def __init__(self, region, track, track2, markType='number', **kwArgs):
self._markType = markType
#r('sink(file("/dev/null", open="wt"), type="message")')
if kwArgs.get('minimal') != True:
silenceRWarnings()
Statistic.__init__(self, region, track, track2, markType=markType, **kwArgs)
def __init__(self, region, track, track2, rawStatistic, randTrackClass=None, assumptions=None, tails=None, numResamplings=2000, randomSeed=None, **kwArgs):
if tails==None:
if 'tail' in kwArgs:
tailTranslator = {'more':'right-tail', 'less':'left-tail', 'different':'two-tail'}
tails = tailTranslator[kwArgs['tail']]
if DebugConfig.VERBOSE:
logMessage('Argument tail provided instead of tails to RandomizationManagerStatUnsplittable', level=logging.DEBUG)
else:
tails = 'right-tail' # or 'two-tail'?
logMessage('No tails argument provided to RandomizationManagerStatUnsplittable', level=logging.DEBUG)
if track2 is None:
self._track2 = None #to allow track2 to be passed on as None to rawStatistics without error. For use by single-track MC-tests..
from gold.util.RandomUtil import getManualSeed, setManualSeed
if randomSeed is not None and randomSeed != 'Random' and getManualSeed() is None:
setManualSeed(int(randomSeed))
if 'mcSetupScheme' in kwArgs:
kwArgs = copy(kwArgs) #to not edit original dict..
if kwArgs['mcSetupScheme'] != 'custom':
assert not 'maxSamples' in kwArgs #check that specific values are not redundantly set
#
Statistic.__init__(self, region, track, track2, rawStatistic=rawStatistic, randTrackClass=randTrackClass, assumptions=assumptions, tails=tails, numResamplings=numResamplings, randomSeed=randomSeed, **kwArgs)
#if type(rawStatistic) is str:
# from gold.statistic.AllStatistics import STAT_CLASS_DICT
# rawStatistic = STAT_CLASS_DICT[rawStatistic]
assert (randTrackClass is None) ^ (assumptions is None) # xor
if assumptions is not None:
assert assumptions.count('_') == 1, assumptions
randTrackClass1, randTrackClass2 = assumptions.split('_')
else:
randTrackClass1 = None
randTrackClass2 = randTrackClass
self._randTrackClass1, self._randTrackClass2 = \
[ ( globals()[clsDef] if clsDef not in ['None',''] else None ) \
if isinstance(clsDef, basestring) else clsDef for clsDef in [randTrackClass1, randTrackClass2]]
assert not (randTrackClass1 is None and randTrackClass2 is None)
for cls in [self._randTrackClass1, self._randTrackClass2]:
assert cls in [None, PermutedSegsAndSampledIntersegsTrack, \
PermutedSegsAndIntersegsTrack, RandomGenomeLocationTrack, SegsSampledByIntensityTrack, ShuffledMarksTrack, SegsSampledByDistanceToReferenceTrack, PointsSampledFromBinaryIntensityTrack]
#print self._randTrackClass1, self._randTrackClass2
self._rawStatistic = self.getRawStatisticClass(rawStatistic)
#self._randTrackList = []
self._tails = tails
if kwArgs.get('minimal') == True:
self._numResamplings = 1
self._kwArgs['maxSamples'] = 1
else:
self._numResamplings = int(numResamplings)
CompBinManager.ALLOW_COMP_BIN_SPLITTING = False
self._randResults = []
self._observation = None
#to load r libraries for McFdr:
McFdr._initMcFdr()
def __init__(self, region, track, track2, rawStatistic=None, **kwArgs):
assert rawStatistic is not None
assert type(rawStatistic) == str
from gold.statistic.AllStatistics import STAT_CLASS_DICT
self._rawStatistic = STAT_CLASS_DICT[rawStatistic]
Statistic.__init__(self, region, track, track2, rawStatistic=rawStatistic, **kwArgs)
def __init__(self,
region,
track,
track2=None,
windowSize=21,
windowBpSize=50000,
sdOfGaussian=20000,
guaranteeBpCoverByWindow='True',
withOverlaps='no',
**kwArgs):
self._windowSize = int(windowSize)
self._windowBpSize = int(windowBpSize)
self._sdOfGaussian = int(sdOfGaussian)
self._guaranteeBpCoverByWindow = ast.literal_eval(
guaranteeBpCoverByWindow)
assert (withOverlaps in ['no', 'yes'])
self._withOverlaps = withOverlaps
Statistic.__init__(self,
region,
track,
track2,
windowSize=windowSize,
windowBpSize=windowBpSize,
sdOfGaussian=sdOfGaussian,
guaranteeBpCoverByWindow=guaranteeBpCoverByWindow,
withOverlaps=withOverlaps,
**kwArgs)
def __init__(self, region, track, track2=None, numDiscreteVals=None, marksStat='MarksListStat', **kwArgs):
self._numDiscreteVals = numDiscreteVals
self._marksStat = marksStat
self._numHistBins = int(self._numDiscreteVals)
Statistic.__init__(self, region, track, track2, numDiscreteVals=numDiscreteVals, marksStat=marksStat, **kwArgs)
def __init__(self, region, track, track2, rawStatistic=None, **kwArgs):
assert rawStatistic is not None
assert isinstance(rawStatistic, basestring)
from gold.statistic.AllStatistics import STAT_CLASS_DICT
self._rawStatistic = STAT_CLASS_DICT[rawStatistic]
Statistic.__init__(self, region, track, track2, rawStatistic=rawStatistic, **kwArgs)
def __init__(self, region, track, track2, childClass, resultKey, **kwArgs):
Statistic.__init__(self, region, track, track2, childClass=childClass, resultKey=resultKey, **kwArgs)
if type(childClass) is str:
childClass = self.getRawStatisticClass(childClass)
self._childClass = childClass
self._resultKey = resultKey
self._kwArgs = kwArgs
def __init__(self, region, track, track2=None, numDiscreteVals=None, marksStat='MarksListStat', **kwArgs):
self._numDiscreteVals = numDiscreteVals
self._marksStat = marksStat
self._numHistBins = int(self._numDiscreteVals)
Statistic.__init__(self, region, track, track2, numDiscreteVals=numDiscreteVals, marksStat=marksStat, **kwArgs)
def __init__(self, region, track, track2=None, numDiscreteVals=None, reducedNumDiscreteVals=None, marksStat='MarksListStat', **kwArgs):
self._numDiscreteVals = numDiscreteVals
self._reducedNumDiscreteVals = reducedNumDiscreteVals
assert numDiscreteVals is not None and numDiscreteVals==reducedNumDiscreteVals
self._marksStat = marksStat
Statistic.__init__(self, region, track, track2, numDiscreteVals=numDiscreteVals, \
reducedNumDiscreteVals=reducedNumDiscreteVals, marksStat=marksStat, **kwArgs)
def __init__(self,
region,
track,
track2,
rawStatistic=None,
normalizationType='zeroToOne',
minimal=False,
**kwArgs):
if minimal == True:
self._globalSource = MinimalBinSource(region.genome)
else:
from gold.application.StatRunner import StatJob
assert StatJob.USER_BIN_SOURCE is not None
self._globalSource = StatJob.USER_BIN_SOURCE
Statistic.__init__(self,
region,
track,
track2,
rawStatistic=rawStatistic,
normalizationType=normalizationType,
minimal=minimal,
**kwArgs)
if type(rawStatistic) is str:
from gold.statistic.AllStatistics import STAT_CLASS_DICT
rawStatistic = STAT_CLASS_DICT[rawStatistic]
self._rawStatistic = rawStatistic
self._normalizationType = normalizationType
def __init__(self,
region,
track,
track2,
rawStatisticTrack1=None,
rawStatisticTrack2=None,
combineOperation=None,
**kwArgs):
#Note: Could take parameter track1kwArgs which could be a dict to be sent further as kwArgs to rawStatisticTrack1, and correspondingly for track2
#assert combineOperation in ['product']
self._combineOperation = combineOperation
Statistic.__init__(self,
region,
track,
track2,
rawStatisticTrack1=rawStatisticTrack1,
rawStatisticTrack2=rawStatisticTrack2,
combineOperation=combineOperation,
**kwArgs)
from gold.statistic.AllStatistics import STAT_CLASS_DICT
if type(rawStatisticTrack1) is str:
rawStatisticTrack1 = STAT_CLASS_DICT[rawStatisticTrack1]
if type(rawStatisticTrack2) is str:
rawStatisticTrack2 = STAT_CLASS_DICT[rawStatisticTrack2]
self._rawStatisticTrack1 = rawStatisticTrack1
self._rawStatisticTrack2 = rawStatisticTrack2
def __init__(self,
region,
track,
track2,
numSubBins=10,
method='pearson',
tail='different',
**kwArgs):
assert method in ['pearson', 'spearman', 'kendall']
assert tail in ['more', 'less', 'different']
tailMapping = {
'more': "greater",
'less': "less",
'different': "two.sided"
}
if kwArgs.get('minimal') != True:
silenceRWarnings()
self._numSubBins = numSubBins
self._method = method
self._rTail = tailMapping[tail]
Statistic.__init__(self,
region,
track,
track2,
method=method,
tail=tail,
**kwArgs)
def __init__(self, region, track, track2, rawStatistic, **kwArgs):
self._rawStatistic = rawStatistic
Statistic.__init__(self,
region,
track,
track2,
rawStatistic=rawStatistic,
**kwArgs)
def __init__(self, region, track, track2, numDiscreteVals=None, **kwArgs):
self._numDiscreteVals = numDiscreteVals
Statistic.__init__(self,
region,
track,
track2,
numDiscreteVals=numDiscreteVals,
**kwArgs)
def __init__(self, region, track, track2, rawStatistic=None, **kwArgs):
assert rawStatistic is not None
assert type(rawStatistic) == str
CompBinManager.ALLOW_COMP_BIN_SPLITTING = False
from gold.statistic.AllStatistics import STAT_CLASS_DICT
self._rawStatistic = STAT_CLASS_DICT[rawStatistic]
Statistic.__init__(self, region, track, track2, rawStatistic=rawStatistic, **kwArgs)
def __init__(self, region, track, track2, markReq=None, **kwArgs):
self._markReq = markReq
Statistic.__init__(self,
region,
track,
track2,
markReq=markReq,
**kwArgs)
def __init__(self, region, track, track2, rawStatistic=None, **kwArgs):
Statistic.__init__(self, region, track, track2, rawStatistic=rawStatistic, **kwArgs)
if type(rawStatistic) is str:
from gold.statistic.AllStatistics import STAT_CLASS_DICT
rawStatistic = STAT_CLASS_DICT[rawStatistic]
self._rawStatistic = rawStatistic
def __init__(self, region, track, track2, track1SummarizerName, track2SummarizerName, *args, **kwArgs):
Statistic.__init__(self, region, track, track2, track1SummarizerName=track1SummarizerName, \
track2SummarizerName=track2SummarizerName, allowIdenticalTracks=True, **kwArgs)
from gold.statistic.AllStatistics import STAT_CLASS_DICT
assert( track1SummarizerName in STAT_CLASS_DICT and track2SummarizerName in STAT_CLASS_DICT)
self._track1Summarizer = STAT_CLASS_DICT[track1SummarizerName]
self._track2Summarizer = STAT_CLASS_DICT[track2SummarizerName]
def __init__(self, region, track, track2=None, withOverlaps='no', markType='number', enforcePoints=True, **kwArgs):
assert( withOverlaps in ['no','yes'] )
self._withOverlaps = withOverlaps
self._markType = markType
if type(enforcePoints) == str:
enforcePoints = eval(enforcePoints)
self._enforcePoints = enforcePoints
Statistic.__init__(self, region, track, track2, withOverlaps=withOverlaps, markType=markType, enforcePoints=enforcePoints, **kwArgs)
def __init__(self, region, track, track2=None, numDiscreteVals=None, reducedNumDiscreteVals=None, \
controlTrackNameList=None, **kwArgs):
assert controlTrackNameList is not None
self._numDiscreteVals = numDiscreteVals
self._reducedNumDiscreteVals = reducedNumDiscreteVals
self._controlTrackNameList = controlTrackNameList
Statistic.__init__(self, region, track, track2, numDiscreteVals=numDiscreteVals, \
reducedNumDiscreteVals=reducedNumDiscreteVals, controlTrackNameList=controlTrackNameList, **kwArgs)
def __init__(self, region, track, track2=None, windowSize=21, windowBpSize=50000, sdOfGaussian=20000, guaranteeBpCoverByWindow='True', withOverlaps='no', **kwArgs):
self._windowSize = int(windowSize)
self._windowBpSize = int(windowBpSize)
self._sdOfGaussian = int(sdOfGaussian)
self._guaranteeBpCoverByWindow = eval(guaranteeBpCoverByWindow)
assert( withOverlaps in ['no','yes'])
self._withOverlaps = withOverlaps
Statistic.__init__(self,region,track,track2,windowSize=windowSize, windowBpSize=windowBpSize, sdOfGaussian=sdOfGaussian, guaranteeBpCoverByWindow=guaranteeBpCoverByWindow, withOverlaps=withOverlaps, **kwArgs)
def __init__(self, region, track, track2, maxRelDifference=0, maxAbsDifference=0, **kwArgs):
self._maxRelDifference = float(maxRelDifference)
self._maxAbsDifference = int(maxAbsDifference)
assert( 0 <= self._maxRelDifference <= 1 )
assert( 0 <= self._maxAbsDifference )
Statistic.__init__(self, region, track, track2, maxRelDifference=maxRelDifference, \
maxAbsDifference=maxAbsDifference, **kwArgs)
def __init__(self, region, track, track2=None, numDiscreteVals=None, reducedNumDiscreteVals=None, \
controlTrackNameList=None, **kwArgs):
assert controlTrackNameList is not None
self._numDiscreteVals = numDiscreteVals
self._reducedNumDiscreteVals = reducedNumDiscreteVals
self._controlTrackNameList = controlTrackNameList
Statistic.__init__(self, region, track, track2, numDiscreteVals=numDiscreteVals, \
reducedNumDiscreteVals=reducedNumDiscreteVals, controlTrackNameList=controlTrackNameList, **kwArgs)
def __init__(self, region, track, track2, method='pearson', tail='different', **kwArgs):
assert method in ['pearson','spearman','kendall']
assert tail in ['more', 'less', 'different']
tailMapping = {'more': "greater", 'less': "less", 'different': "two.sided"}
self._method = method
self._rTail = tailMapping[tail]
Statistic.__init__(self, region, track, track2, method=method, tail=tail, **kwArgs)
def __init__(self, region, track, track2, rawStatistic, randTrackClass=None, assumptions=None, tails=None, numResamplings=2000, randomSeed=None, **kwArgs):
#print 'TEMP RM:',kwArgs
if tails==None:
if 'tail' in kwArgs:
tailTranslator = {'more':'right-tail', 'less':'left-tail', 'different':'two-tail'}
tails = tailTranslator[kwArgs['tail']]
if DebugConfig.VERBOSE:
logMessage('Argument tail provided instead of tails to RandomizationManagerStatUnsplittable', level=logging.DEBUG)
else:
tails = 'right-tail' # or 'two-tail'?
logMessage('No tails argument provided to RandomizationManagerStatUnsplittable', level=logging.DEBUG)
if track2 is None:
self._track2 = None #to allow track2 to be passed on as None to rawStatistics without error. For use by single-track MC-tests..
from gold.util.RandomUtil import getManualSeed, setManualSeed
if randomSeed is not None and randomSeed != 'Random' and getManualSeed() is None:
setManualSeed(int(randomSeed))
Statistic.__init__(self, region, track, track2, rawStatistic=rawStatistic, randTrackClass=randTrackClass, assumptions=assumptions, tails=tails, numResamplings=numResamplings, randomSeed=randomSeed, **kwArgs)
#if type(rawStatistic) is str:
# from gold.statistic.AllStatistics import STAT_CLASS_DICT
# rawStatistic = STAT_CLASS_DICT[rawStatistic]
assert (randTrackClass is None) ^ (assumptions is None) # xor
if assumptions is not None:
assert assumptions.count('_') == 1, assumptions
randTrackClass1, randTrackClass2 = assumptions.split('_')
else:
randTrackClass1 = None
randTrackClass2 = randTrackClass
self._randTrackClass1, self._randTrackClass2 = \
[ ( globals()[clsDef] if clsDef not in ['None',''] else None ) \
if type(clsDef) is str else clsDef for clsDef in [randTrackClass1, randTrackClass2] ]
assert not (randTrackClass1 is None and randTrackClass2 is None)
for cls in [self._randTrackClass1, self._randTrackClass2]:
assert cls in [None, PermutedSegsAndSampledIntersegsTrack, \
PermutedSegsAndIntersegsTrack, RandomGenomeLocationTrack, SegsSampledByIntensityTrack, ShuffledMarksTrack]
#print self._randTrackClass1, self._randTrackClass2
self._rawStatistic = self.getRawStatisticClass(rawStatistic)
#self._randTrackList = []
self._tails = tails
if kwArgs.get('minimal') == True:
self._numResamplings = 1
self._kwArgs['maxSamples'] = 1
else:
self._numResamplings = int(numResamplings)
CompBinManager.ALLOW_COMP_BIN_SPLITTING = False
self._randResults = []
self._observation = None
#to load r libraries for McFdr:
McFdr._initMcFdr()
def __init__(self, region, track, track2, track1SummarizerName,
track2SummarizerName, *args, **kwArgs):
Statistic.__init__(self, region, track, track2, track1SummarizerName=track1SummarizerName, \
track2SummarizerName=track2SummarizerName, allowIdenticalTracks=True, **kwArgs)
from gold.statistic.AllStatistics import STAT_CLASS_DICT
assert (track1SummarizerName in STAT_CLASS_DICT
and track2SummarizerName in STAT_CLASS_DICT)
self._track1Summarizer = STAT_CLASS_DICT[track1SummarizerName]
self._track2Summarizer = STAT_CLASS_DICT[track2SummarizerName]
def __init__(self, region, track, track2, distDirection='both', **kwArgs):
assert (distDirection in ['left', 'right', 'both'])
self._distDirection = distDirection
Statistic.__init__(self,
region,
track,
track2,
distDirection=distDirection,
**kwArgs)
def __init__(self, region, track, track2, minRelSimilarity=1, **kwArgs):
self._minRelSimilarity = float(minRelSimilarity)
assert (0 <= self._minRelSimilarity <= 1)
Statistic.__init__(self,
region,
track,
track2,
minRelSimilarity=minRelSimilarity,
**kwArgs)
def __init__(self, region, track, track2, statClassList=None, **kwArgs):
Statistic.__init__(self, region, track, track2, statClassList=statClassList, **kwArgs)
#self._kwArgs = kwArgs
if type(statClassList) == list:
self._statClassList = statClassList
elif type(statClassList) == str:
from gold.statistic.AllStatistics import STAT_CLASS_DICT
self._statClassList = [STAT_CLASS_DICT[x] for x in \
statClassList.replace(' ','').replace('^','|').split('|')]
else:
raise ShouldNotOccurError
def __init__(self, region, track, track2=None, randTrackClass=None, randomSeed=None, **kwArgs):
#print 'HEI'
from gold.util.RandomUtil import getManualSeed, setManualSeed
if randomSeed is not None and randomSeed != 'Random' and getManualSeed() is None:
setManualSeed(int(randomSeed))
Statistic.__init__(self, region, track, randTrackClass=randTrackClass, randomSeed=randomSeed, **kwArgs)
self._randTrackClass = self.getRandTrackClass(randTrackClass)
CompBinManager.ALLOW_COMP_BIN_SPLITTING = False
def __init__(self, region, track, track2, rawStatistic=None, **kwArgs):
assert rawStatistic is not None
assert isinstance(rawStatistic, basestring)
CompBinManager.ALLOW_COMP_BIN_SPLITTING = False
from gold.statistic.AllStatistics import STAT_CLASS_DICT
self._rawStatistic = STAT_CLASS_DICT[rawStatistic]
Statistic.__init__(self,
region,
track,
track2,
rawStatistic=rawStatistic,
**kwArgs)
def __init__(self, region, track, track2, rawStatistic=None, **kwArgs):
Statistic.__init__(self,
region,
track,
track2,
rawStatistic=rawStatistic,
**kwArgs)
if type(rawStatistic) is str:
from gold.statistic.AllStatistics import STAT_CLASS_DICT
rawStatistic = STAT_CLASS_DICT[rawStatistic]
self._rawStatistic = rawStatistic
def __init__(self,
region,
track,
track2=None,
numDiscreteVals=None,
reducedNumDiscreteVals=None,
marksStat='MarksListStat',
**kwArgs):
self._numDiscreteVals = numDiscreteVals
self._reducedNumDiscreteVals = reducedNumDiscreteVals
assert numDiscreteVals is not None and numDiscreteVals == reducedNumDiscreteVals
self._marksStat = marksStat
Statistic.__init__(self, region, track, track2, numDiscreteVals=numDiscreteVals, \
reducedNumDiscreteVals=reducedNumDiscreteVals, marksStat=marksStat, **kwArgs)
def __init__(self, region, track, track2=None, numDiscreteVals=None, reducedNumDiscreteVals=None, \
marksStat='MarksListStat', controlTrackNameList=None, **kwArgs):
self._numDiscreteVals = int(numDiscreteVals)
self._reducedNumDiscreteVals = int(reducedNumDiscreteVals)
self._marksStat = marksStat
assert controlTrackNameList is not None
self._controlTrackNameList = [x.split('^') for x in controlTrackNameList.split('^^')] \
if type(controlTrackNameList) == str else controlTrackNameList
assert len(controlTrackNameList) > 0
Statistic.__init__(self, region, track, track2, numDiscreteVals=numDiscreteVals, \
reducedNumDiscreteVals=reducedNumDiscreteVals, marksStat=marksStat, **kwArgs)
def __init__(self,
region,
track,
track2,
maxRelDifference=0,
maxAbsDifference=0,
**kwArgs):
self._maxRelDifference = float(maxRelDifference)
self._maxAbsDifference = int(maxAbsDifference)
assert (0 <= self._maxRelDifference <= 1)
assert (0 <= self._maxAbsDifference)
Statistic.__init__(self, region, track, track2, maxRelDifference=maxRelDifference, \
maxAbsDifference=maxAbsDifference, **kwArgs)
def __init__(self, region, track, track2, countMethod='count', normalizePointsBy='rowSum', \
pValueAdjustment='unadjusted', threshold='0.05', **kwArgs):
assert countMethod in ['count', 'binary', 'logOfCount']
self._countMethod = countMethod
assert normalizePointsBy in [
'nothing', 'rowSum', 'rowSumBalanced', 'rowCount',
'rowCountBalanced'
]
self._normalizePointsBy = normalizePointsBy
assert pValueAdjustment in ['unadjusted', 'fdr']
self._pValueAdjustment = pValueAdjustment
self._threshold = float(threshold)
assert 0.0 <= self._threshold <= 1.0
Statistic.__init__(self, region, track, track2, countMethod=countMethod, normalizePointsBy=normalizePointsBy, \
pValueAdjustment=pValueAdjustment, threshold=threshold, **kwArgs)
def __init__(self,
region,
track,
track2=None,
randTrackClass=None,
**kwArgs):
Statistic.__init__(self,
region,
track,
randTrackClass=randTrackClass,
**kwArgs)
self._randTrackClass = self.getRandTrackClass(randTrackClass)
CompBinManager.ALLOW_COMP_BIN_SPLITTING = False
def __init__(self,
region,
track,
track2,
statClass1=None,
statClass2=None,
**kwArgs):
self._statClass1 = statClass1
self._statClass2 = statClass2
Statistic.__init__(self,
region,
track,
track2,
statClass1=statClass1,
statClass2=statClass2,
**kwArgs)
def __init__(self,
region,
track,
track2,
maxCountRegionSize='None',
**kwArgs):
if maxCountRegionSize == 'None':
self._maxCountRegionSize = None
else:
self._maxCountRegionSize = int(maxCountRegionSize)
Statistic.__init__(self,
region,
track,
track2,
maxCountRegionSize=maxCountRegionSize,
**kwArgs)
def __init__(self, region, track, track2, statClassList=None, **kwArgs):
Statistic.__init__(self,
region,
track,
track2,
statClassList=statClassList,
**kwArgs)
#self._kwArgs = kwArgs
if type(statClassList) == list:
self._statClassList = statClassList
elif isinstance(statClassList, basestring):
from gold.statistic.AllStatistics import STAT_CLASS_DICT
self._statClassList = [STAT_CLASS_DICT[x] for x in \
statClassList.replace(' ','').replace('^','|').split('|')]
else:
raise ShouldNotOccurError
def __init__(self, region, track, track2, rawStatistic=None, normalizationType='zeroToOne', minimal=False, **kwArgs):
if minimal == True:
self._globalSource = MinimalBinSource(region.genome)
else:
from gold.application.StatRunner import StatJob
assert StatJob.USER_BIN_SOURCE is not None
self._globalSource = StatJob.USER_BIN_SOURCE
Statistic.__init__(self, region, track, track2, rawStatistic=rawStatistic, normalizationType=normalizationType, minimal=minimal, **kwArgs)
if type(rawStatistic) is str:
from gold.statistic.AllStatistics import STAT_CLASS_DICT
rawStatistic = STAT_CLASS_DICT[rawStatistic]
self._rawStatistic = rawStatistic
self._normalizationType = normalizationType
def __init__(self,
region,
track,
track2,
assumptions='poissonPoints',
tail='different',
**kwArgs):
assert (tail in ['less', 'more', 'different'])
assert assumptions == 'poissonPoints'
self._tail = tail
Statistic.__init__(self,
region,
track,
track2,
assumptions=assumptions,
tail=tail,
**kwArgs)
def __init__(self,
region,
track,
track2=None,
numDiscreteVals=None,
marksStat='MarksListStat',
printIntervals=False,
**kwArgs):
self._numDiscreteVals = int(numDiscreteVals)
self._marksStat = marksStat
assert numDiscreteVals is not None
self._printIntervals = printIntervals
Statistic.__init__(self,
region,
track,
track2,
numDiscreteVals=numDiscreteVals,
marksStat=marksStat,
**kwArgs)
def __init__(self,
region,
track,
track2=None,
withOverlaps='no',
markType='number',
enforcePoints=True,
**kwArgs):
assert (withOverlaps in ['no', 'yes'])
self._withOverlaps = withOverlaps
self._markType = markType
if isinstance(enforcePoints, basestring):
enforcePoints = ast.literal_eval(enforcePoints)
self._enforcePoints = enforcePoints
Statistic.__init__(self,
region,
track,
track2,
withOverlaps=withOverlaps,
markType=markType,
enforcePoints=enforcePoints,
**kwArgs)
def __init__(self,
region,
track,
track2,
method='pearson',
tail='different',
**kwArgs):
assert method in ['pearson', 'spearman', 'kendall']
assert tail in ['more', 'less', 'different']
tailMapping = {
'more': "greater",
'less': "less",
'different': "two.sided"
}
self._method = method
self._rTail = tailMapping[tail]
Statistic.__init__(self,
region,
track,
track2,
method=method,
tail=tail,
**kwArgs)
def __init__(self, region, track, track2=None, numDiscreteVals=None, marksStat='MarksListStat', **kwArgs):
self._numDiscreteVals = int(numDiscreteVals)
self._marksStat = marksStat
assert numDiscreteVals is not None
Statistic.__init__(self, region, track, track2, numDiscreteVals=numDiscreteVals, marksStat=marksStat, **kwArgs)
def __init__(self, region, track, track2, tail='more', **kwArgs):
assert( tail in ['less','more','different'])
self._tail = tail
Statistic.__init__(self, region, track, track2, tail=tail, **kwArgs)
def __init__(self, region, track, track2, rawStatistic, **kwArgs):
self._rawStatistic = rawStatistic
Statistic.__init__(self, region, track, track2, rawStatistic=rawStatistic, **kwArgs)
def __init__(self, region, track, track2, aggregateOperation=None, **kwArgs):
self._aggregateOperation = aggregateOperation
Statistic.__init__(self, region, track, track2, aggregateOperation=aggregateOperation, **kwArgs)
def __init__(self, region, track, track2, **kwArgs):
Statistic.__init__(self, region, track, track2, **kwArgs)
def __init__(self, region, track, track2, numDiscreteVals=None, **kwArgs):
self._numDiscreteVals = numDiscreteVals
Statistic.__init__(self, region, track, track2, numDiscreteVals=numDiscreteVals, **kwArgs)
def __init__(self, region, track, track2, tail='more', **kwArgs):
assert (tail in ['less', 'more', 'different'])
self._tail = tail
Statistic.__init__(self, region, track, track2, tail=tail, **kwArgs)
def __init__(self, region, track, track2, calcPointTotals=False, **kwArgs):
if type(calcPointTotals) == str:
calcPointTotals = eval(calcPointTotals)
self._calcPointTotals = calcPointTotals
Statistic.__init__(self, region, track, track2, calcPointTotals=calcPointTotals, **kwArgs)
def __init__(self, region, track, track2, markReq=None, **kwArgs):
self._markReq = markReq
Statistic.__init__(self, region, track, track2, markReq=markReq, **kwArgs)
def __init__(self, region, track, track2, tail='', **kwArgs):
self._altHyp = tail
if not self._altHyp in ['ha1','ha2','ha3','ha4']:
raise NotSupportedError(self._altHyp)
Statistic.__init__(self, region, track, track2, tail=tail, **kwArgs)
def __init__(self, region, track, track2=None, numDiscreteVals=None, reducedNumDiscreteVals=None, **kwArgs):
self._numDiscreteVals = int(numDiscreteVals)
self._reducedNumDiscreteVals = int(reducedNumDiscreteVals)
assert numDiscreteVals is not None and numDiscreteVals==reducedNumDiscreteVals
Statistic.__init__(self, region, track, track2, numDiscreteVals=numDiscreteVals, reducedNumDiscreteVals=reducedNumDiscreteVals, **kwArgs)
def __init__(self, region, track, track2=None, numSubBins=10, **kwArgs):
#track2 is ignored..
self._numSubBins = int(numSubBins)
Statistic.__init__(self, region, track, track2, numSubBins=numSubBins, **kwArgs)
def __init__(self, region, track, track2, markType='number', **kwArgs):
self._markType = markType
#r('sink(file("/dev/null", open="wt"), type="message")')
silenceRWarnings()
Statistic.__init__(self, region, track, track2, markType=markType, **kwArgs)
def __init__(self, region, track, track2, scriptFn='', **kwArgs):
self._scriptFn = scriptFn
if self._scriptFn != '':
self._useMC = any(["#Use in Monte Carlo" in line for line in open(scriptFn.decode('hex_codec'))])
Statistic.__init__(self, region, track, track2, scriptFn=scriptFn, **kwArgs)
def __init__(self, region, track, track2, distDirection='both', **kwArgs):
assert( distDirection in ['both']) #only supported now..
self._distDirection = distDirection
Statistic.__init__(self, region, track, track2, distDirection=distDirection, **kwArgs)