118 lines
5.8 KiB
Python
118 lines
5.8 KiB
Python
from pkg.dash.func.info_func import *
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from pkg.dash.app_init import app
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from dash.dependencies import Input, Output
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from dash import html
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import pandas as pd
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import re
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from datetime import timedelta
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@app.callback(
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[Output('test-info-tooltip', 'children')],
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[Input('test-button', 'n_clicks'),
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Input('test-date-input', 'date'),
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Input('test-time-input', 'value')]
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)
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def update_test_info(n_clicks, test_date, test_time):
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if n_clicks == 0:
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return [html.Div("Click 'Test' to see historical probability results.")]
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est = pytz.timezone('US/Eastern')
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data = render_data.global_agg_df.copy()
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# 调试：打印输入值
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print(f"test_date: {test_date}, test_time: {test_time}")
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# 检查输入是否为空
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if not test_date or not test_time:
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return [html.Div("Date or time input is empty. Please provide both date (YYYY-MM-DD) and time (HH:MM:SS).")]
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# 验证时间格式
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time_pattern = r'^(?:[01]\d|2[0-3]):[0-5]\d:[0-5]\d$' # HH:MM:SS (00:00:00 to 23:59:59)
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if not re.match(time_pattern, test_time):
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return [html.Div("Invalid time format. Use HH:MM:SS (e.g., 12:00:00) with hours 00-23, minutes 00-59, seconds 00-59.")]
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# 重构 datetime_est，处理夏令时模糊时间
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data['hours'] = data['minute_of_day'] // 60
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data['minutes'] = data['minute_of_day'] % 60
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data['datetime_est'] = pd.to_datetime(
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data['date'].astype(str) + ' ' +
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data['hours'].astype(str) + ':' +
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data['minutes'].astype(str) + ':00',
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errors='coerce'
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).dt.tz_localize(est, ambiguous='NaT')
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if data['datetime_est'].isna().any():
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print("Warning: Some datetime_est values are NaT due to ambiguous time handling")
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# 解析测试日期和时间
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try:
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test_date = pd.to_datetime(test_date, format='%Y-%m-%d').date()
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test_datetime = pd.to_datetime(f"{test_date} {test_time}", format='%Y-%m-%d %H:%M:%S').tz_localize(est, ambiguous=True)
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except ValueError as e:
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print(f"Error parsing date/time: {e}")
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return [html.Div("Invalid date or time format. Use YYYY-MM-DD and HH:MM:SS (e.g., 12:00:00).")]
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# 计算周期开始时间（上一个周五 12:00 PM）
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days_since_last_friday = (test_datetime.weekday() - 4) % 7 # 4 表示周五
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cycle_start = test_datetime - timedelta(days=days_since_last_friday)
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cycle_start = cycle_start.replace(hour=12, minute=0, second=0, microsecond=0)
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# 确保周期结束时间（下周五 12:00 PM EDT）考虑夏令时
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cycle_end = cycle_start + timedelta(days=7)
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# 精确计算夏令时开始（2025年3月9日，第二个星期日）
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first_day = cycle_end.replace(day=1)
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second_sunday = first_day + timedelta(days=((6 - first_day.weekday()) % 7) + 7)
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if cycle_end.month == 3 and cycle_end >= second_sunday.replace(hour=2):
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cycle_end = cycle_end.tz_convert(est) # EDT
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else:
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cycle_end = cycle_end.tz_convert(est) # EST 或 EDT
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# 调试：打印周期信息
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print(f"Cycle Start: {cycle_start}, Cycle End: {cycle_end}")
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# 过滤周期内的数据
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cycle_data = data[(data['datetime_est'] >= cycle_start) & (data['datetime_est'] <= test_datetime)]
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if cycle_data.empty:
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return [html.Div(f"No data available in cycle from {cycle_start} to {test_datetime}")]
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tweet_count = cycle_data['tweet_count'].sum()
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# 计算实际最终推文数（周期结束时的总数）
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actual_data = data[(data['datetime_est'] >= cycle_start) & (data['datetime_est'] <= cycle_end)]
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if actual_data.empty:
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return [html.Div(f"No data available for cycle ending {cycle_end}")]
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actual_end_count = actual_data['tweet_count'].sum()
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# 计算 days_to_next_friday（从 test_datetime 到周期结束）
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days_to_next_friday = (cycle_end - test_datetime).total_seconds() / (24 * 60 * 60)
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# 设置预测范围
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prob_start = actual_end_count * 0.9
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prob_end = actual_end_count * 1.1
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# 计算概率
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probability = calculate_tweet_probability(tweet_count, days_to_next_friday, prob_start, prob_end)
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prob_min, prob_max = map(float, probability.split(" - "))
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formatted_probability = f"{prob_min * 100:.2f}% - {prob_max * 100:.2f}%"
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# 构建测试结果表格（包含 Cycle End）
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test_table_rows = [
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html.Tr([html.Th("Test Date and Time:", colSpan=2), html.Td(str(test_datetime), colSpan=6)]),
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html.Tr([html.Th("Cycle Start:", colSpan=2), html.Td(str(cycle_start), colSpan=6)]),
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html.Tr([html.Th("Cycle End:", colSpan=2), html.Td(str(cycle_end), colSpan=6)]),
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html.Tr([html.Th("Tweet Count at Test Time:", colSpan=2), html.Td(str(tweet_count), colSpan=6)]),
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html.Tr([html.Th("Actual Final Tweet Count:", colSpan=2), html.Td(str(actual_end_count), colSpan=6)]),
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html.Tr([html.Th(f"Predicted Range ({int(prob_start)}-{int(prob_end)}):", colSpan=2), html.Td(formatted_probability, colSpan=6)]),
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html.Tr([html.Th("Does Actual Fall in Range?", colSpan=2),
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html.Td("Yes" if prob_start <= actual_end_count <= prob_end else "No",
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colSpan=6, style={'color': 'green' if prob_start <= actual_end_count <= prob_end else 'red'})])
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]
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if prob_start <= actual_end_count <= prob_end:
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expected_prob = (prob_max + prob_min) / 2
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test_table_rows.append(html.Tr([html.Th("Expected Probability:", colSpan=2),
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html.Td(f"~{expected_prob * 100:.2f}% (should be high if model fits)", colSpan=6)]))
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else:
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test_table_rows.append(html.Tr([html.Th("Note:", colSpan=2),
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html.Td("Model prediction does not match actual outcome.", colSpan=6, style={'color': 'red'})]))
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test_table = html.Table(test_table_rows, style={'width': '100%', 'textAlign': 'left', 'borderCollapse': 'collapse'})
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return [test_table] |