Reporting

# From generator.py:60-75
def get_health_grade(score: int) -> tuple:
    if score >= 90:
        return ('A', colors.green, 'Excellent')
    elif score >= 75:
        return ('B', colors.light_green, 'Good')
    elif score >= 50:
        return ('C', colors.amber, 'Fair')
    elif score >= 25:
        return ('D', colors.orange, 'Poor')
    else:
        return ('F', colors.red, 'Critical')

GET /integration/download-report?type=executive

Content-Type: application/pdf
Content-Disposition: attachment; filename="Report_Motor-01_20260302_1423.pdf"

┌─────────────────────────────────────────────────┐
│     EXECUTIVE HEALTH SUMMARY                    │
│  Asset Health Certificate • Report ID: a3f8b2c1 │
├─────────────────────────────────────────────────┤
│ Asset ID: Motor-01    Report Generated: 2026-03-02 14:23 UTC │
│ Data Capture: 2026-03-02 14:20:15 UTC  Model Version: detector:2.0|baseline:2024-01-15 │
├─────────────────────────────────────────────────┤
│                                                 │
│                      ╔════╗                     │
│                      ║  C ║                     │
│                      ╚════╝                     │
│                  Fair Condition                 │
│               Health Score: 68/100              │
│                                                 │
├────────────┬────────────┬────────────┬──────────┤
│ Max Vibration │ Days to Maint │ Critical Alerts │ Risk Level │
│   0.245 g     │     18.5      │       3         │  MODERATE  │
├───────────────────────────────────────────────────┤
│ Cumulative Prognostics:                           │
│ • DI: 32.0%  • Damage Rate: 0.00162/s  • RUL: 47.3h │
├───────────────────────────────────────────────────┤
│ Recent Maintenance Activity:                      │
│ 2026-03-02 12:15 | Corrective Maint | HIGH | Bearing replaced │
│ 2026-03-01 08:30 | Inspection | MEDIUM | Routine inspection completed │
└───────────────────────────────────────────────────┘

# From generator.py:617-618
is_anomaly = reading.get('is_faulty', False) or reading.get('is_anomaly', False)
status = 'ANOMALY' if is_anomaly else 'NORMAL'

GET /integration/download-report?type=excel

Content-Type: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
Content-Disposition: attachment; filename="Report_Motor-01_20260302_1423.xlsx"

# From generator.py:626-643
ws_summary.column_dimensions['A'].width = 30  # Metric names
ws_summary.column_dimensions['B'].width = 40  # Values

ws_logs.column_dimensions['A'].width = 20     # Event Time
ws_logs.column_dimensions['B'].width = 25     # Type
ws_logs.column_dimensions['C'].width = 12     # Severity
ws_logs.column_dimensions['D'].width = 50     # Technician Note

ws_raw.column_dimensions['A'].width = 20      # Timestamp
for col in ['B', 'C', 'D', 'E', 'F', 'G']:
    ws_raw.column_dimensions[col].width = 15  # Sensor columns

Vibration Variance (σ=0.17g)  ████████████████████████ 45.2%
Current Peak-to-Peak          ████████████████ 28.7%
Power Factor Efficiency       ██████████ 16.3%
Voltage Stability             ████ 9.8%

# From industrial_report.py:1089-1099
MAINTENANCE_ACTIONS = {
    'vibration': [
        ('CRITICAL', 'Inspect bearings and mounting. Check for misalignment.', 'URGENT'),
        ('HIGH', 'Schedule vibration analysis. Order replacement bearings.', 'HIGH'),
        ('MODERATE', 'Increase monitoring frequency. Review lubrication schedule.', 'MEDIUM'),
    ],
    'voltage': [
        ('CRITICAL', 'Check grid connection and transformer. Isolate from critical loads.', 'URGENT'),
        # ...
    ],
    # ...
}

GET /integration/download-report?type=industrial

Content-Type: application/pdf
Content-Disposition: attachment; filename="Report_Motor-01_20260302_1423.pdf"

Report_{AssetID}_{YYYYMMDD_HHMM}.{ext}

# From generator.py:131-137
def generate_filename(asset_id: str, timestamp: datetime, extension: str) -> str:
    ts_str = timestamp.strftime('%Y%m%d_%H%M')
    safe_asset_id = asset_id.replace(' ', '_').replace('/', '-')
    return f"Report_{safe_asset_id}_{ts_str}.{extension}"

# From generator.py:140-165
def generate_pdf_report(
    report: HealthReport,  # <-- Pre-computed, persisted data
    sensor_history: Optional[List[Dict[str, Any]]] = None,
    degradation_index: Optional[float] = None,
    # ...
) -> bytes:
    # Use report.health_score directly (NO re-computation)
    grade, color, desc = get_health_grade(report.health_score)
    
    # Use report.risk_level directly
    risk_color = _get_risk_color(report.risk_level.value)
    
    # Calculate anomaly score from health (inverse relationship)
    # This is for display only, not used for inference
    anomaly_score = max(0, min(100, 100 - report.health_score))

# From reports/constants.py
RISK_COLORS = {
    RiskLevel.LOW: colors.HexColor('#10b981'),      # Green
    RiskLevel.MODERATE: colors.HexColor('#f59e0b'), # Amber
    RiskLevel.HIGH: colors.HexColor('#f97316'),     # Orange
    RiskLevel.CRITICAL: colors.HexColor('#ef4444'), # Red
}

SEVERITY_COLORS = {
    'CRITICAL': colors.HexColor('#ef4444'),  # Red
    'HIGH': colors.HexColor('#f97316'),      # Orange
    'MEDIUM': colors.HexColor('#f59e0b'),    # Amber
    'LOW': colors.HexColor('#10b981'),       # Green
}

# From reports/constants.py (hardcoded per spec)
COST_MAINTENANCE_USD = 2000   # Preventive maintenance
COST_FAILURE_USD = 50000      # Unplanned failure (downtime + repairs)

roi_multiplier = COST_FAILURE_USD / COST_MAINTENANCE_USD  # 25x
savings = COST_FAILURE_USD - COST_MAINTENANCE_USD  # $48,000