forked from Hanabi/hanabi-league
Merge branch 'main' of https://git.abstractnonsen.se/Hanabi/hanabi-league into minimize-loss
This commit is contained in:
commit
edcb230365
11 changed files with 516 additions and 10 deletions
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@ -85,3 +85,7 @@ body {
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.history-bullets ul {
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flex-direction: column;
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}
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.endgame-table td, .endgame-table th {
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border: 1px solid black;
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}
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2
deps/py-hanabi
vendored
2
deps/py-hanabi
vendored
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@ -1 +1 @@
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Subproject commit 3ac51d574e65aff9b3420fdebd467d7b98ea1d28
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Subproject commit 51e09cd94393de64e07191d6ca544139417acb3b
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@ -16,10 +16,15 @@ import fetch_games
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import ratings
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import stats
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import render_site
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import endgames
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from log_setup import logger
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def subcommand_analyze_endgames():
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endgames.work_thread()
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def subcommand_init(force: bool, no_fetch_variants: bool):
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tables = database.get_existing_tables()
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if len(tables) > 0 and not force:
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@ -101,6 +106,7 @@ def get_parser() -> argparse.ArgumentParser:
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subparsers.add_parser('process-ratings', help="Process ratings of all games.")
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subparsers.add_parser('process-stats', help="Process statistics for all players.")
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subparsers.add_parser('generate-site', help="Generate the website from the DB.")
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subparsers.add_parser('analyze-endgames', help="Run endgame analysis on games in DB. Resource intensive!")
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subparsers.add_parser('run', help="Run the automatic suite: Fetch + process games and render site.")
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fetch_parser = subparsers.add_parser('fetch', help='Fetch new data.')
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@ -122,6 +128,7 @@ def main():
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'generate-site': subcommand_generate_site,
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'fetch': subcommand_fetch,
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'run': subcommand_run,
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'analyze-endgames': subcommand_analyze_endgames
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}[args.command]
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if args.verbose:
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@ -359,3 +359,45 @@ CREATE TABLE user_statistics (
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average_game_moves REAL GENERATED ALWAYS AS (CASE WHEN games_played != 0 THEN CAST(total_game_moves AS REAL) / games_played ELSE NULL END) STORED,
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PRIMARY KEY (user_id, variant_type)
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);
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DROP TABLE IF EXISTS endgames;
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CREATE TABLE endgames (
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game_id INTEGER REFERENCES games (id),
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turn SMALLINT,
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/**
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* We want to be able to store probabilities for different actions that can be taken.
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* Action type can be
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0 for play actions
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1 for discard actions
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2 for clues
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*/
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action_type SMALLINT CHECK (0 <= action_type AND action_type <= 2),
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/**
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We store cards as (suit_index, rank) here for uniqueness of representation.
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If we want to refer to known trash, we will use (0,0) as representation.
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*/
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suit_index SMALLINT, /* 0 for clue actions */
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rank SMALLINT, /* 0 for clue actions */
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enumerator BIGINT NOT NULL CHECK (enumerator >= 0),
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denominator BIGINT NOT NULL CHECK (denominator > 0),
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chance REAL GENERATED ALWAYS AS (CAST(enumerator AS REAL) / denominator) STORED,
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PRIMARY KEY (game_id, turn, action_type, suit_index, rank)
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);
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/**
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We store separately whether we analyzed a certain game already and what the termination reason for the analysis was:
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0 if evaluation completed within specified time and memory
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1 if evaluation ran into timeout
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2 if evaluation was empty because state is unreachable
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3 if evaluation ran out of memory
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This is also necessary because for some endgames, because in case 2 we will not have data,
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simply because the game replay ended too early.
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To avoid re-analyzing these seeds, we mark all seeds analyzed in this table.
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*/
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DROP TABLE IF EXISTS endgames_analyzed;
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CREATE TABLE endgames_analyzed (
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game_id INTEGER REFERENCES games (id),
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termination_reason SMALLINT NOT NULL,
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PRIMARY KEY (game_id)
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);
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@ -48,5 +48,9 @@ USER_HISTORY_CACHE_TIME = 60 * 60
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# Fraction of seeds which is assumed to be unwinnable
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UNWINNABLE_SEED_FRACTION = 0.02
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WEBSITE_OUTPUT_DIRECTORY = 'build'
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ENDGAME_MAX_DRAW_PILE_SIZE = 15 # Not interested in game states with more than 15 cards, this should be enough.
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ENDGAME_MEMORY_BYTES = 4 * 1024 * 1024 * 1024 # 4 GB of memory
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ENDGAME_TIMEOUT_SECONDS = 60 * 15 # 15 Minutes per game by default
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ENDGAME_ANALYSIS_QUERY_INTERVAL_MINUTES = 5 # Re-query database every 5 minutes
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254
src/endgames.py
Normal file
254
src/endgames.py
Normal file
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@ -0,0 +1,254 @@
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import json
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import subprocess
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import re
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import resource
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import time
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from typing import List, Dict, Tuple
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from dataclasses import dataclass
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from pathlib import Path
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import platformdirs
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import psycopg2.extras
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import psycopg2.errors
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import hanabi.hanab_game
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import hanabi.constants
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import hanabi.live.compress
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import constants
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import games_db_interface
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from database import conn_manager
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from log_setup import logger
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@dataclass
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class EndgameAction:
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turn: int
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action_type: hanabi.hanab_game.ActionType
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card: hanabi.hanab_game.DeckCard
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enumerator: int
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denominator: int
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@property
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def win_rate(self):
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return self.enumerator / self.denominator
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def analyze_and_store_game(game_id: int) -> int:
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actions, return_code = analyze_game_from_db(game_id)
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store_endgame_actions(game_id, actions, return_code)
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return return_code
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def analyze_game_from_db(game_id: int, refresh_cache=False) -> Tuple[List[EndgameAction], int]:
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# In order to pass the game replay to the endgame analyzer (a C++ program), we use the hanab.live json format.
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# In order to avoid to need to use the /export endpoint of hanab.live, we create the json replay ourselves from
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# the information stored in the database. Note that this is partially lossy, since the GameState class we use here
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# does not support stuff like notes or player names, but we don't care about this anyway and can pass the relevant
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# information, i.e. deck, number of players and actions.
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# We can additionally use the cache dir
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filename = Path(platformdirs.user_cache_dir(constants.APP_NAME)) / "replays" / "{}.json".format(game_id)
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if not filename.exists() or refresh_cache:
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filename.parent.mkdir(exist_ok=True, parents=True)
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game, variant_name = games_db_interface.load_game(game_id)
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game_json = game.to_json()
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game_json["options"]["variant"] = variant_name
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with open(filename, 'w') as f:
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f.write(json.dumps(game_json))
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return analyze_endgame_from_file(str(filename))
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def analyze_endgame_from_file(filename: str) -> Tuple[List[EndgameAction], int]:
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"""
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Analyzes endgame of replay specified in given file using appropriate time and memory limits.
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@param filename: Name of file containing replay of game in hanab.live format
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@return: List of all evaluated actions and return code why evaluation finished:
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0 if evaluation completed within specified time and memory
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1 if evaluation ran into timeout
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2 if evaluation was empty because state is unreachable
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3 if evaluation ran out of memory
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No guarantee can be made on what actions are actually evaluated, these might be more or less depending on
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timeouts and/or resource limitation.
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@raise
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"""
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raw_output: bytes
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return_code = 0
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args = [
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'./endgame-analyzer', '--file', filename,
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'--draw-pile-size', str(constants.ENDGAME_MAX_DRAW_PILE_SIZE), '--list-actions', '--quiet'
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]
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try:
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result = subprocess.run(
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args,
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE,
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timeout=constants.ENDGAME_TIMEOUT_SECONDS,
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preexec_fn=set_memory_limit
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)
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if result.returncode != 0:
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# 2 is the return code to report that the specified game state is not reachable
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# In this case, there is nothing to analyze, so we will return an empty list and normal program termination.
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if result.returncode == 2:
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return [], 2
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# 3 is the return code used by the subprocess to indicate an out of memory exception
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# Since we intentionally limited the memory, this is actually not an exception for us,
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# we will simply parse the results we have and report the OOM exception.
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if result.returncode == 3:
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return_code = 3
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else:
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raise RuntimeError(
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"Abnormal program termination of endgame-analyzer subprocess: Call of\n"
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"{}\n"
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"resulted in returncode '{}' and stderr\n"
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"{}"
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.format(
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" ".join(args),
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result.returncode,
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result.stderr.decode('utf-8')
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)
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)
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raw_output = result.stdout
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except subprocess.TimeoutExpired as time_err:
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return_code = 1
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raw_output = time_err.stdout
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# It could be that we got no output. In that case, we also cannot parse anything
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output = raw_output.decode('utf-8') if raw_output else ""
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pattern = r"Turn (?P<turn>\d+), (?P<type>\w+)(?:\s(?P<card>\w\w))?: (?P<enumerator>\d+)/(?P<denominator>\d+)"
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return [parse_match(match.groupdict()) for match in re.finditer(pattern, output)], return_code
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def set_memory_limit():
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resource.setrlimit(resource.RLIMIT_DATA, (constants.ENDGAME_MEMORY_BYTES, constants.ENDGAME_MEMORY_BYTES))
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def store_endgame_actions(game_id: int, endgame_actions: List[EndgameAction], result_code) -> None:
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values = []
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for action in endgame_actions:
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values.append((game_id, action.turn, action.action_type.value, action.card.suitIndex, action.card.rank, action.enumerator, action.denominator))
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# Remove duplicates (even though we expect none), otherwise this causes errors on insertion.
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values = list(set(values))
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conn = conn_manager.get_connection()
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cur = conn.cursor()
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psycopg2.extras.execute_values(
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cur,
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"INSERT INTO endgames "
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"VALUES %s "
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"ON CONFLICT (game_id, turn, action_type, suit_index, rank) "
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"DO UPDATE "
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"SET (enumerator, denominator) = (EXCLUDED.enumerator, EXCLUDED.denominator)",
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values
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)
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# Mark this game as analyzed.
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cur.execute(
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"INSERT INTO endgames_analyzed "
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"VALUES (%s, %s) "
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"ON CONFLICT (game_id) "
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"DO UPDATE "
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"SET termination_reason = EXCLUDED.termination_reason",
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(game_id, result_code)
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)
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conn.commit()
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def load_endgame_actions(game_id: int) -> List[EndgameAction]:
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cur = conn_manager.get_new_cursor()
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cur.execute(
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"SELECT turn, action_type, suit_index, rank, enumerator, denominator "
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"FROM endgames "
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"WHERE game_id = %s "
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"ORDER BY turn ASC, action_type ASC, suit_index ASC, rank ASC",
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(game_id,)
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)
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ret = []
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for (turn, action_type, suit_index, rank, enumerator, denominator) in cur.fetchall():
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ret.append(EndgameAction(
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turn,
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hanabi.hanab_game.ActionType(action_type),
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hanabi.hanab_game.DeckCard(suit_index, rank),
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enumerator, denominator)
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)
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return ret
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def parse_match(action: Dict) -> EndgameAction:
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turn = action["turn"]
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action_type = parse_action_type(action["type"])
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card = hanabi.hanab_game.DeckCard(0, 0) if action["card"] is None else parse_card(action["card"])
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enumerator = action["enumerator"]
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denominator = action["denominator"]
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return EndgameAction(turn, action_type, card, enumerator, denominator)
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def parse_action_type(action_type: str) -> hanabi.hanab_game.ActionType:
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match action_type:
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case "play":
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return hanabi.hanab_game.ActionType.Play
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case "discard":
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return hanabi.hanab_game.ActionType.Discard
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case "clue":
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return hanabi.hanab_game.ActionType.ColorClue
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raise ValueError("Failed to parse action type: {}".format(action_type))
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def parse_card(card: str) -> hanabi.hanab_game.DeckCard:
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assert len(card) == 2
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if card == "kt":
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return hanabi.hanab_game.DeckCard(0, 0)
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else:
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rank = int(card[1])
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suit = hanabi.constants.COLOR_INITIALS.index(card[0])
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assert suit is not None
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return hanabi.hanab_game.DeckCard(suit, rank)
|
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|
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|
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def print_action_type(action_type: hanabi.hanab_game.ActionType) -> str:
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match action_type:
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case hanabi.hanab_game.ActionType.Play:
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return "Play"
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case hanabi.hanab_game.ActionType.Discard:
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return "Discard"
|
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case hanabi.hanab_game.ActionType.RankClue:
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return "Clue"
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case hanabi.hanab_game.ActionType.ColorClue:
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return "Clue"
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case _:
|
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return "Unknown Action"
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|
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def work_thread():
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"""
|
||||
Will continuously query database to analyze endgames.
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@return:
|
||||
"""
|
||||
conn = conn_manager.get_connection()
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cur = conn.cursor()
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||||
while True:
|
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cur.execute(
|
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"SELECT games.id "
|
||||
"FROM games "
|
||||
"LEFT OUTER JOIN endgames_analyzed "
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" ON endgames_analyzed.game_id = games.id "
|
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"WHERE endgames_analyzed.termination_reason IS NULL "
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"ORDER BY games.league_id DESC "
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"LIMIT 1",
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(False,)
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)
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res = cur.fetchone()
|
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if res is None:
|
||||
logger.info("No game found to analyze. Going to sleep for {} Minutes".format(
|
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constants.ENDGAME_ANALYSIS_QUERY_INTERVAL_MINUTES)
|
||||
)
|
||||
time.sleep(60 * constants.ENDGAME_ANALYSIS_QUERY_INTERVAL_MINUTES)
|
||||
else:
|
||||
(game_id, ) = res
|
||||
logger.info("Analyzing endgame of game {}".format(game_id))
|
||||
return_code = analyze_and_store_game(game_id)
|
||||
print("Finished endgame analysis of {}: Returncode {}".format(game_id, return_code))
|
||||
|
|
@ -81,10 +81,15 @@ def load_deck(seed: str) -> List[hanabi.hanab_game.DeckCard]:
|
|||
return deck
|
||||
|
||||
|
||||
def load_game(game_id: int) -> Tuple[hanabi.hanab_game.HanabiInstance, List[hanabi.hanab_game.Action]]:
|
||||
def load_game_parts(game_id: int) -> Tuple[hanabi.hanab_game.HanabiInstance, List[hanabi.hanab_game.Action], str]:
|
||||
"""
|
||||
Loads information on game from database
|
||||
@param game_id: ID of game
|
||||
@return: Instance (i.e. deck + settings) of game, list of actions, variant name
|
||||
"""
|
||||
cur = conn_manager.get_new_cursor()
|
||||
cur.execute(
|
||||
"SELECT games.num_players, games.seed, variants.clue_starved "
|
||||
"SELECT games.num_players, games.seed, variants.clue_starved, variants.name "
|
||||
"FROM games "
|
||||
"INNER JOIN variants"
|
||||
" ON games.variant_id = variants.id "
|
||||
|
|
@ -98,10 +103,19 @@ def load_game(game_id: int) -> Tuple[hanabi.hanab_game.HanabiInstance, List[hana
|
|||
raise ValueError(err_msg)
|
||||
|
||||
# Unpack results now
|
||||
(num_players, seed, clue_starved) = res
|
||||
(num_players, seed, clue_starved, variant_name) = res
|
||||
|
||||
actions = load_actions(game_id)
|
||||
deck = load_deck(seed)
|
||||
|
||||
instance = hanabi.hanab_game.HanabiInstance(deck, num_players, clue_starved=clue_starved)
|
||||
return instance, actions
|
||||
return instance, actions, variant_name
|
||||
|
||||
|
||||
def load_game(game_id: int) -> Tuple[hanabi.hanab_game.GameState, str]:
|
||||
instance, actions, variant_name = load_game_parts(game_id)
|
||||
game = hanabi.hanab_game.GameState(instance)
|
||||
for action in actions:
|
||||
game.make_action(action)
|
||||
return game, variant_name
|
||||
|
||||
|
|
|
|||
|
|
@ -12,16 +12,18 @@ import requests_cache
|
|||
import platformdirs
|
||||
|
||||
import stats
|
||||
import hanabi.hanab_game
|
||||
|
||||
import constants
|
||||
import config
|
||||
import utils
|
||||
from dataclasses import dataclass
|
||||
import endgames
|
||||
import games_db_interface
|
||||
|
||||
from database import conn_manager
|
||||
|
||||
|
||||
|
||||
@dataclass
|
||||
class PlayerEntry:
|
||||
player_name: str
|
||||
|
|
@ -713,6 +715,111 @@ def build_unique_variants(variant_rows: List[VariantRow]):
|
|||
return [row for row in variant_rows if row.num_players == config.config_manager.get_config().min_player_count]
|
||||
|
||||
|
||||
def render_game_pages(env: jinja2.Environment, out_dir: Path):
|
||||
endgames = get_endgame_page_data()
|
||||
template = env.get_template("game.html")
|
||||
|
||||
for game_id, data in endgames.items():
|
||||
rendered_template = template.render(
|
||||
total_games_played=get_total_games(),
|
||||
total_players=get_num_players(),
|
||||
latest_run=datetime.datetime.now().isoformat(),
|
||||
game_id=game_id,
|
||||
data=data
|
||||
)
|
||||
output_file = out_dir / 'game' / str(game_id) / 'index.html'
|
||||
output_file.parent.mkdir(exist_ok=True, parents=True)
|
||||
with open(output_file, 'w') as f:
|
||||
f.write(rendered_template)
|
||||
|
||||
|
||||
def format_endgame_action(endgame_action: endgames.EndgameAction):
|
||||
|
||||
return "{} {}: {}/{} ~ {}".format(
|
||||
endgames.print_action_type(endgame_action.action_type),
|
||||
endgame_action.card,
|
||||
endgame_action.enumerator,
|
||||
endgame_action.denominator,
|
||||
endgame_action.action_type.value
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class EndgameActionRow:
|
||||
description: str
|
||||
enumerator: int
|
||||
denominator: int
|
||||
marked: bool = False
|
||||
|
||||
@property
|
||||
def win_rate(self):
|
||||
return round(100 * self.enumerator / self.denominator, 3)
|
||||
|
||||
|
||||
def convert_endgame_action(endgame_action: endgames.EndgameAction, game: hanabi.hanab_game.GameState, action: hanabi.hanab_game.Action) -> EndgameActionRow:
|
||||
action_str = endgames.print_action_type(endgame_action.action_type)
|
||||
target_str: str
|
||||
if endgame_action.action_type not in [hanabi.hanab_game.ActionType.ColorClue, hanabi.hanab_game.ActionType.RankClue]:
|
||||
target_str = " {}".format(endgame_action.card)
|
||||
else:
|
||||
target_str = ""
|
||||
description = action_str + target_str
|
||||
|
||||
marked = False
|
||||
# To simplify comparisons, we only work with color clues here. Endgame actions always consist of color clues.
|
||||
if action.type == hanabi.hanab_game.ActionType.RankClue:
|
||||
action.type = hanabi.hanab_game.ActionType.ColorClue
|
||||
if endgame_action.action_type == action.type:
|
||||
if endgame_action.action_type == hanabi.hanab_game.ActionType.ColorClue:
|
||||
marked = True
|
||||
else:
|
||||
game_target = game.instance.deck[action.target]
|
||||
if game.is_trash(game_target):
|
||||
game_target = hanabi.hanab_game.DeckCard(0, 0)
|
||||
if endgame_action.card == game_target:
|
||||
marked = True
|
||||
return EndgameActionRow(description, endgame_action.enumerator, endgame_action.denominator, marked)
|
||||
|
||||
|
||||
def get_endgame_page_data():
|
||||
cur = conn_manager.get_new_cursor()
|
||||
cur.execute(
|
||||
"SELECT games.id, termination_reason "
|
||||
"FROM games "
|
||||
"LEFT OUTER JOIN endgames_analyzed "
|
||||
" ON endgames_analyzed.game_id = games.id "
|
||||
)
|
||||
ret = {}
|
||||
for (game_id, termination_reason) in cur.fetchall():
|
||||
if termination_reason is not None:
|
||||
ret[game_id] = []
|
||||
instance, actions, _ = games_db_interface.load_game_parts(game_id)
|
||||
game = hanabi.hanab_game.GameState(instance)
|
||||
|
||||
endgame_actions = endgames.load_endgame_actions(game_id)
|
||||
while len(endgame_actions) > 0:
|
||||
# Move to current turn and update game
|
||||
cur_turn = endgame_actions[0].turn
|
||||
# Note the -1 here since turns on hanab.live start to count at 1
|
||||
while len(game.actions) < cur_turn - 1:
|
||||
action, *actions = actions
|
||||
game.make_action(action)
|
||||
assert len(actions) > 0
|
||||
|
||||
actions_this_turn: List[endgames.EndgameAction] = []
|
||||
while len(endgame_actions) > 0 and endgame_actions[0].turn == cur_turn:
|
||||
action, *endgame_actions = endgame_actions
|
||||
actions_this_turn.append(action)
|
||||
actions_this_turn.sort(key=lambda a: -a.win_rate)
|
||||
best_action, *other_actions = [convert_endgame_action(a, game, actions[0]) for a in actions_this_turn]
|
||||
ret[game_id].append(
|
||||
(cur_turn, best_action, other_actions)
|
||||
)
|
||||
else:
|
||||
ret[game_id] = None
|
||||
return ret
|
||||
|
||||
|
||||
def render_main_site(env: jinja2.Environment, out_dir: Path):
|
||||
rating_lists = get_rating_lists()
|
||||
streak_lists = get_streak_list()
|
||||
|
|
@ -842,7 +949,8 @@ def render_all():
|
|||
render_main_site(env, out_dir)
|
||||
render_player_pages(env, out_dir)
|
||||
render_variant_pages(env, out_dir)
|
||||
render_game_pages(env, out_dir)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
render_all()
|
||||
render_all()
|
||||
|
|
|
|||
|
|
@ -93,7 +93,7 @@ def analyze_replay(instance: hanab_game.HanabiInstance, actions: List[hanab_game
|
|||
|
||||
def analyze_game_and_store_stats(game_id: int):
|
||||
logger.verbose("Analysing game {} for BDRs and lost crits".format(game_id))
|
||||
instance, actions = games_db_interface.load_game(game_id)
|
||||
instance, actions, _ = games_db_interface.load_game_parts(game_id)
|
||||
analysis = analyze_replay(instance, actions)
|
||||
|
||||
cur = conn_manager.get_new_cursor()
|
||||
|
|
|
|||
73
templates/game.html
Normal file
73
templates/game.html
Normal file
|
|
@ -0,0 +1,73 @@
|
|||
{% extends "layout.html" %}
|
||||
|
||||
{% block navbar %}
|
||||
<nav class="navbar navbar-expand-lg navbar-light bg-light">
|
||||
<div class="container">
|
||||
<a class="navbar-brand" href="/">The Hanabi Pro Hunting League</a><a class="navbar-brand" href="#"><small class="text-muted">- Endgame Statistics for {{game_id}}</small></a>
|
||||
<button class="navbar-toggler" type="button" data-toggle="collapse" data-target="#navbarNav" aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation">
|
||||
<span class="navbar-toggler-icon"></span>
|
||||
</button>
|
||||
<div class="collapse navbar-collapse" id="navbarNav">
|
||||
<ul class="navbar-nav ml-auto">
|
||||
<li class="nav-item">
|
||||
<a class="nav-link" id="Back" href="/">Back</a>
|
||||
</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
</nav>
|
||||
{% endblock %}
|
||||
|
||||
{% block content %}
|
||||
<div class="tab-content" id="myTabContent">
|
||||
<div class="tab-pane fade show active" id="overview">
|
||||
<div class="container my-5">
|
||||
<h3>
|
||||
Statistics for game {{game_id}}
|
||||
</h3>
|
||||
<ul>
|
||||
<li>
|
||||
Replay: <a href="https://hanab.live/replay/{{game_id}}">https://hanab.live/replay/{{game_id}}</a>
|
||||
</li>
|
||||
<li>
|
||||
Shared Replay: <a href="https://hanab.live/shared-replay/{{game_id}}">https://hanab.live/shared-replay/{{game_id}}</a>
|
||||
</li>
|
||||
</ul>
|
||||
<h4>
|
||||
Endgame Analysis table
|
||||
</h4>
|
||||
{% if data %}
|
||||
<table class="endgame-table">
|
||||
<tr>
|
||||
<th>Turn</th>
|
||||
<th>Action</th>
|
||||
<th>Fractional Probability</th>
|
||||
<th>Probability</th>
|
||||
</tr>
|
||||
{% for (turn, best_action, other_actions) in data %}
|
||||
<tr>
|
||||
<td rowspan="{{ other_actions|length + 1 }}"><a href="https://hanab.live/replay/{{game_id}}#{{turn}}">{{ turn }}</a></td>
|
||||
<td>{% if best_action.marked %}<b>{% endif %}{{ best_action.description }}{% if best_action.marked %}</b>{% endif %}</td>
|
||||
<td>{{ best_action.enumerator }}/{{ best_action.denominator }}</td>
|
||||
<td>{{ best_action.win_rate }}%</td>
|
||||
</tr>
|
||||
{% for action in other_actions %}
|
||||
<tr>
|
||||
<td>{% if action.marked %}<b>{% endif %}{{ action.description }}{% if action.marked %}</b>{% endif %}</td>
|
||||
<td>{{ action.enumerator }}/{{ action.denominator }}</td>
|
||||
<td>{{ action.win_rate }}%</td>
|
||||
</tr>
|
||||
{% endfor %}
|
||||
{% endfor %}
|
||||
</table>
|
||||
{% else %}
|
||||
Currently, there is no endgame analysis available for this game. Since the computation is resource extensive,
|
||||
this might take a while, also depending on how many other games have been played recently.
|
||||
<br>
|
||||
Come back later to check again.
|
||||
{% endif %}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{% endblock %}
|
||||
|
|
@ -72,7 +72,7 @@ var table_{{div_id}} = new Tabulator("#table-{{div_id}}", {
|
|||
layout: "fitDataStretch",
|
||||
columns: [
|
||||
{title: "Game", field: "game_id", formatter: "link", formatterParams:{
|
||||
urlPrefix: "https://hanab.live/replay/",
|
||||
urlPrefix: "/game/",
|
||||
target:"_blank"
|
||||
}},
|
||||
{title: "Played", field: "datetime_finished", formatter: "datetime", formatterParams:{
|
||||
|
|
|
|||
Loading…
Reference in a new issue