Mercurial > parpg-source
annotate characterstatistics.py @ 177:62aed6388159
Fixed showing the console.
author | Beliar |
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date | Tue, 06 Mar 2012 15:15:18 +0100 |
parents | 741d7d193bad |
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rev | line source |
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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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Changed CharacterStatistic classes to work with components.
KarstenBock@gmx.net
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22 from components import character_statistics |
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23 |
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24 class AbstractCharacterStatistic(object): |
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25 __metaclass__ = ABCMeta |
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26 |
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27 @abstractmethod |
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28 def __init__(self, description, minimum, maximum): |
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29 self.description = description |
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30 self.minimum = minimum |
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31 self.maximum = maximum |
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32 |
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33 |
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34 class PrimaryCharacterStatistic(AbstractCharacterStatistic): |
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35 def __init__(self, long_name, short_name, description, minimum=0, |
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36 maximum=100): |
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37 AbstractCharacterStatistic.__init__(self, description=description, |
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38 minimum=minimum, maximum=maximum) |
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39 self.long_name = long_name |
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40 self.short_name = short_name |
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41 |
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42 SerializableRegistry.registerClass( |
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43 'PrimaryCharacterStatistic', |
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44 PrimaryCharacterStatistic, |
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45 init_args=[ |
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46 ('long_name', unicode), |
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47 ('short_name', unicode), |
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48 ('description', unicode), |
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49 ('minimum', int), |
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50 ('maximum', int), |
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51 ], |
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52 ) |
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53 |
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54 |
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55 class SecondaryCharacterStatistic(AbstractCharacterStatistic): |
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56 def __init__(self, name, description, unit, mean, sd, stat_modifiers, |
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57 minimum=None, maximum=None): |
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58 AbstractCharacterStatistic.__init__(self, description=description, |
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59 minimum=minimum, maximum=maximum) |
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60 self.name = name |
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61 self.unit = unit |
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62 self.mean = mean |
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63 self.sd = sd |
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64 self.stat_modifiers = stat_modifiers |
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65 |
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66 SerializableRegistry.registerClass( |
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67 'SecondaryCharacterStatistic', |
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68 SecondaryCharacterStatistic, |
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69 init_args=[ |
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70 ('name', unicode), |
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71 ('description', unicode), |
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72 ('unit', unicode), |
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73 ('mean', float), |
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74 ('sd', float), |
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75 ('stat_modifiers', dict), |
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76 ('minimum', float), |
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77 ('maximum', float), |
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78 ], |
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79 ) |
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80 |
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81 |
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82 class AbstractStatisticValue(object): |
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83 __metaclass__ = ABCMeta |
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84 |
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85 @abstractmethod |
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86 def __init__(self, statistic_type, character): |
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87 self.statistic_type = statistic_type |
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88 self.character = weakref(character) |
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89 |
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90 |
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91 class PrimaryStatisticValue(AbstractStatisticValue): |
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92 def value(): |
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93 def fget(self): |
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94 return self._value |
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95 def fset(self, new_value): |
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96 assert 0 <= new_value <= 100 |
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97 self._value = new_value |
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98 |
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99 def __init__(self, statistic_type, character, value): |
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100 AbstractStatisticValue.__init__(self, statistic_type=statistic_type, |
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101 character=character) |
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102 self._value = None |
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103 self.value = value |
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104 |
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105 |
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106 class SecondaryStatisticValue(AbstractStatisticValue): |
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107 def normalized_value(): |
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108 def fget(self): |
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109 return self._normalized_value |
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110 def fset(self, new_value): |
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111 self._normalized_value = new_value |
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112 statistic_type = self.statistic_type |
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113 mean = statistic_type.mean |
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114 sd = statistic_type.sd |
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115 self._value = self.calculate_value(mean, sd, new_value) |
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116 return locals() |
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117 normalized_value = property(**normalized_value()) |
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118 |
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119 def value(): |
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120 def fget(self): |
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121 return self._value |
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122 def fset(self, new_value): |
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123 self._value = new_value |
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124 statistic_type = self.statistic_type |
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125 mean = statistic_type.mean |
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126 sd = statistic_type.sd |
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127 self._normalized_value = self.calculate_value(mean, sd, new_value) |
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128 return locals() |
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129 value = property(**value()) |
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130 |
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131 def __init__(self, statistic_type, character): |
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132 AbstractStatisticValue.__init__(self, statistic_type=statistic_type, |
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133 character=character) |
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134 mean = statistic_type.mean |
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135 sd = statistic_type.sd |
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136 normalized_value = self.derive_value(normalized=True) |
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137 self._normalized_value = normalized_value |
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138 self._value = self.calculate_value(mean, sd, normalized_value) |
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139 |
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140 def derive_value(self, normalized=True): |
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141 """ |
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142 Derive the current value |
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143 """ |
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144 statistic_type = self.statistic_type |
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145 stat_modifiers = statistic_type.stat_modifiers |
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146 character = self.character() |
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147 |
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148 value = sum( |
153
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149 character_statistics.get_statistic(character, name).value * |
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150 modifier for name, modifier in |
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151 stat_modifiers.items() |
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152 ) |
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153 assert 0 <= value <= 100 |
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154 if not normalized: |
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155 mean = statistic_type.mean |
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156 sd = statistic_type.sd |
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157 value = self.calculate_value(mean, sd, value) |
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158 return value |
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159 |
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160 @staticmethod |
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161 def calculate_value(mean, sd, normalized_value): |
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162 value = sd * (normalized_value - 50) + mean |
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163 return value |
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164 |
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165 @staticmethod |
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166 def calculate_normalized_value(mean, sd, value): |
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167 normalized_value = ((value - mean) / sd) + 50 |
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168 return normalized_value |