--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/src/parpg/characterstatistics.py	Sat May 14 01:12:35 2011 -0700
@@ -0,0 +1,165 @@
+#   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
+
+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[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