"""Observation Log — append-only JSONL store for empirical calibration events (Task 59b v1).

Observations are NOT business data and NOT product-DB data — they are CALIBRATION events for the
knowledge base ("ISO27001 -> SDL confirmed", "TISAX -> supplier security refuted"). So they live with the
other versioned knowledge artifacts (hypotheses, transition patterns, vocabulary), NOT in the product
database: an append-only JSONL log under `knowledge/observations/`. NO migration, NO DB. The empirical
DISTRIBUTION and CONFIDENCE are COMPUTED from this log on demand (computed-not-stored) — a hypothesis is
NEVER auto-updated; only REVIEWED observations calibrate (the review gate, enforced in observations.py).

Append-only: each line is one ObservationRecord and lines are NEVER modified in place. A later review is
a NEW line with the same observation_id and reviewed=true; load_observations() reconciles to the latest
per id. You can `rm` the log and recompute, `git diff` it over months, or rebuild confidence under a new
policy. Anonymisation is MANDATORY: customer_archetype is a sector/cert archetype, NEVER a real company
name (this file is committed to git). Time is stamped by the CALLER (no hidden clock) for determinism.
I/O only at the append/load boundary; statistics are pure. Python 3.9 compatible.
"""

from __future__ import annotations

import json
import os
from typing import Dict, List, Optional, Sequence

from pydantic import BaseModel, Field

from .observations import Observation, empirical_confidence, empirical_distribution

_DEFAULT_LOG = os.path.join(
    os.path.dirname(__file__), "..", "..", "knowledge", "observations", "observations.jsonl")


class ObservationRecord(Observation):
    """A persisted observation line: an Observation (with its review gate + observation_type) plus log
    metadata. `observation_id` is stable — a review re-appends the SAME id with reviewed=true."""

    observation_id: str                                  # stable id; a review re-appends the same id
    timestamp: str = ""                                  # ISO 8601, stamped by the CALLER (no hidden clock)
    customer_archetype: str = ""                         # sector/cert archetype — NEVER a real company name
    evidence: str = ""                                   # what backs the answer (reference, not the artifact)
    provenance: str = ""                                 # where the answer came from (audit trail)
    knowledge_version: str = ""                          # hypotheses/vocabulary version observed under


class HypothesisStats(BaseModel):
    """Per-hypothesis empirical rollup — all COMPUTED from the log, nothing stored on the hypothesis."""

    hypothesis_id: str
    distribution: Dict[str, int] = Field(default_factory=dict)   # reviewed counts per observation_type
    confidence: Optional[float] = None                           # None until a for/against obs is reviewed
    reviewed_count: int = 0
    total_count: int = 0


def append_observation(record: ObservationRecord, path: str = _DEFAULT_LOG) -> None:
    """Append ONE record as a JSON line. Append-only — existing lines are never rewritten."""
    os.makedirs(os.path.dirname(path), exist_ok=True)
    line = json.dumps(record.model_dump(mode="json"), ensure_ascii=False, sort_keys=True)
    with open(path, "a", encoding="utf-8") as fh:
        fh.write(line + "\n")


def load_observations(path: str = _DEFAULT_LOG, reconcile: bool = True) -> List[ObservationRecord]:
    """Read all records — a single `.jsonl` file or a directory of monthly `.jsonl` files. With
    reconcile, the LATEST record per observation_id wins (a later reviewed=true supersedes the original).
    Returns deterministic order (by observation_id when reconciled, else append order)."""
    files: List[str] = []
    if os.path.isdir(path):
        files = sorted(os.path.join(path, f) for f in os.listdir(path) if f.endswith(".jsonl"))
    elif os.path.exists(path):
        files = [path]
    records: List[ObservationRecord] = []
    for fpath in files:
        with open(fpath, encoding="utf-8") as fh:
            for raw in fh:
                raw = raw.strip()
                if raw:
                    records.append(ObservationRecord(**json.loads(raw)))
    if not reconcile:
        return records
    latest: Dict[str, ObservationRecord] = {}
    for r in records:                                    # file/append order -> later lines win
        latest[r.observation_id] = r
    return [latest[k] for k in sorted(latest)]


def aggregate_by_hypothesis(records: Sequence[ObservationRecord]) -> List[HypothesisStats]:
    """Per-hypothesis distribution + confidence. The review gate applies inside empirical_distribution/
    empirical_confidence (reviewed-only), so unreviewed observations are counted in total but never
    calibrate. Deterministic order (by hypothesis id)."""
    by_hyp: Dict[str, List[ObservationRecord]] = {}
    for r in records:
        by_hyp.setdefault(r.hypothesis_id, []).append(r)
    out: List[HypothesisStats] = []
    for hyp in sorted(by_hyp):
        obs = by_hyp[hyp]
        out.append(HypothesisStats(
            hypothesis_id=hyp,
            distribution=empirical_distribution(obs),    # reviewed-only (the gate)
            confidence=empirical_confidence(obs),        # None until reviewed for/against
            reviewed_count=sum(1 for o in obs if o.reviewed),
            total_count=len(obs)))
    return out


def review_queue(records: Sequence[ObservationRecord]) -> List[ObservationRecord]:
    """The reviewer's worklist: observations not yet reviewed. Calibration ignores these until a reviewer
    accepts them (Observation -> Review -> Accepted -> Knowledge recomputed), never Observation -> conf++."""
    return [r for r in records if not r.reviewed]