mitai-jinkendo/backend/data_layer/activity_persistence_orchestrator.py

"""
Zentrale Persistenz für activity_log + EAV-Nebenwirkungen (Eval, Spalten→EAV).

Alle Schreibpfade (REST, Universal-CSV, Legacy-Upload) laufen hier zusammen.

Feld-Katalog für CSV-Mappings: get_mappable_activity_field_catalog()
"""
from __future__ import annotations

import logging
import uuid
from typing import Any, Dict, List, Optional

from models import ActivityEntry

from csv_parser.module_registry import get_module_definition
from data_layer.activity_session_metrics import sync_column_backed_session_metrics

logger = logging.getLogger(__name__)

try:
    from evaluation_helper import evaluate_and_save_activity as _evaluate_and_save_activity

    _EVALUATION_AVAILABLE = True
except Exception:  # pragma: no cover
    _evaluate_and_save_activity = None
    _EVALUATION_AVAILABLE = False


def find_activity_duplicate_id(
    cur,
    profile_id: str,
    date_iso: str,
    start_time: Optional[Any],
) -> Optional[str]:
    cur.execute(
        """
        SELECT id FROM activity_log
        WHERE profile_id = %s AND date = %s::date
          AND start_time IS NOT DISTINCT FROM %s::time
        """,
        (profile_id, date_iso, start_time),
    )
    row = cur.fetchone()
    return str(row["id"]) if row else None


def insert_activity_from_entry(cur, profile_id: str, eid: str, e: ActivityEntry) -> None:
    """INSERT activity_log aus ActivityEntry (manueller API-Pfad)."""
    d = e.model_dump()
    cur.execute(
        """INSERT INTO activity_log (id,profile_id,date,start_time,end_time,activity_type,duration_min,kcal_active,kcal_resting,
             hr_avg,hr_max,hr_min,distance_km,pace_min_per_km,cadence,avg_power,elevation_gain,
             temperature_celsius,humidity_percent,avg_hr_percent,kcal_per_km,rpe,source,notes,
             training_type_id,training_category,training_subcategory,created)
            VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,CURRENT_TIMESTAMP)""",
        (
            eid,
            profile_id,
            d["date"],
            d["start_time"],
            d["end_time"],
            d["activity_type"],
            d["duration_min"],
            d["kcal_active"],
            d["kcal_resting"],
            d["hr_avg"],
            d["hr_max"],
            d.get("hr_min"),
            d["distance_km"],
            d.get("pace_min_per_km"),
            d.get("cadence"),
            d.get("avg_power"),
            d.get("elevation_gain"),
            d.get("temperature_celsius"),
            d.get("humidity_percent"),
            d.get("avg_hr_percent"),
            d.get("kcal_per_km"),
            d["rpe"],
            d["source"],
            d["notes"],
            d.get("training_type_id"),
            d.get("training_category"),
            d.get("training_subcategory"),
        ),
    )


def update_activity_from_entry(cur, profile_id: str, eid: str, e: ActivityEntry) -> None:
    """Volles UPDATE aus ActivityEntry (REST PUT)."""
    d = e.model_dump()
    cur.execute(
        f"UPDATE activity_log SET {', '.join(f'{k}=%s' for k in d)} WHERE id=%s AND profile_id=%s",
        list(d.values()) + [eid, profile_id],
    )


def update_activity_columns(
    cur,
    profile_id: str,
    eid: str,
    updates: Dict[str, Any],
) -> None:
    """Teil-UPDATE nur für übergebene Spalten (Importe)."""
    if not updates:
        return
    cols = [f"{k} = %s" for k in updates]
    vals = list(updates.values()) + [eid, profile_id]
    cur.execute(
        f"UPDATE activity_log SET {', '.join(cols)} WHERE id = %s AND profile_id = %s",
        vals,
    )


def insert_activity_csv_minimal(
    cur,
    profile_id: str,
    eid: str,
    *,
    date_iso: str,
    start_time: Any,
    end_time: Any,
    activity_type: str,
    duration_min: Any,
    kcal_active: Any,
    kcal_resting: Any,
    hr_avg: Any,
    hr_max: Any,
    distance_km: Any,
    training_type_id: Any,
    training_category: Any,
    training_subcategory: Any,
    source: str,
) -> None:
    """INSERT minimale activity_log-Zeile (Universal-CSV)."""
    cur.execute(
        """
        INSERT INTO activity_log (
            id, profile_id, date, start_time, end_time, activity_type, duration_min,
            kcal_active, kcal_resting, hr_avg, hr_max, distance_km,
            source, training_type_id, training_category, training_subcategory, created
        )
        VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,CURRENT_TIMESTAMP)
        """,
        (
            eid,
            profile_id,
            date_iso,
            start_time,
            end_time,
            activity_type,
            duration_min,
            kcal_active,
            kcal_resting,
            hr_avg,
            hr_max,
            distance_km,
            source,
            training_type_id,
            training_category,
            training_subcategory,
        ),
    )


def run_activity_post_write_hooks(cur, profile_id: str, eid: str) -> None:
    """Auto-Eval (falls aktiv) + EAV-Spiegel aus activity_log-Spalten."""
    if _EVALUATION_AVAILABLE and _evaluate_and_save_activity:
        cur.execute(
            """
            SELECT id, profile_id, date, training_type_id, duration_min,
                   hr_avg, hr_max, distance_km, kcal_active, kcal_resting,
                   rpe, pace_min_per_km, cadence, elevation_gain
            FROM activity_log
            WHERE id = %s
            """,
            (eid,),
        )
        activity_row = cur.fetchone()
        if activity_row:
            activity_dict = dict(activity_row)
            training_type_id = activity_dict.get("training_type_id")
            if training_type_id:
                try:
                    _evaluate_and_save_activity(cur, eid, activity_dict, training_type_id, profile_id)
                except Exception as eval_error:
                    logger.error("[AUTO-EVAL] activity %s: %s", eid, eval_error)
    sync_column_backed_session_metrics(cur, str(profile_id), str(eid))


def run_activity_post_write_hooks_import(
    cur,
    profile_id: str,
    eid: str,
    *,
    workout_date: str,
    training_type_id: Optional[int],
    duration_min: Any,
    hr_avg: Any,
    hr_max: Any,
    distance_km: Any,
    kcal_active: Any,
    kcal_resting: Any,
) -> None:
    """Eval + EAV nach Legacy-Import mit vorgebautem Kontext-Dict."""
    if _EVALUATION_AVAILABLE and training_type_id and _evaluate_and_save_activity:
        try:
            activity_dict = {
                "id": eid,
                "profile_id": profile_id,
                "date": workout_date,
                "training_type_id": training_type_id,
                "duration_min": duration_min,
                "hr_avg": hr_avg,
                "hr_max": hr_max,
                "distance_km": distance_km,
                "kcal_active": kcal_active,
                "kcal_resting": kcal_resting,
                "rpe": None,
                "pace_min_per_km": None,
                "cadence": None,
                "elevation_gain": None,
            }
            _evaluate_and_save_activity(cur, eid, activity_dict, training_type_id, profile_id)
        except Exception as eval_err:
            logger.warning("[activity import] Auto-Eval fehlgeschlagen: %s", eval_err)
    sync_column_backed_session_metrics(cur, str(profile_id), str(eid))


def merge_activity_csv_module_fields(
    cur,
    static_fields: Dict[str, Any],
) -> Dict[str, Any]:
    """
    activity-Modul für CSV: statische Registry-Felder + alle aktiven training_parameters.

    Gleiche Quelle wie get_mappable_activity_field_catalog.training_parameters — erscheint
    in Admin-CSV-Ziel-Liste, Validierung und Import-Zeilenaggregation.
    """
    out = dict(static_fields)
    cur.execute(
        """
        SELECT key, data_type, unit, name_de
        FROM training_parameters
        WHERE is_active = true
        ORDER BY key
        """
    )
    for row in cur.fetchall():
        k = row["key"]
        if k in out:
            continue
        dt = row["data_type"] or "float"
        if dt == "integer":
            mtype = "int"
        elif dt == "float":
            mtype = "float"
        elif dt == "boolean":
            mtype = "string"
        else:
            mtype = "string"
        spec: Dict[str, Any] = {
            "type": mtype,
            "required": False,
            "from_training_parameter": True,
        }
        if row.get("unit"):
            spec["unit"] = row["unit"]
        if row.get("name_de"):
            spec["label_de"] = row["name_de"]
        out[k] = spec
    return out


def get_mappable_activity_field_catalog(cur, profile_id: str) -> Dict[str, Any]:
    """
    Felder für konfigurierbare Import-Mappings.

    core_fields: module_registry „activity“ → activity_log.
    training_parameters: alle aktiven Parameter (global); bei Anwendung auf eine Session
    werden Keys verworfen, die nicht in resolve_activity_attribute_schema(Kategorie/Typ) liegen.

    profile_id: reserviert für künftige Profil-Filter.
    """
    _ = profile_id
    mod = get_module_definition("activity") or {}
    fields = mod.get("fields") or {}
    core_fields: List[Dict[str, Any]] = []
    for key, spec in fields.items():
        s = spec or {}
        core_fields.append(
            {
                "key": key,
                "target": "activity_log",
                "column": key,
                "data_type": s.get("type", "string"),
                "required": bool(s.get("required")),
                "unit": s.get("unit"),
                "label_de": key,
            }
        )
    core_fields.sort(key=lambda x: x["key"])

    cur.execute(
        """
        SELECT id, key, name_de, name_en, category AS param_category,
               data_type, unit, source_field
        FROM training_parameters
        WHERE is_active = true
        ORDER BY key
        """
    )
    parameters = [dict(r) for r in cur.fetchall()]

    return {
        "core_fields": core_fields,
        "training_parameters": parameters,
        "notes": (
            "training_parameters listet alle aktiven Keys. Pro Session werden Werte ignoriert, "
            "die für deren training_category/training_type_id nicht im Attribut-Schema vorkommen."
        ),
    }


def new_activity_id() -> str:
    return str(uuid.uuid4())