dnd-rolltable/rolltable/types.py

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from collections.abc import Iterable
from typing import List, Optional, Union
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import rich.table
import yaml
from csv2md.table import Table
from random_sets.datasources import DataSource
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class RollTable:
"""
Generate a roll table using weighted distributions of random options.
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Instance Attributes:
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sources - One or more yaml strings to parse as data sources
frequency - The frequency distribution to apply when populating the table
die - The size of the die for which to create a table (default: 20)
headers - An array of header strings
rows - An array of table headers and rows
expanded_rows - An array of table headers and rows, one per die roll value
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Usage:
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table = RollTable(['source.yaml'], die=4)
print(table)
>>> Roll Item
d1 Foo
d2-d4 Bar
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"""
def __init__(
self,
sources: Union[List[str], List[DataSource]],
frequency: str = "default",
die: Optional[int] = 20,
hide_rolls: bool = False,
) -> None:
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self._sources = sources
self.frequency = frequency
self.die = die
self.hide_rolls = hide_rolls
self.data = None
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self._rows = None
self._headers = None
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self._header_excludes = None
self._generated_values = None
self._config()
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@property
def datasources(self) -> List:
return self._data
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@property
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def headers(self) -> List:
return self._headers
@property
def _values(self) -> List:
"""
For each data source, select N random values, where N is the size of the die.
we then zip those random values so that each member of the generated list
contains one value from each data source. So if _data is:
[
['axe', 'shortsword', 'dagger'],
['fire', 'ice', 'poison'],
]
and the die is 2, the resulting generated values might be:
[
['axe', 'fire'],
['dagger', 'ice'],
]
"""
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if not self._generated_values:
self._generated_values = list(zip(*[t.random_values(self.die) for t in self._data]))
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return self._generated_values
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@property
def rows(self) -> List:
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def formatted(lastrow, offset, row, i):
thisrow = [f"d{i}" if offset + 1 == i else f"d{offset+1}-d{i}"]
thisrow += self._flatten(lastrow)
return self._column_filter(thisrow)
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lastrow = None
offset = 0
self._rows = [self._column_filter(["Roll"] + self.headers)]
for face in range(self.die):
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row = self._values[face]
if not lastrow:
lastrow = row
offset = face
continue
if row != lastrow:
self._rows.append(formatted(lastrow, offset, row, face))
lastrow = row
offset = face
self._rows.append(formatted(lastrow, offset, row, face + 1))
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return self._rows
@property
def expanded_rows(self) -> List:
self._rows = [self._column_filter(["Roll"] + self.headers)]
for face in range(self.die):
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row = self._values[face]
self._rows.append(self._column_filter([f"d{face+1}"] + row))
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return self._rows
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def reset(self) -> None:
self._generated_values = None
self._config()
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def as_markdown(self) -> str:
return Table(self.rows).markdown()
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def as_yaml(self, expanded: bool = False) -> dict:
struct = {}
for row in self.rows[1:]:
struct[row[0]] = {}
# pad rows with empty cols as necessary
cols = row[1:] + [""] * (len(self.headers) - len(row[1:]))
for idx, col in enumerate(cols):
struct[row[0]][self.headers[idx] if idx < len(self.headers) else "_"] = col
return yaml.dump(struct, sort_keys=False)
def as_table(self, width: int = 120, expanded: bool = False) -> str:
rows = self.expanded_rows if expanded else self.rows
table = rich.table.Table(*rows[0], width=width)
for row in rows[1:]:
table.add_row(*row)
return table
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def set_headers(self, *headers) -> None:
self._headers = list(headers)
# identify which columns to hide in the output by recording where a
# None header appears
self._header_excludes = []
for i in range(len(self._headers)):
if self.headers[i] is None:
self._header_excludes.append(i + 1)
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def _config(self):
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"""
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Parse data sources, generate headers, and create the column filters
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"""
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# create the datasource objects
self._data = []
for src in self._sources:
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if type(src) is str:
src = [src]
for one_source in src:
ds = DataSource(one_source, frequency=self.frequency)
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ds.load_source()
self._data.append(ds)
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# merge the headers
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headers = []
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for ds in self._data:
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headers += ds.headers
self.set_headers(*headers)
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def _column_filter(self, row):
cols = [col or "" for (pos, col) in enumerate(row) if pos not in self._header_excludes]
# pad the row with empty columns if there are more headers than columns
cols = cols + [""] * (1 + len(self.headers) - len(row))
# strip the leading column if we're hiding the dice rolls
return cols[1:] if self.hide_rolls else cols
def _flatten(self, obj: List) -> List:
for member in obj:
if isinstance(member, Iterable) and not isinstance(member, (str, bytes)):
yield from self._flatten(member)
else:
yield member
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def __repr__(self) -> str:
rows = list(self.rows)
str_format = "\t".join(["{:10s}"] * len(rows[0]))
return "\n".join([str_format.format(*[r or "" for r in row]) for row in rows])