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"CLI, web API and Discord bot for VTES cards and TWDA.",

Project description

KRCG

PyPI version Validation Coverage Python version License Code Style

A Python package build to serve as an interface for the VEKN official card texts and the Tournament Winning Deck Archive (TWDA).

It also contains an ever-growing list of cards rulings, that is kept up to date thanks to the hard work of our contributors.

Portions of the materials are the copyrights and trademarks of Paradox Interactive AB, and are used with permission. All rights reserved. For more information please visit white-wolf.com.

Dark Pack

Offspring projects

The KRCG library has been used in multiple offpsring projects:

  • krcg-cli is a convenient Command Line Interface over the library

  • krcg-static is used to generate easy-to-use static files for web developers. It is available online at static.krcg.org

  • krcg-api is a free RESTful web API exposing to get the most out of the library for web project. It is available online at v2.api.krcg.org

  • krcg-bot is a friendly Discord bot that provides official card text and rulings for free. It is available for free.

Installation

Python 3 is required.

Use pip to install the krcg tool:

pip install krcg

Using the library

KRCG is a Python library for VTES. The code is well-documented and can be explored using Python's built-in help function.

Here are a few quickstart examples to showcase how the library can be used:

VTES

krcg.vtes.VTES is the cards library. It needs to be loaded using the VTES.load() method. Note that this loads the data from the KRCG static server, where it's already available in JSON format for free, for anyone who would want to play with it.

Alternatively, you can use VTES.load_from_vekn() if you want to load directly from the official VEKN CSV files, although that's a bit slower. That's actually the way [krcg-static] does it to generate the static JSON files used for the standard load.

Then you can play around with VTES to access cards, complete card names or search.

>>> from krcg.vtes import VTES
>>> VTES.load()
>>> VTES["Alastor"].to_json()
{
  'id': 100038,
  'name': 'Alastor'
  '_name': 'Alastor',
  'url': 'https://static.krcg.org/card/alastor.jpg',
  'types': ['Political Action'],
  'card_text': (
    'Requires a justicar or Inner Circle member.\n'
    'Choose a ready Camarilla vampire. If this referendum is successful, '
    'search your library for an equipment card and place this card and the equipment '
    'on the chosen vampire. Pay half the cost (round down) of the equipment. '
    'This vampire may enter combat with any vampire controlled by another Methuselah '
    'as a +1 stealth Ⓓ action. This vampire cannot commit diablerie. '
    'A vampire may have only one Alastor.'),
  'artists': ['Monte Moore'],
  'sets': {
    'Gehenna': [{'release_date': '2004-05-17', 'rarity': 'Rare'}],
    'Kindred Most Wanted': [{'release_date': '2005-02-21', 'precon': 'Alastors', 'copies': 1}],
    'Keepers of Tradition': [{'release_date': '2008-11-19', 'rarity': 'Rare'}]},
 'rulings': {
   'text': [
     'If the given weapon costs blood, the target Alastor pays the cost. [LSJ 20040518]',
      'Requirements do not apply. [ANK 20200901]'
    ],
    'links': {
      '[LSJ 20040518]': 'https://groups.google.com/d/msg/rec.games.trading-cards.jyhad/4emymfUPwAM/B2SCC7L6kuMJ',
      '[ANK 20200901]': 'http://www.vekn.net/forum/rules-questions/78830-alastor-and-ankara-citadel#100653'
    }
  }
}
>>> VTES.complete("pentex")
['Pentex™ Loves You!',
 'Pentex™ Subversion',
 'Enzo Giovanni, Pentex Board of Directors',
 'Enzo Giovanni, Pentex Board of Directors (ADV)',
 'Harold Zettler, Pentex Director']
 >>> VTES.search(type=["political action"], sect=["anarch"], artist=["Drew Tucker"])
{<#100790 Free States Rant>}
>>> VTES.search(precon=["Fifth Edition: Nosferatu"])
{<#201534 Aunt Linda>,
 <#201536 Baixinho>,
 <#201537 Belinde>,
 <#100301 Carrion Crows>,
 <#100308 Cats' Guidance>,
 <#102213 Creeping Sabotage>,
 <#100515 Deep Song>,
 <#100698 Fame>,
 <#100863 Guard Dogs>,
 <#100866 Guardian Angel>,
 <#100897 Haven Uncovered>,
 <#201549 Horace Radcliffe>,
 <#100959 Immortal Grapple>,
 <#100995 Instinctive Reaction>,
 <#201553 Larissa Moreira>,
 <#201555 Lenny Burkhead>,
 <#101125 Lost in Crowds>,
 <#101254 Murder of Crows>,
 <#101321 On the Qui Vive>,
 <#101483 Preternatural Strength>,
 <#102214 Protected District>,
 <#101550 Raven Spy>,
 <#101564 Rebel>,
 <#102215 Roundhouse>,
 <#201568 Ryan>,
 <#101808 Slum Hunting Ground>,
 <#101811 Smiling Jack, The Anarch>,
 <#101945 Taste of Vitae>,
 <#201545 The Dowager>,
 <#101070 The Labyrinth>,
 <#102216 The Warrens>,
 <#102065 Underbridge Stray>,
 <#102113 Vessel>,
 <#102149 Warsaw Station>,
 <#201573 Wauneka>}
 >>> VTES.search(set=["Sword of Caine"], rarity=["Rare"])
{<#100167 Black Hand Emissary>,
 <#100314 Census Taker>,
 <#100360 Cloak of Blood>,
 <#100589 Drink the Blood of Ahriman>,
 <#100590 Drop Point Network>,
 <#100655 Epiphany>,
 <#100757 Follow the Blood>,
 <#100787 Framing an Ancient Grudge>,
 <#100865 Guarded Rubrics>,
 <#101024 Joseph Pander>,
 <#101111 Liquefy the Mortal Coil>,
 <#101161 Mantle of the Bestial Majesty>,
 <#101446 Praetorian Backer>,
 <#101489 Prison of the Mind>,
 <#101658 Ruins of Ceoris>,
 <#101724 Seraph's Second>,
 <#102057 The Uncoiling>,
 <#102022 Tribunal Judgment>,
 <#102027 Trophy: Chosen>,
 <#102158 Watchtower: The Wolves Feed>}

TWDA, Analyzer and Deck

krcg.twda.TWDA is the interface to the TWDA. It needs to be loaded in the same way as the VTES instance, using the TWDA.load() method. That time, using TWDA.load_from_vekn() instead is considerably slower.

Once loaded, it can be used to browse the decks in it.

>>> from krcg.twda import TWDA
>>> TWDA.load()
>>> TWDA["2019ecday2pf"]
<Deck #2019ecday2pf: Finnish Politics>
>>> print(TWDA["2019ecday2pf"].to_txt())
EC 2019 - Day 2
Paris, France
August 18th 2019
3R+F
50 players
Otso Saariluoma

-- 2gw8.5 + 1.5vp in the final

Deck Name: Finnish Politics

Crypt (12 cards, min=4, max=38, avg=5.75)
-----------------------------------------
4x Anarch Convert     1 -none-                     Caitiff:ANY
3x Nana Buruku        8 ANI POT PRE                Guruhi:4
2x Nangila Were       9 ANI POT PRE obf ser        Guruhi:4
1x Enkidu, The Noah  11 ANI CEL OBF POT PRO for    Gangrel antitribu:4
1x Black Annis        9 OBF POT ani pro            Nosferatu antitribu:4
1x Andre LeRoux       3 aus                        Toreador:5

Library (65 cards)
Master (26; 6 trifle)
4x Anarch Revolt
1x Archon Investigation
6x Ashur Tablets
1x Dreams of the Sphinx
1x Fame
1x Giant's Blood
1x Information Highway
1x Mbare Market, Harare
2x Pentex(TM) Subversion
1x Powerbase: Luanda
1x Powerbase: Montreal
5x Villein
1x Wider View

Action (5)
3x Deep Song
1x Entrancement
1x Well-Marked

Retainer (1)
1x Mr. Winthrop

Reaction (6)
1x Cats' Guidance
1x Delaying Tactics
2x On the Qui Vive
2x Sense the Savage Way

Combat (26)
1x Canine Horde
5x Carrion Crows
1x Glancing Blow
5x Immortal Grapple
1x Mighty Grapple
1x Slam
1x Stunt Cycle
4x Taste of Vitae
2x Thrown Sewer Lid
4x Torn Signpost
1x Undead Strength

Event (1)
1x Dragonbound

>>> from datetime import date
>>> len([d for d in TWDA.values() if date(2019, 1, 1) < d.date < date(2020, 1, 1) and d.players_count >= 25])
27

The krcg.analyzer can provide some statistics over a collection of decks:

>>> from krcg.analyzer import Analyzer
>>> # You can analyze the whole TWDA, or a fragment of it, or any collection of decks
>>> A = Analyzer([d for d in TWDA.values() if date(2019, 1, 1) < d.date < date(2020, 1, 1)])
>>> # A blank refresh will provide basic statistics
>>> A.refresh()
>>> A.played.most_common(5)
[(<#100588 Dreams of the Sphinx>, 101),
 (<#101384 Pentex™ Subversion>, 96),
 (<#101321 On the Qui Vive>, 86),
 (<#102121 Villein>, 83),
 (<#100824 Giant's Blood>, 74)]
>>> A.average[VTES["Villein"]]
4.409638554216869
>>> A.variance[VTES["Villein"]]
3.6876179416461032
>>> # Refreshing with a list of cards will compute cards affinity using similar decks
>>> # similarity=1 tells the engine to select decks that contains all provided cards
>>> A.refresh(VTES["Aid from Bats"], similarity=1)
>>> # now the candidates method can be used
>>> A.candidates(VTES["Aid from Bats"])[:5]
[(<#100515 Deep Song>, 1.0000000000000002),
 (<#100301 Carrion Crows>, 1.0000000000000002),
 (<#101945 Taste of Vitae>, 0.7777777777777779),
 (<#200185 Beetleman>, 0.6666666666666667),
 (<#100698 Fame>, 0.6666666666666667)]

The krcg.seating module provides functions to compute optimal seatings:

>>> from krcg import seating
>>> # permutations gives you the list of players for each round
>>> seating.permutations(12, 3)
[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]
>>> # things get funny when you have 6, 7 or 11 players: you need more rounds
>>> # but not all players play every round
>>> seating.permutations(7, 3)
[[4, 5, 6, 7],
 [1, 2, 3, 7],
 [3, 4, 5, 6],
 [1, 2, 6, 7],
 [1, 2, 3, 4, 5]]
>>> # you can use the Round class to get tables from the permutations
>>> [seating.Round(p) for p in seating.permutations(14, 3)]
[[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14]],
 [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14]],
 [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14]]]
>>> # and the optimise function to search for an optimal seating
>>> result, score = seating.optimise(seating.permutations(12, 3), iterations=50000)
>>> result
[[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]],
 [[2, 9, 6, 10], [12, 8, 1, 5], [3, 7, 4, 11]],
 [[11, 5, 10, 1], [6, 4, 12, 7], [8, 3, 9, 2]]]
>>> # score.rules gives a score over the nine official rules for optimal seating
>>> score.rules
[0, 0, 0.0, 9, 0, 0, 0, 1.118033988749895, 2]
>>> # you can inspect violations individualy
>>> # for example rule #4 (players are opponents twice) has 9 violations, to see them:
>>> score.R4
[[1, 5], [2, 3], [2, 9], [3, 4], [4, 7], [5, 8], [6, 7], [9, 10], [10, 11]]
>>> # for more details about the Score structure, check the docstring
>>> help(seating.Score)

And finally, the krcg.deck.Deck class can be useful to parse and manipulate any deck.

>>> from krcg.deck import Deck
>>> with open("First_Blood_Nosferatu.txt") as f:
>>>     deck = Deck.from_txt(f)
>>> deck.crypt
[(<#200549 Gustaphe Brunnelle>, 2),
 (<#200571 Harold Tanner>, 2),
 (<#200696 Jeremy "Wix" Wyzchovsky>, 2),
 (<#201116 Petra>, 2),
 (<#200185 Beetleman>, 2),
 (<#200190 Benjamin Rose>, 2)]
>>> deck.library
[(<#100698 Fame>, 2),
 (<#100070 Animalism>, 2),
 (<#101015 J. S. Simmons, Esq.>, 1),
 (<#101070 The Labyrinth>, 1),
 (<#101073 Laptop Computer>, 2),
 (<#101125 Lost in Crowds>, 6),
 (<#100093 Army of Rats>, 2),
 (<#101550 Raven Spy>, 4),
 (<#101808 Slum Hunting Ground>, 1),
 (<#100199 Blood Doll>, 6),
 (<#100029 Aid from Bats>, 12),
 (<#100308 Cats' Guidance>, 4),
 (<#100362 Cloak the Gathering>, 6),
 (<#100390 Computer Hacking>, 4)]
>>> # fetch a deck from Amaranth UID
>>> deck = Deck.from_amaranth("4d3aa426-70da-44b7-8cb7-92377a1a0dbd")
>>> deck.name
'First Blood: Tremere'
>>> deck.crypt
[(<Card #201020 Muhsin Samir>, 2),
 (<Card #201213 Rutor>, 2),
 (<Card #201388 Troius>, 2),
 (<Card #201501 Zane>, 2),
 (<Card #200025 Aidan Lyle>, 2),
 (<Card #200280 Claus Wegener>, 2)]
>>> print(deck.to_txt("lackey"))
1	Academic Hunting Ground
1	Arcane Library
4	Blood Doll
1	Chantry
2	Vast Wealth
12	Govern the Unaligned
1	Thadius Zho
4	.44 Magnum
1	Ivory Bow
2	Sport Bike
1	Charnas the Imp
6	Bonding
4	Enhanced Senses
5	Forced Awakening
5	On the Qui Vive
4	Precognition
4	Spirit's Touch
8	Telepathic Misdirection
8	Apportation
10	Theft of Vitae
2	Walk of Flame
Crypt:
2	Muhsin Samir
2	Rutor
2	Troius
2	Zane
2	Aidan Lyle
2	Claus Wegener

Contribute

Feel free to submit pull requests, they will be merged as long as they pass the tests. Do not hestitate to submit issues or vote on them if you want a feature implemented.

Design considerations

The package uses no database by design. The TWDA, search engine and cards dict are kept in memory for better performances. The whole library generates a memory footprint between 128MB and 256MB.

The package uses external data sources for card list, so that it needs not be updated when new sets are released or official VEKN CSV files are changed: it can use new data sets as soon as they're available.

Contribute Rulings (non-developers)

Please do not hestitate to contribute rulings: all help is welcome.

Open an issue with a ruling you think should be added, provide a link to an online post by one of the rules directors:

Contribute Rulings (developers)

Feel free to contribute rulings as Pull Requests directly, this is very appreciated.

Add the ruling link to rulings-links.yaml, and the ruling itself to cards-rulings.yaml or general-rulings.yaml depending on the case.

The format is mostly self-explanatory:

  • Cards are reference by ID and name in the format ID|Name.

  • Card names inside rulings text should be between bracers, eg. {.44 Magnum}

  • Individual rulings in cards-rulings.yaml must provide one or more references to ruling links at the end of the text, between brackets, eg [LSJ 20100101]

In doing so, please follow the following guidelines:

  • Keep the YAML files clean and alphabetically sorted (you can use a YAML formatter)

  • Make the rulings as concise as possible

  • Prefix the ruling with the discipline level and/or type the ruling applies to (if any), eg. prefix with [PRO] [COMBAT] if the ruling applies only to the card played in combat at superior Protean.

  • Adapt the ruling wording to the cards it applies to (ie. use masculine/feminin forms)

  • You can run the tests with the pytest command to check everything is OK

Project details


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