89 lines
4.0 KiB
Python
89 lines
4.0 KiB
Python
"""
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===============================================================================
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FILE: services/scud_etl/svodka_generator.py
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ROLE: Генератор Ежедневной Сводки (оперативный контроль, текущий срез).
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===============================================================================
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"""
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import os
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import logging
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from typing import Dict, Any, Optional
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import pandas as pd
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from config import DATE_TODAY
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from core.database import load_scud_from_db_by_snapshot
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from services.scud_etl.pipeline import load_best_snapshot_for_date, load_1c_files_for_date
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from services.scud_etl.merger import merge_scud_and_1c, calculate_summary_metrics
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from services.scud_etl.anomaly_detector import detect_registry_anomalies
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from services.snapshots.finder import find_or_create_snapshot_for_time
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from services.excel_exporter import generate_summary_excel, get_dated_reports_dir, format_date_ru
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logger = logging.getLogger("SVODKA_GENERATOR")
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def generate_svodka_service(
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target_date: Optional[str] = None,
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target_time: Optional[str] = None,
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snapshot_id: Optional[str] = None
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) -> Dict[str, Any]:
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"""
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Формирует оперативную сводку:
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- По умолчанию берет ПОСЛЕДНИЙ готовый снапшот из базы SQLite (без долгого опроса MS SQL).
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"""
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date_clean = str(target_date or DATE_TODAY).replace('_', '.')
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applied_note = ""
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# 1. Загрузка среза СКУД строго из готовых в базе
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if snapshot_id:
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df_scud = load_scud_from_db_by_snapshot(date_clean, snapshot_param=snapshot_id)
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elif target_time:
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found_id, note = find_or_create_snapshot_for_time(date_clean, target_time, allow_ondemand_export=False)
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applied_note = note
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df_scud = load_scud_from_db_by_snapshot(date_clean, snapshot_param=found_id)
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else:
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# По умолчанию: берем самый свежий существующий срез за дату
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df_scud = load_best_snapshot_for_date(date_clean, prefer_final_y=False)
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if df_scud is None or df_scud.empty:
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return {
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"status": "error",
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"message": f"Срез СКУД за {date_clean} не найден в базе данных."
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}
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# Извлекаем время фактического среза для имени файла
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actual_snap_time = ""
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if 'snapshot_time' in df_scud.columns and not df_scud.empty:
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raw_st = str(df_scud['snapshot_time'].iloc[0]).strip()
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if " " in raw_st:
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actual_snap_time = raw_st.split()[1][:5].replace(':', '-')
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# 2. Загружаем актуальные кадровые данные 1С (штат + отсутствия + реестры)
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df_staff, df_abs = load_1c_files_for_date(date_clean)
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# 3. Слияние и расчет
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df_merged = merge_scud_and_1c(df_scud, df_staff, df_abs)
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metrics = calculate_summary_metrics(df_merged)
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anomalies = detect_registry_anomalies(df_merged, df_raw_scud=df_scud)
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# 4. Формирование книги Excel
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time_suffix = f" на {actual_snap_time}" if actual_snap_time else ""
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filename = f"{format_date_ru(date_clean)} сводка{time_suffix}.xlsx"
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generate_summary_excel(df_merged, date_str=date_clean, filename=filename)
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target_dir = get_dated_reports_dir(date_clean)
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full_filepath = os.path.join(target_dir, filename)
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return {
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"status": "success",
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"report_type": "SVODKA",
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"date": date_clean,
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"target_time": actual_snap_time,
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"snapshot_id": snapshot_id or "LATEST_READY",
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"filename": filename,
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"filepath": full_filepath,
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"download_url": f"/api/v1/files/download/reports/{os.path.basename(full_filepath)}",
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"metrics": metrics,
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"anomalies_count": len(anomalies),
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"note": applied_note,
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"message": f"Ежедневная сводка на {date_clean} успешно сформирована."
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} |