"""
Compact Analysis - Retroactive measurement of epistemic loss during memory compaction

This module analyzes pre-compact snapshots vs post-compact PREFLIGHT assessments
to measure what knowledge is lost and recovered during Claude Code memory compaction.

Data Quality Filtering:
- Excludes test sessions (ai_id patterns: test*, *-test, storage-*)
- Requires complete CASCADE loop (has POSTFLIGHT)
- Requires actual work evidence (findings or unknowns logged)
- Filters rapid-fire sessions (< 5 min duration)
"""

import argparse
import json
import sqlite3
from datetime import datetime
from pathlib import Path

# AI IDs that indicate test/non-production sessions
TEST_AI_PATTERNS = [
    'test%',
    '%test%',
    'storage%',
    'mcp-%test',
    'cli-e2e%',
    'cli-tester',
]

# Minimum session duration to be considered "real" (seconds)
MIN_SESSION_DURATION = 300  # 5 minutes


def get_ref_docs_path() -> Path:
    """Get the ref-docs directory for pre-compact snapshots via unified context resolver."""
    try:
        from empirica.utils.session_resolver import InstanceResolver as R
        context = R.context()
        project_path = context.get('project_path')
        if project_path:
            ref_docs = Path(project_path) / '.empirica' / 'ref-docs'
            if ref_docs.exists():
                return ref_docs
    except Exception:
        pass

    # Fallback to CWD-based
    local_path = Path.cwd() / '.empirica' / 'ref-docs'
    if local_path.exists():
        return local_path

    global_path = Path.home() / '.empirica' / 'ref-docs'
    if global_path.exists():
        return global_path

    raise FileNotFoundError("No ref-docs directory found")


def is_test_session(ai_id: str) -> bool:
    """Check if AI ID indicates a test session."""
    ai_lower = ai_id.lower()
    return (
        ai_lower.startswith('test') or
        '-test' in ai_lower or
        'test-' in ai_lower or
        ai_lower.startswith('storage') or
        ai_lower in ('cli-tester', 'empirica-tester', 'mcp-full-test', 'mcp-quick-test')
    )


def load_pre_compact_snapshots(ref_docs_path: Path) -> list[dict]:
    """Load all pre-compact snapshots."""
    snapshots = []

    for snapshot_file in ref_docs_path.glob('pre_summary_*.json'):
        try:
            with open(snapshot_file) as f:
                data = json.load(f)
                data['_file'] = str(snapshot_file)
                snapshots.append(data)
        except (OSError, json.JSONDecodeError):
            continue

    # Sort by timestamp
    snapshots.sort(key=lambda x: x.get('timestamp', ''), reverse=True)
    return snapshots


def get_post_compact_sessions(db_path: Path, after_timestamp: float) -> list[dict]:
    """
    Find sessions with PREFLIGHT shortly after a compact (within 30 minutes).
    Uses reflex timestamp since session start_time is ISO string.
    """
    conn = sqlite3.connect(db_path)
    cursor = conn.cursor()

    # Find PREFLIGHT reflexes within 30 minutes after the snapshot
    # (compact can take time, and session creation may be delayed)
    window_end = after_timestamp + 1800  # 30 minutes

    cursor.execute("""
        SELECT
            s.session_id,
            s.ai_id,
            s.start_time,
            r.phase,
            r.know,
            r.uncertainty,
            r.context,
            r.timestamp as reflex_timestamp
        FROM sessions s
        JOIN reflexes r ON s.session_id = r.session_id
        WHERE r.timestamp > ? AND r.timestamp < ?
        AND r.phase = 'PREFLIGHT'
        AND r.round = 1
        ORDER BY r.timestamp ASC
    """, (after_timestamp, window_end))

    sessions = []
    for row in cursor.fetchall():
        sessions.append({
            'session_id': row[0],
            'ai_id': row[1],
            'start_time': row[2],
            'phase': row[3],
            'know': row[4],
            'uncertainty': row[5],
            'context': row[6],
            'reflex_timestamp': row[7]
        })

    conn.close()
    return sessions


def get_post_check_vectors(db_path: Path, session_id: str) -> dict | None:
    """Get the first CHECK vectors for a session (post-context-load state)."""
    conn = sqlite3.connect(db_path)
    cursor = conn.cursor()

    cursor.execute("""
        SELECT know, uncertainty, context, timestamp
        FROM reflexes
        WHERE session_id = ? AND phase = 'CHECK'
        ORDER BY timestamp ASC
        LIMIT 1
    """, (session_id,))

    row = cursor.fetchone()
    conn.close()

    if row:
        return {
            'know': row[0],
            'uncertainty': row[1],
            'context': row[2],
            'timestamp': row[3]
        }
    return None


def get_session_quality_metrics(db_path: Path, session_id: str) -> dict:
    """Get quality metrics for a session to filter out tests."""
    conn = sqlite3.connect(db_path)
    cursor = conn.cursor()

    # Check for findings
    cursor.execute("""
        SELECT COUNT(*) FROM project_findings WHERE session_id = ?
    """, (session_id,))
    findings_count = cursor.fetchone()[0]

    # Check for unknowns
    cursor.execute("""
        SELECT COUNT(*) FROM project_unknowns WHERE session_id = ?
    """, (session_id,))
    unknowns_count = cursor.fetchone()[0]

    # Check for complete loop (POSTFLIGHT)
    cursor.execute("""
        SELECT COUNT(*) FROM reflexes WHERE session_id = ? AND phase = 'POSTFLIGHT'
    """, (session_id,))
    has_postflight = cursor.fetchone()[0] > 0

    # Session duration
    cursor.execute("""
        SELECT MIN(timestamp), MAX(timestamp) FROM reflexes WHERE session_id = ?
    """, (session_id,))
    row = cursor.fetchone()
    duration = (row[1] - row[0]) if row[0] and row[1] else 0

    conn.close()

    return {
        'findings_count': findings_count,
        'unknowns_count': unknowns_count,
        'has_postflight': has_postflight,
        'duration_seconds': duration,
        'has_work_evidence': findings_count > 0 or unknowns_count > 0
    }


def analyze_compact_events(
    include_tests: bool = False,
    min_findings: int = 0,
    limit: int = 20
) -> list[dict]:
    """
    Analyze compact events by matching pre-compact snapshots to post-compact sessions.

    Returns list of compact events with:
    - pre_vectors: Epistemic state before compact
    - post_preflight_vectors: State immediately after compact (before context load)
    - post_check_vectors: State after CHECK (context loaded)
    - loss: Delta from pre to post-preflight
    - recovery: Delta from post-preflight to post-check
    - net_loss: Delta from pre to post-check (permanent loss)
    """
    from empirica.config.path_resolver import get_session_db_path
    db_path = get_session_db_path()
    ref_docs_path = get_ref_docs_path()

    snapshots = load_pre_compact_snapshots(ref_docs_path)
    events = []

    for snapshot in snapshots[:limit * 2]:  # Check more than limit to allow for filtering
        # Parse snapshot timestamp
        ts_str = snapshot.get('timestamp', '')
        try:
            # Format: 2026-01-21T17-34-30
            dt = datetime.strptime(ts_str, '%Y-%m-%dT%H-%M-%S')
            snapshot_timestamp = dt.timestamp()
        except ValueError:
            continue

        # Get pre-compact vectors
        pre_vectors = snapshot.get('vectors_canonical') or snapshot.get('checkpoint', {})
        if not pre_vectors:
            live_state = snapshot.get('live_state', {})
            pre_vectors = live_state.get('vectors', {})

        if not pre_vectors.get('know'):
            continue

        # Find post-compact session
        post_sessions = get_post_compact_sessions(db_path, snapshot_timestamp)

        for post_session in post_sessions:
            ai_id = post_session['ai_id']

            # Filter test sessions unless explicitly included
            if not include_tests and is_test_session(ai_id):
                continue

            # Get quality metrics
            quality = get_session_quality_metrics(db_path, post_session['session_id'])

            # Filter by quality
            if not include_tests:
                if quality['duration_seconds'] < MIN_SESSION_DURATION:
                    continue
                if min_findings > 0 and quality['findings_count'] < min_findings:
                    continue

            # Get post-CHECK vectors
            post_check = get_post_check_vectors(db_path, post_session['session_id'])

            # Calculate deltas
            pre_know = pre_vectors.get('know', 0)
            pre_unc = pre_vectors.get('uncertainty', 0)
            pre_ctx = pre_vectors.get('context', 0)

            post_pf_know = post_session.get('know', 0)
            post_pf_unc = post_session.get('uncertainty', 0)
            post_pf_ctx = post_session.get('context', 0)

            event = {
                'timestamp': ts_str,
                'pre_session_id': snapshot.get('session_id'),
                'post_session_id': post_session['session_id'],
                'ai_id': ai_id,
                'pre_vectors': {
                    'know': pre_know,
                    'uncertainty': pre_unc,
                    'context': pre_ctx
                },
                'post_preflight_vectors': {
                    'know': post_pf_know,
                    'uncertainty': post_pf_unc,
                    'context': post_pf_ctx
                },
                'immediate_loss': {
                    'know': round(post_pf_know - pre_know, 3),
                    'uncertainty': round(post_pf_unc - pre_unc, 3),
                    'context': round(post_pf_ctx - pre_ctx, 3) if post_pf_ctx and pre_ctx else None
                },
                'quality': quality
            }

            if post_check:
                post_ck_know = post_check.get('know', 0)
                post_ck_unc = post_check.get('uncertainty', 0)
                post_ck_ctx = post_check.get('context', 0)

                event['post_check_vectors'] = {
                    'know': post_ck_know,
                    'uncertainty': post_ck_unc,
                    'context': post_ck_ctx
                }
                event['recovery'] = {
                    'know': round(post_ck_know - post_pf_know, 3),
                    'uncertainty': round(post_ck_unc - post_pf_unc, 3),
                    'context': round(post_ck_ctx - post_pf_ctx, 3) if post_ck_ctx and post_pf_ctx else None
                }
                event['net_loss'] = {
                    'know': round(post_ck_know - pre_know, 3),
                    'uncertainty': round(post_ck_unc - pre_unc, 3),
                    'context': round(post_ck_ctx - pre_ctx, 3) if post_ck_ctx and pre_ctx else None
                }

            events.append(event)
            break  # Only take first matching post-compact session

        if len(events) >= limit:
            break

    return events


def calculate_aggregate_stats(events: list[dict]) -> dict:
    """Calculate aggregate statistics across all compact events."""
    if not events:
        return {'error': 'No events to analyze'}

    # Collect deltas
    immediate_know_loss = []
    immediate_unc_gain = []
    recovery_know = []
    recovery_unc = []
    net_know_loss = []
    net_unc_gain = []

    for event in events:
        il = event.get('immediate_loss', {})
        if il.get('know') is not None:
            immediate_know_loss.append(il['know'])
        if il.get('uncertainty') is not None:
            immediate_unc_gain.append(il['uncertainty'])

        rec = event.get('recovery', {})
        if rec.get('know') is not None:
            recovery_know.append(rec['know'])
        if rec.get('uncertainty') is not None:
            recovery_unc.append(rec['uncertainty'])

        net = event.get('net_loss', {})
        if net.get('know') is not None:
            net_know_loss.append(net['know'])
        if net.get('uncertainty') is not None:
            net_unc_gain.append(net['uncertainty'])

    def stats(values):
        """Calculate mean, min, max, count for a list of numeric values."""
        if not values:
            return None
        return {
            'mean': round(sum(values) / len(values), 3),
            'min': round(min(values), 3),
            'max': round(max(values), 3),
            'count': len(values)
        }

    return {
        'events_analyzed': len(events),
        'immediate_loss': {
            'know': stats(immediate_know_loss),
            'uncertainty': stats(immediate_unc_gain)
        },
        'recovery': {
            'know': stats(recovery_know),
            'uncertainty': stats(recovery_unc)
        },
        'net_loss': {
            'know': stats(net_know_loss),
            'uncertainty': stats(net_unc_gain)
        },
        'interpretation': {
            'avg_knowledge_lost': f"{abs(immediate_stats['mean']) * 100:.1f}%" if (immediate_stats := stats(immediate_know_loss)) else 'N/A',
            'avg_knowledge_recovered': f"{abs(recovery_stats['mean']) * 100:.1f}%" if (recovery_stats := stats(recovery_know)) else 'N/A',
            'avg_permanent_loss': f"{abs(net_stats['mean']) * 100:.1f}%" if (net_stats := stats(net_know_loss)) else 'N/A'
        }
    }


def format_human_readable(events: list[dict], stats: dict) -> str:
    """Format analysis results for human reading."""
    lines = []
    lines.append("=" * 60)
    lines.append("COMPACT ANALYSIS - Epistemic Loss Measurement")
    lines.append("=" * 60)
    lines.append("")

    # Summary stats
    lines.append("AGGREGATE STATISTICS")
    lines.append("-" * 40)
    interp = stats.get('interpretation', {})
    lines.append(f"Events analyzed: {stats.get('events_analyzed', 0)}")
    lines.append(f"Avg immediate knowledge loss: {interp.get('avg_knowledge_lost', 'N/A')}")
    lines.append(f"Avg knowledge recovered (via CHECK): {interp.get('avg_knowledge_recovered', 'N/A')}")
    lines.append(f"Avg permanent loss after recovery: {interp.get('avg_permanent_loss', 'N/A')}")
    lines.append("")

    # Recent events
    lines.append("RECENT COMPACT EVENTS")
    lines.append("-" * 40)

    for i, event in enumerate(events[:10], 1):
        lines.append(f"\n{i}. {event['timestamp']}")
        lines.append(f"   AI: {event['ai_id']}")

        pre = event.get('pre_vectors', {})
        post_pf = event.get('post_preflight_vectors', {})
        post_ck = event.get('post_check_vectors', {})

        lines.append(f"   Pre-compact:     know={pre.get('know', 'N/A'):.2f}, unc={pre.get('uncertainty', 'N/A'):.2f}")
        lines.append(f"   Post-PREFLIGHT:  know={post_pf.get('know', 'N/A'):.2f}, unc={post_pf.get('uncertainty', 'N/A'):.2f}")

        if post_ck:
            lines.append(f"   Post-CHECK:      know={post_ck.get('know', 'N/A'):.2f}, unc={post_ck.get('uncertainty', 'N/A'):.2f}")

        il = event.get('immediate_loss', {})
        lines.append(f"   Immediate loss:  Δknow={il.get('know', 'N/A'):+.2f}, Δunc={il.get('uncertainty', 'N/A'):+.2f}")

        if event.get('net_loss'):
            nl = event['net_loss']
            lines.append(f"   Net loss:        Δknow={nl.get('know', 'N/A'):+.2f}, Δunc={nl.get('uncertainty', 'N/A'):+.2f}")

        q = event.get('quality', {})
        lines.append(f"   Quality: {q.get('findings_count', 0)} findings, {q.get('unknowns_count', 0)} unknowns, {'complete' if q.get('has_postflight') else 'incomplete'} loop")

    lines.append("")
    lines.append("=" * 60)

    return "\n".join(lines)


def handle_compact_analysis(args: argparse.Namespace) -> dict:
    """CLI handler for compact-analysis command."""
    try:
        events = analyze_compact_events(
            include_tests=getattr(args, 'include_tests', False),
            min_findings=getattr(args, 'min_findings', 0),
            limit=getattr(args, 'limit', 20)
        )

        stats = calculate_aggregate_stats(events)

        output_format = getattr(args, 'output', 'human')

        if output_format == 'json':
            return {
                'ok': True,
                'events': events,
                'stats': stats
            }
        else:
            print(format_human_readable(events, stats))
            return {'ok': True}

    except FileNotFoundError as e:
        return {'ok': False, 'error': str(e)}
    except Exception as e:
        return {'ok': False, 'error': f"Analysis failed: {e}"}


