%load_ext nb_black
import pandas as pd
import numpy as np

player_count_df = pd.read_excel("./output/seasonal_player_counts.xlsx").set_index(
    "season"
)
start_stop_df = pd.read_excel("./data/season_start_stop.xlsx").set_index("season")
start_stop_df["length"] = start_stop_df["stop"] - start_stop_df["start"]
start_stop_df["length"] = start_stop_df["length"].apply(
    lambda x: x.days
)  # convert time delta to int in days

seasonal_stats_df = (
    pd.read_excel("./output/seasonal_stats.xlsx")
    .drop(columns=["Unnamed: 0"])
    .set_index("season")
)

diversity_df = pd.read_excel("./output/seasonal_diversity_of_nations.xlsx").set_index(
    "season"
)

merged_df = pd.merge(
    player_count_df, start_stop_df, how="inner", left_index=True, right_index=True
)
merged_df = pd.merge(
    merged_df, seasonal_stats_df, how="inner", left_index=True, right_index=True
)
merged_df = pd.merge(
    merged_df, diversity_df, how="inner", left_index=True, right_index=True
)
merged_df

merged_df["pro_players_per_day"] = (merged_df["players_total"] - 500) / merged_df[
    "length"
]
merged_df["top2860_matches_per_day"] = (merged_df["num_matches"] - 500) / merged_df[
    "length"
]

merged_df

import seaborn as sns
import matplotlib.pyplot as plt

sns.lineplot(data=merged_df, x="start", y="pro_players_per_day")

merged_df["season_readable"] = [
    " ".join(x.split()[1:]) for x in merged_df.reset_index()["season"]
]
merged_df.to_excel("./output/seasonal_popularity.xlsx")
merged_df

merged_df["series"] = [
    x.split("_")[0].replace("M", "Masters 0") for x in merged_df.index
]

sns.barplot(
    data=merged_df,
    x="season_readable",
    y="pro_players_per_day",
    hue="series",
    dodge=False,
)

plt.xticks(rotation=45, rotation_mode="anchor", ha="right")
plt.xlabel("Season")
plt.ylabel("Avg. Daily Increase of Pro Players")
plt.title("Gwent: Number of Players Entering Pro Rank (avg. per day)")
plt.tight_layout()
plt.savefig("./images/popularity_plot.png", dpi=300)
plt.show()

sns.barplot(
    data=merged_df, x="season_readable", y="players_total", hue="series", dodge=False,
)

plt.xticks(rotation=45, rotation_mode="anchor", ha="right")
plt.xlabel("Season")
plt.ylabel("Number of Players")
plt.title("Gwent: Number of Players in Pro Rank")
plt.tight_layout()
plt.savefig("./images/player_count_plot.png", dpi=300)
plt.show()

sns.barplot(
    data=merged_df,
    x="season_readable",
    y="top2860_matches_per_day",
    hue="series",
    dodge=False,
)

plt.xticks(rotation=45, rotation_mode="anchor", ha="right")
plt.xlabel("Season")
plt.ylabel("Avg. Games played per day")
plt.title("Gwent: Games Played per day (Top 2860 Pro Rank)")
plt.tight_layout()
plt.savefig("./images/daily_games_plot.png", dpi=300)
plt.show()

sns.barplot(
    data=merged_df,
    x="season_readable",
    y="shannon_diversity",
    hue="series",
    dodge=False,
)

plt.xticks(rotation=45, rotation_mode="anchor", ha="right")
plt.xlabel("Season")
plt.ylabel("Diversity")
plt.ylim(ymax=3.2, ymin=2.5)
plt.title("Gwent: Diversity of Nationalities (Top 2860 Pro Rank)")
plt.tight_layout()
plt.savefig("./images/diversity_per_season.png", dpi=300)
plt.show()

sns.barplot(
    data=merged_df, x="season_readable", y="richness", hue="series", dodge=False,
)

plt.xticks(rotation=45, rotation_mode="anchor", ha="right")
plt.xlabel("Season")
plt.ylabel("Nationalities")
plt.ylim(ymax=90, ymin=60)
plt.title("Gwent: Number of Nationalities (Top 2860 Pro Rank)")
plt.tight_layout()
plt.savefig("./images/number_of_nationalities_per_season.png", dpi=300)
plt.show()