import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import matplotlib.dates as mdates
import seaborn as sns

sns.set_theme(context = 'paper', style = 'ticks', palette = 'deep', color_codes = True)
plt.rcParams['figure.autolayout'] = True
plt.rcParams['figure.dpi'] = 300

head_coach = pd.read_csv('data/head_coach.csv', parse_dates=['Appointed', 'EndDate'])
head_coach_dismissed = head_coach[head_coach['EndDate'].notnull()]

long_tenure = head_coach[head_coach['Tenure'] > 3000].shape[0]

# Useful to add xtick months to dayofyear plot
months = pd.date_range('2022-01-01', '2022-12-31', freq='ME').strftime('%b').tolist()

plt.figure()
plt.grid(axis='y', linestyle='--', alpha=0.8)
sns.histplot(head_coach['Appointed'].dt.month, bins=11, color = 'b', kde=False, stat='density', alpha = 1)
plt.gca().yaxis.set_major_formatter(mticker.PercentFormatter(xmax=1))
plt.xticks(range(1, 13), months)
plt.title('Monthly Distribution of Head Coach Appointments')
plt.xlabel('Month')
plt.ylabel('Proportion');

# Plot for Head Coach dismissal distribution
plt.figure()
plt.grid(axis='y', linestyle='--', alpha=0.8)
sns.histplot(head_coach['EndDate'].dt.month, bins=11, color = 'r', kde=False, stat='density', alpha = 1)
plt.xticks(range(1, 13), months)
plt.gca().yaxis.set_major_formatter(mticker.PercentFormatter(xmax=1))
plt.title('Monthly Distribution of Head Coach Dismissals')
plt.xlabel('Month')
plt.ylabel('Proportion');

# Proportion of in-season vs off-season head coach dismissal per league
head_coach_dismissed['OffSeason'] = head_coach_dismissed['EndDate'].dt.month.isin([5, 6, 7])
season_break = head_coach_dismissed.groupby(['League'])['OffSeason'].value_counts(normalize=True).unstack().fillna(0)
season_break.columns = ['In-Season', 'Off-Season']
season_break = season_break.sort_values(by='Off-Season', ascending=False)
# plot season_break for each league
season_break.plot(kind='barh', stacked=True, color=['r', 'b'])
plt.legend(labels=['In-Season (Aug-Apr)', 'Off-Season (May-Jul)'], loc='lower center', bbox_to_anchor=(0.5, 1.0), ncol=2)
plt.gca().xaxis.set_major_formatter(mticker.PercentFormatter(xmax=1))
plt.tick_params(axis='y', which='both', length=0)
plt.title('Proportion of In-Season vs Off-Season Head Coach Dismissals', y = 1.10)
plt.xlabel('Proportion')
plt.ylabel('')
plt.grid(axis='x', linestyle='--', alpha=0.8);

head_coach['AppointedDay'] = head_coach['Appointed'].dt.dayofyear
head_coach_dismissed['DismissedDay'] = head_coach_dismissed['EndDate'].dt.dayofyear

# KDE Plot of head coach appointment/dismissed days of the year versus league
leagues = head_coach['League'].unique()
fig, ax = plt.subplots(len(leagues), 2, figsize=(8, 1.8*len(leagues),), sharex=True, sharey=True)

for i, league in enumerate(leagues):
    sns.kdeplot(data=head_coach[head_coach['League'] == league], x='AppointedDay', ax=ax[i, 0], fill=True, color='b', alpha=0.5, bw_adjust=0.25, clip=(0, 365), label = 'Appointments')
    sns.kdeplot(data=head_coach_dismissed[head_coach_dismissed['League'] == league], x='DismissedDay', ax=ax[i, 1], fill=True, color='r', alpha=0.5, bw_adjust=0.25, clip=(0, 365), label = 'Dismissals')
    ax[i, 0].set_xlim(0, 365)
    ax[i, 1].set_xlim(0, 365)
    # Major formatter for x-axis
    ax[i, 0].xaxis.set_major_locator(mdates.MonthLocator())
    ax[i, 0].xaxis.set_major_formatter(mdates.DateFormatter('%b'))
    ax[i, 1].xaxis.set_major_locator(mdates.MonthLocator())
    ax[i, 1].xaxis.set_major_formatter(mdates.DateFormatter('%b'))
    ax[i, 0].set_ylabel(league, rotation=0, labelpad=40)
    # Hide y-axis label and ticks
    ax[i, 0].set_yticklabels([])
    ax[i, 1].set_yticklabels([])
    # ax[i, 0].set_yticks([])
    # ax[i, 1].set_yticks([])
    ax[i, 0].grid(axis='y', linestyle='--', alpha=0.8)
    ax[i, 1].grid(axis='y', linestyle='--', alpha=0.8)
    # Remove x-axis label
    ax[i, 0].set_xlabel('')
    ax[i, 1].set_xlabel('')

    if i > 0:
        ax[i, 0].legend().remove()
        ax[i, 1].legend().remove()
    else:
        ax[i, 0].legend()
        ax[i, 1].legend()
        # Place each legend centered on top of their respective axes
        ax[i, 0].legend(loc='upper center', bbox_to_anchor=(0.5, 1.3), ncol=2)
        ax[i, 1].legend(loc='upper center', bbox_to_anchor=(0.5, 1.3), ncol=2)

# Plot ECDF of head_coach tenure 
plt.figure(figsize=(8, 4))

sns.ecdfplot(data=head_coach_dismissed, x='Tenure', stat = 'percent', alpha = 1, linewidth = 2)
plt.ylabel('Percentage of Head Coaches')
# Format percentage
plt.gca().yaxis.set_major_formatter(mticker.PercentFormatter(xmax=100))
# Grid
plt.grid(axis='y', linestyle='-', alpha=0.8)
plt.grid(axis='x', linestyle='-', alpha=0.8)
plt.xticks(range(0, max(head_coach_dismissed['Tenure']) + 365, 365))
plt.xlim(0, head_coach_dismissed['Tenure'].quantile(0.99))
plt.title('Cumulative distribution of Head Coaches tenure (2015 - 2023)')
plt.xlabel('Head Coaches tenure (days)');

# Average days in post per league 

# Calculate average days in post per league
avg_days_in_post = head_coach_dismissed.groupby('League')['Tenure'].mean()
avg_days_in_post = avg_days_in_post.sort_values()

# Plot average days in post per league
plt.figure()
sns.barplot(y=avg_days_in_post.index, x=avg_days_in_post.values, orient='h')
plt.title('Average Head Coach Tenure for Completed Appointments per League (2015 - 2023)')
plt.xlabel('Head Coach tenure (days)')
plt.tick_params(axis='y', which='both', length=0)
# Disable ylabel
plt.ylabel('')
plt.grid(axis='x', linestyle='--', alpha=0.8)

# Number of clubs per Head Coach

# Group by coach_name and count the number of clubs
club_per_coach = head_coach.groupby('HeadCoach').size().reset_index(name='count')

plt.figure()
sns.histplot(x='count', data = club_per_coach, discrete = True, stat="probability", alpha = 1)
plt.xticks(range(1, club_per_coach['count'].max() + 1))
plt.gca().yaxis.set_major_formatter(mticker.PercentFormatter(xmax=1))
plt.title('Proportion of Head Coaches by Number of Club Appointments (2015 - 2023)')
plt.xlabel('Number of clubs')
plt.ylabel('Proportion of Head Coaches')
plt.grid(axis='y', linestyle='--', alpha=0.8)

# Number of Head Coachs per club

# Group by team and count the number of head coach

coach_per_club = head_coach.groupby('Team').size().reset_index(name='count')

plt.figure()
sns.histplot(x='count', data = coach_per_club, discrete=True, stat="probability", alpha = 1)
plt.xticks(range(1, coach_per_club['count'].max() + 1))
plt.gca().yaxis.set_major_formatter(mticker.PercentFormatter(xmax=1))
plt.title(f'Proportion of Clubs by Number of Head Coaches Appointed (2015 - 2023)')
plt.xlabel('Number of head coach')
plt.ylabel('Proportion of clubs');

# Average number of coach per club per league 

# Calculate average number of coach per club per league
coach_per_team = head_coach.groupby(['League', 'Team']).size()
avg_number_of_coach_per_club_per_league = coach_per_team.groupby('League').mean().sort_values()

# Plot average number of coach per club per league
plt.figure()
sns.barplot(x=avg_number_of_coach_per_club_per_league.values, y=avg_number_of_coach_per_club_per_league.index, orient='h')
plt.title('Average Number of Head Coaches Appointed per Club versus League (2015 - 2023)')
plt.ylabel('')
plt.xlabel('Average number of coach per club');