Refactor and enhance CalMiner application

- Updated README.md to reflect new features and usage instructions.
- Removed deprecated Dashboard.html component and integrated dashboard functionality directly into the main application.
- Revised architecture documentation for clarity and added module map and request flow diagrams.
- Enhanced maintenance model to include equipment association and cost tracking.
- Updated requirements.txt to include new dependencies (httpx, pandas, numpy).
- Improved consumption, maintenance, production, and reporting routes with better validation and response handling.
- Added unit tests for maintenance and production routes, ensuring proper CRUD operations and validation.
- Enhanced reporting service to calculate and return detailed summary statistics.
- Redesigned Dashboard.html for improved user experience and integrated Chart.js for visualizing simulation results.

services/reporting.py

@@ -1,15 +1,57 @@
-from typing import List, Dict
+from statistics import mean, median, pstdev
+from typing import Dict, Iterable, List, Union
 
 
-def generate_report(simulation_results: List[Dict[str, float]]) -> Dict[str, float]:
-    """
-    Generate summary report from simulation results.
+def _extract_results(simulation_results: Iterable[Dict[str, float]]) -> List[float]:
+    values: List[float] = []
+    for item in simulation_results:
+        if not isinstance(item, dict):
+            continue
+        value = item.get("result")
+        if isinstance(value, (int, float)):
+            values.append(float(value))
+    return values
 
-    Args:
-        simulation_results: List of dicts with 'iteration' and 'result'.
 
-    Returns:
-        Dictionary with summary statistics (e.g., mean, median).
-    """
-    # TODO: implement reporting logic (e.g., calculate mean, median, percentiles)
-    return {}
+def _percentile(values: List[float], percentile: float) -> float:
+    if not values:
+        return 0.0
+    sorted_values = sorted(values)
+    if len(sorted_values) == 1:
+        return sorted_values[0]
+    index = (percentile / 100) * (len(sorted_values) - 1)
+    lower = int(index)
+    upper = min(lower + 1, len(sorted_values) - 1)
+    weight = index - lower
+    return sorted_values[lower] * (1 - weight) + sorted_values[upper] * weight
+
+
+def generate_report(simulation_results: List[Dict[str, float]]) -> Dict[str, Union[float, int]]:
+    """Aggregate basic statistics for simulation outputs."""
+
+    values = _extract_results(simulation_results)
+
+    if not values:
+        return {
+            "count": 0,
+            "mean": 0.0,
+            "median": 0.0,
+            "min": 0.0,
+            "max": 0.0,
+            "std_dev": 0.0,
+            "percentile_10": 0.0,
+            "percentile_90": 0.0,
+        }
+
+    summary: Dict[str, Union[float, int]] = {
+        "count": len(values),
+        "mean": mean(values),
+        "median": median(values),
+        "min": min(values),
+        "max": max(values),
+        "percentile_10": _percentile(values, 10),
+        "percentile_90": _percentile(values, 90),
+    }
+
+    summary["std_dev"] = pstdev(values) if len(values) > 1 else 0.0
+    return summary