HA-008 HA-009: add discovery and history pipeline

Closes #17

Closes #18

app/api/v1/entities.py

@@ -1,10 +1,13 @@
 from __future__ import annotations
 
+from datetime import datetime
 from typing import List
 
-from fastapi import APIRouter, Depends
+from fastapi import APIRouter, Depends, HTTPException, Query, status
 
 from app.dependencies import get_ha_reader
+from app.ha.discovery import DiscoveredEntity
+from app.ha.history import EntityHistorySeries
 from app.ha.models import HaEntitySummary
 from app.ha.reader import HaReader
 
@@ -19,3 +22,43 @@ router = APIRouter(prefix="/v1", tags=["entities"])
 )
 def list_entities(ha_reader: HaReader = Depends(get_ha_reader)) -> List[HaEntitySummary]:
     return list(ha_reader.read_entities())
+
+
+@router.get(
+    "/discovery",
+    summary="Home-Assistant-Entities klassifizieren",
+    description="Klassifiziert Entities nach Lernrelevanz, Kontextquelle und Aktor-Rolle.",
+    response_model=List[DiscoveredEntity],
+)
+def discovery(
+    domain: List[str] | None = Query(default=None),
+    learnable: bool | None = None,
+    ha_reader: HaReader = Depends(get_ha_reader),
+) -> List[DiscoveredEntity]:
+    return list(
+        ha_reader.discover(
+            domains=set(domain) if domain else None,
+            learnable=learnable,
+        )
+    )
+
+
+@router.get(
+    "/history",
+    summary="Numerische Home-Assistant-Historie lesen",
+    description="Lädt und normalisiert numerische Zustände ausgewählter Entities.",
+    response_model=List[EntityHistorySeries],
+)
+def history(
+    entity_id: List[str] = Query(),
+    start_time: datetime = Query(),
+    end_time: datetime = Query(),
+    ha_reader: HaReader = Depends(get_ha_reader),
+) -> List[EntityHistorySeries]:
+    try:
+        return list(ha_reader.read_history(entity_id, start_time, end_time))
+    except ValueError as exc:
+        raise HTTPException(
+            status_code=status.HTTP_422_UNPROCESSABLE_CONTENT,
+            detail=str(exc),
+        ) from exc

app/ha/client.py

@@ -2,6 +2,9 @@ from __future__ import annotations
 
 import logging
 from dataclasses import dataclass
+from datetime import datetime
+import re
+from urllib.parse import quote
 
 import requests
 
@@ -14,6 +17,9 @@ from app.ha.exceptions import (
 
 logger = logging.getLogger(__name__)
 
+_ENTITY_ID_PATTERN = re.compile(r"^[a-z0-9_]+\.[a-z0-9_]+$")
+_MAX_HISTORY_SECONDS = 31 * 24 * 60 * 60
+
 
 @dataclass(frozen=True)
 class HaClientSettings:
@@ -35,9 +41,58 @@ class HaClient:
         self._session.close()
 
     def list_entities(self) -> list[dict[str, object]]:
+        payload = self._get_json("/api/states")
+        if not isinstance(payload, list):
+            raise HaUnexpectedPayloadError(
+                "Antwort von Home Assistant hat unerwartetes Format."
+            )
+        return payload
+
+    def get_history(
+        self,
+        entity_ids: list[str],
+        start_time: datetime,
+        end_time: datetime,
+    ) -> list[object]:
+        if not entity_ids:
+            raise ValueError("Mindestens eine entity_id ist erforderlich.")
+        if len(entity_ids) > 100:
+            raise ValueError("Es können höchstens 100 Entities abgefragt werden.")
+        if any(not _ENTITY_ID_PATTERN.fullmatch(entity_id) for entity_id in entity_ids):
+            raise ValueError("entity_id enthält ein ungültiges Format.")
+        if start_time.tzinfo is None or end_time.tzinfo is None:
+            raise ValueError("start_time und end_time müssen eine Zeitzone enthalten.")
+        if end_time <= start_time:
+            raise ValueError("end_time muss nach start_time liegen.")
+        if (end_time - start_time).total_seconds() > _MAX_HISTORY_SECONDS:
+            raise ValueError("History-Abfragen sind auf 31 Tage begrenzt.")
+
+        start = quote(start_time.isoformat(), safe=":+")
+        payload = self._get_json(
+            f"/api/history/period/{start}",
+            params={
+                "filter_entity_id": ",".join(entity_ids),
+                "end_time": end_time.isoformat(),
+                "minimal_response": "1",
+                "no_attributes": "1",
+            },
+        )
+        if not isinstance(payload, list):
+            raise HaUnexpectedPayloadError(
+                "History-Antwort von Home Assistant hat unerwartetes Format."
+            )
+        return payload
+
+    def _get_json(
+        self,
+        path: str,
+        *,
+        params: dict[str, str] | None = None,
+    ) -> object:
         try:
             response = self._session.get(
-                f"{self._settings.url.rstrip('/')}/api/states",
+                f"{self._settings.url.rstrip('/')}{path}",
+                params=params,
                 timeout=self._settings.timeout_seconds,
             )
         except requests.Timeout as exc:
@@ -69,9 +124,4 @@ class HaClient:
                 "Antwort von Home Assistant ist kein gültiges JSON."
             ) from exc
 
-        if not isinstance(payload, list):
-            raise HaUnexpectedPayloadError(
-                "Antwort von Home Assistant hat unerwartetes Format."
-            )
-
         return payload

app/ha/discovery.py

@@ -0,0 +1,184 @@
+from __future__ import annotations
+
+from enum import StrEnum
+
+from pydantic import BaseModel
+
+from app.ha.models import HaEntitySummary
+
+
+class EntityRole(StrEnum):
+    MEASUREMENT = "measurement"
+    BINARY_CONTEXT = "binary_context"
+    CONTEXT = "context"
+    ACTUATOR = "actuator"
+    UNSUPPORTED = "unsupported"
+
+
+class DiscoveredEntity(BaseModel):
+    entity_id: str
+    domain: str
+    device_class: str | None = None
+    state_class: str | None = None
+    unit_of_measurement: str | None = None
+    role: EntityRole
+    learnable: bool
+    reason: str
+
+
+_MEASUREMENT_CLASSES = frozenset({
+    "apparent_power",
+    "atmospheric_pressure",
+    "battery",
+    "carbon_dioxide",
+    "carbon_monoxide",
+    "current",
+    "distance",
+    "duration",
+    "energy",
+    "frequency",
+    "gas",
+    "humidity",
+    "illuminance",
+    "moisture",
+    "monetary",
+    "nitrogen_dioxide",
+    "nitrogen_monoxide",
+    "nitrous_oxide",
+    "ozone",
+    "pm1",
+    "pm10",
+    "pm25",
+    "power",
+    "precipitation",
+    "pressure",
+    "reactive_power",
+    "signal_strength",
+    "sound_pressure",
+    "speed",
+    "sulphur_dioxide",
+    "temperature",
+    "volatile_organic_compounds",
+    "voltage",
+    "volume",
+    "volume_flow_rate",
+    "water",
+    "weight",
+    "wind_speed",
+})
+_BINARY_CONTEXT_CLASSES = frozenset({
+    "door",
+    "garage_door",
+    "lock",
+    "motion",
+    "occupancy",
+    "opening",
+    "presence",
+    "problem",
+    "safety",
+    "smoke",
+    "sound",
+    "vibration",
+    "window",
+})
+_ACTUATOR_DOMAINS = frozenset({
+    "button",
+    "climate",
+    "cover",
+    "fan",
+    "humidifier",
+    "light",
+    "lock",
+    "scene",
+    "select",
+    "siren",
+    "switch",
+    "valve",
+})
+_CONTEXT_DOMAINS = frozenset({"device_tracker", "person", "sun", "weather", "zone"})
+_LEARNABLE_CONTEXT_DOMAINS = frozenset({"device_tracker", "person", "weather"})
+_NUMERIC_STATE_CLASSES = frozenset({"measurement", "total", "total_increasing"})
+
+
+def classify_entity(entity: HaEntitySummary) -> DiscoveredEntity:
+    if entity.domain == "sensor" and (
+        entity.state_class in _NUMERIC_STATE_CLASSES
+        or entity.device_class in _MEASUREMENT_CLASSES
+        or entity.unit_of_measurement is not None
+    ):
+        return _result(
+            entity,
+            EntityRole.MEASUREMENT,
+            learnable=True,
+            reason="Numerischer Messsensor für Zeitreihen und Training.",
+        )
+
+    if entity.domain == "binary_sensor" and entity.device_class in _BINARY_CONTEXT_CLASSES:
+        return _result(
+            entity,
+            EntityRole.BINARY_CONTEXT,
+            learnable=True,
+            reason="Binärer Kontextsensor für Zustands- und Anwesenheitsmuster.",
+        )
+
+    if entity.domain in _CONTEXT_DOMAINS:
+        learnable = entity.domain in _LEARNABLE_CONTEXT_DOMAINS
+        return _result(
+            entity,
+            EntityRole.CONTEXT,
+            learnable=learnable,
+            reason=(
+                "Kontextquelle für Training und Erklärungen."
+                if learnable
+                else "Kontextquelle ohne direkte Trainingsfreigabe."
+            ),
+        )
+
+    if entity.domain in _ACTUATOR_DOMAINS:
+        return _result(
+            entity,
+            EntityRole.ACTUATOR,
+            learnable=False,
+            reason="Aktor ist ein mögliches Automationsziel, aber kein Trainingssensor.",
+        )
+
+    return _result(
+        entity,
+        EntityRole.UNSUPPORTED,
+        learnable=False,
+        reason="Entity-Typ ist noch nicht für Lernen oder Automationen klassifiziert.",
+    )
+
+
+def discover_entities(
+    entities: list[HaEntitySummary],
+    domains: set[str] | None = None,
+    learnable: bool | None = None,
+) -> list[DiscoveredEntity]:
+    normalized_domains = {domain.strip().lower() for domain in domains or set() if domain.strip()}
+    discovered = [classify_entity(entity) for entity in entities]
+    return [
+        entity
+        for entity in discovered
+        if (not normalized_domains or entity.domain in normalized_domains)
+        and (learnable is None or entity.learnable is learnable)
+    ]
+
+
+def _result(
+    entity: HaEntitySummary,
+    role: EntityRole,
+    *,
+    learnable: bool,
+    reason: str,
+) -> DiscoveredEntity:
+    return DiscoveredEntity(
+        entity_id=entity.entity_id,
+        domain=entity.domain,
+        device_class=entity.device_class,
+        state_class=entity.state_class,
+        unit_of_measurement=entity.unit_of_measurement,
+        role=role,
+        learnable=learnable,
+        reason=reason,
+    )

app/ha/history.py

@@ -0,0 +1,91 @@
+from __future__ import annotations
+
+import math
+from datetime import datetime
+
+from pydantic import BaseModel
+
+from app.ha.exceptions import HaUnexpectedPayloadError
+
+
+class NumericHistoryPoint(BaseModel):
+    timestamp: datetime
+    value: float
+
+
+class EntityHistorySeries(BaseModel):
+    entity_id: str
+    points: list[NumericHistoryPoint]
+
+
+def normalize_history_payload(payload: object) -> list[EntityHistorySeries]:
+    if not isinstance(payload, list):
+        raise HaUnexpectedPayloadError("History-Payload muss eine Liste sein.")
+
+    normalized: list[EntityHistorySeries] = []
+    for raw_series in payload:
+        if not isinstance(raw_series, list):
+            raise HaUnexpectedPayloadError("History-Serie muss eine Liste sein.")
+        series = _normalize_series(raw_series)
+        if series is not None:
+            normalized.append(series)
+
+    return sorted(normalized, key=lambda item: item.entity_id)
+
+
+def _normalize_series(raw_series: list[object]) -> EntityHistorySeries | None:
+    entity_id: str | None = None
+    points: list[NumericHistoryPoint] = []
+
+    for raw_entry in raw_series:
+        if not isinstance(raw_entry, dict):
+            raise HaUnexpectedPayloadError("History-Eintrag muss ein Objekt sein.")
+
+        raw_entity_id = raw_entry.get("entity_id")
+        if raw_entity_id is not None:
+            if not isinstance(raw_entity_id, str) or "." not in raw_entity_id:
+                raise HaUnexpectedPayloadError("History-Eintrag enthält ungültige entity_id.")
+            if entity_id is not None and entity_id != raw_entity_id:
+                raise HaUnexpectedPayloadError("History-Serie enthält mehrere Entities.")
+            entity_id = raw_entity_id
+
+        raw_state = raw_entry.get("state")
+        value = _finite_float(raw_state)
+        if value is None:
+            continue
+        if entity_id is None:
+            raise HaUnexpectedPayloadError("History-Serie enthält keine entity_id.")
+
+        raw_timestamp = raw_entry.get("last_changed") or raw_entry.get("last_updated")
+        timestamp = _parse_timestamp(raw_timestamp)
+        points.append(NumericHistoryPoint(timestamp=timestamp, value=value))
+
+    if entity_id is None or not points:
+        return None
+
+    points.sort(key=lambda point: point.timestamp)
+    return EntityHistorySeries(entity_id=entity_id, points=points)
+
+
+def _finite_float(value: object) -> float | None:
+    if isinstance(value, bool) or value is None:
+        return None
+    if not isinstance(value, (str, int, float)):
+        return None
+    try:
+        converted = float(value)
+    except (TypeError, ValueError):
+        return None
+    return converted if math.isfinite(converted) else None
+
+
+def _parse_timestamp(value: object) -> datetime:
+    if not isinstance(value, str):
+        raise HaUnexpectedPayloadError("Numerischer History-Eintrag enthält keinen Zeitstempel.")
+    try:
+        parsed = datetime.fromisoformat(value.replace("Z", "+00:00"))
+    except ValueError as exc:
+        raise HaUnexpectedPayloadError("History-Eintrag enthält ungültigen Zeitstempel.") from exc
+    if parsed.tzinfo is None:
+        raise HaUnexpectedPayloadError("History-Zeitstempel muss eine Zeitzone enthalten.")
+    return parsed

app/ha/reader.py

@@ -1,9 +1,12 @@
 from __future__ import annotations
 
 from collections.abc import Sequence
+from datetime import datetime
 from typing import Any
 
 from app.ha.client import HaClient
+from app.ha.discovery import DiscoveredEntity, discover_entities
+from app.ha.history import EntityHistorySeries, normalize_history_payload
 from app.ha.models import HaEntitySummary
 
 
@@ -33,6 +36,22 @@ class HaReader:
             )
         return summaries
 
+    def discover(
+        self,
+        domains: set[str] | None = None,
+        learnable: bool | None = None,
+    ) -> Sequence[DiscoveredEntity]:
+        return discover_entities(list(self.read_entities()), domains=domains, learnable=learnable)
+
+    def read_history(
+        self,
+        entity_ids: list[str],
+        start_time: datetime,
+        end_time: datetime,
+    ) -> Sequence[EntityHistorySeries]:
+        payload = self._client.get_history(entity_ids, start_time, end_time)
+        return normalize_history_payload(payload)
+
 
 def _optional_str(value: object) -> str | None:
     if value is None or value == "":