from datetime import datetime, timedelta
import pandas as pd
from pkg.config import render_data
import pytz

def aggregate_data(data, interval):
    all_minutes = pd.DataFrame({'interval_group': range(0, 1440, interval)})
    result = []

    if data.empty or 'date' not in data.columns:
        complete_data = all_minutes.copy()
        complete_data['tweet_count'] = 0
        complete_data['date'] = datetime.now().date()
        return complete_data

    for date in data['date'].unique():
        day_data = data[data['date'] == date].copy()
        day_data['interval_group'] = (day_data['minute_of_day'] // interval) * interval
        agg = day_data.groupby('interval_group').size().reset_index(name='tweet_count')
        complete_data = all_minutes.merge(agg, on='interval_group', how='left').fillna({'tweet_count': 0})
        complete_data['date'] = date
        result.append(complete_data)

    if not result:
        complete_data = all_minutes.copy()
        complete_data['tweet_count'] = 0
        complete_data['date'] = data['date'].iloc[0] if not data.empty else datetime.now().date()
        return complete_data

    return pd.concat(result, ignore_index=True)


def generate_xticks(interval):
    if interval <= 5:
        tick_step = 60
    elif interval <= 10:
        tick_step = 60
    elif interval <= 30:
        tick_step = 120
    else:
        tick_step = 240
    ticks = list(range(0, 1440, tick_step))
    tick_labels = [f"{m // 60:02d}:{m % 60:02d}" for m in ticks]
    return ticks, tick_labels


def minutes_to_time(minutes):
    hours = minutes // 60
    mins = minutes % 60
    return f"{hours:02d}:{mins:02d}"


def get_tweets_since_last_friday():
    est = pytz.timezone('US/Eastern')
    now_est = datetime.now(est)
    today = now_est.date()
    days_since_last_friday = (today.weekday() - 4) % 7
    last_friday = today - timedelta(days=days_since_last_friday)
    last_friday_datetime = est.localize(datetime.combine(last_friday, datetime.strptime("12:00", "%H:%M").time()))
    this_friday = today - timedelta(days=(today.weekday() - 4) % 7)
    this_friday_datetime = est.localize(datetime.combine(this_friday, datetime.strptime("12:00", "%H:%M").time()))
    if now_est < this_friday_datetime and today.weekday() != 4:
        last_friday -= timedelta(days=7)
        last_friday_datetime = est.localize(datetime.combine(last_friday, datetime.strptime("12:00", "%H:%M").time()))
    if hasattr(render_data, 'global_df') and not render_data.global_df.empty:
        df = render_data.global_df.copy()
        mask = df['datetime_est'] >= last_friday_datetime
        filtered_df = df[mask]
        tweet_count = len(filtered_df)
        return int(tweet_count)
    return 0