import streamlit as st import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from datetime import date, timedelta import random # [All the scheduling functions and analytics functions here] # 1. create_schedule def create_schedule(num_teams, num_conferences, num_inter_games): full_schedule = [] for i in range(num_conferences): conference_name = chr(65 + i) # 'A', 'B', 'C', 'D', ... combined_schedule = combine_schedules(conference_name, num_teams, num_inter_games) assigned_dates = assign_dates_to_matches(combined_schedule) full_schedule.extend(assigned_dates) return pd.DataFrame(full_schedule, columns=["Team 1", "Team 2", "Date"]) # 2. combine_schedules def combine_schedules(conference_name, num_teams, num_inter_games): intra_conf_matches = generate_intra_conference_schedule(conference_name, num_teams) inter_conf_matches = generate_inter_conference_schedule(conference_name, num_teams, num_inter_games) return intra_conf_matches + inter_conf_matches # 3. generate_intra_conference_schedule def generate_intra_conference_schedule(conference_name, num_teams): teams = [f"{conference_name}{i}" for i in range(1, num_teams + 1)] matches = [] for i in range(len(teams)): for j in range(i+1, len(teams)): matches.append((teams[i], teams[j])) matches.append((teams[j], teams[i])) # Home and away return matches # 4. generate_inter_conference_schedule def generate_inter_conference_schedule(conference_name, num_teams, num_inter_games): current_conf_teams = [f"{conference_name}{i}" for i in range(1, num_teams + 1)] other_confs = [chr(65 + i) for i in range(4) if chr(65 + i) != conference_name] other_conf_teams = [f"{conf}{i}" for conf in other_confs for i in range(1, num_teams + 1)] matches = [] for team in current_conf_teams: opponents = random.sample(other_conf_teams, num_inter_games) for opp in opponents: matches.append((team, opp)) return matches # 5. assign_dates_to_matches def assign_dates_to_matches(matches): start_date = date(2022, 11, 6) end_date = date(2023, 3, 1) available_dates = [start_date + timedelta(days=i) for i in range((end_date - start_date).days) if (start_date + timedelta(days=i)).weekday() in [0, 2, 3, 5]] random.shuffle(available_dates) # Ensure cyclic reuse of dates extended_dates = available_dates * (len(matches) // len(available_dates) + 1) return [(match[0], match[1], extended_dates[i]) for i, match in enumerate(matches)] # 6. generate_mock_historical_data def generate_mock_historical_data(num_teams, num_conferences, num_inter_games, start_date, end_date): full_schedule = [] for i in range(num_conferences): conference_name = chr(65 + i) combined_schedule = combine_schedules(conference_name, num_teams, num_inter_games) shuffled_dates = assign_dates_to_matches(combined_schedule) random.shuffle(shuffled_dates) for match in shuffled_dates: full_schedule.append({ "Team 1": match[0], "Team 2": match[1], "Date": match[2] }) return pd.DataFrame(full_schedule) # Team Workload Analysis def team_workload_analysis(schedule_df): # Check if the DataFrame is None if schedule_df is None: plt.figure(figsize=(10, 6)) plt.text(0.5, 0.5, 'Please generate the schedule first before viewing analytics.', horizontalalignment='center', verticalalignment='center', fontsize=14, color='red') plt.axis('off') plt.tight_layout() plt.show() return """Generate a bar chart showing the number of matches each team has per week.""" schedule_df['Week'] = schedule_df['Date'].dt.isocalendar().week team_counts = schedule_df.groupby(['Week', 'Team 1']).size().unstack().fillna(0) # Plot team_counts.plot(kind='bar', stacked=True, figsize=(15, 7), cmap='Oranges') plt.title('Team Workload Analysis') plt.ylabel('Number of Matches') plt.xlabel('Week Number') plt.tight_layout() plt.legend(title='Teams', bbox_to_anchor=(1.05, 1), loc='upper left') plt.show() # Match Distribution def match_distribution(schedule_df): # Check if the DataFrame is None if schedule_df is None: plt.figure(figsize=(10, 6)) plt.text(0.5, 0.5, 'Please generate the schedule first before viewing analytics.', horizontalalignment='center', verticalalignment='center', fontsize=14, color='red') plt.axis('off') plt.tight_layout() plt.show() return """Generate a histogram showing match distribution across months.""" schedule_df['Month'] = schedule_df['Date'].dt.month_name() month_order = ['November', 'December', 'January', 'February', 'March'] # Plot plt.figure(figsize=(10, 6)) sns.countplot(data=schedule_df, x='Month', order=month_order, palette='Oranges_r') plt.title('Match Distribution Across Months') plt.ylabel('Number of Matches') plt.xlabel('Month') plt.tight_layout() plt.show() # Inter-Conference Match Analysis def inter_conference_analysis(schedule_df): # Check if the DataFrame is None if schedule_df is None: plt.figure(figsize=(10, 6)) plt.text(0.5, 0.5, 'Please generate the schedule first before viewing analytics.', horizontalalignment='center', verticalalignment='center', fontsize=14, color='red') plt.axis('off') plt.tight_layout() plt.show() return """Generate a heatmap showing inter-conference match frequencies.""" # Extract the conference from the team names schedule_df['Conference 1'] = schedule_df['Team 1'].str[0] schedule_df['Conference 2'] = schedule_df['Team 2'].str[0] # Filter out intra-conference matches inter_conference_df = schedule_df[schedule_df['Conference 1'] != schedule_df['Conference 2']] # Create a crosstab for the heatmap heatmap_data = pd.crosstab(inter_conference_df['Conference 1'], inter_conference_df['Conference 2']) # Ensure every conference combination has a value all_conferences = schedule_df['Conference 1'].unique() for conf in all_conferences: if conf not in heatmap_data.columns: heatmap_data[conf] = 0 if conf not in heatmap_data.index: heatmap_data.loc[conf] = 0 heatmap_data = heatmap_data.sort_index().sort_index(axis=1) # Plot plt.figure(figsize=(8, 6)) sns.heatmap(heatmap_data, annot=True, cmap='Oranges', linewidths=.5, cbar_kws={'label': 'Number of Matches'}) plt.title('Inter-Conference Match Analysis') plt.ylabel('Conference 1') plt.xlabel('Conference 2') plt.show() # Commissioner Analytics def commissioner_analytics(schedule_df, commissioners): # Check if the DataFrame is None if schedule_df is None: plt.figure(figsize=(10, 6)) plt.text(0.5, 0.5, 'Please generate the schedule first before viewing analytics.', horizontalalignment='center', verticalalignment='center', fontsize=14, color='red') plt.axis('off') plt.tight_layout() plt.show() return """Generate a bar chart showing matches overseen by each commissioner.""" # Assuming each commissioner oversees a specific conference comm_dict = {f"Conference {chr(65+i)}": comm for i, comm in enumerate(commissioners)} schedule_df['Commissioner'] = schedule_df['Conference 1'].map(comm_dict) # Count matches overseen by each commissioner commissioner_counts = schedule_df['Commissioner'].value_counts() # Plot using matplotlib plt.figure(figsize=(10, 6)) plt.bar(commissioner_counts.index, commissioner_counts.values, color='orange') plt.title('Matches Overseen by Each Commissioner') plt.ylabel('Number of Matches') plt.xlabel('Commissioner') plt.xticks(rotation=45) plt.tight_layout() plt.show() # Streamlit App st.title("Basketball Game Schedule Generator") st.set_option('deprecation.showPyplotGlobalUse', False) if 'num_teams' not in st.session_state: st.session_state.num_teams = 10 if 'num_conferences' not in st.session_state: st.session_state.num_conferences = 4 if 'num_inter_games' not in st.session_state: st.session_state.num_inter_games = 3 # Initialize session state for schedule_df and st.session_state.historical_data if 'schedule_df' not in st.session_state: st.session_state.schedule_df = None if 'st.session_state.historical_data' not in st.session_state: st.session_state.historical_data = None if st.session_state.historical_data is None: st.session_state.historical_data = generate_mock_historical_data(st.session_state.num_teams, st.session_state.num_conferences, st.session_state.num_inter_games, date(2022, 11, 6), date(2023, 3, 1)) st.session_state.historical_data['Date'] = pd.to_datetime(st.session_state.historical_data['Date']) # Configuration UI st.header("Configuration") st.session_state.num_teams = st.number_input("Number of teams per conference:", min_value=2, value=st.session_state.num_teams) st.session_state.num_conferences = st.number_input("Number of conferences:", min_value=2, value=st.session_state.num_conferences) st.session_state.num_inter_games = st.number_input("Number of inter-conference games per team:", min_value=1, value=st.session_state.num_inter_games) commissioners = st.multiselect("Add commissioners:", options=[], default=[]) add_commissioner = st.text_input("New commissioner name:") if add_commissioner: commissioners.append(add_commissioner) st.session_state.commissioners = commissioners # Make sure to update session state # Schedule Generation if st.button("Generate Schedule"): st.session_state.schedule_df = create_schedule(st.session_state.num_teams, st.session_state.num_conferences, st.session_state.num_inter_games) if st.session_state.schedule_df is not None and not st.session_state.schedule_df.empty: st.session_state.schedule_df['Date'] = pd.to_datetime(st.session_state.schedule_df['Date']) st.success("Schedule generated successfully!") print("Stored schedule in session state.") else: st.error("Failed to generate schedule. Please check input parameters.") # Schedule Viewing st.header("View Schedule") # Generating the list of conferences dynamically based on the number of conferences in session state conference_options = ["All"] + [f"Conference {chr(65+i)}" for i in range(st.session_state.num_conferences)] conference_selector = st.selectbox("Select conference to view schedule:", options=conference_options) # Check if the schedule DataFrame exists and is not empty if st.session_state.schedule_df is not None and not st.session_state.schedule_df.empty: if conference_selector == "All": # Display the entire schedule if "All" is selected st.dataframe(st.session_state.schedule_df) else: # Filter the schedule based on the selected conference filtered_schedule = st.session_state.schedule_df[ (st.session_state.schedule_df["Team 1"].str.startswith(conference_selector)) | (st.session_state.schedule_df["Team 2"].str.startswith(conference_selector)) ] if filtered_schedule.empty: st.write(f"No matches found for {conference_selector}.") # Provide feedback if no matches are found else: st.dataframe(filtered_schedule) # Display the filtered schedule else: # Display a message if no schedule is available st.write("No schedule available. Please generate the schedule.") # Analytics & Comparisons st.header("Analytics & Comparisons") analytics_option = st.selectbox("Choose an analysis type:", ["Team Workload Analysis", "Match Distribution", "Inter-Conference Match Analysis", "Commissioner Analytics"]) st.session_state.historical_data['Date'] = pd.to_datetime(st.session_state.historical_data['Date']) if analytics_option == "Team Workload Analysis": if st.session_state.historical_data is not None and not st.session_state.historical_data.empty: st.subheader("Historical Data") st.pyplot(team_workload_analysis(st.session_state.historical_data)) if st.session_state.schedule_df is not None and not st.session_state.schedule_df.empty: st.subheader("Current Data") st.pyplot(team_workload_analysis(st.session_state.schedule_df)) else: st.warning("No current data to display. Generate the schedule first.") elif analytics_option == "Match Distribution": st.subheader("Historical Data") st.pyplot(match_distribution(st.session_state.historical_data)) st.subheader("Current Data") st.pyplot(match_distribution(st.session_state.schedule_df)) elif analytics_option == "Inter-Conference Match Analysis": st.subheader("Historical Data") st.pyplot(inter_conference_analysis(st.session_state.historical_data)) st.subheader("Current Data") st.pyplot(inter_conference_analysis(st.session_state.schedule_df)) elif analytics_option == "Commissioner Analytics": st.subheader("Historical Data") st.pyplot(commissioner_analytics(st.session_state.historical_data, commissioners)) st.subheader("Current Data") st.pyplot(commissioner_analytics(st.session_state.schedule_df, commissioners)) else: st.warning("Please generate the schedule first before viewing analytics.") # Export functionality can be added later