src/load_options.py

from dataclasses import dataclass
import pandas as pd
import streamlit as st

from domain.constants import SEASON
from domain.playoffs import (
    PLAYOFF_WEEK_TO_NAME,
    ROSTER_WEEK_TO_PLAYOFF_WEEK,
    PLAYOFFS_TEAMS,
    PLAYOFF_TEAM_DEF_PLAYER,
)
from domain.teams import SCHEDULE_NAME_TO_PFR_NAME_MAP
from queries.nflverse.github_data import get_weekly_rosters
from queries.pfr.league_schedule import get_season_time_map


@dataclass
class PlayerOption:
    full_name: str
    gsis_id: str
    headshot_url: str
    position: str
    team: str
    gametime: pd.Timestamp | None
    week: int | None

    @classmethod
    def from_series(cls, input_series):
        return cls(
            full_name=input_series.full_name,
            gsis_id=input_series.gsis_id,
            headshot_url=input_series.headshot_url,
            position=input_series.position,
            team=input_series.team,
            gametime=input_series.gametime,
            week=int(input_series.week),
        )

    @classmethod
    def empty_player(cls, week: int | None = None):
        return cls(full_name="", gsis_id="", headshot_url="", position="", team="", gametime=None, week=week)

    def is_locked(self):
        if not self.gametime:
            return False
        else:
            date_compare = (pd.Timestamp.now(tz="America/New_York")) + pd.Timedelta(days=0, hours=0)
            return self.gametime < date_compare


def initialize_empty_options_map() -> dict[str, dict[int, list[PlayerOption]]]:
    options_map: dict[str, dict[int, list[PlayerOption]]] = {}
    for pos in ["QB", "RB", "WR", "TE", "K", "DEF"]:
        options_map[pos] = {}
        for week in PLAYOFF_WEEK_TO_NAME.keys():
            options_map[pos][int(week)] = [PlayerOption.empty_player(week=week)]
    return options_map


def player_options_from_df(df_options) -> dict[str, dict[int, list[PlayerOption]]]:
    options_map = initialize_empty_options_map()
    for pos, pos_week_map in options_map.items():
        for week in pos_week_map.keys():
            df_pos_week = df_options[((df_options.week == week) & (df_options.position == pos))]
            if len(df_pos_week) > 0:
                player_options_list = df_pos_week.apply(PlayerOption.from_series, axis=1).tolist()
                options_map[pos][int(week)].extend(player_options_list)
    return options_map


def modify_defensive_players_to_be_team_defense(df_options):
    for team, player_id in PLAYOFF_TEAM_DEF_PLAYER:
        if player_id in df_options.gsis_id.values:
            df_options.loc[df_options.gsis_id == player_id, "position"] = "DEF"
            df_options.loc[df_options.gsis_id == player_id, "full_name"] = team.team_name


def display_player(player_opt: PlayerOption | None):
    if player_opt:
        if player_opt.headshot_url:
            st.image(player_opt.headshot_url)
        if player_opt.full_name:
            st.write(player_opt.full_name)
            st.write(f"{player_opt.team} - {player_opt.gametime.strftime('%-m/%-d %-I:%M %p')}")


@st.cache_data(ttl=60 * 60 * 24)
def load_options():
    df_rosters = get_weekly_rosters()

    # get game schedules
    week_game_times = get_season_time_map(SEASON)
    latest_game_time_defaults = {k: max(v.values()) for k, v in week_game_times.items() if v}

    # sort
    sort_by_cols = ["position", "fantasy_points", "week"]
    df_rosters.sort_values(sort_by_cols, ascending=False, inplace=True)

    # filter data from non-playoffs
    df_rosters = df_rosters[df_rosters.week.isin(ROSTER_WEEK_TO_PLAYOFF_WEEK.keys())]
    df_rosters["week"] = df_rosters["week"].map(ROSTER_WEEK_TO_PLAYOFF_WEEK)

    # Filter out duplicates which occur for week 1 (bye players come from week 18)
    df_rosters = df_rosters.drop_duplicates(subset=["gsis_id", "week"])

    # set gametime
    if len(df_rosters) == 0:
        return initialize_empty_options_map()
    df_rosters["gametime"] = df_rosters.apply(
        lambda x: week_game_times.get(x.week, {}).get(
            SCHEDULE_NAME_TO_PFR_NAME_MAP[x.team], latest_game_time_defaults.get(x.week, None)
        ),
        axis=1,
    )

    df_rosters["in_playoffs"] = df_rosters.apply(lambda x: x.team in PLAYOFFS_TEAMS[x.week], axis=1)

    df_rosters = df_rosters[df_rosters.in_playoffs]
    modify_defensive_players_to_be_team_defense(df_rosters)
    player_options = player_options_from_df(df_rosters)
    return player_options