97 lines
3.5 KiB
Python
97 lines
3.5 KiB
Python
from datetime import datetime, timedelta
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import pandas as pd
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from pkg.config import render_data
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import pytz
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def aggregate_data(data, interval):
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all_minutes = pd.DataFrame({'interval_group': range(0, 1440, interval)})
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result = []
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if data.empty or 'date' not in data.columns:
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complete_data = all_minutes.copy()
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complete_data['tweet_count'] = 0
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complete_data['date'] = datetime.now().date()
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return complete_data
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for date in data['date'].unique():
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day_data = data[data['date'] == date].copy()
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day_data['interval_group'] = (day_data['minute_of_day'] // interval) * interval
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agg = day_data.groupby('interval_group').size().reset_index(name='tweet_count')
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complete_data = all_minutes.merge(agg, on='interval_group', how='left').fillna({'tweet_count': 0})
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complete_data['date'] = date
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result.append(complete_data)
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if not result:
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complete_data = all_minutes.copy()
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complete_data['tweet_count'] = 0
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complete_data['date'] = data['date'].iloc[0] if not data.empty else datetime.now().date()
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return complete_data
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return pd.concat(result, ignore_index=True)
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def generate_xticks(interval):
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if interval <= 5:
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tick_step = 60
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elif interval <= 10:
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tick_step = 60
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elif interval <= 30:
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tick_step = 120
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else:
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tick_step = 240
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ticks = list(range(0, 1440, tick_step))
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tick_labels = [f"{m // 60:02d}:{m % 60:02d}" for m in ticks]
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return ticks, tick_labels
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def minutes_to_time(minutes):
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hours = minutes // 60
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mins = minutes % 60
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return f"{hours:02d}:{mins:02d}"
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def get_tweets_since_last_friday():
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est = pytz.timezone('US/Eastern')
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now_est = datetime.now(est)
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today = now_est.date()
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days_since_friday = (today.weekday() - 4) % 7
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this_friday = today - timedelta(days=days_since_friday)
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this_friday_datetime = est.localize(datetime.combine(this_friday, datetime.strptime("12:00", "%H:%M").time()))
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last_friday = this_friday - timedelta(days=7)
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last_friday_datetime = est.localize(datetime.combine(last_friday, datetime.strptime("12:00", "%H:%M").time()))
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if now_est < this_friday_datetime:
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start_datetime = last_friday_datetime
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else:
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start_datetime = this_friday_datetime
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if hasattr(render_data, 'global_df') and not render_data.global_df.empty:
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df = render_data.global_df.copy()
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mask = df['datetime_est'] >= start_datetime
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filtered_df = df[mask]
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tweet_count = len(filtered_df)
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return int(tweet_count)
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return 0
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def get_time_since_last_tweet():
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est = pytz.timezone('US/Eastern')
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now_est = datetime.now(est)
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if (not hasattr(render_data, 'global_df') or
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render_data.global_df is None or
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render_data.global_df.empty):
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return 0.0
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df = render_data.global_df
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if 'datetime_est' not in df.columns:
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return 0.0
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latest_tweet_time = df['datetime_est'].max()
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time_diff = now_est - latest_tweet_time
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days_diff = time_diff.total_seconds() / (24 * 60 * 60) # 转换为天数
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return days_diff
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def format_time_str(days_to_next_friday):
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total_seconds = days_to_next_friday * 24 * 60 * 60
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days = int(total_seconds // (24 * 60 * 60))
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hours = int((total_seconds % (24 * 60 * 60)) // (60 * 60))
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minutes = int((total_seconds % (60 * 60)) // 60)
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seconds = int(total_seconds % 60)
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total_hours = round(days_to_next_friday * 24, 2)
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return f"{days}d {hours:02d}h {minutes:02d}m {seconds:02d}s ({total_hours}h)" |