249 lines
9.2 KiB
Python
249 lines
9.2 KiB
Python
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from direct.showbase.PythonUtil import randFloat, normalDistrib, Enum
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from direct.showbase.PythonUtil import clampScalar
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from toontown.toonbase import TTLocalizer, ToontownGlobals
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import random, copy
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TraitDivisor = 10000
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def getTraitNames():
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if not hasattr(PetTraits, 'TraitNames'):
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traitNames = []
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for desc in PetTraits.TraitDescs:
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traitNames.append(desc[0])
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PetTraits.TraitNames = traitNames
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return PetTraits.TraitNames
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def uniform(min, max, rng):
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return randFloat(min, max, rng.random)
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def gaussian(min, max, rng):
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return normalDistrib(min, max, rng.gauss)
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class TraitDistribution:
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TraitQuality = Enum('VERY_BAD, BAD, AVERAGE, GOOD, VERY_GOOD')
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TraitTypes = Enum('INCREASING, DECREASING')
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Sz2MinMax = None
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TraitType = None
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TraitCutoffs = {TraitTypes.INCREASING: {TraitQuality.VERY_BAD: 0.1,
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TraitQuality.BAD: 0.25,
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TraitQuality.GOOD: 0.75,
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TraitQuality.VERY_GOOD: 0.9},
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TraitTypes.DECREASING: {TraitQuality.VERY_BAD: 0.9,
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TraitQuality.BAD: 0.75,
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TraitQuality.GOOD: 0.25,
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TraitQuality.VERY_GOOD: 0.1}}
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def __init__(self, rndFunc = gaussian):
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self.rndFunc = rndFunc
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if not hasattr(self.__class__, 'GlobalMinMax'):
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_min = 1.0
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_max = 0.0
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minMax = self.Sz2MinMax
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for sz in minMax:
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thisMin, thisMax = minMax[sz]
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_min = min(_min, thisMin)
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_max = max(_max, thisMax)
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self.__class__.GlobalMinMax = [_min, _max]
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def getRandValue(self, szId, rng = random):
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min, max = self.getMinMax(szId)
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return self.rndFunc(min, max, rng)
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def getHigherIsBetter(self):
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return self.TraitType == TraitDistribution.TraitTypes.INCREASING
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def getMinMax(self, szId):
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return (self.Sz2MinMax[szId][0], self.Sz2MinMax[szId][1])
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def getGlobalMinMax(self):
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return (self.GlobalMinMax[0], self.GlobalMinMax[1])
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def _getTraitPercent(self, traitValue):
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gMin, gMax = self.getGlobalMinMax()
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if traitValue < gMin:
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gMin = traitValue
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elif traitValue > gMax:
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gMax = traitValue
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return (traitValue - gMin) / (gMax - gMin)
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def getPercentile(self, traitValue):
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if self.TraitType is TraitDistribution.TraitTypes.INCREASING:
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return self._getTraitPercent(traitValue)
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else:
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return 1.0 - self._getTraitPercent(traitValue)
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def getQuality(self, traitValue):
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TraitQuality = TraitDistribution.TraitQuality
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TraitCutoffs = self.TraitCutoffs[self.TraitType]
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percent = self._getTraitPercent(traitValue)
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if self.TraitType is TraitDistribution.TraitTypes.INCREASING:
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if percent <= TraitCutoffs[TraitQuality.VERY_BAD]:
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return TraitQuality.VERY_BAD
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elif percent <= TraitCutoffs[TraitQuality.BAD]:
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return TraitQuality.BAD
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elif percent >= TraitCutoffs[TraitQuality.VERY_GOOD]:
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return TraitQuality.VERY_GOOD
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elif percent >= TraitCutoffs[TraitQuality.GOOD]:
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return TraitQuality.GOOD
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else:
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return TraitQuality.AVERAGE
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elif percent <= TraitCutoffs[TraitQuality.VERY_GOOD]:
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return TraitQuality.VERY_GOOD
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elif percent <= TraitCutoffs[TraitQuality.GOOD]:
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return TraitQuality.GOOD
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elif percent >= TraitCutoffs[TraitQuality.VERY_BAD]:
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return TraitQuality.VERY_BAD
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elif percent >= TraitCutoffs[TraitQuality.BAD]:
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return TraitQuality.BAD
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else:
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return TraitQuality.AVERAGE
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def getExtremeness(self, traitValue):
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percent = self._getTraitPercent(traitValue)
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if percent < 0.5:
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howExtreme = (0.5 - percent) * 2.0
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else:
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howExtreme = (percent - 0.5) * 2.0
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return clampScalar(howExtreme, 0.0, 1.0)
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class PetTraits:
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class StdIncDistrib(TraitDistribution):
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TraitType = TraitDistribution.TraitTypes.INCREASING
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Sz2MinMax = {ToontownGlobals.ToontownCentral: (0.2, 0.65),
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ToontownGlobals.DonaldsDock: (0.3, 0.7),
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ToontownGlobals.DaisyGardens: (0.4, 0.75),
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ToontownGlobals.MinniesMelodyland: (0.5, 0.8),
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ToontownGlobals.TheBrrrgh: (0.6, 0.85),
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ToontownGlobals.DonaldsDreamland: (0.7, 0.9)}
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class StdDecDistrib(TraitDistribution):
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TraitType = TraitDistribution.TraitTypes.DECREASING
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Sz2MinMax = {ToontownGlobals.ToontownCentral: (0.35, 0.8),
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ToontownGlobals.DonaldsDock: (0.3, 0.7),
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ToontownGlobals.DaisyGardens: (0.25, 0.6),
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ToontownGlobals.MinniesMelodyland: (0.2, 0.5),
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ToontownGlobals.TheBrrrgh: (0.15, 0.4),
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ToontownGlobals.DonaldsDreamland: (0.1, 0.3)}
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class ForgetfulnessDistrib(TraitDistribution):
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TraitType = TraitDistribution.TraitTypes.DECREASING
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Sz2MinMax = {ToontownGlobals.ToontownCentral: (0.0, 1.0),
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ToontownGlobals.DonaldsDock: (0.0, 0.9),
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ToontownGlobals.DaisyGardens: (0.0, 0.8),
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ToontownGlobals.MinniesMelodyland: (0.0, 0.7),
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ToontownGlobals.TheBrrrgh: (0.0, 0.6),
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ToontownGlobals.DonaldsDreamland: (0.0, 0.5)}
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TraitDescs = (('forgetfulness', ForgetfulnessDistrib(), True),
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('boredomThreshold', StdIncDistrib(), True),
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('restlessnessThreshold', StdIncDistrib(), True),
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('playfulnessThreshold', StdDecDistrib(), True),
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('lonelinessThreshold', StdIncDistrib(), True),
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('sadnessThreshold', StdIncDistrib(), True),
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('fatigueThreshold', StdIncDistrib(), True),
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('hungerThreshold', StdIncDistrib(), True),
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('confusionThreshold', StdIncDistrib(), True),
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('excitementThreshold', StdDecDistrib(), True),
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('angerThreshold', StdIncDistrib(), True),
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('surpriseThreshold', StdIncDistrib(), False),
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('affectionThreshold', StdDecDistrib(), True))
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NumTraits = len(TraitDescs)
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class Trait:
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def __init__(self, index, traitsObj, value = None):
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self.name, distrib, self.hasWorth = PetTraits.TraitDescs[index]
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if value is not None:
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self.value = value
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else:
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szId = traitsObj.safeZoneId
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self.value = distrib.getRandValue(szId, traitsObj.rng)
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self.value = int(self.value * TraitDivisor) / float(TraitDivisor)
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self.higherIsBetter = distrib.getHigherIsBetter()
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self.percentile = distrib.getPercentile(self.value)
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self.quality = distrib.getQuality(self.value)
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self.howExtreme = distrib.getExtremeness(self.value)
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return
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def __repr__(self):
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return 'Trait: %s, %s, %s, %s' % (self.name,
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self.value,
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TraitDistribution.TraitQuality.getString(self.quality),
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self.howExtreme)
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def __init__(self, traitSeed, safeZoneId, traitValueList = []):
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self.traitSeed = traitSeed
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self.safeZoneId = safeZoneId
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self.rng = random.Random(self.traitSeed)
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self.traits = {}
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for i in xrange(len(PetTraits.TraitDescs)):
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if i < len(traitValueList) and traitValueList[i] > 0.0:
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trait = PetTraits.Trait(i, self, traitValueList[i])
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else:
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trait = PetTraits.Trait(i, self)
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self.traits[trait.name] = trait
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self.__dict__[trait.name] = trait.value
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extremeTraits = []
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for trait in self.traits.values():
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if not trait.hasWorth:
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continue
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if trait.quality == TraitDistribution.TraitQuality.AVERAGE:
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continue
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i = 0
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while i < len(extremeTraits) and extremeTraits[i].howExtreme > trait.howExtreme:
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i += 1
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extremeTraits.insert(i, trait)
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self.extremeTraits = []
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for trait in extremeTraits:
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self.extremeTraits.append((trait.name, trait.quality))
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def getValueList(self):
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traitValues = []
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for desc in PetTraits.TraitDescs:
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traitName = desc[0]
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traitValues.append(self.traits[traitName].value)
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return traitValues
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def getTraitValue(self, traitName):
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return self.traits[traitName].value
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def getExtremeTraits(self):
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return copy.copy(self.extremeTraits)
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def getOverallValue(self):
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total = 0
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numUsed = 0
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for trait in self.traits.values():
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if trait.hasWorth:
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if trait.higherIsBetter:
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value = trait.value
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else:
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value = 1.0 - trait.value
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total += value
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numUsed += 1
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value = total / len(self.traits.values())
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return value
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def getExtremeTraitDescriptions(self):
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descs = []
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TraitQuality = TraitDistribution.TraitQuality
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Quality2index = {TraitQuality.VERY_BAD: 0,
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TraitQuality.BAD: 1,
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TraitQuality.GOOD: 2,
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TraitQuality.VERY_GOOD: 3}
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for name, quality in self.extremeTraits:
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descs.append(TTLocalizer.PetTrait2descriptions[name][Quality2index[quality]])
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return descs
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