Mercurial > parpg-source
view characterstatistics.py @ 183:7b6aba7839ea
getGameEnvironment of GameState now returns a locals dictionary that is empty at the beginning of the game and will be saved in save games. The vals and funcs are now both in the globals dictionary.
This WILL break old saves.
author | Beliar <KarstenBock@gmx.net> |
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date | Thu, 15 Mar 2012 16:24:05 +0100 |
parents | 741d7d193bad |
children |
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# This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. """ Provides classes that define character stats and traits. """ from abc import ABCMeta, abstractmethod from weakref import ref as weakref from .serializers import SerializableRegistry from components import character_statistics class AbstractCharacterStatistic(object): __metaclass__ = ABCMeta @abstractmethod def __init__(self, description, minimum, maximum): self.description = description self.minimum = minimum self.maximum = maximum class PrimaryCharacterStatistic(AbstractCharacterStatistic): def __init__(self, long_name, short_name, description, minimum=0, maximum=100): AbstractCharacterStatistic.__init__(self, description=description, minimum=minimum, maximum=maximum) self.long_name = long_name self.short_name = short_name SerializableRegistry.registerClass( 'PrimaryCharacterStatistic', PrimaryCharacterStatistic, init_args=[ ('long_name', unicode), ('short_name', unicode), ('description', unicode), ('minimum', int), ('maximum', int), ], ) class SecondaryCharacterStatistic(AbstractCharacterStatistic): def __init__(self, name, description, unit, mean, sd, stat_modifiers, minimum=None, maximum=None): AbstractCharacterStatistic.__init__(self, description=description, minimum=minimum, maximum=maximum) self.name = name self.unit = unit self.mean = mean self.sd = sd self.stat_modifiers = stat_modifiers SerializableRegistry.registerClass( 'SecondaryCharacterStatistic', SecondaryCharacterStatistic, init_args=[ ('name', unicode), ('description', unicode), ('unit', unicode), ('mean', float), ('sd', float), ('stat_modifiers', dict), ('minimum', float), ('maximum', float), ], ) class AbstractStatisticValue(object): __metaclass__ = ABCMeta @abstractmethod def __init__(self, statistic_type, character): self.statistic_type = statistic_type self.character = weakref(character) class PrimaryStatisticValue(AbstractStatisticValue): def value(): def fget(self): return self._value def fset(self, new_value): assert 0 <= new_value <= 100 self._value = new_value def __init__(self, statistic_type, character, value): AbstractStatisticValue.__init__(self, statistic_type=statistic_type, character=character) self._value = None self.value = value class SecondaryStatisticValue(AbstractStatisticValue): def normalized_value(): def fget(self): return self._normalized_value def fset(self, new_value): self._normalized_value = new_value statistic_type = self.statistic_type mean = statistic_type.mean sd = statistic_type.sd self._value = self.calculate_value(mean, sd, new_value) return locals() normalized_value = property(**normalized_value()) def value(): def fget(self): return self._value def fset(self, new_value): self._value = new_value statistic_type = self.statistic_type mean = statistic_type.mean sd = statistic_type.sd self._normalized_value = self.calculate_value(mean, sd, new_value) return locals() value = property(**value()) def __init__(self, statistic_type, character): AbstractStatisticValue.__init__(self, statistic_type=statistic_type, character=character) mean = statistic_type.mean sd = statistic_type.sd normalized_value = self.derive_value(normalized=True) self._normalized_value = normalized_value self._value = self.calculate_value(mean, sd, normalized_value) def derive_value(self, normalized=True): """ Derive the current value """ statistic_type = self.statistic_type stat_modifiers = statistic_type.stat_modifiers character = self.character() value = sum( character_statistics.get_statistic(character, name).value * modifier for name, modifier in stat_modifiers.items() ) assert 0 <= value <= 100 if not normalized: mean = statistic_type.mean sd = statistic_type.sd value = self.calculate_value(mean, sd, value) return value @staticmethod def calculate_value(mean, sd, normalized_value): value = sd * (normalized_value - 50) + mean return value @staticmethod def calculate_normalized_value(mean, sd, value): normalized_value = ((value - mean) / sd) + 50 return normalized_value