"""Anomaly detection for financial reports.

Compares each row's current-period amount to its comparison-period
amount and flags variances exceeding a threshold. Uses both:
- Absolute threshold ($X minimum movement)
- Percentage threshold (Y% min variance)

Pure-Python: callers pass the engine's compute_*() result; we return
a list of anomaly dicts."""

from dataclasses import dataclass


@dataclass
class Anomaly:
    row_id: str
    label: str
    current_amount: float
    comparison_amount: float
    variance_amount: float
    variance_pct: float
    severity: str  # 'low', 'medium', 'high'
    direction: str  # 'increase', 'decrease'

    def to_dict(self):
        return {
            'row_id': self.row_id, 'label': self.label,
            'current_amount': self.current_amount,
            'comparison_amount': self.comparison_amount,
            'variance_amount': self.variance_amount,
            'variance_pct': self.variance_pct,
            'severity': self.severity, 'direction': self.direction,
        }


# Defaults -- tunable per company via ir.config_parameter
DEFAULT_MIN_ABSOLUTE_THRESHOLD = 100.0
DEFAULT_MIN_PCT_THRESHOLD = 10.0  # 10%
DEFAULT_HIGH_PCT_THRESHOLD = 50.0  # 50%+ flagged 'high'


def detect(report_result: dict, *, min_absolute: float = None,
           min_pct: float = None, high_pct: float = None) -> list[dict]:
    """Detect anomalies in a report_result dict (engine output).

    Returns list of anomaly dicts ordered by severity desc, variance_amount desc.
    Returns empty list if no comparison period was computed."""
    if not report_result.get('comparison_period'):
        return []
    min_absolute = min_absolute if min_absolute is not None else DEFAULT_MIN_ABSOLUTE_THRESHOLD
    min_pct = min_pct if min_pct is not None else DEFAULT_MIN_PCT_THRESHOLD
    high_pct = high_pct if high_pct is not None else DEFAULT_HIGH_PCT_THRESHOLD

    anomalies = []
    for row in report_result.get('rows', []):
        comparison = row.get('amount_comparison')
        current = row.get('amount', 0.0)
        if comparison is None:
            continue
        variance_amount = current - comparison
        variance_pct = abs(row.get('variance_pct') or 0.0)
        if abs(variance_amount) < min_absolute:
            continue
        if variance_pct < min_pct:
            continue
        severity = 'high' if variance_pct >= high_pct else 'medium' if variance_pct >= min_pct * 2 else 'low'
        direction = 'increase' if variance_amount > 0 else 'decrease'
        anomalies.append(Anomaly(
            row_id=row['id'],
            label=row.get('label', ''),
            current_amount=current,
            comparison_amount=comparison,
            variance_amount=variance_amount,
            variance_pct=variance_pct,
            severity=severity,
            direction=direction,
        ).to_dict())

    severity_order = {'high': 0, 'medium': 1, 'low': 2}
    anomalies.sort(key=lambda a: (severity_order[a['severity']], -abs(a['variance_amount'])))
    return anomalies