Mercurial > parpg-core
annotate src/parpg/characterstatistics.py @ 157:db6403c1a7a1
Fixed that dropped items cannot be picked up.
author | KarstenBock@gmx.net |
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date | Fri, 07 Oct 2011 14:36:36 +0200 |
parents | 1fd2201f5c36 |
children | 756ce052ac85 |
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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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 |