removing tables.py
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evilchili
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32c9832daa
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blinker==1.4
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commonmark==0.9.1
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docutils==0.19
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feedgenerator==2.0.0
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importlib-metadata==4.12.0
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invoke==1.7.1
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Jinja2==3.1.2
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Markdown==3.3.7
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MarkupSafe==2.1.1
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pelican==4.7.2
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pelican-drafts==0.1.1
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pelican-sitemap==1.0.2
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pelican-yaml-metadata @ git+https://github.com/pR0Ps/pelican-yaml-metadata.git@cdc1b9708916410e455e8e258e3d39a9d575c7b5
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Pygments==2.12.0
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python-dateutil==2.8.2
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pytz==2022.1
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PyYAML==5.4.1
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rich==12.4.4
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six==1.16.0
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telisar @ file:///home/greg/dev/telisar
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tornado==6.2
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Unidecode==1.3.4
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zipp==3.8.0
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import yaml
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import random
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from collections.abc import Generator
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from typing import Optional, Mapping, List
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class RollTable:
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"""
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Generate a roll table using weighted distributions of random options.
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Usage:
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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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- Category
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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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"""
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def __init__(self, path: str, frequency: str = 'default',
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die: Optional[int] = None, collapsed: bool = True):
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"""
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Initialize a RollTable instance.
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Args:
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path - the path to the source file
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frequency - the name of the frequency distribution to use; must
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be defined in the source file's metadata.
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die - specify a die size
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collapsed - If True, collapse multiple die values with the same
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options into a single line.
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"""
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self._path = path
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self._frequency = frequency
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self._die = die
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self._collapsed = collapsed
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self._metadata = None
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self._source = None
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self._values = None
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def _load_source(self) -> None:
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"""
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Cache the yaml source and parsed or generated the metadata.
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"""
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if self._source:
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return
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with open(self._path, 'r') as source:
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self._source = yaml.safe_load(source)
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def _defaults():
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num_keys = len(self._source.keys())
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default_freq = num_keys/100
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return {
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'headers': [''] * num_keys,
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'die': self._die,
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'frequencies': {
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'default': [(k, default_freq) for k in self._source.keys()]
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}
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}
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self._metadata = self._source.pop('metadata', _defaults())
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def _collapsed_lines(self) -> Generator[list]:
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"""
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Generate an array of column values for each row of the table but
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sort the values and squash multiple rows with the same values into one,
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with a range for the die roll instead of a single die. That is,
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d1 foo bar baz
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d2 foo bar baz
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becomes
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d1-d2 foo bar baz
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"""
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def collapsed(last_val, offset, val, i):
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(cat, option) = last_val
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(k, v) = list(*option.items())
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if offset + 1 == i:
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return [f'd{i}', cat, k, v]
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else:
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return [f'd{offset+1}-d{i}', cat, k, v]
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last_val = None
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offset = 0
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for (i, val) in enumerate(self.values):
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if not last_val:
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last_val = val
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offset = i
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continue
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if val != last_val:
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yield collapsed(last_val, offset, val, i)
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last_val = val
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offset = i
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yield collapsed(last_val, offset, val, i+1)
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@property
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def freqtable(self):
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return self.metadata['frequencies'][self._frequency]
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@property
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def source(self) -> Mapping:
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"""
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The parsed source data
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"""
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if not self._source:
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self._load_source()
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return self._source
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@property
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def metadata(self) -> Mapping:
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"""
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The parsed or generated metadata
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"""
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if not self._metadata:
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self._load_source()
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return self._metadata
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@property
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def values(self) -> List:
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"""
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Randomly pick values from the source data following the frequency
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distrubtion of the options.
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"""
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if not self._values:
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weights = []
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options = []
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for (option, weight) in self.freqtable.items():
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weights.append(weight)
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options.append(option)
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freqs = random.choices(options, weights=weights,
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k=self._die or self.metadata['die'])
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self._values = []
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for option in freqs:
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self._values += [(option, random.choice(self.source[option]))]
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return sorted(self._values, key=lambda val: list(val[1].values())[0])
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@property
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def lines(self) -> Generator[List]:
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"""
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Yield a list of table rows suitable for formatting as output.
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"""
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yield self.metadata['headers']
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if self._collapsed:
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for line in self._collapsed_lines():
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yield line
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else:
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for (i, item) in enumerate(self.values):
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(cat, option) = item
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(k, v) = list(option.items())[0]
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yield [f'd{i+1}', cat, k, v]
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def __str__(self) -> str:
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"""
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Return the lines as a single string.
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"""
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return "\n".join([
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'{:10s}\t{:8s}\t{:20s}\t{:s}'.format(*line) for line in self.lines
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])
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if __name__ == '__main__':
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import sys
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print(RollTable(path=sys.argv[1], die=int(sys.argv[2])))
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