89 lines
2.6 KiB
Markdown
89 lines
2.6 KiB
Markdown
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Generate a roll table using weighted distributions of random options.
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Given source.yaml containing options such as:
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option1:
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- key1: description
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- key2: description
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...
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...
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Generate a random table:
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>>> print(RollTable(path='source.yaml'))
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d1 option6 key3 description
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d2 option2 key2 description
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d3 option3 key4 description
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...
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You can customize the frequency distribution, headers, and table size by
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defining metadata in your source file.
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Using Metadata:
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By default options are given uniform distribution and random keys will be
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selected from each option with equal probability. This behaviour can be
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changed by adding an optional metadata section to the source file:
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metadata:
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frequenceis:
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default:
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option1: 0.5
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option2: 0.1
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option3: 0.3
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option4: 0.1
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This will guarantee that random keys from option1 are selected 50% of the
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time, from option 2 10% of the time, and so forth. Frequencies should add
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up to 1.0.
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If the metadata section includes 'frequencies', The 'default' distribution
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must be defined. Additional optional distributions may also be defined, if
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you want to provide alternatives for specific use cases.
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metadata:
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frequenceis:
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default:
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option1: 0.5
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option2: 0.1
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option3: 0.3
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option4: 0.1
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inverted:
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option1: 0.1
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option2: 0.3
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option3: 0.1
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option4: 0.5
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A specific frequency distribution can be specifed by passing the 'frequency'
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parameter at instantiation:
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>>> t = RollTable('source.yaml', frequency='inverted')
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The metadata section can also override the default size of die to use for
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the table (a d20). For example, this creates a 100-row table:
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metadata:
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die: 100
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This too can be overridden at instantiation:
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>>> t = RollTable('source.yaml', die=64)
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Finally, headers for your table columns can also be defined in metadata:
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metadata:
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headers:
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- Roll
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- Frequency
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- Description
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- Effect
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This will yield output similar to:
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>>> print(RollTable(path='source.yaml'))
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Roll Category Name Effect
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d1 option6 key3 description
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d2 option2 key2 description
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d3 option3 key4 description
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...
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