Split 49 modules/suites into independent git repos; untrack from monorepo

Each top-level module/suite folder is now its own private repo on GitHub
(gsinghpal/<name>) and gitea (admin/<name>), with a fresh single initial
commit. The monorepo no longer tracks them (added to .gitignore + git rm
--cached); working-tree files are retained on disk and managed in their
own repos. The monorepo keeps shared root files (CLAUDE.md, docs/, scripts/,
tools/, AGENTS.md, WIP/obsolete dirs) and full history.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

fusion_accounting/fusion_accounting_assets/services/anomaly_detection.py

@@ -1,96 +0,0 @@
-"""Asset utilization anomaly detection.
-
-Flags assets where actual usage / posted depreciation deviates significantly
-from the expected schedule. Three signal types:
-- behind_schedule: actual depreciation < expected by > threshold pct
-- ahead_of_schedule: actual > expected (over-depreciated; scrap or recompute)
-- low_utilization: units_used < expected_units_per_period (waste alert)
-"""
-
-from dataclasses import dataclass
-
-
-@dataclass
-class AssetAnomaly:
-    asset_id: int
-    asset_name: str
-    anomaly_type: str
-    severity: str
-    expected: float
-    actual: float
-    variance_pct: float
-    detail: str
-
-    def to_dict(self):
-        return {
-            'asset_id': self.asset_id,
-            'asset_name': self.asset_name,
-            'anomaly_type': self.anomaly_type,
-            'severity': self.severity,
-            'expected': self.expected,
-            'actual': self.actual,
-            'variance_pct': self.variance_pct,
-            'detail': self.detail,
-        }
-
-
-DEFAULT_LOW_THRESHOLD_PCT = 10.0
-DEFAULT_MEDIUM_THRESHOLD_PCT = 25.0
-DEFAULT_HIGH_THRESHOLD_PCT = 50.0
-
-
-def detect_schedule_variance(*, asset_id: int, asset_name: str,
-                             expected_accumulated: float,
-                             actual_accumulated: float) -> AssetAnomaly | None:
-    """Compare expected accumulated depreciation vs actual posted."""
-    if expected_accumulated <= 0:
-        return None
-    variance_amt = actual_accumulated - expected_accumulated
-    variance_pct = abs(variance_amt) / expected_accumulated * 100
-    if variance_pct < DEFAULT_LOW_THRESHOLD_PCT:
-        return None
-    direction = 'ahead_of_schedule' if variance_amt > 0 else 'behind_schedule'
-    if variance_pct >= DEFAULT_HIGH_THRESHOLD_PCT:
-        severity = 'high'
-    elif variance_pct >= DEFAULT_MEDIUM_THRESHOLD_PCT:
-        severity = 'medium'
-    else:
-        severity = 'low'
-    detail = f"Posted ${actual_accumulated:,.2f} vs expected ${expected_accumulated:,.2f}"
-    return AssetAnomaly(
-        asset_id=asset_id,
-        asset_name=asset_name,
-        anomaly_type=direction,
-        severity=severity,
-        expected=expected_accumulated,
-        actual=actual_accumulated,
-        variance_pct=round(variance_pct, 1),
-        detail=detail,
-    )
-
-
-def detect_low_utilization(*, asset_id: int, asset_name: str,
-                           expected_units: float,
-                           actual_units: float) -> AssetAnomaly | None:
-    """For units-of-production assets: flag low actual usage."""
-    if expected_units <= 0:
-        return None
-    if actual_units >= expected_units * 0.9:
-        return None
-    deficit_pct = (expected_units - actual_units) / expected_units * 100
-    if deficit_pct >= 50:
-        severity = 'high'
-    elif deficit_pct >= 25:
-        severity = 'medium'
-    else:
-        severity = 'low'
-    return AssetAnomaly(
-        asset_id=asset_id,
-        asset_name=asset_name,
-        anomaly_type='low_utilization',
-        severity=severity,
-        expected=expected_units,
-        actual=actual_units,
-        variance_pct=round(deficit_pct, 1),
-        detail=f"Used {actual_units:.0f} of expected {expected_units:.0f} units",
-    )