227 lines
8.9 KiB
Python
227 lines
8.9 KiB
Python
import argparse
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from datetime import date, datetime
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import random
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from time import sleep
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from tqdm import tqdm
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from data.database import dal2
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import lottery_predictor.analyze as analyze
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from lottery_predictor.config import PROJECT_VARIABLES, GAME_INFO
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from util.scrape import scrape_game_history
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BAR_FORMAT = PROJECT_VARIABLES["LP_BAR_FORMAT"]
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def dry_run_fill_game_data(game: str, year: int | None = None) -> None:
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"""
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:param game: Name of the game to process
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:param year: A starting year or None
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:return: Nothing
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A dummy job to go through the motions of updating without doing anything
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"""
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if year is None:
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year = GAME_INFO[game]['start_year']
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elif not isinstance(year, int):
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raise ValueError(f"Year must be a valid year or None, received {year}")
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elif year not in range(1992, 2030):
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raise ValueError(f"Year must be within the range 1992 and 2030, received {year}")
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for year in range(year, datetime.today().year + 1):
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for _ in tqdm(
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range(random.randint(1, 106)),
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desc=f"\tProcessing year {year}",
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bar_format=BAR_FORMAT
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):
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sleep(0.25)
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if year != datetime.now().year:
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for _ in tqdm(
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range(5),
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desc='\tPseudo API backoff',
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bar_format=BAR_FORMAT
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):
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sleep(1)
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else:
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print()
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def fill_game_data(game: str, year: int | None = None) -> None:
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"""
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:param game: Name of the game to process
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:param year: A starting year or None
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:return: Nothing
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If the year is None, all game data will be scraped for each year the game has data available.
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Otherwise, the scrape will be limited to a range of years starting with the year specified
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Not all data scraped will be used for predictions, but could be useful later.
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"""
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if year is None:
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year = GAME_INFO[game]['start_year']
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elif not isinstance(year, int):
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raise ValueError(f"Year must be a valid year or None, received {year}")
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elif year not in range(1992, 2030):
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raise ValueError(f"Year must be within the range 1992 and 2030, received {year}")
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# scrape records by year and insert into the database
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for year in range(year, datetime.today().year + 1):
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scrape_data = scrape_game_history(game=GAME_INFO[game]['scrape_name'], year=year)
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# convert and insert records
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for _ in tqdm(
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range(len(scrape_data)),
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desc=f"Processing year {year}",
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bar_format=BAR_FORMAT
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):
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for draw_date, numbers in scrape_data.items():
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record = GAME_INFO[game]['object_function'](draw_date=draw_date, draw_result=numbers)
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dal2.add(record)
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# sleep for 30 seconds to avoid overtaxing the endpoint, if the processed year is not the current one
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if year != datetime.now().year:
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for _ in tqdm(
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range(30),
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desc='\tAPI backoff',
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bar_format=BAR_FORMAT
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):
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sleep(1)
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def update_games(dry_run: bool = False) -> None:
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"""
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:return: None
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Loop over games in the game_info dictionary and update the stored records
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"""
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date_format = PROJECT_VARIABLES["LP_DATE_INSERT_FORMAT"]
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for game, info in GAME_INFO.items():
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print(f"Updating {game} data...\n")
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if dal2.count(table_name=info['table']) == 0:
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if not dry_run:
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fill_game_data(game=game)
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else:
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dry_run_fill_game_data(game=game)
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else:
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most_recent = dal2.most_recent(table_name=info['table'])
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start_year = datetime.strptime(str(most_recent[0]), date_format).year
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if not dry_run:
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fill_game_data(game=game, year=start_year)
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else:
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dry_run_fill_game_data(game=game, year=start_year)
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def get_table_counts(from_date: date | str | None) -> dict[str, int]:
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"""
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Loops over the tables in the database and gets a record count for each one.
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:return: A dictionary of table names and record counts
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"""
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counts = {}
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for game, info in GAME_INFO.items():
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if isinstance(from_date, date):
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counts[game] = dal2.count(table_name=info['table'], from_date=from_date)
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elif from_date == 'rule-change':
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rule_date = datetime.strptime(info['rule_change'], PROJECT_VARIABLES["LP_DATE_INSERT_FORMAT"])
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counts[game] = dal2.count(table_name=info['table'], from_date=rule_date)
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else:
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counts[game] = dal2.count(table_name=info['table'])
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return counts
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def get_prediction(game: str, window_size: int, test_case: bool = False) -> str:
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# Get the list of drawings to make the prediction from
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from_date = GAME_INFO[game]['rule_change']
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if test_case:
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if window_size == -1:
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drawings = analyze.load_dataframe_by_dates(game=game, start_date=from_date)
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else:
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drawings = analyze.load_dataframe_most_recent(game=game, limit=window_size)
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window_size = len(drawings) if window_size == -1 else window_size
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test_case = drawings.iloc[0]
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game_ball_out = 'powerball' if game == 'Powerball' else 'mega_ball'
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case_string = (f"Draw Date: {test_case['draw_date']}, "
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f"Main Balls: [{test_case['main_ball1']}, {test_case['main_ball2']}, {test_case['main_ball3']}, "
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f"{test_case['main_ball4']}, {test_case['main_ball5']}], Game Ball: {test_case[game_ball_out]}")
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predicted = analyze.make_prediction(data_frame=drawings.iloc[1:], window_size=(window_size - 2))
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main_balls = ", ".join(predicted[0].astype(str))
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game_ball = "".join(predicted[1].astype(str))
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return f"{case_string}\n Prediction: Main Balls: [{main_balls}], Game Ball: {game_ball}\n"
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else:
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if window_size == -1:
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drawings = analyze.load_dataframe_by_dates(game=game, start_date=from_date)
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else:
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drawings = analyze.load_dataframe_most_recent(game=game, limit=window_size)
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window_size = len(drawings) if window_size == -1 else window_size
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predicted = analyze.make_prediction(data_frame=drawings, window_size=(window_size - 1))
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main_balls = ", ".join(predicted[0].astype(str))
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game_ball = "".join(predicted[1].astype(str))
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return f"Prediction: Main Balls: {main_balls}, Game Ball: {game_ball}\n"
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def main():
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parser = argparse.ArgumentParser(description="A lottery prediction tool.")
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subparsers = parser.add_subparsers(dest="command", required=True)
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update_parser = subparsers.add_parser("update", help="Update database records")
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update_parser.add_argument("-y", "--year", type=int, help="Year to start updates with")
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update_parser.add_argument(
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"-d", "--dryrun",
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action="store_true",
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help="Run the update process without actually making any database or API calls"
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)
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record_parser = subparsers.add_parser("record-count", help="Get record counts by table in the database")
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record_parser.add_argument(
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"-f", "--from-date",
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type=str,
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help="Date from which to base the count on (ex. 2026-01-01). \
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The default value is rule-change for the date when game rules last changed"
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)
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predict_parser = subparsers.add_parser("predict", help="Predict the next drawing")
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predict_parser.add_argument(
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"--game",
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type=str,
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required=True,
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help="The game to predict the next result for [MegaMillions, Powerball]"
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)
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predict_parser.add_argument(
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"--window-size",
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type=int,
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default=-1,
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help="The number of records to use in the prediction model, defaults to -1 for all records"
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)
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predict_parser.add_argument(
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"--test",
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action="store_true",
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help="Use the most recent drawing as a test subject to find the right window_size"
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)
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args = parser.parse_args()
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match args.command:
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case "record-count":
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# load the date format for the database
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date_format = PROJECT_VARIABLES["LP_DATE_INSERT_FORMAT"]
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if args.from_date:
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# if args.from_date is 'rule-change' use that date from vars, otherwise use the date provided
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if args.from_date != 'rule-change':
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from_date = datetime.strptime(args.from_date, date_format)
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# get the table row counts from the date specified
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table_counts = get_table_counts(from_date=from_date)
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else:
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table_counts = get_table_counts(from_date=args.from_date)
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else:
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table_counts = get_table_counts(from_date=None)
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for table, count in table_counts.items():
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print(f"{table}:\t{count}")
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case "update":
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update_games(dry_run=args.dryrun)
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case "predict":
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print(get_prediction(game=args.game, window_size=args.window_size, test_case=args.test))
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if __name__ == "__main__":
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main()
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