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d20's Introduction

d20

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A fast, powerful, and extensible dice engine for D&D, d20 systems, and any other system that needs dice!

Key Features

  • Quick to start - just use d20.roll()!
  • Optimized for speed and memory efficiency
  • Highly extensible API for custom behaviour and dice stringification
  • Built-in execution limits against malicious dice expressions
  • Tree-based dice representation for easy traversal

Installing

Requires Python 3.6+.

python3 -m pip install -U d20

Quickstart

>>> import d20
>>> result = d20.roll("1d20+5")
>>> str(result)
'1d20 (10) + 5 = `15`'
>>> result.total
15
>>> result.crit
<CritType.NORMAL: 0>
>>> str(result.ast)
'1d20 + 5'

Documentation

Check out the docs on Read the Docs!

Dice Syntax

This is the grammar supported by the dice parser, roughly ordered in how tightly the grammar binds.

Numbers

These are the atoms used at the base of the syntax tree.

Name Syntax Description Examples
literal INT, DECIMAL A literal number. 1, 0.5, 3.14
dice INT? "d" (INT | "%") A set of die. d20, 3d6
set "(" (num ("," num)* ","?)? ")" A set of expressions. (), (2,), (1, 3+3, 1d20)

Note that (3d6) is equivalent to 3d6, but (3d6,) is the set containing the one element 3d6.

Set Operations

These operations can be performed on dice and sets.

Grammar

Name Syntax Description Examples
set_op operation selector An operation on a set (see below). kh3, ro<3
selector seltype INT A selection on a set (see below). 3, h1, >2

Operators

Operators are always followed by a selector, and operate on the items in the set that match the selector.

Syntax Name Description
k keep Keeps all matched values.
p drop Drops all matched values.
rr reroll Rerolls all matched values until none match. (Dice only)
ro reroll once Rerolls all matched values once. (Dice only)
ra reroll and add Rerolls up to one matched value once, keeping the original roll. (Dice only)
e explode on Rolls another die for each matched value. (Dice only)
mi minimum Sets the minimum value of each die. (Dice only)
ma maximum Sets the maximum value of each die. (Dice only)

Selectors

Selectors select from the remaining kept values in a set.

Syntax Name Description
X literal All values in this set that are literally this value.
hX highest X The highest X values in the set.
lX lowest X The lowest X values in the set.
>X greater than X All values in this set greater than X.
<X less than X All values in this set less than X.

Unary Operations

Syntax Name Description
+X positive Does nothing.
-X negative The negative value of X.

Binary Operations

Syntax Name
X * Y multiplication
X / Y division
X // Y int division
X % Y modulo
X + Y addition
X - Y subtraction
X == Y equality
X >= Y greater/equal
X <= Y less/equal
X > Y greater than
X < Y less than
X != Y inequality

Examples

>>> from d20 import roll
>>> r = roll("4d6kh3")  # highest 3 of 4 6-sided dice
>>> r.total
14
>>> str(r)
'4d6kh3 (4, 4, **6**, ~~3~~) = `14`'

>>> r = roll("2d6ro<3")  # roll 2d6s, then reroll any 1s or 2s once
>>> r.total
9
>>> str(r)
'2d6ro<3 (**~~1~~**, 3, **6**) = `9`'

>>> r = roll("8d6mi2")  # roll 8d6s, with each die having a minimum roll of 2
>>> r.total
33
>>> str(r)
'8d6mi2 (1 -> 2, **6**, 4, 2, **6**, 2, 5, **6**) = `33`'

>>> r = roll("(1d4 + 1, 3, 2d6kl1)kh1")  # the highest of 1d4+1, 3, and the lower of 2 d6s
>>> r.total
3
>>> str(r)
'(1d4 (2) + 1, ~~3~~, ~~2d6kl1 (2, 5)~~)kh1 = `3`'

Custom Stringifier

By default, d20 stringifies the result of each dice roll formatted in Markdown, which may not be useful in your application. To change this behaviour, you can create a subclass of d20.Stringifier (or d20.SimpleStringifier as a starting point), and implement the _str_* methods to customize how your dice tree is stringified.

Then, simply pass an instance of your stringifier into the roll() function!

>>> import d20
>>> class MyStringifier(d20.SimpleStringifier):
...     def _stringify(self, node):
...         if not node.kept:
...             return 'X'
...         return super()._stringify(node)
...
...     def _str_expression(self, node):
...         return f"The result of the roll {self._stringify(node.roll)} was {int(node.total)}"

>>> result = d20.roll("4d6e6kh3", stringifier=MyStringifier())
>>> str(result)
'The result of the roll 4d6e6kh3 (X, 5, 6!, 6!, X, X) was 17'

Annotations and Comments

Each dice node supports value annotations - i.e., a method to "tag" parts of a roll with some indicator. For example,

>>> from d20 import roll
>>> str(roll("3d6 [fire] + 1d4 [piercing]"))
'3d6 (3, 2, 2) [fire] + 1d4 (3) [piercing] = `10`'

>>> str(roll("-(1d8 + 3) [healing]"))
'-(1d8 (7) + 3) [healing] = `-10`'

>>> str(roll("(1 [one], 2 [two], 3 [three])"))
'(1 [one], 2 [two], 3 [three]) = `6`'

are all examples of valid annotations. Annotations are purely visual and do not affect the evaluation of the roll by default.

Additionally, when allow_comments=True is passed to roll(), the result of the roll may have a comment:

>>> from d20 import roll
>>> result = roll("1d20 I rolled a d20", allow_comments=True)
>>> str(result)
'1d20 (13) = `13`'
>>> result.comment
'I rolled a d20'

Note that while allow_comments is enabled, AST caching is disabled, which may lead to slightly worse performance.

Traversing Dice Results

The raw results of dice rolls are returned in Expression objects, which can be accessed as such:

>>> from d20 import roll
>>> result = roll("3d6 + 1d4 + 3")
>>> str(result)
'3d6 (4, **6**, **6**) + 1d4 (**1**) + 3 = `20`'
>>> result.expr
<Expression roll=<BinOp left=<BinOp left=<Dice num=3 size=6 values=[<Die size=6 values=[<Literal 4>]>, <Die size=6 values=[<Literal 6>]>, <Die size=6 values=[<Literal 6>]>] operations=[]> op=+ right=<Dice num=1 size=4 values=[<Die size=4 values=[<Literal 1>]>] operations=[]>> op=+ right=<Literal 3>> comment=None>

or, in a easier-to-read format,

<Expression 
    roll=<BinOp
        left=<BinOp
            left=<Dice
                num=3
                size=6
                values=[
                    <Die size=6 values=[<Literal 4>]>,
                    <Die size=6 values=[<Literal 6>]>,
                    <Die size=6 values=[<Literal 6>]>
                ]
                operations=[]
            >
            op=+
            right=<Dice
                num=1
                size=4
                values=[
                    <Die size=4 values=[<Literal 1>]>
                ]
                operations=[]
            >
        >
        op=+
        right=<Literal 3>
    >
    comment=None
>

From here, Expression.children returns a tree of nodes representing the expression from left to right, each of which may have children of their own. This can be used to easily search for specific dice, look for the left-most operand, or modify the result by adding in resistances or other modifications.

Examples

Finding the left and right-most operands:

>>> from d20 import roll

>>> binop = roll("1 + 2 + 3 + 4")
>>> left = binop.expr
>>> while left.children:
...     left = left.children[0]
>>> left
<Literal 1>

>>> right = binop.expr
>>> while right.children:
...     right = right.children[-1]
>>> right
<Literal 4>

>>> from d20 import utils  # these patterns are available in the utils submodule:
>>> utils.leftmost(binop.expr)
<Literal 1>
>>> utils.rightmost(binop.expr)
<Literal 4>

Searching for the d4:

>>> from d20 import roll, Dice, SimpleStringifier, utils

>>> mixed = roll("-1d8 + 4 - (3, 1d4)kh1")
>>> str(mixed)
'-1d8 (**8**) + 4 - (3, ~~1d4 (3)~~)kh1 = `-7`'
>>> root = mixed.expr
>>> result = utils.dfs(root, lambda node: isinstance(node, Dice) and node.num == 1 and node.size == 4)
>>> result
<Dice num=1 size=4 values=[<Die size=4 values=[<Literal 3>]>] operations=[]>
>>> SimpleStringifier().stringify(result)
'1d4 (3)'

As a note, even though a Dice object is the parent of Die objects, Dice.children returns an empty list, since it's more common to look for the dice, and not each individual component of that dice.

Performance

By default, the parser caches the 256 most frequently used dice expressions in an LFU cache, allowing for a significant speedup when rolling many of the same kinds of rolls. This caching is disabled when allow_comments is True.

With caching:

$ python3 -m timeit -s "from d20 import roll" "roll('1d20')"
10000 loops, best of 5: 21.6 usec per loop
$ python3 -m timeit -s "from d20 import roll" "roll('100d20')"
500 loops, best of 5: 572 usec per loop
$ python3 -m timeit -s "from d20 import roll; expr='1d20+'*50+'1d20'" "roll(expr)"
500 loops, best of 5: 732 usec per loop
$ python3 -m timeit -s "from d20 import roll" "roll('10d20rr<20')"
1000 loops, best of 5: 1.13 msec per loop

Without caching:

$ python3 -m timeit -s "from d20 import roll" "roll('1d20')"
5000 loops, best of 5: 61.6 usec per loop
$ python3 -m timeit -s "from d20 import roll" "roll('100d20')"
500 loops, best of 5: 620 usec per loop
$ python3 -m timeit -s "from d20 import roll; expr='1d20+'*50+'1d20'" "roll(expr)"
500 loops, best of 5: 2.1 msec per loop
$ python3 -m timeit -s "from d20 import roll" "roll('10d20rr<20')"
1000 loops, best of 5: 1.26 msec per loop

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d20's Issues

[Enhancement] Random.py Package Improvement

Background

A friend and I were looking into the avrae/d20 package. We noticed some concerning code flow with regards to how Random is being used. The package being used (random.py) is the old version of the MT algorithm which tends towards statistically lower outcomes (as compared to the 2002 or newer versions). It's also hit or miss given the lack of seeding which means we'd expect the randomness to basically end up being subsets of older rolls. (Deterministic for the purpose of statistical modeling.) Also, it's one of the slowest random algorithms.

There's lots of alternatives that are easy to use and provide more randomness and quicker throughput.

Suggested Improvement

Upgrade to a new random package to be used on lines 405 and 407 of expression.py. A package in the PCG family would likely be optimal for Avrae's usage.

Quote from the page on comparison to other algorithms:

PCG was developed by applying TestU01 to reduced-size variants,[7] and determining the minimum number of internal state bits required to pass BigCrush. BigCrush examines enough data to detect a period of 235, so even an ideal generator requires 36 bits of state to pass it. Some very poor generators can pass if given a large enough state;[8] passing despite a small state is a measure of an algorithm's quality, and shows how large a safety margin exists between that lower limit and the state size used in practical applications.

PCG-RXS-M-XS (with 32-bit output) passes BigCrush with 36 bits of state (the minimum possible), PCG-XSH-RR (pcg32() above) requires 39, and PCG-XSH-RS (pcg32_fast() above) requires 49 bits of state. For comparison, xorshift*, one of the best of the alternatives, requires 40 bits of state,[5]:โ€Š19โ€Š and Mersenne twister fails despite 19937 bits of state.[9]

This could avoid the known issues of many-zero state recovery in pre-2002 MT algorithms of which random.py uses. The best outcome for Avrae would be faster generation.

Detecting natural ones

Does this library have a way to check for natural ones on rolls? I was walking the syntax tree, but it doesn't seep all that efficient.

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