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annotate characterstatistics.py @ 0:7a89ea5404b1

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Initial commit of parpg-core.
author M. George Hansen <technopolitica@gmail.com>
date Sat, 14 May 2011 01:12:35 -0700
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children 741d7d193bad
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7a89ea5404b1 Initial commit of parpg-core.
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1 # This program is free software: you can redistribute it and/or modify
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2 # it under the terms of the GNU General Public License as published by
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3 # the Free Software Foundation, either version 3 of the License, or
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4 # (at your option) any later version.
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5
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6 # This program is distributed in the hope that it will be useful,
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7 # but WITHOUT ANY WARRANTY; without even the implied warranty of
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8 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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9 # GNU General Public License for more details.
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10
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11 # You should have received a copy of the GNU General Public License
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12 # along with this program. If not, see <http://www.gnu.org/licenses/>.
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13 """
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14 Provides classes that define character stats and traits.
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15 """
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16
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17 from abc import ABCMeta, abstractmethod
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18 from weakref import ref as weakref
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19
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20 from .serializers import SerializableRegistry
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21
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22 class AbstractCharacterStatistic(object):
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23 __metaclass__ = ABCMeta
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24
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25 @abstractmethod
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26 def __init__(self, description, minimum, maximum):
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27 self.description = description
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28 self.minimum = minimum
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29 self.maximum = maximum
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30
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31
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32 class PrimaryCharacterStatistic(AbstractCharacterStatistic):
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33 def __init__(self, long_name, short_name, description, minimum=0,
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34 maximum=100):
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35 AbstractCharacterStatistic.__init__(self, description=description,
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36 minimum=minimum, maximum=maximum)
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37 self.long_name = long_name
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38 self.short_name = short_name
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39
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40 SerializableRegistry.registerClass(
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41 'PrimaryCharacterStatistic',
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42 PrimaryCharacterStatistic,
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43 init_args=[
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44 ('long_name', unicode),
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45 ('short_name', unicode),
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46 ('description', unicode),
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47 ('minimum', int),
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48 ('maximum', int),
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49 ],
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50 )
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51
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52
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53 class SecondaryCharacterStatistic(AbstractCharacterStatistic):
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54 def __init__(self, name, description, unit, mean, sd, stat_modifiers,
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55 minimum=None, maximum=None):
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56 AbstractCharacterStatistic.__init__(self, description=description,
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57 minimum=minimum, maximum=maximum)
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58 self.name = name
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59 self.unit = unit
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60 self.mean = mean
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61 self.sd = sd
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62 self.stat_modifiers = stat_modifiers
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63
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64 SerializableRegistry.registerClass(
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65 'SecondaryCharacterStatistic',
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66 SecondaryCharacterStatistic,
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67 init_args=[
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68 ('name', unicode),
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69 ('description', unicode),
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70 ('unit', unicode),
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71 ('mean', float),
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72 ('sd', float),
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73 ('stat_modifiers', dict),
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74 ('minimum', float),
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75 ('maximum', float),
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76 ],
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77 )
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78
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79
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80 class AbstractStatisticValue(object):
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81 __metaclass__ = ABCMeta
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82
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83 @abstractmethod
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84 def __init__(self, statistic_type, character):
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85 self.statistic_type = statistic_type
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86 self.character = weakref(character)
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87
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88
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89 class PrimaryStatisticValue(AbstractStatisticValue):
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90 def value():
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91 def fget(self):
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92 return self._value
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93 def fset(self, new_value):
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94 assert 0 <= new_value <= 100
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95 self._value = new_value
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96
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97 def __init__(self, statistic_type, character, value):
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98 AbstractStatisticValue.__init__(self, statistic_type=statistic_type,
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99 character=character)
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100 self._value = None
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101 self.value = value
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102
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103
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104 class SecondaryStatisticValue(AbstractStatisticValue):
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105 def normalized_value():
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106 def fget(self):
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107 return self._normalized_value
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108 def fset(self, new_value):
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109 self._normalized_value = new_value
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110 statistic_type = self.statistic_type
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111 mean = statistic_type.mean
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112 sd = statistic_type.sd
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113 self._value = self.calculate_value(mean, sd, new_value)
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114 return locals()
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115 normalized_value = property(**normalized_value())
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116
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117 def value():
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118 def fget(self):
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119 return self._value
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120 def fset(self, new_value):
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121 self._value = new_value
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122 statistic_type = self.statistic_type
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123 mean = statistic_type.mean
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124 sd = statistic_type.sd
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125 self._normalized_value = self.calculate_value(mean, sd, new_value)
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126 return locals()
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127 value = property(**value())
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128
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129 def __init__(self, statistic_type, character):
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130 AbstractStatisticValue.__init__(self, statistic_type=statistic_type,
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131 character=character)
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132 mean = statistic_type.mean
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133 sd = statistic_type.sd
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134 normalized_value = self.derive_value(normalized=True)
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135 self._normalized_value = normalized_value
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136 self._value = self.calculate_value(mean, sd, normalized_value)
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137
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138 def derive_value(self, normalized=True):
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139 """
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140 Derive the current value
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141 """
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142 statistic_type = self.statistic_type
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143 stat_modifiers = statistic_type.stat_modifiers
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144 character = self.character()
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145
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146 value = sum(
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147 character.statistics[name].value * modifier for name, modifier in
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148 stat_modifiers.items()
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149 )
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150 assert 0 <= value <= 100
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151 if not normalized:
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152 mean = statistic_type.mean
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153 sd = statistic_type.sd
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154 value = self.calculate_value(mean, sd, value)
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155 return value
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156
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157 @staticmethod
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158 def calculate_value(mean, sd, normalized_value):
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159 value = sd * (normalized_value - 50) + mean
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160 return value
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161
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162 @staticmethod
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163 def calculate_normalized_value(mean, sd, value):
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164 normalized_value = ((value - mean) / sd) + 50
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165 return normalized_value