Compute outcome probabilities for Risus: The Anything RPG
Project description
This is a simple set of functions to compute probabilities for Risus: The Anything RPG.
Risus was designed by S. John Ross and is currently published by Big Dice games: https://www.risusrpg.com/
Installing
This project is on pypi at https://pypi.org/project/risus-py/. It can be installed thus:
pip install risus-py
This risus.engine depends on icepool
(https://highdiceroller.github.io/icepool/), and risus.table depends
on numpy and pandas. These should all be automatically installed by pip.
Testing
All the modules are tested from their docstring examples. After
installation, you can execute the tests by running risus as a
module:
python3 -m risus
If all goes well, this should appear to hang for an instant and return nothing. It will complain in case of a problem.
Development
Clone the git repository.
git clone https://git.sr.ht/~p0mf/risus-py
When trying out new features, I use pip install -e to install an
editable copy of risus in a virtual environment:
pip install -e .
I build risus-py using the PyPA build
(https://github.com/pypa/build) module and upload it to PyPI using
twine (https://twine.readthedocs.io/en/stable/):
python3 -m build && twine upload dist/*
This monster is the entire release pipeline: it installs locally, tests, then builds for distribution and uploads.
pip install -e . && python -m risus && rm -rf dist/* && python -m build && twine upload dist/*
Wishlist
Features
- Pumps and double pumps.
- Factor out rule variations (explosions, breakthroughs).
- Web application using pyodide and chart.js, like the other
icepoolapplications.
Efficiency
- Parallelized computation, and other optimizations. Is it possible to do better than quadratic time (in the maximum value of the potencies considered) when constructing a table?
Polish
- Clean up documentation: there's still some quirkiness to the way the README is integrated into the module documentation. Given how easy it is to pull up documentation locally with pdoc, static site hosting is a low priority.
- Rationalize rules variations: for now, the engine passes around a whole mess of configuration flags.
Infrastructure
- Automate build test and release, potentially as a continuous integration job on sourcehut.
Basic API Calls
See the documentation for the module for an overview of possible
calls. It can be accessed with pdoc (https://pdoc.dev/), from your
terminal once risus-py has been installed. This command will open
the documentation in your default browser:
pdoc risus
In short, there are functions to compute win/loss chances for
combat, team_combat (including inappropriate clichés),
single_action_conflict, and target_number rolls. All the calls
return float, and take a bool argument percent to indicate
whether the returned value should be scaled 0 to 100 (True) or 0 to
1 (False). The default is False.
Tables
More complex variations are possible: the risus.table module contains
function to generate xarrays of victory probabilities. The file
app/dataset.py is an example of simple web interface using dtale. Here are
some example tables.
Combat:
Ordinary
As an example, here is how you could calculate an ordinary combat table for up to (10) versus (10).
>>> import pandas as pd
>>> from risus.table import make_combat_table
>>> make_combat_table(10, percent=True).round(1).to_pandas()
enemy_potency 1 2 3 4 5 6 7 8 9 10
attack_potency
1 50.0 5.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2 95.0 50.0 8.2 0.3 0.0 0.0 0.0 0.0 0.0 0.0
3 99.9 91.8 50.0 10.5 0.7 0.0 0.0 0.0 0.0 0.0
4 100.0 99.7 89.5 50.0 12.2 1.1 0.0 0.0 0.0 0.0
5 100.0 100.0 99.3 87.8 50.0 13.7 1.5 0.1 0.0 0.0
6 100.0 100.0 100.0 98.9 86.3 50.0 14.8 1.9 0.1 0.0
7 100.0 100.0 100.0 100.0 98.5 85.2 50.0 15.8 2.3 0.1
8 100.0 100.0 100.0 100.0 99.9 98.1 84.2 50.0 16.7 2.7
9 100.0 100.0 100.0 100.0 100.0 99.9 97.7 83.3 50.0 17.5
10 100.0 100.0 100.0 100.0 100.0 100.0 99.9 97.3 82.5 50.0
Boxcars and Breakthroughs
A similar series of manipulations produces one with boxcars and breakthroughs in effect.
>>> make_combat_table(10, percent=True, breakthrough=True).round(1).to_pandas()
enemy_potency 1 2 3 4 5 6 7 8 9 10
attack_potency
1 51.6 9.4 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2 95.2 52.3 9.6 0.5 0.0 0.0 0.0 0.0 0.0 0.0
3 99.9 92.2 50.9 10.9 0.7 0.0 0.0 0.0 0.0 0.0
4 100.0 99.7 89.7 50.3 12.4 1.1 0.0 0.0 0.0 0.0
5 100.0 100.0 99.4 87.8 50.1 13.7 1.5 0.1 0.0 0.0
6 100.0 100.0 100.0 98.9 86.4 50.0 14.8 1.9 0.1 0.0
7 100.0 100.0 100.0 100.0 98.5 85.2 50.0 15.8 2.3 0.1
8 100.0 100.0 100.0 100.0 99.9 98.1 84.2 50.0 16.7 2.7
9 100.0 100.0 100.0 100.0 100.0 99.9 97.7 83.3 50.0 17.5
10 100.0 100.0 100.0 100.0 100.0 100.0 99.9 97.3 82.5 50.0
Single-action conflict:
The function make_single_action_conflict_table takes a number representing to
size of the square table to produce.
Ordinary
enemy_potency 1 2 3 4 5 6 7 8 9 10
attack_potency
1 50.0 10.0 1.2 0.1 0.0 0.0 0.0 0.0 0.0 0.0
2 90.0 50.0 16.3 3.7 0.6 0.1 0.0 0.0 0.0 0.0
3 98.8 83.7 50.0 20.5 6.3 1.5 0.3 0.0 0.0 0.0
4 99.9 96.3 79.5 50.0 23.5 8.6 2.6 0.6 0.1 0.0
5 100.0 99.4 93.7 76.5 50.0 25.7 10.7 3.7 1.1 0.3
6 100.0 99.9 98.5 91.4 74.3 50.0 27.5 12.6 4.9 1.6
7 100.0 100.0 99.7 97.4 89.3 72.5 50.0 28.9 14.2 6.0
8 100.0 100.0 100.0 99.4 96.3 87.4 71.1 50.0 30.1 15.7
9 100.0 100.0 100.0 99.9 98.9 95.1 85.8 69.9 50.0 31.2
10 100.0 100.0 100.0 100.0 99.7 98.4 94.0 84.3 68.8 50.0
Boxcars and Breakthroughs
enemy_potency 1 2 3 4 5 6 7 8 9 10
attack_potency
1 51.6 18.2 6.9 2.7 1.1 0.0 0.0 0.0 0.0 0.0
2 90.0 50.0 17.4 5.4 2.1 0.9 0.4 0.1 0.1 0.0
3 98.8 83.7 50.0 20.6 6.5 1.8 0.6 0.3 0.1 0.1
4 99.9 96.3 79.5 50.0 23.5 8.6 2.6 0.7 0.2 0.1
5 100.0 99.4 93.7 76.5 50.0 25.7 10.7 3.7 1.1 0.3
6 100.0 99.9 98.5 91.4 74.3 50.0 27.5 12.6 4.9 1.6
7 100.0 100.0 99.7 97.4 89.3 72.5 50.0 28.9 14.2 6.0
8 100.0 100.0 100.0 99.4 96.3 87.4 71.1 50.0 30.1 15.7
9 100.0 100.0 100.0 99.9 98.9 95.1 85.8 69.9 50.0 31.2
10 100.0 100.0 100.0 100.0 99.7 98.4 94.0 84.3 68.8 50.0
Target number:
Here's a target number table for games with big pumpers: you feelin' lucky, punk?
attack_potency 1 2 3 4 5 6 7 8 9 10 11 12
enemy_potency
1 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
2 83.3 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
3 66.7 97.2 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
4 50.0 91.7 99.5 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
5 33.3 83.3 98.1 99.9 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
6 16.7 72.2 95.4 99.6 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
7 0.0 58.3 90.7 98.8 99.9 100.0 100.0 100.0 100.0 100.0 100.0 100.0
8 0.0 41.7 83.8 97.3 99.7 100.0 100.0 100.0 100.0 100.0 100.0 100.0
9 0.0 27.8 74.1 94.6 99.3 99.9 100.0 100.0 100.0 100.0 100.0 100.0
10 0.0 16.7 62.5 90.3 98.4 99.8 100.0 100.0 100.0 100.0 100.0 100.0
11 0.0 8.3 50.0 84.1 96.8 99.5 100.0 100.0 100.0 100.0 100.0 100.0
12 0.0 2.8 37.5 76.1 94.1 99.0 99.9 100.0 100.0 100.0 100.0 100.0
13 0.0 0.0 25.9 66.4 90.2 98.0 99.7 100.0 100.0 100.0 100.0 100.0
14 0.0 0.0 16.2 55.6 84.8 96.4 99.4 99.9 100.0 100.0 100.0 100.0
15 0.0 0.0 9.3 44.4 77.9 93.9 98.8 99.8 100.0 100.0 100.0 100.0
16 0.0 0.0 4.6 33.6 69.5 90.4 97.8 99.6 100.0 100.0 100.0 100.0
17 0.0 0.0 1.9 23.9 60.0 85.5 96.2 99.3 99.9 100.0 100.0 100.0
18 0.0 0.0 0.5 15.9 50.0 79.4 93.9 98.6 99.8 100.0 100.0 100.0
19 0.0 0.0 0.0 9.7 40.0 72.1 90.6 97.6 99.5 99.9 100.0 100.0
20 0.0 0.0 0.0 5.4 30.5 63.7 86.3 96.1 99.1 99.9 100.0 100.0
21 0.0 0.0 0.0 2.7 22.1 54.6 80.8 93.9 98.5 99.7 100.0 100.0
22 0.0 0.0 0.0 1.2 15.2 45.4 74.3 90.9 97.5 99.5 99.9 100.0
23 0.0 0.0 0.0 0.4 9.8 36.3 66.8 87.0 96.1 99.1 99.8 100.0
24 0.0 0.0 0.0 0.1 5.9 27.9 58.6 82.1 94.0 98.4 99.7 99.9
25 0.0 0.0 0.0 0.0 3.2 20.6 50.0 76.2 91.3 97.5 99.4 99.9
26 0.0 0.0 0.0 0.0 1.6 14.5 41.4 69.5 87.7 96.1 99.0 99.8
27 0.0 0.0 0.0 0.0 0.7 9.6 33.2 62.0 83.3 94.2 98.4 99.6
28 0.0 0.0 0.0 0.0 0.3 6.1 25.7 54.0 78.0 91.7 97.5 99.4
29 0.0 0.0 0.0 0.0 0.1 3.6 19.2 46.0 71.8 88.4 96.2 99.0
30 0.0 0.0 0.0 0.0 0.0 2.0 13.7 38.0 65.0 84.3 94.4 98.3
31 0.0 0.0 0.0 0.0 0.0 1.0 9.4 30.5 57.6 79.5 92.0 97.5
32 0.0 0.0 0.0 0.0 0.0 0.5 6.1 23.8 50.0 73.9 89.0 96.2
33 0.0 0.0 0.0 0.0 0.0 0.2 3.8 17.9 42.4 67.6 85.3 94.6
34 0.0 0.0 0.0 0.0 0.0 0.1 2.2 13.0 35.0 60.8 80.9 92.4
35 0.0 0.0 0.0 0.0 0.0 0.0 1.2 9.1 28.2 53.6 75.8 89.6
36 0.0 0.0 0.0 0.0 0.0 0.0 0.6 6.1 22.0 46.4 70.0 86.2
37 0.0 0.0 0.0 0.0 0.0 0.0 0.3 3.9 16.7 39.2 63.6 82.2
38 0.0 0.0 0.0 0.0 0.0 0.0 0.1 2.4 12.3 32.4 56.9 77.4
39 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.4 8.7 26.1 50.0 72.1
40 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.7 6.0 20.5 43.1 66.2
41 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 3.9 15.7 36.4 59.9
42 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 2.5 11.6 30.0 53.3
43 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 1.5 8.3 24.2 46.7
44 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9 5.8 19.1 40.1
45 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 3.9 14.7 33.8
46 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 2.5 11.0 27.9
47 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 1.6 8.0 22.6
48 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9 5.6 17.8
49 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 3.8 13.8
50 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 2.5 10.4
51 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 1.6 7.6
52 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 1.0 5.4
53 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6 3.8
54 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 2.5
55 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 1.7
56 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 1.0
57 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6
58 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4
59 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2
60 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file risus-py-0.1.0.tar.gz.
File metadata
- Download URL: risus-py-0.1.0.tar.gz
- Upload date:
- Size: 26.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.10.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 84bb17ab40b1b3c1f992ad6932ae39f385fdd833bfe30c6d2a7ab348ee5ba68a |
|
| MD5 | 16893001c45a9a298465a222275c51ad |
|
| BLAKE2b-256 | 926eb11d6f7961c5f14ba1eada630e9cdfc2b3e36296d8fb5335b4fa7cc703a1 |
Provenance
File details
Details for the file risus_py-0.1.0-py3-none-any.whl.
File metadata
- Download URL: risus_py-0.1.0-py3-none-any.whl
- Upload date:
- Size: 24.8 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.10.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | e4c46b8691aac113589782b74fc45664c3a7f9059e2d69927866d100da17fa63 |
|
| MD5 | 4b65d5216f3de090dc3e60e14f1d09a4 |
|
| BLAKE2b-256 | 3fa8829e1440c4ae4a429a40486fb091bb5309919fcfb78ce8d036482bc3e93c |
