomass)
nll_run = "./nllfast_" + energy + " %s %s %s %s" % nllinput
nll_output = runNLLfast(nll_run, nllpath)
kfacs = getKfactorsFrom(nll_output)
kFacsVector.append(
[dcpl_mass,
kfacs]) #Second point for interpolation (non-decoupled grid)
if len(kFacsVector) < 2:
logger.warning("Not enough points for interpolation in the decoupling "
"limit")
return (None, None)
else:
# Interpolate k-factors
kFacs = interpolateKfactors(kFacsVector, max(squarkmass, gluinomass))
return kFacs
def ulSigma (self, expected=False, workspace_index=None):
"""
Compute the upper limit on the signal strength modifier with:
- by default, the combination of the workspaces contained into self.workspaces
- if workspace_index is specified, self.workspace[workspace_index] (useful for computation of the best upper limit)
:param expected: - if set to `True`: uses expected SM backgrounds as signals
- else: uses `self.nsignals`
:param workspace_index: - if different from `None`: index of the workspace to use for upper limit
- else: all workspaces are combined
:return: the upper limit at `self.cl` level (0.95 by default)
"""
startUL = time.time()
logger.debug("Calling ulSigma")
if self.data.errorFlag or self.workspaces == None: # For now, this flag can only be turned on by PyhfData.checkConsistency
return None
if self.nWS == 1:
if self.zeroSignalsFlag[0] == True:
logger.warning("There is only one workspace but all signals are zeroes")
return None
else:
if workspace_index == None:
logger.error("There are several workspaces but no workspace index was provided")
return None
elif self.zeroSignalsFlag[workspace_index] == True:
logger.debug("Workspace number %d has zero signals" % workspace_index)
return None
def updateWorkspace():
if self.nWS == 1:
return self.workspaces[0]
else:
return self.workspaces[workspace_index]
workspace = updateWorkspace()
def root_func(mu):
# Same modifiers_settings as those use when running the 'pyhf cls' command line
msettings = {'normsys': {'interpcode': 'code4'}, 'histosys': {'interpcode': 'code4p'}}
model = workspace.model(modifier_settings=msettings)
start = time.time()
stat = "qtilde" # by default
args = { "return_expected": expected }
pver = float ( pyhf.__version__[:3] )
if pver < 0.6:
args["qtilde"]=True
else:
args["test_stat"]=stat
with np.testing.suppress_warnings() as sup:
if pyhfinfo["backend"] == "numpy":
sup.filter ( RuntimeWarning, r'invalid value encountered in log')
result = pyhf.infer.hypotest(mu, workspace.data(model), model, **args )
end = time.time()
logger.debug("Hypotest elapsed time : %1.4f secs" % (end - start))
if expected:
logger.debug("expected = {}, mu = {}, result = {}".format(expected, mu, result))
try:
CLs = float(result[1].tolist())
except TypeError:
CLs = float(result[1][0])
else:
logger.debug("expected = {}, mu = {}, result = {}".format(expected, mu, result))
CLs = float(result)
# logger.debug("Call of root_func(%f) -> %f" % (mu, 1.0 - CLs))
return 1.0 - self.cl - CLs
# Rescaling singals so that mu is in [0, 10]
factor = 10.
wereBothLarge = False
wereBothTiny = False
nattempts = 0
nNan = 0
lo_mu, hi_mu = .2, 5.
while "mu is not in [lo_mu,hi_mu]":
nattempts += 1
if nNan > 5:
logger.warn("encountered NaN 5 times while trying to determine the bounds for brent bracketing. now trying with q instead of qtilde test statistic")
stat = "q"
nattempts = 0
if nattempts > 10:
logger.warn ( "tried 10 times to determine the bounds for brent bracketing. we abort now." )
return None
# Computing CL(1) - 0.95 and CL(10) - 0.95 once and for all
rt1 = root_func(lo_mu)
rt10 = root_func(hi_mu)
if rt1 < 0. and 0. < rt10: # Here's the real while condition
break
if self.alreadyBeenThere:
factor = 1 + (factor-1)/2
logger.debug("Diminishing rescaling factor")
if np.isnan(rt1):
nNan += 1
self.rescale(factor)
workspace = updateWorkspace()
continue
if np.isnan(rt10):
nNan += 1
self.rescale(1/factor)
workspace = updateWorkspace()
continue
# Analyzing previous values of wereBoth***
if rt10 < 0 and rt1 < 0 and wereBothLarge:
factor = 1 + (factor-1)/2
logger.debug("Diminishing rescaling factor")
if rt10 > 0 and rt1 > 0 and wereBothTiny:
factor = 1 + (factor-1)/2
logger.debug("Diminishing rescaling factor")
# Preparing next values of wereBoth***
wereBothTiny = rt10 < 0 and rt1 < 0
wereBothLarge = rt10 > 0 and rt1 > 0
# Main rescaling code
if rt10 < 0.:
self.rescale(factor)
workspace = updateWorkspace()
continue
if rt1 > 0.:
self.rescale(1/factor)
workspace = updateWorkspace()
continue
# Finding the root (Brent bracketing part)
logger.debug("Final scale : %f" % self.scale)
logger.debug("Starting brent bracketing")
ul = optimize.brentq(root_func, lo_mu, hi_mu, rtol=1e-3, xtol=1e-3)
endUL = time.time()
logger.debug("ulSigma elpased time : %1.4f secs" % (endUL - startUL))
return ul*self.scale # self.scale has been updated whithin self.rescale() method