from pkg.dash.func.info_func import * from pkg.dash.app_init import app from dash.dependencies import Input, Output from dash import html import os import csv import pandas as pd import re from datetime import timedelta @app.callback( [Output('test-info-tooltip', 'children')], [Input('test-button', 'n_clicks'), Input('test-date-input', 'date'), Input('test-time-input', 'value')] ) def update_test_info(n_clicks, test_date, test_time): if n_clicks == 0: return [html.Div("Click 'Test' to see historical probability results.")] est = pytz.timezone('US/Eastern') data = render_data.global_agg_df.copy() # 调试：打印输入值 print(f"test_date: {test_date}, test_time: {test_time}") # 检查输入是否为空 if not test_date or not test_time: return [html.Div("Date or time input is empty. Please provide both date (YYYY-MM-DD) and time (HH:MM:SS).")] # 验证时间格式 time_pattern = r'^(?:[01]\d|2[0-3]):[0-5]\d:[0-5]\d$' # HH:MM:SS (00:00:00 to 23:59:59) if not re.match(time_pattern, test_time): 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.")] # 重构 datetime_est，处理夏令时模糊时间 data['hours'] = data['minute_of_day'] // 60 data['minutes'] = data['minute_of_day'] % 60 data['datetime_est'] = pd.to_datetime( data['date'].astype(str) + ' ' + data['hours'].astype(str) + ':' + data['minutes'].astype(str) + ':00', errors='coerce' ).dt.tz_localize(est, ambiguous='NaT') if data['datetime_est'].isna().any(): print("Warning: Some datetime_est values are NaT due to ambiguous time handling") # 解析测试日期和时间 try: test_date = pd.to_datetime(test_date, format='%Y-%m-%d').date() test_datetime = pd.to_datetime(f"{test_date} {test_time}", format='%Y-%m-%d %H:%M:%S').tz_localize(est, ambiguous=True) except ValueError as e: print(f"Error parsing date/time: {e}") return [html.Div("Invalid date or time format. Use YYYY-MM-DD and HH:MM:SS (e.g., 12:00:00).")] # 计算周期开始时间（上一个或当前周五 12:00 PM） test_day_of_week = test_datetime.weekday() # 0 = Monday, 4 = Friday test_hour = test_datetime.hour days_since_last_friday = (test_day_of_week - 4) % 7 # 4 表示周五 if test_hour < 12 and test_day_of_week == 4: # Before 12 PM on Friday cycle_start = test_datetime - timedelta(days=days_since_last_friday + 7) # Previous Friday else: cycle_start = test_datetime - timedelta(days=days_since_last_friday) # Current or next Friday cycle_start = cycle_start.replace(hour=12, minute=0, second=0, microsecond=0) # 确保周期结束时间（下周五 12:00 PM EDT）考虑夏令时 cycle_end = cycle_start + timedelta(days=7) # 精确计算夏令时开始（2025年3月9日，第二个星期日） first_day = cycle_end.replace(day=1) second_sunday = first_day + timedelta(days=((6 - first_day.weekday()) % 7) + 7) if cycle_end.month == 3 and cycle_end >= second_sunday.replace(hour=2): cycle_end = cycle_end.tz_convert(est) # EDT else: cycle_end = cycle_end.tz_convert(est) # EST 或 EDT # 调试：打印周期信息 print(f"Cycle Start: {cycle_start}, Cycle End: {cycle_end}") # 过滤周期内的数据（从周期开始到测试时间） cycle_data = data[(data['datetime_est'] >= cycle_start) & (data['datetime_est'] <= test_datetime)] if cycle_data.empty: return [html.Div(f"No data available in cycle from {cycle_start} to {test_datetime}")] tweet_count = cycle_data['tweet_count'].sum() # 计算实际最终推文数（周期结束时的总数） actual_data = data[(data['datetime_est'] >= cycle_start) & (data['datetime_est'] <= cycle_end)] if actual_data.empty: return [html.Div(f"No data available for cycle ending {cycle_end}")] actual_end_count = actual_data['tweet_count'].sum() # 计算 days_to_next_friday（从 test_datetime 到周期结束） days_to_next_friday = (cycle_end - test_datetime).total_seconds() / (24 * 60 * 60) # 设置预测范围 prob_start = actual_end_count * 0.9 prob_end = actual_end_count * 1.1 # 计算概率 probability = calculate_tweet_probability(tweet_count, days_to_next_friday, prob_start, prob_end) prob_min, prob_max = map(float, probability.split(" - ")) formatted_probability = f"{prob_min * 100:.2f}% - {prob_max * 100:.2f}%" # 构建测试结果表格（包含 Cycle End） test_table_rows = [ html.Tr([html.Th("Cycle Start:", colSpan=4), html.Td(str(cycle_start), colSpan=6)]), html.Tr([html.Th("Test Date and Time:", colSpan=4), html.Td(str(test_datetime), colSpan=6)]), html.Tr([html.Th("Cycle End:", colSpan=4), html.Td(str(cycle_end), colSpan=6)]), html.Tr([html.Th("Tweet Count at Test Time:", colSpan=4), html.Td(str(tweet_count), colSpan=6)]), html.Tr([html.Th("Actual Final Tweet Count:", colSpan=4), html.Td(str(actual_end_count), colSpan=6)]), html.Tr([html.Th(f"Predicted Range ({int(prob_start)}-{int(prob_end)}):", colSpan=4), html.Td(formatted_probability, colSpan=6)]), html.Tr([html.Th("Does Actual Fall in Range?", colSpan=4), html.Td("Yes" if prob_start <= actual_end_count <= prob_end else "No", colSpan=6, style={'color': 'green' if prob_start <= actual_end_count <= prob_end else 'red'})]) ] if prob_start <= actual_end_count <= prob_end: expected_prob = (prob_max + prob_min) / 2 test_table_rows.append(html.Tr([html.Th("Expected Probability:", colSpan=4), html.Td(f"~{expected_prob * 100:.2f}% (should be high if model fits)", colSpan=6)])) else: test_table_rows.append(html.Tr([html.Th("Note:", colSpan=4), html.Td("Model prediction does not match actual outcome.", colSpan=6, style={'color': 'red'})])) test_table = html.Table(test_table_rows, style={'width': '100%', 'textAlign': 'left', 'borderCollapse': 'collapse'}) return [test_table] def run_loop_test(start_date="2024-10-01", end_date="2025-03-12", interval_hours=1, output_file="test_results.csv"): est = pytz.timezone('US/Eastern') # 生成测试时间序列 start_dt = pd.to_datetime(start_date).tz_localize(est) end_dt = pd.to_datetime(end_date).tz_localize(est) time_points = [] current_dt = start_dt while current_dt <= end_dt: time_points.append(current_dt) current_dt += timedelta(hours=interval_hours) # 准备 CSV 文件 headers = [ "Test Date", "Test Time", "Cycle Start", "Cycle End", "Tweet Count at Test Time", "Actual Final Tweet Count", "Predicted Range Start", "Predicted Range End", "Probability Min (%)", "Probability Max (%)", "Actual in Range", "Expected Probability (%)", "Note" ] # 如果文件不存在，写入表头 if not os.path.exists(output_file): with open(output_file, 'w', newline='') as f: writer = csv.writer(f) writer.writerow(headers) # 循环测试 for test_datetime in time_points: test_date = test_datetime.date().strftime('%Y-%m-%d') test_time = test_datetime.time().strftime('%H:%M:%S') n_clicks = 1 # 假设已点击 # 调用原始函数 result = update_test_info(n_clicks, test_date, test_time) # 解析结果 if isinstance(result[0], html.Table): table = result[0] rows = table.children # 提取数据 cycle_start = str(rows[0].children[1].children) test_dt = str(rows[1].children[1].children) cycle_end = str(rows[2].children[1].children) tweet_count = int(rows[3].children[1].children) actual_end_count = int(rows[4].children[1].children) prob_range = rows[5].children[1].children # 例如 "2.74% - 3.25%" # 移除 % 符号并转换为浮点数 prob_min, prob_max = [float(x.strip('%')) for x in prob_range.split(" - ")] # 从表头提取预测范围 prob_start, prob_end = map(int, rows[5].children[0].children.split("(")[1].split(")")[0].split("-")) in_range = rows[6].children[1].children == "Yes" # 检查是否有 Expected Probability 或 Note expected_prob = None note = "" if len(rows) > 7: if "Expected" in rows[7].children[0].children: expected_prob = float(rows[7].children[1].children.split()[0][1:-1]) # 移除 "~" 和 "%" elif "Note" in rows[7].children[0].children: note = rows[7].children[1].children # 写入 CSV with open(output_file, 'a', newline='') as f: writer = csv.writer(f) writer.writerow([ test_date, test_time, cycle_start, cycle_end, tweet_count, actual_end_count, prob_start, prob_end, prob_min, prob_max, "Yes" if in_range else "No", expected_prob if expected_prob is not None else "", note ]) else: # 如果返回错误信息，也记录 with open(output_file, 'a', newline='') as f: writer = csv.writer(f) writer.writerow([test_date, test_time, "", "", "", "", "", "", "", "", "", "", result[0].children]) print(f"Processed: {test_date} {test_time}") # 运行测试 if __name__ == "__main__": run_loop_test(start_date="2024-10-01", end_date="2025-03-12", interval_hours=1, output_file="test_results.csv")