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)