Complete rewrite: SnapRAID SMART logger with BackBlaze algorithm

Fetches SMART data from Scrutiny's InfluxDB and calculates failure
probabilities using the exact BackBlaze tables from SnapRAID source.

Key features:
- Calculates both v12 (with attr 193) and v13 (without attr 193) algorithms
- v12 matches SnapRAID pre-v13.0 (includes Load Cycle Count)
- v13 matches modern SnapRAID v13.0+ (excludes Load Cycle Count)
- Stores both values for trending and comparison
- Correctly applies bit masks (16-bit for 187/188, 32-bit for others)
- Annualizes monthly rates and applies Poisson distribution
- Logs to PostgreSQL with device metadata

Solved the mystery: SnapRAID v12 uses attribute 193 which was removed
in v13.0. Load cycle count has massive impact on failure predictions
for some drives (80% vs 4% difference).

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

explore_influx.py

@@ -0,0 +1,112 @@
+#!/usr/bin/env python3
+"""
+Explore Scrutiny's InfluxDB schema to understand how SMART data is stored.
+Run this first to understand the data structure before running the main logger.
+"""
+
+import os
+from influxdb_client import InfluxDBClient
+from dotenv import load_dotenv
+
+load_dotenv()
+
+def explore_influxdb():
+    """Connect to InfluxDB and explore the schema."""
+    url = os.getenv('INFLUXDB_URL')
+    token = os.getenv('INFLUXDB_TOKEN')
+    org = os.getenv('INFLUXDB_ORG', 'scrutiny')
+    bucket = os.getenv('INFLUXDB_BUCKET', 'metrics')
+
+    print(f"Connecting to InfluxDB at {url}")
+    print(f"Organization: {org}")
+    print(f"Bucket: {bucket}\n")
+
+    client = InfluxDBClient(url=url, token=token, org=org)
+    query_api = client.query_api()
+
+    # 1. List all measurements in the bucket
+    print("=" * 80)
+    print("STEP 1: Discovering measurements in bucket")
+    print("=" * 80)
+
+    query_measurements = f'''
+    import "influxdata/influxdb/schema"
+    schema.measurements(bucket: "{bucket}")
+    '''
+
+    try:
+        result = query_api.query(query_measurements)
+        measurements = []
+        for table in result:
+            for record in table.records:
+                measurement = record.get_value()
+                measurements.append(measurement)
+                print(f"  - {measurement}")
+
+        if not measurements:
+            print("  No measurements found!")
+            return
+
+        print(f"\nFound {len(measurements)} measurement(s)\n")
+
+        # 2. For each measurement, show field keys
+        print("=" * 80)
+        print("STEP 2: Discovering fields for each measurement")
+        print("=" * 80)
+
+        for measurement in measurements[:5]:  # Limit to first 5 measurements
+            print(f"\nMeasurement: {measurement}")
+            query_fields = f'''
+            import "influxdata/influxdb/schema"
+            schema.measurementFieldKeys(
+                bucket: "{bucket}",
+                measurement: "{measurement}"
+            )
+            '''
+
+            result = query_api.query(query_fields)
+            for table in result:
+                for record in table.records:
+                    field = record.get_value()
+                    print(f"  Field: {field}")
+
+        # 3. Show sample data from the first measurement
+        print("\n" + "=" * 80)
+        print("STEP 3: Sample data from first measurement")
+        print("=" * 80)
+
+        first_measurement = measurements[0]
+        query_sample = f'''
+        from(bucket: "{bucket}")
+            |> range(start: -24h)
+            |> filter(fn: (r) => r._measurement == "{first_measurement}")
+            |> limit(n: 10)
+        '''
+
+        print(f"\nSample records from '{first_measurement}':\n")
+        result = query_api.query(query_sample)
+        for table in result:
+            for record in table.records:
+                print(f"Time: {record.get_time()}")
+                print(f"  Measurement: {record.get_measurement()}")
+                print(f"  Field: {record.get_field()}")
+                print(f"  Value: {record.get_value()}")
+                print(f"  Tags: {record.values}")
+                print()
+
+    except Exception as e:
+        print(f"Error querying InfluxDB: {e}")
+        import traceback
+        traceback.print_exc()
+
+    finally:
+        client.close()
+
+
+if __name__ == '__main__':
+    if not os.getenv('INFLUXDB_URL'):
+        print("ERROR: Please create a .env file with your InfluxDB configuration")
+        print("Copy .env.example to .env and fill in your details")
+        exit(1)
+
+    explore_influxdb()